a = self.approx.set_size_and_deterministic(a, nmc, 0, kwargs.get("more_replacements"))

flat2rand = self.make_size_and_deterministic_replacements(s, d, more_replacements)

elementwise addition
Generalizes a scalar `Op` to tensors.

    All the inputs must have the same number of dimensions. When the
    `Op` is performed, for each dimension, each input's size for that
    dimension must be the same. As a special case, it can also be one
    but only if the input's `broadcastable` flag is ``True`` for that
    dimension. In that case, the tensor is (virtually) replicated
    along that dimension to match the size of the others.

    The dtypes of the outputs mirror those of the scalar `Op` that is
    being generalized to tensors. In particular, if the calculations
    for an output are done in-place on an input, the output type must
    be the same as the corresponding input type (see the doc of
    `ScalarOp` to get help about controlling the output type)

    Notes
    -----
    -``Elemwise(add)``: represents ``+`` on tensors ``x + y``
    -``Elemwise(add, {0 : 0})``: represents the ``+=`` operation ``x += y``
    -``Elemwise(add, {0 : 1})``: represents ``+=`` on the second argument ``y += x``
    -``Elemwise(mul)(np.random.random((10, 5)), np.random.random((1, 5)))``:
    the second input is completed along the first dimension to match the first input
    -``Elemwise(true_div)(np.random.random(10, 5), np.random.random(10, 1))``: same but along the
    second dimension
    -``Elemwise(int_div)(np.random.random((1, 5)), np.random.random((10, 1)))``:
    the output has size ``(10, 5)``.
    -``Elemwise(log)(np.random.random((3, 4, 5)))``

elementwise modulo

    Notes
    -----

        (?P<option>.*?)                    # very permissive!
        \s*(?P<vi>=|:)\s*              # any number of space/tab,
                                           # followed by any of the
                                           # allowed delimiters,
                                           # followed by any space/tab
        (?P<value>.*)$                     # everything up to eol
        
Default floating-point precision for python casts.

Note: float16 support is experimental, use at your own risk.

Do an action when a tensor variable with float64 dtype is created.

Dump test values while pickling model. If True, test values will be dumped with model.

If `more`, sometimes we will select some implementation that are more deterministic, but slower.  Also see the dnn.conv.algo* flags to cover more cases.

If True, AbstractConv* ops will verify that user-provided shapes match the runtime shapes (debugging option, may slow down compilation)

Raise an error/warning if there is a CPU op in the computational graph.

Replace unpickled Aesara functions with None. This is useful to unpickle old graphs that pickled them when it shouldn't

The C++ compiler to use. Currently only g++ is supported, but supporting additional compilers should not be too difficult. If it is empty, no C++ code is compiled.

Do we default to delete intermediate results during Aesara function calls? Doing so lowers the memory requirement, but asks that we reallocate memory at the next function call. This is implemented for the default linker, but may not work for all linkers.

Default optimizer. If not None, will use this optimizer with the Mode

What to do when an optimization crashes: warn and skip it, raise the exception, or fall into the pdb debugger.

Suppress the deletion of code files that did not compile cleanly

What to do if a variable in the 'inputs' list of  aesara.function() is not used in the graph.

 -Wno-c++11-narrowing -fno-exceptions -fno-unwind-tables -fno-asynchronous-unwind-tables

If True, will print a warning when compiling one or more Op with C code that can't be cached because there is no c_code_cache_version() function associated to at least one of those Ops.

If True, will remove the -O* parameter passed to g++.This is useful to debug in gdb modules compiled by Aesara.The parameter -g is passed by default to g++

In seconds. The time after which Aesara won't reuse a compile c module.

If True, define a DEBUG macro (if not exists) for any compiled C code.

In seconds, time that a process will wait before deciding to
    override an existing lock. An override only happens when the existing
    lock is held by the same owner *and* has not been 'refreshed' by this
    owner for more than this period. Refreshes are done every half timeout
    period for running processes.

Directory which contains the root of Baidu CTC library. It is assumed         that the compiled library is either inside the build, lib or lib64         subdirectory, and the header inside the include directory.

Relax aesara.tensor.math._allclose (0) not at all, (1) a bit, (2) more

Enable or not in fast_run mode(fast_run optimization) the elemwise fusion optimization

The number of stack to trace to keep during compilation. -1 mean all. If greater then 0, will also make us save Aesara internal stack trace.

DEPRECATED: If True, enable the experimental optimization local_alloc_elemwise. Generates error if not True. Use optimizer_excluding=local_alloc_elemwise to disable.

When the local_alloc_elemwise is applied, add an assert to highlight shape errors.

If 'None', we warn about all Aesara bugs found by default. If 'all', we don't warn about Aesara bugs found by default. If a version, we print only the warnings relative to Aesara bugs found after that version. Warning for specific bugs can be configured with specific [warn] flags.

If 'low', the text of exceptions will generally refer to apply nodes with short names such as Elemwise{add_no_inplace}. If 'high', some exceptions will also refer to apply nodes with long descriptions  like:
        A. Elemwise{add_no_inplace}
                B. log_likelihood_v_given_h
                C. log_likelihood_h

If 'True', the __eval__ of an Aesara variable will return its test_value when this is available. This has the practical conseguence that, e.g., in debugging `my_var` will print the same as `my_var.tag.test_value` when a test value is defined.

If 'True', Aesara will run each op at graph build time, using Constants, SharedVariables and the tag 'test_value' as inputs to the function. This helps the user track down problems in the graph before it gets optimized.

For debugging Aesara optimization only. Same as compute_test_value, but is used during Aesara optimization

Specify if types should check their input in their C code. It can be used to speed up compilation, reduce overhead (particularly for scalars) and reduce the number of generated C files.

Check that Python- and C-produced ndarrays have same strides. On difference: (0) - ignore, (1) warn, or (2) raise error

Generate a warning when destroy_map or view_map says that an op works inplace, but the op did not reuse the input for its output.

Test thunks with pre-allocated memory as output storage. This is a list of strings separated by ":". Valid values are: "initial" (initial storage in storage map, happens with Scan),"previous" (previously-returned memory), "c_contiguous", "f_contiguous", "strided" (positive and negative strides), "wrong_size" (larger and smaller dimensions), and "ALL" (all of the above).

When testing with "strided" preallocated output memory, test all combinations of strides over that number of (inner-most) dimensions. You may want to reduce that number to reduce memory or time usage, but it is advised to keep a minimum of 2.

For the memory profile, do not print Apply nodes if the size
                 of their outputs (in bytes) is lower than this threshold

warn: print a warning and use the default value. raise: raise an error

Allow (or not) parallel computation on the CPU with OpenMP. This is the default value used when creating an Op that supports OpenMP parallelization. It is preferable to define it via the Aesara configuration file ~/.aesararc or with the environment variable AESARA_FLAGS. Parallelization is only done for some operations that implement it, and even for operations that implement parallelism, each operation is free to respect this flag or not. You can control the number of threads used with the environment variable OMP_NUM_THREADS. If it is set to 1, we disable openmp in Aesara by default.

If OpenMP is enabled, this is the minimum size of vectors for which the openmp parallelization is enabled in element wise ops.

When using the default mode, we will remove optimizer with these tags. Separate tags with ':'.

When using the default mode, we will add optimizer with these tags. Separate tags with ':'.

When using the default mode, we will require optimizer with these tags. Separate tags with ':'.

Where to stop eariler during optimization. It represent the position of the optimizer where to stop.

If cycle_detection is set to regular, most inplaces are allowed,but it is slower. If cycle_detection is set to faster, less inplacesare allowed, but it makes the compilation faster.The interaction of which one give the lower peak memory usage iscomplicated and not predictable, so if you are close to the peakmemory usage, triyng both could give you a small gain.

A flag for checking the stack trace during the optimization process. default (off): does not check the stack trace of any optimization log: inserts a dummy stack trace that identifies the optimizationthat inserted the variable that had an empty stack trace.warn: prints a warning if a stack trace is missing and also a dummystack trace is inserted that indicates which optimization insertedthe variable that had an empty stack trace.raise: raises an exception if a stack trace is missing

0 for silent, 1 for only warnings, 2 for full output withtimings and selected implementation

Useful only for the VM Linkers. When lazy is None, auto detect if lazy evaluation is needed and use the appropriate version. If the C loop isn't being used and lazy is True, use the Stack VM; otherwise, use the Loop VM.

Seed to use for randomized unit tests. Special value 'random' means using a seed of None.

Warn when using `tensor.round` with the default mode. Round changed its default from `half_away_from_zero` to `half_to_even` to have the same default as NumPy.

Format string for platform-dependent compiled module subdirectory
(relative to base_compiledir). Available keys: aesara_version, device,
gxx_version, hostname, numpy_version, platform, processor,
python_bitwidth, python_int_bitwidth, python_version, short_platform.
Defaults to compiledir_%(short_platform)s-%(processor)s-%(python_versi
on)s-%(python_bitwidth)s.

-L/hps/software/users/gerstung/alomakin/miniconda3/envs/bassis/lib -lmkl_rt -lpthread -lm -lm

Check for openmp library conflict.
WARNING: Setting this to False leaves you open to wrong results in blas-related operations.

Allow/disallow memory preallocation for outputs inside of scan (default: True)

xi = [pm.Gamma('xi_{}'.format(s), mu=0.5, sigma=1, shape=n_genes) for s in range(n_samples)]

rv_out, dims, observed, resize_shape = _make_rv_and_resize_shape(

rv_var.tag.scaling = _get_scaling(total_size, shape=rv_var.shape, ndim=rv_var.ndim)

Instantiation of :class:`aeppl.transforms.LogTransform`
for use in the ``transform`` argument of a random variable.

elementwise [true] division (inverse of multiplication)

    Notes
    -----

r_de = [pm.Lognormal('r_de_{}'.format(s), mu=0, sigma=0.05, shape=n_genes) for s in range(n_samples)]

f_f = [[gp_f[s][i].prior(name, Xs=tiles_axes[s]) for i, name in enumerate(['f_f_{}_{}'.format(i, s) for i in range(n_factors - 1 + n_aug)])] for s in range(n_samples)]

v = pm.Normal(name + "_rotated_", mu=0.0, sigma=1.0, size=self.N, **kwargs)

lm_n = [pm.Gamma('lm_n_{}'.format(s), mu=50, sigma=100, shape=len(cells_counts[s])) for s in range(n_samples)]

E = [E0 * pm.Beta(f'E_{s}',0.01,1, shape=(n_aug, n_genes)) for s in range(n_samples)]

Instantiation of :class:`aeppl.transforms.LogOddsTransform`

M_aug_de = [pm.Lognormal(f'M_aug_{s}', mu=0.0, sigma=0.05, shape=(n_factors, n_genes)) for s in range(n_samples)]

z = 1 - pm.Beta('z', 1, 25, shape=n_genes)/4 ## ie ~1% probe confusion

#section support_code_apply

int APPLY_SPECIFIC(cpu_dimshuffle)(PyArrayObject *input, PyArrayObject **res,
                                   PARAMS_TYPE *params) {

  // This points to either the original input or a copy we create below.
  // Either way, this is what we should be working on/with.
  PyArrayObject *_input;

  if (*res)
    Py_XDECREF(*res);

  if (params->inplace) {
    _input = input;
    Py_INCREF((PyObject *)_input);
  } else {
    _input = (PyArrayObject *)PyArray_FromAny(
        (PyObject *)input, NULL, 0, 0, NPY_ARRAY_ALIGNED | NPY_ARRAY_ENSURECOPY,
        NULL);
  }

  PyArray_Dims permute;

  if (!PyArray_IntpConverter((PyObject *)params->transposition, &permute)) {
    return 1;
  }

  /*
    res = res.transpose(self.transposition)
  */
  PyArrayObject *transposed_input =
      (PyArrayObject *)PyArray_Transpose(_input, &permute);

  Py_DECREF(_input);

  PyDimMem_FREE(permute.ptr);

  npy_intp *res_shape = PyArray_DIMS(transposed_input);
  npy_intp N_shuffle = PyArray_SIZE(params->shuffle);
  npy_intp N_augment = PyArray_SIZE(params->augment);
  npy_intp N = N_augment + N_shuffle;
  npy_intp *_reshape_shape = PyDimMem_NEW(N);

  if (_reshape_shape == NULL) {
    PyErr_NoMemory();
    return 1;
  }

  /*
    shape = list(res.shape[: len(self.shuffle)])
    for augm in self.augment:
        shape.insert(augm, 1)
  */
  npy_intp aug_idx = 0;
  int res_idx = 0;
  for (npy_intp i = 0; i < N; i++) {
    if (aug_idx < N_augment &&
        i == *((npy_intp *)PyArray_GetPtr(params->augment, &aug_idx))) {
      _reshape_shape[i] = 1;
      aug_idx++;
    } else {
      _reshape_shape[i] = res_shape[res_idx];
      res_idx++;
    }
  }

  PyArray_Dims reshape_shape = {.ptr = _reshape_shape, .len = (int)N};

  /* res = res.reshape(shape) */
  *res = (PyArrayObject *)PyArray_Newshape(transposed_input, &reshape_shape,
                                           NPY_CORDER);

  Py_DECREF(transposed_input);

  PyDimMem_FREE(reshape_shape.ptr);

  if (!*res) {
    return 1;
  }

  return 0;
}

  if (*res)

  } else {
        NULL);
  }

    return 1;
  }

  /*
  */

    return 1;
  }

  /*
  */
    } else {
    }
  }

    return 1;
  }

  return 0;
}

chols = [cholesky(stabilize(cov(X), jitter)) for cov, X in zip(self.cov_funcs, Xs)]

e^`a`

    Notes
    -----

elementwise multiplication

    Notes
    -----

    Clip x to be between min and max.

    Note that when `x` is equal to the boundaries, the output is considered
    to be `x`, so at these points, the gradient of the cost wrt the output
    will be propagated to `x`, not to `min` nor `max`. In other words,
    on these points, the gradient wrt `x` will be equal to the gradient wrt
    the output, and the gradient wrt `min` and `max` will be zero.

    Notes
    -----

cov_func2_f = [[pm.gp.cov.ExpQuad(1, ls=2.5*np.sqrt(scale))  for i in range(n_factors-1 + n_aug)] for s in range(n_samples)]

square of a

    Notes
    -----

cov_func1_f = [[pm.gp.cov.ExpQuad(1, ls=2.5*np.sqrt(scale)) for i in range(n_factors-1 + n_aug)] for s in range(n_samples)]

F = [pm.Deterministic('F_{}'.format(s), at.exp(F_matrix[s] * 2) / at.exp(F_matrix[s] * 2).sum(axis=1)[:,None]) for s in range(n_samples)]

pois_n = [pm.Poisson('n_{}'.format(s), lm_n[s], observed=cells_counts[s]) for s in range(n_samples)]

theta_pure = [pm.Deterministic('theta_{}'.format(s), at.dot(F[s], at.concatenate([M,E[s]]))) for s in range(n_samples)]

theta = [pm.Deterministic('theta_mod_{}'.format(s), at.dot(F[s], at.concatenate([M_aug * M_aug_de[s] , E[s]], axis=0))) for s in range(n_samples)] #* M_aug_de[s]

M_de = at.pow(M_de, at.concatenate([at.ones((n_factors-n_wt,1)), 2 * at.ones((n_wt,1))],axis=0))

elementwise power

    Notes
    -----

beta_prior = [pm.Potential('beta_prior_{}'.format(s), beta_sum_logp(T_freq[s][:], (mask[s].sum()), (wgs_mut[s]/u + 1), (wgs_wt[s]/u + 1)) ) for s in range(n_samples)]

beta_logp = logpow(value, alpha - 1) + logpow(1 - value, beta - 1) - betaln(alpha, beta)

elementwise subtraction

    Notes
    -----

return at.switch(at.eq(x, 0), at.switch(at.eq(m, 0), 0.0, -np.inf), m * at.log(x))

if cond then ift else iff

    Notes
    -----

a == b

    Notes
    -----

T_freq = [T[s][[np.where(tree.columns == gene + 'mut')[0][0] for gene in wgs_names]] \

T = [at.dot(F[s].sum(axis=0), at.concatenate([M, E[s]], axis=0)) for s in range(n_samples)] ## Just sum actual genotypes

/ (T[s][[np.where(tree.columns == gene + 'wt')[0][0] for gene in wgs_names]] \

+ T[s][[np.where(tree.columns == gene + 'mut')[0][0] for gene in wgs_names]]) for s in range(n_samples)]

base e logarithm of a

    Notes
    -----

log gamma function

    Notes
    -----

lm_er = [pm.Deterministic('lm_er_{}'.format(s), at.dot(lm[s], confusion_matrix) + xi[s][None,:]) for s in range(n_samples)]

lm = [theta[s]  * lm_n[s][:, None] * r[s][None,:] for s in range(n_samples)]

confusion_matrix = at.set_subtensor(confusion_matrix[i, j], z[i])#

confusion_matrix = at.set_subtensor(confusion_matrix[i, j], 1-z[i])

confusion_matrix = at.set_subtensor(confusion_matrix[i, j], 1-z[i])#

confusion_matrix = at.set_subtensor(confusion_matrix[i, j], z[i])

signal = [pm.NegativeBinomial('exp_{}'.format(s), mu = lm_er[s][mask[s],:], alpha=o, observed=iss_data[s][mask[s],:]) for s in range(n_samples)]

vr = self.input[..., start_idx : start_idx + size].reshape(shape).astype(dtype)

self.input = self._input_type(self.__class__.__name__ + "_symbolic_input")

self.shared_params = self.create_shared_params(self._kwargs.get("start", None))