Source code for splot._viz_giddy_mpl

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from esda.moran import Moran_Local
import ipywidgets as widgets
from ipywidgets import interact, fixed
from giddy.directional import Rose

from ._viz_utils import moran_hot_cold_spots
from ._viz_esda_mpl import lisa_cluster

"""
Lightweight visualizations for pysal dynamics using Matplotlib and Geopandas

TODO
* implement LIMA
* allow for different patterns or list of str
    in dynamic_lisa_composite_explore()
"""

__author__ = ("Stefanie Lumnitz <stefanie.lumitz@gmail.com>")


def _dynamic_lisa_heatmap_data(moran_locy, moran_locx, p=0.05):
    '''
    Utility function to calculate dynamic lisa heatmap table
    and diagonal color mask
    '''
    clustery = moran_hot_cold_spots(moran_locy, p=p)
    clusterx = moran_hot_cold_spots(moran_locx, p=p)

    # to put into seaborn function
    # and set diagonal elements to zero to see the rest better
    heatmap_data = np.zeros((5, 5), dtype=int)
    mask = np.zeros((5, 5), dtype=bool)
    for row in range(5):
        for col in range(5):
            yr1 = clustery == row
            yr2 = clusterx == col
            heatmap_data[row, col] = (yr1 & yr2).sum()
            if row == col:
                mask[row, col] = True
    return heatmap_data, mask


def _moran_loc_from_rose_calc(rose):
    """
    Calculate esda.moran.Moran_Local values from giddy.rose object
    """
    old_state = np.random.get_state()
    moran_locy = Moran_Local(rose.Y[:, 0], rose.w)
    np.random.set_state(old_state)
    moran_locx = Moran_Local(rose.Y[:, 1], rose.w)
    np.random.set_state(old_state)
    return moran_locy, moran_locx


[docs]def dynamic_lisa_heatmap(rose, p=0.05, ax=None, **kwargs): """ Heatmap indicating significant transition of LISA values over time inbetween Moran Scatterplot quadrants Parameters ---------- rose : giddy.directional.Rose instance A ``Rose`` object, which contains (among other attributes) LISA values at two points in time, and a method to perform inference on those. p : float, optional The p-value threshold for significance. Default =0.05 ax : Matplotlib Axes instance, optional If given, the figure will be created inside this axis. Default =None. **kwargs : keyword arguments, optional Keywords used for creating and designing the heatmap. These are passed on to `seaborn.heatmap()`. See `seaborn` documentation for valid keywords. Note: "Start time" refers to `y1` in `Y = np.array([y1, y2]).T` with `giddy.Rose(Y, w, k=5)`, "End time" referst to `y2`. Returns ------- fig : Matplotlib Figure instance Heatmap figure ax : matplotlib Axes instance Axes in which the figure is plotted Examples -------- >>> import geopandas as gpd >>> import pandas as pd >>> from libpysal.weights.contiguity import Queen >>> from libpysal import examples >>> import numpy as np >>> import matplotlib.pyplot as plt >>> from giddy.directional import Rose >>> from splot.giddy import dynamic_lisa_heatmap get csv and shp files >>> shp_link = examples.get_path('us48.shp') >>> df = gpd.read_file(shp_link) >>> income_table = pd.read_csv(examples.get_path("usjoin.csv")) calculate relative values >>> for year in range(1969, 2010): ... income_table[str(year) + '_rel'] = ( ... income_table[str(year)] / income_table[str(year)].mean()) merge to one gdf >>> gdf = df.merge(income_table,left_on='STATE_NAME',right_on='Name') retrieve spatial weights and data for two points in time >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' >>> y1 = gdf['1969_rel'].values >>> y2 = gdf['2000_rel'].values calculate rose Object >>> Y = np.array([y1, y2]).T >>> rose = Rose(Y, w, k=5) plot >>> dynamic_lisa_heatmap(rose) >>> plt.show() customize plot >>> dynamic_lisa_heatmap(rose, cbar='GnBu') >>> plt.show() """ moran_locy, moran_locx = _moran_loc_from_rose_calc(rose) fig, ax = _dynamic_lisa_heatmap(moran_locy, moran_locx, p=p, ax=ax, **kwargs) return fig, ax
def _dynamic_lisa_heatmap(moran_locy, moran_locx, p, ax, **kwargs): """ Create dynamic_lisa_heatmap figure from esda.moran.Moran_local values """ heatmap_data, diagonal_mask = _dynamic_lisa_heatmap_data(moran_locy, moran_locx, p) # set default plot style annot = kwargs.pop('annot', True) cmap = kwargs.pop('cmap', "YlGnBu") mask = kwargs.pop('mask', diagonal_mask) cbar = kwargs.pop('cbar', False) square = kwargs.pop('square', True) # set name for tick labels xticklabels = kwargs.pop('xticklabels', ['ns', 'HH', 'HL', 'LH', 'LL']) yticklabels = kwargs.pop('yticklabels', ['ns', 'HH', 'HL', 'LH', 'LL']) ax = sns.heatmap(heatmap_data, annot=annot, cmap=cmap, xticklabels=xticklabels, yticklabels=yticklabels, mask=mask, ax=ax, cbar=cbar, square=square, **kwargs) ax.set_xlabel('End time') ax.set_ylabel('Start time') fig = ax.get_figure() return fig, ax
[docs]def dynamic_lisa_rose(rose, attribute=None, ax=None, **kwargs): """ Plot dynamic LISA values in a rose diagram. Parameters ---------- rose : giddy.directional.Rose instance A ``Rose`` object, which contains (among other attributes) LISA values at two points in time, and a method to perform inference on those. attribute : (n,) ndarray, optional Points will be colored by chosen attribute values. Variable to specify colors of the colorbars. Default =None. ax : Matplotlib Axes instance, optional If given, the figure will be created inside this axis. Default =None. Note: This axis should have a polar projection. **kwargs : keyword arguments, optional Keywords used for creating and designing the `matplotlib.pyplot.scatter()`. Note: 'c' and 'color' cannot be passed when attribute is not None. Returns ------- fig : Matplotlib Figure instance LISA rose plot figure ax : matplotlib Axes instance Axes in which the figure is plotted Examples -------- >>> import geopandas as gpd >>> import pandas as pd >>> from libpysal.weights.contiguity import Queen >>> from libpysal import examples >>> import numpy as np >>> import matplotlib.pyplot as plt >>> from giddy.directional import Rose >>> from splot.giddy import dynamic_lisa_rose get csv and shp files >>> shp_link = examples.get_path('us48.shp') >>> df = gpd.read_file(shp_link) >>> income_table = pd.read_csv(examples.get_path("usjoin.csv")) calculate relative values >>> for year in range(1969, 2010): ... income_table[str(year) + '_rel'] = ( ... income_table[str(year)] / income_table[str(year)].mean()) merge to one gdf >>> gdf = df.merge(income_table,left_on='STATE_NAME',right_on='Name') retrieve spatial weights and data for two points in time >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' >>> y1 = gdf['1969_rel'].values >>> y2 = gdf['2000_rel'].values calculate rose Object >>> Y = np.array([y1, y2]).T >>> rose = Rose(Y, w, k=5) plot >>> dynamic_lisa_rose(rose, attribute=y1) >>> plt.show() customize plot >>> dynamic_lisa_rose(rose, c='r') >>> plt.show() """ # save_old default values old_gridcolor = mpl.rcParams['grid.color'] old_facecolor = mpl.rcParams['axes.facecolor'] old_edgecolor = mpl.rcParams['axes.edgecolor'] # define plotting style mpl.rcParams['grid.color'] = 'w' mpl.rcParams['axes.edgecolor'] = 'w' mpl.rcParams['axes.facecolor'] = '#E5E5E5' alpha = kwargs.pop('alpha', 0.9) cmap = kwargs.pop('cmap', 'YlGnBu') if ax is None: fig = plt.figure() ax = fig.add_subplot(111, projection='polar') can_insert_colorbar = True else: fig = ax.get_figure() can_insert_colorbar = False ax.set_rlabel_position(315) if attribute is None: c = ax.scatter(rose.theta, rose.r, alpha=alpha, cmap=cmap, **kwargs) else: if 'c' in kwargs.keys() or 'color' in kwargs.keys(): raise ValueError('c and color are not valid keywords here; ' 'attribute is used for coloring') c = ax.scatter(rose.theta, rose.r, c=attribute, alpha=alpha, cmap=cmap, **kwargs) if can_insert_colorbar: fig.colorbar(c) # reset style to old default values mpl.rcParams['grid.color'] = old_gridcolor mpl.rcParams['axes.facecolor'] = old_facecolor mpl.rcParams['axes.edgecolor'] = old_edgecolor return fig, ax
def _add_arrow(line, position=None, direction='right', size=15, color=None): """ add an arrow to a line. line: Line2D object position: x-position of the arrow. If None, mean of xdata is taken direction: 'left' or 'right' size: size of the arrow in fontsize points color: if None, line color is taken. """ if color is None: color = line.get_color() xdata = line.get_xdata() ydata = line.get_ydata() line.axes.annotate('', xytext=(xdata[0], ydata[0]), xy=(xdata[1], ydata[1]), arrowprops=dict(arrowstyle="->", color=color), size=size)
[docs]def dynamic_lisa_vectors(rose, ax=None, arrows=True, **kwargs): """ Plot vectors of positional transition of LISA values in Moran scatterplot Parameters ---------- rose : giddy.directional.Rose instance A ``Rose`` object, which contains (among other attributes) LISA values at two points in time, and a method to perform inference on those. ax : Matplotlib Axes instance, optional If given, the figure will be created inside this axis. Default =None. arrows : boolean, optional If True show arrowheads of vectors. Default =True **kwargs : keyword arguments, optional Keywords used for creating and designing the `matplotlib.pyplot.plot()` Note: 'c' and 'color' cannot be passed when attribute is not None. Returns ------- fig : Matplotlib Figure instance Figure of dynamic LISA vectors ax : matplotlib Axes instance Axes in which the figure is plotted Examples -------- >>> import geopandas as gpd >>> import pandas as pd >>> from libpysal.weights.contiguity import Queen >>> from libpysal import examples >>> import numpy as np >>> import matplotlib.pyplot as plt >>> from giddy.directional import Rose >>> from splot.giddy import dynamic_lisa_vectors get csv and shp files >>> shp_link = examples.get_path('us48.shp') >>> df = gpd.read_file(shp_link) >>> income_table = pd.read_csv(examples.get_path("usjoin.csv")) calculate relative values >>> for year in range(1969, 2010): ... income_table[str(year) + '_rel'] = ( ... income_table[str(year)] / income_table[str(year)].mean()) merge to one gdf >>> gdf = df.merge(income_table,left_on='STATE_NAME',right_on='Name') retrieve spatial weights and data for two points in time >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' >>> y1 = gdf['1969_rel'].values >>> y2 = gdf['2000_rel'].values calculate rose Object >>> Y = np.array([y1, y2]).T >>> rose = Rose(Y, w, k=5) plot >>> dynamic_lisa_vectors(rose) >>> plt.show() customize plot >>> dynamic_lisa_vectors(rose, arrows=False, c='r') >>> plt.show() """ if ax is None: fig = plt.figure() ax = fig.add_subplot(111) can_insert_colorbar = True else: fig = ax.get_figure() can_insert_colorbar = False xlim = [rose.Y.min(), rose.Y.max()] ylim = [rose.wY.min(), rose.wY.max()] color = kwargs.pop('color', 'b') can_insert_colorbar = False xs = [] ys = [] for i in range(len(rose.Y)): # Plot a vector from xy_start to xy_end xs.append(rose.Y[i, :]) ys.append(rose.wY[i, :]) xs = np.asarray(xs).T ys = np.asarray(ys).T lines = ax.plot(xs, ys, color=color, **kwargs) if can_insert_colorbar: fig.colorbar(lines) if arrows: for line in lines: _add_arrow(line) ax.axis('equal') ax.set_xlim(xlim) ax.set_ylim(ylim) return fig, ax
[docs]def dynamic_lisa_composite(rose, gdf, p=0.05, figsize=(13, 10)): """ Composite visualisation for dynamic LISA values over two points in time. Includes dynamic lisa heatmap, dynamic lisa rose plot, and LISA cluster plots for both, compared points in time. Parameters ---------- rose : giddy.directional.Rose instance A ``Rose`` object, which contains (among other attributes) LISA values at two points in time, and a method to perform inference on those. gdf : geopandas dataframe instance The GeoDataFrame containing information and polygons to plot. p : float, optional The p-value threshold for significance. Default =0.05. figsize: tuple, optional W, h of figure. Default =(13,10) Returns ------- fig : Matplotlib Figure instance Dynamic lisa composite figure. axs : matplotlib Axes instance Axes in which the figure is plotted. Examples -------- >>> import geopandas as gpd >>> import pandas as pd >>> from libpysal.weights.contiguity import Queen >>> from libpysal import examples >>> import numpy as np >>> import matplotlib.pyplot as plt >>> from giddy.directional import Rose >>> from splot.giddy import dynamic_lisa_composite get csv and shp files >>> shp_link = examples.get_path('us48.shp') >>> df = gpd.read_file(shp_link) >>> income_table = pd.read_csv(examples.get_path("usjoin.csv")) calculate relative values >>> for year in range(1969, 2010): ... income_table[str(year) + '_rel'] = ( ... income_table[str(year)] / income_table[str(year)].mean()) merge to one gdf >>> gdf = df.merge(income_table,left_on='STATE_NAME',right_on='Name') retrieve spatial weights and data for two points in time >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' >>> y1 = gdf['1969_rel'].values >>> y2 = gdf['2000_rel'].values calculate rose Object >>> Y = np.array([y1, y2]).T >>> rose = Rose(Y, w, k=5) plot >>> dynamic_lisa_composite(rose, gdf) >>> plt.show() customize plot >>> fig, axs = dynamic_lisa_composite(rose, gdf) >>> axs[0].set_ylabel('1996') >>> axs[0].set_xlabel('2009') >>> axs[1].set_title('LISA cluster for 1996') >>> axs[3].set_title('LISA clsuter for 2009') >>> plt.show() """ # Moran_Local uses random numbers, # which we cannot change between the two years! moran_locy, moran_locx = _moran_loc_from_rose_calc(rose) # initialize figure fig = plt.figure(figsize=figsize) fig.suptitle('Space-time autocorrelation', fontsize=20) axs = [] axs.append(plt.subplot(221)) axs.append(plt.subplot(222)) # save_old default values old_gridcolor = mpl.rcParams['grid.color'] old_facecolor = mpl.rcParams['axes.facecolor'] old_edgecolor = mpl.rcParams['axes.edgecolor'] # define plotting style mpl.rcParams['grid.color'] = 'w' mpl.rcParams['axes.edgecolor'] = 'w' mpl.rcParams['axes.facecolor'] = '#E5E5E5' # define axs[2] axs.append(plt.subplot(223, projection='polar')) # reset style to old default values mpl.rcParams['grid.color'] = old_gridcolor mpl.rcParams['axes.facecolor'] = old_facecolor mpl.rcParams['axes.edgecolor'] = old_edgecolor # define axs[3] axs.append(plt.subplot(224)) # space_time_heatmap _dynamic_lisa_heatmap(moran_locy, moran_locx, p=p, ax=axs[0]) axs[0].xaxis.set_ticks_position('top') axs[0].xaxis.set_label_position('top') # Lisa_cluster maps lisa_cluster(moran_locy, gdf, p=p, ax=axs[1], legend=True, legend_kwds={'loc': 'upper left', 'bbox_to_anchor': (0.92, 1.05)}) axs[1].set_title('Start time') lisa_cluster(moran_locx, gdf, p=p, ax=axs[3], legend=True, legend_kwds={'loc': 'upper left', 'bbox_to_anchor': (0.92, 1.05)}) axs[3].set_title('End time') # Rose diagram: Moran movement vectors: dynamic_lisa_rose(rose, ax=axs[2]) return fig, axs
def _dynamic_lisa_widget_update(rose, gdf, start_time, end_time, p=0.05, figsize=(13, 10)): """ Update rose values if widgets are used """ # determine rose object for (timex, timey), # which comes from interact widgets y1 = gdf[start_time].values y2 = gdf[end_time].values Y = np.array([y1, y2]).T rose_update = Rose(Y, rose.w, k=5) fig, _ = dynamic_lisa_composite(rose_update, gdf, p=p, figsize=figsize)
[docs]def dynamic_lisa_composite_explore(rose, gdf, pattern='', p=0.05, figsize=(13, 10)): """ Interactive exploration of dynamic LISA values for different dates in a dataframe. Note: only possible in jupyter notebooks Parameters ---------- rose : giddy.directional.Rose instance A ``Rose`` object, which contains (among other attributes) weights to calculate `esda.moran.Moran_local` values gdf : geopandas dataframe instance The Dataframe containing information and polygons to plot. pattern : str, optional Option to extract all columns ending with a specific pattern. Only extracted columns will be used for comparison. p : float, optional The p-value threshold for significance. Default =0.05 figsize: tuple, optional W, h of figure. Default =(13,10) Returns ------- None Examples -------- **Note**: this function creates Jupyter notebook widgets, so is meant only to run in a notebook. >>> import geopandas as gpd >>> import pandas as pd >>> from libpysal.weights.contiguity import Queen >>> from libpysal import examples >>> import numpy as np >>> import matplotlib.pyplot as plt If you want to see figures embedded inline in a Jupyter notebook, add a line ``%matplotlib inline`` at the top of your notebook. >>> from giddy.directional import Rose >>> from splot.giddy import dynamic_lisa_composite_explore get csv and shp files >>> shp_link = examples.get_path('us48.shp') >>> df = gpd.read_file(shp_link) >>> income_table = pd.read_csv(examples.get_path("usjoin.csv")) calculate relative values >>> for year in range(1969, 2010): ... income_table[str(year) + '_rel'] = ( ... income_table[str(year)] / income_table[str(year)].mean()) merge to one gdf >>> gdf = df.merge(income_table,left_on='STATE_NAME',right_on='Name') retrieve spatial weights and data for two points in time >>> w = Queen.from_dataframe(gdf) >>> w.transform = 'r' >>> y1 = gdf['1969_rel'].values >>> y2 = gdf['2000_rel'].values calculate rose Object >>> Y = np.array([y1, y2]).T >>> rose = Rose(Y, w, k=5) plot >>> fig = dynamic_lisa_composite_explore(rose, gdf, pattern='rel') >>> # plt.show() """ coldict = {col: col for col in gdf.columns if col.endswith(pattern)} interact(_dynamic_lisa_widget_update, start_time=coldict, end_time=coldict, rose=fixed(rose), gdf=fixed(gdf), p=fixed(p), figsize=fixed(figsize))