def __init__(self, MetricTable):
# Create empty ratio table
nprobs = MetricTable.nprobs
nsolvs = MetricTable.nsolvs
self.ratios = ma.masked_array(1.0 * ma.zeros((nprobs + 1, nsolvs)))
# Compute best relative performance ratios across
# solvers for each problem
for prob in range(nprobs):
metrics = MetricTable.prob_mets(prob)
best_met = ma.minimum(metrics)
if (ma.count(metrics) == nsolvs
and ma.maximum(metrics) <= opts.minlimit):
self.ratios[prob + 1, :] = 1.0
else:
self.ratios[prob + 1, :] = metrics * (1.0 / best_met)
# Sort each solvers performance ratios
for solv in range(nsolvs):
self.ratios[:, solv] = ma.sort(self.ratios[:, solv])
# Compute largest ratio and use to replace failures entries
self.maxrat = ma.maximum(self.ratios)
self.ratios = ma.filled(self.ratios, 1.01 * self.maxrat)
def load_prism(self, d):
"""
load prism data for this day
:param d:
:type d: datetime
:return:
"""
year = d.year
month = d.month
day = d.day
shape = self._shape
prism_base = 'PRISMD2_NMHW2mi_{}{:02n}{:02n}'
name = prism_base.format(year, month, day)
ppt = tif_to_array(self._prism_root, name)
tom = d + timedelta(days=1)
name = prism_base.format(year, tom.month, tom.day)
ppt_tom = tif_to_array(self._prsim_root, name)
ppt = maximum(ppt, zeros(shape))
ppt_tom = maximum(ppt_tom, zeros(shape))
# PRISM to mintemp, maxtemp, temp
if year in YEARS:
name = 'cai_tmin_us_us_30s_{}{:02n}{:02n}'.format(year, month, day)
min_temp = tif_to_array(self._prism_min_temp_root, name)
else:
name = 'TempMin_NMHW2Buff_{}{:02n}{:02n}'.format(year, month, day)
min_temp = tif_to_array(self._prism_min_temp_root, name)
name = 'TempMax_NMHW2Buff_{}{:02n}{:02n}'.format(year, month, day)
max_temp = tif_to_array(self._prism_max_temp_root, name)
mid_temp = (min_temp + max_temp) / 2
return ppt, ppt_tom, max_temp, min_temp, mid_temp
def load_prism(self, d):
"""
load prism data for this day
:param d:
:type d: datetime
:return:
"""
year = d.year
month = d.month
day = d.day
shape = self._shape
prism_base = 'PRISMD2_NMHW2mi_{}{:02n}{:02n}'
name = prism_base.format(year, month, day)
ppt = tif_to_array(self._prism_root, name)
tom = d + timedelta(days=1)
name = prism_base.format(year, tom.month, tom.day)
ppt_tom = tif_to_array(self._prsim_root, name)
ppt = maximum(ppt, zeros(shape))
ppt_tom = maximum(ppt_tom, zeros(shape))
# PRISM to mintemp, maxtemp, temp
if year in YEARS:
name = 'cai_tmin_us_us_30s_{}{:02n}{:02n}'.format(year, month, day)
min_temp = tif_to_array(self._prism_min_temp_root, name)
else:
name = 'TempMin_NMHW2Buff_{}{:02n}{:02n}'.format(year, month, day)
min_temp = tif_to_array(self._prism_min_temp_root, name)
name = 'TempMax_NMHW2Buff_{}{:02n}{:02n}'.format(year, month, day)
max_temp = tif_to_array(self._prism_max_temp_root, name)
mid_temp = (min_temp + max_temp) / 2
return ppt, ppt_tom, max_temp, min_temp, mid_temp
def __init__(self, MetricTable):
# Create empty ratio table
nprobs = MetricTable.nprobs
nsolvs = MetricTable.nsolvs
self.ratios = ma.masked_array(1.0 * ma.zeros((nprobs+1, nsolvs)))
# Compute best relative performance ratios across
# solvers for each problem
for prob in range(nprobs):
metrics = MetricTable.prob_mets(prob)
best_met = ma.minimum(metrics)
if (ma.count(metrics)==nsolvs and
ma.maximum(metrics)<=opts.minlimit):
self.ratios[prob+1,:] = 1.0;
else:
self.ratios[prob+1,:] = metrics * (1.0 / best_met)
# Sort each solvers performance ratios
for solv in range(nsolvs):
self.ratios[:,solv] = ma.sort(self.ratios[:,solv])
# Compute largest ratio and use to replace failures entries
self.maxrat = ma.maximum(self.ratios)
self.ratios = ma.filled(self.ratios, 1.01 * self.maxrat)
def myfunction(d,mx,mn):
from numpy.ma import maximum,minimum,absolute,greater,count
try:
if count(d)==0 : return mx,mn
mx=float(maximum(mx,float(maximum(d))))
mn=float(minimum(mn,float(minimum(d))))
except:
for i in d:
mx,mn=myfunction(i,mx,mn)
return mx,mn
def myfunction(d, mx, mn):
from numpy.ma import maximum, minimum, absolute, greater, count
try:
if count(d) == 0: return mx, mn
mx = float(maximum(mx, float(maximum(d))))
mn = float(minimum(mn, float(minimum(d))))
except:
for i in d:
mx, mn = myfunction(i, mx, mn)
return mx, mn
def myfunction(d,mx,mn):
from numpy.ma import maximum,minimum,masked_where,absolute,greater,count
try:
d=masked_where(greater(absolute(d),9.9E19),d)
if count(d)==0 : return mx,mn
mx=float(maximum(mx,float(maximum(d))))
mn=float(minimum(mn,float(minimum(d))))
except:
for i in d:
mx,mn=myfunction(i,mx,mn)
return mx,mn
def myfunction(d, mx, mn):
from numpy.ma import maximum, minimum, masked_where, absolute, greater, count
try:
d = masked_where(greater(absolute(d), 9.9E19), d)
if count(d) == 0: return mx, mn
mx = float(maximum(mx, float(maximum(d))))
mn = float(minimum(mn, float(minimum(d))))
except:
for i in d:
mx, mn = myfunction(i, mx, mn)
return mx, mn
def test_testMinMax2(self):
# Test of minimum, maximum.
assert_(eq(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3]))
assert_(eq(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9]))
x = arange(5)
y = arange(5) - 2
x[3] = masked
y[0] = masked
assert_(eq(minimum(x, y), where(less(x, y), x, y)))
assert_(eq(maximum(x, y), where(greater(x, y), x, y)))
assert_(minimum.reduce(x) == 0)
assert_(maximum.reduce(x) == 4)
def flux_limiter(
stage,
f_old,
Uf,
z):
"""Applies the flux (numerical and positivity) limiter to the
ith nominal flux matrix Uf, shape = (z1.N, z2.N)
inputs:
f_old -- (ndarray, dim=2) density from previous time step
CFL -- (instance) Courant numbers dictating phase space advection
Uf -- (ndarray, dim=2) the normalized flux (high order or not) used in the CS update
outputs:
Uf -- (ndarray, dim=2) final normalized flux for MC originating at prepoints[i,j]
after numerical and positivity limiter has been applied
Note: for a 768 x 1536 grid
the masked implementation here takes about 285 ms
the looped implementation over all i,j takes 3.85 s for the whole grid
for a 1536 x 3072 grid
the masked implementation here takes about 1.44 s
the looped implementation over all i,j takes 17.9 s for the whole grid
i.e. the computational savings is at least a factor of 10
"""
# local masked array (ma) copy of CFL.frac used to leave CFL.frac unchanged
Uf_ma = ma.array(z.CFL.frac[stage,:,:])
# mask negative values, keep positives
Uf_ma[z.CFL.numbers[stage,:,:] < 0] = ma.masked
# assign the mask to a zero matrix of same size for pairwise ma.minimum/maximum operations below
zeros = ma.zeros(Uf.shape)
zeros.mask = Uf_ma.mask
# operating only on positive values (negatives masked out)
Uf_pos = ma.minimum(ma.maximum(zeros, Uf), 1.0*f_old)
# mask positives, keep negatives
Uf_ma.mask = np.logical_not(Uf_ma.mask)
zeros.mask = Uf_ma.mask
#operating only on negative values
Uf_neg = ma.minimum(ma.maximum(-1.0*f_old, Uf), zeros)
# combine masked values in a single matrix Uf_final
Uf = np.where(Uf_neg.mask == False, Uf_neg.data, Uf_pos.data)
return Uf
def test_testMinMax2(self):
# Test of minimum, maximum.
assert_(eq(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3]))
assert_(eq(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9]))
x = arange(5)
y = arange(5) - 2
x[3] = masked
y[0] = masked
assert_(eq(minimum(x, y), where(less(x, y), x, y)))
assert_(eq(maximum(x, y), where(greater(x, y), x, y)))
assert_(minimum.reduce(x) == 0)
assert_(maximum.reduce(x) == 4)
def __init__(self, MetricTable, opts):
epsilon = 0.0
if opts.cpu:
epsilon = 0.01
# Create empty ratio table
nprobs = MetricTable.nprobs
nsolvs = MetricTable.nsolvs
self.ratios = ma.zeros((nprobs, nsolvs), dtype=numpy.float)
# Compute best relative performance ratios across
# solvers for each problem
for prob in range(nprobs):
metrics = MetricTable.prob_mets(prob) + epsilon
best_met = ma.minimum(metrics)
self.ratios[prob,:] = metrics * (1.0 / best_met)
# Sort each solvers performance ratios
for solv in range(nsolvs):
self.ratios[:,solv] = ma.sort(self.ratios[:,solv])
# Compute largest ratio and use to replace failure entries
self.maxrat = ma.maximum(self.ratios)
self.ratios = ma.filled(self.ratios, 10 * self.maxrat)
def _breakList(self, inList, index, parameter):
par = float(parameter)
array = N.empty(shape=[len(inList),],dtype=N.float64)
i = 0
for parameters in inList:
array[i] = parameters[index]
i = i + 1
greater = MA.masked_less(array, par)
less = MA.masked_greater(array, par)
upper = MA.minimum(greater)
lower = MA.maximum(less)
upperArray = MA.masked_inside(array, par, upper)
lowerArray = MA.masked_inside(array, lower, par)
upperList = []
lowerList = []
i = 0
for parameters in inList:
if upperArray.mask[i]:
upperList.append(parameters)
if lowerArray.mask[i]:
lowerList.append(parameters)
i = i + 1
return upperList, lowerList
def has_close(data, value, rtol=1.e-5, atol=1.e-8):
"""Test if data has any values "equal" to value.
Returns 1 if any of the elements of argument data has a value
"equal" to argument value; returns 0 otherwise. If data or value
is floating point, "equal" means where abs(data-value) <= atol +
rtol * abs(value). This is essentially the same algorithm used
in the Numeric function allclose. If data and value are integer,
"equal" means strict equality.
Positional Input Arguments:
* data: Data. Scalar or Numeric array, Python list/tuple of
any size and shape. Floating or integer type.
* value: Test value. Scalar or 1 element Numeric array, list,
or tuple. Floating or integer type.
Keyword Input Arguments:
* rtol: "Relative" tolerance. Default is 1.e-5. Used in the
comparison between data and value only if the two are
floating point. Floating or integer type.
* atol: "Absolute" tolerance. Default is 1.e-8. Used in the
comparison between data and value only if the two are
floating point. Floating or integer type.
Examples:
>>> from has_close import has_close
>>> data = [20., -32., -1., 2., 5., 29.]
>>> has_close(data, -1.)
1
>>> has_close(data, 10.)
0
"""
#- Make sure data is Numeric type and value is a scalar within the
# function:
dataN = np.array(data)
valueS = np.array(value)
#- Safe compare if floating. Strict compare if integer. Any other
# type returns an error:
if (dataN.dtype == float) or (valueS.dtype == type(1.)):
closemask = np.less_equal(np.abs(dataN - valueS),
atol + rtol * np.abs(valueS))
elif (dataN.dtype == np.int32) and (valueS.dtype == type(1)):
closemask = np.where(dataN == valueS, 1, 0)
else:
print((dataN.dtype))
print((type(valueS)))
raise ValueError("has_close: Inputs must be float or integer")
#- Return true if any elements of data has value:
if ma.maximum(closemask) == 1:
return 1
else:
return 0
def test_testMinMax(self):
# Test minimum and maximum.
(x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
xr = np.ravel(x) # max doesn't work if shaped
xmr = ravel(xm)
# true because of careful selection of data
self.assertTrue(eq(max(xr), maximum(xmr)))
self.assertTrue(eq(min(xr), minimum(xmr)))
def _train(self, blob_generator):
for blob in blob_generator:
if self.min is None or self.max is None:
self.min = blob.data
self.max = blob.data
else:
self.min = minimum(self.min, blob.data)
self.max = maximum(self.max, blob.data)
yield blob
def test_testMinMax(self):
# Test minimum and maximum.
(x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
xr = np.ravel(x) # max doesn't work if shaped
xmr = ravel(xm)
# true because of careful selection of data
self.assertTrue(eq(max(xr), maximum(xmr)))
self.assertTrue(eq(min(xr), minimum(xmr)))
def _contour_args(self, *args):
if self.filled: fn = 'contourf'
else: fn = 'contour'
Nargs = len(args)
if Nargs <= 2:
z = ma.asarray(args[0], dtype=np.float64)
x, y = self._initialize_x_y(z)
elif Nargs <=4:
x,y,z = self._check_xyz(args[:3])
else:
raise TypeError("Too many arguments to %s; see help(%s)" % (fn,fn))
z = ma.masked_invalid(z, copy=False)
self.zmax = ma.maximum(z)
self.zmin = ma.minimum(z)
if self.logscale and self.zmin <= 0:
z = ma.masked_where(z <= 0, z)
warnings.warn('Log scale: values of z <=0 have been masked')
self.zmin = z.min()
self._auto = False
if self.levels is None:
if Nargs == 1 or Nargs == 3:
lev = self._autolev(z, 7)
else: # 2 or 4 args
level_arg = args[-1]
try:
if type(level_arg) == int:
lev = self._autolev(z, level_arg)
else:
lev = np.asarray(level_arg).astype(np.float64)
except:
raise TypeError(
"Last %s arg must give levels; see help(%s)" % (fn,fn))
if self.filled and len(lev) < 2:
raise ValueError("Filled contours require at least 2 levels.")
self.levels = lev
self._levels = list(self.levels)
if self.extend in ('both', 'min'):
self._levels.insert(0, min(self.levels[0],self.zmin) - 1)
if self.extend in ('both', 'max'):
self._levels.append(max(self.levels[-1],self.zmax) + 1)
self._levels = np.asarray(self._levels)
self.vmin = np.amin(self.levels) # alternative would be self.layers
self.vmax = np.amax(self.levels)
if self.extend in ('both', 'min'):
self.vmin = 2 * self.levels[0] - self.levels[1]
if self.extend in ('both', 'max'):
self.vmax = 2 * self.levels[-1] - self.levels[-2]
self.layers = self._levels # contour: a line is a thin layer
if self.filled:
self.layers = 0.5 * (self._levels[:-1] + self._levels[1:])
if self.extend in ('both', 'min'):
self.layers[0] = 0.5 * (self.vmin + self._levels[1])
if self.extend in ('both', 'max'):
self.layers[-1] = 0.5 * (self.vmax + self._levels[-2])
return (x, y, z)
def test_testScalarArithmetic(self):
xm = array(0, mask=1)
#TODO FIXME: Find out what the following raises a warning in r8247
with np.errstate(divide='ignore'):
assert_((1 / array(0)).mask)
assert_((1 + xm).mask)
assert_((-xm).mask)
assert_((-xm).mask)
assert_(maximum(xm, xm).mask)
assert_(minimum(xm, xm).mask)
assert_(xm.filled().dtype is xm._data.dtype)
x = array(0, mask=0)
assert_(x.filled() == x._data)
assert_equal(str(xm), str(masked_print_option))
def test_testScalarArithmetic(self):
xm = array(0, mask=1)
#TODO FIXME: Find out what the following raises a warning in r8247
with np.errstate(divide='ignore'):
assert_((1 / array(0)).mask)
assert_((1 + xm).mask)
assert_((-xm).mask)
assert_((-xm).mask)
assert_(maximum(xm, xm).mask)
assert_(minimum(xm, xm).mask)
assert_(xm.filled().dtype is xm._data.dtype)
x = array(0, mask=0)
assert_(x.filled() == x._data)
assert_equal(str(xm), str(masked_print_option))
def _process_args(self, *args, **kwargs):
"""
Process args and kwargs.
"""
if isinstance(args[0], QuadContourSet):
C = args[0].Cntr
if self.levels is None:
self.levels = args[0].levels
self.zmin = args[0].zmin
self.zmax = args[0].zmax
else:
x, y, z = self._contour_args(args, kwargs)
x0 = ma.minimum(x)
x1 = ma.maximum(x)
y0 = ma.minimum(y)
y1 = ma.maximum(y)
self.ax.update_datalim([(x0,y0), (x1,y1)])
self.ax.autoscale_view(tight=True)
_mask = ma.getmask(z)
if _mask is ma.nomask:
_mask = None
C = _cntr.Cntr(x, y, z.filled(), _mask)
self.Cntr = C
def _cast_values(self, values, arguments):
"""Change the return values to be of type self._return_type.
If "should_check" is defined, first check for
values that are too large for the destination type or
integer wrap-around."""
type = values.dtype.str
if self._return_type == type:
return values
if self.should_check(arguments):
max_value = ma.maximum(values)
if max_value > self._max_storable_value[self._return_type]:
max_value_str = str(max_value)
logger.log_error("Variable '%s' is being cast to type '%s', but contains a value (%s) too large to fit into that type."
% (self.name(), self._return_type, max_value_str))
return values.astype(self._return_type)
def make_solution(case):
N, K = case
increasing = np.array(list(range(N)))
decreasing = np.array(increasing[::-1])
# status variables
free = np.ones(N, dtype=bool)
left = np.zeros(N, dtype=int)
right = np.zeros(N, dtype=int)
# init with initial distances
left[0:N] = increasing
right[0:N] = decreasing
for k in range(K):
# print("customer {}".format(k))
# mask with current occupancy status
left = ma.array(left, mask=~free)
right = ma.array(right, mask=~free)
#print("left",left)
#print("right", right)
mins = ma.minimum(left, right)
maxs = ma.maximum(left, right)
#print("mins", mins)
#print("maxs", maxs)
# candidates are all stalls where mins are maximal
max_mins = mins.max()
candidates = ma.where(mins == max_mins)[0]
#print(max_mins, candidates)
# from those candidates select the one where max is also maximal
selected = ma.argmax(maxs[candidates])
selected = candidates[selected]
#print(selected)
# occupy stall and update left and right
free[selected] = False
if selected > 0:
# left of selected, right distance has to be updated
right[0:selected] = ma.minimum(decreasing[-selected:], right[0:selected])
if selected < N -1:
# right of selected, left distance has to be updated
left[selected+1:] = ma.minimum(increasing[:N-selected -1], left[selected+1:])
if k == K -1:
return maxs[selected], mins[selected]
def _cast_values(self, values, arguments):
"""Change the return values to be of type self._return_type.
If "should_check" is defined, first check for
values that are too large for the destination type or
integer wrap-around."""
type = values.dtype.str
if self._return_type == type:
return values
if self.should_check(arguments):
max_value = ma.maximum(values)
if max_value > self._max_storable_value[self._return_type]:
max_value_str = str(max_value)
logger.log_error(
"Variable '%s' is being cast to type '%s', but contains a value (%s) too large to fit into that type."
% (self.name(), self._return_type, max_value_str))
return values.astype(self._return_type)
def _contour_args(self, args, kwargs):
if self.filled: fn = 'contourf'
else: fn = 'contour'
Nargs = len(args)
if Nargs <= 2:
z = ma.asarray(args[0], dtype=np.float64)
x, y = self._initialize_x_y(z)
args = args[1:]
elif Nargs <=4:
x,y,z = self._check_xyz(args[:3], kwargs)
args = args[3:]
else:
raise TypeError("Too many arguments to %s; see help(%s)" % (fn,fn))
z = ma.masked_invalid(z, copy=False)
self.zmax = ma.maximum(z)
self.zmin = ma.minimum(z)
if self.logscale and self.zmin <= 0:
z = ma.masked_where(z <= 0, z)
warnings.warn('Log scale: values of z <= 0 have been masked')
self.zmin = z.min()
self._contour_level_args(z, args)
return (x, y, z)
def denoise(im, U_init, tolerance=0.1, tau=0.125, tv_weight=100):
"""
An implementation of the Rudin Osher Fatemi(ROF) denoising model using
the numerical procedure presented in eq(11)
"""
m,n = im.shape
U = U_init
Px = im
Py = im
error =1
while(error > tolerance):
Uold = U
# gradient of primal variable
GradUx = roll(U, -1, axis = 1)-U
GradUy = roll(U, -1, axis = 0)-U
PxNew = Px + (tau/tv_weight)*GradUx
PyNew = Py + (tau/tv_weight)*GradUy
NormNew = maximum(1, sqrt(PxNew**2 + PyNew**2))
Px = PxNew/NormNew
Py = PyNew/NormNew
RxPx = roll(Px,1,axis=1)
RyPy = roll(Py,1,axis=0)
DivP = (Px-RxPx) + (Py-RyPy)
U = im + tv_weight*DivP
error = linalg.norm(U-Uold)/sqrt(n*m)
return U,im-U
def _contour_args(self, *args):
if self.filled: fn = 'contourf'
else: fn = 'contour'
Nargs = len(args)
if Nargs <= 2:
z = ma.asarray(args[0], dtype=np.float64)
x, y = self._initialize_x_y(z)
elif Nargs <= 4:
x, y, z = self._check_xyz(args[:3])
else:
raise TypeError("Too many arguments to %s; see help(%s)" %
(fn, fn))
z = ma.masked_invalid(z, copy=False)
self.zmax = ma.maximum(z)
self.zmin = ma.minimum(z)
if self.logscale and self.zmin <= 0:
z = ma.masked_where(z <= 0, z)
warnings.warn('Log scale: values of z <=0 have been masked')
self.zmin = z.min()
self._auto = False
if self.levels is None:
if Nargs == 1 or Nargs == 3:
lev = self._autolev(z, 7)
else: # 2 or 4 args
level_arg = args[-1]
try:
if type(level_arg) == int:
lev = self._autolev(z, level_arg)
else:
lev = np.asarray(level_arg).astype(np.float64)
except:
raise TypeError(
"Last %s arg must give levels; see help(%s)" %
(fn, fn))
if self.filled and len(lev) < 2:
raise ValueError("Filled contours require at least 2 levels.")
self.levels = lev
return (x, y, z)
def _contour_args(self, *args):
if self.filled: fn = 'contourf'
else: fn = 'contour'
Nargs = len(args)
if Nargs <= 2:
z = ma.asarray(args[0], dtype=np.float64)
x, y = self._initialize_x_y(z)
elif Nargs <=4:
x,y,z = self._check_xyz(args[:3])
else:
raise TypeError("Too many arguments to %s; see help(%s)" % (fn,fn))
z = ma.masked_invalid(z, copy=False)
self.zmax = ma.maximum(z)
self.zmin = ma.minimum(z)
if self.logscale and self.zmin <= 0:
z = ma.masked_where(z <= 0, z)
warnings.warn('Log scale: values of z <=0 have been masked')
self.zmin = z.min()
self._auto = False
if self.levels is None:
if Nargs == 1 or Nargs == 3:
lev = self._autolev(z, 7)
else: # 2 or 4 args
level_arg = args[-1]
try:
if type(level_arg) == int:
lev = self._autolev(z, level_arg)
else:
lev = np.asarray(level_arg).astype(np.float64)
except:
raise TypeError(
"Last %s arg must give levels; see help(%s)" % (fn,fn))
if self.filled and len(lev) < 2:
raise ValueError("Filled contours require at least 2 levels.")
self.levels = lev
return (x, y, z)
def load_current_use(self):
"""
:return:
"""
root = self._current_use_root
qDeps = tif_to_array(root, 'Q_deps_std')
min_val = ones(qDeps.shape) * 0.001
qDeps = maximum(qDeps, min_val)
aws = tif_to_array(root, 'aws_mod_4_21_10_0')
aws = maximum(aws, min_val)
self._dataset_params = tif_params(root, 'aws_mod_4_21_10_0')
nlcd_rt_z = tif_to_array(root, 'nlcd_root_dpth_15apr')
nlcd_rt_z = maximum(nlcd_rt_z, min_val)
nlcd_plt_hgt = tif_to_array(root, 'nlcd_plnt_hgt1_250_m_degraded1')
nlcd_plt_hgt = maximum(nlcd_plt_hgt, min_val)
ksat = tif_to_array(root, 'Soil_Ksat_15apr')
ksat = maximum(ksat, min_val)
tew = tif_to_array(root, 'tew_250_15apr')
tew = maximum(tew, min_val)
self._qDeps = qDeps
self._taw = aws
self._nlcd_rt_z = nlcd_rt_z
self._nlcd_plt_hgt = nlcd_plt_hgt
self._ksat = ksat
self._tew = tew
self._min_val = min_val
self._shape = aws.shape
def load_current_use(self):
"""
:return:
"""
root = self._current_use_root
qDeps = tif_to_array(root, 'Q_deps_std')
min_val = ones(qDeps.shape) * 0.001
qDeps = maximum(qDeps, min_val)
aws = tif_to_array(root, 'aws_mod_4_21_10_0')
aws = maximum(aws, min_val)
self._dataset_params = tif_params(root, 'aws_mod_4_21_10_0')
nlcd_rt_z = tif_to_array(root, 'nlcd_root_dpth_15apr')
nlcd_rt_z = maximum(nlcd_rt_z, min_val)
nlcd_plt_hgt = tif_to_array(root, 'nlcd_plnt_hgt1_250_m_degraded1')
nlcd_plt_hgt = maximum(nlcd_plt_hgt, min_val)
ksat = tif_to_array(root, 'Soil_Ksat_15apr')
ksat = maximum(ksat, min_val)
tew = tif_to_array(root, 'tew_250_15apr')
tew = maximum(tew, min_val)
self._qDeps = qDeps
self._taw = aws
self._nlcd_rt_z = nlcd_rt_z
self._nlcd_plt_hgt = nlcd_plt_hgt
self._ksat = ksat
self._tew = tew
self._min_val = min_val
self._shape = aws.shape
def plot_map(self,
dataset,
attribute_data,
min_value=None,
max_value=None,
file=None,
my_title="",
filter=None,
background=None):
""" Plots a 2D image of attribute given by 'name'. matplotlib required.
The dataset must have a method 'get_2d_attribute' defined that returns
a 2D array that is to be plotted. If min_value/max_value are given, all values
that are smaller/larger than these values are set to min_value/max_value.
Argument background is a value to be used for background. If it is not given,
it is considered as a 1/100 under the minimum value of the array.
Filter is a 2D array. Points where filter is > 0 are masked out (put into background).
"""
import matplotlib
matplotlib.use('Qt4Agg')
from matplotlib.pylab import jet, imshow, colorbar, show, axis, savefig, close, figure, title, normalize
from matplotlib.pylab import rot90
attribute_data = attribute_data[filter]
coord_2d_data = dataset.get_2d_attribute(attribute_data=attribute_data)
data_mask = coord_2d_data.mask
# if filter is not None:
# if isinstance(filter, ndarray):
# if not ma.allclose(filter.shape, coord_2d_data.shape):
# raise StandardError, "Argument filter must have the same shape as the 2d attribute."
# filter_data = filter
# else:
# raise TypeError, "The filter type is invalid. A character string or a 2D numpy array allowed."
# filter_data = where(ma.filled(filter_data,1) > 0, 1,0)
# data_mask = ma.mask_or(data_mask, filter_data)
nonmaskedmin = ma.minimum(coord_2d_data) - .2 * (
ma.maximum(coord_2d_data) - ma.minimum(coord_2d_data))
if max_value == None:
max_value = ma.maximum(coord_2d_data)
if min_value == None:
min_value = nonmaskedmin
coord_2d_data = ma.filled(coord_2d_data, min_value)
if background is None:
value_range = max_value - min_value
background = min_value - value_range / 100
coord_2d_data = ma.filled(
ma.masked_array(coord_2d_data, mask=data_mask), background)
# Our data uses NW as 0,0, while matplotlib uses SW for 0,0.
# Rotate the data so the map is oriented correctly.
coord_2d_data = rot90(coord_2d_data, 1)
jet()
figure()
norm = normalize(min_value, max_value)
im = imshow(
coord_2d_data,
origin='lower',
aspect='equal',
interpolation=None,
norm=norm,
)
tickfmt = '%4d'
if isinstance(min_value, float) or isinstance(max_value, float):
tickfmt = '%1.4f'
colorbar(format=tickfmt)
title(my_title)
axis('off')
if file:
savefig(file)
close()
else:
show()
def create(self,
parent=None,
min=None,
max=None,
save_file=None,
thread_it=1,
rate=None,
bitrate=None,
ffmpegoptions=''):
from vcs import minmax
from numpy.ma import maximum, minimum
##from tkMessageBox import showerror
# Cannot "Run" or "Create" an animation while already creating an animation
if self.run_flg == 1: return
if self.vcs_self.canvas.creating_animation() == 1: return
if self.vcs_self.animate_info == []:
str = "No data found!"
showerror("Error Message to User", str)
return
finish_queued_X_server_requests(self.vcs_self)
self.vcs_self.canvas.BLOCK_X_SERVER()
# Stop the (thread) execution of the X main loop (if it is running).
self.vcs_self.canvas.stopxmainloop()
# Force VCS to update its orientation, needed when the user changes the
# VCS Canvas size.
self.vcs_self.canvas.updateorientation()
# Make sure the animate information is up-to-date for creating images
if ((self.gui_popup == 1) and (self.create_flg == 0)):
self.update_animate_display_list()
# Save the min and max values for the graphics methods.
# Will need to restore values back when animation is done.
self.save_original_min_max()
# Set up the animation min and max values by changing the graphics method
# Note: cannot set the min and max values if the default graphics method is set.
do_min_max = 'yes'
try:
if (parent is not None) and (parent.iso_spacing == 'Log'):
do_min_max = 'no'
except:
pass
# Draw specified continental outlines if needed.
self.continents_hold_value = self.vcs_self.canvas.getcontinentstype()
self.vcs_self.canvas.setcontinentstype(self.continents_value)
if (do_min_max == 'yes'):
minv = []
maxv = []
if (min is None) or (max is None):
for i in range(len(self.vcs_self.animate_info)):
minv.append(1.0e77)
maxv.append(-1.0e77)
for i in range(len(self.vcs_self.animate_info)):
dpy, slab = self.vcs_self.animate_info[i]
mins, maxs = minmax(slab)
minv[i] = float(minimum(float(minv[i]), float(mins)))
maxv[i] = float(maximum(float(maxv[i]), float(maxs)))
if isinstance(min, list) or isinstance(max, list):
for i in range(len(self.vcs_self.animate_info)):
try:
minv.append(min[i])
except:
minv.append(min[-1])
try:
maxv.append(max[i])
except:
maxv.append(max[-1])
else:
for i in range(len(self.vcs_self.animate_info)):
minv.append(min)
maxv.append(max)
# Set the min an max for each plot in the page. If the same graphics method is used
# to display the plots, then the last min and max setting of the data set will be used.
for i in range(len(self.vcs_self.animate_info)):
try:
self.set_animation_min_max(minv[i], maxv[i], i)
except Exception, err:
pass # if it is default, then you cannot set the min and max, so pass.
def autoscale_None(self, A):
' autoscale only None-valued vmin or vmax'
if self.vmin is None: self.vmin = ma.minimum(A)
if self.vmax is None: self.vmax = ma.maximum(A)
def press2alt(arg, P0=None, T0=None, missing=1e+20, invert=0):
"""Calculate elevation given pressure (or vice versa).
Calculations are made assuming that the temperature distribution
follows the 1976 Standard Atmosphere. Technically the standard
atmosphere defines temperature distribution as a function of
geopotential altitude, and this routine actually calculates geo-
potential altitude rather than geometric altitude.
Method Positional Argument:
* arg: Numeric floating point vector of any shape and size, or a
Numeric floating point scalar. If invert=0 (the default), arg
is air pressure [hPa]. If invert=1, arg is elevation [m].
Method Keyword Arguments:
* P0: Pressure [hPa] at the surface (altitude equals 0). Numeric
floating point vector of same size and shape as arg or a scalar.
Default of keyword is set to None, in which case the routine
uses the value of instance attribute sea_level_press (converted
to hPa) from the AtmConst class. Keyword value is used if the
keyword is set in the function call. This keyword cannot have
any missing values.
* T0: Temperature [K] at the surface (altitude equals 0). Numeric
floating point vector of same size and shape as arg or a scalar.
Default of keyword is set to None, in which case the routine uses
the value of instance attribute sea_level_temp from the AtmConst
class. Keyword value is used if the keyword is set in the func-
tion call. This keyword cannot have any missing values.
* missing: If arg has missing values, this is the missing value
value. Floating point scalar. Default is 1e+20.
* invert: If set to 1, function calculates pressure [hPa] from
altitude [m]. In that case, positional input variable arg is
altitude [m] and the output is pressure [hPa]. Default value of
invert=0, which means the function calculates altitude given
pressure.
Output:
* If invert=0 (the default), output is elevation [m] at each
element of arg, relative to the surface. If invert=1, output
is the air pressure [hPa]. Numeric floating point array of
the same size and shape as arg.
If there are any missing values in output, those values are set
to the value in argument missing from the input. If there are
missing values in the output due to math errors and missing is
set to None, output will fill those missing values with the MA
default value of 1e+20.
References:
* Carmichael, Ralph (2003): "Definition of the 1976 Standard Atmo-
sphere to 86 km," Public Domain Aeronautical Software (PDAS).
URL: http://www.pdas.com/coesa.htm.
* Wallace, J. M., and P. V. Hobbs (1977): Atmospheric Science:
An Introductory Survey. San Diego, CA: Academic Press, ISBN
0-12-732950-1, pp. 60-61.
Examples:
(1) Calculating altitude given pressure:
>>> from press2alt import press2alt
>>> import Numeric as N
>>> press = N.array([200., 350., 850., 1e+20, 50.])
>>> alt = press2alt(press, missing=1e+20)
>>> ['%.7g' % alt[i] for i in range(5)]
['11783.94', '8117.19', '1457.285', '1e+20', '20575.96']
(2) Calculating pressure given altitude:
>>> alt = N.array([0., 10000., 15000., 20000., 50000.])
>>> press = press2alt(alt, missing=1e+20, invert=1)
>>> ['%.7g' % press[i] for i in range(5)]
['1013.25', '264.3589', '120.443', '54.74718', '0.7593892']
(3) Input is a Numeric floating point scalar, and using a keyword
set surface pressure to a different scalar:
>>> alt = press2alt(N.array(850.), P0=1000.)
>>> ['%.7g' % alt[0]]
['1349.778']
"""
import numpy.ma as MA
import numpy as N
from atmconst import AtmConst
#from is_numeric_float import is_numeric_float
#- Check input is of the correct type:
#if is_numeric_float(arg) != 1:
# raise TypeError, "press2alt: Arg not Numeric floating"
#- Import general constants and set additional constants. h1_std
# is the lower limit of the Standard Atmosphere layer geopoten-
# tial altitude [m], h2_std is the upper limit [m] of the layer,
# and dT/dh is the temperature gradient (i.e. negative of the
# lapse rate) [K/m]:
const = AtmConst()
h1_std = N.array([0., 11., 20., 32., 47., 51., 71.]) * 1000.
h2_std = N.array(MA.concatenate([h1_std[1:], [84.852 * 1000.]]))
dTdh_std = N.array([-6.5, 0.0, 1.0, 2.8, 0.0, -2.8, -2.0]) / 1000.
#- Prep arrays for masked array calculation and set conditions
# at sea-level. Pressures are in hPa and temperatures in K.
# Sea-level conditions arrays are same shape/size as P_or_z.
# If input argument is a scalar, make the local variable used
# for calculations a 1-element vector:
if missing == None: P_or_z = MA.masked_array(arg)
else: P_or_z = MA.masked_values(arg, missing, copy=0)
if P_or_z.shape == ():
P_or_z = MA.reshape(P_or_z, (1, ))
if P0 == None:
P0_use = MA.zeros(P_or_z.shape) \
+ (const.sea_level_press / 100.)
else:
P0_use = MA.zeros(P_or_z.shape) \
+ MA.masked_array(P0)
if T0 == None:
T0_use = MA.zeros(P_or_z.shape) \
+ const.sea_level_temp
else:
T0_use = MA.zeros(P_or_z.shape) \
+ MA.masked_array(T0)
#- Calculate P and T for the boundaries of the 7 layers of the
# Standard Atmosphere for the given P0 and T0 (layer 0 goes from
# P0 to P1, layer 1 from P1 to P2, etc.). These are given as
# 8 element dictionaries P_std and T_std where the key is the
# location (P_std[0] is at the bottom of layer 0, P_std[1] is the
# top of layer 0 and bottom of layer 1, ... and P_std[7] is the
# top of layer 6). Remember P_std and T_std are dictionaries but
# dTdh_std, h1_std, and h2_std are vectors:
P_std = {0: P0_use}
T_std = {0: T0_use}
for i in range(len(h1_std)):
P_std[i+1] = _pfromz_MA( h2_std[i], -dTdh_std[i] \
, P_std[i], T_std[i], h1_std[i] )
T_std[i + 1] = T_std[i] + (dTdh_std[i] * (h2_std[i] - h1_std[i]))
#- Test input is within Standard Atmosphere limits:
if invert == 0:
tmp = MA.where(P_or_z < P_std[len(h1_std)], 1, 0)
if MA.sum(MA.ravel(tmp)) > 0:
raise ValueError, "press2alt: Pressure out-of-range"
else:
tmp = MA.where(P_or_z > MA.maximum(h2_std), 1, 0)
if MA.sum(MA.ravel(tmp)) > 0:
raise ValueError, "press2alt: Altitude out-of-range"
#- What layer number is each element of P_or_z in?
P_or_z_layer = 0 #MA.zeros(P_or_z.shape)
#if invert == 0:
# for i in range(len(h1_std)):
# tmp = MA.where( MA.logical_and( (P_or_z <= P_std[i]) \
# , (P_or_z > P_std[i+1]) ) \
# , i, 0 )
# P_or_z_layer += tmp
#else:
# for i in range(len(h1_std)):
# tmp = MA.where( MA.logical_and( (P_or_z >= h1_std[i]) \
# , (P_or_z < h2_std[i]) ) \
# , i, 0 )
# P_or_z_layer += tmp
#- Fill in the bottom-of-the-layer variables and the lapse rate
# for the layers that the levels are in. The *_actual variables
# are the values of dTdh, P_bott, etc. for each element in the
# P_or_z_flat array:
P_or_z_flat = MA.ravel(P_or_z)
if P_or_z_flat.mask() == None:
P_or_z_flat_mask = MA.make_mask_none(P_or_z_flat.shape)
else:
P_or_z_flat_mask = P_or_z_flat.mask()
P_or_z_layer_flat = MA.ravel(P_or_z_layer)
dTdh_actual = MA.zeros(P_or_z_flat.shape)
P_bott_actual = MA.zeros(P_or_z_flat.shape)
T_bott_actual = MA.zeros(P_or_z_flat.shape)
z_bott_actual = MA.zeros(P_or_z_flat.shape)
for i in xrange(MA.size(P_or_z_flat)):
if P_or_z_flat_mask[i] != 1:
layer_number = P_or_z_layer_flat[i]
dTdh_actual[i] = dTdh_std[layer_number]
P_bott_actual[i] = MA.ravel(P_std[layer_number])[i]
T_bott_actual[i] = MA.ravel(T_std[layer_number])[i]
z_bott_actual[i] = h1_std[layer_number]
else:
dTdh_actual[i] = MA.masked
P_bott_actual[i] = MA.masked
T_bott_actual[i] = MA.masked
z_bott_actual[i] = MA.masked
#- Calculate pressure/altitude from altitude/pressure (output is
# a flat array):
if invert == 0:
output = _zfromp_MA( P_or_z_flat, -dTdh_actual \
, P_bott_actual, T_bott_actual, z_bott_actual )
else:
output = _pfromz_MA( P_or_z_flat, -dTdh_actual \
, P_bott_actual, T_bott_actual, z_bott_actual )
#- Return output as same shape as input positional argument:
return MA.filled(MA.reshape(output, arg.shape), missing)
def flux_limiter(
stage,
f_old,
Uf,
z
):
"""Applies the flux (numerical and positivity) limiter to the
ith nominal flux matrix Uf, shape = (z1.N, z2.N)
inputs:
f_old -- (ndarray, dim=2) density from previous time step
CFL -- (instance) Courant numbers dictating phase space advection
Uf -- (ndarray, dim=2) the normalized flux (high order or not) used in the CS update
outputs:
Uf -- (ndarray, dim=2) final normalized flux for MC originating at prepoints[i,j]
after numerical and positivity limiter has been applied
Note: for a 768 x 1536 grid
the masked implementation here takes about 285 ms
the looped implementation over all i,j takes 3.85 s for the whole grid
for a 1536 x 3072 grid
the masked implementation here takes about 1.44 s
the looped implementation over all i,j takes 17.9 s for the whole grid
i.e. the computational savings is at least a factor of 10
"""
# note that before we apply the limiter, there is no guarantee that sign(Uf) == sign(z.CFL.numbers)
# this is one of the functions of the limiter, to make these signs agree if the unlimited value of Uf
# has been unphysically flipped in sign. Recall that since f >= 0 by initial condition, sign(Uf) == sign(U)
# for all points f > 0, and where f = 0 then Uf = 0.0 and its role in remapping is to add zero contribution.
#
# here, U is the high order correction to z.CFL.frac, i.e.
#
# U = z.CFL.frac + high order terms
#
# there isn't a restriction on the size of each high order term or the sign. It is possible for
# the correction to cause sign(U) != sign(z.CFL.frac) which is not physical.
# Put in other words, this is unphysical because "if f[i] -> f[i+k] lies between grid points i+k and i+k+1, then
# the high order correction df keeps (f[i] + Uf[i]) should also between grid points i+k and i+k+1.
# if U suffers a sign reversal from the high order term contributions, this maps the density packet to
# outside the interval we know to zeroeth order it must lie in (z.CFL.frac gives the remap fraction to zereoth order)
# Finally, note that z.CFL.frac and z.CFL.number by construction have the same sign. We often prefer doing
# sign checks on z.CFL.numbers, but this is not of consequence.
# assign the mask to a zero matrix of same size for pairwise ma.minimum/maximum operations below
zeros = ma.zeros(Uf.shape)
zeros[z.CFL.numbers[stage,:,:] < 0] = ma.masked
# operating only on positive values (negatives masked out), assert the limiter criteria
# note: there is some decision making we let python make here (at the cost of slight efficiency, but at the benefit
# of less storage for local copies of the same arrays). Uf and f_old are ndarrays (not a masked arrays)
# however, having one masked object (here, the zeros array) is sufficient so that the ma.minimum and
# ma.maximum operations will act only on the unmasked entries of zeros and hence will pairwise operate on comparing
# minima and maximum for the nested objects that are compared.
Uf_pos = ma.minimum(ma.maximum(zeros, Uf), 1.0*f_old)
# mask positives, keep negatives
zeros.mask = np.logical_not(zeros.mask)
# operating only on negative values; see note just before U_pos calculation regarding how this operation
# plays out given f_old and Uf are unmasked and zeros is masked.
Uf_neg = ma.minimum(ma.maximum(-1.0*f_old, Uf), zeros)
# combine masked data that has been limited by the operations above
# into a final Uf object to return.
# Here, we can interpret the np.where call as "where Uf_neg is unmasked [i.e. is negative]
# insert the negative data at those entries in Uf, wherever there are masked values [i.e.
# corresponded to masked value], insert the positive data that has been limited at those
# [i,j] locations in Uf"
Uf = np.where(Uf_neg.mask == False, Uf_neg.data, Uf_pos.data)
return Uf
def _contour_args(self, *args):
if self.filled: fn = 'contourf'
else: fn = 'contour'
Nargs = len(args)
if Nargs <= 2:
z = ma.asarray(args[0], dtype=np.float64)
x, y = self._initialize_x_y(z)
elif Nargs <= 4:
x, y, z = self._check_xyz(args[:3])
else:
raise TypeError("Too many arguments to %s; see help(%s)" %
(fn, fn))
self.zmax = ma.maximum(z)
self.zmin = ma.minimum(z)
if self.logscale and self.zmin <= 0:
z = ma.masked_where(z <= 0, z)
warnings.warn('Log scale: values of z <=0 have been masked')
self.zmin = z.min()
self._auto = False
if self.levels is None:
if Nargs == 1 or Nargs == 3:
lev = self._autolev(z, 7)
else: # 2 or 4 args
level_arg = args[-1]
try:
if type(level_arg) == int:
lev = self._autolev(z, level_arg)
else:
lev = np.asarray(level_arg).astype(np.float64)
except:
raise TypeError(
"Last %s arg must give levels; see help(%s)" %
(fn, fn))
if self.filled and len(lev) < 2:
raise ValueError("Filled contours require at least 2 levels.")
# Workaround for cntr.c bug wrt masked interior regions:
#if filled:
# z = ma.masked_array(z.filled(-1e38))
# It's not clear this is any better than the original bug.
self.levels = lev
#if self._auto and self.extend in ('both', 'min', 'max'):
# raise TypeError("Auto level selection is inconsistent "
# + "with use of 'extend' kwarg")
self._levels = list(self.levels)
if self.extend in ('both', 'min'):
self._levels.insert(0, min(self.levels[0], self.zmin) - 1)
if self.extend in ('both', 'max'):
self._levels.append(max(self.levels[-1], self.zmax) + 1)
self._levels = np.asarray(self._levels)
self.vmin = np.amin(self.levels) # alternative would be self.layers
self.vmax = np.amax(self.levels)
if self.extend in ('both', 'min'):
self.vmin = 2 * self.levels[0] - self.levels[1]
if self.extend in ('both', 'max'):
self.vmax = 2 * self.levels[-1] - self.levels[-2]
self.layers = self._levels # contour: a line is a thin layer
if self.filled:
self.layers = 0.5 * (self._levels[:-1] + self._levels[1:])
if self.extend in ('both', 'min'):
self.layers[0] = 0.5 * (self.vmin + self._levels[1])
if self.extend in ('both', 'max'):
self.layers[-1] = 0.5 * (self.vmax + self._levels[-2])
return (x, y, z)
def specify(self, slab, axes, specification, confined_by, aux):
''' First part: confine the slab within a Domain wide enough to do the exact in post'''
import string, copy
from numpy.ma import minimum, maximum
# myconfined is for later, we can't confine a dimension twice with an argument plus a keyword or 2 keywords
myconfined = [None] * len(axes)
self.aux = copy.copy(specification)
# First look at the arguments (i.e not keywords) and confine the dimensions
# in the order of the arguments
for i in range(len(self.args)):
if confined_by[
i] is None: # Check it hasn't been confined by somebody else
myconfined[i] = 1 # dim confined by argument list
confined_by[
i] = self # for cdms I want to confine this dimension
self.aux[i] = specs = list(
self.args[i]) # How do we want to confine this dim ?
if type(specs) == type(slice(0)):
specification[i] = specs # If it's a slicing nothing to do
else: # But if it's not...
if specs[0] is None:
tmp = axes[i].getBounds()
if tmp is None:
raise ValueError, 'Region error, axis:' + axes[
i].id + ' has no bounds'
specs[0] = minimum(minimum(tmp[0], tmp[-1]))
if specs[1] is None:
tmp = axes[i].getBounds()
if tmp is None:
raise ValueError, 'Region error, axis:' + axes[
i].id + ' has no bounds'
specs[1] = maximum(maximum(tmp[0], tmp[-1]))
if axes[i].isTime(): # Time is as always "Special"
import cdtime
tc = type(cdtime.comptime(0)) # component time type
tr = type(cdtime.reltime(
0, 'months since 0')) # relative time type
t = type(specs[0]) # my first spec type
if t == type(
''): #if my first spec is passed as a string
specs[0] = cdtime.s2r(specs[0], axes[i].units)
elif t == tc or t == tr: #if my first spec is passed as a cdtime object
specs[0] = cdtime.torel(specs[0], axes[i].units)
else: # If not it has to be that the users knows the time values in the axis
pass
t = type(specs[1]) # my second spec type
if t == type(
''): #if my second spec is passed as a string
specs[1] = cdtime.s2r(specs[1], axes[i].units)
elif t == tc or t == tr: #if my second spec is passed as a cdtime object
specs[1] = cdtime.torel(specs[1], axes[i].units)
sp = [
specs[0], specs[1], 'oob'
] # Now retrieve the values wide enough for the exact specification[i]=sp # sets the specifications
else:
return 1
for kw in self.kargs.keys():
axis = None
for i in range(len(axes)):
if axes[i].id == kw: axis = i
if axis is None:
if kw == 'time':
for i in range(len(axes)):
if axes[i].isTime(): axis = i
elif kw == 'level':
for i in range(len(axes)):
if axes[i].isLevel(): axis = i
elif kw == 'longitude':
for i in range(len(axes)):
if axes[i].isLongitude(): axis = i
elif kw == 'latitude':
for i in range(len(axes)):
if axes[i].isLatitude(): axis = i
elif not kw in [
'exact', 'atol', 'rtol'
]: # keyword not a recognised keyword or dimension name
raise 'Error, keyword: ' + kw + ' not recognized'
# At this point, if axis is None:
# we are dealing with a keyword for the selector
# so we'll skip it
if not axis is None:
if confined_by[axis] is None:
confined_by[axis] = self
myconfined[axis] = 1
self.aux[axis] = specs = list(self.kargs[kw])
if type(specs) != type(slice(0)):
if specs[0] is None:
tmp = axes[axis].getBounds()
if tmp is None:
raise ValueError, 'Region error, axis:' + axes[
axis].id + ' has no bounds'
specs[0] = minimum(minimum(tmp[0], tmp[-1]))
if specs[1] is None:
tmp = axes[axis].getBounds()
if tmp is None:
raise ValueError, 'Region error, axis:' + axes[
axis].id + ' has no bounds'
specs[1] = maximum(maximum(tmp[0], tmp[-1]))
if axes[axis].isTime():
import cdtime
tc = type(cdtime.comptime(0))
tr = type(cdtime.reltime(0, 'months since 0'))
t = type(specs[0])
if t == type(''):
specs[0] = cdtime.s2r(specs[0], axes[i].units)
elif t == tc or t == tr:
specs[0] = cdtime.torel(
specs[0], axes[i].units)
t = type(specs[1])
if t == type(''):
specs[1] = cdtime.s2r(specs[1], axes[i].units)
elif t == tc or t == tr:
specs[1] = cdtime.torel(
specs[1], axes[i].units)
sp = [specs[0], specs[1], 'oob']
specification[axis] = sp
else:
specification[axis] = specs
else:
if myconfined[axis] == 1:
raise 'Error you are attempting to set the axis: ' + str(
axes[axis].id) + ' more than once'
else:
return 1
return 0
def specify(self, slab, axes, specification, confined_by, aux):
""" First part: confine the slab within a Domain wide enough to do the exact in post"""
import string, copy
from numpy.ma import minimum, maximum
# myconfined is for later, we can't confine a dimension twice with an argument plus a keyword or 2 keywords
myconfined = [None] * len(axes)
self.aux = copy.copy(specification)
# First look at the arguments (i.e not keywords) and confine the dimensions
# in the order of the arguments
for i in range(len(self.args)):
if confined_by[i] is None: # Check it hasn't been confined by somebody else
myconfined[i] = 1 # dim confined by argument list
confined_by[i] = self # for cdms I want to confine this dimension
self.aux[i] = specs = list(self.args[i]) # How do we want to confine this dim ?
if not (isinstance(specs, list) or isinstance(specs, tuple)):
raise Exception, "Error in Selector, you must specify a list or a tuple, you passed:" + str(specs)
elif (
type(specs[0]) == type(cdtime.comptime(1999))
or type(specs[0]) == type(cdtime.reltime(0, "days since 1999"))
or type(specs[0]) == type("")
):
list2 = []
for l in specs:
if type(l) != type(""):
list2.append(l.torel("days since 1900").value)
else:
list2.append(cdtime.s2r(l, "days since 1900").value)
min = minimum(list2)
max = maximum(list2)
specification[i] = cdtime.reltime(min, "days since 1900"), cdtime.reltime(max, "days since 1900")
else: # But if it's not...
specification[i] = minimum(specs), maximum(specs) # sets the specifications
else:
return 1
for kw in self.kargs.keys():
axis = None
for i in range(len(axes)):
if axes[i].id == kw:
axis = i
if axis is None:
if kw == "time":
for i in range(len(axes)):
if axes[i].isTime():
axis = i
elif kw == "level":
for i in range(len(axes)):
if axes[i].isLevel():
axis = i
elif kw == "longitude":
for i in range(len(axes)):
if axes[i].isLongitude():
axis = i
elif kw == "latitude":
for i in range(len(axes)):
if axes[i].isLatitude():
axis = i
elif not kw in ["match"]: # keyword not a recognised keyword or dimension name
raise Exception, "Error, keyword: " + kw + " not recognized"
# At this point, if axis is None:
# we are dealing with a keyword for the selector
# so we'll skip it
if not axis is None:
if confined_by[axis] is None:
confined_by[axis] = self
myconfined[axis] = 1
self.aux[axis] = specs = list(self.kargs[kw])
if (
type(specs[0]) == type(cdtime.comptime(1999))
or type(specs[0]) == type(cdtime.reltime(0, "days since 1999"))
or type(specs[0]) == type("")
):
list2 = []
for l in specs:
if type(l) != type(""):
list2.append(l.torel("days since 1900").value)
else:
list2.append(cdtime.s2r(l, "days since 1900").value)
min = minimum(list2)
max = maximum(list2)
specification[axis] = (
cdtime.reltime(min, "days since 1900"),
cdtime.reltime(max, "days since 1900"),
)
else: # But if it's not...
specification[axis] = minimum(specs), maximum(specs)
else:
if myconfined[axis] == 1:
raise "Error you are attempting to set the axis: " + str(axes[axis].id) + " more than once"
else:
return 1
return 0
def interp(y, x, xinterp, missing=1e+20):
"""Simple linear interpolation for ordinate with missing values.
Vectors x and y are the data describing a piecewise linear function.
Function returns the interpolated values of the ordinate function
at abscissa values in xinterp. Values of xinterp outside the range
of x are returned as missing. Any elements in the output that uses
missing values in y for the interpolation are set to missing.
Positional Input Arguments:
* y: Ordinate values of data. Rank 1 numeric vector. Required.
Can have missing values. Floating or integer type.
* x: Abscissa values of data. Rank 1 numeric vector. Required.
Can have no missing values. Must be monotonically ascending.
Floating or integer type.
* xinterp: Abscissa values to calculate interpolated ordinate
values at. Rank 1 numeric vector or numeric scalar. Required.
Can have no missing values. Can be in any order. Floating or
integer type.
Keyword Input Arguments:
* missing: If input has missing values, this is the missing value
value. Scalar. Floating or integer type. Default is 1e+20.
Output Result:
* Interpolated ordinate values at xinterp. Rank 1 numeric vector
of same length as xinterp (if xinterp is a numeric scalar,
output is also a numeric scalar). Missing values are set to the
value of argument missing. Type is Float, even if argument
missing and inputs are all integer.
References:
* Lin, J. W.-B.: "Simple Interpolation."
Python/CDAT for Earth Scientists: Tips and Examples.
http://www.johnny-lin.com/cdat_tips/tips_math/interp.html
Example with no missing values (gives same output as function
arrayfns.interp):
>>> from interp import interp
>>> import numpy as N
>>> x = N.array([1., 2., 3., 4., 5.])
>>> y = N.array([3., 6., 2.,-5.,-3.])
>>> xint = N.array([3.4, 2.3])
>>> yint = interp(y, x, xint, missing=1e+20)
>>> ['%.7g' % yint[i] for i in range(len(yint))]
['-0.8', '4.8']
Example with missing values:
>>> x = N.array([1., 2., 3., 4., 5.])
>>> y = N.array([3., 1e+20, 2., -5., -3.])
>>> xint = N.array([3.4, 2.3])
>>> yint = interp(y, x, xint, missing=1e+20)
>>> ['%.7g' % yint[i] for i in range(len(yint))]
['-0.8', '1e+20']
Example with values out of range of the data:
>>> x = N.array([1., 2.1, 3., 4., 5.1])
>>> y = N.array([3., 1e+20, 2., -5., -3.])
>>> xint = N.array([3.4, -2.3, 6.])
>>> yint = interp(y, x, xint, missing=1e+20)
>>> ['%.7g' % yint[i] for i in range(len(yint))]
['-0.8', '1e+20', '1e+20']
"""
import arrayfns
import numpy.ma as MA
import numpy as N
from .where_close import where_close
#- Check inputs for possible errors:
if (N.rank(y) != 1) or (N.rank(x) != 1):
raise ValueError("interp: Input(s) not a vector")
if N.rank(xinterp) > 1:
raise ValueError("interp: xinterp not a vector or scalar")
if x[-1] <= x[0]:
raise ValueError("interp: x not monotonically increasing")
#- Establish constants and define xint, a rank 1 version of
# xinterp to be used for the rest of the function:
if N.rank(xinterp) == 0:
xint = N.reshape(xinterp, (1, ))
else:
xint = xinterp
num_xint = N.size(xint)
#- Mask as missing values of xint that are outside of the range
# of x:
yint_outrange_mask = N.logical_or( N.less(xint, x[0]) \
, N.greater(xint, x[-1]) )
#- Mask of elements with missing values in y, if there are any
# missing values in y. If xint equals a value in x, missing
# values mask for that xint is the same as the corresponding
# value in x; and mask elements in xint which fall in an interval
# (whose upper bound index is top_idx) where one of the endpoints
# is missing:
y_miss_mask = where_close(y, missing)
yint_miss_mask = N.zeros(num_xint)
if MA.maximum(y_miss_mask) == 1:
for i in range(num_xint):
if yint_outrange_mask[i] == 0:
x_eq_xint = where_close(x, xint[i])
if MA.maximum(x_eq_xint) == 1:
yint_miss_mask[i] = y_miss_mask[N.nonzero(x_eq_xint)]
else:
top_idx = N.nonzero(N.greater(x, xint[i]))[0]
yint_miss_mask[i] = y_miss_mask[top_idx] or \
y_miss_mask[top_idx-1]
#- Return interpolated values, set to missing values as
# appropriate, and making a scalar if xinterp is a scalar:
yint = arrayfns.interp(y, x, xint)
N.putmask( yint, N.logical_or(yint_miss_mask, yint_outrange_mask) \
, missing)
if N.rank(xinterp) == 0: yint = yint[0]
return yint
def autoscale(self, A):
'''
Set vmin, vmax to min, max of A.
'''
self.vmin = ma.minimum(A)
self.vmax = ma.maximum(A)
def __init__(self, ax, *args, **kwargs):
"""
Draw contour lines or filled regions, depending on
whether keyword arg 'filled' is False (default) or True.
The first argument of the initializer must be an axes
object. The remaining arguments and keyword arguments
are described in ContourSet.contour_doc.
"""
self.ax = ax
self.levels = kwargs.get('levels', None)
self.filled = kwargs.get('filled', False)
self.linewidths = kwargs.get('linewidths', None)
self.linestyles = kwargs.get('linestyles', None)
self.alpha = kwargs.get('alpha', 1.0)
self.origin = kwargs.get('origin', None)
self.extent = kwargs.get('extent', None)
cmap = kwargs.get('cmap', None)
self.colors = kwargs.get('colors', None)
norm = kwargs.get('norm', None)
self.extend = kwargs.get('extend', 'neither')
self.antialiased = kwargs.get('antialiased', True)
self.nchunk = kwargs.get('nchunk', 0)
self.locator = kwargs.get('locator', None)
if (isinstance(norm, colors.LogNorm)
or isinstance(self.locator, ticker.LogLocator)):
self.logscale = True
if norm is None:
norm = colors.LogNorm()
if self.extend is not 'neither':
raise ValueError('extend kwarg does not work yet with log scale')
else:
self.logscale = False
if self.origin is not None: assert(self.origin in
['lower', 'upper', 'image'])
if self.extent is not None: assert(len(self.extent) == 4)
if cmap is not None: assert(isinstance(cmap, colors.Colormap))
if self.colors is not None and cmap is not None:
raise ValueError('Either colors or cmap must be None')
if self.origin == 'image': self.origin = mpl.rcParams['image.origin']
if isinstance(args[0], ContourSet):
C = args[0].Cntr
if self.levels is None:
self.levels = args[0].levels
else:
x, y, z = self._contour_args(*args)
x0 = ma.minimum(x)
x1 = ma.maximum(x)
y0 = ma.minimum(y)
y1 = ma.maximum(y)
self.ax.update_datalim([(x0,y0), (x1,y1)])
self.ax.autoscale_view()
_mask = ma.getmask(z)
if _mask is ma.nomask:
_mask = None
C = _cntr.Cntr(x, y, z.filled(), _mask)
self.Cntr = C
self._process_levels()
if self.colors is not None:
cmap = colors.ListedColormap(self.colors, N=len(self.layers))
if self.filled:
self.collections = cbook.silent_list('collections.PathCollection')
else:
self.collections = cbook.silent_list('collections.LineCollection')
self.labelTexts = []
self.labelCValues = []
kw = {'cmap': cmap}
if norm is not None:
kw['norm'] = norm
cm.ScalarMappable.__init__(self, **kw) # sets self.cmap;
self._process_colors()
if self.filled:
if self.linewidths is not None:
warnings.warn('linewidths is ignored by contourf')
lowers = self._levels[:-1]
uppers = self._levels[1:]
for level, level_upper in zip(lowers, uppers):
nlist = C.trace(level, level_upper, nchunk = self.nchunk)
nseg = len(nlist)//2
segs = nlist[:nseg]
kinds = nlist[nseg:]
paths = self._make_paths(segs, kinds)
col = collections.PathCollection(paths,
antialiaseds = (self.antialiased,),
edgecolors= 'none',
alpha=self.alpha)
self.ax.add_collection(col)
self.collections.append(col)
else:
tlinewidths = self._process_linewidths()
self.tlinewidths = tlinewidths
tlinestyles = self._process_linestyles()
for level, width, lstyle in zip(self.levels, tlinewidths, tlinestyles):
nlist = C.trace(level)
nseg = len(nlist)//2
segs = nlist[:nseg]
col = collections.LineCollection(segs,
linewidths = width,
linestyle = lstyle,
alpha=self.alpha)
col.set_label('_nolegend_')
self.ax.add_collection(col, False)
self.collections.append(col)
self.changed() # set the colors
def create(self, parent=None, min=None, max=None, save_file=None,
thread_it=1, rate=None, bitrate=None, ffmpegoptions=''):
from vcs import minmax
from numpy.ma import maximum, minimum
# Cannot "Run" or "Create" an animation while already creating an
# animation
if self.run_flg == 1:
return
if self.vcs_self.canvas.creating_animation() == 1:
return
if self.vcs_self.animate_info == []:
str = "No data found!"
showerror("Error Message to User", str)
return
# Stop the (thread) execution of the X main loop (if it is running).
self.vcs_self.canvas.stopxmainloop()
# Force VCS to update its orientation, needed when the user changes the
# VCS Canvas size.
self.vcs_self.canvas.updateorientation()
# Make sure the animate information is up-to-date for creating images
if ((self.gui_popup == 1) and (self.create_flg == 0)):
self.update_animate_display_list()
# Save the min and max values for the graphics methods.
# Will need to restore values back when animation is done.
self.save_original_min_max()
# Set up the animation min and max values by changing the graphics method
# Note: cannot set the min and max values if the default graphics
# method is set.
do_min_max = 'yes'
try:
if (parent is not None) and (parent.iso_spacing == 'Log'):
do_min_max = 'no'
except:
pass
# Draw specified continental outlines if needed.
self.continents_hold_value = self.vcs_self.canvas.getcontinentstype()
self.vcs_self.canvas.setcontinentstype(self.continents_value)
if (do_min_max == 'yes'):
minv = []
maxv = []
if (min is None) or (max is None):
for i in range(len(self.vcs_self.animate_info)):
minv.append(1.0e77)
maxv.append(-1.0e77)
for i in range(len(self.vcs_self.animate_info)):
dpy, slab = self.vcs_self.animate_info[i]
mins, maxs = minmax(slab)
minv[i] = float(minimum(float(minv[i]), float(mins)))
maxv[i] = float(maximum(float(maxv[i]), float(maxs)))
if isinstance(min, list) or isinstance(max, list):
for i in range(len(self.vcs_self.animate_info)):
try:
minv.append(min[i])
except:
minv.append(min[-1])
try:
maxv.append(max[i])
except:
maxv.append(max[-1])
else:
for i in range(len(self.vcs_self.animate_info)):
minv.append(min)
maxv.append(max)
# Set the min an max for each plot in the page. If the same graphics method is used
# to display the plots, then the last min and max setting of the
# data set will be used.
for i in range(len(self.vcs_self.animate_info)):
try:
self.set_animation_min_max(minv[i], maxv[i], i)
except:
# if it is default, then you cannot set the min and max, so
# pass.
pass
if save_file is None or save_file.split('.')[-1].lower() == 'ras':
if thread_it:
thread.start_new_thread(
self.vcs_self.canvas.animate_init, (save_file,))
else:
self.vcs_self.canvas.animate_init(save_file)
else: # ffmpeg stuff
save_info = self.vcs_self.animate_info
animation_info = self.animate_info_from_python()
slabs = []
templates = []
dpys = []
for i in range(len(self.vcs_self.animate_info)):
dpy, slab = self.vcs_self.animate_info[i]
slabs.append(slab)
dpys.append(dpy)
templates.append(dpy.template)
sh = slabs[0].shape
if dpy.g_type in ['boxfill', 'isofill', 'isoline', 'meshfill',
'outfill', 'outline', 'taylordiagram', 'vector', ]:
r = len(sh) - 2
else:
r = len(sh) - 1
# now create the list of all previous indices to plot
indices = []
for i in range(r):
this = list(range(sh[i]))
tmp = []
if indices == []:
for k in this:
indices.append([k, ])
else:
for j in range(len(indices)):
for k in this:
tmp2 = copy.copy(indices[j])
tmp2.append(k)
tmp.append(tmp2)
indices = tmp
count = 1
white_square = self.vcs_self.createfillarea()
white_square.color = 240
white_square.x = [0, 1, 1, 0]
white_square.y = [0, 0, 1, 1]
new_vcs = self.vcs_self
if self.vcs_self.orientation() == 'portrait':
new_vcs.portrait()
# self.vcs_self.close()
for index in indices:
new_vcs.clear()
new_vcs.plot(white_square, bg=1)
for i in range(len(save_info)):
slab = slabs[i]
template = templates[i]
gtype = animation_info["gtype"][i].lower()
gname = animation_info["gname"][i]
gm = None # for flake8 to be happy
exec("gm = new_vcs.get%s('%s')" % (gtype, gname))
for j in index:
slab = slab[j]
new_vcs.plot(slab, gm, new_vcs.gettemplate(template), bg=1)
new_vcs.png("tmp_anim_%i" % count)
count += 1
new_vcs.ffmpeg(
save_file,
"tmp_anim_%d.png",
bitrate=bitrate,
rate=rate,
options=ffmpegoptions)
for i in range(count - 1):
os.remove("tmp_anim_%i.png" % (i + 1))
del(new_vcs)
self.create_flg = 1
def specify(self,slab,axes,specification,confined_by,aux):
''' First part: confine the slab within a Domain wide enough to do the exact in post'''
import string,copy
from numpy.ma import minimum,maximum
# myconfined is for later, we can't confine a dimension twice with an argument plus a keyword or 2 keywords
myconfined=[None]*len(axes)
self.aux=copy.copy(specification)
# First look at the arguments (i.e not keywords) and confine the dimensions
# in the order of the arguments
for i in range(len(self.args)):
if confined_by[i] is None : # Check it hasn't been confined by somebody else
myconfined[i]=1 # dim confined by argument list
confined_by[i]=self # for cdms I want to confine this dimension
self.aux[i]=specs=list(self.args[i]) # How do we want to confine this dim ?
if type(specs)==type(slice(0)):
specification[i]=specs # If it's a slicing nothing to do
else: # But if it's not...
if specs[0] is None:
tmp=axes[i].getBounds()
if tmp is None:
raise ValueError, 'Region error, axis:'+axes[i].id+' has no bounds'
specs[0]=minimum(minimum(tmp[0],tmp[-1]))
if specs[1] is None:
tmp=axes[i].getBounds()
if tmp is None:
raise ValueError, 'Region error, axis:'+axes[i].id+' has no bounds'
specs[1]=maximum(maximum(tmp[0],tmp[-1]))
if axes[i].isTime(): # Time is as always "Special"
import cdtime
tc=type(cdtime.comptime(0)) # component time type
tr=type(cdtime.reltime(0,'months since 0')) # relative time type
t=type(specs[0]) # my first spec type
if t==type(''): #if my first spec is passed as a string
specs[0]=cdtime.s2r(specs[0],axes[i].units)
elif t==tc or t==tr: #if my first spec is passed as a cdtime object
specs[0]=cdtime.torel(specs[0],axes[i].units)
else: # If not it has to be that the users knows the time values in the axis
pass
t=type(specs[1]) # my second spec type
if t==type(''): #if my second spec is passed as a string
specs[1]=cdtime.s2r(specs[1],axes[i].units)
elif t==tc or t==tr: #if my second spec is passed as a cdtime object
specs[1]=cdtime.torel(specs[1],axes[i].units)
sp=[specs[0],specs[1],'oob'] # Now retrieve the values wide enough for the exact specification[i]=sp # sets the specifications
else:
return 1
for kw in self.kargs.keys():
axis=None
for i in range(len(axes)):
if axes[i].id==kw : axis=i
if axis is None:
if kw=='time' :
for i in range(len(axes)):
if axes[i].isTime() : axis=i
elif kw=='level' :
for i in range(len(axes)):
if axes[i].isLevel() : axis=i
elif kw=='longitude' :
for i in range(len(axes)):
if axes[i].isLongitude() : axis=i
elif kw=='latitude' :
for i in range(len(axes)):
if axes[i].isLatitude() : axis=i
elif not kw in ['exact','atol','rtol']: # keyword not a recognised keyword or dimension name
raise 'Error, keyword: '+kw+' not recognized'
# At this point, if axis is None:
# we are dealing with a keyword for the selector
# so we'll skip it
if not axis is None :
if confined_by[axis] is None:
confined_by[axis]=self
myconfined[axis]=1
self.aux[axis]=specs=list(self.kargs[kw])
if type(specs)!=type(slice(0)):
if specs[0] is None:
tmp=axes[axis].getBounds()
if tmp is None:
raise ValueError, 'Region error, axis:'+axes[axis].id+' has no bounds'
specs[0]=minimum(minimum(tmp[0],tmp[-1]))
if specs[1] is None:
tmp=axes[axis].getBounds()
if tmp is None:
raise ValueError, 'Region error, axis:'+axes[axis].id+' has no bounds'
specs[1]=maximum(maximum(tmp[0],tmp[-1]))
if axes[axis].isTime():
import cdtime
tc=type(cdtime.comptime(0))
tr=type(cdtime.reltime(0,'months since 0'))
t=type(specs[0])
if t==type(''):
specs[0]=cdtime.s2r(specs[0],axes[i].units)
elif t==tc or t==tr:
specs[0]=cdtime.torel(specs[0],axes[i].units)
t=type(specs[1])
if t==type(''):
specs[1]=cdtime.s2r(specs[1],axes[i].units)
elif t==tc or t==tr:
specs[1]=cdtime.torel(specs[1],axes[i].units)
sp=[specs[0],specs[1],'oob']
specification[axis]=sp
else:
specification[axis]=specs
else:
if myconfined[axis]==1:
raise 'Error you are attempting to set the axis: '+str(axes[axis].id)+' more than once'
else:
return 1
return 0
def autoscale(self, A):
"""
Set *vmin*, *vmax* to min, max of *A*.
"""
self.vmin = ma.minimum(A)
self.vmax = ma.maximum(A)
def interp(y, x, xinterp, missing=1e+20):
"""Simple linear interpolation for ordinate with missing values.
Vectors x and y are the data describing a piecewise linear function.
Function returns the interpolated values of the ordinate function
at abscissa values in xinterp. Values of xinterp outside the range
of x are returned as missing. Any elements in the output that uses
missing values in y for the interpolation are set to missing.
Positional Input Arguments:
* y: Ordinate values of data. Rank 1 numeric vector. Required.
Can have missing values. Floating or integer type.
* x: Abscissa values of data. Rank 1 numeric vector. Required.
Can have no missing values. Must be monotonically ascending.
Floating or integer type.
* xinterp: Abscissa values to calculate interpolated ordinate
values at. Rank 1 numeric vector or numeric scalar. Required.
Can have no missing values. Can be in any order. Floating or
integer type.
Keyword Input Arguments:
* missing: If input has missing values, this is the missing value
value. Scalar. Floating or integer type. Default is 1e+20.
Output Result:
* Interpolated ordinate values at xinterp. Rank 1 numeric vector
of same length as xinterp (if xinterp is a numeric scalar,
output is also a numeric scalar). Missing values are set to the
value of argument missing. Type is Float, even if argument
missing and inputs are all integer.
References:
* Lin, J. W.-B.: "Simple Interpolation."
Python/CDAT for Earth Scientists: Tips and Examples.
http://www.johnny-lin.com/cdat_tips/tips_math/interp.html
Example with no missing values (gives same output as function
arrayfns.interp):
>>> from interp import interp
>>> import numpy as N
>>> x = N.array([1., 2., 3., 4., 5.])
>>> y = N.array([3., 6., 2.,-5.,-3.])
>>> xint = N.array([3.4, 2.3])
>>> yint = interp(y, x, xint, missing=1e+20)
>>> ['%.7g' % yint[i] for i in range(len(yint))]
['-0.8', '4.8']
Example with missing values:
>>> x = N.array([1., 2., 3., 4., 5.])
>>> y = N.array([3., 1e+20, 2., -5., -3.])
>>> xint = N.array([3.4, 2.3])
>>> yint = interp(y, x, xint, missing=1e+20)
>>> ['%.7g' % yint[i] for i in range(len(yint))]
['-0.8', '1e+20']
Example with values out of range of the data:
>>> x = N.array([1., 2.1, 3., 4., 5.1])
>>> y = N.array([3., 1e+20, 2., -5., -3.])
>>> xint = N.array([3.4, -2.3, 6.])
>>> yint = interp(y, x, xint, missing=1e+20)
>>> ['%.7g' % yint[i] for i in range(len(yint))]
['-0.8', '1e+20', '1e+20']
"""
import arrayfns
import numpy.ma as MA
import numpy as N
from where_close import where_close
#- Check inputs for possible errors:
if (N.rank(y) != 1) or (N.rank(x) != 1):
raise ValueError, "interp: Input(s) not a vector"
if N.rank(xinterp) > 1:
raise ValueError, "interp: xinterp not a vector or scalar"
if x[-1] <= x[0]:
raise ValueError, "interp: x not monotonically increasing"
#- Establish constants and define xint, a rank 1 version of
# xinterp to be used for the rest of the function:
if N.rank(xinterp) == 0:
xint = N.reshape(xinterp, (1,))
else:
xint = xinterp
num_xint = N.size(xint)
#- Mask as missing values of xint that are outside of the range
# of x:
yint_outrange_mask = N.logical_or( N.less(xint, x[0]) \
, N.greater(xint, x[-1]) )
#- Mask of elements with missing values in y, if there are any
# missing values in y. If xint equals a value in x, missing
# values mask for that xint is the same as the corresponding
# value in x; and mask elements in xint which fall in an interval
# (whose upper bound index is top_idx) where one of the endpoints
# is missing:
y_miss_mask = where_close(y, missing)
yint_miss_mask = N.zeros(num_xint)
if MA.maximum(y_miss_mask) == 1:
for i in xrange(num_xint):
if yint_outrange_mask[i] == 0:
x_eq_xint = where_close(x, xint[i])
if MA.maximum(x_eq_xint) == 1:
yint_miss_mask[i] = y_miss_mask[N.nonzero(x_eq_xint)]
else:
top_idx = N.nonzero(N.greater(x, xint[i]))[0]
yint_miss_mask[i] = y_miss_mask[top_idx] or \
y_miss_mask[top_idx-1]
#- Return interpolated values, set to missing values as
# appropriate, and making a scalar if xinterp is a scalar:
yint = arrayfns.interp(y, x, xint)
N.putmask( yint, N.logical_or(yint_miss_mask, yint_outrange_mask) \
, missing)
if N.rank(xinterp) == 0: yint = yint[0]
return yint
def create( self, parent=None, min=None, max=None, save_file=None, thread_it = 1, rate=None, bitrate=None, ffmpegoptions='' ):
from vcs import minmax
from numpy.ma import maximum,minimum
##from tkMessageBox import showerror
# Cannot "Run" or "Create" an animation while already creating an animation
if self.run_flg == 1: return
if self.vcs_self.canvas.creating_animation() == 1: return
if self.vcs_self.animate_info == []:
str = "No data found!"
showerror( "Error Message to User", str )
return
finish_queued_X_server_requests( self.vcs_self )
self.vcs_self.canvas.BLOCK_X_SERVER()
# Stop the (thread) execution of the X main loop (if it is running).
self.vcs_self.canvas.stopxmainloop( )
# Force VCS to update its orientation, needed when the user changes the
# VCS Canvas size.
self.vcs_self.canvas.updateorientation()
# Make sure the animate information is up-to-date for creating images
if ((self.gui_popup == 1) and (self.create_flg == 0)):
self.update_animate_display_list( )
# Save the min and max values for the graphics methods.
# Will need to restore values back when animation is done.
self.save_original_min_max()
# Set up the animation min and max values by changing the graphics method
# Note: cannot set the min and max values if the default graphics method is set.
do_min_max = 'yes'
try:
if (parent is not None) and (parent.iso_spacing == 'Log'):
do_min_max = 'no'
except:
pass
# Draw specified continental outlines if needed.
self.continents_hold_value = self.vcs_self.canvas.getcontinentstype( )
self.vcs_self.canvas.setcontinentstype( self.continents_value )
if ( do_min_max == 'yes' ):
minv = []
maxv=[]
if (min is None) or (max is None):
for i in range(len(self.vcs_self.animate_info)):
minv.append( 1.0e77 )
maxv.append( -1.0e77 )
for i in range(len(self.vcs_self.animate_info)):
dpy, slab = self.vcs_self.animate_info[i]
mins, maxs = minmax(slab)
minv[i] = float(minimum(float(minv[i]), float(mins)))
maxv[i] = float(maximum(float(maxv[i]), float(maxs)))
if isinstance(min,list) or isinstance(max,list):
for i in range(len(self.vcs_self.animate_info)):
try:
minv.append( min[i] )
except:
minv.append( min[-1] )
try:
maxv.append( max[i] )
except:
maxv.append( max[-1] )
else:
for i in range(len(self.vcs_self.animate_info)):
minv.append( min )
maxv.append( max )
# Set the min an max for each plot in the page. If the same graphics method is used
# to display the plots, then the last min and max setting of the data set will be used.
for i in range(len(self.vcs_self.animate_info)):
try:
self.set_animation_min_max( minv[i], maxv[i], i )
except Exception,err:
pass # if it is default, then you cannot set the min and max, so pass.
def _plot_with_flag(self, scan, showflag=False):
# total number of panles to plot as a whole
nptot = scan.nrow()
# remaining panels to plot
n = nptot - self._ipanel - 1
ganged = False
maxpanel = 25
if n > 1:
ganged = rcParams['plotter.ganged']
if self._rows and self._cols:
n = min(n,self._rows*self._cols)
self._plotter.set_panels(rows=self._rows,cols=self._cols,
nplots=n,margin=self._margins,ganged=ganged)
else:
n = min(n,maxpanel)
self._plotter.set_panels(rows=n,cols=0,nplots=n,margin=self._margins,ganged=ganged)
else:
self._plotter.set_panels(margin=self._margins)
#r = 0
r = self._startrow
# total row number of scantable
nr = scan.nrow()
panelcount = 0
allylim = []
allxlim = []
while r < nr:
# always plot to new panel
self._plotter.subplot(panelcount)
self._plotter.palette(0)
# title and axes labels
xlab = self._abcissa and self._abcissa[panelcount] \
or scan._getabcissalabel()
if self._offset and not self._abcissa:
xlab += " (relative)"
ylab = self._ordinate and self._ordinate[panelcount] \
or scan._get_ordinate_label()
self._plotter.set_axes('xlabel', xlab)
self._plotter.set_axes('ylabel', ylab)
lbl = self._get_label(scan, r, mode='title', userlabel=self._title)
if type(lbl) in (list, tuple):
if 0 <= panelcount < len(lbl):
lbl = lbl[panelcount]
else:
# get default label
lbl = self._get_label(scan, r, 'title')
self._plotter.set_axes('title',lbl)
panelcount += 1
# Now get data to plot
y = scan._getspectrum(r)
# Check for FLAGROW column
mr = scan._getflagrow(r)
from numpy import ma, array
if mr:
ys = ma.masked_array(y,mask=mr)
if showflag:
yf = ma.masked_array(y, mask=(not mr))
else:
m = scan._getmask(r)
from numpy import logical_not, logical_and
if self._maskselection and len(self._usermask) == len(m):
if d[self._stacking](r) in self._maskselection[self._stacking]:
m = logical_and(m, self._usermask)
ys = ma.masked_array(y,mask=logical_not(array(m,copy=False)))
if showflag:
yf = ma.masked_array(y,mask=m)
x = array(scan._getabcissa(r))
if self._offset:
x += self._offset
#llbl = self._get_label(scan, r, mode='legend', userlabel=self._lmap)
#if type(llbl) in (list, tuple):
# llbl = llbl[0]
#self._plotter.set_line(label=llbl)
self._plotter.set_line(label="data")
#plotit = self._plotter.plot
#if self._hist: plotit = self._plotter.hist
self._plotter.plot(x,ys)
if showflag:
self._plotter.set_line(label="flagged")
self._plotter.plot(x,yf)
ylim = self._minmaxy or [min(y),max(y)]
xlim= self._minmaxx or [min(x),max(x)]
elif mr or ys.mask.all():
ylim = self._minmaxy or []
xlim = self._minmaxx or []
else:
ylim = self._minmaxy or [ma.minimum(ys),ma.maximum(ys)]
xlim= self._minmaxx or [min(x),max(x)]
allylim += ylim
allxlim += xlim
if (panelcount == n) or (r == nr-1):
break
r+=1 # next row
# Set x- and y- limts of subplots
if ganged:
xlim = None
ylim = None
if len(allylim) > 0:
allylim.sort()
ylim = allylim[0],allylim[-1]
if len(allxlim) > 0:
allxlim.sort()
xlim = allxlim[0],allxlim[-1]
self._plotter.set_limits(xlim=xlim,ylim=ylim)
# save the current counter for multi-page plotting
self._startrow = r+1
self._ipanel += panelcount
if self.casabar_exists():
if self._ipanel >= nptot-1:
self._plotter.figmgr.casabar.disable_next()
else:
self._plotter.figmgr.casabar.enable_next()
if self._ipanel + 1 - panelcount > 0:
self._plotter.figmgr.casabar.enable_prev()
else:
self._plotter.figmgr.casabar.disable_prev()
def specify(self, slab, axes, specification, confined_by, aux):
''' First part: confine the slab within a Domain wide enough to do the exact in post'''
import string, copy
from numpy.ma import minimum, maximum
# myconfined is for later, we can't confine a dimension twice with an argument plus a keyword or 2 keywords
myconfined = [None] * len(axes)
self.aux = copy.copy(specification)
# First look at the arguments (i.e not keywords) and confine the dimensions
# in the order of the arguments
for i in range(len(self.args)):
if confined_by[
i] is None: # Check it hasn't been confined by somebody else
myconfined[i] = 1 # dim confined by argument list
confined_by[
i] = self # for cdms I want to confine this dimension
self.aux[i] = specs = list(
self.args[i]) # How do we want to confine this dim ?
if not (isinstance(specs, list) or isinstance(specs, tuple)):
raise Exception, "Error in Selector, you must specify a list or a tuple, you passed:" + str(
specs)
elif type(specs[0]) == type(
cdtime.comptime(1999)) or type(specs[0]) == type(
cdtime.reltime(0, 'days since 1999')) or type(
specs[0]) == type(''):
list2 = []
for l in specs:
if type(l) != type(''):
list2.append(l.torel('days since 1900').value)
else:
list2.append(
cdtime.s2r(l, 'days since 1900').value)
min = minimum(list2)
max = maximum(list2)
specification[i] = cdtime.reltime(
min, 'days since 1900'), cdtime.reltime(
max, 'days since 1900')
else: # But if it's not...
specification[i] = minimum(specs), maximum(
specs) # sets the specifications
else:
return 1
for kw in self.kargs.keys():
axis = None
for i in range(len(axes)):
if axes[i].id == kw: axis = i
if axis is None:
if kw == 'time':
for i in range(len(axes)):
if axes[i].isTime(): axis = i
elif kw == 'level':
for i in range(len(axes)):
if axes[i].isLevel(): axis = i
elif kw == 'longitude':
for i in range(len(axes)):
if axes[i].isLongitude(): axis = i
elif kw == 'latitude':
for i in range(len(axes)):
if axes[i].isLatitude(): axis = i
elif not kw in [
'match'
]: # keyword not a recognised keyword or dimension name
raise Exception, 'Error, keyword: ' + kw + ' not recognized'
# At this point, if axis is None:
# we are dealing with a keyword for the selector
# so we'll skip it
if not axis is None:
if confined_by[axis] is None:
confined_by[axis] = self
myconfined[axis] = 1
self.aux[axis] = specs = list(self.kargs[kw])
if type(specs[0]) == type(cdtime.comptime(1999)) or type(
specs[0]) == type(
cdtime.reltime(0, 'days since 1999')) or type(
specs[0]) == type(''):
list2 = []
for l in specs:
if type(l) != type(''):
list2.append(l.torel('days since 1900').value)
else:
list2.append(
cdtime.s2r(l, 'days since 1900').value)
min = minimum(list2)
max = maximum(list2)
specification[axis] = cdtime.reltime(
min, 'days since 1900'), cdtime.reltime(
max, 'days since 1900')
else: # But if it's not...
specification[axis] = minimum(specs), maximum(specs)
else:
if myconfined[axis] == 1:
raise 'Error you are attempting to set the axis: ' + str(
axes[axis].id) + ' more than once'
else:
return 1
return 0
def run_model(self):
"""
Run the model
:return:
"""
shape = self._shape
tew = self._tew
taw = self._taw
start_time = datetime.now()
pkcb = zeros(shape)
swe = zeros(shape)
tot_snow = zeros(shape)
tot_mass = zeros(shape)
cum_mass = zeros(shape)
ref_et = zeros(shape)
a_min = ones(shape) * 0.45
a_max = ones(shape) * 0.90
a = a_max
pA = a_min
days = list(
rrule.rrule(rrule.DAILY, dtstart=self._start, until=self._end))
nsteps = len(days)
plt_day = zeros(nsteps)
plt_rain = zeros(nsteps)
plt_eta = zeros(nsteps)
plt_snow_fall = zeros(nsteps)
plt_ro = zeros(nsteps)
plt_dr = zeros(nsteps)
plt_de = zeros(nsteps)
plt_drew = zeros(nsteps)
plt_temp = zeros(nsteps)
plt_dp_r = zeros(nsteps)
plt_ks = zeros(nsteps)
plt_pdr = zeros(nsteps)
plt_etrs = zeros(nsteps)
plt_kcb = zeros(nsteps)
plt_ppt = zeros(nsteps)
plt_ke = zeros(nsteps)
plt_kr = zeros(nsteps)
plt_mlt = zeros(nsteps)
plt_swe = zeros(nsteps)
plt_tempm = zeros(nsteps)
plt_fs1 = zeros(nsteps)
plt_mass = zeros(nsteps)
p_mo_et = zeros(shape)
p_mo_precip = zeros(shape)
p_mo_ro = zeros(shape)
p_mo_deps = self._dr + self._de + self._drew
p_mo_infil = zeros(shape)
p_mo_etrs = zeros(shape)
p_yr_et = zeros(shape)
p_yr_precip = zeros(shape)
p_yr_ro = zeros(shape)
p_yr_deps = self._dr + self._de + self._drew
p_yr_infil = zeros(shape)
p_yr_etrs = zeros(shape)
start_month = self._start_month
end_month = self._end_month
for i, dday in enumerate(days):
if i > 0:
pkcb = kcb
doy = dday.timetuple().tm_yday
year = dday.year
month = dday.month
day = dday.day
msg = 'Time : {} day {}_{}'.format(datetime.now() - start_time,
doy, year)
logging.debug(msg)
# -------------- kcb -------------------
if year == 2000:
ndvi = self.calculate_ndvi_2000(doy)
elif year == 2001:
ndvi = self.calculate_ndvi_2001(doy)
else:
ndvi = self.calculate_ndvi(year, doy)
kcb = ndvi * 1.25
kcb = maximum(kcb, self._min_val)
kcb = where(isnan(kcb), pkcb, kcb)
# -------------- PRISM -------------------
ppt, ppt_tom, max_temp, min_temp, mid_temp = self.load_prism(dday)
# -------------- PM -------------------
# PM data to etrs
name = os.path.join('PM{}'.format(year),
'PM_NM_{}_{:03n}'.format(year, doy))
etrs = tif_to_array(self._pm_data_root, name)
etrs = maximum(etrs, self._min_val)
name = os.path.join('PM{}'.format(year),
'RLIN_NM_{}_{:03n}'.format(year, doy))
rlin = tif_to_array(self._pm_data_root, name)
rlin = maximum(rlin, zeros(shape))
name = os.path.join('rad{}'.format(year),
'RTOT_{}_{:03n}'.format(year, doy))
rg = tif_to_array(self._pm_data_root, name)
rg = maximum(rg, zeros(shape))
if i == 0:
# Total evaporable water is depth of water in the evaporable
# soil layer, i.e., the water available to both stage 1 and 2 evaporation
rew = minimum((2 + (tew / 3.)), 0.8 * tew)
# del tew1, tew2
# you should have all these from previous model runs
pDr = self._dr
pDe = self._de
pDrew = self._drew
dr = self._dr
de = self._de
drew = self._drew
nom = 2 if start_month <= doy <= end_month else 6
ksat = self._ksat * nom / 24.
kc_max_1 = kcb + 0.0001
min_val = ones(shape) * 0.0001
kc_max = maximum(min_val, kc_max_1)
self._nlcd_plt_hgt = self._nlcd_plt_hgt * 0.5 + 1
numr = maximum(kcb - self._kc_min, min_val * 10)
denom = maximum((kc_max - self._kc_min), min_val * 10)
fcov_ref = (numr / denom)**self._nlcd_plt_hgt
fcov_min = minimum(fcov_ref, ones(shape))
fcov = maximum(fcov_min, min_val * 10)
few = maximum(1 - fcov, 0.01) # exposed ground
pKr = kr
kr = minimum(((tew - de) / (tew - rew)), ones(shape))
kr = where(isnan(kr), pKr, kr)
pKs = ks
ks_ref = where(((taw - pDr) / (0.6 * taw)) < zeros(shape),
ones(shape) * 0.001, ((taw - pDr) / (0.6 * taw)))
ks_ref = where(isnan(ks), pKs, ks_ref)
ks = minimum(ks_ref, ones(shape))
# Ke evaporation reduction coefficient; stage 1 evaporation
fsa = where(isnan((rew - drew) / (KE_MAX * etrs)), zeros(shape),
(rew - drew) / (KE_MAX * etrs))
fsb = minimum(fsa, ones(shape))
fs1 = maximum(fsb, zeros(shape))
ke = where(
drew < rew,
minimum((fs1 + (1 - fs1) * kr) * (kc_max - ks * kcb),
few * kc_max), zeros(shape))
transp = (ks * kcb) * etrs
et_init = (ks * kcb + ke) * etrs
eta = maximum(et_init, zeros(shape))
evap_init = ke * etrs
evap_min = maximum(evap_init, zeros(shape))
evap = minimum(evap_min, kc_max)
# Load temp, find swe, melt, and precipitation, load Ksat
# Use SNOTEL data for precip and temps:
# df_snow : (stel_date, stel_snow, stel_precip, stel_tobs, stel_tmax, stel_tmin, stel_tavg, stel_snwd)
snow_fall = where(mid_temp <= 0.0, ppt, zeros(shape))
rain = where(mid_temp >= 0.0, ppt, zeros(shape))
pA = a
a = where(snow_fall > 3.0, ones(shape) * a_max, a)
a = where(snow_fall <= 3.0, a_min + (pA - a_min) * exp(-0.12), a)
a = where(snow_fall == 0.0, a_min + (pA - a_min) * exp(-0.05), a)
a = where(a < a_min, a_min, a)
swe += snow_fall
mlt_init = maximum(((1 - a) * rg * 0.2) + (mid_temp - 1.8) * 11.0,
zeros(shape)) # use calibrate coefficients
mlt = minimum(swe, mlt_init)
swe -= mlt
# Find depletions
pDr = dr
pDe = de
pDrew = drew
watr = rain + mlt
deps = dr + de + drew
ro = zeros(shape)
ro = where(watr > ksat + deps, watr - ksat - deps, ro)
ro = maximum(ro, zeros(shape))
dp_r = zeros(shape)
id1 = where(watr > deps, ones(shape), zeros(shape))
id2 = where(ksat > watr - deps, ones(shape), zeros(shape))
dp_r = where(id1 + id2 > 1.99, maximum(watr - deps, zeros(shape)),
dp_r)
dp_r = where(watr > ksat + deps, ksat, dp_r)
dp_r = maximum(dp_r, zeros(shape))
drew_1 = minimum((pDrew + ro + (evap - (rain + mlt))), rew)
drew = maximum(drew_1, zeros(shape))
diff = maximum(pDrew - drew, zeros(shape))
de_1 = minimum((pDe + (evap - (rain + mlt - diff))), tew)
de = maximum(de_1, zeros(shape))
diff = maximum(((pDrew - drew) + (pDe - de)), zeros(shape))
dr_1 = minimum((pDr + ((transp + dp_r) - (rain + mlt - diff))),
taw)
dr = maximum(dr_1, zeros(shape))
# Create cumulative rasters to show net over entire run
infil += dp_r
infil = maximum(infil, zeros(shape))
prev_et = et
ref_et += etrs
et = et + evap + transp
et_ind = et / ref_et
et = where(isnan(et) == True, prev_et, et)
et = where(et > ref_et, ref_et / 2., et)
et = maximum(et, ones(shape) * 0.001)
precip = precip + rain + snow_fall
precip = maximum(precip, zeros(shape))
runoff += ro
runoff = maximum(runoff, zeros(shape))
snow_ras = swe + snow_fall - mlt
snow_ras = maximum(snow_ras, zeros(shape))
tot_snow += snow_fall
mo_date = calendar.monthrange(year, month)
if day == mo_date[1]:
infil_mo = infil - p_mo_infil
infil_mo = maximum(infil_mo, zeros(shape))
ref_et_mo = etrs - p_mo_etrs
et_mo = et - p_mo_et
et_mo = where(isnan(et_mo) == True, p_mo_et, et_mo)
et_mo = where(et_mo > ref_et, ref_et / 2., et_mo)
et_mo = maximum(et_mo, ones(shape) * 0.001)
precip_mo = precip - p_mo_precip
precip_mo = maximum(precip_mo, zeros(shape))
runoff_mo = ro - p_mo_ro
runoff_mo = maximum(runoff_mo, zeros(shape))
snow_ras_mo = swe
snow_ras_mo = maximum(snow_ras_mo, zeros(shape))
deps_mo = drew + de + dr
delta_s_mo = p_mo_deps - deps_mo
outputs = (('infil', infil_mo), ('et', et_mo), ('precip',
precip_mo),
('runoff', runoff_mo), ('snow_ras', snow_ras_mo),
('delta_s_mo', delta_s_mo), ('deps_mo', deps_mo))
self.save_month_step(outputs, month, year)
p_mo_et = et
p_mo_precip = precip
p_mo_ro = ro
p_mo_deps = deps_mo
p_mo_infil = infil
p_mo_etrs = etrs
if day == 31 and month == 12:
infil_yr = infil - p_yr_infil
infil_yr = maximum(infil_yr, zeros(shape))
ref_et_yr = etrs - p_yr_etrs
et_yr = et - p_yr_et
et_yr = where(isnan(et_yr) == True, p_yr_et, et_yr)
et_yr = where(et_yr > ref_et, ref_et / 2., et_yr)
et_yr = maximum(et_yr, ones(shape) * 0.001)
precip_yr = precip - p_yr_precip
precip_yr = maximum(precip_yr, zeros(shape))
runoff_yr = ro - p_yr_ro
runoff_yr = maximum(runoff_yr, zeros(shape))
snow_ras_yr = swe
snow_ras_yr = maximum(snow_ras_yr, zeros(shape))
deps_yr = drew + de + dr
delta_s_yr = p_yr_deps - deps_yr
outputs = (('infil', infil_yr), ('et', et_yr), ('precip',
precip_yr),
('runoff', runoff_yr), ('snow_ras', snow_ras_yr),
('delta_s_yr', delta_s_yr), ('deps_yr', deps_yr))
p_yr_et = et
p_yr_precip = precip
p_yr_ro = ro
p_yr_deps = deps_yr # this was originally p_mo_deps = deps_yr im assuming this is a typo
p_yr_infil = infil
p_yr_etrs = etrs
self.save_year_step(outputs, month, year)
# Check MASS BALANCE for the love of WATER!!!
mass = rain + mlt - (ro + transp + evap + dp_r +
((pDr - dr) + (pDe - de) + (pDrew - drew)))
tot_mass += abs(mass)
cum_mass += mass
plt_day[i] = dday
plt_rain[i] = rain[S, E]
plt_eta[i] = eta[S, E]
plt_snow_fall[i] = snow_fall[S, E]
plt_ro[i] = ro[S, E]
plt_dr[i] = dr[S, E]
plt_de[i] = de[S, E]
plt_drew[i] = drew[S, E]
plt_temp[i] = mid_temp[S, E]
plt_dp_r[i] = dp_r[S, E]
plt_ks[i] = ks[S, E]
plt_pdr[i] = pDr[S, E]
plt_etrs[i] = etrs[S, E]
plt_kcb[i] = kcb[S, E]
plt_ppt[i] = ppt[S, E]
plt_ke[i] = ke[S, E]
plt_kr[i] = kr[S, E]
plt_mlt[i] = mlt[S, E]
plt_swe[i] = swe[S, E]
plt_tempm[i] = max_temp[S, E]
plt_fs1[i] = fs1[S, E]
plt_mass[i] = mass[S, E]
def __init__(self, ax, *args, **kwargs):
"""
Draw contour lines or filled regions, depending on
whether keyword arg 'filled' is False (default) or True.
The first argument of the initializer must be an axes
object. The remaining arguments and keyword arguments
are described in ContourSet.contour_doc.
"""
self.ax = ax
self.levels = kwargs.get('levels', None)
self.filled = kwargs.get('filled', False)
self.linewidths = kwargs.get('linewidths', None)
self.linestyles = kwargs.get('linestyles', None)
self.alpha = kwargs.get('alpha', 1.0)
self.origin = kwargs.get('origin', None)
self.extent = kwargs.get('extent', None)
cmap = kwargs.get('cmap', None)
self.colors = kwargs.get('colors', None)
norm = kwargs.get('norm', None)
self.extend = kwargs.get('extend', 'neither')
self.antialiased = kwargs.get('antialiased', True)
self.nchunk = kwargs.get('nchunk', 0)
self.locator = kwargs.get('locator', None)
if (isinstance(norm, colors.LogNorm)
or isinstance(self.locator, ticker.LogLocator)):
self.logscale = True
if norm is None:
norm = colors.LogNorm()
if self.extend is not 'neither':
raise ValueError(
'extend kwarg does not work yet with log scale')
else:
self.logscale = False
if self.origin is not None:
assert (self.origin in ['lower', 'upper', 'image'])
if self.extent is not None: assert (len(self.extent) == 4)
if cmap is not None: assert (isinstance(cmap, colors.Colormap))
if self.colors is not None and cmap is not None:
raise ValueError('Either colors or cmap must be None')
if self.origin == 'image': self.origin = mpl.rcParams['image.origin']
if isinstance(args[0], ContourSet):
C = args[0].Cntr
if self.levels is None:
self.levels = args[0].levels
else:
x, y, z = self._contour_args(*args)
x0 = ma.minimum(x)
x1 = ma.maximum(x)
y0 = ma.minimum(y)
y1 = ma.maximum(y)
self.ax.update_datalim([(x0, y0), (x1, y1)])
self.ax.autoscale_view()
_mask = ma.getmask(z)
if _mask is ma.nomask:
_mask = None
C = _cntr.Cntr(x, y, z.filled(), _mask)
self.Cntr = C
self._process_levels()
if self.colors is not None:
cmap = colors.ListedColormap(self.colors, N=len(self.layers))
if self.filled:
self.collections = cbook.silent_list('collections.PathCollection')
else:
self.collections = cbook.silent_list('collections.LineCollection')
# label lists must be initialized here
self.labelTexts = []
self.labelCValues = []
kw = {'cmap': cmap}
if norm is not None:
kw['norm'] = norm
cm.ScalarMappable.__init__(self, **kw) # sets self.cmap;
self._process_colors()
if self.filled:
if self.linewidths is not None:
warnings.warn('linewidths is ignored by contourf')
lowers = self._levels[:-1]
uppers = self._levels[1:]
for level, level_upper in zip(lowers, uppers):
nlist = C.trace(level, level_upper, nchunk=self.nchunk)
nseg = len(nlist) // 2
segs = nlist[:nseg]
kinds = nlist[nseg:]
paths = self._make_paths(segs, kinds)
col = collections.PathCollection(
paths,
antialiaseds=(self.antialiased, ),
edgecolors='none',
alpha=self.alpha)
self.ax.add_collection(col)
self.collections.append(col)
else:
tlinewidths = self._process_linewidths()
self.tlinewidths = tlinewidths
tlinestyles = self._process_linestyles()
for level, width, lstyle in zip(self.levels, tlinewidths,
tlinestyles):
nlist = C.trace(level)
nseg = len(nlist) // 2
segs = nlist[:nseg]
#kinds = nlist[nseg:]
col = collections.LineCollection(segs,
linewidths=width,
linestyle=lstyle,
alpha=self.alpha)
col.set_label('_nolegend_')
self.ax.add_collection(col, False)
self.collections.append(col)
self.changed() # set the colors
def autoscale(self, A):
'''
Set *vmin*, *vmax* to min, max of *A*.
'''
self.vmin = ma.minimum(A)
self.vmax = ma.maximum(A)
def run_model(self):
"""
Run the model
:return:
"""
shape = self._shape
tew = self._tew
taw = self._taw
start_time = datetime.now()
pkcb = zeros(shape)
swe = zeros(shape)
tot_snow = zeros(shape)
tot_mass = zeros(shape)
cum_mass = zeros(shape)
ref_et = zeros(shape)
a_min = ones(shape) * 0.45
a_max = ones(shape) * 0.90
a = a_max
pA = a_min
days = list(rrule.rrule(rrule.DAILY, dtstart=self._start, until=self._end))
nsteps = len(days)
plt_day = zeros(nsteps)
plt_rain = zeros(nsteps)
plt_eta = zeros(nsteps)
plt_snow_fall = zeros(nsteps)
plt_ro = zeros(nsteps)
plt_dr = zeros(nsteps)
plt_de = zeros(nsteps)
plt_drew = zeros(nsteps)
plt_temp = zeros(nsteps)
plt_dp_r = zeros(nsteps)
plt_ks = zeros(nsteps)
plt_pdr = zeros(nsteps)
plt_etrs = zeros(nsteps)
plt_kcb = zeros(nsteps)
plt_ppt = zeros(nsteps)
plt_ke = zeros(nsteps)
plt_kr = zeros(nsteps)
plt_mlt = zeros(nsteps)
plt_swe = zeros(nsteps)
plt_tempm = zeros(nsteps)
plt_fs1 = zeros(nsteps)
plt_mass = zeros(nsteps)
p_mo_et = zeros(shape)
p_mo_precip = zeros(shape)
p_mo_ro = zeros(shape)
p_mo_deps = self._dr + self._de + self._drew
p_mo_infil = zeros(shape)
p_mo_etrs = zeros(shape)
p_yr_et = zeros(shape)
p_yr_precip = zeros(shape)
p_yr_ro = zeros(shape)
p_yr_deps = self._dr + self._de + self._drew
p_yr_infil = zeros(shape)
p_yr_etrs = zeros(shape)
start_month = self._start_month
end_month = self._end_month
for i, dday in enumerate(days):
if i > 0:
pkcb = kcb
doy = dday.timetuple().tm_yday
year = dday.year
month = dday.month
day = dday.day
msg = 'Time : {} day {}_{}'.format(datetime.now() - start_time, doy, year)
logging.debug(msg)
# -------------- kcb -------------------
if year == 2000:
ndvi = self.calculate_ndvi_2000(doy)
elif year == 2001:
ndvi = self.calculate_ndvi_2001(doy)
else:
ndvi = self.calculate_ndvi(year, doy)
kcb = ndvi * 1.25
kcb = maximum(kcb, self._min_val)
kcb = where(isnan(kcb), pkcb, kcb)
# -------------- PRISM -------------------
ppt, ppt_tom, max_temp, min_temp, mid_temp = self.load_prism(dday)
# -------------- PM -------------------
# PM data to etrs
name = os.path.join('PM{}'.format(year),
'PM_NM_{}_{:03n}'.format(year, doy))
etrs = tif_to_array(self._pm_data_root, name)
etrs = maximum(etrs, self._min_val)
name = os.path.join('PM{}'.format(year),
'RLIN_NM_{}_{:03n}'.format(year, doy))
rlin = tif_to_array(self._pm_data_root, name)
rlin = maximum(rlin, zeros(shape))
name = os.path.join('rad{}'.format(year),
'RTOT_{}_{:03n}'.format(year, doy))
rg = tif_to_array(self._pm_data_root, name)
rg = maximum(rg, zeros(shape))
if i == 0:
# Total evaporable water is depth of water in the evaporable
# soil layer, i.e., the water available to both stage 1 and 2 evaporation
rew = minimum((2 + (tew / 3.)), 0.8 * tew)
# del tew1, tew2
# you should have all these from previous model runs
pDr = self._dr
pDe = self._de
pDrew = self._drew
dr = self._dr
de = self._de
drew = self._drew
nom = 2 if start_month <= doy <= end_month else 6
ksat = self._ksat * nom / 24.
kc_max_1 = kcb + 0.0001
min_val = ones(shape) * 0.0001
kc_max = maximum(min_val, kc_max_1)
self._nlcd_plt_hgt = self._nlcd_plt_hgt * 0.5 + 1
numr = maximum(kcb - self._kc_min, min_val * 10)
denom = maximum((kc_max - self._kc_min), min_val * 10)
fcov_ref = (numr / denom) ** self._nlcd_plt_hgt
fcov_min = minimum(fcov_ref, ones(shape))
fcov = maximum(fcov_min, min_val * 10)
few = maximum(1 - fcov, 0.01) # exposed ground
pKr = kr
kr = minimum(((tew - de) / (tew - rew)), ones(shape))
kr = where(isnan(kr), pKr, kr)
pKs = ks
ks_ref = where(((taw - pDr) / (0.6 * taw)) < zeros(shape), ones(shape) * 0.001,
((taw - pDr) / (0.6 * taw)))
ks_ref = where(isnan(ks), pKs, ks_ref)
ks = minimum(ks_ref, ones(shape))
# Ke evaporation reduction coefficient; stage 1 evaporation
fsa = where(isnan((rew - drew) / (KE_MAX * etrs)), zeros(shape),
(rew - drew) / (KE_MAX * etrs))
fsb = minimum(fsa, ones(shape))
fs1 = maximum(fsb, zeros(shape))
ke = where(drew < rew, minimum((fs1 + (1 - fs1) * kr) * (kc_max - ks * kcb), few * kc_max),
zeros(shape))
transp = (ks * kcb) * etrs
et_init = (ks * kcb + ke) * etrs
eta = maximum(et_init, zeros(shape))
evap_init = ke * etrs
evap_min = maximum(evap_init, zeros(shape))
evap = minimum(evap_min, kc_max)
# Load temp, find swe, melt, and precipitation, load Ksat
# Use SNOTEL data for precip and temps:
# df_snow : (stel_date, stel_snow, stel_precip, stel_tobs, stel_tmax, stel_tmin, stel_tavg, stel_snwd)
snow_fall = where(mid_temp <= 0.0, ppt, zeros(shape))
rain = where(mid_temp >= 0.0, ppt, zeros(shape))
pA = a
a = where(snow_fall > 3.0, ones(shape) * a_max, a)
a = where(snow_fall <= 3.0, a_min + (pA - a_min) * exp(-0.12), a)
a = where(snow_fall == 0.0, a_min + (pA - a_min) * exp(-0.05), a)
a = where(a < a_min, a_min, a)
swe += snow_fall
mlt_init = maximum(((1 - a) * rg * 0.2) + (mid_temp - 1.8) * 11.0,
zeros(shape)) # use calibrate coefficients
mlt = minimum(swe, mlt_init)
swe -= mlt
# Find depletions
pDr = dr
pDe = de
pDrew = drew
watr = rain + mlt
deps = dr + de + drew
ro = zeros(shape)
ro = where(watr > ksat + deps, watr - ksat - deps, ro)
ro = maximum(ro, zeros(shape))
dp_r = zeros(shape)
id1 = where(watr > deps, ones(shape), zeros(shape))
id2 = where(ksat > watr - deps, ones(shape), zeros(shape))
dp_r = where(id1 + id2 > 1.99, maximum(watr - deps, zeros(shape)), dp_r)
dp_r = where(watr > ksat + deps, ksat, dp_r)
dp_r = maximum(dp_r, zeros(shape))
drew_1 = minimum((pDrew + ro + (evap - (rain + mlt))), rew)
drew = maximum(drew_1, zeros(shape))
diff = maximum(pDrew - drew, zeros(shape))
de_1 = minimum((pDe + (evap - (rain + mlt - diff))), tew)
de = maximum(de_1, zeros(shape))
diff = maximum(((pDrew - drew) + (pDe - de)), zeros(shape))
dr_1 = minimum((pDr + ((transp + dp_r) - (rain + mlt - diff))), taw)
dr = maximum(dr_1, zeros(shape))
# Create cumulative rasters to show net over entire run
infil += dp_r
infil = maximum(infil, zeros(shape))
prev_et = et
ref_et += etrs
et = et + evap + transp
et_ind = et / ref_et
et = where(isnan(et) == True, prev_et, et)
et = where(et > ref_et, ref_et / 2., et)
et = maximum(et, ones(shape) * 0.001)
precip = precip + rain + snow_fall
precip = maximum(precip, zeros(shape))
runoff += ro
runoff = maximum(runoff, zeros(shape))
snow_ras = swe + snow_fall - mlt
snow_ras = maximum(snow_ras, zeros(shape))
tot_snow += snow_fall
mo_date = calendar.monthrange(year, month)
if day == mo_date[1]:
infil_mo = infil - p_mo_infil
infil_mo = maximum(infil_mo, zeros(shape))
ref_et_mo = etrs - p_mo_etrs
et_mo = et - p_mo_et
et_mo = where(isnan(et_mo) == True, p_mo_et, et_mo)
et_mo = where(et_mo > ref_et, ref_et / 2., et_mo)
et_mo = maximum(et_mo, ones(shape) * 0.001)
precip_mo = precip - p_mo_precip
precip_mo = maximum(precip_mo, zeros(shape))
runoff_mo = ro - p_mo_ro
runoff_mo = maximum(runoff_mo, zeros(shape))
snow_ras_mo = swe
snow_ras_mo = maximum(snow_ras_mo, zeros(shape))
deps_mo = drew + de + dr
delta_s_mo = p_mo_deps - deps_mo
outputs = (('infil', infil_mo), ('et', et_mo), ('precip', precip_mo), ('runoff', runoff_mo),
('snow_ras', snow_ras_mo), ('delta_s_mo', delta_s_mo), ('deps_mo', deps_mo))
self.save_month_step(outputs, month, year)
p_mo_et = et
p_mo_precip = precip
p_mo_ro = ro
p_mo_deps = deps_mo
p_mo_infil = infil
p_mo_etrs = etrs
if day == 31 and month == 12:
infil_yr = infil - p_yr_infil
infil_yr = maximum(infil_yr, zeros(shape))
ref_et_yr = etrs - p_yr_etrs
et_yr = et - p_yr_et
et_yr = where(isnan(et_yr) == True, p_yr_et, et_yr)
et_yr = where(et_yr > ref_et, ref_et / 2., et_yr)
et_yr = maximum(et_yr, ones(shape) * 0.001)
precip_yr = precip - p_yr_precip
precip_yr = maximum(precip_yr, zeros(shape))
runoff_yr = ro - p_yr_ro
runoff_yr = maximum(runoff_yr, zeros(shape))
snow_ras_yr = swe
snow_ras_yr = maximum(snow_ras_yr, zeros(shape))
deps_yr = drew + de + dr
delta_s_yr = p_yr_deps - deps_yr
outputs = (('infil', infil_yr), ('et', et_yr), ('precip', precip_yr), ('runoff', runoff_yr),
('snow_ras', snow_ras_yr), ('delta_s_yr', delta_s_yr), ('deps_yr', deps_yr))
p_yr_et = et
p_yr_precip = precip
p_yr_ro = ro
p_yr_deps = deps_yr # this was originally p_mo_deps = deps_yr im assuming this is a typo
p_yr_infil = infil
p_yr_etrs = etrs
self.save_year_step(outputs, month, year)
# Check MASS BALANCE for the love of WATER!!!
mass = rain + mlt - (ro + transp + evap + dp_r + ((pDr - dr) + (pDe - de) + (pDrew - drew)))
tot_mass += abs(mass)
cum_mass += mass
plt_day[i] = dday
plt_rain[i] = rain[S, E]
plt_eta[i] = eta[S, E]
plt_snow_fall[i] = snow_fall[S, E]
plt_ro[i] = ro[S, E]
plt_dr[i] = dr[S, E]
plt_de[i] = de[S, E]
plt_drew[i] = drew[S, E]
plt_temp[i] = mid_temp[S, E]
plt_dp_r[i] = dp_r[S, E]
plt_ks[i] = ks[S, E]
plt_pdr[i] = pDr[S, E]
plt_etrs[i] = etrs[S, E]
plt_kcb[i] = kcb[S, E]
plt_ppt[i] = ppt[S, E]
plt_ke[i] = ke[S, E]
plt_kr[i] = kr[S, E]
plt_mlt[i] = mlt[S, E]
plt_swe[i] = swe[S, E]
plt_tempm[i] = max_temp[S, E]
plt_fs1[i] = fs1[S, E]
plt_mass[i] = mass[S, E]
def plot_map(self, dataset, attribute_data, min_value=None, max_value=None, file=None,
my_title="", filter=None, background=None):
""" Plots a 2D image of attribute given by 'name'. matplotlib required.
The dataset must have a method 'get_2d_attribute' defined that returns
a 2D array that is to be plotted. If min_value/max_value are given, all values
that are smaller/larger than these values are set to min_value/max_value.
Argument background is a value to be used for background. If it is not given,
it is considered as a 1/100 under the minimum value of the array.
Filter is a 2D array. Points where filter is > 0 are masked out (put into background).
"""
import matplotlib
matplotlib.use('Qt4Agg')
from matplotlib.pylab import jet,imshow,colorbar,show,axis,savefig,close,figure,title,normalize
from matplotlib.pylab import rot90
attribute_data = attribute_data[filter]
coord_2d_data = dataset.get_2d_attribute(attribute_data = attribute_data)
data_mask = coord_2d_data.mask
# if filter is not None:
# if isinstance(filter, ndarray):
# if not ma.allclose(filter.shape, coord_2d_data.shape):
# raise StandardError, "Argument filter must have the same shape as the 2d attribute."
# filter_data = filter
# else:
# raise TypeError, "The filter type is invalid. A character string or a 2D numpy array allowed."
# filter_data = where(ma.filled(filter_data,1) > 0, 1,0)
# data_mask = ma.mask_or(data_mask, filter_data)
nonmaskedmin = ma.minimum(coord_2d_data) - .2 * (ma.maximum(coord_2d_data) - ma.minimum(coord_2d_data))
if max_value == None:
max_value = ma.maximum(coord_2d_data)
if min_value == None:
min_value = nonmaskedmin
coord_2d_data = ma.filled(coord_2d_data,min_value)
if background is None:
value_range = max_value-min_value
background = min_value-value_range/100
coord_2d_data = ma.filled(ma.masked_array(coord_2d_data, mask=data_mask), background)
# Our data uses NW as 0,0, while matplotlib uses SW for 0,0.
# Rotate the data so the map is oriented correctly.
coord_2d_data = rot90(coord_2d_data, 1)
jet()
figure()
norm = normalize(min_value, max_value)
im = imshow(coord_2d_data,
origin='lower',
aspect='equal',
interpolation=None,
norm=norm,
)
tickfmt = '%4d'
if isinstance(min_value, float) or isinstance(max_value, float):
tickfmt='%1.4f'
colorbar(format=tickfmt)
title(my_title)
axis('off')
if file:
savefig(file)
close()
else:
show()
def autoscale(self, A):
'''
Set *vmin*, *vmax* to min, max of *A*.
'''
self.vmin = ma.minimum(A)
self.vmax = ma.maximum(A)
def autoscale_None(self, A):
' autoscale only None-valued vmin or vmax'
if self.vmin is None: self.vmin = ma.minimum(A)
if self.vmax is None: self.vmax = ma.maximum(A)
def create(self,
parent=None,
min=None,
max=None,
save_file=None,
thread_it=1,
rate=None,
bitrate=None,
ffmpegoptions=''):
from vcs import minmax
from numpy.ma import maximum, minimum
# Cannot "Run" or "Create" an animation while already creating an
# animation
if self.run_flg == 1:
return
if self.vcs_self.canvas.creating_animation() == 1:
return
if self.vcs_self.animate_info == []:
str = "No data found!"
showerror("Error Message to User", str)
return
# Stop the (thread) execution of the X main loop (if it is running).
self.vcs_self.canvas.stopxmainloop()
# Force VCS to update its orientation, needed when the user changes the
# VCS Canvas size.
self.vcs_self.canvas.updateorientation()
# Make sure the animate information is up-to-date for creating images
if ((self.gui_popup == 1) and (self.create_flg == 0)):
self.update_animate_display_list()
# Save the min and max values for the graphics methods.
# Will need to restore values back when animation is done.
self.save_original_min_max()
# Set up the animation min and max values by changing the graphics method
# Note: cannot set the min and max values if the default graphics
# method is set.
do_min_max = 'yes'
try:
if (parent is not None) and (parent.iso_spacing == 'Log'):
do_min_max = 'no'
except:
pass
# Draw specified continental outlines if needed.
self.continents_hold_value = self.vcs_self.canvas.getcontinentstype()
self.vcs_self.canvas.setcontinentstype(self.continents_value)
if (do_min_max == 'yes'):
minv = []
maxv = []
if (min is None) or (max is None):
for i in range(len(self.vcs_self.animate_info)):
minv.append(1.0e77)
maxv.append(-1.0e77)
for i in range(len(self.vcs_self.animate_info)):
dpy, slab = self.vcs_self.animate_info[i]
mins, maxs = minmax(slab)
minv[i] = float(minimum(float(minv[i]), float(mins)))
maxv[i] = float(maximum(float(maxv[i]), float(maxs)))
if isinstance(min, list) or isinstance(max, list):
for i in range(len(self.vcs_self.animate_info)):
try:
minv.append(min[i])
except:
minv.append(min[-1])
try:
maxv.append(max[i])
except:
maxv.append(max[-1])
else:
for i in range(len(self.vcs_self.animate_info)):
minv.append(min)
maxv.append(max)
# Set the min an max for each plot in the page. If the same graphics method is used
# to display the plots, then the last min and max setting of the
# data set will be used.
for i in range(len(self.vcs_self.animate_info)):
try:
self.set_animation_min_max(minv[i], maxv[i], i)
except:
# if it is default, then you cannot set the min and max, so
# pass.
pass
if save_file is None or save_file.split('.')[-1].lower() == 'ras':
if thread_it:
thread.start_new_thread(self.vcs_self.canvas.animate_init,
(save_file, ))
else:
self.vcs_self.canvas.animate_init(save_file)
else: # ffmpeg stuff
save_info = self.vcs_self.animate_info
animation_info = self.animate_info_from_python()
slabs = []
templates = []
dpys = []
for i in range(len(self.vcs_self.animate_info)):
dpy, slab = self.vcs_self.animate_info[i]
slabs.append(slab)
dpys.append(dpy)
templates.append(dpy.template)
sh = slabs[0].shape
if dpy.g_type in [
'boxfill',
'isofill',
'isoline',
'meshfill',
'outfill',
'outline',
'taylordiagram',
'vector',
]:
r = len(sh) - 2
else:
r = len(sh) - 1
# now create the list of all previous indices to plot
indices = []
for i in range(r):
this = list(range(sh[i]))
tmp = []
if indices == []:
for k in this:
indices.append([
k,
])
else:
for j in range(len(indices)):
for k in this:
tmp2 = copy.copy(indices[j])
tmp2.append(k)
tmp.append(tmp2)
indices = tmp
count = 1
white_square = self.vcs_self.createfillarea()
white_square.color = 240
white_square.x = [0, 1, 1, 0]
white_square.y = [0, 0, 1, 1]
new_vcs = self.vcs_self
if self.vcs_self.orientation() == 'portrait':
new_vcs.portrait()
# self.vcs_self.close()
for index in indices:
new_vcs.clear()
new_vcs.plot(white_square, bg=1)
for i in range(len(save_info)):
slab = slabs[i]
template = templates[i]
gtype = animation_info["gtype"][i].lower()
gname = animation_info["gname"][i]
gm = None # for flake8 to be happy
exec("gm = new_vcs.get%s('%s')" % (gtype, gname))
for j in index:
slab = slab[j]
new_vcs.plot(slab, gm, new_vcs.gettemplate(template), bg=1)
new_vcs.png("tmp_anim_%i" % count)
count += 1
new_vcs.ffmpeg(save_file,
"tmp_anim_%d.png",
bitrate=bitrate,
rate=rate,
options=ffmpegoptions)
for i in range(count - 1):
os.remove("tmp_anim_%i.png" % (i + 1))
del (new_vcs)
self.create_flg = 1
