def main():
from Preprocessing.utilities import nhd_states
# Set parameters
overwrite = True
# Set initial paths
nhd_path = os.path.join("..", "bin", "Preprocessed", "CondensedNHD")
output_path = os.path.join(r"..\bin\Preprocessed\Navigators",
"region_{}.npz")
# Loop through regions
for region in nhd_states.keys():
print(region)
# Designate paths
nhd_table = HydroTable(region, nhd_path)
nodes, times, conversion_dict = get_nodes(nhd_table)
attributes = np.array([times])
outlets = identify_outlets(nhd_table, conversion_dict)
paths, attribute_matrix = upstream_trace(nodes, outlets, attributes)
path_map = map_paths(paths)
paths, attribute_matrix, start_cols = collapse_array(
paths, attribute_matrix)
conversion_array = dict_to_array(conversion_dict)
write_outfile(output_path.format(region), paths, path_map,
conversion_array, attribute_matrix[0])
def main():
from Preprocessing.utilities import nhd_states
# Set initial paths
nhd_path = os.path.join("..", "bin", "Preprocessed", "CondensedNHD")
nav_path = os.path.join("..", "bin", "Preprocessed", "Navigators")
volume_path = os.path.join(r"T:\NationalData\LakeMorphometry",
"region_{}.dbf")
output_path = os.path.join(r"..\bin\Preprocessed\LakeFiles",
"region_{}.npz")
# Loop through regions
for region in nhd_states.keys():
# Read tables and set output path
nhd_table = HydroTable(region, nhd_path)
nav = Navigator(region, nav_path)
volume_table_path = volume_path.format(region)
outfile_path = output_path.format(region)
# Get a table of all lentic reaches, with the COMID of the reach and waterbody
reservoir_table = nhd_table[["comid", "wb_comid", "hydroseq",
"qma"]].rename(columns={'qma': 'flow'})
# Get the outlets for each reservoir
reservoir_table = identify_outlets(reservoir_table)
# Get residence times
reservoir_table = get_residence_times(reservoir_table,
volume_table_path)
# Count number of reservoirs upstream of each reservoir
reservoir_table = make_lake_bins(nav, reservoir_table)
# Save table
save_table(reservoir_table, outfile_path)
def main():
condensed_nhd_path = os.path.join("..", "bin", "Preprocessed", "CondensedNHD")
output_path = os.path.join("..", "bin", "Preprocessed", "FlowFiles")
for region in nhd_states.keys():
nhd_table = HydroTable(region, condensed_nhd_path)
out_table = os.path.join(output_path, "region_{}.npz".format(region))
extract_flow_data(nhd_table, out_table)
def get_nhd(nhd_dir=r"T:\NationalData\NHDPlusV2"):
all_paths = defaultdict()
regions = list(nhd_states.keys())
region_dirs = {"NHDPlus{}".format(region) for region in regions}
for root_dir, sub_dirs, _ in os.walk(nhd_dir):
if set(sub_dirs) & region_dirs:
for sub_dir in sub_dirs:
region = sub_dir.lstrip("NHDPlus")
if region in regions:
all_paths[sub_dir.lstrip("NHDPlus")] = os.path.join(
root_dir, sub_dir)
return OrderedDict(sorted(all_paths.items()))
def main():
nhd_dir = r"..\bin\Preprocessed\CondensedNHD"
scenario_dir = r"..\bin\Preprocessed\ScenarioMatrices"
scenario_format = r"([A-Z]{2})_scenarios_agg_(\d{6}).txt"
recipe_path = r"..\bin\Preprocessed\Recipes"
recipe_format = "recipes_(\d{8})cdl_(\d{4}).txt"
output_dir = os.path.join(r"..\bin\Preprocessed\RecipeMaps")
region_filter = ('07', ) # 'all'
# Get an index of all scenario names for each state and date
scenario_files = get_scenario_index(scenario_dir, scenario_format)
# Identify all recipe files and index by HUC-2 and year
recipe_files = assemble_files(recipe_path, recipe_format,
nhd_states.keys())
for region, states in nhd_states.items():
regional_recipes = recipe_files.get(region)
out_file = os.path.join(output_dir, "region_" + region)
if os.path.exists(out_file):
os.remove(out_file)
if regional_recipes is not None:
if region in region_filter or region_filter == 'all':
# Get a list of all recipes and scenarios in the region for aliasing and structuring output
# scenario_index = compile_scenario_index(states, scenario_files)
#scenario_index.to_csv(r"..\bin\temp_index.txt")
print("Reading scenario index...")
scenario_index = pd.Series.from_csv(r"..\bin\temp_index.txt")
# Get all the reach IDs in the region
#inventory, n_lines = inventory_files(regional_recipes)
#inventory.to_csv(r"..\bin\temp_inventory.txt")
#print(n_lines)
print("Reading inventory file...")
inventory = pd.read_csv(r"..\bin\temp_inventory.txt")
n_lines = 96637799
# Unpack recipe files into matrix
print("Generating recipe map...")
generate_recipe_map(scenario_index, inventory, n_lines,
out_file)