def plot_MDS(Data, cmap="viridis"):
Fmax = max(Data.log_den)
Rho_bord_m = np.copy(Data.log_den_bord)
d_dis = np.zeros((Data.N_clusters, Data.N_clusters), dtype=float)
model = manifold.MDS(n_components=2,
n_jobs=None,
dissimilarity="precomputed")
for i in range(Data.N_clusters):
for j in range(Data.N_clusters):
d_dis[i][j] = Fmax - Rho_bord_m[i][j]
for i in range(Data.N_clusters):
d_dis[i][i] = 0.0
out = model.fit_transform(d_dis)
fig, ax = plt.subplots(nrows=1, ncols=1)
s = []
col = []
for i in range(Data.N_clusters):
s.append(20.0 * np.sqrt(len(Data.cluster_indices[i])))
col.append(i)
plt.scatter(out[:, 0], out[:, 1], s=s, c=col, cmap=cmap)
cmal = cm.get_cmap(cmap, Data.N_clusters)
colors = cmal(np.arange(0, cmal.N))
for i in range(Data.N_clusters):
cc = "k"
r = colors[i][0]
g = colors[i][1]
b = colors[i][2]
luma = (0.2126 * r + 0.7152 * g + 0.0722 * b) * 255
if luma < 156:
cc = "w"
plt.annotate(
i,
(out[i, 0], out[i, 1]),
horizontalalignment="center",
verticalalignment="center",
c=cc,
weight="bold",
)
# for i in range(Data.N_clusters):
# ax.annotate(i, (out[i, 0], out[i, 1]))
# Add edges
rr = np.amax(Rho_bord_m)
if rr > 0.0:
Rho_bord_m = Rho_bord_m / rr * 100.0
start_idx, end_idx = np.where(out)
segments = [[out[i, :], out[j, :]] for i in range(len(out))
for j in range(len(out))]
values = np.abs(Rho_bord_m)
lc = LineCollection(segments,
zorder=0,
norm=plt.Normalize(0, values.max()))
lc.set_array(Rho_bord_m.flatten())
# lc.set_linewidths(np.full(len(segments),0.5))
lc.set_edgecolor(np.full(len(segments), "black"))
lc.set_facecolor(np.full(len(segments), "black"))
lc.set_linewidths(0.02 * Rho_bord_m.flatten())
ax.add_collection(lc)
# fig.savefig('2D.png')
plt.show()
def plot_map(year, disease, color_parameter, std_num_records):
mpl.rcParams['font.size'] = 10.
mpl.rcParams['font.family'] = 'Comic Sans MS'
mpl.rcParams['axes.labelsize'] = 8.
mpl.rcParams['xtick.labelsize'] = 6.
mpl.rcParams['ytick.labelsize'] = 6.
fig = plt.figure(figsize=(11.7, 8.3))
plt.style.use('fivethirtyeight')
plt.subplots_adjust(left=0.05,
right=0.95,
top=0.90,
bottom=0.05,
wspace=0.15,
hspace=0.05)
ax = plt.subplot(111)
m = Basemap(resolution='i',
projection='merc',
llcrnrlon=-132,
llcrnrlat=22,
urcrnrlon=-60,
urcrnrlat=50)
m.etopo()
m.drawcountries()
for record, shape, num in zip(records, shapes, std_num_records):
lons, lats = zip(*shape.points)
data = np.array(m(lons, lats)).T
if len(shape.parts) == 1:
segs = [
data,
]
else:
segs = []
for i in range(1, len(shape.parts)):
index = shape.parts[i - 1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs, antialiaseds=(1, ))
color = colors.hsv_to_rgb((color_parameter, num, 1))
lines.set_facecolor(color)
lines.set_edgecolors('k')
lines.set_linewidth(0.1)
ax.add_collection(lines)
m.drawstates(linewidth=0.5)
m.drawcoastlines(linewidth=0.5)
plt.title('geo-heatmap for {} in {}'.format(disease, year))
plt.savefig('geo-heatmap for {} in {}.pdf'.format(disease, year))
def generate_boundary_artist(vertices, color):
if DIM == 2:
artist = LineCollection(vertices)
artist.set_color(color)
artist.set_linewidth(2.5)
elif DIM == 3:
artist = Poly3DCollection(vertices, linewidths=1)
# transparency alpha must be set BEFORE face/edge color
artist.set_alpha(0.5)
artist.set_facecolor(color)
artist.set_edgecolor('k')
else:
raise ValueError(
"%d dimensions needs special attention for plotting." % int(DIM))
return artist
def generate_boundary_artist(vertices, color):
ndim = len(next(iter(vertices[0])))
vertices = tuple(map(tuple, vertices))
if ndim == 2:
artist = LineCollection(vertices)
artist.set_color(color)
artist.set_linewidth(2.5)
elif ndim == 3:
artist = Poly3DCollection(vertices, linewidth=1)
# transparency alpha must be set BEFORE face/edge color
artist.set_alpha(0.5)
artist.set_facecolor(color)
artist.set_edgecolor('k')
else:
raise ValueError("{:d} dimensions needs special attention for plotting".format(ndim))
return artist
def plot_MDS(Data):
Fmax = max(Data.Rho)
Rho_bord_m = np.copy(Data.out_bord)
d_dis = np.zeros((Data.Nclus_m, Data.Nclus_m), dtype=float)
model = manifold.MDS(n_components=2,
n_jobs=None,
dissimilarity='precomputed')
for i in range(Data.Nclus_m):
for j in range(Data.Nclus_m):
d_dis[i][j] = Fmax - Rho_bord_m[i][j]
for i in range(Data.Nclus_m):
d_dis[i][i] = 0.
out = model.fit_transform(d_dis)
fig, ax = plt.subplots(nrows=1, ncols=1)
s = []
col = []
for i in range(Data.Nclus_m):
s.append(20. * np.sqrt(len(Data.clstruct_m[i])))
col.append(i)
plt.scatter(out[:, 0], out[:, 1], s=s, c=col)
for i in range(Data.Nclus_m):
ax.annotate(i, (out[i, 0], out[i, 1]))
# Add edges
rr = np.amax(Rho_bord_m)
if (rr > 0.):
Rho_bord_m = Rho_bord_m / rr * 100.
start_idx, end_idx = np.where(out)
segments = [[out[i, :], out[j, :]] for i in range(len(out))
for j in range(len(out))]
values = np.abs(Rho_bord_m)
lc = LineCollection(segments,
zorder=0,
norm=plt.Normalize(0, values.max()))
lc.set_array(Rho_bord_m.flatten())
# lc.set_linewidths(np.full(len(segments),0.5))
lc.set_edgecolor(np.full(len(segments), 'black'))
lc.set_facecolor(np.full(len(segments), 'black'))
lc.set_linewidths(0.02 * Rho_bord_m.flatten())
ax.add_collection(lc)
#fig.savefig('2D.png')
plt.show()
def plot_europe_map(country_weights, b=None, ax=None):
"""
Plot a map from shapefiles with coutnries colored by gamma. Inspired by Magnus.
"""
if ax == None:
ax = plt.subplot(111)
m = Basemap(llcrnrlon=-10., llcrnrlat=30., urcrnrlon=50., urcrnrlat=72.,
projection='lcc', lat_1=40., lat_2=60., lon_0=20.,
resolution='l', area_thresh=1000.,
rsphere=(6378137.00, 6356752.3142))
m.drawcoastlines(linewidth=0)
r = shapefile.Reader(r'settings/ne_10m_admin_0_countries/ne_10m_admin_0_countries')
all_shapes = r.shapes()
all_records = r.records()
shapes = []
records = []
for country in all_countries:
shapes.append(all_shapes[shapefile_index[country]])
records.append(all_records[shapefile_index[country]])
country_count = 0
for record, shape in zip(records, shapes):
lons, lats = zip(*shape.points)
data = np.array(m(lons, lats)).T
if len(shape.parts) == 1:
segs = [data, ]
else:
segs = []
for i in range(1, len(shape.parts)):
index = shape.parts[i - 1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs, antialiaseds=(1,))
lines.set_facecolor(cmap(country_weights[country_count]))
lines.set_edgecolors('k')
lines.set_linewidth(0.3)
ax.add_collection(lines)
country_count += 1
if b: plt.text(1.5e5, 4e6, r'$\beta = ' + str(b) + r'$', fontsize=12)
def plot_linestring_collection(ax,
geoms,
colors_or_values,
plot_values,
vmin=None,
vmax=None,
cmap=None,
linewidth=1.0,
**kwargs):
"""
Plots a collection of LineString and MultiLineString geometries to `ax`
Parameters
----------
ax : matplotlib.axes.Axes
where shapes will be plotted
geoms : a sequence of `N` LineStrings and/or MultiLineStrings (can be mixed)
colors_or_values : a sequence of `N` values or RGBA tuples
It should have 1:1 correspondence with the geometries (not their components).
plot_values : bool
If True, `colors_or_values` is interpreted as a list of values, and will
be mapped to colors using vmin/vmax/cmap (which become required).
Otherwise `colors_or_values` is interpreted as a list of colors.
Returns
-------
collection : matplotlib.collections.Collection that was plotted
"""
from matplotlib.collections import LineCollection
components, component_colors_or_values = _flatten_multi_geoms(
geoms, colors_or_values)
# LineCollection does not accept some kwargs.
if 'markersize' in kwargs:
del kwargs['markersize']
segments = [np.array(linestring)[:, :2] for linestring in components]
collection = LineCollection(segments, linewidth=linewidth, **kwargs)
if plot_values:
collection.set_array(np.array(component_colors_or_values))
collection.set_cmap(cmap)
collection.set_clim(vmin, vmax)
else:
# set_color magically sets the correct combination of facecolor and
# edgecolor, based on collection type.
collection.set_color(component_colors_or_values)
# If the user set facecolor and/or edgecolor explicitly, the previous
# call to set_color might have overridden it (remember, the 'color' may
# have come from plot_series, not from the user). The user should be
# able to override matplotlib's default behavior, by setting them again
# after set_color.
if 'facecolor' in kwargs:
collection.set_facecolor(kwargs['facecolor'])
if 'edgecolor' in kwargs:
collection.set_edgecolor(kwargs['edgecolor'])
ax.add_collection(collection, autolim=True)
ax.autoscale_view()
return collection
def plots_topography(dpa, ax_dendrogram, ax_project):
Nclus_m = np.max(dpa.labels_) + 1
cmap = plt.get_cmap('tab10', Nclus_m)
# Convert from border densities to distances
nd = int((Nclus_m * Nclus_m - Nclus_m) / 2)
Dis = np.empty(nd, dtype=float)
nl = 0
Fmax = max(dpa.densities_)
Rho_bord = np.zeros((Nclus_m, Nclus_m), dtype=float)
for row in dpa.topography_:
Rho_bord[row[0]][row[1]] = row[2]
Rho_bord[row[1]][row[0]] = row[2]
Dis[nl] = Fmax - row[2]
nl = nl + 1
# dendrogram representation
DD = sp.cluster.hierarchy.single(Dis)
dn = sp.cluster.hierarchy.dendrogram(DD,
color_threshold=0,
above_threshold_color='k',
ax=ax_dendrogram)
xlbls = ax_dendrogram.get_xmajorticklabels()
dorder = []
for lbl in xlbls:
dorder.append(int(lbl._text))
lbl.set_color(cmap(int(lbl._text)))
lbl.set_weight('bold')
# 2D projection representation of the topography
pop = np.zeros((Nclus_m), dtype=int)
for i in range(len(dpa.labels_)):
pop[dpa.labels_[i]] = pop[dpa.labels_[i]] + 1
d_dis = np.zeros((Nclus_m, Nclus_m), dtype=float)
model = manifold.MDS(n_components=2,
n_jobs=10,
dissimilarity='precomputed')
for i in range(Nclus_m):
for j in range(Nclus_m):
d_dis[i][j] = Fmax - Rho_bord[i][j]
for i in range(Nclus_m):
d_dis[i][i] = 0.
out = model.fit_transform(d_dis)
ax_project.yaxis.set_major_locator(plt.NullLocator())
ax_project.xaxis.set_major_locator(plt.NullLocator())
s = []
col = []
for i in range(Nclus_m):
s.append(20. * sqrt(pop[i]))
col.append(i)
ax_project.scatter(out[:, 0], out[:, 1], s=s, c=col, cmap=cmap)
#plt.colorbar(ticks=range(Nclus_m))
#plt.clim(-0.5, Nclus_m-0.5)
for i in range(Nclus_m):
ax_project.annotate(i, (out[i, 0], out[i, 1]))
for i in range(Nclus_m):
for j in range(Nclus_m):
d_dis[i][j] = Rho_bord[i][j]
rr = np.amax(d_dis)
if (rr > 0.):
d_dis = d_dis / rr * 100.
start_idx, end_idx = np.where(out)
segments = [[out[i, :], out[j, :]] for i in range(len(out))
for j in range(len(out))]
values = np.abs(d_dis)
lc = LineCollection(segments,
zorder=0,
norm=plt.Normalize(0, values.max()))
lc.set_array(d_dis.flatten())
lc.set_edgecolor(np.full(len(segments), 'black'))
lc.set_facecolor(np.full(len(segments), 'black'))
lc.set_linewidths(0.2 * Rho_bord.flatten())
ax_project.add_collection(lc)
return ax_dendrogram, ax_project
def plot_linestring_collection(ax, geoms, colors_or_values, plot_values,
vmin=None, vmax=None, cmap=None,
linewidth=1.0, **kwargs):
"""
Plots a collection of LineString and MultiLineString geometries to `ax`
Parameters
----------
ax : matplotlib.axes.Axes
where shapes will be plotted
geoms : a sequence of `N` LineStrings and/or MultiLineStrings (can be mixed)
colors_or_values : a sequence of `N` values or RGBA tuples
It should have 1:1 correspondence with the geometries (not their components).
plot_values : bool
If True, `colors_or_values` is interpreted as a list of values, and will
be mapped to colors using vmin/vmax/cmap (which become required).
Otherwise `colors_or_values` is interpreted as a list of colors.
Returns
-------
collection : matplotlib.collections.Collection that was plotted
"""
from matplotlib.collections import LineCollection
components, component_colors_or_values = _flatten_multi_geoms(
geoms, colors_or_values)
# LineCollection does not accept some kwargs.
if 'markersize' in kwargs:
del kwargs['markersize']
segments = [np.array(linestring)[:, :2] for linestring in components]
collection = LineCollection(segments,
linewidth=linewidth, **kwargs)
if plot_values:
collection.set_array(np.array(component_colors_or_values))
collection.set_cmap(cmap)
collection.set_clim(vmin, vmax)
else:
# set_color magically sets the correct combination of facecolor and
# edgecolor, based on collection type.
collection.set_color(component_colors_or_values)
# If the user set facecolor and/or edgecolor explicitly, the previous
# call to set_color might have overridden it (remember, the 'color' may
# have come from plot_series, not from the user). The user should be
# able to override matplotlib's default behavior, by setting them again
# after set_color.
if 'facecolor' in kwargs:
collection.set_facecolor(kwargs['facecolor'])
if 'edgecolor' in kwargs:
collection.set_edgecolor(kwargs['edgecolor'])
ax.add_collection(collection, autolim=True)
ax.autoscale_view()
return collection