def cell_padding(input, output, radius=3):
"""Mitigates edge effect by growing an input raster map by radius cells
Args
----
input, output : str
Names of GRASS raster map for input, and padded output
radius : int
Radius in which to expand region and grow raster
Returns
-------
input_grown : str
GRASS raster map which has been expanded by radius cells"""
region = Region()
g.region(n=region.north + (region.nsres * radius),
s=region.south - (region.nsres * radius),
w=region.west - (region.ewres * radius),
e=region.east + (region.ewres * radius))
r.grow(input=input,
output=output,
radius=radius+1,
quiet=True)
return (region)
def main():
g.message("Pocitam NDVI...")
# nastavit region
g.region(rast=options['tm4'])
# vypocitat NDVI
r.mapcalc('ndvi = float({y} - {x}) / ({y} + {x})'.format(x=options['tm3'],
y=options['tm4']),
overwrite=True)
# r.reclass podporuje pouze datovy typ CELL
r.mapcalc('temp1 = 100 * ndvi', overwrite=True)
g.message("Reklasifikuji data...")
# reklasifikovat data
reclass_rules = """-100 thru 5 = 1 bez vegetace, vodni plochy
5 thru 35 = 2 plochy s minimalni vegetaci
35 thru 100 = 3 plochy pokryte vegetaci"""
r.reclass(overwrite=True,
rules='-',
input='temp1',
output='r_ndvi',
stdin_=reclass_rules)
# nastavit tabulku barev
color_rules = """1 red
2 yellow
3 0 136 26"""
r.colors(quiet=True, map='r_ndvi', rules='-', stdin_=color_rules)
# vytiskout zakladni charakteristiku dat
r.report(map='r_ndvi', units=['c', 'p', 'h'])
def main():
g.message("Pocitam NDVI...")
# nastavit region
g.region(rast=options['tm4'])
# vypocitat NDVI
r.mapcalc('ndvi = float({y} - {x}) / ({y} + {x})'.format(x=options['tm3'], y=options['tm4']), overwrite = True)
# r.reclass podporuje pouze datovy typ CELL
r.mapcalc('temp1 = 100 * ndvi', overwrite = True)
g.message("Reklasifikuji data...")
# reklasifikovat data
reclass_rules = """-100 thru 5 = 1 bez vegetace, vodni plochy
5 thru 35 = 2 plochy s minimalni vegetaci
35 thru 100 = 3 plochy pokryte vegetaci"""
r.reclass(overwrite = True, rules = '-',
input = 'temp1', output = 'r_ndvi', stdin_ = reclass_rules)
# nastavit tabulku barev
color_rules = """1 red
2 yellow
3 0 136 26"""
r.colors(quiet = True,
map = 'r_ndvi', rules = '-', stdin_ = color_rules)
# vytiskout zakladni charakteristiku dat
r.report(map = 'r_ndvi', units = ['c', 'p', 'h'])
def cell_padding(input, output, radius=3):
"""Mitigates edge effect by growing an input raster map by radius cells
Args
----
input, output : str
Names of GRASS raster map for input, and padded output
radius : int
Radius in which to expand region and grow raster
Returns
-------
input_grown : str
GRASS raster map which has been expanded by radius cells"""
region = Region()
g.region(n=region.north + (region.nsres * radius),
s=region.south - (region.nsres * radius),
w=region.west - (region.ewres * radius),
e=region.east + (region.ewres * radius))
r.grow(input=input,
output=output,
radius=radius+1,
quiet=True)
return (region)
def main():
"""
Creates a hydrologically correct MODFLOW grid that inlcudes minimum
DEM elevations for all stream cells and mean elevations everywhere else
"""
"""
dem = 'DEM'
grid = 'grid_tmp'
streams = 'streams_tmp'
streams_MODFLOW = 'streams_tmp_MODFLOW'
DEM_MODFLOW = 'DEM_coarse'
resolution = 500
"""
options, flags = gscript.parser()
dem = options['dem']
grid = options['grid']
streams = options['streams']
#resolution = float(options['resolution'])
streams_MODFLOW = options['streams_modflow']
DEM_MODFLOW = options['dem_modflow']
# Get number of rows and columns
colNames = np.array(gscript.vector_db_select(grid, layer=1)['columns'])
colValues = np.array(
gscript.vector_db_select(grid, layer=1)['values'].values())
cats = colValues[:, colNames == 'cat'].astype(int).squeeze()
rows = colValues[:, colNames == 'row'].astype(int).squeeze()
cols = colValues[:, colNames == 'col'].astype(int).squeeze()
nRows = np.max(rows)
nCols = np.max(cols)
gscript.use_temp_region()
# Set the region to capture only the channel
g.region(raster=dem)
v.to_rast(input=streams,
output=streams_MODFLOW,
use='val',
value=1.0,
type='line',
overwrite=gscript.overwrite(),
quiet=True)
r.mapcalc('tmp' + " = " + streams_MODFLOW + " * " + dem, overwrite=True)
g.rename(raster=('tmp', streams_MODFLOW), overwrite=True, quiet=True)
g.region(vector=grid, rows=nRows, cols=nCols, quiet=True)
r.resamp_stats(input=streams_MODFLOW,
output=streams_MODFLOW,
method='average',
overwrite=gscript.overwrite(),
quiet=True)
r.resamp_stats(input=dem,
output=DEM_MODFLOW,
method='average',
overwrite=gscript.overwrite(),
quiet=True)
r.patch(input=streams_MODFLOW + ',' + DEM_MODFLOW,
output=DEM_MODFLOW,
overwrite=True,
quiet=True)
def start_up():
"""Prepare input raster and vector data
"""
# czech republic, resolution 1km
g.region(flags="d", res='1000')
r.random_surface(output='inputraster1')
r.random_surface(output='inputraster2')
def reg_2deg(input = '', size = 2):
# get region center
reg = grass.parse_command('g.region', input = input, flags = 'c')
lat = float()
lon = float()
g.region(n = lat + size/2, s = lat - size/2, w = lon - size/2, e = lon + size/2,
align = input, flags = 'ap')
def cleanup():
gs.message("Deleting intermediate files...")
for f in TMP_RAST:
gs.run_command(
"g.remove", type="raster", name=f, flags='bf', quiet=True)
g.region(**current_reg)
if mask_test:
r.mask(original_mask, overwrite=True, quiet=True)
def main():
"""
Creates a hydrologically correct MODFLOW grid that inlcudes minimum
DEM elevations for all stream cells and mean elevations everywhere else
"""
"""
dem = 'DEM'
grid = 'grid_tmp'
streams = 'streams_tmp'
streams_MODFLOW = 'streams_tmp_MODFLOW'
DEM_MODFLOW = 'DEM_coarse'
resolution = 500
"""
options, flags = gscript.parser()
dem = options['dem']
grid = options['grid']
streams = options['streams']
#resolution = float(options['resolution'])
streams_MODFLOW = options['streams_modflow']
DEM_MODFLOW = options['dem_modflow']
gscript.use_temp_region()
# Set the region to capture only the channel
g.region(raster=dem)
v.to_rast(input=streams,
output=streams_MODFLOW,
use='val',
value=1.0,
type='line',
overwrite=gscript.overwrite(),
quiet=True)
r.mapcalc('tmp' + " = " + streams_MODFLOW + " * " + dem, overwrite=True)
g.rename(raster=('tmp', streams_MODFLOW), overwrite=True, quiet=True)
g.region(raster=DEM_MODFLOW, quiet=True)
print "ALTERED"
r.resamp_stats(input=streams_MODFLOW,
output=streams_MODFLOW,
method='average',
overwrite=gscript.overwrite(),
quiet=True)
r.resamp_stats(input=dem,
output=DEM_MODFLOW,
method='average',
overwrite=gscript.overwrite(),
quiet=True)
r.patch(input=streams_MODFLOW + ',' + DEM_MODFLOW,
output=DEM_MODFLOW,
overwrite=True,
quiet=True)
def compute_solar_irradiation(inputFile, outputFile, day_of_year, crs='32630'):
# Define grass working set
GRASS_GISDB = 'grassdata'
GRASS_LOCATION = 'GEOPROCESSING'
GRASS_MAPSET = 'PERMANENT'
GRASS_ELEVATIONS_FILENAME = 'ELEVATIONS'
os.environ.update(dict(GRASS_COMPRESS_NULLS='1'))
# Clean previously processed data
if os.path.isdir(GRASS_GISDB):
shutil.rmtree(GRASS_GISDB)
with Session(gisdb=GRASS_GISDB,
location=GRASS_LOCATION,
mapset=GRASS_MAPSET,
create_opts='EPSG:32630') as ses:
# Set project projection to match elevation raster projection
g.proj(epsg=crs, flags='c')
# Load raster file into working directory
r.import_(input=inputFile, output=GRASS_ELEVATIONS_FILENAME, flags='o')
# Set project region to match raster region
g.region(raster=GRASS_ELEVATIONS_FILENAME, flags='s')
# Calculate solar irradiation
gscript.run_command('r.slope.aspect',
elevation=GRASS_ELEVATIONS_FILENAME,
slope='slope',
aspect='aspect')
gscript.run_command('r.sun',
elevation=GRASS_ELEVATIONS_FILENAME,
slope='slope',
aspect='aspect',
beam_rad='beam',
step=1,
day=day_of_year)
# Get extraterrestrial irradiation from history metadata
regex = re.compile(r'\d+\.\d+')
output = gscript.read_command("r.info", flags="h", map=["beam"])
splits = str(output).split('\n')
line = next(filter(lambda line: 'Extraterrestrial' in line, splits))
extraterrestrial_irradiance = float(regex.search(line)[0])
# Export generated results into a GeoTiff file
if os.path.isfile(outputFile):
os.remove(outputFile)
r.out_gdal(input='beam', output=outputFile)
return extraterrestrial_irradiance
def setRegion(parcelmap,betriebid):
## set region to parcel layer extent + buffer
reg = Region()
reg.vect(parcelmap.name)
regbuffer = 100
reg.north += regbuffer
reg.east += regbuffer
reg.south -= regbuffer
reg.west -= regbuffer
reg.set_current()
# set_current() not working right now
# so using g.region() :
g.region(n=str(reg.north), s=str(reg.south), w=str(reg.west), e=str(reg.east), res='2', flags='a',quiet=quiet)
g.region(save='B'+betriebid,overwrite=True,quiet=quiet)
def worker(src):
window, src = src
window = dict(window)
window['n'] = window.pop('north')
window['s'] = window.pop('south')
window['e'] = window.pop('east')
window['w'] = window.pop('west')
del (window['top'])
del (window['bottom'])
g.region(**window)
with RasterRow(src) as rs:
arr = np.asarray(rs)
return (arr)
def initGrassSetup(userDemDir,
userid,
lat,
lon,
filename=options['demname'] + '.tif'):
# redudant conections to grass
r, g, gscript = connect2grass(userid)
# r.in.gdal input=/home/justin/Documents/ucmi/geodata/zip/tempEPSG3857/tile.tif output=tile
gscript.run_command(
'r.in.gdal',
input=userDemDir + filename,
output=filename[:-4],
overwrite=True,
)
g.region(raster=filename[:-4] + '@' + str(userid))
g.region(flags='p')
# remove old viewsheds
g.remove(flags='fb', type='raster', pattern='viewshed*')
def firsttimeGRASS(infiles, adminfile, maskfile):
"""
Run a maxlikelihood unsupervised classification on the data
nclasses: number of expected classes
infiles: list of raster files to import and process
firstime: if firsttime, it will import all files in GRASS
"""
from grass_session import Session
from grass.script import core as gcore
from grass.pygrass.modules.shortcuts import raster as r
from grass.pygrass.modules.shortcuts import vector as v
from grass.pygrass.modules.shortcuts import general as g
from grass.pygrass.modules.shortcuts import imagery as i
# create a new location from EPSG code (can also be a GeoTIFF or SHP or ... file)
with Session(gisdb="/tmp", location="loc", create_opts="EPSG:4326"):
# First run, needs to import the files and create a mask
# Import admin boundary
#v.import_(input=adminfile,output="admin",quiet=True,superquiet=True)
gcore.parse_command("v.import",
input=adminfile,
output="admin",
quiet=True)
# Set computational region to admin boundary
g.region(flags="s", vector="admin", quiet=True)
# Keep only file name for output
outmf = maskfile.split("/")[-1]
# Import Mask file
r.in_gdal(input=maskfile, output=outmf, quiet=True)
# Apply Mask
r.mask(raster=outmf, maskcats="0", quiet=True)
# Set computational resolution to mask pixel size
g.region(flags="s", raster=outmf, quiet=True)
# Import files
for f in infiles:
# Keep only file name for output
outf = f.split("/")[-1]
# Landsat files not in Geo lat long needs reproj on import
#r.import_(input=f,output=outf,quiet=True)
gcore.parse_command("r.import", input=f, output=outf, quiet=True)
# Create group
i.group(group="l8", subgroup="l8", input=outf, quiet=True)
def cell_padding(input, output, radius=3):
"""Mitigates edge effect by growing an input raster map by radius cells
Parameters
----------
input, output : str
Names of GRASS raster map for input, and padded output.
radius : int
Radius in which to expand region and grow raster.
Returns
-------
input_grown : str
GRASS raster map which has been expanded by radius cells
"""
g.region(grow=radius)
r.grow(input=input, output=output, radius=radius, quiet=True)
region = Region()
return region
def main():
"""
Creates a hydrologically correct MODFLOW grid that inlcudes minimum
DEM elevations for all stream cells and mean elevations everywhere else
"""
"""
dem = 'DEM'
grid = 'grid_tmp'
streams = 'streams_tmp'
streams_MODFLOW = 'streams_tmp_MODFLOW'
DEM_MODFLOW = 'DEM_coarse'
resolution = 500
"""
options, flags = gscript.parser()
dem = options['dem']
grid = options['grid']
streams = options['streams']
resolution = float(options['resolution'])
streams_MODFLOW = options['streams_modflow']
DEM_MODFLOW = options['dem_modflow']
gscript.use_temp_region()
g.region(raster=dem)
g.region(vector=grid)
v.to_rast(input=streams,
output=streams_MODFLOW,
use='val',
value=1.0,
type='line',
overwrite=gscript.overwrite(),
quiet=True)
r.mapcalc(streams_MODFLOW + " = " + streams_MODFLOW + " * DEM",
overwrite=True)
g.region(res=resolution, quiet=True)
r.resamp_stats(input=streams_MODFLOW,
output=streams_MODFLOW,
method='minimum',
overwrite=gscript.overwrite(),
quiet=True)
r.resamp_stats(input=dem,
output=DEM_MODFLOW,
method='average',
overwrite=gscript.overwrite(),
quiet=True)
r.patch(input=streams_MODFLOW + ',' + DEM_MODFLOW,
output=DEM_MODFLOW,
overwrite=True,
quiet=True)
aspect = 'aspect' # Topographic aspect
HRUs = 'HRUs' # Hydrologic response units
gravity_reservoirs = 'gravity_reservoirs' # Connect HRUs to MODFLOW grid
basin_mask = 'basin_mask' # Mask out non-study-basin cells
pour_point = 'pour_point' # Outlet pour point
bc_cell = 'bc_cell' # Grid cell for MODFLOW b.c.
# Import DEM if required
# And perform the standard starting tasks.
# These take time, so skip if not needed
if Settings.DEM_input != '':
# Import DEM and set region
r.in_gdal(input=Settings.DEM_input,
output=DEM_original_import,
overwrite=True)
g.region(raster=DEM_original_import)
# Build flow accumulation with only fully on-map flow
# Cell areas
r.cell_area(output=cellArea_meters2, units='m2', overwrite=True)
# Flow weights (e.g., precipitation
# Test first if it is an existing raster; if not, import
rastersAll = np.array(
list(set(list(gscript.parse_command('g.list', type='raster')))))
if Settings.flow_weights in rastersAll:
# NOTE: Here, this might not necessarily be called "flow_weights"
r.mapcalc(flow + ' = ' + cellArea_meters2 * Settings.flow_weights,
overwrite=True)
else:
r.in_gdal(input=Settings.flow_weights,
output=flow_weights,
overwrite=True)
margin_top = 2110
margin_bottom = 410
margin_left = 300
margin_right = 3700
# Existing full-extent DEMs
DEMs_fullextent = gscript.parse_command(
'g.list', type='raster', pattern='DEM_fullextent_0??????').keys()
DEMs_fullextent = sorted(DEMs_fullextent)
# x and y values across basin and more
length_y_trimmed = margin_top - margin_bottom
length_x_trimmed = margin_right - margin_left
# Full-extent region: get x and y
g.region(raster=DEMs_fullextent[0])
try:
r.mapcalc('x = x()')
r.mapcalc('y = y()')
except:
pass
g.region(flags='d')
# Set region to limited area
g.region(n=margin_top / 1000.,
s=margin_bottom / 1000.,
w=margin_left / 1000.,
e=margin_right / 1000.,
res=0.001)
reg = gscript.region()
# region aligned to this map
map_for_define_region = 'Neotropic_Hansen_percenttreecoverd_2000_wgs84@PERMANENT'
# input vector with buffers
vector = 'buffers_5km_comm_data_neotro_checked_2020_d11_06'
# For each buffer
for i in buffer_index:
print i, comm_code[i], years[i]
# select feature
v.extract(input = vector, output = 'vector_cat', where = 'cat = ' + str(i+1),
flags = 't', overwrite = True, quiet = True)
# define region
g.region(vector = 'vector_cat', align = map_for_define_region, flags = 'p')
# use vector as a mask
r.mask(vector = 'vector_cat', overwrite = True, quiet = True)
# Cut maps
# tree cover with zero where there was deforestation
expr = comm_code[i] + '_treecover_GFW_2000_deforestation = if(Neotropical_Hansen_treecoverlossperyear_wgs84_2017@PERMANENT > 0 && '+ \
'Neotropical_Hansen_treecoverlossperyear_wgs84_2017@PERMANENT < ' + str(years[i]) + ', 0, Neotropic_Hansen_percenttreecoverd_2000_wgs84@PERMANENT)'
r.mapcalc(expr, overwrite = True)
# thresholds for binary values of natural vegetation
thresholds = [70, 80, 90]
# loop to cut for each one and account for deforestation
for tr in thresholds:
def compute_supply(
base,
recreation_spectrum,
highest_spectrum,
base_reclassification_rules,
reclassified_base,
reclassified_base_title,
flow,
flow_map_name,
aggregation,
ns_resolution,
ew_resolution,
print_only=False,
flow_column_name=None,
vector=None,
supply_filename=None,
use_filename=None,
):
"""
Algorithmic description of the "Contribution of Ecosysten Types"
# FIXME
'''
1 B ← {0, .., m-1} : Set of aggregational boundaries
2 T ← {0, .., n-1} : Set of land cover types
3 WE ← 0 : Set of weighted extents
4 R ← 0 : Set of fractions
5 F ← 0
6 MASK ← HQR : High Quality Recreation
7 foreach {b} ⊆ B do : for each aggregational boundary 'b'
8 RB ← 0
9 foreach {t} ⊆ T do : for each Land Type
10 WEt ← Et * Wt : Weighted Extent = Extent(t) * Weight(t)
11 WE ← WE⋃{WEt} : Add to set of Weighted Extents
12 S ← ∑t∈WEt
13 foreach t ← T do
14 Rt ← WEt / ∑WE
15 R ← R⋃{Rt}
16 RB ← RB⋃{R}
'''
# FIXME
Parameters
----------
recreation_spectrum:
Map scoring access to and quality of recreation
highest_spectrum :
Expected is a map of areas with highest recreational value (category 9
as per the report ... )
base :
Base land types map for final zonal statistics. Specifically to
ESTIMAP's recrceation mapping algorithm
base_reclassification_rules :
Reclassification rules for the input base map
reclassified_base :
Name for the reclassified base cover map
reclassified_base_title :
Title for the reclassified base map
ecosystem_types :
flow :
Map of visits, derived from the mobility function, depicting the
number of people living inside zones 0, 1, 2, 3. Used as a cover map
for zonal statistics.
flow_map_name :
A name for the 'flow' map. This is required when the 'flow' input
option is not defined by the user, yet some of the requested outputs
required first the production of the 'flow' map. An example is the
request for a supply table without requesting the 'flow' map itself.
aggregation :
ns_resolution :
ew_resolution :
statistics_filename :
supply_filename :
Name for CSV output file of the supply table
use_filename :
Name for CSV output file of the use table
flow_column_name :
Name for column to populate with 'flow' values
vector :
If 'vector' is given, a vector map of the 'flow' along with appropriate
attributes will be produced.
? :
Land cover class percentages in ROS9 (this is: relative percentage)
output :
Supply table (distribution of flow for each land cover class)
Returns
-------
This function produces a map to base the production of a supply table in
form of CSV.
Examples
--------
"""
# Inputs
flow_in_base = flow + "_" + base
base_scores = base + ".scores"
# Define lists and dictionaries to hold intermediate data
statistics_dictionary = {}
weighted_extents = {}
flows = []
# MASK areas of high quality recreation
r.mask(raster=highest_spectrum, overwrite=True, quiet=True)
# Reclassify land cover map to MAES ecosystem types
r.reclass(
input=base,
rules=base_reclassification_rules,
output=reclassified_base,
quiet=True,
)
# add to "remove_at_exit" after the reclassified maps!
# Discard areas out of MASK
copy_equation = EQUATION.format(result=reclassified_base,
expression=reclassified_base)
r.mapcalc(copy_equation, overwrite=True)
# Count flow within each land cover category
r.stats_zonal(
base=base,
flags="r",
cover=flow_map_name,
method="sum",
output=flow_in_base,
overwrite=True,
quiet=True,
)
# Set colors for "flow" map
r.colors(map=flow_in_base, color=MOBILITY_COLORS, quiet=True)
# Parse aggregation raster categories and labels
categories = grass.parse_command("r.category",
map=aggregation,
delimiter="\t")
for category in categories:
# Intermediate names
cells = highest_spectrum + ".cells" + "." + category
remove_map_at_exit(cells)
extent = highest_spectrum + ".extent" + "." + category
remove_map_at_exit(extent)
weighted = highest_spectrum + ".weighted" + "." + category
remove_map_at_exit(weighted)
fractions = base + ".fractions" + "." + category
remove_map_at_exit(fractions)
flow_category = "_flow_" + category
flow = base + flow_category
remove_map_at_exit(flow)
flow_in_reclassified_base = reclassified_base + "_flow"
flow_in_category = reclassified_base + flow_category
flows.append(flow_in_category) # add to list for patching
remove_map_at_exit(flow_in_category)
# Output names
msg = "Processing aggregation raster category: {r}"
msg = msg.format(r=category)
grass.debug(_(msg))
# g.message(_(msg))
# First, set region to extent of the aggregation map
# and resolution to the one of the population map
# Note the `-a` flag to g.region: ?
# To safely modify the region: grass.use_temp_region() # FIXME
g.region(
raster=aggregation,
nsres=ns_resolution,
ewres=ew_resolution,
flags="a",
quiet=True,
)
msg = "|! Computational resolution matched to {raster}"
msg = msg.format(raster=aggregation)
grass.debug(_(msg))
# Build MASK for current category & high quality recreation areas
msg = "Setting category '{c}' of '{a}' as a MASK"
grass.verbose(_(msg.format(c=category, a=aggregation)))
masking = "if( {spectrum} == {highest_quality_category} && "
masking += "{aggregation} == {category}, "
masking += "1, null() )"
masking = masking.format(
spectrum=recreation_spectrum,
highest_quality_category=HIGHEST_RECREATION_CATEGORY,
aggregation=aggregation,
category=category,
)
masking_equation = EQUATION.format(result="MASK", expression=masking)
grass.mapcalc(masking_equation, overwrite=True)
# zoom to MASK
g.region(zoom="MASK",
nsres=ns_resolution,
ewres=ew_resolution,
quiet=True)
# Count number of cells within each land category
r.stats_zonal(
flags="r",
base=base,
cover=highest_spectrum,
method="count",
output=cells,
overwrite=True,
quiet=True,
)
cells_categories = grass.parse_command("r.category",
map=cells,
delimiter="\t")
grass.debug(_("Cells: {c}".format(c=cells_categories)))
# Build cell category and label rules for `r.category`
cells_rules = "\n".join([
"{0}:{1}".format(key, value)
for key, value in cells_categories.items()
])
# Discard areas out of MASK
copy_equation = EQUATION.format(result=cells, expression=cells)
r.mapcalc(copy_equation, overwrite=True)
# Reassign cell category labels
r.category(map=cells, rules="-", stdin=cells_rules, separator=":")
# Compute extent of each land category
extent_expression = "@{cells} * area()"
extent_expression = extent_expression.format(cells=cells)
extent_equation = EQUATION.format(result=extent,
expression=extent_expression)
r.mapcalc(extent_equation, overwrite=True)
# Write extent figures as labels
r.stats_zonal(
flags="r",
base=base,
cover=extent,
method="average",
output=extent,
overwrite=True,
verbose=False,
quiet=True,
)
# Write land suitability scores as an ASCII file
temporary_reclassified_base_map = temporary_filename(
filename=reclassified_base)
suitability_scores_as_labels = string_to_file(
SUITABILITY_SCORES_LABELS,
filename=temporary_reclassified_base_map)
remove_files_at_exit(suitability_scores_as_labels)
# Write scores as raster category labels
r.reclass(
input=base,
output=base_scores,
rules=suitability_scores_as_labels,
overwrite=True,
quiet=True,
verbose=False,
)
remove_map_at_exit(base_scores)
# Compute weighted extents
weighted_expression = "@{extent} * float(@{scores})"
weighted_expression = weighted_expression.format(extent=extent,
scores=base_scores)
weighted_equation = EQUATION.format(result=weighted,
expression=weighted_expression)
r.mapcalc(weighted_equation, overwrite=True)
# Write weighted extent figures as labels
r.stats_zonal(
flags="r",
base=base,
cover=weighted,
method="average",
output=weighted,
overwrite=True,
verbose=False,
quiet=True,
)
# Get weighted extents in a dictionary
weighted_extents = grass.parse_command("r.category",
map=weighted,
delimiter="\t")
# Compute the sum of all weighted extents and add to dictionary
category_sum = sum([
float(x) if not math.isnan(float(x)) else 0
for x in weighted_extents.values()
])
weighted_extents["sum"] = category_sum
# Create a map to hold fractions of each weighted extent to the sum
# See also:
# https://grasswiki.osgeo.org/wiki/LANDSAT#Hint:_Minimal_disk_space_copies
r.reclass(
input=base,
output=fractions,
rules="-",
stdin="*=*",
verbose=False,
quiet=True,
)
# Compute weighted fractions of land types
fraction_category_label = {
key: float(value) / weighted_extents["sum"]
for (key, value) in weighted_extents.iteritems()
if key is not "sum"
}
# Build fraction category and label rules for `r.category`
fraction_rules = "\n".join([
"{0}:{1}".format(key, value)
for key, value in fraction_category_label.items()
])
# Set rules
r.category(map=fractions,
rules="-",
stdin=fraction_rules,
separator=":")
# Assert that sum of fractions is ~1
fraction_categories = grass.parse_command("r.category",
map=fractions,
delimiter="\t")
fractions_sum = sum([
float(x) if not math.isnan(float(x)) else 0
for x in fraction_categories.values()
])
msg = "Fractions: {f}".format(f=fraction_categories)
grass.debug(_(msg))
# g.message(_("Sum: {:.17g}".format(fractions_sum)))
assert abs(fractions_sum - 1) < 1.0e-6, "Sum of fractions is != 1"
# Compute flow
flow_expression = "@{fractions} * @{flow}"
flow_expression = flow_expression.format(fractions=fractions,
flow=flow_in_base)
flow_equation = EQUATION.format(result=flow,
expression=flow_expression)
r.mapcalc(flow_equation, overwrite=True)
# Write flow figures as raster category labels
r.stats_zonal(
base=reclassified_base,
flags="r",
cover=flow,
method="sum",
output=flow_in_category,
overwrite=True,
verbose=False,
quiet=True,
)
# Parse flow categories and labels
flow_categories = grass.parse_command("r.category",
map=flow_in_category,
delimiter="\t")
grass.debug(_("Flow: {c}".format(c=flow_categories)))
# Build flow category and label rules for `r.category`
flow_rules = "\n".join([
"{0}:{1}".format(key, value)
for key, value in flow_categories.items()
])
# Discard areas out of MASK
# Check here again!
# Output patch of all flow maps?
copy_equation = EQUATION.format(result=flow_in_category,
expression=flow_in_category)
r.mapcalc(copy_equation, overwrite=True)
# Reassign cell category labels
r.category(map=flow_in_category,
rules="-",
stdin=flow_rules,
separator=":")
# Update title
reclassified_base_title += " " + category
r.support(flow_in_category, title=reclassified_base_title)
# debugging
# r.report(
# flags='hn',
# map=(flow_in_category),
# units=('k','c','p'),
# )
if print_only:
r.stats(
input=(flow_in_category),
output="-",
flags="nacpl",
separator=COMMA,
quiet=True,
)
if not print_only:
if flow_column_name:
flow_column_prefix = flow_column_name + category
else:
flow_column_name = "flow"
flow_column_prefix = flow_column_name + category
# Produce vector map(s)
if vector:
# The following is wrong
# update_vector(vector=vector,
# raster=flow_in_category,
# methods=METHODS,
# column_prefix=flow_column_prefix)
# What can be done?
# Maybe update columns of an existing map from the columns of
# the following vectorised raster map(s)
# ?
raster_to_vector(raster=flow_in_category,
vector=flow_in_category,
type="area")
# get statistics
dictionary = get_raster_statistics(
map_one=aggregation, # reclassified_base
map_two=flow_in_category,
separator="|",
flags="nlcap",
)
# merge 'dictionary' with global 'statistics_dictionary'
statistics_dictionary = merge_two_dictionaries(
statistics_dictionary, dictionary)
# It is important to remove the MASK!
r.mask(flags="r", quiet=True)
# FIXME
# Add "reclassified_base" map to "remove_at_exit" here, so as to be after
# all reclassified maps that derive from it
# remove the map 'reclassified_base'
# g.remove(flags='f', type='raster', name=reclassified_base, quiet=True)
# remove_map_at_exit(reclassified_base)
if not print_only:
r.patch(flags="",
input=flows,
output=flow_in_reclassified_base,
quiet=True)
if vector:
# Patch all flow vector maps in one
v.patch(
flags="e",
input=flows,
output=flow_in_reclassified_base,
overwrite=True,
quiet=True,
)
# export to csv
if supply_filename:
supply_filename += CSV_EXTENSION
nested_dictionary_to_csv(supply_filename, statistics_dictionary)
if use_filename:
use_filename += CSV_EXTENSION
uses = compile_use_table(statistics_dictionary)
dictionary_to_csv(use_filename, uses)
# Maybe return list of flow maps? Requires unique flow map names
return flows
## for testing and debugging I strongly suggest using this resource:
## http://grasswiki.osgeo.org/wiki/GRASS_and_Python
## usage: python importraster.py input=geoTiff output=raster
## description: Creates a raster file and sets the g.region for
## Concurrent raster calculations
#######################################################################
import os
import tempfile
from grass.pygrass.modules.shortcuts import general as g
from grass.pygrass.modules.shortcuts import raster as r
from grass.pygrass.modules import Module
# python importraster.py input=tiff output=raster
for arg in sys.argv:
mySplit = arg.split('=')
if len(mySplit) > 1:
command = mySplit[0]
value = mySplit[1]
if command = "input":
myInput = value
if command = "output":
myOutput = value
r.external(input=myInput, output=myOutput)
g.region(rast=myOutput)
from grass.pygrass.modules.shortcuts import vector as v
#from grass.pygrass.modules.shortcuts import temporal as t
from grass.pygrass.modules.grid import GridModule
file = '/appl/data/geo/mml/dem10m/2019/W3/W33/W3331.tif'
grassfile = 'W3331'
grasscontoursfile = 'W3331_contours'
contoursfile = "/scratch/project_2000599/grass/output/V4132.gpkg"
cpus = 4
# Register external GeoTIFF in current mapset:
r.external(input=file, output=grassfile, flags="e", overwrite=True)
# Set GRASS region
g.region(raster=grassfile)
#Perform GRASS analysis, here calculate contours from DEM, parallelization with GridModule
region = gscript.region()
width = region['cols'] // 2 + 1
height = region['rows'] // 2 + 1
grd = GridModule('r.contour',
width=width,
height=height,
overlap=20,
processes=cpus,
input=grassfile,
output=grasscontoursfile,
minlevel=200,
maxlevel=800,
def main():
"""
Builds river reaches for input to the USGS hydrologic model, GSFLOW.
These reaches link the PRMS stream segments to the MODFLOW grid cells.
"""
##################
# OPTION PARSING #
##################
options, flags = gscript.parser()
segments = options['segment_input']
grid = options['grid_input']
reaches = options['output']
elevation = options['elevation']
Smin = options['s_min']
h_stream = options['h_stream']
x1 = options['upstream_easting_column_seg']
y1 = options['upstream_northing_column_seg']
x2 = options['downstream_easting_column_seg']
y2 = options['downstream_northing_column_seg']
tostream = options['tostream_cat_column_seg']
# Hydraulic paramters
STRTHICK = options['strthick']
STRHC1 = options['strhc1']
THTS = options['thts']
THTI = options['thti']
EPS = options['eps']
UHC = options['uhc']
# Build reach maps by overlaying segments on grid
if len(gscript.find_file(segments, element='vector')['name']) > 0:
v.extract(input=segments,
output='GSFLOW_TEMP__',
type='line',
quiet=True,
overwrite=True)
v.overlay(ainput='GSFLOW_TEMP__',
atype='line',
binput=grid,
output=reaches,
operator='and',
overwrite=gscript.overwrite(),
quiet=True)
g.remove(type='vector', name='GSFLOW_TEMP__', quiet=True, flags='f')
else:
gscript.fatal('No vector file "' + segments + '" found.')
# Start editing database table
reachesTopo = VectorTopo(reaches)
reachesTopo.open('rw')
# Rename a,b columns
reachesTopo.table.columns.rename('a_' + x1, 'x1')
reachesTopo.table.columns.rename('a_' + x2, 'x2')
reachesTopo.table.columns.rename('a_' + y1, 'y1')
reachesTopo.table.columns.rename('a_' + y2, 'y2')
reachesTopo.table.columns.rename('a_NSEG', 'NSEG')
reachesTopo.table.columns.rename('a_ISEG', 'ISEG')
reachesTopo.table.columns.rename('a_stream_type', 'stream_type')
reachesTopo.table.columns.rename('a_type_code', 'type_code')
reachesTopo.table.columns.rename('a_cat', 'rnum_cat')
reachesTopo.table.columns.rename('a_' + tostream, 'tostream')
reachesTopo.table.columns.rename('a_id', 'segment_id')
reachesTopo.table.columns.rename('a_OUTSEG', 'OUTSEG')
reachesTopo.table.columns.rename('b_row', 'row')
reachesTopo.table.columns.rename('b_col', 'col')
reachesTopo.table.columns.rename('b_id', 'cell_id')
# Drop unnecessary columns
cols = reachesTopo.table.columns.names()
for col in cols:
if (col[:2] == 'a_') or (col[:2] == 'b_'):
reachesTopo.table.columns.drop(col)
# Add new columns to 'reaches'
reachesTopo.table.columns.add('KRCH', 'integer')
reachesTopo.table.columns.add('IRCH', 'integer')
reachesTopo.table.columns.add('JRCH', 'integer')
reachesTopo.table.columns.add('IREACH', 'integer')
reachesTopo.table.columns.add('RCHLEN', 'double precision')
reachesTopo.table.columns.add('STRTOP', 'double precision')
reachesTopo.table.columns.add('SLOPE', 'double precision')
reachesTopo.table.columns.add('STRTHICK', 'double precision')
reachesTopo.table.columns.add('STRHC1', 'double precision')
reachesTopo.table.columns.add('THTS', 'double precision')
reachesTopo.table.columns.add('THTI', 'double precision')
reachesTopo.table.columns.add('EPS', 'double precision')
reachesTopo.table.columns.add('UHC', 'double precision')
reachesTopo.table.columns.add('xr1', 'double precision')
reachesTopo.table.columns.add('xr2', 'double precision')
reachesTopo.table.columns.add('yr1', 'double precision')
reachesTopo.table.columns.add('yr2', 'double precision')
# Commit columns before editing (necessary?)
reachesTopo.table.conn.commit()
reachesTopo.close()
# Update some columns that can be done now
reachesTopo.open('rw')
colNames = np.array(gscript.vector_db_select(reaches, layer=1)['columns'])
colValues = np.array(
gscript.vector_db_select(reaches, layer=1)['values'].values())
cats = colValues[:, colNames == 'cat'].astype(int).squeeze()
nseg = np.arange(1, len(cats) + 1)
nseg_cats = []
for i in range(len(cats)):
nseg_cats.append((nseg[i], cats[i]))
cur = reachesTopo.table.conn.cursor()
# Hydrogeologic properties
cur.execute("update " + reaches + " set STRTHICK=" + str(STRTHICK))
cur.execute("update " + reaches + " set STRHC1=" + str(STRHC1))
cur.execute("update " + reaches + " set THTS=" + str(THTS))
cur.execute("update " + reaches + " set THTI=" + str(THTI))
cur.execute("update " + reaches + " set EPS=" + str(EPS))
cur.execute("update " + reaches + " set UHC=" + str(UHC))
# Grid properties
cur.execute("update " + reaches + " set KRCH=1") # Top layer: unchangable
cur.executemany("update " + reaches + " set IRCH=? where row=?", nseg_cats)
cur.executemany("update " + reaches + " set JRCH=? where col=?", nseg_cats)
reachesTopo.table.conn.commit()
reachesTopo.close()
v.to_db(map=reaches, columns='RCHLEN', option='length', quiet=True)
# Still to go after these:
# STRTOP (added with slope)
# IREACH (whole next section dedicated to this)
# SLOPE (need z_start and z_end)
# Now, the light stuff is over: time to build the reach order
v.to_db(map=reaches, option='start', columns='xr1,yr1')
v.to_db(map=reaches, option='end', columns='xr2,yr2')
# Now just sort by category, find which stream has the same xr1 and yr1 as
# x1 and y1 (or a_x1, a_y1) and then find where its endpoint matches another
# starting point and move down the line.
# v.db.select reaches col=cat,a_id,xr1,xr2 where="a_x1 = xr1"
# First, get the starting coordinates of each stream segment
# and a set of river ID's (ordered from 1...N)
colNames = np.array(gscript.vector_db_select(segments, layer=1)['columns'])
colValues = np.array(
gscript.vector_db_select(segments, layer=1)['values'].values())
number_of_segments = colValues.shape[0]
segment_x1s = colValues[:, colNames == 'x1'].astype(float).squeeze()
segment_y1s = colValues[:, colNames == 'y1'].astype(float).squeeze()
segment_ids = colValues[:, colNames == 'id'].astype(float).squeeze()
# Then move back to the reaches map to produce the ordering
colNames = np.array(gscript.vector_db_select(reaches, layer=1)['columns'])
colValues = np.array(
gscript.vector_db_select(reaches, layer=1)['values'].values())
reach_cats = colValues[:, colNames == 'cat'].astype(int).squeeze()
reach_x1s = colValues[:, colNames == 'xr1'].astype(float).squeeze()
reach_y1s = colValues[:, colNames == 'yr1'].astype(float).squeeze()
reach_x2s = colValues[:, colNames == 'xr2'].astype(float).squeeze()
reach_y2s = colValues[:, colNames == 'yr2'].astype(float).squeeze()
segment_ids__reach = colValues[:, colNames == 'segment_id'].astype(
float).squeeze()
for segment_id in segment_ids:
reach_order_cats = []
downstream_directed = []
ssel = segment_ids == segment_id
rsel = segment_ids__reach == segment_id # selector
# Find first segment: x1y1 first here, but not necessarily later
downstream_directed.append(1)
_x_match = reach_x1s[rsel] == segment_x1s[ssel]
_y_match = reach_y1s[rsel] == segment_y1s[ssel]
_i_match = _x_match * _y_match
x1y1 = True # false if x2y2
# Find cat
_cat = int(reach_cats[rsel][_x_match * _y_match])
reach_order_cats.append(_cat)
# Get end of reach = start of next one
reach_x_end = float(reach_x2s[reach_cats == _cat])
reach_y_end = float(reach_y2s[reach_cats == _cat])
while _i_match.any():
_x_match = reach_x1s[rsel] == reach_x_end
_y_match = reach_y1s[rsel] == reach_y_end
_i_match = _x_match * _y_match
if _i_match.any():
_cat = int(reach_cats[rsel][_x_match * _y_match])
reach_x_end = float(reach_x2s[reach_cats == _cat])
reach_y_end = float(reach_y2s[reach_cats == _cat])
reach_order_cats.append(_cat)
print len(reach_order_cats), len(reach_cats[rsel])
# Reach order to database table
reach_number__reach_order_cats = []
for i in range(len(reach_order_cats)):
reach_number__reach_order_cats.append((i + 1, reach_order_cats[i]))
reachesTopo = VectorTopo(reaches)
reachesTopo.open('rw')
cur = reachesTopo.table.conn.cursor()
cur.executemany("update " + reaches + " set IREACH=? where cat=?",
reach_number__reach_order_cats)
reachesTopo.table.conn.commit()
reachesTopo.close()
# TOP AND BOTTOM ARE OUT OF ORDER: SOME SEGS ARE BACKWARDS. UGH!!!!
# NEED TO GET THEM IN ORDER TO GET THE Z VALUES AT START AND END
# 2018.10.01: Updating this to use the computational region for the DEM
g.region(raster=elevation)
# Compute slope and starting elevations from the elevations at the start and
# end of the reaches and the length of each reach]
gscript.message('Obtaining elevation values from raster: may take time.')
v.db_addcolumn(map=reaches,
columns='zr1 double precision, zr2 double precision')
zr1 = []
zr2 = []
for i in range(len(reach_cats)):
_x = reach_x1s[i]
_y = reach_y1s[i]
#print _x, _y
_z = float(
gscript.parse_command('r.what',
map=elevation,
coordinates=str(_x) + ',' +
str(_y)).keys()[0].split('|')[-1])
zr1.append(_z)
_x = reach_x2s[i]
_y = reach_y2s[i]
_z = float(
gscript.parse_command('r.what',
map=elevation,
coordinates=str(_x) + ',' +
str(_y)).keys()[0].split('|')[-1])
zr2.append(_z)
zr1_cats = []
zr2_cats = []
for i in range(len(reach_cats)):
zr1_cats.append((zr1[i], reach_cats[i]))
zr2_cats.append((zr2[i], reach_cats[i]))
reachesTopo = VectorTopo(reaches)
reachesTopo.open('rw')
cur = reachesTopo.table.conn.cursor()
cur.executemany("update " + reaches + " set zr1=? where cat=?", zr1_cats)
cur.executemany("update " + reaches + " set zr2=? where cat=?", zr2_cats)
reachesTopo.table.conn.commit()
reachesTopo.close()
# Use these to create slope -- backwards possible on DEM!
v.db_update(map=reaches, column='SLOPE', value='(zr1 - zr2)/RCHLEN')
v.db_update(map=reaches,
column='SLOPE',
value=Smin,
where='SLOPE <= ' + str(Smin))
# srtm_local_filled_grid = srtm_local_filled @ 200m (i.e. current grid)
# resolution
# r.to.vect in=srtm_local_filled_grid out=srtm_local_filled_grid col=z type=area --o#
# NOT SURE IF IT IS BEST TO USE MEAN ELEVATION OR TOP ELEVATION!!!!!!!!!!!!!!!!!!!!!!!
v.db_addcolumn(map=reaches, columns='z_topo_mean double precision')
v.what_rast(map=reaches, raster=elevation,
column='z_topo_mean') #, query_column='z')
v.db_update(map=reaches,
column='STRTOP',
value='z_topo_mean -' + str(h_stream),
quiet=True)
## END USER SETTINGS
import os
import grass.script as gscript
from grass.pygrass.modules import Module
from grass.pygrass.modules.shortcuts import raster as r, vector as v, general as g, display as d
params['username'] += username
params['password'] += password
dsn = params['dsn'] + ' user=%s password=%s' %(username,password)
g.mapset(flags='c',mapset='Gemeinde_'+bfsnum)
v.in_ogr(dsn=dsn, layer=params['layers']['gemeinde'], output='region',
where='bfs_nummer = 2461',overwrite=True)
g.region(vect='region',flags='a',res='2')
g.region(flags='p')
v.in_ogr(dsn=dsn, layer=params['layers']['fieldblocks'], output='fieldblocks', flags='r')
rinwcs = Module("r.in.wcs")
rinwcs.inputs.url=params['url']
rinwcs.inputs.username=username
rinwcs.inputs.password=password
rinwcs(coverage=params['coverages']['elevation'])
rinwcs(coverage=params['coverages']['rfactor'])
rinwcs(coverage=params['coverages']['kfactor'])
rsoillossbare = Module("r.soilloss.bare")
rsoillossbare.inputs.flowaccmethod='r.terraflow'
def main():
# options and flags
options, flags = gs.parser()
input_raster = options["input"]
minradius = int(options["minradius"])
maxradius = int(options["maxradius"])
steps = int(options["steps"])
output_raster = options["output"]
region = Region()
res = np.mean([region.nsres, region.ewres])
# some checks
if "@" in output_raster:
output_raster = output_raster.split("@")[0]
if maxradius <= minradius:
gs.fatal("maxradius must be greater than minradius")
if steps < 2:
gs.fatal("steps must be greater than 1")
# calculate radi for generalization
radi = np.logspace(np.log(minradius),
np.log(maxradius),
steps,
base=np.exp(1),
dtype=np.int)
radi = np.unique(radi)
sizes = radi * 2 + 1
# multiscale calculation
ztpi_maps = list()
for step, (radius, size) in enumerate(zip(radi[::-1], sizes[::-1])):
gs.message(
"Calculating the TPI at radius {radius}".format(radius=radius))
# generalize the dem
step_res = res * size
step_res_pretty = str(step_res).replace(".", "_")
generalized_dem = gs.tempname(4)
if size > 15:
step_dem = gs.tempname(4)
gg.region(res=str(step_res))
gr.resamp_stats(
input=input_raster,
output=step_dem,
method="average",
flags="w",
)
gr.resamp_rst(
input=step_dem,
ew_res=res,
ns_res=res,
elevation=generalized_dem,
quiet=True,
)
region.write()
gg.remove(type="raster", name=step_dem, flags="f", quiet=True)
else:
gr.neighbors(input=input_raster, output=generalized_dem, size=size)
# calculate the tpi
tpi = gs.tempname(4)
gr.mapcalc(expression="{x} = {a} - {b}".format(
x=tpi, a=input_raster, b=generalized_dem))
gg.remove(type="raster", name=generalized_dem, flags="f", quiet=True)
# standardize the tpi
raster_stats = gr.univar(map=tpi, flags="g",
stdout_=PIPE).outputs.stdout
raster_stats = parse_key_val(raster_stats)
tpi_mean = float(raster_stats["mean"])
tpi_std = float(raster_stats["stddev"])
ztpi = gs.tempname(4)
ztpi_maps.append(ztpi)
RAST_REMOVE.append(ztpi)
gr.mapcalc(expression="{x} = ({a} - {mean})/{std}".format(
x=ztpi, a=tpi, mean=tpi_mean, std=tpi_std))
gg.remove(type="raster", name=tpi, flags="f", quiet=True)
# integrate
if step > 1:
tpi_updated2 = gs.tempname(4)
gr.mapcalc("{x} = if(abs({a}) > abs({b}), {a}, {b})".format(
a=ztpi_maps[step], b=tpi_updated1, x=tpi_updated2))
RAST_REMOVE.append(tpi_updated2)
tpi_updated1 = tpi_updated2
else:
tpi_updated1 = ztpi_maps[0]
RAST_REMOVE.pop()
gg.rename(raster=(tpi_updated2, output_raster), quiet=True)
# set color theme
with RasterRow(output_raster) as src:
color_rules = """{minv} blue
-1 0:34:198
0 255:255:255
1 255:0:0
{maxv} 110:15:0
"""
color_rules = color_rules.format(minv=src.info.min, maxv=src.info.max)
gr.colors(map=output_raster, rules="-", stdin_=color_rules, quiet=True)
margin_bottom = 410
margin_left = 300
margin_right = 3700
# x and y values across basin and more
#sourcedir = '/media/awickert/Elements/Fluvial 2015/151109_MC_IW_01/Processed/'
#sourcedir = '/media/awickert/data3/TerraceExperiment/Fluvial 2015/151109_MC_IW_01/Processed/'
#sourcedirs = sorted(next(os.walk('/media/awickert/data3/TerraceExperiment/Fluvial 2015/'))[1])
#sourcedirs = sorted(glob.glob('/data3/TerraceExperiment/Forgotten/*/Processed/'))
sourcedirs = sorted(glob.glob('/data3/TerraceExperiment/Fluvial 2015/*/Processed/'))
length_y_trimmed = margin_top - margin_bottom
length_x_trimmed = margin_right - margin_left
g.region(w=margin_left/1000., e=margin_right/1000., s=margin_bottom/1000., n=margin_top/1000., res=0.001, flags='s')
# Maps of x and y
g.region(w=0, s=0, e=int(np.floor(margin_right*1.5))/1000., n=int(np.floor(margin_top*1.5))/1000.)
try:
r.mapcalc('x = x()')
r.mapcalc('y = y()')
except:
pass
g.region(flags='d')
errordirs = []
errorfiles = []
for sourcedir in sourcedirs:
DATpaths = sorted(glob.glob(sourcedir+'*.DAT'))
for DATpath in DATpaths:
def main():
"""
Import any raster or vector data set and add its attribute
to a GSFLOW data object
"""
##################
# OPTION PARSING #
##################
options, flags = gscript.parser()
# Parsing
if options['attrtype'] == 'int':
attrtype = 'integer'
elif options['attrtype'] == 'float':
attrtype = 'double precision'
elif options['attrtype'] == 'string':
attrtype = 'varchar'
else:
attrtype = ''
########################################
# PROCESS AND UPLOAD TO DATABASE TABLE #
########################################
if options['vector_area'] is not '':
gscript.use_temp_region()
g.region(vector=options['map'], res=options['dxy'])
v.to_rast(input=options['vector_area'],
output='tmp___tmp',
use='attr',
attribute_column=options['from_column'],
quiet=True,
overwrite=True)
try:
gscript.message("Checking for existing column to overwrite")
v.db_dropcolumn(map=options['map'],
columns=options['column'],
quiet=True)
except:
pass
if attrtype is 'double precision':
try:
gscript.message("Checking for existing column to overwrite")
v.db_dropcolumn(map=options['map'],
columns='tmp_average',
quiet=True)
except:
pass
v.rast_stats(map=options['map'],
raster='tmp___tmp',
column_prefix='tmp',
method='average',
flags='c',
quiet=True)
g.remove(type='raster', name='tmp___tmp', flags='f', quiet=True)
v.db_renamecolumn(map=options['map'],
column=['tmp_average', options['column']],
quiet=True)
else:
try:
v.db_addcolumn(map=options['map'],
columns=options['column'] + ' ' + attrtype,
quiet=True)
except:
pass
gscript.run_command('v.distance',
from_=options['map'],
to=options['vector_area'],
upload='to_attr',
to_column=options['from_column'],
column=options['column'],
quiet=True)
elif options['vector_points'] is not '':
try:
gscript.message("Checking for existing column to overwrite")
v.db_dropcolumn(map=options['map'],
columns=options['column'],
quiet=True)
v.db_addcolumn(map=options['map'],
columns=options['column'] + ' ' + attrtype,
quiet=True)
except:
pass
gscript.run_command('v.distance',
from_=options['map'],
to=options['vector_points'],
upload='to_attr',
to_column=options['from_column'],
column=options['column'],
quiet=True)
elif options['raster'] is not '':
try:
gscript.message("Checking for existing column to overwrite")
v.db_dropcolumn(map=options['map'],
columns=options['column'],
quiet=True)
except:
pass
v.rast_stats(map=options['map'],
raster=options['raster'],
column_prefix='tmp',
method='average',
flags='c',
quiet=True)
v.db_renamecolumn(map=options['map'],
column=['tmp_average', options['column']],
quiet=True)
gscript.message("Done.")
def main():
"""
Builds a grid for the MODFLOW component of the USGS hydrologic model,
GSFLOW.
"""
options, flags = gscript.parser()
basin = options['basin']
pp = options['pour_point']
raster_input = options['raster_input']
dx = options['dx']
dy = options['dy']
grid = options['output']
mask = options['mask_output']
bc_cell = options['bc_cell']
# basin='basins_tmp_onebasin'; pp='pp_tmp'; raster_input='DEM'; raster_output='DEM_coarse'; dx=dy='500'; grid='grid_tmp'; mask='mask_tmp'
"""
# Fatal if raster input and output are not both set
_lena0 = (len(raster_input) == 0)
_lenb0 = (len(raster_output) == 0)
if _lena0 + _lenb0 == 1:
gscript.fatal("You must set both raster input and output, or neither.")
"""
# Fatal if bc_cell set but mask and grid are false
if bc_cell != '':
if (mask == '') or (pp == ''):
gscript.fatal(
'Mask and pour point must be set to define b.c. cell')
# Create grid -- overlaps DEM, three cells of padding
gscript.use_temp_region()
reg = gscript.region()
reg_grid_edges_sn = np.linspace(reg['s'], reg['n'], reg['rows'])
reg_grid_edges_we = np.linspace(reg['w'], reg['e'], reg['cols'])
g.region(vector=basin, ewres=dx, nsres=dy)
regnew = gscript.region()
# Use a grid ratio -- don't match exactly the desired MODFLOW resolution
grid_ratio_ns = np.round(regnew['nsres'] / reg['nsres'])
grid_ratio_ew = np.round(regnew['ewres'] / reg['ewres'])
# Get S, W, and then move the unit number of grid cells over to get N and E
# and include 3 cells of padding around the whole watershed
_s_dist = np.abs(reg_grid_edges_sn - (regnew['s'] - 3. * regnew['nsres']))
_s_idx = np.where(_s_dist == np.min(_s_dist))[0][0]
_s = float(reg_grid_edges_sn[_s_idx])
_n_grid = np.arange(_s, reg['n'] + 3 * grid_ratio_ns * reg['nsres'],
grid_ratio_ns * reg['nsres'])
_n_dist = np.abs(_n_grid - (regnew['n'] + 3. * regnew['nsres']))
_n_idx = np.where(_n_dist == np.min(_n_dist))[0][0]
_n = float(_n_grid[_n_idx])
_w_dist = np.abs(reg_grid_edges_we - (regnew['w'] - 3. * regnew['ewres']))
_w_idx = np.where(_w_dist == np.min(_w_dist))[0][0]
_w = float(reg_grid_edges_we[_w_idx])
_e_grid = np.arange(_w, reg['e'] + 3 * grid_ratio_ew * reg['ewres'],
grid_ratio_ew * reg['ewres'])
_e_dist = np.abs(_e_grid - (regnew['e'] + 3. * regnew['ewres']))
_e_idx = np.where(_e_dist == np.min(_e_dist))[0][0]
_e = float(_e_grid[_e_idx])
# Finally make the region
g.region(w=str(_w),
e=str(_e),
s=str(_s),
n=str(_n),
nsres=str(grid_ratio_ns * reg['nsres']),
ewres=str(grid_ratio_ew * reg['ewres']))
# And then make the grid
v.mkgrid(map=grid, overwrite=gscript.overwrite())
# Cell numbers (row, column, continuous ID)
v.db_addcolumn(map=grid, columns='id int', quiet=True)
colNames = np.array(gscript.vector_db_select(grid, layer=1)['columns'])
colValues = np.array(
gscript.vector_db_select(grid, layer=1)['values'].values())
cats = colValues[:, colNames == 'cat'].astype(int).squeeze()
rows = colValues[:, colNames == 'row'].astype(int).squeeze()
cols = colValues[:, colNames == 'col'].astype(int).squeeze()
nrows = np.max(rows)
ncols = np.max(cols)
cats = np.ravel([cats])
_id = np.ravel([ncols * (rows - 1) + cols])
_id_cat = []
for i in range(len(_id)):
_id_cat.append((_id[i], cats[i]))
gridTopo = VectorTopo(grid)
gridTopo.open('rw')
cur = gridTopo.table.conn.cursor()
cur.executemany("update " + grid + " set id=? where cat=?", _id_cat)
gridTopo.table.conn.commit()
gridTopo.close()
# Cell area
v.db_addcolumn(map=grid, columns='area_m2', quiet=True)
v.to_db(map=grid,
option='area',
units='meters',
columns='area_m2',
quiet=True)
# Basin mask
if len(mask) > 0:
# Fine resolution region:
g.region(n=reg['n'],
s=reg['s'],
w=reg['w'],
e=reg['e'],
nsres=reg['nsres'],
ewres=reg['ewres'])
# Rasterize basin
v.to_rast(input=basin,
output=mask,
use='val',
value=1,
overwrite=gscript.overwrite(),
quiet=True)
# Coarse resolution region:
g.region(w=str(_w),
e=str(_e),
s=str(_s),
n=str(_n),
nsres=str(grid_ratio_ns * reg['nsres']),
ewres=str(grid_ratio_ew * reg['ewres']))
r.resamp_stats(input=mask,
output=mask,
method='sum',
overwrite=True,
quiet=True)
r.mapcalc('tmp' + ' = ' + mask + ' > 0', overwrite=True, quiet=True)
g.rename(raster=('tmp', mask), overwrite=True, quiet=True)
r.null(map=mask, null=0, quiet=True)
# Add mask location (1 vs 0) in the MODFLOW grid
v.db_addcolumn(map=grid,
columns='basinmask double precision',
quiet=True)
v.what_rast(map=grid, type='centroid', raster=mask, column='basinmask')
"""
# Resampled raster
if len(raster_output) > 0:
r.resamp_stats(input=raster_input, output=raster_output, method='average', overwrite=gscript.overwrite(), quiet=True)
"""
# Pour point
if len(pp) > 0:
v.db_addcolumn(map=pp,
columns=('row integer', 'col integer'),
quiet=True)
v.build(map=pp, quiet=True)
v.what_vect(map=pp,
query_map=grid,
column='row',
query_column='row',
quiet=True)
v.what_vect(map=pp,
query_map=grid,
column='col',
query_column='col',
quiet=True)
# Next point downstream of the pour point
# Requires pp (always) and mask (sometimes)
# Dependency set above w/ gscript.fatal
if len(bc_cell) > 0:
########## NEED TO USE TRUE TEMPORARY FILE ##########
# May not work with dx != dy!
v.to_rast(input=pp, output='tmp', use='val', value=1, overwrite=True)
r.buffer(input='tmp',
output='tmp',
distances=float(dx) * 1.5,
overwrite=True)
r.mapcalc('tmp2 = if(tmp==2,1,null()) * ' + raster_input,
overwrite=True)
g.rename(raster=('tmp2', 'tmp'), overwrite=True, quiet=True)
#r.mapcalc('tmp = if(isnull('+raster_input+',0,(tmp == 2)))', overwrite=True)
#g.region(rast='tmp')
#r.null(map=raster_input,
r.drain(input=raster_input,
start_points=pp,
output='tmp2',
overwrite=True)
r.mapcalc('tmp3 = tmp2 * tmp', overwrite=True, quiet=True)
g.rename(raster=('tmp3', 'tmp'), overwrite=True, quiet=True)
#r.null(map='tmp', setnull=0) # Not necessary: center point removed above
r.to_vect(input='tmp',
output=bc_cell,
type='point',
column='z',
overwrite=gscript.overwrite(),
quiet=True)
v.db_addcolumn(map=bc_cell,
columns=('row integer', 'col integer',
'x double precision', 'y double precision'),
quiet=True)
v.build(map=bc_cell, quiet=True)
v.what_vect(map=bc_cell, query_map=grid, column='row', \
query_column='row', quiet=True)
v.what_vect(map=bc_cell, query_map=grid, column='col', \
query_column='col', quiet=True)
v.to_db(map=bc_cell, option='coor', columns=('x,y'))
# Find out if this is diagonal: finite difference works only N-S, W-E
colNames = np.array(gscript.vector_db_select(pp, layer=1)['columns'])
colValues = np.array(
gscript.vector_db_select(pp, layer=1)['values'].values())
pp_row = int(colValues[:, colNames == 'row'].astype(int).squeeze())
pp_col = int(colValues[:, colNames == 'col'].astype(int).squeeze())
colNames = np.array(
gscript.vector_db_select(bc_cell, layer=1)['columns'])
colValues = np.array(
gscript.vector_db_select(bc_cell, layer=1)['values'].values())
bc_row = int(colValues[:, colNames == 'row'].astype(int).squeeze())
bc_col = int(colValues[:, colNames == 'col'].astype(int).squeeze())
# Also get x and y while we are at it: may be needed later
bc_x = float(colValues[:, colNames == 'x'].astype(float).squeeze())
bc_y = float(colValues[:, colNames == 'y'].astype(float).squeeze())
if (bc_row != pp_row) and (bc_col != pp_col):
# If not diagonal, two possible locations that are adjacent
# to the pour point
_col1, _row1 = str(bc_col), str(pp_row)
_col2, _row2 = str(pp_col), str(bc_row)
# Check if either of these is covered by the basin mask
_ismask_1 = gscript.vector_db_select(grid,
layer=1,
where='(row == ' + _row1 +
') AND (col ==' + _col1 + ')',
columns='basinmask')
_ismask_1 = int(_ismask_1['values'].values()[0][0])
_ismask_2 = gscript.vector_db_select(grid,
layer=1,
where='(row == ' + _row2 +
') AND (col ==' + _col2 + ')',
columns='basinmask')
_ismask_2 = int(_ismask_2['values'].values()[0][0])
# If both covered by mask, error
if _ismask_1 and _ismask_2:
gscript.fatal(
'All possible b.c. cells covered by basin mask.\n\
Contact the developer: awickert (at) umn(.)edu')
# Otherwise, those that keep those that are not covered by basin
# mask and set ...
# ... wait, do we want the point that touches as few interior
# cells as possible?
# maybe just try setting both and seeing what happens for now!
else:
# Get dx and dy
dx = gscript.region()['ewres']
dy = gscript.region()['nsres']
# Build tool to handle multiple b.c. cells?
bcvect = vector.Vector(bc_cell)
bcvect.open('rw')
_cat_i = 2
if not _ismask_1:
# _x should always be bc_x, but writing generalized code
_x = bc_x + dx * (int(_col1) - bc_col) # col 1 at w edge
_y = bc_y - dy * (int(_row1) - bc_row) # row 1 at n edge
point0 = Point(_x, _y)
bcvect.write(
point0,
cat=_cat_i,
attrs=(None, _row1, _col1, _x, _y),
)
bcvect.table.conn.commit()
_cat_i += 1
if not _ismask_2:
# _y should always be bc_y, but writing generalized code
_x = bc_x + dx * (int(_col2) - bc_col) # col 1 at w edge
_y = bc_y - dy * (int(_row2) - bc_row) # row 1 at n edge
point0 = Point(_x, _y)
bcvect.write(
point0,
cat=_cat_i,
attrs=(None, _row2, _col2, _x, _y),
)
bcvect.table.conn.commit()
# Build database table and vector geometry
bcvect.build()
bcvect.close()
g.region(n=reg['n'],
s=reg['s'],
w=reg['w'],
e=reg['e'],
nsres=reg['nsres'],
ewres=reg['ewres'])
def main():
"""
Builds a grid for the MODFLOW component of the USGS hydrologic model,
GSFLOW.
"""
options, flags = gscript.parser()
basin = options["basin"]
pp = options["pour_point"]
raster_input = options["raster_input"]
dx = options["dx"]
dy = options["dy"]
grid = options["output"]
mask = options["mask_output"]
bc_cell = options["bc_cell"]
# basin='basins_tmp_onebasin'; pp='pp_tmp'; raster_input='DEM'; raster_output='DEM_coarse'; dx=dy='500'; grid='grid_tmp'; mask='mask_tmp'
"""
# Fatal if raster input and output are not both set
_lena0 = (len(raster_input) == 0)
_lenb0 = (len(raster_output) == 0)
if _lena0 + _lenb0 == 1:
gscript.fatal("You must set both raster input and output, or neither.")
"""
# Fatal if bc_cell set but mask and grid are false
if bc_cell != "":
if (mask == "") or (pp == ""):
gscript.fatal(
"Mask and pour point must be set to define b.c. cell")
# Create grid -- overlaps DEM, three cells of padding
g.region(raster=raster_input, ewres=dx, nsres=dy)
gscript.use_temp_region()
reg = gscript.region()
reg_grid_edges_sn = np.linspace(reg["s"], reg["n"], reg["rows"])
reg_grid_edges_we = np.linspace(reg["w"], reg["e"], reg["cols"])
g.region(vector=basin, ewres=dx, nsres=dy)
regnew = gscript.region()
# Use a grid ratio -- don't match exactly the desired MODFLOW resolution
grid_ratio_ns = np.round(regnew["nsres"] / reg["nsres"])
grid_ratio_ew = np.round(regnew["ewres"] / reg["ewres"])
# Get S, W, and then move the unit number of grid cells over to get N and E
# and include 3 cells of padding around the whole watershed
_s_dist = np.abs(reg_grid_edges_sn - (regnew["s"] - 3.0 * regnew["nsres"]))
_s_idx = np.where(_s_dist == np.min(_s_dist))[0][0]
_s = float(reg_grid_edges_sn[_s_idx])
_n_grid = np.arange(_s, reg["n"] + 3 * grid_ratio_ns * reg["nsres"],
grid_ratio_ns * reg["nsres"])
_n_dist = np.abs(_n_grid - (regnew["n"] + 3.0 * regnew["nsres"]))
_n_idx = np.where(_n_dist == np.min(_n_dist))[0][0]
_n = float(_n_grid[_n_idx])
_w_dist = np.abs(reg_grid_edges_we - (regnew["w"] - 3.0 * regnew["ewres"]))
_w_idx = np.where(_w_dist == np.min(_w_dist))[0][0]
_w = float(reg_grid_edges_we[_w_idx])
_e_grid = np.arange(_w, reg["e"] + 3 * grid_ratio_ew * reg["ewres"],
grid_ratio_ew * reg["ewres"])
_e_dist = np.abs(_e_grid - (regnew["e"] + 3.0 * regnew["ewres"]))
_e_idx = np.where(_e_dist == np.min(_e_dist))[0][0]
_e = float(_e_grid[_e_idx])
# Finally make the region
g.region(
w=str(_w),
e=str(_e),
s=str(_s),
n=str(_n),
nsres=str(grid_ratio_ns * reg["nsres"]),
ewres=str(grid_ratio_ew * reg["ewres"]),
)
# And then make the grid
v.mkgrid(map=grid, overwrite=gscript.overwrite())
# Cell numbers (row, column, continuous ID)
v.db_addcolumn(map=grid, columns="id int", quiet=True)
colNames = np.array(gscript.vector_db_select(grid, layer=1)["columns"])
colValues = np.array(
gscript.vector_db_select(grid, layer=1)["values"].values())
cats = colValues[:, colNames == "cat"].astype(int).squeeze()
rows = colValues[:, colNames == "row"].astype(int).squeeze()
cols = colValues[:, colNames == "col"].astype(int).squeeze()
nrows = np.max(rows)
ncols = np.max(cols)
cats = np.ravel([cats])
_id = np.ravel([ncols * (rows - 1) + cols])
_id_cat = []
for i in range(len(_id)):
_id_cat.append((_id[i], cats[i]))
gridTopo = VectorTopo(grid)
gridTopo.open("rw")
cur = gridTopo.table.conn.cursor()
cur.executemany("update " + grid + " set id=? where cat=?", _id_cat)
gridTopo.table.conn.commit()
gridTopo.close()
# Cell area
v.db_addcolumn(map=grid, columns="area_m2 double precision", quiet=True)
v.to_db(map=grid,
option="area",
units="meters",
columns="area_m2",
quiet=True)
# Basin mask
if len(mask) > 0:
# Fine resolution region:
g.region(
n=reg["n"],
s=reg["s"],
w=reg["w"],
e=reg["e"],
nsres=reg["nsres"],
ewres=reg["ewres"],
)
# Rasterize basin
v.to_rast(
input=basin,
output=mask,
use="val",
value=1,
overwrite=gscript.overwrite(),
quiet=True,
)
# Coarse resolution region:
g.region(
w=str(_w),
e=str(_e),
s=str(_s),
n=str(_n),
nsres=str(grid_ratio_ns * reg["nsres"]),
ewres=str(grid_ratio_ew * reg["ewres"]),
)
r.resamp_stats(input=mask,
output=mask,
method="sum",
overwrite=True,
quiet=True)
r.mapcalc("tmp" + " = " + mask + " > 0", overwrite=True, quiet=True)
g.rename(raster=("tmp", mask), overwrite=True, quiet=True)
r.null(map=mask, null=0, quiet=True)
# Add mask location (1 vs 0) in the MODFLOW grid
v.db_addcolumn(map=grid,
columns="basinmask double precision",
quiet=True)
v.what_rast(map=grid, type="centroid", raster=mask, column="basinmask")
"""
# Resampled raster
if len(raster_output) > 0:
r.resamp_stats(input=raster_input, output=raster_output, method='average', overwrite=gscript.overwrite(), quiet=True)
"""
# Pour point
if len(pp) > 0:
v.db_addcolumn(map=pp,
columns=("row integer", "col integer"),
quiet=True)
v.build(map=pp, quiet=True)
v.what_vect(map=pp,
query_map=grid,
column="row",
query_column="row",
quiet=True)
v.what_vect(map=pp,
query_map=grid,
column="col",
query_column="col",
quiet=True)
# Next point downstream of the pour point
# Requires pp (always) and mask (sometimes)
# Dependency set above w/ gscript.fatal
# g.region(raster='DEM')
# dx = gscript.region()['ewres']
# dy = gscript.region()['nsres']
if len(bc_cell) > 0:
########## NEED TO USE TRUE TEMPORARY FILE ##########
# May not work with dx != dy!
v.to_rast(input=pp, output="tmp", use="val", value=1, overwrite=True)
r.buffer(input="tmp",
output="tmp",
distances=float(dx) * 1.5,
overwrite=True)
r.mapcalc("tmp2 = if(tmp==2,1,null()) * " + raster_input,
overwrite=True)
# r.mapcalc('tmp = if(isnull('+raster_input+',0,(tmp == 2)))', overwrite=True)
# g.region(rast='tmp')
# r.null(map=raster_input,
# g.region(raster=raster_input)
# r.resample(input=raster_input, output='tmp3', overwrite=True)
r.resamp_stats(input=raster_input,
output="tmp3",
method="minimum",
overwrite=True)
r.drain(input="tmp3", start_points=pp, output="tmp", overwrite=True)
# g.region(w=str(_w), e=str(_e), s=str(_s), n=str(_n), nsres=str(grid_ratio_ns*reg['nsres']), ewres=str(grid_ratio_ew*reg['ewres']))
# r.resamp_stats(input='tmp2', output='tmp3', overwrite=True)
# g.rename(raster=('tmp3','tmp2'), overwrite=True, quiet=True)
r.mapcalc("tmp3 = tmp2 * tmp", overwrite=True, quiet=True)
g.rename(raster=("tmp3", "tmp"), overwrite=True, quiet=True)
# r.null(map='tmp', setnull=0) # Not necessary: center point removed above
r.to_vect(
input="tmp",
output=bc_cell,
type="point",
column="z",
overwrite=gscript.overwrite(),
quiet=True,
)
v.db_addcolumn(
map=bc_cell,
columns=(
"row integer",
"col integer",
"x double precision",
"y double precision",
),
quiet=True,
)
v.build(map=bc_cell, quiet=True)
v.what_vect(map=bc_cell,
query_map=grid,
column="row",
query_column="row",
quiet=True)
v.what_vect(map=bc_cell,
query_map=grid,
column="col",
query_column="col",
quiet=True)
v.to_db(map=bc_cell, option="coor", columns=("x,y"))
# Of the candidates, the pour point is the closest one
# v.db_addcolumn(map=bc_cell, columns=('dist_to_pp double precision'), quiet=True)
# v.distance(from_=bc_cell, to=pp, upload='dist', column='dist_to_pp')
# Find out if this is diagonal: finite difference works only N-S, W-E
colNames = np.array(gscript.vector_db_select(pp, layer=1)["columns"])
colValues = np.array(
gscript.vector_db_select(pp, layer=1)["values"].values())
pp_row = colValues[:, colNames == "row"].astype(int).squeeze()
pp_col = colValues[:, colNames == "col"].astype(int).squeeze()
colNames = np.array(
gscript.vector_db_select(bc_cell, layer=1)["columns"])
colValues = np.array(
gscript.vector_db_select(bc_cell, layer=1)["values"].values())
bc_row = colValues[:, colNames == "row"].astype(int).squeeze()
bc_col = colValues[:, colNames == "col"].astype(int).squeeze()
# Also get x and y while we are at it: may be needed later
bc_x = colValues[:, colNames == "x"].astype(float).squeeze()
bc_y = colValues[:, colNames == "y"].astype(float).squeeze()
if (bc_row != pp_row).all() and (bc_col != pp_col).all():
if bc_row.ndim > 0:
if len(bc_row) > 1:
for i in range(len(bc_row)):
"""
UNTESTED!!!!
And probably unimportant -- having 2 cells with river
going through them is most likely going to happen with
two adjacent cells -- so a side and a corner
"""
_col1, _row1 = str(bc_col[i]), str(pp_row[i])
_col2, _row2 = str(pp_col[i]), str(bc_row[i])
# Check if either of these is covered by the basin mask
_ismask_1 = gscript.vector_db_select(
grid,
layer=1,
where="(row == " + _row1 + ") AND (col ==" +
_col1 + ")",
columns="basinmask",
)
_ismask_1 = int(_ismask_1["values"].values()[0][0])
_ismask_2 = gscript.vector_db_select(
grid,
layer=1,
where="(row == " + _row2 + ") AND (col ==" +
_col2 + ")",
columns="basinmask",
)
_ismask_2 = int(_ismask_2["values"].values()[0][0])
# check if either of these is the other point
"""
NOT DOING THIS YET -- HAVEN'T THOUGHT THROUGH IF
ACTUALLY NECESSARY. (And this is an edge case anyway)
"""
# If both covered by mask, error
if _ismask_1 and _ismask_2:
gscript.fatal(
"All possible b.c. cells covered by basin mask.\n\
Contact the developer: awickert (at) umn(.)edu"
)
# If not diagonal, two possible locations that are adjacent
# to the pour point
_col1, _row1 = str(bc_col), str(pp_row)
_col2, _row2 = str(pp_col), str(bc_row)
# Check if either of these is covered by the basin mask
_ismask_1 = gscript.vector_db_select(
grid,
layer=1,
where="(row == " + _row1 + ") AND (col ==" + _col1 + ")",
columns="basinmask",
)
_ismask_1 = int(_ismask_1["values"].values()[0][0])
_ismask_2 = gscript.vector_db_select(
grid,
layer=1,
where="(row == " + _row2 + ") AND (col ==" + _col2 + ")",
columns="basinmask",
)
_ismask_2 = int(_ismask_2["values"].values()[0][0])
# If both covered by mask, error
if _ismask_1 and _ismask_2:
gscript.fatal(
"All possible b.c. cells covered by basin mask.\n\
Contact the developer: awickert (at) umn(.)edu")
# Otherwise, those that keep those that are not covered by basin
# mask and set ...
# ... wait, do we want the point that touches as few interior
# cells as possible?
# maybe just try setting both and seeing what happens for now!
else:
# Get dx and dy
# dx = gscript.region()['ewres']
# dy = gscript.region()['nsres']
# Build tool to handle multiple b.c. cells?
bcvect = vector.Vector(bc_cell)
bcvect.open("rw")
_cat_i = 2
if _ismask_1 != 0:
# _x should always be bc_x, but writing generalized code
_x = bc_x + float(dx) * (int(_col1) - bc_col
) # col 1 at w edge
_y = bc_y - float(dy) * (int(_row1) - bc_row
) # row 1 at n edge
point0 = Point(_x, _y)
bcvect.write(
point0,
cat=_cat_i,
attrs=(None, _row1, _col1, _x, _y),
)
bcvect.table.conn.commit()
_cat_i += 1
if _ismask_2 != 0:
# _y should always be bc_y, but writing generalized code
_x = bc_x + float(dx) * (int(_col2) - bc_col
) # col 1 at w edge
_y = bc_y - float(dy) * (int(_row2) - bc_row
) # row 1 at n edge
point0 = Point(_x, _y)
bcvect.write(
point0,
cat=_cat_i,
attrs=(None, _row2, _col2, _x, _y),
)
bcvect.table.conn.commit()
# Build database table and vector geometry
bcvect.build()
bcvect.close()
g.region(
n=reg["n"],
s=reg["s"],
w=reg["w"],
e=reg["e"],
nsres=reg["nsres"],
ewres=reg["ewres"],
)
def main():
"""
Import any raster or vector data set and add its attribute
to a GSFLOW data object
"""
##################
# OPTION PARSING #
##################
options, flags = gscript.parser()
# Parsing
if options["attrtype"] == "int":
attrtype = "integer"
elif options["attrtype"] == "float":
attrtype = "double precision"
elif options["attrtype"] == "string":
attrtype = "varchar"
else:
attrtype = ""
########################################
# PROCESS AND UPLOAD TO DATABASE TABLE #
########################################
if options["vector_area"] is not "":
gscript.use_temp_region()
g.region(vector=options["map"], res=options["dxy"])
v.to_rast(
input=options["vector_area"],
output="tmp___tmp",
use="attr",
attribute_column=options["from_column"],
quiet=True,
overwrite=True,
)
try:
gscript.message("Checking for existing column to overwrite")
v.db_dropcolumn(map=options["map"],
columns=options["column"],
quiet=True)
except:
pass
if attrtype is "double precision":
try:
gscript.message("Checking for existing column to overwrite")
v.db_dropcolumn(map=options["map"],
columns="tmp_average",
quiet=True)
except:
pass
v.rast_stats(
map=options["map"],
raster="tmp___tmp",
column_prefix="tmp",
method="average",
flags="c",
quiet=True,
)
g.remove(type="raster", name="tmp___tmp", flags="f", quiet=True)
v.db_renamecolumn(
map=options["map"],
column=["tmp_average", options["column"]],
quiet=True,
)
else:
try:
v.db_addcolumn(
map=options["map"],
columns=options["column"] + " " + attrtype,
quiet=True,
)
except:
pass
gscript.run_command(
"v.distance",
from_=options["map"],
to=options["vector_area"],
upload="to_attr",
to_column=options["from_column"],
column=options["column"],
quiet=True,
)
elif options["vector_points"] is not "":
try:
gscript.message("Checking for existing column to overwrite")
v.db_dropcolumn(map=options["map"],
columns=options["column"],
quiet=True)
v.db_addcolumn(
map=options["map"],
columns=options["column"] + " " + attrtype,
quiet=True,
)
except:
pass
gscript.run_command(
"v.distance",
from_=options["map"],
to=options["vector_points"],
upload="to_attr",
to_column=options["from_column"],
column=options["column"],
quiet=True,
)
elif options["raster"] is not "":
try:
gscript.message("Checking for existing column to overwrite")
v.db_dropcolumn(map=options["map"],
columns=options["column"],
quiet=True)
except:
pass
v.rast_stats(
map=options["map"],
raster=options["raster"],
column_prefix="tmp",
method="average",
flags="c",
quiet=True,
)
v.db_renamecolumn(map=options["map"],
column=["tmp_average", options["column"]],
quiet=True)
gscript.message("Done.")
def main():
"""
Builds a grid for the MODFLOW component of the USGS hydrologic model,
GSFLOW.
"""
options, flags = gscript.parser()
basin = options['basin']
pp = options['pour_point']
raster_input = options['raster_input']
dx = options['dx']
dy = options['dy']
grid = options['output']
mask = options['mask_output']
bc_cell = options['bc_cell']
# basin='basins_tmp_onebasin'; pp='pp_tmp'; raster_input='DEM'; raster_output='DEM_coarse'; dx=dy='500'; grid='grid_tmp'; mask='mask_tmp'
"""
# Fatal if raster input and output are not both set
_lena0 = (len(raster_input) == 0)
_lenb0 = (len(raster_output) == 0)
if _lena0 + _lenb0 == 1:
grass.fatal("You must set both raster input and output, or neither.")
"""
# Create grid -- overlaps DEM, one cell of padding
gscript.use_temp_region()
reg = gscript.region()
reg_grid_edges_sn = np.linspace(reg['s'], reg['n'], reg['rows'])
reg_grid_edges_we = np.linspace(reg['w'], reg['e'], reg['cols'])
g.region(vector=basin, ewres=dx, nsres=dy)
regnew = gscript.region()
# Use a grid ratio -- don't match exactly the desired MODFLOW resolution
grid_ratio_ns = np.round(regnew['nsres'] / reg['nsres'])
grid_ratio_ew = np.round(regnew['ewres'] / reg['ewres'])
# Get S, W, and then move the unit number of grid cells over to get N and E
# and include 3 cells of padding around the whole watershed
_s_dist = np.abs(reg_grid_edges_sn - (regnew['s'] - 3. * regnew['nsres']))
_s_idx = np.where(_s_dist == np.min(_s_dist))[0][0]
_s = float(reg_grid_edges_sn[_s_idx])
_n_grid = np.arange(_s, reg['n'] + 3 * grid_ratio_ns * reg['nsres'],
grid_ratio_ns * reg['nsres'])
_n_dist = np.abs(_n_grid - (regnew['n'] + 3. * regnew['nsres']))
_n_idx = np.where(_n_dist == np.min(_n_dist))[0][0]
_n = float(_n_grid[_n_idx])
_w_dist = np.abs(reg_grid_edges_we - (regnew['w'] - 3. * regnew['ewres']))
_w_idx = np.where(_w_dist == np.min(_w_dist))[0][0]
_w = float(reg_grid_edges_we[_w_idx])
_e_grid = np.arange(_w, reg['e'] + 3 * grid_ratio_ew * reg['ewres'],
grid_ratio_ew * reg['ewres'])
_e_dist = np.abs(_e_grid - (regnew['e'] + 3. * regnew['ewres']))
_e_idx = np.where(_e_dist == np.min(_e_dist))[0][0]
_e = float(_e_grid[_e_idx])
# Finally make the region
g.region(w=str(_w),
e=str(_e),
s=str(_s),
n=str(_n),
nsres=str(grid_ratio_ns * reg['nsres']),
ewres=str(grid_ratio_ew * reg['ewres']))
# And then make the grid
v.mkgrid(map=grid, overwrite=gscript.overwrite())
# Cell numbers (row, column, continuous ID)
v.db_addcolumn(map=grid, columns='id int', quiet=True)
colNames = np.array(gscript.vector_db_select(grid, layer=1)['columns'])
colValues = np.array(
gscript.vector_db_select(grid, layer=1)['values'].values())
cats = colValues[:, colNames == 'cat'].astype(int).squeeze()
rows = colValues[:, colNames == 'row'].astype(int).squeeze()
cols = colValues[:, colNames == 'col'].astype(int).squeeze()
nrows = np.max(rows)
ncols = np.max(cols)
cats = np.ravel([cats])
_id = np.ravel([ncols * (rows - 1) + cols])
_id_cat = []
for i in range(len(_id)):
_id_cat.append((_id[i], cats[i]))
gridTopo = VectorTopo(grid)
gridTopo.open('rw')
cur = gridTopo.table.conn.cursor()
cur.executemany("update " + grid + " set id=? where cat=?", _id_cat)
gridTopo.table.conn.commit()
gridTopo.close()
# Cell area
v.db_addcolumn(map=grid, columns='area_m2', quiet=True)
v.to_db(map=grid,
option='area',
units='meters',
columns='area_m2',
quiet=True)
# Basin mask
if len(mask) > 0:
# Fine resolution region:
g.region(n=reg['n'],
s=reg['s'],
w=reg['w'],
e=reg['e'],
nsres=reg['nsres'],
ewres=reg['ewres'])
# Rasterize basin
v.to_rast(input=basin,
output=mask,
use='val',
value=1,
overwrite=gscript.overwrite(),
quiet=True)
# Coarse resolution region:
g.region(w=str(_w),
e=str(_e),
s=str(_s),
n=str(_n),
nsres=str(grid_ratio_ns * reg['nsres']),
ewres=str(grid_ratio_ew * reg['ewres']))
r.resamp_stats(input=mask,
output=mask,
method='sum',
overwrite=True,
quiet=True)
r.mapcalc(mask + ' = ' + mask + ' > 0', overwrite=True, quiet=True)
"""
# Resampled raster
if len(raster_output) > 0:
r.resamp_stats(input=raster_input, output=raster_output, method='average', overwrite=gscript.overwrite(), quiet=True)
"""
# Pour point
if len(pp) > 0:
v.db_addcolumn(map=pp,
columns=('row integer', 'col integer'),
quiet=True)
v.build(map=pp, quiet=True)
v.what_vect(map=pp,
query_map=grid,
column='row',
query_column='row',
quiet=True)
v.what_vect(map=pp,
query_map=grid,
column='col',
query_column='col',
quiet=True)
# Next point downstream of the pour point
if len(bc_cell) > 0:
########## NEED TO USE TRUE TEMPORARY FILE ##########
# May not work with dx != dy!
v.to_rast(input=pp, output='tmp', use='val', value=1, overwrite=True)
r.buffer(input='tmp',
output='tmp',
distances=float(dx) * 1.5,
overwrite=True)
r.mapcalc('tmp = (tmp == 2) * ' + raster_input, overwrite=True)
r.drain(input=raster_input,
start_points=pp,
output='tmp2',
overwrite=True)
r.mapcalc('tmp = tmp2 * tmp', overwrite=True)
r.null(map='tmp', setnull=0)
r.to_vect(input='tmp',
output=bc_cell,
type='point',
column='z',
overwrite=gscript.overwrite(),
quiet=True)
v.db_addcolumn(map=bc_cell,
columns=('row integer', 'col integer'),
quiet=True)
v.build(map=bc_cell, quiet=True)
v.what_vect(map=bc_cell, query_map=grid, column='row', \
query_column='row', quiet=True)
v.what_vect(map=bc_cell, query_map=grid, column='col', \
query_column='col', quiet=True)
g.region(n=reg['n'],
s=reg['s'],
w=reg['w'],
e=reg['e'],
nsres=reg['nsres'],
ewres=reg['ewres'])
def rasterizeCs(reso):
g.region(res=reso)
v.to_rast(csmap, output = 'cr', use='attr',attribute_column=fcol, overwrite = True)
# x and y values across basin and more
#sourcedir = '/media/awickert/Elements/Fluvial 2015/151109_MC_IW_01/Processed/'
#sourcedir = '/media/awickert/data3/TerraceExperiment/Fluvial 2015/151109_MC_IW_01/Processed/'
#sourcedirs = sorted(next(os.walk('/media/awickert/data3/TerraceExperiment/Fluvial 2015/'))[1])
#sourcedirs = sorted(glob.glob('/data3/TerraceExperiment/Forgotten/*/Processed/'))
sourcedirs = sorted(
glob.glob('/data3/TerraceExperiment/Fluvial 2015/*/Processed/'))
length_y_trimmed = margin_top - margin_bottom
length_x_trimmed = margin_right - margin_left
g.region(w=margin_left / 1000.,
e=margin_right / 1000.,
s=margin_bottom / 1000.,
n=margin_top / 1000.,
res=0.001,
flags='s')
# Maps of x and y
g.region(w=0,
s=0,
e=int(np.floor(margin_right * 1.5)) / 1000.,
n=int(np.floor(margin_top * 1.5)) / 1000.)
try:
r.mapcalc('x = x()')
r.mapcalc('y = y()')
except:
pass
g.region(flags='d')
reaches = reaches_df['Reach_no']
n = reaches_df['N']
s = reaches_df['S']
e = reaches_df['E']
w = reaches_df['W']
for i, reach_no in enumerate(reaches):
# make the directory for this reach
this_dir = DataDirectory + 'Upper_Miss_reach' + str(reach_no) + '/'
try:
os.mkdir(this_dir)
except:
pass
# set the region
g.region(n=str(n[i]), s=str(s[i]), e=str(e[i]), w=str(w[i]), flags='p')
# now output the ENVI bil file
r.out_gdal(input="Upper_Miss_filled",
format="ENVI",
type="Float32",
nodata=-9999,
output=this_dir + 'Upper_Miss_reach' + str(reach_no) + '.bil')
# clip the baseline to this region
v.in_region(output='region_tmp', overwrite=True)
v.overlay(ainput='Mississippi_River',
atype='line',
binput='region_tmp',
output='Mississippi_River_clip_tmp',
operator='and',
def main():
elevation = options['elevation']
slope = options['slope']
flat_thres = float(options['flat_thres'])
curv_thres = float(options['curv_thres'])
filter_size = int(options['filter_size'])
counting_size = int(options['counting_size'])
nclasses = int(options['classes'])
texture = options['texture']
convexity = options['convexity']
concavity = options['concavity']
features = options['features']
# remove mapset from output name in case of overwriting existing map
texture = texture.split('@')[0]
convexity = convexity.split('@')[0]
concavity = concavity.split('@')[0]
features = features.split('@')[0]
# store current region settings
global current_reg
current_reg = parse_key_val(g.region(flags='pg', stdout_=PIPE).outputs.stdout)
del current_reg['projection']
del current_reg['zone']
del current_reg['cells']
# check for existing mask and backup if found
global mask_test
mask_test = gs.list_grouped(
type='rast', pattern='MASK')[gs.gisenv()['MAPSET']]
if mask_test:
global original_mask
original_mask = temp_map('tmp_original_mask')
g.copy(raster=['MASK', original_mask])
# error checking
if flat_thres < 0:
gs.fatal('Parameter thres cannot be negative')
if filter_size % 2 == 0 or counting_size % 2 == 0:
gs.fatal(
'Filter or counting windows require an odd-numbered window size')
if filter_size >= counting_size:
gs.fatal(
'Filter size needs to be smaller than the counting window size')
if features != '' and slope == '':
gs.fatal('Need to supply a slope raster in order to produce the terrain classification')
# Terrain Surface Texture -------------------------------------------------
# smooth the dem
gs.message("Calculating terrain surface texture...")
gs.message(
"1. Smoothing input DEM with a {n}x{n} median filter...".format(
n=filter_size))
filtered_dem = temp_map('tmp_filtered_dem')
gs.run_command("r.neighbors", input = elevation, method = "median",
size = filter_size, output = filtered_dem, flags='c',
quiet=True)
# extract the pits and peaks based on the threshold
pitpeaks = temp_map('tmp_pitpeaks')
gs.message("2. Extracting pits and peaks with difference > thres...")
r.mapcalc(expression='{x} = if ( abs({dem}-{median})>{thres}, 1, 0)'.format(
x=pitpeaks, dem=elevation, thres=flat_thres, median=filtered_dem),
quiet=True)
# calculate density of pits and peaks
gs.message("3. Using resampling filter to create terrain texture...")
window_radius = (counting_size-1)/2
y_radius = float(current_reg['ewres'])*window_radius
x_radius = float(current_reg['nsres'])*window_radius
resample = temp_map('tmp_density')
r.resamp_filter(input=pitpeaks, output=resample, filter=['bartlett','gauss'],
radius=[x_radius,y_radius], quiet=True)
# convert to percentage
gs.message("4. Converting to percentage...")
r.mask(raster=elevation, overwrite=True, quiet=True)
r.mapcalc(expression='{x} = float({y} * 100)'.format(x=texture, y=resample),
quiet=True)
r.mask(flags='r', quiet=True)
r.colors(map=texture, color='haxby', quiet=True)
# Terrain convexity/concavity ---------------------------------------------
# surface curvature using lacplacian filter
gs.message("Calculating terrain convexity and concavity...")
gs.message("1. Calculating terrain curvature using laplacian filter...")
# grow the map to remove border effects and run laplacian filter
dem_grown = temp_map('tmp_elevation_grown')
laplacian = temp_map('tmp_laplacian')
g.region(n=float(current_reg['n']) + (float(current_reg['nsres']) * filter_size),
s=float(current_reg['s']) - (float(current_reg['nsres']) * filter_size),
w=float(current_reg['w']) - (float(current_reg['ewres']) * filter_size),
e=float(current_reg['e']) + (float(current_reg['ewres']) * filter_size))
r.grow(input=elevation, output=dem_grown, radius=filter_size, quiet=True)
r.mfilter(
input=dem_grown, output=laplacian,
filter=string_to_rules(laplacian_matrix(filter_size)), quiet=True)
# extract convex and concave pixels
gs.message("2. Extracting convexities and concavities...")
convexities = temp_map('tmp_convexities')
concavities = temp_map('tmp_concavities')
r.mapcalc(
expression='{x} = if({laplacian}>{thres}, 1, 0)'\
.format(x=convexities, laplacian=laplacian, thres=curv_thres),
quiet=True)
r.mapcalc(
expression='{x} = if({laplacian}<-{thres}, 1, 0)'\
.format(x=concavities, laplacian=laplacian, thres=curv_thres),
quiet=True)
# calculate density of convexities and concavities
gs.message("3. Using resampling filter to create surface convexity/concavity...")
resample_convex = temp_map('tmp_convex')
resample_concav = temp_map('tmp_concav')
r.resamp_filter(input=convexities, output=resample_convex,
filter=['bartlett','gauss'], radius=[x_radius,y_radius],
quiet=True)
r.resamp_filter(input=concavities, output=resample_concav,
filter=['bartlett','gauss'], radius=[x_radius,y_radius],
quiet=True)
# convert to percentages
gs.message("4. Converting to percentages...")
g.region(**current_reg)
r.mask(raster=elevation, overwrite=True, quiet=True)
r.mapcalc(expression='{x} = float({y} * 100)'.format(x=convexity, y=resample_convex),
quiet=True)
r.mapcalc(expression='{x} = float({y} * 100)'.format(x=concavity, y=resample_concav),
quiet=True)
r.mask(flags='r', quiet=True)
# set colors
r.colors_stddev(map=convexity, quiet=True)
r.colors_stddev(map=concavity, quiet=True)
# Terrain classification Flowchart-----------------------------------------
if features != '':
gs.message("Performing terrain surface classification...")
# level 1 produces classes 1 thru 8
# level 2 produces classes 5 thru 12
# level 3 produces classes 9 thru 16
if nclasses == 8: levels = 1
if nclasses == 12: levels = 2
if nclasses == 16: levels = 3
classif = []
for level in range(levels):
# mask previous classes x:x+4
if level != 0:
min_cla = (4*(level+1))-4
clf_msk = temp_map('tmp_clf_mask')
rules = '1:{0}:1'.format(min_cla)
r.recode(
input=classif[level-1], output=clf_msk,
rules=string_to_rules(rules), overwrite=True)
r.mask(raster=clf_msk, flags='i', quiet=True, overwrite=True)
# image statistics
smean = r.univar(
map=slope, flags='g', stdout_=PIPE).outputs.stdout.split(os.linesep)
smean = [i for i in smean if i.startswith('mean=') is True][0].split('=')[1]
cmean = r.univar(
map=convexity, flags='g', stdout_=PIPE).outputs.stdout.split(os.linesep)
cmean = [i for i in cmean if i.startswith('mean=') is True][0].split('=')[1]
tmean = r.univar(
map=texture, flags='g', stdout_=PIPE).outputs.stdout.split(os.linesep)
tmean = [i for i in tmean if i.startswith('mean=') is True][0].split('=')[1]
classif.append(temp_map('tmp_classes'))
if level != 0:
r.mask(flags='r', quiet=True)
classification(level+1, slope, smean, texture, tmean,
convexity, cmean, classif[level])
# combine decision trees
merged = []
for level in range(0, levels):
if level > 0:
min_cla = (4*(level+1))-4
merged.append(temp_map('tmp_merged'))
r.mapcalc(
expression='{x} = if({a}>{min}, {b}, {a})'.format(
x=merged[level], min=min_cla, a=merged[level-1], b=classif[level]))
else:
merged.append(classif[level])
g.rename(raster=[merged[-1], features], quiet=True)
del TMP_RAST[-1]
# Write metadata ----------------------------------------------------------
history = 'r.terrain.texture '
for key,val in options.iteritems():
history += key + '=' + str(val) + ' '
r.support(map=texture,
title=texture,
description='generated by r.terrain.texture',
history=history)
r.support(map=convexity,
title=convexity,
description='generated by r.terrain.texture',
history=history)
r.support(map=concavity,
title=concavity,
description='generated by r.terrain.texture',
history=history)
if features != '':
r.support(map=features,
title=features,
description='generated by r.terrain.texture',
history=history)
# write color and category rules to tempfiles
r.category(
map=features,
rules=string_to_rules(categories(nclasses)),
separator='pipe')
r.colors(
map=features, rules=string_to_rules(colors(nclasses)), quiet=True)
return 0
def main():
"""
Builds river reaches for input to the USGS hydrologic model, GSFLOW.
These reaches link the PRMS stream segments to the MODFLOW grid cells.
"""
##################
# OPTION PARSING #
##################
options, flags = gscript.parser()
segments = options["segment_input"]
grid = options["grid_input"]
reaches = options["output"]
elevation = options["elevation"]
Smin = options["s_min"]
h_stream = options["h_stream"]
x1 = options["upstream_easting_column_seg"]
y1 = options["upstream_northing_column_seg"]
x2 = options["downstream_easting_column_seg"]
y2 = options["downstream_northing_column_seg"]
tostream = options["tostream_cat_column_seg"]
# Hydraulic paramters
STRTHICK = options["strthick"]
STRHC1 = options["strhc1"]
THTS = options["thts"]
THTI = options["thti"]
EPS = options["eps"]
UHC = options["uhc"]
# Build reach maps by overlaying segments on grid
if len(gscript.find_file(segments, element="vector")["name"]) > 0:
v.extract(
input=segments,
output="GSFLOW_TEMP__",
type="line",
quiet=True,
overwrite=True,
)
v.overlay(
ainput="GSFLOW_TEMP__",
atype="line",
binput=grid,
output=reaches,
operator="and",
overwrite=gscript.overwrite(),
quiet=True,
)
g.remove(type="vector", name="GSFLOW_TEMP__", quiet=True, flags="f")
else:
gscript.fatal('No vector file "' + segments + '" found.')
# Start editing database table
reachesTopo = VectorTopo(reaches)
reachesTopo.open("rw")
# Rename a,b columns
reachesTopo.table.columns.rename("a_" + x1, "x1")
reachesTopo.table.columns.rename("a_" + x2, "x2")
reachesTopo.table.columns.rename("a_" + y1, "y1")
reachesTopo.table.columns.rename("a_" + y2, "y2")
reachesTopo.table.columns.rename("a_NSEG", "NSEG")
reachesTopo.table.columns.rename("a_ISEG", "ISEG")
reachesTopo.table.columns.rename("a_stream_type", "stream_type")
reachesTopo.table.columns.rename("a_type_code", "type_code")
reachesTopo.table.columns.rename("a_cat", "rnum_cat")
reachesTopo.table.columns.rename("a_" + tostream, "tostream")
reachesTopo.table.columns.rename("a_id", "segment_id")
reachesTopo.table.columns.rename("a_OUTSEG", "OUTSEG")
reachesTopo.table.columns.rename("b_row", "row")
reachesTopo.table.columns.rename("b_col", "col")
reachesTopo.table.columns.rename("b_id", "cell_id")
# Drop unnecessary columns
cols = reachesTopo.table.columns.names()
for col in cols:
if (col[:2] == "a_") or (col[:2] == "b_"):
reachesTopo.table.columns.drop(col)
# Add new columns to 'reaches'
reachesTopo.table.columns.add("KRCH", "integer")
reachesTopo.table.columns.add("IRCH", "integer")
reachesTopo.table.columns.add("JRCH", "integer")
reachesTopo.table.columns.add("IREACH", "integer")
reachesTopo.table.columns.add("RCHLEN", "double precision")
reachesTopo.table.columns.add("STRTOP", "double precision")
reachesTopo.table.columns.add("SLOPE", "double precision")
reachesTopo.table.columns.add("STRTHICK", "double precision")
reachesTopo.table.columns.add("STRHC1", "double precision")
reachesTopo.table.columns.add("THTS", "double precision")
reachesTopo.table.columns.add("THTI", "double precision")
reachesTopo.table.columns.add("EPS", "double precision")
reachesTopo.table.columns.add("UHC", "double precision")
reachesTopo.table.columns.add("xr1", "double precision")
reachesTopo.table.columns.add("xr2", "double precision")
reachesTopo.table.columns.add("yr1", "double precision")
reachesTopo.table.columns.add("yr2", "double precision")
# Commit columns before editing (necessary?)
reachesTopo.table.conn.commit()
reachesTopo.close()
# Update some columns that can be done now
reachesTopo.open("rw")
colNames = np.array(gscript.vector_db_select(reaches, layer=1)["columns"])
colValues = np.array(gscript.vector_db_select(reaches, layer=1)["values"].values())
cats = colValues[:, colNames == "cat"].astype(int).squeeze()
nseg = np.arange(1, len(cats) + 1)
nseg_cats = []
for i in range(len(cats)):
nseg_cats.append((nseg[i], cats[i]))
cur = reachesTopo.table.conn.cursor()
# Hydrogeologic properties
cur.execute("update " + reaches + " set STRTHICK=" + str(STRTHICK))
cur.execute("update " + reaches + " set STRHC1=" + str(STRHC1))
cur.execute("update " + reaches + " set THTS=" + str(THTS))
cur.execute("update " + reaches + " set THTI=" + str(THTI))
cur.execute("update " + reaches + " set EPS=" + str(EPS))
cur.execute("update " + reaches + " set UHC=" + str(UHC))
# Grid properties
cur.execute("update " + reaches + " set KRCH=1") # Top layer: unchangable
cur.executemany("update " + reaches + " set IRCH=? where row=?", nseg_cats)
cur.executemany("update " + reaches + " set JRCH=? where col=?", nseg_cats)
reachesTopo.table.conn.commit()
reachesTopo.close()
v.to_db(map=reaches, columns="RCHLEN", option="length", quiet=True)
# Still to go after these:
# STRTOP (added with slope)
# IREACH (whole next section dedicated to this)
# SLOPE (need z_start and z_end)
# Now, the light stuff is over: time to build the reach order
v.to_db(map=reaches, option="start", columns="xr1,yr1")
v.to_db(map=reaches, option="end", columns="xr2,yr2")
# Now just sort by category, find which stream has the same xr1 and yr1 as
# x1 and y1 (or a_x1, a_y1) and then find where its endpoint matches another
# starting point and move down the line.
# v.db.select reaches col=cat,a_id,xr1,xr2 where="a_x1 = xr1"
# First, get the starting coordinates of each stream segment
# and a set of river ID's (ordered from 1...N)
colNames = np.array(gscript.vector_db_select(segments, layer=1)["columns"])
colValues = np.array(gscript.vector_db_select(segments, layer=1)["values"].values())
number_of_segments = colValues.shape[0]
segment_x1s = colValues[:, colNames == "x1"].astype(float).squeeze()
segment_y1s = colValues[:, colNames == "y1"].astype(float).squeeze()
segment_ids = colValues[:, colNames == "id"].astype(float).squeeze()
# Then move back to the reaches map to produce the ordering
colNames = np.array(gscript.vector_db_select(reaches, layer=1)["columns"])
colValues = np.array(gscript.vector_db_select(reaches, layer=1)["values"].values())
reach_cats = colValues[:, colNames == "cat"].astype(int).squeeze()
reach_x1s = colValues[:, colNames == "xr1"].astype(float).squeeze()
reach_y1s = colValues[:, colNames == "yr1"].astype(float).squeeze()
reach_x2s = colValues[:, colNames == "xr2"].astype(float).squeeze()
reach_y2s = colValues[:, colNames == "yr2"].astype(float).squeeze()
segment_ids__reach = colValues[:, colNames == "segment_id"].astype(float).squeeze()
for segment_id in segment_ids:
reach_order_cats = []
downstream_directed = []
ssel = segment_ids == segment_id
rsel = segment_ids__reach == segment_id # selector
# Find first segment: x1y1 first here, but not necessarily later
downstream_directed.append(1)
_x_match = reach_x1s[rsel] == segment_x1s[ssel]
_y_match = reach_y1s[rsel] == segment_y1s[ssel]
_i_match = _x_match * _y_match
x1y1 = True # false if x2y2
# Find cat
_cat = int(reach_cats[rsel][_x_match * _y_match])
reach_order_cats.append(_cat)
# Get end of reach = start of next one
reach_x_end = float(reach_x2s[reach_cats == _cat])
reach_y_end = float(reach_y2s[reach_cats == _cat])
while _i_match.any():
_x_match = reach_x1s[rsel] == reach_x_end
_y_match = reach_y1s[rsel] == reach_y_end
_i_match = _x_match * _y_match
if _i_match.any():
_cat = int(reach_cats[rsel][_x_match * _y_match])
reach_x_end = float(reach_x2s[reach_cats == _cat])
reach_y_end = float(reach_y2s[reach_cats == _cat])
reach_order_cats.append(_cat)
_message = str(len(reach_order_cats)) + " " + str(len(reach_cats[rsel]))
gscript.message(_message)
# Reach order to database table
reach_number__reach_order_cats = []
for i in range(len(reach_order_cats)):
reach_number__reach_order_cats.append((i + 1, reach_order_cats[i]))
reachesTopo = VectorTopo(reaches)
reachesTopo.open("rw")
cur = reachesTopo.table.conn.cursor()
cur.executemany(
"update " + reaches + " set IREACH=? where cat=?",
reach_number__reach_order_cats,
)
reachesTopo.table.conn.commit()
reachesTopo.close()
# TOP AND BOTTOM ARE OUT OF ORDER: SOME SEGS ARE BACKWARDS. UGH!!!!
# NEED TO GET THEM IN ORDER TO GET THE Z VALUES AT START AND END
# 2018.10.01: Updating this to use the computational region for the DEM
g.region(raster=elevation)
# Compute slope and starting elevations from the elevations at the start and
# end of the reaches and the length of each reach]
gscript.message("Obtaining elevation values from raster: may take time.")
v.db_addcolumn(map=reaches, columns="zr1 double precision, zr2 double precision")
zr1 = []
zr2 = []
for i in range(len(reach_cats)):
_x = reach_x1s[i]
_y = reach_y1s[i]
# print _x, _y
_z = float(
gscript.parse_command(
"r.what", map=elevation, coordinates=str(_x) + "," + str(_y)
)
.keys()[0]
.split("|")[-1]
)
zr1.append(_z)
_x = reach_x2s[i]
_y = reach_y2s[i]
_z = float(
gscript.parse_command(
"r.what", map=elevation, coordinates=str(_x) + "," + str(_y)
)
.keys()[0]
.split("|")[-1]
)
zr2.append(_z)
zr1_cats = []
zr2_cats = []
for i in range(len(reach_cats)):
zr1_cats.append((zr1[i], reach_cats[i]))
zr2_cats.append((zr2[i], reach_cats[i]))
reachesTopo = VectorTopo(reaches)
reachesTopo.open("rw")
cur = reachesTopo.table.conn.cursor()
cur.executemany("update " + reaches + " set zr1=? where cat=?", zr1_cats)
cur.executemany("update " + reaches + " set zr2=? where cat=?", zr2_cats)
reachesTopo.table.conn.commit()
reachesTopo.close()
# Use these to create slope -- backwards possible on DEM!
v.db_update(map=reaches, column="SLOPE", value="(zr1 - zr2)/RCHLEN")
v.db_update(map=reaches, column="SLOPE", value=Smin, where="SLOPE <= " + str(Smin))
# srtm_local_filled_grid = srtm_local_filled @ 200m (i.e. current grid)
# resolution
# r.to.vect in=srtm_local_filled_grid out=srtm_local_filled_grid col=z type=area --o#
# NOT SURE IF IT IS BEST TO USE MEAN ELEVATION OR TOP ELEVATION!!!!!!!!!!!!!!!!!!!!!!!
v.db_addcolumn(map=reaches, columns="z_topo_mean double precision")
v.what_rast(
map=reaches, raster=elevation, column="z_topo_mean"
) # , query_column='z')
v.db_update(
map=reaches, column="STRTOP", value="z_topo_mean -" + str(h_stream), quiet=True
)
# get only those for zone 23
maps = [i for i in all_maps if 'zone_23' in i]
regions = [
i for i in all_regions if i.endswith('zone_23') and i.startswith('region')
]
# loop to reproject regions
for i in regions:
v.proj(location='newLocation_wgs84',
mapset='variables_cut',
input=i,
output=i,
overwrite=True)
g.region(vector=(regions))
categorical = [
'Drainage', 'Ecoregions', 'Landcover', 'Protected_areas', 'roads',
'Treecover', 'Tree_plantations', 'Water_presence'
]
#len(grass.list_grouped(type = "raster", pattern = "*"+"101"+"*")["PERMANENT"])
# loop to reproject
for reg in regions:
# define region
g.region(vector=reg, res=30, flags='ap')
# get prefix for maps
reg_pref = reg.split("buffer")[1]
def main():
r_elevation = options["elevation"]
mrvbf = options["mrvbf"].split("@")[0]
mrrtf = options["mrrtf"].split("@")[0]
t_slope = float(options["t_slope"])
t_pctl_v = float(options["t_pctl_v"])
t_pctl_r = float(options["t_pctl_r"])
t_vf = float(options["t_rf"])
t_rf = float(options["t_rf"])
p_slope = float(options["p_slope"])
p_pctl = float(options["p_pctl"])
moving_window_square = flags["s"]
min_cells = int(options["min_cells"])
global current_region, TMP_RAST, L
TMP_RAST = {}
current_region = Region()
# some checks
if (t_slope <= 0 or t_pctl_v <= 0 or t_pctl_r <= 0 or t_vf <= 0
or t_rf <= 0 or p_slope <= 0 or p_pctl <= 0):
gs.fatal("Parameter values cannot be <= 0")
if min_cells < 2:
gs.fatal(
"Minimum number of cells in generalized DEM cannot be less than 2")
if min_cells > current_region.cells:
gs.fatal(
"Minimum number of cells in the generalized DEM cannot exceed the ungeneralized number of cells"
)
# calculate the number of levels
levels = 2
remaining_cells = current_region.cells
while remaining_cells >= min_cells:
levels += 1
g.region(nsres=Region().nsres * 3, ewres=Region().ewres * 3)
remaining_cells = Region().cells
current_region.write()
if levels < 3:
gs.fatal(
"MRVBF algorithm requires a greater level of generalization. Reduce number of min_cells or use a larger computational region."
)
gs.message("Parameter Settings")
gs.message("------------------")
gs.message("min_cells = %d will result in %d generalization steps" %
(min_cells, levels))
# intermediate outputs
Xres_step = list()
Yres_step = list()
DEM = list()
SLOPE = list()
F = list()
PCTL = list()
PVF = list()
PVF_RF = list()
VF = list()
VF_RF = list()
MRVBF = list()
MRRTF = list()
# step 1 at base resolution -------------------------------------------------------
L = 0
TMP_RAST[L] = list()
Xres_step.append(current_region.ewres)
Yres_step.append(current_region.nsres)
DEM.append(r_elevation)
radius = 3
step_message(L, Xres_step[L], Yres_step[L], current_region.cells, t_slope)
# calculation of slope (S1) and calculation of flatness (F1) (equation 2)
SLOPE.append(calc_slope(DEM[L]))
F.append(flatness(SLOPE[L], t_slope, p_slope))
# calculation of elevation percentile PCTL for step 1
PCTL.append(elevation_percentile(DEM[L], radius, moving_window_square))
# transform elevation percentile to local lowness for step 1 (equation 3)
PVF.append(prelim_flatness_valleys(F[L], PCTL[L], t_pctl_v, p_pctl))
if mrrtf != "":
PVF_RF.append(prelim_flatness_ridges(F[L], PCTL[L], t_pctl_r, p_pctl))
# calculation of the valley flatness step 1 VF1 (equation 4)
VF.append(valley_flatness(PVF[L], t_vf, p_slope))
MRVBF.append(None)
if mrrtf != "":
VF_RF.append(valley_flatness(PVF_RF[L], t_rf, p_slope))
MRRTF.append(None)
# step 2 at base scale resolution -------------------------------------------------
L = 1
TMP_RAST[L] = list()
Xres_step.append(current_region.ewres)
Yres_step.append(current_region.nsres)
DEM.append(r_elevation)
t_slope /= 2.0
radius = 6
step_message(L, Xres_step[L], Yres_step[L], current_region.cells, t_slope)
# calculation of flatness for step 2 (equation 5)
SLOPE.append(SLOPE[L - 1])
F.append(flatness(SLOPE[L], t_slope, p_slope))
# calculation of elevation percentile PCTL for step 2 (radius of 6 cells)
PCTL.append(elevation_percentile(r_elevation, radius,
moving_window_square))
# PVF for step 2 (equation 6)
PVF.append(prelim_flatness_valleys(F[L], PCTL[L], t_pctl_v, p_pctl))
if mrrtf != "":
PVF_RF.append(prelim_flatness_ridges(F[L], PCTL[L], t_pctl_r, p_pctl))
# calculation of the valley flatness VF for step 2 (equation 7)
VF.append(valley_flatness(PVF[L], t_vf, p_slope))
if mrrtf != "":
VF_RF.append(valley_flatness(PVF_RF[L], t_rf, p_slope))
# calculation of MRVBF for step 2
MRVBF.append(calc_mrvbf(VF1=VF[L - 1], VF2=VF[L], t=t_pctl_v))
if mrrtf != "":
MRRTF.append(calc_mrvbf(VF1=VF_RF[L - 1], VF2=VF_RF[L], t=t_pctl_r))
# update flatness for step 2 with combined flatness from F1 and F2 (equation 10)
F[L] = combined_flatness(F[L - 1], F[L])
# remaining steps -----------------------------------------------------------------
# for steps >= 2, each step uses the smoothing radius of the current step
# but at the dem resolution of the previous step
remaining_cells = current_region.cells
while remaining_cells >= min_cells:
L += 1
TMP_RAST[L] = list()
t_slope /= 2.0
Xres_step.append(Xres_step[L - 1] * 3)
Yres_step.append(Yres_step[L - 1] * 3)
radius = 6
# delete temporary maps from L-2
for tmap in TMP_RAST[L - 2]:
if len(gs.find_file(tmap)["fullname"]) > 0:
g.remove(type="raster", name=tmap, flags="f", quiet=True)
# coarsen resolution to resolution of previous step (step L-1) and smooth DEM
if L > 2:
g.region(ewres=Xres_step[L - 1], nsres=Yres_step[L - 1])
step_message(L, Xres_step[L], Yres_step[L], remaining_cells, t_slope)
DEM.append(smooth_dem(DEM[L - 1]))
# calculate slope at coarser resolution
SLOPE.append(calc_slope(DEM[L]))
# refine slope back to base resolution
if L > 2:
SLOPE[L] = refine(SLOPE[L], current_region, method="bilinear")
# coarsen resolution to current step L and calculate PCTL
g.region(ewres=Xres_step[L], nsres=Yres_step[L])
remaining_cells = Region().cells
DEM[L] = refine(DEM[L], Region(), method="average")
PCTL.append(elevation_percentile(DEM[L], radius, moving_window_square))
# refine PCTL to base resolution
PCTL[L] = refine(PCTL[L], current_region, method="bilinear")
# calculate flatness F at the base resolution
F.append(flatness(SLOPE[L], t_slope, p_slope))
# update flatness with combined flatness CF from the previous step
F[L] = combined_flatness(F1=F[L - 1], F2=F[L])
# calculate preliminary valley flatness index PVF at the base resolution
PVF.append(prelim_flatness_valleys(F[L], PCTL[L], t_pctl_v, p_pctl))
if mrrtf != "":
PVF_RF.append(
prelim_flatness_ridges(F[L], PCTL[L], t_pctl_r, p_pctl))
# calculate valley flatness index VF
VF.append(valley_flatness(PVF[L], t_vf, p_slope))
if mrrtf != "":
VF_RF.append(valley_flatness(PVF_RF[L], t_rf, p_slope))
# calculation of MRVBF
MRVBF.append(calc_mrvbf(VF1=MRVBF[L - 1], VF2=VF[L], t=t_pctl_v))
if mrrtf != "":
MRRTF.append(calc_mrvbf(VF1=MRRTF[L - 1], VF2=VF_RF[L],
t=t_pctl_r))
# output final MRVBF --------------------------------------------------------------
current_region.write()
gs.mapcalc("$x = $y", x=mrvbf, y=MRVBF[L])
if mrrtf != "":
gs.mapcalc("$x = $y", x=mrrtf, y=MRRTF[L])
owsConnections = {}
owsConnections['default'] = {
'dsn' : 'PG:host=523.riedackerhof.ch dbname=gis user=gis password=gisHoch3',
'url' : 'https://523.riedackerhof.ch:4433/ows?',
'username' :'gis',
'password' : 'gisHoch3',
'layers' : {
'cultures' : 'ch_gelan_kulturen_2014',
'fieldblocks' : 'ch_blw_erk2_feldblockkarte',
'borders' : 'ch_swisstopo_kantone'
},
}
params = owsConnections['default']
dsn = params['dsn']
g.mapset(flags='c',mapset='solothurn')
v.in_ogr(dsn=dsn, layer=params['layers']['borders'], output='region',
where="kantonsnum = 11")
g.region(vect='region')
g.region(flags='p')
v.in_ogr(dsn=dsn, layer=params['layers']['cultures'], output='cultures', flags='r')
v.in_ogr(dsn=dsn, layer=params['layers']['fieldblocks'],output='fieldblocks', flags='r')
v.overlay(ainput='fieldblocks', binput = 'cultures', operator='not', output='fff')