def shapely_shapes_to_shapefile(shapely_shapes, shp_path, sf=None,
prj_path=None):
if sf:
w = shapefile.Writer(sf.shapeType)
for field in sf.fields:
w.field(*field)
for record in sf.iterRecords():
w.record(*record)
else:
shape_type = shapely_to_pyshp(shapely_shapes[0]).shapeType
w = shapefile.Writer(shape_type)
w.field(str("id"), str("N"), 255, 0)
for id_shape in xrange(len(shapely_shapes)):
w.record(*[id_shape])
for shape in shapely_shapes:
if shape.type == "Polygon":
pyshp_shape = shapely_to_pyshp(shape.buffer(0.00000001))
w._shapes.append(pyshp_shape)
elif shape.type == "Point":
coords = shape.coords[0]
w.point(coords[0], coords[1])
else:
pyshp_shape = shapely_to_pyshp(shape)
w._shapes.append(pyshp_shape)
# return w
w.save(shp_path)
if prj_path:
copy_prj(prj_path, shp_path)
def create_buffered_shp(directory, distance, buffer_dir=BUFFER_DIR,
resolution=16, recalculate=False,
context_shp_or_polygon=None):
context_polygon = create_context_polygon(context_shp_or_polygon)
shp_path = pf.find_shp_path(directory)
shp_name = _create_shp_name(shp_path, distance)
# print("\n".join([directory, shp_name, buffer_dir]))
buffer_shp_path = _create_shp_path(directory, shp_name, buffer_dir)
# print(buffer_shp_path)
if not os.path.isfile(buffer_shp_path + ".shp") or recalculate:
# read shapefile with pyshp
sf_est = shapefile.Reader(shp_path)
# create buffers from shapely shapes
buffer_shapes = []
for shape in geo_utils.get_shapely_shapes(sf_est):
if not context_polygon or context_polygon.contains(shape):
buffer_shapes.append(create_buffer(shape, distance,
resolution,
context_polygon))
write_shapefile(sf_est, buffer_shapes, buffer_shp_path)
utils.copy_prj(shp_path, buffer_shp_path)
def create_buffered_shp(directory,
distance,
buffer_dir=BUFFER_DIR,
resolution=16,
recalculate=False,
context_shp_or_polygon=None):
context_polygon = create_context_polygon(context_shp_or_polygon)
shp_path = pf.find_shp_path(directory)
shp_name = _create_shp_name(shp_path, distance)
# print("\n".join([directory, shp_name, buffer_dir]))
buffer_shp_path = _create_shp_path(directory, shp_name, buffer_dir)
# print(buffer_shp_path)
if not os.path.isfile(buffer_shp_path + ".shp") or recalculate:
# read shapefile with pyshp
sf_est = shapefile.Reader(shp_path)
# create buffers from shapely shapes
buffer_shapes = []
for shape in geo_utils.get_shapely_shapes(sf_est):
if not context_polygon or context_polygon.contains(shape):
buffer_shapes.append(
create_buffer(shape, distance, resolution,
context_polygon))
write_shapefile(sf_est, buffer_shapes, buffer_shp_path)
utils.copy_prj(shp_path, buffer_shp_path)
def merge_shapefiles(shp_paths, output_path, merging_field="orig_sf",
replacements=None, group_fields=None,
group_fields_by_sf=None, prj_path=None):
"""Merge shapefiles in a single shapefile.
There is a merging_field retaining the name of the original field taken
from shp_paths keys. Fields not shared by all merged shape files will take
a None value.
Args:
shp_paths (dict or str):
If dict, paths to shps to merge as {"RADIO": "radio_indic_path"}
If str, dir where all the subdirs are shps to be merged.
output_path (str): Path of the merged shapefile.
merging_field (str): Name of the field retaining name of original shps.
replacements (dict): Only if shp_paths is str. Replace long names with
short ones, to put in the merging_field. {"long_name": "short"}
group_fields (dict): Fields with different names that should be grouped
into the same field (group_field: ["name1", "name2", "name3"])
group_fields_by_sf (dict): Field name replacements for each shapefile
merged like in
{"RADIO": {"co_frac_ra": "id_div"},
"FRAC": {"co_fracc": "id_div"}}
where "id_div" is the column gathering all ids.
prj_path (str): Path to a prj file to be used. If none is provided, the
prj file of the first shapefile to be merged will be used.
"""
if type(shp_paths) == str or type(shp_paths) == unicode:
shp_paths = create_shp_paths_dict(shp_paths, replacements)
sf_first = shapefile.Reader(shp_paths.values()[0])
w = shapefile.Writer(sf_first.shapeType)
if not prj_path:
copy_prj(shp_paths.values()[0], output_path)
else:
copy_prj(prj_path, output_path)
# write all the fields first
write_fields(w, shp_paths, merging_field, group_fields, group_fields_by_sf)
# now write shapes and records
new_fields = [new_field[0] for new_field in w.fields]
# print(new_fields)
for id_sf, sf_path in shp_paths.iteritems():
sf = shapefile.Reader(sf_path)
print("Merging", sf.shapeType, id_sf.ljust(15),
os.path.basename(sf_path))
gfsf = group_fields_by_sf
orig_fields = [translate(f, group_fields,
gfsf[id_sf] if gfsf else None)[0]
for f in sf.fields if f[0] != "DeletionFlag"]
# print(orig_fields)
# extend writing shapefile with all new shapes
write_curated_shapes(w, sf)
for sr in sf.iterShapeRecords():
record = sr.record
# shape = sr.shape
dict_record = {orig_field: value for orig_field, value in
zip(orig_fields, record)}
# add elements to the record in the writing sf fields order
new_record = []
for field in new_fields:
if field == merging_field:
new_record.append(id_sf)
elif field in dict_record:
new_record.append(dict_record[field])
else:
new_record.append(None)
w.record(*new_record)
# return w
w.save(output_path)
def recalculate_indicators(new_shp_dir,
area_level,
skip=None,
subcategory=None,
omit_fields=None,
by_area=None):
skip = skip or []
by_area = by_area or []
# some fields are omitted always
if omit_fields:
if not type(omit_fields) == list:
omit_fields = [omit_fields]
omit_fields = OMIT_FIELDS
omit_fields.extend(omit_fields)
new_shp_path = find_shp_path(new_shp_dir)
shp_name = os.path.basename(new_shp_path)
sf = shapefile.Reader(new_shp_path)
df_indicators = get_indicators(area_level)
weights = get_weights(new_shp_path, area_level)
w = shapefile.Writer(shapefile.POLYGON)
indicators = _get_indicator_names(df_indicators)
for field in sf.fields[1:]:
w.field(*field)
for indicator in indicators:
field = [str(indicator), str("N"), 20, 18]
# print(indicator)
w.field(*field)
w.field(str("area_km2"), str("N"), 20, 18)
w.field(str("hab_km2"), str("N"), 20, 18)
# print(w.fields)
for record_shape in sf.iterShapeRecords():
record = record_shape.record
shape = record_shape.shape
# print(record[0])
if type(record[0]) == int:
id_record = unicode(record[0])
else:
id_record = unicode(record[0].decode("utf-8"))
if len(weights[id_record]) > 0:
calculated_indicators = _calc_indicators(indicators, df_indicators,
weights[id_record],
area_level, skip, by_area)
# print(calculated_indicators)
record.extend(calculated_indicators)
area = calculate_area(shape) / 1000000
record.append(area)
population = calculated_indicators[indicators.index(POPULATION)]
pop_density = population / area
record.append(pop_density)
w.record(*record)
w.poly(shapeType=shapefile.POLYGON, parts=[shape.points])
path = get_indicators_shp_path(shp_name, subcategory)
w.save(path)
utils.copy_prj(new_shp_path.decode("utf-8"), path)
def merge_shapefiles(shp_paths, output_path, merging_field="orig_sf",
replacements=None, group_fields=None,
group_fields_by_sf=None, skip_fields=None,
fields_to_keep=None):
"""Merge shapefiles in a single shapefile.
There is a merging_field retaining the name of the original field taken
from shp_paths keys. Fields not shared by all merged shape files will take
a None value.
Args:
shp_paths (dict or str):
If dict, paths to shps to merge as {"RADIO": "radio_indic_path"}
If str, dir where all the subdirs are shps to be merged.
output_path (str): Path of the merged shapefile.
merging_field (str): Name of the field retaining name of original shps.
replacements (dict): Only if shp_paths is str. Replace long names with
short ones, to put in the merging_field. {"long_name": "short"}
group_fields (dict): Fields with different names that should be grouped
into the same field (group_field: ["name1", "name2", "name3"])
"""
skip_fields = skip_fields or []
if type(shp_paths) == str or type(shp_paths) == unicode:
shp_paths = create_shp_paths_dict(shp_paths, replacements)
sf_first = shapefile.Reader(shp_paths.values()[0])
w = shapefile.Writer(sf_first.shapeType)
# print(sf_first.shapeType)
copy_prj(shp_paths.values()[0], output_path)
# write all the fields first
write_fields(w, shp_paths, merging_field, group_fields, group_fields_by_sf,
skip_fields, fields_to_keep)
# now write shapes and records
new_fields = [new_field[0] for new_field in w.fields]
# print(new_fields)
for id_sf, sf_path in shp_paths.iteritems():
sf = shapefile.Reader(sf_path)
print("Merging", sf.shapeType, id_sf.ljust(15),
os.path.basename(sf_path))
gfsf = group_fields_by_sf
orig_fields = [translate(f, group_fields,
gfsf[id_sf] if gfsf else None)[0]
for f in sf.fields if f[0] != "DeletionFlag"]
# print(orig_fields)
# extend writing shapefile with all new shapes
write_curated_shapes(w, sf)
for sr in sf.iterShapeRecords():
record = sr.record
# shape = sr.shape
dict_record = {orig_field: value for orig_field, value in
zip(orig_fields, record)}
# add elements to the record in the writing sf fields order
new_record = []
for field in new_fields:
if field == merging_field:
new_record.append(id_sf)
elif field in dict_record:
new_record.append(dict_record[field])
else:
new_record.append(None)
w.record(*new_record)
# return w
w.save(output_path)
def recalculate_indicators(new_shp_dir, area_level, skip=None,
subcategory=None, omit_fields=None, by_area=None):
skip = skip or []
by_area = by_area or []
# some fields are omitted always
if omit_fields:
if not type(omit_fields) == list:
omit_fields = [omit_fields]
omit_fields = OMIT_FIELDS
omit_fields.extend(omit_fields)
new_shp_path = find_shp_path(new_shp_dir)
shp_name = os.path.basename(new_shp_path)
sf = shapefile.Reader(new_shp_path)
df_indicators = get_indicators(area_level)
weights = get_weights(new_shp_path, area_level)
w = shapefile.Writer(shapefile.POLYGON)
indicators = _get_indicator_names(df_indicators)
for field in sf.fields[1:]:
w.field(*field)
for indicator in indicators:
field = [str(indicator), str("N"), 20, 18]
# print(indicator)
w.field(*field)
w.field(str("area_km2"), str("N"), 20, 18)
w.field(str("hab_km2"), str("N"), 20, 18)
# print(w.fields)
for record_shape in sf.iterShapeRecords():
record = record_shape.record
shape = record_shape.shape
# print(record[0])
if type(record[0]) == int:
id_record = unicode(record[0])
else:
id_record = unicode(record[0].decode("utf-8"))
if len(weights[id_record]) > 0:
calculated_indicators = _calc_indicators(indicators,
df_indicators,
weights[id_record],
area_level,
skip, by_area)
# print(calculated_indicators)
record.extend(calculated_indicators)
area = calculate_area(shape) / 1000000
record.append(area)
population = calculated_indicators[indicators.index(POPULATION)]
pop_density = population / area
record.append(pop_density)
w.record(*record)
w.poly(shapeType=shapefile.POLYGON, parts=[shape.points])
path = get_indicators_shp_path(shp_name, subcategory)
w.save(path)
utils.copy_prj(new_shp_path.decode("utf-8"), path)
