Exemplo n.º 1
0
def create_basin_csvs(basin_ls, gdrive, folder):
    """Determines if a basin csv exists.

        This function

        Parameters:
            nabd_dams (pandas.DataFrame): 
                
        
        Returns:
    """   
    ## If the specified basin csv does not exist, extract it
    os.chdir(gdrive+folder)
    read_flag = False

    for basin in basin_ls:
        if os.path.isfile(basin+'.csv'):  #does it exist?
            #Read specified basin 
            print(basin + ': Exists')

        else:
            if read_flag == False:
                flowlines, dams = read.read_lines_dams(gdrive)
                read_flag = True
            print('\n', basin +  ': Does not exist')
            nabd_nhd = ex.join_dams_flowlines(basin, flowlines, dams)
Exemplo n.º 2
0
def create_basin_csvs(basin_ls, gdrive, folder):
    """Determines if a basin csv exists and create it if needed.

        This function passes in a list of basins and determines if a csv with each
        basin name exists. If the csv does not exist, it is created with read.py.
        The read_flag ensures that read.py is only executed once.

        Parameters:
            basin_ls (List):
                List of basins to be analyzed.
            gdrive (string):
                Location on the Google Drive to save the csvs.
            folder (string):
                Folder on the Google Drive where csvs will be saved.
            read_flag (boolean): 
                If False, flowlines and dams will be read in with read.py.
                If True, flowlines and dams will not be read in because they
                    already have been.
            flowlines (pandas.DataFrame):
                Dataframe containing all NHD flowlines from read.py.
            dams (pandas.DataFrame):
                Dataframe containing all dams from read.py.
                
        
        Returns:
            Csvs for each of the basins listed in basin_ls in a specified folder
                on the Google Drive.
    """
    ## If the specified basin csv does not exist, extract it
    os.chdir(gdrive + folder)
    read_flag = False

    for basin in basin_ls:
        if os.path.isfile(basin + '.csv'):  #does it exist?
            #Read specified basin
            print(basin + ': Exists')

        else:
            if read_flag == False:
                flowlines, dams = read.read_lines_dams(gdrive)
                read_flag = True
            print('\n', basin + ': Does not exist')
            ex.join_dams_flowlines(basin, flowlines, dams)
# function = ex.my_function()

# If the specified basin csv does not exist, extract it
if os.path.isfile(run_name + '.csv'):  #does it exist?
    #Read specified basin
    print('exists')
    segments = pd.read_csv(run_name + '.csv',
                           usecols=[
                               'Hydroseq', 'UpHydroseq', 'DnHydroseq',
                               'LENGTHKM', 'StartFlag', 'Coordinates', 'DamID',
                               'DamCount'
                           ])
else:
    print('does not exist')
    nabd_nhd = ex.join_dams_flowlines(run_name)
    filtered_join = ex.filter_join(nabd_nhd, run_name)  #filter the joined data
    segments = ex.filter_join(nabd_nhd, run_name)  #filter the joined data
    # segments = pd.read_csv(run_name+'.csv', usecols=['Hydroseq', 'UpHydroseq',
    #                                                  'DnHydroseq', 'LENGTHKM',
    #                                                  'StartFlag','Coordinates',
    #                                                  'DamID', 'DamCount'])

#%%
# STEP1:  Make Fragments
segments = bfc.make_fragments(segments, exit_id=99900000)

# STEP 2: Making a fragment data frame and aggregated by fragment
fragments = bfc.agg_by_frag(segments)

# STEP 3: Map Upstream Fragments
# %%
# Read in data
## If the specified basin csv does not exist, extract it
if os.path.isfile(run_name + '.csv'):  #does it exist?
    #Read specified basin
    print('Exists')
    segments = pd.read_csv(run_name + 'test.csv',
                           usecols=[
                               'Hydroseq', 'UpHydroseq', 'DnHydroseq',
                               'LENGTHKM', 'StartFlag', 'Coordinates', 'DamID',
                               'DamCount'
                           ])
else:
    print('Does not exist')
    nabd_nhd = ex.join_dams_flowlines(flowlines, run_name, nabd)
    segments = pd.read_csv(run_name + 'test.csv',
                           usecols=[
                               'Hydroseq', 'UpHydroseq', 'DnHydroseq',
                               'LENGTHKM', 'StartFlag', 'Coordinates', 'DamID',
                               'DamCount'
                           ])

#%%
from time import time

t1 = time()
# STEP1:  Make Fragments
segments = bfup.make_fragments(segments, exit_id=99900000)
t2 = time()
print("Make Fragments:", (t2 - t1))
Exemplo n.º 5
0
flowlines['REACHCODE'] = flowlines['REACHCODE'] / (
    10**12)  #convert Reachcode to HUC 2 format
# flowlines['REACHCODE'] = flowlines['REACHCODE']/(10**10) #convert Reachcode to HUC 4 format
flowlines['REACHCODE'] = flowlines['REACHCODE'].apply(
    np.floor)  #round down to integer
#round the hydroseq values because of bug
flowlines[['UpHydroseq', 'DnHydroseq',
           'Hydroseq']] = flowlines[['UpHydroseq', 'DnHydroseq',
                                     'Hydroseq']].round(decimals=0)
# flowlines = flowlines[flowlines['FTYPE']!= 'Coastline']  #filter out coastlines

# %%
# Read in data
# basin_ls = ['Red']
# basin_ls = ['California', 'Colorado', 'Columbia', 'Great Basin', 'Great Lakes',
# 'Gulf Coast','Mississippi', 'North Atlantic', 'Red', 'Rio Grande','South Atlantic']

basin_ls = ['Columbia']

## If the specified basin csv does not exist, extract it
for basin in basin_ls:
    if os.path.isfile(basin + '.csv'):  #does it exist?
        #Read specified basin
        print(basin + ' Exists')

    else:
        print(basin + ' Does not exist')
        nabd_nhd = ex.join_dams_flowlines(flowlines, basin, nabd)

# %%