def test_from_wkb():
expected = pygeos.points(1, 1)
actual = pygeos.from_wkb(POINT11_WKB)
assert pygeos.equals(actual, expected)
def test_from_wkb_none():
# None propagates
assert pygeos.from_wkb(None) is None
def test_from_wkb_exceptions():
with pytest.raises(TypeError, match="Expected bytes, got int"):
pygeos.from_wkb(1)
with pytest.raises(pygeos.GEOSException):
pygeos.from_wkb(b"\x01\x01\x00\x00\x00\x00")
def _convex_hull_all_single(self):
return pg.from_wkb(convex_hull_all(self._active_geometry))
def test_to_wkb_point_empty_srid():
expected = pygeos.set_srid(empty_point, 4236)
wkb = pygeos.to_wkb(expected, include_srid=True)
actual = pygeos.from_wkb(wkb)
assert pygeos.get_srid(actual) == 4236
def get_coordinates(arr):
return pg.get_coordinates(pg.from_wkb(arr))
def time_read_from_wkb(self):
pygeos.from_wkb(self.to_read_wkb)
def test_from_wkb_empty(wkt):
wkb = pygeos.to_wkb(pygeos.Geometry(wkt))
geom = pygeos.from_wkb(wkb)
assert pygeos.is_geometry(geom).all()
assert pygeos.is_empty(geom).all()
assert pygeos.to_wkb(geom) == wkb
def constructive(arr, operation, *args, **kwargs):
if operation == 'boundary':
geometries = pg.boundary(pg.from_wkb(arr), **kwargs)
elif operation == 'buffer':
geometries = pg.buffer(pg.from_wkb(arr), *args, **kwargs)
elif operation == 'build_area':
geometries = pg.build_area(pg.from_wkb(arr), **kwargs)
elif operation == 'centroid':
geometries = pg.centroid(pg.from_wkb(arr), **kwargs)
elif operation == 'clip_by_rect':
geometries = pg.clip_by_rect(pg.from_wkb(arr), *args, **kwargs)
elif operation == 'convex_hull':
geometries = pg.convex_hull(pg.from_wkb(arr), **kwargs)
elif operation == 'delaunay_triangles':
geometries = pg.delaunay_triangles(pg.from_wkb(arr), **kwargs)
elif operation == 'envelope':
geometries = pg.envelope(pg.from_wkb(arr), **kwargs)
elif operation == 'extract_unique_points':
geometries = pg.extract_unique_points(pg.from_wkb(arr), **kwargs)
elif operation == 'make_valid':
geometries = pg.make_valid(pg.from_wkb(arr), **kwargs)
elif operation == 'normalize':
geometries = pg.normalize(pg.from_wkb(arr), **kwargs)
elif operation == 'offset_curve':
geometries = pg.offset_curve(pg.from_wkb(arr), *args, **kwargs)
elif operation == 'point_on_surface':
geometries = pg.point_on_surface(pg.from_wkb(arr), **kwargs)
elif operation == 'reverse':
geometries = pg.reverse(pg.from_wkb(arr), **kwargs)
elif operation == 'simplify':
geometries = pg.simplify(pg.from_wkb(arr), *args, **kwargs)
elif operation == 'snap':
geometries = pg.snap(pg.from_wkb(arr), *args, **kwargs)
elif operation == 'voronoi_polygons':
geometries = pg.voronoi_polygons(pg.from_wkb(arr), **kwargs)
else:
warnings.warn('Operation %s not supported.' % (operation))
return None
return pg.to_wkb(geometries)
def from_wkb(arr):
return pg.from_wkb(arr)
def within(arr, geometry):
geometry = pg.from_wkb(geometry)
return pg.within(from_wkb(arr), geometry)
def convex_hull_all(arr):
if isinstance(arr, LazyObj):
arr = arr.values()
points = pg.union_all(pg.extract_unique_points(pg.from_wkb(arr)))
return pg.to_wkb(pg.convex_hull(points))
def total_bounds(arr):
if isinstance(arr, LazyObj):
arr = arr.values()
return pg.to_wkb(pg.box(*pg.total_bounds(pg.from_wkb(arr))))
def test_from_wkb_on_invalid_unsupported_option():
with pytest.raises(ValueError, match="not a valid option"):
pygeos.from_wkb(b"\x01\x01\x00\x00\x00\x00", on_invalid="unsupported_option")
def plot_percolation_full():
# set data paths to results
data_path_perc = r'C:\Data\percolation_results_random_attack_regular'
data_path_met= r'C:\Data\percolation_metrics'
data_path_net = r'C:\Data\percolation_networks'
# file to get full country names
glob_info = pd.read_excel(r'C:\Projects\trails\data\global_information.xlsx')
# get all files from data paths
perc_files = os.listdir(data_path_perc)
met_files = os.listdir(data_path_met)
net_files = os.listdir(data_path_net)
# save the failed ones, so we can check them later
save_failed = []
# set x-axis
x = np.arange(1,100,1)
# create figure
fig, axs = plt.subplots(3,2,figsize=(15,20))
for iter1,file in enumerate(perc_files):
# get name of percolation analysis
net_name = file[:5]
try:
if os.path.isfile(os.path.join('..','..','figures','{}_results.png'.format(net_name))):
print(net_name+" already finished!")
continue
# load metrics
df_metrics = pd.read_csv(os.path.join(data_path_met,[x for x in met_files if file[:5] in x][0]))
# load percolation results
df = pd.read_csv(os.path.join(data_path_perc,file),index_col=[0])
df.frac_counter = df.frac_counter*100
# remove all results where it is pretty much done, so we can zoom onto the interesting part
df = df.loc[df.pct_isolated < 99.5]
max_frac_counter = df.frac_counter.max()
df_isolated = pd.DataFrame([df.frac_counter.values,df.pct_isolated.values,df.pct_unaffected.values,df.pct_delayed.values]).T
df_isolated.columns = ['frac_counter','pct_isolated','pct_unaffected','pct_delayed']
df_sloss = pd.DataFrame([df.frac_counter.values,df.total_pct_surplus_loss_e1.values,df.total_pct_surplus_loss_e2.values]).T
df_sloss.columns = ['frac_counter','total_pct_surplus_loss_e1','total_pct_surplus_loss_e2']
# load network
network = pd.read_feather(os.path.join(data_path_net,[x for x in net_files if file[:5] in x][0]))
network.geometry = pygeos.from_wkb(network.geometry)
network = gpd.GeoDataFrame(network)
network.crs = 4326
# get mean,max,min values
y_unaff = df_isolated.groupby('frac_counter').max()['pct_unaffected'].values
y_del = df_isolated.groupby('frac_counter').mean()['pct_delayed'].values
y_iso = df_isolated.groupby('frac_counter').min()['pct_isolated'].values
mainnet = 'yes'
if net_name[4] != '0':
mainnet = 'no, #{}'.format(net_name)
if iter1 > 0:
for iter2,ax in enumerate(axs.flatten()):
ax.clear()
#and plot
for iter2,ax in enumerate(axs.flatten()):
if iter2 == 0:
sns.boxplot(x="frac_counter", y="pct_isolated", data=df_isolated,ax=ax,fliersize=0,order=np.arange(max_frac_counter+2),palette="rocket_r",linewidth=0.5)
ax.set_ylabel('Percentage of isolated trips', fontsize=13)
ax.set_xlabel('Percentage of network removed', fontsize=13)
ax.set_title('Boxplots of isolated trips', fontsize=15, fontweight='bold')
ax.set_xlim([0, max_frac_counter+2])
ax.set_xticks(np.arange(0, max_frac_counter+2, 5))
ax.set_ylim([0, 102])
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
elif iter2 == 1:
network.plot(column='highway',legend=True,ax=ax)
try:
cx.add_basemap(ax, crs=network.crs.to_string(),alpha=0.5)
except:
cx.add_basemap(ax, crs=network.crs.to_string(),alpha=0.5,zoom=10)
ax.set_title('Road network', fontsize=15, fontweight='bold')
elif iter2 == 2:
sns.boxplot(x="frac_counter", y="pct_unaffected", data=df_isolated,ax=ax,fliersize=0,order=np.arange(max_frac_counter+2),palette="rocket_r",linewidth=0.5)
ax.set_ylabel('Percentage of unaffected trips', fontsize=13)
ax.set_xlabel('Percentage of network removed', fontsize=13)
ax.set_title('Boxplots of unaffected trips', fontsize=15, fontweight='bold')
ax.set_xlim([0, max_frac_counter+2])
ax.set_xticks(np.arange(0, max_frac_counter+2, 5))
ax.set_ylim([0, 102])
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
elif iter2 == 3:
sns.boxplot(x="frac_counter", y="pct_delayed", data=df_isolated,ax=ax,fliersize=0,order=np.arange(max_frac_counter+2),palette="rocket_r",linewidth=0.5)
ax.set_ylabel('Percentage of delayed trips', fontsize=13)
ax.set_xlabel('Percentage of network removed', fontsize=13)
ax.set_title('Boxplots of delayed trips', fontsize=15, fontweight='bold')
ax.set_xlim([0, max_frac_counter+2])
ax.set_xticks(np.arange(0, max_frac_counter+2, 5))
ax.set_ylim([0, 102])
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
elif iter2 == 4:
sns.boxplot(x="frac_counter", y="total_pct_surplus_loss_e1", data=df_sloss,ax=ax,fliersize=0,order=np.arange(max_frac_counter+2),palette="rocket_r",linewidth=0.5)
ax.set_ylabel('Percentage of surpluss loss (e1)', fontsize=13)
ax.set_xlabel('Percentage of network removed', fontsize=13)
ax.set_xlim([0, max_frac_counter+2])
ax.set_xticks(np.arange(0, max_frac_counter+2, 5))
ax.set_ylim([0, 102])
ax.set_title('Boxplots of surpluss loss e1', fontsize=15, fontweight='bold')
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
elif iter2 == 5:
sns.boxplot(x="frac_counter", y="total_pct_surplus_loss_e2", data=df_sloss,ax=ax,fliersize=0,order=np.arange(max_frac_counter+2),palette="rocket_r",linewidth=0.5)
ax.set_ylabel('Percentage of surpluss loss (e2)', fontsize=13)
ax.set_xlabel('Percentage of network removed', fontsize=13)
ax.set_xlim([0, max_frac_counter+2])
ax.set_xticks(np.arange(0, max_frac_counter+2, 5))
ax.set_ylim([0, 102])
ax.set_title('Boxplots of surpluss loss e2', fontsize=15, fontweight='bold')
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.text(max_frac_counter*0.5, 10.5,'Main Network: {} \nEdges: {} \nDensity: {} \nClique_No: {} \nAssortativity: {} \nDiameter: {} \nMax_Degree: {}'.format(
mainnet,
df_metrics.Edge_No.values[0],
np.round(df_metrics.Density.values[0],7),
df_metrics.Clique_No.values[0],
np.round(df_metrics.Assortativity.values[0],7),
df_metrics.Diameter.values[0],
df_metrics.Max_Degree.values[0],
), fontsize=15)
if net_name[4] == '0':
if net_name[:3] in ['HKG','TWN','MNP','MAC','MHL','GUM']:
name_dict_errors = {'HKG': "Hong Kong",
'TWN': "Taiwan",
'MNP': "Northern Mariana Islands",
'MAC': "Macau",
'MHL': "Marshall Islands",
'GUM' : "Guam"
}
plt.suptitle('Main network of {}'.format(name_dict_errors[net_name[:3]]), fontsize=20, fontweight='bold',y=0.92)
else:
plt.suptitle('Main network of {}'.format(dict(zip(glob_info.ISO_3digit,glob_info.Country))[net_name[:3]]), fontsize=20, fontweight='bold',y=0.92)
else:
plt.suptitle('Subnetwork of {}'.format(dict(zip(glob_info.ISO_3digit,glob_info.Country))[net_name[:3]]), fontsize=20, fontweight='bold',y=0.92)
plt.savefig(os.path.join('..','..','figures','{}_results.png'.format(net_name)))
except Exception as e:
print(net_name+" failed because of {}".format(e))
print(traceback.format_exc())
save_failed.append(net_name)
print(save_failed)
def test_from_wkb_all_types(geom, use_hex, byte_order):
wkb = pygeos.to_wkb(geom, hex=use_hex, byte_order=byte_order)
actual = pygeos.from_wkb(wkb)
assert pygeos.equals(actual, geom)
def main():
# set data paths to results
data_path_perc = r'C:\Data\percolation_results'
data_path_met= r'C:\Data\percolation_metrics'
data_path_net = r'C:\Data\percolation_networks'
# file to get full country names
glob_info = pd.read_excel(r'C:\Projects\trails\data\global_information.xlsx')
# get all files from data paths
perc_files = os.listdir(data_path_perc)
met_files = os.listdir(data_path_met)
net_files = os.listdir(data_path_net)
# save the failed ones, so we can check them later
save_failed = []
# set x-axis
x = np.arange(1,100,1)
# create figure
fig, axs = plt.subplots(2,2,figsize=(15,15))
for iter1,file in enumerate(perc_files):
# get name of percolation analysis
net_name = file[:5]
if os.path.isfile(os.path.join('..','..','figures','{}_results.png'.format(net_name))):
print(net_name+" already finished!")
continue
try:
# load metrics
df_metrics = pd.read_csv(os.path.join(data_path_met,[x for x in met_files if file[:5] in x][0]))
# load percolation results
df = pd.read_csv(os.path.join(data_path_perc,file),index_col=[0])
df_isolated = pd.DataFrame([df.frac_counter.values,df.pct_isolated.values]).T
df_isolated.columns = ['frac_counter','pct_isolated']
df_isolated = df_isolated*100
df_sloss = pd.DataFrame([df.frac_counter.values,df.total_pct_surplus_loss_e1.values]).T
df_sloss.columns = ['frac_counter','total_pct_surplus_loss_e1']
df_sloss.total_pct_surplus_loss_e1 = df_sloss.total_pct_surplus_loss_e1
df_sloss = df_sloss*100
# load network
network = pd.read_feather(os.path.join(data_path_net,[x for x in net_files if file[:5] in x][0]))
network.geometry = pygeos.from_wkb(network.geometry)
network = gpd.GeoDataFrame(network)
network.crs = 4326
# get mean,max,min values
max_iso = df_isolated.groupby('frac_counter').max()['pct_isolated'].values
y_iso = df_isolated.groupby('frac_counter').mean()['pct_isolated'].values
min_iso = df_isolated.groupby('frac_counter').min()['pct_isolated'].values
mainnet = 'yes'
if net_name[4] != '0':
mainnet = 'no, #{}'.format(net_name)
if iter1 > 0:
for iter2,ax in enumerate(axs.flatten()):
ax.clear()
#and plot
for iter2,ax in enumerate(axs.flatten()):
if iter2 == 0:
ax.plot(df_isolated.frac_counter.unique(), y_iso, 'r-')
ax.fill_between(df_isolated.frac_counter.unique(), min_iso, max_iso)
ax.text(50, 0.2,'Main Network: {} \nEdges: {} \nDensity: {} \nClique_No: {} \nAssortativity: {} \nDiameter: {} \nMax_Degree: {}'.format(
mainnet,
df_metrics.Edge_No.values[0],
np.round(df_metrics.Density.values[0],7),
df_metrics.Clique_No.values[0],
np.round(df_metrics.Assortativity.values[0],7),
df_metrics.Diameter.values[0],
df_metrics.Max_Degree.values[0],
), fontsize=15)
ax.set_ylabel('Percentage of isolated trips', fontsize=13, fontweight='bold')
ax.set_xlabel('Percentage of network removed', fontsize=13, fontweight='bold')
ax.set_xticks(np.arange(0, 100+2, 10))
ax.set_title('Max,mean and min isolated trips', fontsize=15, fontweight='bold')
elif iter2 == 1:
network.plot(column='highway',legend=True,ax=ax)
cx.add_basemap(ax, crs=network.crs.to_string(),alpha=0.5)
ax.set_title('Road network', fontsize=15, fontweight='bold')
elif iter2 == 2:
sns.boxplot(x="frac_counter", y="pct_isolated", data=df_isolated,ax=ax,fliersize=0)
ax.set_ylabel('Percentage of isolated trips', fontsize=13, fontweight='bold')
ax.set_xlabel('Percentage of network removed', fontsize=13, fontweight='bold')
ax.set_xticks(np.arange(-1, 100+2, 10))
ax.set_title('Boxplots of isolated trips', fontsize=15, fontweight='bold')
elif iter2 == 3:
#ax.plot(x, y_sloss, 'r-')
#ax.fill_between(x, min_sloss, max_sloss)
sns.boxplot(x="frac_counter", y="total_pct_surplus_loss_e1", data=df_sloss,ax=ax,fliersize=0)
ax.set_ylabel('Percentage of surpluss loss', fontsize=13, fontweight='bold')
ax.set_xlabel('Percentage of network removed', fontsize=13, fontweight='bold')
ax.set_xticks(np.arange(-1, 100+2, 10))
ax.set_title('Boxplots of surpluss loss e1', fontsize=15, fontweight='bold')
plt.suptitle(dict(zip(glob_info.ISO_3digit,glob_info.Country))[net_name[:3]], fontsize=20, fontweight='bold')
plt.savefig(os.path.join('..','..','figures','{}_results.png'.format(net_name)))
#plt.pause(5)
except Exception as e:
print(net_name+" failed because of {}".format(e))
save_failed.append(net_name)
print(save_failed)
def test_from_wkb_point_empty(wkb, expected_type):
geom = pygeos.from_wkb(wkb)
# POINT (nan nan) transforms to an empty point
# Note that the dimensionality (2D/3D) is GEOS-version dependent
assert pygeos.is_empty(geom)
assert pygeos.get_type_id(geom) == expected_type
def __setstate__(self, state):
geoms = pygeos.from_wkb(state[0])
self._crs = state[1]
self.data = geoms
self.base = None
def shapely_wkb_loads_mock(wkb):
geom = pygeos.from_wkb(wkb)
return ShapelyGeometryMock(geom)
def convex_hull(arr):
return pg.to_wkb(pg.convex_hull(pg.from_wkb(arr)))
