def convert(response, category_formatter=str):
"""
:param response:
:return:
"""
if hasattr(response.aggregations, "categories"):
for category in response.aggregations.categories:
for bucket in category.grids:
x, y = lnglat_to_meters(bucket.centroid.location.lon,
bucket.centroid.location.lat)
yield {
"lon": bucket.centroid.location.lon,
"lat": bucket.centroid.location.lat,
"x": x,
"y": y,
"c": bucket.centroid.count,
"t": category_formatter(category.key),
}
else:
for bucket in response.aggregations.grids:
lon = bucket.centroid.location.lon
lat = bucket.centroid.location.lat
x, y = lnglat_to_meters(lon, lat)
yield {
"lon": lon,
"lat": lat,
"x": x,
"y": y,
"c": bucket.centroid.count
}
def project_to_meters(self, df, x, y):
try:
df.loc[:, "x_meters"], df.loc[:, "y_meters"] = lnglat_to_meters(
df[x], df[y])
geometry = [Point(xy) for xy in zip(df.x_meters, df.y_meters)]
web_merc = {"init": "epsg:3857"}
return gpd.GeoDataFrame(df, crs=web_merc, geometry=geometry)
except:
print(
"""Could not project specified coordinate fields to meters. Make sure the X Field and Y Field
values have been set correctly.\n""")
def convert_composite(response, categorical, filter_buckets,
histogram_interval, category_type, category_format):
if categorical and filter_buckets == False:
# Convert a regular terms aggregation
for bucket in response:
for category in bucket.categories:
lon, lat = geotile_bucket_to_lonlat(bucket)
x, y = lnglat_to_meters(lon, lat)
raw = category.key
# Bin the data
if histogram_interval is not None:
# Format with pynumeral if provided
if category_format:
label = "%s-%s" % (
pynumeral.format(float(raw), category_format),
pynumeral.format(
float(raw) + histogram_interval,
category_format),
)
else:
label = "%s-%s" % (float(raw),
float(raw) + histogram_interval)
else:
if category_type == "number":
try:
label = pynumeral.format(to_32bit_float(raw),
category_format)
except ValueError:
label = str(raw)
else:
label = str(raw)
yield {
"lon": lon,
"lat": lat,
"x": x,
"y": y,
"c": category.doc_count,
"t": label,
}
elif categorical and filter_buckets == True:
# Convert a filter bucket aggregation
for bucket in response:
for key in bucket.categories.buckets:
category = bucket.categories.buckets[key]
if category.doc_count > 0:
lon, lat = geotile_bucket_to_lonlat(bucket)
x, y = lnglat_to_meters(lon, lat)
if category_type == "number":
try:
label = pynumeral.format(to_32bit_float(key),
category_format)
except ValueError:
label = str(key)
else:
label = str(key)
yield {
"lon": lon,
"lat": lat,
"x": x,
"y": y,
"c": category.doc_count,
"t": label,
}
else:
# Non-categorical
for bucket in response:
lon, lat = geotile_bucket_to_lonlat(bucket)
x, y = lnglat_to_meters(lon, lat)
yield {
"lon": lon,
"lat": lat,
"x": x,
"y": y,
"c": bucket.doc_count
}
def transform_coords(df):
df = df.copy()
df.loc[:, 'longitude'], df.loc[:, 'latitude'] = \
du.lnglat_to_meters(df.longitude,df.latitude)
return df
def to_meters(row):
return lnglat_to_meters(row["LON"], row["LAT"])
export = partial(export_image, background=background, export_path="export")
#cm = partial(colormap_select, reverse=(background!="black"))
cm = partial(colormap_select, reverse=(background != "black"))
display(HTML("<style>.container {width:100%} !important; }</style>"))
cvs = ds.Canvas(plot_width, plot_height, *FFM)
agg = cvs.points(new_df, 'RECHTSWERT', 'HOCHWERT')
export(tf.shade(agg, cmap=cm(fire, 0.4), how='log'),
"Frankfurt_Baumbestand_nach_2000")
# In[17]:
from datashader.utils import lnglat_to_meters
df.loc[:, 'lon'], df.loc[:, 'lat'] = lnglat_to_meters(df.lon, df.lat)
# In[12]:
import holoviews as hv
from holoviews.element.tiles import EsriImagery
from holoviews.operation.datashader import datashade
#from datashader.utils import lnglat_to_meters
hv.extension('bokeh')
from colorcet import fire
#df.loc[:, 'lon'], df.loc[:, 'lat'] = lnglat_to_meters(df.lon,df.lat)
map_tiles = EsriImagery().opts(alpha=0.5,
width=900,
height=600,
bgcolor='black')