Exemplo n.º 1
0
 def plot(self,
          xys,
          neighborhoods=None,
          shpfile=None,
          outfile=None,
          title=None):
     if self.happy_ending:
         cm = get_cmap('RdBu')
         cm = get_cmap('Accent')
         f = plt.figure()
         ax = f.add_subplot(111)
         grid = ax.scatter(xys[:, 0], xys[:, 1], alpha=0.75, linewidths=0, \
                 s=80, marker=None, color='0.6', cmap=cm)
         axys = xys[self.agent_xyids, :]
         ax.scatter(axys[:, 0], axys[:, 1], alpha=0.8, linewidths=0, \
                 s=20, marker='o', c=self.group_map, cmap=cm)
         props = self.prop_groups + [1. - sum(self.prop_groups)]
         if title == None:
             title = "%i agents | %s share | %.2f similar wanted"\
                     %(self.pop_size, '_'.join(map(str, props)), \
                             self.pct_similar_wanted)
         plt.title(title, color='0.3')
         ax.set_frame_on(False)
         ax.axes.get_yaxis().set_visible(False)
         ax.axes.get_xaxis().set_visible(False)
         if neighborhoods:
             nr, nc = neighborhoods
             r = len(set(xys[:, 1]))
             ir = int(np.round((r * 1.) / nr))
             nrb = [i + ir for i in range(-ir, r, ir)]
             nrb[-1] += 0.5
             nrb[0] -= 0.5
             c = len(set(xys[:, 0]))
             ic = int(np.round((c * 1.) / nc))
             ncb = [i + ic for i in range(-ic, c, ic)]
             ncb[0] -= 0.5
             ncb[-1] += 0.5
             # Horizontal lines
             for i in nrb:
                 plt.hlines(i - 0.5, xmin=-1, xmax=c, color='k')
             # Vertical lines
             for i in ncb:
                 plt.vlines(i - 0.5, ymin=-1, ymax=r, color='k')
         elif shpfile:
             from pysal.contrib.viz import mapping as viz
             shp = ps.open(shpfile)
             patchco = viz.map_poly_shp(shp)
             patchco.set_facecolor('none')
             patchco.set_edgecolor('0.3')
             patchco.set_linewidth(0.3)
             ax = viz.setup_ax([patchco], ax)
         fc = '1'
         f.set_facecolor(fc)
         if not outfile:
             plt.show()
         else:
             plt.savefig(outfile, facecolor=fc)
     else:
         print 'No use in plotting a bad ending'
Exemplo n.º 2
0
 def plot(self, xys, neighborhoods=None, shpfile=None, outfile=None,
         title=None):
     if self.happy_ending:
         cm = get_cmap('RdBu')
         cm = get_cmap('Accent')
         f = plt.figure()
         ax = f.add_subplot(111)
         grid = ax.scatter(xys[:, 0], xys[:, 1], alpha=0.75, linewidths=0, \
                 s=80, marker=None, color='0.6', cmap=cm)
         axys = xys[self.agent_xyids, :]
         ax.scatter(axys[:, 0], axys[:, 1], alpha=0.8, linewidths=0, \
                 s=20, marker='o', c=self.group_map, cmap=cm)
         props = self.prop_groups + [1. - sum(self.prop_groups)]
         if title == None:
             title = "%i agents | %s share | %.2f similar wanted"\
                     %(self.pop_size, '_'.join(map(str, props)), \
                             self.pct_similar_wanted)
         plt.title(title, color='0.3')
         ax.set_frame_on(False)
         ax.axes.get_yaxis().set_visible(False)
         ax.axes.get_xaxis().set_visible(False)
         if neighborhoods:
             nr, nc = neighborhoods
             r = len(set(xys[:, 1]))
             ir = int(np.round((r*1.)/nr))
             nrb = [i+ir for i in range(-ir, r, ir)]
             nrb[-1] += 0.5
             nrb[0] -= 0.5
             c = len(set(xys[:, 0]))
             ic = int(np.round((c*1.)/nc))
             ncb = [i+ic for i in range(-ic, c, ic)]
             ncb[0] -= 0.5
             ncb[-1] += 0.5
             # Horizontal lines
             for i in nrb:
                 plt.hlines(i-0.5, xmin=-1, xmax=c, color='k')
             # Vertical lines
             for i in ncb:
                 plt.vlines(i-0.5, ymin=-1, ymax=r, color='k')
         elif shpfile:
             from pysal.contrib.viz import mapping as viz
             shp = ps.open(shpfile)
             patchco = viz.map_poly_shp(shp)
             patchco.set_facecolor('none')
             patchco.set_edgecolor('0.3')
             patchco.set_linewidth(0.3)
             ax = viz.setup_ax([patchco], ax)
         fc = '1'
         f.set_facecolor(fc)
         if not outfile:
             plt.show()
         else:
             plt.savefig(outfile, facecolor=fc)
     else:
         print 'No use in plotting a bad ending'
Exemplo n.º 3
0
def lisa_mapa(variavel, shapefile, p_thres=0.05, **kws):

    w = ps.queen_from_shapefile(shapefile)
    lisa = ps.Moran_Local(variavel, w)

    fig = plt.figure(figsize=(9, 7))
    shp = ps.open(shapefile)
    base = maps.map_poly_shp(shp)
    base = maps.base_lisa_cluster(base, lisa, p_thres=p_thres)
    base.set_edgecolor('1')
    base.set_linewidth(0.1)
    ax = maps.setup_ax([base], [shp.bbox])

    boxes, labels = maps.lisa_legend_components(lisa, p_thres=p_thres)
    plt.legend(boxes, labels, fancybox=True, **kws)

    plt.show();
Exemplo n.º 4
0
def lisa_mapa(variavel, shapefile, p_thres=0.05, **kws):

    w = ps.queen_from_shapefile(shapefile)
    lisa = ps.Moran_Local(variavel, w)

    fig = plt.figure(figsize=(9, 7))
    shp = ps.open(shapefile)
    base = maps.map_poly_shp(shp)
    base = maps.base_lisa_cluster(base, lisa, p_thres=p_thres)
    base.set_edgecolor('1')
    base.set_linewidth(0.1)
    ax = maps.setup_ax([base], [shp.bbox])

    boxes, labels = maps.lisa_legend_components(lisa, p_thres=p_thres)
    plt.legend(boxes, labels, fancybox=True, **kws)

    plt.show()
Exemplo n.º 5
0
def visualize_adjacency_graph(mggg_graph, out_dir=None):
    ''' Visualize an adjacency graph
        Input: 
            mggg_graph (Graph): A graph object from adjacency_graphs.algorithms
            out_dir (string, optional): Path to the output file. If none provided,
                                        no file will be created
        Output:
            fig (matplotlib.pyplot): An object which can be plotted
    '''
    # open the file and obtain pysal geometries
    shp = mggg_graph.loaded_geodata
    # setting up matplot figure
    fig = plt.figure(figsize=(9, 9))
    fig.set_facecolor('white')
    base = maps.map_poly_shp(shp)
    base.set_linewidth(0.75)
    base.set_facecolor('none')
    base.set_edgecolor('0.8')

    # Build a dictionary to associate geoid and index.
    data = mggg_graph.shape_df
    gti = {}
    for index, row in data.iterrows():
        gti[row[mggg_graph.id_column]] = index
    # graph contains polygons matched to their neighbors, uses polygon identifiers
    graph = mggg_graph.neighbors
    # obtain the centroids of polygons

    polygon_centroids = {x: y.centroid for x, y in enumerate(shp)}

    # connect centroids of the polygons using LineCollection
    edge_list = [(polygon_centroids[gti[poly1]], polygon_centroids[gti[poly2]])
                 for poly1, neighbors in graph.items() for poly2 in neighbors]

    edge_list = LineCollection(edge_list)
    edge_list.set_linewidth(0.20)
    ax = maps.setup_ax([base, edge_list], [shp.bbox, shp.bbox])
    fig.add_axes(ax)

    # save your output
    if (out_dir is not None):
        savefig(out_dir)
    return fig
Exemplo n.º 6
0
def choropleth(shp, bbox, data, title=None):
    data = 1.0 * np.nan_to_num(np.array(data))
    normed = (data - data.min()) / data.ptp()

    if np.min(data) < 0:
        cmap = 'PRGn'
    else:
        cmap = 'viridis'
    shapes = maps.map_poly_shp(shp)
    zips = maps.base_choropleth_classless(shapes,
                                       values=normed,
                                       cmap=cmap)
    zips.set_linewidth(0.75)
    zips.set_edgecolor('0.8')
    fig = plt.figure(figsize=(7, 7))
    ax = maps.setup_ax([zips], [bbox])
    fig.add_axes(ax)
    if title:
        plt.title(title)
    m = mpl.cm.ScalarMappable(cmap=cmap)
    m.set_array(np.unique(data))
    plt.colorbar(m)
    plt.show()
Exemplo n.º 7
0
def visualize_adjacency_graph(mggg_graph, fig=None, out_dir=None):
    """
    visualize_adjacency_graph
    create and visualize an adjacency graph of geometries in an shp file.

    @param file_dir a string directory for the shp file which one wishes to
            analyze.
    @param out_dir a string directory where one wishes to save the output
            image. Image will not be saved if this is None.
    """
    if not fig:
        fig = plt.figure(figsize=(9, 9))
    fig.set_facecolor('white')
    base = maps.map_poly_shp(mggg_graph.loaded_geodata)
    base.set_linewidth(0.75)
    base.set_facecolor('none')
    base.set_edgecolor('0.8')

    # obtain the centroids of polygons
    polygon_centroids = {
        x: y.centroid
        for x, y in enumerate(mggg_graph.loaded_geodata)
    }

    # connect centroids of the polygons using LineCollection
    edge_list = ((polygon_centroids[poly1], polygon_centroids[poly2])
                 for poly1, neighbors in mggg_graph.neighbors.items()
                 for poly2 in neighbors)
    edge_list = LineCollection(edge_list)

    edge_list.set_linewidth(0.20)
    ax = maps.setup_ax(
        [base, edge_list],
        [mggg_graph.loaded_geodata.bbox, mggg_graph.loaded_geodata.bbox])
    fig.add_axes(ax)
    return fig
Exemplo n.º 8
0
w = ps.queen_from_shapefile(shp_link)
lisa = ps.Moran_Local(TotPop, w, permutations=9999)

# In[7]:

maps.plot_lisa_cluster(shp_link, lisa, figsize=(9, 6))

# In[11]:

orig_crs = ccrs.PlateCarree()
projection = ccrs.LambertConformal()
p_thres = 0.1

shp = ps.open(shp_link)
polys = maps.map_poly_shp(shp)
polys = maps.base_lisa_cluster(polys, lisa, p_thres=p_thres)
polys.set_edgecolor('1')
polys.set_linewidth(0.2)
polys.set_transform(orig_crs)

f = plt.figure(figsize=(12, 8))

ax = plt.axes(projection=projection)
extent = [shp.bbox[0], shp.bbox[2], shp.bbox[1], shp.bbox[3]]
ax.set_extent(extent, crs=ccrs.PlateCarree())
ax.add_collection(polys)
ax.outline_patch.set_visible(False)

boxes, labels = maps.lisa_legend_components(lisa, p_thres=p_thres)
plt.legend(boxes, labels, loc='lower left', fancybox=True)
Exemplo n.º 9
0
lisa = ps.Moran_Local(hr90, w, permutations=9999)

# _ = maps.plot_lisa_cluster(shp_link, lisa, figsize=(9, 6))




import matplotlib.pyplot as plt
import cartopy.crs as ccrs

orig_crs = ccrs.PlateCarree()
projection = ccrs.LambertConformal()
p_thres = 0.01

shp = ps.open(shp_link)
polys = maps.map_poly_shp(shp)
polys = maps.base_lisa_cluster(polys, lisa, p_thres=p_thres)
polys.set_edgecolor('1')
polys.set_linewidth(0.2)
polys.set_transform(orig_crs)

f = plt.figure(figsize=(12, 8))

ax = plt.axes(projection=projection)
extent = [shp.bbox[0], shp.bbox[2], shp.bbox[1], shp.bbox[3]]
ax.set_extent(extent, crs=ccrs.PlateCarree())
ax.add_collection(polys)
ax.outline_patch.set_visible(False)

boxes, labels = maps.lisa_legend_components(lisa, p_thres=p_thres)
plt.legend(boxes, labels, loc='lower left', fancybox=True)
Exemplo n.º 10
0
def visualize_adjacency_graph(mggg_graph, bokeh_graph=False, out_dir=None):
    ''' Visualize an adjacency graph
        Input: 
            mggg_graph (Graph): A graph object from adjacency_graphs.algorithms
            out_dir (string, optional): Path to the output file. If none provided,
                                        no file will be created
        Output:
            fig (matplotlib.pyplot): An object which can be plotted
    '''
    # open the file and obtain pysal geometries
    shp = mggg_graph.loaded_geodata
    # setting up matplot figure
    fig = plt.figure(figsize=(9, 9))
    fig.set_facecolor('white')
    base = maps.map_poly_shp(shp)
    base.set_linewidth(0.75)
    base.set_facecolor('none')
    base.set_edgecolor('0.8')

    # Build a dictionary to associate geoid and index.
    data = mggg_graph.shape_df
    gti = {}
    for index, row in data.iterrows():
        gti[row[mggg_graph.id_column]] = index
    # graph contains polygons matched to their neighbors, uses polygon identifiers
    graph = mggg_graph.neighbors
    # obtain the centroids of polygons

    polygon_centroids = {x: y.centroid for x, y in enumerate(shp)}

    if bokeh_graph == False:
        edge_list = [
            (polygon_centroids[gti[poly1]], polygon_centroids[gti[poly2]])
            for poly1, neighbors in graph.items() for poly2 in neighbors
        ]

        edge_list = LineCollection(edge_list)
        edge_list.set_linewidth(0.20)
        ax = maps.setup_ax([base, edge_list], [shp.bbox, shp.bbox])
        fig.add_axes(ax)

        # save your output
        if (out_dir is not None):
            savefig(out_dir)
        return fig
    else:
        edge_list_bokeh_x = [[
            polygon_centroids[gti[poly1]][0], polygon_centroids[gti[poly2]][0]
        ] for poly1, neighbors in graph.items() for poly2 in neighbors]
        edge_list_bokeh_y = [[
            polygon_centroids[gti[poly1]][1], polygon_centroids[gti[poly2]][1]
        ] for poly1, neighbors in graph.items() for poly2 in neighbors]

        lons, lats = gpd_bokeh(mggg_graph.shape_df)
        source = ColumnDataSource(
            data=dict(x=lons, y=lats, name=mggg_graph.shape_df.NAME))

        TOOLS = "pan, wheel_zoom, box_zoom, reset, hover, save"
        p = figure(title="Adjecency Graph",
                   tools=TOOLS,
                   plot_width=900,
                   plot_height=900)

        p.patches('x',
                  'y',
                  source=source,
                  fill_alpha=0.7,
                  color='white',
                  line_color='black',
                  line_width=0.5)

        hover = p.select_one(HoverTool)
        hover.point_policy = 'follow_mouse'
        hover.tooltips = [
            ("Name", "@name"),
            ("(Long, Lat)", "($x, $y)"),
        ]

        p.multi_line(edge_list_bokeh_x, edge_list_bokeh_y, line_width=1)

        output_file("adjacency.html", title="Adjacency Graph")
        show(p)
Exemplo n.º 11
0
import numpy as np
import pysal as ps
import random as rdm
from pysal.contrib.viz import mapping as maps
import matplotlib.pyplot as plt

shp_link = 'transportation.shp'
shp = ps.open(shp_link)

fig = plt.figure()

base = maps.map_poly_shp(shp)
base.set_facecolor('none')
base.set_linewidth(0.75)
base.set_edgecolor('0.8')

ax = maps.setup_ax([base])
fig.add_axes(ax)
plt.show()
def plot_lisa(lisa, st, msa, outfile=None, thr=0.05, title=''):
    '''
    Plot LISA results for MSAs on background map of US states

    NOTE: shapefiles hardcoded linked to paths inside the function
    ...

    Arguments
    ---------
    lisa    : Moran_Local
              LISA object from PySAL
    st      : str
              Path to states shape
    msa     : str
              Path to MSA points shape
    outfile : str
              [Optional] Path to png to be written
    thr     : float
              [Optional] Significance value to identify clusters
    title   : str
              [Optional] Title for the figure
    title   : str
    Returns
    -------
    None
    '''
    sig = (lisa.p_sim < thr) * 1
    vals = pd.Series(lisa.q * sig)

    states = ps.open(st)
    pts = ps.open(msa)

    fig = plt.figure(figsize=(9, 5))

    base = maps.map_poly_shp(states)
    base.set_facecolor('0.85')
    base.set_linewidth(0.75)
    base.set_edgecolor('0.95')

    msas = pd.np.array([pt for pt in ps.open(msa)])
    sizes = vals.apply(lambda x: 4 if x==0 else 50)
    colors = vals.map(q_mapper)
    colors = pd.np.array(list(colors))
    pts = []
    for clas in q_mapper:
        i = vals[vals==clas].index
        p = plt.scatter(msas[i, 0], msas[i, 1], s=sizes[i], \
                c=colors[i, :], label=q_names[clas])
        p.set_linewidth(0)
        pts.append(p)
    plt.legend(loc=3, ncol=2, fontsize=14, scatterpoints=1, frameon=False)

    ax = maps.setup_ax([base] + pts)
    #ax = maps.setup_ax(pts)
    fig.add_axes(ax)
    if title:
        plt.title(title)
    if outfile:
        plt.savefig(outfile)
    else:
        plt.show()
    return None