def mmm_wrap(_ax,
_graph,
_layer_spec,
_n_iters,
_spans,
_bands=9,
_tiers=3,
_title='',
_random_seed=0):
landuse_state = mmm_layercake_diversity(_graph,
_layer_spec,
_n_iters,
_spans,
_bands=_bands,
_tiers=_tiers,
random_seed=_random_seed)
print('done')
xs = []
for n, d in _graph.nodes(data=True):
xs.append(d['x'])
plotter(_ax,
_n_iters,
xs,
_res_factor=1,
_plot_maps=[
pop_map, capacitance_maps[0], landuse_maps[0],
spillover_maps[0]
])
_ax.set_title(_title + f' sum l.u: {landuse_maps[0].sum()}')
def mmm_single(_graph,
_iters,
_layer_specs,
_title='',
random_seed=0,
figsize=(12, 20),
theme=None,
path=None,
dark=False):
xs = []
for n, d in _graph.nodes(data=True):
xs.append(d['x'])
util_funcs.plt_setup(dark=dark)
if isinstance(_layer_specs, dict):
pop_map, landuse_maps, capacitance_maps, flow_maps = mmm_layercake_phd(_graph,
_iters,
_layer_specs=_layer_specs,
random_seed=random_seed)
fig, ax = plt.subplots(1, 1, figsize=figsize)
caps = capacitance_maps[0]
lus = landuse_maps[0]
flows = flow_maps[0]
plotter(ax, _iters, xs, _res_factor=1,
_plot_maps=[pop_map, caps, lus, flows],
_plot_scales=(1, 1.5, 0.75, 1))
title = _title + f'l.u: {np.round(np.sum(landuse_maps), 2)}'
style_ax(ax, title, _iters, dark=dark)
else:
assert isinstance(_layer_specs, (list, tuple))
n_ax = len(_layer_specs)
fig, axes = plt.subplots(1, n_ax, figsize=figsize)
if isinstance(_title, str):
_title = [_title] * n_ax
else:
assert isinstance(_title, (list, tuple))
assert len(_title) == len(_layer_specs)
for ax, title, layer_spec in zip(axes, _title, _layer_specs):
pop_map, landuse_maps, capacitance_maps, flow_maps = mmm_layercake_phd(_graph,
_iters,
_layer_specs=layer_spec,
random_seed=random_seed)
caps = capacitance_maps[0]
lus = landuse_maps[0]
flows = flow_maps[0]
plotter(ax, _iters, xs, _res_factor=1, _plot_maps=[pop_map, caps, lus, flows])
title = title + f'l.u: {np.round(np.sum(landuse_maps), 2)}'
style_ax(ax, title, _iters, dark=dark)
if theme is not None:
fig.suptitle(theme)
if path is not None:
plt.savefig(path, dpi=300)
def mmm_nested_split(_graph,
_iters,
_layer_specs,
_title='',
random_seed=0,
figsize=(30, 20),
theme=None,
path=None,
dark=False):
xs = []
for n, d in _graph.nodes(data=True):
xs.append(d['x'])
util_funcs.plt_setup(dark=dark)
assert isinstance(_layer_specs, (list, tuple))
fig, axes = plt.subplots(1, 3, figsize=figsize)
pop_map, landuse_maps, capacitance_maps, flow_maps = mmm_layercake_phd(_graph,
_iters,
_layer_specs=
_layer_specs[0],
random_seed=random_seed)
plotter(axes[0], _iters, xs, _res_factor=1, _plot_maps=[pop_map,
capacitance_maps[0],
landuse_maps[0]],
_plot_colours=['#555555', '#d32f2f', '#2e7d32'])
title = _title + f'l.u: {np.round(np.sum(landuse_maps), 2)}'
style_ax(axes[0], title, _iters, dark=dark)
# both
pop_map, landuse_maps, capacitance_maps, flow_maps = mmm_layercake_phd(_graph,
_iters,
_layer_specs=_layer_specs,
random_seed=random_seed)
plotter(axes[1], _iters, xs, _res_factor=1, _plot_maps=[pop_map,
np.sum(capacitance_maps, axis=0),
landuse_maps[0],
landuse_maps[1]],
_plot_colours=['#555555', '#d32f2f', '#2e7d32', '#0064b7'])
title = _title + f'l.u: {np.round(np.sum(landuse_maps), 2)}'
style_ax(axes[1], title, _iters, dark=dark)
pop_map, landuse_maps, capacitance_maps, flow_maps = mmm_layercake_d(_graph,
_iters,
_layer_specs=
_layer_specs[1],
random_seed=random_seed)
plotter(axes[2], _iters, xs, _res_factor=1, _plot_maps=[pop_map,
capacitance_maps[0],
landuse_maps[0]],
_plot_colours=['#555555', '#d32f2f', '#0064b7'])
title = _title + f'l.u: {np.round(np.sum(landuse_maps), 2)}'
style_ax(axes[2], title, _iters, dark=dark)
if theme is not None:
fig.suptitle(theme)
if path is not None:
plt.savefig(path, dpi=300)
def mmm_single(_graph,
_iters,
_layer_specs,
_title='',
seed=False,
random_seed=0,
figsize=(12, 20),
theme=None,
path=None,
dark=False):
xs = []
for n, d in _graph.nodes(data=True):
xs.append(d['x'])
background_col = '#ffffff'
if dark:
background_col = '#2e2e2e'
util_funcs.plt_setup()
if isinstance(_layer_specs, dict):
pop_map, landuse_maps, capacitance_maps = mmm_layercake_b(
_graph,
_iters,
_layer_specs=_layer_specs,
seed=seed,
random_seed=random_seed)
fig, ax = plt.subplots(1, 1, figsize=figsize, facecolor=background_col)
caps = capacitance_maps[0]
lus = landuse_maps[0] * caps
plotter(ax, _iters, xs, _res_factor=1, _plot_maps=[pop_map, caps, lus])
title = _title + f'l.u: {landuse_maps[0].sum()}'
style_ax(ax, title, _iters)
else:
assert isinstance(_layer_specs, (list, tuple))
n_ax = len(_layer_specs)
fig, axes = plt.subplots(1,
n_ax,
figsize=figsize,
facecolor=background_col)
if isinstance(_title, str):
_title = [_title] * n_ax
else:
assert isinstance(_title, (list, tuple))
assert len(_title) == len(_layer_specs)
for ax, title, layer_spec in zip(axes, _title, _layer_specs):
pop_map, landuse_maps, capacitance_maps = mmm_layercake_b(
_graph,
_iters,
_layer_specs=layer_spec,
seed=seed,
random_seed=random_seed)
caps = capacitance_maps[0]
lus = landuse_maps[0] * caps
plotter(ax,
_iters,
xs,
_res_factor=1,
_plot_maps=[pop_map, caps, lus])
title = title + f'l.u: {landuse_maps[0].sum()}'
style_ax(ax, title, _iters)
if theme is not None:
fig.suptitle(theme)
if path is not None:
plt.savefig(path,
facecolor=fig.get_facecolor(),
edgecolor='none',
dpi=300,
transparent=True)
plt.show()
dens[np.isnan(dens)] = 0
# set the centrality weights accordingly
Netw_Layer.weights = dens / np.nanmax(dens) # normalised
# calculate the density weighted centrality
Netw_Layer.gravity_index()
cent_pop_wt = Netw_Layer.metrics['centrality']['gravity_index'][400]
# reassign people from low to high pressure
pressure_reallocation(cent_pop_wt,
Netw_Layer._nodes,
Pop_Layer._data,
_iters=50,
_max=40)
plotter(axes[ax_n],
iters,
Netw_Layer.x_arr,
_res_factor=1,
_grey_map=pop_map)
theme = 'density_agglomeration'
fig.suptitle(theme)
plt.savefig(f'./src/explore/H_mmm/exploratory_plots/{theme}.png', dpi=300)
plt.show()
# %%
'''
2 - density vis-a-vis mixed-uses
Scenarios: sequential, random, random with topo stretch
1) People pursue mixed uses
2) Centrality is aggregate weighted by people, and destination weighted by respective R, G, B flows
