httk.core package

Submodules

httk.core.basic module

Basic help functions

httk.core.basic.anonymous_symbol_to_int(symb)[source]
httk.core.basic.breath_first_idxs(dim=1, start=None, end=None, perm=True, negative=False)[source]
httk.core.basic.create_tmpdir()[source]
httk.core.basic.destroy_tmpdir(tmpdir)[source]
httk.core.basic.flatten(l)[source]
httk.core.basic.int_to_anonymous_symbol(i)[source]
httk.core.basic.is_sequence(arg)[source]
httk.core.basic.is_unary(e)[source]
httk.core.basic.main()[source]
httk.core.basic.micro_pyawk(ioa, search, results=None, debug=False, debugfunc=None, postdebugfunc=None)[source]

Small awk-mimicking search routine.

‘f’ is stream object to search through. ‘search’ is the “search program”, a list of lists/tuples with 3 elements; i.e., [[regex,test,run],[regex,test,run],...] ‘results’ is a an object that your search program will have access to for storing results.

Here regex is either as a Regex object, or a string that we compile into a Regex. test and run are callable objects.

This function goes through each line in filename, and if regex matches that line and test(results,line)==True (or test == None) we execute run(results,match), where match is the match object from running Regex.match.

The default results is an empty dictionary. Passing a results object let you interact with it in run() and test(). Hence, in many occasions it is thus clever to use results=self.

Returns: results

httk.core.basic.mkdir_p(path)[source]
httk.core.basic.nested_split(s, start, stop)[source]
httk.core.basic.parse_parexpr(string)[source]

Generate parenthesized contents in string as pairs (level, contents).

httk.core.basic.tuple_to_str(t)[source]

httk.core.citation module

Keep track of citation information for different parts of httk, so that this info can be printed out on program exit. Turn on either explicitly by calling httk.config.print_citations_at_exit() from your program, or implicitly for all software using httk by setting ‘auto_print_citations_at_exit=yes’ in httk.cfg

Right now this is mostly a proof of concept code, and was added in response to a concern that co-authors of the software would not get credit. We should extend this to add a facility to make it easier to track and acknowledge citations also of the data being used.

httk.core.citation.add_ext_citation(software, author)[source]
httk.core.citation.add_src_citation(module, author)[source]
httk.core.citation.dont_print_citations_at_exit()[source]
httk.core.citation.print_citations()[source]
httk.core.citation.print_citations_at_exit()[source]

httk.core.code module

class httk.core.code.Code(name, version)[source]

Bases: httk.core.httkobject.HttkObject

Object for keeping track of httk data about a computer software or script

add_ref(ref)[source]
add_refs(refs)[source]
add_tag(tag, val)[source]
add_tags(tags)[source]
classmethod create(name, version, refs=None, tags=None)[source]

Create a Computation object.

get_refs()[source]
get_tag(tag)[source]
get_tags()[source]
class httk.core.code.CodeRef(code, reference)[source]

Bases: httk.core.httkobject.HttkObject

class httk.core.code.CodeTag(structure, tag, value)[source]

Bases: httk.core.httkobject.HttkObject

httk.core.code.main()[source]

httk.core.computation module

class httk.core.computation.Computation(computation_date, description, code, manifest_hash, signatures, keys, relpath, project_counter, added_date=None)[source]

Bases: httk.core.httkobject.HttkObject

Object for keeping track of httk data about a specific computation run

add_project(project)[source]
add_projects(projects)[source]
add_ref(ref)[source]
add_refs(refs)[source]
add_tag(tag, val)[source]
add_tags(tags)[source]
added_date
classmethod create(computation_date, description, code, manifest_hash, signatures, keys, project_counter, relpath, added_date=None)[source]

Create a Computation object.

get_projects()[source]
get_refs()[source]
get_tag(tag)[source]
get_tags()[source]
class httk.core.computation.ComputationProject(computation, project)[source]

Bases: httk.core.httkobject.HttkObject

classmethod create(computation, project)[source]

Create a Computation object.

class httk.core.computation.ComputationRef(computation, reference)[source]

Bases: httk.core.httkobject.HttkObject

class httk.core.computation.ComputationRelated(main_computation, other_computation, relation)[source]

Bases: httk.core.httkobject.HttkObject

Object for keeping track of httk data about a specific computation run

classmethod create(main_computation, other_computation, relation)[source]

Create a Computation object.

class httk.core.computation.ComputationTag(computation, tag, value)[source]

Bases: httk.core.httkobject.HttkObject

class httk.core.computation.Result(computation)[source]

Bases: httk.core.httkobject.HttkObject

Intended as a base class for results tables for computations

classmethod create(computation)[source]

Create a Computation object.

httk.core.computation.main()[source]

httk.core.crypto module

Provides a few central and very helpful functions for cryptographic hashes, etc.

httk.core.crypto.generate_keys(public_key_path, secret_key_path)[source]

Generates a public and a private key pair and stores them in respective files

httk.core.crypto.get_crypto_signature(message, secret_key_path)[source]
httk.core.crypto.hexhash_ioa(ioa, prepend=None)[source]
httk.core.crypto.hexhash_str(data, prepend=None)[source]
httk.core.crypto.main()[source]
httk.core.crypto.manifest_dir(basedir, manifestfile, excludespath, keydir, sk, pk, debug=False, force=False)[source]
httk.core.crypto.read_keys(keydir)[source]
httk.core.crypto.sha256file(filename)[source]
httk.core.crypto.tuple_to_hexhash(t)[source]
httk.core.crypto.tuple_to_str(t)[source]
httk.core.crypto.verify_crytpo_signature(signature, message, public_key)[source]
httk.core.crypto.verify_crytpo_signature_old(signature, message, public_key_path)[source]

httk.core.ed25519 module

httk.core.ed25519.H(m)[source]
httk.core.ed25519.Hint(m)[source]
httk.core.ed25519.bit(h, i)[source]
httk.core.ed25519.checkvalid(s, m, pk)[source]
httk.core.ed25519.decodeint(s)[source]
httk.core.ed25519.decodepoint(s)[source]
httk.core.ed25519.edwards(P, Q)[source]
httk.core.ed25519.encodeint(y)[source]
httk.core.ed25519.encodepoint(P)[source]
httk.core.ed25519.expmod(b, e, m)[source]
httk.core.ed25519.inv(x)[source]
httk.core.ed25519.isoncurve(P)[source]
httk.core.ed25519.main()[source]
httk.core.ed25519.publickey(sk)[source]
httk.core.ed25519.scalarmult(P, e)[source]
httk.core.ed25519.signature(m, sk, pk)[source]
httk.core.ed25519.xrecover(y)[source]

httk.core.fracvector module

class httk.core.fracvector.FracScalar(nom, denom)[source]

Bases: httk.core.fracvector.FracVector

Represents the fractional number nom/denom. This is a subclass of FracVector with the purpose of making it clear when a scalar fracvector is needed/used.

classmethod create(nom, denom=None, simplify=True)[source]

Create a FracScalar.

FracScalar(something)
something may be any object that can be used in the constructor of the Python Fraction class (also works with strings!).
class httk.core.fracvector.FracVector(noms, denom=1)[source]

Bases: object

FracVector is a general immutable N-dimensional vector (tensor) class for performing linear algebra with fractional numbers.

A FracVector consists of a multidimensional tuple of integer nominators, and a single shared integer denominator.

Since FracVectors are immutable, every operation on a FracVector returns a new FracVector with the result of the operation. A created FracVector never changes. Hence, they are safe to use as keys in dictionaries, to use in sets, etc.

Note: most methods returns FracVector results that are not simplified (i.e., the FracVector returned does not have the smallest possible integer denominator). To return a FracVector with the smallest possible denominator, just call FracVector.simplify() at the last step.

T()[source]

Returns the transpose, A^T.

argmax()[source]

Return the index of the maximum element across all dimensions in the FracVector.

argmin()[source]

Return the index of the minimum element across all dimensions in the FracVector.

ceil()[source]

Returns the integer that is equal to or just below the value stored in a scalar FracVector.

classmethod chain_vecs(vecs)[source]

Optimized chaining of FracVectors.

vecs: a list (or tuple) of fracvectors.

Returns the same thing as
FracVector.create(vecs,chain=True)
i.e., removes outermost dimension and chain the sub-sequences. If input=[[1 2 3],[4,5,6]], then
FracVector.chain(input) -> [1,2,3,4,5,6]

but this method assumes all vectors share the same denominator (it raises an exception if this is not true)

classmethod create(noms, denom=None, simplify=True, chain=False)[source]

Create a FracVector from various types of sequences.

Simplest use:

FracVector.create(some_kind_of_sequence)

where ‘some_kind_of_sequence’ can be any nested list or tuple of objects that can be used in the constructor of the Python Fraction class (also works with strings!). If any object found while traveling the items has a .to_fractions() method, it will be called and is expected to return a fraction or list or tuple of fractions.

Optional parameters:

  • Invocation with denominator: FracVector.create(nominators,denominator) nominators is any sequence, and denominator a common denominator to divide all nominators with
  • simplify: boolean, return a FracVector with the smallest possible denominator.
  • chain: boolean, remove outermost dimension and chain the sub-sequences. I.e., if input=[[1 2 3],[4,5,6]], then FracVector.create(input) -> [1,2,3,4,5,6]

Relevant: FracVector itself implements .to_fractions(), and hence, the same constructor allows stacking several FracVector objects like this:

vertical_fracvector = FracVector([[fracvector1],[fracvector2]])
horizontal_fracvector = FracVector([fracvector1,fracvector2],chain=True)
cross(other)[source]

Returns the vector cross product of the 3-element 1D vector with the 3-element 1D vector ‘other’, i.e., A x B.

det()[source]

Returns the determinant of the FracVector as a scalar FracVector.

dim

This property returns a tuple with the dimensionality of each dimension of the FracVector (the noms are assumed to be a nested list of rectangular shape).

dot(other)[source]

Returns the vector dot product of the 1D vector with the 1D vector ‘other’, i.e., A . B or A cdot B. The same as A * B.T().

classmethod eye(dims)[source]

Create a diagonal one-matrix with the given dimensions

flatten()[source]

Returns a FracVector that has been flattened out to a single rowvector

floor()[source]

Returns the integer that is equal to or just below the value stored in a scalar FracVector.

classmethod from_floats(l, resolution=4294967296)[source]

Create a FracVector from a (nested) list or tuple of floats. You can convert a numpy array with this method if you use A.tolist()

resolution: the resolution used for interpreting the given floating point numbers. Default is 2^32.

classmethod from_tuple(t)[source]

Return a FracVector created from the tuple representation: (denom, ...noms...), returned by the to_tuple() method.

ged_prestacked(other)[source]
ged_stackedinsert(pos, other)[source]
get_append(other)[source]
get_extend(other)[source]
get_insert(pos, other)[source]
get_prepend(other)[source]
get_prextend(other)[source]
get_stacked(other)[source]
inv()[source]

Returns the matrix inverse, A^-1

lengthsqr()[source]

Returns the square of the length of the vector. The same as A * A.T()

limit_denominator(max_denom=1000000000)[source]

Returns a FracVector of reduced resolution.

resolution: each element in the returned FracVector is the closest numerical approximation that can is allowed by a fraction with maximally this denominator. Note: since all elements must be put on a common denominator, the result may have a larger denominator than max_denom

max()[source]

Return the maximum element across all dimensions in the FracVector. max(fracvector) works for a 1D vector.

metric_product(vecA, vecB)[source]
Returns the result of the metric product using the present square FracVector as the metric matrix. The same as
vecA*self*vecB.T().
min()[source]

Return the minimum element across all dimensions in the FracVector. max(fracvector) works for a 1D vector.

mul(other)[source]

Returns the result of multiplying the vector with ‘other’ using matrix multiplication.

Note that for two 1D FracVectors, A.dot(B) is not the same as A.mul(B), but rather: A.mul(B.T()).

nargmax()[source]

Return a list of indices of all maximum elements across all dimensions in the FracVector.

nargmin()[source]

Return a list of indices for all minimum elements across all dimensions in the FracVector.

static nested_map(op, *ls)

Map an operator over a nested tuple. (i.e., the same as the built-in map(), but works recursively on a nested tuple)

static nested_map_fractions(op, *ls)

Map an operator over a nested tuple, but checks every element for a method to_fractions() and uses this to further convert objects into tuples of Fraction.

nom

Returns the integer nominator of a scalar FracVector.

normalize()[source]

Add/remove an integer +/-N to each element to place it in the range [0,1)

normalize_half()[source]

Add/remove an integer +/-N to each element to place it in the range [-1/2,1/2)

This is useful to find the shortest vector C between two points A, B in a space with periodic boundary conditions [0,1):
C = (A-B).normalize_half()
classmethod random(dims, minnom=-100, maxnom=100, denom=100)[source]

Create a zero matrix with the given dimensions

reciprocal()[source]
classmethod set_common_denom(A, B)[source]

Used internally to combine two different FracVectors.

Returns a tuple (A2,B2,denom) where A2 is numerically equal to A, and B2 is numerically equal to B, but A2 and B2 are both set on the same shared denominator ‘denom’ which is the product of the denominator of A and B.

set_denominator(set_denom=1000000000)[source]

Returns a FracVector of reduced resolution where every element is the closest numerical approximation using this denominator.

sign()[source]

Returns the sign of the scalar FracVector: -1, 0 or 1.

simplify()[source]

Returns a reduced FracVector. I.e., each element has the same numerical value but the new FracVector represents them using the smallest possible shared denominator.

classmethod stack_vecs(vecs)[source]

Optimized stacking of FracVectors.

vecs = a list (or tuple) of fracvectors.

Returns the same thing as:

FracVector.create(vecs)

but only works if all vectors share the same denominator (raises an exception if this is not true)

to_float()[source]

Converts a scalar ExactVector to a single float.

to_floats()[source]

Converts the ExactVector to a list of floats.

to_fraction()[source]

Converts scalar FracVector to a fraction.

to_fractions()[source]

Converts the FracVector to a list of fractions.

to_int()[source]

Converts scalar FracVector to an integer (truncating as necessary).

to_ints()[source]

Converts the FracVector to a list of integers, rounded off as best possible.

to_tuple()[source]

Return a FracVector on tuple representation: (denom, ...noms...).

classmethod use(old)[source]

Make sure variable is a FracVector, and if not, convert it.

validate()[source]
classmethod zeros(dims)[source]

Create a zero matrix with the given dimensions

httk.core.fracvector.main()[source]
httk.core.fracvector.nested_map_fractions_list(op, *ls)[source]

Map an operator over a nested list, but checks every element for a method to_fractions() and uses this to further convert objects into lists of Fraction.

httk.core.fracvector.nested_map_fractions_tuple(op, *ls)[source]

Map an operator over a nested tuple, but checks every element for a method to_fractions() and uses this to further convert objects into tuples of Fraction.

httk.core.fracvector.nested_map_list(op, *ls)[source]

Map an operator over a nested list. (i.e., the same as the built-in map(), but works recursively on a nested list)

httk.core.fracvector.nested_map_tuple(op, *ls)[source]

Map an operator over a nested tuple. (i.e., the same as the built-in map(), but works recursively on a nested tuple)

httk.core.fracvector.nested_reduce(op, l, initializer=None)[source]

Same as built-in reduce, but operates on a nested tuple/list/sequence.

httk.core.fracvector.nested_reduce_fractions(op, l, initializer=None)[source]

Same as built-in reduce, but operates on a nested tuple/list/sequence. Also checks every element for a method to_fractions() and uses this to further convert such elements to lists of fractions.

httk.core.fracvector.nested_reduce_levels(op, l, level=1, initializer=None)[source]

Same as built-in reduce, but operates on a nested tuple/list/sequence.

httk.core.fracvector.tuple_eye(dims, onepos=0)[source]

Create a matrix with the given dimensions and 1 on the diagonal

httk.core.fracvector.tuple_index(dims, uppidx=())[source]

Create a nested tuple where every element is a tuple indicating the position of that tuple

httk.core.fracvector.tuple_random(dims, minval, maxval)[source]

Create a nested tuple with the given dimensions filled with random numbers between minval and maxval

httk.core.fracvector.tuple_slice(l, key)[source]

Given a python slice (i.e., what you get to __getitem__ when you write A[3:2]), cut out the relevant nested tuple.

httk.core.fracvector.tuple_zeros(dims)[source]

Create a netsted tuple with the given dimensions filled with zeroes

httk.core.geometry module

Basic geometry helper functions

httk.core.geometry.hull_z(points, zs)[source]

points: a list of points=(x,y,..) with zs= a list of z values; a convex half-hull is constructed over negative z-values

returns data on the following format.:

{
  'hull_points': indices in points list for points that make up the convex hull, 
   'interior_points':indices for points in the interior, 
   'interior_zs':interior_zs
   'zs_on_hull': hull z values for each point (for points on the hull, the value of the hull if this point is excluded)
   'closest_points': list of best linear combination of other points for each point 
   'closest_weights': weights of best linear combination of other points for each point 
}

where hull_points and interior_points are lists of the points on the hull and inside the hull. and

hull_zs is a list of z-values that the hull would have at that point, had this point not been included. interior_zs is a list of z-values that the hull has at the interior points.
httk.core.geometry.is_any_part_of_cube_inside_cell(cell, midpoint, side)[source]

Checks if any part of a cube is inside the cell spanned by the vectors in cell

httk.core.geometry.is_point_inside_cell(cell, point)[source]

Checks if a given triple-vector is inside the cell given by the basis matrix in cell

httk.core.geometry.is_point_inside_tetra(tetra, point)[source]

Checks if a point is inside the tretrahedra spanned by the coordinates in tetra

httk.core.geometry.numpy_quickhull_2d(sample)[source]
httk.core.geometry.simplex_le_solver(a, b, c)[source]

Minimizie func = a[0]*x + a[1]*y + a[2]*z + ... With constraints:

b[0,0]x + b[0,1]y + b[0,2]z + ... <= c[0]
b[1,0]x + b[1,1]y + b[1,2]z + ... <= c[1]
...
x,y,z, ... >= 0

Algorithm adapted from ‘taw9’, http://taw9.hubpages.com/hub/Simplex-Algorithm-in-Python

httk.core.httkobject module

class httk.core.httkobject.HttkObject[source]

Bases: object

get_codependent_data()[source]
hexhash[source]
classmethod new_from(other)[source]
to(newtype)[source]
to_tuple(use_hexhash=False)[source]
classmethod types()[source]
classmethod use(old)[source]
class httk.core.httkobject.HttkPlugin[source]

Bases: object

class httk.core.httkobject.HttkPluginPlaceholder(plugininfo=None)[source]

Bases: object

class httk.core.httkobject.HttkPluginWrapper(plugin=None)[source]

Bases: object

class httk.core.httkobject.HttkTypedProperty(property_type, fget=None, fset=None, fdel=None, doc=None)[source]

Bases: property

httk.core.httkobject.httk_typed_init(t, **kargs)[source]
httk.core.httkobject.httk_typed_init_delayed(t, **kargs)[source]
httk.core.httkobject.httk_typed_property(t)[source]
httk.core.httkobject.httk_typed_property_delayed(t)[source]
httk.core.httkobject.httk_typed_property_resolve(cls, propname)[source]

httk.core.ioadapters module

class httk.core.ioadapters.IoAdapterFileAppender(f, name=None)[source]

Bases: object

Io adapter for access to data as a python file object

close()[source]
classmethod use(other)[source]
class httk.core.ioadapters.IoAdapterFileReader(f, name=None, deletefilename=None, close=False)[source]

Bases: object

Io adapter for easy handling of io.

close()[source]
classmethod use(other)[source]
class httk.core.ioadapters.IoAdapterFileWriter(f, name=None, close=False)[source]

Bases: object

Io adapter for access to data as a python file object

close()[source]
classmethod use(other)[source]
class httk.core.ioadapters.IoAdapterFilename(filename, name=None, deletefilename=None)[source]

Bases: object

Universal io adapter, helps handling the passing of filenames, files, and strings to functions that deal with io

close()[source]
classmethod use(other)[source]
class httk.core.ioadapters.IoAdapterString(string=None, name=None)[source]

Bases: object

Universal io adapter, helps handling the passing of filenames, files, and strings to functions that deal with io

close()[source]
string
classmethod use(other)[source]
class httk.core.ioadapters.IoAdapterStringList(stringlist, name=None)[source]

Bases: object

Universal io adapter, helps handling the passing of filenames, files, and strings to functions that deal with io

classmethod use(other)[source]
httk.core.ioadapters.cleveropen(filename, mode, *args)[source]
httk.core.ioadapters.universal_opener(other)[source]
httk.core.ioadapters.zdecompressor(f, mode, *args)[source]

Read a classic unix compress .Z type file.

httk.core.mutablefracvector module

class httk.core.mutablefracvector.MutableFracVector(noms, denom)[source]

Bases: httk.core.fracvector.FracVector

Same as FracVector, only, this version allow assignment of elements, e.g.,

mfracvec[2,7] = 5

and, e.g.,

mfracvec[:,7] = [1,2,3,4] 

Other than this, the FracVector methods exist and do the same, i.e., they return copies of the fracvector, rather than modifying it.

However, methods have also been added named with set_* prefixes which performs mutating operations, e.g.,

A.set_T()

replaces A with its own transpose, whereas

A.T()

just returns a new MutableFracVector that is the transpose of A, leaving A unmodified.

classmethod from_FracVector(other)[source]

Create a MutableFracVector from a FracVector.

invalidate()[source]

Internal method to call when MutableFracVector is changed in such a way that cached properties are invalidated (e.g., _dim)

static nested_inmap(op, *ls)

Like inmap, but work for nested lists

static nested_map(op, *ls)

Map an operator over a nested list. (i.e., the same as the built-in map(), but works recursively on a nested list)

static nested_map_fractions(op, *ls)

Map an operator over a nested list, but checks every element for a method to_fractions() and uses this to further convert objects into lists of Fraction.

set_T()[source]

Changes MutableFracVector inline into own transpose: self -> self.T

set_inv()[source]

Changes MutableFracVector inline into own inverse: self -> self^-1

set_negative()[source]

Changes MutableFracVector inline into own negative: self -> -self

set_normalize()[source]

Add/remove an integer +/-N to each element to place it in the range [0,1)

set_normalize_half()[source]

Add/remove an integer +/-N to each element to place it in the range [-1/2,1/2)

This is useful to find the shortest vector C between two points A, B in a space with periodic boundary conditions [0,1):
C = (A-B).normalize_half()
set_set_denominator(resolution=1000000000)[source]

Changes MutableFracVector; reduces resolution.

resolution is the new denominator, each element becomes the closest numerical approximation using this denominator.
set_simplify()[source]

Changes MutableFracVector; reduces any common factor between denominator and all nominators

to_FracVector()[source]

Return a FracVector with the values of this MutableFracVector.

classmethod use(old)[source]

Make sure variable is a MutableFracVector, and if not, convert it.

validate()[source]
httk.core.mutablefracvector.inmap(f, x)[source]

Like built-in map, but work on a list and replace the elements in the list with the result of the mapping.

httk.core.mutablefracvector.list_set_slice(l, key, values)[source]
Given:
l = list, key = python slice (i.e., what you get to __setitem__ when you write A[3:2]=[2,5]) values = a list of values,

change the elements specified by the slice in key to those given by values.

httk.core.mutablefracvector.list_slice(l, key)[source]

Given a python slice (i.e., what you get to __getitem__ when you write A[3:2]), cut out the relevant nested list.

httk.core.mutablefracvector.main()[source]
httk.core.mutablefracvector.nested_inmap_list(op, *ls)[source]

Like inmap, but work for nested lists

httk.core.project module

class httk.core.project.Project(name, description, project_key, keys)[source]

Bases: httk.core.httkobject.HttkObject

add_ref(ref)[source]
add_refs(refs)[source]
add_tag(tag, val)[source]
add_tags(tags)[source]
classmethod create(name, description, project_key, keys)[source]

Create a Project object.

get_refs()[source]
get_tag(tag)[source]
get_tags()[source]
class httk.core.project.ProjectOwner(project, owner_key)[source]

Bases: httk.core.httkobject.HttkObject

classmethod create(project, owner)[source]

Create a Project object.

class httk.core.project.ProjectRef(project, reference)[source]

Bases: httk.core.httkobject.HttkObject

class httk.core.project.ProjectTag(project, tag, value)[source]

Bases: httk.core.httkobject.HttkObject

httk.core.project.main()[source]

httk.core.reference module

class httk.core.reference.Author(last_name, given_names)[source]

Bases: httk.core.httkobject.HttkObject

Object for keeping track of tags for other objects

classmethod create(last_name, given_names)[source]

Create a Author object.

class httk.core.reference.Reference(ref, authors=None, authorsstr=None, journal=None, volume=None, firstpage=None, lastpage=None, year=None, publisher=None, publisher_extra=None)[source]

Bases: httk.core.httkobject.HttkObject

A reference citation

classmethod create(ref, authors=None)[source]

Create a Reference object.

httk.core.reference.main()[source]

httk.core.signature module

class httk.core.signature.Signature(signature_data, key)[source]

Bases: httk.core.httkobject.HttkObject

classmethod create(signature_data, key)[source]

Create a Computation object.

class httk.core.signature.SignatureKey(keydata, description)[source]

Bases: httk.core.httkobject.HttkObject

classmethod create(keydata, description)[source]

Create a Computation object.

httk.core.signature.main()[source]

httk.core.template module

httk.core.template.apply_template(template, output, envglobals=None, envlocals=None)[source]

Simple Python template engine.

The file ‘template’ is turned into a new file ‘output’ replacing the following: $name -> the value of the variable ‘name’ in the scope provided by locals and globals. $(python statement) -> result of evaluating the python statment. ${some python code} -> text on stdout from running that python code.

Note: it is safe for the code inside the template to load the file it eventually will replace.

httk.core.template.apply_templates(inputpath, outpath, template_suffixes='template', envglobals=None, envlocals=None, mkdir=True)[source]

Apply one or a series of templates throughout directory tree.

template_suffixes: string or list of strings that are the suffixes of templates that are to be applied. name: subdirectory in which to apply the template, defaults to last subrun created, or ‘.’ if no subrun have been created.

Module contents

httk core module

Basic utilities and data definitions that are used throughout the httk code.

A few of the most important components:
fracvector: our general matrix object used to allow exact representation of arrays to allow, e.g., exact matching
of coordinates to existing structures in the database.

ioadapters: our classes for generic handling of IO to files, streams, etc.

structure: our basic definition of a “structure of atoms”