\":\n ids.append(l[1:-1])\n return ids\n\n\ndef get_closest_residue_position(hier, resindex, terminus=\"N\"):\n '''\n this function works with plain hierarchies, as read from the pdb,\n no multi-scale hierarchies\n '''\n p = []\n niter = 0\n while len(p) == 0:\n niter += 1\n sel = IMP.atom.Selection(hier, residue_index=resindex,\n atom_type=IMP.atom.AT_CA)\n\n if terminus == \"N\":\n resindex += 1\n if terminus == \"C\":\n resindex -= 1\n\n if niter >= 10000:\n print(\"get_closest_residue_position: exiting while loop without result\")\n break\n p = sel.get_selected_particles()\n\n if len(p) == 1:\n return IMP.core.XYZ(p[0]).get_coordinates()\n elif len(p) == 0:\n print(\"get_closest_residue_position: got NO residues for hierarchy %s and residue %i\" % (hier, resindex))\n raise Exception(\"get_closest_residue_position: got NO residues for hierarchy %s and residue %i\" % (\n hier, resindex))\n else:\n raise ValueError(\"got multiple residues for hierarchy %s and residue %i; the list of particles is %s\" % (hier, resindex, str([pp.get_name() for pp in p])))\n\ndef get_terminal_residue(representation, hier, terminus=\"C\", resolution=1):\n '''\n Get the particle of the terminal residue at the GIVEN resolution\n (NOTE: not the closest resolution!).\n To get the terminal residue at the closest resolution use:\n particles=IMP.pmi.tools.select_by_tuple(representation,molecule_name)\n particles[0] and particles[-1] will be the first and last particles\n corresponding to the two termini.\n It is needed for instance to determine the last residue of a pdb.\n @param hier hierarchy containing the terminal residue\n @param terminus either 'N' or 'C'\n @param resolution resolution to use.\n '''\n termresidue = None\n termparticle = None\n\n ps=select(representation,\n resolution=resolution,\n hierarchies=[hier])\n\n for p in ps:\n if IMP.pmi.Resolution(p).get_resolution() == resolution:\n residues = IMP.pmi.tools.get_residue_indexes(p)\n if terminus == \"C\":\n if termresidue is None:\n termresidue = max(residues)\n termparticle = p\n elif max(residues) >= termresidue:\n termresidue = max(residues)\n termparticle = p\n elif terminus == \"N\":\n if termresidue is None:\n termresidue = min(residues)\n termparticle = p\n elif min(residues) <= termresidue:\n termresidue = min(residues)\n termparticle = p\n else:\n raise ValueError(\"terminus argument should be either N or C\")\n return termparticle\n\ndef get_terminal_residue_position(representation, hier, terminus=\"C\",\n resolution=1):\n \"\"\"Get XYZ coordinates of the terminal residue at the GIVEN resolution\"\"\"\n p = get_terminal_residue(representation, hier, terminus, resolution)\n return IMP.core.XYZ(p).get_coordinates()\n\ndef get_residue_gaps_in_hierarchy(hierarchy, start, end):\n '''\n Return the residue index gaps and contiguous segments in the hierarchy.\n\n @param hierarchy hierarchy to examine\n @param start first residue index\n @param end last residue index\n\n @return A list of lists of the form\n [[1,100,\"cont\"],[101,120,\"gap\"],[121,200,\"cont\"]]\n '''\n gaps = []\n for n, rindex in enumerate(range(start, end + 1)):\n sel = IMP.atom.Selection(hierarchy, residue_index=rindex,\n atom_type=IMP.atom.AT_CA)\n\n if len(sel.get_selected_particles()) == 0:\n if n == 0:\n # set the initial condition\n rindexgap = start\n rindexcont = start - 1\n if rindexgap == rindex - 1:\n # residue is contiguous with the previously discovered gap\n gaps[-1][1] += 1\n else:\n # residue is not contiguous with the previously discovered gap\n # hence create a new gap tuple\n gaps.append([rindex, rindex, \"gap\"])\n # update the index of the last residue gap\n rindexgap = rindex\n else:\n if n == 0:\n # set the initial condition\n rindexgap = start - 1\n rindexcont = start\n if rindexcont == rindex - 1:\n # residue is contiguous with the previously discovered\n # continuous part\n gaps[-1][1] += 1\n else:\n # residue is not contiguous with the previously discovered continuous part\n # hence create a new cont tuple\n gaps.append([rindex, rindex, \"cont\"])\n # update the index of the last residue gap\n rindexcont = rindex\n return gaps\n\n\nclass map(object):\n\n def __init__(self):\n self.map = {}\n\n def set_map_element(self, xvalue, yvalue):\n self.map[xvalue] = yvalue\n\n def get_map_element(self, invalue):\n if type(invalue) == float:\n n = 0\n mindist = 1\n for x in self.map:\n dist = (invalue - x) * (invalue - x)\n\n if n == 0:\n mindist = dist\n minx = x\n if dist < mindist:\n mindist = dist\n minx = x\n n += 1\n return self.map[minx]\n elif type(invalue) == str:\n return self.map[invalue]\n else:\n raise TypeError(\"wrong type for map\")\n\n\ndef select(representation,\n resolution=None,\n hierarchies=None,\n selection_arguments=None,\n name=None,\n name_is_ambiguous=False,\n first_residue=None,\n last_residue=None,\n residue=None,\n representation_type=None):\n '''\n this function uses representation=SimplifiedModel\n it returns the corresponding selected particles\n representation_type=\"Beads\", \"Res:X\", \"Densities\", \"Representation\", \"Molecule\"\n '''\n\n if resolution is None:\n allparticles = IMP.atom.get_leaves(representation.prot)\n resolution_particles = None\n hierarchies_particles = None\n names_particles = None\n residue_range_particles = None\n residue_particles = None\n representation_type_particles = None\n\n if not resolution is None:\n resolution_particles = []\n hs = representation.get_hierarchies_at_given_resolution(resolution)\n for h in hs:\n resolution_particles += IMP.atom.get_leaves(h)\n\n if not hierarchies is None:\n hierarchies_particles = []\n for h in hierarchies:\n hierarchies_particles += IMP.atom.get_leaves(h)\n\n if not name is None:\n names_particles = []\n if name_is_ambiguous:\n for namekey in representation.hier_dict:\n if name in namekey:\n names_particles += IMP.atom.get_leaves(\n representation.hier_dict[namekey])\n elif name in representation.hier_dict:\n names_particles += IMP.atom.get_leaves(representation.hier_dict[name])\n else:\n print(\"select: component %s is not there\" % name)\n\n if not first_residue is None and not last_residue is None:\n sel = IMP.atom.Selection(representation.prot,\n residue_indexes=range(first_residue, last_residue + 1))\n residue_range_particles = [IMP.atom.Hierarchy(p)\n for p in sel.get_selected_particles()]\n\n if not residue is None:\n sel = IMP.atom.Selection(representation.prot, residue_index=residue)\n residue_particles = [IMP.atom.Hierarchy(p)\n for p in sel.get_selected_particles()]\n\n if not representation_type is None:\n representation_type_particles = []\n if representation_type == \"Molecule\":\n for name in representation.hier_representation:\n for repr_type in representation.hier_representation[name]:\n if repr_type == \"Beads\" or \"Res:\" in repr_type:\n h = representation.hier_representation[name][repr_type]\n representation_type_particles += IMP.atom.get_leaves(h)\n\n elif representation_type == \"PDB\":\n for name in representation.hier_representation:\n for repr_type in representation.hier_representation[name]:\n if repr_type == \"Res:\" in repr_type:\n h = representation.hier_representation[name][repr_type]\n representation_type_particles += IMP.atom.get_leaves(h)\n\n else:\n for name in representation.hier_representation:\n h = representation.hier_representation[\n name][\n representation_type]\n representation_type_particles += IMP.atom.get_leaves(h)\n\n selections = [hierarchies_particles, names_particles,\n residue_range_particles, residue_particles, representation_type_particles]\n\n if resolution is None:\n selected_particles = set(allparticles)\n else:\n selected_particles = set(resolution_particles)\n\n for s in selections:\n if not s is None:\n selected_particles = (set(s) & selected_particles)\n\n return list(selected_particles)\n\n\ndef select_by_tuple(\n representation,\n tupleselection,\n resolution=None,\n name_is_ambiguous=False):\n if isinstance(tupleselection, tuple) and len(tupleselection) == 3:\n particles = IMP.pmi.tools.select(representation, resolution=resolution,\n name=tupleselection[2],\n first_residue=tupleselection[0],\n last_residue=tupleselection[1],\n name_is_ambiguous=name_is_ambiguous)\n elif isinstance(tupleselection, str):\n particles = IMP.pmi.tools.select(representation, resolution=resolution,\n name=tupleselection,\n name_is_ambiguous=name_is_ambiguous)\n else:\n raise ValueError('you passed something bad to select_by_tuple()')\n # now order the result by residue number\n particles = IMP.pmi.tools.sort_by_residues(particles)\n\n return particles\n\ndef select_by_tuple_2(hier,tuple_selection,resolution):\n \"\"\"New tuple format: molname OR (start,stop,molname,copynum,statenum)\n Copy and state are optional. Can also use 'None' for them which will get all.\n You can also pass -1 for stop which will go to the end.\n Returns the particles\n \"\"\"\n kwds = {} # going to accumulate keywords\n kwds['resolution'] = resolution\n if type(tuple_selection) is str:\n kwds['molecule'] = tuple_selection\n elif type(tuple_selection) is tuple:\n rbegin = tuple_selection[0]\n rend = tuple_selection[1]\n kwds['molecule'] = tuple_selection[2]\n try:\n copynum = tuple_selection[3]\n if copynum is not None:\n kwds['copy_index'] = copynum\n except:\n pass\n try:\n statenum = tuple_selection[4]\n if statenum is not None:\n kwds['state_index'] = statenum\n except:\n pass\n if rend==-1:\n if rbegin>1:\n s = IMP.atom.Selection(hier,**kwds)\n s -= IMP.atom.Selection(hier,\n residue_indexes=range(1,rbegin),\n **kwds)\n return s.get_selected_particles()\n else:\n kwds['residue_indexes'] = range(rbegin,rend+1)\n s = IMP.atom.Selection(hier,**kwds)\n return s.get_selected_particles()\n\n\n\ndef get_db_from_csv(csvfilename):\n import csv\n outputlist = []\n with open(csvfilename) as fh:\n csvr = csv.DictReader(fh)\n for l in csvr:\n outputlist.append(l)\n return outputlist\n\n\nclass HierarchyDatabase(object):\n \"\"\"Store the representations for a system.\"\"\"\n\n def __init__(self):\n self.db = {}\n # this dictionary map a particle to its root hierarchy\n self.root_hierarchy_dict = {}\n self.preroot_fragment_hierarchy_dict = {}\n self.particle_to_name = {}\n self.model = None\n\n def add_name(self, name):\n if name not in self.db:\n self.db[name] = {}\n\n def add_residue_number(self, name, resn):\n resn = int(resn)\n self.add_name(name)\n if resn not in self.db[name]:\n self.db[name][resn] = {}\n\n def add_resolution(self, name, resn, resolution):\n resn = int(resn)\n resolution = float(resolution)\n self.add_name(name)\n self.add_residue_number(name, resn)\n if resolution not in self.db[name][resn]:\n self.db[name][resn][resolution] = []\n\n def add_particles(self, name, resn, resolution, particles):\n resn = int(resn)\n resolution = float(resolution)\n self.add_name(name)\n self.add_residue_number(name, resn)\n self.add_resolution(name, resn, resolution)\n self.db[name][resn][resolution] += particles\n for p in particles:\n (rh, prf) = self.get_root_hierarchy(p)\n self.root_hierarchy_dict[p] = rh\n self.preroot_fragment_hierarchy_dict[p] = prf\n self.particle_to_name[p] = name\n if self.model is None:\n self.model = particles[0].get_model()\n\n def get_model(self):\n return self.model\n\n def get_names(self):\n names = list(self.db.keys())\n names.sort()\n return names\n\n def get_particles(self, name, resn, resolution):\n resn = int(resn)\n resolution = float(resolution)\n return self.db[name][resn][resolution]\n\n def get_particles_at_closest_resolution(self, name, resn, resolution):\n resn = int(resn)\n resolution = float(resolution)\n closestres = min(self.get_residue_resolutions(name, resn),\n key=lambda x: abs(float(x) - float(resolution)))\n return self.get_particles(name, resn, closestres)\n\n def get_residue_resolutions(self, name, resn):\n resn = int(resn)\n resolutions = list(self.db[name][resn].keys())\n resolutions.sort()\n return resolutions\n\n def get_molecule_resolutions(self, name):\n resolutions = set()\n for resn in self.db[name]:\n resolutions.update(list(self.db[name][resn].keys()))\n resolutions.sort()\n return resolutions\n\n def get_residue_numbers(self, name):\n residue_numbers = list(self.db[name].keys())\n residue_numbers.sort()\n return residue_numbers\n\n def get_particles_by_resolution(self, name, resolution):\n resolution = float(resolution)\n particles = []\n for resn in self.get_residue_numbers(name):\n result = self.get_particles_at_closest_resolution(\n name,\n resn,\n resolution)\n pstemp = [p for p in result if p not in particles]\n particles += pstemp\n return particles\n\n def get_all_particles_by_resolution(self, resolution):\n resolution = float(resolution)\n particles = []\n for name in self.get_names():\n particles += self.get_particles_by_resolution(name, resolution)\n return particles\n\n def get_root_hierarchy(self, particle):\n prerootfragment = particle\n while IMP.atom.Atom.get_is_setup(particle) or \\\n IMP.atom.Residue.get_is_setup(particle) or \\\n IMP.atom.Fragment.get_is_setup(particle):\n if IMP.atom.Atom.get_is_setup(particle):\n p = IMP.atom.Atom(particle).get_parent()\n elif IMP.atom.Residue.get_is_setup(particle):\n p = IMP.atom.Residue(particle).get_parent()\n elif IMP.atom.Fragment.get_is_setup(particle):\n p = IMP.atom.Fragment(particle).get_parent()\n prerootfragment = particle\n particle = p\n return (\n (IMP.atom.Hierarchy(particle), IMP.atom.Hierarchy(prerootfragment))\n )\n\n def get_all_root_hierarchies_by_resolution(self, resolution):\n hierarchies = []\n resolution = float(resolution)\n particles = self.get_all_particles_by_resolution(resolution)\n for p in particles:\n rh = self.root_hierarchy_dict[p]\n if rh not in hierarchies:\n hierarchies.append(IMP.atom.Hierarchy(rh))\n return hierarchies\n\n def get_preroot_fragments_by_resolution(self, name, resolution):\n fragments = []\n resolution = float(resolution)\n particles = self.get_particles_by_resolution(name, resolution)\n for p in particles:\n fr = self.preroot_fragment_hierarchy_dict[p]\n if fr not in fragments:\n fragments.append(fr)\n return fragments\n\n def show(self, name):\n print(name)\n for resn in self.get_residue_numbers(name):\n print(resn)\n for resolution in self.get_residue_resolutions(name, resn):\n print(\"----\", resolution)\n for p in self.get_particles(name, resn, resolution):\n print(\"--------\", p.get_name())\n\n\ndef get_prot_name_from_particle(p, list_of_names):\n '''Return the component name provided a particle and a list of names'''\n root = p\n protname = root.get_name()\n is_a_bead = False\n while not protname in list_of_names:\n root0 = root.get_parent()\n if root0 == IMP.atom.Hierarchy():\n return (None, None)\n protname = root0.get_name()\n\n # check if that is a bead\n # this piece of code might be dangerous if\n # the hierarchy was called Bead :)\n if \"Beads\" in protname:\n is_a_bead = True\n root = root0\n return (protname, is_a_bead)\n\n\ndef get_residue_indexes(hier):\n '''\n Retrieve the residue indexes for the given particle.\n\n The particle must be an instance of Fragment,Residue, Atom or Molecule\n or else returns an empty list\n '''\n resind = []\n if IMP.atom.Fragment.get_is_setup(hier):\n resind = IMP.atom.Fragment(hier).get_residue_indexes()\n elif IMP.atom.Residue.get_is_setup(hier):\n resind = [IMP.atom.Residue(hier).get_index()]\n elif IMP.atom.Atom.get_is_setup(hier):\n a = IMP.atom.Atom(hier)\n resind = [IMP.atom.Residue(a.get_parent()).get_index()]\n elif IMP.atom.Molecule.get_is_setup(hier):\n resind_tmp=IMP.pmi.tools.OrderedSet()\n for lv in IMP.atom.get_leaves(hier):\n for ind in get_residue_indexes(lv):\n resind_tmp.add(ind)\n resind=list(resind_tmp)\n else:\n resind = []\n return resind\n\n\ndef sort_by_residues(particles):\n particles_residues = [(p, IMP.pmi.tools.get_residue_indexes(p))\n for p in particles]\n sorted_particles_residues = sorted(\n particles_residues,\n key=lambda tup: tup[1])\n particles = [p[0] for p in sorted_particles_residues]\n return particles\n\n\ndef get_residue_to_particle_map(particles):\n # this function returns a dictionary that map particles to residue indexes\n particles = sort_by_residues(particles)\n particles_residues = [(p, IMP.pmi.tools.get_residue_indexes(p))\n for p in particles]\n return dict(zip(particles_residues, particles))\n\n#\n# Parallel Computation\n#\n\n\ndef scatter_and_gather(data):\n \"\"\"Synchronize data over a parallel run\"\"\"\n from mpi4py import MPI\n comm = MPI.COMM_WORLD\n rank = comm.Get_rank()\n number_of_processes = comm.size\n comm.Barrier()\n if rank != 0:\n comm.send(data, dest=0, tag=11)\n\n elif rank == 0:\n for i in range(1, number_of_processes):\n data_tmp = comm.recv(source=i, tag=11)\n if type(data) == list:\n data += data_tmp\n elif type(data) == dict:\n data.update(data_tmp)\n else:\n raise TypeError(\"data not supported, use list or dictionaries\")\n\n for i in range(1, number_of_processes):\n comm.send(data, dest=i, tag=11)\n\n if rank != 0:\n data = comm.recv(source=0, tag=11)\n return data\n\ndef scatter_and_gather_dict_append(data):\n \"\"\"Synchronize data over a parallel run\"\"\"\n from mpi4py import MPI\n comm = MPI.COMM_WORLD\n rank = comm.Get_rank()\n number_of_processes = comm.size\n comm.Barrier()\n if rank != 0:\n comm.send(data, dest=0, tag=11)\n elif rank == 0:\n for i in range(1, number_of_processes):\n data_tmp = comm.recv(source=i, tag=11)\n for k in data:\n data[k]+=data_tmp[k]\n\n for i in range(1, number_of_processes):\n comm.send(data, dest=i, tag=11)\n\n if rank != 0:\n data = comm.recv(source=0, tag=11)\n return data\n\n\n#\n### Lists and iterators\n#\n\ndef sublist_iterator(l, lmin=None, lmax=None):\n '''\n Yield all sublists of length >= lmin and <= lmax\n '''\n if lmin is None:\n lmin = 0\n if lmax is None:\n lmax = len(l)\n n = len(l) + 1\n for i in range(n):\n for j in range(i + 1, n):\n if len(l[i:j]) <= lmax and len(l[i:j]) >= lmin:\n yield l[i:j]\n\n\ndef flatten_list(l):\n return [item for sublist in l for item in sublist]\n\n\ndef list_chunks_iterator(list, length):\n \"\"\" Yield successive length-sized chunks from a list.\n \"\"\"\n for i in range(0, len(list), length):\n yield list[i:i + length]\n\n\ndef chunk_list_into_segments(seq, num):\n seq = list(seq)\n avg = len(seq) / float(num)\n out = []\n last = 0.0\n\n while last < len(seq):\n out.append(seq[int(last):int(last + avg)])\n last += avg\n\n return out\n\n\nclass Segments(object):\n\n ''' This class stores integers\n in ordered compact lists eg:\n [[1,2,3],[6,7,8]]\n the methods help splitting and merging the internal lists\n Example:\n s=Segments([1,2,3]) is [[1,2,3]]\n s.add(4) is [[1,2,3,4]] (add right)\n s.add(3) is [[1,2,3,4]] (item already existing)\n s.add(7) is [[1,2,3,4],[7]] (new list)\n s.add([8,9]) is [[1,2,3,4],[7,8,9]] (add item right)\n s.add([5,6]) is [[1,2,3,4,5,6,7,8,9]] (merge)\n s.remove(3) is [[1,2],[4,5,6,7,8,9]] (split)\n etc.\n '''\n\n def __init__(self,index):\n '''index can be a integer or a list of integers '''\n if type(index) is int:\n self.segs=[[index]]\n elif type(index) is list:\n self.segs=[[index[0]]]\n for i in index[1:]:\n self.add(i)\n\n def add(self,index):\n '''index can be a integer or a list of integers '''\n if type(index) is int:\n mergeleft=None\n mergeright=None\n for n,s in enumerate(self.segs):\n if index in s:\n return 0\n else:\n if s[0]-index==1:\n mergeleft=n\n if index-s[-1]==1:\n mergeright=n\n if mergeright is None and mergeleft is None:\n self.segs.append([index])\n if not mergeright is None and mergeleft is None:\n self.segs[mergeright].append(index)\n if not mergeleft is None and mergeright is None:\n self.segs[mergeleft]=[index]+self.segs[mergeleft]\n if not mergeleft is None and not mergeright is None:\n self.segs[mergeright]=self.segs[mergeright]+[index]+self.segs[mergeleft]\n del self.segs[mergeleft]\n\n for n in range(len(self.segs)):\n self.segs[n].sort()\n\n self.segs.sort(key=lambda tup: tup[0])\n\n elif type(index) is list:\n for i in index:\n self.add(i)\n\n def remove(self,index):\n '''index can be a integer'''\n for n,s in enumerate(self.segs):\n if index in s:\n if s[0]==index:\n self.segs[n]=s[1:]\n elif s[-1]==index:\n self.segs[n]=s[:-1]\n else:\n i=self.segs[n].index(index)\n self.segs[n]=s[:i]\n self.segs.append(s[i+1:])\n for n in range(len(self.segs)):\n self.segs[n].sort()\n if len(self.segs[n])==0:\n del self.segs[n]\n self.segs.sort(key=lambda tup: tup[0])\n\n def get_flatten(self):\n ''' Returns a flatten list '''\n return [item for sublist in self.segs for item in sublist]\n\n\n\n#\n# COORDINATE MANIPULATION\n#\n\n\ndef translate_hierarchy(hierarchy, translation_vector):\n '''\n Apply a translation to a hierarchy along the input vector.\n '''\n rbs = set()\n xyzs = set()\n if type(translation_vector) == list:\n transformation = IMP.algebra.Transformation3D(\n IMP.algebra.Vector3D(translation_vector))\n else:\n transformation = IMP.algebra.Transformation3D(translation_vector)\n for p in IMP.atom.get_leaves(hierarchy):\n if IMP.core.RigidBody.get_is_setup(p):\n rbs.add(IMP.core.RigidBody(p))\n elif IMP.core.RigidMember.get_is_setup(p):\n rb = IMP.core.RigidMember(p).get_rigid_body()\n rbs.add(rb)\n else:\n xyzs.add(p)\n for xyz in xyzs:\n IMP.core.transform(IMP.core.XYZ(xyz), transformation)\n for rb in rbs:\n IMP.core.transform(rb, transformation)\n\n\ndef translate_hierarchies(hierarchies, translation_vector):\n for h in hierarchies:\n IMP.pmi.tools.translate_hierarchy(h, translation_vector)\n\n\ndef translate_hierarchies_to_reference_frame(hierarchies):\n xc = 0\n yc = 0\n zc = 0\n nc = 0\n for h in hierarchies:\n for p in IMP.atom.get_leaves(h):\n coor = IMP.core.XYZ(p).get_coordinates()\n nc += 1\n xc += coor[0]\n yc += coor[1]\n zc += coor[2]\n xc = xc / nc\n yc = yc / nc\n zc = zc / nc\n IMP.pmi.tools.translate_hierarchies(hierarchies, (-xc, -yc, -zc))\n\n\n#\n# Tools to simulate data\n#\n\ndef normal_density_function(expected_value, sigma, x):\n return (\n 1 / math.sqrt(2 * math.pi) / sigma *\n math.exp(-(x - expected_value) ** 2 / 2 / sigma / sigma)\n )\n\n\ndef log_normal_density_function(expected_value, sigma, x):\n return (\n 1 / math.sqrt(2 * math.pi) / sigma / x *\n math.exp(-(math.log(x / expected_value) ** 2 / 2 / sigma / sigma))\n )\n\n\ndef get_random_residue_pairs(representation, resolution,\n number,\n max_distance=None,\n avoid_same_particles=False,\n names=None):\n\n particles = []\n if names is None:\n names=list(representation.hier_dict.keys())\n\n for name in names:\n prot = representation.hier_dict[name]\n particles += select(representation,name=name,resolution=resolution)\n random_residue_pairs = []\n while len(random_residue_pairs)<=number:\n p1 = random.choice(particles)\n p2 = random.choice(particles)\n if max_distance is not None and \\\n core.get_distance(core.XYZ(p1), core.XYZ(p2)) > max_distance:\n continue\n r1 = random.choice(IMP.pmi.tools.get_residue_indexes(p1))\n r2 = random.choice(IMP.pmi.tools.get_residue_indexes(p2))\n if r1==r2 and avoid_same_particles: continue\n name1 = representation.get_prot_name_from_particle(p1)\n name2 = representation.get_prot_name_from_particle(p2)\n random_residue_pairs.append((name1, r1, name2, r2))\n\n return random_residue_pairs\n\n\ndef get_random_data_point(\n expected_value,\n ntrials,\n sensitivity,\n sigma,\n outlierprob,\n begin_end_nbins_tuple,\n log=False,\n loggrid=False):\n import random\n\n begin = begin_end_nbins_tuple[0]\n end = begin_end_nbins_tuple[1]\n nbins = begin_end_nbins_tuple[2]\n\n if not loggrid:\n fmod_grid = get_grid(begin, end, nbins, True)\n else:\n fmod_grid = get_log_grid(begin, end, nbins)\n\n norm = 0\n cumul = []\n cumul.append(0)\n\n a = []\n for i in range(0, ntrials):\n a.append([random.random(), True])\n\n if sigma != 0.0:\n for j in range(1, len(fmod_grid)):\n fj = fmod_grid[j]\n fjm1 = fmod_grid[j - 1]\n df = fj - fjm1\n\n if not log:\n pj = normal_density_function(expected_value, sigma, fj)\n pjm1 = normal_density_function(expected_value, sigma, fjm1)\n else:\n pj = log_normal_density_function(expected_value, sigma, fj)\n pjm1 = log_normal_density_function(expected_value, sigma, fjm1)\n\n norm += (pj + pjm1) / 2.0 * df\n cumul.append(norm)\n # print fj, pj\n\n random_points = []\n\n for i in range(len(cumul)):\n # print i,a, cumul[i], norm\n for aa in a:\n if (aa[0] <= cumul[i] / norm and aa[1]):\n random_points.append(\n int(fmod_grid[i] / sensitivity) * sensitivity)\n aa[1] = False\n\n else:\n random_points = [expected_value] * ntrials\n\n for i in range(len(random_points)):\n if random.random() < outlierprob:\n a = random.uniform(begin, end)\n random_points[i] = int(a / sensitivity) * sensitivity\n print(random_points)\n '''\n for i in range(ntrials):\n if random.random() > OUTLIERPROB_:\n r=truncnorm.rvs(0.0,1.0,expected_value,BETA_)\n if r>1.0: print r,expected_value,BETA_\n else:\n r=random.random()\n random_points.append(int(r/sensitivity)*sensitivity)\n '''\n\n rmean = 0.\n rmean2 = 0.\n for r in random_points:\n rmean += r\n rmean2 += r * r\n\n rmean /= float(ntrials)\n rmean2 /= float(ntrials)\n stddev = math.sqrt(max(rmean2 - rmean * rmean, 0.))\n return rmean, stddev\n\ndef print_multicolumn(list_of_strings, ncolumns=2, truncate=40):\n\n l = list_of_strings\n\n cols = ncolumns\n # add empty entries after l\n for i in range(len(l) % cols):\n l.append(\" \")\n\n split = [l[i:i + len(l) / cols] for i in range(0, len(l), len(l) / cols)]\n for row in zip(*split):\n print(\"\".join(str.ljust(i, truncate) for i in row))\n\nclass ColorChange(object):\n '''Change color code to hexadecimal to rgb'''\n def __init__(self):\n self._NUMERALS = '0123456789abcdefABCDEF'\n self._HEXDEC = dict((v, int(v, 16)) for v in (x+y for x in self._NUMERALS for y in self._NUMERALS))\n self.LOWERCASE, self.UPPERCASE = 'x', 'X'\n\n def rgb(self,triplet):\n return float(self._HEXDEC[triplet[0:2]]), float(self._HEXDEC[triplet[2:4]]), float(self._HEXDEC[triplet[4:6]])\n\n def triplet(self,rgb, lettercase=None):\n if lettercase is None: lettercase=self.LOWERCASE\n return format(rgb[0]<<16 | rgb[1]<<8 | rgb[2], '06'+lettercase)\n\n# -------------- Collections --------------- #\n\nclass OrderedSet(collections.MutableSet):\n\n def __init__(self, iterable=None):\n self.end = end = []\n end += [None, end, end] # sentinel node for doubly linked list\n self.map = {} # key --> [key, prev, next]\n if iterable is not None:\n self |= iterable\n\n def __len__(self):\n return len(self.map)\n\n def __contains__(self, key):\n return key in self.map\n\n def add(self, key):\n if key not in self.map:\n end = self.end\n curr = end[1]\n curr[2] = end[1] = self.map[key] = [key, curr, end]\n\n def discard(self, key):\n if key in self.map:\n key, prev, next = self.map.pop(key)\n prev[2] = next\n next[1] = prev\n\n def __iter__(self):\n end = self.end\n curr = end[2]\n while curr is not end:\n yield curr[0]\n curr = curr[2]\n\n def __reversed__(self):\n end = self.end\n curr = end[1]\n while curr is not end:\n yield curr[0]\n curr = curr[1]\n\n def pop(self, last=True):\n if not self:\n raise KeyError('set is empty')\n if last:\n key = self.end[1][0]\n else:\n key = self.end[2][0]\n self.discard(key)\n return key\n\n def __repr__(self):\n if not self:\n return '%s()' % (self.__class__.__name__,)\n return '%s(%r)' % (self.__class__.__name__, list(self))\n\n def __eq__(self, other):\n if isinstance(other, OrderedSet):\n return len(self) == len(other) and list(self) == list(other)\n return set(self) == set(other)\n\n\nclass OrderedDefaultDict(OrderedDict):\n \"\"\"Store objects in order they were added, but with default type.\n Source: http://stackoverflow.com/a/4127426/2608793\n \"\"\"\n def __init__(self, *args, **kwargs):\n if not args:\n self.default_factory = None\n else:\n if not (args[0] is None or callable(args[0])):\n raise TypeError('first argument must be callable or None')\n self.default_factory = args[0]\n args = args[1:]\n super(OrderedDefaultDict, self).__init__(*args, **kwargs)\n\n def __missing__ (self, key):\n if self.default_factory is None:\n raise KeyError(key)\n self[key] = default = self.default_factory()\n return default\n\n def __reduce__(self): # optional, for pickle support\n args = (self.default_factory,) if self.default_factory else ()\n return self.__class__, args, None, None, self.iteritems()\n\n# -------------- PMI2 Tools --------------- #\n\ndef set_coordinates_from_rmf(hier,rmf_fn,frame_num=0):\n \"\"\"Extract frame from RMF file and fill coordinates. Must be identical topology.\n @param hier The (System) hierarchy to fill (e.g. after you've built it)\n @param rmf_fn The file to extract from\n @param frame_num The frame number to extract\n \"\"\"\n rh = RMF.open_rmf_file_read_only(rmf_fn)\n IMP.rmf.link_hierarchies(rh,[hier])\n IMP.rmf.load_frame(rh, RMF.FrameID(frame_num))\n del rh\n\ndef input_adaptor(stuff,\n pmi_resolution=0,\n flatten=False,\n selection_tuple=None,\n warn_about_slices=True):\n \"\"\"Adapt things for PMI (degrees of freedom, restraints, ...)\n Returns list of list of hierarchies, separated into Molecules if possible.\n The input can be a list, or a list of lists (iterable of ^1 or iterable of ^2)\n (iterable of ^2) Hierarchy -> returns input as list of list of hierarchies,\n only one entry, not grouped by molecules.\n (iterable of ^2) PMI::System/State/Molecule/TempResidue ->\n returns residue hierarchies, grouped in molecules, at requested resolution\n @param stuff Can be one of the following inputs:\n IMP Hierarchy, PMI System/State/Molecule/TempResidue, or a list/set (of list/set) of them.\n Must be uniform input, however. No mixing object types.\n @param pmi_resolution For selecting, only does it if you pass PMI objects. Set it to \"all\"\n if you want all resolutions!\n @param flatten Set to True if you just want all hierarchies in one list.\n @param warn_about_slices Print a warning if you are requesting only part of a bead.\n Sometimes you just don't care!\n \\note since this relies on IMP::atom::Selection, this will not return any objects if they weren't built!\n But there should be no problem if you request unbuilt residues, they should be ignored.\n \"\"\"\n\n if stuff is None:\n return stuff\n\n if hasattr(stuff,'__iter__'):\n if len(stuff)==0:\n return stuff\n thelist=list(stuff)\n\n # iter of iter of should be ok\n if all(hasattr(el,'__iter__') for el in thelist):\n thelist = [i for sublist in thelist for i in sublist]\n elif any(hasattr(el,'__iter__') for el in thelist):\n raise Exception('input_adaptor: input_object must be a list or a list of lists')\n\n stuff = thelist\n else:\n stuff = [stuff]\n\n # check that it is a hierarchy homogenously:\n try:\n is_hierarchy=all(IMP.atom.Hierarchy.get_is_setup(s) for s in stuff)\n except NotImplementedError:\n is_hierarchy=False\n # get the other types homogenously\n is_system=all(isinstance(s, IMP.pmi.topology.System) for s in stuff)\n is_state=all(isinstance(s, IMP.pmi.topology.State) for s in stuff)\n is_molecule=all(isinstance(s, IMP.pmi.topology.Molecule) for s in stuff)\n is_temp_residue=all(isinstance(s, IMP.pmi.topology.TempResidue) for s in stuff)\n\n # now that things are ok, do selection if requested\n hier_list = []\n pmi_input = False\n if is_system or is_state or is_molecule or is_temp_residue:\n # if PMI, perform selection using gathered indexes\n pmi_input = True\n indexes_per_mol = OrderedDefaultDict(list) #key is Molecule object, value are residues\n if is_system:\n for system in stuff:\n for state in system.get_states():\n mdict = state.get_molecules()\n for molname in mdict:\n for copy in mdict[molname]:\n indexes_per_mol[copy] += [r.get_index() for r in copy.get_residues()]\n elif is_state:\n for state in stuff:\n mdict = state.get_molecules()\n for molname in mdict:\n for copy in mdict[molname]:\n indexes_per_mol[copy] += [r.get_index() for r in copy.get_residues()]\n elif is_molecule:\n for molecule in stuff:\n indexes_per_mol[molecule] += [r.get_index() for r in molecule.get_residues()]\n elif is_temp_residue:\n for tempres in stuff:\n indexes_per_mol[tempres.get_molecule()].append(tempres.get_index())\n for mol in indexes_per_mol:\n if pmi_resolution=='all':\n # because you select from the molecule,\n # this will start the search from the base resolution\n ps = select_at_all_resolutions(mol.get_hierarchy(),\n residue_indexes=indexes_per_mol[mol])\n else:\n sel = IMP.atom.Selection(mol.get_hierarchy(),\n resolution=pmi_resolution,\n residue_indexes=indexes_per_mol[mol])\n ps = sel.get_selected_particles()\n\n # check that you don't have any incomplete fragments!\n if warn_about_slices:\n rset = set(indexes_per_mol[mol])\n for p in ps:\n if IMP.atom.Fragment.get_is_setup(p):\n fset = set(IMP.atom.Fragment(p).get_residue_indexes())\n if not fset <= rset:\n print('BAD')\n minset = min(fset)\n maxset = max(fset)\n found = fset&rset\n minf = min(found)\n maxf = max(found)\n resbreak = maxf if minf==minset else minset-1\n print('WARNING: You are trying to select only part of the bead %s:%i-%i.\\n'\n 'The residues you requested are %i-%i. You can fix this by:\\n'\n '1) requesting the whole bead/none of it or\\n'\n '2) break the bead up by passing bead_extra_breaks=[\\'%i\\'] in '\n 'molecule.add_representation()'\n %(mol.get_name(),minset,maxset,minf,maxf,resbreak))\n hier_list.append([IMP.atom.Hierarchy(p) for p in ps])\n elif is_hierarchy:\n #check\n ps=[]\n if pmi_resolution=='all':\n for h in stuff:\n ps+=select_at_all_resolutions(h)\n else:\n for h in stuff:\n ps+=IMP.atom.Selection(h,resolution=pmi_resolution).get_selected_particles()\n hier_list=[IMP.atom.Hierarchy(p) for p in ps]\n if not flatten:\n hier_list = [hier_list]\n else:\n raise Exception('input_adaptor: you passed something of wrong type or a list with mixed types')\n\n if flatten and pmi_input:\n return [h for sublist in hier_list for h in sublist]\n else:\n return hier_list\n\n\ndef get_sorted_segments(mol):\n \"\"\"Returns sequence-sorted segments array, each containing the first particle\n the last particle and the first residue index.\"\"\"\n\n from operator import itemgetter\n hiers=IMP.pmi.tools.input_adaptor(mol)\n if len(hiers)>1:\n raise Exception(\"IMP.pmi.tools.get_sorted_segments: only pass stuff from one Molecule, please\")\n hiers = hiers[0]\n SortedSegments = []\n for h in hiers:\n try:\n start = IMP.atom.Hierarchy(h).get_children()[0]\n except:\n start = IMP.atom.Hierarchy(h)\n\n try:\n end = IMP.atom.Hierarchy(h).get_children()[-1]\n except:\n end = IMP.atom.Hierarchy(h)\n\n startres = IMP.pmi.tools.get_residue_indexes(start)[0]\n endres = IMP.pmi.tools.get_residue_indexes(end)[-1]\n SortedSegments.append((start, end, startres))\n SortedSegments = sorted(SortedSegments, key=itemgetter(2))\n return SortedSegments\n\ndef display_bonds(mol):\n \"\"\"Decorate the sequence-consecutive particles from a PMI2 molecule with a bond,\n so that they appear connected in the rmf file\"\"\"\n SortedSegments=get_sorted_segments(mol)\n for x in range(len(SortedSegments) - 1):\n\n last = SortedSegments[x][1]\n first = SortedSegments[x + 1][0]\n\n p1 = last.get_particle()\n p2 = first.get_particle()\n if not IMP.atom.Bonded.get_is_setup(p1):\n IMP.atom.Bonded.setup_particle(p1)\n if not IMP.atom.Bonded.get_is_setup(p2):\n IMP.atom.Bonded.setup_particle(p2)\n\n if not IMP.atom.get_bond(IMP.atom.Bonded(p1),IMP.atom.Bonded(p2)):\n IMP.atom.create_bond(\n IMP.atom.Bonded(p1),\n IMP.atom.Bonded(p2),1)\n\n\nclass ThreeToOneConverter(defaultdict):\n \"\"\"This class converts three to one letter codes, and return X for any unknown codes\"\"\"\n def __init__(self,is_nucleic=False):\n\n if not is_nucleic:\n threetoone = {'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D',\n 'CYS': 'C', 'GLU': 'E', 'GLN': 'Q', 'GLY': 'G',\n 'HIS': 'H', 'ILE': 'I', 'LEU': 'L', 'LYS': 'K',\n 'MET': 'M', 'PHE': 'F', 'PRO': 'P', 'SER': 'S',\n 'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V', 'UNK': 'X'}\n else:\n threetoone = {'ADE': 'A', 'URA': 'U', 'CYT': 'C', 'GUA': 'G',\n 'THY': 'T', 'UNK': 'X'}\n\n defaultdict.__init__(self,lambda: \"X\", threetoone)\n\n\n\n\ndef get_residue_type_from_one_letter_code(code,is_nucleic=False):\n threetoone=ThreeToOneConverter(is_nucleic)\n one_to_three={}\n for k in threetoone:\n one_to_three[threetoone[k]] = k\n return IMP.atom.ResidueType(one_to_three[code])\n\n\ndef get_all_leaves(list_of_hs):\n \"\"\" Just get the leaves from a list of hierarchies \"\"\"\n lvs = list(itertools.chain.from_iterable(IMP.atom.get_leaves(item) for item in list_of_hs))\n return lvs\n\n\ndef select_at_all_resolutions(hier=None,\n hiers=None,\n **kwargs):\n \"\"\"Perform selection using the usual keywords but return ALL resolutions (BEADS and GAUSSIANS).\n Returns in flat list!\n \"\"\"\n\n if hiers is None:\n hiers = []\n if hier is not None:\n hiers.append(hier)\n if len(hiers)==0:\n print(\"WARNING: You passed nothing to select_at_all_resolutions()\")\n return []\n ret = OrderedSet()\n for hsel in hiers:\n try:\n htest = IMP.atom.Hierarchy.get_is_setup(hsel)\n except:\n raise Exception('select_at_all_resolutions: you have to pass an IMP Hierarchy')\n if not htest:\n raise Exception('select_at_all_resolutions: you have to pass an IMP Hierarchy')\n if 'resolution' in kwargs or 'representation_type' in kwargs:\n raise Exception(\"don't pass resolution or representation_type to this function\")\n selB = IMP.atom.Selection(hsel,resolution=IMP.atom.ALL_RESOLUTIONS,\n representation_type=IMP.atom.BALLS,**kwargs)\n selD = IMP.atom.Selection(hsel,resolution=IMP.atom.ALL_RESOLUTIONS,\n representation_type=IMP.atom.DENSITIES,**kwargs)\n ret |= OrderedSet(selB.get_selected_particles())\n ret |= OrderedSet(selD.get_selected_particles())\n return list(ret)\n\n\ndef get_particles_within_zone(hier,\n target_ps,\n sel_zone,\n entire_residues,\n exclude_backbone):\n \"\"\"Utility to retrieve particles from a hierarchy within a\n zone around a set of ps.\n @param hier The hierarchy in which to look for neighbors\n @param target_ps The particles for zoning\n @param sel_zone The maximum distance\n @param entire_residues If True, will grab entire residues\n @param exclude_backbone If True, will only return sidechain particles\n \"\"\"\n\n test_sel = IMP.atom.Selection(hier)\n backbone_types=['C','N','CB','O']\n if exclude_backbone:\n test_sel -= IMP.atom.Selection(hier,atom_types=[IMP.atom.AtomType(n)\n for n in backbone_types])\n test_ps = test_sel.get_selected_particles()\n nn = IMP.algebra.NearestNeighbor3D([IMP.core.XYZ(p).get_coordinates()\n for p in test_ps])\n zone = set()\n for target in target_ps:\n zone|=set(nn.get_in_ball(IMP.core.XYZ(target).get_coordinates(),sel_zone))\n zone_ps = [test_ps[z] for z in zone]\n if entire_residues:\n final_ps = set()\n for z in zone_ps:\n final_ps|=set(IMP.atom.Hierarchy(z).get_parent().get_children())\n zone_ps = [h.get_particle() for h in final_ps]\n return zone_ps\n\n\ndef get_rbs_and_beads(hiers):\n \"\"\"Returns unique objects in original order\"\"\"\n rbs = set()\n beads = []\n rbs_ordered = []\n if not hasattr(hiers,'__iter__'):\n hiers = [hiers]\n for p in get_all_leaves(hiers):\n if IMP.core.RigidMember.get_is_setup(p):\n rb = IMP.core.RigidMember(p).get_rigid_body()\n if rb not in rbs:\n rbs.add(rb)\n rbs_ordered.append(rb)\n elif IMP.core.NonRigidMember.get_is_setup(p):\n rb = IMP.core.NonRigidMember(p).get_rigid_body()\n if rb not in rbs:\n rbs.add(rb)\n rbs_ordered.append(rb)\n beads.append(p)\n else:\n beads.append(p)\n return rbs_ordered,beads\n\ndef get_molecules(input_objects):\n \"This function returns the parent molecule hierarchies of given objects\"\n stuff=input_adaptor(input_objects, pmi_resolution='all',flatten=True)\n molecules=set()\n for h in stuff:\n is_root=False\n is_molecule=False\n while not (is_molecule or is_root):\n root=IMP.atom.get_root(h)\n if root == h:\n is_root=True\n is_molecule=IMP.atom.Molecule.get_is_setup(h)\n if is_molecule:\n molecules.add(IMP.atom.Molecule(h))\n h=h.get_parent()\n return list(molecules)\n\ndef get_molecules_dictionary(input_objects):\n moldict=defaultdict(list)\n for mol in IMP.pmi.tools.get_molecules(input_objects):\n name=mol.get_name()\n moldict[name].append(mol)\n return moldict\n\ndef get_selections_dictionary(input_objects):\n moldict=IMP.pmi.tools.get_molecules_dictionary(input_objects)\n seldict=defaultdict(list)\n for name, mols in moldict.items():\n for m in mols:\n seldict[name].append(IMP.atom.Selection(m))\n return seldict\n\ndef get_densities(input_objects):\n \"\"\"Given a list of PMI objects, returns all density hierarchies within\n these objects. The output of this function can be inputted into\n things such as EM restraints. This function is intended to gather density particles\n appended to molecules (and not other hierarchies which might have been appended to the root node directly).\n \"\"\"\n # Note that Densities can only be selected at the Root or Molecule level and not at the Leaves level.\n # we'll first get all molecule hierarchies corresponding to the leaves.\n molecules=get_molecules(input_objects)\n densities=[]\n for i in molecules:\n densities+=IMP.atom.Selection(i,representation_type=IMP.atom.DENSITIES).get_selected_particles()\n return densities\n\ndef shuffle_configuration(objects,\n max_translation=300., max_rotation=2.0 * pi,\n avoidcollision_rb=True, avoidcollision_fb=False,\n cutoff=10.0, niterations=100,\n bounding_box=None,\n excluded_rigid_bodies=[],\n hierarchies_excluded_from_collision=[],\n hierarchies_included_in_collision=[],\n verbose=False,\n return_debug=False):\n \"\"\"Shuffle particles. Used to restart the optimization.\n The configuration of the system is initialized by placing each\n rigid body and each bead randomly in a box with a side of\n max_translation angstroms, and far enough from each other to\n prevent any steric clashes. The rigid bodies are also randomly rotated.\n @param objects Can be one of the following inputs:\n IMP Hierarchy, PMI System/State/Molecule/TempResidue, or a list/set of them\n @param max_translation Max translation (rbs and flexible beads)\n @param max_rotation Max rotation (rbs only)\n @param avoidcollision_rb check if the particle/rigid body was\n placed close to another particle; uses the optional\n arguments cutoff and niterations\n @param avoidcollision_fb Advanced. Generally you want this False because it's hard to shuffle beads.\n @param cutoff Distance less than this is a collision\n @param niterations How many times to try avoiding collision\n @param bounding_box Only shuffle particles within this box. Defined by ((x1,y1,z1),(x2,y2,z2)).\n @param excluded_rigid_bodies Don't shuffle these rigid body objects\n @param hierarchies_excluded_from_collision Don't count collision with these bodies\n @param hierarchies_included_in_collision Hierarchies that are not shuffled, but should be included in collision calculation (for fixed regions)\n @param verbose Give more output\n \\note Best to only call this function after you've set up degrees of freedom\n For debugging purposes, returns: <shuffled indexes>, <collision avoided indexes>\n \"\"\"\n\n ### checking input\n hierarchies = IMP.pmi.tools.input_adaptor(objects,\n pmi_resolution='all',\n flatten=True)\n rigid_bodies,flexible_beads = get_rbs_and_beads(hierarchies)\n if len(rigid_bodies)>0:\n mdl = rigid_bodies[0].get_model()\n elif len(flexible_beads)>0:\n mdl = flexible_beads[0].get_model()\n else:\n raise Exception(\"Could not find any particles in the hierarchy\")\n if len(rigid_bodies) == 0:\n print(\"shuffle_configuration: rigid bodies were not intialized\")\n\n ### gather all particles\n gcpf = IMP.core.GridClosePairsFinder()\n gcpf.set_distance(cutoff)\n\n # Add particles from excluded hierarchies to excluded list\n collision_excluded_hierarchies = IMP.pmi.tools.input_adaptor(hierarchies_excluded_from_collision,\n pmi_resolution='all',\n flatten=True)\n\n collision_included_hierarchies = IMP.pmi.tools.input_adaptor(hierarchies_included_in_collision,\n pmi_resolution='all',\n flatten=True)\n\n collision_excluded_idxs = set([l.get_particle().get_index() for h in collision_excluded_hierarchies \\\n for l in IMP.core.get_leaves(h)])\n\n collision_included_idxs = set([l.get_particle().get_index() for h in collision_included_hierarchies \\\n for l in IMP.core.get_leaves(h)])\n\n # Excluded collision with Gaussians\n all_idxs = [] #expand to representations?\n for p in IMP.pmi.tools.get_all_leaves(hierarchies):\n if IMP.core.XYZ.get_is_setup(p):\n all_idxs.append(p.get_particle_index())\n if IMP.core.Gaussian.get_is_setup(p):\n collision_excluded_idxs.add(p.get_particle_index())\n\n if bounding_box is not None:\n ((x1, y1, z1), (x2, y2, z2)) = bounding_box\n ub = IMP.algebra.Vector3D(x1, y1, z1)\n lb = IMP.algebra.Vector3D(x2, y2, z2)\n bb = IMP.algebra.BoundingBox3D(ub, lb)\n\n all_idxs = set(all_idxs) | collision_included_idxs\n all_idxs = all_idxs - collision_excluded_idxs\n debug = []\n print('shuffling', len(rigid_bodies), 'rigid bodies')\n for rb in rigid_bodies:\n if rb not in excluded_rigid_bodies:\n # gather particles to avoid with this transform\n if avoidcollision_rb:\n rb_idxs = set(rb.get_member_particle_indexes()) - \\\n collision_excluded_idxs\n other_idxs = all_idxs - rb_idxs\n if not other_idxs:\n continue\n\n # iterate, trying to avoid collisions\n niter = 0\n while niter < niterations:\n rbxyz = (rb.get_x(), rb.get_y(), rb.get_z())\n\n # local transform\n if bounding_box:\n translation = IMP.algebra.get_random_vector_in(bb)\n # First move to origin\n transformation_orig = IMP.algebra.Transformation3D(IMP.algebra.get_identity_rotation_3d(),\n -IMP.core.XYZ(rb).get_coordinates())\n IMP.core.transform(rb, transformation_orig)\n old_coord = IMP.core.XYZ(rb).get_coordinates()\n rotation = IMP.algebra.get_random_rotation_3d()\n transformation = IMP.algebra.Transformation3D(rotation,\n translation)\n\n else:\n transformation = IMP.algebra.get_random_local_transformation(\n rbxyz,\n max_translation,\n max_rotation)\n\n debug.append([rb, other_idxs if avoidcollision_rb else set()])\n IMP.core.transform(rb, transformation)\n\n # check collisions\n if avoidcollision_rb:\n mdl.update()\n npairs = len(gcpf.get_close_pairs(mdl,\n list(other_idxs),\n list(rb_idxs)))\n #print(\"NPAIRS:\", npairs)\n if npairs==0:\n break\n else:\n niter += 1\n if verbose:\n print(\"shuffle_configuration: rigid body placed close to other %d particles, trying again...\" % npairs)\n print(\"shuffle_configuration: rigid body name: \" + rb.get_name())\n if niter == niterations:\n raise ValueError(\"tried the maximum number of iterations to avoid collisions, increase the distance cutoff\")\n else:\n break\n\n print('shuffling', len(flexible_beads), 'flexible beads')\n for fb in flexible_beads:\n # gather particles to avoid\n if avoidcollision_fb:\n fb_idxs = set(IMP.get_indexes([fb]))\n other_idxs = all_idxs - fb_idxs\n if not other_idxs:\n continue\n\n # iterate, trying to avoid collisions\n niter = 0\n while niter < niterations:\n if bounding_box:\n translation = IMP.algebra.get_random_vector_in(bb)\n transformation = IMP.algebra.Transformation3D(translation)\n else:\n fbxyz = IMP.core.XYZ(fb).get_coordinates()\n transformation = IMP.algebra.get_random_local_transformation(fbxyz,\n max_translation,\n max_rotation)\n\n # For gaussians, treat this fb as an rb\n if IMP.core.NonRigidMember.get_is_setup(fb):\n xyz=[fb.get_value(IMP.FloatKey(4)), fb.get_value(IMP.FloatKey(5)), fb.get_value(IMP.FloatKey(6))]\n xyz_transformed=transformation.get_transformed(xyz)\n if bounding_box:\n # Translate to origin\n fb.set_value(IMP.FloatKey(4),xyz_transformed[0]-xyz[0])\n fb.set_value(IMP.FloatKey(5),xyz_transformed[1]-xyz[1])\n fb.set_value(IMP.FloatKey(6),xyz_transformed[2]-xyz[2])\n debug.append([xyz,other_idxs if avoidcollision_fb else set()])\n xyz2=[fb.get_value(IMP.FloatKey(4)), fb.get_value(IMP.FloatKey(5)), fb.get_value(IMP.FloatKey(6))]\n else:\n fb.set_value(IMP.FloatKey(4),xyz_transformed[0])\n fb.set_value(IMP.FloatKey(5),xyz_transformed[1])\n fb.set_value(IMP.FloatKey(6),xyz_transformed[2])\n debug.append([xyz,other_idxs if avoidcollision_fb else set()])\n else:\n d =IMP.core.XYZ(fb)\n if bounding_box:\n # Translate to origin first\n IMP.core.transform(d, -d.get_coordinates())\n d =IMP.core.XYZ(fb)\n debug.append([d,other_idxs if avoidcollision_fb else set()])\n IMP.core.transform(d, transformation)\n\n if avoidcollision_fb:\n mdl.update()\n npairs = len(gcpf.get_close_pairs(mdl,\n list(other_idxs),\n list(fb_idxs)))\n\n if npairs==0:\n break\n else:\n niter += 1\n print(\"shuffle_configuration: floppy body placed close to other %d particles, trying again...\" % npairs)\n if niter == niterations:\n raise ValueError(\"tried the maximum number of iterations to avoid collisions, increase the distance cutoff\")\n else:\n break\n if return_debug:\n return debug\n\ndef color2rgb(colorname):\n \"\"\"Given a chimera color name, return RGB\"\"\"\n d = {'aquamarine': (0.4980392156862745, 1.0, 0.8313725490196079),\n 'black': (0.0, 0.0, 0.0),\n 'blue': (0.0, 0.0, 1.0),\n 'brown': (0.6470588235294118, 0.16470588235294117, 0.16470588235294117),\n 'chartreuse': (0.4980392156862745, 1.0, 0.0),\n 'coral': (1.0, 0.4980392156862745, 0.3137254901960784),\n 'cornflower blue': (0.39215686274509803, 0.5843137254901961, 0.9294117647058824),\n 'cyan': (0.0, 1.0, 1.0),\n 'dark cyan': (0.0, 0.5450980392156862, 0.5450980392156862),\n 'dark gray': (0.6627450980392157, 0.6627450980392157, 0.6627450980392157),\n 'dark green': (0.0, 0.39215686274509803, 0.0),\n 'dark khaki': (0.7411764705882353, 0.7176470588235294, 0.4196078431372549),\n 'dark magenta': (0.5450980392156862, 0.0, 0.5450980392156862),\n 'dark olive green': (0.3333333333333333, 0.4196078431372549, 0.1843137254901961),\n 'dark red': (0.5450980392156862, 0.0, 0.0),\n 'dark slate blue': (0.2823529411764706, 0.23921568627450981, 0.5450980392156862),\n 'dark slate gray': (0.1843137254901961, 0.30980392156862746, 0.30980392156862746),\n 'deep pink': (1.0, 0.0784313725490196, 0.5764705882352941),\n 'deep sky blue': (0.0, 0.7490196078431373, 1.0),\n 'dim gray': (0.4117647058823529, 0.4117647058823529, 0.4117647058823529),\n 'dodger blue': (0.11764705882352941, 0.5647058823529412, 1.0),\n 'firebrick': (0.6980392156862745, 0.13333333333333333, 0.13333333333333333),\n 'forest green': (0.13333333333333333, 0.5450980392156862, 0.13333333333333333),\n 'gold': (1.0, 0.8431372549019608, 0.0),\n 'goldenrod': (0.8549019607843137, 0.6470588235294118, 0.12549019607843137),\n 'gray': (0.7450980392156863, 0.7450980392156863, 0.7450980392156863),\n 'green': (0.0, 1.0, 0.0),\n 'hot pink': (1.0, 0.4117647058823529, 0.7058823529411765),\n 'khaki': (0.9411764705882353, 0.9019607843137255, 0.5490196078431373),\n 'light blue': (0.6784313725490196, 0.8470588235294118, 0.9019607843137255),\n 'light gray': (0.8274509803921568, 0.8274509803921568, 0.8274509803921568),\n 'light green': (0.5647058823529412, 0.9333333333333333, 0.5647058823529412),\n 'light sea green': (0.12549019607843137, 0.6980392156862745, 0.6666666666666666),\n 'lime green': (0.19607843137254902, 0.803921568627451, 0.19607843137254902),\n 'magenta': (1.0, 0.0, 1.0),\n 'medium blue': (0.19607843137254902, 0.19607843137254902, 0.803921568627451),\n 'medium purple': (0.5764705882352941, 0.4392156862745098, 0.8588235294117647),\n 'navy blue': (0.0, 0.0, 0.5019607843137255),\n 'olive drab': (0.4196078431372549, 0.5568627450980392, 0.13725490196078433),\n 'orange red': (1.0, 0.27058823529411763, 0.0),\n 'orange': (1.0, 0.4980392156862745, 0.0),\n 'orchid': (0.8549019607843137, 0.4392156862745098, 0.8392156862745098),\n 'pink': (1.0, 0.7529411764705882, 0.796078431372549),\n 'plum': (0.8666666666666667, 0.6274509803921569, 0.8666666666666667),\n 'purple': (0.6274509803921569, 0.12549019607843137, 0.9411764705882353),\n 'red': (1.0, 0.0, 0.0),\n 'rosy brown': (0.7372549019607844, 0.5607843137254902, 0.5607843137254902),\n 'salmon': (0.9803921568627451, 0.5019607843137255, 0.4470588235294118),\n 'sandy brown': (0.9568627450980393, 0.6431372549019608, 0.3764705882352941),\n 'sea green': (0.1803921568627451, 0.5450980392156862, 0.3411764705882353),\n 'sienna': (0.6274509803921569, 0.3215686274509804, 0.17647058823529413),\n 'sky blue': (0.5294117647058824, 0.807843137254902, 0.9215686274509803),\n 'slate gray': (0.4392156862745098, 0.5019607843137255, 0.5647058823529412),\n 'spring green': (0.0, 1.0, 0.4980392156862745),\n 'steel blue': (0.27450980392156865, 0.5098039215686274, 0.7058823529411765),\n 'tan': (0.8235294117647058, 0.7058823529411765, 0.5490196078431373),\n 'turquoise': (0.25098039215686274, 0.8784313725490196, 0.8156862745098039),\n 'violet red': (0.8156862745098039, 0.12549019607843137, 0.5647058823529412),\n 'white': (1.0, 1.0, 1.0),\n 'yellow': (1.0, 1.0, 0.0)}\n return d[colorname]\n\nclass Colors(object):\n def __init__(self):\n self.colors={\n \"reds\":[(\"maroon\",\"#800000\",(128,0,0)),(\"dark red\",\"#8B0000\",(139,0,0)),\n (\"brown\",\"#A52A2A\",(165,42,42)),(\"firebrick\",\"#B22222\",(178,34,34)),\n (\"crimson\",\"#DC143C\",(220,20,60)),(\"red\",\"#FF0000\",(255,0,0)),\n (\"tomato\",\"#FF6347\",(255,99,71)),(\"coral\",\"#FF7F50\",(255,127,80)),\n (\"indian red\",\"#CD5C5C\",(205,92,92)),(\"light coral\",\"#F08080\",(240,128,128)),\n (\"dark salmon\",\"#E9967A\",(233,150,122)),(\"salmon\",\"#FA8072\",(250,128,114)),\n (\"light salmon\",\"#FFA07A\",(255,160,122)),(\"orange red\",\"#FF4500\",(255,69,0)),\n (\"dark orange\",\"#FF8C00\",(255,140,0))],\n \"yellows\":[(\"orange\",\"#FFA500\",(255,165,0)),(\"gold\",\"#FFD700\",(255,215,0)),\n (\"dark golden rod\",\"#B8860B\",(184,134,11)),(\"golden rod\",\"#DAA520\",(218,165,32)),\n (\"pale golden rod\",\"#EEE8AA\",(238,232,170)),(\"dark khaki\",\"#BDB76B\",(189,183,107)),\n (\"khaki\",\"#F0E68C\",(240,230,140)),(\"olive\",\"#808000\",(128,128,0)),\n (\"yellow\",\"#FFFF00\",(255,255,0)),(\"antique white\",\"#FAEBD7\",(250,235,215)),\n (\"beige\",\"#F5F5DC\",(245,245,220)),(\"bisque\",\"#FFE4C4\",(255,228,196)),\n (\"blanched almond\",\"#FFEBCD\",(255,235,205)),(\"wheat\",\"#F5DEB3\",(245,222,179)),\n (\"corn silk\",\"#FFF8DC\",(255,248,220)),(\"lemon chiffon\",\"#FFFACD\",(255,250,205)),\n (\"light golden rod yellow\",\"#FAFAD2\",(250,250,210)),(\"light yellow\",\"#FFFFE0\",(255,255,224))],\n \"greens\":[(\"yellow green\",\"#9ACD32\",(154,205,50)),(\"dark olive green\",\"#556B2F\",(85,107,47)),\n (\"olive drab\",\"#6B8E23\",(107,142,35)),(\"lawn green\",\"#7CFC00\",(124,252,0)),\n (\"chart reuse\",\"#7FFF00\",(127,255,0)),(\"green yellow\",\"#ADFF2F\",(173,255,47)),\n (\"dark green\",\"#006400\",(0,100,0)),(\"green\",\"#008000\",(0,128,0)),\n (\"forest green\",\"#228B22\",(34,139,34)),(\"lime\",\"#00FF00\",(0,255,0)),\n (\"lime green\",\"#32CD32\",(50,205,50)),(\"light green\",\"#90EE90\",(144,238,144)),\n (\"pale green\",\"#98FB98\",(152,251,152)),(\"dark sea green\",\"#8FBC8F\",(143,188,143)),\n (\"medium spring green\",\"#00FA9A\",(0,250,154)),(\"spring green\",\"#00FF7F\",(0,255,127)),\n (\"sea green\",\"#2E8B57\",(46,139,87)),(\"medium aqua marine\",\"#66CDAA\",(102,205,170)),\n (\"medium sea green\",\"#3CB371\",(60,179,113)),(\"light sea green\",\"#20B2AA\",(32,178,170)),\n (\"dark slate gray\",\"#2F4F4F\",(47,79,79)),(\"teal\",\"#008080\",(0,128,128)),\n (\"dark cyan\",\"#008B8B\",(0,139,139))],\n \"blues\":[(\"dark turquoise\",\"#00CED1\",(0,206,209)),\n (\"turquoise\",\"#40E0D0\",(64,224,208)),(\"medium turquoise\",\"#48D1CC\",(72,209,204)),\n (\"pale turquoise\",\"#AFEEEE\",(175,238,238)),(\"aqua marine\",\"#7FFFD4\",(127,255,212)),\n (\"powder blue\",\"#B0E0E6\",(176,224,230)),(\"cadet blue\",\"#5F9EA0\",(95,158,160)),\n (\"steel blue\",\"#4682B4\",(70,130,180)),(\"corn flower blue\",\"#6495ED\",(100,149,237)),\n (\"deep sky blue\",\"#00BFFF\",(0,191,255)),(\"dodger blue\",\"#1E90FF\",(30,144,255)),\n (\"light blue\",\"#ADD8E6\",(173,216,230)),(\"sky blue\",\"#87CEEB\",(135,206,235)),\n (\"light sky blue\",\"#87CEFA\",(135,206,250)),(\"midnight blue\",\"#191970\",(25,25,112)),\n (\"navy\",\"#000080\",(0,0,128)),(\"dark blue\",\"#00008B\",(0,0,139)),\n (\"medium blue\",\"#0000CD\",(0,0,205)),(\"blue\",\"#0000FF\",(0,0,255)),(\"royal blue\",\"#4169E1\",(65,105,225)),\n (\"aqua\",\"#00FFFF\",(0,255,255)),(\"cyan\",\"#00FFFF\",(0,255,255)),(\"light cyan\",\"#E0FFFF\",(224,255,255))],\n \"violets\":[(\"blue violet\",\"#8A2BE2\",(138,43,226)),(\"indigo\",\"#4B0082\",(75,0,130)),\n (\"dark slate blue\",\"#483D8B\",(72,61,139)),(\"slate blue\",\"#6A5ACD\",(106,90,205)),\n (\"medium slate blue\",\"#7B68EE\",(123,104,238)),(\"medium purple\",\"#9370DB\",(147,112,219)),\n (\"dark magenta\",\"#8B008B\",(139,0,139)),(\"dark violet\",\"#9400D3\",(148,0,211)),\n (\"dark orchid\",\"#9932CC\",(153,50,204)),(\"medium orchid\",\"#BA55D3\",(186,85,211)),\n (\"purple\",\"#800080\",(128,0,128)),(\"thistle\",\"#D8BFD8\",(216,191,216)),\n (\"plum\",\"#DDA0DD\",(221,160,221)),(\"violet\",\"#EE82EE\",(238,130,238)),\n (\"magenta / fuchsia\",\"#FF00FF\",(255,0,255)),(\"orchid\",\"#DA70D6\",(218,112,214)),\n (\"medium violet red\",\"#C71585\",(199,21,133)),(\"pale violet red\",\"#DB7093\",(219,112,147)),\n (\"deep pink\",\"#FF1493\",(255,20,147)),(\"hot pink\",\"#FF69B4\",(255,105,180)),\n (\"light pink\",\"#FFB6C1\",(255,182,193)),(\"pink\",\"#FFC0CB\",(255,192,203))],\n \"browns\":[(\"saddle brown\",\"#8B4513\",(139,69,19)),(\"sienna\",\"#A0522D\",(160,82,45)),\n (\"chocolate\",\"#D2691E\",(210,105,30)),(\"peru\",\"#CD853F\",(205,133,63)),\n (\"sandy brown\",\"#F4A460\",(244,164,96)),(\"burly wood\",\"#DEB887\",(222,184,135)),\n (\"tan\",\"#D2B48C\",(210,180,140)),(\"rosy brown\",\"#BC8F8F\",(188,143,143)),\n (\"moccasin\",\"#FFE4B5\",(255,228,181)),(\"navajo white\",\"#FFDEAD\",(255,222,173)),\n (\"peach puff\",\"#FFDAB9\",(255,218,185)),(\"misty rose\",\"#FFE4E1\",(255,228,225)),\n (\"lavender blush\",\"#FFF0F5\",(255,240,245)),(\"linen\",\"#FAF0E6\",(250,240,230)),\n (\"old lace\",\"#FDF5E6\",(253,245,230)),(\"papaya whip\",\"#FFEFD5\",(255,239,213)),\n (\"sea shell\",\"#FFF5EE\",(255,245,238))],\n \"greys\":[(\"black\",\"#000000\",(0,0,0)),(\"dim gray / dim grey\",\"#696969\",(105,105,105)),\n (\"gray / grey\",\"#808080\",(128,128,128)),(\"dark gray / dark grey\",\"#A9A9A9\",(169,169,169)),\n (\"silver\",\"#C0C0C0\",(192,192,192)),(\"light gray / light grey\",\"#D3D3D3\",(211,211,211)),\n (\"gainsboro\",\"#DCDCDC\",(220,220,220)),(\"white smoke\",\"#F5F5F5\",(245,245,245)),\n (\"white\",\"#FFFFFF\",(255,255,255))]}\n\n def assign_color_group(self,color_group,representation,component_names):\n for n,p in enumerate(component_names):\n s=IMP.atom.Selection(representation.prot,molecule=p)\n psel=s.get_selected_particles()\n ctuple=self.colors[color_group][n]\n print(\"Assigning \"+p+\" to color \"+ctuple[0])\n c=ctuple[2]\n color=IMP.display.Color(float(c[0])/255,float(c[1])/255,float(c[2])/255)\n for part in psel:\n if IMP.display.Colored.get_is_setup(part):\n IMP.display.Colored(part).set_color(color)\n else:\n IMP.display.Colored.setup_particle(part,color)\n\n def get_list_distant_colors(self):\n cnames = ['#F0F8FF', '#FAEBD7', '#00FFFF', '#7FFFD4', '#F0FFFF', '#F5F5DC',\n '#FFE4C4', '#000000', '#FFEBCD', '#0000FF', '#8A2BE2', '#A52A2A', '#DEB887',\n '#5F9EA0', '#7FFF00', '#D2691E', '#FF7F50', '#6495ED', '#FFF8DC', '#DC143C',\n '#00FFFF', '#00008B', '#008B8B', '#B8860B', '#A9A9A9', '#006400', '#BDB76B',\n '#8B008B', '#556B2F', '#FF8C00', '#9932CC', '#8B0000', '#E9967A', '#8FBC8F',\n '#483D8B', '#2F4F4F', '#00CED1', '#9400D3', '#FF1493', '#00BFFF', '#696969',\n '#1E90FF', '#B22222', '#FFFAF0', '#228B22', '#FF00FF', '#DCDCDC', '#F8F8FF',\n '#FFD700', '#DAA520', '#808080', '#008000', '#ADFF2F', '#F0FFF0', '#FF69B4',\n '#CD5C5C', '#4B0082', '#FFFFF0', '#F0E68C', '#E6E6FA', '#FFF0F5', '#7CFC00',\n '#FFFACD', '#ADD8E6', '#F08080', '#E0FFFF', '#FAFAD2', '#90EE90', '#D3D3D3',\n '#FFB6C1', '#FFA07A', '#20B2AA', '#87CEFA', '#778899', '#B0C4DE', '#FFFFE0',\n '#00FF00', '#32CD32', '#FAF0E6', '#FF00FF', '#800000', '#66CDAA', '#0000CD',\n '#BA55D3', '#9370DB', '#3CB371', '#7B68EE', '#00FA9A', '#48D1CC', '#C71585',\n '#191970', '#F5FFFA', '#FFE4E1', '#FFE4B5', '#FFDEAD', '#000080', '#FDF5E6',\n '#808000', '#6B8E23', '#FFA500', '#FF4500', '#DA70D6', '#EEE8AA', '#98FB98',\n '#AFEEEE', '#DB7093', '#FFEFD5', '#FFDAB9', '#CD853F', '#FFC0CB', '#DDA0DD',\n '#B0E0E6', '#800080', '#FF0000', '#BC8F8F', '#4169E1', '#8B4513', '#FA8072',\n '#FAA460', '#2E8B57', '#FFF5EE', '#A0522D', '#C0C0C0', '#87CEEB', '#6A5ACD',\n '#708090', '#FFFAFA', '#00FF7F', '#4682B4', '#D2B48C', '#008080', '#D8BFD8',\n '#FF6347', '#40E0D0', '#EE82EE', '#F5DEB3', '#FFFFFF', '#F5F5F5', '#FFFF00',\n '#9ACD32']\n return 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text stringlengths 5.09k 178k | domain stringclasses 106
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|---|---|
# -*- coding: utf-8 -*-
# * This Program is free software; you can redistribute it and/or modify
# * it under the terms of the GNU General Public License as published by
# * the Free Software Foundation; either version 2, or (at your option)
# * any later version.
# *
# * This Program is distributed in the hope th... | codeparrot/github-code-clean |
# -*- coding: utf-8 -*-
## Comments and reviews for records.
## This file is part of Invenio.
## Copyright (C) 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014 CERN.
##
## Invenio is free software; you can redistribute it and/or
## modify it under the terms of the GNU General Public License as
## published b... | codeparrot/github-code-clean |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#Para probar en windows
import re
import ayuda
import sys
from PyQt4.QtCore import *
from PyQt4.QtGui import *
import subprocess
import platform
import threading
import string as s
import windows.main as win
from functools import partial
from config import config
impor... | codeparrot/github-code-clean |
#!/usr/bin/env python
#
import base64
import os
import pprint
import readline
import argparse
class Address(object):
"""
Class to contain address definitions
for hosts or subnets
"""
def __init__(self,addrname,zone,mapto=''):
if 'debug' in globals() and debug > 1:
print "init ... | codeparrot/github-code-clean |
# -*- coding: utf-8 -*-
# Copyright (C) 2016 Mathew Topper, Vincenzo Nava, Adam Collin
# Copyright (C) 2017-2021 Mathew Topper
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, e... | codeparrot/github-code-clean |
import traceback
import tempfile
import operator
import copy
import sys
import os
import random
import re
import numpy
import glob
from collections import OrderedDict
import csv
from subprocess import check_call, Popen, PIPE
import utils
import indelutils
# ------------------------------------------------------------... | codeparrot/github-code-clean |
# orm/query.py
# Copyright (C) 2005-2013 the SQLAlchemy authors and contributors <see AUTHORS file>
#
# This module is part of SQLAlchemy and is released under
# the MIT License: http://www.opensource.org/licenses/mit-license.php
"""The Query class and support.
Defines the :class:`.Query` class, the central
construct... | codeparrot/github-code-clean |
# -*- coding: utf-8 -*-
from south.utils import datetime_utils as datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding model 'SourceRegionPolygon'
db.create_table('source_region_polyg... | codeparrot/github-code-clean |
"""
Instructor Views
"""
## NOTE: This is the code for the legacy instructor dashboard
## We are no longer supporting this file or accepting changes into it.
# pylint: skip-file
from contextlib import contextmanager
import csv
import json
import logging
import os
import re
import requests
from collections import defau... | codeparrot/github-code-clean |
# Copyright (c) 2012 NTT DOCOMO, INC.
# Copyright 2011 OpenStack Foundation
# Copyright 2011 Ilya Alekseyev
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http:... | codeparrot/github-code-clean |
#
# Copyright (c) 2014 Juniper Networks, Inc. All rights reserved.
#
"""
This file contains implementation of data model for physical router
configuration manager
"""
from device_conf import DeviceConf
from ansible_base import AnsibleBase
from dm_utils import PushConfigState
from dm_utils import DMUtils
from dm_utils ... | codeparrot/github-code-clean |
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | codeparrot/github-code-clean |
# Copyright (c) 2014-2019, Ruslan Baratov & Luca Martini
# Copyright (c) 2014-2019, Michele Caini
# Copyright (c) 2017-2019, Robert Nitsch
# Copyright (c) 2018-2019, David Hirvonen
# Copyright (c) 2018-2019, Richard Hodges
# Copyright (c) 2020, Clemens Arth
# All rights reserved.
import os
import platform
class Toolc... | codeparrot/github-code-clean |
from sympy import sqrt, Abs
from sympy.core import S, Rational
from sympy.integrals.intpoly import (decompose, best_origin, distance_to_side,
polytope_integrate, point_sort,
hyperplane_parameters, main_integrate3d,
... | codeparrot/github-code-clean |
# -*- coding: utf-8 -*-
#
# Authors: Denis A. Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Juergen Dammers <j.dammers@fz-juelich.de>
#
# License: BSD (3-clause)
from inspect import isfunction
from collections import namedtuple
from copy import d... | codeparrot/github-code-clean |
# -*- coding: utf-8 -*-
import sys
from nose.plugins.skip import SkipTest
sys.path[0:0] = [""]
import datetime
import unittest
import uuid
import math
import itertools
import re
try:
import dateutil
except ImportError:
dateutil = None
from decimal import Decimal
from bson import Binary, DBRef, ObjectId
fr... | codeparrot/github-code-clean |
# -*- coding: utf-8 -*-
## This file is part of Invenio.
## Copyright (C) 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2019, 2020 CERN.
##
## Invenio is free software; you can redistribute it and/or
## modify it under the terms of the GNU General Public License as
## published by the Free Software... | codeparrot/github-code-clean |
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not... | codeparrot/github-code-clean |
# -*- encoding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the ... | codeparrot/github-code-clean |
"""Copyright 2008 Orbitz WorldWide
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software... | codeparrot/github-code-clean |
# Copyright (c) 2011 X.commerce, a business unit of eBay Inc.
# Copyright 2010 United States Government as represented by the
# Administrator of the National Aeronautics and Space Administration.
# All Rights Reserved.
# Copyright 2013 IBM Corp.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you... | codeparrot/github-code-clean |
"""
ltisys -- a collection of classes and functions for modeling linear
time invariant systems.
"""
#
# Author: Travis Oliphant 2001
#
# Feb 2010: Warren Weckesser
# Rewrote lsim2 and added impulse2.
# Apr 2011: Jeffrey Armstrong <jeff@approximatrix.com>
# Added dlsim, dstep, dimpulse, cont2discrete
# Aug 2013: Jua... | codeparrot/github-code-clean |
# Copyright (c) 2010-2013 OpenStack Foundation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agree... | codeparrot/github-code-clean |
#
# Author: Travis Oliphant 2002-2011 with contributions from
# SciPy Developers 2004-2011
#
from __future__ import division, print_function, absolute_import
import warnings
import numpy as np
from scipy.misc.doccer import inherit_docstring_from
from scipy import optimize
from scipy import integrate
impor... | codeparrot/github-code-clean |
# Copyright (c) Twisted Matrix Laboratories.
# See LICENSE for details.
"""
Test running processes.
"""
import gzip
import os
import sys
import signal
import StringIO
import errno
import gc
import stat
import operator
try:
import fcntl
except ImportError:
fcntl = process = None
else:
from twisted.internet... | codeparrot/github-code-clean |
<<<<<<< HEAD
<<<<<<< HEAD
# Copyright 2001-2013 by Vinay Sajip. All Rights Reserved.
#
# Permission to use, copy, modify, and distribute this software and its
# documentation for any purpose and without fee is hereby granted,
# provided that the above copyright notice appear in all copies and that
# both that copyright... | codeparrot/github-code-clean |
# _ __
# | |/ /___ ___ _ __ ___ _ _ ®
# | ' </ -_) -_) '_ \/ -_) '_|
# |_|\_\___\___| .__/\___|_|
# |_|
#
# Keeper Commander
# Copyright 2021 Keeper Security Inc.
# Contact: ops@keepersecurity.com
#
import itertools
import json
import base64
import collections
import re
import getpass
im... | codeparrot/github-code-clean |
#!/usr/bin/env python
#coding=utf8
# Copyright (C) 2010-2014 GRNET S.A.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later versio... | codeparrot/github-code-clean |
# Copyright (c), Michael DeHaan <michael.dehaan@gmail.com>, 2012-2013
# Copyright (c), Toshio Kuratomi <tkuratomi@ansible.com> 2016
# Simplified BSD License (see licenses/simplified_bsd.txt or https://opensource.org/licenses/BSD-2-Clause)
from __future__ import absolute_import, division, print_function
__metaclass__ =... | codeparrot/github-code-clean |
#
# Copyright (C) 2010-2015 Netcetera Switzerland (info@netcetera.com)
#
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the Free
# Software Foundation; either version 3 of the License, or (at your option)
# any later version.... | codeparrot/github-code-clean |
# Taken from periodictable-1.5.0
MASS_DICT = {
'x': 1, # dummy atom
'n': 14.0067, 'h': 1.00794, 'he': 4.002602, 'li': 6.941, 'be': 9.012182,
# 'n': 14.0067, 'h': 1.007825, 'he': 4.002602, 'li': 6.941, 'be': 9.012182,
'b': 10.811, 'c': 12.0107, 'o': 15.9994, 'f': 18.9984032, 'ne': 20.1797,
# 'b': 10.... | codeparrot/github-code-clean |
# =============================================================================
# AUSTRALIAN NATIONAL UNIVERSITY OPEN SOURCE LICENSE (ANUOS LICENSE)
# VERSION 1.3
#
# The contents of this file are subject to the ANUOS License Version 1.3
# (the "License"); you may not use this file except in compliance with
# the Lice... | codeparrot/github-code-clean |
# -*- coding: utf-8 -*-
""" License
Copyright (C) 2013 YunoHost
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any l... | codeparrot/github-code-clean |
import os
import sys
import json
import time
import uuid
import base64
import gevent
import itertools
from hashlib import sha256
from datetime import datetime
from collections import namedtuple
from flask import (request, g, Blueprint, make_response, Response,
stream_with_context)
from flask import ... | codeparrot/github-code-clean |
# Copyright 2012 OpenStack Foundation
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | codeparrot/github-code-clean |
import itertools
import unittest
from parameterized import parameterized
import math
import random
import torch
import torch.nn as nn
#import torch_dct as dct
from orthonormalTransform import OrthonormalTransform
from nsoltUtility import OrthonormalMatrixGenerationSystem
from nsoltSynthesis2dNetwork import NsoltSynthes... | codeparrot/github-code-clean |
from __future__ import absolute_import, print_function, division
import errno
import os
import sys
import logging
import numpy
import platform
import textwrap
import re
import socket
import struct
import warnings
from six import string_types
import theano
from theano.configparser import (AddConfigVar, BoolParam, Conf... | codeparrot/github-code-clean |
# -*- encoding: utf-8 -*-
"""Test class for Smart/Puppet Class Parameter
:Requirement: Classparameters
:CaseAutomation: Automated
:CaseLevel: Acceptance
:CaseComponent: UI
:TestType: Functional
:CaseImportance: High
:Upstream: No
"""
import yaml
from fauxfactory import gen_integer, gen_string
from nailgun impor... | codeparrot/github-code-clean |
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
import copy
import re
import json
from StringIO import StringIO
from mock import Mock, patch, call
from nose.tools impo... | codeparrot/github-code-clean |
"""The request for analysis by a client. It contains analysis instances.
"""
import datetime
from base_olims_model import BaseOLiMSModel
from fields.string_field import StringField
from fields.text_field import TextField
from fields.boolean_field import BooleanField
from fields.date_time_field import DateTimeField
from... | codeparrot/github-code-clean |
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | codeparrot/github-code-clean |
"""
Modulestore backed by Mongodb.
Stores individual XModules as single documents with the following
structure:
{
'_id': <location.as_dict>,
'metadata': <dict containing all Scope.settings fields>
'definition': <dict containing all Scope.content fields>
'definition.children': <list of all child locati... | codeparrot/github-code-clean |
## This file is part of Invenio.
## Copyright (C) 2005, 2006, 2007, 2008, 2009, 2010, 2011 CERN.
##
## Invenio is free software; you can redistribute it and/or
## modify it under the terms of the GNU General Public License as
## published by the Free Software Foundation; either version 2 of the
## License, or (at your ... | codeparrot/github-code-clean |
# This code is part of Ansible, but is an independent component.
# This particular file snippet, and this file snippet only, is BSD licensed.
# Modules you write using this snippet, which is embedded dynamically by Ansible
# still belong to the author of the module, and may assign their own license
# to the complete wo... | codeparrot/github-code-clean |
"""
Test SciPy functions versus mpmath, if available.
"""
import numpy as np
from numpy.testing import assert_, assert_allclose
from numpy import pi
import pytest
import itertools
from scipy._lib import _pep440
import scipy.special as sc
from scipy.special._testutils import (
MissingModule, check_version, FuncDa... | codeparrot/github-code-clean |
# -*- coding: utf-8 -*-
"""
***************************************************************************
test_qgsdelimitedtextprovider_wanted.py
---------------------
Date : May 2013
Copyright : (C) 2013 by Chris Crook
Email : ccrook at linz dot govt dot nz
... | codeparrot/github-code-clean |
def foo(): pass # keep the line number constant
import os, time, sys, yaml
from StringIO import StringIO
from datetime import timedelta
from twisted.trial import unittest
from twisted.internet import defer, reactor
from twisted.python.failure import Failure
from twisted.python import log
from pycryptopp.hash.sha256 i... | codeparrot/github-code-clean |
#===============================================================================
# #
# Author : Angus Johnson #
# Version : 5.1.6 ... | codeparrot/github-code-clean |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright: (c) 2012, Stephen Fromm <sfromm@gmail.com>
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
from __future__ import absolute_import, division, print_function
__metaclass__ = type
ANSIBLE_METADATA = {'metadata_version': ... | codeparrot/github-code-clean |
''' DIRAC Transformation DB
Transformation database is used to collect and serve the necessary information
in order to automate the task of job preparation for high level transformations.
This class is typically used as a base class for more specific data processing
databases
'''
import re, time, thre... | codeparrot/github-code-clean |
#!/usr/bin/env python
# SSSD
#
# SSSD Config API tests
#
# Copyright (C) Red Hat
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) a... | codeparrot/github-code-clean |
# coding=utf-8
# !/usr/bin/python
# module: PiConnect.py
# TODO: Dodac swieta
# TODO: Dodac mozlwiosc wlaczenia przyciskiem
# TODO: Dodac regule, jezeli czujka ruchu wykrywa obecnosc domownika
import time
from datetime import datetime
from datetime import timedelta
import ConfigParser
import logging
import MySQLdb
... | codeparrot/github-code-clean |
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this file,
# You can obtain one at http://mozilla.org/MPL/2.0/.
# Common codegen classes.
import os
import string
import operator
from WebIDL import *
from Configuration import NoSuc... | codeparrot/github-code-clean |
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use ... | codeparrot/github-code-clean |
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright 2012 United States Government as represented by the
# Administrator of the National Aeronautics and Space Administration.
# All Rights Reserved.
#
# Copyright 2012 Nebula, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# no... | codeparrot/github-code-clean |
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not... | codeparrot/github-code-clean |
# Copyright 2012 OpenStack Foundation
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | codeparrot/github-code-clean |
r"""
This module contains specific inner product matrices for the different bases in
the Legendre family.
A naming convention is used for the first three capital letters for all matrices.
The first letter refers to type of matrix.
- Mass matrices start with `B`
- One derivative start with `C`
- Stiffness ... | codeparrot/github-code-clean |
# -*- coding: utf-8 -*-
# transformations.py
# Copyright (c) 2006-2013, Christoph Gohlke
# Copyright (c) 2006-2013, The Regents of the University of California
# Produced at the Laboratory for Fluorescence Dynamics
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modifica... | codeparrot/github-code-clean |
from sympy.abc import t, w, x, y, z, n, k, m, p, i
from sympy.assumptions import (ask, AssumptionsContext, Q, register_handler,
remove_handler)
from sympy.assumptions.assume import global_assumptions
from sympy.assumptions.ask import (compute_known_facts, known_facts_cnf,
know... | codeparrot/github-code-clean |
"""
Objects for dealing with Chebyshev series.
This module provides a number of objects (mostly functions) useful for
dealing with Chebyshev series, including a `Chebyshev` class that
encapsulates the usual arithmetic operations. (General information
on how this module represents and works with such polynomials is in... | codeparrot/github-code-clean |
# -*- coding: utf-8 -*-
#
# This file is part of Invenio.
# Copyright (C) 2012, 2013, 2014 CERN.
#
# Invenio is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or (at your optio... | codeparrot/github-code-clean |
#!/usr/bin/env python2
# vim:fileencoding=UTF-8:ts=4:sw=4:sta:et:sts=4:ai
from __future__ import (unicode_literals, division, absolute_import,
print_function)
'''
Created on 29 Jun 2012
@author: charles
'''
import socket, select, json, os, traceback, time, sys, random
import posixpath
from coll... | codeparrot/github-code-clean |
# Unix SMB/CIFS implementation.
# backend code for provisioning a Samba AD server
# Copyright (C) Jelmer Vernooij <jelmer@samba.org> 2007-2012
# Copyright (C) Andrew Bartlett <abartlet@samba.org> 2008-2009
# Copyright (C) Oliver Liebel <oliver@itc.li> 2008-2009
#
# Based on the original in EJS:
# Copyright (C) Andrew ... | codeparrot/github-code-clean |
"""Tools for working with epoched data"""
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Matti Hamalainen <msh@nmr.mgh.harvard.edu>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
# Denis Engemann <denis.engemann@gmail.com>
# Mainak Jas <mainak@neuro... | codeparrot/github-code-clean |
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | codeparrot/github-code-clean |
import numpy as np
from numpy.testing import (assert_, assert_approx_equal,
assert_allclose, assert_array_equal, assert_equal,
assert_array_almost_equal_nulp, suppress_warnings)
import pytest
from pytest import raises as assert_raises
from scipy import signal
from ... | codeparrot/github-code-clean |
from toontown.toonbase import TTLocalizer
from toontown.coghq.SpecImports import *
GlobalEntities = {1000: {'type': 'levelMgr',
'name': 'LevelMgr',
'comment': '',
'parentEntId': 0,
'cogLevel': 0,
'farPlaneDistance': 1500.0,
'modelFilename': 'phase_9/models/cogHQ/SelbotLeg... | codeparrot/github-code-clean |
import maya.cmds as cmds
import maya.mel as mel
from functools import partial
import xml.dom.minidom as xd
from random import randint
import MASH.api as mapi
import flux.ui.core as fui
""" windows not saving my shelf buttons.
import cva
reload(cva)
cva.cvaUI()
To do:
-error check on :
-cva name field. no bad names... | codeparrot/github-code-clean |
# This version uses the newer trigrid2 class, and doesn't bother with
# orthomaker (relevant parts of orthomaker are moved into trigrid2.)
################################################################################
# Maintain a live constrained delaunay triangulation of the grid.
# designed as a mixin
class Miss... | codeparrot/github-code-clean |
#
# Copyright 2014 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | codeparrot/github-code-clean |
#!/usr/bin/env python
"""
Synopsis:
Generate Python classes from XML Schema definition.
Input is read from in_xsd_file or, if "-" (dash) arg, from stdin.
Output is written to files named in "-o" and "-s" options.
Usage:
python generateDS.py [ options ] <xsd_file>
python generateDS.py [ options ] -
O... | codeparrot/github-code-clean |
# -*- coding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
#... | codeparrot/github-code-clean |
###############################################################################
##
## Copyright (C) 2011-2014, NYU-Poly.
## Copyright (C) 2006-2011, University of Utah.
## All rights reserved.
## Contact: contact@vistrails.org
##
## This file is part of VisTrails.
##
## "Redistribution and use in source and binary for... | codeparrot/github-code-clean |
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | codeparrot/github-code-clean |
#!/usr/bin/env python
# Copyright (c) 2012 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Smoke tests for gclient.py.
Shell out 'gclient' and run basic conformance tests.
This test assumes GClientSmokeBase.URL_BASE i... | codeparrot/github-code-clean |
# Author: Steven J. Bethard <steven.bethard@gmail.com>.
import codecs
import inspect
import os
import shutil
import stat
import sys
import textwrap
import tempfile
import unittest
import argparse
from io import StringIO
from test import support
class StdIOBuffer(StringIO):
pass
class TestCase(unittest.TestCase)... | codeparrot/github-code-clean |
from header_common import *
from header_animations import *
####################################################################################################################
# There are two animation arrays (one for human and one for horse). Each animation in these arrays contains the following fields:
# 1) Anima... | codeparrot/github-code-clean |
from __future__ import division, print_function, absolute_import
import warnings
from distutils.version import LooseVersion
import numpy as np
from numpy.testing import (TestCase, assert_array_almost_equal,
assert_array_equal, assert_array_less,
assert_raises, ass... | codeparrot/github-code-clean |
##### Pylint Configurations #################
# For the complete list of pylint error messages, http://pylint-messages.wikidot.com/all-codes
# To disable pylint "Line too long (%s/%s)" error message.
# pylint: disable=C0301
# To disable too many modules error message.
# pylint: disable=C0302
# To disable Anomalous back... | codeparrot/github-code-clean |
#!/usr/bin/env python
# The MIT License (MIT)
#
# Copyright (c) 2016 Paul Watkins, National Institutes of Health / NINDS
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction... | codeparrot/github-code-clean |
from astropy.table import Table
def vizq(_ra,_dec,catalogue,radius):
import os,string,re,subprocess
_site='vizier.u-strasbg.fr'
# _site='vizier.cfa.harvard.edu'
cat={'usnoa2':['I/252/out','USNO-A2.0','Rmag'],\
'2mass':['II/246/out','2MASS','Jmag'],\
'usnob1':['I/284/out','USNO-B1.0','R... | codeparrot/github-code-clean |
# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
This module defines the VaspInputSet abstract base class and a concrete
implementation for the parameters developed and tested by the core team
of pymatgen, including the Materials Virtual Lab, Materials Pr... | codeparrot/github-code-clean |
## This file is part of Invenio.
## Copyright (C) 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012 CERN.
##
## Invenio is free software; you can redistribute it and/or
## modify it under the terms of the GNU General Public License as
## published by the Free Software Foundation; either version 2 of the
## License, ... | codeparrot/github-code-clean |
import os
import time
import cPickle as pickle
from enigma import eServiceReference, eServiceCenter, eTimer, eSize, iPlayableService, iServiceInformation, getPrevAsciiCode, eRCInput, eListboxPythonMultiContent, gFont, RT_HALIGN_CENTER, RT_HALIGN_LEFT, RT_VALIGN_CENTER, RT_WRAP
from Screen import Screen
from Component... | codeparrot/github-code-clean |
"""
Interface for Cobbler's XMLRPC API(s).
there are two:
a read-only API that koan uses
a read-write API that requires logins
Copyright 2007-2008, Red Hat, Inc
Michael DeHaan <mdehaan@redhat.com>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public ... | codeparrot/github-code-clean |
# -*- coding: utf-8 -*-
##
##
## This file is part of Indico.
## Copyright (C) 2002 - 2014 European Organization for Nuclear Research (CERN).
##
## Indico is free software; you can redistribute it and/or
## modify it under the terms of the GNU General Public License as
## published by the FreeSoftware Foundation; eithe... | codeparrot/github-code-clean |
# -*- coding: utf-8 -*-
from odoo import api, fields, models, Command, _
from odoo.exceptions import RedirectWarning, UserError, ValidationError, AccessError
from odoo.tools import float_compare, date_utils, email_split, email_re, html_escape, is_html_empty
from odoo.tools.misc import formatLang, format_date, get_lang... | codeparrot/github-code-clean |
"""Universal feed parser
Handles RSS 0.9x, RSS 1.0, RSS 2.0, CDF, Atom 0.3, and Atom 1.0 feeds
Visit https://code.google.com/p/feedparser/ for the latest version
Visit http://packages.python.org/feedparser/ for the latest documentation
Required: Python 2.4 or later
Recommended: iconv_codec <http://cjkpython.i18n.org... | codeparrot/github-code-clean |
# Copyright (C) 2012 Nippon Telegraph and Telephone Corporation.
# Copyright (C) 2012, 2013 Isaku Yamahata <yamahata at valinux co jp>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# h... | codeparrot/github-code-clean |
# Copyright 2012 OpenStack Foundation
# All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requi... | codeparrot/github-code-clean |
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (c) 2010 Citrix Systems, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/L... | codeparrot/github-code-clean |
# Copyright (c) 2011 X.commerce, a business unit of eBay Inc.
# Copyright 2010 United States Government as represented by the
# Administrator of the National Aeronautics and Space Administration.
# Copyright 2014 IBM Corp.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you... | codeparrot/github-code-clean |
# -*- coding: utf-8 -*-
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Matti Hämäläinen <msh@nmr.mgh.harvard.edu>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Denis Engemann <denis.engemann@gmail.com>
# Teon Brooks <teon.brooks@gmail.com>
# Marijn van Vliet... | codeparrot/github-code-clean |
# Author: Dennis Lutter <lad1337@gmail.com>
# Author: Jonathon Saine <thezoggy@gmail.com>
# URL: http://code.google.com/p/sickbeard/
#
# This file is part of SickRage.
#
# SickRage is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free S... | codeparrot/github-code-clean |
# -*- coding: utf-8 -*-
# Resource object code
#
# Created: Fri Aug 3 00:15:09 2012
# by: The Resource Compiler for PyQt (Qt v4.8.0)
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore
qt_resource_data = "\
\x00\x00\x08\x4a\
\x3c\
\x3f\x78\x6d\x6c\x20\x76\x65\x72\x73\x69\x6f\x6e\x3... | codeparrot/github-code-clean |
from collections import OrderedDict
from datetime import datetime, timedelta
from decimal import Decimal
from doctest import DocTestSuite
from fractions import Fraction
from functools import partial, reduce
from heapq import merge
from io import StringIO
from itertools import (
accumulate,
chain,
count,
... | codeparrot/github-code-clean |
# OpenShot Video Editor is a program that creates, modifies, and edits video files.
# Copyright (C) 2009 Jonathan Thomas
#
# This file is part of OpenShot Video Editor (http://launchpad.net/openshot/).
#
# OpenShot Video Editor is free software: you can redistribute it and/or modify
# it under the terms of the GNU G... | codeparrot/github-code-clean |
#coding=UTF-8
from pyspark import SparkContext, SparkConf, SQLContext, Row, HiveContext
from pyspark.sql.types import *
from datetime import date, datetime, timedelta
import sys, re, os
st = datetime.now()
conf = SparkConf().setAppName('PROC_A_PROD_CHANNEL_INFO').setMaster(sys.argv[2])
sc = SparkContext(conf = conf)
s... | codeparrot/github-code-clean |
#!/usr/bin/env python
"""@namespace IMP.pmi.tools
Miscellaneous utilities.
"""
from __future__ import print_function
import IMP
import IMP.algebra
import IMP.isd
import IMP.pmi
import IMP.pmi.topology
import collections
import itertools
from math import log,pi,sqrt,exp
import sys,os
import random
import ast
import... | codeparrot/github-code-clean |
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