def load_shape_arrays(shapename,rowname='row',colname='column'):
#--get the field names and make sure rowname and colname are found
field_names = shapefile.get_fieldnames(grid_shapename)
assert rowname in field_names
assert colname in field_names
#--get the decimal of each field - to get the array type later
grid_shp = shapefile.Reader(shapename)
header = grid_shp.dbfHeader()
h_dict = {}
for item in header:
h_dict[item[0]] = int(item[-1])
#--load all of the records as a dict
records = shapefile.load_as_dict(shapename,loadShapes=False)
#--get nrow and ncol
nrow = max(records[rowname])
ncol = max(records[colname])
#--row and column maps
row,col = records[rowname],records[colname]
#--setup a dict for all of the arrays and map the values
array_dict = {}
for key,record in records.iteritems():
decimal = h_dict[key]
if decimal == 0:
arr = np.zeros((nrow,ncol),dtype=np.int)
else:
arr = np.zeros((nrow,ncol))
print key,arr.dtype
try:
for r,c,val in zip(row,col,record):
arr[r-1,c-1] = val
array_dict[key] = arr.copy()
except:
print 'couldnt cast '+str(key)+' field to array'
return array_dict
def load_shape_arrays(shapename, rowname='row', colname='column'):
#--get the field names and make sure rowname and colname are found
field_names = shapefile.get_fieldnames(grid_shapename)
assert rowname in field_names
assert colname in field_names
#--get the decimal of each field - to get the array type later
grid_shp = shapefile.Reader(shapename)
header = grid_shp.dbfHeader()
h_dict = {}
for item in header:
h_dict[item[0]] = int(item[-1])
#--load all of the records as a dict
records = shapefile.load_as_dict(shapename, loadShapes=False)
#--get nrow and ncol
nrow = max(records[rowname])
ncol = max(records[colname])
#--row and column maps
row, col = records[rowname], records[colname]
#--setup a dict for all of the arrays and map the values
array_dict = {}
for key, record in records.iteritems():
decimal = h_dict[key]
if decimal == 0:
arr = np.zeros((nrow, ncol), dtype=np.int)
else:
arr = np.zeros((nrow, ncol))
print key, arr.dtype
try:
for r, c, val in zip(row, col, record):
arr[r - 1, c - 1] = val
array_dict[key] = arr.copy()
except:
print 'couldnt cast ' + str(key) + ' field to array'
return array_dict
import pandas
from shapely.geometry import Polygon
import shapefile
nex_shapename = '..\\shapes\\NEXRAD_pixels_tsala'
grid_shapename = '..\\shapes\\join_all2'
df = pandas.read_csv('NEXRAD.csv', nrows=1)
df_keys = list(df.keys())
#--build nexrad polygons - just duplicate multipart polys
print 'loading grid shapefile'
nex_shapes, nex_recs = shapefile.load_as_dict(nex_shapename)
nex_polys, nex_pixelnums = [], []
print 'building nexrad polygons'
for shape, pnum in zip(nex_shapes, nex_recs['Pixel']):
if str(pnum) in df_keys:
if len(shape.parts) > 1:
points = shape.points
for i1, i2 in zip(shape.parts[:-1], shape.parts[1:]):
poly = Polygon(shape.points[i1:i2])
if not poly.is_valid:
raise Exception('invalid nexrad geometry' + str(pnum))
nex_polys.append(poly)
nex_pixelnums.append(pnum)
#raise Exception('multipart nexrad shape'+str(rec))
else:
poly = Polygon(shape.points)
if not poly.is_valid:
raise Exception('invalid nexrad geometry' + str(pnum))
nex_polys.append(poly)
mpl.rcParams['font.monospace'] = 'Courier New'
mpl.rcParams['pdf.compression'] = 0
mpl.rcParams['pdf.fonttype'] = 42
ticksize = 6
mpl.rcParams['legend.fontsize'] = 6
mpl.rcParams['axes.labelsize'] = 8
mpl.rcParams['xtick.labelsize'] = ticksize
mpl.rcParams['ytick.labelsize'] = ticksize
#--load well locations and pandas dataframe
well_shapename = '..\\..\\_gis\\shapes\\pws_combine'
well_points = sf.load_shape_list(well_shapename)
#shp = sf.reader(well_shapename)
#print sf.get_fieldnames(well_shapename)
records = sf.load_as_dict(well_shapename, loadShapes=False)
well_names = records['DPEP_NAME']
well_zbots = records['zbot']
float_zbots = []
for i, wb in enumerate(well_zbots):
float_zbots.append(float(wb))
well_zbots = np.array(float_zbots)
well_rows, well_cols = records['row'], records['column']
pump = pandas.read_csv('..\\..\\_pumpage\\pws_filled_zeros.csv',
index_col=0,
parse_dates=True)
#--load lines and active dates
line_shapename = '..\\..\\_gis\shapes\sw_reaches'
lines = sf.load_shape_list(line_shapename)
shp = sf.Reader(line_shapename)
coastal_df = pandas.read_csv('..\\..\\_noaa\\noaa_slr.csv',
index_col=0,
parse_dates=True)
coastal_stages = coastal_df[flow.slr_scenario].values
#--process stage records for WCA ghbs - these records should have been presampled to stress period dimensions
print 'processing EDEN records'
df_eden = pandas.read_csv('..\\..\\_eden\\eden_sp.csv',
index_col=0,
parse_dates=True)
df_eden_monthly = df_eden.groupby(lambda x: x.month).mean()
print df_eden_monthly.index
print 'loading grid info for coastal GHBs'
g_att = shapefile.load_as_dict('..\\..\\_gis\\shapes\\broward_grid_master',
loadShapes=False,
attrib_name_list=['row', 'column', 'ibound_CS'])
icoast_rowcol, atlant_rowcol = [], []
for r, c, i in zip(g_att['row'], g_att['column'], g_att['ibound_CS']):
if i in [51, 52, 53, 54, 55]:
icoast_rowcol.append([r, c, i])
elif i == 2:
atlant_rowcol.append([r, c, i])
print 'loading grid info for WCA GHBs'
recs = shapefile.load_as_dict('..\\..\\_gis\\scratch\\broward_grid_eden',
loadShapes=False)
wca_r, wca_c, eden_fracs, eden_cells = recs['row'], recs['column'], recs[
'eden_fracs'], recs['eden_cells']
#--cast fracs and cells
print 'casting fractions and cell numbers'
#--calc monthly average water levels for ns ghbs
df_ns_monthly = df_ns_data.groupby(lambda x:x.month).mean()
print df_ns_monthly.index
#--process stage record for coastal GHBs - presampled
coastal_df = pandas.read_csv('..\\..\\_noaa\\noaa_slr.csv',index_col=0,parse_dates=True)
coastal_stages = coastal_df[flow.slr_scenario].values
#--process stage records for WCA ghbs - these records should have been presampled to stress period dimensions
print 'processing EDEN records'
df_eden = pandas.read_csv('..\\..\\_eden\\eden_sp.csv',index_col=0,parse_dates=True)
df_eden_monthly = df_eden.groupby(lambda x:x.month).mean()
print df_eden_monthly.index
print 'loading grid info for coastal GHBs'
g_att = shapefile.load_as_dict('..\\..\\_gis\\shapes\\broward_grid_master',loadShapes=False,attrib_name_list=['row','column','ibound_CS'])
icoast_rowcol,atlant_rowcol = [],[]
for r,c,i in zip(g_att['row'],g_att['column'],g_att['ibound_CS']):
if i in [51,52,53,54,55]:
icoast_rowcol.append([r,c,i])
elif i == 2:
atlant_rowcol.append([r,c,i])
print 'loading grid info for WCA GHBs'
recs = shapefile.load_as_dict('..\\..\\_gis\\scratch\\broward_grid_eden',loadShapes=False)
wca_r,wca_c,eden_fracs,eden_cells = recs['row'],recs['column'],recs['eden_fracs'],recs['eden_cells']
#--cast fracs and cells
print 'casting fractions and cell numbers'
for i,fracs in enumerate(eden_fracs):
raw = fracs.split()
def setup():
'''write an ssm key file and extract stress period nss lists to binary
'''
from bro import seawat as cal
from bro_pred import seawat as pred
#--build a swr reach tidal key
f = open('..\\_BCDPEP\\BCDPEP_reach_conc.dat', 'r')
f.readline()
tidal_conc = {}
for line in f:
raw = line.strip().split(',')
tidal_conc[int(raw[0])] = float(raw[1])
f.close()
#--group the tidal source reaches if the conc is the same
tidal_rc, tidal_names = [], []
concs, groups = [], []
for sreach, conc in tidal_conc.iteritems():
if conc in concs:
groups[concs.index(conc)].append(sreach)
else:
groups.append([sreach])
concs.append(conc)
tidal_rc.append([])
tidal_names.append([])
import shapefile
shapename = '..\\_gis\\scratch\\sw_reaches_conn_swrpolylines_2'
recs = shapefile.load_as_dict(shapename, loadShapes=False)
for r, c, sreach, sstruct, sname in zip(recs['ROW'], recs['COLUMN'],
recs['SRC_reach'],
recs['SRC_struct'],
recs['SRC_name']):
if sstruct == -1:
for i, group in enumerate(groups):
if sreach in group:
tidal_rc[i].append((r, c))
if sname not in tidal_names[i]:
tidal_names[i].append(sname)
f = open('misc\\ssm_riv.key', 'w', 0)
f.write('group_name,row,col,names\n')
for i, [tups, names] in enumerate(zip(tidal_rc, tidal_names)):
name = '_'.join(names).replace(' ', '_').replace(',', '_')
for (r, c) in tups:
f.write('riv_cn_#' + str(i + 1) + ',' + str(r) + ',' + str(c) +
',' + name + '\n')
f.close()
#--build intercoastal ghb tidal key
ibound = np.loadtxt('..\\_model\\bro.03\\seawatref\\ibound_CS.ref',
dtype=np.int)
ic_groups = {}
for i in range(ibound.shape[0]):
for j in range(ibound.shape[1]):
name = 'ghb_cn_#' + str(ibound[i, j])
if name in ic_groups.keys():
ic_groups[name].append((i + 1, j + 1))
else:
ic_groups[name] = [(i + 1, j + 1)]
f = open('misc\\ssm_ghb.key', 'w', 0)
f.write('name,row,col\n')
for name, tups in ic_groups.iteritems():
for (r, c) in tups:
f.write(name + ',' + str(r) + ',' + str(c) + '\n')
f.close()
#--write an ssm template file - monthly
par_dict = {}
tpl_entries = {}
months = calendar.month_abbr
pnames = []
tpl_dict = {}
for riv_grp in range(len(tidal_rc)):
tpl_entries = []
for mn in months[1:]:
pname = 'rcn_' + str(riv_grp + 1) + '_' + mn
assert len(pname) <= 10, pname
pnames.append(pname)
tpl_entry = '~{0:25s}~'.format(pname)
tpl_entries.append(tpl_entry)
tpl_dict['riv_cn_#' + str(riv_grp + 1)] = tpl_entries
par_dict['riv_conc'] = pnames
pnames = []
for ghb_grp in ic_groups.keys():
grp_num = int(ghb_grp.split('#')[1])
tpl_entries = []
for mn in months[1:]:
pname = 'gcn_' + str(grp_num) + '_' + mn
assert len(pname) <= 10, pname
pnames.append(pname)
tpl_entry = '~{0:25s}~'.format(pname)
tpl_entries.append(tpl_entry)
tpl_dict['ghb_cn_#' + str(ghb_grp)] = tpl_entries
par_dict['ghb_conc'] = pnames
#--save the template file
df = pandas.DataFrame(tpl_dict)
df.index = df.index + 1
f = open('tpl\\ghbwel_ssm.tpl', 'w', 0)
f.write('ptf ~\n')
df.to_csv(f, index_label='month')
f.close()
#--save a generic par file for testing
for col in df.columns:
df[col] = 1.0
df.to_csv('par\\ghbwell_ssm.csv', index_label='month')
#--write the pst components
f_grp = open('pst_components\\ghbwel_ssm_grps.dat', 'w', 0)
f_par = open('pst_components\\ghbwel_ssm_pars.dat', 'w', 0)
pargps = par_dict.keys()
pargps.sort()
for pargp in pargps:
pnames = par_dict[pargp]
f_grp.write(
'{0:<20s} factor 0.01 0.001 switch 2.0 parabolic\n'.format(
pargp))
for pname in pnames:
f_par.write(
'{0:<20s} log factor 1.0 1.0e-10 1.0e+10 {1:<20s} 1.0 0.0 0\n'
.format(pname, pargp))
f_grp.close()
f_par.close()
#--extract sp data and zip to binary
#--load the key files into a dict
itype_dict = {'riv': 4, 'ghb': 5}
files = os.listdir('misc\\')
key_files = []
for f in files:
if 'key' in f and 'ssm' in f:
key_files.append(f)
key_dict = {}
for key_file in key_files:
ptype = key_file.split('.')[0].split('_')[1]
itype = itype_dict[ptype]
f = open('misc\\' + key_file, 'r')
header = f.readline()
for line in f:
raw = line.strip().split(',')
r, c = int(raw[1]), int(raw[2])
key_dict[(r, c, itype)] = raw[0]
f.close()
sp_lists = [cal.sp_start, pred.sp_start]
for ssm_file, list_dir, sp_list in zip(SSM_FILES, LIST_DIRS, sp_lists):
f = open(ssm_file, 'r')
#--read the header info
logicals = f.readline()
maxssm = int(f.readline().strip())
#--read the rch,ets junk
rchets_lines = []
for i in range(4):
rchets_lines.append(f.readline().strip())
#--start the sp loop
kper = 0
while True:
try:
nss = int(f.readline().strip())
except:
break
lines = []
#line_str = ''
for i in range(nss):
line = parse_ssm_line(f.readline())
try:
line[-1] = key_dict[(line[1], line[2], line[4])]
except:
pass
#line_str += line
lines.append(tuple(line))
arr = np.array(lines, dtype=ssm_dtype_extend)
dt = sp_list[kper]
fname = list_dir + 'ssm_' + dt.strftime('%Y%m%d') + '_' + str(
nss) + '.dat'
print 'writing', fname
#--for testing
#np.savetxt('test.dat',arr,fmt=' %9d %9d %9d %15.6E %9d %20s')
arr.tofile(fname)
#--read the repeat rch and ets lines
rch = f.readline()
rch = f.readline()
kper += 1
import numpy as np
import calendar
import shapefile
import pestUtil
#--load the grid shapefile that has the nexrad groups
shapename = 'shapes\\cwm_grid_groups'
records = shapefile.load_as_dict(shapename, ['row', 'column', 'nex_group'],
loadShapes=False)
#--load the grid info
ginfo = pestUtil.load_grid_spec('misc\grid.spc')
#--fill in the groups array
grp_arr = np.zeros((ginfo['nrow'], ginfo['ncol']))
for r, c, g in zip(records['row'], records['column'], records['nex_group']):
grp_arr[r - 1, c - 1] = g
np.savetxt('ref\UMD_nexrad.ref', grp_arr, fmt=' %2.0f')
num_grps = np.unique(grp_arr).shape[0]
f = open('fac\\nex_fac.dat', 'w')
f.write('cl_nexpts\n')
f.write('cl_mf_grid\n')
f.write('{0:10.0f}{1:10.0f}\n'.format(1, num_grps))
f.write('{0:10.0f}{1:10.0f}\n'.format(1, ginfo['nrow'] * ginfo['ncol']))
f.write('{0:10.0f}{1:10.0f}\n'.format(num_grps, ginfo['nrow'] * ginfo['ncol']))
f.write('{0:10.0f}\n'.format(1))
cell_num = 1
for i in range(ginfo['nrow']):
mpl.rcParams['pdf.compression'] = 0
mpl.rcParams['pdf.fonttype'] = 42
ticksize = 6
mpl.rcParams['legend.fontsize'] = 6
mpl.rcParams['axes.labelsize'] = 8
mpl.rcParams['xtick.labelsize'] = ticksize
mpl.rcParams['ytick.labelsize'] = ticksize
#--load well locations and pandas dataframe
well_shapename = '..\\..\\_gis\\shapes\\pws_combine'
well_points = sf.load_shape_list(well_shapename)
#shp = sf.reader(well_shapename)
#print sf.get_fieldnames(well_shapename)
records = sf.load_as_dict(well_shapename,loadShapes=False)
well_names = records['DPEP_NAME']
well_zbots = records['zbot']
float_zbots = []
for i,wb in enumerate(well_zbots):
float_zbots.append(float(wb))
well_zbots = np.array(float_zbots)
well_rows, well_cols = records['row'],records['column']
pump = pandas.read_csv('..\\..\\_pumpage\\pws_filled_zeros.csv',index_col=0,parse_dates=True)
#--load lines and active dates
line_shapename = '..\\..\\_gis\shapes\sw_reaches'
lines = sf.load_shape_list(line_shapename)
shp = sf.Reader(line_shapename)
fnames = sf.get_fieldnames(line_shapename,ignorecase=True)
#for i,fn in enumerate(fnames):
def main(num_plots):
#--load well locations and pandas dataframe
well_shapename = '..\\..\\_gis\\shapes\\pws_combine'
well_points = sf.load_shape_list(well_shapename)
#shp = sf.reader(well_shapename)
#print sf.get_fieldnames(well_shapename)
records = sf.load_as_dict(well_shapename, loadShapes=False)
well_names = records['DPEP_NAME']
well_aban = records['ABAN_YEAR']
act_wells = []
for pt, ab in zip(well_points, well_aban):
if ab == '':
act_wells.append(pt)
#--load lines and active dates
line_shapename = '..\\..\\_gis\shapes\sw_reaches'
lines = sf.load_shape_list(line_shapename)
#--head stuff
#--use bot of Q5 to check for dry cells
#hds_elev = np.loadtxt(flow.ref_dir+'Q5_bot.ref')
#hds_layer_idx = 0
#head_file = flow.root+'.hds'
#headObj = mfb.MODFLOW_Head(flow.nlay,flow.nrow,flow.ncol,head_file)
#htimes = headObj.get_time_list()
#--conc stuff
conc_lay_idxs = [0, 2, 3, 4, 5]
conc_file = 'MT3D001.UCN'
concObj = mfb.MT3D_Concentration(seawat.nlay, seawat.nrow, seawat.ncol,
conc_file)
ctimes = concObj.get_time_list()
#-- stress period step
sp_step = 1
plt_dir = 'png\\results\\seawat\\'
#--for ffmpeg - sequentially numbered
plt_num = 1
istart = 0
q_args = []
for i, [start, end] in enumerate(zip(seawat.sp_start, seawat.sp_end)):
if i >= istart and i % sp_step == 0:
print 'building args list for stress period ending on ', end
#--find the conc output nearest the end of the stress period
try:
kper_seekpoints = ctimes[np.where(ctimes[:, 2] == i + 1), -1]
c_seekpoint = long(kper_seekpoints[0][-1])
except:
break
fig_name = plt_dir + 'sp{0:03.0f}_conc.png'.format(plt_num)
fig_title = 'stress period ' + str(
i + 1) + ' start date ' + start.strftime('%d/%m/%Y')
args = [
fig_name, c_seekpoint, conc_lay_idxs, lines, act_wells,
fig_title
]
q_args.append(args)
plt_num += 1
if num_plots != None and i > num_plots:
break
jobq = mp.JoinableQueue()
#--for testing
#if num_plots != None:
# jobq.put_nowait(q_args[0])
# jobq.put_nowait(None)
# plot_worker(jobq,1,conc_file)
# return
procs = []
num_procs = 4
for i in range(num_procs):
#--pass the woker function jobq and a PID
p = mp.Process(target=plot_worker, args=(jobq, i, conc_file))
p.daemon = True
print 'starting process', p.name
p.start()
procs.append(p)
for q in q_args:
jobq.put(q)
for p in procs:
jobq.put(None)
#--block until all finish
for p in procs:
p.join()
print p.name, 'Finished'
anim_name = 'png\\demo_conc.avi'
if os.path.exists(anim_name):
os.remove(anim_name)
cmd_line = 'ffmpeg.exe -i png\\results\\seawat\\sp%03d_conc.png -r 24 ' + anim_name + ' -y'
os.system(cmd_line)
return
import os
import pandas
import shapefile
import dbhydro_util
df_dir = 'stage_dfs_navd\\'
smp_dir = 'stage_smp_navd\\'
ngvd_2_navd = -1.5
#--load the attributes of the structure shapefile
shapename = '..\\_gis\\shapes\\sw_structures'
records = shapefile.load_as_dict(shapename,attrib_name_list=['system','struct_num','dbhydro'],loadShapes=False)
#--get a list of stage records
stg_dir = 'SW\\STG\\'
stg_files = os.listdir(stg_dir)
#--build a list of station names
rec_attribs = []
for f in stg_files:
fdict = dbhydro_util.parse_fname(f)
rec_attribs.append(fdict)
#--find all the records for each primary structure, headwater only
wl_stats = ['DA','BK','DWR','INST','FWM','MEAN']
for system,name in zip(records['system'],records['dbhydro']):
if system == 1 and name != None:
match_h,match_t = [],[]
for i,rec in enumerate(rec_attribs):
station = rec['STATION']
if '_' in station:
import math
import shapefile
def dist(point1, point2):
xx = (point1[0] - point2[0])**2
yy = (point1[1] - point2[1])**2
return math.sqrt(xx + yy)
line_shapename = '..\\_gis\\shapes\\sw_reaches'
#shp_lines = shapefile.Reader(line_shapename)
#lines = shp_lines.shapes()
#l_records = shp_lines.records()
#l_name_idx,reach_idx = 0,2
lines, l_records = shapefile.load_as_dict(line_shapename)
#--tolerance distance
warn_dist = 50.0
tol_dist = 0.0
#--set the writer instance
in_shapename = '..\\_gis\\shapes\\sw_structures'
out_shapename = '..\\_gis\\scratch\\sw_structures_reaches'
shp_points = shapefile.Reader(in_shapename)
points = shp_points.shapes()
p_records = shp_points.records()
p_name_idx, dwnstr_idx = 4, 6
wr = shapefile.writer_like(in_shapename)
wr.field('upstream', fieldType='N', size=10)
import os
import pandas
import shapefile
import dbhydro_util
df_dir = 'stage_dfs_navd\\'
smp_dir = 'stage_smp_navd\\'
ngvd_2_navd = -1.5
#--load the attributes of the structure shapefile
shapename = '..\\_gis\\shapes\\sw_structures'
records = shapefile.load_as_dict(
shapename,
attrib_name_list=['system', 'struct_num', 'dbhydro'],
loadShapes=False)
#--get a list of stage records
stg_dir = 'SW\\STG\\'
stg_files = os.listdir(stg_dir)
#--build a list of station names
rec_attribs = []
for f in stg_files:
fdict = dbhydro_util.parse_fname(f)
rec_attribs.append(fdict)
#--find all the records for each primary structure, headwater only
wl_stats = ['DA', 'BK', 'DWR', 'INST', 'FWM', 'MEAN']
for system, name in zip(records['system'], records['dbhydro']):
if system == 1 and name != None:
match_h, match_t = [], []
for i, rec in enumerate(rec_attribs):
import pandas
from shapely.geometry import Polygon
import shapefile
nex_shapename = '..\\shapes\\NEXRAD_pixels_tsala'
grid_shapename = '..\\shapes\\join_all2'
df = pandas.read_csv('NEXRAD.csv',nrows=1)
df_keys = list(df.keys())
#--build nexrad polygons - just duplicate multipart polys
print 'loading grid shapefile'
nex_shapes,nex_recs = shapefile.load_as_dict(nex_shapename)
nex_polys,nex_pixelnums = [],[]
print 'building nexrad polygons'
for shape,pnum in zip(nex_shapes,nex_recs['Pixel']):
if str(pnum) in df_keys:
if len(shape.parts) > 1:
points = shape.points
for i1,i2 in zip(shape.parts[:-1],shape.parts[1:]):
poly = Polygon(shape.points[i1:i2])
if not poly.is_valid:
raise Exception('invalid nexrad geometry'+str(pnum))
nex_polys.append(poly)
nex_pixelnums.append(pnum)
#raise Exception('multipart nexrad shape'+str(rec))
else:
poly = Polygon(shape.points)
if not poly.is_valid:
raise Exception('invalid nexrad geometry'+str(pnum))
nex_polys.append(poly)
def main():
#--load well locations and pandas dataframe
well_shapename = '..\\..\\_gis\\shapes\\pws_combine'
well_points = sf.load_shape_list(well_shapename)
#shp = sf.reader(well_shapename)
#print sf.get_fieldnames(well_shapename)
records = sf.load_as_dict(well_shapename,loadShapes=False)
well_names = records['DPEP_NAME']
well_zbots = records['zbot']
float_zbots = []
for i,wb in enumerate(well_zbots):
float_zbots.append(float(wb))
well_zbots = np.array(float_zbots)
well_rows, well_cols = records['row'],records['column']
pump = pandas.read_csv('..\\..\\_pumpage\\dataframes\\pws_filled_zeros.csv',index_col=0,parse_dates=True)
#--load lines and active dates
line_shapename = '..\\..\\_gis\shapes\sw_reaches'
lines = sf.load_shape_list(line_shapename)
shp = sf.Reader(line_shapename)
fnames = sf.get_fieldnames(line_shapename,ignorecase=True)
#for i,fn in enumerate(fnames):
# print i,fn
a_idx = fnames.index('ACTIVE_ST')
line_active = []
for i in range(shp.numRecords):
rec = shp.record(i)
year = int(rec[a_idx])
if year < flow.start.year:
year = flow.start.year
dt = datetime(year=year,month=1,day=1)
line_active.append(dt)
#--head stuff
#--use bot of Q5 to check for dry cells
hds_elev = np.loadtxt(flow.ref_dir+'Q5_bot.ref')
hds_layer_idx = 0
head_file = flow.root+'.hds'
headObj = mfb.MODFLOW_Head(flow.nlay,flow.nrow,flow.ncol,head_file)
htimes = headObj.get_time_list()
#--zeta stuff
zta_layer_idx = 0
zta_elev = np.loadtxt(flow.ref_dir+'Q1_bot.ref')
zeta_file = flow.root+'.zta'
zetaObj = mfb.MODFLOW_CBB(flow.nlay,flow.nrow,flow.ncol,zeta_file)
zta_text = ' ZETAPLANE 1'
z1times = zetaObj.get_time_list(zta_text)
#zeta_file = None
#-- stress period step
sp_step = 1
plt_dir = 'png\\results\\'
#--for ffmpeg - sequentially numbered
plt_num = 1
istart = 0
q_args = []
for i,dt in enumerate(flow.sp_start):
if i >= istart and i%sp_step == 0:
print 'building args list for ',dt
try:
h_seekpoint = long(htimes[i,3])
except:
break
if zeta_file:
z_seekpoint = long(z1times[i,3])
else:
z_seekpoint = None
act_lines = []
for ldt,line in zip(line_active,lines):
if ldt <= dt:
act_lines.append(line)
act_wells = []
if i == 0:
plt_start = dt
else:
plt_start = flow.sp_start[i-sp_step]
plt_end = flow.sp_end[i]
pump_plt = pump[plt_start:plt_end]
pump_plt_sum = pump_plt.sum()
for wname,wpoint,wrow,wcol,wzbot in zip(well_names,well_points,well_rows,well_cols,well_zbots):
if wname in pump_plt.keys() and pump_plt_sum[wname] != 0:
act_wells.append(wpoint)
fig_name = plt_dir+'sp{0:03.0f}.png'.format(plt_num)
#fig_title = 'stress period '+str(i+1)+' start date '+dt.strftime('%d/%m/%Y')
fig_title = str(dt.year)
args = [fig_name,h_seekpoint,z_seekpoint,act_lines,act_wells,hds_layer_idx,zta_layer_idx,fig_title]
q_args.append(args)
plt_num += 1
jobq = mp.JoinableQueue()
#--for testing
#jobq.put_nowait(q_args[0])
#jobq.put_nowait(None)
#plot_worker(jobq,0,head_file,None,hds_elev,zta_elev)
#return
procs = []
num_procs = 6
for i in range(num_procs):
#--pass the woker function jobq and a PID
p = mp.Process(target=plot_worker,args=(jobq,i,head_file,zeta_file,hds_elev,zta_elev))
p.daemon = True
print 'starting process',p.name
p.start()
procs.append(p)
for q in q_args:
jobq.put(q)
for p in procs:
jobq.put(None)
#--block until all finish
for p in procs:
p.join()
print p.name,'Finished'
cmd_line = 'ffmpeg.exe -i results\sp%03d.png -r 24 demo.avi -y'
os.system(cmd_line)
return
def main(num_plots):
#--load well locations and pandas dataframe
well_shapename = '..\\..\\_gis\\shapes\\pws_combine'
well_points = sf.load_shape_list(well_shapename)
#shp = sf.reader(well_shapename)
#print sf.get_fieldnames(well_shapename)
records = sf.load_as_dict(well_shapename,loadShapes=False)
well_names = records['DPEP_NAME']
well_zbots = records['zbot']
float_zbots = []
for i,wb in enumerate(well_zbots):
float_zbots.append(float(wb))
well_zbots = np.array(float_zbots)
well_rows, well_cols = records['row'],records['column']
pump = pandas.read_csv('..\\..\\_pumpage\\dataframes\\pws_filled_zeros.csv',index_col=0,parse_dates=True)
#--load lines and active dates
line_shapename = '..\\..\\_gis\shapes\sw_reaches'
lines = sf.load_shape_list(line_shapename)
shp = sf.Reader(line_shapename)
fnames = sf.get_fieldnames(line_shapename,ignorecase=True)
#for i,fn in enumerate(fnames):
# print i,fn
a_idx = fnames.index('ACTIVE_ST')
line_active = []
for i in range(shp.numRecords):
rec = shp.record(i)
year = int(rec[a_idx])
if year < flow.start.year:
year = flow.start.year
dt = datetime(year=year,month=1,day=1)
line_active.append(dt)
#--head stuff
#--use bot of Q5 to check for dry cells
#hds_elev = np.loadtxt(flow.ref_dir+'Q5_bot.ref')
#hds_layer_idx = 0
#head_file = flow.root+'.hds'
#headObj = mfb.MODFLOW_Head(flow.nlay,flow.nrow,flow.ncol,head_file)
#htimes = headObj.get_time_list()
#--conc stuff
conc_lay_idxs = [0,5,9,11]
conc_file = 'MT3D001.UCN'
concObj = mfb.MT3D_Concentration(seawat.nlay,seawat.nrow,seawat.ncol,conc_file)
ctimes = concObj.get_time_list()
#--zeta stuff
#zta_layer_idx = 0
#zta_elev = np.loadtxt(flow.ref_dir+'Q1_bot.ref')
#zeta_file = flow.root+'.zta'
#zetaObj = mfb.MODFLOW_CBB(flow.nlay,flow.nrow,flow.ncol,zeta_file)
#zta_text = ' ZETAPLANE 1'
#z1times = zetaObj.get_time_list(zta_text)
#zeta_file = None
#-- stress period step
sp_step = 1
plt_dir = 'png\\results\\seawat\\'
#--for ffmpeg - sequentially numbered
plt_num = 1
istart = 0
q_args = []
for i,[start,end] in enumerate(zip(seawat.sp_start,seawat.sp_end)):
if i >= istart and i%sp_step == 0:
print 'building args list for stress period ending on ',end
#--find the conc output nearest the end of the stress period
try:
kper_seekpoints = ctimes[np.where(ctimes[:,2]==i+1),-1]
c_seekpoint = long(kper_seekpoints[0][-1])
except:
break
act_lines = []
for ldt,line in zip(line_active,lines):
if ldt <= start:
act_lines.append(line)
act_wells = []
if i == 0:
plt_start = start
else:
plt_start = seawat.sp_start[i-sp_step]
plt_end = seawat.sp_end[i]
pump_plt = pump[plt_start:plt_end]
pump_plt_sum = pump_plt.sum()
for wname,wpoint,wrow,wcol,wzbot in zip(well_names,well_points,well_rows,well_cols,well_zbots):
if wname in pump_plt.keys() and pump_plt_sum[wname] != 0:
act_wells.append(wpoint)
fig_name = plt_dir+'sp{0:03.0f}_conc.png'.format(plt_num)
fig_title = 'stress period '+str(i+1)+' start date '+start.strftime('%d/%m/%Y')
args = [fig_name,c_seekpoint,conc_lay_idxs,act_lines,act_wells,fig_title]
q_args.append(args)
plt_num += 1
if num_plots != None and i > num_plots:
break
jobq = mp.JoinableQueue()
#--for testing
if num_plots != None:
jobq.put_nowait(q_args[0])
jobq.put_nowait(None)
plot_worker(jobq,1,conc_file)
return
procs = []
num_procs = 3
for i in range(num_procs):
#--pass the woker function jobq and a PID
p = mp.Process(target=plot_worker,args=(jobq,i,conc_file))
p.daemon = True
print 'starting process',p.name
p.start()
procs.append(p)
for q in q_args:
jobq.put(q)
for p in procs:
jobq.put(None)
#--block until all finish
for p in procs:
p.join()
print p.name,'Finished'
anim_name = 'png\\demo_conc.avi'
if os.path.exists(anim_name):
os.remove(anim_name)
cmd_line = 'ffmpeg.exe -i png\\results\\seawat\\sp%03d_conc.png -r 24 '+anim_name+' -y'
os.system(cmd_line)
return
import os
import shutil
from datetime import datetime
import pandas
import shapefile
from simple import grid
f = open('settings.fig','w',0)
f.write('date = dd/mm/yyy\ncolrow=no\n')
f.close()
points,records = shapefile.load_as_dict('shapes\\simple_obs')
ids = records['Id']
#--set the obs at the bottom of upper
for olayer in range(grid.nlay):
if grid.lay_key[olayer] != 'upper':
break
print os.path.abspath('.\\')
f = open('_misc\\bore_coords.dat','w',0)
onames = []
for point,id in zip(points,ids):
point = point.points[0]
x,y = point
oname = 'obs_'+str(id)
onames.append(oname+'up')
onames.append(oname+'lw')
f.write('{0:25s} {1:15.5E} {2:15.5E} {3:6d}\n'.format(oname+'up',x,y,olayer))
f.write('{0:25s} {1:15.5E} {2:15.5E} {3:6d}\n'.format(oname+'lw',x,y,9))
f.close()
top = np.loadtxt('..\\_model\\bro.01\\ref\\top_mod.ref')
xsec_dir = 'xsec_navd\\'
xsec_files = os.listdir(xsec_dir)
xsecs = {}
for xname in xsec_files:
header,xsec = swr.load_xsec(xsec_dir+xname)
xsecs[xname] = xsec
shapename = '..\\_gis\\scratch\\sw_reaches_conn_SWRpolylines'
shapes,records = shapefile.load_as_dict(shapename)
wr = shapefile.Writer()
wr.field('reach',fieldType='N',size=10,decimal=0)
wr.field('top',fieldType='N',size=20,decimal=10)
wr.field('xsec_min',fieldType='N',size=20,decimal=10)
wr.field('xsec_max',fieldType='N',size=20,decimal=10)
wr.field('xsec_depth',fieldType='N',size=20,decimal=10)
wr.field('mod_depth',fieldType='N',size=20,decimal=10)
wr.field('depth_diff',fieldType='N',size=20,decimal=10)
wr.field('xsec_perm',fieldType='N',size=20,decimal=10)
wr.field('mod_perm',fieldType='N',size=20,decimal=10)
wr.field('perm_diff',fieldType='N',size=20,decimal=10)
#print shapefile.get_fieldnames(shapename)
reaches = records['REACH']
rows = records['ROW']
cols = records['COLUMN']
try:
if sys.argv[1].upper() == 'R':
rst = True
except:
pass
arr_prefix = 'nexrad_rech_inch_day\\rech'
pixel_file = 'pixel_data\\nexrad_inc138246_96_12_rainfall_ord.dat'
#arr_prefix = 'mm_day_pet\\pet'
#pixel_file = 'pet_pixel_all_ord.txt'
#--name of model grid shapefile with pixels attached - from make_pixel_map.py
print 'loading grid shapefile...'
shapefile_name = '..\\..\\_gis\\shapes\\broward_grid_master'
shapes, records = shapefile.load_as_dict(
shapefile_name, attrib_name_list=['row', 'column', 'pixels', 'fractions'])
nrow, ncol = records['row'].max(), records['column'].max()
print 'done'
print 'nrow,ncol', nrow, ncol
#--load the pixel timeseries with
#--pixel, value and ord date index values for the timeseries file
p_idx, v_idx, d_idx = 0, 1, 2
print 'loading pixel timeseries file', pixel_file
pixel = np.loadtxt(pixel_file, usecols=[0, 2, 3], delimiter=',')
print 'done - ', pixel.shape[0], ' records loaded'
#--get a list of the unique ordinal days in pixel time series
pixel_days = np.unique(pixel[:, d_idx])
#--process each day
import os
import numpy as np
from scipy.interpolate import Rbf
import shapefile
import simple
#--first write pilot points file from topot_points shape
shapename = 'shapes\\simple_topo_points'
f = open('misc\\topo_points.dat','w',0)
points,rec_dict = shapefile.load_as_dict(shapename)
xs,ys,ztops = [],[],[]
for pt,id,ztop in zip(points,rec_dict['Id'],rec_dict['ztop']):
x,y = pt.points[0][0],pt.points[0][1]
if x in xs and y in ys:
print 'dup',id
else:
xs.append(x)
ys.append(y)
ztops.append(ztop)
pname = 'topo_{0:02d}'.format(id)
f.write('{0:20s} {1:15E} {2:15E} 1 {3:15E}\n'.format(pname,x,y,float(ztop)))
f.close()
xs,ys,ztops = np.array(xs),np.array(ys),np.array(ztops)
zs = np.ones(xs.shape)
#--write the grid file
f = open('misc\\simple.grd','w',0)
f.write('{0:10d} {1:10d}\n'.format(simple.grid.nrow,simple.grid.ncol))
f.write('{0:15.5G} {1:15.5G} 0.0\n'.format(simple.grid.xmin,simple.grid.cols[-1]))
for dr in simple.grid.delr:
def write():
#--get the well locations
shapename = '..\\shapes\\simple_well_grid_join'
records = shapefile.load_as_dict(shapename, loadShapes=False)
rows, cols, ztops, zbots, ids, hydros = records['row'], records[
'column_'], records['ztop'], records['zbot'], records['Id'], records[
'hydro']
#--random sampling
#--first create normal distributions for the wells
upper_mean, upper_std = -500.0, 10.0
lower_mean, lower_std = -5000.0, 50.0
nper = len(grid.sp_start)
well_records = []
for id, hydro in zip(ids, hydros):
if hydro == 3:
rec = np.random.normal(lower_mean, lower_std, nper)
if hydro == 1:
rec = np.random.normal(upper_mean, upper_std, nper)
rec = add_zeros(rec)
well_records.append(rec)
well_records = np.array(well_records).transpose()
well_records[np.where(well_records > 0.0)] = 0.0
mnw_ds2 = []
names = []
for id, r, c, top, bot, hydro in zip(ids, rows, cols, ztops, zbots,
hydros):
#--mnw
if hydro == 1:
name = 'upper_' + str(id)
else:
name = 'lower_' + str(id)
names.append(name)
line_2a = '{0:20s}{1:10f}{2:>33s}\n'.format(name, -1, '#2a')
mnw_ds2.append(line_2a)
line_2b = '{0:20s}{1:10d}{2:10d}{3:10d}{4:10d} #2b\n'.format(
'THIEM', 0, 0, 0, 0)
mnw_ds2.append(line_2b)
line_2c = '{0:10.4f}{1:>53s}\n'.format(1.0, '#2c')
mnw_ds2.append(line_2c)
line_2d2 = ' {0:9.4f} {1:9.4f} {2:9.0f} {3:9.0f}{4:>26s}\n'.format(
float(top), float(bot), int(float(r)), int(float(c)), '#2d-2\n')
mnw_ds2.append(line_2d2)
f_mnw = open(grid.modelname + '.mnw', 'w', 0)
f_mnw.write('# ' + sys.argv[0] + ' ' + str(datetime.now()) + '\n')
f_mnw.write(' {0:9.0f} {1:9.0f} {2:9.0f}\n'.format(len(ids), 0, 0))
for line in mnw_ds2:
f_mnw.write(line)
upper_layers = [3, 4, 5, 6]
lower_layers = [10, 11, 12, 13, 14, 15, 16, 17, 18]
f_wel = open(grid.modelname + '.wel', 'w', 0)
f_wel.write('# ' + sys.argv[0] + ' ' + str(datetime.now()) + '\n')
f_wel.write(' {0:9.0f} {1:9.0f} {2:9.0f}\n'.format(300, 0, 0))
for i, slice in enumerate(well_records):
lines = []
for name, rate in zip(names, slice):
lines.append('{0:20s}{1:15.4G}\n'.format(name, rate))
f_mnw.write('{0:10d} {1:20s} {2:3d}\n'.format(len(lines),
'#3 Stress Period',
i + 1))
for line in lines:
f_mnw.write(line)
lines = []
for name, rate, row, col in zip(names, slice, rows, cols):
if 'upper' in name:
layers = upper_layers
else:
layers = lower_layers
rate /= float(len(layers))
for lay in layers:
line = '{0:10d}{1:10d}{2:10d}{3:15.4E} #{4:20s}\n'\
.format(lay,int(float(row)),int(float(col)),rate,name)
lines.append(line)
f_wel.write('{0:10d}{1:10d} #{2:20s}{3:4d}\n'.format(
len(lines), 0, 'stress period ', i + 1))
for line in lines:
f_wel.write(line)
f_mnw.close()
f_wel.close()
import os
from datetime import datetime,timedelta
import numpy as np
from scipy.io.netcdf import netcdf_file as ncdf
import pandas
import shapefile
#--get a list of the eden masterid numbers that are needed
recs = shapefile.load_as_dict('..\\_gis\\scratch\\broward_grid_eden',attrib_name_list=['eden_cells'],loadShapes=False)
masterids = []
for r in recs['eden_cells']:
raw = r.split()
for rr in raw:
if int(rr) not in masterids:
masterids.append(int(rr))
#--get the x,y of the masterids
recs = shapefile.load_as_dict('..\\_gis\\shapes\\EDEN_grid_poly_Jan_10_sp',loadShapes=False)
xs,ys = [],[]
for x,y,m in zip(recs['X_COORD'],recs['Y_COORD'],recs['MASTERID']):
if int(m) in masterids:
xs.append(float(x))
ys.append(float(y))
cdf_dir = 'surface_netcdf\\'
cdf_files = os.listdir(cdf_dir)
#--group files by year
cdf_years = []
def main(num_plots):
#--load well locations and pandas dataframe
well_shapename = '..\\..\\_gis\\shapes\\pws_combine'
well_points = sf.load_shape_list(well_shapename)
#shp = sf.reader(well_shapename)
#print sf.get_fieldnames(well_shapename)
records = sf.load_as_dict(well_shapename, loadShapes=False)
well_names = records['DPEP_NAME']
well_zbots = records['zbot']
float_zbots = []
for i, wb in enumerate(well_zbots):
float_zbots.append(float(wb))
well_zbots = np.array(float_zbots)
well_rows, well_cols = records['row'], records['column']
pump = pandas.read_csv(
'..\\..\\_pumpage\\dataframes\\pws_filled_zeros.csv',
index_col=0,
parse_dates=True)
#--load lines and active dates
line_shapename = '..\\..\\_gis\shapes\sw_reaches'
lines = sf.load_shape_list(line_shapename)
shp = sf.Reader(line_shapename)
fnames = sf.get_fieldnames(line_shapename, ignorecase=True)
#for i,fn in enumerate(fnames):
# print i,fn
a_idx = fnames.index('ACTIVE_ST')
line_active = []
for i in range(shp.numRecords):
rec = shp.record(i)
year = int(rec[a_idx])
if year < flow.start.year:
year = flow.start.year
dt = datetime(year=year, month=1, day=1)
line_active.append(dt)
#--head stuff
#--use bot of Q5 to check for dry cells
#hds_elev = np.loadtxt(flow.ref_dir+'Q5_bot.ref')
#hds_layer_idx = 0
#head_file = flow.root+'.hds'
#headObj = mfb.MODFLOW_Head(flow.nlay,flow.nrow,flow.ncol,head_file)
#htimes = headObj.get_time_list()
#--conc stuff
conc_lay_idxs = [0, 5, 9, 11]
conc_file = 'MT3D001.UCN'
concObj = mfb.MT3D_Concentration(seawat.nlay, seawat.nrow, seawat.ncol,
conc_file)
ctimes = concObj.get_time_list()
#--zeta stuff
#zta_layer_idx = 0
#zta_elev = np.loadtxt(flow.ref_dir+'Q1_bot.ref')
#zeta_file = flow.root+'.zta'
#zetaObj = mfb.MODFLOW_CBB(flow.nlay,flow.nrow,flow.ncol,zeta_file)
#zta_text = ' ZETAPLANE 1'
#z1times = zetaObj.get_time_list(zta_text)
#zeta_file = None
#-- stress period step
sp_step = 1
plt_dir = 'png\\results\\seawat\\'
#--for ffmpeg - sequentially numbered
plt_num = 1
istart = 0
q_args = []
for i, [start, end] in enumerate(zip(seawat.sp_start, seawat.sp_end)):
if i >= istart and i % sp_step == 0:
print 'building args list for stress period ending on ', end
#--find the conc output nearest the end of the stress period
try:
kper_seekpoints = ctimes[np.where(ctimes[:, 2] == i + 1), -1]
c_seekpoint = long(kper_seekpoints[0][-1])
except:
break
act_lines = []
for ldt, line in zip(line_active, lines):
if ldt <= start:
act_lines.append(line)
act_wells = []
if i == 0:
plt_start = start
else:
plt_start = seawat.sp_start[i - sp_step]
plt_end = seawat.sp_end[i]
pump_plt = pump[plt_start:plt_end]
pump_plt_sum = pump_plt.sum()
for wname, wpoint, wrow, wcol, wzbot in zip(
well_names, well_points, well_rows, well_cols, well_zbots):
if wname in pump_plt.keys() and pump_plt_sum[wname] != 0:
act_wells.append(wpoint)
fig_name = plt_dir + 'sp{0:03.0f}_conc.png'.format(plt_num)
fig_title = 'stress period ' + str(
i + 1) + ' start date ' + start.strftime('%d/%m/%Y')
args = [
fig_name, c_seekpoint, conc_lay_idxs, act_lines, act_wells,
fig_title
]
q_args.append(args)
plt_num += 1
if num_plots != None and i > num_plots:
break
jobq = mp.JoinableQueue()
#--for testing
if num_plots != None:
jobq.put_nowait(q_args[0])
jobq.put_nowait(None)
plot_worker(jobq, 1, conc_file)
return
procs = []
num_procs = 3
for i in range(num_procs):
#--pass the woker function jobq and a PID
p = mp.Process(target=plot_worker, args=(jobq, i, conc_file))
p.daemon = True
print 'starting process', p.name
p.start()
procs.append(p)
for q in q_args:
jobq.put(q)
for p in procs:
jobq.put(None)
#--block until all finish
for p in procs:
p.join()
print p.name, 'Finished'
anim_name = 'png\\demo_conc.avi'
if os.path.exists(anim_name):
os.remove(anim_name)
cmd_line = 'ffmpeg.exe -i png\\results\\seawat\\sp%03d_conc.png -r 24 ' + anim_name + ' -y'
os.system(cmd_line)
return
#--load the botm's - for the standard wel package
botm = np.zeros((flow.nlay+1,flow.nrow,flow.ncol)) - 1.0e10
botm[0,:,:] = np.loadtxt(flow.ref_dir+'top_layering.ref')
for i,prefix in enumerate(flow.layer_botm_names):
lay_botm = np.loadtxt(flow.ref_dir+prefix+'_bot.ref')
botm[i+1,:,:] = lay_botm
botm2 = np.zeros((seawat.nlay+1,seawat.nrow,seawat.ncol)) - 1.0e10
botm2[0,:,:] = np.loadtxt(seawat.ref_dir+'top_layering.ref')
for i,prefix in enumerate(seawat.layer_botm_names):
lay_botm = np.loadtxt(seawat.ref_dir+prefix+'_bot.ref')
botm2[i+1,:,:] = lay_botm
pws_shapename = '..\\..\\_gis\\shapes\\pws_combine'
pws_recs = shapefile.load_as_dict(pws_shapename,loadShapes=False)
mnw_ds2 = []
dfs = []
#f = open('test.dat','w')
count = 1
inactive = []
wel_rcl = {}
wel_rcl2 = {}
nwells_flow,nwells_seawat = 0,0
for dep_name,r,c,top,bot,ibnd in zip(pws_recs['DPEP_NAME'],pws_recs['row'],pws_recs['column'],pws_recs['ztop'],pws_recs['zbot'],pws_recs['ibound_CS']):
if ibnd > 0:
if dep_name in pump_df.keys():
#--mnw
line_2a = dep_name.ljust(20)+'{0:10.0f}'.format(-1)+'{0:>33s}'.format('#2a')+'\n'
mnw_ds2.append(line_2a)
line_2b = 'THIEM'.ljust(19)+'0'.ljust(10)+'0'.ljust(10)+'0'.ljust(10)+'0'.ljust(10)+' #2b\n'
for the area of each polygon.
genereate using pixel_grid_union.py
'''
#--name of unioned pixel-grid shapefile
pixelgrid_shapename = '..\\..\\_gis\\scratch\\broward_pixel_grid'
#--name of pixels shape
pixel_shapename = '..\\..\\_gis\\shapes\\NEXRAD_pixels_broward'
#--name of grid shapefile
grid_shapename = '..\\..\\_gis\\shapes\\broward_grid_master'
#--name of new grid shapefile with pixel info attached
new_grid_shapename = '..\\..\\_gis\\scratch\\broward_grid_pixelmap'
#--load the pixel shapes into shapely - need to use centroid attribute later
print 'loading pixel shapefile...'
pixel_shapes,pixel_records = shapefile.load_as_dict(pixel_shapename)
pixel_polys = []
for p_shape in pixel_shapes:
pixel_polys.append(Polygon(p_shape.points))
print 'done'
print 'loading pixel grid shapefile...'
pg_shapes,pg_records = shapefile.load_as_dict(pixelgrid_shapename,attrib_name_list=['pixel','cellnum','area'])
print pg_records.keys()
#pg_row,pg_col = np.array(pg_records['row']),np.array(pg_records['column'])
pg_pixel,pg_area = np.array(pg_records['pixel']),np.array(pg_records['area'])
pg_cellnum = np.array(pg_records['cellnum'])
#pg_delx,pg_dely = np.array(pg_records['delx']),np.array(pg_records['dely'])
print 'done'
# --load the botm's - for the standard wel package
botm = np.zeros((flow.nlay + 1, flow.nrow, flow.ncol)) - 1.0e10
botm[0, :, :] = np.loadtxt(flow.ref_dir + "top_layering.ref")
for i, prefix in enumerate(flow.layer_botm_names):
lay_botm = np.loadtxt(flow.ref_dir + prefix + "_bot.ref")
botm[i + 1, :, :] = lay_botm
botm2 = np.zeros((seawat.nlay + 1, seawat.nrow, seawat.ncol)) - 1.0e10
botm2[0, :, :] = np.loadtxt(seawat.ref_dir + "top_layering.ref")
for i, prefix in enumerate(seawat.layer_botm_names):
lay_botm = np.loadtxt(seawat.ref_dir + prefix + "_bot.ref")
botm2[i + 1, :, :] = lay_botm
pws_shapename = "..\\..\\_gis\\shapes\\pws_combine"
pws_recs = shapefile.load_as_dict(pws_shapename, loadShapes=False)
mnw_ds2 = []
dfs = []
# f = open('test.dat','w')
count = 1
inactive = []
wel_rcl = {}
wel_rcl2 = {}
nwells_flow, nwells_seawat = 0, 0
for dep_name, r, c, top, bot, ibnd in zip(
pws_recs["DPEP_NAME"],
pws_recs["row"],
pws_recs["column"],
pws_recs["ztop"],
pws_recs["zbot"],
pws_recs["ibound_CS"],
import os
import numpy as np
from scipy.interpolate import Rbf
import shapefile
import simple
#--first write pilot points file from topot_points shape
shapename = 'shapes\\simple_topo_points'
f = open('misc\\topo_points.dat', 'w', 0)
points, rec_dict = shapefile.load_as_dict(shapename)
xs, ys, ztops = [], [], []
for pt, id, ztop in zip(points, rec_dict['Id'], rec_dict['ztop']):
x, y = pt.points[0][0], pt.points[0][1]
if x in xs and y in ys:
print 'dup', id
else:
xs.append(x)
ys.append(y)
ztops.append(ztop)
pname = 'topo_{0:02d}'.format(id)
f.write('{0:20s} {1:15E} {2:15E} 1 {3:15E}\n'.format(
pname, x, y, float(ztop)))
f.close()
xs, ys, ztops = np.array(xs), np.array(ys), np.array(ztops)
zs = np.ones(xs.shape)
#--write the grid file
f = open('misc\\simple.grd', 'w', 0)
f.write('{0:10d} {1:10d}\n'.format(simple.grid.nrow, simple.grid.ncol))
f.write('{0:15.5G} {1:15.5G} 0.0\n'.format(simple.grid.xmin,
def main():
#--load well locations and pandas dataframe
well_shapename = '..\\..\\_gis\\shapes\\pws_combine'
well_points = sf.load_shape_list(well_shapename)
#shp = sf.reader(well_shapename)
#print sf.get_fieldnames(well_shapename)
records = sf.load_as_dict(well_shapename, loadShapes=False)
well_names = records['DPEP_NAME']
well_zbots = records['zbot']
float_zbots = []
for i, wb in enumerate(well_zbots):
float_zbots.append(float(wb))
well_zbots = np.array(float_zbots)
well_rows, well_cols = records['row'], records['column']
pump = pandas.read_csv(
'..\\..\\_pumpage\\dataframes\\pws_filled_zeros.csv',
index_col=0,
parse_dates=True)
#--load lines and active dates
line_shapename = '..\\..\\_gis\shapes\sw_reaches'
lines = sf.load_shape_list(line_shapename)
shp = sf.Reader(line_shapename)
fnames = sf.get_fieldnames(line_shapename, ignorecase=True)
#for i,fn in enumerate(fnames):
# print i,fn
a_idx = fnames.index('ACTIVE_ST')
line_active = []
for i in range(shp.numRecords):
rec = shp.record(i)
year = int(rec[a_idx])
if year < flow.start.year:
year = flow.start.year
dt = datetime(year=year, month=1, day=1)
line_active.append(dt)
#--head stuff
#--use bot of Q5 to check for dry cells
hds_elev = np.loadtxt(flow.ref_dir + 'Q5_bot.ref')
hds_layer_idx = 0
head_file = flow.root + '.hds'
headObj = mfb.MODFLOW_Head(flow.nlay, flow.nrow, flow.ncol, head_file)
htimes = headObj.get_time_list()
#--zeta stuff
zta_layer_idx = 0
zta_elev = np.loadtxt(flow.ref_dir + 'Q1_bot.ref')
zeta_file = flow.root + '.zta'
zetaObj = mfb.MODFLOW_CBB(flow.nlay, flow.nrow, flow.ncol, zeta_file)
zta_text = ' ZETAPLANE 1'
z1times = zetaObj.get_time_list(zta_text)
#zeta_file = None
#-- stress period step
sp_step = 1
plt_dir = 'png\\results\\'
#--for ffmpeg - sequentially numbered
plt_num = 1
istart = 0
q_args = []
for i, dt in enumerate(flow.sp_start):
if i >= istart and i % sp_step == 0:
print 'building args list for ', dt
try:
h_seekpoint = long(htimes[i, 3])
except:
break
if zeta_file:
z_seekpoint = long(z1times[i, 3])
else:
z_seekpoint = None
act_lines = []
for ldt, line in zip(line_active, lines):
if ldt <= dt:
act_lines.append(line)
act_wells = []
if i == 0:
plt_start = dt
else:
plt_start = flow.sp_start[i - sp_step]
plt_end = flow.sp_end[i]
pump_plt = pump[plt_start:plt_end]
pump_plt_sum = pump_plt.sum()
for wname, wpoint, wrow, wcol, wzbot in zip(
well_names, well_points, well_rows, well_cols, well_zbots):
if wname in pump_plt.keys() and pump_plt_sum[wname] != 0:
act_wells.append(wpoint)
fig_name = plt_dir + 'sp{0:03.0f}.png'.format(plt_num)
#fig_title = 'stress period '+str(i+1)+' start date '+dt.strftime('%d/%m/%Y')
fig_title = str(dt.year)
args = [
fig_name, h_seekpoint, z_seekpoint, act_lines, act_wells,
hds_layer_idx, zta_layer_idx, fig_title
]
q_args.append(args)
plt_num += 1
jobq = mp.JoinableQueue()
#--for testing
#jobq.put_nowait(q_args[0])
#jobq.put_nowait(None)
#plot_worker(jobq,0,head_file,None,hds_elev,zta_elev)
#return
procs = []
num_procs = 6
for i in range(num_procs):
#--pass the woker function jobq and a PID
p = mp.Process(target=plot_worker,
args=(jobq, i, head_file, zeta_file, hds_elev,
zta_elev))
p.daemon = True
print 'starting process', p.name
p.start()
procs.append(p)
for q in q_args:
jobq.put(q)
for p in procs:
jobq.put(None)
#--block until all finish
for p in procs:
p.join()
print p.name, 'Finished'
cmd_line = 'ffmpeg.exe -i results\sp%03d.png -r 24 demo.avi -y'
os.system(cmd_line)
return
import shapefile
#--shapenames
sc_shapename = '..\\_gis\\scratch\\pws_K_locations'
apt_shapename = '..\\_gis\\scratch\\apt_K_locations'
sc_shapes,sc_records = shapefile.load_as_dict(sc_shapename)
apt_shapes,apt_records = shapefile.load_as_dict(apt_shapename)
wr = shapefile.Writer()
wr.field('top',fieldType='N',size=10,decimal=1)
wr.field('bot',fieldType='N',size=10,decimal=1)
wr.field('K_ftday',fieldType='N',size=30,decimal=10)
for shape,top,bot,k in zip(sc_shapes,sc_records['top'],sc_records['bot'],sc_records['K']):
wr.poly([shape.points],shapeType=shape.shapeType)
wr.record([top,bot,k])
for shape,top,bot,k in zip(apt_shapes,apt_records['top'],apt_records['bot'],apt_records['K']):
wr.poly([shape.points],shapeType=shape.shapeType)
wr.record([top,bot,k])
wr.save('..\\_gis\\scratch\\all_K_locations')
def prep():
upper_layers = [3,4,5,6]
lower_layers = [10,11,12,13]
print 'loading pumping well locations'
shapename = 'shapes\\simple_well_grid_join'
records = shapefile.load_as_dict(shapename,loadShapes=False)
rows,cols,ztops,zbots,ids,hydros = \
records['row'],records['column_'],records['ztop'],\
records['zbot'],records['Id'],records['hydro']
upper_flux,lower_flux = -500.0,-1000.0
upper_std,lower_std = 10.0,10.0
wel_row,wel_col,wel_lay,wel_name = [],[],[],[]
wel_flux = []
pred_name = 'lower_7'
pred_rate = -2500.0
for r,c,id,hydro in zip(rows,cols,ids,hydros):
if id == pred_rate:
layers = lower_layers
name = 'pred_'+str(id)
flux = pred_rate
elif hydro == 1:
layers = upper_layers
name = 'upper_'+str(id)
flux = upper_flux
else:
layers = lower_layers
name = 'lower_'+str(id)
flux = lower_flux
for l in layers:
wel_row.append(int(float(r)))
wel_col.append(int(float(c)))
wel_lay.append(int(float(l)))
wel_name.append(name)
wel_flux.append(flux)
print 'building ghb and wel lrc lists from ibounds'
ghb_stage,ghb_cond = 95.0,50000.0
ghb_rows,ghb_cols,ghb_lays,ghb_stages,ghb_conds,ghb_names = [],[],[],[],[],[]
flux_rows,flux_cols,flux_lays,flux_names = [],[],[],[]
ghb_stage_rate = 0.0005
dwn_j = 204
for k,iname in enumerate(grid.ibound_names):
arr = np.loadtxt('_model\\'+iname)
for i in range(grid.nrow):
for j in range(grid.ncol):
if arr[i,j] == 4:
ghb_rows.append(i+1)
ghb_cols.append(j+1)
ghb_lays.append(k+1)
if j < dwn_j:
ghb_stages.append(ghb_stage + ((dwn_j - j) * ghb_stage_rate))
elif j == dwn_j:
ghb_stages.append(ghb_stage)
elif j > dwn_j:
ghb_stages.append(ghb_stage + ((j - dwn_j) * ghb_stage_rate))
elif arr[i,j] == 2 and grid.lay_key[k] == 'upper':
flux_rows.append(i+1)
flux_cols.append(j+1)
flux_lays.append(k+1)
flux_names.append('flux_'+str(k+1))
wel_row.extend(flux_rows)
wel_col.extend(flux_cols)
wel_lay.extend(flux_lays)
wel_name.extend(flux_names)
#--calc the flux on the north
ncells = len(flux_rows)
xsec = 5.0 * 100.0
target_rate = 0.02
flux = xsec * target_rate
#wel_flux.extend([flux]*len(flux_rows))
df_wel = pandas.DataFrame({'layer':wel_lay,'row':wel_row,'column':wel_col,'name':wel_name})
df_wel.to_csv('_misc\\well_locs.csv')
df_ghb = pandas.DataFrame({'layer':ghb_lays,'row':ghb_rows,'column':ghb_cols,'stage':ghb_stages})
df_ghb['conductance'] = ghb_cond
df_ghb.to_csv('_misc\\ghb_locs.csv')
#--write tpl files for each
step = relativedelta(months=1)
day = grid.start
d_count = 1
p_count = 1
wel_pnames = []
pdict = {}
while day < grid.end:
day_entries = []
last = wel_name[0]
for i,name in enumerate(wel_name):
if 'flux' in name:
name = name.split('_')[0]
if name != last:
p_count += 1
last = name
pname = 'w{0:04d}_{1:04d}'.format(p_count,d_count)
if pname not in wel_pnames:
wel_pnames.append(pname)
tpl_string = '~{0:20s}~'.format(pname)
day_entries.append(tpl_string)
pdict[day.strftime('%Y%m%d')] = day_entries
day += step
d_count += 1
wel_tpl = pandas.DataFrame(pdict,index=wel_name)
grouped = wel_tpl.groupby(level=0).last()
f = open('tpl\\wel.tpl','w')
f.write('ptf ~\n')
grouped.to_csv(f,index_label='wel_name')
f.close()
wel_tpl.dtype = np.float64
for i,col in enumerate(wel_tpl.columns):
wel_tpl[col] = 1.0
grouped = wel_tpl.groupby(level=0).last()
grouped.to_csv('par\\wel.dat',index_label='wel_name')
#--generate markov series for the upper/lower and flux
#--set the last entry to the max for the prediction
flux_series = [float(flux)]
beta = 0.5
for i in range(wel_tpl.shape[1]-1):
innov = np.random.normal(0.0,flux*0.1)
val = float(flux_series[-1]) + (beta * innov)
print val
flux_series.append(val)
flux_series[-1] = min(flux_series)
lower_series = [lower_flux]
beta = 0.5
for i in range(wel_tpl.shape[1]-1):
innov = np.random.normal(0.0,lower_std)
val = lower_series[-1] + (beta * innov)
lower_series.append(val)
#lower_series = np.array(lower_series) - lower_flux
wnames = []
for wname in wel_tpl.index:
if wname == pred_name:
wseries = np.zeros((len(lower_series)))
wseries[-1] = pred_rate
wel_tpl.ix[wname] = wseries
elif 'upper' in wname:
wseries = lower_series + np.random.normal(0.0,10.0,wel_tpl.shape[1])
wseries[np.where(wseries > 0.0)] = 0.0
wseries[-1] = np.min(wseries)
wel_tpl.ix[wname] = wseries
elif 'lower' in wname:
wseries = lower_series + np.random.normal(0.0,10.0,wel_tpl.shape[1])
wseries[np.where(wseries > 0.0)] = 0.0
wseries[-1] = np.min(wseries)
wel_tpl.ix[wname] = wseries - (lower_flux - upper_flux)
elif 'flux' in wname:
wel_tpl.ix[wname] = flux_series
wnames.append(wname)
grouped = wel_tpl.groupby(level=0).last()
grouped.to_csv('base\\wel.dat',index_label='wel_name')
#wel_tpl.dtype = np.float64
#wel_tpl.ix['flux_1'].T.plot(legend=False)
#wel_tpl.ix['upper_1'].T.plot(legend=False)
#wel_tpl.ix['upper_5'].T.plot(legend=False)
#wel_tpl.ix['lower_2'].T.plot(legend=False)
#pylab.show()
pdict = {}
day = grid.start
d_count = 1
ghb_pnames = []
while day < grid.end:
day_entries = []
for ptype in ['stg','cnd']:
pname = '{0:3s}_{1:04d}'.format(ptype,d_count)
ghb_pnames.append(pname)
tpl_string = '~{0:20s}~'.format(pname)
day_entries.append(tpl_string)
pdict[day.strftime('%Y%m%d')] = day_entries
day += step
d_count += 1
ghb_tpl = pandas.DataFrame(pdict,index=('stage','conductance'))
f = open('tpl\\ghb.tpl','w')
f.write('ptf ~\n')
ghb_tpl.to_csv(f,index_label='ptype')
f.close()
for i,col in enumerate(ghb_tpl.columns):
ghb_tpl[col] = 1
ghb_tpl.to_csv('par\\ghb.dat',index_label='ptype')
f = open('pst_components\\ghbwel_pars.dat','w',0)
for pname in wel_pnames:
f.write('{0:20s} log factor 1.0 1.0e-10 1.0e+10 well_mult 1.0 0.0 1\n'.format(pname))
for pname in ghb_pnames:
f.write('{0:20s} log factor 1.0 1.0e-10 1.0e+10 ghb_mult 1.0 0.0 1\n'.format(pname))
f.close()
f = open('pst_components\\ghbwel_grps.dat','w',0)
f.write('well_mult relative 1.0000E-02 0.000 switch 2.000 parabolic\n')
f.write('ghb_mult relative 1.0000E-02 0.000 switch 2.000 parabolic\n')
f.close()
try:
if sys.argv[1].upper() == 'R':
rst = True
except:
pass
arr_prefix = 'nexrad_rech_inch_day\\rech'
pixel_file = 'pixel_data\\nexrad_inc138246_96_12_rainfall_ord.dat'
#arr_prefix = 'mm_day_pet\\pet'
#pixel_file = 'pet_pixel_all_ord.txt'
#--name of model grid shapefile with pixels attached - from make_pixel_map.py
print 'loading grid shapefile...'
shapefile_name = '..\\..\\_gis\\shapes\\broward_grid_master'
shapes,records = shapefile.load_as_dict(shapefile_name,attrib_name_list=['row','column','pixels','fractions'])
nrow,ncol = records['row'].max(),records['column'].max()
print 'done'
print 'nrow,ncol',nrow,ncol
#--load the pixel timeseries with
#--pixel, value and ord date index values for the timeseries file
p_idx,v_idx,d_idx = 0,1,2
print 'loading pixel timeseries file',pixel_file
pixel = np.loadtxt(pixel_file,usecols=[0,2,3],delimiter=',')
print 'done - ',pixel.shape[0],' records loaded'
#--get a list of the unique ordinal days in pixel time series
pixel_days = np.unique(pixel[:,d_idx])
#--process each day
import sys
import math
import shapefile
def dist(point1,point2):
xx = (point1[0] - point2[0])**2
yy = (point1[1] - point2[1])**2
return math.sqrt(xx+yy)
line_shapename = '..\\_gis\\shapes\\sw_reaches'
#shp_lines = shapefile.Reader(line_shapename)
#lines = shp_lines.shapes()
#l_records = shp_lines.records()
#l_name_idx,reach_idx = 0,2
lines,l_records = shapefile.load_as_dict(line_shapename)
#--tolerance distance
warn_dist = 50.0
tol_dist = 0.0
#--set the writer instance
in_shapename = '..\\_gis\\shapes\\sw_structures'
out_shapename = '..\\_gis\\scratch\\sw_structures_reaches'
shp_points = shapefile.Reader(in_shapename)
points = shp_points.shapes()
p_records = shp_points.records()
p_name_idx,dwnstr_idx = 4,6
wr = shapefile.writer_like(in_shapename)
wr.field('upstream',fieldType='N',size=10)
#--coordinate information
x0, y0 = 539750.0, 2785750.0
dx, dy = 500., 500.
xcell, ycell = mfd.cell_coordinates(nrow, ncol, dx, dy)
xcell += x0
ycell += y0
xedge, yedge = mfd.edge_coordinates(nrow, ncol, dx, dy)
xedge += x0
yedge += y0
xmin, xmax = xedge.min(), xedge.max()
ymin, ymax = yedge.min(), yedge.max()
#--read shapefile with cross-section data
shape_name = os.path.join('..', 'GIS', 'UMDCrossSections')
print shape_name
xsect_list = sf.load_shape_list(shape_name)
shapes, records = sf.load_as_dict(shape_name)
nxsect = len(shapes)
xsect_name = []
xsect_label = []
for idx in xrange(0, nxsect):
xsect_name.append(records['XSECT'][idx].replace(' ', ''))
xsect_label.append(records['Label'][idx])
#--create crossection figures
dxsect = 5.
ifigure = 0
lay_width = [0.5, 0.5, 0.5, 0.5]
lay_color = ['k', 'k', 'k', 'k']
# fresh brackish saltwater
#surf_color = ['#40d3f7','#4E8975','#F76541']
surf_color = ['#40d3f7', '#40d3f7', '#F76541']
for idx, xsect in enumerate(xsect_list):
def prep():
upper_layers = [3, 4, 5, 6]
lower_layers = [10, 11, 12, 13]
print 'loading pumping well locations'
shapename = 'shapes\\simple_well_grid_join'
records = shapefile.load_as_dict(shapename, loadShapes=False)
rows,cols,ztops,zbots,ids,hydros = \
records['row'],records['column_'],records['ztop'],\
records['zbot'],records['Id'],records['hydro']
upper_flux, lower_flux = -500.0, -1000.0
upper_std, lower_std = 10.0, 10.0
wel_row, wel_col, wel_lay, wel_name = [], [], [], []
wel_flux = []
pred_name = 'lower_7'
pred_rate = -2500.0
for r, c, id, hydro in zip(rows, cols, ids, hydros):
if id == pred_rate:
layers = lower_layers
name = 'pred_' + str(id)
flux = pred_rate
elif hydro == 1:
layers = upper_layers
name = 'upper_' + str(id)
flux = upper_flux
else:
layers = lower_layers
name = 'lower_' + str(id)
flux = lower_flux
for l in layers:
wel_row.append(int(float(r)))
wel_col.append(int(float(c)))
wel_lay.append(int(float(l)))
wel_name.append(name)
wel_flux.append(flux)
print 'building ghb and wel lrc lists from ibounds'
ghb_stage, ghb_cond = 95.0, 50000.0
ghb_rows,ghb_cols,ghb_lays,ghb_stages,ghb_conds,ghb_names = [],[],[],[],[],[]
flux_rows, flux_cols, flux_lays, flux_names = [], [], [], []
ghb_stage_rate = 0.0005
dwn_j = 204
for k, iname in enumerate(grid.ibound_names):
arr = np.loadtxt('_model\\' + iname)
for i in range(grid.nrow):
for j in range(grid.ncol):
if arr[i, j] == 4:
ghb_rows.append(i + 1)
ghb_cols.append(j + 1)
ghb_lays.append(k + 1)
if j < dwn_j:
ghb_stages.append(ghb_stage +
((dwn_j - j) * ghb_stage_rate))
elif j == dwn_j:
ghb_stages.append(ghb_stage)
elif j > dwn_j:
ghb_stages.append(ghb_stage +
((j - dwn_j) * ghb_stage_rate))
elif arr[i, j] == 2 and grid.lay_key[k] == 'upper':
flux_rows.append(i + 1)
flux_cols.append(j + 1)
flux_lays.append(k + 1)
flux_names.append('flux_' + str(k + 1))
wel_row.extend(flux_rows)
wel_col.extend(flux_cols)
wel_lay.extend(flux_lays)
wel_name.extend(flux_names)
#--calc the flux on the north
ncells = len(flux_rows)
xsec = 5.0 * 100.0
target_rate = 0.02
flux = xsec * target_rate
#wel_flux.extend([flux]*len(flux_rows))
df_wel = pandas.DataFrame({
'layer': wel_lay,
'row': wel_row,
'column': wel_col,
'name': wel_name
})
df_wel.to_csv('_misc\\well_locs.csv')
df_ghb = pandas.DataFrame({
'layer': ghb_lays,
'row': ghb_rows,
'column': ghb_cols,
'stage': ghb_stages
})
df_ghb['conductance'] = ghb_cond
df_ghb.to_csv('_misc\\ghb_locs.csv')
#--write tpl files for each
step = relativedelta(months=1)
day = grid.start
d_count = 1
p_count = 1
wel_pnames = []
pdict = {}
while day < grid.end:
day_entries = []
last = wel_name[0]
for i, name in enumerate(wel_name):
if 'flux' in name:
name = name.split('_')[0]
if name != last:
p_count += 1
last = name
pname = 'w{0:04d}_{1:04d}'.format(p_count, d_count)
if pname not in wel_pnames:
wel_pnames.append(pname)
tpl_string = '~{0:20s}~'.format(pname)
day_entries.append(tpl_string)
pdict[day.strftime('%Y%m%d')] = day_entries
day += step
d_count += 1
wel_tpl = pandas.DataFrame(pdict, index=wel_name)
grouped = wel_tpl.groupby(level=0).last()
f = open('tpl\\wel.tpl', 'w')
f.write('ptf ~\n')
grouped.to_csv(f, index_label='wel_name')
f.close()
wel_tpl.dtype = np.float64
for i, col in enumerate(wel_tpl.columns):
wel_tpl[col] = 1.0
grouped = wel_tpl.groupby(level=0).last()
grouped.to_csv('par\\wel.dat', index_label='wel_name')
#--generate markov series for the upper/lower and flux
#--set the last entry to the max for the prediction
flux_series = [float(flux)]
beta = 0.5
for i in range(wel_tpl.shape[1] - 1):
innov = np.random.normal(0.0, flux * 0.1)
val = float(flux_series[-1]) + (beta * innov)
print val
flux_series.append(val)
flux_series[-1] = min(flux_series)
lower_series = [lower_flux]
beta = 0.5
for i in range(wel_tpl.shape[1] - 1):
innov = np.random.normal(0.0, lower_std)
val = lower_series[-1] + (beta * innov)
lower_series.append(val)
#lower_series = np.array(lower_series) - lower_flux
wnames = []
for wname in wel_tpl.index:
if wname == pred_name:
wseries = np.zeros((len(lower_series)))
wseries[-1] = pred_rate
wel_tpl.ix[wname] = wseries
elif 'upper' in wname:
wseries = lower_series + np.random.normal(0.0, 10.0,
wel_tpl.shape[1])
wseries[np.where(wseries > 0.0)] = 0.0
wseries[-1] = np.min(wseries)
wel_tpl.ix[wname] = wseries
elif 'lower' in wname:
wseries = lower_series + np.random.normal(0.0, 10.0,
wel_tpl.shape[1])
wseries[np.where(wseries > 0.0)] = 0.0
wseries[-1] = np.min(wseries)
wel_tpl.ix[wname] = wseries - (lower_flux - upper_flux)
elif 'flux' in wname:
wel_tpl.ix[wname] = flux_series
wnames.append(wname)
grouped = wel_tpl.groupby(level=0).last()
grouped.to_csv('base\\wel.dat', index_label='wel_name')
#wel_tpl.dtype = np.float64
#wel_tpl.ix['flux_1'].T.plot(legend=False)
#wel_tpl.ix['upper_1'].T.plot(legend=False)
#wel_tpl.ix['upper_5'].T.plot(legend=False)
#wel_tpl.ix['lower_2'].T.plot(legend=False)
#pylab.show()
pdict = {}
day = grid.start
d_count = 1
ghb_pnames = []
while day < grid.end:
day_entries = []
for ptype in ['stg', 'cnd']:
pname = '{0:3s}_{1:04d}'.format(ptype, d_count)
ghb_pnames.append(pname)
tpl_string = '~{0:20s}~'.format(pname)
day_entries.append(tpl_string)
pdict[day.strftime('%Y%m%d')] = day_entries
day += step
d_count += 1
ghb_tpl = pandas.DataFrame(pdict, index=('stage', 'conductance'))
f = open('tpl\\ghb.tpl', 'w')
f.write('ptf ~\n')
ghb_tpl.to_csv(f, index_label='ptype')
f.close()
for i, col in enumerate(ghb_tpl.columns):
ghb_tpl[col] = 1
ghb_tpl.to_csv('par\\ghb.dat', index_label='ptype')
f = open('pst_components\\ghbwel_pars.dat', 'w', 0)
for pname in wel_pnames:
f.write(
'{0:20s} log factor 1.0 1.0e-10 1.0e+10 well_mult 1.0 0.0 1\n'
.format(pname))
for pname in ghb_pnames:
f.write(
'{0:20s} log factor 1.0 1.0e-10 1.0e+10 ghb_mult 1.0 0.0 1\n'
.format(pname))
f.close()
f = open('pst_components\\ghbwel_grps.dat', 'w', 0)
f.write(
'well_mult relative 1.0000E-02 0.000 switch 2.000 parabolic\n'
)
f.write(
'ghb_mult relative 1.0000E-02 0.000 switch 2.000 parabolic\n'
)
f.close()
def setup():
'''write an ssm key file and extract stress period nss lists to binary
'''
from bro import seawat as cal
from bro_pred import seawat as pred
#--build a swr reach tidal key
f = open('..\\_BCDPEP\\BCDPEP_reach_conc.dat','r')
f.readline()
tidal_conc = {}
for line in f:
raw = line.strip().split(',')
tidal_conc[int(raw[0])] = float(raw[1])
f.close()
#--group the tidal source reaches if the conc is the same
tidal_rc,tidal_names = [],[]
concs,groups = [],[]
for sreach,conc in tidal_conc.iteritems():
if conc in concs:
groups[concs.index(conc)].append(sreach)
else:
groups.append([sreach])
concs.append(conc)
tidal_rc.append([])
tidal_names.append([])
import shapefile
shapename = '..\\_gis\\scratch\\sw_reaches_conn_swrpolylines_2'
recs = shapefile.load_as_dict(shapename,loadShapes=False)
for r,c,sreach,sstruct,sname in zip(recs['ROW'],recs['COLUMN'],recs['SRC_reach'],recs['SRC_struct'],recs['SRC_name']):
if sstruct == -1:
for i,group in enumerate(groups):
if sreach in group:
tidal_rc[i].append((r,c))
if sname not in tidal_names[i]:
tidal_names[i].append(sname)
f = open('misc\\ssm_riv.key','w',0)
f.write('group_name,row,col,names\n')
for i,[tups,names] in enumerate(zip(tidal_rc,tidal_names)):
name = '_'.join(names).replace(' ','_').replace(',','_')
for (r,c) in tups:
f.write('riv_cn_#'+str(i+1)+','+str(r)+','+str(c)+','+name+'\n')
f.close()
#--build intercoastal ghb tidal key
ibound = np.loadtxt('..\\_model\\bro.03\\seawatref\\ibound_CS.ref',dtype=np.int)
ic_groups = {}
for i in range(ibound.shape[0]):
for j in range(ibound.shape[1]):
name = 'ghb_cn_#'+str(ibound[i,j])
if name in ic_groups.keys():
ic_groups[name].append((i+1,j+1))
else:
ic_groups[name] = [(i+1,j+1)]
f = open('misc\\ssm_ghb.key','w',0)
f.write('name,row,col\n')
for name,tups in ic_groups.iteritems():
for (r,c) in tups:
f.write(name+','+str(r)+','+str(c)+'\n')
f.close()
#--write an ssm template file - monthly
par_dict = {}
tpl_entries = {}
months = calendar.month_abbr
pnames = []
tpl_dict = {}
for riv_grp in range(len(tidal_rc)):
tpl_entries = []
for mn in months[1:]:
pname = 'rcn_'+str(riv_grp+1)+'_'+mn
assert len(pname) <= 10,pname
pnames.append(pname)
tpl_entry = '~{0:25s}~'.format(pname)
tpl_entries.append(tpl_entry)
tpl_dict['riv_cn_#'+str(riv_grp+1)] = tpl_entries
par_dict['riv_conc'] = pnames
pnames = []
for ghb_grp in ic_groups.keys():
grp_num = int(ghb_grp.split('#')[1])
tpl_entries = []
for mn in months[1:]:
pname = 'gcn_'+str(grp_num)+'_'+mn
assert len(pname) <= 10,pname
pnames.append(pname)
tpl_entry = '~{0:25s}~'.format(pname)
tpl_entries.append(tpl_entry)
tpl_dict['ghb_cn_#'+str(ghb_grp)] = tpl_entries
par_dict['ghb_conc'] = pnames
#--save the template file
df = pandas.DataFrame(tpl_dict)
df.index = df.index + 1
f = open('tpl\\ghbwel_ssm.tpl','w',0)
f.write('ptf ~\n')
df.to_csv(f,index_label='month')
f.close()
#--save a generic par file for testing
for col in df.columns:
df[col] = 1.0
df.to_csv('par\\ghbwell_ssm.csv',index_label='month')
#--write the pst components
f_grp = open('pst_components\\ghbwel_ssm_grps.dat','w',0)
f_par = open('pst_components\\ghbwel_ssm_pars.dat','w',0)
pargps = par_dict.keys()
pargps.sort()
for pargp in pargps:
pnames = par_dict[pargp]
f_grp.write('{0:<20s} factor 0.01 0.001 switch 2.0 parabolic\n'.format(pargp))
for pname in pnames:
f_par.write('{0:<20s} log factor 1.0 1.0e-10 1.0e+10 {1:<20s} 1.0 0.0 0\n'.format(pname,pargp))
f_grp.close()
f_par.close()
#--extract sp data and zip to binary
#--load the key files into a dict
itype_dict = {'riv':4,'ghb':5}
files = os.listdir('misc\\')
key_files = []
for f in files:
if 'key' in f and 'ssm' in f:
key_files.append(f)
key_dict = {}
for key_file in key_files:
ptype = key_file.split('.')[0].split('_')[1]
itype = itype_dict[ptype]
f = open('misc\\'+key_file,'r')
header = f.readline()
for line in f:
raw = line.strip().split(',')
r,c = int(raw[1]),int(raw[2])
key_dict[(r,c,itype)] = raw[0]
f.close()
sp_lists = [cal.sp_start,pred.sp_start]
for ssm_file,list_dir,sp_list in zip(SSM_FILES,LIST_DIRS,sp_lists):
f = open(ssm_file,'r')
#--read the header info
logicals = f.readline()
maxssm = int(f.readline().strip())
#--read the rch,ets junk
rchets_lines = []
for i in range(4):
rchets_lines.append(f.readline().strip())
#--start the sp loop
kper = 0
while True:
try:
nss = int(f.readline().strip())
except:
break
lines = []
#line_str = ''
for i in range(nss):
line = parse_ssm_line(f.readline())
try:
line[-1] = key_dict[(line[1],line[2],line[4])]
except:
pass
#line_str += line
lines.append(tuple(line))
arr = np.array(lines,dtype=ssm_dtype_extend)
dt = sp_list[kper]
fname = list_dir+'ssm_'+dt.strftime('%Y%m%d')+'_'+str(nss)+'.dat'
print 'writing',fname
#--for testing
#np.savetxt('test.dat',arr,fmt=' %9d %9d %9d %15.6E %9d %20s')
arr.tofile(fname)
#--read the repeat rch and ets lines
rch = f.readline()
rch = f.readline()
kper += 1
import numpy as np
import shapefile
shapename = '..\\_gis\\scratch\\sw_reaches_coastal_conc'
records = shapefile.load_as_dict(shapename,loadShapes=False)
print records.keys()
conc_key = 'max_relcon'
reach_key = 'reach'
tidal_key = 'stage_rec'
f = open('BCDPEP_reach_conc.dat','w',0)
f.write('source_reach,rel_conc\n')
for conc,reach,tidal in zip(records[conc_key],records[reach_key],records[tidal_key]):
if 'COASTAL' in tidal.upper():
f.write(str(int(float(reach)))+','+str(conc)+'\n')
f.close()
#--coordinate information
x0, y0 = 539750.0, 2785750.0
dx,dy = 500., 500.
xcell, ycell = mfd.cell_coordinates(nrow,ncol,dx,dy)
xcell += x0
ycell += y0
xedge, yedge = mfd.edge_coordinates(nrow,ncol,dx,dy)
xedge += x0
yedge += y0
xmin,xmax = xedge.min(),xedge.max()
ymin,ymax = yedge.min(),yedge.max()
#--read shapefile with cross-section data
shape_name = os.path.join( '..','GIS','UMDCrossSections' )
print shape_name
xsect_list = sf.load_shape_list(shape_name)
shapes,records = sf.load_as_dict(shape_name)
nxsect = len(shapes)
xsect_name = []
xsect_label = []
for idx in xrange( 0, nxsect ):
xsect_name.append( records['XSECT'][idx].replace(' ','') )
xsect_label.append( records['Label'][idx] )
#--create crossection figures
dxsect = 5.
ifigure = 0
lay_width = [0.5,0.5,0.5,0.5]
lay_color = ['k','k','k','k']
# fresh brackish saltwater
#surf_color = ['#40d3f7','#4E8975','#F76541']
surf_color = ['#40d3f7','#40d3f7','#F76541']
for idx,xsect in enumerate( xsect_list ):
else:
groups[i] = [intrv]
midpoints[i] = mid
return midpoints, groups
#--build the geo array
geom = np.zeros((seawat.nlay + 1, seawat.nrow, seawat.ncol))
geom[0, :, :] = np.loadtxt(seawat.top_name)
for i, lay in enumerate(seawat.layer_botm_names):
arr = np.loadtxt(seawat.ref_dir + lay + '_bot.ref')
geom[i + 1, :, :] = arr
#--load ftl salt grid shapefile
shapename = '..\\..\\_gis\\scratch\\ftl_salt_grid'
shapes, records = shapefile.load_as_dict(shapename)
wellnums, rows, cols = records['Id'], records['row'], records['column_']
xs, ys = [], []
for s in shapes:
x, y = s.points[0]
xs.append(x)
ys.append(y)
#--load the relconc dataframes
df_dir = '..\\..\\_ftl_salt\\dataframes\\'
files = os.listdir(df_dir)
dfs = {}
for f in files:
if 'relconc' in f:
print f
wellnum = int(f.split('_')[0].split('-')[1])
import numpy as np
import calendar
import shapefile
import pestUtil
#--load the grid shapefile that has the nexrad groups
shapename = 'shapes\\cwm_grid_groups'
records = shapefile.load_as_dict(shapename,['row','column','nex_group'],loadShapes=False)
#--load the grid info
ginfo = pestUtil.load_grid_spec('misc\grid.spc')
#--fill in the groups array
grp_arr = np.zeros((ginfo['nrow'],ginfo['ncol']))
for r,c,g in zip(records['row'],records['column'],records['nex_group']):
grp_arr[r-1,c-1] = g
np.savetxt('ref\UMD_nexrad.ref',grp_arr,fmt=' %2.0f')
num_grps = np.unique(grp_arr).shape[0]
f = open('fac\\nex_fac.dat','w')
f.write('cl_nexpts\n')
f.write('cl_mf_grid\n')
f.write('{0:10.0f}{1:10.0f}\n'.format(1,num_grps))
f.write('{0:10.0f}{1:10.0f}\n'.format(1,ginfo['nrow']*ginfo['ncol']))
f.write('{0:10.0f}{1:10.0f}\n'.format(num_grps,ginfo['nrow']*ginfo['ncol']))
f.write('{0:10.0f}\n'.format(1))
cell_num = 1
for i in range(ginfo['nrow']):
for j in range(ginfo['ncol']):
def write():
#--get the well locations
shapename = '..\\shapes\\simple_well_grid_join'
records = shapefile.load_as_dict(shapename,loadShapes=False)
rows,cols,ztops,zbots,ids,hydros = records['row'],records['column_'],records['ztop'],records['zbot'],records['Id'],records['hydro']
#--random sampling
#--first create normal distributions for the wells
upper_mean,upper_std = -500.0,10.0
lower_mean,lower_std = -5000.0,50.0
nper = len(grid.sp_start)
well_records = []
for id,hydro in zip(ids,hydros):
if hydro == 3:
rec = np.random.normal(lower_mean,lower_std,nper)
if hydro == 1:
rec = np.random.normal(upper_mean,upper_std,nper)
rec = add_zeros(rec)
well_records.append(rec)
well_records = np.array(well_records).transpose()
well_records[np.where(well_records>0.0)] = 0.0
mnw_ds2 = []
names = []
for id,r,c,top,bot,hydro in zip(ids,rows,cols,ztops,zbots,hydros):
#--mnw
if hydro ==1:
name = 'upper_'+str(id)
else:
name = 'lower_'+str(id)
names.append(name)
line_2a = '{0:20s}{1:10f}{2:>33s}\n'.format(name,-1,'#2a')
mnw_ds2.append(line_2a)
line_2b = '{0:20s}{1:10d}{2:10d}{3:10d}{4:10d} #2b\n'.format('THIEM',0,0,0,0)
mnw_ds2.append(line_2b)
line_2c = '{0:10.4f}{1:>53s}\n'.format(1.0,'#2c')
mnw_ds2.append(line_2c)
line_2d2 = ' {0:9.4f} {1:9.4f} {2:9.0f} {3:9.0f}{4:>26s}\n'.format(float(top),float(bot),int(float(r)),int(float(c)),'#2d-2\n')
mnw_ds2.append(line_2d2)
f_mnw = open(grid.modelname+'.mnw','w',0)
f_mnw.write('# '+sys.argv[0]+' '+str(datetime.now())+'\n')
f_mnw.write(' {0:9.0f} {1:9.0f} {2:9.0f}\n'.format(len(ids),0,0))
for line in mnw_ds2:
f_mnw.write(line)
upper_layers = [3,4,5,6]
lower_layers = [10,11,12,13,14,15,16,17,18]
f_wel = open(grid.modelname+'.wel','w',0)
f_wel.write('# '+sys.argv[0]+' '+str(datetime.now())+'\n')
f_wel.write(' {0:9.0f} {1:9.0f} {2:9.0f}\n'.format(300,0,0))
for i,slice in enumerate(well_records):
lines = []
for name,rate in zip(names,slice):
lines.append('{0:20s}{1:15.4G}\n'.format(name,rate))
f_mnw.write('{0:10d} {1:20s} {2:3d}\n'.format(len(lines),'#3 Stress Period',i+1))
for line in lines:
f_mnw.write(line)
lines = []
for name,rate,row,col in zip(names,slice,rows,cols):
if 'upper' in name:
layers = upper_layers
else:
layers = lower_layers
rate /= float(len(layers))
for lay in layers:
line = '{0:10d}{1:10d}{2:10d}{3:15.4E} #{4:20s}\n'\
.format(lay,int(float(row)),int(float(col)),rate,name)
lines.append(line)
f_wel.write('{0:10d}{1:10d} #{2:20s}{3:4d}\n'.format(len(lines),0,'stress period ',i+1))
for line in lines:
f_wel.write(line)
f_mnw.close()
f_wel.close()
import os
from datetime import datetime, timedelta
import numpy as np
from scipy.io.netcdf import netcdf_file as ncdf
import pandas
import shapefile
#--get a list of the eden masterid numbers that are needed
recs = shapefile.load_as_dict('..\\_gis\\scratch\\broward_grid_eden',
attrib_name_list=['eden_cells'],
loadShapes=False)
masterids = []
for r in recs['eden_cells']:
raw = r.split()
for rr in raw:
if int(rr) not in masterids:
masterids.append(int(rr))
#--get the x,y of the masterids
recs = shapefile.load_as_dict('..\\_gis\\shapes\\EDEN_grid_poly_Jan_10_sp',
loadShapes=False)
xs, ys = [], []
for x, y, m in zip(recs['X_COORD'], recs['Y_COORD'], recs['MASTERID']):
if int(m) in masterids:
xs.append(float(x))
ys.append(float(y))
cdf_dir = 'surface_netcdf\\'
cdf_files = os.listdir(cdf_dir)
#--group files by year
