def define_terrain_defunct(width, length):
"""
"""
n = math.ceil(math.log(width, 2))
data = mpd(n)
dem = data[0:width, 0:length]
x, y = terrain_min(dem)
mask = np.where(dem < dem.mean(), 1, 0)
flood = floodFill(x, y, mask)
out_number = str(np.random.randint(low=100000, high=999999))
out_raster = 'temp/' + out_number
out_shp = 'temp/' + out_number + '.shp'
write_array(flood, out_raster)
ds = gdal.Open(out_raster + '.tif')
band = ds.GetRasterBand(1)
drv = ogr.GetDriverByName("ESRI Shapefile")
out = drv.CreateDataSource(out_shp)
out_layer = out.CreateLayer('terrains', srs=None)
fd = ogr.FieldDefn("DN", ogr.OFTInteger)
out_layer.CreateField(fd)
gdal.Polygonize(band, None, out_layer, 0, [], callback=None)
out = None
gdf = gpd.read_file(out_shp)
return band
def load_geojson_state(df_uri):
df = gpd.read_file(df_uri)
return df
from shapely import geometry
from geopandas import gpd
import urllib
import matplotlib.pyplot as plt
# from main import *
from PIL import Image
route = gpd.read_file( "../output/for_Presentation_留仙洞.geojson" )
points = gpd.GeoDataFrame(route[['RID']].merge(DB_panos, on='RID'))
# points.query( "DIR != 0" ).reset_index().to_file( '../output/points_liuxiandong_presetation.geojson', driver="GeoJSON" )
points.query( "DIR != 0", inplace=True )
points.info()
def draw_polygon_by_bbox(bbox=[113.93306,22.57437, 113.9383, 22.58037]):
# extract the roads of intrest
from shapely.geometry import LineString
coords = [bbox[:2], [bbox[0], bbox[3]],
bbox[2:], [bbox[2], bbox[1]], bbox[:2]]
area = gpd.GeoDataFrame( [{'name': 'presetation area', 'geometry':LineString(coords)}] )
return area
def get_staticimage(id, heading, folder=pano_dir):
file_name = f"{folder}/{id}.jpg"
if os.path.exists(file_name):
data['complaint_date'] = pd.to_datetime(data['complaint_date'], format='%Y%m%dT%H:%M:%S.%f')
data['complaint_year'] = data.complaint_date.dt.year
data = data.drop(['age_of_complainant','assignment','case_type','complaint_month','complaint_day','complaint_hour',
'current_status','finding_code','police_shooting','sex_of_complainant'],axis=1)
return data
def create_shapefile(fileloc):
"""
Reads the shapefile at the filelocation and returns it as a geopandas shapefile
:param fileloc: file path of shapefile
:return: Geopandas shapefile
"""
logger.info('Importing shapefile')
shapefile = gpd.read_file(fileloc)
shapefile['beat_num'] = pd.to_numeric(shapefile['beat_num'])
print(type(shapefile))
return shapefile
def add_race(shapefile, data):
"""
Appends aggregated race tabulations of police complaints to a beats-level dataframe
:param shapefile: Basic beats Shapefile
:param data: Cleaned and processed police complaints dataset
:return: Police beats shapefile with aggregated race dataset appended
"""
logger.info('Add race aggregates to shapefile')
race = data[['beat','race_of_complainant']].dropna()
drv = ogr.GetDriverByName("ESRI Shapefile")
srs = osr.SpatialReference()
srs.ImportFromWkt(ds.GetProjectionRef())
out = drv.CreateDataSource('test.shp')
out_layer = out.CreateLayer('new', srs=None)
fd = ogr.FieldDefn("DN", ogr.OFTInteger)
out_layer.CreateField(fd)
gdal.Polygonize(band, None, out_layer, 0, [], callback=None)
out = None
gdf = gpd.read_file('test.shp')
# set up plot
fig1 = plt.figure(1)
ax1 = fig1.add_subplot(111, projection='3d')
fig2, axes = plt.subplots(1, 2, sharex=True, sharey=True)
ax2, ax3 = axes
ax1.plot_surface(X,
Y,
data,
cmap=cm.coolwarm,
rstride=1,
cstride=1,
linewidth=0)
def test_classify():
# data
link_to_data = examples.get_path('columbus.shp')
gdf = gpd.read_file(link_to_data)
x = gdf['HOVAL'].values
# box_plot
a = classify(x, 'box_plot')
b = mapclassify.BoxPlot(x)
_assertions(a, b)
# EqualInterval
a = classify(x, "EqualInterval", k=3)
b = mapclassify.EqualInterval(x, k=3)
_assertions(a, b)
# FisherJenks
a = classify(x, "FisherJenks", k=3)
b = mapclassify.FisherJenks(x, k=3)
_assertions(a, b)
a= classify(x, "FisherJenksSampled", k=3, pct_sampled=0.5, truncate=False)
b = mapclassify.FisherJenksSampled(x, k=3, pct=0.5,truncate=False)
_assertions(a, b)
# headtail_breaks
a = classify(x, 'headtail_breaks')
b = mapclassify.HeadTailBreaks(x)
_assertions(a, b)
# quantiles
a = classify(x, 'quantiles',k=3)
b = mapclassify.Quantiles(x, k=3)
_assertions(a, b)
# percentiles
a = classify(x, 'percentiles', pct=[25,50,75,100])
b = mapclassify.Percentiles(x, pct=[25,50,75,100])
_assertions(a, b)
#JenksCaspall
a = classify(x, 'JenksCaspall', k=3)
b = mapclassify.JenksCaspall(x, k=3)
_assertions(a, b)
a = classify(x, 'JenksCaspallForced', k=3)
b = mapclassify.JenksCaspallForced(x, k=3)
_assertions(a, b)
a = classify(x, 'JenksCaspallSampled', pct_sampled=0.5)
b = mapclassify.JenksCaspallSampled(x, pct=0.5)
_assertions(a, b)
# natural_breaks, max_p_classifier
a = classify(x, 'natural_breaks')
b = mapclassify.NaturalBreaks(x)
_assertions(a, b)
a = classify(x, 'max_p', k=3, initial=50)
b = mapclassify.MaxP(x, k=3, initial=50)
_assertions(a, b)
# std_mean
a = classify(x, 'std_mean', multiples=[-1,-0.5,0.5,1])
b = mapclassify.StdMean(x, multiples=[-1,-0.5,0.5,1])
_assertions(a, b)
# user_defined
a = classify(x, 'user_defined', bins=[20, max(x)])
b = mapclassify.UserDefined(x, bins=[20, max(x)])
_assertions(a, b)