def cmp_(cls0 , cls1):
if np.issubclass_(cls0, cls1):
return -1
elif np.issubclass_(cls1, cls0):
return 1
else:
return 0 # do not change order
def sd_bias_model(self, val):
if not isinstance(val, str) and not issubclass_(val, bias.ScaleDepBias) and val is not None:
raise ValueError("scale_dependenent_bias must be a subclass of bias.ScaleDepBias")
elif isinstance(val, str):
model = get_model_(val, "halomod.bias")
if not issubclass_(model, bias.ScaleDepBias):
raise ValueError("scale_dependenent_bias must be a subclass of bias.ScaleDepBias")
return model
return val
def _get_db_type_from_dataframe(self, col_type):
"""Helper method to get SQL data type from DataFrame."""
if np.issubclass_(col_type, np.floating):
return self._get_db_type(float)
elif np.issubclass_(col_type, np.integer):
return self._get_db_type(int)
elif np.issubclass_(col_type, np.datetime64):
return self._get_db_type(datetime)
else:
return self._get_db_type(str)
def alter_dndm(self, val):
"""
A model for empirical recalibration of the HMF.
:type: None, str, or :class`WDMRecalibrateMF` subclass.
"""
if not np.issubclass_(val, WDMRecalibrateMF) and val is not None and not np.issubclass_(val, str):
raise TypeError("alter_dndm must be a WDMRecalibrateMF subclass, string, or None")
else:
return val
def alter_dndm(self, val):
"""
A model for empirical recalibration of the HMF.
:type: None, str, or :class`WDMRecalibrateMF` subclass.
"""
if not np.issubclass_(val, WDMRecalibrateMF) and val is not None and not np.issubclass_(val, basestring):
raise TypeError("alter_dndm must be a WDMRecalibrateMF subclass, string, or None")
else:
return val
def cm(self):
"""A class containing the elements necessary to calculate the concentration-mass relation"""
this_filter = copy(self.filter)
this_filter.power = self._power0
this_profile = self.profile_model(None, self.mean_density0,
self.delta_halo, self.z,
**self.profile_params)
if issubclass_(self.concentration_model, CMRelation):
return self.concentration_model(filter0=this_filter,
mean_density0=self.mean_density0,
growth=self.growth,
delta_c=self.delta_c,
profile=this_profile,
cosmo=self.cosmo,
delta_halo=self.delta_halo,
**self.concentration_params)
else:
return get_model(self.concentration_model,
"halomod.concentration",
filter0=this_filter,
mean_density0=self.mean_density0,
growth=self.growth,
delta_c=self.delta_c,
profile=this_profile,
cosmo=self.cosmo,
delta_halo=self.delta_halo,
**self.concentration_params)
def bias(self):
"""A class containing the elements necessary to calculate the halo bias"""
if issubclass_(self.bias_model, bias.Bias):
return self.bias_model(nu=self.nu,
delta_c=self.delta_c,
m=self.m,
mstar=self.mass_nonlinear,
delta_halo=self.delta_halo,
n=self.n,
Om0=self.cosmo.Om0,
h=self.cosmo.h,
sigma_8=self.sigma_8,
**self.bias_params).bias()
else:
# FIXME: this is an ugly hack just to get things fast for the paper.
if self.bias_model in ["Jing98", "Seljak04"]:
mstar = self.mass_nonlinear
else:
mstar = None
return get_model(self.bias_model,
"halomod.bias",
nu=self.nu,
delta_c=self.delta_c,
m=self.m,
mstar=mstar,
delta_halo=self.delta_halo,
n=self.n,
Om0=self.cosmo.Om0,
h=self.cosmo.h,
sigma_8=self.sigma_8,
**self.bias_params).bias()
def sd_bias_model(self, val):
if not isinstance(val, basestring) and not issubclass_(
val, bias.ScaleDepBias) and val is not None:
raise ValueError(
"scale_dependenent_bias must be a subclass of bias.ScaleDepBias"
)
return val
def default(self, obj):
if isinstance(obj, BaseP2GObject):
mod_str = str(obj.__class__.__module__)
mod_str = mod_str + "." if mod_str != __name__ else ""
cls_str = str(obj.__class__.__name__)
obj = obj.copy(as_dict=True)
# object should now be a builtin dict
obj["__class__"] = mod_str + cls_str
return obj
# return super(P2GJSONEncoder, self).encode(obj)
elif isinstance(obj, datetime):
return obj.isoformat()
elif numpy.issubclass_(obj, numpy.number) or isinstance(
obj, numpy.dtype):
return dtype_to_str(obj)
elif hasattr(obj, 'dtype'):
if obj.size > 1:
return obj.tolist()
return int(obj) if numpy.issubdtype(obj,
numpy.integer) else float(obj)
else:
try:
return super(P2GJSONEncoder, self).default(obj)
except TypeError as e:
print("TypeError:", str(e), type(obj))
print(obj)
raise
def bias_model(self, val):
if not isinstance(val, str) and not issubclass_(val, bias.Bias):
raise ValueError("bias_model must be a subclass of bias.Bias")
elif isinstance(val, str):
return get_model_(val, "halomod.bias")
else:
return val
def __init__(self, *args, **kwargs):
self.__enums = dict((f, t) for f, t in self._fields_
if issubclass_(t, Enum))
for field, val in iteritems(self._defaults_):
setattr(self, field, val)
Structure.__init__(self, *args, **kwargs)
def default(self, obj):
if isinstance(obj, BaseP2GObject):
mod_str = str(obj.__class__.__module__)
mod_str = mod_str + "." if mod_str != __name__ else ""
cls_str = str(obj.__class__.__name__)
obj = obj.copy(as_dict=True)
# object should now be a builtin dict
obj["__class__"] = mod_str + cls_str
return obj
# return super(P2GJSONEncoder, self).encode(obj)
elif isinstance(obj, datetime):
return obj.isoformat()
elif numpy.issubclass_(obj, numpy.number) or isinstance(obj, numpy.dtype):
return dtype_to_str(obj)
elif hasattr(obj, 'dtype'):
if obj.size > 1:
return obj.tolist()
return int(obj) if numpy.issubdtype(obj, numpy.integer) else float(obj)
else:
try:
return super(P2GJSONEncoder, self).default(obj)
except TypeError as e:
print("TypeError:", str(e), type(obj))
print(obj)
raise
def cm(self):
this_filter = copy(self.filter)
this_filter.power = self._power0
this_profile = self.profile_model(None, self.mean_density0,
self.delta_halo, self.z,
**self.profile_params)
kwargs = dict(filter0=this_filter,
mean_density0=self.mean_density0,
growth=self.growth,
delta_c=self.delta_c,
profile=this_profile,
cosmo=self.cosmo,
delta_halo=self.delta_halo,
**self.concentration_params)
if np.issubclass_(self.concentration_model, CMRelation):
if self.concentration_model.__class__.__name__.endswith("WDM"):
cm = self.concentration_model(m_hm=self.wdm.m_hm, **kwargs)
else:
cm = self.concentration_model(**kwargs)
elif self.concentration_model.endswith("WDM"):
cm = CMRelationWDMRescaled(self.concentration_model[:-3],
m_hm=self.wdm.m_hm,
**kwargs)
else:
cm = get_model(self.concentration_model,
"halomod.halo_concentration", **kwargs)
return cm
def __init__(self, *args, **kwargs):
self.__enums = dict(
(f, t) for f, t in self._fields_ if issubclass_(t, Enum))
for field, val in iteritems(self._defaults_):
setattr(self, field, val)
Structure.__init__(self, *args, **kwargs)
def mf_fit(self, val):
if not issubclass_(val, FittingFunction) and not isinstance(
val, basestring):
raise ValueError(
"mf_fit must be a FittingFunction or string, got %s" %
type(val))
return val
def build_sequential_model(model_states, input_state, output_state,
model_compile_dict, **kwargs):
"""
Parameters
----------
model_states: a list of _operators sampled from operator space
input_state:
output_state: specifies the output tensor, e.g. Dense(1, activation='sigmoid')
model_compile_dict: a dict of `loss`, `optimizer` and `metrics`
Returns
---------
Keras.Model
"""
inp = get_layer(None, input_state)
x = inp
model_space = kwargs.pop("model_space", None)
for i, state in enumerate(model_states):
if issubclass(type(state), Operation):
x = get_layer(x, state)
elif issubclass(type(state), int) or np.issubclass_(
type(state), np.integer):
assert model_space is not None, "if provided integer model_arc, must provide model_space in kwargs"
x = get_layer(x, model_space[i][state])
else:
raise Exception("cannot understand %s of type %s" %
(state, type(state)))
out = get_layer(x, output_state)
model = Model(inputs=inp, outputs=out)
if not kwargs.pop('stop_compile', False):
model.compile(**model_compile_dict)
return model
def growth(self):
"The instantiated growth model"
if np.issubclass_(self.growth_model, gf.GrowthFactor):
return self.growth_model(self.cosmo, **self.growth_params)
else:
return get_model(self.growth_model, "hmf.growth_factor", cosmo=self.cosmo,
**self.growth_params)
def type_to_builtin_type(type):
# Infer from numpy type if it is one
if type.__module__ == np.__name__:
return numpy_type_to_builtin_type(type)
# Otherwise, try to infer from a few generic python types
if np.issubclass_(type, bool):
return types_bool
elif np.issubclass_(type, int):
return types_int32
elif np.issubclass_(type, str):
return types_str
elif np.issubclass_(type, float):
return types_fp32
else:
raise TypeError("Could not determine builtin type for " + str(type))
def stack_petab(dict_of_petabs, keystostack, keytosort=None):
'''
stack peTabs along new dimension.
dict_of_petabs - the peTabs to stack. The keys of dict_of_petabs do not matter
keystostack - a list of peTab keys that have to be stacked. Values should have the same shape
keytosort - a key that is used to sort peTabs along new dimension.
If None, will be sorted in ascending order of the keys of dict_of_petabs.
'''
if keytosort is not None:
dict_of_petabs_keys = sorted(
dict_of_petabs.keys(),
key=lambda k: dict_of_petabs[k].eval_expr(keytosort))
else:
dict_of_petabs_keys = sorted(dict_of_petabs.keys())
LMO_loen_ptb = dict_of_petabs[list(dict_of_petabs_keys)[0]].copy()
keys = list(LMO_loen_ptb.keys())
print(keys)
for key in keys:
if key in keystostack:
LMO_loen_ptb[key] = np.stack(
[dict_of_petabs[i][key] for i in dict_of_petabs_keys], axis=0)
if len(LMO_loen_ptb[key].shape) == 1:
LMO_loen_ptb[key] = LMO_loen_ptb[key].reshape(
(list(LMO_loen_ptb[key].shape) + [1, 1]))
elif np.issubclass_(type(LMO_loen_ptb[key]), np.ndarray):
LMO_loen_ptb[key] = LMO_loen_ptb[key].reshape(
([1] + list(LMO_loen_ptb[key].shape)))
return LMO_loen_ptb
def __truediv__(self, other):
"""Divides the spectra by another object.
Parameters
----------
other : ndarray, float, int, list or Spectra
Returns
-------
Spectra
New :class:`Spectra` instance which has the divided spectra.
Notes
-----
This special function, together with :meth:`Spectra.__rtruediv__`, allows the use fo the symbol ``/`` to divide :class:`Spectra` objects.
See Also
--------
:meth:`Spectra.__rtruediv__`
"""
if np.issubclass_(type(other), Spectra):
inv_spectra = Spectra([])
inv_spectra._eng = other.eng
inv_spectra._in_eng = other.in_eng
inv_spectra._grid_vals = 1 / other.grid_vals
inv_spectra._rs = other.rs
inv_spectra._spec_type = other.spec_type
inv_spectra._N_underflow = 0
inv_spectra._eng_underflow = 0
return self * inv_spectra
else:
return self * (1 / other)
def view(self, dtype=None, type=None, fill_value=None):
""" Return a view of the PDS_marray data.
Subclassed to fix a NumPy bug that breaks setting fill_value when subselecting a field from a
structured array.
"""
try:
obj = super(PDS_marray, self).view(dtype=dtype,
type=type,
fill_value=fill_value)
# Fix bug in NumPy < v1.10, which resets fill value on ``view`` if mask is not nomask
if ((dtype is None) or ((type is None) and np.issubclass_(dtype, np.ma.MaskedArray))) and \
(fill_value is None):
obj._fill_value = self._fill_value
except TypeError:
# NumPy < v1.8 did not have a fill value attribute for ``view``
obj = super(PDS_marray, self).view(dtype=dtype, type=type)
return obj
def concentration_model(self, val):
"""A concentration-mass relation"""
if not isinstance(val, basestring) and not issubclass_(
val, CMRelation):
raise ValueError(
"concentration_model must be a subclass of concentration.CMRelation"
)
return val
def _wdm(self):
if issubclass_(self.wdm_transfer, WDM):
return self.wdm_transfer(self.wdm_mass, self.omegac, self.h, self.mean_dens,
**self.wdm_params)
elif isinstance(self.wdm_transfer, basestring):
return get_wdm(self.wdm_transfer, mx=self.wdm_mass, omegac=self.omegac,
h=self.h, rho_mean=self.mean_dens,
**self.wdm_params)
def halo_concentration_model(self, val):
"""A halo_concentration-mass relation"""
if not isinstance(val, str) and not issubclass_(val, CMRelation):
raise ValueError("halo_concentration_model must be a subclass of halo_concentration.CMRelation")
elif isinstance(val, str):
return get_model_(val, "halomod.concentration")
else:
return val
def hod_model(self, val):
""":class:`~hod.HOD` class"""
if not isinstance(val, str) and not issubclass_(val, hod.HOD):
raise ValueError("hod_model must be a subclass of hod.HOD")
elif isinstance(val, str):
return get_model_(val, "halomod.hod")
else:
return val
def halo_profile_model(self, val):
"""The halo density halo_profile model"""
if not isinstance(val, str) and not issubclass_(val, profiles.Profile):
raise ValueError("halo_profile_model must be a subclass of profiles.Profile")
elif isinstance(val, str):
return get_model_(val, "halomod.profiles")
else:
return val
def replace_dim(self, olddim, newdim):
if isinstance(olddim, string_types):
olddim = self.dims_index(olddim)
olddim = self.dims[olddim]
if np.issubclass_(newdim, DimBase):
newdim = newdim(olddim)
self.dims = replace_dim(self.dims, olddim, newdim)
return self.dims
def sd_bias(self):
"""A class containing relevant methods to calculate scale-dependent bias corrections"""
if self.sd_bias_model is None:
return None
elif issubclass_(self.sd_bias_model, bias.ScaleDepBias):
return self.sd_bias_model(self.corr_mm_base, **self.sd_bias_params)
else:
return get_model(self.sd_bias_model, "halomod.bias",
xi_dm=self.corr_mm_base, **self.sd_bias_params)
def growth_model(self, val):
"""
The model to use to calculate the growth function/growth rate.
:type: str or `hmf.growth_factor.GrowthFactor` subclass
"""
if not np.issubclass_(val, gf.GrowthFactor) and not isinstance(val, str):
raise ValueError("growth_model must be a GrowthFactor or string, got %s" % type(val))
return val
def profile_model(self, val):
"""The halo density profile model"""
if not isinstance(val, basestring) and not issubclass_(val, profiles.Profile):
raise ValueError("profile_model must be a subclass of profiles.Profile")
if isinstance(val, basestring):
return get_model_(val, "halomod.profiles")
else:
return val
def transfer(self):
"""
The instantiated transfer model
"""
if np.issubclass_(self.transfer_model, tm.TransferComponent):
return self.transfer_model(self.cosmo, **self.transfer_params)
elif isinstance(self.transfer_model, str):
return get_model(self.transfer_model, "hmf.transfer_models", cosmo=self.cosmo,
**self.transfer_params)
def cross_hod_model(self, val):
if not isinstance(val, str) and not np.issubclass_(val, HODCross):
raise ValueError(
"cross_hod_model must be a subclass of cross_correlations.HODCross"
)
elif isinstance(val, str):
return get_model_(val, "")
else:
return val
def warp(gray_img, old_pts, pt1, A):
"""
Args:
gray_img: (W, H) The grayscaled input image.
old_pts : (pts, 2) Old/original point coordinates.
pt1 : (2,) The first reference point (tl - top left).
A : (24,) The 24 mapping coefficients.
Returns:
Dewarped image
"""
# Asserting input
if gray_img.max() <= 1.0:
gray_img = gray_img * 255
if not np.issubclass_(gray_img.dtype.type, np.uint8):
gray_img = gray_img.astype(np.uint8)
# Init.
width, height = gray_img.shape
old_x, old_y = old_pts[:, 0], old_pts[:, 1]
# preparation for the place of dewarping values (1 Megapixel = 1000x1000 pixels).
w_grid, h_grid = np.meshgrid(np.arange(0, width), np.arange(0, height))
# calculating the new X and Y coordinates of the dewarped image.
x = w_grid - old_x[pt1]
y = h_grid - old_y[pt1]
new_x, new_y = nl_trans(x, y, A)
new_x = np.round(new_x + old_x[pt1])
new_y = np.round(new_y + old_y[pt1])
# recover the image area that are lost during the mapping with black color.
mask_x = np.multiply((new_x >= 0), (new_x <= (width - 1)))
mask_y = np.multiply((new_y >= 0), (new_y <= (height - 1)))
fill_x = np.logical_or((new_x > (width - 1)), (new_x < 0))
fill_y = np.logical_or((new_y > (height - 1)), (new_y < 0))
new_x = np.multiply(mask_x, new_x) + np.multiply(fill_x, (width - 1))
new_y = np.multiply(mask_y, new_y) + np.multiply(fill_y, (height - 1))
# dewarping the raw images into the new symetrical image.
gray_img = np.transpose(gray_img)
dewarp = np.reshape(gray_img, width * height) # Vector
i = new_y + (new_x * height)
i = np.transpose(i)
i = np.int64(np.reshape(i, width * height))
i_min, i_max = np.amin(i), np.amax(i)
min_pos = np.where(i == i_min)
dewarp_new = dewarp[i]
dewarp_new = np.reshape(dewarp_new, [height, width]) # Array
dewarp_new = np.transpose(dewarp_new)
return dewarp_new
def hmf_model(self, val):
"""
A model to use as the fitting function :math:`f(\sigma)`
:type: str or `hmf.fitting_functions.FittingFunction` subclass
"""
if not issubclass_(val, ff.FittingFunction) and not isinstance(val, basestring):
raise ValueError("hmf_model must be a ff.FittingFunction or string, got %s" % type(val))
return val
def filter_model(self, val):
"""
A model for the window/filter function.
:type: str or :class:`hmf.filters.Filter` subclass
"""
if not issubclass_(val, Filter) and not isinstance(val, basestring):
raise ValueError("filter must be a Filter or string, got %s" % type(val))
return val
def exclusion_model(self, val):
"""A string identifier for the type of halo exclusion used (or None)"""
if val is None:
val = "NoExclusion"
if issubclass_(val, Exclusion):
return val
else:
return get_model_(val, "halomod.halo_exclusion")
def wdm_model(self, val):
"""
A model for the WDM effect on the transfer function.
:type: str or :class:`WDM` subclass
"""
if not np.issubclass_(val, WDM) and not isinstance(val, basestring):
raise ValueError("wdm_model must be a WDM subclass or string, got %s" % type(val))
return val
def wdm_model(self, val):
"""
A model for the WDM effect on the transfer function.
:type: str or :class:`WDM` subclass
"""
if not np.issubclass_(val, WDM) and not isinstance(val, str):
raise ValueError("wdm_model must be a WDM subclass or string, got %s" % type(val))
return val
def str_to_dtype(dtype_str):
if numpy.issubclass_(dtype_str, numpy.number):
# if they gave us a numpy dtype
return dtype_str
try:
return str2dtype[dtype_str]
except KeyError:
raise ValueError("Not a valid data type string: %s" % (dtype_str,))
def filter(self):
"""
Instantiated model for filter/window functions.
"""
if issubclass_(self.filter_model, Filter):
return self.filter_model(self.k,self._unnormalised_power, **self.filter_params)
elif isinstance(self.filter_model, basestring):
return get_model(self.filter_model, "hmf.filters", k=self.k,
power=self._unnormalised_power, **self.filter_params)
def exclusion_model(self, val):
"""A string identifier for the type of halo exclusion used (or None)"""
if val is None:
val = "NoExclusion"
if issubclass_(val, Exclusion):
return val
else:
return get_model_(val, "halomod.halo_exclusion")
def get_np(self, dtype=None, start=0, n=None):
'''
Return vv data in a numpy array
:param start: index of first value, must be >=0
:param n: number of values wanted
:param dtype: numpy data type wanted
:returns: (data, (fid_start, fid_incr))
.. versionadded:: 9.1
'''
if dtype is None:
dtype = self._dtype
else:
dtype = np.dtype(dtype)
if n is None:
n = self.length - start
else:
n = min((self.length - start), n)
if (n <= 0) or (start < 0):
raise VVException(
_t('Cannot get (start,n) ({},{}) from vv of length {}').format(
start, n, self.length))
# strings wanted
if dtype.type is np.str_:
if self._sr is None:
self._sr = gxapi.str_ref()
npd = np.empty((n, ), dtype=dtype)
for i in range(start, start + n):
self._vv.get_string(i, self._sr)
npd[i - start] = self._sr.value
# numeric wanted
else:
# strings to numeric
if self._gxtype < 0:
if np.issubclass_(dtype.type, np.integer):
vvd = gxapi.GXVV.create_ext(gxapi.GS_LONG, n)
else:
vvd = gxapi.GXVV.create_ext(gxapi.GS_DOUBLE, n)
vvd.copy(self._vv) # this will do the conversion
npd = vvd.get_data_np(start, n, dtype)
# numeric to numeric
else:
npd = self._vv.get_data_np(start, n, dtype)
fid = self.fid
start = fid[0] + start * fid[1]
return npd, (start, fid[1])
def _unnormalised_lnT(self):
"""
The un-normalised transfer function
This wraps the individual transfer_fit methods to provide unified access.
"""
if issubclass_(self.transfer_fit, GetTransfer):
return self.transfer_fit(self).lnt(self.lnk)
elif isinstance(self.transfer_fit, basestring):
return get_transfer(self.transfer_fit, self).lnt(self.lnk)
def get_fill_value(self, item, default_dtype=numpy.float32):
"""If the caller of `get_swath_data` doesn't force the output fill value then they need to know what it is now.
Defaults version expects a 'data_type' attribute in the value returned from `file_type_info`.
- Unsigned Integers: Highest valid integer (i.e. -1 converted to unsigned integer)
- Signed Integers: -999
- Float: NaN
:raises: RuntimeError if unknown data type
"""
data_type = self.get_data_type(item, default_dtype=default_dtype)
if numpy.issubclass_(data_type, numpy.unsignedinteger):
return data_type(-1)
elif numpy.issubclass_(data_type, numpy.integer):
return -999
elif numpy.issubclass_(data_type, numpy.floating):
return numpy.nan
else:
raise RuntimeError("Unknown data type for %s: %s" % (item, data_type))
def __init__(self, theta, phi, r, w=None, s=0.0, eps=1e-16):
if np.issubclass_(w, float):
w = ones(len(theta)) * w
nt_, tt_, np_, tp_, c, fp, ier = dfitpack.spherfit_smth(theta, phi, r, w=w, s=s, eps=eps)
if not ier in [0, -1, -2]:
message = _spherefit_messages.get(ier, "ier=%s" % (ier))
raise ValueError(message)
self.fp = fp
self.tck = tt_[:nt_], tp_[:np_], c[: (nt_ - 4) * (np_ - 4)]
self.degrees = (3, 3)
def filter_mod(self):
if issubclass_(self.filter, Filter):
filter = self.filter(self.mean_dens, self.delta_c, self.lnk, self._lnP_0,
**self.filter_params)
elif isinstance(self.filter, basestring):
filter = get_filter(self.filter, rho_mean=self.mean_dens,
delta_c=self.delta_c, lnk=self.lnk, lnp=self._lnP_0,
**self.filter_params)
return filter
def create_output_from_product(self, gridded_product, output_pattern=None,
data_type=None, inc_by_one=None, fill_value=None, **kwargs):
inc_by_one = inc_by_one or False
data_type = data_type or gridded_product["data_type"]
fill_value = fill_value or gridded_product["fill_value"]
same_fill = numpy.isnan(fill_value) and numpy.isnan(gridded_product["fill_value"]) or fill_value == gridded_product["fill_value"]
grid_def = gridded_product["grid_definition"]
if not output_pattern:
output_pattern = DEFAULT_OUTPUT_PATTERN
if "{" in output_pattern:
# format the filename
of_kwargs = gridded_product.copy(as_dict=True)
of_kwargs["data_type"] = data_type
output_filename = self.create_output_filename(output_pattern,
grid_name=grid_def["grid_name"],
rows=grid_def["height"],
columns=grid_def["width"],
**of_kwargs)
else:
output_filename = output_pattern
if os.path.isfile(output_filename):
if not self.overwrite_existing:
LOG.error("Geotiff file already exists: %s", output_filename)
raise RuntimeError("Geotiff file already exists: %s" % (output_filename,))
else:
LOG.warning("Geotiff file already exists, will overwrite: %s", output_filename)
# if we have a floating point data type, then scaling doesn't make much sense
if data_type == gridded_product["data_type"] and same_fill:
LOG.info("Saving product %s to binary file %s", gridded_product["product_name"], output_filename)
shutil.copyfile(gridded_product["grid_data"], output_filename)
return output_filename
elif numpy.issubclass_(data_type, numpy.floating):
# we didn't rescale any data, but we need to convert it
data = gridded_product.get_data_array()
else:
try:
LOG.debug("Scaling %s data to fit data type", gridded_product["product_name"])
data = self.rescaler.rescale_product(gridded_product, data_type,
inc_by_one=inc_by_one, fill_value=fill_value)
data = clip_to_data_type(data, data_type)
except ValueError:
if not self.keep_intermediate and os.path.isfile(output_filename):
os.remove(output_filename)
raise
LOG.info("Saving product %s to binary file %s", gridded_product["product_name"], output_filename)
data = data.astype(data_type)
fill_mask = gridded_product.get_data_mask()
data[fill_mask] = fill_value
data.tofile(output_filename)
return output_filename
def filter_model(self, val):
"""
A model for the window/filter function.
:type: :class:`hmf.filters.Filter` subclass
"""
if not issubclass_(val, Filter) and not isinstance(val, str):
raise ValueError("filter must be a Filter or string, got %s" % type(val))
elif isinstance(val, str):
return get_model_(val, "hmf.filters")
else:
return val
def wdm(self):
"""
The instantiated WDM model.
Contains quantities relevant to WDM.
"""
if np.issubclass_(self.wdm_transfer, WDM):
return self.wdm_transfer(self.wdm_mass, self.cosmo,self.z,
**self.wdm_params)
elif isinstance(self.wdm_transfer, basestring):
return get_model(self.wdm_model, __name__, mx=self.wdm_mass, cosmo=self.cosmo,
z=self.z,**self.wdm_params)
def str_to_dtype(dtype_str):
if numpy.issubclass_(dtype_str, numpy.number):
# if they gave us a numpy dtype
return dtype_str
elif isinstance(dtype_str, str) and hasattr(numpy, dtype_str):
# they gave us the numpy name of the dtype
return getattr(numpy, dtype_str)
try:
return str2dtype[dtype_str]
except KeyError:
raise ValueError("Not a valid data type string: %s" % (dtype_str,))
def cm(self):
if np.issubclass_(self.cm_relation, CMRelation):
cm = self.cm_relation(self.filter_mod, delta_c=self.delta_c, z=self.z,
cdict=self.cosmolopy_dict, m_hm=self._wdm.m_hm,
**self.cm_params)
elif isinstance(self.cm_relation, basestring):
cm = get_cm(self.cm_relation, filter=self.filter_mod,
delta_c=self.delta_c, z=self.z, cdict=self.cosmolopy_dict,
m_hm=self._wdm.m_hm,
** self.cm_params)
return cm
def wdm_model(self, val):
"""
A model for the WDM effect on the transfer function.
:type: str or :class:`WDM` subclass
"""
if np.issubclass_(val, WDM):
return val
elif isinstance(val, str):
return get_model_(val, "hmf.wdm")
else:
raise ValueError("wdm_model must be a WDM subclass or string, got %s" % type(val))
def cm(self):
"""A class containing the elements necessary to calculate the concentration-mass relation"""
if issubclass_(self.cm_relation, CMRelation):
cm = self.cm_relation(self.filter_mod, delta_c=self.delta_c, z=self.z,
cdict=self.cosmolopy_dict, m_hm=None,
**self.cm_params)
elif isinstance(self.cm_relation, basestring):
cm = get_cm(self.cm_relation, filter=self.filter_mod, delta_c=self.delta_c,
z=self.z, cdict=self.cosmolopy_dict, m_hm=None,
**self.cm_params)
return cm
def _fit(self):
"""The actual fitting function class (as opposed to string identifier)"""
if issubclass_(self.mf_fit, FittingFunction):
fit = self.mf_fit(M=self.M, nu2=self.nu, z=self.z,
delta_halo=self.delta_halo, omegam_z=self.omegam_z,
delta_c=self.delta_c, sigma=self.sigma, n_eff=self.n_eff,
** self._fsig_params)
elif isinstance(self.mf_fit, basestring):
fit = get_fit(self.mf_fit, M=self.M, nu2=self.nu, z=self.z,
delta_halo=self.delta_halo, omegam_z=self.omegam_z,
delta_c=self.delta_c, sigma=self.sigma, n_eff=self.n_eff,
** self._fsig_params)
return fit
def alter_model(self, val):
"""
A model for empirical recalibration of the HMF.
:type: None, str, or :class`WDMRecalibrateMF` subclass.
"""
if np.issubclass_(val, WDMRecalibrateMF):
return val
elif val is None:
return None
elif isinstance(val, str):
return get_model_(val, __name__)
else:
raise TypeError("alter_model must be a WDMRecalibrateMF subclass, string, or None")
