def plot_2D(df, basemap=False, figsize=(10, 10), save=False, fname='2D'):
# specify line and direction query
linename = df['PublishedLineName'].unique()[0]
direction = df['DirectionRef'].unique()[0]
# read route shapefile
gdf = gpd.read_file("MTA_shp/bus_routes_nyc_aug2017.shp")
gdf.to_crs(epsg=4326, inplace=True)
# plot route
route_shp = gdf[gdf['route_dir'] == '%s_%s' % (linename, direction)]
route_shp.plot(color='red', figsize=figsize)
# save figure locally if specified
if save:
plt.savefig("%s.png" % (fname), dpi=300)
else:
pass
# plot on a basemap or not
# requires "mplleaflet" package
if basemap:
mlf.display()
else:
plt.show()
def draw_road_graph(graph, draw_edges=True, draw_nodes=True):
"""
Plots nodes and edges of a road graph, overlays the graph over the world map.
:param graph: the road graph that shall be plotted
:type graph: RoadGraphX
:param draw_edges: should plot edges
:param draw_nodes: should plot nodes
:return:
"""
pos = {}
for node in graph.graph.nodes():
pos[node] = [float(i) for i in node]
colors = []
for start, finish, edge_data in graph.graph.edges(data=True):
colors.append(edge_data['fun_factor'])
if draw_nodes:
nx.draw_networkx_nodes(graph.graph, pos=pos, node_size=10)
if draw_edges:
nx.draw_networkx_edges(graph.graph,
pos=pos,
edge_color=colors,
width=5)
return mplleaflet.display()
def leaflet_plot_stations(binsize, hashid):
df = pd.read_csv('data/C2A2_data/BinSize_d{}.csv'.format(binsize))
station_locations_by_hash = df[df['hash'] == hashid]
lons = station_locations_by_hash['LONGITUDE'].tolist()
lats = station_locations_by_hash['LATITUDE'].tolist()
plt.figure(figsize=(8, 8))
plt.scatter(lons, lats, c='r', alpha=0.7, s=200)
return mplleaflet.display()
def plot_stations(longitudes, latitudes, embedded = False):
if embedded:
plt.figure(figsize = (8, 8))
plt.scatter(longitudes, latitudes,
c = 'b',
marker = 'D',
alpha = 0.7,
s = 200)
return mplleaflet.display() if embedded else mplleaflet.show()
def plot_gpx(df):
'''
Takes in a gpx dataframe and plots on mplleaflet background
'''
fig, ax = plt.subplots(figsize=(5, 5))
df = df.dropna()
ax.plot(df['Longitude'],
df['Latitude'],
color='darkorange',
linewidth=5,
alpha=0.5)
sub = 10
return mplleaflet.display(fig=fig, tiles='esri_aerial')
def leaflet_plot_stations(binsize, hashid):
df = pd.read_csv('data/C2A2_data/BinSize_d{}.csv'.format(binsize))
station_locations_by_hash = df[df['hash'] == hashid]
lons = station_locations_by_hash['LONGITUDE'].tolist()
lats = station_locations_by_hash['LATITUDE'].tolist()
plt.figure(figsize=(8,8))
plt.scatter(lons, lats, c='r', alpha=0.7, s=200)
return mplleaflet.display()
def test_plot_leaflet(location= ''):
lon = -84.0156
lat = 41.9164
plt.figure(figsize= (10, 10))
# print(ml.data['latitude'][:10])
# print(ml.data['longitude'][:10])
plt.scatter(ml.data['longitude'][:600], ml.data['latitude'][:600], c= 'r', alpha= 0.7, s =1)
# plt.hist2d(ml.data['longitude'][:100], ml.data['latitude'][:100],
# bins= 50)
# plt.scatter(lon, lat, c= 'r', alpha= 0.7, s =5)
# img_to_save = os.path.join(os.getcwd(), 'static', 'test1.png')
# fig.savefig(img_to_save, format= 'png')
# encoded = base64.b64encode(tmpfile.getvalue())
return mplleaflet.display()
def map(self,
map_type='osm',
embed=False,
ax=None,
size=(10, 10),
plot='plot',
**kwargs):
"""Plot trajectory on map.
Parameters
----------
- map_type can be e.g. osm, esri_aerial, esri_worldtopo, etc. see:
https://github.com/jwass/mplleaflet/blob/master/mplleaflet/maptiles.py
- embed: if True, embed plot in Jupyter. If False (default), open in
browser.
- ax: if not None, use provided matplotlib axes.
- size: when embedded, size of the figure.
- plot: 'plot' or 'scatter'
- **kwargs: any plt.plot or plt.scatter keyword arguments
"""
if ax is None:
fig, ax = plt.subplots(figsize=size)
else:
fig = ax.figure
if plot == 'plot':
ax.plot(self.longitude, self.latitude, '.-r', **kwargs)
elif plot == 'scatter':
ax.scatter(self.longitude, self.latitude, **kwargs)
else:
raise ValueError(f'Unrecognized plot type: {plot}')
parameters = {'fig': fig, 'tiles': map_type}
if embed:
leaflet = mplleaflet.display(**parameters)
else:
leaflet = mplleaflet.show(**parameters)
return leaflet
def leaflet_plot_stations(binsize, hashid):
df = pd.read_csv('data/C2A2_data/BinSize_d{}.csv'.format(binsize))
#print (df.head())
#Index(['ID', 'LATITUDE', 'LONGITUDE', 'ELEVATION', 'STATE', 'NAME', 'GSNFLAG',
# 'HCNFLAG', 'WMOID', 'x', 'y', 'x_group', 'y_group', 'xy_group', 'hash'],
# dtype='object')
station_locations_by_hash = df[df['hash'] == hashid]
lons = station_locations_by_hash['LONGITUDE'].tolist()
lats = station_locations_by_hash['LATITUDE'].tolist()
plt.figure(figsize=(8,8))
plt.scatter(lons, lats, c='r', alpha=0.7, s=200)
return mplleaflet.display()
def plot_geodata(self, embed=False):
"""Plot users on the map.
Args:
embed: True if map should be drawn in IPython Notebook cell.
"""
counter = Counter(u.city_id for u in self.get_users())
data = []
for city_id, count in counter.items():
if np.isnan(city_id):
continue
name, lat, lon = self._cities[city_id]
if (lat, lon) != (None, None):
data.append((lon, lat, count * 3))
xs, ys, sizes = list(zip(*data))
plt.scatter(xs, ys, s=sizes)
if embed:
return mplleaflet.display()
else:
return mplleaflet.show()
def choropleth(df, grid_size,
bounds=None,
latitude="latitude",
longitude="longitude",
tiles='cartodb_positron',
vmax=10,
cmap="viridis",
column="latitude",
crs=None,
figsize=None):
if figsize is None:
figsize = [10,10]
fig = plt.figure(figsize=figsize)
grid = create_grid(df, grid_size, bounds, latitude, longitude, column, crs, vmax)
if len(grid) > 0:
for idx, r in grid.iterrows():
color = plt.get_cmap(cmap).colors[int(r.color) if not np.isnan(r.color) else 0]
plt.gca().add_patch(PolygonPatch(r.geometry, alpha=0.5,color=color, zorder=2))
return mplleaflet.display(fig, tiles=tiles)
def draw_road_graph(graph, draw_edges=True, draw_nodes=True):
"""
Plots nodes and edges of a road graph, overlays the graph over the world map.
:param graph: the road graph that shall be plotted
:type graph: RoadGraphX
:param draw_edges: should plot edges
:param draw_nodes: should plot nodes
:return:
"""
pos = {}
for node in graph.graph.nodes():
pos[node] = [float(i) for i in node]
colors = []
for start, finish, edge_data in graph.graph.edges(data=True):
colors.append(edge_data['fun_factor'])
if draw_nodes:
nx.draw_networkx_nodes(graph.graph, pos=pos, node_size=10)
if draw_edges:
nx.draw_networkx_edges(graph.graph, pos=pos, edge_color=colors, width=5)
return mplleaflet.display()
V = (f * vamp *
np.cos(v + thours[k] * omega + u - vpha * np.pi / 180)).sum(axis=1)
w = units['knots'] * (U + 1j * V)
wf = np.NaN * np.ones_like(lonf, dtype=w.dtype)
wf[inbox] = w
# FIXME: Cannot use masked arrays and tricontour!
# wf = ma.masked_invalid(wf)
# cs = ax.tricontour(lonf, latf, trif, np.abs(wf).filled(fill_value=0))
# fig.colorbar(cs)
q = plt.quiver(lon, lat, U, V, scale=vscale, color='white')
plt.axis(bbox) # Vineyard sound 2.
#q.set_title('{}'.format(glocals[k]))
mplleaflet.display(fig=fig, tiles='esri_aerial')
# In[ ]:
js = mplleaflet.fig_to_geojson(fig=fig)
with open("test.json", "w") as outfile:
json.dump(js, outfile, indent=4)
# In[ ]:
get_ipython().magic(u'matplotlib inline')
import matplotlib.pyplot as plt
from JSAnimation import IPython_display
from matplotlib.animation import FuncAnimation
[0.76, 0.15, 0.05, 0.38]])
# weight = np.around(np.random.uniform(low=0.01, high=0.99, size=(5, 4)), decimals=2)
R = 1
Alpha = 0.1
c = 0.1
Et = 18
E0 = 1
model = som.train_SOM(weight, dataset.values, Alpha, c, R, Et, E0)
klaster = som.test_SOM(model, dataset.values)
klaster = np.ravel(klaster)
sil = silhouette_score(dataset.values, klaster, metric='euclidean')
print(sil)
dataset['klaster'] = klaster
fig, ax = plt.subplots(figsize=(17, 10))
print(set(dataset['klaster'].values))
plot = ax.scatter(data['longitude'],
data['latitude'],
s=dataset['confidence'] * 100,
Alpha=0.5,
c=dataset['klaster'],
cmap='jet')
plt.colorbar(plot)
plt.show()
mplleaflet.display(fig=fig)
# unique, counts = np.unique(klaster, return_counts=True)
# print(dict(zip(unique, counts)))
# print(dataset.groupby(['klaster']).mean())
lonc = cube.mesh.face_coordinates[:,0]
latc = cube.mesh.face_coordinates[:,1]
# In[16]:
subsample = 5
# find velocity points in bounding box
ind = np.argwhere((lonc >= bbox[0]) & (lonc <= bbox[1]) & (latc >= bbox[2]) & (latc <= bbox[3]))
np.random.shuffle(ind)
Nvec = int(len(ind) / subsample)
idv = ind[:Nvec]
# In[17]:
import mplleaflet
fig = plt.figure(figsize=(10,5))
vscale = 30 # smaller makes arrows bigger
q = plt.quiver(lonc[idv], latc[idv], u[idv], v[idv], scale=vscale, color='white')
plt.axis(bbox)
#q.set_title('{}'.format(glocals[k]))
mplleaflet.display(fig=fig, tiles='esri_aerial')
# In[ ]:
plt.title("roomtype")
plt.xlabel("id")
plt.ylabel("count")
sns.plt.show()
# In[ ]:
# In[42]:
proprytypeDF = edadata.groupby('property_type').id.count()
proprytypeDF.hist()
# In[47]:
roomPrptyDF = edadata.groupby(['property_type', 'room_type']).price.mean()
roomPrptyDF.hist()
# In[10]:
from pandas.tools.plotting import scatter_matrix
scatter_matrix(edadata, alpha=0.2, figsize=(10, 10), diagonal='kde')
# In[ ]:
import mplleaflet
sample = edadata.sample(1000)
plt.scatter(sample['longitude'], sample['latitude'])
mplleaflet.display()
overlay = nx.read_gml(overlay_path).to_undirected()
mapping = {}
for ii, node in enumerate(underlay.nodes()):
mapping[str(ii)] = node
overlay = nx.relabel_nodes(overlay, mapping).to_undirected()
fig, ax = plt.subplots()
nx.draw_networkx_nodes(overlay,
pos=pos_dict,
node_size=10,
node_color='red',
edge_color='k',
alpha=.5,
with_labels=True)
nx.draw_networkx_edges(overlay,
pos=pos_dict,
edge_color='blue',
alpha=1,
width=5.0)
nx.draw_networkx_labels(overlay, pos=pos_dict, label_pos=10.3)
mplleaflet.display(fig=ax.figure)
mplleaflet.save_html(fig=ax.figure,
fileobj=os.path.join(
"results", args.underlay,
"{}.html".format(args.architecture)))
# In[12]:
plt.figure(figsize=(16,6))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent(bbox)
ax.coastlines(resolution='10m')
plt.tricontourf(triang, zcube.data, levels=levs)
plt.colorbar(fraction=0.046, pad=0.04)
plt.tricontour(triang, zcube.data, colors='k',levels=levs)
tstr = tvar.units.num2date(tvar.points[itime])
gl = ax.gridlines(draw_labels=True)
gl.xlabels_top = False
gl.ylabels_right = False
plt.title('%s: %s: %s' % (var,tstr,zcube.attributes['title']));
# In[13]:
import mplleaflet
fig = plt.figure(figsize=(6,8))
plt.tricontourf(triang, zcube.data, levels=levs)
plt.axis(bbox) # Vineyard sound 2.
mplleaflet.display()
# In[ ]:
m = folium.Map(location=[53.235048, -1.421629], zoom_start=13)
for i in range(0, df_links.shape[0]):
link = df_links.iloc[i]
lons = link['the_geom'].coords.xy[0] #coordinates in latlon
lats = link['the_geom'].coords.xy[1]
#gid = int(link.gid)
xs = []
ys = []
n_segments = len(lons)
for j in range(0, n_segments):
(x, y) = pyproj.transform(
wgs84, bng, lons[j], lats[j]
) #project to BNG -- uses nonISO lonlat convention #TODO faster to cache this!
xs.append(x)
ys.append(y)
color = 'r'
folium.PolyLine(locations=[ys, xs]).add_to(m)
m.save("chesterfield5.html")
plotSelectedODRoutes(con, cur)
mplleaflet.display(fig=ax.figure, crs=df.crs, tiles="cartodb_positron")
# Playing with folium
import folium
map_osm = folium.Map(location=[53.235048, -1.421629],
zoom_start=13,
tiles="cartodbpositron")
map_osm.save("chesterfield.html")
map_osm1 = folium.Map(location=[53.235048, -1.421629], zoom_start=13)
map_osm1.save("chesterfield1.html")
