def __convert_gdf_to_bokeh_data(
input_gdf: gpd.GeoDataFrame,
get_gdf_structure: bool = False) -> ColumnDataSource:
assert isinstance(
input_gdf, gpd.GeoDataFrame
), f"use a GeoDataframe please => found {type(input_gdf)}"
if get_gdf_structure:
input_gdf = input_gdf.head(1)
bokeh_data = ColumnDataSource({
**{
"x":
input_gdf["geometry"].apply(lambda x: geometry_2_bokeh_format(
x, "x")).tolist(),
"y":
input_gdf["geometry"].apply(lambda x: geometry_2_bokeh_format(
x, "y")).tolist(),
},
**{
column: input_gdf[column].to_list()
for column in input_gdf.columns if column != "geometry"
},
})
return bokeh_data
frames = [guiddf, pubdatedf, isodf, countrydf, etypedf, epidf, evidf, bboxdf,coordf, aleveldf, ascoredf]
df = pd.concat(frames, axis=1, ignore_index = True, names=[frames])
return df[df[2].isin(wfpiso3)] #!IMPORTANT!#
#return df
data = get_data()
data
#f = 'C:/Users/Michael/Desktop/Notebooks/test.csv'
#data.to_csv(f, encoding='utf-8')
geometry = [Point(xy) for xy in zip(data[8])]
gdacsdf = data.drop([8], axis=1)
crs = {'init': 'epsg:4326'}
gdf = GeoDataFrame(gdacsdf, crs=crs, geometry=geometry)
gdf.head()
#Same as line 75/56
#geodata = gpd.GeoDataFrame(gdf)
#geodata.head(3)
jsondata = geodata.to_json()
m = folium.Map(tiles='stamentoner') #cartodbpositron #stamentoner #cartodbdark_matter
folium.GeoJson(jsondata).add_to(m)
m.fit_bounds(m.get_bounds())
#m.save(os.path.join('results', 'geopandas_2.html'))
m
def log_data_frame(gdf: GeoDataFrame) -> None:
logger.debug(gdf.head())
buffer = StringIO()
gdf.info(buf=buffer)
logger.debug(buffer.getvalue())
api = tweepy.API(auth)
keywords = ['earthquake', 'quake', 'magnitude', 'epicenter', 'magnitude', 'aftershock']
# Collect 100 tweets using the keywords:
search_results = api.search(q=' OR '.join(keywords), count=10)
df = pd.DataFrame([ {'id': result.id, 'created_at': result.created_at, 'user': '******'+result.user.name, 'text': result.text } for result in search_results])[['id', 'created_at', 'user', 'text']]
df.display(df.head())
# What is the weather 500mm around Lyon?:
keywords2 = ['weather' , 'forcast', 'sun', 'rain', 'clouds', 'storm']
# Only in english please !
lang = 'en'
# Get tweets around Lyon (latitide,longitude,radius):
geocode = '45.76,4.84,500km'
# Collect tweets using the keywords:
search_results2 = api.search(q=' OR '.join(keywords2), geocode=geocode, lang=lang, count=1500)
# Convert to GeoPandas:
df2 = pd.DataFrame([ {'id': result.id, 'created_at': result.created_at, 'user': '******'+result.user.name, 'text': result.text, 'geometry': result.coordinates } for result in search_results2])[['id', 'created_at', 'user', 'text', 'geometry']]
df2['geometry'] = df2['geometry'].apply(lambda coords: np.nan if coords is None else Point(coords['coordinates']))
df2 = df2.dropna() # Remove documents without geometry point (the twitter API may obtain location using user details rather than the tweet location.).
df2 = GeoDataFrame(df2, crs = {'init': 'epsg:2263'})
display(df2.head())
import pandas as pd
os.chdir("path to working directory")
# ### Create GeoDataFrames
from geopandas import GeoDataFrame
from shapely.geometry import Point, LineString
shipping_gdf = GeoDataFrame(
shipping, geometry=[Point(xy) for xy in zip(shipping.Long, shipping.Lat)])
noShipping_gdf = GeoDataFrame(
noShipping,
geometry=[Point(xy) for xy in zip(noShipping.Long, noShipping.Lat)])
hq_gdf = GeoDataFrame(hq, geometry=[Point(xy) for xy in zip(hq.Long, hq.Lat)])
hq_gdf.head()
# ### Get adjusted lat/long coordinates
# https://stackoverflow.com/questions/30740046/calculate-distance-to-nearest-feature-with-geopandas
def nearest_poly(point, polygons):
min_dist = polygons.distance(point).min()
index = polygons.distance(point)[polygons.distance(point) ==
min_dist].index[0]
return polygons.iat[index, 0]
def getXY(pt):
return (pt.x, pt.y)
# #convert to geodataframe
# In[6]:
geometry = [
Point(xy) for xy in zip(df_stations.LATITUDE, df_stations.LONGITUDE)
]
df_stations = df_stations.drop(['LATITUDE', 'LONGITUDE'], axis=1)
crs = {'init': 'epsg:4326'}
geodf_stations = GeoDataFrame(df_stations, crs=crs, geometry=geometry)
# In[7]:
geodf_stations.info()
geodf_stations.head()
# In[8]:
#add a new geometry to geodf_stations of a circle of X miles around each station
#new design uses polygons that will be loaded from a shape file so drawing buffer circles around the stations will not be required
#X = 0.01
#geodf_stations['CIRCLE'] = geodf_stations.geometry.buffer(X)
#geodf_stations.geometry.name
#geodf_stations = geodf_stations.rename(columns={'geometry':'POINT'}).set_geometry('CIRCLE')
#geodf_stations.geometry.name
#geodf_stations.info()
#geodf_stations.head()
# # LOAD STATIONS FROM TRANSIT DATA
df_stations.columns = [
'STATION_ID', 'STOP_ID', 'STOP_NAME', 'BOROUGH', 'LATITUDE', 'LONGITUDE'
]
# convert to geodataframe
geometry = [
Point(xy) for xy in zip(df_stations.LATITUDE, df_stations.LONGITUDE)
]
df_stations = df_stations.drop(['LATITUDE', 'LONGITUDE'], axis=1)
crs = {'init': 'epsg:4326'}
geodf_stations = GeoDataFrame(df_stations, crs=crs, geometry=geometry)
geodf_stations.info()
geodf_stations.head()
# In[8]:
#add a new geometry to geodf_stations of a circle of X miles around each station
#new design uses polygons that will be loaded from a shape file so drawing buffer circles around the stations will not be required
#X = 0.01
#geodf_stations['CIRCLE'] = geodf_stations.geometry.buffer(X)
#geodf_stations.geometry.name
#geodf_stations = geodf_stations.rename(columns={'geometry':'POINT'}).set_geometry('CIRCLE')
#geodf_stations.geometry.name
#geodf_stations.info()
#geodf_stations.head()
# # LOAD STATIONS FROM TRANSIT DATA
# #convert to geodataframe
# In[5]:
geometry = [
LineString(build_coord_tuples(x)) for x in df_traffic_links.LINK_POINTS
]
crs = {'init': 'epsg:4326'}
geodf_traffic_links = GeoDataFrame(df_traffic_links.drop('LINK_POINTS',
axis=1),
crs=crs,
geometry=geometry)
geodf_traffic_links.info()
geodf_traffic_links.head()
# In[6]:
geodf_traffic_links.plot(color='r')
plt.show()
# # JOIN TRANSIT STATIONS WITH TRAFFIC LINKS
# In[7]:
#LOAD STATION GEO DF (ALREADY PROCESSED IN STATIONS NOTEBOOK)
#file = root + 'transit/Stations_geomerged.geojson'
#geodf_stations = GeoDataFrame.from_file(file)[['STATION','geometry']]
#geodf_stations.head()
def makeGrid(ipoints, experiment, gridsize):
# Projections
gridproj = {'init': 'epsg:3740', 'no_defs': True}
wgs84 = {'datum':'WGS84', 'no_defs':True, 'proj':'longlat'}
# import grid script
sys.path.insert(0, os.getcwd()+'/mapping/libs/')
import grid as g
opath = os.getcwd() + '/diysco2-db/campaigns/'+experiment+'/diysco2-grid'
if(os.path.isdir(opath)):
print "already a folder!"
else:
os.mkdir(opath)
# gridsize = 200
ogridname = "grid_"+str(gridsize)+"m.shp"
ofile = opath + "/" + ogridname
print "making grid"
g.main(ofile, ipoints.total_bounds[0], ipoints.total_bounds[2],
ipoints.total_bounds[1], ipoints.total_bounds[3],
gridsize, gridsize)
print "grid complete! "
# read in the grid that was just made
grid = GeoDataFrame.from_file(ofile)
grid.crs = gridproj
# create grid id to groupby
grid['id'] = [i for i in range(len(grid))]
# Read in transect to spatial subset grids in transect
transect = GeoDataFrame.from_file(os.getcwd()+'/diysco2-db/_main_/study-area/' +'transect_epicc2sp_woss.shp')
transect.crs = gridproj
# subset grid
# transectgrid = grid[grid.geometry.intersects(transect.geometry)]; print transectgrid
sagrid = []
for i in range(len(grid)):
if np.array(transect.intersects(grid.geometry[i]))[0] != False:
sagrid.append(grid.geometry[i])
transectgrid = GeoDataFrame(sagrid)
transectgrid.columns = ['geometry']
transectgrid['id'] = [i for i in range(len(transectgrid))]
transectgrid.crs = gridproj
transectgrid.to_file(ofile[:-4]+"_transect.shp")
# transectgrid.to_file(ofile[:-4]+"_transect.geojson",driver="GeoJSON")
## !!!Some weird things with reading in data makes the sjoin work !!! :(
transectgrid = GeoDataFrame.from_file(ofile[:-4]+"_transect.shp")
transectgrid.crs = gridproj
print transectgrid.head()
ipoints = GeoDataFrame.from_file( os.getcwd() + '/diysco2-db/campaigns/'+experiment+'/diysco2-filtered-points/all_20150528.shp')
ipoints.crs = gridproj
print ipoints.head()
# ipoints['id'] = [i for i in range(len(ipoints))]
# Spatial join points to grid
oname = "gridjoin_"+str(gridsize)+"m.shp"
# join_inner_df = sjoin(transectgrid, ipoints, how="inner")
join_inner_df = sjoin(transectgrid, ipoints, how="left", op='intersects')
# join_inner_df.to_file(opath+ "/"+oname)
return join_inner_df
def makePoints(experiment):
path = os.getcwd() + '/diysco2-db/campaigns/'+experiment+'/diysco2-filtered/'
ipaths = [os.path.join(path,i) for i in os.listdir(path) if i.endswith('.csv')]
# experiment data
data = [readdata(i) for i in ipaths]
# copy data to ldata
ldata = [i.copy() for i in data]
# --- print number of measurements --- #
featcount(ldata)
''' ------------- Spatial Operations ------------- '''
for i in range(0,len(ldata)):
ldata[i].lon = ldata[i].lon.astype('float')
ldata[i].lat = ldata[i].lat.astype('float')
# need to keep datetime field
# create geopoints
for i in ldata:
i['datetime'] = i.index
for i in ldata:
i.index = [j for j in range(len(i))]
i['geometry'] = GeoSeries([Point(x, y) for x, y in zip(i.lon, i.lat)])
# convert datetime string to iso format
i['datetime'] = i.datetime.map(lambda x: datetime.strftime(x, '%Y-%m-%dT%H:%M:%SZ'))
print ldata[0].head()
# Projections
gridproj = {'init': 'epsg:3740', 'no_defs': True}
wgs84 = {'datum':'WGS84', 'no_defs':True, 'proj':'longlat'}
# create geodataframe from data
ldata = [GeoDataFrame(i) for i in ldata]
# set projection as wgs84
for i in ldata:
i.crs = wgs84
# reproject to utm zone 10N
for i in ldata:
i.geometry = i.geometry.to_crs(epsg=3740)
# i.geometry = i.geometry.to_crs(epsg=4326)
for i in ldata:
i = i[pd.isnull(i.geometry) == False]
# --- Merge geodata together --- #
mergedgeo = pd.concat([ldata[0], ldata[1],ldata[2],ldata[3],ldata[4]])
mergedgeo = GeoDataFrame(mergedgeo)
mergedgeo.crs = gridproj
print len(mergedgeo)
mergedgeo = mergedgeo[pd.isnull(mergedgeo.lat)==False]
print len(mergedgeo)
# mergedgeo['date'] = mergedgeo['date'].str.replace('/', '-').astype(str)
# mergedgeo['datetime'] = mergedgeo['datetime'].astype(str)
print mergedgeo.head()
# mergedgeo.to_crs(wgs84)
opath = os.getcwd() + '/diysco2-db/campaigns/'+experiment+'/diysco2-filtered-points/'
print opath
if(os.path.isdir(opath)):
print "already a folder!"
else:
os.mkdir(opath)
if(os.path.isfile(opath + 'all_20150528.geojson')):
os.remove(opath + 'all_20150528.geojson')
mergedgeo.to_file(opath + 'all_20150528.geojson', driver="GeoJSON")
# with open(opath + 'all_20150528.geojson', 'w') as f:
# f.write(mergedgeo.to_json())
mergedgeo.to_file(opath + 'all_20150528.shp', driver='ESRI Shapefile')
return mergedgeo
del mergedgeo
# ### Convert to GeoDataFrame
# In[160]:
from geopandas import GeoDataFrame
from shapely.geometry import Point
# In[161]:
# Initialize the geographic reference system as WGS 1984
crs = {'init': 'epsg:4326'}
# Project ot Mercator system
stations_gdf = GeoDataFrame(stations, crs=crs,
geometry='geometry').to_crs(epsg=3857)
stations_gdf.head()
# ### Join Trip Counts to Station Data
# In[162]:
# Count trips by station
by_startStation = indego.groupby('start_station').size().reset_index(
name='counts')
by_startStation.head()
# In[163]:
stations_gdf = stations_gdf.merge(by_startStation,
left_on='kioskId',
right_on='start_station')
# change the CRS of the shapefile to the specified projected one all_countries.to_crs(crs=target_crs, inplace=True) # In[6]: # create a geometry column in our point data set for geopandas to use rs['geometry'] = rs.apply(lambda row: Point(row['lon'], row['lat']), axis=1) # create a new geopandas geodataframe from the point data points = GeoDataFrame(rs) # you must specify its original CRS to convert it to a different (projected) one later points.crs = original_crs points.head() # In[7]: # convert the point data to the same projected CRS we specified earlier for our shapefile points.to_crs(crs=target_crs, inplace=True) # convert the projected points into discrete x and y columns for easy matplotlib scatterplotting points['x'] = points['geometry'].map(lambda point: point.x) points['y'] = points['geometry'].map(lambda point: point.y) points.head() # In[8]:
all_rps.head()
#Create geometries for sps
geometry = [Point(xy) for xy in zip(all_sps.Easting, all_sps.Northing)]
crs = {'init': 'epsg:23031'}
#Create points for sps
all_sps_points = GeoDataFrame(all_sps, crs=crs, geometry=geometry)
#Create lines for sps
all_sps_lines = all_sps_points.groupby(
['Line Number'])['geometry'].apply(lambda x: LineString(x.tolist()))
all_sps_lines = GeoDataFrame(all_sps_lines, geometry='geometry')
#Check
all_sps_points.head()
all_sps_lines.head()
#Create shapefiles for sps
all_sps_points.to_file(driver='ESRI Shapefile',
filename="result_SPS_points.shp")
all_sps_lines.to_file(driver='ESRI Shapefile', filename="result_SPS_lines.shp")
#Create geometries for rps
geometry = [Point(xy) for xy in zip(all_rps.Easting, all_rps.Northing)]
crs = {'init': 'epsg:23031'}
#Create points for rps
all_rps_points = GeoDataFrame(all_rps, crs=crs, geometry=geometry)
#Create lines for rps
