def main(args):
'Runs calculation chain'
try:
action = args[1]
config = args[2]
except IndexError:
action = config = None
inm_log = InmarsatLog.from_csv('data', 'inmarsat-su-log-redacted.csv')
time_step = timedelta(seconds=10)
bin_log = inm_log.bin_data(time_step)
traj_time_step = time_step / 2
traj = Trajectory.from_csv('data',
acars='acars.csv',
adsb='all-combined.csv',
radar='route.csv')
int_traj = traj.int_data(bin_log.data[0].time - traj_time_step,
traj_time_step)
r_btos = RadialDistance.from_bto(bin_log.data)
r_known = RadialDistance.from_traj(int_traj.data)
r_flight = r_known.append(r_btos).take_after(T0['take-off'])
trend_times = [(2014, 3, 7, 16, 42, 0), (2014, 3, 7, 17, 23, 0),
(2014, 3, 7, 18, 28, 15), (2014, 3, 7, 19, 41, 5),
(2014, 3, 7, 20, 41, 5), (2014, 3, 7, 21, 41, 25),
(2014, 3, 7, 22, 41, 25), (2014, 3, 8, 0, 19, 35)]
r_trend = r_flight.filter_by_list(
[datetime(*item) for item in trend_times])
r_interp = r_flight.interpolate(r_trend, T0['off-radar'])
if action == 'polygon':
with open(config, 'rt') as cfg_file:
cfg = load(cfg_file)
from shapefile import Writer, POLYGON
shp_file = Writer(POLYGON)
shp_file.field('TIME', 'C', '20')
for contour in cfg['times']:
contour_pts = []
ind_a = 0
ind_b = 1
contour_time = datetime(*contour['time'])
if contour_time > r_interp.data[-1][0]:
contour_time = r_interp.data[-1][0]
for offset in cfg['time_delta']:
time_offset = timedelta(minutes=offset)
cur_time = contour_time + time_offset
contour_pts += [
r_interp.find_loc(cur_time, lat) for lat in frange(
contour['lat_bounds'][ind_a], contour['lat_bounds']
[ind_b], cfg['lat_step'] * (ind_b - ind_a))
]
ind_a, ind_b = ind_b, ind_a
shp_file.poly(parts=[contour_pts])
poly_name = '{}_{}_{}'.format(*contour['time'][2:5])
shp_file.record(poly_name)
shp_file.save(cfg['save_to_file'])
def merge_shapes(
inputfile,
outputfile=None,
overwrite=False,
verbose=True,
vverbose=False,
):
"""
Merges all the shapes in a shapefile into a single shape.
"""
if outputfile is None: output = '{}/merged'.format(os.getcwd())
if os.path.isfile(outputfile + '.shp') and not overwrite:
if verbose:
print('combined watershed shapefile {} exists'.format(outputfile))
return
if verbose:
print('combining shapes from {}\n'.format(inputfile) +
'this may take a while...\n')
# start by copying the projection files
shutil.copy(inputfile + '.prj', outputfile + '.prj')
# load the catchment and flowline shapefiles
r = Reader(inputfile, shapeType=5)
try:
combined = combine_shapes(r.shapes(), verbose=vverbose)
except:
print('error: unable to combine shapes')
raise
# create the new file with the merged shapes
w = Writer(shapeType=5)
w.poly(shapeType=5, parts=[combined])
# copy the fields from the original and then the first record; note this
# can be adapted as needed
for field in r.fields:
w.field(*field)
w.record(*r.record(0))
w.save(outputfile)
if verbose:
its = inputfile, outputfile
print('successfully combined shapes from {} to {}\n'.format(*its))
def extract_catchments(
self,
source,
destination,
flowlinefile,
verbose=True,
):
"""
Extracts the catchments from the source data file to the destination
using the list of comids for the query.
"""
# make a list of the comids
comids = self.get_comids(flowlinefile)
# open the catchment shapefile
if verbose: print('reading the catchment shapefile\n')
shapefile = Reader(source)
# get the index of the feature id, which links to the flowline comid
featureid_index = shapefile.fields.index(['FEATUREID', 'N', 9, 0]) - 1
# go through the comids from the flowlines and add the corresponding
# catchment to the catchment list
if verbose: print('searching the catchments in the watershed\n')
records = shapefile.records()
indices = []
i = 0
for record in records:
if record[featureid_index] in comids: indices.append(i)
i += 1
if len(indices) == 0:
print('query returned no values, returning\n')
raise
# create the new shapefile
if verbose: print('writing the new catchment shapefile\n')
w = Writer()
for field in shapefile.fields:
w.field(*field)
for i in indices:
shape = shapefile.shape(i)
w.poly(shapeType=5, parts=[shape.points])
w.record(*records[i])
w.save(destination)
def extract_flowlines(self, source, destination, HUC8, verbose = True):
"""Extracts flowlines from the source datafile to the destination using
the HUC8 for the query."""
# open the flowline file
if verbose: print('reading the flowline file\n')
shapefile = Reader(source, shapeType = 3)
records = shapefile.records()
# figure out which field codes are the Reach code and comid
reach_index = shapefile.fields.index(['REACHCODE', 'C', 14, 0]) - 1
# go through the reach indices, add add them to the list of flowlines
# if in the watershed; also make a list of the corresponding comids
if verbose: print('searching for flowlines in the watershed\n')
indices = []
i = 0
for record in records:
if record[reach_index][:8] == HUC8: indices.append(i)
i+=1
if len(indices) == 0:
if verbose: print('error: query returned no values')
raise
# write the data from the HUC8 to a new shapefile
w = Writer(shapeType = 3)
for field in shapefile.fields: w.field(*field)
for i in indices:
shape = shapefile.shape(i)
w.poly(shapeType = 3, parts = [shape.points])
record = records[i]
# little work around for blank GNIS_ID and GNIS_NAME values
if isinstance(record[3], bytes):
record[3] = record[3].decode('utf-8')
if isinstance(record[4], bytes):
record[4] = record[4].decode('utf-8')
w.record(*record)
w.save(destination)
if verbose:
l = len(indices)
print('queried {} flowlines from original shapefile\n'.format(l))
def extract_flowlines(self, source, destination, HUC8, verbose = True):
"""Extracts flowlines from the source datafile to the destination using
the HUC8 for the query."""
# open the flowline file
if verbose: print('reading the flowline file\n')
shapefile = Reader(source, shapeType = 3)
records = shapefile.records()
# figure out which field codes are the Reach code and comid
reach_index = shapefile.fields.index(['REACHCODE', 'C', 14, 0]) - 1
# go through the reach indices, add add them to the list of flowlines
# if in the watershed; also make a list of the corresponding comids
if verbose: print('searching for flowlines in the watershed\n')
indices = []
i = 0
for record in records:
if record[reach_index][:8] == HUC8: indices.append(i)
i+=1
if len(indices) == 0:
if verbose: print('error: query returned no values')
raise
# write the data from the HUC8 to a new shapefile
w = Writer(shapeType = 3)
for field in shapefile.fields: w.field(*field)
for i in indices:
shape = shapefile.shape(i)
w.poly(shapeType = 3, parts = [shape.points])
record = records[i]
# little work around for blank GNIS_ID and GNIS_NAME values
if isinstance(record[3], bytes):
record[3] = record[3].decode('utf-8')
if isinstance(record[4], bytes):
record[4] = record[4].decode('utf-8')
w.record(*record)
w.save(destination)
if verbose:
l = len(indices)
print('queried {} flowlines from original shapefile\n'.format(l))
def extract_catchments(self,
source,
destination,
flowlinefile,
verbose = True,
):
"""
Extracts the catchments from the source data file to the destination
using the list of comids for the query.
"""
# make a list of the comids
comids = self.get_comids(flowlinefile)
# open the catchment shapefile
if verbose: print('reading the catchment shapefile\n')
shapefile = Reader(source)
# get the index of the feature id, which links to the flowline comid
featureid_index = shapefile.fields.index(['FEATUREID', 'N', 9, 0]) - 1
# go through the comids from the flowlines and add the corresponding
# catchment to the catchment list
if verbose: print('searching the catchments in the watershed\n')
records = shapefile.records()
indices = []
i = 0
for record in records:
if record[featureid_index] in comids: indices.append(i)
i+=1
if len(indices) == 0:
print('query returned no values, returning\n')
raise
# create the new shapefile
if verbose: print('writing the new catchment shapefile\n')
w = Writer()
for field in shapefile.fields: w.field(*field)
for i in indices:
shape = shapefile.shape(i)
w.poly(shapeType = 5, parts = [shape.points])
w.record(*records[i])
w.save(destination)
def merge_shapes(inputfile,
outputfile = None,
overwrite = False,
verbose = True,
vverbose = False,
):
"""
Merges all the shapes in a shapefile into a single shape.
"""
if outputfile is None: output = '{}/merged'.format(os.getcwd())
if os.path.isfile(outputfile + '.shp') and not overwrite:
if verbose:
print('combined watershed shapefile {} exists'.format(outputfile))
return
if verbose: print('combining shapes from {}\n'.format(inputfile) +
'this may take a while...\n')
# start by copying the projection files
shutil.copy(inputfile + '.prj', outputfile + '.prj')
# load the catchment and flowline shapefiles
r = Reader(inputfile, shapeType = 5)
try:
combined = combine_shapes(r.shapes(), verbose = vverbose)
except:
print('error: unable to combine shapes')
raise
# create the new file with the merged shapes
w = Writer(shapeType = 5)
w.poly(shapeType = 5, parts = [combined])
# copy the fields from the original and then the first record; note this
# can be adapted as needed
for field in r.fields: w.field(*field)
w.record(*r.record(0))
w.save(outputfile)
if verbose:
its = inputfile, outputfile
print('successfully combined shapes from {} to {}\n'.format(*its))
def clip_value(in_file, ot_dir, min_height, max_height):
"""
オンライン学習４ ベクタデータのフィルタリング
浸水・土砂崩れベクタデータをGISデータの属性値(値)を使用してフィルタリングするプログラムを実行します。
関数 : clip_value
引数1 : 浸水・土砂崩れベクタデータ(*.shp)
引数2 : 出力ディレクトリ名
引数3 : 出力対象となる値の最小値
引数4 : 出力対象となる値の最大値
"""
# Get actual file path
in_file = path.join(DATA_PATH_BASE, in_file)
ot_dir = path.join(DATA_PATH_BASE, ot_dir)
makedirs(ot_dir, exist_ok=True)
ot_file = path.join(
ot_dir, "{0}v.tif".format(path.splitext(path.basename(in_file))[0]))
reader = ShpReader(in_file, encoding='cp932')
writer = ShpWriter(ot_file, encoding='cp932')
# Create DBF schema
height_col_id = None
for i, col in enumerate(
(col for col in reader.fields if col[0] != "DeletionFlag")):
if col[0] != "DeletionFlag":
writer.field(col[0], col[1], col[2], col[3])
if col[0] == "height":
height_col_id = i
if height_col_id is None:
print("height column not found in polygon shapefile")
return
# Filtering
n_mesh = reader.numRecords
cnt_mesh = 0
for data in reader.iterShapeRecords():
height = data.record[height_col_id]
if (height is not None) and (min_height <= height <= max_height):
# This polygon is output target.
writer.shape(data.shape)
writer.record(*data.record)
cnt_mesh = cnt_mesh + 1
if cnt_mesh % 100000 == 0:
print("{0}K / {1}K".format(cnt_mesh / 1000, n_mesh / 1000))
writer.close()
def makeSHP(dic):
shpname = saveSHP()
shpWriter = Writer()
shpWriter.autoBalance = 1
shpWriter.field(headerEntry.get(), 'C', '255')
shpWriter.field('Longitude', 'F')
shpWriter.field('Latitude', 'F')
geomtype =1
shpWriter.shapeType = geomtype
parsedGeometryList = []
dicValList = []
dicKeyList = []
for k in dic.keys():
dicValList.append(dic[k])
valist = k,dic[k][0],dic[k][1]
dicKeyList.append(valist)
[parsedGeometryList.append(filez) for filez in dicValList]
[shpWriter.point(*parsedGeometry) for parsedGeometry in parsedGeometryList]
[shpWriter.record(*dList) for dList in dicKeyList]
shpWriter.save(shpname)
prj = generatePRJ(int(sridEntry.get()))
if prj != None:
prjfile = shpname.replace('.shp','') + '.prj'
prjfileOpen = open(prjfile, 'w')
prjfileOpen.write(prj)
prjfileOpen.close()
return shpname
def clip_shape(in_mesh, in_mask, ot_dir, flg_mask):
"""
オンライン学習４ ベクタデータのフィルタリング
浸水・土砂崩れベクタデータをGISデータの形状を利用してフィルタリングします。
関数 : clip_shape
引数1 : 浸水・土砂崩れベクタデータ(*.shp)
引数2 : GISデータ(*.shp)
引数3 : 出力ディレクトリ名
引数4 : 出力フラグ(True：GISデータと重なる部分を出力、False：GISデータと重ならない部分を出力)
"""
# Get actual file path
in_mesh = path.join(DATA_PATH_BASE, in_mesh)
in_mask = path.join(DATA_PATH_BASE, in_mask)
ot_dir = path.join(DATA_PATH_BASE, ot_dir)
makedirs(ot_dir, exist_ok=True)
ot_file = path.join(ot_dir, "{0}s.tif".format(path.splitext(path.basename(in_mesh))[0]))
reader_mesh = ShpReader(in_mesh, encoding='cp932')
reader_mask = ShpReader(in_mask, encoding='cp932')
writer = ShpWriter(ot_file, encoding='cp932')
# Create DBF schema
for col in reader_mesh.fields:
if col[0] != "DeletionFlag":
writer.field(col[0], col[1], col[2], col[3])
# Create set of mask polygon
maskdata = []
for data in reader_mask.iterShapes():
points = data.points
points_split = list(data.parts) + [len(points)]
poly_list = []
for i in range(len(points_split) - 1):
points_part = points[points_split[i]:points_split[i + 1]]
poly_list.append(Polygon(points_part))
# Use as mask polygon only when all key conditions are satisfied.
# Memorize shape and bbox of polygon.
x_range = min(points, key=lambda x: x[0])[0], max(points, key=lambda x: x[0])[0]
y_range = min(points, key=lambda x: x[1])[1], max(points, key=lambda x: x[1])[1]
maskdata.append((x_range, y_range, poly_list))
# Filtering
n_mesh = reader_mesh.numRecords
cnt_mesh = 0
for data in reader_mesh.iterShapeRecords():
center = Polygon(data.shape.points).centroid
x = center.x
y = center.y
masked = False
for x_range, y_range, mask_polys in maskdata:
# Primary screening by mask polygon bbox.
if x < x_range[0] or x > x_range[1] or y < y_range[0] or y > y_range[1]:
continue
mask_count = sum(poly.contains(center) for poly in mask_polys)
if mask_count % 2 == 1:
masked = True
break
if masked == flg_mask:
# This polygon is output target.
writer.shape(data.shape)
writer.record(*data.record)
cnt_mesh = cnt_mesh + 1
if cnt_mesh % 100000 == 0:
print("{0}K / {1}K".format(cnt_mesh/1000, n_mesh/1000))
writer.close()
def extract_HUC8(
self,
HUC8,
output,
gagefile='gagestations',
verbose=True,
):
"""
Extracts the USGS gage stations for a watershed from the gage
station shapefile into a shapefile for the 8-digit hydrologic unit
code of interest.
"""
# make sure the metadata exist locally
self.download_metadata()
# make sure the output destination exists
if not os.path.isdir(output): os.mkdir(output)
sfile = '{}/{}'.format(output, gagefile)
if not os.path.isfile(sfile + '.shp'):
# copy the projection
shutil.copy(self.NWIS + '.prj', sfile + '.prj')
# read the file
gagereader = Reader(self.NWIS, shapeType=1)
gagerecords = gagereader.records()
# pull out the HUC8 record to parse the dataset
HUC8_index = gagereader.fields.index(['HUC', 'C', 8, 0]) - 1
# iterate through the field and find gages in the watershed
its = HUC8, sfile
print('extracting gage stations in {} to {}\n'.format(*its))
gage_indices = []
i = 0
for record in gagerecords:
if record[HUC8_index] == HUC8: gage_indices.append(i)
i += 1
# write the data from the HUC8 to a new shapefile
w = Writer(shapeType=1)
for field in gagereader.fields:
w.field(*field)
for i in gage_indices:
point = gagereader.shape(i).points[0]
w.point(*point)
w.record(*gagerecords[i])
w.save(sfile)
if verbose:
print('successfully extracted NWIS gage stations\n')
elif verbose:
print('gage station file {} exists\n'.format(sfile))
self.set_metadata(sfile)
def trans_vector(in_file, ot_dir, output_flg, dem_path, flood_flg):
"""
オンライン学習3 被害領域の抽出、ラスタベクタ変換
二値画像からポリゴンを生成します
関数 ： trans_vector
引数1 ： 入力ファイル名(.tif)
引数2 ： 出力ディレクトリ名
引数3 ： 出力フラグ(0：被災領域、1：非被災領域)
引数4 ： 数値標高ﾓﾃﾞﾙ名(.shp)
引数5 ： 災害フラグ(True：浸水、False：土砂崩れ)
"""
# Get destination file name
filename = path.splitext(path.basename(in_file))[0]
if filename.lower().startswith("sendai"):
basename = "Sendai"
elif filename.lower().startswith("kumamoto"):
basename = "Kumamoto"
else:
basename = filename
# Get actual file path
in_file = path.join(DATA_PATH_BASE, in_file)
ot_dir = path.join(DATA_PATH_BASE, ot_dir)
dem_path = path.join(DATA_PATH_BASE, dem_path)
makedirs(ot_dir, exist_ok=True)
print("creating shapefile ...")
# Create shapefile information of output area
fn_tmp = path.join(ot_dir, "tmp.shp")
writer = ShpWriter(target=fn_tmp, shapeType=POLYGON)
writer.field("id", "C", "20", 0)
writer.field("type", "C", "10", 0)
writer.field("format", "C", "10", 0)
writer.field("dis_tf", "C", "8", 0)
writer.field("dis_tt", "C", "8", 0)
writer.field("proc", "C", "8", 0)
writer.field("pre_dn", "C", "10", 0)
writer.field("pre_st", "C", "10", 0)
writer.field("post_dn", "C", "10", 0)
writer.field("post_st", "C", "10", 0)
"""
オンライン学習3 被害領域の抽出、ラスタベクタ変換
ポリゴンに付与する属性情報を定義するプログラムを実行します
関数 ： get_flood_record
関数 ： get_land_slide_record
"""
if flood_flg:
# flood processing
record = get_flood_record()
else:
# landslide processing
record = get_land_slide_record()
# Read binary image and get coordinate information
bin = imread(in_file)
rect_tiff = RectGeoTiffCoordinateCalculator(in_file)
# Create rectangle polygon of output area and output to shapefile
n_shape = bin.shape[0] * bin.shape[1]
cnt = 0
for x_index, y_index in itertools.product(range(bin.shape[1]),
range(bin.shape[0])):
"""
オンライン学習3 被害領域の抽出、ラスタベクタ変換
二値画像の各ピクセル四隅座標(緯度、経度)を計算するプログラムを実行します
関数 ： create_polygon_points
引数1 ： 対象ピクセルのx番号
引数2 ： 対象ピクセルのy番号
引数3 ： 二値画像の図形情報(ファイル名、画像サイズ等)を持つインスタンス
"""
points = create_polygon_points(x_index, y_index, rect_tiff)
"""
オンライン学習3 被害領域の抽出、ラスタベクタ変換
二値画像からメッシュを作成します
bin[y_index, x_index] == 255)：ピクセル値が255の場合
output_flg == "0"：被災領域のメッシュを作成
output_flg == "1"：非被災領域のメッシュを作成
"""
if (bin[y_index, x_index] == 255) == (output_flg == "0"):
# This pixel is output target.
writer.poly([points])
writer.record(*record)
cnt = cnt + 1
if cnt % 100000 == 0:
print("{0}K / {1}K".format(cnt / 1000, n_shape / 1000))
writer.close()
print("created shapefile .")
if output_flg == "0":
fn_out = path.join(
ot_dir, "{0}_{1}cbp.shp".format(basename,
time.strftime("%Y%m%d%H%M%S")))
else:
fn_out = path.join(
ot_dir, "{0}_{1}cbpr.shp".format(basename,
time.strftime("%Y%m%d%H%M%S")))
# Attach elevation value
"""
オンライン学習3 被害領域の抽出、ラスタベクタ変換
メッシュデータに標高値を付与するプログラムを実行します
関数 ： add_height_vector
引数1 ： 入力メッシュデータ名(.tif)
引数2 ： 数値標高ﾓﾃﾞﾙ名(.shp)
引数3 ： 出力ファイル名(.shp)
"""
add_height_vector([fn_tmp], dem_path, fn_out)
if not DEV_FLAG:
# Delete temporary file.
remove("{0}.shp".format(path.splitext(fn_tmp)[0]))
remove("{0}.shx".format(path.splitext(fn_tmp)[0]))
remove("{0}.dbf".format(path.splitext(fn_tmp)[0]))
def extract_HUC8(self, HUC8, output, gagefile = 'gagestations',
verbose = True):
"""Extracts the USGS gage stations for a watershed from the gage
station shapefile into a shapefile for the 8-digit hydrologic unit
code of interest.
"""
# make sure the metadata exist locally
self.download_metadata()
# make sure the output destination exists
if not os.path.isdir(output): os.mkdir(output)
sfile = '{}/{}'.format(output, gagefile)
if not os.path.isfile(sfile + '.shp'):
# copy the projection
shutil.copy(self.NWIS + '.prj', sfile + '.prj')
# read the file
gagereader = Reader(self.NWIS, shapeType = 1)
gagerecords = gagereader.records()
# pull out the HUC8 record to parse the dataset
HUC8_index = gagereader.fields.index(['HUC', 'C', 8, 0]) - 1
# iterate through the field and find gages in the watershed
its = HUC8, sfile
print('extracting gage stations in {} to {}\n'.format(*its))
gage_indices = []
i = 0
for record in gagerecords:
if record[HUC8_index] == HUC8: gage_indices.append(i)
i+=1
# write the data from the HUC8 to a new shapefile
w = Writer(shapeType = 1)
for field in gagereader.fields: w.field(*field)
for i in gage_indices:
point = gagereader.shape(i).points[0]
w.point(*point)
w.record(*gagerecords[i])
w.save(sfile)
if verbose:
print('successfully extracted NWIS gage stations\n')
elif verbose:
print('gage station file {} exists\n'.format(sfile))
self.set_metadata(sfile)
def add_height_vector(in_polys, in_hpoint, dst_fn):
"""
オンライン学習3 被害領域の抽出、ラスタベクタ変換
メッシュデータに標高値を付与します。
関数 ： add_height_vector
引数1 ： 入力メッシュデータ名(.tif)
引数2 ： 数値標高ﾓﾃﾞﾙ名(.shp)
引数3 ： 出力ファイル名(.shp)
"""
# Read DEM data
print("loading DEM data ...")
dem = GridData()
dem_reader = ShpReader(in_hpoint, encoding='cp932')
n_p = dem_reader.numRecords
cnt_p = 0
for data in dem_reader.iterShapeRecords():
point = Point(data.shape.points[0])
p_val = data.record
dem.add_data(point.x, point.y, p_val)
cnt_p = cnt_p + 1
if cnt_p % 100000 == 0:
print("{0}K / {1}K".format(cnt_p/1000, n_p/1000))
print("loaded DEM data .")
print()
# Process each polygon shapefile
for in_poly in in_polys:
print("processing {0} ...".format(in_poly))
poly_reader = ShpReader(in_poly)
poly_writer = ShpWriter(target=dst_fn)
# Create DBF schema
for col in poly_reader.fields:
if col[0] != "DeletionFlag":
poly_writer.field(col[0], col[1], col[2], col[3])
poly_writer.field("height", "N", 18, 9)
# Attach elevation value
n_poly = poly_reader.numRecords
cnt_poly = 0
for data in poly_reader.iterShapeRecords():
center = Polygon(data.shape.points).centroid
key_x = dem.search_nearest_x(center.coords[0][0])
key_y = dem.search_nearest_y(center.coords[0][1])
dem_record = dem.get_data(key_x, key_y)
if dem_record:
# Nearest grid point has elevation value
record = data.record + dem_record
else:
# Nearest grid point doesn't have elevation value
record = data.record + [None]
poly_writer.shape(data.shape)
poly_writer.record(*record)
cnt_poly = cnt_poly + 1
if cnt_poly % 100000 == 0:
print("{0}K / {1}K".format(cnt_poly/1000, n_poly/1000))
poly_writer.close()
print("processed {0} .".format(in_poly))
print()
def extract_bbox(self, bbox, output, verbose=True):
"""Extracts the NID dam locations for a watershed from the dam
shapefile and the 8-digit hydrologic unit code of interest.
"""
self.download_compressed()
xmin, ymin, xmax, ymax = bbox
# copy the projection files
if verbose: print('copying the projections from the NID source\n')
projection = self.source + '.prj'
shutil.copy(projection, output + '.prj')
# get the dams within the watershed
if verbose: print('reading the dam file\n')
sf = Reader(self.source, shapeType=1)
# work around for issues with pyshp
damrecords = []
for i in range(len(sf.shapes())):
try:
damrecords.append(sf.record(i))
except:
damrecords.append([-100 for i in range(len(sf.fields))])
name_index = sf.fields.index(['DAM_NAME', 'C', 65, 0]) - 1
nid_index = sf.fields.index(['NIDID', 'C', 7, 0]) - 1
long_index = sf.fields.index(['LONGITUDE', 'N', 19, 11]) - 1
lat_index = sf.fields.index(['LATITUDE', 'N', 19, 11]) - 1
river_index = sf.fields.index(['RIVER', 'C', 65, 0]) - 1
owner_index = sf.fields.index(['OWN_NAME', 'C', 65, 0]) - 1
type_index = sf.fields.index(['DAM_TYPE', 'C', 10, 0]) - 1
purp_index = sf.fields.index(['PURPOSES', 'C', 254, 0]) - 1
year_index = sf.fields.index(['YR_COMPL', 'C', 10, 0]) - 1
high_index = sf.fields.index(['NID_HEIGHT', 'N', 19, 11]) - 1
mstor_index = sf.fields.index(['MAX_STOR', 'N', 19, 11]) - 1
nstor_index = sf.fields.index(['NORMAL_STO', 'N', 19, 11]) - 1
area_index = sf.fields.index(['SURF_AREA', 'N', 19, 11]) - 1
# iterate through the fields and determine which points are in the box
if verbose: print('extracting dams into new file\n')
dam_indices = []
i = 0
for record in damrecords:
lat = record[lat_index]
lon = record[long_index]
if self.inside_box([xmin, ymin], [xmax, ymax], [lon, lat]):
dam_indices.append(i)
i += 1
# write the data from the bbox to a new shapefile
w = Writer(output, shapeType=1)
for field in sf.fields:
w.field(*field)
for i in dam_indices:
point = sf.shape(i).points[0]
w.point(*point)
values = damrecords[i]
rs = []
for value in values:
if isinstance(value, bytes): value = value.decode('utf-8')
rs.append(value)
w.record(*rs)
w.close()
if verbose:
print('successfully extracted NID dam locations to new file\n')
def extract_bbox(self, bbox, output, verbose = True):
"""Extracts the NID dam locations for a watershed from the dam
shapefile and the 8-digit hydrologic unit code of interest.
"""
self.download_compressed()
xmin, ymin, xmax, ymax = bbox
# copy the projection files
if verbose: print('copying the projections from the NID source\n')
projection = self.source + '.prj'
shutil.copy(projection, output + '.prj')
# get the dams within the watershed
if verbose: print('reading the dam file\n')
sf = Reader(self.source, shapeType = 1)
# work around for issues with pyshp
damrecords = []
for i in range(len(sf.shapes())):
try: damrecords.append(sf.record(i))
except: damrecords.append([-100 for i in range(len(sf.fields))])
name_index = sf.fields.index(['DAM_NAME', 'C', 65, 0]) - 1
nid_index = sf.fields.index(['NIDID', 'C', 7, 0]) - 1
long_index = sf.fields.index(['LONGITUDE', 'N', 19, 11]) - 1
lat_index = sf.fields.index(['LATITUDE', 'N', 19, 11]) - 1
river_index = sf.fields.index(['RIVER', 'C', 65, 0]) - 1
owner_index = sf.fields.index(['OWN_NAME', 'C', 65, 0]) - 1
type_index = sf.fields.index(['DAM_TYPE', 'C', 10, 0]) - 1
purp_index = sf.fields.index(['PURPOSES', 'C', 254, 0]) - 1
year_index = sf.fields.index(['YR_COMPL', 'C', 10, 0]) - 1
high_index = sf.fields.index(['NID_HEIGHT', 'N', 19, 11]) - 1
mstor_index = sf.fields.index(['MAX_STOR', 'N', 19, 11]) - 1
nstor_index = sf.fields.index(['NORMAL_STO', 'N', 19, 11]) - 1
area_index = sf.fields.index(['SURF_AREA', 'N', 19, 11]) - 1
# iterate through the fields and determine which points are in the box
if verbose: print('extracting dams into new file\n')
dam_indices = []
i = 0
for record in damrecords:
lat = record[lat_index]
lon = record[long_index]
if self.inside_box([xmin, ymin], [xmax, ymax], [lon, lat]):
dam_indices.append(i)
i+=1
# write the data from the bbox to a new shapefile
w = Writer(shapeType = 1)
for field in sf.fields: w.field(*field)
for i in dam_indices:
point = sf.shape(i).points[0]
w.point(*point)
values = damrecords[i]
rs = []
for value in values:
if isinstance(value, bytes): value = value.decode('utf-8')
rs.append(value)
w.record(*rs)
w.save(output)
if verbose:
print('successfully extracted NID dam locations to new file\n')
def merge_shapes(inputfile, outputfile = None, overwrite = False,
verbose = True, vverbose = False):
"""Merges all the shapes in a shapefile into a single shape."""
if outputfile is None: output = '{}/merged'.format(os.getcwd())
if os.path.isfile(outputfile + '.shp') and not overwrite:
if verbose: print('combined watershed shapefile %s exists' % outputfile)
return
if verbose: print('combining shapes from %s\n' % inputfile)
# start by copying the projection files
shutil.copy(inputfile + '.prj', outputfile + '.prj')
# load the catchment and flowline shapefiles
r = Reader(inputfile, shapeType = 5)
n = len(r.records())
try:
shapes = []
records = []
bboxes = []
for i in range(n):
shape = r.shape(i)
record = r.record(i)
shape_list = format_shape(shape.points)
for sh in shape_list:
shapes.append(sh)
records.append(record)
bboxes.append(shape.bbox)
try: combined = combine_shapes(shapes, bboxes,
verbose = vverbose)
except:
if verbose: print('trying alternate trace method')
combined = combine_shapes(shapes, bboxes, skip = True,
verbose = vverbose)
except:
if verbose: print('trying alternate trace method')
shapes = []
records = []
bboxes = []
for i in range(n):
shape = r.shape(i)
record = r.record(i)
shape_list = format_shape(shape.points, omit = True)
for sh in shape_list:
shapes.append(sh)
records.append(record)
bboxes.append(shape.bbox)
try: combined = combine_shapes(shapes, bboxes, verbose = vverbose)
except:
if verbose: print('trying alternate trace method')
combined = combine_shapes(shapes, bboxes, skip = True,
verbose = vverbose)
# create the new file with the merged shapes
w = Writer(shapeType = 5)
w.poly(shapeType = 5, parts = [combined])
# copy the fields from the original and then the first record; note this
# can be adapted as needed
for field in r.fields: w.field(*field)
w.record(*r.record(0))
w.save(outputfile)
if verbose:
print('successfully combined shapes from %s to %s\n' %
(inputfile, outputfile))
def merge_shapes(inputfile, outputfile = None, overwrite = False,
verbose = True, vverbose = False):
"""Merges all the shapes in a shapefile into a single shape."""
if outputfile is None: output = '{}/merged'.format(os.getcwd())
if os.path.isfile(outputfile + '.shp') and not overwrite:
if verbose: print('combined watershed shapefile %s exists' % outputfile)
return
if verbose: print('combining shapes from {}\n'.format(inputfile) +
'this may take a while...\n')
# start by copying the projection files
shutil.copy(inputfile + '.prj', outputfile + '.prj')
# load the catchment and flowline shapefiles
r = Reader(inputfile, shapeType = 5)
n = len(r.records())
try:
shapes = []
records = []
bboxes = []
for i in range(n):
shape = r.shape(i)
record = r.record(i)
shape_list = format_shape(shape.points)
for sh in shape_list:
shapes.append(sh)
records.append(record)
bboxes.append(shape.bbox)
try: combined = combine_shapes(shapes, bboxes,
verbose = vverbose)
except:
if verbose: print('trying alternate trace method')
combined = combine_shapes(shapes, bboxes, skip = True,
verbose = vverbose)
except:
if verbose: print('trying alternate trace method')
shapes = []
records = []
bboxes = []
for i in range(n):
shape = r.shape(i)
record = r.record(i)
shape_list = format_shape(shape.points, omit = True)
for sh in shape_list:
shapes.append(sh)
records.append(record)
bboxes.append(shape.bbox)
try: combined = combine_shapes(shapes, bboxes, verbose = vverbose)
except:
if verbose: print('trying alternate trace method')
combined = combine_shapes(shapes, bboxes, skip = True,
verbose = vverbose)
# create the new file with the merged shapes
w = Writer(shapeType = 5)
w.poly(shapeType = 5, parts = [combined])
# copy the fields from the original and then the first record; note this
# can be adapted as needed
for field in r.fields: w.field(*field)
w.record(*r.record(0))
w.save(outputfile)
if verbose:
print('successfully combined shapes from %s to %s\n' %
(inputfile, outputfile))
def export_data(self, query):
def get_label(item):
label = item.descriptor
if label is None:
return None
return label.label
def abbrev_to(name, chars, columns_abbrev):
if len(name) > chars:
n = 1
while True:
name_new = name[:chars - len(str(n))] + str(n)
if not name_new in columns_abbrev.values():
return name_new
n += 1
return name
path = as_path(self.url, check_if_exists = False)
if path is None:
return
geometries = [] # [[coords, geometry_type], ...]
row_idxs = [] # [row_idx, ...]
for row_idx, row in enumerate(query):
for column in row:
label = get_label(row[column])
if label.__class__.__name__ == "DGeometry":
geometries.append(label.coords)
row_idxs.append(row_idx)
break
if not row_idxs:
return
columns_abbrev = {} # {column: column_abbrev, ...}; abbreviated column names
for column in query.columns:
column_abbrev = column
if len(column_abbrev) > 10:
if "." in column_abbrev:
column_abbrev = column_abbrev.split(".")
column_abbrev = "_".join([abbrev_to(column_abbrev[0], 4, columns_abbrev), abbrev_to(column_abbrev[1], 5, columns_abbrev)])
else:
column_abbrev = abbrev_to(column_abbrev, 10, columns_abbrev)
column_abbrev = column_abbrev.replace(".", "_")
columns_abbrev[column] = column_abbrev
shapeType = -1
shape_types = {
"POINT": 1,
"LINESTRING": 3,
"POLYGON": 5,
"MULTIPOINT": 8,
"POINTZ": 11,
"LINESTRINGZ": 13,
"POLYGONZ": 15,
"MULTIPOINTZ": 18,
"POINTM": 21,
"LINESTRINGM": 23,
"POLYGONM": 25,
"MULTIPOINTM": 28,
}
for _, geometry_type in geometries:
if geometry_type not in shape_types:
raise Exception("Unknown geometry type")
if shapeType > -1:
if shape_types[geometry_type] != shapeType:
raise Exception("Geometries must be of the same type")
else:
shapeType = shape_types[geometry_type]
sf = Writer(shapeType = shapeType)
types = {} # {column: type, ...}
shp_types = {bool: "C", int: "N", float: "N", str: "C"}
conv_order = ["N", "C"]
for row in query:
for column in row:
label = get_label(row[column])
if label.__class__.__name__ != "DString":
continue
value = label.try_numeric
typ = type(value)
typ = shp_types[typ] if typ in shp_types else "C"
if (not column in types) or ((typ != types[column]) and (conv_order.index(typ) > conv_order.index(types[column]))):
types[column] = typ
for column in types:
sf.field(columns_abbrev[column], fieldType = types[column], size = "128")
for i in range(len(geometries)):
row = query[row_idxs[i]]
coords = geometries[i][0]
if shapeType in [1, 11, 21]: # point types
sf.point(*coords[0], shapeType = shapeType)
else:
sf.poly(shapeType = shapeType, parts = [coords])
if types:
record = []
for column in types:
label = get_label(row[column])
if label is not None:
label = label.value
record.append(label)
sf.record(*record)
sf.save(path)
def combine_catchments(catchmentfile,
flowfile,
elevationfile,
comid,
output=None,
overwrite=False,
verbose=True):
"""Combines together all the catchments in a basin catchment shapefile.
Creates a new shapefile called "combined" in the same directory as the
original file. Uses the elevation data from the raster file and the flow
data file to estimate the length and average slope of the overland flow
plane.
"""
t0 = time.time()
numpy.seterr(all='raise')
if output is None: output = os.getcwd() + r'\combined'
if os.path.isfile(output + '.shp') and not overwrite:
if verbose: print('combined catchment shapefile %s exists' % output)
return
if verbose: print('combining catchments from %s\n' % catchmentfile)
# start by copying the projection files
shutil.copy(catchmentfile + '.prj', output + '.prj')
# load the catchment and flowline shapefiles
c = Reader(catchmentfile, shapeType=5)
f = Reader(flowfile, shapeType=3)
# make lists of the comids and featureids
featureid_index = c.fields.index(['FEATUREID', 'N', 9, 0]) - 1
comid_index = f.fields.index(['COMID', 'N', 9, 0]) - 1
featureids = [r[featureid_index] for r in c.records()]
comids = [r[comid_index] for r in f.records()]
# check that shapes are traceable--don't have multiple points and start
# and end at the same place--then make an appropriate list of shapes
# and records--note it's more memory efficient to read one at a time
n = len(c.records())
shapes = []
records = []
bboxes = []
try:
for i in range(n):
catchment = c.shape(i)
record = c.record(i)
shape_list = format_shape(catchment.points)
for s in shape_list:
shapes.append(s)
records.append(record)
bboxes.append(catchment.bbox)
try:
combined = combine_shapes(shapes, bboxes, verbose=verbose)
except:
combined = combine_shapes(shapes,
bboxes,
skip=True,
verbose=verbose)
except:
shapes = []
records = []
bboxes = []
for i in range(n):
catchment = c.shape(i)
record = c.record(i)
shape_list = format_shape(catchment.points, omit=True)
for s in shape_list:
shapes.append(s)
records.append(record)
bboxes.append(catchment.bbox)
try:
combined = combine_shapes(shapes, bboxes, verbose=verbose)
except:
combined = combine_shapes(shapes,
bboxes,
skip=True,
verbose=verbose)
# iterate through the catchments and get the elevation data from NED
# then estimate the value of the overland flow plane length and slope
lengths = numpy.empty((n), dtype='float')
slopes = numpy.empty((n), dtype='float')
for i in range(n):
catchment = c.shape(i)
flowline = f.shape(comids.index(featureids[i]))
catchpoints = get_raster_on_poly(elevationfile,
catchment.points,
verbose=verbose)
catchpoints = numpy.array([p for p in catchpoints])
zs = get_raster(elevationfile, flowline.points)
flowpoints = numpy.array([[p[0], p[1], z]
for p, z in zip(flowline.points, zs)])
# iterate through the raster values and find the closest flow point
closest = numpy.empty((len(catchpoints), 3), dtype='float')
for point, j in zip(catchpoints, range(len(catchpoints))):
closest[j] = flowpoints[numpy.dot(flowpoints[:, :2],
point[:2]).argmin()]
# estimate the slope and overland flow plane length
length, slope = get_overland_vector(catchpoints, closest)
if verbose:
print('avg slope and length =', slope.mean(), length.mean())
lengths[i], slopes[i] = length.mean(), slope.mean()
if verbose: print('\nfinished overland flow plane calculations\n')
# get area of the subbasin from the catchment metadata
areasq_index = c.fields.index(['AreaSqKM', 'N', 19, 6]) - 1
areas = numpy.array([r[areasq_index] for r in c.records()])
# take the area weighted average of the slopes and flow lengths
tot_area = round(areas.sum(), 2)
avg_length = round(1000 * numpy.sum(areas * lengths) / tot_area, 1)
avg_slope = round(numpy.sum(areas * slopes) / tot_area, 4)
# get the centroid and the average elevation
combined = [[float(x), float(y)] for x, y in combined]
centroid = get_centroid(numpy.array(combined))
Cx, Cy = round(centroid[0], 4), round(centroid[1], 4)
elev_matrix, origin = get_raster_in_poly(elevationfile,
combined,
verbose=verbose)
elev_matrix = elev_matrix.flatten()
elev_matrix = elev_matrix[elev_matrix.nonzero()]
avg_elev = round(elev_matrix.mean() / 100., 2)
# write the data to the shapefile
w = Writer(shapeType=5)
fields = [['ComID', 'N', 9, 0], ['PlaneLenM', 'N', 8, 2],
['PlaneSlope', 'N', 9, 6], ['AreaSqKm', 'N', 10, 2],
['CenX', 'N', 12, 6], ['CenY', 'N', 12, 6],
['AvgElevM', 'N', 8, 2]]
record = [comid, avg_length, avg_slope, tot_area, Cx, Cy, avg_elev]
for field in fields:
w.field(*field)
w.record(*record)
w.poly(shapeType=5, parts=[combined])
w.save(output)
if verbose:
print('\ncompleted catchment combination in %.1f seconds\n' %
(time.time() - t0))
def plot_gage_subbasin(self, hspfmodel, folder):
"""Makes a plot of the subbasin area."""
subbasinfile = '{}/subbasins'.format(folder)
boundaryfile = '{}/boundary'.format(folder)
flowfile = '{}/flowlines'.format(folder)
combinedfile = '{}/combined'.format(folder)
watershedplot = '{}/watershed.png'.format(folder)
# make a shapefile of the subbasins for the watershed
f = '{0}/{1}/{1}subbasins'.format(self.directory, self.HUC8)
for out in (subbasinfile, boundaryfile, flowfile, combinedfile):
if not os.path.isfile(out + '.prj'):
shutil.copy(f + '.prj', out + '.prj')
if not os.path.isfile(subbasinfile + '.shp'):
subshapes = []
subrecords = []
for subbasin in hspfmodel.subbasins:
f = '{0}/{1}/{2}/combined'.format(self.directory, self.HUC8,
subbasin)
s = Reader(f, shapeType=5)
subshapes.append(s.shape(0).points)
subrecords.append(s.record(0))
w = Writer(shapeType=5)
for field in s.fields:
w.field(*field)
for record in subrecords:
w.record(*record)
for shape in subshapes:
w.poly(shapeType=5, parts=[shape])
w.save(subbasinfile)
if not os.path.isfile(combinedfile + '.shp'):
fshapes = []
frecords = []
for subbasin in hspfmodel.subbasins:
f = '{0}/{1}/{2}/combined_flowline'.format(
self.directory, self.HUC8, subbasin)
r = Reader(f, shapeType=3)
fshapes.append(r.shape(0).points)
frecords.append(r.record(0))
w = Writer(shapeType=3)
for field in r.fields:
w.field(*field)
for record in frecords:
w.record(*record)
for shape in fshapes:
w.poly(shapeType=3, parts=[shape])
w.save(combinedfile)
# merge the shapes into a watershed
if not os.path.exists(boundaryfile + '.shp'):
merge_shapes(subbasinfile, outputfile=boundaryfile)
# make a flowline file for the subbasins for the watershed
if not os.path.isfile(flowfile + '.shp'):
shapes = []
records = []
for subbasin in hspfmodel.subbasins:
f = '{0}/{1}/{2}/flowlines'.format(self.directory, self.HUC8,
subbasin)
r = Reader(f, shapeType=3)
for shape in r.shapes():
shapes.append(shape.points)
for record in r.records():
records.append(record)
w = Writer(shapeType=3)
for field in r.fields:
w.field(*field)
for record in records:
w.record(*record)
for shape in shapes:
w.poly(shapeType=3, parts=[shape])
w.save(flowfile)
if not os.path.isfile(watershedplot):
plot_gage_subbasin(folder,
self.HUC8,
self.gageid,
hspfmodel,
output=watershedplot)
def extract_aquifers(directory, HUC8, aquifers, pad=0.2, verbose=True):
"""Extracts aquifers from the source datafile to the destination using
the HUC8 boundaries for the query."""
start = time.time()
# open up the HUC8 boundary shapefile and use it to get the bounding box
shapefile = Reader(directory + '/%s/%scatchments' % (HUC8, HUC8))
xmin, ymin, xmax, ymax = get_boundaries(shapefile.shapes())
# convert to bounding corners for testing
p1 = [xmin - pad * (xmax - xmin), ymin - pad * (ymax - ymin)]
p2 = [xmax + pad * (xmax - xmin), ymax + pad * (ymax - ymin)]
shapefile = None
# start by copying the projection files
if verbose: print('\ncopying the projections\n')
shutil.copy(directory + '/%s/%scatchments.prj' % (HUC8, HUC8),
directory + '/%s/%saquifers.prj' % (HUC8, HUC8))
# open the flowline file
if verbose: print('reading the aquifer file\n')
shapefile = Reader(aquifers, shapeType=5)
# work around for issues with pyshp
records = []
for i in range(len(shapefile.shapes())):
try:
records.append(shapefile.record(i))
except:
records.append('')
# use the bounding boxes to see if the shapes are within the watershed area
if verbose: print('searching for aquifers in the watershed\n')
bboxes = [shapefile.shape(i).bbox for i in range(len(records))]
corners = [[[b[0], b[1]], [b[0], b[3]], [b[2], b[1]], [b[2], b[3]]]
for b in bboxes]
indices = [
i for i, c in zip(range(len(corners)), corners)
if any([inside_box(p1, p2, p) for p in c])
or all([inside_box(c[0], c[3], p1),
inside_box(c[0], c[3], p2)])
]
# remove any non aquifers
indices = [i for i in indices if shapefile.record(i)[4] != 999]
# find a record for the non aquifer
i = 0
while shapefile.record(i)[4] != 999:
i += 1
nonrecord = shapefile.record(i)
nonrecord[1] = nonrecord[1].decode('utf-8')
nonrecord[5] = 0
nonrecord[6] = 0
if len(indices) == 0:
if verbose: print('query returned no values, returning\n')
return
# write the data from the HUC8 to a new shapefile
w = Writer(directory + '/%s/%saquifers' % (HUC8, HUC8), shapeType=5)
for field in shapefile.fields:
w.field(*field)
for i in indices:
shape = shapefile.shape(i)
# check for multiple parts
if len(shape.parts) > 1:
parts = [
shape.points[i:j]
for i, j in zip(shape.parts[:-1], shape.parts[1:])
]
else:
parts = [shape.points]
record = records[i]
# little work around for blank binary values
if isinstance(record[1], bytes):
record[1] = record[1].decode('utf-8')
w.poly(shapeType=5, parts=parts)
w.record(*record)
# add a shape for the bounding box showing no aquifer locations
part = [p1, [p1[0], p2[1]], p2, [p2[0], p1[1]]]
w.poly(shapeType=5, parts=[part])
w.record(*nonrecord)
w.close()
end = time.time()
if verbose:
print('successfully queried data in %.2f seconds\n' % (end - start))
def plot_gage_subbasin(self, hspfmodel, folder):
"""Makes a plot of the subbasin area."""
subbasinfile = '{}/subbasins'.format(folder)
boundaryfile = '{}/boundary'.format(folder)
flowfile = '{}/flowlines'.format(folder)
combinedfile = '{}/combined'.format(folder)
watershedplot = '{}/watershed.png'.format(folder)
# make a shapefile of the subbasins for the watershed
f = '{0}/{1}/{1}subbasins'.format(self.directory, self.HUC8)
for out in (subbasinfile, boundaryfile, flowfile, combinedfile):
if not os.path.isfile(out + '.prj'):
shutil.copy(f + '.prj', out + '.prj')
if not os.path.isfile(subbasinfile + '.shp'):
subshapes = []
subrecords = []
for subbasin in hspfmodel.subbasins:
f = '{0}/{1}/{2}/combined'.format(self.directory, self.HUC8,
subbasin)
s = Reader(f, shapeType = 5)
subshapes.append(s.shape(0).points)
subrecords.append(s.record(0))
w = Writer(shapeType = 5)
for field in s.fields: w.field(*field)
for record in subrecords: w.record(*record)
for shape in subshapes: w.poly(shapeType = 5, parts = [shape])
w.save(subbasinfile)
if not os.path.isfile(combinedfile + '.shp'):
fshapes = []
frecords = []
for subbasin in hspfmodel.subbasins:
f = '{0}/{1}/{2}/combined_flowline'.format(self.directory,
self.HUC8,
subbasin)
r = Reader(f, shapeType = 3)
fshapes.append(r.shape(0).points)
frecords.append(r.record(0))
w = Writer(shapeType = 3)
for field in r.fields: w.field(*field)
for record in frecords: w.record(*record)
for shape in fshapes: w.poly(shapeType = 3, parts = [shape])
w.save(combinedfile)
# merge the shapes into a watershed
if not os.path.exists(boundaryfile + '.shp'):
merge_shapes(subbasinfile, outputfile = boundaryfile)
# make a flowline file for the subbasins for the watershed
if not os.path.isfile(flowfile + '.shp'):
shapes = []
records = []
for subbasin in hspfmodel.subbasins:
f = '{0}/{1}/{2}/flowlines'.format(self.directory,
self.HUC8, subbasin)
r = Reader(f, shapeType = 3)
for shape in r.shapes(): shapes.append(shape.points)
for record in r.records(): records.append(record)
w = Writer(shapeType = 3)
for field in r.fields: w.field(*field)
for record in records: w.record(*record)
for shape in shapes: w.poly(shapeType = 3, parts = [shape])
w.save(flowfile)
if not os.path.isfile(watershedplot):
plot_gage_subbasin(folder, self.HUC8, self.gageid, hspfmodel,
output = watershedplot)
def extract_aquifers(directory, HUC8, aquifers, pad = 0.2, verbose = True):
"""Extracts aquifers from the source datafile to the destination using
the HUC8 boundaries for the query."""
start = time.time()
# open up the HUC8 boundary shapefile and use it to get the bounding box
shapefile = Reader(directory + '/%s/%scatchments' % (HUC8, HUC8))
xmin, ymin, xmax, ymax = get_boundaries(shapefile.shapes())
# convert to bounding corners for testing
p1 = [xmin - pad * (xmax - xmin), ymin - pad * (ymax - ymin)]
p2 = [xmax + pad * (xmax - xmin), ymax + pad * (ymax - ymin)]
shapefile = None
# start by copying the projection files
if verbose: print('\ncopying the projections\n')
shutil.copy(directory + '/%s/%scatchments.prj' % (HUC8, HUC8),
directory + '/%s/%saquifers.prj' % (HUC8, HUC8))
# open the flowline file
if verbose: print('reading the aquifer file\n')
shapefile = Reader(aquifers, shapeType = 5)
# work around for issues with pyshp
records = []
for i in range(len(shapefile.shapes())):
try: records.append(shapefile.record(i))
except: records.append('')
# use the bounding boxes to see if the shapes are within the watershed area
if verbose: print('searching for aquifers in the watershed\n')
bboxes = [shapefile.shape(i).bbox for i in range(len(records))]
corners = [[[b[0], b[1]], [b[0], b[3]], [b[2], b[1]], [b[2], b[3]]]
for b in bboxes]
indices = [i for i, c in zip(range(len(corners)), corners) if
any([inside_box(p1, p2, p) for p in c]) or
all([inside_box(c[0], c[3], p1), inside_box(c[0], c[3], p2)])]
# remove any non aquifers
indices = [i for i in indices if shapefile.record(i)[4] != 999]
# find a record for the non aquifer
i = 0
while shapefile.record(i)[4] != 999: i+=1
nonrecord = shapefile.record(i)
nonrecord[1] = nonrecord[1].decode('utf-8')
nonrecord[5] = 0
nonrecord[6] = 0
if len(indices) == 0:
if verbose: print('query returned no values, returning\n')
return
# write the data from the HUC8 to a new shapefile
w = Writer(shapeType = 5)
for field in shapefile.fields: w.field(*field)
for i in indices:
shape = shapefile.shape(i)
# check for multiple parts
if len(shape.parts) > 1:
parts = [shape.points[i:j]
for i, j in zip(shape.parts[:-1], shape.parts[1:])]
else: parts = [shape.points]
record = records[i]
# little work around for blank binary values
if isinstance(record[1], bytes):
record[1] = record[1].decode('utf-8')
w.poly(shapeType = 5, parts = parts)
w.record(*record)
# add a shape for the bounding box showing no aquifer locations
part = [p1, [p1[0], p2[1]], p2, [p2[0], p1[1]]]
w.poly(shapeType = 5, parts = [part])
w.record(*nonrecord)
w.save(directory + '/%s/%saquifers' % (HUC8, HUC8))
end = time.time()
if verbose:
print('successfully queried data in %.2f seconds\n' % (end - start))