From 21e384b6ad55f08b447ae76b81c4ec9388928b73 Mon Sep 17 00:00:00 2001 From: my-name Date: Mon, 6 May 2024 18:33:31 +0200 Subject: [PATCH] Debugging error_2 --- cell2mol/c2m_driver.py | 30 + cell2mol/cell_reconstruction.py | 23 +- cell2mol/classes.py | 6 +- cell2mol/connectivity.py | 10 +- cell2mol/missingH.py | 3 + cell2mol/test/check_Cell_object.ipynb | 600 ++- cell2mol/test/check_radius.ipynb | 223 + cell2mol/test/cif_file.ipynb | 4050 ++++++++++++++++++- cell2mol/test/error_2/APRCOB.search6.cif | 112 + cell2mol/test/error_2/BOFFOS.search5.cif | 249 ++ cell2mol/test/error_2/BOXSAK.search5.cif | 184 + cell2mol/test/error_2/BOXSEO.search5.cif | 184 + cell2mol/test/error_2/CIVREF.search2.cif | 152 + cell2mol/test/error_2/CORLIE.search2.cif | 195 + cell2mol/test/error_2/CSCNCC01.search6.cif | 302 ++ cell2mol/test/error_2/DUGPIF.search5.cif | 194 + cell2mol/test/error_2/EQOQIK.search2.cif | 114 + cell2mol/test/error_2/FIYFEB.search5.cif | 153 + cell2mol/test/error_2/FURPEO.search5.cif | 344 ++ cell2mol/test/error_2/FUYTOK.niquel_all.cif | 234 ++ cell2mol/test/error_2/GAPZEC.search6.cif | 121 + cell2mol/test/error_2/HAFRAI.search5.cif | 163 + cell2mol/test/error_2/HESMIB.search3.cif | 113 + cell2mol/test/error_2/IFUFIY.search2.cif | 198 + cell2mol/test/error_2/ITEREF.search2.cif | 122 + cell2mol/test/error_2/JEDJAE.search2.cif | 126 + cell2mol/test/error_2/KAGHOS.search6.cif | 183 + cell2mol/test/error_2/KIPLIH.search3.cif | 150 + cell2mol/test/error_2/LAPNIZ.search6.cif | 144 + cell2mol/test/error_2/LOJLEE.search2.cif | 152 + cell2mol/test/error_2/MAKBUW.search5.cif | 195 + cell2mol/test/error_2/QEHBEM.search5.cif | 197 + cell2mol/test/error_2/QUSHET.search5.cif | 177 + cell2mol/test/error_2/RUKLAK.search3.cif | 134 + cell2mol/test/error_2/SEZVEY.search3.cif | 184 + cell2mol/test/error_2/TAVHOO.search5.cif | 163 + cell2mol/test/error_2/URUKIC.search6.cif | 123 + cell2mol/test/error_2/VEYTAW.search5.cif | 249 ++ cell2mol/test/error_2/WOVMAY.search2.cif | 210 + 39 files changed, 10344 insertions(+), 122 deletions(-) create mode 100644 cell2mol/test/check_radius.ipynb create mode 100755 cell2mol/test/error_2/APRCOB.search6.cif create mode 100755 cell2mol/test/error_2/BOFFOS.search5.cif create mode 100755 cell2mol/test/error_2/BOXSAK.search5.cif create mode 100755 cell2mol/test/error_2/BOXSEO.search5.cif create mode 100755 cell2mol/test/error_2/CIVREF.search2.cif create mode 100755 cell2mol/test/error_2/CORLIE.search2.cif create mode 100755 cell2mol/test/error_2/CSCNCC01.search6.cif create mode 100755 cell2mol/test/error_2/DUGPIF.search5.cif create mode 100755 cell2mol/test/error_2/EQOQIK.search2.cif create mode 100755 cell2mol/test/error_2/FIYFEB.search5.cif create mode 100755 cell2mol/test/error_2/FURPEO.search5.cif create mode 100755 cell2mol/test/error_2/FUYTOK.niquel_all.cif create mode 100755 cell2mol/test/error_2/GAPZEC.search6.cif create mode 100755 cell2mol/test/error_2/HAFRAI.search5.cif create mode 100755 cell2mol/test/error_2/HESMIB.search3.cif create mode 100755 cell2mol/test/error_2/IFUFIY.search2.cif create mode 100755 cell2mol/test/error_2/ITEREF.search2.cif create mode 100755 cell2mol/test/error_2/JEDJAE.search2.cif create mode 100755 cell2mol/test/error_2/KAGHOS.search6.cif create mode 100755 cell2mol/test/error_2/KIPLIH.search3.cif create mode 100755 cell2mol/test/error_2/LAPNIZ.search6.cif create mode 100755 cell2mol/test/error_2/LOJLEE.search2.cif create mode 100755 cell2mol/test/error_2/MAKBUW.search5.cif create mode 100755 cell2mol/test/error_2/QEHBEM.search5.cif create mode 100755 cell2mol/test/error_2/QUSHET.search5.cif create mode 100755 cell2mol/test/error_2/RUKLAK.search3.cif create mode 100755 cell2mol/test/error_2/SEZVEY.search3.cif create mode 100755 cell2mol/test/error_2/TAVHOO.search5.cif create mode 100755 cell2mol/test/error_2/URUKIC.search6.cif create mode 100755 cell2mol/test/error_2/VEYTAW.search5.cif create mode 100755 cell2mol/test/error_2/WOVMAY.search2.cif diff --git a/cell2mol/c2m_driver.py b/cell2mol/c2m_driver.py index c5cf02da..5b658679 100644 --- a/cell2mol/c2m_driver.py +++ b/cell2mol/c2m_driver.py @@ -108,7 +108,37 @@ print(f"ENTERING cell2mol with debug={debug}") cell = cell2mol(newcell, reconstruction=True, charge_assignment=True, spin_assignment=True, debug=debug) cell.assess_errors() + print("*** Cell ***") cell.save(cell_fname) + print(cell) + print("*** Reference molecules ***") + print(cell.refmoleclist) + print("*** Molecules ***") + for idx, mol in enumerate(cell.moleclist): + if mol.iscomplex: + print(f"{idx}: {mol.subtype}({mol.type}) {mol.formula} {mol.is_haptic=} {mol.totcharge=} {mol.spin=}") #\n {mol.adjnum=}\n {mol.madjnum=} \n {mol.smiles=}") + # print(mol.adjnum) + for lig in mol.ligands: + print(f"|- {lig.subtype}({lig.type}) {lig.formula} {lig.is_haptic=} {lig.denticity=} {lig.totcharge=}")# \n {lig.smiles=}") + # print(f"|- {lig.connected_idx}") + # print(lig.groups) + for group in lig.groups: + print(f"|-- {group.subtype} ({group.type}) {group.formula} {group.is_haptic=} {group.denticity=} {group.closest_metal.label}") + for met in group.metals: + print(f"|--- {met.label} {met.mconnec=}") + print("") + for metal in mol.metals: + print(f"|# {metal.subtype}({metal.type}) {metal.label} {metal.coord_nr=} {metal.coord_geometry} {metal.charge=} {metal.spin=} {metal.coord_sphere_formula} {metal.mconnec=} {metal.connec=}") + # print(f"|# {metal.get_coord_sphere_formula()}") + # print(f"|# {metal.coord_sphere_formula}") + # print(f"|# {metal.mconnec=} {metal.connec=}") + # print(metal.metal_adjacency) + # for bond in metal.bonds: + # print(f"|--- {bond}") + else: + print(f"{idx}: {mol.subtype}({mol.type}) {mol.formula} {mol.totcharge=} {mol.spin=}\n {mol.smiles}") + print("") + output.close() sys.stdout = stdout diff --git a/cell2mol/cell_reconstruction.py b/cell2mol/cell_reconstruction.py index 6a162fbc..08e17566 100644 --- a/cell2mol/cell_reconstruction.py +++ b/cell2mol/cell_reconstruction.py @@ -196,7 +196,7 @@ def fragments_reconstruct(moleclist: list, fraglist: list, Hlist: list, refmolec newmols, remfrag = sequential(fraglist, refmoleclist, cellvec, factor, metal_factor, "Heavy", debug) print(f"FRAG_RECONSTRUCT. {len(newmols)} molecules and {len(remfrag)} fragments out of SEQUENTIAL with Heavy") moleclist.extend(newmols) - + print(f"FRAG_RECONSTRUCT. {remfrag=}") # After the first step, fraglist is made of the remaining molecules in the first step, and the list of H atoms fraglist = [] fraglist.extend(remfrag) @@ -210,16 +210,16 @@ def fragments_reconstruct(moleclist: list, fraglist: list, Hlist: list, refmolec for frag in fraglist: print("FRAG_RECONSTRUCT.", frag.formula, frag.subtype, frag.labels) - finalmols, remfrag = sequential(fraglist, refmoleclist, cellvec, factor, metal_factor, "All", debug) + finalmols, remfrag = sequential(fraglist, refmoleclist, cellvec, factor, metal_factor, "All", debug=2) moleclist.extend(finalmols) print(f"FRAG_RECONSTRUCT. {moleclist=}") print(f"FRAG_RECONSTRUCT. {remfrag=}") - # if len(remfrag) > 0: - # for i, mol in enumerate(moleclist): - # writexyz(os.getcwd(), f"moleclist_{i}.xyz", mol.labels, mol.coord) + if len(remfrag) > 0: + for i, mol in enumerate(moleclist): + writexyz(os.getcwd(), f"moleclist_{i}.xyz", mol.labels, mol.coord) - # for i, rem in enumerate(remfrag): - # writexyz(os.getcwd(), f"remfrag_{i}.xyz", rem.labels, rem.coord) + for i, rem in enumerate(remfrag): + writexyz(os.getcwd(), f"remfrag_{i}.xyz", rem.labels, rem.coord) if len(remfrag) > 0: Warning = True; print("FRAG_RECONSTRUCT. Remaining after Hydrogen reconstruction",remfrag) elif len(moleclist) == 0: Warning = True; print("FRAG_RECONSTRUCT. No Molecules after Hydrogen reconstruction", moleclist) else: Warning = False; print("FRAG_RECONSTRUCT. No remaining Molecules after Hydrogen reconstruction") @@ -253,7 +253,7 @@ def assign_subtype(mol: object, references: list) -> str: else: return "Other" ####################################################### -def sequential(fragmentlist: list, refmoleclist: list, cellvec: list, factor: float=1.3, metal_factor: float=1.0, typ: str="All", debug: int=1): +def sequential(fragmentlist: list, refmoleclist: list, cellvec: list, factor: float=1.3, metal_factor: float=1.0, typ: str="All", debug: int=2): # Crappy function that controls the reconstruction process. It is called sequential because pairs of fragments are sent one by one. Ideally, a parallel version would be desirable. # Given a list of fragments(fragmentlist), a list of reference molecules(refmoleclist), and some other minor parameters, the function sends pairs of fragments and evaluates if they... # ...form a bigger fragment. If so, the bigger fragment is evaluated. If it coincides with one of the molecules in refmoleclist, than it means that it is a full molecule that requires no further work. @@ -380,7 +380,10 @@ def sequential(fragmentlist: list, refmoleclist: list, cellvec: list, factor: fl # Here, the function "combine" is called. It will try cell translations of one fragment, and check whether it eventually combines with the second fragment into either a bigger fragment or a molecule ################# goodlist, avglist, badlist = combine(sublist, refmoleclist, cellvec, threshold_tmat, factor, metal_factor, debug=debug) - + if debug >=2 : + print("SEQUENTIAL: goodlist", len(goodlist), [g.formula for g in goodlist]) + print("SEQUENTIAL: avglist", len(avglist), [a.formula for a in avglist]) + print("SEQUENTIAL: badlist", len(badlist), [b.formula for b in badlist]) ################# # This part handles the results of combine ################# @@ -522,7 +525,7 @@ def combine(tobemerged: list, references: list, cellvec: list, threshold_tmat: f lig.get_denticity(debug=debug) for met in reordered_newmolec.metals: met.get_coordination_geometry(debug=debug) - if debug >= 1: print(f"COMBINE: {reordered_newmolec.fomula=}") + if debug >= 1: print(f"COMBINE: {reordered_newmolec.formula=}") if debug >= 1: print(f"COMBINE: {reordered_newmolec=}") issame = compare_species(reordered_newmolec, ref, debug=debug) diff --git a/cell2mol/classes.py b/cell2mol/classes.py index 45aea6aa..a44cae30 100644 --- a/cell2mol/classes.py +++ b/cell2mol/classes.py @@ -1454,9 +1454,9 @@ def reconstruct(self, cov_factor: float=None, metal_factor: float=None, debug: i for f in fragments: if not hasattr(f,"frac_coord"): f.get_fractional_coord(self.cellvec) molecules, fragments, hydrogens = classify_fragments(fragments, self.refmoleclist, debug=debug) - if debug > 0: print(f"CELL.RECONSTRUCT: {molecules=}") - if debug > 0: print(f"CELL.RECONSTRUCT: {fragments=}") - if debug > 0: print(f"CELL.RECONSTRUCT: {hydrogens=}") + if debug > 0: print(f"CELL.RECONSTRUCT: {len(molecules)} {molecules=}") + if debug > 0: print(f"CELL.RECONSTRUCT: {len(fragments)} {fragments=}") + if debug > 0: print(f"CELL.RECONSTRUCT: {len(hydrogens)} {hydrogens=}") ## Determines if Reconstruction is necessary if len(fragments) > 0 or len(hydrogens) > 0: self.is_fragmented = True diff --git a/cell2mol/connectivity.py b/cell2mol/connectivity.py index ded47f22..b9265da9 100644 --- a/cell2mol/connectivity.py +++ b/cell2mol/connectivity.py @@ -7,6 +7,7 @@ from cell2mol.other import inv, extract_from_list from cell2mol.elementdata import ElementData from cell2mol.read_write import writexyz +import os elemdatabase = ElementData() ####################################################### @@ -219,7 +220,9 @@ def get_adjmatrix(labels: list, pos: list, cov_factor: float=1.3, radii="default b = np.array(pos[j]) dist = np.linalg.norm(a - b) if (elemdatabase.elementgroup[labels[i]] == 1 or elemdatabase.elementgroup[labels[j]] == 1 ) and (labels[i] != "H" and labels[j] != "H"): - cov_factor = 0.6 + cov_factor = 1.05 + elif (elemdatabase.elementgroup[labels[i]] == 1 and elemdatabase.elementgroup[labels[j]] == 1 ) and (labels[i] == "H" or labels[j] == "H"): + cov_factor = 1.05 else : cov_factor = 1.3 thres = (radii[i] + radii[j]) * cov_factor @@ -421,6 +424,7 @@ def compare_species(mol1, mol2, check_coordinates: bool=False, debug: int=0): print(mol1.formula) print(mol2.formula) + # a pair of species is compared on the basis of: # 1) the total number of atoms if (mol1.natoms != mol2.natoms): @@ -439,7 +443,7 @@ def compare_species(mol1, mol2, check_coordinates: bool=False, debug: int=0): if elem != mol2.element_count[kdx]: if debug > 0: print(f"COMPARE_SPECIES. FALSE, different {elem} count:") return False - + # writexyz(os.getcwd(), f"reordered.xyz", mol1.labels, mol1.coord) # 4) the number of adjacencies between each pair of element types if not hasattr(mol1,"adj_types"): mol1.set_adj_types() if not hasattr(mol2,"adj_types"): mol2.set_adj_types() @@ -447,7 +451,7 @@ def compare_species(mol1, mol2, check_coordinates: bool=False, debug: int=0): if debug == 2: print(f"{mol2.adj_types=}") count = 0 - # if debug > 0: print("COMPARE_SPECIES. kdx ldx elem1 - elem2 : reordered - reference") + if debug > 0: print("COMPARE_SPECIES. kdx ldx elem1 - elem2 : reordered - reference") for kdx, (elem, row1) in enumerate(zip(elems, mol1.adj_types)): for ldx, (elem2, val1) in enumerate(zip(elems, row1)): val2 = mol2.adj_types[kdx, ldx] diff --git a/cell2mol/missingH.py b/cell2mol/missingH.py index 58021eef..bf5b07cd 100755 --- a/cell2mol/missingH.py +++ b/cell2mol/missingH.py @@ -129,6 +129,9 @@ def check_missingH(refmoleclist: list, debug: int=0): bonded_atom_labels.append(ref.atoms[adj].label) print("Adjacency", a.adjacency, bonded_atom_labels) ismissingH, report = get_missingH_from_adjacency(a.atnum, a.coord, bonded_atom_coord) + if ismissingH: + for label, coord in zip(bonded_atom_labels, bonded_atom_coord): + print("Dist", f"{a.label}-{label}", get_dist(a.coord, coord)) if ismissingH: if debug >= 2: print("") if debug >= 2: print(f"WARNING in Missing H function for: {ref.type}, {idx}, {ref.labels}") diff --git a/cell2mol/test/check_Cell_object.ipynb b/cell2mol/test/check_Cell_object.ipynb index 863a8c24..9112c619 100644 --- a/cell2mol/test/check_Cell_object.ipynb +++ b/cell2mol/test/check_Cell_object.ipynb @@ -13,15 +13,33 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/ycho/cell2mol/cell2mol/test\n" + ] + } + ], + "source": [ + "! pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ - "name = \"AFUGIS\"\n", - "cell = np.load(f\"{name}/Cell_{name}.cell\", allow_pickle=True)" + "name = \"BOXSAK\"\n", + "\n", + "cell = np.load(f\"error_2/{name}/Cell_{name}.cell\", allow_pickle=True)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -30,21 +48,27 @@ "------------- Cell2mol CELL Object ----------------\n", " Version = 0.1\n", " Type = cell\n", - " Name (Refcode) = AFUGIS\n", - " Num Atoms = 230\n", - " Cell Parameters a:c = [10.878, 12.781, 15.368]\n", - " Cell Parameters al:ga = [98.705, 99.605, 107.216]\n", - " # Molecules: = 2\n", + " Name (Refcode) = BOXSAK\n", + " Num Atoms = 200\n", + " Cell Parameters a:c = [9.8268, 12.5116, 17.0871]\n", + " Cell Parameters al:ga = [93.498, 97.872, 99.113]\n", + " # Molecules: = 6\n", " With Formulae: \n", - " 0: H68-C42-N4-Fe \n", - " 1: H68-C42-N4-Fe \n", + " 0: K \n", + " 1: K \n", + " 2: H24-C12-O6 \n", + " 3: H24-C12-O6 \n", + " 4: H36-C12-N2-Si4-Fe-I2 \n", + " 5: H36-C12-N2-Si4-Fe-I2 \n", "---------------------------------------------------\n", - " # of Ref Molecules: = 1\n", + " # of Ref Molecules: = 3\n", " With Formulae: \n", - " 0: H68-C42-N4-Fe " + " 0: H24-C12-O6 \n", + " 1: H36-C12-N2-Si4-Fe-I2 \n", + " 2: K " ] }, - "execution_count": 3, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -55,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -65,15 +89,37 @@ " Version = 0.1\n", " Type = specie\n", " Sub-Type = molecule\n", - " Number of Atoms = 115\n", - " Formula = H68-C42-N4-Fe\n", + " Number of Atoms = 42\n", + " Formula = H24-C12-O6\n", + " Covalent Radii Factor = 1.3\n", + " Metal Radii Factor = 1.0\n", + " Has Adjacency Matrix = YES\n", + " ---------------------------------------------------,\n", + " ------------- Cell2mol MOLECULE Object --------------\n", + " Version = 0.1\n", + " Type = specie\n", + " Sub-Type = molecule\n", + " Number of Atoms = 57\n", + " Formula = H36-C12-N2-Si4-Fe-I2\n", + " Covalent Radii Factor = 1.3\n", + " Metal Radii Factor = 1.0\n", " Has Adjacency Matrix = YES\n", - " Number of Ligands = 2\n", + " Number of Ligands = 4\n", " Number of Metals = 1\n", + " ---------------------------------------------------,\n", + " ------------- Cell2mol MOLECULE Object --------------\n", + " Version = 0.1\n", + " Type = specie\n", + " Sub-Type = molecule\n", + " Number of Atoms = 1\n", + " Formula = K\n", + " Covalent Radii Factor = 1.3\n", + " Metal Radii Factor = 1.0\n", + " Has Adjacency Matrix = YES\n", " ---------------------------------------------------]" ] }, - "execution_count": 4, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -84,24 +130,139 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[------------- Cell2mol MOLECULE Object --------------\n", + " Version = 0.1\n", + " Type = specie\n", + " Sub-Type = molecule\n", + " Number of Atoms = 1\n", + " Formula = K\n", + " Covalent Radii Factor = 1.3\n", + " Metal Radii Factor = 1.0\n", + " Has Adjacency Matrix = YES\n", + " Total Charge = 1\n", + " Spin = 1\n", + " Smiles = [K+]\n", + " Origin = cell.reconstruct\n", + " ---------------------------------------------------,\n", + " ------------- Cell2mol MOLECULE Object --------------\n", + " Version = 0.1\n", + " Type = specie\n", + " Sub-Type = molecule\n", + " Number of Atoms = 1\n", + " Formula = K\n", + " Covalent Radii Factor = 1.3\n", + " Metal Radii Factor = 1.0\n", + " Has Adjacency Matrix = YES\n", + " Total Charge = 1\n", + " Spin = 1\n", + " Smiles = [K+]\n", + " Origin = cell.reconstruct\n", + " ---------------------------------------------------,\n", + " ------------- Cell2mol MOLECULE Object --------------\n", + " Version = 0.1\n", + " Type = specie\n", + " Sub-Type = molecule\n", + " Number of Atoms = 42\n", + " Formula = H24-C12-O6\n", + " Covalent Radii Factor = 1.3\n", + " Metal Radii Factor = 1.0\n", + " Has Adjacency Matrix = YES\n", + " Total Charge = 0\n", + " Spin = 1\n", + " Smiles = [H]C1([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC1([H])[H]\n", + " Origin = cell.reconstruct\n", + " ---------------------------------------------------,\n", + " ------------- Cell2mol MOLECULE Object --------------\n", + " Version = 0.1\n", + " Type = specie\n", + " Sub-Type = molecule\n", + " Number of Atoms = 42\n", + " Formula = H24-C12-O6\n", + " Covalent Radii Factor = 1.3\n", + " Metal Radii Factor = 1.0\n", + " Has Adjacency Matrix = YES\n", + " Total Charge = 0\n", + " Spin = 1\n", + " Smiles = [H]C1([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC1([H])[H]\n", + " Origin = cell.reconstruct\n", + " ---------------------------------------------------,\n", + " ------------- Cell2mol MOLECULE Object --------------\n", + " Version = 0.1\n", + " Type = specie\n", + " Sub-Type = molecule\n", + " Number of Atoms = 57\n", + " Formula = H36-C12-N2-Si4-Fe-I2\n", + " Covalent Radii Factor = 1.3\n", + " Metal Radii Factor = 1.0\n", + " Has Adjacency Matrix = YES\n", + " Total Charge = -1\n", + " Spin = 6\n", + " Smiles = ['[H]C([H])([H])[Si]([N-][Si](C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H]', '[H]C([H])([H])[Si]([N-][Si](C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H]', '[I-]', '[I-]']\n", + " Origin = cell.reconstruct\n", + " Number of Ligands = 4\n", + " Number of Metals = 1\n", + " ---------------------------------------------------,\n", + " ------------- Cell2mol MOLECULE Object --------------\n", + " Version = 0.1\n", + " Type = specie\n", + " Sub-Type = molecule\n", + " Number of Atoms = 57\n", + " Formula = H36-C12-N2-Si4-Fe-I2\n", + " Covalent Radii Factor = 1.3\n", + " Metal Radii Factor = 1.0\n", + " Has Adjacency Matrix = YES\n", + " Total Charge = -1\n", + " Spin = 6\n", + " Smiles = ['[H]C([H])([H])[Si]([N-][Si](C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H]', '[H]C([H])([H])[Si]([N-][Si](C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H]', '[I-]', '[I-]']\n", + " Origin = cell.reconstruct\n", + " Number of Ligands = 4\n", + " Number of Metals = 1\n", + " ---------------------------------------------------]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cell.moleclist" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "molecule(specie) H68-C42-N4-Fe False\n", - "|- ligand(specie) H15-C7-N2 False 2\n", - "|= group (specie) N False 1 Fe\n", - "|= group (specie) N False 1 Fe\n", + "molecule(specie) H24-C12-O6\n", "\n", - "|- ligand(specie) H53-C35-N2 False 2\n", + "molecule(specie) H36-C12-N2-Si4-Fe-I2 False\n", + "|- ligand(specie) H18-C6-N-Si2 False 1\n", "|= group (specie) N False 1 Fe\n", + "\n", + "|- ligand(specie) H18-C6-N-Si2 False 1\n", "|= group (specie) N False 1 Fe\n", "\n", - "|# metal(atom) Fe 4 Seesaw\n", - "|# N4\n", + "|- ligand(specie) I False 1\n", + "|= group (specie) I False 1 Fe\n", + "\n", + "|- ligand(specie) I False 1\n", + "|= group (specie) I False 1 Fe\n", + "\n", + "|# metal(atom) Fe 4 Tetrahedral\n", + "|# N2-I2\n", + "\n", + "molecule(specie) K\n", "\n" ] } @@ -111,6 +272,7 @@ " if ref.iscomplex:\n", " print(f\"{ref.subtype}({ref.type}) {ref.formula} {ref.is_haptic}\")\n", " for lig in ref.ligands:\n", + " # print(lig)\n", " print(f\"|- {lig.subtype}({lig.type}) {lig.formula} {lig.is_haptic} {lig.denticity}\")\n", " # print(lig.groups)\n", " for group in lig.groups:\n", @@ -126,114 +288,131 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0: molecule(specie) H68-C42-N4-Fe False 0 5\n", - "[5 3 3 4 1 4 1 4 1 1 1 4 1 1 1 3 1 4 1 1 1 4 1 1 1 3 3 4 3 4 1 3 3 4 1 1 1\n", - " 4 1 1 1 4 1 1 1 3 1 1 1 4 1 1 1 4 1 1 1 3 4 4 1 3 1 3 1 3 4 1 4 1 1 1 4 1\n", - " 1 1 3 1 3 1 1 1 4 1 1 1 3 3 3 1 4 1 3 1 4 4 1 4 1 1 1 4 1 1 1 3 1 4 1 1 1\n", - " 4 1 1 1]\n", - "|- ligand(specie) H53-C35-N2 False 2 -1\n", - "|- [0, 1]\n", - "|-- group (specie) N False 1 Fe\n", - "|--- Fe 5\n", - "|-- group (specie) N False 1 Fe\n", - "|--- Fe 5\n", + "0: molecule(specie) K mol.totcharge=1 mol.spin=1\n", + " [K+]\n", + "\n", + "1: molecule(specie) K mol.totcharge=1 mol.spin=1\n", + " [K+]\n", + "\n", + "2: molecule(specie) H24-C12-O6 mol.totcharge=0 mol.spin=1\n", + " [H]C1([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC1([H])[H]\n", "\n", - "|- ligand(specie) H15-C7-N2 False 2 -1\n", - "|- [0, 1]\n", - "|-- group (specie) N False 1 Fe\n", - "|--- Fe 5\n", - "|-- group (specie) N False 1 Fe\n", - "|--- Fe 5\n", + "3: molecule(specie) H24-C12-O6 mol.totcharge=0 mol.spin=1\n", + " [H]C1([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC1([H])[H]\n", "\n", - "|# metal(atom) Fe 4 Seesaw 2.0 5 N4 5 5\n", - "|# N4\n", - "|# N4\n", - "|# metal.mconnec=5 metal.connec=5\n", - "[1, 2, 25, 26, 105]\n", + "4: molecule(specie) H36-C12-N2-Si4-Fe-I2 False -1 6\n", + "|- ligand(specie) H18-C6-N-Si2 lig.is_haptic=False lig.denticity=1 lig.totcharge=-1\n", + "|-- group (specie) N group.is_haptic=False group.denticity=1 Fe\n", + "|--- Fe met.mconnec=4\n", "\n", - "1: molecule(specie) H68-C42-N4-Fe False 0 5\n", - "[5 3 3 3 1 4 1 4 1 1 1 4 1 1 1 4 1 4 1 1 1 4 1 1 1 3 3 4 3 3 1 3 4 4 1 1 1\n", - " 4 1 1 1 4 1 1 1 4 1 1 1 4 1 1 1 4 1 1 1 3 3 3 1 3 1 3 1 3 4 1 4 1 1 1 4 1\n", - " 1 1 4 1 4 1 1 1 4 1 1 1 3 3 3 1 3 1 3 1 3 4 1 4 1 1 1 4 1 1 1 4 1 4 1 1 1\n", - " 4 1 1 1]\n", - "|- ligand(specie) H15-C7-N2 False 2 -1\n", - "|- [0, 1]\n", - "|-- group (specie) N False 1 Fe\n", - "|--- Fe 5\n", - "|-- group (specie) N False 1 Fe\n", - "|--- Fe 5\n", + "|- ligand(specie) H18-C6-N-Si2 lig.is_haptic=False lig.denticity=1 lig.totcharge=-1\n", + "|-- group (specie) N group.is_haptic=False group.denticity=1 Fe\n", + "|--- Fe met.mconnec=4\n", "\n", - "|- ligand(specie) H53-C35-N2 False 2 -1\n", - "|- [0, 1]\n", - "|-- group (specie) N False 1 Fe\n", - "|--- Fe 5\n", - "|-- group (specie) N False 1 Fe\n", - "|--- Fe 5\n", + "|- ligand(specie) I lig.is_haptic=False lig.denticity=1 lig.totcharge=-1\n", + "|-- group (specie) I group.is_haptic=False group.denticity=1 Fe\n", + "|--- Fe met.mconnec=4\n", "\n", - "|# metal(atom) Fe 4 Seesaw 2.0 5 N4 5 5\n", - "|# N4\n", - "|# N4\n", - "|# metal.mconnec=5 metal.connec=5\n", - "[1, 2, 3, 25, 26]\n", + "|- ligand(specie) I lig.is_haptic=False lig.denticity=1 lig.totcharge=-1\n", + "|-- group (specie) I group.is_haptic=False group.denticity=1 Fe\n", + "|--- Fe met.mconnec=4\n", + "\n", + "|# metal(atom) Fe metal.coord_nr=4 Tetrahedral metal.charge=3.0 metal.spin=6 N2-I2 metal.mconnec=4 metal.connec=4\n", + "\n", + "5: molecule(specie) H36-C12-N2-Si4-Fe-I2 False -1 6\n", + "|- ligand(specie) H18-C6-N-Si2 lig.is_haptic=False lig.denticity=1 lig.totcharge=-1\n", + "|-- group (specie) N group.is_haptic=False group.denticity=1 Fe\n", + "|--- Fe met.mconnec=4\n", + "\n", + "|- ligand(specie) H18-C6-N-Si2 lig.is_haptic=False lig.denticity=1 lig.totcharge=-1\n", + "|-- group (specie) N group.is_haptic=False group.denticity=1 Fe\n", + "|--- Fe met.mconnec=4\n", + "\n", + "|- ligand(specie) I lig.is_haptic=False lig.denticity=1 lig.totcharge=-1\n", + "|-- group (specie) I group.is_haptic=False group.denticity=1 Fe\n", + "|--- Fe met.mconnec=4\n", + "\n", + "|- ligand(specie) I lig.is_haptic=False lig.denticity=1 lig.totcharge=-1\n", + "|-- group (specie) I group.is_haptic=False group.denticity=1 Fe\n", + "|--- Fe met.mconnec=4\n", + "\n", + "|# metal(atom) Fe metal.coord_nr=4 Tetrahedral metal.charge=3.0 metal.spin=6 N2-I2 metal.mconnec=4 metal.connec=4\n", "\n" ] } ], "source": [ "for idx, mol in enumerate(cell.moleclist):\n", + " \n", " if mol.iscomplex:\n", " print(f\"{idx}: {mol.subtype}({mol.type}) {mol.formula} {mol.is_haptic} {mol.totcharge} {mol.spin}\") #\\n {mol.adjnum=}\\n {mol.madjnum=} \\n {mol.smiles=}\")\n", - " print(mol.adjnum)\n", + " # print(mol.adjnum)\n", " for lig in mol.ligands:\n", - " print(f\"|- {lig.subtype}({lig.type}) {lig.formula} {lig.is_haptic} {lig.denticity} {lig.totcharge}\")# \\n {lig.smiles=}\")\n", - " print(f\"|- {lig.connected_idx}\")\n", + " print(f\"|- {lig.subtype}({lig.type}) {lig.formula} {lig.is_haptic=} {lig.denticity=} {lig.totcharge=}\")# \\n {lig.smiles=}\")\n", + " # print(f\"|- {lig.connected_idx}\")\n", " # print(lig.groups)\n", " for group in lig.groups:\n", - " print(f\"|-- {group.subtype} ({group.type}) {group.formula} {group.is_haptic} {group.denticity} {group.closest_metal.label}\")\n", + " print(f\"|-- {group.subtype} ({group.type}) {group.formula} {group.is_haptic=} {group.denticity=} {group.closest_metal.label}\")\n", " for met in group.metals:\n", - " print(f\"|--- {met.label} {met.mconnec}\")\n", + " print(f\"|--- {met.label} {met.mconnec=}\")\n", " print(\"\")\n", " for metal in mol.metals:\n", - " print(f\"|# {metal.subtype}({metal.type}) {metal.label} {metal.coord_nr} {metal.coord_geometry} {metal.charge} {metal.spin} {metal.coord_sphere_formula} {metal.connec} {metal.mconnec}\")\n", - " print(f\"|# {metal.get_coord_sphere_formula()}\")\n", - " print(f\"|# {metal.coord_sphere_formula}\")\n", - " print(f\"|# {metal.mconnec=} {metal.connec=}\")\n", - " print(metal.metal_adjacency)\n", + " print(f\"|# {metal.subtype}({metal.type}) {metal.label} {metal.coord_nr=} {metal.coord_geometry} {metal.charge=} {metal.spin=} {metal.coord_sphere_formula} {metal.mconnec=} {metal.connec=}\")\n", + " # print(f\"|# {metal.get_coord_sphere_formula()}\")\n", + " # print(f\"|# {metal.coord_sphere_formula}\")\n", + " # print(f\"|# {metal.mconnec=} {metal.connec=}\")\n", + " # print(metal.metal_adjacency)\n", " # for bond in metal.bonds:\n", " # print(f\"|--- {bond}\")\n", " \n", " else:\n", - " print(f\"{idx}: {mol.subtype}({mol.type}) {mol.formula} {mol.totcharge} {mol.spin}\\n {mol.smiles}\")\n", + " print(f\"{idx}: {mol.subtype}({mol.type}) {mol.formula} {mol.totcharge=} {mol.spin=}\\n {mol.smiles}\")\n", " print(\"\")" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Fe-N 0\n", - "Fe-N 0\n", - "Fe-N 0\n", - "Fe-N 0\n", - "Fe-C 0\n", + "Co-P 0\n", + "Co-P 0\n", + "Co-P 0\n", + "Co-P 0\n", + "Co-Cl 0\n", + "Co-Cl 0\n", "\n", - "Fe-N 0\n", - "Fe-N 0\n", - "Fe-C 0\n", - "Fe-N 0\n", - "Fe-N 0\n", + "Co-P 0\n", + "Co-P 0\n", + "Co-P 0\n", + "Co-P 0\n", + "Co-Cl 0\n", + "Co-Cl 0\n", + "\n", + "Co-P 0\n", + "Co-P 0\n", + "Co-P 0\n", + "Co-P 0\n", + "Co-Cl 0\n", + "Co-Cl 0\n", + "\n", + "Co-P 0\n", + "Co-P 0\n", + "Co-P 0\n", + "Co-P 0\n", + "Co-Cl 0\n", + "Co-Cl 0\n", "\n" ] } @@ -249,7 +428,218 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from cell2mol.read_write import writexyz" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0. , 0. , 0.44415413],\n", + " [ 5.615 , 9.3705 , 6.08944587],\n", + " [ 1.4870766 , 17.86279674, 6.16987449],\n", + " [ 9.7429234 , 0.87820326, 6.16987449],\n", + " [ 7.1020766 , 10.24870326, 0.36372551],\n", + " [ 4.1279234 , 8.49229674, 0.36372551],\n", + " [ 2.7256333 , 18.09968298, 6.42056872],\n", + " [ 8.5043667 , 0.64131702, 6.42056872],\n", + " [ 8.3406333 , 10.01181702, 0.11303128],\n", + " [ 2.8893667 , 8.72918298, 0.11303128],\n", + " [ 3.6579479 , 17.13358443, 5.67704504],\n", + " [ 7.5720521 , 1.60741557, 5.67704504],\n", + " [ 9.2729479 , 10.97791557, 0.85655496],\n", + " [ 1.9570521 , 7.76308443, 0.85655496],\n", + " [ 3.383599 , 15.89405469, 5.99327128],\n", + " [ 7.846401 , 2.84694531, 5.99327128],\n", + " [ 8.998599 , 12.21744531, 0.54032872],\n", + " [ 2.231401 , 6.52355469, 0.54032872],\n", + " [ 4.9054886 , 17.3148099 , 5.91682816],\n", + " [ 6.3245114 , 1.4261901 , 5.91682816],\n", + " [10.5204886 , 10.7966901 , 0.61677184],\n", + " [ 0.7095114 , 7.9443099 , 0.61677184],\n", + " [ 3.5036477 , 17.22185454, 4.37947208],\n", + " [ 7.7263523 , 1.51914546, 4.37947208],\n", + " [ 9.1186477 , 10.88964546, 2.15412792],\n", + " [ 2.1113523 , 7.85135454, 2.15412792],\n", + " [ 3.2418764 , 0.30266715, 0.68341456],\n", + " [ 7.9881236 , 18.43833285, 0.68341456],\n", + " [ 8.8568764 , 9.06783285, 5.85018544],\n", + " [ 2.3731236 , 9.67316715, 5.85018544],\n", + " [ 4.111303 , 0.4235466 , 0.8363008 ],\n", + " [ 7.118697 , 18.3174534 , 0.8363008 ],\n", + " [ 9.726303 , 8.9469534 , 5.6972992 ],\n", + " [ 1.503697 , 9.7940466 , 5.6972992 ],\n", + " [ 2.4524074 , 1.25808333, 1.37336272],\n", + " [ 8.7775926 , 17.48291667, 1.37336272],\n", + " [ 8.0674074 , 8.11241667, 5.16023728],\n", + " [ 3.1625926 , 10.62858333, 5.16023728],\n", + " [ 3.0510787 , 2.39659908, 2.15020776],\n", + " [ 8.1789213 , 16.34440092, 2.15020776],\n", + " [ 8.6660787 , 6.97390092, 4.38339224],\n", + " [ 2.5639213 , 11.76709908, 4.38339224],\n", + " [ 3.735098 , 3.38312532, 1.1727812 ],\n", + " [ 7.494902 , 15.35787468, 1.1727812 ],\n", + " [ 9.350098 , 5.98737468, 5.3608188 ],\n", + " [ 1.879902 , 12.75362532, 5.3608188 ],\n", + " [ 3.802478 , 4.2860667 , 1.568064 ],\n", + " [ 7.427522 , 14.4549333 , 1.568064 ],\n", + " [ 9.417478 , 5.0844333 , 4.965536 ],\n", + " [ 1.812522 , 13.6565667 , 4.965536 ],\n", + " [ 3.15563 , 3.3583872 , 0.3658816 ],\n", + " [ 8.07437 , 15.3826128 , 0.3658816 ],\n", + " [ 8.77063 , 6.0121128 , 6.1677184 ],\n", + " [ 2.45937 , 12.7288872 , 6.1677184 ],\n", + " [ 4.487508 , 2.9142255 , 0.9016368 ],\n", + " [ 6.742492 , 15.8267745 , 0.9016368 ],\n", + " [10.102508 , 6.4562745 , 5.6319632 ],\n", + " [ 1.127492 , 12.2847255 , 5.6319632 ],\n", + " [ 4.079859 , 1.92376365, 3.17990312],\n", + " [ 7.150141 , 16.81723635, 3.17990312],\n", + " [ 9.694859 , 7.44673635, 3.35369688],\n", + " [ 1.535141 , 11.29426365, 3.35369688],\n", + " [ 3.446487 , 1.3643448 , 3.822156 ],\n", + " [ 7.783513 , 17.3766552 , 3.822156 ],\n", + " [ 9.061487 , 8.0061552 , 2.711444 ],\n", + " [ 2.168513 , 10.7348448 , 2.711444 ],\n", + " [ 4.513337 , 2.7642975 , 3.7045512 ],\n", + " [ 6.716663 , 15.9767025 , 3.7045512 ],\n", + " [10.128337 , 6.6062025 , 2.8290488 ],\n", + " [ 1.101663 , 12.1347975 , 2.8290488 ],\n", + " [ 4.35724 , 1.293129 , 2.3455624 ],\n", + " [ 6.87276 , 17.447871 , 2.3455624 ],\n", + " [ 9.97224 , 8.077371 , 4.1880376 ],\n", + " [ 1.25776 , 10.663629 , 4.1880376 ],\n", + " [ 1.8580035 , 2.9629521 , 2.76501952],\n", + " [ 9.3719965 , 15.7780479 , 2.76501952],\n", + " [ 7.4730035 , 6.4075479 , 3.76858048],\n", + " [ 3.7569965 , 12.3334521 , 3.76858048],\n", + " [ 1.8712549 , 4.26151599, 2.96494768],\n", + " [ 9.3587451 , 14.47948401, 2.96494768],\n", + " [ 7.4862549 , 5.10898401, 3.56865232],\n", + " [ 3.7437451 , 13.63201599, 3.56865232],\n", + " [ 1.866426 , 4.83030534, 4.17562376],\n", + " [ 9.363574 , 13.91069466, 4.17562376],\n", + " [ 7.481426 , 4.54019466, 2.35797624],\n", + " [ 3.748574 , 14.20080534, 2.35797624],\n", + " [ 2.78504 , 4.4678544 , 4.8413976 ],\n", + " [ 8.44496 , 14.2731456 , 4.8413976 ],\n", + " [ 8.40004 , 4.9026456 , 1.6922024 ],\n", + " [ 2.82996 , 13.8383544 , 1.6922024 ],\n", + " [ 1.11177 , 4.3198005 , 4.704192 ],\n", + " [10.11823 , 14.4211995 , 4.704192 ],\n", + " [ 6.72677 , 5.0506995 , 1.829408 ],\n", + " [ 4.50323 , 13.6903005 , 1.829408 ],\n", + " [ 1.536264 , 5.8115841 , 4.5212512 ],\n", + " [ 9.693736 , 12.9294159 , 4.5212512 ],\n", + " [ 7.151264 , 3.5589159 , 2.0123488 ],\n", + " [ 4.078736 , 15.1820841 , 2.0123488 ],\n", + " [ 0.6549336 , 2.43801669, 2.11884648],\n", + " [10.5750664 , 16.30298331, 2.11884648],\n", + " [ 6.2699336 , 6.93248331, 4.41475352],\n", + " [ 4.9600664 , 11.80851669, 4.41475352],\n", + " [10.882993 , 1.99273053, 3.15180864],\n", + " [ 0.347007 , 16.74826947, 3.15180864],\n", + " [ 5.267993 , 7.37776947, 3.38179136],\n", + " [ 5.962007 , 11.36323053, 3.38179136],\n", + " [ 0.32567 , 1.1338305 , 3.626148 ],\n", + " [10.90433 , 17.6071695 , 3.626148 ],\n", + " [ 5.94067 , 8.2366695 , 2.907452 ],\n", + " [ 5.28933 , 10.5043305 , 2.907452 ],\n", + " [10.20807 , 1.7560317 , 2.744112 ],\n", + " [ 1.02193 , 16.9849683 , 2.744112 ],\n", + " [ 4.59307 , 7.6144683 , 3.789488 ],\n", + " [ 6.63693 , 11.1265317 , 3.789488 ],\n", + " [10.677484 , 2.6912076 , 3.9266936 ],\n", + " [ 0.552516 , 16.0497924 , 3.9266936 ],\n", + " [ 5.062484 , 6.6792924 , 2.6069064 ],\n", + " [ 6.167516 , 12.0617076 , 2.6069064 ],\n", + " [ 0.047166 , 3.46764723, 1.110712 ],\n", + " [11.182834 , 15.27335277, 1.110712 ],\n", + " [ 5.662166 , 5.90285277, 5.422888 ],\n", + " [ 5.567834 , 12.83814723, 5.422888 ],\n", + " [10.97171 , 4.3066818 , 1.5549968 ],\n", + " [ 0.25829 , 14.4343182 , 1.5549968 ],\n", + " [ 5.35671 , 5.0638182 , 4.9786032 ],\n", + " [ 5.87329 , 13.6771818 , 4.9786032 ],\n", + " [10.467483 , 3.036042 , 0.6272256 ],\n", + " [ 0.762517 , 15.704958 , 0.6272256 ],\n", + " [ 4.852483 , 6.334458 , 5.9063744 ],\n", + " [ 6.377517 , 12.406542 , 5.9063744 ],\n", + " [ 1.0107 , 3.298416 , 0.0392016 ],\n", + " [10.2193 , 15.442584 , 0.0392016 ],\n", + " [ 6.6257 , 6.072084 , 6.4943984 ],\n", + " [ 4.6043 , 12.668916 , 6.4943984 ],\n", + " [ 1.1437755 , 1.28432073, 1.36748248],\n", + " [10.0862245 , 17.45667927, 1.36748248],\n", + " [ 6.7587755 , 8.08617927, 5.16611752],\n", + " [ 4.4712245 , 10.65482073, 5.16611752]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cell.coord" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'MUFXIU/MUFXIU_cell.xyz'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [37]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mwritexyz\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m_cell.xyz\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoord\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/cell2mol/read_write.py:73\u001b[0m, in \u001b[0;36mwritexyz\u001b[0;34m(fdir, fname, labels, pos, charge, spin)\u001b[0m\n\u001b[1;32m 71\u001b[0m natoms \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(labels)\n\u001b[1;32m 72\u001b[0m fullname \u001b[38;5;241m=\u001b[39m fdir \u001b[38;5;241m+\u001b[39m fname\n\u001b[0;32m---> 73\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfullname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mw\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m fil:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28mprint\u001b[39m(natoms, file\u001b[38;5;241m=\u001b[39mfil)\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28mprint\u001b[39m(charge, spin, file\u001b[38;5;241m=\u001b[39mfil)\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'MUFXIU/MUFXIU_cell.xyz'" + ] + } + ], + "source": [ + "writexyz(f\"{name}\", f\"{name}_cell.xyz\", cell.labels, cell.coord)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'MUFXIU/MUFXIU_0.xyz'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [38]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx, mol \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(cell\u001b[38;5;241m.\u001b[39mmoleclist):\n\u001b[0;32m----> 2\u001b[0m \u001b[43mwritexyz\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43midx\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m.xyz\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmol\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmol\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoord\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmol\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtotcharge\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmol\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mspin\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/cell2mol/read_write.py:73\u001b[0m, in \u001b[0;36mwritexyz\u001b[0;34m(fdir, fname, labels, pos, charge, spin)\u001b[0m\n\u001b[1;32m 71\u001b[0m natoms \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(labels)\n\u001b[1;32m 72\u001b[0m fullname \u001b[38;5;241m=\u001b[39m fdir \u001b[38;5;241m+\u001b[39m fname\n\u001b[0;32m---> 73\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfullname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mw\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m fil:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28mprint\u001b[39m(natoms, file\u001b[38;5;241m=\u001b[39mfil)\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28mprint\u001b[39m(charge, spin, file\u001b[38;5;241m=\u001b[39mfil)\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'MUFXIU/MUFXIU_0.xyz'" + ] + } + ], + "source": [ + "for idx, mol in enumerate(cell.moleclist):\n", + " writexyz(f\"{name}\", f\"{name}_{idx}.xyz\", mol.labels, mol.coord, mol.totcharge, mol.spin)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -259,21 +649,21 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "smiles='[H]C1=C(C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])[N-](c2c(C([H])(C([H])([H])[H])C([H])([H])[H])c([H])c([H])c([H])c2C([H])(C([H])([H])[H])C([H])([H])[H])~[Fe+2]23(~N(c4c(C([H])(C([H])([H])[H])C([H])([H])[H])c([H])c([H])c([H])c4C([H])(C([H])([H])[H])C([H])([H])[H])=C1C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])~N(C([H])(C([H])([H])[H])C([H])([H])[H])=C~2([H])[N-]~3C([H])(C([H])([H])[H])C([H])([H])[H]'\n" + "mol.formula='H32-C22-N4-O4-F6-Ni' smiles='[H]C1=C(C(F)(F)F)[O-]~[Ni+2]2(~O=C(C(F)(F)F)C([H])=C3[N-]~2C(C([H])([H])[H])(C([H])([H])[H])N(OC([H])([H])[H])C3(C([H])([H])[H])C([H])([H])[H])~N2=C1C(C([H])([H])[H])(C([H])([H])[H])N(OC([H])([H])[H])C2(C([H])([H])[H])C([H])([H])[H]'\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -283,17 +673,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "['Fe', 'N', 'N', 'C', 'H', 'C', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'N', 'N', 'C', 'C', 'C', 'H', 'C', 'C', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'C', 'C', 'H', 'C', 'H', 'C', 'H', 'C', 'C', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'C', 'C', 'H', 'C', 'H', 'C', 'H', 'C', 'C', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H']\n", - "smiles_2='[H]C1=C(C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])[N-](c2c(C([H])(C([H])([H])[H])C([H])([H])[H])c([H])c([H])c([H])c2C([H])(C([H])([H])[H])C([H])([H])[H])~[Fe+2]23(~N(c4c(C([H])(C([H])([H])[H])C([H])([H])[H])c([H])c([H])c([H])c4C([H])(C([H])([H])[H])C([H])([H])[H])=C1C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])~N(C([H])(C([H])([H])[H])C([H])([H])[H])=C~2([H])[N-]~3C([H])(C([H])([H])[H])C([H])([H])[H]'\n", + "['Ni', 'O', 'C', 'C', 'F', 'F', 'F', 'C', 'H', 'C', 'C', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'N', 'O', 'C', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'N', 'O', 'C', 'C', 'F', 'F', 'F', 'C', 'H', 'C', 'C', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'N', 'O', 'C', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'N']\n", + "smiles_2='[H]C1=C(C(F)(F)F)[O-]~[Ni+2]2(~O=C(C(F)(F)F)C([H])=C3[N-]~2C(C([H])([H])[H])(C([H])([H])[H])N(OC([H])([H])[H])C3(C([H])([H])[H])C([H])([H])[H])~N2=C1C(C([H])([H])[H])(C([H])([H])[H])N(OC([H])([H])[H])C2(C([H])([H])[H])C([H])([H])[H]'\n", "True\n", - "smiles='[H]C1=C(C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])[N-](c2c(C([H])(C([H])([H])[H])C([H])([H])[H])c([H])c([H])c([H])c2C([H])(C([H])([H])[H])C([H])([H])[H])~[Fe+2]23(~N(c4c(C([H])(C([H])([H])[H])C([H])([H])[H])c([H])c([H])c([H])c4C([H])(C([H])([H])[H])C([H])([H])[H])=C1C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])~N(C([H])(C([H])([H])[H])C([H])([H])[H])=C~2([H])[N-]~3C([H])(C([H])([H])[H])C([H])([H])[H]'\n" + "mol.formula='H32-C22-N4-O4-F6-Ni' smiles='[H]C1=C2[N-](~[Ni+2]3(~O=C1C(F)(F)F)~O=C(C(F)(F)F)C([H])=C1[N-]~3C(C([H])([H])[H])(C([H])([H])[H])N(OC([H])([H])[H])C1(C([H])([H])[H])C([H])([H])[H])C(C([H])([H])[H])(C([H])([H])[H])N(OC([H])([H])[H])C2(C([H])([H])[H])C([H])([H])[H]'\n" ] }, { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAASwAAAEsCAIAAAD2HxkiAAAABmJLR0QA/wD/AP+gvaeTAAAgAElEQVR4nOyde1zM2f/H3zPTdFUpilJJ2FZhkXvWNbdV67KyWbf9uuSWCm1hpVhWYemyoVgrd7nnEkq0pURRSal0v6emmu7TzJzfH6c+RjfTzGcafj7PRw+P+XzmzDmnzPtzbu/3601DCAEFBYX0oEu7AxQUXzuUEVJQSBnKCCkopAxlhBQUUoYyQgoKKUMZIQWFlKGMkIJCylBGSEEhZSgjpKCQMpQRUlBIGcoIKSikDGWEFBRShjJCCgopQxkhBYWUoYyQgkLKUEZIQSFlKCOkoJAylBFSUEgZyggpKKQMZYQUFFKGMkIKCilDGSEFhZShjJCCQspQRkhBIWUoI6SgkDKUEVJQSBnKCCkopAxlhBQUUoYyQgoKKUMZIQWFlKGMkIJCylBGSEEhZSgjpKCQMpQRUlBIGcoIKSikDGWEFBRShjJCCgopQxkhBYWUoYyQgkLKUEZIQSFlKCOkoJAylBFSUEgZygg/a/h8voODQ0lJybFjx/bu3VtbWyvtHlGQD2WEnzUnT56UkZGpqamxsrIyNjaOiYmRdo8oyIcyws+X3NzcuLg4eXn50tJSJSWl6Ojo0aNHS7tTFORDGeHni7Ky8qpVqwCgurr6p59+GjBgQElJibQ7RUE+NISQtPtA0REcDodGo1VXVwOAkpKSrKystHtEQTKUEVJQSBlqOvplcPToUTabjV83Njb+9ddf0u0PBYlQI6F0CAgIaGhoWLx48ZkzZ/T09MzMzDou36dPn8jIyL59+wJATU2Nurp6Q0NDl/SUQuJQI6EUqK6uHj16dF5e3vHjx4cOHRoWFkaMchRfITLS7sDXSLdu3TIyMpKSkrZu3Xr16tWsrKyysjIVFZWOP/Xff/9pamoCADUG/j+DMkIpUF5eXllZ+e233wLAsmXLjh07pqur+8lPhYaGKisrAwCXy5V4Fym6EGpNKB0iIyPl5OQMDQ3DwsLGjBnTs2fPjstTa8L/x1AjoXQYP348fjFnzhzp9oRC6lBG2HU8Ki/P53Dway1Z2elqatLtD8VnAmWEXcfN0lJdefkB8vIAIE/v3L50enq6nJwcfq2kpHTr1q3a2lpFRUXye0nR5VBHFF2KqYrKAg2NBRoa36uqduqD8vLyNBoNv16xYsXs2bPPnTsngQ5SSAHKCLuU+Jqa0IqK0IqK4uZ5qQjMmjULADw9PbtgU83T0zMmJub8+fMHDx5MSEiQdHNfJ5QRdilZdXUJ1dUJ1dXlYhwzWFpa6ujoJCUlhYSEkNi31oSFhVVXVxcUFERFRY0fP/758+cSbe6rhTLCLmVuz572Ojr2OjrfirGck5GRWb9+PQB4enqS17WWNDQ0nDp1ql+/fsXFxerq6rGxsQoKCpJr7muGMsIvknXr1ikqKt67d+/t27eSa8XW1ha/KCoqUlNTKy0tlVxbXzPUYX3XsSktzUpT07STWzLtYW1tfeLECRsbG29vb1IqbBMej8fn82k0WkVFxSc9CihEgzLCLiK8snK4srIijUZv3uQUkzdv3gwZMkRBQSE3N1ddXZ2UOimkAjUd7QqSa2u3vHv3v+RkEus0NjaeNm1abW3tv//+S2K1rSktLRWciBYUFFAxH+TytRhhSkqKt7f33bt3nz175uHhER4e3pWtnygoQAATu3cnaxjE2NnZAYC3tzePxyOx2hZ4enp6eXkRl5s3bw4MDJRcc18hX4sRYtGk0NDQGzduLFiw4MGDB13WdEptbXhlpQKdvqRXL3JrnjNnjqGhYXZ2NmUVXzRfixFqa2vfvHlTQ0NDR0fnwoULxCKqkMM5nJtL/BRIIDTBt7AQAVhqaqrLkOwkSKPRNm7cCBI+q6CQNF+LET5+/FhdXb2wsDAzM3PEiBGFhYX4/vvGxqds9igVFfzTjWw7SamtDa+oUKDTl5I9DGJ+/fVXVVXVsLAwieoCHz161LCZoKAgyTX0dfK1GOGkSZP69evn7u7u7u6uo6Ozb98+4i1lBuN7VVX8o8JgkNuuHx4GNTRIHwYxysrKK1euBAAfHx9J1I/ZsGFDSjOzZ8+WXENfJ1+LEdLpdENDQ3l5eSaTaWRkJKjeyWpsvFxScrmk5B6LRW6jqbW1/1VUyNPpS3v3JrdmQWxtbRkMxsWLF4uKiiTXCoXk+FqMsAMQABch/AMAHPIOTiU9DGL09fUtLCwaGhp8fX0l1wqF5PjaD+sTamoO5+ae/vZb4s661FQ6jWajrW2kpCROzel1dYuTkmTo9MDBg3symWL3tCOePHkyZcoUTU3N7OxseXl5cit//fo1jUYbPHgwvoyIiNDV1cVCG+KTl5dXXFysr68vLy/f0NDwdXodUCPhRxRyOG9qap6z2Svevt2Snv5WjFRke+ztB7x9+0v37pK2QACYPHnysGHDSkpKAgIC8J1r166dPn06ODjYzc1NzMqHDBmCLZDP5//666+WlpYkmsrJkyfLy8tra2uPHj169epVsqr9svjajVAGQFFgM0ZLVvb2kCHWWlqKDMZ/FRVLk5M3pKUl1dR0ttrExMRzJ0/eWL16QXM4vKTBztYeHh4AUFhYWFhYmJWVNWXKlPr6erKaoNPpGRkZRUVFJG6QamhoxMXF5efnL1myhKw6vzi+diM0UlI6OnCg4J3uMjLW2tq3Bg+21tJSYjCes9nL377dkJb2pjOm6OLiwufzN2zYoK2tTXaX2+aXX37R1NR89epVeHj4zp07DQwMsrKyZMhei86fPx8Abty4QVaFS5cu3bhx41fubPC1G2F7tDbFFUKb4ps3b27evCkvL79169Yu6CqGx+OpqKhoa2ufP3/ezs6uV69e8vLyUVFRsbGxsbGxZLUyf/58Go129+5dsgQXQ0JCfHx8Nm3a9ODBAzab/ZV6pSKKT1He2Oibnz/p1SuTmJilQUFmZmbR0dEdlP/pp58AYPPmzV3Ww4qKClNTUwCg0WgMBiMjIwMhVFtbW1tby2Kx6urqSGxr2LBhABAUFERine3R2Nhoa2vLZrMvXbp05MiR8vLyLmi062G4urpK9ynw+SNPp5soK8/r2ZNOo93duvVZaOjJkydfvHgxcODAPn36tCj85s0bOzs7OTm5y5cvKysrv3379uLFi+np6VlZWWFhYU+fPh0zZgy53WOxWLNmzXr27Jment7kyZOTk5OZTOaMGTOYTCaTyVRQUCB3UlpSUvLkyRMlJSULCwsSq22To0eP1tTUGBgYPH782NzcnKwt2c8OaT8FvjBKS0tdXFxUmwNzzczMnj17Jlhg4cKFAGBvb48vWSxWSUnJ1q1bEUIZGRn+/v7k9qewsHDIkCEA0K9fv4yMDDzz7N69e1VVFbkNEcTHxwNAr169uFwuKRX6+vpOnDgxKysLXzo6OuL9pHfv3tna2jo5OV2/ft3e3t7b2zskJISUFj83KCMUBWyK3bt3x6Zoamr6+PFjhFBiYiKdTpeXl8/LyyMK379/39nZGSG0Z88ecmeGWVlZAwYMAAAjI6P8/Hx8E89LfXx8SGyoBQMHDgSAiIgIUmr766+/5quqclVUkJoaUlN7NnCgPJPJZrPLyspiYmKcnJxiY2PXrVv3+++/x8fHk9Li5wZlhKJTVlbWwhQnT54MAHZ2dkSZ//7778KFCzY2NiwWy8PDg8TW3759i9PImJiYvH//nriPjwoHDhzI4/FIbE4QvOGEh3fxubJ+fTWNhgCIn2kyMmw2G79bX1/P5/MbGhqIO///oIxQXMrKynbu3IkTm9HpdDqdfuvWLcECxcXFXC63sbGxsbGRrEYTExPx4cf3339fWVkp+BaXy+3Xrx8A3Lt3j6zmWhAREQEA+vr6pNR2zdoaAUQqKIw1NHypqIgAptLp/49NrjVfrxHW1dUlJiby+fzKysqUlBQxaysqKurduze9Wdx++vTpZM3WWvPixYsePXoAwNSpU9tc+7m7uwPAzJkzJdQBHo+npaUFAGLOD6urq0tKSrARFqipBZqbl/boQRnhV0RAQEBAQIC3t/e///578ODB0NBQcWrbs2cPAOjp6e3Zs0etOdOLqanpo0ePyOowJiwsDI+6FhYW7a0wWSyWkpISjUZLSkoit3UCa2trANi9e7doHy8pKXFxcenRo8fq1avP29vzBeaifBrNRGA6+jXw9RphfX39unXrIiIiiouLrayscnNzRa4qKSkJ54p4+PAhQojNZru5uUnCFO/du4cVeBcvXtxicltVVXX27Fnict26dQCwbt06UtptsycAMGzYsM5+8O3bt9bW1oSX+ZQpUw4dOrROWfn9ggXI2hpZW/tNm8ZkMikj/CpgsVjV1dW///47i8WKjY0V/AZ3isbGxlGjRgHAhg0bBO+3NkUxd9hv3bqFEzOtXbu29aaLoaEhADx58gRfpqSk0Ol0RUXF0tJScRptDw6Hg3ek0tPThfxITEzMsmXLGAwGXjybm5vjGXtwcPCQIUMSEhJwsbNnz/bp06e6uloS3f48+XqN8MaNGwcPHkxLS/vnn38OHToksjfGn3/+CQB9+/Zt8+FNlimeP38en7nb2Njw+fzWBbDTxfz584k7M2fOBAB3d3cRmhMGKysrADh8+HDHxXg8XmBgID44AQA5Oblly5a1N0/m8XjPnj2T3HL68+TrNUJSSE5OxhPR+/fvd1CsqqrKzc2NiAAyNTUNDAwUvpVjx47hLR8nJ6f2yhQXF8vLy9PpdGJowjPGPn36cDgc4dsSnsuXL+Pt2fYK1NfX+/v7f9scq6mqqmpra0ucZ7YJjmaaPHmyBPr7+UIZoejweDz8gF+zZo0w5UUzRbzVSaPRDh061HHJFStWAMCWLVvwJZ/PHzRoEAAEBAQI073OUlVVhc2+sLCwxVvv3793c3MjIkj69evn4eEhzAyzsrJSVlZWRkaGxWJJos+fJ1+1EdbV1e3bt4+4zMvLO378uPAfP3DgAB5qOjWVraqq8vDw6NUsvtaxKeKQXBqN5uXl9cmaX758CQDKysrEySFWfzI1NRW+e51izpw5AHDixAniTkZGhq2tLZFCePjw4f7+/p06IJ06dSoAXLx4UQL9/Uz5qo2wvLxcSUmJuIyOjh4+fLiQn82uq1tx966yhoZoZ+ItTHH8+PEtTJHP52/evBkAGAzG6dOnhax24sSJAEBYbE1NDR54W4d9PH369OjRo0+ePPH19d27d68IvwJC6OTJkwAwZ84chFBsbOyyZcvwwpVGo5mZmXVqyk3w119/AcDSpUtF69KXCGWEohghD6FVb9+axMS4paaK04H2TJHL5a5evRoAZGVlr127JnyF165dA4ABAwYQ26cODg6tv9P19fV79uxxdXVFCLHZ7E9urrTH+/fvGQwGk8kU3HdZuXLlmzdvRKsQIfTu3TsAUFdXJ9HBqD1ycnJ8fX1DQ0PT0tKOHj1aUlIi6Rbb5Gs3Qnl5+evNHDhwQEgjPF9cbBITMys+vpKMSAI2m71//34i8dj48eOnT58OAIqKig8ePOhUVVwu18DAAADu3LmD7+D4eiaTKehT7urqeufOnbVr1zY0NPz999+tF3WfpLGxMTw83NbWVlZWFh+cqKio2NrainPcSvDNN98AQHh4uPhVdcy5c+eys7MRQpGRkSwWa9u2bZJusU2+9sh6Ho8X3UxiYqIwHyloaDienw8A2/T0SBELVlZW3rZtW1ZWloeHR+/evSMjI2NiYlRUVB48eDBjxoxOVcVgMDZs2AACwvh9+/adO3duY2OjoCCilZVV7969AYDL5bJYrN5Cy6Ky2ezLly9bWVn16NHj+++/9/Ly4nA4DQ0Nw4cPz8vL8/T01NHR6VSH28Tc3BwA7t69K35VAIAQGjVqFGpWFczOzl6wYAF+jQXFXVxcxo0bl5CQMGLECFJaFKWLXy0iTEd5CK1JSTGJiXHJzJREl6qqqrC0mcjne+Xl5d26dQMBx87//vsPADQ0NFq4udXV1fF4vIaGhk/WWVpa6u/vb25uLiegW2VkZOTi4hIYGEij0ZSVlVtUfvz48fr6+qNHj7q5uXX2jOTRo0cAMHjw4E59qj1wyipifp6cnNy/f3/8+tWrV5mZmZs2bQoMDDx69KjkgjA7hjLCzhnhpeJik5iYmfHxlRJbsWD/mxaxwp0CZ4mxtrZuUeepU6c6VQ8enM3MzIjYfDqdbmpq6ubmJujyjgUvBDeoAgICli5dWllZuW/fvjZdCzqGcMfJJONJ14ERlpWVXb9+vbi4OCgoKCAg4OnTp+I3JwJftRFWVFR069aNuHz+/PmIESM6KJ/f0PD9y5cmMTGhYoudeHt7b9++PSkp6dChQ4cPHxb0tsGTw44PtTsmNTWVTqcrKCgQPmv+/v54bBHGJBITE93c3ExNTWnN2RTl5eXNzMw8PDzaXD3u3r1b0OZLS0vd3NwOHz5cXl5+7NixDRs25OTkdPZXWLRoEQD8/fffnf1ga7ARrm7G0tKSMMLPhK96TaiqqlpVVYVf43i/58+ft1cYAezLzq7l82erq09pDuQVmV9//bV79+5v377NycnR1tZWalb75nA4JSUlTCaT2DK9efNmQEBAWVmZ8JUPHDhw1qxZdXV1fn5++A5eBCYmJo4cOTI6Opooqa+vj2vm8/mxsbGurq7ffvvt4MGDt23b9vTpUwUFBXNzc39//+Li4uDgYDs7uzZXj/PmzQOAW7du4a87zngVGhoaHh5ubGysr69fUVHR2b8PPoEUf1mYk5ODXyxdunTZsmXLli378ccfxayTfKT9FPhcwCuxmJiY9grceP/eJCZmenx8BRkT0bq6ujNnznh6eh44cOD27dvEZmZGRgYA9O3blyg5fPhwAHjx4kWn6sdZULW1tYn1GHYuVVNTE/TMVFVVjYuLW716taamJvGV0NLSWrt27f3794VZLmJaC16kpKRwOJzw8PCXL192queY0tJSBoMhJycn2joNO6yamZkpKCgUFxdDO9PRz4SveiQUZPTo0QAQFhbWXoHJ3bvPVFffpqurKrZ4GULo+PHjKSkpY8eOLSkpefr06XfffYffys3NBQCsW9HeHWGYMWPGkCFDCgoKrl+/ju+sX79eXl6+vLw8Pz9fsKSCgsLp06dLSkr09fVtbW2Dg4NzcnKOHz8+c+ZMwdxVHYOHF0FR4G+++YbJZE6YMAE/RDpLjx49xowZ09DQgDdphIfFYrm7u+vr6//4448hISHy8vJCbnpLE2k/BT4X8Krpxx9/JO6UNTY6pqcTPwldElxz7tw5AFi8eDG+rKuro9FocnJyImxvHD9+HADGjRtH3MHOpZqampObkZGRKSkp8ff3f/36tTjdJlfwAoNzSArpl4sQSk1NtbW1JSb2AwcOxA6rfD7/hx9+IP6Aubm5q1evJrGf4kMZYRN48aCmpkbMW/Lr62fGx6fU1OCfKpIU/jpm//79AODo6IgvU1NTAcDAwECEqmpqarAKBrHRip1LFRQU3r59m5+fn5+fr6KiQoqbCFmCF4JgbUUtLa2OH0B8Pj84ONjc3BxvIxEecyI8tqTFlz0dzcrKOnz48G+//fbixYtff/1VnKpwuq/y8vLXr18TN2VotG8UFfFPt4/P5bOzs/Pz8+vq6p4+fUqWJjy0mnzm5eVB5+eiGEVFRez75uXlhe8MHz4cH+g9fPhQW1tbW1ub2P8UEzqdjrWAb968SUqFADB06FB9ff3CwkL87GhNdXW1n5/f4MGDp0+ffufOHRyp+Pr16+DgYAsLC7J+tS7gyzZCfX39mTNn8ni8UaNGYYkxcZg0aRJ8vCys4vG88vO98vOPFxQIlmxoaHB3d79z586uXbvodDqJKuZ4QCasTrQFIcGmTZuYTOaVK1ewMQMA3nT18vLi8/kkdFcAvEdKYq4YAPjhhx8A4M6dOy3uFxQUuLq69u3bd+3atUlJSdra2i4uLnl5eWfOnDE2NiaxA13Dl22EANCzZ0/s9tHmuwUFBX/++efGjRufPHni7u5O+Cu1SWsjZNJogxQVBykqGjbH5mC8vb3xIMPn88eNG8fhcDrV59raWj8/v/v37xcVFXl7e7969Yp4a4aKyhJTU0M9PXwpphH26dNn/vz5jY2NeH0IAGvXrtXT03v37h0O+V2/fr3ix7+ayJiZmXXv3j0uLg5v8JJC64OK2NjY5cuX9+3bd/fu3SwWy8TExN/fPzs729XVFc+9v0ikPR8Wi9TU1EOHDm3dujUpKWn58uVYZ6k1zs7ONTU1lZWVR44c6aA27L/fo0cPvCzMr6+f0yx8IgiXy7Wxsfn9999XrVplY2PD5XJtbW3brDAqKopYYdbX1xPnH1lZWdXV1Rs3bkxISMA5Tz58Rl0dAaDmdVqZvf0zY+P0f//t8M/QEU+fPgWAnj171tbW4juHDh0CADMzM5HrbA8hBS+Ep66uDsvGZWVlBQQEjB07Fn9pZWVlLS0to6KiyGpIunzZRigMT58+xe5anp6eRUVFHRfW09MDALxV2J4RYths9rNnz5KTk48cOfLu3bs2yzCZzJqaGvw6MzOzT58+xFt3797Ftpeenn7w4MGmuzU1CADJyyNiU+GHHxAAun1bqF+1HfDpy8mTJ/FlZWUlFk0kXVX+k4IXIoCVcnCHAaBXr14uLi4ihH18zkjWCHk83smTJ318fPh8PpfL/eOPPySnzd4mL1++nDNnjpub2/v374VRocf5YrG3VDGHs0G8cMH2jPDNmzfx8fGrV69+9uzZ1q1biVwo6O1bBIAGDPhQxZAhCADFxYnTDXzsIeizhp1Lhd/9F5IOBC9E4OXLl9bW1rKystiPdMiQIb6+vsR4/v8JyRphVVVVfn6+j4/P27dvfXx8Vq9eLYlIzby8PMEo0tTUVJEdf7Gfl6WlJSkdYzKZpaWlNTU1NTU1ycnJhBFyudzg4OCMjAy8lfchcC44GAGgqVM/VKGqigBQWZk43eBwODjCiJA/JZxLBZNYkAJexRGjrghwudyrV69+//33eOhjMBjYCH/++WcS+/lZIdmNmW7dumlqaqanp9NoNDab3bt3bxJTqBPcu3fv4MGDxKWfn9/FixdFq4rYm0HN4WdiMm3aNFNTU1NTU5w5FMNgMMzMzPr16zd48GAzM7MJEyY0vZGbCwBAbMNUVUFlJSgqQrM2lGgwmcy1a9eCQJAh4Vx64sQJcWpujTh7pGw229PTc8CAAQsXLgwPD8dRwu/evXv+/Lmqqurly5e9vb3J7e3ngkRNnMVirVix4uHDh3FxcQEBAVZWVuIIH7SHn5/frFmziBWOg4PD0qVLMzIyBCdFdXV1Qj71sUZYcnKyaJ0pKChwd3c3NTVtaGjoYE3YNrt3IwC0c2fT5Zs3CAAZGorWE0FKSkqwNCMRgtTauZQUsOCFnJxcizQ1HZOWlibo7NK/f383NzdB+aybN2/SaDQmk/n/UpJUskZYWVkZExMTExODQ3UKCgok4cfg5+c3cODAfzZurHZ2rnZ2vjlunJme3qlTp/T09Igyd+/eFXI/8OeffwaATsmuIYQaGhquXbtmbm5OhN7duHGj00a4ejUCQL6+TZf37yMARNI25sqVK+HjtG04u6iYumZxcXHYxzo1NbW4uBghhGeSly9fFubj4eHhlpaWjGZHCFNT04CAgDbTj2LZK11dXWkpwUiO/w+7o35+fuPk5IKVlIikIsUMxh4HBw0NjYqKClxGeCM8evQoCHhvfpKkpCQnJyciCkFWVtbc3DwgIIDD4XTaCGfMQACIiI59/BhNmICaXdjE5PXr1zgEnvib4MPDsWPHilxnWlqahYVFXFxcRUXF8uXLr1y5ghA6fPgwAFhZWXXwwbq6On9/fxy5As2y3B37rzY2NmLznjZtGllJgj8Tus4I79+/XyawwYC/pp2qoa6ubu/evTY2NikpKVu3bt2yZQvWk/Xz89vTqxcCqPrmm9wlS2plZBDA3BEjAIDBYBgZGVlbW2/fvn3SpEnCtJKUlAQAWlpaHRerqKjw9fUlVMYAwMjIyM3NDY8GmODgYGI3uK6uDi81O2LQIASAxPOl7oApU6YAAHFYWltb28K5lODRo0c4dVwHfebz+du3b7969WpcXNyuXbuePn2KjTAzMxNbe319fetPFRQU4HxM+I+mpaXl4uIi5EqhsLAQBzRinbj/N3SdEQ4bNuz58+fEpYKCAvFI7hRnz54NDg5es2bNjh07WhhhqJHRqlWrCplMBGAzdy6NRhN0IKTRaKNHj3Z0dLx3714HUWp8Ph/7dqW2dT7B4/HCw8Otra0JRxNVVVVra2typMGUlREAEunPIgzYsVNfX58YSZycnFoP+1FRUebm5tnZ2ffu3XN3d29vgMrMzPzjjz/Wr19/4sQJZ2dnFxeXXbt24bew4EVQUJBgeZwQhpiuY2eX9h7ELBbLwsKiqqpq9+7drq6uRGRjaGgog8Gg0+ktKv+i+cKMMDs7e+/evZGRkWfPnt29ezfOK0IYIZKTQ2pqfBoNAVw+dEhPT6+2tjY8PNzNzW3YsGFEBk88QpqYmNja2gYEBLTuxsKFC+FjYWmEUE5OjpubG+GhSqfTzczM/P39iQmnuPB4yN8f/fknObW13QKvf//+AEDkEs7Ly2MymUwmk5AqbGhocHR09PX1LSgoyMjIsLS07HiLJSUlBQ/+79+/f/v2Lb6JnWmx4EVDQ0NAQMC4cePw343JZFpaWn5SzcXV1dXR0RGPrleuXBHMHolTQaqrq5OiQPM50KVGGBAQkNSMvLx8Z42wuLjY3Nz8+PHjYWFhjo6O27dvx2v0oqIiu7lz2djhCwABhPXocfrkyRYbM1OmTMEGaWZmJhiuig3SyckpMDAQf+HwVjgWzK2rqwsICDAzMyMGVV1dXScnp4yMDDL/Oi9fom3b0NKlyMUFSfK7deTIEQCYKnAUidVcduzYgS/j4+OdnZ0XLFhw5cqV7Ozs69evi5A+FUchaWho/Pnnn4QIYvfu3W1tbagAv7kAACAASURBVIXRmwkLC1u7du2iRYsSExOPHTu2efPmx48fE+/y+fy5c+cCwOjRo4WP/f+c6VIjnDRp0txmGAyGaNPRNlm/fv3fhw4hFgv/TJ86tePd0erq6uDgYBcXlxYGKSMjY2JigjcSNTU1bW1tifwt8vLylpaWwcHB5G/wRkYibW30778oOhodPIh690ZkSOi2SWVlpbKyMjS75qFm51J1dXXBIT0uLo7NZl+8ePH06dOd2gV59+7dpUuXHBwccDJTzNChQ0+ePNleXuHWVFRUpKen29vbl5SUHDlyxMnJqcWslcVi4SlJe167XxZf2HS0Paqrq/H/MYfDYbPZbDa7rKzs0qVLRIHc3Nz2kkZgg3RycjI1NWUymYJrSGL14uvrK8HcsdOmIcEUpdu2oY0bJdUWQseOHQsMDBT0H8TOpS2m30KSn58fGBjo4uJibm6uoaEBAsjKyvbs2ZP0hOGY58+fYxFUkbO7fj5I3wjT0tKOHDkSFBT08uXLQ4cOdVYXKCIiwsfHB78ODg4GgO+++w5flpeXm5iYrF+/XvjaKioqbt++vWXLFuwxbGlpSaxzJIia2kdD34MHaPx4iTcqAHYuNTY2FmaQLygoCAwM3LVr1w8//CAoD0Xsdpqbm7u6ugYEBGCbvH79uoS6jXNOdevWTRIeIF2J9I0wOzubz+fb29s7ODgghDZv3typas+cOUMcScXExIDA6UJ4eDgAYAn0zoLDBTt7ZN8xQUFB7u7ukZGRJ06c8PX1bUoOwecjBQUkeAAdFoaETg5FCoRzaZtZhCsqKsLDwz08PJYtW2ZkZNTC6lRVVU1NTW1tbf39/RMTEwU/iI3EwMCgzbMKUli+fDkAGBoaftE57sUVDhOemzdvElqaAHDv3j0839PT03v9+vU333yjo6Pj6en5/v17kZvAp091dXX4EstsESfCnaJv374AkJ2dLXJnWjNu3Dhtbe2HDx+WlpYuWLCgKYcmjQYDB8KbNzB5clO5xET45hsS2/0k2LnU2dnZ09Nz2rRpbDY7ISEhthnswUcUVlZWHjp0qEkzRkZG7QlJrF279tixY4mJiX///ffWrVsl0fOjR4/Gxsa+efPG2tpaZIdh6SMV09+zZ4+MjAyOLQoJCVm+fHlYWFhiYuL169c7K7p85syZ4cOHHzp06NChQ3v37gUAWVlZ/JaNjQ0AdJzgtrGxER821tfXC+4cYPG1X375pdO/W/vw+fwTJ078888/b968uX37tp+fX9Mbfn5o/PimUIl375C+PpJ8QqIWvH//XkFBgUaj9e3bt4VRdevWbeLEiVu2bLlw4UJqamqn9qVCQkIAoHv37qSHaxCkpKTgtcOxY8ck1ISk6bqRUJChQ4dyuVwPDw8bG5uhQ4e6uLgwGAwVFRU5Obn58+d3tjYZGRm846esrEyn0zkcDofDkZWVxZJN2EOyTTgcjo+PT0ZGxtKlSy9cuAAATk5OeICSxEh46dIlBoNRXl4eFhZWXl4+cuTIpjdWrYLiYpgwAbhcUFCA3buBiKvoKnr27Dl9+vRXr15lZ2czmcyBAwfigW7ChAnDhg1jiJp8atq0abNmzbp//76LiwuenZLON9984+vru3jx4ujo6HXr1kmiCYkjFdNvfWosMoJrQoQQ9sTH8RM441/HSR1SU1N/+eUXf3//iIiIqKgoYkM1MzMTAHR1dcXsniB8Ph877jU2Nn5CWLqoCHUmNygpYFUlLy8vcmM+k5KSmEwmg8EQU9q0Y548eVJbWyu4q8disbAvx+ePdISe6HQ6nisSEW5kgY+nysrKCgsLS0tL1dTUmpZe7aCvrz9v3ryysjIWi8Visbo3J5no06cPg8EoKChobGwkq280Gg2fOsrIyODsZW3DZsOePTBhApAU0ygkWFRq4sSJMmJLjAsyaNCgNWvW8Hg8R0dHEqttwaRJk7Kzsy0tLYk74eHhElqIko7U1NZWrVqloqISGhoaHx+/cuVKnEUEALhcLlaw/CTXrl3z9/dXUVERzFKClwdlZWV4V6aDuSgA1NXVeXt7p6amrlixIiUlJSkpadq0afgtJpOpra3N4/FaiMZ3BSoq8PffcOsWNHemaxBT2a0Ddu/e3b1796CgoJCQZ6RX/v8BKY7Ctra2ALBq1aq+ffumpaXhmw0NDTQa7ZOfvXTpkoyMDIPBiI2NFbxPRHb/9ddfALBhwwaRu4cjJJ48eSJyDaJTV4cWL0aJiairYnZwdipFRUUJ1e/tfWHcuFxjYyS5RPTJyckGBga1zVy5cmX27NmSaoxUpKk7umnTJjqdfu7cOZxSS3guX768dOlSLpe7devWFimO8XyvrKzszZs3IOr5BEYSezPCIi8PFy5AUREMGgTNyb0kSgvRYdKxtl5cWqrz5g2QrafxEXl5eeObwQEiXwTS2R3FDBgwYM6cObdv366urj58+LCamhoAfFIZ+tKlS8uWLeNyuU5OTosWLSosLNTU1Hzx4oWhoaGamho+KiSmo+IbYU6X2EDbvHgBXl6gowNcLpC6TmuN5OaiGFlZcHeHBQtg5074+WcxRXPaRVdXl1BSDgwMJCSPP3OkrMBtZ2cHAFVVVYaGhsOHDx8+fDiRJKympqZ1+VOnTi1ZsoTL5bq6ui5btozFYu3YsSM6OppGozk7O0PzeX1paSmOzRVHFB1rkIo/EvJ4UF7+4bK+Hpq9CT7Ftm1gYADff886ckTMPnwSSRshAMyfD9OmAYsF+/dLrpEvEikb4bRp04YOHcrj8fh8/sKFCxcuXIiV6tlstq6uroWFRWRkJFH45MmTa9as4fP5e/bscXFxMTY2njZtmoqKyvjx41NSUnBWIGyE2dnZ1dXVffr0URfjkUvWdPT1a1BXB8Kdw90d3N2F/SwqLr6ppHSJJKX6DugCIwSAQ4eATgcvL0hLa+Pd8vJynNO3oqKCy+VKtCefF9JelDZJfQ4ePBhf4o2Z27dvEwENpqamV65cOX78OI7K3bdvHy5ZX1/v5uaWkZGBTwKxhNG1a9cAAOulz5o1S5yO4VWlodhiZ69eob59kYEBwuphrq7IxQVdvIjs7dH27Z/+eFlZWXl5+a5du8iVRWvB//73PxA1kKJT/PorAkALFrS8z2az16xZExgYGBUVdfr06U2bNnW25pqaGkF9g5KSEtGSBHc90k8Is3TpUllZ2cTExKioKACg0+mjR482NzfPycnBYiRPnz61tLRct24dQmjfvn07duzAH3zx4gWO2i4tLfXy8sJDKB4Ji4qKQLwFIQisCZHY53W9e8OyZdDccQAAKys4cgT+/PPTn1VXV583bx6LxZLo4NA1IyEAuLmBsjJcvw4tMvCy2ezu3btnZGSMGTNGRkZGhOwuioqKEwQ8jTQ0NERLEiwFpP0UQAghbFdtSizX1dX98ssvRG+VlZWtra07CC/CrmqqqqoA8K8YeVQw+KsgqN0kAq9eoTFjUE0NMjBAz541jYSdorq6urGxce/evQnt58YQk4kTa7/7rjwpqfzTRcVmzx4EgIYNQ4IpEbhcLp/Pd3d3x0kBOhWtGxMT8+rVK4RQWloaiUGqXYb0R0IA2LhxI5PJvHbtGn4eC+Lj43PhwgUajWZjY2Nubo7zQhoZGVlYWGDn4BZgs8GbOh2f1AuDmMvC16/h8uWm14qKcPgwrF8PxO7v5s0QEgIsFlRUfKIeJSWlgwcPRkZG4g1kSfDypUJ8fHcdne4Sql8QBwfo2xfi4uDMmQ83i4uLDx06pKSkVFJS4uHhMWrUKCFrY7PZZ8+eraqqunPnTkhIyLZt20h0cuoipP0UaAJn1dq2bZvgzQMHDgAAjUYjwnbj4+Otra0J6YThw4f7+voKRj8QiQrpdHq12Fnm8dE/VvITHh4PBQcjc3NEoyFlZRQejsaMaXrLwgIZGiIXF8TlIg0NlJWF9uxBW7eisjLU/Cu2DRZTuXDhgpeXl4i/TPuUlSEA1L076RW3y/nzaPFilJ1NQlWFhYW7du3au3evo6NjQ0ODj48P4fjxpfC5GOGLFy8AQE1NjbAcNzc3bEs4sZkgxcXFbm5uhFNo7969XVxcSktL8bvYRPv37y9+r/AJSsfBUIKw2cjTEw0Y0KQ4payMNm1Cjx9/MMLsbKSk1DQdxUpRNjYoLAydP4/mzkU8Hho7FhUX17UZXBsXFzd06NAXL160fquhocHBwWHDhg3//POPCL9mXBwCQEOGiPBR0QkNRZs3f3CgOXIEiZZ5oK6ujsPhHDhwYNeuXZmZmbt27RLUz/8i+FyMECE0ZswYAPD19UXNFshgME6fPt1e+YaGBn9/f2LOiVWcExMTcfDEzJkzxe8SFpMWZn2Sno6cnJCaWpP59euH3NyaIgRLS9GNGx9KBgej5mShH4iKQk+eoGfP0JAhKCgoaMKECQihjRs3lpeXC8Y0nDx5klCtb2xs3LlzZ3Fxsbu7O5vNxjEKU6ZMQQhlZ2d3Sgf19m0EgH74QfhPkICXF5KTQ8TzzcwMtfXk+TQlJSWHDx8+c+YMm80+cuRIazWNhISE4OBgHL8SHx/fqSQZXcNnZIQ4os/IyAgfuzMYjDNnznzyU3w+/+HDhz/88AMORaXRaFj/Z9myZeJ36erVqwAwd+7cDso8ecKZOxfR6U3mN3kyunEDtc7COH8+WrYMdfwF4PFQXh66ffv2mTNnUlNT+/Tpw+fzzc3NHzx4kJaW9ubNm5kzZ3br1g0ndamrq1NQUKisrLx79y7+OJvN/vHHH3Nzc/39/Xfs2CF8II+PDwJAa9cKWZwcvLzQ//6HdHWbJqUiG+EncXFxSU9Pr6ury8jImD9/fnR0tESaEYPPyAgbGxsJjUoZGRkhM4oQCGb2wUGoampqpqam1tbWHh4e4eHhnwjhaws8SR42bFjrt+rr6/39/YcOHTp58gMAJCuLLC1RKzn5JjgcRKcjGRnU2IhKS0vd3d1/++03DoeTk5OzaNGiNj/CYrHCwsI4HE7v3r0rKys3bdq0f//+mTNnbty4cdq0aajZCN+/f48XzPicLTU1NSUlxcvLy9bWtrW4PULoyZMnHh4eZWVlfn5+fn5+WHNt2zYEgPbu7eyfRyy8vJCDA/L2RvgRh43wzh10+zZKSEAsVudrLCtDhw6hX39Fjo5IQKfUx8dnz549sbGxv/32261btz5DI/wsdkcBgM1m+/j41NTUKCoq0un0y5cvY1Fa4RkwYICnp2d2dvbBgwfnz5+vrKxcXl7+9OlTPz8/e3v777//XlVVdcCAAfPmzfv9998vX76cmJj4yW20Nt1HCwoKnJ2ddXV1V6xYkZCQUFz8x+7dKDcXAgJgzJi268nPBz4ftLVBRgZ69OhhZWWFd+R9fX2/aUdORk1NbeLEiVgbW0VFRUNDA+8SLVy4kMfjnT9/HhcrLS319fXNz883NTXt06fP48eP9fX158yZY2FhMaZVb8rLy588ebJ69eqampqioqL6+vq8vDxolRaxK1m/HnJy4M6dpksnJ7CwgKFDQV0dunUDY2OYORNWrYLduyH30lN4+BCSk6G2to2K6upg/HgoKYE1a8DEBFauhEuX8DsWFhZWVla3b99WVlYOCQlJSEjoql9OWKTpwI15+/atl5fXmTNn8LkCdosR+Zy9R48eDg4O+HVBQUFSUtKbN2/wvy9fvkxPT09PT7916xYuICMjo6enZ2RkZGJiYmxsbGRkNGjQIEG1fA0NDSUlJRaLxWazVVRUXr586evre+bMGZzqdPjw4evWrVu+fLm8fNtKRwQtvuUyMjJMJvPixYuNjY1ZWVmZmZmEun5rcIgtnqJj/v7771mzZpmZmRF35OTkbG1t8dk0g8EwMDAwMDBoqxu5ubm5+/btmzx5Mla5x5vSUjRCBgOOHoVffwWsATZ9OujpQU4O5OYCmw1JSZCU1FRyw8h9EBPUdKGuDjo6oKcHurqgowOzZ0NEBAwZ8sEhUEcHFi8GKysAiI2NLSoqcnJykpeXz8jIEFQl/kyQphFGRES4u7vjJQ0AmJqa2tnZPXjw4J9//vH29hYtLaufn19BQcH//ve/J0+e4HxdxJeVy+Xm5OQQNhkbG5uSkpKRkZGRkXGn+VEsKys7YMAAwiaNjY11dXXfvn179uzZmzdv4pNJOp1ubm5uZ2cnaAYdI/gtLywsPHfuXH19vbm5+dy5c728vDqO/W+NsbHx4sWLsaoVJi4u7tSpU5GRkVu3bu3fv7+Kiorgo4SgX79+ioqKffr0yczMNDAwkJWVzcjI0NDQkKIRAsDYsTBxIpw6BQAg6KnOZkNuLuTkQF4e5OWBXIUJqHIgLw9ycoDFAhYLiDGtRw+IjYUpUz58eNw4KC2F9++heQaBafPZJH26fgaMc9MR8Q0tctMlJibSaLRu3bqJttFcXV0dFxfn6enp6Oj433//dVy4pqbmxYsX//77r4ODw4wZM/r06dP67yOoya2mpubo6Jjd+eMtNzcEgH77TYRf6CNmzpyJszJUVVX169dPQUEhOTl56NChgYGB//zzT0ZGRu/evXk83vz58+/fv5+RkUHk5SXIzc3F6qAJCQn4BZ+PlJQQjYZqa8XtnpBkZaHi4o+S35SWosGD0aeSxAhQUoJevkSBgcjbGzk5oVev0KJF6OTJj8qoqZFzECl5utQI28xNR5zvEWCNib/++kuEJrhcrrOzc2RkpIODw9mzZwMDAzv18YqKipiYGH9/f1tbWzMzMyyU2qNHD319fQ8PD5FP/zduRABI/GP2n376iTh7uHbtmpaWVmpqKj7PQAiVlZUFBwc3NjZqamqyWCx7e/s//vhj+3YUFoYqK9uwsfp6hCVzuVyUn4+qqpAkXcSbKC5GAweigQPJNpDff0dbtny4zMtDioptbFJ/lnSREXYqN11gYCAA9O3bt7MJWblc7uzZs/ft2xcdHb1hwwZHR8cWmtCdBauP9uvXT8wIBgsLBPDRaSFZ8Pn81j4iWPF6165d8fGve/VCWVnojz/Q1q2ovBz5+38oduwYkpVFxEHG7NlI0jn/2GxkYoIA0NChiOQT9cxMpKnZdAJbX48WLSJh4tFVSNYIRctNx+fzDQ0NQew0BkJaDo/H8/X1/fPPP+vq6v744w/CPYDL5eJuCHNc2THDhiGANs7oxWf//v3jx49/9+5dm+/y+Qi7fK9ciUJD0cWL6McfEY+HzMwQm42OHUOGhmjqVITlfLER7t+P7O0RqfL/TTQ0NKUD798fFRaSXz+6dw+NHIkMDdG336KNG5HQSaCkjrhG+OjRI0EppICAALy6w55lxLmfhoaGk5NT69x0fD7/wIEDmzZtioyM3LJlC5HBB0shCpngWkw4HE5JSUlAQMDdu3d3795d2PwFwcPggAEDxNfh7NEDASDxgjHapqamhsfjBQUF+QuOce3w+DF6+BBFRyNjY4QQOnYM2dqimTObUkJJdCTkcpGlJQJA2tqI3MyObVNfj+7fl3wz5CCuEe7YsUMwgfjChQv9/f3/97//ycvLY/MTJjfdw4cPg4KCoqKi7jf/4dhsNg5HatNVknQaGxt/++03fIy2adOmkpISYhgU5svdMTU1TUmESclrWFtb+9tvvzk6OhLC8q9everXr5/wqTzr6xEeOLERJiUhHR1UViZZI9y0CQEgVVX06pWkmvgAl4v690c0GvpCsjWRf1jPZDLfvHnD4XDMzMwCAwPj4uJWrVpF2GRr8vLyoqOjZ8yYIXhTWVkZZ+qUkHa6IJWVlYsXLx41alR2dnZWVhaXy5WXl79w4UJKSkr//v0FoxlFIy8PAEBHB9rJm9I5nj59OnHixJ9//jkoqOnQbNiwYcnJyZqamlhb4JM1yMlB//4fLgcNgiVLwMWFqB9I15fbuRO8vUFBAW7fhmHDSK68DRgMmDkTEAIvL8k3RgIknBO+ffv25s2b+HVBQQEA+Pj4qKmp9Rf8r26Hurq6LVu2TJs27fXr1yEhIRwOZ/r06fiMy8bGJvfZs9WvX0NxMQikcyIdBQUFd3d3AOjVq1fv3r0XLlwoLy+/b98+AHB2dhZfjprcUzg6nc7j8Xg8nuBJIJ1OX7x4MdZxFQFnZxg6FBgM4PPh118hIwN++AF27mzXAahTHD0K+/YBgwHnz8P335NQoVDY28Px43D2LOzbB50P0u9qxBxJd+zYYWpq6tiMoaHhhQsXyBiiEUIIzZuHANDu3aRVKBxnz54FgP79+5OSleHffxEAWr5c/JoQQqihoWHnzp07d+5scY7K5XKLi4ttV6+uz8oSsio8HcVcu4YA0NWraOlSxGQ2OaNPm4aCgsSaRV+4cNnY+AWNhsSe1Hee2bMRAHJz67IGAwICfH19CwoK7t+/f/jwYeFTJnbCCImU1BUVFcTGY+s1IZlG+PgxAkCaml2508Xlcr/99lsgQx0Dg9Ucfv+dlMo64sK2bRw9PST0+eqrVyg4+MPlsWNNWyaFhcjF5UNY1pAhyNdXlP+BoKAgJpPZrZvy0aPSODS/fx8BoD59uuL0EyGE0J49e96/f19UVLR3715WZzzQhV0TFhQUnDhxYt26dVFRUQEBASLPfDrH5Mnw3XdQUgJXrnRFcwAAcP16ubJy//79+y9dupSUCrvMKWzx1q3MY8fA3h4SE4UpP2wYCDrerVsH2IO1d29wdYXsbPDwAF1deP0a1q4FfX1wdf1IQLVjnj9/bmlp2djYaGOzcf16vc7+LiQwYwYMGgT5+XDjBom1Xr161dramrh0d3d3b3ZYHT169Llz5/z9/QsKCnDidyHrFNYItbW17e3ttbS0RowYsWbNGsILlkajCeaUbHFJAtjaPTzIrLN9eDxwcen54sUdF5dYspITdZ1nZs+eMGkSTJ4M27aJX5myMtjZwbt3cPo0DBkCxcWwezcsWpTi4ODQWgqoBUlJSbNnz66url62bNmfwkjKiUFmZmaFgEpPcnJyU6pmGg02bQIAIDXzV319PZvNJi5ramoIleqKioru3bv36tVLRkZGTU0NCS/SJ/ygeeXKlaCgID6f//fff7/qip1mhBBC9fWoVy8E0DXJay9cQADIwIDMvCXGxggASUwnrRV37iAeDz19Ss6RCEIIIT4fBQWhadNQv36zAIDJZC5dujQuLq7Nwrm5uVi83MLCgtxUh20yf/58IqskQsjY2PjDl7OmpumIlrwYwrNnzwrKAjo7Ozs7O+PXRUVFOJC6oKCgPfeJNhF2JExISIiOjs7Jybl27VpJScnz589R12TPk5MDPPqTncmwNXx+kxDo77+Tmfqhq2MU5swBBwdYvhyKisiqkkaDWbMgJARu3HBbtmwZAJw7d27YsGETJky4ffu24DehtLR0xowZOTk548aNw5mzCF1tHP/VpSgqwqpVAEDuWcWTJ0+mN3Pu3Dnifq9evQYNGgQAWlpawhwNfICsJ4QEKShAsrKIwZC0q8XFiwgA9e2LGhpIq7OiAgGgbt1Iq1AoXrxA1dUoPh7l5kqieuyITyRUHTp0KNa8q6mpGT9+PAAMGTIE70yw2ewlS5acPXv2+vXrBw8edHR0JL0z8+fPv3jxIq+Zj0ZChFB2dq6OzrapUwUTNh85csTX17e8vPz333/vQMSoBSUlJcnJyWfPnrWwsMhuxs7OjhgJReZziazvCC0tsLQEHg+OHZNcI8Qw6OwMsrKkVSudUL2RIyEoCI4fB2VlSVSvpaXl6uqamZm5f/9+LS2thISEtWvXDhw4cOTIkZGRkQYGBg8ePMASqRUVFZqamtXV1WlpaWPHjsWO5qT3x8HBwbiZd+/effSent7W8ePdQkOPCXx5srOzTUxMOBwOj8fDWSg7JiUlxc7OTl9ff8OGDQCgqKio1wxOSisuYhpxF/H8OQJAPXqg+noJtXDpEvnDIELo3j0EgGbMILNOocCLsbo6dOUK2rMH+fm1jh26desW1rYTB6x5h5UQ1NTUGAyGYPgY1rA5fPhwTk5OWFiYjY2NmM21pqM1IUIIofDwcADQ0NCobY7mSkhI8PPzu3PnTnx8/JYtWwrbcSfn8Xg3b96cOnUqthQ6nW5hYfHvv/+2tyYUmS9hJASAUaPgwAF4+hTk5CRRPZ8P+/YBAOzcSeYwCFJUjpCRgYYGGD8ebt6Efv2gsBBGjYLoaMEi06dPJ7KUi4ysrOzy5cuvX7/OZDLLy8t5PJ6VldU///yD3y0oKDhw4ACNRqutrY2NjZ0zZ46YzQmChBtUJ0yYMGrUqPfv31+8eBEAuFxuSEjIu3fvFBQUwsLCampqlFvNF6qqqvz8/IyNjefNmxcaGoqTLyQmJgYGBhobG8+cOZMoOWbMmNZaPqL8Jl8GfD7y8UFz5qBx49Dq1aj9dBQicPkyAkB6eiQPgwihnTsRABJwZ+hCPD3R/PkfLs+fR+PHtyjiSlLPsHUtX76cOED+6aefJCrCW1BQMGHChJCQkE+OhAihM2fOAMDgwYP5fD5CiMPh4JDL2traFjGr6enpTk5ORLoBAwMDNze3Tp28i8CXY4RubmVGRvEXLyIWC/n5IS2tc97endoI7oARIxAAEntq1gYrViAAJJIutthYWiJBp5/qaiQjI/iYiYyMXLRoUYbY210PHjwAABUVlYKCAoTQlStX8J6Nvr5+VFSUmJW3yYsXL7AWiYmJSXR0tOCmy+PHj1vL+3I4HFy+g1iT8PBwS0tLLJYJAKampgEBAV1wxIK+GCPk8ZCq6k4rq6tbtiAnJ+TkhAYPvtanz8OHD0mpPjMT/fYb+cMgQmjqVASASOpmJzEzQ9eufbjk85GsbJMqOEIIoerqahaLVS/eMruhoQGrNh4+fJi4mZmZiSdpTCbTzc2NT96JJUIoICBAUVER24nwCbN2794Nbek4E/qx2Paw4lF8fDyJHf4kX4gR5uQgdXX3uXNzDA2bPBoBOHR6qJcXOnQIXbqEIiJQbi4SWg4jKgqNHImIcdTNDXUy6Yuw+p+KjgAAIABJREFUDByIAMidOwvNmjXowIEPl1lZksj5sn//fgAYNGhQCx2D+vp6W1tb7D71448/tlYSEgE+n+/m5obrXLNmTUNnnpolJSXy8vJ0Op2YPRUWFrq4uOCkCQDQq1cvFxeXkpIS8fvZWb4QI8zNRaqql8aPRwClhoYJS5ZwmUwEELZ6NWGTCADJyCAdHWRqin7+udLlL09PdOMGevGiDTGFBw+Qmhoi0lVs3PiJpEgio6iIAFDntb/JICICGRigoiKEEOLx0MqVaPNmclsoLCzEe/T32wljv3nzJs5Yrqur26n0GK2pqqqaP38+ADAYDLdWsRExMTEeHh5JSUnnz593c3NrM4IBZyO2t7fHikeEjt6IESNa5PbqYj47I6ysrDx//vy7d+/YbPaZM2dy8XEzj4e6d787YgQCiBw1aseOHRxshLt3I3t7tHAhGjsWaWt/yAgBUDJytqB5ysmh/v3RpElo2TK0dy968ACZm6MpUxBe0kvICN+/RwBIXZ38moXl4EGkp4fMzJChIZo37xOpMDoPjnj+6aefOiiTk5ODz+JkZGRcXFx4Iimg5ebmmpiYAIC6unqwYOhHM1u2bMnIyOByubm5uU+ePLnRlqjW69evaTQaseqTkZFZtGhRZGSkCP0hl8/OCKOjo4uKijZu3Pjbb78VFhZu3Lix6Y39+0tUVD4a9wDCmlMUXb161dPTk1df/9fOnT5btvDPnUs/et/GBv34Ixo+HGlofPQ5I6MmI0xIQHp6qKJCUkb48iUCQN99R37NnYDDQTk5kogFi4iIoNFoCgoKn9zaaWxsdHFxwVHIU6dOxfs3nWoIa08OHDgwuZ38aXPmzLl06dKRI0f4fL6zs3Pr2S+fz/fw8KDT6UpKSgoKCra2tiLox0qIz+6ccPTo0XQ6vWfPnjwer3fv3h90MZycIg0NGwXOCe9qaXHU1fHrefPmlZeXP/rvPwVt7fHLltGWLDFYP9PbG27dgoiI2sJCXm0tiogofPQI/P0/5I4fMgQWLgRXV0n9LjiHhbSUrZvQ1oaxY6E5dypZ8Pl8e3t7hJCTk1MHGv4YGRkZV1fXhw8famlphYaGfvfdd/fv3xeyoZMnT06dOrW4uHjmzJnPnz/HoZ6tweko5eTkVq5c2a9fv9qP81WwWCxzc3Pc4Zqaml69eh0+fBh7mX8WSPkh0Ip3795t3769pKTE0dGxqKhI0Mdi/fr1x44dIy7NzMwEk2m6urpeuHDh1KlTzs7OmZmZ+GZeXt6qVavi4uJ27tzp7+/v6emJ7+ORECHEZiN9fWRmJpGR0NsbAaD168mvWViqqhAAUlQkvWKs/aOrq1tTUyP8p7AtAQCNRrO1te1Yk5LL5To5OeFvqbW1dcenBXV1de/eveNyuTjdiOApRWxsLFa/79Gjx71797BrdWdVoSXKZ2eEjx8/9vX1PXv2LF4T5uXltS7T2Nj4+vVrwSPUkJAQCwuLx48f29nZ2djYCK7Lb9y4ERcXZ2dn9+jRo1WrVuGbhBGiZoc1SRihoyMC+CD2Ti6hoaFXr15FCN25cyeIkEl7/hzduoWIP1pSEgJA33xDbtNlZWV4U/Ga4BGIcOBpId4UGT16dHtT2crKSnNzcwCQk5NrnapZePz9/XHsq4mJCW4LJ37F6eU+Ez47IxSGn3/+GQDOYrnMj2lsbGyx9MdGiHNUEG5+8fFo//4PZbZsQQ8ekN/PxYsRAGqrm+ISEhJy+fJlNpt9+vTpp0+fEg+dxIULVwIQ2r1B+/a5YK0YMmjg8+OqquKqqpa5unb77rspxGMMIYRQYGDggQMHCOXYjnn27Jm+vj4AqKqqXml1OpSWloZjgnr27CmoatspamtrsWAfHkiJ8wycYAsAuvgwsAM+uzWhMOCj1VevXrV+S0ZGRlCGrKKiIjo6+v79+xERESEhIevWrWuuoSn6nM2Gs2ehXz/4WHKRHCTnOBoTE5OXl7d9+/aEhIT4+HgiQ1NVXl68QJMl6emp5PWgtLHRJi3tZGpqBJ2u8dNP9tjdtpm7d+/Kysp2oG0pyJgxY168eGFubl5ZWblo0SI7O7uG5lXrw4cPR40alZyc/N1338XExEyaNEmErqampo4ZM+bUqVPdunW7ePGir6+vbLNPsLKy8ooVKwBAtLRfEkHaTwFRuHfvHgDg/OxikpnZdIogidwhEyey1dV5JLnWfcSlS5cePHjg4ODg7u6ekJCwadMmfD/KwMBEIIzff968xQBo1y5SGs1vaPghIQFbxeZWR4549e7g4CB8hXhqis1jxIgRaWlpvr6+WFVk4cKFIqffuXHjBlaONjQ0JLJ9CZKWlkan0+Xk5IR3uJEoX6QRFhUVAUD37t1J8YfS00MAqK3/LLHg8XiysrI0Gk1Mv7A24fP5165de/XqFYfDuXjxIpECLUpJSQNg/cqV69evX79+/TRt7cUA6MQJUhrNb2iYFBXF7NFDy9Awo9UZQGhoqLu7uwjpQ6Kjo/H+qpycHADQaDRXV1fR/mcbGxuJvZxffvmlgwTpeMG5t4tThLfDF2mECKHevXsDgPjOxwihJUsksjGTn58PAL169SK53g6oqIgC+IZOD2nGSV9/MUDYwYOLFy8+f/58mYDjqAiklZebPHv27alTc8LC1rfKfPhJqqqqPDw8rly5kpGR4e7uLuhkU1lZOXbsWLyDMmHCBNHGwNzcXBzXLyMj09qlpgXBwcEAoK2t3SnfNwnxpRrh7NmzQaTdudYcP44AkJWV+DV9xLNnzwBg1KhRJNfbAa9fRwGYyMsTN/y1tBYDbFqyBA8ODAbDxMTExcUlJiZGhKHGds+eIXfvmpiYiOb14u7uHhsbW1VVZWdnx+VyiSk0QqimpkZXVxeaU7IOHjz4TSfTSISGhuIDfV1dXSFDN/DOApkyuaLyRW7MAMCIESOgnb2ZzjJxIgBAWJj4NX0E1gXU7cqjerwRJBiVXF4OAJudnDw9PadPny4jIxMbG7t79+6RI0caGBhs3Ljx/v37Quovpaennzp1CgB8fHzazMX9SQoKCvLy8rZu3Yp9x5hMJmqOynV3d8/NzR09evSrV68GDx6cmJg4cuRIT+GkvRBC7u7u06dPLy4unjp1akxMzNixY4X5oI2NDQDgEwspI+2ngIhcvXoVAObMmSN+VXw+0tREAIjcHRT8v4uTtHQNF06dGty//8QRI/BlPYvl0a/f9p49iQI1NTWBgYHW1taCicEVFBTMzMw8PDxaJ64TZM6cObLa2mNFPTBACJ07d+7kyZO2trZ///33xYsXtzQn1s3Ly1NSUgIA7OFdW1srfGRwaWnprFmzAIBGozk5OXVqiK6trcWnnRIKehSeL9UI09PTAUBbW5uU2hYsQACIJNn7JjZv3gwAhw4dIrPSDtm5cycAEMHycXFxADB48OA2CycmJrq5uZmamgoOa0ZGRk5OTjjntmDh27dvA0D3nj2DhU500SbZ2dl4myozM5Nwl8FK54LCLQihK1eu4B1OfX39Z8+eIYTS0tIE44xev3797Nkz7HqmoaHRplf3J9m+fTsAWJG+FOkkX6oR8vl8rEFQhEN1xOPIEQSAVq4Uv6YPLFy4EAAuX75MZqUdgo+//mkO48eWM3v27I4/VVJS4u/vb2lpKSgc1rNnT0tLS39///LyciJs9+Dhw1NevdqQmkpiiG5sbCydTpeVlW0tkpCSkjJs2DAAkJOT8/DwWLp0qaDrzOjRo+/fv29gYDBy5EjCS7Gz5OfnM5lMGRmZjmcBkuZLNUKEED6wai+SrVPExCAANGCA+DV9AIeWd2WkDNYFI9QGjh49CgDW1tZCfry+vv7BgwebNm3CnpYYWVlZ7GxpZGQUXVZmEhOzhEhyTwbff/89AGzbtq29LhGRwTo6Ot7e3sRbo0ePjoiIyMjIEDIpentYWVkBwPbt28WpREy+YCO0t7cHgP2C7meiwuUiFRVEo6H8fNI0RbS1tQEgVzLyu20ycOBAACCCffBc648//hChqvT0dA8PDzMzM3ySLicnN2bMGJ/kZJOYGHfyIoACAgIAQFNTs6KiooNiN27cwJHB6urqjx49qqurq6urGzVqVEREhPh9iIqKAgA1NTWRfQPE5ws2QpxTftGiRYI3k5OTa2pq0tLSgoOD23T+bo81a/5VV/9GULdLHDgcDoPBYDAYXaMUhMHKK8QJNV5rCa8w3SbYOQkHwqro6Aw6c+aueIeNBA0NDQMGDAAAYbRPs7OzNTQ08AZMr169vvvuO0VFRVKMEDXPWcSXYBWZL/WIAgCGDx8OAIIJqLKzs318fPLz8y9dumRgYIBX9kKir1/AYqVioVjxyc/P5/F42traZKV2+iSlpaW1tbVqamrdunXDd0g5I/nvv/8AYNWqVePGjWPn5b1dvfrFv/+K31sAOHLkyLt374yMjAg36w7Q09ObMWPG3LlzaTRacXFxjx49DA0NSekGANjZ2QEADnMjq85O8QUb4aBBg+Tl5XHwGL7Tt2/fcePGAYCuru6NGzciIiKErw0vTsgywq4/JKyrq/vhhx8mT57cug/v378/ffp0Dg4x7iR37twBACsrq0vBwb1XrEAcjquDw9KlS4l8YKLx/v17rBB15MgRIZ9TNBpt7ty5Dx8+7N27d2hoaGJiYvTHWsYis3DhQh0dnaSkpEePHpFSYaeR1hBMCqNGjQKA//77j7hz/vz51NTU3NxcNpvdqdV2fX29vLw8jUYT07eL6Aa02nbvSvh8PnbFrKmpiYqKYrFYgh4qbZY/derU8ePHEUIVFRW7du1CCGVnZwOAiooKh8MJKisziYmxOHoUzy++/fbbxMREkbu3du1a6OQxL7E7WlxcPOP/2jvzsCauvY8fQgyyhs3QgIIb1UgBFbEu3CqKvmi5ilastIUWq9Jq1WoVfbgoteh7odeN0irQ4gviqygCKiCi8EBxAw2ripFNKRJIhEAgIYEkzPvHucybyxJDEhiB83n4YyaZnJyQfGfO/NaVK4FymcFKcuzYMQBAYGCg+kOpwPBdCYVCIV7YGABQXFw8f/58NceEK1I8bobH4/F4vPz8/BcvXsTGxu7fv1/5oXR0dObPn49h2P3799WcFSAkXOY/4XK5nZ2d5ubmenp6CxYs4PP5iqs5ZGZmUqlUaCqMiIiA5SGgk8Pd3X3cuHFPhEIAwLp16woKCuzt7Vks1oIFCxISElSY2/Pnz2NiYshkMt7jthcSiWTt2rWnT5+GQeEw2DogIABm5dNotJs3b27ZsoVEIv3yyy/+/v4hISHwfAHZuXPnYNuw+fv7371791hPcpZMJpPvBDrUaF6Eb968CQsLk0qlN2/ePHXq1OvXr+HjGIb9u4UqAACA7u5u9RvW9RKhqanpd9995+Pjs3z58p07d8prXhk++ugjoKEVKeEihD9KGo0GACguLo6Njd2yZYuC42tqathsdkBAwM2bN42MjCQSiVAoTE9PBwDAEvdPBAIAgIO+/owZMwoKCjZv3iwQCLy9vf39/bu6ugY1tz179kil0m+//dbOzq7fA5qbm+l0urGx8bJly/z8/OAE7O3tocEZAKCtrf37779nZ2dPmzZt586dSUlJb968wV8eExMz2CmRSCQYjQx5/Pixq6vroEZQB82LEMOwzs5OqVSampq6evVqeDYdInqJUE00eFtIrAi7urp+/PFHGxub58+fb9y4sby83M7O7tGjRwpe4uzs3N7erqOjY2pqSqfTGxoa6uvrc3NzSSSSu7t7Z3d3pUhE0tJi6OsDAHR1dWNiYmDliOjo6EWLFr18+VLJud28eTMzM9PY2Pjw4cMDHfPee+9FRka2tra+evUqLi4OFlbsy5IlS1gsFvwNjGg0b7uj0WjwbsTU1DQ6OtrNzQ1/qqurCxpOAADwzj47O9vNzW38+PEmcujq6ip+RFdXF17lHBwcyGRyeXk5vKNTc+aLFi0ik8lMJlMgEOA2RtUgUIQikeiTTz7JyMjQ1dUlk8mJiYlJSUmbNm2CQW0D4ezsTKPRDA0NTU1NFyxY4OLi8vjxY5FItHDhQhqNVlRTM6WzU5dK1ZOLcfP19f3ggw+8vLwKCwudnZ3j4+PlLyb9IpPJAgICAACHDx/GS1/3hc1mx8fHSyQSS0tLY2NjBVZu3Khz9epV/CwjlUoVT+NdQ/Mi5PP59fX1lZWVbDb7q6++evDgAf7dUCgUuMIBAJSWlu7evbu9vR0AIBaLGxoaBtWmi0KhGBoaUqlUMpksFotXrlw5adIkQ0NDY2NjIyMjIyMjQ0NDQ0NDIyMjExMTfBsGCg+EgYHB7NmzmUxmQUHB8uXLVf0HAECcCIVC4dq1a7Ozs2k0WmZmppmZ2fHjx6Ojoy9evJiQkLB69eqQkBAYC9YXGxsbfJtOp8uvRbOTki4FBHz77bfgzBn5l8ydO7eoqMjPzy8lJeXjjz8OCAg4duwYXl23L5GRkc+ePZs2bRrstjkQlpaWeG7u1q1blfngUqlUIpEoc+RACIVC3O0hFosVnCM0jhamad8In8+vqqoyMDAwNzd/9erVnDlzYIiwQCAwNzfH7wMLCwv9/PzKyspEIpFYLBaJRC099Nod6BH8HQ0MDAQCgTJzI5FIVCqVSqXKqxTqFm5nZGTcvn370KFDP/30k8r/AbFYrKenR6FQRCIRDLkaHlpbW1etWpWfn0+n02/fvg0bdwIA6urqjh8//vvvv4tEIhKJtHr16p9++knxKg7DMGtr69evX5eUlDg6On7yySfJyclxcXG+vr4ymYzH40HXOX7wL7/8sm/fPqlU6urqevHiRZhy3Xd6tra2TU1NKSkpnp6eGvzgs2fP/uOPP+bNmwd39fT0GhsbB9VDt6WlxdraGl4SAAD5+fk7duwoLCzU4CQVMWx2WHjLge8ymUx7e3uVRxOJRFwut6qqCq5Lz507Fx8ff+bMmdDQ0IMHD27fvt3Hx8fT03PZsmVOTk7Tp0+3sLCAidtvRUdHx9zcPD09XeW5VVRUAACmTp2q8ggq0NjY6OjoCACYPHlyvx3jGhsbDxw4AP8JJBLJw8OjqKhooNHg72/ixIkw9xemPlVUVGAYFhUVdezYsf8vsthDbm4unU4HAFhZWfUby/L06VMGg7F06VK1Pmd/ODo6Pn78GN/V1dXt2x1NMTwez8DAAN99+PDh3J6MsGFg+ETY2dn5j3/8A999/fr1qVOn1BwTlpAwVbrbg0Qi4fF41dXVJSUleXl56enpCQkJUVFRYWFhQUFBu3fvxhckJBLpwIEDqgWdQZ/vkiVLVHitarDZbGhpnDlzpuJoVQ6Hg0tRS0vLw8OjsLCw72FwIfDNN99gGPbq1SsAgJmZGRQkj8f78ssvX/RX3qK+vt7FxQUAMG7cuIiICBaLJR9en5+ff/fuXY1kvfRixYoV8vULp02bBmP3kpKSzp8/39zcHBkZqThpcKyIcCjIyMjQ4M+9s7PTzMwMAHDgwAFYZ8HZ2bm6unqw48TGxgIAfHx8NDKrt1JTUwMTHebMmaNkZ6+3ShGGU6ampmIYBrtM4451sVjMYrF+k6vJk5+fHx0dzefzo6KiEhISgoODtbS0zp49y2AwLCwssKQk7MoV7MoV13HjhvPElJ6enpGRIZFInj9/LpFI5Eu590UsFkf2FGvFMKyxsVFTUcTKMLJF+PPPPwMAFMeCKE9ycjIAYPbs2ZhcCTAqlTrY7yMkJAQMV/hFeXk5XCs6OzsPNtZnIClyOBwSiaSrqwtL3MPQSjwbIyoq6p///CfeTaWqqurEiRMdHR1Pnjy5fv06fLCgoADDMAaDsdnYGKNQYCMeGQA/zpqlqQ/+VsLCwo4fP/711193dXXFxcUdOXJk2N56sIzg2FEAwJMnTwAA9vb2GhkNxprB5ANY78TLy4vP52/atMnX11c+0kAxw2YaLSoqWrJkSX19/ZIlS7Kzs0172uMoCY1GCw0NffXqFZRiWlravHnz/v73v//222/d3d3Lli2DaRmwYhXuW9q2bdvBgwfx+JuysjIul7tv3z6ZTFZXVwe7aOGxUL/x+aCrS7RqlcTOjgSAXU+U7zDAYDDodLqBgcGjR48WLlw4KNt7a2tra2srAKCpqWmwfn9VIPosoBbQFAHLH6hJa2urrq4uiUTqlQAVFRUF3Z52dnZKRktCl0xaWpr6s1JAQUEBVN3HH3/c0dGh5mhcLvfAgQNQdRCYaCsWi3V0dEgk0kCmjtLS0tOnT584ceLOnTtVVVU7duzAiwgyGIwOADAAzPT09lMoGACJVlZqznNQ5OXlVVdXw9Af+b72iunq6vrqq69SUlIyMzNDQ0OVz4pWmREswq6uLgqFQiKRFNR4VZ6oqCgAwIoVK/o+VVhYCPNlDQ0N+22A0QvoGxjSVgc5OTmGhoYAgE8//VQjEcwQLpe7f/9+3K3i5uYWExMDAHBwcFDwqry8vAcPHojF4qSkJJZcZ3AGgyHS0sIAwObMwSZNGn4RqsabN29++OGHkydPlpaWent7D6qmuGqMYBGWlZUBAGxtbTUyGgwcjY2NbWpqCgoKKi8vv3fv3vHjx+F9Tltb2+c91Tt9fHwUZ2HDCA/5plGaJTU1FYYHffHFFxpPGoZVcSdMmACvilCQ69evV2EoBoNxffx4vDlrJwABajilhg2ZTCaTySIiImBzvr179w6q/ZsKjGARcrncs2fPyhu1VKa2tpZEIunp6bW1tUml0qysrLt370J7z65du/DD4uLiYMzNzJkzS0pK+h0KRt/r6+sPdg5SqbSpqam6uprJZCpYXl66dAlabrdv365aEV7FwKIhgYGBb968CQ4O1tbWhl5vNze3R48eDfSqjo6OwMDAiIiIp0+fbty4Ec6fwWBYWVhgTCb8mzm81lGV4XA4//rXv8LDw8vLy0NDQ3/99dehfsdhyvtWjaKiomnTpj18+LC9vd3a2hrazfl8/t69e2NiYiZMmPDNN9+wWKygoCC8LZFqXLhwobu729PTE67xIDDQh/Sf0ZLz58//9NNPy8rKFixYEBoaCi2H8shbZQQCAZvNbm1t5fP5ra2tLS0t+Da+ge/KB/2UlpbC+tC9iI+P9/Pzk8lkBw4cCA0NVecjDwT0+qxevdrc3NzZ2RleFvT19bOysrKzsz08PIKDg2H7eHnu3Lmjra1tampqZ2fn5OSEYRgA4OeffzY2NgY9BwfGxOBBLe8yNBpt3759cBt2aBtyhlrlKlNfXx8UFFRYWMjn858/f46fkBobG01MTPDD8vLyPvzwQzXfC97FwSiZjo6OPXv2HDx4MDIyMjw8vG8hKZFIhFenXb9+fa9lJ/wRw3vLxMRE5b8ILS0tExOTqVOnzpkzp9/L7K+//goXh3hl0aGgoaHh3LlzUqkU6zFynjx5El4V8UAwNzc36ITASUxMzMzMPHv27PPnz8PCwoZ6/TbKeHdFiGHYjRs3oOcqLCwMt85pXIRMJhMAMGHChL4WDgUNla5evWpsbAwAsLGxka9rGB0dDQDYvHkznNv06dPnzZvn5ua2YcOGLVu27Nu3LyQkJCIi4vz58zdu3MjLyystLa2trX1rmBW87mlpaakfZtSX9PR0f39/fDciIiI0NBQGcFtYWOCKUiDFlpaWwMDAoKAgFovl7e39P5qtozzaGQEiFAqFYWFh+IONjY26urr/3cO2bdvUFCEslS1/76ckr169gm0PyGRycHAwvEM7dOgQACA4OFidKfUCjqmtrY0X9tUsly9f9vT0xHdDQkICAgLg4v/EiRO9DobODDzVy2PNmmdyzckRKvDuOusFAkFDQwPsTQ9rS+OQSCR6DzDQTGVkMhkMy4I++kFhY2OTl5cHWyAcOXLE3d2dw+FoylMvlUqbm5urqqq2bdsWEhJCoVAiIyMdHR0LCwtlMpmag7+VmpqagoICCwsLvLcxzoQJE6CLPzg4mEqldjs6+lZUbK+sfKpe6aexjOZTmYYaDofDYDB4PB7cvXv37v79+2FUhwpkZma6u7vb2trC1AfVuHHjxubNm5ubmydOnGhhYVFYWJiZmbmyTwPuXtlY/YInbXE4nO7ubgCApaVlU1NTcnKys7NzXV1ddHT0mTNnFOTsqcCVK1e+//57eOkDALx48aKtra2+vv748eM//PCDghc2NTWl8HjxHR0dMhkAYBGVuo1O/0Bh0iaiL++0dXQYuHDhAgDAx8dHnUHWrFlTVFT02Wef3b9/HyZ2nDx58sSJE/LGT7wnu5JAO42uri6bzba0tHR3d9fW1iaTyY6OjiorsLS0NDU11cHBgc/ns9nsxsbGU6dOwadmzZqFlzkKDAy8fv26ubk5rImmAHNz863m5hul0stc7iUu9wGf/4DPdzQw2G5p6SRnZ0YoZuSJkEwmyxd+1dPTg5HWCrh27dqtW7cOHTp0+PDhnTt34qnlQqHw2rVrWlpauCNeZaytrXNzc48ePRoeHi4SiTIzM/se06tmRy96lfCwsLDQ1tbGMGzmzJkVFRWpqamenp6xsbHKlModiAsXLtja2tJotDVr1mRlZcFwPIihoeGsWbPgNmznJH/jpxgqmbzN0nITjZbA5V7icksFAv+KClyKrVLp73Jxm57m5rbKJXaOIYi+KR0moKXk2rVrxcXF+IPx8fEAgMWLF2vwjfh8/oULFxISEs6fP3/58uXy8nI2m62Oyf706dMAAJgLizd7UY0dO3aIxWLYMjEoKAh/XN4w87C+3mL6dD09PdV6M/AlkjP19R8VFzsxmU5M5o6Kisdtbf9VWnq/tRX+NWsuyG7U8O4aZvqFzWa/ePGis7MzLS2tpqYGf/z+/fuwVCYAAOuJvXor8mkTmsLIyOjzzz93dXV9+vRpdXW1RCKh0+nygdGDZfPmzVQqNTc3t6SkZMWKFerMzdvb+8SJEzNmzKitrZUvbzFlyhTY0QkAEN3ebrR9+9ZjxxTX4xkIIzL5W0vLDHsPDYzDAAAJUklEQVT7nVZWhtraZQLBeG1tHRJpEZUK/0zHjVPnI4xKRpJhBsOw5ORkFovl7u5uaGh48uTJyMhI+NTUqVMzMjLgMlUqlcq3YgYA/Pnnn8nJyd7e3rdu3TI1NYWudi6Xa2VlRSKR2Gy2mibWvsAA1EmTJllbW6t5wwkA2LNnz+nTp/38/GDD6qEjv63tu8pKYzL5hr29nko9seVpk8lYQiFdR+crFutrOh0AQNHS2iBXnAbxb4i9EA8WiURy9OhRDMMEAkFAQAD++JQpU/D4fVh1661DQZuEvH9MswiFwvj4eI2kWb18+VJbW1tHR2coakPIs4XFcmIy4xoaNDjmX2LxspKSixzORQ7nqnKJ/2ONEbYchXC53FOnTvUqpNna2trU1NTU1NTc3KzMINAuqr5Jpl8wDIuNjaVQKLjdXx0mT57s4eHR2dkJw3GGiIK2tmKBgEoma/xiZaCt7U2jedNon6DLYH+MpOUoACAxMbGlpWXSpEl1dXVkMhk3FU6dOtXExASv/wvjyIKDg+fOnQvL1/Yah8ViMRgMIyMjGH8zrJ9BJXJycpYtW0aj0f766y95q6YG2friRbFAsNPK6sv+ChaqTF1n53eVldd76i/2gsfj7dq1C54QxywjzEXh5eU10FMXL16Uvyesrq7Ga4fS6XQXF5fFixe7uLjAOqjwW9+4ceOIUCAAwNXV1dHRsbS0NDExUbOWJMijnsugl6YvVtoAGA3s2AwPD8c7TIxZRuRyVBnGjx8fFBTk6uqqr6/f0NCQmJj4/fffz5s3z9TUdNWqVbCf+1D8mocOaE/C3eua5Y+GBgCAj4WFnkZjcQAAljo68QMkBOXk5Lx586a2trayslKzbzrCIPieVEMoMMxIpdKnT59GRUX5+Pjg/mjoM5gxY0ZwcHBlZSVBsx4cYrHYwsICAKCpNtE4BW1tTkzmspISoVSq2ZEV09raWl1dvXv3bsU960c9o0SETCZTJBLhuwp+pq9fv4Y9anA/mJaWlouLS2RkpEbagw4p0Bbl5eWl2WG3slhOTOY5jRpFEcozwgwz6tPV1WVpadnc3FxaWtrW1hYfH3/p0iXYhIBCoaxcudLLy2vDhg3quNeHjoaGhsmTJ8tkssrKyrcG6ylPiUBwkcv90cZG42tRhFIQfRYYbpKSkgAAc+bMwR/p6Oi4cuWKh4fHuJ5gDiqV6uPjc+fOHVj4/Z0C+lTkfaSq8b8czv6qKvh3WmHlfMRQM+ZEuH79etBfriqGYU1NTVFRUYsXL8bPUBMnTty1a5eCxinDD6wDYGxsrFpsJ05gTc25hoYXHR0vOjpq5VbyiOFnbImQx+PBUra9Kvz2ory8PDg4GDZ4gMyaNSs0NJTNZuPHCIVC+eZHPB5PcScWDbJo0SIAwJkzZ9QZJLCm5uY7fw88RhhbIoSxpitXrlTyeCaTuWvXLrxfJIlEWrx4cVRUVFtb27179+bPn48fGRMT88UXXwzNrHtz+fJlAMD777+vTsnDwJqa7ysrw+vqwuvqSjRRPRmhMqPWT9gvMG1C+VA1Jyen8PDw169fJyUleXp6ksnk+/fv+/v7W1pawvqihLB+/Xpra+uKiorbt2+rM46Vjo6dvr6dvj7KbCCWMSTC2trae/fu6enprVu3blAv1NHRWb9+fUpKCofDiYuLc3Nzc3JyGlQjWM1CJpNhu+nw8HB1xrHT119uYrLcxGTS0MTBIZRkhIWtqcOFCxcwDFu3bp2hqpUXjI2NfX19fX19Ozs7mUxmWVkZnuPf1tYG3Y/Dw9atW8vKyhR3fkeMFMbQlRBWVdNI2gQMoXZwcHjRA16gBcJkMru7u+vr67OysoaiOJqpqSmVSp0+fTrclUgkmzZtGtQI71Eohsgr+G4wVkTIZDKfPXtGo9HUTE5XhmfPnoWHh0skkpKSkvb2dmhH0ThpaWl45XyZTDaoat8AgJ1WVi5U6hDMCzFoxooIoUnG29ubTB7yFbidnR3sIL9w4cKUlBT5QhIIRF/GxD2hVCpNSEgAGk2bmDlzpnxLluXLl/dt4dLS0hIcHJyWljZEfUXOnDkD+4RKpVIlX5KTk/Pw4cMPP/xw+fLlQzElhAqMCRHeuXOnsbHR1tZWg12BzMzMXF1d8V0bGxsbGxu4XVdXp6end+vWLQqFUlNTo06RQsXQ6XSYrwwTR5QBNoEzMTEZoikhVIFoR+VwAI0xISEhRE9Ek0yaNAkP2RGJRCQSSZlXdXd3d3d379mzZyinhhgco/9KKBQKr1+/rqWl9dlnnxE9F+L5888/i4uL5asnIwhn9IswOTlZIBD87W9/mzp1KtFz0STvv/8+hUKB2yQSCZqC3srSpUuXLl06hNNCDJ7RL8IhrapGIFlZWfg2hUIpKysjcDIIdRjlSb0cDmfixIlDVOEXgdAIo/xKWFNTY21t7eDgMOoVeO/evYKCgmnTpnl6ehI9F8TgGIXO+s7OzsePH8PthQsXFhYW7t+/n9gpDQM5OTkfffRRcXEx0RNBDJpRKEIOh7N27Vp8l8lkBgYGEjif4cHMzOzJkycktRtIIIYf9J2NEjgcjo6ODh5NihhBjM57QgzD8J+jWCwmdjLDw5EjR7hcLvKFjkRGpwibm5uXLFkCt9vb28dIofW+LTcQI4LRKUJzc/PCwkK4nZWVdfToUWLng0AoAN0TIhAEg0SIQBDMKIyYgX5CFxcXuNvS0vLy5cu5c+cSOysEYiBGoQgRiJHFaFuO4umt7e3t6PyCGBGMKhFevXp17969AICXL1+6uroSWJ8XgVCeUSXCDRs2wJL1f/zxh7e3N9HTQSCUYlSJEJKXl9fY2Jibm/vo0SOi54JAvJ1R5ay/ceMGm802MjKKiYlJTk6G3YsQiHccZB1FIAhmFC5HEYiRBRIhAkEwSIQIBMEgESIQBINEiEAQDBIhAkEwSIQIBMEgESIQBINEiEAQDBIhAkEwSIQIBMEgESIQBINEiEAQDBIhAkEwSIQIBMEgESIQBINEiEAQDBIhAkEwSIQIBMEgESIQBINEiEAQDBIhAkEwSIQIBMEgESIQBINEiEAQDBIhAkEwSIQIBMEgESIQBINEiEAQDBIhAkEwSIQIBMEgESIQBINEiEAQDBIhAkEwSIQIBMEgESIQBINEiEAQDBIhAkEwSIQIBMEgESIQBINEiEAQDBIhAkEw/wf7KMJdVt2D7gAAA9d6VFh0cmRraXRQS0wgcmRraXQgMjAyMi4wMy41AAB4nO3TbUxTVxgH8Ke31N7bO8pLKdiiBUuBlrbTCrSOlt6DK4pa68s00WaZq5vbjNPwwfky9oJQERSmMpwGcTNOl00DQU2MDug9sC06+bAlwyi+gJtsM9mU6MKYM770Pg1f+sUP+7qTnDy/+z9Pz2nvvR2NtA9BdCRCbMiiszw6K6KzWjYlWxWtDLDZZmmRmRSrCROVi1VZfJ1Yl8Vdx9eJ9fj6rHVlrMrjv9ez9vvv5xZEyxNg/r8hcTeEhxxWC77olEWnkdXKGJZh5MAoQZ4AcgXIJ4GSBSXHMpwKOCOoeFAlgUoD/HPAJwKvhqRkSEqBpFTQpIFGB2la0KaDNgO0k0GnB50BdEbQZ4J+CuingiELDNlgmAY5JpYx5YLJArl5kJsPuWawFIDFyjJWG1h9YLODzQG2YrA/D/bpYJ8BjpngKARHERQ7odgNThe4ZoHrBXCVgNsDbgJuH3hKweMFjwCkDMhsIC9CWgLncxfbrBZTDihQnFGnUXFKRgYKVMYceez/g6M88/cUb8+F217p4kzTFsHVtUCUXOFdKDxakkolp3g50mAoRe+xrSLaP95Eq251C+G6BvT9B1NJ3ncR/Kw51BzZuGEZ5vvzD3uZkk/QQfeIqF4eRIcWCXR89jT0g8ur6MmWKvS1/nq6urwSLdQp6FH1R+h5M6qp86/jaOe+3fTYk0z0u4cP0PnDm/DckKaFWmcFMO+qEemy7nT0zr7l9IcrO7DnRnMrdax+Hb22q51mvzWOv90RGaTVNX/2SF5Qu4se+eBbQfLYLUrNv95BPx4roD3Je7B/5fkwrdzIYd5RuJX+OAZE8t/z6mkgk0H7T3ZSVmjGnsDbTtpy9Bf05t4Gmvg9iz3vbTgnfrpNg1asW0gH6wLo6zePiRF/CPvNrdPFf8uuorvmJ4i2sB97XmbuimcXb0a3j/rpEfVetG/tAbHtsy/QTWNWOlCajzaaUoWli3eg+5Qj3fqXTqODzE3hlXAf7k9TAmRo+Da63+Igrz1chD0lJEyuDsTO6vCcENZrCtGXytLIum/2ob/kPybKXj36XEUn8Zcewn2S12wnwsVNmFeqRZKVWoauefV9srdNjj2DoQ4yp7UT7+2anb1k90gtPiN70x1Sxd2ISC7aepD8dOUfzO+dGiCXx3X4fPsbPaSxLQNzMhwm79zvQbtrgqSqcSb2ZP32FXHWKzHXbzlBlvKTMX/jOk9q597D/Ay/gqgSCzHfXyQIQ5e+xjxv7s8Cczr2Pn8evChc+3DUIzn9KSE9PaIrH7GoAAAFuHpUWHRNT0wgcmRraXQgMjAyMi4wMy41AAB4nH1Yy24dNwzd+yvmBzwQn6IWXSRO2hRtbaBN8w/Z9//RQ2msuUGJXt8AMzTF5+ER46cjP39++u37P8f+8Kenp+No//MdYxzfpLX29MeRD8fHz7/8+nq8fP3w8V3y8vb369e/Dh9HZ5zBz4+6H76+/fEuoeP1+9FOUtYQPKipW8dDmx8cfvnyy0/8rs7HG343ehvEx3M7+7Bge1B/V5TjBdJo1AWKfHI4mxaKCkWGf1WSVBymI0ahaMfPh5wa8G3Hs5zO0lkKRYcindrN+zie9eTRvbdCsUORT/WQoOMZR0aTQYViIEYky2ExIn0LDVYuNMfxBVIhjsbp24daVCappU06e0hzSptKKEKpSqmKysRgbvlkNDpV7iF8mbZGN3QRT0NQrip5VPvLrE7XCE3VbspS9ZI0Vfls4Ug8D6kMjl6p2rLaGkoFq3aKMpX9JM9YYSt8uKZ9la49KtW+rHYfNlq2AidEy1hjqTp38YkTZqbaajYLEY7Q3iIDEDMuraLsr7PvMnL+0Dc0TVqFZyaMCAJAX5UjY0XbqAQqz27ZqYxpympiBBllKTRnsxzzpOo9jRLZGtj/qOoqgNjovWMQTAzKlaYto417cGoC3male18IdKDOM2kOkrKmsDObSjpEUtPE8VRpzkYJqiMdcwp0kbVWao4Lqeg9TkFTULAKffjOjhqqqIKMQGugk0rzmqmwJg4onL031yoj0NeqZwNQPakKtNOrOZXZJNAIFDFRnEd6VPUUXblzG0m2iVEfJU1gHl/TJgP4k2cjWuv/4ednOr59+P2mafEEYTs9bKhnes1IyvT64jYj75gXOWkAA2VxYzFWmtQMOlDjVrGQDBDrTA/AH4cCBIIyV+TfUhN9Gi0JzXAEhahAAGb8eUIQtXVGcTF/VLZWeV49hpkGVgEb0LZV+NfZrzbxj+js9CZJWoVmXlIoIgCKG5WSf91LkwZFJE5IIvEXBPBXJVKf1173oO55AoxNVX80qQ/zJBwoFoqOO8/LUuY8ERiq98hx6iPyYikUc5wATnKDRzAlLulWJWN5RaF5w3CJ50VJ0aSyaASLmLoWAVKgrLiWM2+8kgEsYyLIvJUFN5kWx4QtFBvQWbtWzAduOVDD5DgjpVbVEQh4g0VVgDc9Cp64grllZ0CKGriOJ9NjjKnMJlvTwd6STAwgqVCvNWPmnfwKhMAmgqCSwSyb46djKdG5DmmzbhWCvE0EYbzbmN5BEFxizWn2kYwZlpKgQoaXmtmf3ESU8pYDiLDvcNUgXw3CciGYzPTOQ7m0qUvTAinPSxaLWbllua1VMEzQmWSa0DEqZGIAMk5Y6uh6xpkrXmmzz4Wso57DZ+4Gyi3jXPPTbYy1Dgn6ytWkYa9+TRKIFisPHvwDu5WU/Pn10w+L91rFP769frpX8fwnfu/a+e7jXqnzfRvUpW73fgxaPOTegvHLQ+9dN1/j3mgJBvbZMV+3Z2rzvT9soIQjOxJKTzDxuEzSPEUPO+OU3PGhUzQt7QzIL8ntuC8JP+5zeQQeHta2Jdm5cbsk2xdnwGl+pwBsLskuAcsl2Wlxxsx5+mFFWpK7KRkzzPP9X6KMGeZ5R8hxSe4IxyXZEc5+wxjvCIWWRLZ34Utyn8qY4ZB3FqKX5IaJLYnc9/HEAr73ftAvyY5Z4pLsmHGLcx6VHaFOwMHy9q60JHqjMmOGMdnx5CKWEn28TpdkZ6oZM47qjgcXJE/JjkczZhzVXQ2NJblHRCeQDacfLrEpscfbakm2ZcuY4VB3piaXZGdqekm2L8uYLe09XB1Lsqth/ZLsvCxjhjG753YsiT9SO6V521l4xgzztiN0viQ7QpdLsiP0jNnz9APFLsn27n5J7lMZMxz6zsLjktzkMi7JzS+Tqwz2toQuyY45d6cp2TEnKT5SYL6//yUDz0//AgddK9ngyLv6AAAEBXpUWHRTTUlMRVMgcmRraXQgMjAyMi4wMy41AAB4nJWUT2tdRwzFv0qXz/T6Mvo7I5msDCErp3vjRRdddFFSSpYhn71Hc02fSKFQbOw3mpH0m6Mz7/XT2zN9eObXl8e32/fXl99/5je5ff/84Zmebx8f8PvxoVa3f1bPt9dPbw/Yr5TvUjsV2H/2hx/WL7fPP0Se6ccz/1r+/5r83zV/+nYbpxCvwcejnh5q63h6HCcvW7GOR8FusCJG51wyvEJKpFeIw4KPca41xpzH0zhJWZcgpKZuOxRzBKH+OGfYYqvYGjQFMUYnrxAjU5UqEqYRxxP6LGRWQ2eZfDzRqdN8FSnH9FlZ6ksQwVYMic3uy0L9APAw0h0y8ul2yElhBYXzdagiy1UQkJMHkxx6msi4bkcxJh2GHY+ShVEaPwf+k9C+h82BAshiKGa712AIhSZ2+pAVhT0N8gDIKNwrYGtgp84GS0mOW6vXZVEymCHNk55kDGnqbkvCtyKQtySaGA6O2CkjRHcWh/IVsnXpKBArtrILd6o6a70ri625z8xLR4wGQqJHNbOAVJs63i3AUhoJJhOjxg1ZaaxqphqwAjyED4xCfg5dEB11BmSk42meivRVjCo0ITFuBnfZvhmyYQ2kOfprEemwadWMKWAkQFOAHteYvmhhYqwxL32EkYMRTvijiEXmjKNuteBUpAi5Mabj8FtlWBhNHFBaQ4rfxloLTTA+3WRU7qmaVuPAiSgXITBgIblMMIn9qDdCMssXyNJihDDiGxairahNRp7tIwztpNaCmSOC3kywDtWT2dZhNB2yjTuHX1nQcBUwOvHlW1V4EBHszHg3brkK//myKQZ2DcBLbBTBu8XlMIAVTCy7uXLQfqZLgIGQ402qzjpGZPWGd3+LWfqYGNE+te1dY8EIfVMHym5Xwt6TNgAmZPurImDQ9/tPXdtN05QvIfF0ZH/vqATHPjUGvncO8Ili/juE+5YxMTRBiR2aDicWaeDx6lXfecIEJQczrX3HWHq5XMzwHfZw/Pr1yx+//PXlzxxnfXz58vW3cyXdFzP5voiU+0I09b4aafcFpd8XnLMl5bovNOO+sKTG4EkNQrDZMAS7HWQmNRIBf2ORSGo0OpIaj1JSJ+KkxqSS3KFWcqOySG5UPpIblVNyo3JOblSOyo3KNblRuSU3KvfkRuUzpVGZpHQqTWlUZimNyjylURlKNSpbKV0rlOpaoVTXCqW6VjO1UelKbVQaqZ1qpHYqSu1UnNqo2FK7nzy1UTH6NipG30bFkdaoBDbtvqK07itO676StEZFKNWpUKpToVSnQqlOhVKdStMbFY30RkV4Po2KOL1RkaR3KpTqL8/SGxV5eqOimd6oaH37G4xnefVj2JstAAAAAElFTkSuQmCC", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -303,8 +693,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "['Fe', 'N', 'N', 'C', 'H', 'C', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'N', 'N', 'C', 'C', 'C', 'H', 'C', 'C', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'C', 'C', 'H', 'C', 'H', 'C', 'H', 'C', 'C', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'C', 'C', 'H', 'C', 'H', 'C', 'H', 'C', 'C', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H']\n", - "smiles_2='[H]C1=C(C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])[N-](c2c(C([H])(C([H])([H])[H])C([H])([H])[H])c([H])c([H])c([H])c2C([H])(C([H])([H])[H])C([H])([H])[H])~[Fe+2]23(~N(c4c(C([H])(C([H])([H])[H])C([H])([H])[H])c([H])c([H])c([H])c4C([H])(C([H])([H])[H])C([H])([H])[H])=C1C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])~N(C([H])(C([H])([H])[H])C([H])([H])[H])=C~2([H])[N-]~3C([H])(C([H])([H])[H])C([H])([H])[H]'\n", + "['Ni', 'O', 'C', 'C', 'F', 'F', 'F', 'C', 'H', 'C', 'C', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'N', 'O', 'C', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'N', 'O', 'C', 'C', 'F', 'F', 'F', 'C', 'H', 'C', 'C', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'N', 'O', 'C', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'C', 'H', 'H', 'H', 'N']\n", + "smiles_2='[H]C1=C2[N-](~[Ni+2]3(~O=C1C(F)(F)F)~O=C(C(F)(F)F)C([H])=C1[N-]~3C(C([H])([H])[H])(C([H])([H])[H])N(OC([H])([H])[H])C1(C([H])([H])[H])C([H])([H])[H])C(C([H])([H])[H])(C([H])([H])[H])N(OC([H])([H])[H])C2(C([H])([H])[H])C([H])([H])[H]'\n", "True\n" ] } diff --git a/cell2mol/test/check_radius.ipynb b/cell2mol/test/check_radius.ipynb new file mode 100644 index 00000000..584e1a25 --- /dev/null +++ b/cell2mol/test/check_radius.ipynb @@ -0,0 +1,223 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from cell2mol.elementdata import ElementData\n", + "from cell2mol.coordination_sphere import covalent_factor_for_metal_v2" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'H': 1.19,\n", + " 'D': 1.19,\n", + " 'He': 1.68,\n", + " 'Li': 0.99,\n", + " 'Be': 0.95,\n", + " 'B': 1.1,\n", + " 'C': 1.22,\n", + " 'N': 1.19,\n", + " 'O': 1.21,\n", + " 'F': 1.14,\n", + " 'Ne': 1.56,\n", + " 'Na': 0.88,\n", + " 'Mg': 1.04,\n", + " 'Al': 1.32,\n", + " 'Si': 1.06,\n", + " 'P': 1.14,\n", + " 'S': 1.19,\n", + " 'Cl': 1.19,\n", + " 'Ar': 1.5,\n", + " 'K': 0.79,\n", + " 'Ca': 0.97,\n", + " 'Sc': 1.3,\n", + " 'Ti': 1.3,\n", + " 'V': 1.3,\n", + " 'Cr': 1.3,\n", + " 'Mn': 1.3,\n", + " 'Fe': 1.3,\n", + " 'Co': 1.3,\n", + " 'Ni': 1.3,\n", + " 'Cu': 1.3,\n", + " 'Zn': 1.3,\n", + " 'Ga': 1.26,\n", + " 'Ge': 1.24,\n", + " 'As': 1.12,\n", + " 'Se': 1.12,\n", + " 'Br': 1.21,\n", + " 'Kr': 1.6,\n", + " 'Rb': 1.02,\n", + " 'Sr': 0.97,\n", + " 'Y': 1.3,\n", + " 'Zr': 1.3,\n", + " 'Nb': 1.3,\n", + " 'Mo': 1.3,\n", + " 'Tc': 1.3,\n", + " 'Ru': 1.3,\n", + " 'Rh': 1.3,\n", + " 'Pd': 1.3,\n", + " 'Ag': 1.3,\n", + " 'Cd': 1.3,\n", + " 'In': 1.35,\n", + " 'Sn': 1.29,\n", + " 'Sb': 1.04,\n", + " 'Te': 1.06,\n", + " 'I': 1.21,\n", + " 'Xe': 1.51,\n", + " 'Cs': 1.03,\n", + " 'Ba': 0.99,\n", + " 'La': 1.3,\n", + " 'Ce': 1.3,\n", + " 'Pr': 1.3,\n", + " 'Nd': 1.3,\n", + " 'Pm': 1.3,\n", + " 'Sm': 1.3,\n", + " 'Eu': 1.3,\n", + " 'Gd': 1.3,\n", + " 'Tb': 1.3,\n", + " 'Dy': 1.3,\n", + " 'Ho': 1.3,\n", + " 'Er': 1.3,\n", + " 'Tm': 1.3,\n", + " 'Yb': 1.3,\n", + " 'Lu': 1.3,\n", + " 'Hf': 1.3,\n", + " 'Ta': 1.3,\n", + " 'W': 1.3,\n", + " 'Re': 1.3,\n", + " 'Os': 1.3,\n", + " 'Ir': 1.3,\n", + " 'Pt': 1.3,\n", + " 'Au': 1.3,\n", + " 'Hg': 1.3,\n", + " 'Tl': 1.3,\n", + " 'Pb': 1.29,\n", + " 'Bi': 1.28,\n", + " 'Po': 1.38,\n", + " 'At': 1.34,\n", + " 'Rn': 1.63,\n", + " 'Fr': 1.09,\n", + " 'Ra': 1.16,\n", + " 'Ac': 1.3,\n", + " 'Th': 1.3,\n", + " 'Pa': 1.3,\n", + " 'U': 1.3,\n", + " 'Np': 1.3,\n", + " 'Pu': 1.3,\n", + " 'Am': 1.3,\n", + " 'Cm': 1.3,\n", + " 'Bk': 1.3,\n", + " 'Cf': 1.3,\n", + " 'Es': 1.3,\n", + " 'Fm': 1.3,\n", + " 'Md': 1.3,\n", + " 'No': 1.3,\n", + " 'Lr': 1.3,\n", + " 'Rf': 1.3,\n", + " 'Db': 1.3,\n", + " 'Sg': 1.3,\n", + " 'Bh': 1.3,\n", + " 'Hs': 1.3,\n", + " 'Mt': 1.3}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "covalent_factor_for_metal_v2" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "elemdatabase = ElementData()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "H 0.23 0.31 1.35\n", + "D 0.23 0.31 1.35\n", + "Li 0.68 1.28 1.88\n", + "Be 0.35 0.96 2.74\n", + "Na 0.68 1.66 2.44\n", + "Mg 0.88 1.41 1.6\n", + "K 0.68 2.03 2.99\n", + "Ca 0.99 1.76 1.78\n", + "Rb 1.47 2.2 1.5\n", + "Sr 1.12 1.95 1.74\n", + "Cs 1.67 2.44 1.46\n", + "Ba 1.34 2.15 1.6\n", + "Fr 2.0 2.6 1.3\n", + "Ra 1.9 2.21 1.16\n" + ] + } + ], + "source": [ + "for elem in elemdatabase.elementgroup.keys():\n", + " if elemdatabase.elementgroup[elem] == 1 or elemdatabase.elementgroup[elem] == 2:\n", + " print(elem, elemdatabase.CovalentRadius2[elem], elemdatabase.CovalentRadius3[elem], \n", + " round(elemdatabase.CovalentRadius3[elem]/elemdatabase.CovalentRadius2[elem],2),)\n", + " # covalent_factor_for_metal_v2[elem],\n", + " # round(elemdatabase.CovalentRadius3[elem]*covalent_factor_for_metal_v2[elem],2),\n", + " # round(elemdatabase.CovalentRadius3[elem]*0.6, 2) )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + " \"Na1+\": 0.970,\n", + " \"Mg1+\": 0.820,\n", + " \"Mg2+\": 0.660,\n", + " \"K1+\": 1.330,\n", + " \"Ca1+\": 1.180,\n", + " \"Ca2+\": 0.990," + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cell2mol", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/cell2mol/test/cif_file.ipynb b/cell2mol/test/cif_file.ipynb index cdde1791..739774cb 100644 --- a/cell2mol/test/cif_file.ipynb +++ b/cell2mol/test/cif_file.ipynb @@ -76,10 +76,4058 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import ase.io\n", + "from ase.spacegroup" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "input_path= \"error_4/BOFFOS/BOFFOS.cif\"" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 0 and 59 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 0 and 60 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 2 and 61 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 2 and 62 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 3 and 63 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 3 and 64 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 4 and 65 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 4 and 66 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 5 and 67 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 5 and 68 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 6 and 69 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 6 and 70 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 7 and 71 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 7 and 72 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 8 and 73 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 8 and 74 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 9 and 75 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 9 and 76 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 10 and 77 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 10 and 78 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 11 and 79 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 11 and 80 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 15 and 81 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 15 and 82 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 16 and 83 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 16 and 84 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 17 and 85 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 17 and 86 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 18 and 87 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 18 and 88 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 19 and 89 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 19 and 90 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 20 and 91 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 20 and 92 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 21 and 93 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 21 and 94 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 22 and 95 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 22 and 96 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 23 and 97 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 23 and 98 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 24 and 99 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 24 and 100 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 25 and 101 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 25 and 102 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 26 and 103 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 26 and 104 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 27 and 105 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 27 and 106 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 28 and 107 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 28 and 108 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 29 and 109 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 29 and 110 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 30 and 111 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 30 and 112 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 31 and 113 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 31 and 114 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 32 and 115 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 32 and 116 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 33 and 117 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 33 and 118 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 34 and 119 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 34 and 120 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 35 and 121 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 35 and 122 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 36 and 123 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 36 and 124 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 37 and 125 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 37 and 126 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 38 and 127 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 38 and 128 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 39 and 129 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 39 and 130 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 40 and 131 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 40 and 132 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 41 and 133 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 41 and 134 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 42 and 135 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 42 and 136 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 43 and 137 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 43 and 138 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 44 and 139 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 44 and 140 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 45 and 141 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 45 and 142 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 46 and 143 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 46 and 144 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 47 and 145 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 47 and 146 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 48 and 147 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 48 and 148 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 49 and 149 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 49 and 150 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 50 and 151 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 50 and 152 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 51 and 153 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 51 and 154 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 52 and 155 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 52 and 156 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n" + ] + } + ], + "source": [ + "from ase.io import read, write\n", + "from ase.spacegroup import crystal, get_spacegroup\n", + "cif_file_path = \"error_2/BOFFOS/BOFFOS.cif\"\n", + "xyz_file_path = \"error_2/BOFFOS/Ref_All_BOFFOS.xyz\"\n", + "# Load the initial structure from CIF file\n", + "initial_structure = read(cif_file_path, format='cif')\n", + "\n", + "# Extract cell parameters and atomic positions from the CIF file\n", + "cell = initial_structure.cell.cellpar()\n", + "# symbols = initial_structure.get_chemical_symbols()\n", + "# positions = initial_structure.get_positions()\n", + "space_group = initial_structure.info['spacegroup']" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 24.369, 24.369, 9.748, 90. , 90. , 120. ])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cell" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Spacegroup(146, setting=1), 146, 'R 3')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "space_group, space_group.no, space_group.symbol" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([[1, 0, 0],\n", + " [0, 1, 0],\n", + " [0, 0, 1]]), array([0., 0., 0.]))\n", + "(array([[ 0, -1, 0],\n", + " [ 1, -1, 0],\n", + " [ 0, 0, 1]]), array([0., 0., 0.]))\n", + "(array([[-1, 1, 0],\n", + " [-1, 0, 0],\n", + " [ 0, 0, 1]]), array([0., 0., 0.]))\n", + "(array([[1, 0, 0],\n", + " [0, 1, 0],\n", + " [0, 0, 1]]), array([0.66666667, 0.33333333, 0.33333333]))\n", + "(array([[ 0, -1, 0],\n", + " [ 1, -1, 0],\n", + " [ 0, 0, 1]]), array([0.66666667, 0.33333333, 0.33333333]))\n", + "(array([[-1, 1, 0],\n", + " [-1, 0, 0],\n", + " [ 0, 0, 1]]), array([0.66666667, 0.33333333, 0.33333333]))\n", + "(array([[1, 0, 0],\n", + " [0, 1, 0],\n", + " [0, 0, 1]]), array([0.33333333, 0.66666667, 0.66666667]))\n", + "(array([[ 0, -1, 0],\n", + " [ 1, -1, 0],\n", + " [ 0, 0, 1]]), array([0.33333333, 0.66666667, 0.66666667]))\n", + "(array([[-1, 1, 0],\n", + " [-1, 0, 0],\n", + " [ 0, 0, 1]]), array([0.33333333, 0.66666667, 0.66666667]))\n" + ] + } + ], + "source": [ + "# Access symmetry operations\n", + "symmetry_operations = space_group.get_symop()\n", + "for op in symmetry_operations:\n", + " print(op)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Symmetry operation #1\n", + "Rotation matrix: [1 0 0]\n", + " [0 1 0]\n", + " [0 0 1]\n", + "Translation vector: [0. 0. 0.]\n", + "\n", + "Symmetry operation #2\n", + "Rotation matrix: [ 0 -1 0]\n", + " [ 1 -1 0]\n", + " [0 0 1]\n", + "Translation vector: [0. 0. 0.]\n", + "\n", + "Symmetry operation #3\n", + "Rotation matrix: [-1 1 0]\n", + " [-1 0 0]\n", + " [0 0 1]\n", + "Translation vector: [0. 0. 0.]\n", + "\n", + "Symmetry operation #4\n", + "Rotation matrix: [1 0 0]\n", + " [0 1 0]\n", + " [0 0 1]\n", + "Translation vector: [0.66666667 0.33333333 0.33333333]\n", + "\n", + "Symmetry operation #5\n", + "Rotation matrix: [ 0 -1 0]\n", + " [ 1 -1 0]\n", + " [0 0 1]\n", + "Translation vector: [0.66666667 0.33333333 0.33333333]\n", + "\n", + "Symmetry operation #6\n", + "Rotation matrix: [-1 1 0]\n", + " [-1 0 0]\n", + " [0 0 1]\n", + "Translation vector: [0.66666667 0.33333333 0.33333333]\n", + "\n", + "Symmetry operation #7\n", + "Rotation matrix: [1 0 0]\n", + " [0 1 0]\n", + " [0 0 1]\n", + "Translation vector: [0.33333333 0.66666667 0.66666667]\n", + "\n", + "Symmetry operation #8\n", + "Rotation matrix: [ 0 -1 0]\n", + " [ 1 -1 0]\n", + " [0 0 1]\n", + "Translation vector: [0.33333333 0.66666667 0.66666667]\n", + "\n", + "Symmetry operation #9\n", + "Rotation matrix: [-1 1 0]\n", + " [-1 0 0]\n", + " [0 0 1]\n", + "Translation vector: [0.33333333 0.66666667 0.66666667]\n", + "\n" + ] + } + ], + "source": [ + "for i, (rot, trans) in enumerate(space_group.get_symop()):\n", + " print(\"Symmetry operation #{}\".format(i + 1))\n", + " print(\"Rotation matrix: \", rot[0,:])\n", + " print(\" \", rot[1,:])\n", + " print(\" \", rot[2,:])\n", + " print(\"Translation vector: \", trans)\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "# Example lattice vectors (you should replace these with your actual lattice vectors)\n", + "a = np.array([cell[0], 0, 0]) # length of vector a in Angstroms\n", + "b = np.array([0, cell[1], 0]) # length of vector b in Angstroms\n", + "c = np.array([0, 0, cell[2]]) # length of vector c in Angstroms\n", + "\n", + "# Create a matrix of lattice vectors\n", + "lattice_vectors = np.array([a, b, c]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[24.369, 0. , 0. ],\n", + " [ 0. , 24.369, 0. ],\n", + " [ 0. , 0. , 9.748]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lattice_vectors" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert each operation to Cartesian coordinates\n", + "sym_ops_cartesian = []\n", + "sym_ops_fractional = space_group.get_symop()\n", + "for rot_frac, trans_frac in sym_ops_fractional:\n", + " rot_cart = lattice_vectors @ rot_frac @ np.linalg.inv(lattice_vectors)\n", + " trans_cart = lattice_vectors @ trans_frac\n", + " sym_ops_cartesian.append((rot_cart, trans_cart))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]),\n", + " array([0., 0., 0.])),\n", + " (array([[ 0., -1., 0.],\n", + " [ 1., -1., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([0., 0., 0.])),\n", + " (array([[-1., 1., 0.],\n", + " [-1., 0., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([0., 0., 0.])),\n", + " (array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]),\n", + " array([16.246 , 8.123 , 3.24933333])),\n", + " (array([[ 0., -1., 0.],\n", + " [ 1., -1., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([16.246 , 8.123 , 3.24933333])),\n", + " (array([[-1., 1., 0.],\n", + " [-1., 0., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([16.246 , 8.123 , 3.24933333])),\n", + " (array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]),\n", + " array([ 8.123 , 16.246 , 6.49866667])),\n", + " (array([[ 0., -1., 0.],\n", + " [ 1., -1., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([ 8.123 , 16.246 , 6.49866667])),\n", + " (array([[-1., 1., 0.],\n", + " [-1., 0., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([ 8.123 , 16.246 , 6.49866667]))]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sym_ops_cartesian" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "labels = atoms.get_chemical_symbols(),\n", + "positions = atoms.positions" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]),\n", + " array([0., 0., 0.])),\n", + " (array([[ 0., -1., 0.],\n", + " [ 1., -1., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([0., 0., 0.])),\n", + " (array([[-1., 1., 0.],\n", + " [-1., 0., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([0., 0., 0.])),\n", + " (array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]),\n", + " array([16.246 , 8.123 , 3.24933333])),\n", + " (array([[ 0., -1., 0.],\n", + " [ 1., -1., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([16.246 , 8.123 , 3.24933333])),\n", + " (array([[-1., 1., 0.],\n", + " [-1., 0., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([16.246 , 8.123 , 3.24933333])),\n", + " (array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]),\n", + " array([ 8.123 , 16.246 , 6.49866667])),\n", + " (array([[ 0., -1., 0.],\n", + " [ 1., -1., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([ 8.123 , 16.246 , 6.49866667])),\n", + " (array([[-1., 1., 0.],\n", + " [-1., 0., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([ 8.123 , 16.246 , 6.49866667]))]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sym_ops_cartesian" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(['K',\n", + " 'Fe',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'K',\n", + " 'K',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'O',\n", + " 'C',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'C',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'H',\n", + " 'C',\n", + " 'C',\n", + " 'C',\n", + " 'C'],)" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[14.586187, 10.49194 , 6.840074],\n", + " [12.185718, 7.034021, 9.395122],\n", + " [15.035307, 9.651149, 4.231314],\n", + " [12.463647, 10.436225, 4.932293],\n", + " [12.009409, 11.296853, 7.554505],\n", + " [14.172645, 12.408199, 9.007249],\n", + " [16.712138, 11.83121 , 8.150303],\n", + " [17.233391, 10.873925, 5.55441 ],\n", + " [15.732626, 8.578424, 8.425196],\n", + " [13.601557, 7.986663, 8.331421],\n", + " [13.773237, 6.633253, 10.549481],\n", + " [15.920268, 7.135743, 10.792011],\n", + " [18.384461, 8.708004, 9.464333],\n", + " [ 8.947566, 13.331506, 4.549392],\n", + " [18.450623, 6.839229, 2.009063],\n", + " [13.971844, 9.667611, 3.294824],\n", + " [14.124272, 8.994599, 2.613439],\n", + " [13.92932 , 10.535203, 2.861038],\n", + " [12.69698 , 9.39389 , 3.992781],\n", + " [11.968834, 9.35548 , 3.352337],\n", + " [12.746205, 8.540859, 4.450937],\n", + " [11.209131, 10.29947 , 5.581705],\n", + " [11.173186, 9.450449, 6.050584],\n", + " [10.495728, 10.31572 , 4.92469 ],\n", + " [11.032334, 11.413137, 6.543832],\n", + " [11.128104, 12.263635, 6.087626],\n", + " [10.144815, 11.375149, 6.934727],\n", + " [11.859296, 12.265956, 8.574341],\n", + " [10.985545, 12.181329, 8.988631],\n", + " [11.934718, 13.158452, 8.200018],\n", + " [12.920322, 12.046895, 9.57741 ],\n", + " [12.747424, 12.586529, 10.364074],\n", + " [12.933847, 11.115568, 9.844505],\n", + " [15.239642, 12.253927, 9.925414],\n", + " [15.303732, 11.32872 , 10.207131],\n", + " [15.085629, 12.803902, 10.710128],\n", + " [16.497447, 12.671579, 9.248902],\n", + " [16.423488, 13.593198, 8.952563],\n", + " [17.243504, 12.607633, 9.865951],\n", + " [17.95581 , 12.006586, 7.511809],\n", + " [18.66178 , 12.058924, 8.175648],\n", + " [17.951424, 12.833448, 7.005888],\n", + " [18.20413 , 10.875825, 6.612068],\n", + " [19.09433 , 10.946735, 6.23677 ],\n", + " [18.148813, 10.043476, 7.108242],\n", + " [17.347194, 9.746118, 4.713158],\n", + " [17.226446, 8.935507, 5.232726],\n", + " [18.232886, 9.720582, 4.316414],\n", + " [16.321381, 9.814707, 3.643802],\n", + " [16.372313, 10.67238 , 3.191495],\n", + " [16.477099, 9.114892, 2.989712],\n", + " [14.762862, 7.989407, 8.87068 ],\n", + " [14.865943, 7.181961, 10.194458],\n", + " [17.555428, 8.673815, 9.21186 ],\n", + " [ 8.921491, 14.1609 , 4.24038 ],\n", + " [17.579797, 6.668919, 1.90086 ],\n", + " [18.538717, 8.253842, 10.176912],\n", + " [ 8.113659, 13.588977, 4.67904 ],\n", + " [18.422964, 6.795544, 2.895156],\n", + " [ 7.98962 , 7.386038, 6.840074],\n", + " [13.977693, 3.226195, 6.840074],\n", + " [ 8.493206, 8.195384, 4.231314],\n", + " [13.024987, 3.25764 , 4.231314],\n", + " [ 9.099141, 5.575723, 4.932293],\n", + " [14.990712, 5.092226, 4.932293],\n", + " [ 8.580934, 4.752027, 7.554505],\n", + " [15.963157, 5.055294, 7.554505],\n", + " [ 6.536862, 6.069771, 9.007249],\n", + " [15.843993, 2.626203, 9.007249],\n", + " [ 5.766802, 8.557531, 8.150303],\n", + " [14.07456 , 0.715431, 8.150303],\n", + " [ 6.335209, 9.487592, 5.55441 ],\n", + " [12.9849 , 0.742656, 5.55441 ],\n", + " [ 9.073553, 9.335642, 8.425196],\n", + " [11.74732 , 3.190107, 8.425196],\n", + " [10.651568, 7.785963, 8.331421],\n", + " [12.300375, 5.331547, 8.331421],\n", + " [11.737816, 8.611347, 10.549481],\n", + " [11.042447, 5.859574, 10.549481],\n", + " [10.404101, 3.748945, 10.792011],\n", + " [10.229131, 10.219485, 10.792011],\n", + " [ 9.010681, 7.266167, 3.294824],\n", + " [13.570974, 4.170396, 3.294824],\n", + " [ 9.517313, 7.734679, 2.613439],\n", + " [12.911915, 4.374895, 2.613439],\n", + " [ 8.280586, 6.795544, 2.861038],\n", + " [14.343593, 3.773426, 2.861038],\n", + " [ 9.885163, 6.298963, 3.992781],\n", + " [13.971357, 5.411321, 3.992781],\n", + " [14.302166, 6.061119, 3.352337],\n", + " [10.2825 , 5.687575, 3.352337],\n", + " [13.207998, 5.795206, 4.450937],\n", + " [10.599297, 6.768108, 4.450937],\n", + " [ 9.844832, 4.557657, 5.581705],\n", + " [15.499537, 6.247046, 5.581705],\n", + " [10.598078, 4.951039, 6.050584],\n", + " [14.782235, 6.702685, 6.050584],\n", + " [10.18746 , 3.931707, 4.92469 ],\n", + " [15.870311, 6.856746, 4.92469 ],\n", + " [ 8.968767, 3.847713, 6.543832],\n", + " [16.5524 , 5.843323, 6.543832],\n", + " [ 8.184329, 3.505403, 6.087626],\n", + " [17.241067, 5.335135, 6.087626],\n", + " [ 9.445424, 3.098093, 6.934727],\n", + " [16.963261, 6.630931, 6.934727],\n", + " [ 7.816722, 4.137473, 8.574341],\n", + " [16.877482, 4.700744, 8.574341],\n", + " [ 8.326887, 3.423097, 8.988631],\n", + " [17.241067, 5.499748, 8.988631],\n", + " [ 7.006088, 3.756543, 8.200018],\n", + " [17.612695, 4.189178, 8.200018],\n", + " [ 7.475922, 5.165879, 9.57741 ],\n", + " [16.157256, 3.891398, 9.57741 ],\n", + " [16.711042, 3.771316, 10.364074],\n", + " [ 7.095034, 4.746329, 10.364074],\n", + " [15.343941, 4.345349, 9.844505],\n", + " [ 8.275712, 5.643256, 9.844505],\n", + " [ 6.136967, 7.070953, 9.925414],\n", + " [15.176891, 1.779293, 9.925414],\n", + " [ 6.906175, 7.589061, 10.207131],\n", + " [14.343593, 2.186392, 10.207131],\n", + " [ 5.737681, 6.662587, 10.710128],\n", + " [15.730189, 1.637684, 10.710128],\n", + " [ 5.146367, 7.951419, 9.248902],\n", + " [14.909685, 0.481175, 9.248902],\n", + " [ 4.385202, 7.426559, 8.952563],\n", + " [15.744811, 0.084417, 8.952563],\n", + " [ 4.828717, 8.629496, 9.865951],\n", + " [14.481278, -0.132956, 9.865951],\n", + " [ 4.993086, 9.546895, 7.511809],\n", + " [13.604603, -0.449308, 7.511809],\n", + " [ 4.594775, 10.132113, 8.175648],\n", + " [13.296945, -1.086865, 8.175648],\n", + " [ 4.279196, 9.129665, 7.005888],\n", + " [14.32288 , -0.85894 , 7.005888],\n", + " [ 5.848194, 10.327327, 6.612068],\n", + " [12.501175, -0.098979, 6.612068],\n", + " [12.117485, -0.905369, 6.23677 ],\n", + " [ 5.341685, 11.062808, 6.23677 ],\n", + " [11.807999, 0.365102, 7.108242],\n", + " [ 6.596688, 10.695595, 7.108242],\n", + " [11.951289, 1.208003, 4.713158],\n", + " [ 7.255017, 10.150052, 4.713158],\n", + " [11.309653, 1.71788 , 5.232726],\n", + " [ 8.017401, 10.450787, 5.232726],\n", + " [11.486328, 0.45374 , 4.316414],\n", + " [ 6.834286, 10.929851, 4.316414],\n", + " [12.523595, 2.062089, 3.643802],\n", + " [ 7.708524, 9.227378, 3.643802],\n", + " [13.240896, 1.589144, 3.191495],\n", + " [ 6.940291, 8.842649, 3.191495],\n", + " [11.839679, 2.27714 , 2.989712],\n", + " [ 8.236722, 9.71214 , 2.989712],\n", + " [10.06854 , 8.79031 , 8.87068 ],\n", + " [11.722098, 4.324456, 8.87068 ],\n", + " [10.716268, 9.283304, 10.194458],\n", + " [10.971289, 4.638908, 10.194458]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "positions" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# List to collect all transformed structures and their labels\n", + "all_transformed_positions = []\n", + "all_labels = []\n", + "\n", + "# Apply all symmetry operations\n", + "for rot_cart, trans_cart in sym_ops_cartesian:\n", + " transformed_positions = (rot_cart @ positions.T + trans_cart[:, np.newaxis]).T\n", + " all_transformed_positions.append(transformed_positions)\n", + " all_labels.append(labels) # Copy labels directly since symmetry operations don't alter the type of atoms\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]),\n", + " array([0., 0., 0.])),\n", + " (array([[ 0., -1., 0.],\n", + " [ 1., -1., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([0., 0., 0.])),\n", + " (array([[-1., 1., 0.],\n", + " [-1., 0., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([0., 0., 0.])),\n", + " (array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]),\n", + " array([16.246 , 8.123 , 3.24933333])),\n", + " (array([[ 0., -1., 0.],\n", + " [ 1., -1., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([16.246 , 8.123 , 3.24933333])),\n", + " (array([[-1., 1., 0.],\n", + " [-1., 0., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([16.246 , 8.123 , 3.24933333])),\n", + " (array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]),\n", + " array([ 8.123 , 16.246 , 6.49866667])),\n", + " (array([[ 0., -1., 0.],\n", + " [ 1., -1., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([ 8.123 , 16.246 , 6.49866667])),\n", + " (array([[-1., 1., 0.],\n", + " [-1., 0., 0.],\n", + " [ 0., 0., 1.]]),\n", + " array([ 8.123 , 16.246 , 6.49866667]))]" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sym_ops_cartesian" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(all_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "from cell2mol.read_write import writexyz" + ] + }, + { + "cell_type": "code", + "execution_count": 56, "metadata": {}, "outputs": [], + "source": [ + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'os' has no attribute 'getpwd'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [58]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetpwd\u001b[49m()\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'os' has no attribute 'getpwd'" + ] + } + ], + "source": [ + "os." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['K', 'Fe', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'H', 'H', 'K', 'K', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'C', 'C']\n", + "[[14.586187 10.49194 6.840074]\n", + " [12.185718 7.034021 9.395122]\n", + " [15.035307 9.651149 4.231314]\n", + " [12.463647 10.436225 4.932293]\n", + " [12.009409 11.296853 7.554505]\n", + " [14.172645 12.408199 9.007249]\n", + " [16.712138 11.83121 8.150303]\n", + " [17.233391 10.873925 5.55441 ]\n", + " [15.732626 8.578424 8.425196]\n", + " [13.601557 7.986663 8.331421]\n", + " [13.773237 6.633253 10.549481]\n", + " [15.920268 7.135743 10.792011]\n", + " [18.384461 8.708004 9.464333]\n", + " [ 8.947566 13.331506 4.549392]\n", + " [18.450623 6.839229 2.009063]\n", + " [13.971844 9.667611 3.294824]\n", + " [14.124272 8.994599 2.613439]\n", + " [13.92932 10.535203 2.861038]\n", + " [12.69698 9.39389 3.992781]\n", + " [11.968834 9.35548 3.352337]\n", + " [12.746205 8.540859 4.450937]\n", + " [11.209131 10.29947 5.581705]\n", + " [11.173186 9.450449 6.050584]\n", + " [10.495728 10.31572 4.92469 ]\n", + " [11.032334 11.413137 6.543832]\n", + " [11.128104 12.263635 6.087626]\n", + " [10.144815 11.375149 6.934727]\n", + " [11.859296 12.265956 8.574341]\n", + " [10.985545 12.181329 8.988631]\n", + " [11.934718 13.158452 8.200018]\n", + " [12.920322 12.046895 9.57741 ]\n", + " [12.747424 12.586529 10.364074]\n", + " [12.933847 11.115568 9.844505]\n", + " [15.239642 12.253927 9.925414]\n", + " [15.303732 11.32872 10.207131]\n", + " [15.085629 12.803902 10.710128]\n", + " [16.497447 12.671579 9.248902]\n", + " [16.423488 13.593198 8.952563]\n", + " [17.243504 12.607633 9.865951]\n", + " [17.95581 12.006586 7.511809]\n", + " [18.66178 12.058924 8.175648]\n", + " [17.951424 12.833448 7.005888]\n", + " [18.20413 10.875825 6.612068]\n", + " [19.09433 10.946735 6.23677 ]\n", + " [18.148813 10.043476 7.108242]\n", + " [17.347194 9.746118 4.713158]\n", + " [17.226446 8.935507 5.232726]\n", + " [18.232886 9.720582 4.316414]\n", + " [16.321381 9.814707 3.643802]\n", + " [16.372313 10.67238 3.191495]\n", + " [16.477099 9.114892 2.989712]\n", + " [14.762862 7.989407 8.87068 ]\n", + " [14.865943 7.181961 10.194458]\n", + " [17.555428 8.673815 9.21186 ]\n", + " [ 8.921491 14.1609 4.24038 ]\n", + " [17.579797 6.668919 1.90086 ]\n", + " [18.538717 8.253842 10.176912]\n", + " [ 8.113659 13.588977 4.67904 ]\n", + " [18.422964 6.795544 2.895156]\n", + " [ 7.98962 7.386038 6.840074]\n", + " [13.977693 3.226195 6.840074]\n", + " [ 8.493206 8.195384 4.231314]\n", + " [13.024987 3.25764 4.231314]\n", + " [ 9.099141 5.575723 4.932293]\n", + " [14.990712 5.092226 4.932293]\n", + " [ 8.580934 4.752027 7.554505]\n", + " [15.963157 5.055294 7.554505]\n", + " [ 6.536862 6.069771 9.007249]\n", + " [15.843993 2.626203 9.007249]\n", + " [ 5.766802 8.557531 8.150303]\n", + " [14.07456 0.715431 8.150303]\n", + " [ 6.335209 9.487592 5.55441 ]\n", + " [12.9849 0.742656 5.55441 ]\n", + " [ 9.073553 9.335642 8.425196]\n", + " [11.74732 3.190107 8.425196]\n", + " [10.651568 7.785963 8.331421]\n", + " [12.300375 5.331547 8.331421]\n", + " [11.737816 8.611347 10.549481]\n", + " [11.042447 5.859574 10.549481]\n", + " [10.404101 3.748945 10.792011]\n", + " [10.229131 10.219485 10.792011]\n", + " [ 9.010681 7.266167 3.294824]\n", + " [13.570974 4.170396 3.294824]\n", + " [ 9.517313 7.734679 2.613439]\n", + " [12.911915 4.374895 2.613439]\n", + " [ 8.280586 6.795544 2.861038]\n", + " [14.343593 3.773426 2.861038]\n", + " [ 9.885163 6.298963 3.992781]\n", + " [13.971357 5.411321 3.992781]\n", + " [14.302166 6.061119 3.352337]\n", + " [10.2825 5.687575 3.352337]\n", + " [13.207998 5.795206 4.450937]\n", + " [10.599297 6.768108 4.450937]\n", + " [ 9.844832 4.557657 5.581705]\n", + " [15.499537 6.247046 5.581705]\n", + " [10.598078 4.951039 6.050584]\n", + " [14.782235 6.702685 6.050584]\n", + " [10.18746 3.931707 4.92469 ]\n", + " [15.870311 6.856746 4.92469 ]\n", + " [ 8.968767 3.847713 6.543832]\n", + " [16.5524 5.843323 6.543832]\n", + " [ 8.184329 3.505403 6.087626]\n", + " [17.241067 5.335135 6.087626]\n", + " [ 9.445424 3.098093 6.934727]\n", + " [16.963261 6.630931 6.934727]\n", + " [ 7.816722 4.137473 8.574341]\n", + " [16.877482 4.700744 8.574341]\n", + " [ 8.326887 3.423097 8.988631]\n", + " [17.241067 5.499748 8.988631]\n", + " [ 7.006088 3.756543 8.200018]\n", + " [17.612695 4.189178 8.200018]\n", + " [ 7.475922 5.165879 9.57741 ]\n", + " [16.157256 3.891398 9.57741 ]\n", + " [16.711042 3.771316 10.364074]\n", + " [ 7.095034 4.746329 10.364074]\n", + " [15.343941 4.345349 9.844505]\n", + " [ 8.275712 5.643256 9.844505]\n", + " [ 6.136967 7.070953 9.925414]\n", + " [15.176891 1.779293 9.925414]\n", + " [ 6.906175 7.589061 10.207131]\n", + " [14.343593 2.186392 10.207131]\n", + " [ 5.737681 6.662587 10.710128]\n", + " [15.730189 1.637684 10.710128]\n", + " [ 5.146367 7.951419 9.248902]\n", + " [14.909685 0.481175 9.248902]\n", + " [ 4.385202 7.426559 8.952563]\n", + " [15.744811 0.084417 8.952563]\n", + " [ 4.828717 8.629496 9.865951]\n", + " [14.481278 -0.132956 9.865951]\n", + " [ 4.993086 9.546895 7.511809]\n", + " [13.604603 -0.449308 7.511809]\n", + " [ 4.594775 10.132113 8.175648]\n", + " [13.296945 -1.086865 8.175648]\n", + " [ 4.279196 9.129665 7.005888]\n", + " [14.32288 -0.85894 7.005888]\n", + " [ 5.848194 10.327327 6.612068]\n", + " [12.501175 -0.098979 6.612068]\n", + " [12.117485 -0.905369 6.23677 ]\n", + " [ 5.341685 11.062808 6.23677 ]\n", + " [11.807999 0.365102 7.108242]\n", + " [ 6.596688 10.695595 7.108242]\n", + " [11.951289 1.208003 4.713158]\n", + " [ 7.255017 10.150052 4.713158]\n", + " [11.309653 1.71788 5.232726]\n", + " [ 8.017401 10.450787 5.232726]\n", + " [11.486328 0.45374 4.316414]\n", + " [ 6.834286 10.929851 4.316414]\n", + " [12.523595 2.062089 3.643802]\n", + " [ 7.708524 9.227378 3.643802]\n", + " [13.240896 1.589144 3.191495]\n", + " [ 6.940291 8.842649 3.191495]\n", + " [11.839679 2.27714 2.989712]\n", + " [ 8.236722 9.71214 2.989712]\n", + " [10.06854 8.79031 8.87068 ]\n", + " [11.722098 4.324456 8.87068 ]\n", + " [10.716268 9.283304 10.194458]\n", + " [10.971289 4.638908 10.194458]]\n", + "['K', 'Fe', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'H', 'H', 'K', 'K', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'C', 'C']\n", + "[[-1.0491940e+01 4.0942470e+00 6.8400740e+00]\n", + " [-7.0340210e+00 5.1516970e+00 9.3951220e+00]\n", + " [-9.6511490e+00 5.3841580e+00 4.2313140e+00]\n", + " [-1.0436225e+01 2.0274220e+00 4.9322930e+00]\n", + " [-1.1296853e+01 7.1255600e-01 7.5545050e+00]\n", + " [-1.2408199e+01 1.7644460e+00 9.0072490e+00]\n", + " [-1.1831210e+01 4.8809280e+00 8.1503030e+00]\n", + " [-1.0873925e+01 6.3594660e+00 5.5544100e+00]\n", + " [-8.5784240e+00 7.1542020e+00 8.4251960e+00]\n", + " [-7.9866630e+00 5.6148940e+00 8.3314210e+00]\n", + " [-6.6332530e+00 7.1399840e+00 1.0549481e+01]\n", + " [-7.1357430e+00 8.7845250e+00 1.0792011e+01]\n", + " [-8.7080040e+00 9.6764570e+00 9.4643330e+00]\n", + " [-1.3331506e+01 -4.3839400e+00 4.5493920e+00]\n", + " [-6.8392290e+00 1.1611394e+01 2.0090630e+00]\n", + " [-9.6676110e+00 4.3042330e+00 3.2948240e+00]\n", + " [-8.9945990e+00 5.1296730e+00 2.6134390e+00]\n", + " [-1.0535203e+01 3.3941170e+00 2.8610380e+00]\n", + " [-9.3938900e+00 3.3030900e+00 3.9927810e+00]\n", + " [-9.3554800e+00 2.6133540e+00 3.3523370e+00]\n", + " [-8.5408590e+00 4.2053460e+00 4.4509370e+00]\n", + " [-1.0299470e+01 9.0966100e-01 5.5817050e+00]\n", + " [-9.4504490e+00 1.7227370e+00 6.0505840e+00]\n", + " [-1.0315720e+01 1.8000800e-01 4.9246900e+00]\n", + " [-1.1413137e+01 -3.8080300e-01 6.5438320e+00]\n", + " [-1.2263635e+01 -1.1355310e+00 6.0876260e+00]\n", + " [-1.1375149e+01 -1.2303340e+00 6.9347270e+00]\n", + " [-1.2265956e+01 -4.0666000e-01 8.5743410e+00]\n", + " [-1.2181329e+01 -1.1957840e+00 8.9886310e+00]\n", + " [-1.3158452e+01 -1.2237340e+00 8.2000180e+00]\n", + " [-1.2046895e+01 8.7342700e-01 9.5774100e+00]\n", + " [-1.2586529e+01 1.6089500e-01 1.0364074e+01]\n", + " [-1.1115568e+01 1.8182790e+00 9.8445050e+00]\n", + " [-1.2253927e+01 2.9857150e+00 9.9254140e+00]\n", + " [-1.1328720e+01 3.9750120e+00 1.0207131e+01]\n", + " [-1.2803902e+01 2.2817270e+00 1.0710128e+01]\n", + " [-1.2671579e+01 3.8258680e+00 9.2489020e+00]\n", + " [-1.3593198e+01 2.8302900e+00 8.9525630e+00]\n", + " [-1.2607633e+01 4.6358710e+00 9.8659510e+00]\n", + " [-1.2006586e+01 5.9492240e+00 7.5118090e+00]\n", + " [-1.2058924e+01 6.6028560e+00 8.1756480e+00]\n", + " [-1.2833448e+01 5.1179760e+00 7.0058880e+00]\n", + " [-1.0875825e+01 7.3283050e+00 6.6120680e+00]\n", + " [-1.0946735e+01 8.1475950e+00 6.2367700e+00]\n", + " [-1.0043476e+01 8.1053370e+00 7.1082420e+00]\n", + " [-9.7461180e+00 7.6010760e+00 4.7131580e+00]\n", + " [-8.9355070e+00 8.2909390e+00 5.2327260e+00]\n", + " [-9.7205820e+00 8.5123040e+00 4.3164140e+00]\n", + " [-9.8147070e+00 6.5066740e+00 3.6438020e+00]\n", + " [-1.0672380e+01 5.6999330e+00 3.1914950e+00]\n", + " [-9.1148920e+00 7.3622070e+00 2.9897120e+00]\n", + " [-7.9894070e+00 6.7734550e+00 8.8706800e+00]\n", + " [-7.1819610e+00 7.6839820e+00 1.0194458e+01]\n", + " [-8.6738150e+00 8.8816130e+00 9.2118600e+00]\n", + " [-1.4160900e+01 -5.2394090e+00 4.2403800e+00]\n", + " [-6.6689190e+00 1.0910878e+01 1.9008600e+00]\n", + " [-8.2538420e+00 1.0284875e+01 1.0176912e+01]\n", + " [-1.3588977e+01 -5.4753180e+00 4.6790400e+00]\n", + " [-6.7955440e+00 1.1627420e+01 2.8951560e+00]\n", + " [-7.3860380e+00 6.0358200e-01 6.8400740e+00]\n", + " [-3.2261950e+00 1.0751498e+01 6.8400740e+00]\n", + " [-8.1953840e+00 2.9782200e-01 4.2313140e+00]\n", + " [-3.2576400e+00 9.7673470e+00 4.2313140e+00]\n", + " [-5.5757230e+00 3.5234180e+00 4.9322930e+00]\n", + " [-5.0922260e+00 9.8984860e+00 4.9322930e+00]\n", + " [-4.7520270e+00 3.8289070e+00 7.5545050e+00]\n", + " [-5.0552940e+00 1.0907863e+01 7.5545050e+00]\n", + " [-6.0697710e+00 4.6709100e-01 9.0072490e+00]\n", + " [-2.6262030e+00 1.3217790e+01 9.0072490e+00]\n", + " [-8.5575310e+00 -2.7907290e+00 8.1503030e+00]\n", + " [-7.1543100e-01 1.3359129e+01 8.1503030e+00]\n", + " [-9.4875920e+00 -3.1523830e+00 5.5544100e+00]\n", + " [-7.4265600e-01 1.2242244e+01 5.5544100e+00]\n", + " [-9.3356420e+00 -2.6208900e-01 8.4251960e+00]\n", + " [-3.1901070e+00 8.5572130e+00 8.4251960e+00]\n", + " [-7.7859630e+00 2.8656050e+00 8.3314210e+00]\n", + " [-5.3315470e+00 6.9688280e+00 8.3314210e+00]\n", + " [-8.6113470e+00 3.1264690e+00 1.0549481e+01]\n", + " [-5.8595740e+00 5.1828730e+00 1.0549481e+01]\n", + " [-3.7489450e+00 6.6551560e+00 1.0792011e+01]\n", + " [-1.0219485e+01 9.6460000e-03 1.0792011e+01]\n", + " [-7.2661670e+00 1.7445140e+00 3.2948240e+00]\n", + " [-4.1703960e+00 9.4005780e+00 3.2948240e+00]\n", + " [-7.7346790e+00 1.7826340e+00 2.6134390e+00]\n", + " [-4.3748950e+00 8.5370200e+00 2.6134390e+00]\n", + " [-6.7955440e+00 1.4850420e+00 2.8610380e+00]\n", + " [-3.7734260e+00 1.0570167e+01 2.8610380e+00]\n", + " [-6.2989630e+00 3.5862000e+00 3.9927810e+00]\n", + " [-5.4113210e+00 8.5600360e+00 3.9927810e+00]\n", + " [-6.0611190e+00 8.2410470e+00 3.3523370e+00]\n", + " [-5.6875750e+00 4.5949250e+00 3.3523370e+00]\n", + " [-5.7952060e+00 7.4127920e+00 4.4509370e+00]\n", + " [-6.7681080e+00 3.8311890e+00 4.4509370e+00]\n", + " [-4.5576570e+00 5.2871750e+00 5.5817050e+00]\n", + " [-6.2470460e+00 9.2524910e+00 5.5817050e+00]\n", + " [-4.9510390e+00 5.6470390e+00 6.0505840e+00]\n", + " [-6.7026850e+00 8.0795500e+00 6.0505840e+00]\n", + " [-3.9317070e+00 6.2557530e+00 4.9246900e+00]\n", + " [-6.8567460e+00 9.0135650e+00 4.9246900e+00]\n", + " [-3.8477130e+00 5.1210540e+00 6.5438320e+00]\n", + " [-5.8433230e+00 1.0709077e+01 6.5438320e+00]\n", + " [-3.5054030e+00 4.6789260e+00 6.0876260e+00]\n", + " [-5.3351350e+00 1.1905932e+01 6.0876260e+00]\n", + " [-3.0980930e+00 6.3473310e+00 6.9347270e+00]\n", + " [-6.6309310e+00 1.0332330e+01 6.9347270e+00]\n", + " [-4.1374730e+00 3.6792490e+00 8.5743410e+00]\n", + " [-4.7007440e+00 1.2176738e+01 8.5743410e+00]\n", + " [-3.4230970e+00 4.9037900e+00 8.9886310e+00]\n", + " [-5.4997480e+00 1.1741319e+01 8.9886310e+00]\n", + " [-3.7565430e+00 3.2495450e+00 8.2000180e+00]\n", + " [-4.1891780e+00 1.3423517e+01 8.2000180e+00]\n", + " [-5.1658790e+00 2.3100430e+00 9.5774100e+00]\n", + " [-3.8913980e+00 1.2265858e+01 9.5774100e+00]\n", + " [-3.7713160e+00 1.2939726e+01 1.0364074e+01]\n", + " [-4.7463290e+00 2.3487050e+00 1.0364074e+01]\n", + " [-4.3453490e+00 1.0998592e+01 9.8445050e+00]\n", + " [-5.6432560e+00 2.6324560e+00 9.8445050e+00]\n", + " [-7.0709530e+00 -9.3398600e-01 9.9254140e+00]\n", + " [-1.7792930e+00 1.3397598e+01 9.9254140e+00]\n", + " [-7.5890610e+00 -6.8288600e-01 1.0207131e+01]\n", + " [-2.1863920e+00 1.2157201e+01 1.0207131e+01]\n", + " [-6.6625870e+00 -9.2490600e-01 1.0710128e+01]\n", + " [-1.6376840e+00 1.4092505e+01 1.0710128e+01]\n", + " [-7.9514190e+00 -2.8050520e+00 9.2489020e+00]\n", + " [-4.8117500e-01 1.4428510e+01 9.2489020e+00]\n", + " [-7.4265590e+00 -3.0413570e+00 8.9525630e+00]\n", + " [-8.4417000e-02 1.5660394e+01 8.9525630e+00]\n", + " [-8.6294960e+00 -3.8007790e+00 9.8659510e+00]\n", + " [ 1.3295600e-01 1.4614234e+01 9.8659510e+00]\n", + " [-9.5468950e+00 -4.5538090e+00 7.5118090e+00]\n", + " [ 4.4930800e-01 1.4053911e+01 7.5118090e+00]\n", + " [-1.0132113e+01 -5.5373380e+00 8.1756480e+00]\n", + " [ 1.0868650e+00 1.4383810e+01 8.1756480e+00]\n", + " [-9.1296650e+00 -4.8504690e+00 7.0058880e+00]\n", + " [ 8.5894000e-01 1.5181820e+01 7.0058880e+00]\n", + " [-1.0327327e+01 -4.4791330e+00 6.6120680e+00]\n", + " [ 9.8979000e-02 1.2600154e+01 6.6120680e+00]\n", + " [ 9.0536900e-01 1.3022854e+01 6.2367700e+00]\n", + " [-1.1062808e+01 -5.7211230e+00 6.2367700e+00]\n", + " [-3.6510200e-01 1.1442897e+01 7.1082420e+00]\n", + " [-1.0695595e+01 -4.0989070e+00 7.1082420e+00]\n", + " [-1.2080030e+00 1.0743286e+01 4.7131580e+00]\n", + " [-1.0150052e+01 -2.8950350e+00 4.7131580e+00]\n", + " [-1.7178800e+00 9.5917730e+00 5.2327260e+00]\n", + " [-1.0450787e+01 -2.4333860e+00 5.2327260e+00]\n", + " [-4.5374000e-01 1.1032588e+01 4.3164140e+00]\n", + " [-1.0929851e+01 -4.0955650e+00 4.3164140e+00]\n", + " [-2.0620890e+00 1.0461506e+01 3.6438020e+00]\n", + " [-9.2273780e+00 -1.5188540e+00 3.6438020e+00]\n", + " [-1.5891440e+00 1.1651752e+01 3.1914950e+00]\n", + " [-8.8426490e+00 -1.9023580e+00 3.1914950e+00]\n", + " [-2.2771400e+00 9.5625390e+00 2.9897120e+00]\n", + " [-9.7121400e+00 -1.4754180e+00 2.9897120e+00]\n", + " [-8.7903100e+00 1.2782300e+00 8.8706800e+00]\n", + " [-4.3244560e+00 7.3976420e+00 8.8706800e+00]\n", + " [-9.2833040e+00 1.4329640e+00 1.0194458e+01]\n", + " [-4.6389080e+00 6.3323810e+00 1.0194458e+01]]\n", + "['K', 'Fe', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'H', 'H', 'K', 'K', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'C', 'C']\n", + "[[-4.0942470e+00 -1.4586187e+01 6.8400740e+00]\n", + " [-5.1516970e+00 -1.2185718e+01 9.3951220e+00]\n", + " [-5.3841580e+00 -1.5035307e+01 4.2313140e+00]\n", + " [-2.0274220e+00 -1.2463647e+01 4.9322930e+00]\n", + " [-7.1255600e-01 -1.2009409e+01 7.5545050e+00]\n", + " [-1.7644460e+00 -1.4172645e+01 9.0072490e+00]\n", + " [-4.8809280e+00 -1.6712138e+01 8.1503030e+00]\n", + " [-6.3594660e+00 -1.7233391e+01 5.5544100e+00]\n", + " [-7.1542020e+00 -1.5732626e+01 8.4251960e+00]\n", + " [-5.6148940e+00 -1.3601557e+01 8.3314210e+00]\n", + " [-7.1399840e+00 -1.3773237e+01 1.0549481e+01]\n", + " [-8.7845250e+00 -1.5920268e+01 1.0792011e+01]\n", + " [-9.6764570e+00 -1.8384461e+01 9.4643330e+00]\n", + " [ 4.3839400e+00 -8.9475660e+00 4.5493920e+00]\n", + " [-1.1611394e+01 -1.8450623e+01 2.0090630e+00]\n", + " [-4.3042330e+00 -1.3971844e+01 3.2948240e+00]\n", + " [-5.1296730e+00 -1.4124272e+01 2.6134390e+00]\n", + " [-3.3941170e+00 -1.3929320e+01 2.8610380e+00]\n", + " [-3.3030900e+00 -1.2696980e+01 3.9927810e+00]\n", + " [-2.6133540e+00 -1.1968834e+01 3.3523370e+00]\n", + " [-4.2053460e+00 -1.2746205e+01 4.4509370e+00]\n", + " [-9.0966100e-01 -1.1209131e+01 5.5817050e+00]\n", + " [-1.7227370e+00 -1.1173186e+01 6.0505840e+00]\n", + " [-1.8000800e-01 -1.0495728e+01 4.9246900e+00]\n", + " [ 3.8080300e-01 -1.1032334e+01 6.5438320e+00]\n", + " [ 1.1355310e+00 -1.1128104e+01 6.0876260e+00]\n", + " [ 1.2303340e+00 -1.0144815e+01 6.9347270e+00]\n", + " [ 4.0666000e-01 -1.1859296e+01 8.5743410e+00]\n", + " [ 1.1957840e+00 -1.0985545e+01 8.9886310e+00]\n", + " [ 1.2237340e+00 -1.1934718e+01 8.2000180e+00]\n", + " [-8.7342700e-01 -1.2920322e+01 9.5774100e+00]\n", + " [-1.6089500e-01 -1.2747424e+01 1.0364074e+01]\n", + " [-1.8182790e+00 -1.2933847e+01 9.8445050e+00]\n", + " [-2.9857150e+00 -1.5239642e+01 9.9254140e+00]\n", + " [-3.9750120e+00 -1.5303732e+01 1.0207131e+01]\n", + " [-2.2817270e+00 -1.5085629e+01 1.0710128e+01]\n", + " [-3.8258680e+00 -1.6497447e+01 9.2489020e+00]\n", + " [-2.8302900e+00 -1.6423488e+01 8.9525630e+00]\n", + " [-4.6358710e+00 -1.7243504e+01 9.8659510e+00]\n", + " [-5.9492240e+00 -1.7955810e+01 7.5118090e+00]\n", + " [-6.6028560e+00 -1.8661780e+01 8.1756480e+00]\n", + " [-5.1179760e+00 -1.7951424e+01 7.0058880e+00]\n", + " [-7.3283050e+00 -1.8204130e+01 6.6120680e+00]\n", + " [-8.1475950e+00 -1.9094330e+01 6.2367700e+00]\n", + " [-8.1053370e+00 -1.8148813e+01 7.1082420e+00]\n", + " [-7.6010760e+00 -1.7347194e+01 4.7131580e+00]\n", + " [-8.2909390e+00 -1.7226446e+01 5.2327260e+00]\n", + " [-8.5123040e+00 -1.8232886e+01 4.3164140e+00]\n", + " [-6.5066740e+00 -1.6321381e+01 3.6438020e+00]\n", + " [-5.6999330e+00 -1.6372313e+01 3.1914950e+00]\n", + " [-7.3622070e+00 -1.6477099e+01 2.9897120e+00]\n", + " [-6.7734550e+00 -1.4762862e+01 8.8706800e+00]\n", + " [-7.6839820e+00 -1.4865943e+01 1.0194458e+01]\n", + " [-8.8816130e+00 -1.7555428e+01 9.2118600e+00]\n", + " [ 5.2394090e+00 -8.9214910e+00 4.2403800e+00]\n", + " [-1.0910878e+01 -1.7579797e+01 1.9008600e+00]\n", + " [-1.0284875e+01 -1.8538717e+01 1.0176912e+01]\n", + " [ 5.4753180e+00 -8.1136590e+00 4.6790400e+00]\n", + " [-1.1627420e+01 -1.8422964e+01 2.8951560e+00]\n", + " [-6.0358200e-01 -7.9896200e+00 6.8400740e+00]\n", + " [-1.0751498e+01 -1.3977693e+01 6.8400740e+00]\n", + " [-2.9782200e-01 -8.4932060e+00 4.2313140e+00]\n", + " [-9.7673470e+00 -1.3024987e+01 4.2313140e+00]\n", + " [-3.5234180e+00 -9.0991410e+00 4.9322930e+00]\n", + " [-9.8984860e+00 -1.4990712e+01 4.9322930e+00]\n", + " [-3.8289070e+00 -8.5809340e+00 7.5545050e+00]\n", + " [-1.0907863e+01 -1.5963157e+01 7.5545050e+00]\n", + " [-4.6709100e-01 -6.5368620e+00 9.0072490e+00]\n", + " [-1.3217790e+01 -1.5843993e+01 9.0072490e+00]\n", + " [ 2.7907290e+00 -5.7668020e+00 8.1503030e+00]\n", + " [-1.3359129e+01 -1.4074560e+01 8.1503030e+00]\n", + " [ 3.1523830e+00 -6.3352090e+00 5.5544100e+00]\n", + " [-1.2242244e+01 -1.2984900e+01 5.5544100e+00]\n", + " [ 2.6208900e-01 -9.0735530e+00 8.4251960e+00]\n", + " [-8.5572130e+00 -1.1747320e+01 8.4251960e+00]\n", + " [-2.8656050e+00 -1.0651568e+01 8.3314210e+00]\n", + " [-6.9688280e+00 -1.2300375e+01 8.3314210e+00]\n", + " [-3.1264690e+00 -1.1737816e+01 1.0549481e+01]\n", + " [-5.1828730e+00 -1.1042447e+01 1.0549481e+01]\n", + " [-6.6551560e+00 -1.0404101e+01 1.0792011e+01]\n", + " [-9.6460000e-03 -1.0229131e+01 1.0792011e+01]\n", + " [-1.7445140e+00 -9.0106810e+00 3.2948240e+00]\n", + " [-9.4005780e+00 -1.3570974e+01 3.2948240e+00]\n", + " [-1.7826340e+00 -9.5173130e+00 2.6134390e+00]\n", + " [-8.5370200e+00 -1.2911915e+01 2.6134390e+00]\n", + " [-1.4850420e+00 -8.2805860e+00 2.8610380e+00]\n", + " [-1.0570167e+01 -1.4343593e+01 2.8610380e+00]\n", + " [-3.5862000e+00 -9.8851630e+00 3.9927810e+00]\n", + " [-8.5600360e+00 -1.3971357e+01 3.9927810e+00]\n", + " [-8.2410470e+00 -1.4302166e+01 3.3523370e+00]\n", + " [-4.5949250e+00 -1.0282500e+01 3.3523370e+00]\n", + " [-7.4127920e+00 -1.3207998e+01 4.4509370e+00]\n", + " [-3.8311890e+00 -1.0599297e+01 4.4509370e+00]\n", + " [-5.2871750e+00 -9.8448320e+00 5.5817050e+00]\n", + " [-9.2524910e+00 -1.5499537e+01 5.5817050e+00]\n", + " [-5.6470390e+00 -1.0598078e+01 6.0505840e+00]\n", + " [-8.0795500e+00 -1.4782235e+01 6.0505840e+00]\n", + " [-6.2557530e+00 -1.0187460e+01 4.9246900e+00]\n", + " [-9.0135650e+00 -1.5870311e+01 4.9246900e+00]\n", + " [-5.1210540e+00 -8.9687670e+00 6.5438320e+00]\n", + " [-1.0709077e+01 -1.6552400e+01 6.5438320e+00]\n", + " [-4.6789260e+00 -8.1843290e+00 6.0876260e+00]\n", + " [-1.1905932e+01 -1.7241067e+01 6.0876260e+00]\n", + " [-6.3473310e+00 -9.4454240e+00 6.9347270e+00]\n", + " [-1.0332330e+01 -1.6963261e+01 6.9347270e+00]\n", + " [-3.6792490e+00 -7.8167220e+00 8.5743410e+00]\n", + " [-1.2176738e+01 -1.6877482e+01 8.5743410e+00]\n", + " [-4.9037900e+00 -8.3268870e+00 8.9886310e+00]\n", + " [-1.1741319e+01 -1.7241067e+01 8.9886310e+00]\n", + " [-3.2495450e+00 -7.0060880e+00 8.2000180e+00]\n", + " [-1.3423517e+01 -1.7612695e+01 8.2000180e+00]\n", + " [-2.3100430e+00 -7.4759220e+00 9.5774100e+00]\n", + " [-1.2265858e+01 -1.6157256e+01 9.5774100e+00]\n", + " [-1.2939726e+01 -1.6711042e+01 1.0364074e+01]\n", + " [-2.3487050e+00 -7.0950340e+00 1.0364074e+01]\n", + " [-1.0998592e+01 -1.5343941e+01 9.8445050e+00]\n", + " [-2.6324560e+00 -8.2757120e+00 9.8445050e+00]\n", + " [ 9.3398600e-01 -6.1369670e+00 9.9254140e+00]\n", + " [-1.3397598e+01 -1.5176891e+01 9.9254140e+00]\n", + " [ 6.8288600e-01 -6.9061750e+00 1.0207131e+01]\n", + " [-1.2157201e+01 -1.4343593e+01 1.0207131e+01]\n", + " [ 9.2490600e-01 -5.7376810e+00 1.0710128e+01]\n", + " [-1.4092505e+01 -1.5730189e+01 1.0710128e+01]\n", + " [ 2.8050520e+00 -5.1463670e+00 9.2489020e+00]\n", + " [-1.4428510e+01 -1.4909685e+01 9.2489020e+00]\n", + " [ 3.0413570e+00 -4.3852020e+00 8.9525630e+00]\n", + " [-1.5660394e+01 -1.5744811e+01 8.9525630e+00]\n", + " [ 3.8007790e+00 -4.8287170e+00 9.8659510e+00]\n", + " [-1.4614234e+01 -1.4481278e+01 9.8659510e+00]\n", + " [ 4.5538090e+00 -4.9930860e+00 7.5118090e+00]\n", + " [-1.4053911e+01 -1.3604603e+01 7.5118090e+00]\n", + " [ 5.5373380e+00 -4.5947750e+00 8.1756480e+00]\n", + " [-1.4383810e+01 -1.3296945e+01 8.1756480e+00]\n", + " [ 4.8504690e+00 -4.2791960e+00 7.0058880e+00]\n", + " [-1.5181820e+01 -1.4322880e+01 7.0058880e+00]\n", + " [ 4.4791330e+00 -5.8481940e+00 6.6120680e+00]\n", + " [-1.2600154e+01 -1.2501175e+01 6.6120680e+00]\n", + " [-1.3022854e+01 -1.2117485e+01 6.2367700e+00]\n", + " [ 5.7211230e+00 -5.3416850e+00 6.2367700e+00]\n", + " [-1.1442897e+01 -1.1807999e+01 7.1082420e+00]\n", + " [ 4.0989070e+00 -6.5966880e+00 7.1082420e+00]\n", + " [-1.0743286e+01 -1.1951289e+01 4.7131580e+00]\n", + " [ 2.8950350e+00 -7.2550170e+00 4.7131580e+00]\n", + " [-9.5917730e+00 -1.1309653e+01 5.2327260e+00]\n", + " [ 2.4333860e+00 -8.0174010e+00 5.2327260e+00]\n", + " [-1.1032588e+01 -1.1486328e+01 4.3164140e+00]\n", + " [ 4.0955650e+00 -6.8342860e+00 4.3164140e+00]\n", + " [-1.0461506e+01 -1.2523595e+01 3.6438020e+00]\n", + " [ 1.5188540e+00 -7.7085240e+00 3.6438020e+00]\n", + " [-1.1651752e+01 -1.3240896e+01 3.1914950e+00]\n", + " [ 1.9023580e+00 -6.9402910e+00 3.1914950e+00]\n", + " [-9.5625390e+00 -1.1839679e+01 2.9897120e+00]\n", + " [ 1.4754180e+00 -8.2367220e+00 2.9897120e+00]\n", + " [-1.2782300e+00 -1.0068540e+01 8.8706800e+00]\n", + " [-7.3976420e+00 -1.1722098e+01 8.8706800e+00]\n", + " [-1.4329640e+00 -1.0716268e+01 1.0194458e+01]\n", + " [-6.3323810e+00 -1.0971289e+01 1.0194458e+01]]\n", + "['K', 'Fe', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'H', 'H', 'K', 'K', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'C', 'C']\n", + "[[30.832187 18.61494 10.08940733]\n", + " [28.431718 15.157021 12.64445533]\n", + " [31.281307 17.774149 7.48064733]\n", + " [28.709647 18.559225 8.18162633]\n", + " [28.255409 19.419853 10.80383833]\n", + " [30.418645 20.531199 12.25658233]\n", + " [32.958138 19.95421 11.39963633]\n", + " [33.479391 18.996925 8.80374333]\n", + " [31.978626 16.701424 11.67452933]\n", + " [29.847557 16.109663 11.58075433]\n", + " [30.019237 14.756253 13.79881433]\n", + " [32.166268 15.258743 14.04134433]\n", + " [34.630461 16.831004 12.71366633]\n", + " [25.193566 21.454506 7.79872533]\n", + " [34.696623 14.962229 5.25839633]\n", + " [30.217844 17.790611 6.54415733]\n", + " [30.370272 17.117599 5.86277233]\n", + " [30.17532 18.658203 6.11037133]\n", + " [28.94298 17.51689 7.24211433]\n", + " [28.214834 17.47848 6.60167033]\n", + " [28.992205 16.663859 7.70027033]\n", + " [27.455131 18.42247 8.83103833]\n", + " [27.419186 17.573449 9.29991733]\n", + " [26.741728 18.43872 8.17402333]\n", + " [27.278334 19.536137 9.79316533]\n", + " [27.374104 20.386635 9.33695933]\n", + " [26.390815 19.498149 10.18406033]\n", + " [28.105296 20.388956 11.82367433]\n", + " [27.231545 20.304329 12.23796433]\n", + " [28.180718 21.281452 11.44935133]\n", + " [29.166322 20.169895 12.82674333]\n", + " [28.993424 20.709529 13.61340733]\n", + " [29.179847 19.238568 13.09383833]\n", + " [31.485642 20.376927 13.17474733]\n", + " [31.549732 19.45172 13.45646433]\n", + " [31.331629 20.926902 13.95946133]\n", + " [32.743447 20.794579 12.49823533]\n", + " [32.669488 21.716198 12.20189633]\n", + " [33.489504 20.730633 13.11528433]\n", + " [34.20181 20.129586 10.76114233]\n", + " [34.90778 20.181924 11.42498133]\n", + " [34.197424 20.956448 10.25522133]\n", + " [34.45013 18.998825 9.86140133]\n", + " [35.34033 19.069735 9.48610333]\n", + " [34.394813 18.166476 10.35757533]\n", + " [33.593194 17.869118 7.96249133]\n", + " [33.472446 17.058507 8.48205933]\n", + " [34.478886 17.843582 7.56574733]\n", + " [32.567381 17.937707 6.89313533]\n", + " [32.618313 18.79538 6.44082833]\n", + " [32.723099 17.237892 6.23904533]\n", + " [31.008862 16.112407 12.12001333]\n", + " [31.111943 15.304961 13.44379133]\n", + " [33.801428 16.796815 12.46119333]\n", + " [25.167491 22.2839 7.48971333]\n", + " [33.825797 14.791919 5.15019333]\n", + " [34.784717 16.376842 13.42624533]\n", + " [24.359659 21.711977 7.92837333]\n", + " [34.668964 14.918544 6.14448933]\n", + " [24.23562 15.509038 10.08940733]\n", + " [30.223693 11.349195 10.08940733]\n", + " [24.739206 16.318384 7.48064733]\n", + " [29.270987 11.38064 7.48064733]\n", + " [25.345141 13.698723 8.18162633]\n", + " [31.236712 13.215226 8.18162633]\n", + " [24.826934 12.875027 10.80383833]\n", + " [32.209157 13.178294 10.80383833]\n", + " [22.782862 14.192771 12.25658233]\n", + " [32.089993 10.749203 12.25658233]\n", + " [22.012802 16.680531 11.39963633]\n", + " [30.32056 8.838431 11.39963633]\n", + " [22.581209 17.610592 8.80374333]\n", + " [29.2309 8.865656 8.80374333]\n", + " [25.319553 17.458642 11.67452933]\n", + " [27.99332 11.313107 11.67452933]\n", + " [26.897568 15.908963 11.58075433]\n", + " [28.546375 13.454547 11.58075433]\n", + " [27.983816 16.734347 13.79881433]\n", + " [27.288447 13.982574 13.79881433]\n", + " [26.650101 11.871945 14.04134433]\n", + " [26.475131 18.342485 14.04134433]\n", + " [25.256681 15.389167 6.54415733]\n", + " [29.816974 12.293396 6.54415733]\n", + " [25.763313 15.857679 5.86277233]\n", + " [29.157915 12.497895 5.86277233]\n", + " [24.526586 14.918544 6.11037133]\n", + " [30.589593 11.896426 6.11037133]\n", + " [26.131163 14.421963 7.24211433]\n", + " [30.217357 13.534321 7.24211433]\n", + " [30.548166 14.184119 6.60167033]\n", + " [26.5285 13.810575 6.60167033]\n", + " [29.453998 13.918206 7.70027033]\n", + " [26.845297 14.891108 7.70027033]\n", + " [26.090832 12.680657 8.83103833]\n", + " [31.745537 14.370046 8.83103833]\n", + " [26.844078 13.074039 9.29991733]\n", + " [31.028235 14.825685 9.29991733]\n", + " [26.43346 12.054707 8.17402333]\n", + " [32.116311 14.979746 8.17402333]\n", + " [25.214767 11.970713 9.79316533]\n", + " [32.7984 13.966323 9.79316533]\n", + " [24.430329 11.628403 9.33695933]\n", + " [33.487067 13.458135 9.33695933]\n", + " [25.691424 11.221093 10.18406033]\n", + " [33.209261 14.753931 10.18406033]\n", + " [24.062722 12.260473 11.82367433]\n", + " [33.123482 12.823744 11.82367433]\n", + " [24.572887 11.546097 12.23796433]\n", + " [33.487067 13.622748 12.23796433]\n", + " [23.252088 11.879543 11.44935133]\n", + " [33.858695 12.312178 11.44935133]\n", + " [23.721922 13.288879 12.82674333]\n", + " [32.403256 12.014398 12.82674333]\n", + " [32.957042 11.894316 13.61340733]\n", + " [23.341034 12.869329 13.61340733]\n", + " [31.589941 12.468349 13.09383833]\n", + " [24.521712 13.766256 13.09383833]\n", + " [22.382967 15.193953 13.17474733]\n", + " [31.422891 9.902293 13.17474733]\n", + " [23.152175 15.712061 13.45646433]\n", + " [30.589593 10.309392 13.45646433]\n", + " [21.983681 14.785587 13.95946133]\n", + " [31.976189 9.760684 13.95946133]\n", + " [21.392367 16.074419 12.49823533]\n", + " [31.155685 8.604175 12.49823533]\n", + " [20.631202 15.549559 12.20189633]\n", + " [31.990811 8.207417 12.20189633]\n", + " [21.074717 16.752496 13.11528433]\n", + " [30.727278 7.990044 13.11528433]\n", + " [21.239086 17.669895 10.76114233]\n", + " [29.850603 7.673692 10.76114233]\n", + " [20.840775 18.255113 11.42498133]\n", + " [29.542945 7.036135 11.42498133]\n", + " [20.525196 17.252665 10.25522133]\n", + " [30.56888 7.26406 10.25522133]\n", + " [22.094194 18.450327 9.86140133]\n", + " [28.747175 8.024021 9.86140133]\n", + " [28.363485 7.217631 9.48610333]\n", + " [21.587685 19.185808 9.48610333]\n", + " [28.053999 8.488102 10.35757533]\n", + " [22.842688 18.818595 10.35757533]\n", + " [28.197289 9.331003 7.96249133]\n", + " [23.501017 18.273052 7.96249133]\n", + " [27.555653 9.84088 8.48205933]\n", + " [24.263401 18.573787 8.48205933]\n", + " [27.732328 8.57674 7.56574733]\n", + " [23.080286 19.052851 7.56574733]\n", + " [28.769595 10.185089 6.89313533]\n", + " [23.954524 17.350378 6.89313533]\n", + " [29.486896 9.712144 6.44082833]\n", + " [23.186291 16.965649 6.44082833]\n", + " [28.085679 10.40014 6.23904533]\n", + " [24.482722 17.83514 6.23904533]\n", + " [26.31454 16.91331 12.12001333]\n", + " [27.968098 12.447456 12.12001333]\n", + " [26.962268 17.406304 13.44379133]\n", + " [27.217289 12.761908 13.44379133]]\n", + "['K', 'Fe', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'H', 'H', 'K', 'K', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'C', 'C']\n", + "[[ 5.75406 12.217247 10.08940733]\n", + " [ 9.211979 13.274697 12.64445533]\n", + " [ 6.594851 13.507158 7.48064733]\n", + " [ 5.809775 10.150422 8.18162633]\n", + " [ 4.949147 8.835556 10.80383833]\n", + " [ 3.837801 9.887446 12.25658233]\n", + " [ 4.41479 13.003928 11.39963633]\n", + " [ 5.372075 14.482466 8.80374333]\n", + " [ 7.667576 15.277202 11.67452933]\n", + " [ 8.259337 13.737894 11.58075433]\n", + " [ 9.612747 15.262984 13.79881433]\n", + " [ 9.110257 16.907525 14.04134433]\n", + " [ 7.537996 17.799457 12.71366633]\n", + " [ 2.914494 3.73906 7.79872533]\n", + " [ 9.406771 19.734394 5.25839633]\n", + " [ 6.578389 12.427233 6.54415733]\n", + " [ 7.251401 13.252673 5.86277233]\n", + " [ 5.710797 11.517117 6.11037133]\n", + " [ 6.85211 11.42609 7.24211433]\n", + " [ 6.89052 10.736354 6.60167033]\n", + " [ 7.705141 12.328346 7.70027033]\n", + " [ 5.94653 9.032661 8.83103833]\n", + " [ 6.795551 9.845737 9.29991733]\n", + " [ 5.93028 8.303008 8.17402333]\n", + " [ 4.832863 7.742197 9.79316533]\n", + " [ 3.982365 6.987469 9.33695933]\n", + " [ 4.870851 6.892666 10.18406033]\n", + " [ 3.980044 7.71634 11.82367433]\n", + " [ 4.064671 6.927216 12.23796433]\n", + " [ 3.087548 6.899266 11.44935133]\n", + " [ 4.199105 8.996427 12.82674333]\n", + " [ 3.659471 8.283895 13.61340733]\n", + " [ 5.130432 9.941279 13.09383833]\n", + " [ 3.992073 11.108715 13.17474733]\n", + " [ 4.91728 12.098012 13.45646433]\n", + " [ 3.442098 10.404727 13.95946133]\n", + " [ 3.574421 11.948868 12.49823533]\n", + " [ 2.652802 10.95329 12.20189633]\n", + " [ 3.638367 12.758871 13.11528433]\n", + " [ 4.239414 14.072224 10.76114233]\n", + " [ 4.187076 14.725856 11.42498133]\n", + " [ 3.412552 13.240976 10.25522133]\n", + " [ 5.370175 15.451305 9.86140133]\n", + " [ 5.299265 16.270595 9.48610333]\n", + " [ 6.202524 16.228337 10.35757533]\n", + " [ 6.499882 15.724076 7.96249133]\n", + " [ 7.310493 16.413939 8.48205933]\n", + " [ 6.525418 16.635304 7.56574733]\n", + " [ 6.431293 14.629674 6.89313533]\n", + " [ 5.57362 13.822933 6.44082833]\n", + " [ 7.131108 15.485207 6.23904533]\n", + " [ 8.256593 14.896455 12.12001333]\n", + " [ 9.064039 15.806982 13.44379133]\n", + " [ 7.572185 17.004613 12.46119333]\n", + " [ 2.0851 2.883591 7.48971333]\n", + " [ 9.577081 19.033878 5.15019333]\n", + " [ 7.992158 18.407875 13.42624533]\n", + " [ 2.657023 2.647682 7.92837333]\n", + " [ 9.450456 19.75042 6.14448933]\n", + " [ 8.859962 8.726582 10.08940733]\n", + " [13.019805 18.874498 10.08940733]\n", + " [ 8.050616 8.420822 7.48064733]\n", + " [12.98836 17.890347 7.48064733]\n", + " [10.670277 11.646418 8.18162633]\n", + " [11.153774 18.021486 8.18162633]\n", + " [11.493973 11.951907 10.80383833]\n", + " [11.190706 19.030863 10.80383833]\n", + " [10.176229 8.590091 12.25658233]\n", + " [13.619797 21.34079 12.25658233]\n", + " [ 7.688469 5.332271 11.39963633]\n", + " [15.530569 21.482129 11.39963633]\n", + " [ 6.758408 4.970617 8.80374333]\n", + " [15.503344 20.365244 8.80374333]\n", + " [ 6.910358 7.860911 11.67452933]\n", + " [13.055893 16.680213 11.67452933]\n", + " [ 8.460037 10.988605 11.58075433]\n", + " [10.914453 15.091828 11.58075433]\n", + " [ 7.634653 11.249469 13.79881433]\n", + " [10.386426 13.305873 13.79881433]\n", + " [12.497055 14.778156 14.04134433]\n", + " [ 6.026515 8.132646 14.04134433]\n", + " [ 8.979833 9.867514 6.54415733]\n", + " [12.075604 17.523578 6.54415733]\n", + " [ 8.511321 9.905634 5.86277233]\n", + " [11.871105 16.66002 5.86277233]\n", + " [ 9.450456 9.608042 6.11037133]\n", + " [12.472574 18.693167 6.11037133]\n", + " [ 9.947037 11.7092 7.24211433]\n", + " [10.834679 16.683036 7.24211433]\n", + " [10.184881 16.364047 6.60167033]\n", + " [10.558425 12.717925 6.60167033]\n", + " [10.450794 15.535792 7.70027033]\n", + " [ 9.477892 11.954189 7.70027033]\n", + " [11.688343 13.410175 8.83103833]\n", + " [ 9.998954 17.375491 8.83103833]\n", + " [11.294961 13.770039 9.29991733]\n", + " [ 9.543315 16.20255 9.29991733]\n", + " [12.314293 14.378753 8.17402333]\n", + " [ 9.389254 17.136565 8.17402333]\n", + " [12.398287 13.244054 9.79316533]\n", + " [10.402677 18.832077 9.79316533]\n", + " [12.740597 12.801926 9.33695933]\n", + " [10.910865 20.028932 9.33695933]\n", + " [13.147907 14.470331 10.18406033]\n", + " [ 9.615069 18.45533 10.18406033]\n", + " [12.108527 11.802249 11.82367433]\n", + " [11.545256 20.299738 11.82367433]\n", + " [12.822903 13.02679 12.23796433]\n", + " [10.746252 19.864319 12.23796433]\n", + " [12.489457 11.372545 11.44935133]\n", + " [12.056822 21.546517 11.44935133]\n", + " [11.080121 10.433043 12.82674333]\n", + " [12.354602 20.388858 12.82674333]\n", + " [12.474684 21.062726 13.61340733]\n", + " [11.499671 10.471705 13.61340733]\n", + " [11.900651 19.121592 13.09383833]\n", + " [10.602744 10.755456 13.09383833]\n", + " [ 9.175047 7.189014 13.17474733]\n", + " [14.466707 21.520598 13.17474733]\n", + " [ 8.656939 7.440114 13.45646433]\n", + " [14.059608 20.280201 13.45646433]\n", + " [ 9.583413 7.198094 13.95946133]\n", + " [14.608316 22.215505 13.95946133]\n", + " [ 8.294581 5.317948 12.49823533]\n", + " [15.764825 22.55151 12.49823533]\n", + " [ 8.819441 5.081643 12.20189633]\n", + " [16.161583 23.783394 12.20189633]\n", + " [ 7.616504 4.322221 13.11528433]\n", + " [16.378956 22.737234 13.11528433]\n", + " [ 6.699105 3.569191 10.76114233]\n", + " [16.695308 22.176911 10.76114233]\n", + " [ 6.113887 2.585662 11.42498133]\n", + " [17.332865 22.50681 11.42498133]\n", + " [ 7.116335 3.272531 10.25522133]\n", + " [17.10494 23.30482 10.25522133]\n", + " [ 5.918673 3.643867 9.86140133]\n", + " [16.344979 20.723154 9.86140133]\n", + " [17.151369 21.145854 9.48610333]\n", + " [ 5.183192 2.401877 9.48610333]\n", + " [15.880898 19.565897 10.35757533]\n", + " [ 5.550405 4.024093 10.35757533]\n", + " [15.037997 18.866286 7.96249133]\n", + " [ 6.095948 5.227965 7.96249133]\n", + " [14.52812 17.714773 8.48205933]\n", + " [ 5.795213 5.689614 8.48205933]\n", + " [15.79226 19.155588 7.56574733]\n", + " [ 5.316149 4.027435 7.56574733]\n", + " [14.183911 18.584506 6.89313533]\n", + " [ 7.018622 6.604146 6.89313533]\n", + " [14.656856 19.774752 6.44082833]\n", + " [ 7.403351 6.220642 6.44082833]\n", + " [13.96886 17.685539 6.23904533]\n", + " [ 6.53386 6.647582 6.23904533]\n", + " [ 7.45569 9.40123 12.12001333]\n", + " [11.921544 15.520642 12.12001333]\n", + " [ 6.962696 9.555964 13.44379133]\n", + " [11.607092 14.455381 13.44379133]]\n", + "['K', 'Fe', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'H', 'H', 'K', 'K', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'C', 'C']\n", + "[[ 1.21517530e+01 -6.46318700e+00 1.00894073e+01]\n", + " [ 1.10943030e+01 -4.06271800e+00 1.26444553e+01]\n", + " [ 1.08618420e+01 -6.91230700e+00 7.48064733e+00]\n", + " [ 1.42185780e+01 -4.34064700e+00 8.18162633e+00]\n", + " [ 1.55334440e+01 -3.88640900e+00 1.08038383e+01]\n", + " [ 1.44815540e+01 -6.04964500e+00 1.22565823e+01]\n", + " [ 1.13650720e+01 -8.58913800e+00 1.13996363e+01]\n", + " [ 9.88653400e+00 -9.11039100e+00 8.80374333e+00]\n", + " [ 9.09179800e+00 -7.60962600e+00 1.16745293e+01]\n", + " [ 1.06311060e+01 -5.47855700e+00 1.15807543e+01]\n", + " [ 9.10601600e+00 -5.65023700e+00 1.37988143e+01]\n", + " [ 7.46147500e+00 -7.79726800e+00 1.40413443e+01]\n", + " [ 6.56954300e+00 -1.02614610e+01 1.27136663e+01]\n", + " [ 2.06299400e+01 -8.24566000e-01 7.79872533e+00]\n", + " [ 4.63460600e+00 -1.03276230e+01 5.25839633e+00]\n", + " [ 1.19417670e+01 -5.84884400e+00 6.54415733e+00]\n", + " [ 1.11163270e+01 -6.00127200e+00 5.86277233e+00]\n", + " [ 1.28518830e+01 -5.80632000e+00 6.11037133e+00]\n", + " [ 1.29429100e+01 -4.57398000e+00 7.24211433e+00]\n", + " [ 1.36326460e+01 -3.84583400e+00 6.60167033e+00]\n", + " [ 1.20406540e+01 -4.62320500e+00 7.70027033e+00]\n", + " [ 1.53363390e+01 -3.08613100e+00 8.83103833e+00]\n", + " [ 1.45232630e+01 -3.05018600e+00 9.29991733e+00]\n", + " [ 1.60659920e+01 -2.37272800e+00 8.17402333e+00]\n", + " [ 1.66268030e+01 -2.90933400e+00 9.79316533e+00]\n", + " [ 1.73815310e+01 -3.00510400e+00 9.33695933e+00]\n", + " [ 1.74763340e+01 -2.02181500e+00 1.01840603e+01]\n", + " [ 1.66526600e+01 -3.73629600e+00 1.18236743e+01]\n", + " [ 1.74417840e+01 -2.86254500e+00 1.22379643e+01]\n", + " [ 1.74697340e+01 -3.81171800e+00 1.14493513e+01]\n", + " [ 1.53725730e+01 -4.79732200e+00 1.28267433e+01]\n", + " [ 1.60851050e+01 -4.62442400e+00 1.36134073e+01]\n", + " [ 1.44277210e+01 -4.81084700e+00 1.30938383e+01]\n", + " [ 1.32602850e+01 -7.11664200e+00 1.31747473e+01]\n", + " [ 1.22709880e+01 -7.18073200e+00 1.34564643e+01]\n", + " [ 1.39642730e+01 -6.96262900e+00 1.39594613e+01]\n", + " [ 1.24201320e+01 -8.37444700e+00 1.24982353e+01]\n", + " [ 1.34157100e+01 -8.30048800e+00 1.22018963e+01]\n", + " [ 1.16101290e+01 -9.12050400e+00 1.31152843e+01]\n", + " [ 1.02967760e+01 -9.83281000e+00 1.07611423e+01]\n", + " [ 9.64314400e+00 -1.05387800e+01 1.14249813e+01]\n", + " [ 1.11280240e+01 -9.82842400e+00 1.02552213e+01]\n", + " [ 8.91769500e+00 -1.00811300e+01 9.86140133e+00]\n", + " [ 8.09840500e+00 -1.09713300e+01 9.48610333e+00]\n", + " [ 8.14066300e+00 -1.00258130e+01 1.03575753e+01]\n", + " [ 8.64492400e+00 -9.22419400e+00 7.96249133e+00]\n", + " [ 7.95506100e+00 -9.10344600e+00 8.48205933e+00]\n", + " [ 7.73369600e+00 -1.01098860e+01 7.56574733e+00]\n", + " [ 9.73932600e+00 -8.19838100e+00 6.89313533e+00]\n", + " [ 1.05460670e+01 -8.24931300e+00 6.44082833e+00]\n", + " [ 8.88379300e+00 -8.35409900e+00 6.23904533e+00]\n", + " [ 9.47254500e+00 -6.63986200e+00 1.21200133e+01]\n", + " [ 8.56201800e+00 -6.74294300e+00 1.34437913e+01]\n", + " [ 7.36438700e+00 -9.43242800e+00 1.24611933e+01]\n", + " [ 2.14854090e+01 -7.98491000e-01 7.48971333e+00]\n", + " [ 5.33512200e+00 -9.45679700e+00 5.15019333e+00]\n", + " [ 5.96112500e+00 -1.04157170e+01 1.34262453e+01]\n", + " [ 2.17213180e+01 9.34100000e-03 7.92837333e+00]\n", + " [ 4.61858000e+00 -1.02999640e+01 6.14448933e+00]\n", + " [ 1.56424180e+01 1.33380000e-01 1.00894073e+01]\n", + " [ 5.49450200e+00 -5.85469300e+00 1.00894073e+01]\n", + " [ 1.59481780e+01 -3.70206000e-01 7.48064733e+00]\n", + " [ 6.47865300e+00 -4.90198700e+00 7.48064733e+00]\n", + " [ 1.27225820e+01 -9.76141000e-01 8.18162633e+00]\n", + " [ 6.34751400e+00 -6.86771200e+00 8.18162633e+00]\n", + " [ 1.24170930e+01 -4.57934000e-01 1.08038383e+01]\n", + " [ 5.33813700e+00 -7.84015700e+00 1.08038383e+01]\n", + " [ 1.57789090e+01 1.58613800e+00 1.22565823e+01]\n", + " [ 3.02821000e+00 -7.72099300e+00 1.22565823e+01]\n", + " [ 1.90367290e+01 2.35619800e+00 1.13996363e+01]\n", + " [ 2.88687100e+00 -5.95156000e+00 1.13996363e+01]\n", + " [ 1.93983830e+01 1.78779100e+00 8.80374333e+00]\n", + " [ 4.00375600e+00 -4.86190000e+00 8.80374333e+00]\n", + " [ 1.65080890e+01 -9.50553000e-01 1.16745293e+01]\n", + " [ 7.68878700e+00 -3.62432000e+00 1.16745293e+01]\n", + " [ 1.33803950e+01 -2.52856800e+00 1.15807543e+01]\n", + " [ 9.27717200e+00 -4.17737500e+00 1.15807543e+01]\n", + " [ 1.31195310e+01 -3.61481600e+00 1.37988143e+01]\n", + " [ 1.10631270e+01 -2.91944700e+00 1.37988143e+01]\n", + " [ 9.59084400e+00 -2.28110100e+00 1.40413443e+01]\n", + " [ 1.62363540e+01 -2.10613100e+00 1.40413443e+01]\n", + " [ 1.45014860e+01 -8.87681000e-01 6.54415733e+00]\n", + " [ 6.84542200e+00 -5.44797400e+00 6.54415733e+00]\n", + " [ 1.44633660e+01 -1.39431300e+00 5.86277233e+00]\n", + " [ 7.70898000e+00 -4.78891500e+00 5.86277233e+00]\n", + " [ 1.47609580e+01 -1.57586000e-01 6.11037133e+00]\n", + " [ 5.67583300e+00 -6.22059300e+00 6.11037133e+00]\n", + " [ 1.26598000e+01 -1.76216300e+00 7.24211433e+00]\n", + " [ 7.68596400e+00 -5.84835700e+00 7.24211433e+00]\n", + " [ 8.00495300e+00 -6.17916600e+00 6.60167033e+00]\n", + " [ 1.16510750e+01 -2.15950000e+00 6.60167033e+00]\n", + " [ 8.83320800e+00 -5.08499800e+00 7.70027033e+00]\n", + " [ 1.24148110e+01 -2.47629700e+00 7.70027033e+00]\n", + " [ 1.09588250e+01 -1.72183200e+00 8.83103833e+00]\n", + " [ 6.99350900e+00 -7.37653700e+00 8.83103833e+00]\n", + " [ 1.05989610e+01 -2.47507800e+00 9.29991733e+00]\n", + " [ 8.16645000e+00 -6.65923500e+00 9.29991733e+00]\n", + " [ 9.99024700e+00 -2.06446000e+00 8.17402333e+00]\n", + " [ 7.23243500e+00 -7.74731100e+00 8.17402333e+00]\n", + " [ 1.11249460e+01 -8.45767000e-01 9.79316533e+00]\n", + " [ 5.53692300e+00 -8.42940000e+00 9.79316533e+00]\n", + " [ 1.15670740e+01 -6.13290000e-02 9.33695933e+00]\n", + " [ 4.34006800e+00 -9.11806700e+00 9.33695933e+00]\n", + " [ 9.89866900e+00 -1.32242400e+00 1.01840603e+01]\n", + " [ 5.91367000e+00 -8.84026100e+00 1.01840603e+01]\n", + " [ 1.25667510e+01 3.06278000e-01 1.18236743e+01]\n", + " [ 4.06926200e+00 -8.75448200e+00 1.18236743e+01]\n", + " [ 1.13422100e+01 -2.03887000e-01 1.22379643e+01]\n", + " [ 4.50468100e+00 -9.11806700e+00 1.22379643e+01]\n", + " [ 1.29964550e+01 1.11691200e+00 1.14493513e+01]\n", + " [ 2.82248300e+00 -9.48969500e+00 1.14493513e+01]\n", + " [ 1.39359570e+01 6.47078000e-01 1.28267433e+01]\n", + " [ 3.98014200e+00 -8.03425600e+00 1.28267433e+01]\n", + " [ 3.30627400e+00 -8.58804200e+00 1.36134073e+01]\n", + " [ 1.38972950e+01 1.02796600e+00 1.36134073e+01]\n", + " [ 5.24740800e+00 -7.22094100e+00 1.30938383e+01]\n", + " [ 1.36135440e+01 -1.52712000e-01 1.30938383e+01]\n", + " [ 1.71799860e+01 1.98603300e+00 1.31747473e+01]\n", + " [ 2.84840200e+00 -7.05389100e+00 1.31747473e+01]\n", + " [ 1.69288860e+01 1.21682500e+00 1.34564643e+01]\n", + " [ 4.08879900e+00 -6.22059300e+00 1.34564643e+01]\n", + " [ 1.71709060e+01 2.38531900e+00 1.39594613e+01]\n", + " [ 2.15349500e+00 -7.60718900e+00 1.39594613e+01]\n", + " [ 1.90510520e+01 2.97663300e+00 1.24982353e+01]\n", + " [ 1.81749000e+00 -6.78668500e+00 1.24982353e+01]\n", + " [ 1.92873570e+01 3.73779800e+00 1.22018963e+01]\n", + " [ 5.85606000e-01 -7.62181100e+00 1.22018963e+01]\n", + " [ 2.00467790e+01 3.29428300e+00 1.31152843e+01]\n", + " [ 1.63176600e+00 -6.35827800e+00 1.31152843e+01]\n", + " [ 2.07998090e+01 3.12991400e+00 1.07611423e+01]\n", + " [ 2.19208900e+00 -5.48160300e+00 1.07611423e+01]\n", + " [ 2.17833380e+01 3.52822500e+00 1.14249813e+01]\n", + " [ 1.86219000e+00 -5.17394500e+00 1.14249813e+01]\n", + " [ 2.10964690e+01 3.84380400e+00 1.02552213e+01]\n", + " [ 1.06418000e+00 -6.19988000e+00 1.02552213e+01]\n", + " [ 2.07251330e+01 2.27480600e+00 9.86140133e+00]\n", + " [ 3.64584600e+00 -4.37817500e+00 9.86140133e+00]\n", + " [ 3.22314600e+00 -3.99448500e+00 9.48610333e+00]\n", + " [ 2.19671230e+01 2.78131500e+00 9.48610333e+00]\n", + " [ 4.80310300e+00 -3.68499900e+00 1.03575753e+01]\n", + " [ 2.03449070e+01 1.52631200e+00 1.03575753e+01]\n", + " [ 5.50271400e+00 -3.82828900e+00 7.96249133e+00]\n", + " [ 1.91410350e+01 8.67983000e-01 7.96249133e+00]\n", + " [ 6.65422700e+00 -3.18665300e+00 8.48205933e+00]\n", + " [ 1.86793860e+01 1.05599000e-01 8.48205933e+00]\n", + " [ 5.21341200e+00 -3.36332800e+00 7.56574733e+00]\n", + " [ 2.03415650e+01 1.28871400e+00 7.56574733e+00]\n", + " [ 5.78449400e+00 -4.40059500e+00 6.89313533e+00]\n", + " [ 1.77648540e+01 4.14476000e-01 6.89313533e+00]\n", + " [ 4.59424800e+00 -5.11789600e+00 6.44082833e+00]\n", + " [ 1.81483580e+01 1.18270900e+00 6.44082833e+00]\n", + " [ 6.68346100e+00 -3.71667900e+00 6.23904533e+00]\n", + " [ 1.77214180e+01 -1.13722000e-01 6.23904533e+00]\n", + " [ 1.49677700e+01 -1.94554000e+00 1.21200133e+01]\n", + " [ 8.84835800e+00 -3.59909800e+00 1.21200133e+01]\n", + " [ 1.48130360e+01 -2.59326800e+00 1.34437913e+01]\n", + " [ 9.91361900e+00 -2.84828900e+00 1.34437913e+01]]\n", + "['K', 'Fe', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'H', 'H', 'K', 'K', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'C', 'C']\n", + "[[22.709187 26.73794 13.33874067]\n", + " [20.308718 23.280021 15.89378867]\n", + " [23.158307 25.897149 10.72998067]\n", + " [20.586647 26.682225 11.43095967]\n", + " [20.132409 27.542853 14.05317167]\n", + " [22.295645 28.654199 15.50591567]\n", + " [24.835138 28.07721 14.64896967]\n", + " [25.356391 27.119925 12.05307667]\n", + " [23.855626 24.824424 14.92386267]\n", + " [21.724557 24.232663 14.83008767]\n", + " [21.896237 22.879253 17.04814767]\n", + " [24.043268 23.381743 17.29067767]\n", + " [26.507461 24.954004 15.96299967]\n", + " [17.070566 29.577506 11.04805867]\n", + " [26.573623 23.085229 8.50772967]\n", + " [22.094844 25.913611 9.79349067]\n", + " [22.247272 25.240599 9.11210567]\n", + " [22.05232 26.781203 9.35970467]\n", + " [20.81998 25.63989 10.49144767]\n", + " [20.091834 25.60148 9.85100367]\n", + " [20.869205 24.786859 10.94960367]\n", + " [19.332131 26.54547 12.08037167]\n", + " [19.296186 25.696449 12.54925067]\n", + " [18.618728 26.56172 11.42335667]\n", + " [19.155334 27.659137 13.04249867]\n", + " [19.251104 28.509635 12.58629267]\n", + " [18.267815 27.621149 13.43339367]\n", + " [19.982296 28.511956 15.07300767]\n", + " [19.108545 28.427329 15.48729767]\n", + " [20.057718 29.404452 14.69868467]\n", + " [21.043322 28.292895 16.07607667]\n", + " [20.870424 28.832529 16.86274067]\n", + " [21.056847 27.361568 16.34317167]\n", + " [23.362642 28.499927 16.42408067]\n", + " [23.426732 27.57472 16.70579767]\n", + " [23.208629 29.049902 17.20879467]\n", + " [24.620447 28.917579 15.74756867]\n", + " [24.546488 29.839198 15.45122967]\n", + " [25.366504 28.853633 16.36461767]\n", + " [26.07881 28.252586 14.01047567]\n", + " [26.78478 28.304924 14.67431467]\n", + " [26.074424 29.079448 13.50455467]\n", + " [26.32713 27.121825 13.11073467]\n", + " [27.21733 27.192735 12.73543667]\n", + " [26.271813 26.289476 13.60690867]\n", + " [25.470194 25.992118 11.21182467]\n", + " [25.349446 25.181507 11.73139267]\n", + " [26.355886 25.966582 10.81508067]\n", + " [24.444381 26.060707 10.14246867]\n", + " [24.495313 26.91838 9.69016167]\n", + " [24.600099 25.360892 9.48837867]\n", + " [22.885862 24.235407 15.36934667]\n", + " [22.988943 23.427961 16.69312467]\n", + " [25.678428 24.919815 15.71052667]\n", + " [17.044491 30.4069 10.73904667]\n", + " [25.702797 22.914919 8.39952667]\n", + " [26.661717 24.499842 16.67557867]\n", + " [16.236659 29.834977 11.17770667]\n", + " [26.545964 23.041544 9.39382267]\n", + " [16.11262 23.632038 13.33874067]\n", + " [22.100693 19.472195 13.33874067]\n", + " [16.616206 24.441384 10.72998067]\n", + " [21.147987 19.50364 10.72998067]\n", + " [17.222141 21.821723 11.43095967]\n", + " [23.113712 21.338226 11.43095967]\n", + " [16.703934 20.998027 14.05317167]\n", + " [24.086157 21.301294 14.05317167]\n", + " [14.659862 22.315771 15.50591567]\n", + " [23.966993 18.872203 15.50591567]\n", + " [13.889802 24.803531 14.64896967]\n", + " [22.19756 16.961431 14.64896967]\n", + " [14.458209 25.733592 12.05307667]\n", + " [21.1079 16.988656 12.05307667]\n", + " [17.196553 25.581642 14.92386267]\n", + " [19.87032 19.436107 14.92386267]\n", + " [18.774568 24.031963 14.83008767]\n", + " [20.423375 21.577547 14.83008767]\n", + " [19.860816 24.857347 17.04814767]\n", + " [19.165447 22.105574 17.04814767]\n", + " [18.527101 19.994945 17.29067767]\n", + " [18.352131 26.465485 17.29067767]\n", + " [17.133681 23.512167 9.79349067]\n", + " [21.693974 20.416396 9.79349067]\n", + " [17.640313 23.980679 9.11210567]\n", + " [21.034915 20.620895 9.11210567]\n", + " [16.403586 23.041544 9.35970467]\n", + " [22.466593 20.019426 9.35970467]\n", + " [18.008163 22.544963 10.49144767]\n", + " [22.094357 21.657321 10.49144767]\n", + " [22.425166 22.307119 9.85100367]\n", + " [18.4055 21.933575 9.85100367]\n", + " [21.330998 22.041206 10.94960367]\n", + " [18.722297 23.014108 10.94960367]\n", + " [17.967832 20.803657 12.08037167]\n", + " [23.622537 22.493046 12.08037167]\n", + " [18.721078 21.197039 12.54925067]\n", + " [22.905235 22.948685 12.54925067]\n", + " [18.31046 20.177707 11.42335667]\n", + " [23.993311 23.102746 11.42335667]\n", + " [17.091767 20.093713 13.04249867]\n", + " [24.6754 22.089323 13.04249867]\n", + " [16.307329 19.751403 12.58629267]\n", + " [25.364067 21.581135 12.58629267]\n", + " [17.568424 19.344093 13.43339367]\n", + " [25.086261 22.876931 13.43339367]\n", + " [15.939722 20.383473 15.07300767]\n", + " [25.000482 20.946744 15.07300767]\n", + " [16.449887 19.669097 15.48729767]\n", + " [25.364067 21.745748 15.48729767]\n", + " [15.129088 20.002543 14.69868467]\n", + " [25.735695 20.435178 14.69868467]\n", + " [15.598922 21.411879 16.07607667]\n", + " [24.280256 20.137398 16.07607667]\n", + " [24.834042 20.017316 16.86274067]\n", + " [15.218034 20.992329 16.86274067]\n", + " [23.466941 20.591349 16.34317167]\n", + " [16.398712 21.889256 16.34317167]\n", + " [14.259967 23.316953 16.42408067]\n", + " [23.299891 18.025293 16.42408067]\n", + " [15.029175 23.835061 16.70579767]\n", + " [22.466593 18.432392 16.70579767]\n", + " [13.860681 22.908587 17.20879467]\n", + " [23.853189 17.883684 17.20879467]\n", + " [13.269367 24.197419 15.74756867]\n", + " [23.032685 16.727175 15.74756867]\n", + " [12.508202 23.672559 15.45122967]\n", + " [23.867811 16.330417 15.45122967]\n", + " [12.951717 24.875496 16.36461767]\n", + " [22.604278 16.113044 16.36461767]\n", + " [13.116086 25.792895 14.01047567]\n", + " [21.727603 15.796692 14.01047567]\n", + " [12.717775 26.378113 14.67431467]\n", + " [21.419945 15.159135 14.67431467]\n", + " [12.402196 25.375665 13.50455467]\n", + " [22.44588 15.38706 13.50455467]\n", + " [13.971194 26.573327 13.11073467]\n", + " [20.624175 16.147021 13.11073467]\n", + " [20.240485 15.340631 12.73543667]\n", + " [13.464685 27.308808 12.73543667]\n", + " [19.930999 16.611102 13.60690867]\n", + " [14.719688 26.941595 13.60690867]\n", + " [20.074289 17.454003 11.21182467]\n", + " [15.378017 26.396052 11.21182467]\n", + " [19.432653 17.96388 11.73139267]\n", + " [16.140401 26.696787 11.73139267]\n", + " [19.609328 16.69974 10.81508067]\n", + " [14.957286 27.175851 10.81508067]\n", + " [20.646595 18.308089 10.14246867]\n", + " [15.831524 25.473378 10.14246867]\n", + " [21.363896 17.835144 9.69016167]\n", + " [15.063291 25.088649 9.69016167]\n", + " [19.962679 18.52314 9.48837867]\n", + " [16.359722 25.95814 9.48837867]\n", + " [18.19154 25.03631 15.36934667]\n", + " [19.845098 20.570456 15.36934667]\n", + " [18.839268 25.529304 16.69312467]\n", + " [19.094289 20.884908 16.69312467]]\n", + "['K', 'Fe', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'H', 'H', 'K', 'K', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'C', 'C']\n", + "[[-2.36894 20.340247 13.33874067]\n", + " [ 1.088979 21.397697 15.89378867]\n", + " [-1.528149 21.630158 10.72998067]\n", + " [-2.313225 18.273422 11.43095967]\n", + " [-3.173853 16.958556 14.05317167]\n", + " [-4.285199 18.010446 15.50591567]\n", + " [-3.70821 21.126928 14.64896967]\n", + " [-2.750925 22.605466 12.05307667]\n", + " [-0.455424 23.400202 14.92386267]\n", + " [ 0.136337 21.860894 14.83008767]\n", + " [ 1.489747 23.385984 17.04814767]\n", + " [ 0.987257 25.030525 17.29067767]\n", + " [-0.585004 25.922457 15.96299967]\n", + " [-5.208506 11.86206 11.04805867]\n", + " [ 1.283771 27.857394 8.50772967]\n", + " [-1.544611 20.550233 9.79349067]\n", + " [-0.871599 21.375673 9.11210567]\n", + " [-2.412203 19.640117 9.35970467]\n", + " [-1.27089 19.54909 10.49144767]\n", + " [-1.23248 18.859354 9.85100367]\n", + " [-0.417859 20.451346 10.94960367]\n", + " [-2.17647 17.155661 12.08037167]\n", + " [-1.327449 17.968737 12.54925067]\n", + " [-2.19272 16.426008 11.42335667]\n", + " [-3.290137 15.865197 13.04249867]\n", + " [-4.140635 15.110469 12.58629267]\n", + " [-3.252149 15.015666 13.43339367]\n", + " [-4.142956 15.83934 15.07300767]\n", + " [-4.058329 15.050216 15.48729767]\n", + " [-5.035452 15.022266 14.69868467]\n", + " [-3.923895 17.119427 16.07607667]\n", + " [-4.463529 16.406895 16.86274067]\n", + " [-2.992568 18.064279 16.34317167]\n", + " [-4.130927 19.231715 16.42408067]\n", + " [-3.20572 20.221012 16.70579767]\n", + " [-4.680902 18.527727 17.20879467]\n", + " [-4.548579 20.071868 15.74756867]\n", + " [-5.470198 19.07629 15.45122967]\n", + " [-4.484633 20.881871 16.36461767]\n", + " [-3.883586 22.195224 14.01047567]\n", + " [-3.935924 22.848856 14.67431467]\n", + " [-4.710448 21.363976 13.50455467]\n", + " [-2.752825 23.574305 13.11073467]\n", + " [-2.823735 24.393595 12.73543667]\n", + " [-1.920476 24.351337 13.60690867]\n", + " [-1.623118 23.847076 11.21182467]\n", + " [-0.812507 24.536939 11.73139267]\n", + " [-1.597582 24.758304 10.81508067]\n", + " [-1.691707 22.752674 10.14246867]\n", + " [-2.54938 21.945933 9.69016167]\n", + " [-0.991892 23.608207 9.48837867]\n", + " [ 0.133593 23.019455 15.36934667]\n", + " [ 0.941039 23.929982 16.69312467]\n", + " [-0.550815 25.127613 15.71052667]\n", + " [-6.0379 11.006591 10.73904667]\n", + " [ 1.454081 27.156878 8.39952667]\n", + " [-0.130842 26.530875 16.67557867]\n", + " [-5.465977 10.770682 11.17770667]\n", + " [ 1.327456 27.87342 9.39382267]\n", + " [ 0.736962 16.849582 13.33874067]\n", + " [ 4.896805 26.997498 13.33874067]\n", + " [-0.072384 16.543822 10.72998067]\n", + " [ 4.86536 26.013347 10.72998067]\n", + " [ 2.547277 19.769418 11.43095967]\n", + " [ 3.030774 26.144486 11.43095967]\n", + " [ 3.370973 20.074907 14.05317167]\n", + " [ 3.067706 27.153863 14.05317167]\n", + " [ 2.053229 16.713091 15.50591567]\n", + " [ 5.496797 29.46379 15.50591567]\n", + " [-0.434531 13.455271 14.64896967]\n", + " [ 7.407569 29.605129 14.64896967]\n", + " [-1.364592 13.093617 12.05307667]\n", + " [ 7.380344 28.488244 12.05307667]\n", + " [-1.212642 15.983911 14.92386267]\n", + " [ 4.932893 24.803213 14.92386267]\n", + " [ 0.337037 19.111605 14.83008767]\n", + " [ 2.791453 23.214828 14.83008767]\n", + " [-0.488347 19.372469 17.04814767]\n", + " [ 2.263426 21.428873 17.04814767]\n", + " [ 4.374055 22.901156 17.29067767]\n", + " [-2.096485 16.255646 17.29067767]\n", + " [ 0.856833 17.990514 9.79349067]\n", + " [ 3.952604 25.646578 9.79349067]\n", + " [ 0.388321 18.028634 9.11210567]\n", + " [ 3.748105 24.78302 9.11210567]\n", + " [ 1.327456 17.731042 9.35970467]\n", + " [ 4.349574 26.816167 9.35970467]\n", + " [ 1.824037 19.8322 10.49144767]\n", + " [ 2.711679 24.806036 10.49144767]\n", + " [ 2.061881 24.487047 9.85100367]\n", + " [ 2.435425 20.840925 9.85100367]\n", + " [ 2.327794 23.658792 10.94960367]\n", + " [ 1.354892 20.077189 10.94960367]\n", + " [ 3.565343 21.533175 12.08037167]\n", + " [ 1.875954 25.498491 12.08037167]\n", + " [ 3.171961 21.893039 12.54925067]\n", + " [ 1.420315 24.32555 12.54925067]\n", + " [ 4.191293 22.501753 11.42335667]\n", + " [ 1.266254 25.259565 11.42335667]\n", + " [ 4.275287 21.367054 13.04249867]\n", + " [ 2.279677 26.955077 13.04249867]\n", + " [ 4.617597 20.924926 12.58629267]\n", + " [ 2.787865 28.151932 12.58629267]\n", + " [ 5.024907 22.593331 13.43339367]\n", + " [ 1.492069 26.57833 13.43339367]\n", + " [ 3.985527 19.925249 15.07300767]\n", + " [ 3.422256 28.422738 15.07300767]\n", + " [ 4.699903 21.14979 15.48729767]\n", + " [ 2.623252 27.987319 15.48729767]\n", + " [ 4.366457 19.495545 14.69868467]\n", + " [ 3.933822 29.669517 14.69868467]\n", + " [ 2.957121 18.556043 16.07607667]\n", + " [ 4.231602 28.511858 16.07607667]\n", + " [ 4.351684 29.185726 16.86274067]\n", + " [ 3.376671 18.594705 16.86274067]\n", + " [ 3.777651 27.244592 16.34317167]\n", + " [ 2.479744 18.878456 16.34317167]\n", + " [ 1.052047 15.312014 16.42408067]\n", + " [ 6.343707 29.643598 16.42408067]\n", + " [ 0.533939 15.563114 16.70579767]\n", + " [ 5.936608 28.403201 16.70579767]\n", + " [ 1.460413 15.321094 17.20879467]\n", + " [ 6.485316 30.338505 17.20879467]\n", + " [ 0.171581 13.440948 15.74756867]\n", + " [ 7.641825 30.67451 15.74756867]\n", + " [ 0.696441 13.204643 15.45122967]\n", + " [ 8.038583 31.906394 15.45122967]\n", + " [-0.506496 12.445221 16.36461767]\n", + " [ 8.255956 30.860234 16.36461767]\n", + " [-1.423895 11.692191 14.01047567]\n", + " [ 8.572308 30.299911 14.01047567]\n", + " [-2.009113 10.708662 14.67431467]\n", + " [ 9.209865 30.62981 14.67431467]\n", + " [-1.006665 11.395531 13.50455467]\n", + " [ 8.98194 31.42782 13.50455467]\n", + " [-2.204327 11.766867 13.11073467]\n", + " [ 8.221979 28.846154 13.11073467]\n", + " [ 9.028369 29.268854 12.73543667]\n", + " [-2.939808 10.524877 12.73543667]\n", + " [ 7.757898 27.688897 13.60690867]\n", + " [-2.572595 12.147093 13.60690867]\n", + " [ 6.914997 26.989286 11.21182467]\n", + " [-2.027052 13.350965 11.21182467]\n", + " [ 6.40512 25.837773 11.73139267]\n", + " [-2.327787 13.812614 11.73139267]\n", + " [ 7.66926 27.278588 10.81508067]\n", + " [-2.806851 12.150435 10.81508067]\n", + " [ 6.060911 26.707506 10.14246867]\n", + " [-1.104378 14.727146 10.14246867]\n", + " [ 6.533856 27.897752 9.69016167]\n", + " [-0.719649 14.343642 9.69016167]\n", + " [ 5.84586 25.808539 9.48837867]\n", + " [-1.58914 14.770582 9.48837867]\n", + " [-0.66731 17.52423 15.36934667]\n", + " [ 3.798544 23.643642 15.36934667]\n", + " [-1.160304 17.678964 16.69312467]\n", + " [ 3.484092 22.578381 16.69312467]]\n", + "['K', 'Fe', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'H', 'H', 'K', 'K', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'C', 'C']\n", + "[[ 4.028753 1.659813 13.33874067]\n", + " [ 2.971303 4.060282 15.89378867]\n", + " [ 2.738842 1.210693 10.72998067]\n", + " [ 6.095578 3.782353 11.43095967]\n", + " [ 7.410444 4.236591 14.05317167]\n", + " [ 6.358554 2.073355 15.50591567]\n", + " [ 3.242072 -0.466138 14.64896967]\n", + " [ 1.763534 -0.987391 12.05307667]\n", + " [ 0.968798 0.513374 14.92386267]\n", + " [ 2.508106 2.644443 14.83008767]\n", + " [ 0.983016 2.472763 17.04814767]\n", + " [-0.661525 0.325732 17.29067767]\n", + " [-1.553457 -2.138461 15.96299967]\n", + " [12.50694 7.298434 11.04805867]\n", + " [-3.488394 -2.204623 8.50772967]\n", + " [ 3.818767 2.274156 9.79349067]\n", + " [ 2.993327 2.121728 9.11210567]\n", + " [ 4.728883 2.31668 9.35970467]\n", + " [ 4.81991 3.54902 10.49144767]\n", + " [ 5.509646 4.277166 9.85100367]\n", + " [ 3.917654 3.499795 10.94960367]\n", + " [ 7.213339 5.036869 12.08037167]\n", + " [ 6.400263 5.072814 12.54925067]\n", + " [ 7.942992 5.750272 11.42335667]\n", + " [ 8.503803 5.213666 13.04249867]\n", + " [ 9.258531 5.117896 12.58629267]\n", + " [ 9.353334 6.101185 13.43339367]\n", + " [ 8.52966 4.386704 15.07300767]\n", + " [ 9.318784 5.260455 15.48729767]\n", + " [ 9.346734 4.311282 14.69868467]\n", + " [ 7.249573 3.325678 16.07607667]\n", + " [ 7.962105 3.498576 16.86274067]\n", + " [ 6.304721 3.312153 16.34317167]\n", + " [ 5.137285 1.006358 16.42408067]\n", + " [ 4.147988 0.942268 16.70579767]\n", + " [ 5.841273 1.160371 17.20879467]\n", + " [ 4.297132 -0.251447 15.74756867]\n", + " [ 5.29271 -0.177488 15.45122967]\n", + " [ 3.487129 -0.997504 16.36461767]\n", + " [ 2.173776 -1.70981 14.01047567]\n", + " [ 1.520144 -2.41578 14.67431467]\n", + " [ 3.005024 -1.705424 13.50455467]\n", + " [ 0.794695 -1.95813 13.11073467]\n", + " [-0.024595 -2.84833 12.73543667]\n", + " [ 0.017663 -1.902813 13.60690867]\n", + " [ 0.521924 -1.101194 11.21182467]\n", + " [-0.167939 -0.980446 11.73139267]\n", + " [-0.389304 -1.986886 10.81508067]\n", + " [ 1.616326 -0.075381 10.14246867]\n", + " [ 2.423067 -0.126313 9.69016167]\n", + " [ 0.760793 -0.231099 9.48837867]\n", + " [ 1.349545 1.483138 15.36934667]\n", + " [ 0.439018 1.380057 16.69312467]\n", + " [-0.758613 -1.309428 15.71052667]\n", + " [13.362409 7.324509 10.73904667]\n", + " [-2.787878 -1.333797 8.39952667]\n", + " [-2.161875 -2.292717 16.67557867]\n", + " [13.598318 8.132341 11.17770667]\n", + " [-3.50442 -2.176964 9.39382267]\n", + " [ 7.519418 8.25638 13.33874067]\n", + " [-2.628498 2.268307 13.33874067]\n", + " [ 7.825178 7.752794 10.72998067]\n", + " [-1.644347 3.221013 10.72998067]\n", + " [ 4.599582 7.146859 11.43095967]\n", + " [-1.775486 1.255288 11.43095967]\n", + " [ 4.294093 7.665066 14.05317167]\n", + " [-2.784863 0.282843 14.05317167]\n", + " [ 7.655909 9.709138 15.50591567]\n", + " [-5.09479 0.402007 15.50591567]\n", + " [10.913729 10.479198 14.64896967]\n", + " [-5.236129 2.17144 14.64896967]\n", + " [11.275383 9.910791 12.05307667]\n", + " [-4.119244 3.2611 12.05307667]\n", + " [ 8.385089 7.172447 14.92386267]\n", + " [-0.434213 4.49868 14.92386267]\n", + " [ 5.257395 5.594432 14.83008767]\n", + " [ 1.154172 3.945625 14.83008767]\n", + " [ 4.996531 4.508184 17.04814767]\n", + " [ 2.940127 5.203553 17.04814767]\n", + " [ 1.467844 5.841899 17.29067767]\n", + " [ 8.113354 6.016869 17.29067767]\n", + " [ 6.378486 7.235319 9.79349067]\n", + " [-1.277578 2.675026 9.79349067]\n", + " [ 6.340366 6.728687 9.11210567]\n", + " [-0.41402 3.334085 9.11210567]\n", + " [ 6.637958 7.965414 9.35970467]\n", + " [-2.447167 1.902407 9.35970467]\n", + " [ 4.5368 6.360837 10.49144767]\n", + " [-0.437036 2.274643 10.49144767]\n", + " [-0.118047 1.943834 9.85100367]\n", + " [ 3.528075 5.9635 9.85100367]\n", + " [ 0.710208 3.038002 10.94960367]\n", + " [ 4.291811 5.646703 10.94960367]\n", + " [ 2.835825 6.401168 12.08037167]\n", + " [-1.129491 0.746463 12.08037167]\n", + " [ 2.475961 5.647922 12.54925067]\n", + " [ 0.04345 1.463765 12.54925067]\n", + " [ 1.867247 6.05854 11.42335667]\n", + " [-0.890565 0.375689 11.42335667]\n", + " [ 3.001946 7.277233 13.04249867]\n", + " [-2.586077 -0.3064 13.04249867]\n", + " [ 3.444074 8.061671 12.58629267]\n", + " [-3.782932 -0.995067 12.58629267]\n", + " [ 1.775669 6.800576 13.43339367]\n", + " [-2.20933 -0.717261 13.43339367]\n", + " [ 4.443751 8.429278 15.07300767]\n", + " [-4.053738 -0.631482 15.07300767]\n", + " [ 3.21921 7.919113 15.48729767]\n", + " [-3.618319 -0.995067 15.48729767]\n", + " [ 4.873455 9.239912 14.69868467]\n", + " [-5.300517 -1.366695 14.69868467]\n", + " [ 5.812957 8.770078 16.07607667]\n", + " [-4.142858 0.088744 16.07607667]\n", + " [-4.816726 -0.465042 16.86274067]\n", + " [ 5.774295 9.150966 16.86274067]\n", + " [-2.875592 0.902059 16.34317167]\n", + " [ 5.490544 7.970288 16.34317167]\n", + " [ 9.056986 10.109033 16.42408067]\n", + " [-5.274598 1.069109 16.42408067]\n", + " [ 8.805886 9.339825 16.70579767]\n", + " [-4.034201 1.902407 16.70579767]\n", + " [ 9.047906 10.508319 17.20879467]\n", + " [-5.969505 0.515811 17.20879467]\n", + " [10.928052 11.099633 15.74756867]\n", + " [-6.30551 1.336315 15.74756867]\n", + " [11.164357 11.860798 15.45122967]\n", + " [-7.537394 0.501189 15.45122967]\n", + " [11.923779 11.417283 16.36461767]\n", + " [-6.491234 1.764722 16.36461767]\n", + " [12.676809 11.252914 14.01047567]\n", + " [-5.930911 2.641397 14.01047567]\n", + " [13.660338 11.651225 14.67431467]\n", + " [-6.26081 2.949055 14.67431467]\n", + " [12.973469 11.966804 13.50455467]\n", + " [-7.05882 1.92312 13.50455467]\n", + " [12.602133 10.397806 13.11073467]\n", + " [-4.477154 3.744825 13.11073467]\n", + " [-4.899854 4.128515 12.73543667]\n", + " [13.844123 10.904315 12.73543667]\n", + " [-3.319897 4.438001 13.60690867]\n", + " [12.221907 9.649312 13.60690867]\n", + " [-2.620286 4.294711 11.21182467]\n", + " [11.018035 8.990983 11.21182467]\n", + " [-1.468773 4.936347 11.73139267]\n", + " [10.556386 8.228599 11.73139267]\n", + " [-2.909588 4.759672 10.81508067]\n", + " [12.218565 9.411714 10.81508067]\n", + " [-2.338506 3.722405 10.14246867]\n", + " [ 9.641854 8.537476 10.14246867]\n", + " [-3.528752 3.005104 9.69016167]\n", + " [10.025358 9.305709 9.69016167]\n", + " [-1.439539 4.406321 9.48837867]\n", + " [ 9.598418 8.009278 9.48837867]\n", + " [ 6.84477 6.17746 15.36934667]\n", + " [ 0.725358 4.523902 15.36934667]\n", + " [ 6.690036 5.529732 16.69312467]\n", + " [ 1.790619 5.274711 16.69312467]]\n" + ] + } + ], + "source": [ + "for idx, (lab, pos) in enumerate(zip(all_labels, all_transformed_positions)):\n", + " writexyz(\"error_2/BOFFOS\", f\"BOFFOS_symmetry_operation_{idx}.xyz\", lab[0], pos)\n", + " print(lab[0])\n", + " print(pos)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(all_transformed_positions)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5., 5., 5.],\n", + " [ 5., 5., 15.]])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transformed_positions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from ase.visualize.plot import plot_atoms\n", + "plot_atoms(initial_structure)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Cell([0.0, 0.0, 0.0])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[14.586187, 10.49194 , 6.840074],\n", + " [12.185718, 7.034021, 9.395122],\n", + " [15.035307, 9.651149, 4.231314],\n", + " [12.463647, 10.436225, 4.932293],\n", + " [12.009409, 11.296853, 7.554505],\n", + " [14.172645, 12.408199, 9.007249],\n", + " [16.712138, 11.83121 , 8.150303],\n", + " [17.233391, 10.873925, 5.55441 ],\n", + " [15.732626, 8.578424, 8.425196],\n", + " [13.601557, 7.986663, 8.331421],\n", + " [13.773237, 6.633253, 10.549481],\n", + " [15.920268, 7.135743, 10.792011],\n", + " [18.384461, 8.708004, 9.464333],\n", + " [ 8.947566, 13.331506, 4.549392],\n", + " [18.450623, 6.839229, 2.009063],\n", + " [13.971844, 9.667611, 3.294824],\n", + " [14.124272, 8.994599, 2.613439],\n", + " [13.92932 , 10.535203, 2.861038],\n", + " [12.69698 , 9.39389 , 3.992781],\n", + " [11.968834, 9.35548 , 3.352337],\n", + " [12.746205, 8.540859, 4.450937],\n", + " [11.209131, 10.29947 , 5.581705],\n", + " [11.173186, 9.450449, 6.050584],\n", + " [10.495728, 10.31572 , 4.92469 ],\n", + " [11.032334, 11.413137, 6.543832],\n", + " [11.128104, 12.263635, 6.087626],\n", + " [10.144815, 11.375149, 6.934727],\n", + " [11.859296, 12.265956, 8.574341],\n", + " [10.985545, 12.181329, 8.988631],\n", + " [11.934718, 13.158452, 8.200018],\n", + " [12.920322, 12.046895, 9.57741 ],\n", + " [12.747424, 12.586529, 10.364074],\n", + " [12.933847, 11.115568, 9.844505],\n", + " [15.239642, 12.253927, 9.925414],\n", + " [15.303732, 11.32872 , 10.207131],\n", + " [15.085629, 12.803902, 10.710128],\n", + " [16.497447, 12.671579, 9.248902],\n", + " [16.423488, 13.593198, 8.952563],\n", + " [17.243504, 12.607633, 9.865951],\n", + " [17.95581 , 12.006586, 7.511809],\n", + " [18.66178 , 12.058924, 8.175648],\n", + " [17.951424, 12.833448, 7.005888],\n", + " [18.20413 , 10.875825, 6.612068],\n", + " [19.09433 , 10.946735, 6.23677 ],\n", + " [18.148813, 10.043476, 7.108242],\n", + " [17.347194, 9.746118, 4.713158],\n", + " [17.226446, 8.935507, 5.232726],\n", + " [18.232886, 9.720582, 4.316414],\n", + " [16.321381, 9.814707, 3.643802],\n", + " [16.372313, 10.67238 , 3.191495],\n", + " [16.477099, 9.114892, 2.989712],\n", + " [14.762862, 7.989407, 8.87068 ],\n", + " [14.865943, 7.181961, 10.194458],\n", + " [17.555428, 8.673815, 9.21186 ],\n", + " [ 8.921491, 14.1609 , 4.24038 ],\n", + " [17.579797, 6.668919, 1.90086 ],\n", + " [18.538717, 8.253842, 10.176912],\n", + " [ 8.113659, 13.588977, 4.67904 ],\n", + " [18.422964, 6.795544, 2.895156],\n", + " [ 7.98962 , 7.386038, 6.840074],\n", + " [13.977693, 3.226195, 6.840074],\n", + " [ 8.493206, 8.195384, 4.231314],\n", + " [13.024987, 3.25764 , 4.231314],\n", + " [ 9.099141, 5.575723, 4.932293],\n", + " [14.990712, 5.092226, 4.932293],\n", + " [ 8.580934, 4.752027, 7.554505],\n", + " [15.963157, 5.055294, 7.554505],\n", + " [ 6.536862, 6.069771, 9.007249],\n", + " [15.843993, 2.626203, 9.007249],\n", + " [ 5.766802, 8.557531, 8.150303],\n", + " [14.07456 , 0.715431, 8.150303],\n", + " [ 6.335209, 9.487592, 5.55441 ],\n", + " [12.9849 , 0.742656, 5.55441 ],\n", + " [ 9.073553, 9.335642, 8.425196],\n", + " [11.74732 , 3.190107, 8.425196],\n", + " [10.651568, 7.785963, 8.331421],\n", + " [12.300375, 5.331547, 8.331421],\n", + " [11.737816, 8.611347, 10.549481],\n", + " [11.042447, 5.859574, 10.549481],\n", + " [10.404101, 3.748945, 10.792011],\n", + " [10.229131, 10.219485, 10.792011],\n", + " [ 9.010681, 7.266167, 3.294824],\n", + " [13.570974, 4.170396, 3.294824],\n", + " [ 9.517313, 7.734679, 2.613439],\n", + " [12.911915, 4.374895, 2.613439],\n", + " [ 8.280586, 6.795544, 2.861038],\n", + " [14.343593, 3.773426, 2.861038],\n", + " [ 9.885163, 6.298963, 3.992781],\n", + " [13.971357, 5.411321, 3.992781],\n", + " [14.302166, 6.061119, 3.352337],\n", + " [10.2825 , 5.687575, 3.352337],\n", + " [13.207998, 5.795206, 4.450937],\n", + " [10.599297, 6.768108, 4.450937],\n", + " [ 9.844832, 4.557657, 5.581705],\n", + " [15.499537, 6.247046, 5.581705],\n", + " [10.598078, 4.951039, 6.050584],\n", + " [14.782235, 6.702685, 6.050584],\n", + " [10.18746 , 3.931707, 4.92469 ],\n", + " [15.870311, 6.856746, 4.92469 ],\n", + " [ 8.968767, 3.847713, 6.543832],\n", + " [16.5524 , 5.843323, 6.543832],\n", + " [ 8.184329, 3.505403, 6.087626],\n", + " [17.241067, 5.335135, 6.087626],\n", + " [ 9.445424, 3.098093, 6.934727],\n", + " [16.963261, 6.630931, 6.934727],\n", + " [ 7.816722, 4.137473, 8.574341],\n", + " [16.877482, 4.700744, 8.574341],\n", + " [ 8.326887, 3.423097, 8.988631],\n", + " [17.241067, 5.499748, 8.988631],\n", + " [ 7.006088, 3.756543, 8.200018],\n", + " [17.612695, 4.189178, 8.200018],\n", + " [ 7.475922, 5.165879, 9.57741 ],\n", + " [16.157256, 3.891398, 9.57741 ],\n", + " [16.711042, 3.771316, 10.364074],\n", + " [ 7.095034, 4.746329, 10.364074],\n", + " [15.343941, 4.345349, 9.844505],\n", + " [ 8.275712, 5.643256, 9.844505],\n", + " [ 6.136967, 7.070953, 9.925414],\n", + " [15.176891, 1.779293, 9.925414],\n", + " [ 6.906175, 7.589061, 10.207131],\n", + " [14.343593, 2.186392, 10.207131],\n", + " [ 5.737681, 6.662587, 10.710128],\n", + " [15.730189, 1.637684, 10.710128],\n", + " [ 5.146367, 7.951419, 9.248902],\n", + " [14.909685, 0.481175, 9.248902],\n", + " [ 4.385202, 7.426559, 8.952563],\n", + " [15.744811, 0.084417, 8.952563],\n", + " [ 4.828717, 8.629496, 9.865951],\n", + " [14.481278, -0.132956, 9.865951],\n", + " [ 4.993086, 9.546895, 7.511809],\n", + " [13.604603, -0.449308, 7.511809],\n", + " [ 4.594775, 10.132113, 8.175648],\n", + " [13.296945, -1.086865, 8.175648],\n", + " [ 4.279196, 9.129665, 7.005888],\n", + " [14.32288 , -0.85894 , 7.005888],\n", + " [ 5.848194, 10.327327, 6.612068],\n", + " [12.501175, -0.098979, 6.612068],\n", + " [12.117485, -0.905369, 6.23677 ],\n", + " [ 5.341685, 11.062808, 6.23677 ],\n", + " [11.807999, 0.365102, 7.108242],\n", + " [ 6.596688, 10.695595, 7.108242],\n", + " [11.951289, 1.208003, 4.713158],\n", + " [ 7.255017, 10.150052, 4.713158],\n", + " [11.309653, 1.71788 , 5.232726],\n", + " [ 8.017401, 10.450787, 5.232726],\n", + " [11.486328, 0.45374 , 4.316414],\n", + " [ 6.834286, 10.929851, 4.316414],\n", + " [12.523595, 2.062089, 3.643802],\n", + " [ 7.708524, 9.227378, 3.643802],\n", + " [13.240896, 1.589144, 3.191495],\n", + " [ 6.940291, 8.842649, 3.191495],\n", + " [11.839679, 2.27714 , 2.989712],\n", + " [ 8.236722, 9.71214 , 2.989712],\n", + " [10.06854 , 8.79031 , 8.87068 ],\n", + " [11.722098, 4.324456, 8.87068 ],\n", + " [10.716268, 9.283304, 10.194458],\n", + " [10.971289, 4.638908, 10.194458]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "atoms.get_scaled_positions()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Load your initial atomic positions; assuming these are stored in an XYZ file\n", + "atoms = read(xyz_file_path, format='xyz')\n", + "\n", + "# Create the crystal structure using the symmetry of the space group\n", + "crystal_structure = crystal(symbols=atoms.get_chemical_symbols(),\n", + " basis=atoms.get_positions(),\n", + " spacegroup=space_group,\n", + " cellpar=cell)\n", + "\n", + "# # # Save the complete structure to a new XYZ file\n", + "# cell_xyz_file_path = \"error_2/BOFFOS/BOFFOS_complete_structure.xyz\"\n", + "# write(cell_xyz_file_path, crystal_structure, format='xyz')\n", + "\n", + "# print(f\"XYZ file with complete structure saved as {xyz_file_path}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAGdCAYAAAD65LGrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd3yN5/vH38/ZWRIRRCQh9g5Cgth7b0rsvWkVRSnab4tWtdraas9SO6gtxEyMEKNmJCJBEtlnP78/VH5VK+OcoH3er1f+yDn3uJ6zrue+7+v6XIIoiiISEhISEhIS/0pk79oACQkJCQkJCeshOXoJCQkJCYl/MZKjl5CQkJCQ+BcjOXoJCQkJCYl/MZKjl5CQkJCQ+BcjOXoJCQkJCYl/MZKjl5CQkJCQ+BcjOXoJCQkJCYl/MYp3bcA/MZvNREdH4+DggCAI79ocCQkJCQmJd4IoiiQnJ+Pm5oZMlv11+Xvn6KOjo/Hw8HjXZkhISEhISLwXREZG4u7unu3+752jd3BwAJ5dWJ48ed6xNRISEhISEu+GpKQkPDw8MvxidnnvHP3z7fo8efJIjl5CQkJC4j9PTo+xpWA8CQkJCQmJfzGSo5eQkJCQkPgXIzl6CQkJCQmJfzGSo5eQkJCQkPgX894F40lISGSfhIQEIiMjMRqNFChQIEcpORISEv8OpBW9hMQHjiiKBAUF0a1bNwoWLIi3tzc+Pj54eHjg7+/P2rVr0Wq179pMCQmJd4Tk6CUkPmD0ej39+vWjXr16XLhwgW+//ZbTp08TEhLCxo0bsbW1pVevXvj6+hIVFfWuzZWQkHgHZNnRBwUF0aZNG9zc3BAEge3bt7/U5tq1a7Rt2xZHR0fs7OyoXr069+/ft4S9EhISf2E2m+nbty/r169n5cqVXL9+nY8//hg/Pz98fHz46KOPOHDgABcuXCAxMZEGDRrw+PHjd222hIRELpNlR5+amoq3tzfz589/5fO3b9+mdu3alClThqNHjxIWFsbUqVPRaDQ5NlZCQuL/Wb9+PRs2bGD9+vX06dPntaIalStX5ujRoyQlJfHxxx/nrpESEhLvHEEURTHbnQWBbdu20b59+4zHunXrhlKpZM2aNdkaMykpCUdHRxITEyVlPAmJN1CzZk0cHBzYv39/ptrPnTuXiRMnEhUVRYECBaxsnYSERE6xlD+06Bm92WwmMDCQUqVK0axZMwoUKICfn98rt/efo9PpSEpKeuFPQkLizVy8eJHTp08zfPjwTPfp27cvcrmc5cuXW9EyCQmJ9w2LOvpHjx6RkpLCrFmzaN68Ofv376dDhw507NiRY8eOvbLPzJkzcXR0zPiTKtdJSLydixcvAtCiRYtM93F2dqZmzZoZfSUkJP4bWHxFD9CuXTs++eQTKleuzMSJE2ndujWLFi16ZZ9JkyaRmJiY8RcZGWlJkyQk/pWkpaWhUChQqVRZ6mdnZ0daWpqVrJKQkHgfsaijd3FxQaFQUK5cuRceL1u27Guj7tVqdUalOqlinYRE5nBycsJoNBIfH5+lfjExMTg6OlrJKgkJifcRizp6lUpF9erVuXHjxguP//nnnxQpUsSSU0lI/Kdp2LAhCoWCtWvXZrrPtWvXCAkJydJ2v4SExIdPliVwU1JSuHXrVsb/d+/e5eLFizg7O+Pp6cn48eP56KOPqFu3Lg0aNGDfvn3s2rWLo0ePWtJuCYn/NK6urnTq1IkFCxYwcuRITCYTly9fJjQ0lFu3bpGeno5CocDFxYWqVavi4+PDggULyJ8/P506dXrX5ktISOQiWU6vO3r0KA0aNHjp8T59+rBy5UoAli9fzsyZM4mKiqJ06dLMmDGDdu3aZWp8Kb1OQiJznD59Gn9/f0qUKEHE3bvoDAbkgoCXSoUNYAKiTSaeGo3As7v6lm3bsm7dOuzt7d+l6RISEpnAUv4wR3n01kBy9BL/NXQ6HTt37uT69evo9Xry5ctHu3bt8PLyem2fK1euMKh/f06fO4enIDBMqaSeQoG3TIbt34RzRFHkrihyzmRindFIoMmEnY0NX379NaNGjUIul+fGJUr8hzGZTOzZs4fDhw+TnJyMnZ0dfn5+dOrUCbVa/a7Ne6+xmD8U3zMSExNFQExMTHzXpkhIWJXExERx8uTJYoECBURALFCggOjh4SGq1WpREASxVatW4vHjx1/oYzQaxa+//lpUKhRiOZVKDLSxEU329qLo4JCpvwg7O3G4UikCYu2aNcWbN2++o6uX+LdjMpnE77//XvT09BQBsVixYqKvr69YpkwZERDz588vTpkyRdRqte/a1PcWS/lDqaiNhMQ7ICYmhjp16vDTTz/x0UcfcfXqVWJjY7l//z5xcXEsXbqUqKgo6tevz6pVq4BnBWy6d+vG1ClT+FQmI1SloqVCgew10revwlMmY75GwzEbGx6GhuJXrRrnzp2z1mVK/EcxGo0EBAQwbtw4GjZsyNmzZ7l9+zZnzpzh2rVrXL16le7du/Pdd9/RvHlzUlNT37XJ/2qkrXsJiVwmNTWVOnXqEBsby/79+ylfvvwr2xmNRoYNG8by5cvZunUra1atYteOHfymUtFOqcyxHQmiSCu9nqsqFcdOnMDb2zvHY0pIAIwaNYqFCxeyadOmNwZ/njhxghYtWtCgQQN27Njx2noN/1WkM3oJiQ+UBQsWMHr0aEJDQ9/qXE0mE61ateL8+fM8efyYbRqNRZz8c5JEkYZ6PU/y5+fytWs4ODhYbGyJ/yb37t2jWLFizJkzh7Fjx761/e+//07nzp05ceIE/v7+uWDhh8N7qXUvISHxZkRRZMGCBbRr1y5TK2i5XM6UKVN4/Pgx3RUKizp5gDyCwGalkicxMYwfN86iY0v8N1m8eDF58uRhyJAhmWrfoUMHSpYsyYIFC6xs2X8XydFLSOQioaGhhIeHM3To0Ez38ff3p2yZMpisZJOXTMa3CgWLlyzhxIkTVppF4r/C2rVr6dWrF3Z2dplqL5PJGDx4MJs3b0ar1VrZuv8mkqOXkMhFnktBV61aNdN9BEHAp1o1omXW+7oOVSqpoFLxw/ffW20OiX8/oigSExPzkgz62yhXrhwGgyHLks4SmUNy9BISucjzkJisBh0JgoA1g2lkgsAwQWDHzp1ERUVZcSaJ/wJZDf16XhBNCsazDpKjl5DIRdzc3AAIDw/PUr+rYWEU+uvH0Fr0VCpRCQIbN2606jwS/14EQcDT05PQ0NAs9btw4QK2trbky5fPSpb9t5EcvYRELuLn50fx4sVZvHhxpvuEhIQQeukSvSwciPdP8ggC1eTyDz6vPiYmhq+//prSpUtjb2+Pg4MDZcuWZdasWTx+/Phdm/evp1+/fmzYsIGnT5++8LgoisTGxnLr1i1u3LhBREQEer0eo9HIkiVL6NGjR5bLLktkDsnRS0jkIjKZjGHDhrF58+YXikO9DlEUmTVzJp5KJS1zQa7WRxQJPX3a6vNYA7PZzJQpU/D09OTrr7+mVq1afPnll8yYMQNfX19mzJiBu7s7X375ZZa3liUyz8CBAzEYDMyZM4cLFy4wZcoUmjdvTv78+XF1daVkyZKUKVOGokWL4uDgQPHixYmKisLLywudTveuzf9XIuXRS0jkMomJifj6+mI2mzl48OBrSziLosikSZOYPXs26zQaAqy8ogdYptczSKfDYDCgUGS5uOU7QxRFhg0bxuLFi5k2bRpjxowhb968L7SJi4tj7ty5fPPNN4wePZoff/xROhO2Anq9nm7durF9+3ZEUcTBwQFPT0/c3d0pXLgwNjY2yGQy9Ho9T548ITIykoiICGJjY3FxcWHgwIEMGzYMT0/Pd30p7xxJMEdC4gPmzp07NG7cmJSUFEaNGsXAgQMpVKgQ8EwkZ/fu3fzwww8cO3aM79VqxubSluYGg4EArZaUlJRMp0e9DyxbtoxBgwaxfPly+vXr98a2CxcuZPjw4axZs4aePXvmkoX/Dc6fP0/v3r0JDw+nVKlS1K5dm/Lly2eqeFJsbCzBwcGEhIRgMpmYOXMmo0eP/k8XXpIcvYTEB05sbCxTp05l3bp16PV6SpcujVqtJioqikePHuHq6ordkyfcsrHJNZvWGgz00mrRarUfTGUxURQpX7485cqVY8uWLZnq07p1ax48eMD58+elVb0FMBqNfPnll3zzzTe4ubnx0Ucf4e7unq2xdDodgYGBHD9+HD8/P9asWUOJEiUsbPGHgeToJST+JSQmJrJu3Tpu3LiBTqfDxcWFDh06sHXrVlZ89x3RGk2u2fKdXs8MmYzk1NQPxgEePXqUBg0acPjwYRo0aJCpPnv37qVly5acOnWKGjVqWNnCfzc6nY7u3buzY8cOmjZtSpMmTSyyCr99+zYbN25EFEX2799PlSpVLGDth4Wl/OGHcwgnIfEvxdHRkeHDh7/0+L179/jGYOChSkUhK4rl/J1Qk4kqVat+ME4e4ODBg7i6ulK/fv1M92nWrBl58+bl4MGDkqPPAUajka5du7J371769+9PhQoVLDZ28eLF+fjjj1myZAkNGzYkKCiIihUrWmz8/xJS1L2ExHuKj48PACFWzp//O+dkMnx8fXNtPkuQlJRE/vz5s3RzIpPJcHFxITEx0YqWvZ/odDpiYmJ48uQJRqMxR2ONGzeOwMBA+vXrZ1En/xw7OzuGDBmCg4MDzZo1y5Fy3sOHD/nqq69o2bIl9erVo3Xr1syZM4e4uDgLWvx+Ijl6CYn3lCJFilC8SBE25fDHOLOcM5m4o9fTuHHjXJnPUtjZ2ZGcnJylPqIokpSUhL29vZWssgyiKJKQkEBMTAxxcXHZdsyiKHLs2DE++ugj7O3tKVSoEPnz58fFxYWPP/6YGzduZHnMY8eOMW/ePNq0aZNlydusYGtry4ABA0hKSmLUqFFZ7h8fH09AQACenp7MmjULhUKBh4cHZrOZzz//nMKFCzN48GBSU1OtYP37geToJSTeUwRBYNioUWw2mXiUC6v6hQYDnm5utGjRwupzWZJq1apx7949wsLCAHjy5Am3bt3i+vXr3L17l/T09Jf6nDt3jtjYWKpVq5bb5r6VCxcuMHHiRBo3bEg+JyecnZ0pVKgQLi4u2Nva4ufjw/Dhw9m9ezcm09tLHSUmJtK8eXPq16/PpUuXmDVrFjt27GDr1q0MHTqUdevWUaZMGcaPH58hRfs2UlNT6du3LyVKlKBu3bo5veS34uTkRLt27Vi/fj07duzIdL9Hjx7h7+/P/v37mTt3LtHR0ezcuZO1a9eyZ88eoqKimD59OuvXr6dhw4ZZvmH8UJCC8SQk3mPi4uJwd3PjY2CmFaPg75nNlE1PZ+pXXzF58mSrzWMNrly5Qq1atXB2dkar1RIbG/vC83K5nDJlylC9enVq167NRx99xMiRIzl27Bi3bt16L9K3RFHkt99+48fvv+f0uXMUVCqpCVQVBErLZNgIAgZRJFIUCTWZOCuXc0Ono0jhwgwdOZLRo0dja2v70rhpaWk0aNCAGzdusHr1atq0afPSEYdOp+Onn37is88+w9XVlW7dulGtWjX8/f1fq/Ewc+ZMpk2bxoQJE8ifP781XpKXEEWRZcuWkZKSwu3bt9/6vpnNZmrVqkVERATHjh2jVKlSr20bGhpKw4YNqVevHjt37rS06dlGirqXkPiPMGPGDL6aMYMzNjb4WMEpmUWRJno9t1xcuHzt2gfxvTObzezYsYP58+dz6NAhNBoNnp6eGcIsdnZ2yGQyjEYjcXFxREZG8uDBA6KiolCr1Wi1WsaOHcucOXPe9aUQGRnJoP79+ePgQRqrVIyQyWitUKB4S8zBOZOJBQYDG0wmPDw9WbFmDbVr136hzZgxY1i2bBlBQUEZMR+v47kWQZ48eUhKSgKeBS2OGDGCli1bZjhWk8lE0aJFcXNzIyAgIAdXnnXu3bvHjz/+SGBgIC1btnxj23379tGiRYtMZ2Ns3LiR7t27c+HCBSpXrmwhi3OG5OglJP4jGAwGfH18MF6/zmm1GjsLR8T/pNczRqdj//79NGnSxKJjW4ObN2/Sr18/goOD8fLywt/fn8qVK2dKyS8hIYGTJ09y8uRJtFotn332GV988cUbNQNu3LhBVFQUoiji7u5OmTJlLHYtu3fvpke3bjjo9SxRKGiZDTXCG2Yz/Q0GThkMTJkyhRkzZiAIAsnJyRQuXJgxY8bw1VdfZWqsevXq8eDBA/r378+VK1c4deoU9+7do1ixYvz666/Ur1+fXbt20bZtW8aOHZur6nUGg4GLFy+ydetW7OzsqFatGmXLlmXw4MGvfE/atWtHREQEFy5cyFSgptFopGjRorRq1SpLtSisieToJST+Q1y+fJlafn7UMBrZqVZjYyFn/5vBQHedjlGjRvHjvHkWGdNaiKLITz/9xMSJE3FwcKBr166ULFkyW2MZjUYOHTrEgQMHKFWqFGvXrn0hT1un07Fp0yYWLFjAmTNnXuhbrVo1hg8fTvfu3dHkQONgy5YtdPvoI9rI5axQq3HKwXtqEkVm6fVM0esZ/dd7OXnyZL799lsiIiIyLV6zefNmunbtyoQJEzIqLd6/f5+dO3dy69YtRo4cyYMHDwgNDWXs2LHZtjcriKLIoUOHOHbsGMnJydSoUYOiRYui1WoJDg7m8ePHNGzYkAULFlC6dGkAtFotdnZ2/Pjjj1kK4JsyZQoLFy58byLxLeUPpWA8CYkPgIoVK7Jrzx5OyuU01+t5mMPgPFEU+UWvp7tOR0BAAHN/+MFClloHs9nMsGHD+Pjjj6levTrjxo3LtpMHUCgUNGvWjLFjx5KcnEzt2rU5ePAgAI8fP6Z+/fr06dMHR0dHtm7dyu3bt7l9+zbbt2/HxcWFAQMG4O3tzY4dO7KV8nXs2DECunenq1zO5hw6eQC5IPC5Ws1CtZqffv6Z7t27M3v2bKpWrZolhbp27doBEBERkfGYp6cnw4cPp0OHDixZsoQ9e/ZQuHDhHNmbWURRZNOmTezevZvevXtz/fp1Tp06xYYNG9i2bRuRkZGsW7eOBw8eUKtWLS5cuAA827kxm814eXllab6iRYsSHx+fqSDHDwlpRS8h8QFx8uRJOrRtiyExkZ8VCgIUiiyL20SYzQw0GDj41+pv7g8/vBcBaa9DFEWGDh3KsmXL6Nq1q8UFbvR6PStWrMhw5FOmTCEqKoqdO3fi5+f3yj7nz5+nefPmGU7B09OT6tWr06FDBzp37vzGo4Dk5GQqli1L0UePOKBWo7TwUUz7tDR2mEw4OTlRvXp19u/fn6X+tra2NGvW7JUCRDExMcyfPx9BEPj0009xdHS0kNWvZv/+/ezZs4fVq1fTq1ev17ZLSEigadOmREVFcenSJeRyOS4uLvz+++907NgReJZHf/78eR49eoROp0OlUpEvXz6qVKmCh4cHgiCwcOFCRo8ejV6vfy9Eo6StewmJ/yhxcXGMGjmSDRs34vdX8FYXhQLNW36YLv0VvLXWbMY5f36WrVxJs2bNcsnq7DNnzhzGjx9Pt27drKZiZzQaWbp0KXfu3EGlUhEcHIy3t/cb+4SHh1OjRg08PT0pUKAAERER3L17FxcXFwYNGsSYMWMoWLDgS/2GDR3KmmXLuKzR4GVhxcM/jEZapafj4+uLKIqYTCZCQ0Mz3T89PR1bW9s3vtZxcXHMmzcPOzs7PvnkE6vVkNfpdEyfPp2hQ4cyd+7ct7aPiYmhWLFifP7550ycOJGCBQvi7++PXC7n1KlTxMTEZLSVyWQvpBLmy5cPPz8/nj59Snx8PNeuXbPKNWUVydFLSPzH2bdvHz/MmcP+Q4dwUijwEwR8BIFyMhl2gAGI/isdK0Qm45pej1uBAgwZMYIxY8ZYfTVmCa5du0blypXx9/fP2Fa2FikpKcyYMYNhw4YxL5PxCuPHj2fhwoVMmzYNlUr1QgU2tVrN/Pnz6datW8bq8Pbt25QoUYJ5ajWjLewgE0SRsunpOBcvzqChQzl37hwbNmzg1q1bFC9ePFNjrF69mj59+vD555+/MW3u4cOHzJ07l1q1atGhQwdLXcILnDp1it9++427d+++Ns3vnwwcOJD9+/czYsQIZs6cSWJiIh4eHpQuXRoPDw/c3d1xcnJCLpdjNptJSkoiKiqKyMhIbt26xe3bt3FwcOCTTz5h3LhxODg4WOXaMovk6CUkJAD4888/2bBhA+fOniX07FlinjzJeE6tVFKpQgV8/Pxo0qQJbdq0QZkLde0tgdFopFatWkRGRjJ27FirrRyf83yb+Nq1a5mOrH/uuAMCAvD9m3RwSkoKW7du5fz587Rr144VK1aQN29exo8fz/IffyRKo7FYQOVzemm1bJXJmDBpEk5OTuj1er788kuGDBmSqTRCURTx9fUlJSWFoUOHvrX9kSNH2LlzJyNHjsz0jURWWLRoEe7u7hw4cCDTfU6dOkWtWrWQy+VUrVqV2rVr4+npmelt+IcPHxIcHMy5c+coUKAAK1asoFGjRtm9hBwjFbWRkJAAoFSpUkybNi3j/5SUFNLT01Eqldjb22cq7ex9wmAwEBcXx6ZNmzh37hxjxoyxupP/888/2bdvHwULFsxS+lzx4sUpWrToC9vCAPb29vTu3Rtvb29+++036tatS2BgIMuXLqW/IFjcyR81GllrMNC9e3ecnJwAUKlU1KxZk59++omWLVvSsGHDN47x7bffEhISwuDBgzM1Z7169QgLC2PTpk1MnDgRmYWPIdLS0rJ8A/E8+K5du3bZUuwrVKgQnTt3pkGDBmzcuJHGjRszfPhwfvzxxw/mBvlVSFH3EhL/Muzt7cmfPz9OTk4flJO/fPkyw4cPz5B8/eSTTyhZsmSWI6ezyp07d1i6dCmOjo7Y2dllub+Njc1rNei9vb0ZOXIkUVFRNGjQgPjERD6ygsOYZzDgVqDAC7sKAM2bN6d48eK0bNmSZcuWodPpXuobHx/P+PHjmThxIk2bNs20br1MJqNt27Y8evQoW1r5b0Mul6PX67PU53n7nKr15cuXj2HDhtGpUycWL15Mp06dXvnafShIjl5CQuKdYjAY6NatG5UqVWLTpk306dOHH374AVEUqVOnjlXnTkpKYtmyZXh6euLn58ejR4+y5FyMRiMxMTGvlJ99TqFChRg6dCixsbHIBIEKFl7NR5nN7DQaqVW37ktb1HK5nAEDBlCxYkUGDRqEu7s7EyZMYNmyZSxatIh+/frh5ubGjz/+SNu2bbNc5+C5Ql5wcLAlLwl45myDgoIyrb8PcPz4cSDnjh6e3cjUqVOHAQMGsG/fPgICAnJc7e9dITl6CQmJd0JKSgrz58/H1dWVTZs2Ac9Wl/Pnz+f777/H1tY2QwDFGjzXl5fJZPTr1w9vb29SUlLYvn17psfYvXs3CQkJlC9f/o3tXF1dCQgIwCyKrLdwjvYKgwGVUvnaAj1KpZKePXsyceJEypYty6JFixg0aBAjRowgMDCQxo0bM23aNBo2bJjllDJBEKhduzbh4eEWL/lbq1Ytbt++zeHDhzPdZ/78+ZQuXRoXFxeL2VGuXDn69OnD9u3bmTlzpsXGzU0kRy8hIZHrRERE4Ovry5gxY6hbty779u3LSE/bsmULJUuWJC0tjWXLlpGWlmYVG0JDQ7ly5QpdunTJKN1aokQJfvrpJzITo/xcqc/LywsPD4+3ti9fvjzVq1fnY72eSAtWIzxhNlO8RIm3qvS5urrSqVMnvvrqK+bOncv333/P559/TpMmTXIUXV6uXDlEUXxBZMcSeHl54ebmxpQpU9BqtW9tv2vXroxgPEtToUIFGjVqxFdffZVRJfFDQnL0EhISucpzyVKtVsvly5fZtm0bzZo1w9PTk6JFi9KpUycOHz5MUFAQT5484ddff8VgMFjUBrPZTGBgIN7e3i/kyzds2JDg4GAmTZr0RmcviiLTpk3jyJEjbw1y+zsdOnRA0GiYlcWz5zfZESKKuGdRc14mk1lMEMbR0REHBwciIyMtMt5zBEGgS5cuXLhwgY4dO5KSkvLatoGBgXz00UdUqlSJihUrWtSO59SrVw9bW1saNWrEyJEjmTFjBpcvX7bKXJYmy44+KCiINm3a4ObmhiAIb9zmGjp0KIIg8OOPP+bARAkJiX8TEydOJCkpiUOHDlG2bNnXtqtTpw579+7l/v37HDt2zKI2XL16lYSEhJdSp8qVK0e7du2YPXs2PXr04MqVKy/1vXbtGn369OGrr76idevWWXIstra21Khdm1UmE8kWyGyOFEXiTaZM7ShYC0EQ8PDwsLijh2er+v79+3PkyBG8vLyYPHkyN2/eRKvV8vTpU7Zt20bjxo1p3bo1JUqUoGfPnhaP/k9JSWHLli3873//Izk5GTs7O44cOcK8efOoVKkSdevW5Y8//rDonJYmy69Iamoq3t7ezJ8//43ttm3bxunTpzMKI0hISEgkJCSwfv16Pvnkk0xF0/v5+dGjRw9OnTqVpaCstxEcHJxR1vafNGjQgICAAPbs2UPFihXx9/dn5MiRjBo1inr16lGuXDl27NhBt27daNy4cZbnrlmzJumiyDoL7FJE/3Wz4OzsnOOxcoKzs3NGaVtLU6ZMGcaNG0fZsmX57rvvKFWqFDY2NuTNm5eOHTty9+5devbsSb9+/SyehhkXF8dPP/1EeHg448eP5969e9y7d4/w8HBiY2P57bffMJvNtGjR4q0+8V2S5dybFi1avDUy88GDB4waNYo//viDVq1aZds4CQmJfxdr1qzBZDIxYMCATPcZPnw4K1eu5Pr165lO/XoTWq2W69ev06VLl9e28fX1pWrVqly+fJlz586xY8cOABwcHOjVqxfe3t7ZTl3MmzcvZUqXZtOtW7xdlubNaP9y9O86x1upVFr8eOXvuLi4YGNjg2g04iaTEWM2U9Hbm2bNmlltMZmens7SpUuxtbXl8OHDL92YKpVKunTpQqdOnfj0008ZOXIkBQsWpHPnzlaxJydYPMnWbDbTq1cvxo8f/9ZIVHimZ/z3/ERr3RVKSEi8e8LDw6lYseIrNeBfR/Xq1bG3tycmJsYijj4yMhJRFN+6o6BQKKhSpcoL5WsthVfx4gT9+SdmUUSWg7Py5+79XVdbM5lMVi2MFBERwcEDB/hCpWKQUknZ1FREs9mqO8anT58mLi6OK1euvPGzIpPJmDt3Lnfu3GHChAl07NjR4scHOcXi1syePRuFQsHo0aMz1X7mzJk4Ojpm/L3LsyYJCQnrotVqs1XDXa1WWyyHOSoqCpVKlaWbDUvj4eFBstnM7Rye0+f96yYhNTXVEmZlm5SUFGxsbKwydmxsLMsWLaKaXM5klQo3mYz5Gg1hly9bLQLebDZz6tQpOnfuTKlSpd7aXhAEJk2axN27d9/L83qLOvrQ0FDmzZvHypUrMx3ROWnSJBITEzP+rBHQISEh8X6gVCqJiIjIVPrac1JSUkhMTHyjKE1WiI6OplChQu901fW8RnxYDlfipWQy1ILwzn83IyIiMBqNWXpfM0NUVBTz583Dw2Bgj0aTUdK3h0JBY4WCA3v3WnxOeHY9jx49ypTm/3P8/Pzw9vZm9erVFrcnp1j0k378+HEePXqEp6cnCoUChUJBREQEn376KUWLFn1lH7VaTZ48eV74k5CQ+HdhNptZsGABa9eu5cGDBxw5ciTTfdetW4fZbM7UUWBm0Ol0FrtpyC7PV7/JORxHIQhUksuJiorKuVHZRKvVEh8fT2RkJEuWLOHp06c5HtNsNnPkyBF++uEHSuj1HFOryfe3xaMgCIxVKol8+NDi+ftAhvhPVjIqBEGgQoUKREdHW9yenGJRR9+rVy/CwsK4ePFixp+bmxvjx49/L7czJCQkrE9UVBSNGjVixIgRVK1aFVdX10yn3BqNRn7++WfKly9P3rx5LWKPyWR652eoz+c3WmA16icI3P3zT4tmJWSFO3fuIIoiv/zyC/fu3WPmzJkEBwdnOzgvMjKSX+bNY+eOHQyXyTiu0eDyivermVxOUbmckydP5vQSLIql9AksSZaD8VJSUrh161bG/3fv3uXixYs4Ozvj6elJvnz5XmivVCpxdXW1qpSlhITE+8nNmzdp2LAhqampDB8+nFKlShESEsLatWv57rvvGD9+/Gv7ms1mRowYwbVr1xg5cqTFbFIqlVZT28ssz+MN7lrAOXdXKvklKYkbN268UZfAWpw8eRK5XE65cuVQK5UUSUlh8+bN7Nu9G99atahRowYuLi5vdIA6nY6wsDBOBgVxNzKS4nI5x2xsqPOGzAaZINBGJuO3v/kjS/G8AuClS5eoX79+pvqIosilS5eoVKmSxe3JKVl29CEhITRo0CDj/7FjxwLQp08fVq5caTHDJCQkPmzu3btHvXr1EEWRMWPGZPx4VqtWjdjYWCZMmMCNGzeYMGHCSwFPISEhTJ8+ncDAQLp160axYsUsZpeTkxP379+32HjZ4fHjxwDMNZhorDDSIAdVBmvKZFRUKAg+fjzXHX1CQgLh4eEUsC1K8+Yt0Ot1rLGxoaRMxiK9nuVHjnDo0CEcbGwo7OGBm7s7tra2yGQy9Ho9jx8/JjoigpgnTxCBJgoFP2g0tFIoUGRiZewjl/NzfDzp6ekWDQYsUqQIrq6uLFiwINOOPjg4mCtXrvD9999bzA5LkeVPV/369bMU/HDv3r2sTiEhIfGBo9VqadmyJSaTiREjRuDo6PjC861atSJPnjxs2rSJX3/9lfr161OuXDlMJhPnzp3j/PnzGX0sXWrXw8ODo0ePkpqamq2ytJYgMjISAQEPJx+aPQ1hmkrJZypVppzbPxEEgdrAwqtXiYqKygj0yw0OHTqESm7DiEqrWHJlCFH6a1SRySgsk/GDRsPXosghk4lQk4mQO3cIu3OHFFHEJIqoBYGigkBHQcBHo6GuXE7xLB6pVPmr/cOHDy16MygIAjVr1mTbtm2Eh4e/NT7EbDbzzTffULJkyWyJKFmb9yvZT0JC4l/BjBkzuHnzJv369XvJyT+nTp06TJs2jZ49exIbG8uuXbvYu3cvRqORgQMHMm3aNKpWrcrWrVstqq/xPIX3XQawRUVFkd/Ok8GVllDPcwBf6A34pus4lMXI9esmEwHpWhYajdgo7Vm/bkOulVK9ffs2J06coEWRUTionantFgCA899uVmwFgTYKBdPVanbb2HDfxoZ4W1sS7ex4ZGvLWRsbftFo6KdUZtnJw/+nF1qjVryfnx8FCxakefPm/Pnnn69t9/xmdt++fcyZM+edx3+8ivfPIgkJiQ+as2fP8u2339KsWTMKFSr0xrbKv8qrDhkyhE8//ZSxY8fSv39/KlSogEwmyxAf2bRpk8WCzVxcXMiTJ887K0hiNpu5EhZOsTzVUciUtCr2MWOqruexpgiN09MpnaZjjl7PaZOJ9H84fZMoEm4ysdJgoEGalrJpaezGloAy3zCs4q/ExDzkwIEDVr8GnU7H+nUb8HKqQh33HgDIBYvrr72V57cU1kix02g0DBo0CJPJRLVq1Rg/fjy3b9/OeF6r1bJ69Wr8/PxYunQpS5cupW3btha3wxJIjl5CQsKifPbZZxQuXDhLVd1eh729Pd27d+fq1ats3brVIj/oMpmMmjVrcu7cuUyVP7U0V69eJSExnlpu/y/B65mnImOrbWNk5ZXY5WvAJL2RmmlpOKSk4pWqo2yajhJpOhxS06iQlkY/rZb7dmXpWXY2X9Q6QnXXtnjkKU8TzyHs37+fixcvWs1+o9HIypWrSE5MoXup/yETnrkRteLZMchTKzjd1/F8LrVabZXxnZycGDVqFNWqVWPRokWUKFECNzc3PD09cXFxoU+fPri4uHDo0KEsyTrnNrl/CyYhIfHBI4oiISEhhIeHo9VqcXZ2pnHjxsTGxnL06FF69eplMUnU8uXL07VrVzZt2oTJZKJLly453h6tWbMmBw4cICQkhNq1a1vEzsxy4vgJPPOUx8PhxXNfQRAo7lSN4k7VMJr1PEy9SWTyVeLSozCadchlSsornfFwKE9hh7LYKF6uId+06DCepN9n9eo1iKJocfleg8HAqpWr+fPGTQZVmE9+2yIZzxWyKwHAJbOZQrm0fX3xr12et+0c5QRbW1vatWtHixYtCAsL49ChQyQlJfHFF1/QoUMHSpYsabW5LYXk6CUkJDKN0Whk1apVLFiwgPPnzwPPHJQoimg0Gry8vLCzs3uhxrslqFmzJjKZjI0bNxIdHU1AQECOJGydnJzw9vZm7969VK5cGXt7ewta+3rCw8O5fuM6vcp++8Z2CpkKD4eXbwbehkyQ0b3M/+CGwOpVq4mOjqZZs2ZvDGjUarXcunWL1NRUlEolhQoVeqXjjI2NZf26DUQ/iKZ/+XmUdq71wvP5NB7YyW0JNRlpbuEAytcRajKR38kpVwSQVCoVpUuXZuPGjcyaNYtPP/3U6nNaCsnRS0hIZIq0tDQ++ugjAgMDadGiBYGBgTRq1AiVSsXDhw9ZtWoVv/zyC1qtlvDwcIs7ez8/P/Lnz8+ihQv59ttvadGiBbVr186Wdn5sbCypycmkpaXx+5Yt9Onb16K2voq0tDR+27iZMs7+VCnw5gqgOUEuUxJQ5hsK2Hix/9BCroSF071Ht5dK8sbGxhIUFERoaOhLRxjFihWjVq1aVK1aFVEUCQoKYk/gHpxUhRheaQVFHV9+bwVBoEieKuxOOsvnVru6/0cURXabTHhYMNr+bWzbtg0HBwf69euXa3NaAkG0RhRDDkhKSsLR0ZHExERJDldC4j3BZDLRuXNn9u/fz++//07z5s1f2U6n09G7d29+//13Bg8ebHGhrD///JMFCxbQRaHgd6MRtUqFj68vNWvWfKt+vV6v59q1awQHBfHn7dsUksvpLpMx12CgV69e+Pj4WNTWv2M2m1mzZg3XLv/JZ9V24KRxtdpcfyc65Qbrb0zmQfINSpcqQ+06/pQrV45r166xevVqnJycGDp0KH369MHDw4P09HQOHjzIL7/8wpEjRyhUqBDpqVoSk55S170XLb1GoZK/Pl/90uMDrAz/hIu2tnhbsZodwGGjkUbp6dSpU4dOnTpZdS54Jp6zYsUK1q9fT/fu3a0+H1jOH0oregkJibeya9cutm/fzs6dO1/r5OFZUNS6deuIi4tjy5YtTJo0yaLpRsHHj1NWoWCTRkOUKLLUYGDRqVOcOHECG5UKd3d3Cnt6Ymdnh1wux2AwEBcXx4OICB4+eoRZFKmpULBOo6GTQoEKiAU2rFuHjY2NRcrg/hNRFNmxYwcXLlygd7nvcs3JA7jZl+aTKhsJfbSHkw838uuvv2KjsUOnT6dNmzZs2LDhBaEZpVJJx44d6dixIzt27KBr1644yPPzSdVFeOR5+zFChXz1cVI686M+iRVWqmb3nB/0epTAqVOnqFGjBoULF7baXHFxcWzatAlBEAgPD0en01ktANAaSCt6CQmJt9K0aVOSk5M5depUptqfOnWKWrVqMWTIEIuptaWmpjJ1yhTmqVSMVKkyHteLIsdMJkJMJkLMZi4AiaKI8S9RlsKCgK8g4COT4S+XU/4fK02DKNJRq2Wf2UyPXr0sGsBmMpn4/fffOXnyJJ1KTqF24W4WGzs73E+6zNLwYVT1q8TBgwdRKpVvbL9lyxa6dOnCwIrzKZ+vXqbmCIpay7ZbszhqY0M9K53VbzcY6KDV0k4u55gootdoGD16NK6ulr+Jevr0Kb/88guiKFKpUiWCgoIoXbo0v/32m8UKLb0OS/lDydFLSEi8kYiICIoWLcqqVavo3bt3pvqIokjlypURBMFi55nXr19n0aJF/GlnR0kLR3XrRZE+Oh0bDQZ8q1enfYcOrwzwSkhI4OzZszx58gSz2Yy9vT1VqlShSJEiL2m5R0dHs27tBh4+fEDXUtPxK9TRojZnhxvxJ1kUNpjjx49nOtvAp2o1tJF2DKqwIFPtzaKJ+Rd6YU65xhVbNXYWLvISJ4qUT03FVyZjh40NcaJIPZ2O+0olAwYPxsvLy2JzxcTEsGTJEsxmM6NGjSJfvnw8ePCAdevWkZaWxh9//IGvr6/F5vsn0ta9xL+SyMhIAgMDiY+PR6PRUL58eRo3bmyxVC2JrHPnzh0AatWq9ZaW/48gCPj7+7N7926L2REZGYmDTEZxK1QHUwkC69VqmsjlfBwayp/XrtGqXTuqVKmCQqHg8ePH7Nq1iytXrmBjY0OFChVQqVTcvHmTY8eO4eHhQbNmzahQoQJJSUkcP36cw4cOU8DWi4+rbshy9Ly1OB3zO+XLVcDf3z/TfUaOGsGAAQNI0D4kr+btaWwyQU63MjP5PqQjnbU6dmjUqCz0nqWKIm3T0jCIIos1GgRBwEUQOK7R0Eqr5eeffqJBw4Y0b978rbsVb8JkMnHkyBH27duHi4sLQ4YMyaieWLhwYUaOHMmyZcto2rQpx44ds3jgqaWRHL3Ee8HJkyf57rvv2LlzJ4IgkDdvXtLT00lNTcXLy4uhQ4cyevTobEVYS+SM55KqWdWcVyqVFi2dGhMTQwWZDJmVyoAKgkB/pZKmcjnDdTrWrVvH9q1bKVehAuHh4eTPn59ffvmFnj174uDwLIfdbDbzxx9/MGfOHJYtW4a7uzsPox8iExQ09BhI0yJDUMhUb5k594jT36d1vfpZKqVau3ZtRFEkThuVKUcPkN+2CP0q/MKyy8Nom65ji40a+xy+bwmiSJv0dMLMZg7a2r6Qq+/8l7P/Tq9n2uHDhIWF0aJFCypVqpSlz63ZbObatWv88ccfREZGUr9+fVq0aIFK9eJ7aGtry+DBg1mwYAEtW7bk6tWrr5V6fh+QlPEk3jkrVqygbt263Lp1iwULFpCQkMDjx49JTk7m9OnT1KlTh6lTp9KkSROePn36rs39z5E/f36AF+Q/M8PNmzctWjRGr9eTGz+l7jIZOzUartvZUVGn48KFC5QtW5bz588zbNiwDCcPz1T2WrRowYEDBxg7dixRUVH4FGjDjBpHaOk16r1y8gAms+Elp/U2nq+MTeas1Zcv7VyTgRUXclSUUTFNx7EcaPDvMRqpkJrKVZOJg7a2+L1ih08hCExSqxkslxMXF8fq1auZMWMGgYGB3L59+7V6+Hq9nnv37nHgwAG++uorli5diiiKjB49mnbt2r329dJoNPTt25eEhISMKq7vK9KKXuKdsn37dgYMGMCgQYOYP3/+C3ffgiDg5+eHn58fQ4cOpWXLlnTs2JE//vgjR9tyb+P+/fts3bqVx48fo1AoKFGiBJ06dcoVUY73kUqVKlGqVCmWLVtGo0aNMtUnMjKSP/74w6JpT4IgkJsBRaVlMmwFAVtbW/bu3Yuzs/Nr28pkMubMmcPVq9e4FHwZzStU694HbBV5uX37Tpb63L17FwA7Zd4sz1fauSafVtvKpuuTqZ90iX4KBWNUqkyl3omiyFmzmR/0ejYZjTSTy1mq0eDxhviMMJOJJSYTjRo3pmrVqgQHBxMUFMSBAwcQBIH8+fOTN29e5HI5JpOJ5ORkYmJiMJvNKJVKqlSpQu3atV/SHHgdzs7OtGnThuXLl9OlSxe8vb1ZunQpW7Zs4cmTJ6hUKkqWLMngwYNp3769VX+33oQUjCfxzjCZTHh5eeHt7c2OHTvemoZ19OhRGjRowIYNG+jWzfLRy2fPnuXrr79m9+7dqFQqChYsiNFoJDo6GkdHR/r27cvkyZMzVrj/JX788UcmTJjAn3/+SdGiRd/afty4cSxYsIBp06ZZ7Lhl/fr1GC5c4HwuHd+kiyL5tVqGjhnDnDlzMtXnyJEjNGzYkBHeyymR13pBWtnleNR6dtyZzf3I+7i5uWWqT0BADw7tCuYzn10ZuvZZxSyaOfFgA0ciFvPUEI+fQkErmQwfuZzKMhlOf93EJYgiF8xmQk0mdhmNnDeb8RIEpqjV9FMo3nrk0CE9nVOOjoyfNClj0WAymYiNjSUyMpLIyEhSUlIwGo3I5XLs7OwoXLgwHh4euLm5ZasksiiKLFiwgKdPnxIfH49araZz5854eXlhMBgICgrixIkTuLq6snTpUlq3bp3psaWoe4kPnp07d9KuXTtCQkIyLVbSsGFDDAYDx48ft6gtmzdvpmfPnpQsWZLRo0cTEBCQIYt69+5dFi9ezJIlS8iXLx/79++3aGTvh8DTp0+pWrUqNjY2HDp06I1pTKtWraJv3760bNmSpk2bWsyGY8eOEbh9O8l2dhYL7noTO41G2qWnc+PGDUqVKpWpPqIoUrJEKfKnV+aj0tOta2A2SDcmM+NMIz75dDQzZ858a/vIyEiKFytOyyIfU9+jT47nN5mNLL8ymtvJp7HRqHmanPzKdvkEgdpyOYOVSprJ5cgz8X5Hmc0USU2lY+fOuV6/4OrVqyxZsoQRI0bwv//9Dycnpxeev3z5MpMmTWLv3r2sW7cu0wsVS/lD6Yxe4p2xfPlyqlevniVFsmHDhnHixAlu3ryJyWQiKiqKmzdvcuvWLR48eJCt4K8jR44QEBBA586dOX/+PIMHD35B+9zLy4tZs2YRGhqKKIo0b96chISELM/zIePk5MSePXtISEjAz8+PRYsWkZKS8kKby5cvM3jwYPr27UvVqlVp0qSJRW3w8PBAL4qEWzDA7008NJsRBIESJUpkuo8gCJQpW4Zk/RMrWpZ9bBQO1HPrzezZs9m4ceMb2z5+/JhWLVvjoHLB17W9ReaXyxQ0KTIYnUHP7r17ad+uHQXkclar1azTaNhpY8M9Ozse29mx3caGlgpFppw8wBKDAdVfZY9zmzJlypAvXz6Sk5NfcvIAFStWZMeOHQQEBNCnT59cL5EsOXqJd0ZERESWnLzZbM5wLh06dCBPnjx4eHhQqlQpSpYsibu7O05OTtSvX59x48Zx5MiRt5Y1FUWRcePGUaNGDVauXPnGQCUvL6+MaNwFCzKXU/xvokyZMpw+fZqqVasyYsQI3NzcaNasGe3ataNatWpUqlSJXbt2oVKpyJcvX5YiuzODu7s7GqWS7TkI6soKEaKI+NdfVjCZTNne4s4NmhUdjk/B1gQEBDB69Gj+/PPPF55PT09n5cqV+Fb3I+LWAwaWW4Ct0nJhkJ55KpJH48ymTZvYExjIOIWCXioVAUolbRQKishk2frsbDKbqezj804yc2QyGX5+fmzZsgWTyfTKNnK5nF9//ZUCBQrw888/5659uTqbhMTfMJlMmcqPj4+P57vvvqN48eL0798fJycnlEoljRo1YtCgQYwcOZIRI0YwcOBA6taty9OnT1mxYgUNGzakTJky/PTTTyQlJb1y7MOHD3P+/Hlq167NgQMHePjw4RttKV68OAEBASxevPi1X+h/M56enmzbto27d+/y8ccfY29vj9FopGzZsvz222/cv3+foUOHcvz4ceLj4y06t0qlopqfH4tMJgy5cOJ49q8bitDQ0Ez3MRgMXDh/gbzqzJ1/vwtkgozupb+mRdFRrFiyltKlS1OrZi0++ugj2rZpi5trYfr164dNSmFGea+lkL1ly7DKBDllnOqy9fft6I1GullAPS9ZFLlpNL7TI7WiRYuSlpbGjRs3XttGpVIxePBg1q1bl6sZRNIZvcQ7o1mzZphMJg4ePPjaNps3b2bYsGEkJiZSpUoV/P39X6lC9k9EUeT27dsEBwcTFhZGgQIF+PXXX2nR4lnVsLCwMH7++WdWrVqFwfD/aUMKhYIOHTowatQo6tSp88qxQ0JCqF69OgcOHKBx48bZuPJ/N4mJiZQrVw4HBweGDh1q0ZX9w4cPmT17Nis1GvpYM/PCbKZ4aiqCQklAQAArV63MVL/nkrHjq/2Om71lC/pYA4NJx6XH+7kSd5R0UyIKQYWrbQlqunXGxSZzkefZ4fLjQywPH0M+hYLHfwnf5IQgo5F66elMmDAh00GGliYtLY3JkyezevVqevXq9dp29+7dw8vLix07dtC2bds3jikp40l88AQEBNC3b19u3br10jloXFwcQ4cOZcuWLXh7ezNmzJgsfdCfn62WKFGC+Ph4fvvtN1q2bEnfvn3x9fVl1KhRFCpUiC+++IJu3bplnK/t2LGDBQsWULduXb744gumT5/+0o/QcxWsM2fO4OjoiMlkwsbGhiJFirzyfO6/hqOjI8uXL6d58+acOHHitTdM2aFQoUJU8fZm7OXLNJfLKWhhKVx4dpM4UKtFBTgoPdiwcSMTPpvw1oI36enpfPP1TIrnrfpBOHkApVxNNdc2VHNtk6vzFnN6dmTnLooWuRG8bDajkMkoWLBgjsfKLra2tuTPn5+wsLA3tnseyJqbK3rJ0Uu8M7p27crYsWP57rvvWLx4ccbj0dHRNGrUiKioKHr37k2VKlVy9GPg7OzMkCFDOH36NOvWrWPlypUMHjyYX3755YW81rx58zJq1ChGjhzJrFmzmDx5MhqNhkmTJmE2mzl8+DBbt27l7NmzCILAlClTmDJlygtzFStWjOrVq9OgQQMCAgJeEFf5t/HkyRNCQ0O5ePEiT58+xWw2Y2trS9myZfHx8WHUqFHMnz8/Qw/eUnTq0oVvb95kiF7PNrXa4rEASwwGDphM2CDDu0BTwhMO07RJMw4c3P/aAj2pqal07dKV8CtXGV5phUXt+Tdip3TCUVUAk9EyQYtJooiNSvXOpbJtbGxIfk0mwXNSU1MBclWXQ3L0Eu8MGxsbpk+fzujRoyldujRjx44lNjaWevXqER8fz6hRoyx2hy4IAlWrVmXHjh106tSJRYsWvdZBCILApEmTSEtL4/PPPyc1NZVNmzZx69YtChQogKenJ+3bt8fNzQ0bGxtkMhl6vZ7Hjx8TGRlJSEgImzdv5tNPP6VPnz4Z1/dvIDExkdWrV7N4/nzC/zqLdJDLya9QIAOSzWZi/zoKcXFyolSpUqxevRq9Xo+fn59FbLC3t6dzt24sX76cccAcCzr7QKORkTod3RUKNhiNeDlWoaZbF5ZeGYJP1Wr07tOLESNGULFiReBZZPqvv/7K/F8W8PhRHP3KzaNInooWseXfTpE8lXgcd8QiY5kAwQq7O1lFJpNlSEa/jr179wLPhKhyC+mMXuKdIooikydPZtasWbRv357r16/z8OFDRo0ahYuLi0XnCg4O5vfff+fu3buZUr5KS0ujUKFCJCcnZ8QHFCtWLFNOJSEhgVOnTnHmzBnS0tKYOnUqEydOfGfKWDnFYDAwa9YsZs+ciVarpYNCQSe5nGpyOcUE4QX9+UdmM6FmMweNRlaKIvFGIwJQsVIlPvrooxzL4iYlJfHbb79x5coVAIYplfyoznnhlA0GA320WloqFHSWy+ml0/E//xPYKZ1INyZzLGoNZ2K38DT9ETYaW5QKJUkpiagUaiq7tKCBRz9c7YrnyIb/EgcjlrH/7k9o7W1zXL9gjl7PF6LIzG+/tZB12eO7776jQ4cOb4yqr1WrFnZ2dhw4cOCt40ln9BL/CgRBYObMmZQtW5Zx48bx5MkTxowZY3EnD3D69Glat26daXlLW1tbBg4cyOLFi+nRo0eWtgXz5s2bIRjzxx9/MH36dH7//XfWr19PuXLlSE9PZ9OmTSxfvpyIiAhMJhOurq4ZcQtvklvNbS5fvkyfHj0Iu3KFTxQKPrG1xe0Nq6cCMhktZDJaKBT8TxRZbzAw0WgkPCyML2/coHPnzhlV4bKCXq8nJCSE3bt3I5PJ6NevH2lpaSz57TdOiCKrVSoqZ2PrNk4UGa3Vst5opIdCwQqNhm/0ehwVjtgpnYBn+efNiw6niecgrsYfJy49CpNoxKGwM+Vd6me0k8g8rnYlMGAmVhQplENHX0QQSNfpSE5OfmfHZWazmSdPnlCkSJHXtlmzZg2nTp1i69atuWiZ5Ogl3hOqVq3K06dPadCgQaYkVrNDbGwsDRo0yFKf+vXrM3fuXFJSUrJVnUqhUNCqVSsqVqzIhg0b8Pf3zzi7jo+Pp1mzZvTq1Qu5XM7169eZOHEin3/+ORMmTGDatGlvlQW2NgcOHKB927YUM5s5Y2ODTxYdqY0gMEClop1CwUidjk3Pq8Jt306tWrXw8fGhQIECr71Ok8lETEwMISEhGbsjPj4+dOjQIUPUyMPDg41r11I9NpZeCgUjlMpM2RltNrPUYGC+wYBRFFmr0RDwl8zqQ1Ekj7rAS33kMiUVXRpm6TWQeDV5VM9u5iPM5hcq0WWH5+93ZGTkW4MmrUVsbCw6ne6V2iBms5lly5YxfPhwBgwYQPv27XPVNsnRS7wXTJgwgXz58mWkv1kaURQxGo2o1eos9Xve/m3nbm/D09OTUaNG8f333/PVV1/Rq1cvpk2bRvHiL271Pnr0iHnz5vHll1/y8OFDFi9ebPFgs8xy5MgR2rRqRSNBYItKhU0O7HCRydhoY0N1vZ5xOh3Ozs4cP36cAwcOoFarKVy4MK6urhmvt06n4+HDh0RFRWEwGLC1tcXPzw9/f/+Xdnvc3d35eNw4goKC2HH0KCuSkqgkl1NPJqOaXE4ZmQwbwMCztLlQs5mzJhOHTCY0QE+lkmkq1QvOJtos4qB+dxHc/wUc/7qROmIyUSOHufRegoCTTPZOHX1kZCQAM2fO5OnTpxQtWjRDrnvhwoXcvn2boUOH8vPPP+f6d1py9BLvnDt37rBv3z66detmtTNsQRCwt7cnIiIiS/2et7dEudWoqCji4+MZP348s2fPfuWXvUCBAnz99deUKlWKvn374u3tzYgRI3I8d1aJjo6mY7t21AG2qlSoLfTD9KlKhRmYEBlJjx49cHR0zCg2cu/ePQwGA4IgoFQqKViwIJUqVcLd3Z0iRYq8UbVQoVDQsGFD6tWrx7Vr1wgNDWVpWBg/G14urVpQEPCRyfhRraaXUonjK67tATLySI7eqtgrnQGBbUYjk7J4A/5PBEGgrkzGhYsXadq06Tu5Ob5y5QoeHh7ExcXRsWPHjMdVKhVdunRh9erV1KxZ853YJjl6iXfOokWLsLW1tWgK1quoUKECK1eu5Kuvvsr0DcWyZcsoXbq0RWQ1jx49SqVKlZg1a9Zbv+x9+vTh4MGDzJkzh6FDh+Zq2pAoigweOBCNVssmtdpiTv4545RKTpvN7Ny6lQmTJ2e6YExmkMvlVKhQgQoVKnD27FnWr1/PTo2GAjIZKqCAIOAmCG99/WNFkXLKfBazS+Jl5DIF9monQnUJxJjNuOZw+36oUknLhw+JiIiw2vHf60hISODKlSvMnz+fIUOGcO/ePeLi4lCpVHh4eJA3b9ZL/FqSd5+PIPGfRhRFVq9ejY+PzxtXbJagdu3axMbGsnnz5ky1P3PmDGfPnsXf3z/Hcz958oRr164xevToTJ+7jxw5knv37rFv374cz58VNm3aRODevSxWKHC2wupDEAQWqFQodTq2WzEoqXLlythrNBw1mfCTy6kil1M4kzrqekAhy9kqU+LtyFEgAmP1+hyP1Uwup6hczvGgoJwblkVOnjyJra0tPXr0QBAEvLy8Muo/vGsnD5Kjl3jHREdHExsbm6UKYdnFzc2NihUrMnToUM6fP//GthEREXTu3Bl3d3fKly+f47mvX7+OTCbLdHlKAF9fX0qVKsWePXtyPH9mEUWR72bOpLlSSVsLaJC/joIyGV8qlVz4S2zHGqhUKipVrUpgNjKIjaL4Xhem+bcgIEPkWWrj9lccs2QFmSAwVqHg/Pnz3L171zIGZoInT54QFBTEkCFD3luBLOmTLGERzGYz+/fvp0uXLlSpUoWKFSvSsGFDFi1a9EalqOcFQzw8PHLFzh49epAvXz7q1q3LnDlzXiq8kpqaytKlS/H19UWr1TJw4ECLbJunp6fj4OCQJTUsQRBwdXXNVanMc+fOcT4sjJFWdPLP6a1UYiMInDx50mpzeHp68qfRSEoWnb0MEHmvJEb+lQgygUqVKmGjUtFPq+VaDgtFDVcqqa5QsHHtWvQW2CV4G2azmY0bN+Lq6sr06dOtPl92kRy9RI45fPgwpUuXplmzZvz555/UqFGD+vXrY2Njw4gRIyhcuDBfffXVK8t9XrhwAXt7+1zTiNdoNAwbNowKFSowadIk3NzcaNeuHQMGDKBLly64ubkxZMgQChYsyJgxYyxml1KpJD09PcslT1NTU3NVKnPNmjV4KJU0z4WYAAdBoKdcTuiZM1abw93dHRG4mEUHohAETGLulMP9L2MWTRQqVIjPJk/GbGtLvfR0wnPg7OWCwCqVioT4eHbu3Jnl71tWOXLkCLdu3WLlypUZ6Z7vI1m+bQ8KCuK7774jNDSUhw8fsm3btoycQIPBwJQpU9izZw937tzB0dGRxo0bM2vWrHdWUUjCumzbto2uXbtSp04dVq5cSa1atV44A42MjGTevHl88cUXREREsHTp0heeT0hIIE+ePLkaiapWq+nevTutW7fmzJkz3Lhxgz/++AMHBweqV69OrVq1yJfPsoFYhQsXRqfTcezYMerXr5+pPtHR0Vy8eJF+/fpZ1JY3cfbkSeqLIvJcej8ayuUsTkwkJSXFKj+UhQoVAuC62UztLPTLIwikG9+sWS6Rc9INKWg0GpycnBg/aRI//fADfvHx/KzR0PcvTYOsUlomo5dczq8nTmBvb0/z5s1f2S4tLY2zZ88SFhZGamoqMpmMfPnyUaNGDcqVK/fWWJozZ86wa9cuJk6cmOnv9Lsiy44+NTUVb29v+vfv/0IKATx74c6fP8/UqVPx9vYmISGBMWPG0LZtW0JCQixmtMT7QXh4OAEBAXTo0IF169a9MpLdw8ODOXPmUKlSJfr06ZNxNp6eno4gCMTFxZGSksLRo0fx8PDA09Mz12RiHRwcaNy4MY0bN2b69OlUrVqVli1bWmWuEiVK4Orqyvz58zP9o7B06VI0Gg09e/a0ik3/xGAwcOnyZQJyMcL/70InrysYkxPkcjkquZz0LPYrjEii7rHF7ZH4f7TGVPSm9AwhKgcHB/oPGsSPc+fSX6tlk0LBbJUK7yx8Hq+ZTEwxGNhqNFLI1ZV9+/ah1+tp3bp1huM2m83s2bOHoKAgRFGkdevWlChRAqPRSFBQEMuWLSNfvnx07tz5lZ9JURQJCgpi27ZtDB48mG+++cYyL4gVybKjb9GixWtFTRwdHV/S7/3ll1/w9fXl/v37mZYelfgw+PHHH3FxcWH16tVvdc69e/fm5MmTrFixgsqVK2foNjs6OvLgwQMCAwPfKoxiTYxGY5YlWbOCIAj4+/uzbds29u/fT9OmTd/Y/vr16/z444/06tUrW4p82SEiIgKdwUAFG5tcmQ+gmCCgEQRiY2Ot4uifk9UNXDdBJEoXaxVbJJ6RpH8EkPFbkJSUxK+//oqdgwPtGjfm4L59VE5KooZCwUC5nJpyOaVlshd2m8yiyE1R5LTJxAqjkWNGI452dvTt0YPKlStz9OhRduzYwZ07d+jevTv58+dn3bp1XLhwgc8//5wRI0a8VDgrJCSEKVOmsHTpUnr37k3lypUznnv69CmbN28mPDycsWPHMmfOnHcmaJUVrB5xk5iYiCAIrz3r1Ol06HS6jP+TkpKsbZKEBXj69Cnr1q3LKOWaGUaNGsXixYspU6YMVatWfeE5k8nEw4cPCQ0N5cyZMxw5coSqVavSsWNHq5996XQ6UlNTrR4xW6tWLa5du0b79u1Zu3YtHTp0eOWPxNmzZ2nfvj2FCxdm5syZVrXp76SlpQFgn4s/XIIgYCeTYchhxPXrMJlM6E0mbLN4E+cmCCTrJUdvTZL+2jFxdHTEbDbz66+/YjAYGD16NPny5cPPz48rV65w8vhxBt66BYCtTIaXXI4a0AERJhMpZjMAxb286F2nDpUqVcq4aa9fvz6enp5s2LCBOXPmULRoUW7dusWmTZvo0qXLK+2qVq0agYGB9OrVi7Vr11KgQAGcnZ05c+YM+/fvx97enh07dtC2bVurv0aWwqqOXqvV8tlnn9G9e/fXVt6ZOXMmM2bMsKYZElbg4MGDpKen07dv30z3KV++PD4+PoSHh7/k6OVyOe7u7ri7u9OiRQtCQ0PZtWsXs2bNokuXLnh7e1v4Cv6f6OhoRFG0euS/XC6nb9++rF27lk6dOlGhQgVGjBhBtWrVMrTulyxZwtGjR6lWrRq7d+/OtSDF5/bBs5KfuYkRrKbpHxMTAzw7t80KHjIZCbrHmMxG5DJJV8waxGujgWcr+qNHj3L//v0MJw/PPo/e3t54e3uTlpZGVFQUUVFRJCQkYDQasVMo8HJ0xMPDA3d399eqVxYrVozx48eza9cuTp06xciRI1/r5J8jl8tZsWIFx48fZ+XKlSQlJWEwGOjZsydz5859r4pOZQarfYINBgNdu3ZFFEUWLlz42naTJk1i7NixGf8nJSXlWqqVRPZJSEgAyHKQpaenJ9evX39jG5VKRc2aNalQoQKbN29mxYoV1KtXj/bt21tlm+z+/fvI5XJcXV0tPvY/UalU9O3bl5s3b3LixAlGjBiB+a8VCUCdOnXYsGEDHTt2tLqA0D95/gMbYzZDLp3Tp4kiySaT1TILIiMjEYAqWbweb5kMvagjNu0ObvaWU+6T+H+iUq6S36UgCQkJ7Nmzh/r16+Pl5fXKtra2tpQqVSrbKooqlYoiRYpw/PjxTEtKq9Vqhg0bxhdffMGnn37K6NGjKVy4cLbmf9dY5Tb6uZOPiIjgwIEDb6yjq1aryZMnzwt/Eu8/z7frs5qrmp6enumzcAcHB/r160enTp04duwYmzdvtkq6zNWrVxFFkSWLFmVZCz87yGQySpcuzYABA/jyyy/x8vKibt263L9/n6CgILp165brTh6gYMGCuLq4EPq3Gw9rc8lsxsyzNDhrcP/+fbwUiiwfR1SRyxGAqOSrVrFLAiJTruDhWZi9e/fi6OhotYJWzwkPD8fPz4/SpUtnuk/v3r0xmUz4+vp+sE4erODonzv5mzdvcvDgQYunKUm8HzyvEHXw4MFM90lNTeXkyZNZWjkLgkCdOnXo3r07J0+eZO/evVm29U08efKEP2/cYKBMhubePeb9+CO7du2y2pnxP7G3t0er1VK+fHk8PDx48uQJixcvZtq0aUyfPp3ly5fnWtyKIAj4+PoSYuXc478TajIhEwTu379vcWEgg8HAhQsXEM3mLN8g5hEESshURKZIjt4amMxGHqTcIH/+/Fy+fJn69etb/eY2PT09yzeUz3csc1O0yhpkees+JSWFW38FRgDcvXuXixcv4uzsTKFChejcuTPnz59n9+7dGbWkAZydnd/JKkXCOvj4+FCtWjXmz59P69atM9Vnw4YNJCcnU6NGjSzP5+fnR1JSEnv27KFMmTIUK1Ysy2O8ipMnT+Iok/GDRoMK+E6vZ/rhw9y8do1Bw4ZZPUBPp9MRExND4cKF6dOnD5s2bcJkMlGgQAFEUSQ2NpbRo0fTq1cvJk+e/NZjrejoaHbv3k1oaCihZ84QGRmJ3mBApVRSuHBhfPz88PHxoXXr1q/80WvUuDET9+3jkdlMASudm/+dDUYjKoWCLVu28Pvvv1OhQgVq165NyZIlc3xMc+nSJdLT07kLfKbTMVutztKY1WVmTiZezJENEq8mJu0WBpOOhIQEFAoF1apVs/qcCoWC1NTULPVJT3+WmGmTi5ko1iDL3+SQkBCqVKmSUWls7NixVKlShS+++IIHDx6wc+dOoqKiqFy5MoUKFcr4s6bMpcS7YcCAAfzxxx+ZWmU/evSIr776ivLly2d7l6dRo0YUKVKEDRs2WETeMi4ujpPHjzNILsdWEFAIApPUas7Y2KCNjWXBTz+RkpKS43nexIMHDxBFka+//pqjR49m1KF/8OAB0dHR3L9/n3HjxrF161b8/PwICwt75ThBQUF07dKFIp6eDBs6lJOrVlEhPJyRKSlM1usZlZJC5WvXOLt6NSOHD6dokSJ06tCBI0eOvDBO7969kSkULM+FHY3LJhMnTSa69ejBN998Q8eOHXn06BELFixg3rx5xMZmP+rdbDZz/PhxGjVqxLx58/jOYOATnQ5jFlb2jRUKIlKukaKPf3tjiSxxLe4EKqWae/fuUbly5VxxpG5ubgQHB2fpO71//36AD/5IOcuOvn79+oii+NLfypUrKVq06CufE0XxvVcOksg69+7dQyaT0alTJ3bu3PnadhERETRo0IDExMSXRJaygkwmo3v37jx9+vQlvYasYjab2bR+PS5mM1P+UQu7slzOcY0GY3w8y5cuxZRD/e03ER4ejkwmIz09nXLlytGtW7cX9AMKFy7M9OnTCQsLw9XVlWbNmhEZGZnxfHx8PD179KBevXpc3rmTuQoFcXZ2XFapWGljw1S1mvEqFVPUapbb2HBJrSbezo6flEpu7NlDw4YN+ahrVx4/fpbqlC9fProFBPCLKJJs5S382QYDjnZ2VKxYERsbG2rXrs1nn33GsGHDSElJYc6cORw5cuSFYMXMcvLkSSIiIpg8eTKjR49m/vz5/GQ0UisLeuqt5HJA5Gpc7ldD+7cTHn+YkiVL8PjxY4oXL54rc9aoUYPU1FTWr1+f6T7z58/Hzs6OTz755I01O953JK17iWyRnp7O4sWL8ff3p2TJkrRr1w5fX19Wr17NrVu3iIiI4PDhw3Tv3p2SJUsSGxvLsGHDcpyWUrBgQWrVqsXJkydzdI5+4sQJ/rx9mxUqFXlesZ1bSiZju1rN3YgIjh49mgOLX4/BYOD06dPUqlWLgIAAzp49S/ny5Vm6dOlLZ8oFCxZk7969mM1mZs2aBTzT2S5XujSBv/3GKo2GqyoVo1QqnN6yPZ1HEBiuUnFZpWKdRsPBbdsoX6ZMxupl6tSpJMjlfPY3fQtLE2g0ss5goEWbNi8UDRIEgdKlSzNhwgRq1arFzp07Wb16NUZj5nXn4+Li2LVrF4MGDaJhw4YADB8+nC1btnDRZKJyWhqfaLXcfMsNRAGZDF+5kvAnh7N3kRKvJFkfR0TiZQq5FcqVtNbnODs7U65cuYxds7exbds2Dh06xPTp07lz5w5r167NBSutg+ToJbLFpk2bePr0KXXr1qVv374MGDCA5ORk+vTpQ8mSJSlatCiNGjXi8OHDtGrVik8//dRi6Wv+/v6kpqZy8eLFbPW/dOkS27dtY5RSSeM3ZADUkssZq1Syb8+eHG0jv8mO1NRU6tSpg6+vL+PHj6dChQoMHjyYzp07vyAkBc+c/ZAhQ1i9ejWbN2+medOmlE9MJFytprdSmeUzbUEQCFAqCVerqZqcTOtWrdi2bRvFihVj9nffsdBg4I8sONjM8thsZqBeT9nSpfHz83tlG5VKRYcOHejXrx+XL19m1apVmdpZ0ev1rF27lvz58zNnzpyMx0VRZP78+dg7OVG7YUOWyeWUSk2lYXo6X+p07DEauWc288hsJtpsJsRkYrFej1k0cTXhBHpTVkV0JV7H1bhjwLOMK7lc/pIynTXp1KkTaWlp1K9f/4VYs78jiiIbN26ke/fudOnShbFjx9K2bVsWLFhg9SI51kIQ3zPLk5KScHR0JDEx8YM/F/k3U6NGDZKSkhgyZMgLj8fFxREXF4fJZMLe3p7ChQtbRQxl4cKF6PV6xowZk6V+oaGhrF+7lk4KBevV6rcWb0kXRUqmp+NerRrdu3fPickvoNPpmD17NgUKFGDo0KEvPBcWFsaaNWuoX78+u3btQv23o4WoqCg8PT1RyGS0lsnYqFajsoC2gEEU6anTsU0U2btvHw0aNKBls2YEHznCQbUaPwvl1SeIIo20Wm6rVHwyfjx58+Z9a5+rV6+ybNky/Pz8+Oijj17bTq/Xs2LFiozdpL/fRCxZsoQhQ4YwdOhQypQpg16v5+LFi5wPCSHy/n1StdqXxpMJAgWcnYmJi6Nb6a/wK9Qhexct8QLzLgSgLKClVKmSHDt2jP/973+5Ov/jx49ZunQpcXFxtGnThkGDBlG8eHEMBgPBwcEsWLCAsLAwAgICWL58OWq1mj179tCqVSvCw8MzMo5yA0v5Q0nySSLLaLVaQkND6dDh5R++fPny5UpKZYUKFdi+fXumNeq1Wi07tm/n1OnT9FEqWZYJJw9gIwgMl8uZERJC27ZtX6u+lVV27dpFSkoKw4cPf+m5SpUqMWjQIJYuXUpAQABbtmzJWK3nyZMHpSDgJwhssJCTB1AKAmvValrodPTq3p3wGzfYsm0bzRo3pnFICJtUKlrmsBbAXbOZdjoddxUKho0YkSknD89SOTt16sTmzZuxs7N7ZZZHYmIiq1evzsg6+LuTNxqNzJgxg2rVqlGmTBng2Y6Br68vvr6+iKJIfHw8jx8/xmAwIJfLsbOzo1ChQqhUKhYvWkJw9AbJ0VuAqORr3EsMo3+n/kRHR1tNEfFN5M+fn7FjxxISEsLJkyfZvn17xnMymYw2bdrw3Xff0aRJk4zvXYkSJYBnQcW56egthbR1L5FlwsLCMBqN71TB0MPDI0Mf/02YzWbCwsL49ptvCDt7lkVqNSvUahRZcJADlEpEs5nQ0NCcmg08K1hz4sQJWrdu/drCPaVKlaJXr15s3bqVJUuWZDw+7tNPUZjNrFarUVtYJVApCKxUqUhLSOCTjz/G3t6e/YcOUa9JE1qlpzNQqyUxGxuAZlFkoV5PhfR0ou3sGDFmTJbFR2rVqkXp0qU5fPjwC9oNoihy9uxZvv32W5KTkzl06BANGzZEFEVOnz7NpEmTaNGiBdHR0a8NCBYEgXz58lGmTBkqVqxIuXLlKFKkSEY6cO06/kQmXeV+0uUsX7vEiwRHb8IxjxPly5dHoVBkKfbCkmg0GmrXrs348eMpUKAAnTt35uTJk0RFRbF9+3aaNm36wlHY83ig3KqsaWkkRy+RZUJCQpDL5Rm1vt8Fbm5uyGQy7t+//8rnExMTOXToEN98+SXLly+ncloaV2xsGKJSZfksu6BMRnW5nLt37+bY7nv37rFixQrKlClD7dpvrpBeqVIlatSowaeffkpERASXLl1i6bJlfKdWU9RKKyF3mYy5CgWrVq/m3Llz2NnZsWvPHpYsWcJvSiWldDq+0OmIykQkfJoostxgoKpWy3Cdjsp+foyfNClbnxtBEOjWrRtKpZLdu3cTFhbG1atXWbRoEevXr6d9+/Zcu3aNmjVrsmXLFnx8fKhZsyYrV67k7NmzeHp6Zlt9r2zZsuR1ysfxBxuy1V/iGWmGRM4/2k0t/5rI5XKcnJxIS0vLcm67JRFFkcTERPz8/KhZs+ZrP5unT59GEASKFi2auwZaCGnrXiLLPHjwgLx5877Tu1uVSoW9vT2hoaHodDpEUUSn0/EwOpoHERHEJyejEgS6KRQMt7XFVybLkQBLdUFgYw7lcW/evMmyZctwc3OjX79+mdq2bN++PX/++SfDhg3D09MTV7mcQVZ+3fsoFHypVLJgwQJWrFiBIAgMGjSIZs2aMXv2bOYuX87Xqan4yuVUl8moKpeTXxCQA8miSJjZTIjZzEmzmWSzmXJlyzKiYUNKliyZI7vy5s1L7dq1CQoKYu3atej1ery9vV+oJPa///2PqVOn0qxZM/bs2YO/vz9OTk40a9Ys2/PKZDLq1a/Dzh07aVJkEAVsX63HLvFmDkcuB5mZWrVqAWTsCEZFRWVJltaSPHr0CJ1Oh4+Pz2vbPA/kbNmy5Qcrgys5eokso9VqrVq7PbMoFQpi7t4l7t49EARsgHKCQAuZDB+NhoZyOS4WWvlWkcuZFx+PXq/PssKjwWBg3759HD58mJIlSzJgwIAXAuzehEajoWXLlqxduxYbtZrP5HKUVi4jKxcEhgoCM9avZ86cOTg4OBAYGMipU6e4evUqBrMZZDLSypdnc1QUP8e/KCiTx9YWd09PahUtSvXq1S0as1GrVi0OHz6MKIosW7aM/v37Z9zA/frrr0ydOpUZM2YwdepUBEHgxIkTiKJIkSJFcjSvv78/R48cY8/dn+lbfq4lLuU/RaLuEUEP1lK/Yb0Mtcn8+fOjVquJjIx8Z47+uSbFP6tp/p1t27Zx4cIFvvrqq9wyy+K8+19riQ8OhUKRLRETSyMzmxmjVPJNJp1mTsj7lzPR6XSZdvRms5kbN26wY8cOHj9+TMuWLWnYsOELeeOZoXLlyvz++++kp6fTy0LBgG+jt1LJpNRUBg8eTFBQEE+ePCFfvnwULlwYpVJJ5cqVCQgIAJ69JlqtFrPZjEqlwtbW1ipVBuFZsGe5cuW4desW4eHhGfPo9Xo+//xzevXqleHk4VmWhVKpzPExk1KppEXL5mzYsIH7SVfwzFMhx9fyX+KPewtRqhQZugbwbKfEy8uL8PBwGjdu/E7sunz5MuXLl8fR0fGVzwcGBtKzZ086depk9aI71kQ6o5fIEnFxcYSHh5OUlPROc0pFUSQ1LY3cSsB87raioqLemM8tiiJPnz7l6NGjzJw5k8WLF6NWqxk3bhxNmjTJcPJmsxmtVktqaiopKSmkpaWh1Wpf+ZoqFApcXV1xArysvJqHZ6l2yw0GBEEgMDCQsmXLMnHiRKZOnUrXrl1JTU2lbNmyGe3VajWOjo7kzZsXOzs7qzn555QqVQqDwfBCIObWrVuJjY1l4sSJL8x/9epVXF1ds3xz9SqqV69OwQKF2HVnzgebT/0uiE29zZmY32nStPFLUrc1a9bk7t27REVF5bpdT58+5fLly8TExPDtt98SGxuLKIro9XoCAwNp2bIlbdq0oWnTpqxZs+adZAhYCmlFL5EpIiMjmTp1Khs3bsRkMmE0GomPj39n1Qnj4uJI0+vxzqViEwl//bAvXrwYpVKJm5sbhQsXxsbGBkEQMBgMPHr0iMjISFJSUpDL5VSqVIlmzZphNps5ffo08fHxJD1NJjEpieTkREzml28Y5HIFeRwcyZMnD45ODuTLlw93d3eMBgPV5XKrO9HLJhO99XrCTCYaNWpEkyZNXjhmeL7V+a4zLsxm8wtBXGvXrqVOnTovpT6lpKRklFTOKTKZjA4d27Fo0SJOPdxCLbcuFhn334xZNLHhzynky+fyyuDTChUq4OjoSHBw8Bs1EqzBqVOnUKvVNGnShC+++ILPPvsMuVyecSNftWpVli5dSt++fS1yo/gukRy9xFu5evUqTZo0wWw2M336dFq1akWlSpWIiop6Z47+ucPxyaW77IsmEw4aDX0HDSIyMpLIyEju3buHXq/HbDajUChwdnamYsWK6PV6njyOI/zKVS5cuACAi607LpoiFFQVp6RjfhwLFMBW4YhCpkQQ5IiiCaPZQIohgWT9Y5L0T0iMesSlm9c5kn4EBdDcykF4h4xG2up0OObLx8e9euHp6flSm6ioKGxsbN5p+enChQsjCMILK6zo6Gh8fX1famvplXeZMmXw86vBztBvKePsj7PGzaLj/9s4GrmK+4lXGD1m9CuDd+VyOXXq1MkInMxuZkRWiY+P59ixYwwcOJCff/6Z6OhovvvuOy5dusSD+/d58OAB58+fZ+DAgYwaMYIK5crh4+dHrVq16Nixo8X0NHILydFLvJFHjx7RokULnJ2d2b9/f8ZZp6urK7dv38bb2/ud2HXnzh1c5PJcKaUKcNZkooCHB8WLF3+pCEdkZCTnzp3jyuWrXL9+HbXChlJOtajk0REPh/K425fFRpn9Q4ZUw1N+CumCyvwkp5fxWo4YjbTUailesiR93xAsmJycjJOTk9V3Ft6EWq1Go9G8EMAlCMIrnbqNjU2OaiK8ivbt23Hj2g02/fkFQysufaevxftMbOpt9t77mfoN6uPl9fpMhfr16z9TrFy/nk8//dTqq2dRFPntt99wdnZm9OjRTJkyhaULF/IoPp4SKhU+ZjP9ZTLyqdUIwFPg0uXLBF29yuJFixg1fDh9+vdnzJgxFiuXbW0kRy/xRn755Rfi4+MJDg5+IaApICCAJUuW0Lp16yxHoecUvV7PuXPnKJNL56RPzGbOms00+Esd67kNFy9e5MTxYO5HRuCoyU8F50aUd29Ayby+KGSWe03slE7YKp3Q6qzj6K+bTLTR6ShWsiQDBg9+Y0aF0Wh8L7YxlUolcrmcnTt3EhISQnRUFOvDwzmwZw9KpRJXNzd8fH1JTk4mJiYGs9lssTNWGxsbPurelcWLF3M0ciUNPPtZZNz3Ha0xhTRDImZMKGUaHFQuyIRXv6Y6Uxprrk8gr3PetwaxKRQKAgIC+OGHH9i7d+8rlQ+fk5KSQkxMDHq9HhsbG9zd3bOc5nvixAmuX7/OhAkT8KtWDWNqKn1kMoba2lL+LZ/tewoFS/R6li1cyNLFi5k5ezajRo1678/vJUcv8Vr0ej1Lly6ld+/eL22pDR06lLlz53Lx4sVXbplaA6PRSHR0NEFBQWi1Wi4DHdLSyCMIlJHJ8JHLqSaX42zhFdYKoxET4OPjg06n4+jRoxw7GkRaeiplnGsxoMI4yjrXQS6z3tfJycaTa+mvLsKRE0yiSF+9Hvu8eek3YMBb0yb/fob5rnj8+DHp6enMmzuXmTodBZRKqooiZQQBzaNHGESR+5GR7Dh3jjt6PQKwbOlSGjdpYrEVWNmyZWnUqBG7Dv+Aq10JyuarY5Fx3yd0pjQuxO7hz4TTPEgO45E2+oXnbeQ2FLYvi0ceb6oVbIObfSng2Yp5w/UpPNbe4+NhYzK1EPDw8KBRo0YcOHCAPHnyULdu3Reev3v3LidOnODSpUsvqOnZ29vj6+uLv79/po6TLly4wNatWylRrBjffvst3ZRKftZoMp2GW1Qm4xu1ms9FkYk6HR9//DFbN2/m9+3bX6ty+T4gOXqJ13Lo0CFiYmJeKlwDULJkSZo2bcrRo0fx8fGx6irv8ePHBAcHc+70aVK1WuRAObkcVyAViDWb2WY0ksyzNJK2CgUjlEoayuXIcuj0daLIfKMRM8+kf48dDSI9XUutQl2pUziA/LY5y8/OLO4O5Tn85AAmUcyURn9m+cFg4KzRyKgePTKV229nZ5eRcZHbW9ZGo5EDBw5wcP9+7EWRISoVA+3sKCEIr7XlsVLJOqORn//8k5+uXaOKtzedunTB3t4+x/a0atWKhw9jWH1tHB9X2UBBuw9jG/dtJOoec+j+MkJitqIzpeMnV9JdBlU1Ggr9JYyUAlw1mQhJDSMoOYwjkSsonsebeh79iU65yaXH++nfvz9ubm+PYRBFkQsXLhAcHAw8y6DQ6/U0atQIgL1797J//368vLyYNWsWrVq1wt7entjYWNatW8fy5csJDg6md+/elC9f/rXznDlzho0bN1LAxYXoiAg2azR0zmbci50g8LNGQyeFgq5nzlDP358jx49ToECBbI1nbaTqdRKv5ddff2XgwIEYDIZXrvRCQ0Px8/OjSZMmNG/e3OLzp6WlsWP7ds6cPUtemYyBcjmdlEq8ZTI0//hhN4sit0SRg0YjCw0GrpjNVJXJWK7R4J2Dm5ApOh1f6/XIBDmiKFLdtQ3Ni44kryZ35X9vJpxhwaUBnLe1pYqFbqqemM24p6VRo27dVxYoehVXrlxh2bJlTJs2LdNFaSxBbGwsa1asICY2lslKJZNUqpc+A2/CLIpsNBoZqddj0mjo1rOnRYqTaLVafpj7I+YUFaMrr8Ve5ZzjMd8VoigSEruL7Te/RmPWMVQpY7BSSZG3rHYNoshOo5GfDSaOmQwIQIOGDTPUCl/bz2Dg4sWLBAcHc+/ePVq2bMmePXvo0aMH69ato2zZshQsWDAjVXXChAmv3CJPSUmhV69e7Nq1iyFDhlCqVKkXnk9NTWXr1q2EhobiWbgw8TEx/KFWU8tC36MbZjP19Xpcy5Qh+PRpbG1tLTIuWM4fvt8HCxLvlOerpNfdC/r4+DBp0iQOHDhg8TzY69ev893MmYSHhDBfreaBrS3fajT4yeWv/IGXCQKlZDKGq1SE2dpy1MYGA1AtLY0vdTpM2bifPWw08o1eD0AJJ1/GV99K9zJf57qTB/ByrIqjMi/LLBhYttxgwCyT0aRJk0z3eX6E8zzrITd48OAB8+fNw/bxY87a2DBDrc6Sk4dnn48ApZJrNjb46/UsW7rUIkWKNBoNAwcNQCt7yqLLg0gzJOZ4zHeB0axnzdVPWX99Mu0EPX/aafharX6rk4dnxZA6KZUctdXwm0aDExAcFERgYCAPHz4kMTGRlJQUEhISuHXrFkeOHGHNmjXMmDGDdevWUbJkSQIDA1m+fDkAXbt2JTAwkMePH3P06FG+/PJLJk6c+NpzcHt7e3777TcaNGjA+vXrM46WDAYDZ86cYfbs2dy+fZuePXty/8EDtqpUFnPyAKVlMv5QKrkeHs6UKVMsNq4lkVb0Eq/lwIEDNG3alHPnzlGtWrVXttHr9fj4+BATE8OoUaNeqzCVFc6ePcvGDRtoKJfzq1qNZzYDXfSiyP/0er7W6+mqULBao8m0fOxyvZ4heiNymZoOJSbh69r+nUdX7737C8H3lxJtZ0OeHNpiFkWKpadToEoVevTsmel+oigyffp0KlWqRKdOnXJkQ2Z48uQJP82di5dezyGNhnwWeA+Mokh/nY51RiMDBg5843ZvZnn48CG//DSfvAp3hlZahp3SKcdj5hZGs55fL4/gbsIZ1mhUdMlhGucjs5nOWi3BJhOv0s/UaDRUrlyZunXrMnDgwIwaCKGhoVSrVo2goCDq1KnDwIED2bVrF5GRkZk657906RKVK1emXbt2pKSkcPbsWZKTk+nQoQPjxo2jccOGDDKbmWchXYV/8r1ez3i9nqCgoLcWrMos0opewuo0aNAADw8PFi5c+No2KpWKwMBA1Go1CxcuJCEhIUdznj9/ng3r19NPoWCfRpNtJw+gEgS+VKvZrNHwu9FIP60W81vua82iyLD0dAbodBR38mNi9Z34Ferwzp08QM1CndEj8KVOl+OxjptMRJhM1PL3z1I/QRCoVq0a586dQ2cBO96E2Wxm3erV5NPpOKhWW8TJAygEgRVqNa3kcjasWUNSUlKOxyxUqBDDRw7jqfkB8y/1JVH3yAKWWp9ngXOTuZNwhj026hw7eYACMhn7bWxoIJdjo1Lxyy+/sG3bNvbu3UtYWBjJycmcOnWK2bNnv1DoaNWqVeTPnx9fX1/S0tLYtGkTQ4YMyXRWj729PUWLFmXnzp2cOHGCJk2acPXqVbZu3cqPP/xAQbPZqnLZHyuV+CoUfDpmjNXmyC6So5d4LQqFgqFDh7J+/Xpu3rz52naenp4cPXoUpVLJDz/8wI0bNzKee673fvjwYfbt28exY8eIjY195TixsbFsXLeO7kolS9RqiwWddVQqWafRsM5o5Jc3bH0nms1UTktnkdFE62KfMKTSYpw0rhaxwRI4aVxp5jWauQYDJ3MY+X7GZEKjVGar2Iu/vz86nY7z58/nyIa3cezYMe7dv88alcpixYmeIxcElqvV2BgMbPntN4sI6xQuXJiRo0aQJnvC3PNdifgA6tdfeLSX84/2sUqjoqEFC1VpBIEdNjYUNZlYsXQprVq1onnz5lSsWPGV8T6JiYmsWrWKgQMHZhS6SUlJyQjIexOHDh2iRYsWlChRgqioKJycnFAoFGzdupV27doxY8YMtm3bxliZDDsr3rDLBYHJCgVnz58nJCTEavNkB8nRS7yR4cOHU6RIEZo3b86dO3de287T05MqVaqQkpLCwoUL2bBhAwcOHGDWrFksXLiQo0ePcuHCBfbs2cPMmTNZsGABV69ezehvNpvZuG4d7oLAUrU6x9Hy/6SLUslIpZKJOh23/lGQxyiKLNHpcEvTch0lAyv8TCPPAe/FKv6fNPDoQxGH8vTQGojJQWGhELMZ98KFs5X/6+zsTPny5Tl06BD6v2IYLE1KSgp7d+9mjFKJv5UqJbrIZCxUKgm7coXr169bZExXV1c+/mQMRlk6P1/oRUjMLouMaw2S9U/Y9ueXdFEo6WYF1UU7QWC1Ws3FsDC+/fbb17bTarV07doVgGHDhmU8Brykjf9P5s2bR5MmTXj8+DErVqwgKSmJ+Ph4kpOTOX78ONWqVePLL79EaTbTOxfKareSy/FUKlm4YIHV58oKkqOXeCNOTk7s27cPuVxO1apVGT9+PLdv3854Pjk5mYULF1KlShX27dvHhg0b+OGHHwgNDWXfvn00btyY4OBgnj59SkxMDImJiaxbtw5nZ2eWLFnCwYMHgWe60/fu32eVUomtlRzsLLWaQoLAYK2WG2Yz6w0Gxmq1eGi1DDOYUKsKMNZnE+Vd6ltlfksgE+T0LPc9T+V5aJSuz7azDwEK56B0a5s2bUhMTCQwMDDbY7yJ06dPg9nMFCtXJuyoUFBJoSD4+PFXPp+SksKJEycIDAwkMDCQ4ODgFzT2/4nJZGLXrl3oDToaNGrAuuuT2HpzJnqT1lqXkG2ORK5Ebk5nvtp6glfV5HI+USj4cvr0lxYKoihy9OhRGjRowPHjx9m2bVtGDQVn52fZC28K+ly9ejUff/wxn376KWfPnqVv374ZNwaCIFC7dm3Wr19PuVKlaCOX45gLN+5yQaCbIPCHlb4X2UUKxpPIFE+ePGHWrFksX76cp0+fUrBgQZRKJY8ePcJgMNCuXTsmTZqEj48P7du35/Dhw+zcufOFspR/RxRFpk2bxldffUX16tW5fvkyTYxGfrdykZoNBgMB2v//0S1coAAJSanYy/MzvNJyHNXvZx7sP4lNvcPiS/2wNyayWq2kfhZXvY6pqdRv3fq1709mOHLkCDt37mTkyJEvyQLnBLPZzDdffkmr1FRWWilw6u8s1esZotMxZerUDNGVR48esX//fi5dugRAwYIFAYiJiUEmk1G5cmWaNm36gkhKeno669ev59q1a2zYsIFOnTrx888/M378BPKqCvFRya/wcqxi9evJDHqTlq9O1meITMccK7/G0WYznqmpCAoFHTp0wMPDA61Wy5EjR7h27Rply5ZlxYoV+Pn5ZfQRRREfHx8KFy7Mrl0v74potVrc3d1p3rw5a9asee3um8lkIo+9PV+KIp/mkoLnFoOBLlotMTExGZ+b7CIF40nkKi4uLsyZM4cHDx6wZs0ahg0bRp8+fZg9ezb37t1j69atVK9enQMHDrBr1y7WrVv3RiciCAIzZsygf//+nD9/nmStllG5sLXWUaEgv1xOu3btCAkJwSxT4qh0ZaT3qg/GyQMUtCvGyKobUNtXpEF6OiO0Wh5nYXWvF8UcixzVq1cPLy8vli9fTkxMTI7G+jvR0dE8efqUPlbasv8nAUolCkHIOEq6c+cO8+bNIyYmhq+//pro6OiMQkYPHjxgxowZREVF8eOPPxIREQHAtWvX+Pbbb7l79y7bt2+nc+fOCILA6NGjuXTpIkXLFuTni73Zces7dKY0q12LWTTzKO0eNxPOcDUuiOvxwdxLvITO+OKclx7vJ8WUwtBccH5uMhkdlUpcnJyIiorK2BmpVKkSR44cITw8/AUnD89+H4YPH05gYCC3br2sCLl582bi4uL44osv3njEduPGDdK02lwrfgXg89f3yhLpm5ZCWtFLWJR27doRERHBhQsXMnXGfe/ePYoVK4YdMFOppJBMRlW5nKJvUDvLKZ/pdPxqY0NelwIkxeoZXnEljur8VpnL2phFMycebGDPne8RzQY6KxQMUCrwlcuxf8XrlyqKnDOZaKbT0TyHK3p4Jkby888/k5aWxtChQzOlhPY2Tp8+zaaNG0myt3/lNViDymlpCBUr0qJFC3766SeqVq3Krl27XpsumpCQQMuWLQkLC8PLy4vw8HAaNWrE8uXLX1n1z2QyMXfuXKZMmYqNPA9N3IdQo1An5LKc39zGa6M583ArdxJDiEq5htb48tGCgEAB+6J42FWgcoHmXHq0H/2TvVy0te7RyHM2Gwx0zeIqNzU1lfLly+Pg4MCRI0de2D1p0KABCoWCAwcOvHGM50cDN+3sKJFLzt4giqhSUli5ciV9+vTJ0ViW8oeSBK6ExXj8+DG7d+9mwYIFmXbSRYsWpXnz5hw8cIBPjEaMf61K88rl1BQEBioUtFEoUFjwB7+OXM63iYmkppkYW2XTB+vkAWSCjLruPfAp2IqzD7dx4MF61qc/RABKyVWUxIRaENCLIreQc8Okxwwo5UqePn2a4/nt7OwYMWIEixYtYt68eXTo0AE/P78c3aRFRkZSSqHINScP4CuTseLiRe7evYuLi8sbnTxA3rx5CQwMpEKFCty9e5fFixczaNCg1163XC5n/PjxdOnShS+mfsGatf/j2MPVNPcYSeUCzZAJWd9duZlwlmNRq7gafxy1Sk2p0qVo5FEfDw8PXFxcUCgUmM1mUlNTefDgAVFRUdy+FUrI5V0o5Soqi0aSRRUOufA6/32V27Jly0z1sbOzY+/evdSrV49atWoxc+ZM2rZti1KpJCIiIlP165/r4udmGabnTtXSVRNzguToJSzGgwcPMJvNVK1aNUv9qlWrxtmzZ5k6dSpJSUkZ26TXwsPpGBmJm1zOSLmcT7Ioe/o6nm/j1XHrkWta9dbGTulEA89+1PPow8PUP4lKvkpk8lXu6mIwmXXIZWryqgvQxb4c7g7lOXh/KVGRt98+cCZwcHBg1KhRbN26lY0bN3Lx4kW6dOmS7Zr1sbGxlMzljUYvQUCj0ZCcnMyMGTMyJfzk7OzMmDFjmDZtGp06dXqlk09LS2Pbtm2cPHmS0DNnuHL1Kqnp6QAkpj9g/bUJbL45g3LO9Whd7JNMqS6mGRLZdms2IbE7cSvkTteuXahatepraxU4OztnBLmJosj9+/c5ceIEF86fp3x6OitUKhpZ+ZjESxDIq1Bw4cKFTDt6eFY86NSpU/Tt25fOnTvj5uZG8+bNSUhIyFTGiOav+IP0XPw8pf9j7vcBydFLWAzzX6vxrKZsyWSyjL558uShfPnylC9fnubNmxMVFUXwiRN8cfYsq81mVqlU+ObwbLmQTIa9IEMjt8vROO8jMkFGYfsyFLYvg1+hjq9t5+FQgQNRQRYr36rRaAgICKBy5cps2rSJ//3vf1SoUIHatWtTsmTJt85hNpu5evUqwcHB3Lp1i4q5dD7/HJUgYDKZUKlUWdpu7d+/P1OnTmXTpk0MHz484/F79+4xb948Vv76K4nJyZRRq/Exmegsl+P0l0OOF0UuCAJnRS3nH+0h7NEe8tkWp6FHPyoXaIZK/nJg6o34k2y48Tk6UujevTu+vr5Z2j0RBIEiRYpkpMxuXLeOxnfuMFSp5Ee1GrWVVveCIFBALs+WoFbx4sU5fvw4ly5dYuHChVy4cAGj0fhCeu6b+gJcM5spl83fDZMo8kAUSRFF7AUBd0F4Y/rv1b9+y0r8raz1u0Zy9BIWw9X1mbjM1atX8fHxyXS/q1ev4uDg8Mrn3N3d+ahbN+rWq8eGtWup+eABU1UqpqlUOdoethHkGM3WyQH/ECiapxI6vZaIiAi8vLwsNm65cuWYNGkSISEhBAcHs3DhQuzt7fHw8MDDwwNXV1fUajWiKKLT6YiOjs7YVk5JSaFatWpUqVIF7eXcFZvRiSIymQyPokVxcnLKdL/8+fNTuHDhjFoPZrOZhQsX8tn48WgMBgbLZAyxs6PYW250rplMLDIY+DX9Dr/dmMJvN6dTJq8/FfI1oLiTLy42Hlx4tJd11ydRomQJAgJGZMnOV5EvXz6GjRzJyZMn+XXbNm5ptWzXaKwmKiOHHJU49vb2ZtGiRQD8/PPPjB07lujo6DfGhRQqVIhC+fMTmphIVgWbY81mfjUYWCyK3P/bNryXUslQQaC/UvlKIadQkwm5TIa3t3cWZ7QekqOXsBhubm40aNCAJUuW0KtXr0z1iY2NZfv27bRq1eqN7QoVKsSYsWM5cOAAM/btI1oUWZQDYR2jKCIT/rsffy/HquSzLUxwcLBFHT08W93Xrl0bf39/7ty5w40bN4iMjOTkyZOkpKS80LZgwYL4+vrSvXt3WrZsSbVq1Rg9ejT7wsMtatPbuGU245BNxykIAqIokpCQQOcOHTh87BhDlUq+1Wgyff5dVi5nnlzONFFktE7HOoOBWPNlrt4MQhRFVAoNeqOOatV86N69u8XKQstkMmrXrk3BggX5dckSOmi17NJorLKyTxHFtwrgZJbevXszceJEfvjhB7777rs3tq1eowbH9u3L0vg7jUa663SYFQq69+hBp86dcXJyIj4+ni2bN/PFxo18pdWyRaWi2T92n46bzZQvU8Zi12oJ/ru/dBJWYeDAgfTo0YPg4GD8M6Gj/sMPPyAIAr6+vm9tK5fLad68OXnz5mXZhg1ogHlqdZZX9imiyFPRiMMHXFI0p8gEGf6u3Qi8MI/27dtbpD77PxEEgeLFi2dsn4qiiFarRafT8eOPP1K9enW2b9/+UllPHx8fftbrSVSpckXkBCBULqdI8eKEhYWRlJSU6Qjn+Ph4Hjx4QJ48eahfpw4PbtzgoI1Nts+8nQWBtRoNneVyeiUl4uvjQ0CvXowdO5bKlb0JCAiwyFHLPylZsiQDBg9myaJFfKLTscDC58uJosh9vZ6yZctaZDxHR0cmT57MlClTKFWqFIMGDXpt27r16jFu1y7ClUrKZ+IGKdBopINWS/t27Vj6668Z4j3PadOmDd/PnUu/Pn1ovXcvezUaGv/1fj8xm9liMjGjd++cXaCFkfLoJSxKcHAwCoWC9u3bc/kt26/Lli1j9uzZNGrUKEs1nP38/OjcpQs/Gwys/SuqNitcNJkQAXeHnNcj/5DxLdQBGXL279+fK/MJgoCNjQ3Xrl3j6dOnHDhwAAcHB1o0a8auXbsytnWfH/ucyaGef2ZJFEXC9Xrat2+PXq9n7dq1me67cuVKzGYzv61fz8MbNwiyUGBbe6WSQ2o14efP88XUqRQoUIAePXpYxck/p2TJkrRt356FBgNHsvG9ehPn//HeWoLJkyczYsQIBg8eTPfu3Tl58uQLNQsePHjA9OnT+eqrr1DL5SzIRBR8kijSXa+nTevW/LZly0tO/jkuLi5s27GDxo0b85HBQNpf864wGkEuZ8CAAZa5SAuR5U9NUFAQbdq0wc3NDUEQ2L59+wvPi6LIF198QaFChbCxsaFx48ZvLIgi8e/h2LFjLFiwgKZNm2JjY0OtWrWYPHlyhqgIPPt8nDhxgo8++ohBgwZRu3ZtmjZtmuW5/P398alShVF6PQ+zKAN71mxGKShwtbWcmtuHiJ3SiZZFR3M86PgLssbWJCEhgZ3bttFboSDU1paflUrijh6lbdu2lChalL1791K+fHlKFy/Ocgs7m9ex2mBAEAQ6depEu3btmDdv3htlbp+TnJzML7/8QsmSJbl69Sr7VKpsB3y9Cl+5nG4yGUnJyXTr1g1lLghK+fv7U7JYMfrq9aRYMFL9sMmEg60tpUuXttiYgiDw888/s2jRIs6dO4e/vz8lSpSgdu3aVK1alSJFivD9998TEBDAp599xlKTiStvuXlc/ZfTnr9w4VuPRxQKBQsWLybBZGKDwcBDs5mZJhM9e/V6Ief/fSDLgjl79+4lODgYHx8fOnbsyLZt22jfvn3G87Nnz2bmzJmsWrUKLy8vpk6dyuXLl7l69Wqm0g0kwZwPk7S0NMqXL49cLmfEiBHo9Xr27NnDuXPn0Gq1lC1bFjs7O2JiYoiMjKRgwYLUq1ePmjVrZjuoLjU1lW+/+Ya6Oh07M3keJooiVdJ1GB39GFhpUbbm/TdhFk38crEPyfIHjJvwqVVTgsxmM0sWLSLp9m3CbWxw+tv7HmIyMcVo5A+9nn59+1KqdGmmTp5MpK0trlZcxYqiSKnUVG6LIggCXp4e3H8QTfHixVm5ciU1atR4Zb/ndc5PnTqFNj2dr5VKJlpYlz9RFHFLS8O3bt0XfmOtzZMnT5j1zTfMUiotIhtrEEU8dTo6DBzIAisVezGbzRw4cIA9e/bw9OlTbGxsqFSpEj179iRPnjzodDqqVqqEzb17nFapXqvLUV6no1zr1mz+/fdMz926ZUseHjyIO3AmTx7Cb9zIdmrpP7GUP8yRMp4gCC84elEUcXNz49NPP2XcuHHAs/KDBQsWZOXKlXTr1u2tY0qO/sNk8eLFDB8+nM8+++wF5avn5Uyjo6PR6/VoNBrKlSuXqZSrzHD+/HlWr15NqK0tVTOxmjptMlEzLY1BFRdQLl/dHM//b+BR2j3mnu+Kp5c7gwcPssrKURRFNm/ezOmTJ9lrY0PTV2xvi6LIcoOBsSYTHiVKEBERQTejkaVWvPlYZTDQV6tldt0iCEDYkzQux2m5+jgFg1mkWNEiTJz8Ob169UKj0ZCYmMiaNWuYN28ejx49ooi7O6qbNzmtVltU1AngF72eMXo906ZPz1RevyVZs3o18ZcuccvGJseVJDcZDHTTagkLC6NixYoWsjDrnDt3jho1ajBSLufHV8T2pIsittlQtFu8eDHDhg5FhJcWvjnlvVTGu3v3LjExMTRu3DjjMUdHR/z8/Dh16tQrHb1Op0On02X8n5SUZEmTJHIBURT55ZdfKF++/Evylmq1mpo1a1ptbm9vb5wdHFig1bLsLY5eFEW+1BtwUbtSxvntgYL/FQrYFmVghfksCRvC0iXLGDCw/2vFV7KD2Wxmy+bNnDx1il81mlc6eXi2cBigUlHTZKLJ7dv/x955R0V1vH/4udspUkRUELA37F1RYk1ijbHEEkvUKBaM6flG04up9ti7xt5j1MRoNCpYAXtvKE1EpbP93t8fCD+NgpRdxHifc/Z43J07M3d32c/MO2+hRMmSLIyPp7dK9Yhnsy2IEUXeNhrpWaUkfWtkmlr73H/NLErsuZnMsjO3CQoKYuyY0ZRwdSMtLQ2LxUKPHj3o168fvXv3ZoNOZ3ORlySJmVYrdevUKXKRB2gVGMj0iAh2Wa2Feu9TJIkPrVZe7tDhqYo8QJMmTZg1axajR49GA/z0L7HPqgaQX8dUZ2dnJOC9994rUstLfrCpTSyrsMW/f+zLlCmTY9GL77//HldX1+xHVgYnmWeHQ4cOcebMGQICAop8bKVSSfPAQFZaLCQ+wTj1q8XCHxYzr1SdUKCUo/9lqrg1IajuPG5cj2LypKlERkbapN87d+4wZ+ZMDh86xGKdjmF5sBb4K5X8rVZjunMHz5IlGWaxEFXAcrw5YZQkBhkN6LRKvmj56G+OWiHwcgU3VnWtxt99ajGwhgfG1CQsZjNvv/02K1euZNeuXXir1bxih0XIWVHkosVCMzsuknOjQoUKeHl6sraQaVw/MBpJVKmYt3ChjWZWOEaNGsWMGTOYZDbTw2h8qMxz1n45ISEhX33evn0bhULxxDC/p8lT97ofP348ycnJ2Y/c6g/LFE+2bt2Km5ubTR1t8kOTJk0wSBJ7cnHeuiaKjDOaaVS6M3VKFa6Qy3+VKm5NeKfBanRGD6ZPn87WrVvR6/VPvvAxWCwW9u/fz88//IDxxg3+dnBgaD6OBGoolSzTaEi4dw+zszMdTCaibST2Bkmij8HAQVFkeodKuGpzF+rKbjo+D/Dl6IA6BNcvw4xp02jauBFbN22iD6C2Qwhg2P17tXWOg7wiCAIVqlThWCH6WGgyscBsZtLUqZQvX3xSTb/11lts2bKFQyVKUMtoZJHJhFGSUAsCbTQaVi1fnq/+VqxYwcsvv2zXiIjCYtOZZWVGi4+Pf+j5+Pj47Nf+jVarxcXF5aGHzLNFWFgYvr6+T+2L7u7ujpuzM+E5CMFNUaSd3oRW60WPqp8U8eyeLco6VWZc/ZV0qfgO+/45wBeff8m6deuIjY3N0/X37t1j+/btfP3552zatIlhgsBZBwfaFGDX20Wl4g21Gr3BQIaHBwEmE/8U0hP/qijSQa/nL9HK3Jcq09z78RkZH4ejWsl7jb3Z8mp1THGR3EpI4KYkYbJDHvVwq5WyHh5PNV+6j48P5y2WAuWJX2AyEWQ0EjxmDEFBQXaYXeHo3r07Zy9e5OXevRluNOJrNDLeaKQDcODQIU6ePJmnfo4cOUJERMRD6Y+LIza1OVWsWJGyZcvy999/U79+fSDzzP3IkSOMHj3alkPJFBMkSSI8PNyu5/B5oZyfH2GXLj3y/BGrlV4GE0Z1KUbXX4yTuujPO581lAoV7f3epHGZbhyK28Ch8HUcPHgQZydn/Mr74efnh7u7O0qlEqvVSmpqKtFRUcTcuEFCUhIlFAqGKJWMdnSkZiHDzSZrtazNyOC1/v1ZvmQJbZOSGKNW861Wi3s+dtImSWKe2czHJiMejmpWtq9Co7IFSxJUu5Qjv3Wvyszjt5h9PI5Wej07dLrHpkMtKCclCa/HlLstSnx8fLCSmbu9UR4/xyRJ4n2jkcVmM2ODg5k+Y4bdyk0XllKlSrFq9Wo+/+IL5s6dy5xFi0hOS0OlUtGvb19CDx7MMY4eMk38gwcPpmbNmnTq1KkIZ55/8i30aWlpXLlyJfv/169f58SJE5QsWRI/Pz/eeecdvv32W6pWrZodXuft7V1snRRkCkd8fDxJSUk2qUNeGLzLlePMA0JvkCS+MJmYZDLh61yT4XVm4qbNWx1smUxctaXpWGEML/qN4MK9UHbfXMCVy+eJi4t7qDiJk0JBfYWCgYJAE52ObjYsMeshCPRQKJg7axaCZGWgvydLLt5hSbqZfio1I9RqGisUjzWfi5LEZUniV7OZBRYzt0WJQf6l+LiZD07qwi1ANEoF7zX2pkN5V4btuMwLBj1/6RzwsZHYJwsCJfKRRMoeZCWxSs3Djt4gSay3WBhvtZKiVrNg9mzefPPNYivyD1KjRg2mTZvGTz/9xJkzZ9i2bRs//vgjAQEBzJs3jxdeeOGh+5AkiT179jBq1ChSUlI4ePCgzVIS24t8C31YWBht27bN/v97770HwBtvvMHSpUv56KOPSE9PJygoiKSkJFq1asWff/5ZrEr2ydiOrNzlT/vz1el0ZIgiUaLIfLOZOSYTyYKSThXfoa3vEJQKOdtzQVEq1NQq1QYntRvTjw9k9s+zGT58ONvve9Db2uP8QRIliXBJRIXEii5VqVfaibcberHu4h1Wnk1gSUYGWgHqKJXUQMBBEDBJEjeRiBBFkkUJZ5WCXjVLMdDfk6ruts0/XtfTifWv1mDg75cINOg5YCOxt5D/KpAPYrVaiY+P5/bt25hMmcWbNBoNpUuXpkyZMnkSpqzxt1osuAsC/v9aUMWLIuGiyF6LhaWSxB2Lha6dOzNrzhz8nrI1oiBoNBoaNmxIw4YN6devH7169aJNmzb4+/vTs2dP3N3dM3Pdb9jAxYsXqV+/Pjt37qRSpUpPe+pPJN+/fm3atCG30HtBEPj666/5+uuvCzUxmWeDQqRhsCmCIJAGlE9PR6NSoVCX4IP6qyjjVHBnplvpV7mZchqTqEerdKSyWxNK6p6u5eJp4udSBxcHD9auXYtaEGhvZ5FPlyS66PXcVgqsf6U6NUpmirSno5rgBl6MrFeWE7fTOX0ng9MJ6VxKMWK0iKiVCko5qgnydKR2KUcal3Uu9A4+Nyq66lj3ag36bLnAiwY9+3UOeBZS7HWAOZ8e78nJyRw6dIjz588TGxub4/VqtRpvb29q1KhBixYtcqyCl3X9VLOZqWYzWoUCT5UKJZmfzZ37r3u4uTFoyBBGjRr11BxybU21atU4deoUe/fuZfbs2SxatIjU1FRKlChBYGAg8+fPJzAw8JmwWIBc1EamkGRVaMrvj5KtMZlMaHW6zBKhH31MdfWLBRJ5SZI4dWc3oXGruXzvKAAqpQqL1YJCUODv8QIvlBtEVfdmtr6FYo9CUFLDNZCjR/dSR622W+1yyPwc+hoMnBIkVnauli3yD6JSCDQu60zjAp6125JyzhpWdKvGa1su0NlgINTBAU0h3h8/SeJcHsO8oqKi+Pvvvzl16hQqlYpatWrRoEGDh8oCQ2bOkqzMlDdv3mTv3r3s2rWLOnXq0L59+0d24Xfv3gXgzJkz3Lt3j+PHj3P37l0sFguO99PZNmrUiAoVKjwzgpcfBEGgXbt2tGv37EfpyEIvUyjKli2LTqcjPj4ef/+nVyTm1q1bVKxYkWvXrnErPg6jw05O3NmBVbSgUmhw0XhSzskfvxK1qODagLJOj+a5FyWRTZe/IzR2DS0DWvH1W6vp3r07Dg4OpKSksGbNGn6ZMZPZJ9+ka6V3ae9XvApXFAWVXBsSfmsLtewQO/4gC8xmtlssLO5Yhfqlnew6lq2o6KpjUaeq9PztAt+ZTHxZiKRDjRUK/oqORpKkHEXUYrHwxx9/sGfPHkqVKsWrr75KkyZNciyPqlKpHqomqNfrCQsL48CBA0ydOpW2bdvSqVOn7MyIUVFRuLu74+/vjyAIBAYGPtSfXq/nzp07XL9+Ha1WS5kyZVDZ+XshUzDkT0WmUKhUKurXr//U8h+kpaVx+PBhzp49i9ls5tKlS/j6+uLn54ezszNKpRKLxUJiYiLXb4Ry+PIGJEmigmtdWnr1p57nS6iVmT/Iv1+dzMG4tcyfP/+RspcuLi4EBQUxYsQIPv/8c7799lu0Skdalev/NG77qeFTwh8F2MzZ7nHcEEXeN5noW82Ddn7PVpREvdJOBDfwYmJEHK+qVNQvoJNWI6WSjPtC6unp+cjrt27dYunSpSQkJNCpUyfat2+fb4cwBwcHAgMDCQgIYO/evfzxxx+cOXOGIUOG4O3tTVRUFI0bN85eaJjNZrZu3cr27dsJO3SIc5cuYX0gpNVBq6VenTo0bt6cPn360KpVq//kTv9ZRBZ6mQIRGRnJvHnzOHDgAFeuXCnys3qr1cru3bvZtWsXAA0aNCAgIAA/P79cf/CMRiMXLlwgNOQgKy+MZ+v1SfSu8hmlHSvyT/QyJk2alGtta0EQ+Oabb7h37x4L5k2iYenOOD5HIXuZFf8E7FVAVpIk3jQaKKFT8UnAs5klc2yDsuy6nsiQFANHHRwLZMJvplSiEQROnz79iOn45s2bzJ07F1dXVz744AO8vLwKNV+lUkmHDh2oXbs2y5cvZ+bMmbzxxhtcuXKF/v37k5aWxpQpU5g3axaxt29TT6OhuSQRrFZTXqFADRiAS6JI+IkTbD91ipkzZ1K7Rg3Gvfcew4YNK/Ze6f91ClXUxh7IRW2KN/fu3WP48OFs2bKFEiVK0KVLF5KTk9mxYwfvv/9+kaQwjo2NZdWqVcTGxtK2bVvatWuHk1P+zbvx8fH8vnUbZ86extOhPJJjBjGx0WjyULErPj4eXx9fOpd/lza+gwtyG88sn4Q0px0Gfs9jxcD8MN9kYqTRyLJOVWjt++wuoM7cyaD75vN8qtYU2IQ/yGBgp7Mz4z/7LNsDPjY2lpkzZ+Lp6cnIkSOzQ+BshV6vZ/78+URHRyOKIqtXr+aj994jPi6ONxQKRqvV1M1DTYk9ViuzLBa2mM20aNaMJcuXU61aNZvO9XnAVnpYfHP2yRQ7bt++TcuWLTlw4ABz587NFtzffvsNb29vQkND7T6H06dPM2XKFCwWC++88w7dunUrkMhDZg2GN4cPo3///twzRRM0ckSeRD7r2p69enE0YVOBxn6WcdF4cczGuechM9nKByYTfap5PNMiD5lJdYIbeDHRZOJ6Ad+rMWo1CYmJXLhwAci0Ri1atAh3d3e7iDxkmvNHjhxJqVKlEASBPn36UP72bU7rdMzR6Z4o8pBp9WqvUrFJp2OfgwO3IyKoV6cOq1atsvl8ZfKGLPQyeUKSJHr27EliYiIHDx4kKCgoW2BVKhWjR48mIiIiO67eHpw8eZIlS5ZQq1Yt3n//fZvE6gqCQI0aNbBarfnO7hcQ0IK76c9fbQZPRz/iRZE4G4v9crMZPRIfNC1n036fFqPrl8VRrWBeASNSmisUNFGp+G3LFiwWC7///jupqakMHTrULiKfhVarpVy5clgsFn7QaNij0VC5gOGCgSoVJ7VaXpMkBg4cyOLFi208W5m8IAu9TJ7Yv38/oaGhLFu2jKpVqz7yelBQEDqdjt9++80u41+7do3ly5dTr1493njjDZvWTBfvC1Z+PYZVKhUW0VJscgkUFb7OtRCAdYXMO/8gkiQx22Lm5QpulHa03Wf7NHFQKehdvRSLLGYMBfyO1ARuJySwevVqQkJC6Nq1K6VKlbLtRP/Frl27CAsLY75Wy/+02kLXo3cUBJZqtQSpVIwYPpzt27fbaKYyeUV2xpPJE7Nnz6Z69eq89NJLj329dOnSTJ8+naFDh1KvXj1q165ts7GNRiMrV67Ez8+PAQMG2Lx4jqOjIwqF4qHUznnhypUruOg8njvPYk/HCkjAbFFkXC7hX/lhr9XKRavIV7VKF36CxYgB/p4sPnObDRYLA/OxOJUkiU9MJpZbLHTu3Jk///yT8uXL06pVKzvONtPR788//uBzjYYReTzGygsKQWC2VksU8OYbb3D24kU8PDxs1r8t0ev1nDp1ivDwcG7evInRaESj0eDj40OjRo2oX7++XS0q9kAWepknIkkSW7Zs4auvvsr1R/2NN95g/fr1rF+/nnLlyuHu7m6T8bdt20ZKSgqjRo2yS5yuVqulVq1azJs3j+Dg4DwJl8FgYNnSX6nn0dnm8ynulNBkFvq4ZDazRamkhw2sK7PNZqq5amnm9fST39iSym46WnmXYPbtjDwL/W1RZLTJxCazmUmTJtG2bVt27NhB27Zt7Voh0mKxsGblSuoolXxqQ5HPQiEILNBoqJWczLi33mJlMTqzF0WRv/76i9kzZ7Ljjz+wiiIqQcBXo0EHGIFosxmTKKJQKHixXTvGvPUWXbp0eSYiCmTTvcwTSU9Px2QyPfFMXBAEFi1ahJubG3PnziUpKanQY0dFRXHgwAG6du362HhiW9GyZUvOnDnD33//naf2y5cv517iHQK8+9htTsUVlSJTBF5o1YoxViv3Cnl0ESeKbLFYGFi79H/SOjKwlieHLFZOWnMPSjRJEsvMZmoZjex3dmb9+vW8//77LFy4EDc3N+rUqWPXeYaHhxMXH88yjeaxRYJsgbdCwWSVilWrV3P69Gm7jJFf9u7dS82qVenUqRM3d+1iilrNMUdHUp2cuKbRcE6j4apGQ6qjIxGOjsxUq0ncv5/u3btTpUIFduzY8bRv4YnIQi/zRLJSaBoMhie2zSpTrFKp+OWXX7h161ahxj5w4ADu7u6PZOWyNdWqVaNy5cr069ePM2fO5Np27969jBv3No3LdqO0YwW7zqs4ohAydzATPv0UvVZLsNFYKD+FA1YrVqBTRdtYgIob7cu7oVEI7HqMT4NekjhitfLp/ZroQwwG2nbvztkLF+jduzeSJLFhwwYaNmxo953jwf37eVmlop6dxxmkUlFWrWbOnDl2HedJpKWlETxmDO3atcMrOppQR0eOazSM02horFSi+9diRyMINFAqGa3RcESr5ZijI9Xj4+nSpQtDhwyxycbGXshCL/NE1Go1VatWZc+ePXlqX7lyZQ4ePIgoivz000/s3r0b6xN2M48jPT2d48ePExAQYFeTJWRW6ho2bBgODg4EBAQwceJE4uPjH2pz/fp1PvroI15+6WUqOjekT9Uv7Tqn4oooZTovenl5MXfBAtaYzXxYCLEPF0W8HdV4/kec8P6NWiFQvaQDH5lM1DOZaG008oLRSG2TiRLp6TTPyGCGSkWfkSM5e/Ys6zZsoHTpTF+F2NhYEhISqFChgl3nGBUVxY2YGIJt6OSaE2pBIEgQ+HXpUrtG6eTG3bt3ade6NUvnz+cXrZY9Gg0BSmW+LEqNlUr+0GpZpNOxacUKAgMCiIuLs+OsC44s9DJ5YuTIkaxfv57bt2/nqb2TkxMZGRm0atWKHTt2MGPGDE6dOpUvwY+IiEAURZo3b17QaecLJycnxo4dS+3atfn666/x8fGhVatWdO3ajWZNm1O5cmVmT5/HC96DGV5rVnbq3OcNq5QZLqZUKunXrx/Tp09nstnMKKOxQN7l4aKV2p7PlnNTfqlf2omyZUrTsH9/jA0aEOfnxx1nZ9QqFUqFAqPJxMa1a/nwgw/4/PPP2b9/P5IkER4eDmD3RFSXL1/GUaGgUxGdN/dRqUi7n2u/qElJSeHl9u25fuoUB7Raxmo0BY4sEASBYWo1h7Ra7l25Qoc2bbhz546NZ1x4ZGc8mTwxdOhQPv30U8aPH8/ChQtzXflKksQnn3yCJEmsX7+ea9eu8e6777J48WLc3d1p2rQpVapUwdfX97F17FNSUoiKimLfvn34+PhQokQJe97aQ+h0Ovr27Uu3bt04ePAg27Ztw9upGmWdqtKn2pc0LN0ZjdL2GeGeJTLMKQDZzpbjxo3DycmJMaNGsd9oZIlaTfM8CoYkZdaNH+b5bBSuKSildCri4uNYu2oVRrOZWhoNraxWaimVOCkUiEBccjLhu3czd/duvvnmG/yrVaNStWo4OjrmWErWVty8eZP6CgXKIvKRqKFQ4KhUEhYWRps2bYpkzCyGDxvGlbNn2afR2OyYwl+pZI8gEHjtGgP69+fPv/4qVv4mstDL5ImSJUsyd+5chgwZgqOjI1OmTHlsLLvFYuGjjz5i3rx5LFiwgNKlS1O6dGkOHTrE8ePHmTNnDqtWrWLnzp1AZoY5Z2dnFAoFVquVxMREEhMTgcwjg5o1axbpfWbh6OhIhw4dCDsaQQVVfV6r9vlTmUdxJMWUgCAI2eZlgDfffJMWLVowZNAgWh4/TneVijEqFe2fYA69JookihK1S/03d/RGq8j08DjmnbiFlyAQDAx3dKRMLkdRkiSxV6Vi9vXrbLl0CaVSSWRkJBUr5r/scl65dfMmfYpQmJSCQH2lkuPHjxfZmEBmVNDGjazR6Wzui1BdoWCZWk3n3btZtGgRw4cPt2n/hUEWepk888Ybb2A0Ghk9ejSbN28mKCiI1157DXd3d5KSkti4cSPz5s0jJiaGmTNnPvJFb9CgAfPnz2fOnDlcuHCB8PBwjh8/TmJiIiaTCZ1OR5kyZWjUqBHVqlWjXr16RZI7Pzd8y5cj+uLZpzqH4kaKKQHPUqUfCXX09/fn4JEjLFq0iF+mTuXFixfx02hoLoo0UiioolCgFQSMksQ1SSLcaiXL66PWf1DoI5MNBP15levJBr7UaPhfHr3ZBUGgnUpFO5WK82o1gw0GZkyfzosvvUSnTp3sslNM1+spW8Q70DKiSOK9e0U2XnJyMmNGjqSXWk0fO5XT7aRSMVSt5r2336Zbt26UKVPGLuPkF1noZfJFUFAQzZs3Z9asWfz000988cUX2a85ODgwYMAAgoODqV+/fo59KJVKatWqRa1atRg8+PEFYS5dugRkWhKeJiVLluSy6cZTnUNxI8WYgFfZx1dMU6lUjBw5kqCgIEJCQti0aRPHDh9m2/HjZDwQteGo09GgcWPqu7jw119/4aot/rHI+eFKop7Xf7+Em1kk3NGROgXcPdZUKjns6MiPJhOf/PUXaampvNanj83FXhTFInfYUgBWG2ZXfBK//voriUlJTHN0zPX9M0gSGywWVlmt3FIoUALlRZFhKhUvK5VPPN6YpNWyRq9n0aJFTJgwwcZ3UTBkZzyZfFO3bt3snfvevXvZunUre/fuJTY2lgULFuQq8nnFaDQC+U9La2tUKhVm0fRU51DcuGuIwmDSs23btkciE7IQBAFPT09EUeT0uXNk3P88ASpUqMCXX3/N1u3b6d27N4IAGkXxOc8sLLfSTQzcdonSZpEDOocCi3wWSkFgglbLEp2Og4cOsW3bNhvN9P/RqNUUtf97OuBQwIJU+UWSJGbPmEEPlQqfXI5NZptM+BoMDDIY0DdvTrOhQ2kweDDXatSgi15PVaORbU9YnJQUBPorFMydObNA0Ub2QBZ6mQLj6upKmzZt6NatG23atLGpw1DW+f/T/kOxWq2oFLLhKwtJkriZeobr16/TrVs3vLy86NSpE9u3b3/os5o2bRr+/v6sXr2a4OBgDhw4QEREBH/88QctW7bk008/pVq1aly4cAG1QlGsHJcKgyRJfLzvBoLRyl86B0rbMCx0iFrNz1otf//9d7bFy1aULluW03aoSJgbpxUKu/jgSJKUXb8ii4iICM5fvszIXDYOnxiNBBuNdH/jDS5cuMDe/fuZM2cO8+fPJ/zkSQ4fPkyNdu3objDw6xMKFY1Sq4mKi2Pfvn02uafCIv+CyRRLshYN/46zjYuLIzQ0lMjIyOwc1Fk5wL29vW0+j7S0NHSqovP6L+4kGmIxWNIYPnw4Xl5eXLp0iUOHDtG1a1fKly/P7NmzuXTpEu+++y4ffvgh33zzTXbCpSw6duzIpEmT6Nu3LzNnzsRkFZFslDP/abP+0l3+iU5hm4MDXnbI/fCeWs3vVivrVq3iw/HjH3lvC0o5Pz/CIiNt0ldeiBdFYkwmGjVq9NjXRVEkJiaG1NRUnJ2dKVeuXK4Jg5KTk1m+fDnz58/n4sWLWCwWSpYsSc+ePRkzZgxHjx5FJQi0yqGP5WYz35lM/Pzzz3zwwQePvC4IAs2aNeP37dsJGjGCYUuXUk2hoFkO/TVSKHBWKjl27Bjt2rXLwztiX2ShlymWlClTBk9PT6Kjo6lXrx4pKSmsWrWKCxcu4OnpSY8ePShZsiSJiYls2bKFgwcPUq1aNV5//XWbWhaio2Io5+hvs/6edaLSzgHg5+eHi4sLLVq0oHnz5pnFUP78ky5duiAIAh9++CE//fRTjv2ULVuWP/74g7p163L16lWMVgmd6tkWeoNF5IdD0QxSq+hipyMnhSCwRKvFPzmZffv25VhkKr+UL1+ePRYLl0WRqnZOTgWw5771p0mTJg89f/fuXZYsWcKcOXO4du1a9vO+vr6MHDmS4cOHP+LgtmzZMsaMGYPJZKJnz56MGjUKjUbDjRs3WLZsGQsWLMDX15faavUj2e4g0wLwndVKj+7dHyvyD6JUKpk7bx6HQkL4OTKSDTkIvUIQaHg/fLA4IJvuZYolgiDQuHFjoqOjSUpKYsaMGSQlJbF69Wqio6OZN28e33//PXPnziUqKop169aRlpbGjBkzssPzCovVaiUmJgbfErLQZ3Ej5RSuJdxwcXHJfk4QBMqXL09QUBDVqlXD1dWVb7755ol9OTo6MmrUKADuGYrOKcte/HE9kXsmK59q7JtIqZJCwQCVisMHDtjsaKtmzZo463TMMxWNP8pcUaR1q1aUL18++7ndu3dTqVIlPvnkEwICAti6dSshISFs376dl156iYkTJ1KxYkU2b96cfc2cOXMYMmQIffr04caNG6xdu5bg4GBGjBjBt99+y/Xr11mzZg3xsbHUyuFoYq/VykWzmbfffTdPc1er1QS//TZbLBZicjnuqC1JXHxCOu2iQhZ6mWJL06ZNuXHjBosXL0aj0XDkyBH69euH5l+VtdRqNa+99hpHjx7F2dmZRYsWPXJGVxBu3ryJ2WLCt4TtSu4+65xL3Ef1mtUe+5rZbCY6OpqRI0fm2aTcp09mUaCzdzJsNsenxa9nbtNBpaRaEeyIg9Vq7qWmcvasbUI/NRoNTQMCWGS1kl7IIkVP4pTVyn6TieBx47Kf27t3L507dyYgIICoqCh+/fVXunXrRsuWLencuTMLFy4kJiaGrl270rt3b7Zu3Up4eDhjx45l3LhxLF68+LFHdyqVir59+1KlUiWcczga+s1ioaKPDy+88EKe72HQoEFIgsD2XBzznAWBjIzi8b2WhV6m2NK/f38yMjK4efNmdj363ChXrlz2jv/ChQuFHv/QoUO4O3hR0bV+ofv6L5CQcZP4tGvUrv34hU9iYiIZGRl07pz30r2+vr44Ozpw+hkX+mSjhYiEDAaqiiZff0OlkuoqlU2+51m0bNmSDEHgkwciJGyNKEkEWyxULl+eV199FcgsltW3b19at27N1q1bH0rE9CDu7u6sWbOGHj168PrrrzN58mT8/PyYMmXKE/07nEuUICfbx11JwtfPL18+Ii4uLrg5O+daudEiSU89aigLWehlii3Vq1endOnS1KxZk9atW+fpmhYtWtCgQQNCQ0MLNXZ6ejrHI44TULZvdrW2552zd/9BpVRTvXr1x75uum/2dcpHyJQgCHiXK8ephHSbzPFpkWWRaFIEu/ksmgMxN2yX48HDw4PO3boxw2zmgJ3i22eYzYSYTCxevjw7smbt2rUkJCQwe/bsx2bbfBCFQsHMmTMxGo2sW7eO0aNH56mqn4enJzmVm9EJAvp87rwlScJgMj32zD+LW5KER6lS+erXXshCL1NskSSJe/fuMXTo0DyvtgVBYMiQIZw7d65Q5vu9e/ciihLNvXoWuI//Gmfu/k3ValVzNMs7OGTWAMhvaWIXVzdOJWQUqtTt0+bMnQwchcw0qEVFI6WSmLg4m4agvvDCC1QqX54+RiORNg6322ux8LHZzFtvvfWQmXzOnDm89NJLVK1aNU/9lC1blp49M/8us45+nkSDRo2IyOE1f4WCk2fO5KsYzZEjR8gwGKiZy+cdrlTSsGnTPPdpT2Shlym2ZGRkYLFYKFeuXL6u8/b2zlxxP5CJLT9ERUXx999/YxUtbLz0NcnGvFXs+y8Tn36Nq0nhNGrUMMc2JUuWpEyZMvz666957tdoNHL58mXuGSzcSs89Nrk4c0dvwbsIi8IA+AoCFlFEr9fbrE+FQkGFKlVIEEVe0Ou5YiOx322x0M1konW7dvz888/Zz0uSRFhYGN26dctXf927d8dqtT7ir5MTjRo1Is5sfqzz3GCVCkEUWbJkSZ7HnzN7NhXVal7MwZqQIklcNBpzDB8samShlym2ZFW2y69DS9YP35PMgI/DYrGwesUKaiuVzNZoiL33Dz8d7caxW1uf6R1nYTkYuw4nR+dcsx4qFApatGjBpk2biI2NzVO/GzZsIDk5GY1azdarRZf33NaYRYmijg5U3V9U2GpHb7Va2bp1K3///TeBbdpgLlmSxno9v5rNBf7umySJL4xGOhkM1G/alG7du/POO+/Qt29fevToQZ8+fbBarTg7O+er36z2OWVm/DeBgYGoVSrWPeZIopRCQV+lksk//sjNmzef2Nfhw4dZs3o1owQhx/K2681mBEEo8sp8OSELvUyxRalU4u/vz19//ZWv63bu3Enp0qXzLfSiKLJi+XLu3L7NCo2G0Vot5x11vCqYWHVhAluvTnouxd5ozeDo7S00b9Hsic5FTZs2RafT0atXL9LTcz93P3fuHG+99RZdu3alb9++rLyYiPiMvr9apYC+iKeuv/9e3bNBYZhbt24xffp09u7dS/fu3Xn11Vd56913qVK/PoMNBroajYTlY0FhlSR2WCw0Nhr51mxGUigIPXSIt99+m99//52TJ09y8eJFTp8+jUKhICEhIV/zvX0708r2zz//5Km9p6cn3bt35xeT6bHfsR81GhxSUmjfujWXL1/OsZ/9+/fT+eWXaSoIjMvBmiBJErMlic4dO1KhQoU8zc/eFA+XQBmZHBg1ahTvvfcesbGxecp8l5CQwPr16+nUqVO+xrFaraxasYLTp06xQaej7n2TXElBYKWDjgCTgrHRy5CQ6F75w/9EFre8Eh6/DaM5nYCAgCe2dXR05M0332Tu3Lm88MILTJs2jVatWj30fmU5Ur399tuULl2aV155hb179xKdlEHb1WfQKRU4qhVUKulAnVKO1CvtRH3P3AuRPG3Ku2iJsoqkSxJORTTPC6KIRqHgl19+oX379rRs2TLfyaKSk5MJDQ1lz549lCxZknHjxmWXw3VycmLQ4MHUb9CALRs20CQ5mUYqFUOUSpoqldRVKB5yRkuWJCKsVkKtVhZYrdy0WlECLm5utGzZkqpVq+Lt7f2IuX3hwoWsWLGCDz74IM+f8apVqyhdujQLFy7knXfeydN1vn5+bJAktlgs9PzXJqCsQsHfGg2dYmLwr1mTHq++StCoUdSoUQOr1UpYWBhzZs7k73/+oY1azWatNkdHvL+tViJMJraPHZuneykKBKmYbVFSUlJwdXUlOTn5oaQcMs8nycnJlCtXjm7durFy5UoUT6jj/cYbb7BmzRq+/PLLPHt/37lzhzUrV3IjMpJVWi29c7AEzDaZCDYa6Vv9K5p79SrQ/TxrmK1GvjvWifLVvRk6dEier4uOjmbFihXcunULf39/unbtirOzM/Hx8axdu5Y7d+7gVaYM8bdvI0oSFTQaGogiXoKAEkiVJM4gccpqxSRBZRctA2p50quaB67a4rc/OXMng66bzhPq6EiAjeuc58Srej2pLVvSqGlTpk6ditVqpW7dujRq1Ag/Pz9cXV0fK4DJycncvHmT8PBwTp06hUqlIjAwkJdffjnHM29RFDl37hyh+/dz6coVrKKIShDwVCpRk1nx7fb9Hb9KocAiitmLOH9//1z/bs+fP8+8efMICQmhZcuWT7zvs2fPUrt2bT777DO++eYbvvvuO8aPH5/rNVevXqV58+ZoVCrEu3c5q9VS8jHvTYoksdRsZo4kceFfyYMCNBpGKxT0UanQ5CDyaZJEHaOR8s2asWffvlzvOy/YSg9loZcp9qxfv56+ffsyePBg5syZk+3d/SAGg4G33nqLhQsXMmjQoDw5wYiimJl5a+tWykoSv2o0BD7BND1Ub2CtpOLDJltx1z2+VOt/ib1RS9l2bQofj/84x/jmnBBFkcuXLxMaGkpcXBwpKSmo1WrcXVyIio2lrkbDGEGgt1qNRw4/nCZJItRqZZ7ZzEaLBReNkm9eKE+XSu62uD2bYbKK1Flygq9Uav5no/zzuWGWJHyMRoa9/z7ff/89N2/epEOHDly9ejU72sTV1ZUyZcpkR0kYDAZiY2Ozj1TKlClDq1ataNy48WP/pnIc22wmLi6OqKgoUlJSMgs/qVRotVoOHDhASkoKXbp0oXXr1nkSOlEUmTRpEjqdjkOHDuX6PUtKSqJ169YkJydz8eJFvv/+e7766is++eQTxo8f/8jiXpIkQkJC6NevH87OzmzcuJHAgABeNhhYrdXmaAmQJInjosgtScosUysI1HjCAk6SJIKMRlYplZw+e5ZKlSo98d6fhCz0Ms8Vq1atYsiQIZQoUYKhQ4fy2muvZee637hxIwsWLCAlJYU+ffrQrFmzXPtKTU3lyJEjHDpwgLvJyYxRq/lRq80xc9aDJEkS/hkG3N2aMaLuPFvdXrFEb0nl2yMvU69xbfr2zVsYU24sWLCAC2fPUk6tZt792t75McfHiiLjjEY2Wix0rujGj60rUEJTfHIcjPv7Gucik7no4Jijk5at2Gg209tgICIiggYNGgCZRyK//vor06ZNeyRjnkqlomTJklSvXp0qVarg6+uLu7u7zY5D7ty5w8yZM1EqlQQFBT2Sjz4v1//yyy+4u7szf/582rdv/9AiIUuwx4wZQ0xMDPv376d27dpIksSPP/7IhAkTcHFxYciQIbRu3RqtVsv169dZvHgxERERNG7cmN9//52yZcuybt06+vbty3tqNZNyEfv88rXRyBcmEwsWLGD48OE26bPYCr3VauXLL7/MNtt5e3szZMgQPv300zy9obLQy+TE1atXmTt3LosXL37IAUmr1WZXsqtUsSK+fn6ZGdecnVEoFFitVu7evUt0dDQxN25wIyoKpSTRX6lkrEZDo3yaWleZzQwwGPioyRa8nKrY+jaLDVuvTiYkbiWffvYJrq6uhepr165dbN++nWEqFdN0OkoU8MdVkiTWWSwEGY1UKKnj167Vio0pP+xWGr23XuRPBwdetnNGtNYZGYRardSoVYu3336b4cOHZ/++SpLE9OnTeffdd+nZsydlypShcuXKdsvSlpKSwrRp01AqlYwdO7bA35W7d++ybNkybt68SZUqVejbty8lS5YkOTmZDRs2cO7cOZydndm/f3/24iaL69evM3/+fBYuXJgdDy8IAp07d2bMmDG8/PLLDyXWmTlzJm+99RYj1Gqma7U4FELsTZLEeKORKWYzEydOZMKECQXu698UW6H/7rvvmDJlCsuWLaNWrVqEhYUxdOhQJk6cyLgHchvnhCz0Mk9Cr9dz7tw5UlNTKVGiBDVr1iQsLIzWrVtTS6HgtiCQ8BgP4UoqFU2BAKWS13MxFz8JkyThk2GgetnX6FXtk0LeTfHkRsppph8fQKdOHQtdIW3fvn1s3ryZLzUaPtdobLKDOm618qJBj5+HA6u6VcdB9fQDiCRJovum80hJRsIcHHM8xy0sv1ssvKLX8+mnn3LmzBm2bNnC4MGDWbx4cbaYTZ06lfHjx/PDDz/Y1YlRkiQWLFhAdHQ07777Lu7uhTtSkSSJa9euERoayrVr19Dr9Tg4OODn50eVKlXYunUr77zzzkOx+A9itVpJTEzEaDRSsmTJXI8klixZwuiRI6kALFWraV4A34rjVitDLBbOWa1MnjIlTxqXH2ylhzZf4h08eJDu3bvTpUsXACpUqMDq1as5evSorYeSeU5xcHB45Az+0KFDOCmVHHJwwBmIlySSJAkTmSkuywgCrjb6wdMIAiNUCqbGb6FH1Y//cylyzVYjqy9+QjnvcrRv375QfUVFRfHbli28r1bzhQ3PrhsolfypcyDwTgY/HY3hiwBfm/VdUARB4PvWFei26TwTTSa+ssNZ/T1JIshioUunTnz99dcIgsDq1asZNGgQrq6uzJgxA8hcDGtstKjKjWPHjnHu3DmGDx9eaJGHzPewcuXKVK5c+bGvm0wmpkyZQs+ePWnRosUjryuVSkrlMe3s0KFDadasGUMHDyYgPJzuajXBKhXtlMpcj14kSeKA1cpsi4UNFgu1/f05tmJFrjkmnjY2XwYHBATw999/c+nSJQBOnjxJSEhIjuFORqORlJSUhx4yMvnl8KFDNFcoKCEICIJAWYWCGkoldZWZ1cRsJfJZdFAq0Vv13M6ItGm/xYGdN2Zzx3CTAQNfz1Me8ZzISj5UR6nkezuIXmOlku80Wpaeuc3RuFSb918QEg0WROA7s5mdNs4Xb5Yk3jAa0Wu1zFuwIFvE+/fvz6RJk/jll1+yY8CLIhQxLS2NzZs307hx4xwLHdmatm3b4ufnxxtvvIHFBu+vv78/oYcPM2fuXC5XqsSLej0VTSb66vX8ZDSyyWxmu8XCZrOZySYTr+v1VDGZaK3Xc9zXlynTpnE0IqJYizzYQeg//vhj+vXrR40aNVCr1TRo0IB33nmHAQMGPLb9999/j6ura/bD1/fpr8xlnj3CjxyhURHGWTe8L4BRqbYpE1pcOH83hD1RS+jY8WW8vAoXVXDgwAFu377Nco0GtZ0+m3FqNS1USj7df/OpJzM6lZDOqL8jad+uHS937EgPk4m/bCT2Rkmiv9HITklizfr1j6SFHjVqFB4eHsyZMwfIjIE3GAw2KdecE4cOHcJisWRXoSsKlEolvXr14vLly2zbts0mfapUKkaOHMnp8+fZv38/PceMIa5pU75WKullMNBVr6enwcBnQGSDBnQOCmLXrl1cuHKFcePG5TkN79PE5kK/bt06Vq5cyapVq4iIiGDZsmVMmjSJZcuWPbb9+PHjSU5Ozn5ERUXZekoy/3HS09OJunWL2kVYUMRVEPBVqInPuFpkY9qb2xnXWX7hA2rWqFFok70oioTu20c/lSo7+ZA9UAoC36o1XEo2cDguzW7jPIlL9/QM/uMqzq5ulPXyonLVqtRv3JiuRiM/GI1YCrEIuSSKvJCRwWazGfeSJTl9+jR37959qI1Op2Po0KEsXboUSZLw9/fHbDbnO+NcXhFFkYMHD9KgQYN8p68tLL6+vlSsWJFZs2bZtF9BEAgMDGTq1KnsDw0lOTWVO3fuEBMTQ0JCAilpaRw8epRffvmFDh06FOsETv/G5r+MH374Yfauvk6dOgwaNIh3332X77///rHttVotLi4uDz1kZPJDVi78EkU8rrMgYLIWrHBOcUNvTmH+qTGotQoGDR5U6EQfFy5c4E5SEsEFqDeQX9ooldRUKlhx9ukUHzqVkE7vrRdJM1koXdaLa9eusXHjRg4dOYKbhwefmM0EmEwctlrzZXVIkyR+Npmom5HBdVdXevfpQ/ny5ZkwYQJVqlRhxYoVD/VXv359EhMT0ev1NGyYWXwoOjra5vcLmUluEhMTadWqlV36fxIBAQHs3r2bK1eu2G0MpVKJh4cH3t7elCpVqtjUli8INhf6jIyMR34klEqlXU1IMs83Wd+3ov6GifCfcMSziCaWn3uPdGMsqWkpNnGcPX36NNVVKpoVgZVFEASGqtTsvpGMWSxa8/0/Ucn0/f0S7mW9OXfhIidPnuTgwYPcvHmTnTt30rhxY0RJ4maJErTIyKBBRgYLTSauiOJjRT9dkgixWHjLYMArI4OPTSaaBQby4fjxBAQEMGDAAL744gsqV67MoEGD6N69e3ZZ4KwdpiiKlCxZkvLlyxMZGWmX+7506RIeHh74+fnZpf8nUbduXRQKBXv37n0q4z9r2PyvsFu3bkycOJHt27cTGRnJ5s2bmTJlCj169LD1UDIyQGYlK6VCwe0iPKOVJIkEUcRBZXs7giRJxKVf4UriUa4nHyfZaB/zK4BVNLP87HtcSzzKdp2GjzQaNm/enO1MW1CiIyMJoGicwgCaKxQYRYnLibYr2ZobqSYrH++PZMgfV2jeKpAzZ889VE9doVDw0ksvsX37dr788kvi75vaY93cCDIaqZqejltGBi0MBl7U62mn11NTr8clLY1AvZ7lajUt2rfns88/p2fPng+dA5coUYJBgwYxbNgw9u/fT7Nmzbh69Spnz56lRIkS2dnhXn31VU6cOGETp7V/Ex0d/VT9qbRaLWXLliU8PPypzeFZwua2iF9++YXPPvuMMWPGcPv2bby9vRk5ciSff/65rYeSkQEy/+j9q1Xj+NWiOy+PkiTuSRbKOdfAYEknJu08UanniM+4hsmqByTUCi2lHMrjW8IfnxK1cFLnnkjEYEnj6K3fOBS/llup17KfVwgKanm0IcC7L9XdA2wmnhbRxK9n3+fC3X1scdDRTqWijVLJEVFk7cqVfDh+fHap4Hz1a7EQFx9PoyIw22fRQKlEAM4kZODv4WjXsfZHp/DR/pvcTjfyzTff8Mknn+T4mQiCwOeff86FCxfYtm0bH3/yCXq9nqioKKKiorh9+zYJZjMKhYJSTk7U9/HBx8cHLy+vJ0Y81K1bF19fX+bOnUtgYCBms5lBgwZlz2X06NFMnz6dEydO0LhxY5vdvyiKREdH8+KLL9qsz4JQrlw5OWw7j9hc6EuUKMG0adOYNm2arbuWkcmRRs2bE3bt2pMb2ogj93dJf0ctYvGZcUhIqFVqypQui65EpjiaTGZOxPyB0ZR5ju/jUpOWXv1oWLozGuXDiTzi06+x8NxoEvW3eLVHD0aMmE3FihUxm80cOHCAmb/MYt6pkTQu05W+1b9BpSiciBos6fx69j2uJB5is4OOzvfPHxWCwBKtltqpqezYvp2evfJfvCc1NRWLKFK5CJ0jnQWBskoFcemmJzcuILczzEwNi2X1hTs4OTjQ7ZXufPrpp0+8ThAE/ve//7FmzRrOnDlDvXr1qFmzJjVr1iz0nNzd3RkzZgwzZswgMTGRoUOHZr9WvXp12rZty4EDB2jYsGGh/S6ySE9Px2g05jvNra0pU6YMISEhT3UOzwrPrneBjMwDtG3blmVLl3JNraaSHQVGlCTmmM18aDYDIJRMoU/HTCepMmXKPLILE0WRO3fucPPmTSLCj7Pu/JdsvfYzgd4DebF8ECqFhnv6GOaceRPv8p4c2b7/kWIYtWrVYuTIkaxatYqhQ4dhvWBlYM0fUQgFu887+iiWnB5Div4m2xy0vPgvJ6OKCgX/U6mYeOgQHTt1wtExfztk6/2shPYv7fIwKgmMVtt6akiSxNFbaSw/m8DOyGR0Djo++ugjfvrpJ95///0891O/fn2aNm1KeHg49erVs+kcXV1dCQoK4ueff2bFihUP7d6/+OIL2rRpw4EDB2jdurVNxjPf/+6ri9Bi8yBWqxWDwYBCoUCvL5qjmmcdWehl/hP07t2bd956i7l6PT8VwNycF66KIsOMRvZbLDRp0oQ2bdo8Es/8bxQKBaVLl6Z06dI0btyYu3fvEhISwt/7FnDq7i76V5vIzhuzcC3lyD/79uZYuUsQBAYMGIBWq+W1116jTql2NCj9+CRUuXE58SjLz4yjtGRkl4MW/xzMw0FqNd9kZHDs2LF8C0TWYseY79kVDr0ksfLCPVy1Kl6pXJKyTuoCH3Pc0Zv541oiv55J4FKyAZ1CwMHBkT3//JPtyV6jRo189env78++ffsKNJ8nUaZMGbp27cqMGTPo1asXgYGBALRu3ZoxY8awcOFC/P398fT0LPRYD+bULypEUeT8+fOEhoZy/vz57LHVajWTJk1i6NCheHh4FNl8njWefoJoGRkb4OjoyNDhw1koSdy1ww/QJrOZOhkZnHN2Jjg4mAEDBjxR5B+Hh4cH3bt35/0P3kflamJaRH/O3w3hy6++yFMZ2N69exPY6gUOxq3N17hW0cLuGwuZd3I4zTByzDFnkQcoq1DwqkpF+JEj+RoHMo/vlILA1SKMtEm7/7nXrN+IH4/G0mLVaZr+eophf1xmSlgsuyKTiEszYbD8v7e7JEkYrSK3M8z8E5XMLxFxBO28QsCKUzT+9RRfhkZRO93CbgcHInUO+JhM9HzlFVJTU7Ovzw+iKNrMfP44XnjhBSpWrEhwcDCSJCFJEuHh4VSpUgWtVsvSpUsxGAofDprlGGiLvvJCYmIiU6ZMYcGCBajVaqZNm8b69etZvnw5ffr04ZNPPsHHx4cVK1YUyXyeReQdvcx/hg8++IDFCxYwzmBgpQ139SvMZt4wGKhXrx79Xn89u753YfD29ubd995h+vTpJCYm0rdv3zxfO/atYPr27cut9KuUdXp8TvAHuZV+lTXnPyYq7TwfajR8q9GgysNOt71CweZbtzCZTHnO/mU2m9mxYweCJBH+mMJC9uKE1YoENG3alCOHDzNRoyFDhPC4dFbFpHLnX2F3GoWAWZR48FlXhUAjhYKBCiWNdDpaKZV4PyDMfwkC9RISWLF8OQDh4eE5pvb+N5IkcezYsUJXAcwNhULBiy++yLx583j//ff5c9s2zl++jEoQqKxUcj09nXlz5zJy1KgCOVlmce/ePbRaLX/s2MH2LVtISUvDIoqoVSo8S5bEu3x5/Pz8qFu3bqHzoiQnJzNz5kwcHR0JCQkhIOBhZ9RBgwYxdepUPvjgAwYNGoTZbH7IT0EmE1noZf4zeHl5MWPWLAYPHkxPpZJeNjhD3GqxMMRgoEnTpvTt18+mOzKlUomzszP16tXL1zl4VjW5uPTLuQq9RTTxT9QydkbOopIgEOromK8KXY2USqxGI7GxsVSoUOGJ7SMjI1mzZg337t2jYdOmHD5xAkmSiiTE7rAoolGpmDNrFh9qNEx4YDEmSRJRksQJUSRFktBLEkZAAzgIAs5AXaWSSvfrJOSEj0LBTJWK13fvpkKFCsyZMyfPQh8SEsL58+cZPXp04W70Cej1etSCwIypU+mhVvOLgwMtlUp0gsBhq5WXb95k9owZDBo6NF9mfKvVSnh4OAcPHCAyKgpnQaDGvXs0VSjwU6lQAXrgwr17HLt7l9+OHWPzxo3UrVePF1q3pmLFigW6n5UrV6LVajl48CA+Pj6PbePp6cnSpUvR6XQEBQUREBBA9erVCzTefxVZ6GX+UwwcOJCtW7Yw6Lff8BQEXihENqtYUWSw0Uit2rVtLvJZWCyWfDu7ZZXeNIuPPwUXJSvh8dvYeX06icbbfKDR8JVGgy6fglvn/v3evn07R6EXRZELFy5w8OBBzp49S8OGDdm9ezeRkZF07dqVoyoVzeyYAhcyhXyxJOFZujS6hAS++pf1QRAE/AQBPxt8fv1UKlar1RxNTeX333/n2LFjNGnSJNdrrFYrX3/9NWXKlHko1t6WGAwG1qxaxYlTp3hVpeIXrRaff91vc6WS/TodPeLj+fnHH+nctSsvvPDCE7/Xt27dYs2KFURGR/OiSsU0nY4uKlWuVqEkSWKZ2czMM2eYfuIELVq0oHv37vmyJMTFxXHp0iXWrFmTo8hnIQgCM2bMYNOmTcydO5epU6fmeZznAVnoZf5TCILA8hUr6NKxIx1DQlgDvFIAsZckiSCjERwc6Nu/v93OVnU6HTExMfm6JjY2FuCRZD2iZOXs3X38eW0qsRnX6aFS852jIzUKKLQ6QUBB5g99YmIiKpUKi8VCSkpKdhz4tWvXSEhIoG7dusyfP58hQ4agUqmoUaMGFX19mX3rlt2Ffp/VynmTCUVcHNMLsKDJD4Ig8D+VilZ371K9enW6du3Kn3/+SYMGDR7b3mw2M2LECPbs2cPw4cPt8j1KT09n3uzZJMXFsUano49KlaNlop5SyWkHByYYjczYsoXwo0cJbNOG+vXrP/Z4JiQkhN82b6aCIHDQ0ZEWefws3QSBtzUa3pIk5pnNfHDkCJfOnWPI8OF5TrQTGhpK6dKl85xsTavVMnz4cObMmcO3336bnThIBgTpaZd8+hcpKSm4urqSnJws572XKTB6vZ7+ffvy2++/M1StZopWi1s+BGC12czrBgNvvvkmderUsds8jxw5wpo1a7h8+XKONbj/zZdffskP3/3MF83+xkFVgjRTIkdubeZwzCruGG/RWqnmR6260AIrSRKKtJwLxfj7+xMYGMiQIUNo1qzZI+IyadIkJvzvf4TrdNSxk9iLkkRrk4nLJUqQmphInKMjLnY+KpAkiQYmE95t25Jw9y5nzpxh4MCBBAcHZ5crTU1NZcWKFcyYMYPLly/Tv39/myatycJoNDLnl19IjYtjt1ZL/Xy8zyEWC9+YzfxlseCs01G3YUPKly+Pr68vZcqU4e+//2bHjh2MVav5SavFoRDva6Qo0tdo5LRCwYhRox4JIX0ckydPplOnTsybNy/P44SFhdGkSROOHTtml/e7qLGVHspCL/OfRZIkFi1axHtvv42j2cwYQWCEWo3XE3ZVFlGkmsGArmpVRo4aZdc5mkwmvvrqK0aMGJEnc6PBYKBC+YpUULTA3+MFTiXs4lTCXwiSSD+VkjEaNU0VCpuci98WRcqkp/PTTz9Rt25djEYjWq0WpVJJ//79qV69On/++WeO1cuMRiO1qlfHOSqKY46OdilVO91k4h2jkbq1a1PtwgXWOzg8+SIb8J3RyE8aDVGxsUybNo158+YRExODi4sLSqWS1NRURFGkTp06tG3bNk8+DgVhw/r1hB86RIhOl106Ob9cEUXmmEz8LklcsViQAAGQgK81Gj6zgfMpZEZGdDEYOKZQMO7ddx8pg2w2m4mNjSU1NRWz2czWrVsJCgrihx9+yPu9XLlC1apV2bNnD23btrXJvJ8mttJD2XQv859FEASGDx/OSy+9xLfffsuPv/7KNxkZtFapaCRkelh7CQJKQSBdkjgrioRbreySJOKtVkbaKMFIbmg0GgIDA5kxYwaBgYH07Nkzx7YWi4VBAwdx504Ct8UtHLm1mUoKFd+qFQxTayllY7Nw+P3wuF69ej2yA9u2bRsdOnSgdevWTJo0iTZt2jy0uEhPT2fFihXE3r6NQRT5xGi0eX6DCKuV8RYLY8eOZdnixbxehJn4GimVJKelcevWLT777DPGjx/Pjh07WLVqFWvXrqVHjx7Uq1cPNzc3u83h0qVLhISGMkOrLbDIA1RRKJis0zEZSJEktpjNvGk0Mkqt5lMb1lp3FgS26XQ0MxhYtXw573zwAXFxcRw+fJjr168TFxf3UPEztVrNvXv38jVGYmIikBniKfP/yDt6meeG5ORkfv31V/7evZuww4eJjo9/6HWdRkO9OnVISkvj7t27TJgwwa5xz1mIosiKFSs4ceIEn376KcHBwQ/F1EuSxNGjR5kwYQJ79+59JH7bjUzh6apS8YZajbuNds7fGI1M1em4m5T0WAvBiRMnGDBgAOfOnaNGjRp07NgRR0dH4uLi2LhxI6mpqfTr14+6desyfvx4vtFo+ESjsYm14aTVSgeTiUr16jF/0SLq16/PXw4Oj2T5sxd3RBHP9HTWrl1Lnz59sp9ftGgRw4cPZ8qUKXb97oiiyI8TJ1ItOZl/dDoUtqp/IEm0zMggGTju6Fgoc31OhFksNNXrcXVzIykpCTc3N2rUqIGPjw++vr6ULFkSlUrFb7/9xpUrV4iOjs5zSOtHH33ElClTuHDhAlWqVLH53IsaeUcvI5NPXF1dGTt2LGPHjgUgISGBe/fuYbFYcHBwwNfXF7VaTdmyZalXr16RiDxkxj8PHDgQNzc3fvjhB7777jteeeUVKlWqhNls5p9//uHkyZO4q1R8plLxmlqNThAwSxKRkkSE1coRq5WPjEYmGI0MUKv5WqN54hFFbkiSxHqgVWAg169fx2g0olarcXd3z85AVr9+fc6cOcP+/fuZM2cOf/75J3q9Hjc3N4KDgwkKCso2WZvNZj77/HNuSBJTtVqcCyEg681mRlgsVKlVix07d3L58mUAvIqoUh5AKYUCpSBw935VuiyyBMliseQ590BBuHjxIvF377LRwcFmIg+w2mLhqChy0E4if0MU+Z/ZjERmWFyvXr3w9/d/bAGfNm3acPjwYTZu3Mjrr7/+xL71ej2LFi3CarVy+/bt/4TQ2wpZ6GWeWzw9PR+JJY6LiyM+Pp7OnTsX6VwUCgWvvPIK7du3Z/fu3fz2228oFQrcBIE6osg2Bwc6KpUo//XjWxPodH8XGy+KLDKbmW42s9FsZoZOx4BcPLAfR5IksdpsZovFwllJ4syOHfy+fftDbby9vWncuDFNmzalf//+tG7dOtc0uZIk4ePjg06nY7HBwE6rlYVaLS8qlfma2y1R5C2TiQ1mMz1ffZUly5bh4uLChQsXgKJP86kShEdKwGZZYpKSkvKU6bCghIaEUEelIsDGTo6zTSZeUirz7F2fH5aZzQSbTGidnRn1+utPTCFctmxZatSowXvvvUfLli0pX758jm0lSSI4ODg7a2FWPn6ZTGShl5F5gKz61k+r1raTkxNdu3blwIEDfCAITMyHI1QZhYIJWi0jNRreMhgYZDCwS6VioU73REe4k1Yrv5jNrLRYMAPlfHwIuO+B7e7ujkqlwmq1kpaWRnR0NBcvXmTXrl189tlnvPzyywQHB9OlS5eHhFsURdauXcu7775LfHw8jRs3JiAggD+2bePla9eoq1IxVqmkdy7HDWZJ4pDVyhyzmQ33HcW8vb356OOPs02ZWXkF0vP8ThUesyRhFMVHciA0bNgQyKzXnpPQS5JEUlISMTExJCYmkpKSQnJyMikpqegzDIiiFUmUUCiVKJVKSrg44eLigouLC66urri5uXHu3Dmm2ugYJIvjViuHRZHNdqgV8aPRyMcmE02bNKFHz57Zn9mTGDBgANOnTycgIIAVK1Y84gsCmYvz9957jzVr1vD+++8zefJkuy6ynkVkoZeReYCrV6+i0Whwd3d/anNQKpWU9fAgKZ+OSFl4CAKrHBzoYjYzxGAgw2BgtU732AQn6ZLEBKORX8xm3EuUoF1gIM2bN8/1PDArZtxkMhEREcHBgwfp1q0bHTp0YNGiRfj5+REeHk6vXr2IiorC0dGRESNGUKtWLQBGjx3LxYsXCT1wgJHnzhFkNFJRpaIxUEYQUAJpwElJ4pTVikmSKOPhQdfAQMqXL8/mzZsJDAxk5cqVvPbaa1StWhVBEDgjijSxc8x+FmfvO439e1daqlQpfH19iYqKyhZ9q9XK9evXuXTpEjdvRhF9M5q0jMydp1KhwkVbChdNKVzU3rioSqAUMovxiBYRi8lIWspdrppvkWI8Q7opKXusKWaRY1Y9zZRKuqhUVCzkUdMfFgsuQFcb+zlMNZn42GTi5ZdfpmPHjvlanJQoUYK33nqLxYsX065dO/z9/RkyZAje3t7o9Xp27tzJ5s2bkSSJRo0acfnyZerWrZvvgkP/dWShl5F5AL1ej8bGO6WCoNZo0BfST3aAWk0JQaCnXs8HRiPT/rVTO2i1MtBoJEYQ6P7qqwQGBj72rDQnNBoNzZs3p1mzZpw/f57169dTq1YtgoODmT59OhaLhXLlyjFy5MiHQvAUCkV2PfbExESuXr1KVFQUJ6Ki0KenI4kiKo0GTy8vuvj64ufnR/ny5bN9Jt5++21WrVpF3759sVqt9OvXj2oVKxIeFcXQIiqdGm61olAosuPmHyQgIID9+/fj6+vL2bPnOH/2PBmGdJw1bviVqEuAZ2t8S9TCx7kmLtrS+So3bBFNxGdcIzr1HFGp5whJPcWatIu8ZTTir1TzqlLgFZWqQCGW4aJIQ6UyT3UQ8spui4X3jEbat2+f53TB/8bV1ZV33nmHy5cvExoayoQJE7KPTLy8vOjevTsuLi78+uuvhIeHM3fu3Kf+91vckIVeRuYBBEEo0vKbOSFJkk2crF5RqZii1fK20Uh3lYq293drm81m+hmN+Pj58eHAgYUqXyoIAv7+/nz00Uds2LCBn3/+GZVKhZeXF2PGjMnVTOvu7k7jxo3zldxEqVQyYMAAFAoFgwYNomzZsjRt2ZK9a9cWWW79vaJIrerVH8m+du7cOfR6Pbfi4lm+fDneJarRqswganm0waeEf75E/XGoFBrKOdegnHMNmnllhmIaLOlcTDzImTt7mHH3H77LSKWmQs1YtYKBanWeEwhFWK30suFuPlWSGGoyUa1yZbp06VKovgRBoFq1alSrVg1JkjCZTKhUqocWprdv32bHjh3ybv4xyEIvI/MAzs7OGI3Gx5YUzcjI4Pr169m7/nLlytmtBrbRYMBWCTzHqtVstFgYZjBwxsmJv61WXjMYqFuvHgMGDUJlox93BwcHvLy8OH78eHaBkbyexeYXhUJBv379uHfvHp07d0alUpFmNqMymdAKAj5KJU3IDDt8QamkkY2SCEFmaN16q5WJw4YBmYuybdu2MXnSZPbt34eLzoMOfsNp5tUTD4fcc7TbAp3KiXqeL1LP80VEycrlxCMcjF3DW3f+4SOThcEqBf/TaCj/BNN+vCTZpB5AFh8YjSQoFHz0+us2jWARBOGx4Xbt27fn9OnTjBo1ihMnTtikyuR/BVnoZWQewN/fH4vFwu3btylbtiyQmVt+//79REREYDKZstsKgkDNmjVp2bIl/v7+NhMSk8lE/N271LJReJZCEFik01EjPZ3vjUZ+tlioXacOAwcPzpep/kmIosg///yDKIq89tprdk9aolQqef311/nhhx8oVaoUL730EgqFArPZzJ07dzh04wYbYmMxGY3Uu+/497pajWMhP6eFZjOCUsnQoUMJCQnho4/+x6FDB6nk3oBBNX+irueLqBRFc4TwbxSCkuolA6heMoAkwy0OxW1gZcxqFqanEKxW8YlGk2NiJTO2E4TrosgCs5lXe/Sw22L432RlbPz5559ZtWqVXK72AYo6IkVGpliT5UAVFRUFZObOnjJlCjdv3uSzzz7j2rVrpKenEx8fz6JFi9BqtSxYsIBNmzY9lNWrMMTGxiJKEo1sKMJVFApeUSqZZjbjUaoUg954w6YiD5me5unp6TRq1Miu9QEexMPDg+7duxMVFUWFChVo2bIlbdq0oXfv3rz9/vt8/9NPBAUFYa1alSCjkQp6PZsKEXoVJYp8b7Xyao8evDH4DQIDA4k+l8jIuvMYW3c5Dct0fmoi/2/cdGXpVHEsE5rvokOFscyzKqmYYeAboxHDY46ndGSWmrUF88xmHLRaWrRoYaMe84a3tzc1a9Zk5syZRTpucUcWehmZB3B1daVSpUpERkZy6tQpVq5cyYABA4iMjOTTTz+lYsWKODo6Urp0aYYOHUpERARz584lJCSErVu32mQOkZGRqAWB2jZO2DNGoyEdaN2+vc3M9Q9y4sQJgEKfx+aXgIAA3N3dOXDgwCOvKZVK/P39GT5yJJ9+9hleNWvSy2Cgr8HAnXwuzCRJYoTZjMLBgU2bt3B03ykG+//MO/XXUqNky2LrAKZVOfJShZFMaL6LxuUG8rXZSoMMI0et1ofaVVYouGCDxapBklhotdKkeXO7Jg3KiZYtWxIREcGxY8eKfOziiiz0MjL/okePHpw4cYJ169bRvXt3Fi9ejDoXb+6RI0cyZcoU/vnnH6Kjows1tiRJhB0+zMtKJVobC0c7pRJHQSAtl4p0BUWSJE6fPo2/vz8lS5a0ef+5oVAoaNmyJcePHyc9Pedoeg8PD4a++SaDBg1iu1JJE4OB6/kQti9NJnaaTCSlpdGyTH8+aLiZBqU7FdrBrqhw1rjTvcqHvNdoAxmOlWmRkcH4B3b3jZRKwv8l/gVhv9XKXauV5s2bF7qvglCzZk3c3NxYv379Uxm/OPJsfENlZIqQUaNGkZ6eTlpaGj/88EOeHInGjh2Lt7c3ISEhhRo7MjKS6Fu3GGuHMDGFINBAqST6/rGELYmOjiYhIYFWrVrZvO+80KxZM0RRJCIiItd2giDQqFEj3vvwQzJcXWllMBD5BLGXJImvjUa+Nplw1rgzrsGvdK/yIRql7RPLFAVezlUZ13ANHSuOY5LZShO9kUhRpLFCwWlRJLGQUSdhVisOGg1lypSx0Yzzh0KhwNfXl7CwsKcyfnFEFnoZmX9RpUoVPDw8aNu2LdWrV8/TNSqVitGjRxMREYHRaCzw2Hv37kWlUHDJakW0Q5hfY0Hglh2EPjIyEqVSSdWqVW3ed14oUaIEPj4+REZG5qm9h4cHwW+/jcXFhfYGAyk5vNe3RJGeBgNfmEz4lPDns2a7qOjawIYzfzooFSpeLB/Eu43WEa8qReMMI2UEAYHMVLWFIVwU8fX1LbJaEY/D19eXiIiIYhEqWxyQhV5GJgfatGmTr/aBgYGYTCaSkpIKNN6ZM2c4deoUFSpWZJzJRLt8mpbzgocgYCjEQiQnoqKi8Pb2tsvZf17x8/PLdqLMC66urowYM4YYhYIP//WepEsSc00mamZk8LvFQtOyPXiv4dpndhefE97O1Xm78Xo8XOrT12CknkLBbJOpUIvMk4CXj/3DCnOjXLlyJCcnF/oo7b+CLPQyMo9BFMV8OxJlxe1aC3DOmZ6ezrp16/D39yd47FjGjBnDGScn6uj17P1X4ZTCIIBdLAXR0dH4POUfdx8fHxISEjAYDHm+xtPTk27duzPfbGa92cwOi4W3DQbK6fWMNhpJE5QM9J9C/xrfFFtnu8LipHYjqN4CWpTrT5goclmSmFuIXX2qJD1SA6CoycrfkFXk5nlHFnoZmcdQqlSpPJuBs7h+/TrAI9nSnoTFYmHFihWYzWb69u2bnQXsw/Hj8a1cmY4GA7ttJPbJkoTWDp7QqampT7U+AGRm2ZMkKVeHvMcREBBAlcqV6Wcy0UWvZ7WzMwoXV0poPfig0Ubql37JTjMuPtwzxFHWqQo1SwYC8J7RyLV8LliTJYl/LBYyRJHo6GiOHTvG2bNnSUxMLHITetaxwb+rCz6vyAlzZGQeQ69evZgzZw6TJ0/Os3AvXLiQihUr4urqmudxLBYLv/76K5cuXSIoKOiha3U6HcNHjmTxwoW8cvEiBxwcCh1bf0KSKFOuXKH6eBwWi+Wpmu2B7PHzW6JUoVDQtVs3pk2bxrRp05gzey63oxN5q95iPB1zLo36X+DCvVD2xSznwt1QFAoFJZxcUKnUGC1m6plMfK9UMkatfmw6ZkmSOGi1stBs5gBw9QFRPX36NKdPn87+v7OzM35+fjRs2JD69evb/buS9R2wV2bGZw1Z6GVkHsOoUaP48ccfWbJkCWPHjn1i+1OnTrFnzx4GDhyY5zGSk5NZuXIlV69eZciQIY91/FOpVAwZNoxfpk1jwO3bnNDp0BXQhCxJEsesVmrlYyGSV5RKZYGOLGxJ1vgFEZHy90vyfvH5F2BSM6bu0v+8yP99cxHbrk2lYYNGLJ28lD59+uDg4IAoivz111/MmjWLcdu387coslarRXP/eydKEkvMZqZZrZyxWCjt7k6NOnVo5uODr68vHh4eqFQqRFHMLmt88+ZNrl69yooVK9i8eTPNmzenXbt2+bZ+5ZWEhAQUCgXl7LCofRaRhV5G5jGUL1+eIUOG8MEHH1CrVi3atm2bY9vo6Gi6d++Ol5fXY6uZ/RtJkggLC2PTpk2oVCpGjhxJtWrVcmyv0Wh4fdAgJv/8M1+ZTHxfwBzeYaJIsiRx8OBBYmJiaNu2LfXq1bPJ2bOjoyMpKSmF7qcwZJ3HFmQXJwgCAQEBrF27ltH1FlDasYKNZ1e8CI1Zy7ZrU/nss8/46quvHvoOKBQKOnbsSMeOHdm8eTN9+/Shp8HA7zodVyWJIUYjoRYLtf39GRkYSPXq1R/rYa9QKHBzc8PNzY3atWsDEB8fT2hoKCEhIRw7dow+ffpkv2ZLoqKiqFmz5lP3FSguyGf0MjI5MGfOHF544QU6duzIF198QWxs7EOvp6ens2DBApo2bUpqairDhw/PdTdptVo5ceIEv/zyCytXrsTf35+PP/44V5HPwsvLi5c7deInk4kzBdw5zzabKeniwtChQ9FqtSxdupRFixaRnJxcoP4epFy5ck/dwzkqKgp3d/cC7xKzqp6ZrHl35nsWMVjS2BY5haCgIL7++utcF3o9evRg/oIFbLdYCMjIoE5GBpdKlGDs2LEMDwqiZs2a+QqjK1OmDD179mT8+PH4+PiwcOFCVq5c+VANCVtw48YNzp07R5UqVZg4cSK3bt2yaf/PGrLQy8jkgFarZdu2bQQHBzN58mT8/Px46aWXGDRoED169KBMmTIEBQVhNBpp2bIler3+IecfURS5desWx44dY9OmTXz11VcsXboUgBEjRjBo0KB8iVK7du1wcXZmVgE8ohNEkdUWCy0CA6lXrx5jxoxh2LBhREZG8uOPPz50nloQfH19iYmJyc73L0lSkTtgRUdH4+vrW+Dr3dzccHJ0Jir1rA1nVfwIi9+G2Wrgs88+y1P7wYMH4+fnx2FRpGGLFnzw8cdUqVKlUHNwc3NjxIgRvP7665w4cYL58+cXKv/Eg9y5c4e4uDgGDBhA69atmThxIr6+vnzzzTfPbVy9bLqXkckFjUbDlClT+OKLL1ixYgV//vknO3bswGg0Ur9+fSRJ4tq1a/z+++/ZtdBVKhWCIGA2m7N/WEqVKkWdOnVo2bIl3t7eBZqLUqmkWcuWLN+1ix8lKc91xgGCTSbUOt1DRUbq1q1L5cqVWbt2LYsXL6Zfv340a9Ys3/OyWCyYzWZMJhNTfv6Ze/fuob//o63TaChbtiw+5ctToUIF6tSpY5f850ajkRs3bvDSSwX3kBcEAV9fH6LvnLPhzIofx25volu3bnkOh1QoFIwdO5bx48fTrVs3m5V/FQSBpk2bUqpUKebNm8fChQsJCgrKNd10Xjh48CAuLi7MmzcPR0dHJk2axOTJk/n8889JSkpi8uTJNpn/s4RdhD4mJob//e9//PHHH2RkZFClShWWLFlC48aN7TGcjIzdcXV1JTg4mI4dO1KlShUGDRpEo0aNsl83Go3ExsYSHx+P0WhEkiTUajWlS5emXLlyNjsrbNGiBbv++ovVZjMj8yiYG+7HiA/u3x9nZ+eHXnNycmLIkCGsX7+eNWvWoFarsyv4PYmUlBQOHDjAkdBQUjIy8BMEmick0EChoKRWiwAkShInY2M5FhvLgQMHcNbpaNKiBS+88IJNw/HCwsIwm80PfSYFwc3djdi4uzaaVfHkniGWps3y7jQK0KRJE6xWK6mpqTb3ZK9UqRJBQUHMnTuX3377jd69exe4L5PJxNGjRxkxYkT235y7uzvffvstZcqUYdy4cTRr1ow+ffrYavrPBDYX+sTERFq2bEnbtm35448/8PT05PLly089xlZGxhaEhIQgCAK1atV66HmtVkvFihWpWLGiXcd3c3OjvI8PIXFxjMxD+zCrlaEmE/Xq1qVBg8enblUoFLz22muYzWZWrlyJt7c3ZcuWzbHPLGfCLRs2oDCbGaJUMsrREf8nhP5dEUXmmkws2rePQyEhdHv1VQICAgrtDChJEqGhodSqVavQvzMqlQqLZNvz4uKGKFnzXaI4q72tSjH/m8qVK9O1a1c2b95MvXr1CpxKeefOnZhMJoKDgx957a233uK3335j2rRpz53Q2/yM/scff8TX15clS5bQtGlTKlasyEsvvUTlypVtPZSMTJETFhZGmTJl0OmeXirUcuXLk5cCnAetVjoYDHj6+PD6gAG5CqpCoaBPnz54eHiwatWqHEPljEYjSxYtYuXKlXQXRSIdHZmh0z1R5AGqKBRM0um44ejIQGD9+vXMmz2bjIyMPNxNzpw5c4bY2FibFNSxWq0oheJRT95euGhLcfHixXxdk9W+RIkS9pgSkJlCunLlyqxZs6ZA5/WRkZHs3buXL7/8Mke9CQ4O5tChQxw/fryw032msLnQb926lcaNG/Paa69RunRpGjRowIIFC3JsbzQaSUlJeeghI1NcCQsLe+qxuT4+PlyyWEjNwbHIJEl8aTTSWq/Hs3x5Ro4Zk6dzVY1Gw+uvv05UVBR79+595HWDwcD82bO5eu4cm3U6Vup0lCzAbtxFEFig07HTwYFbV68y55df8p3NLov09HTWr19PrVq18lyAKDdSUlJxUrkVup/iTH2PzqxetSZf0RZz586lZs2adot7h8zFZr9+/UhMTOTo0aP5ujYjI4M1a9ZQv359PvzwwxzbdevWDQcHh8d+v//L2Fzor127xpw5c6hatSo7d+5k9OjRjBs3jmXLlj22/ffff4+rq2v2ozBeszIy9iYqKgoPD4+nOodSpUohAbf+JfRmSWK92Uwjg4FvzGbavfgio4KD82V9qFChAq1ateLvv/9+KORJFEWWLFzI7ago9uh0vGqDMrovqVQc0OnIuH2bBXPn5jtdqSRJbN68GbPZTJ8+fWySDyD6ZjTlnGsWup/iTHOvXphMJmbPnp2n9vv27SM8PJyWLVvaeWaZtQfq1KlDSEhInj3kDQYDCxcuxGAwsHLlylxDXFUqFW5ubs/dhtLmQi+KIg0bNuS7776jQYMGBAUFMWLECObOnfvY9uPHjyc5OTn7kZ/qUzIyRY3RaHzqqV6zvJLTRJGrosg6s5kPDAZ89Xr6GAwYfHx497336Ny5c4Hm2rp1awwGw0O13ffu3cvlK1f4TaulaSHT8D5IbaWSP7RaYqKj2blzZ76u3b17N2FhYfTq1StfaYdzIiUlheTUJHycaz258TOMq7Y0rbxf57NPP2Pjxo25tj1z5gy9e/emcuXK+Pv7F8n8WrVqRXx8PFeuXHli29TUVObMmUNCQgJ//vlndi6EnBBFkdTU1EecUv/r2PwXy8vL65EvRM2aNXP8Qmm1WpuFa8jI2IOEhAT27t1LUlISFovFbg5JeSXr/LyhXp/9nIeLC9Vr1+aNVq0KHL6XRalSpahZsyYhISE0a9aM+Ph4/tyxg/fUatraYZHTRKnkc7WaL3bvpk6dOvj5+eXaXpIkdu3axY4dO+jYsaPNonkuX74MgJ+L7TO1FTdeqfw+qeY7vPbaa7z55psEBwc/lNUxOjqaBQsWMHXqVFxdXRk2bJjN68tLksTly5cJCwsjNTUVQRBwc3OjadOmuLu7c+rUqRyd8iRJ4vjx46xfvx4HBwf++eefPEWL7Nmzh7S0tAKFkT7L2PyvtmXLlo84ely6dIny5f/beaNl/nuEhYUxbdo01q9fj8lkQhAEFApFgc+TbUWW81r37t0zY9R9fGzuJNW0aVOWLl1KUlIS237/HV/gazsuyP+n0bBeFPl9yxaCx43LsV16ejobN24kIiKCzp078+KLL9psDgdDD1HZrREldYVbKD0LKAQlA2r8QDmnGmxYuYKFCxdSo3pNypYtQ3JyMidPnUSj0dC4cWO6du1qc+fTU6dOsWPHDm7dukW1atWoVasWoigSERHBwYMHcXZ2zl54/ZuoqCh27drFqVOn0Gq19OrVK88hobNmzaJ27do2cdx8lrC50L/77rsEBATw3Xff0adPH44ePcr8+fOZP3++rYeSkbEbS5cuZfjw4VSoUIHvvvuOgQMHUrp0aV577bWn7rEbFRWFk5MTbdq0sVuN9AoVKgBw/vx5zp07xxyNBgc71mNXCwITVCr6XbtGXFwcXl5eD70uSRKnTp1iw4YNWK3WR/IYFJa4uDiuXrvCYP9JNuuzuKMQFLTzG0Zrn0GcvfsPF+4dJDkxjRspl3F3L8mHH35gl+iSkJAQNmzYQKdOnfj4448JDAzM/h5brVb+/PNPvv32W44cOcKJEyeoV68ed+/e5cqVKxw+fJjIyEjKlSvHunXrOHnyJJMmTeKNN954og/Bxo0b+e2335gzZ47d/m6KKzYX+iZNmrB582bGjx/P119/TcWKFZk2bRoDBgyw9VAyMnZh48aNDBs2jOHDhzN79uyHzrmbNWvG9u3bEUXR5qbMvJKV6tWeP1aurq44Oztz7NgxnASB123gfPckeqhUeCqVhIaGZidNMRgMHDt2jNDQUG7dukWtWrXo06ePTc7kH+TPP3fioi1FnVLtbdrvs4BSoaau54vU9cy0jvwQ1o1q/pXsIvJnz55l48aNvPPOO0yZMuWR77BSqaRLly68+OKLvP766yxfvhyNRoPBkFl/4KWXXmLatGl06dIFlUrFK6+8QkhICB07dmTp0qX06NHjkb9Li8XC4sWLCQ4Opm/fvowYMcLm91XcsYtXUdeuXenatas9upaRsStGo5HRo0fTo0cP5s6d+8iPRpMmTTAYDMTExDyVCBGr1UpkZCQBAQF2HUcQBMqVK0fMjRv0VChwLoIdkEYQ6KdQsDwiAoVCQVRUFNHR0VitVurUqUPPnj2pWrWqzRc4J06c4OTJEwz2/xmV4r8dQ58XTFa93eq479q1ixdeeIHJkyfn+jlqNBpWrlxJrVq1cHBw4KeffqJRo0aULl36oXZZ9Shef/11evfuTdWqVQm6X2xHFEWOHz/O/PnziYmJ4c0332TOnDlPbYH+NJFz3cvIPMDGjRtJSEjg22+/fewPQqtWrShbtiyHDh16KkJ/+vRp0tPTc8xyZ0s0Gg0ZBgNNitBZtolSyS8ZGYSEhFCqVCk6d+5MgwYNcHNzs8t4qamprF+3gbqeHajv2dEuYzxriJLVLtaiqKgoIiMjmT59ep7EVqvVMm7cON5//33q16//iMhn4ezszG+//UZoaCizZ89mwoQJmO8XfnJ0dGTgwIGMHj06TyWk/6s8f0sbGZlcWLhwIW3btqVmzcfHUqtUKkaNGkVERAT6B7zei4rQ0FAqVapUaM/6vGA0GhGBRjYMp3sSje8LwPDhw7l9+zYODg52E3mDwcC8ufMRLGp6V/30uTu3zQm1QpstlLbkxIkTeHp60qVLlzxfM3jwYAB+++23XNsJgkCrVq1YtWoVKSkpxMTEEBcXR2JiIvPmzXuuRR5koZeReYgrV6480Sw+YsQIzGYzBw4csPt8Hiz3GhkZyeXLl4skcQmQfS7qW4QC6HNf6Nu2bcuoUaNYu3YtBw8etPk46enpzJk9lzu3ExlVZwElNKVsPsazSkmtD7fjb9u837S0NCpUqJCvPPtubm54eHiQkJCQ52t0Ol12vQZ7VEp8FpFN9zIyD2CxWJ6YZMbb25v33nuPqVOnUrdu3VwLwBSEu3fvcvDgQSIiIrLTlLq7u2MwGPD29i6y3UnW2EW3n///HySLxcKsWbNQKBTMnj2b6OhoXnnlFZs4iF29epVVK1ZhSLcypu4iypXIPcnK84ZviVqERW2yeb9KpbJAOezNZrMs2IVE3tHLyDyAp6cn165de2K7r776iooVK7J69eocC8DkF6vVyoYNG/j2228JCwtj4MCBzJo1i5kzZ9KjRw+sVivx8fH89ddfeU4PWlD0en220BflAUXWWDqdDoVCwcyZM5k5cybHjh3j+++/58KFCwW+d4PBwMaNG/nll19ITEqkc/m38S3x386CVxB8nGuRnJKYr1z4ecHT05Pz589z7969PF+T1V4uilY4ZKGXkXmAPn36sGHDBhITE3Nt5+DgwPLly4mOjmb9+vWFFl5RFFmxYgWHDx9m8uTJxMbGMmPGDEaNGsXo0aNZsGABt27dYsKECezcuZPNmzfbVewfTEV9oQgzAZ6/v2iqUqVK9nOnTp7EYjbjlZbG3LlzmfLTTxw6dCjPu8PY2FjWr1/Pl599xrGQEH7WaHhFoWDzlW+5cC/ULvfxLFPZrSEKQcnJkydt2m/jxo2xWq0sXbo0z9fMmTOH0qVL88orr9h0Ls8bgmTvrUE+SUlJwdXVleTkZFxcXJ72dGSeM27duoWfnx9fffUV48ePf2L7iRMn8umnn9KyZUt69epV4NCdvXv3snXrVjZs2EDPnj1zbTtnzhzGjBlj86QxD7Ju3TquXr2KKSODd41GPisiz/sZJhMfiSKp6emo1Wrmz5/PyJEjWazT8YZKxU6rlVlmMzssFtQqFT7lyuFTvjzlypXDyckJhUKB2Wzmzp07REVFEXvjBrcTEymtVDJKqWSEWo2PQoFZkuhhMLJHUvFhk62467yePLnniKVn3yOOU4yf8D+bOin++uuvxMbGcvz4ccqUKZNr24sXL9K4cWPGjRvHxIkTbTaHZwlb6aEs9DIy/+Kdd95h5syZbNq0KdedxI0bN2jbti0Gg4H4+Hj8/f3p06dPvr+3oigyceJEunXrxpIlS/J0zUsvvcTly5d555138jVWXtDr9Xz11Vd8/PHHHAwJQdy3j112SJ7yOF4zGLhZpw5HwsO5ceMGtWvWpJ/FwoJ/jR8pimy0WAizWjkGXP1X5TsnhYL6CgVNBIFApZJuKhXqfwlWkiThn2HA1bUJQXXny173D3Al8SizTg5jzJgxVKtWzWb9JiYmMn36dHx8fPjzzz9zLPl8/vx5OnbsiLOzM6GhoXaLvCju2EoPZdO9jMy/mDRpEq+++io9evTg3XfffSTndnJyMr/88gvNmzcH4NChQ/z222/Ex8fz448/EhYWli+z+rlz57h79y7BwcF5vmbs2LFERkZy8+bNPF+TV44ePYrFYmHEiBH0HzCAv81mrhaB+f6WKPKb1crr90Oqxo4ejZvFwqTHWBMqKBS8r9Gw2sGBKw4OpDs7E+/kRLSTE3ednUl2dCTEwYGpOh091epHRB7ATRBYqFVzIfEQEbe32/3+niUquzXBp0RNNm/cku/ywbnh7u7OyJEjiYmJoXr16owZM4aTJ09iMpkwGAwcPHiQgQMHUr9+fVxcXNi5c+dzK/K2RN7Ry8g8BqvVyrfffsuMGTO4d+8ezZs3p2zZsqSnpxMaGorJZKJnz57MmDEj2wR5584dxo4dy9q1a/Hx8SEgIIBGjRo9sTrj5s2buXHjBtevX8/X/FxcXOjQoQPt2rUr1L0+SHJyMj/99BO9evVi2bJl6PV6ypUtyzC9nkl23tV/azTynSAQe+sW8fHx1KhRg2U6HYPtnH63k97AWW0l3m28Qd7VP0Bs2kWmRPSlXfu2+Yp9zwupqamEhIRw+PDhR5z+SpUqxf/+9z+CgoKeew2QTfcyMkWAXq9n/fr1/PHHHyQlJeHk5ES9evV48803c0xas3v3bmbMmMH27dvRarVUrlwZHx8ffH19KVmyJCqVCqvVSnp6OlFRUYSEhODj45PvYjnlypWjdu3adO7cOd/3ZTQaCQsL48yZM+j1epRKJZ6enty+fZu0tDTOnTuHh4cHABMmTGDqjz9y0sGBanZKHxolitQ2GBgYFMSs2bN55513WDlrFlE6HTo7i+92i4Wuej3vNFxFeZe6dh3rWeOvyLnsvDGb4LHBdvF8t1qtXLp0iTt37rBr1y68vLw4deqUHE53H1noZWSKOTdu3GDJkiWEhoYSHh7+WE9+tVpNyZIlKVGiRI5lOR+HJEm4urrSpk0b2rfPeyEWURTZuXMn+/fvx2g00r59e8qXL4/RaGTv3r1ER0dTtWpV1q1blx2vn5GRQV1/f8rExbFfo0FpY+GVJInORiOn3N05e/Eirq6ulPHwYHBamt2tCABWSaJ8hpFKXq/Rs+oEu4/3LGEVzcw7PZKojNOMCR6Nn5+fzcfQ6/XMmzeP1NRUDh48aFOfgGcd+YxeRqaYU758eb788kt27drF3bt3uX79OgcPHmTPnj2EhITQuHFjKlasyMSJE7ly5Qrnzp3Lc9979uwhNTWV8uXL5/kaURRZuXIlf/31F2PHjuX69ev89ddfLFiwgOXLlxMZGcnvv/+Ok5MTgYGBhIZmhp45Ojqy5NdfOWQ284nJlO/34Un8YDLxp9nMgiVLcHNz48aNGyQkJtLmCYmLbIVSEGitkIhOsW042X8BpULNsFq/UFZXlVkzZ3Px4kWb9p+UlMTs2bNJTExk586dssjbCVnoZWSKAEEQqFChAi1atKBt27a0bNmSFStWcOfOHWbPnk2pUqWYO3dunvubPXs23t7e+TKn7ty5k4iICNauXctPP/30yO5MqVTStWtXDhw4QKNGjXjllVeIiYkBIDAwkMmTJ/OjycTnRqPNYvgnm0xMMJn4/PPPs48gwsPDAWhUhFXGGimVxKRdxirazvHsv4JO5USPyhNwVnoyZ84cNmzYUKAMdw8iSRJHjx7lp59+wmw2s3//fruFisrIQi8j89SoXr06u3fvJjY2lsTERObOncv+/fufeN2mTZvYvHkzLVu2zLPzmNFoZP/+/XzwwQe89tprubZ1dnZm06ZNmM3mhxYf7777Lj/88APfmEz0Mxq5UwhP/CRJYpjBwAdGI+PHj+fLL7/Mfu3KlSu4qVR4FaHQ11IoMEkmkk22z/H+rCJJEmfu7GX28TeYGtEPqz4Wf4WCI6Gh/PzDD4SFheW7+I0kSVy7do0FCxawatUqevTowdmzZ6lbV/aNsCey0MvIPEUaNGjA6dOn+eqrr1Cr1bz88susW7cO8TEiarFYmD9/Pn379gXg8uXLD2Wwy42wsDCMRiNjx47NU/uSJUsyePBgFixYgOkBc/3//vc/1qxZw25HR/yNRlaYzZjzsbu3SBLrzWZqG41s1GhYtGgR33333UMLFr1ej2MR1wx3vP+v2Woo0nGLK6mmOyw9+w6LzrxF6bRTrNbpiHd24KyTE+ccHWmUlsaKFSv4+vPP+f3337l69WqOu3yr1UpsbCwHDhxg8uTJzJgxA4PBwG+//caKFSsoWbJkEd/d84fsjCcjU0xISUmhe/fu/PPPP1SqVImRI0fi7++PKIocP36cBQsWEBMTw5tvvknz5s358ssviYmJoUKFCjRt2pQKFSpQpkyZx1YHmzVrFhUqVOCvv/7K83wiIiJo1KgR+/bt44UXXnjotVu3bhE8ejSbtmyhrFrNCEGgq0pFXYXiES95kyRxRhTZYbEwT5KINpvp+OKLzF+0CF9f30fG/fbbb5n59dfcKqIkPQB7LRba6fV80mwHpRxs73D2LHEl6RjLzoxDa9UzV6uidw7hjRdFkbkmE0usVpJFEQEoW6oUbqVKodZosFgspKSkEB8fj9lsRqFQ0K1bN4KDg2nfvn2Bs0g+T9hKD+XqdTIyxQQXFxf27NnD4cOHmT17Np999ln2btrR0ZGBAwcyZswY6tWrB8CQIUPYsWMHs2bNys63r9Fo8Pb2RqfTIQgCFouFhIQE0tLSaN26db7mk3WGf/fu3UdeK1u2LBs3b+bMmTPMmTOHqUuX8k1GBipBwF+jwV2SEIAkQeCc2YxJFHHQahkwaBCjx4yhYcOGOY5bqlQp7losGCTJ7qF1WcTe3+84qdyKZLziysV7B1l8OphWCljjqMUzFzGurlAwVafjJ0nivCgSJoqEJycTl5SEHtBIErckiRhRpEOHDqxfv15OfvOUkHf0MjLFFKPRyL179xAEgZIlS+YaW5ySksLx48cJDw/n9OnTpKWlYbVa0el0VKxYkS1bttCoUSOWL1+e5/FjYmLw8fFhx44ddOrUKde2BoOBU6dOER4ezsmTJ0lNTUWSJJydnalTpw6NGzemXr16ODo65toPZGbma9asGUcdHWmSj9rlheE9g4EVCg8mtNhdJOMVR6JTzzPz+ADaKSQ267RobbTIWmc2099oZNy4cUydNs0mfT4vyDt6GZn/OFqtFi+vvBVbcXFxoXXr1jnu2m/fvs2uXbuwWq2PNe0/jr179wLkKeRJp9PRtGlTmjZtmqe+c6Nu3bqolEqOWa1FJvRHRfB2e34dwiyiidXnP6KGILHRhiIP0EetJk6SeGf6dLp265avvA8ytkE+JJGReQ4ICgrixo0b/PHHH3m+Zvbs2XTo0KHIa4HrdDoCW7ZkTREZG2+IIoesZqq5tyiS8Yojf0XOJSEjkuVaNQ52OC55S62mrUbDm2+8QWpqqs37l8kdWehlZJ4DmjRpQuPGjfnss89IS0t7YvuNGzdy6NAhxowZUwSze5TRY8dywGTi9P369PZkntmMVulAo9K2zef+rJBqusveqMV8rFFT104WFIUgsEit5lZcHLNnz7bLGDI5Iwu9jMxzwoIFC7hy5Qpdu3bl3r17ObbbuHEjAwYMoF+/fnTv3r0IZ/j/vPrqq3h5evJdPuO088sdUWSeWaRx2R5oVU/2H/gvciRuM0pJ5G0755evqFDQT6lk3qxZWItgASfz/8hCLyPznFC/fn3+/PNPTp8+TYUKFRg7diwRERHcuXOHmJgYVqxYQUBAAL179+bVV19l6dKlTy0ESq1W8+Pkyawxm/nNjmI/1mjCpNDRwS/IbmMUZyRJ4nDsKvqrlHgUQYTDGLWa61FR7Ny50+5jyfw/stDLyDxHtGzZklOnTvH222+zYcMGGjVqhKenJz4+PgwaNCg7K96qVaueWF7X3gwcOJCunTsz0molrhBZ+HJijdnMWouZV6t9hou2lM37fxa4o4/irvE2fdVF45fdRKGggkbD33//XSTjyWQih9fJyDynmEwmDh8+zN27d9HpdFSrVq3IHe+eRGxsLE0bNqRkYiJ/q9W5xnXnh78sFrrqDdQt3ZEBNX96buvQR8Tv4NfzH5Hg5ESpIrLevKbXk9C8Of8cOFAk4z3LyOF1MjIyhUKj0TyS8a644e3tza69e2kbGMgLKSlsVqupUQiHMUmS+NViYbjBRLWSrehX47vnVuQBYtIu4KNQF5nIQ2axou8iIopsPBnZdC8jI1PMqVmzJgcOHUL09aW+wcBPRiPWAhgi40SR7gYjbxgM1C/TlSG1Z6BSPD696/NCujkJryJe6JRVKEjNyMh3QRyZgiMLvYyMTLGnatWqnDhzhrHvvsvHZjM1TSammkwk5kHwz1itBBsMVE5P5x90DK01jddrfvfcizyAhFjkIpBlj7FY5JLARYVsupeRkXkmcHBwYNKkSfTt25cpkyfzv40bGW8200ilopEkUUehwFkQEIHbkkS41cphq5WrkoSjRoNBEOjoO4S6nh2e9q08NURJ5I7+JtGp57ijv0ls2kWMooXvjOCvUNBIqcRHEOx6nJEuSQiC8NSdPZ8nZGc8GRmZZ5L4+HhWr17N+nXrOHboEA8agnUaDdWqVOH0+fPUrFmTYcOGsWTJUpJuWni/4fpn8lzeKlo4e/cfolLPYLTq0amcqebWjMpuTXK9H1ESuZx4mNDYtVxOOozBkg6As2MJVBoVomjFajSSfr/MrIdSSXeFgmC1moZ2SKDzlsHALl9fLly9avO+/2vIzngyMjLPNWXKlKFu3bp8+OGH9B80iHnz5mXn8lcoFDRr1gwvLy+GDRuGSqWiZcsA5p+dz42UU1Rwrfe0p59nrKKZPVFLOBS/lsSMeHy8fSlRogR3791l1415eJWoTKDXIJp79XpI8EVJ5EjcJvZGLyYh4yZeZb1p/3IbfH198fX1xcnJKbutJEkkJycTFRXFjRs32HLkCItTU2mmUvGlWk1Hle2kIlwQaNzi+U03/DSQhV5GRuaZZfz48TRr1ozFixejekCMJk2axOnTp3n33Xezn69RowYl3UsRGrvmmRF6k1XPknNvczX5GEOGDmHMmDHUr18fyBTnffv28csvM1m36Utupp7htWqfoxAU3NVHs+bSZ1xJPEb9+g3o/8KrVKxYMcedvyAIuLm54ebmRp06dejUqRPnzp1j3549dLp+nSFqNVO1WtwKaQlJkiSOWyz0bdKkUP3I5A+7+2H88MMPCILAO++8Y++hZGRkniPCwsI4evQoH3300UMib7FYmDZtGk2aNMHX1zf7eYVCQavAAI4n/EmaKecUwMUFSZJYeWE8N9JPsPOvncyfPz9b5CFTnNu0acPGjRtYunQpR25tZMf16Ry//Qc/hfXgrnSV0aNHM2TIG1SqVClfxxVKpZI6deoQPG4c/fr1Y60g4K/Xc7SQqWuXmc1YBIG+ffsWqh+Z/GFXoT927Bjz5s2jbt3nt/yjjIyMfVixYgXlypWjS5eHi9Fs376dmJgYWrVq9cg1zZo1Q6lUsDdqaRHNsuBcTz7OqYTdLF22hLZt2+ba9o033uDLL79kb/RSlp/7kNp1a/K/jz+ievXqhZqDIAg0b96cj8aPR1euHG0NBv4poLe8KEnMkSR69epF2bJlCzUvmfxhN6FPS0tjwIABLFiwAHd3d3sNIyMj85wSExNDzZo1Uf7LYWzevHlUqFDhod18Fk5OTrRp25oDsStIMtwqqqkWiJDY1VSuVIXevXvnqf24cePQaNT4+fkxcNBAdDqdzebi7u7OqOBgfCtVoqvRyLEC7OxnmM1cNJl49733bDYvmbxhN6EPDg6mS5cudOjw/IayyMjI2I/HmaJFUeTAgQPUrl07x+vatWuHRqth54059pxeoTBaMzh9Zzejx4zKc2EhNzc3Bg4ciMFgsEsxIq1Wy7ARI/AsV45eRiOp+QjYuiyKTLBYGDduHM2aNbP53GRyxy5Cv2bNGiIiIvj++++f2NZoNJKSkvLQQ0ZGRuZJ+Pr6cubMmYcyrF2+fJm0tLTH7uaz0Ol0vPhSB47e2kx8evEM8Uo3JWIRzdSpUydf19WuXZukpCT7TIpMsR80ZAi3FQo+uB+O9yQSJYneZjNe5crx3Xff2W1uMjljc6GPiori7bffZuXKlXkyHX3//fe4urpmP3L7A5WRkZHJYvDgwdy6dYutW7dmPxceHg6Aj49Prte2atWKkiU9WHPpc0SpGNZGL8Zx/h4eHnTr3p35ZjN7nnBef0cUedFoJNrBga07djwU0idTdNhc6MPDw7l9+zYNGzZEpVKhUqnYt28fM2bMQKVSYf3X2c748eNJTk7OfkRFRdl6SjIyMv9B6tWrR8uWLfnxxx8x3t9d3rx5EycnpycKikql4vUB/bmRfIr90SuKYrr5wlntjkqp4eTJk/m67sSJE0XiExUQEEB5Hx8m5pKvfrfFQiOTiZslSvD3P/9Qq1Ytu89L5vHYXOjbt2/P6dOnOXHiRPajcePGDBgwgBMnTjziOKPVanFxcXnoISMjI5NFSkoKFy9e5OzZsyQkJDz02o8//sjJkycZMGAABoMBo9GIRqPJU7+VKlXihdYvsCNyOrczIu0w84KjUTpQr9SLzJk9F1EU83RNYmIia9asoVGjRnaeXWaoYmCbNuyxWLjwr83bNVFkhMHAi3o9lVu04Gh4+ENhgTJFj82FvkSJEtSuXfuhh5OTEx4eHrk6yMjIyMhkIUkShw4dYtCgQXh6elKjRg1q165N6dKl6dChA5s2bcJisdCyZUvWrVvH9u3badCgAeHh4Y9YDXOjS5cuuLq5svLCx5itl7EQrAAAL0tJREFUeTtzLipaevfjeuQ11q5dm6f2U6dOxWw207x5czvPLJN69epRwsGBOWYzF6xWVpjNdDYaqZKezgadjtmzZ7N7714qVKhQJPORyRk5M56MjEyxwmw2M3LkSJYsWUKlSpX49ttv78e/K7l8+TLz58+nV69eNGnShN9//53u3bsTEhLCxIkT2bx5M4IgsG7dOmJjY9Hr9VitVtRqNS4uLvj4+ODr60uFChVwc3NDo9Ew+I1B/DLjF9Zf+or+NSYWmzz4FVzqU7/0ywwb+ialSpXixRdfzLHtggUL+Oabb3jppZfsYhWVJInIyEgSEhKwWq04OztTrVo16jVqxOxDh5iRkQFA4/r1WTRuHH379sXR0dHm85ApGHJRGxkZmWKDKIoMHDiQDRs2MHfuXIYMGfLYULHQ0FB69+6Nh4cHO3bsYNu2bcyaNYtz584hCAKlS5fGx8cHZ2dnlEolFouFe/fuERUVRXJyMoIgULNmTVq1akWNGjU4fvw4v/76K90rf0gb3zeewp0/HrPVyNJz73Ap6RADBgxgTPAYmjTJLGIjiiK7du1i1qxZ/P7777Rq1YpevXrZdKFiNps5cuQIBw8eJDY29qHXHB0d8fHx4dKlS2zZsoXAwEBKlixps7FlbKeHstDLyMgUG5YtW8aQIUNYt24dr732Wq5tL1y4QIsWLUhLS0MURerUqUPz5s2pVKlSriVQU1NTOXv2LCEhIURHR+Pp6UmfPn24cOECe/bsZUTtWdT0CLT1rRUYq2hhX/RyDsWv5U56DKU9y1CihAt37yaQlJxEuXLlaN26dfYCwFZkZGSwaNEiIiMj6d69O2PGjKFVq1aoVCpu3LjBwoULmT9/PklJSUyaNElOc24HZKGXkZH5z9G0aVM8PDz4448/8tR+4sSJfPnll7z//vt4eXnlayxJkrhx4wa///47V69epWXLliTeS+TypasE1ZlLFbfiVXhFlKycvxdCVMoZjNYMriSFYdDe5pNPJ9j8uMFisTB37lzu3r3Ltm3bCAgIeGy7lJQUunfvzuHDhwkNDaVhw4Y2ncfzjq300O5FbWRkZGTyQlhYGMeOHWPMmDF5vubNN99EEAQuXryY7/EEQaBChQoEBwfTo0cPjh49yt17d/H182HB6TFcTQrLd5/2RCEoqeXRmo4Vg+le5UPKOlXCxcXFLj4FR44c4dq1a7mKPICLiwvbtm2jevXqvP322zafh4xtkIVeRkamWBAREYEgCHTq1CnP15QtW5ZGjRoRHR1d4HEVCgWtW7fm/fffx2g0kpScSDlfL+adHsnFewcL3K+9kbCPMTYr4qFr1665inwWTk5OfPrpp4SEhHDq1Cm7zEmmcMhCLyMjUyz4v/buOyqqa98D+PfAFHoRIkgvNuxPWhCJCRp7EDWIXmsEw0WMMZqXaNQYV+I1phgTC2AENUGxJLZrS7BAFEVRNBYUQZGigEroZep+f3jhXaTNwAwOk99nLdaCM/vswp4zv5kzu1RXV0NfX7/BlrOKMDExgVgsbnf51tbWWLhwIeRyOaprquDi6oStN8OR/Eix6W0dTaCjD5Go/e1+UW5uLvLz85W6szJhwgR069YN27ZtU3l9SPtRoCeEaARTU1NUV1ejsrJSqfMKCwtVtlNbly5dEB4ejpKSEph3MYfv0CH4JfNz/HLvc8jkza8C9zJ0NXDCkydFSq0boIhnz54BQJPb/DaHz+fD29sbWVlZKq0LUQ0K9IQQjeDv7w8dHR3s3r1b4XNu376NGzduoHfv3iqrh5WVFd566y2kpKSgf//+CA4ORkrhL4i8EYpSUZHKymkvO+M+kEjFePLkiUrzrXvjoOydFT6f32CDIaI5KNATQjSCo6Mjxo8fj82bNyu87OuWLVtgamqKAQMGqLQuvr6+6N69O/bu3QsvLy9ELIjAU1kW1l2ZgMuFh6EJk5XsjNzAgUNubq5K8zUyMgIA3L+v3M5+9+/fxyuvvKLSuhDVoEBPCNEYixcvxs2bN7F06dJWg+mhQ4cQFRVVP7dblXR0dBAYGIji4mLcvn0bLi4u+HjpR+g3sDfi7y7HtlvzX/qnez2eEZxMB+LKlasqzbdHjx4wMjJCTEyMwuekpaUhLS0NU6ZMUWldiGpQoCeEaIxhw4Zh/fr1+PrrrzF37lzk5OQ0SlNWVoZ169bh7bffxoABAzB8+HC11MXOzg6Ojo5ITk4G8Hx0+YwZMxAaGoo80Z9Ye3k8TmZvRq1UuTEFquRrMw2ZmfdQVKS6Nx18Ph9eXl6IiYnBX3/9pdA53333Hezs7DB+/HiV1YOoDgV6QohGWbRoEWJiYnDgwAG4uLhg/PjxWLNmDdatW4eQkBDY2Nhg+fLl8PX1xcyZM5tcIldVhg4dioyMDBQXF9cfc3Z2hmt3F4hlNfg9JxpfXB6NpPyfIZWrfgR8awa+8iaMBOY4d+6cSvP18/ODTCZDQEAAysvLW0z7zTffIC4uDp9++qnK76wQ1aCV8QghGqmyshJvvPEG0tPTwePxIJPJYGxsjEGDBsHHxwempqZqr0N1dTU++eQTBAQE4OnTp7hz4wbKqqqanMFuyDeHr00wfLq9DTM9a6XLkjMZCqoykV+RjryKdJSJnkDGxODpCGAm7AZ74z6wN+4HKwOXBovknM6NwbHsDXj//fdVulPcw4cPsXXrVjg6OmLFihWYPHlyg6WFr1y5gvXr1yM+Ph6ffPIJ1qxZo7KyyXO0BC4hROu5uLjA3t4ekyZNeinl5+XlYdP330MklcIUwBQ+Hx46OnDX1YUNx4EHoBrAb1IpYiQSpMkBGYB+lq/Dp1sQeph7g6cjaLGMSvFfuFR4ECmPduOZqAgcgJ66ArhADj0OqGHAPcbhwX+m91nrO8DHdjo8rQOgzzOGTC7F99f/AbHeX/jwf5eAz+errP0FBQU4dOgQMjIyYGFhAS8vLwiFQty7dw/p6elwcHDAypUrERoaqrIyyf+jQE8I0WqlpaUwNzfH9OnT4enZsevOS6VS/P777ziVkIBeHIdlAgGCeDzotbLcbDlj+E4sxkaJHMVMCr6OHty6+KGf5Rtw6+IHI4F5fVo5kyEp7yecyP4BHJNhGk8Xs/k8eOrqwqiJckoZQ4pMhhiJFIekz/Me3/0jOBr3x6XCAzj/eA+8vb0QHBys8mVxi4qKkJKSgidPnuDhw4eQy+WIiYnBlClToKurq9KyyP9TVTykL1QIIRqpbltUCwuLDi23qqoK26KjkZeXh5V8Pj4RCCBQMHCacBxWCYX4VMBwRCrFArEYt4rP4MazBABAV2MH2Oi7wVxoi7vPzqCw5iEW8flYLjSARStlmHEcRvN4GM3joUAuxwqxGLH3VkMHgFDPAO7ug5GSkgJ9fX0EBASoNNhbWVlh7Nix2LFjB2QyGU6dOgVfX1+V5U/UiwbjEUI0kkgkAgCV3opuTXV1NaI2bUJZfj6S9fXxmVCocJD/bxzHYQKfj4cGBljIe/4yO2PGDPwjZBLQrQjn8rZDWJuD8wYGWK+n12qQf1E3HR3E6OnhhL4+rHg8dLXsgujoaAQGBuLs2bPYs2ePSpYFrlNeXo4ff/wRWVlZOHToEAX5ToY+0RNCNFLdCG5VL/HaHLlcjh2xsagoKsIfenror4Jb0roch/VCIfgAvo6Lw6ZNmxC3cycG8nk4KRDAsp0zBkbzeDjDcfB5/Lh+P3pHR0dcuXIFDx48wLRp0+Di4tLm/BljSEtLw8GDB6Gvr4+EhAT4+fm1q86k41GgJ4RopLrvJKuqqjqkvOTkZNzLykKCvr5KgnwdjuOwTihEOmNY8v77cNHRwW9CodKf4ptyTirFbLEYVRyHkW++CV9fXxgbG6OoqAjx8fHYuHEjPDw88Nprr8He3l7hfOVyOe7du4ekpCTcuXMHwcHB2LRpEywtLdtdZ9LxKNATQjSSg4MDTE1N8ejRI/Tt21etZRUXF+PYkSMI4/MxQg1zwTmOQw+Ow0mpFLtVEOQZY1gnFuMTsRgujo74aMaMBsvPWllZYeHChTh37hzOnj2L1NRUODg4YPDgwXBwcICtrW2DqXLA8zdUeXl5yMnJQWpqKp49e4Z+/frh119/fWmzHohqUKAnhGgkjuPg7u6OvLw8tZeVmJgII7kcX+vrqyX/e3I5NkokWCkQYJAK7hasFIuxRizGyJEjMXr06CYXDdLR0cGwYcMwdOhQ3LlzB+fPn8fRo0chlUrBcRzMzc0hEAjAGENtbS3KysoAPP/KRCqV4t1330VUVJTKR/CTjkeBnhCisTw9PREVFQWZTKa2aVwikQhXLl3CB7q6MFZTUIsUi2HOcVgqaHlOvSK+/U+QDwgIgL+/f6vpdXV10a9fP/Tr1w8ymQyFhYXIy8vD06dPIZFIwHEchEIhrKysYG9vDwsLCyQkJGDr1q0YNGgQwsPD211n8nJRoCeEaKypU6di3bp1uH37tsp3qKtz/fp1iMRivGtoqJb8qxjDdokE4QIBhO18I5Emk+FjkQj+/v4KBfkX6erqwtbWFra2ti2mGzVqFCorK7Fo0SIMGzYMffr0aWuViQag6XWEEI01aNAgvPrqq/Uby6hDdnY2+vF4cFTTmvkJUinKAIS2c5qgmDHMEoth060bxo0bp5rKNYPjOEyYMAFdunTB7NmzIZVK1VoeUS8K9IQQjRYREYGMjIwmd7JThUcPH8JTjd9DX5HLYc1xcG3nG4mvxGLclckwdcaMDlmNjs/nY+rUqUhLS8PGjRvVXh5RHwr0hBCNNnXqVAwePBh79uyBRCJRad5yuRyPnzzBIDXugHdVJoN7O/MXMYbvZTL4+Pq2ettdlZycnODh4YFvv/22w9YzIKpHgZ4QotF4PB527NiBp0+f4uTJkyrNWyqVQiaXo4saP9E/kMvRq52B/oBUimcyGYYOHaqiWinOz88Pjx49wrFjxzq8bKIaFOgJIRpNJBJh3bp1kEqlOH36NK5evdpi2qdPn6KwsBCVlZWt5t0Re3qJgFY3w2nNFqkUPV1dYW2t/Pa37WVvbw8nJydERkZ2eNlENWjUPSFEY8lkMkydOhUnTpxAbGwszp49i927dwMA3N3d69Pl5+fj/PnzSEtLa7DGe48ePeDr64v+/fs3+b12eXk5OAAxEgmipVIUcRxEjEEAwAKAO8fBQ1cXXjo6cGvj9+J8AJJ2vKGoZQwpUikmDBrU5jzaa8CAAUhISFDrNEeiPhToCSEaa/PmzThy5AiOHDmCcePGYebMmeA4Dj///DMKCwvx5ptv4ujRo/jjjz9gY2ODFStWYOjQoeDz+Xjw4AG2bt2KHTt2wM7ODqGhoTAzM4NUKsWNGzeQnJyM+/fvAwCuGxvDwdER1mZm4PF4kMlkKC8vxy85OdhUXAwAGMzjYQGPh2AeDwZKfEK31tFBbjsC/U25HFI8XynwZbGzs0NNTQ0yMjJoql0nRIGeEKKR5HI5Nm3ahClTptRPJ+PxeNi+fTu6d++O1atXIzk5GbW1tfj+++8xf/78+o1wAGDo0KGYNWsWUlNTMXHiRGzZsgUjRozAsWPHUFZWBldXV8yaNQu9evWCYQtz6Gtra5GVlYUL588j5O5dLNbRwTd8Puby+QqtGjdYRwfH2zE97apMBl0dHdjY2LQ5j/ays7N7XperVynQd0IU6AkhGun06dPIzMxETExMg+M6OjpYuXIljI2N8cEHH2Dbtm0ICQlpNh9PT0+cO3cOnp6eiI+PR58+ffDPf/4T3bp1U6geenp69SvLPXv2DL+dPInQK1ewXybDNqEQdq0MtPPQ1cUPEgn+YqxNg/6yGYOFiUmHbtf7IgMDA5iYmCA7O/ul1YG0HQ3GI4RopNTUVJibmzc70vzYsWPw8fFpMcjXcXZ2xqeffgqO4xAUFKRwkH+RpaUlps+YgXnz5uGSnh4G1NTgcivTzt7U1QUPwK42Tg2sZeylBvk6AoEAtbW1L7sapA0o0BNCNFJVVRWMjY2bvD1+7949nDp1CvPnz1c4v1mzZkEoFOLSpUvtrlvfvn3xv8uWwdzeHv61tbjYQrC31tHBJB4PWySSNo3y10XHzA5oDWOMBuJ1UhToCSEaycTEBCUlJU0u1JKWlgYAeOuttxTOz8zMDK+99prKdsMzNDRE2Pz5sHZ0xJjaWmTI5c2mjeDzcVcux942fFdvzHGorqlpT1XbjTGGyspK/PDDDzAzM4OdnR2Cg4ORlJSkEW9CSMtUHujXrl0LT09PGBsbo2vXrggMDERGRoaqiyGEaDl/f39UVFTg+PHjjR6rrq4GABgZGSmVp4mJiUpX1xMKhQh9913omZtjlkgEWTNBz09XF3Ych/kiEZ608IagKf10dFBaVYWKigpVVLlNiouLIRKJMHr0aKxYsQKzZ8/Gn3/+iddffx2DBw9GVlbWS6sbaZ3KA31SUhIiIiKQkpKChIQESCQSjBw5ElVVVaouihCixTw9PeHh4YFNmzY1eszU1BQA8OTJE6XyLCwshJ6enkrqV0dfXx9TZ8xAqlSK9S+8iUiTyfC5SAS36mo8AlDOGObW1jb7hqAp7v+5Xa6qOxFtkZ+fDwD44Ycf8OGHH2LNmjW4c+cOTp06herqagwZMgT37t17afUjLVN5oD958iTmzJmDvn37YuDAgdixYwdyc3NbXM2KEEKasnjxYvz+++/YvHlzg+N+fn4QCAT1i+coIicnB8nJyejVq5eqqwlnZ2e8/sYbWCEWI1Mmw08SCTxrauBeXY21HAeZiwveeOMN+L3+Ok7I5Qhp4dN/o7w5DmY6Onj48KHK662o7OxsdOvWDVZWVvXHOI7D8OHDkZycDAsLC7z11lu0y52GUvv0urKyMgBAly5dmnxcJBJBJBLV/11eXq7uKhFCOolp06bh8uXLWLBgAR4/fowlS5agS5cu6Nq1K4KCghAZGYlFixYpNEgsOjoaQqGwwYp6qjR69GgknzuHV0Ui/CWToXfPngh97TW4ubk1qJ+dnR1+3rULpYwhRk8PFq1MueM4DpN0dXEoJQWjRo2qz4sxhurqaojFYgiFQujr6ys0r19ZUqkU165dw5w5c5p83NLSErt27YK7uzuOHj2KwMBAldeBtA/H1DiSQi6XIyAgAKWlpTh//nyTaT777DOsXr260fGysjKYmJioq2qEkE6CMYYvvvgCn3/+OXR1dTF16lQMGjQIubm52LBhA8LCwrB58+YWg9zp06cxZswY+Pn5ISAgQOV1FIvFOHbsGJKSkuDo6Ihp06a1uC79rVu3EB8XBwOJBNF8PgJ5vBbrnyaTwb26GiEhIXB1dUVqaiouXryIwsLC+jQ2NjYYMmQIPDw8VPr1xNWrV/Hzzz/jzp076N27d7PpfHx8YGxsjN9//11lZf/dlZeXw9TUtN3xUK2BPjw8HCdOnMD58+frV1Z6UVOf6O3t7SnQE0IaKCoqQmxsLLZv3478/HzI5XIYGRmhuLgY//jHP/DFF1/A2dm5wTlVVVXYvn07lixZAldXV4SGhqp8ilhtbS1+/PFH5OTkYNy4cRg2bBh0FNitrqysDPv37sWt9HQM5PHwnq4upvH5zS6v61VTg3wLC5SWl0MikWDixImYPHkyzMzM8Ndff2Hfvn04cuQIDAwM8M4778DV1bXdbZPL5diwYQNcXV1x5syZFtNu2rQJCxcuhEQioWl4KqLxgX7BggU4fPgw/vjjj0YXX0tU1TBCyN9DfHw85s+fj7KyMowePRpDhw6FQCDAgwcPsGvXLlRUVMDLywtBQUENlshVBZFIhKioKBQUFCAsLEyp1zrg+d2Ku3fvIvmPP3D7zh3wOQ59eTx4AHDhOAj+s8lOplyOE3I5ihjDyJEjsX379iYX/cnLy8PMmTNx4cIFhIeHw8XFpV3tO3v2LI4cOYJz587B19e3xbT79+/HlClTUFJSAjMzs3aVS57T2EDPGMN7772HgwcPIjExET169FDqfAr0hBBlVVdXY8+ePVi6dCnKysqgq6sLY2Nj9O/fH0OGDIGFhYXKy2SMISYmBpmZmYiIiGj3pjPFxcVIT09HXl4eHuXkoKysDNL/rHMvFovBdHXh7++Po0ePtrhSnkgkwsiRI/Hnn39i5cqVbX5zU1RUhG+//RYRERFYv359q+ljY2MREhICsVisESv5aQONDfTz58/H7t27cfjw4QajW01NTaGvr9/q+RToCSFt5ezsDEdHR0ycOFHtZV2+fBm7d+/G3LlzMWDAALWWdeDAAZw7dw45OTmwt7dvNX1GRgZ69+6NGTNmwMPDQ+nyqqqqsHHjRhgaGuLGjRswMDBo9ZxJkyYhMzMTN2/eVLo80jRVxUOVT6+LjIxEWVkZXn/9dXTr1q3+Z+/evaouihBCGnj8+HGzM3xUqaysDAcPHoSHh4fagzxjDFlZWQgICFAoyANAr1696qe+KauyshJRUVF48uQJtm7dqlCQz8/Px+HDh5Vakph0HJUHesZYkz/NTc0ghBBVYIx12G3j48ePg8fjdcidA4lEgsePHytd1sSJE5GTk6PUErX5+fnYtGkTqqurYWBggO+++67VufEymQwREREwMjLCjBkzlKoj6Ri01j0hRCtwHAcejwe5kkvMKquqqgppaWkYNmxYi/vYq4pYLAYApW/dmpiYQC6XK7Tkr1QqxYkTJ/Ddd9/hlVdeQXJyMvbv348TJ04gKCgIf/31V5PnlZSUICgoCMeOHUN8fDyMjY2VqiPpGLQfPSFEa5iYmKCyslKtZVy+fBlyuRze3t5qLaeOUCgE8HywnjKKi4uhq6vb4h2OqqoqXL58GRcuXEBJSQlWrFiBTz75BAKBAL169cKhQ4cQHBwMW1tbTJs2DUFBQTA3N0dJSQl++eUXxMfHg+M4HDx4EGPHjm1XO4n6UKAnhGiNgQMH4tGjR2otIzU1FQMHDuywT698Ph/Ozs7Ys2cPQkNDFT5v165dsLW1RVVVFQQCAYDnI/ILCgqQl5eH3Nxc3LlzBwAQFBSEjz76qNF4g/Hjx+PBgweIjY1FVFQUtm/fXv+Yvb09VqxYgZCQkAZL4xLNQ4GeEKI1PD09ERMTo7b86wKln5+f2spoiq+vL+Li4nD37t0WV6erc/XqVVy5cgUAsGLFikaPGxoaYtCgQfjss88QEhKCrl27NpuXlZUVli1bho8++gh5eXmoqKiAsbEx7OzsVL4uAVEP6iVCiNbw8PDAV199heLiYrXMnX/06BEYYwqPfleVQYMG4dixY5g+fTqSkpJa3J63tLQUc+bMQffu3fHrr7/i/v37qK6uBsdx0NfXR+/evdGzZ0+lV6/T1dWFk5NTO1tCXgYK9IQQrTFmzBgYGxsjJSUF48aNU3n+jx49gq6ubovr2KsDj8dDSEgINm/eDD8/P2zbtq3R5jyMMVy6dAmhoaEoKCjA+fPn4ebmpvbpf0Tz0ah7QojWMDIywpw5c3Dp0iW1bJlaVVUFIyOjl3LL2s7ODgsWLEB+fj48PDzg6emJr776Clu3bsWXX34Jd3d3+Pj4QCqVIjk5GW5ubh1eR6KZKNATQrRKeHg4ysvLkZKSovK8ZTLZS92wxdbWFsuWLYOFhQWePn2KL774AmFhYfjXv/4FW1tbHD9+HOnp6Qp9j0/+PujWPSFEq7i5uWHu3LnYvXs3+vTpo9KV8nR1ddVyp0DZOvD5fLz99tv45ptvwBhTyz70RHvQJ3pCiNZZv349unTpgr179yq1MlxrjIyMUFlZqdAiNOoil8tRXFyMmJgYrFq1Su3TCUnnR4GeEKJ1TE1NERsbi4yMDBw5ckRlwd7e3h5yuRwFBQUqya8tnj59ColEAl9fX6xfvx5OTk5Yvny52lcEJJ0XBXpCiFYaOXIkxo4di7Nnz+Lo0aMqCfY2NjbQ0dFBXl6eCmrYNnVl//TTT3j8+DFWrVqFf/3rX3jvvfdUeveCaA8K9IQQrbR48WIcP34cI0aMwOnTp7Fz5852L4/L5/Nha2uLu3fvqqiWysvIyED37t3RpUsXGBsbY+XKlfjxxx+xZcsWxMXFvbR6Ec1FgZ4QonX27NmDDRs2YPPmzUhISMDevXuRk5ODr776Cn/++We7Pvk6Ozvj1q1bKC0tVV2FFVRVVYU///wTISEhDY6HhoZi3Lhx+O677+hTPWmEYxr2rCgvL4epqSnKysqU3q2JEEIAwMvLC+bm5vjtt9/qjxUWFiIsLAxHjhyBg4MDhgwZgsGDB9evA98SqVSKGzdu4MKFC8jKygKPx8Pw4cMxZsyY+jRisRhpaWlISUnB06dPIZPJYGhoiP79+8PX1xevvPJKu9t19uxZnDhxAnl5eY2WrT1+/DjGjRuHlJSUDttwh6iXquIhTa8jhGiV1NRUpKam4siRIw2OW1tb49ChQzh58iQ2btyIvXv34t///jecnJxgZ2cHe3t7mJmZgcfjQSaToby8HPn5+cjLy8PDhw9RXl6O1157DWvWrEFSUhJ27tyJIUOGwNTUFNevX8e+fftQU1OD0aNHIywsDAKBAA8fPkRcXBwSExPh7u6O4OBghd5YNKWqqgqJiYkIDg5ucm36UaNGwcTEBImJiRToSQP0iZ4QolXWrVuHNWvWoKSkpMXFbR48eICdO3fi4sWLuHLlCkpKShqlMTY2hru7O7y9vTFz5kz07dsXwPMtYPv06QMrKyv069cP8fHxCAoKwpdffgkXF5cGedTU1CAuLg4LFy6Evb09wsLC2rSyXlxcHDIzM5Geng4bG5sm0zg5OWH69OlYs2aN0vkTzUOf6AkhpAkVFRUwNzdvdQU7FxcXrF69GsDzdeL79++PHj16YMmSJRAIBLCwsICzszN0dBoPZbKwsEB0dDQmTpyI27dvIzQ0FFu3bm1y4Rp9fX3MmzcPbm5uGDFiBA4fPozJkycr1aYbN27gypUr2LlzZ7NBnjGGsrKyFje8IX9PNBiPEKJVDA0NUVFRodSgNI7jIBKJ4OLigqFDh8LLywuurq5NBvk6gYGB6N69O6ytrREZGdnq6nRDhw7F8uXLcenSJVRXVytct+zsbOzatQsTJ07EzJkzm02XmJiI0tJSvPrqqwrnTf4eKNATQrSKp6cnSkpKcP78eYXPSU9PR1ZWFjw9PRU+p6SkBPn5+ViwYIHCt+LnzZsHuVyO1NRUhdJnZmYiOjoaXl5e2LVrV4tvJrZs2QI3Nze8/vrrCuVN/j4o0BNCtIq/vz969uyJLVu2KHxOZGQkunbtikmTJil8TnJyMmprazFt2jSFz7G2toa/vz8yMjJaTCeVSnHy5Els2bIFffr0wYkTJ6Cvr99s+oMHD+LXX3/Fe++9R+vek0Yo0BNCtIqOjg4WLFiAffv24ejRo62mP3/+PH788cf6kfKKKi8vBwClp81ZWVlBJBI1+/jDhw+xYcMGJCQkwNnZGenp6Th8+DBkMlmjtBKJBNHR0ZgyZQqCgoIQFhamVF3I3wMNxiOEaJ358+fjzJkzePvttxEVFYWZM2c2GpzHGMPBgwcxe/Zs+Pj4YPny5UqVYWBgAACorKyEoaGhwudVVFSAz+c3OCYWi3H9+nVcuHABDx8+RN++fXH48GG4ublh1qxZmD59OpYvX4558+ahb9++YIzh2rVr2LZtGx4/foyQkBBERka2OKaA/H3R9DpCiFYSiUQICQnBrl274OjoiLCwMAwaNAgcxyE9PR3R0dG4d+8exo8fjz179igVrAEgJycHzs7O2Lp1K0JDQxU6p7KyEtbW1nB0dETPnj1RWFiIR48e4fHjx5DJZBg1ahQiIiIwduzY+jcmjDGkpqZiy5Yt2LNnT/3dAENDQ8ycORPh4eEYMGCAcv8c0imoKh5SoCeEaLW6IBkfH18fJHk8HiZPnoz58+fDz8+vzd9rBwQEIC8vD2lpaQrlER0djfDwcDDGoKenh549e8LT0xPu7u5488030b179xbPl0gkKCkpAcdxMDc3b9N8fNJ5UKAnhBAl1NTU4NmzZ2CMwdLSsv7We3v8/vvvGDVqFDZu3IgFCxa0mDY3NxevvvoqvL29ceDAARo0R1pFC+YQQogS9PX1YW9vr9I8R44ciQ8++AALFy5EVVUVPvjggyYH9KWlpWHSpEkQCoWIioqiIE86FAV6Qghph2+++QZCoRBLly7Fhg0bMG/ePPj5+UEoFOLhw4fYtm0bzp07h379+uH48eOwsrJ62VUmfzN0654QQlTg1q1biIyMxE8//dRg3/thw4YhIiICgYGBjUbbE9IS+o6eEEI0UE1NDYqKiiASiWBhYQFLS8uXXSXSSdF39IQQooH09fXh5OT0sqtBSD1aXYEQQgjRYhToCSGEEC2mtkC/efNmODk5QU9PD97e3rh8+bK6iiKEEEJIM9QS6Pfu3YvFixdj1apVSEtLw8CBAzFq1Cg8efJEHcURQgghpBlqCfTr16/HvHnz8M4776BPnz6IioqCgYEBYmNj1VEcIYQQQpqh8lH3YrEYV69exbJly+qP6ejoYMSIEbh48WKj9CKRqMGWjWVlZQD+fwtIQggh5O+oLg62dxa8ygP9s2fPIJPJGq3+ZGVlhbt37zZKv3btWqxevbrRcVUvVUkIIYR0RhUVFTA1NW3z+S99Hv2yZcuwePHi+r9LS0vh6OiI3NzcdjVMU5WXl8Pe3h55eXlatyCQNrcN0O72aXPbAO1unza3DdDu9rXWNsYYKioqYGNj065yVB7oLS0toauri6KiogbHi4qKYG1t3Si9UCiEUChsdNzU1FTrOvW/mZiYaG37tLltgHa3T5vbBmh3+7S5bYB2t6+ltqniA6/KB+MJBAK4u7vj9OnT9cfkcjlOnz4NHx8fVRdHCCGEkBao5db94sWLMXv2bHh4eMDLywsbNmxAVVUV3nnnHXUURwghhJBmqCXQBwcH4+nTp/j0009RWFiIQYMG4eTJkwptzygUCrFq1aomb+drA21unza3DdDu9mlz2wDtbp82tw3Q7vZ1VNs0bvc6QgghhKgOrXVPCCGEaDEK9IQQQogWo0BPCCGEaDEK9IQQQogWeymBXtktbPfv34/evXtDT08P/fv3x/HjxzuopspZu3YtPD09YWxsjK5duyIwMBAZGRktnrNjxw5wHNfgR09Pr4NqrJzPPvusUV179+7d4jmdpe+cnJwatY3jOERERDSZXpP77Y8//sBbb70FGxsbcByHQ4cONXicMYZPP/0U3bp1g76+PkaMGIHMzMxW89WUradbap9EIsHHH3+M/v37w9DQEDY2Npg1axYeP37cYp5teW6rQ2t9N2fOnEb1HD16dKv5doa+A9DkNchxHL7++utm89SUvlPk9b+2thYRERGwsLCAkZERJk+e3GhxuRe19Xr9bx0e6JXdwvbChQuYNm0aQkJCcO3aNQQGBiIwMBC3bt3q4Jq3LikpCREREUhJSUFCQgIkEglGjhyJqqqqFs8zMTFBQUFB/U9OTk4H1Vh5ffv2bVDX8+fPN5u2M/Vdampqg3YlJCQAAIKCgpo9R1P7raqqCgMHDsTmzZubfPyrr77CDz/8gKioKFy6dAmGhoYYNWoUamtrm81Tk7aebql91dXVSEtLw8qVK5GWloYDBw4gIyMDAQEBrearzHNbXVrrOwAYPXp0g3rGx8e3mGdn6TsADdpVUFCA2NhYcByHyZMnt5ivJvSdIq//H3zwAf79739j//79SEpKwuPHjzFp0qQW823L9doI62BeXl4sIiKi/m+ZTMZsbGzY2rVrm0w/ZcoUNm7cuAbHvL29WVhYmFrrqQpPnjxhAFhSUlKzabZv385MTU07rlLtsGrVKjZw4ECF03fmvnv//feZq6srk8vlTT7eWfoNADt48GD933K5nFlbW7Ovv/66/lhpaSkTCoUsPj6+2XyUvW47yovta8rly5cZAJaTk9NsGmWf2x2hqbbNnj2bTZgwQal8OnPfTZgwgfn7+7eYRhP7jrHGr/+lpaWMz+ez/fv316e5c+cOA8AuXrzYZB5tvV5f1KGf6Ou2sB0xYkT9sZa2sAWAixcvNkgPAKNGjWo2vSap23K3S5cuLaarrKyEo6Mj7O3tMWHCBNy+fbsjqtcmmZmZsLGxgYuLC6ZPn47c3Nxm03bWvhOLxYiLi8PcuXPBcVyz6TpTv9XJzs5GYWFhg34xNTWFt7d3s/3SlutWk5SVlYHjOJiZmbWYTpnn9suUmJiIrl27olevXggPD0dxcXGzaTtz3xUVFeHYsWMICQlpNa0m9t2Lr/9Xr16FRCJp0Be9e/eGg4NDs33Rluu1KR0a6FvawrawsLDJcwoLC5VKrynkcjkWLVoEX19f9OvXr9l0vXr1QmxsLA4fPoy4uDjI5XIMGTIE+fn5HVhbxXh7e2PHjh04efIkIiMjkZ2dDT8/P1RUVDSZvrP23aFDh1BaWoo5c+Y0m6Yz9dt/q/vfK9MvbbluNUVtbS0+/vhjTJs2rcUNUZR9br8so0ePxk8//YTTp09j3bp1SEpKwpgxYyCTyZpM35n7bufOnTA2Nm711rYm9l1Tr/+FhYUQCASN3nC2Fv/q0ih6TlNe+ja12ioiIgK3bt1q9bsiHx+fBpv9DBkyBG5uboiOjsbnn3+u7moqZcyYMfW/DxgwAN7e3nB0dMS+ffsUetfdWcTExGDMmDEtbg3Zmfrt70oikWDKlClgjCEyMrLFtJ3luT116tT63/v3748BAwbA1dUViYmJGD58+EusmerFxsZi+vTprQ5y1cS+U/T1v6N06Cd6ZbewBQBra2ul0muCBQsW4OjRozh79izs7OyUOpfP5+N//ud/kJWVpabaqY6ZmRl69uzZbF07Y9/l5OTg1KlTCA0NVeq8ztJvdf97ZfqlLdfty1YX5HNycpCQkKD09qatPbc1hYuLCywtLZutZ2fsOwA4d+4cMjIylL4OgZffd829/ltbW0MsFqO0tLRB+tbiX10aRc9pSocG+rZsYevj49MgPQAkJCRo5Ja3jDEsWLAABw8exJkzZ+Ds7Kx0HjKZDDdv3kS3bt3UUEPVqqysxP3795uta2fquzrbt29H165dMW7cOKXO6yz95uzsDGtr6wb9Ul5ejkuXLjXbL51t6+m6IJ+ZmYlTp07BwsJC6Txae25rivz8fBQXFzdbz87Wd3ViYmLg7u6OgQMHKn3uy+q71l7/3d3dwefzG/RFRkYGcnNzm+2LtlyvzVWuQ+3Zs4cJhUK2Y8cOlp6ezt59911mZmbGCgsLGWOMzZw5ky1durQ+fXJyMuPxeOybb75hd+7cYatWrWJ8Pp/dvHmzo6veqvDwcGZqasoSExNZQUFB/U91dXV9mhfbt3r1avbbb7+x+/fvs6tXr7KpU6cyPT09dvv27ZfRhBYtWbKEJSYmsuzsbJacnMxGjBjBLC0t2ZMnTxhjnbvvGHs+GtnBwYF9/PHHjR7rTP1WUVHBrl27xq5du8YAsPXr17Nr167Vjzr/8ssvmZmZGTt8+DC7ceMGmzBhAnN2dmY1NTX1efj7+7ONGzfW/93adasp7ROLxSwgIIDZ2dmx69evN7gORSJRs+1r7bmtCW2rqKhgH374Ibt48SLLzs5mp06dYoMHD2Y9evRgtbW1zbats/RdnbKyMmZgYMAiIyObzENT+06R1/9//vOfzMHBgZ05c4ZduXKF+fj4MB8fnwb59OrVix04cKD+b0Wu19Z0eKBnjLGNGzcyBwcHJhAImJeXF0tJSal/bNiwYWz27NkN0u/bt4/17NmTCQQC1rdvX3bs2LEOrrFiADT5s3379vo0L7Zv0aJF9f8LKysrNnbsWJaWltbxlVdAcHAw69atGxMIBMzW1pYFBwezrKys+sc7c98xxthvv/3GALCMjIxGj3Wmfjt79myTz8O6+svlcrZy5UpmZWXFhEIhGz58eKM2Ozo6slWrVjU41tJ125Faal92dnaz1+HZs2fr83ixfa09tzWhbdXV1WzkyJHslVdeYXw+nzk6OrJ58+Y1Ctidte/qREdHM319fVZaWtpkHprad4q8/tfU1LD58+czc3NzZmBgwCZOnMgKCgoa5fPf5yhyvbaGtqklhBBCtBitdU8IIYRoMQr0hBBCiBajQE8IIYRoMQr0hBBCiBajQE8IIYRoMQr0hBBCiBajQE8IIYRoMQr0hBBCiBajQE8IIYRoMQr0hBBCiBajQE8IIYRoMQr0hBBCiBb7PwRRJtKXoJDrAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_atoms(atoms)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_atoms(crystal_structure)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from ase.visualize import view\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "view(atoms)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "157" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(atoms)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[14.586187, 10.49194 , 6.840074],\n", + " [12.185718, 7.034021, 9.395122],\n", + " [15.035307, 9.651149, 4.231314],\n", + " [12.463647, 10.436225, 4.932293],\n", + " [12.009409, 11.296853, 7.554505],\n", + " [14.172645, 12.408199, 9.007249],\n", + " [16.712138, 11.83121 , 8.150303],\n", + " [17.233391, 10.873925, 5.55441 ],\n", + " [15.732626, 8.578424, 8.425196],\n", + " [13.601557, 7.986663, 8.331421],\n", + " [13.773237, 6.633253, 10.549481],\n", + " [15.920268, 7.135743, 10.792011],\n", + " [18.384461, 8.708004, 9.464333],\n", + " [ 8.947566, 13.331506, 4.549392],\n", + " [18.450623, 6.839229, 2.009063],\n", + " [13.971844, 9.667611, 3.294824],\n", + " [14.124272, 8.994599, 2.613439],\n", + " [13.92932 , 10.535203, 2.861038],\n", + " [12.69698 , 9.39389 , 3.992781],\n", + " [11.968834, 9.35548 , 3.352337],\n", + " [12.746205, 8.540859, 4.450937],\n", + " [11.209131, 10.29947 , 5.581705],\n", + " [11.173186, 9.450449, 6.050584],\n", + " [10.495728, 10.31572 , 4.92469 ],\n", + " [11.032334, 11.413137, 6.543832],\n", + " [11.128104, 12.263635, 6.087626],\n", + " [10.144815, 11.375149, 6.934727],\n", + " [11.859296, 12.265956, 8.574341],\n", + " [10.985545, 12.181329, 8.988631],\n", + " [11.934718, 13.158452, 8.200018],\n", + " [12.920322, 12.046895, 9.57741 ],\n", + " [12.747424, 12.586529, 10.364074],\n", + " [12.933847, 11.115568, 9.844505],\n", + " [15.239642, 12.253927, 9.925414],\n", + " [15.303732, 11.32872 , 10.207131],\n", + " [15.085629, 12.803902, 10.710128],\n", + " [16.497447, 12.671579, 9.248902],\n", + " [16.423488, 13.593198, 8.952563],\n", + " [17.243504, 12.607633, 9.865951],\n", + " [17.95581 , 12.006586, 7.511809],\n", + " [18.66178 , 12.058924, 8.175648],\n", + " [17.951424, 12.833448, 7.005888],\n", + " [18.20413 , 10.875825, 6.612068],\n", + " [19.09433 , 10.946735, 6.23677 ],\n", + " [18.148813, 10.043476, 7.108242],\n", + " [17.347194, 9.746118, 4.713158],\n", + " [17.226446, 8.935507, 5.232726],\n", + " [18.232886, 9.720582, 4.316414],\n", + " [16.321381, 9.814707, 3.643802],\n", + " [16.372313, 10.67238 , 3.191495],\n", + " [16.477099, 9.114892, 2.989712],\n", + " [14.762862, 7.989407, 8.87068 ],\n", + " [14.865943, 7.181961, 10.194458],\n", + " [17.555428, 8.673815, 9.21186 ],\n", + " [ 8.921491, 14.1609 , 4.24038 ],\n", + " [17.579797, 6.668919, 1.90086 ],\n", + " [18.538717, 8.253842, 10.176912],\n", + " [ 8.113659, 13.588977, 4.67904 ],\n", + " [18.422964, 6.795544, 2.895156],\n", + " [ 7.98962 , 7.386038, 6.840074],\n", + " [13.977693, 3.226195, 6.840074],\n", + " [ 8.493206, 8.195384, 4.231314],\n", + " [13.024987, 3.25764 , 4.231314],\n", + " [ 9.099141, 5.575723, 4.932293],\n", + " [14.990712, 5.092226, 4.932293],\n", + " [ 8.580934, 4.752027, 7.554505],\n", + " [15.963157, 5.055294, 7.554505],\n", + " [ 6.536862, 6.069771, 9.007249],\n", + " [15.843993, 2.626203, 9.007249],\n", + " [ 5.766802, 8.557531, 8.150303],\n", + " [14.07456 , 0.715431, 8.150303],\n", + " [ 6.335209, 9.487592, 5.55441 ],\n", + " [12.9849 , 0.742656, 5.55441 ],\n", + " [ 9.073553, 9.335642, 8.425196],\n", + " [11.74732 , 3.190107, 8.425196],\n", + " [10.651568, 7.785963, 8.331421],\n", + " [12.300375, 5.331547, 8.331421],\n", + " [11.737816, 8.611347, 10.549481],\n", + " [11.042447, 5.859574, 10.549481],\n", + " [10.404101, 3.748945, 10.792011],\n", + " [10.229131, 10.219485, 10.792011],\n", + " [ 9.010681, 7.266167, 3.294824],\n", + " [13.570974, 4.170396, 3.294824],\n", + " [ 9.517313, 7.734679, 2.613439],\n", + " [12.911915, 4.374895, 2.613439],\n", + " [ 8.280586, 6.795544, 2.861038],\n", + " [14.343593, 3.773426, 2.861038],\n", + " [ 9.885163, 6.298963, 3.992781],\n", + " [13.971357, 5.411321, 3.992781],\n", + " [14.302166, 6.061119, 3.352337],\n", + " [10.2825 , 5.687575, 3.352337],\n", + " [13.207998, 5.795206, 4.450937],\n", + " [10.599297, 6.768108, 4.450937],\n", + " [ 9.844832, 4.557657, 5.581705],\n", + " [15.499537, 6.247046, 5.581705],\n", + " [10.598078, 4.951039, 6.050584],\n", + " [14.782235, 6.702685, 6.050584],\n", + " [10.18746 , 3.931707, 4.92469 ],\n", + " [15.870311, 6.856746, 4.92469 ],\n", + " [ 8.968767, 3.847713, 6.543832],\n", + " [16.5524 , 5.843323, 6.543832],\n", + " [ 8.184329, 3.505403, 6.087626],\n", + " [17.241067, 5.335135, 6.087626],\n", + " [ 9.445424, 3.098093, 6.934727],\n", + " [16.963261, 6.630931, 6.934727],\n", + " [ 7.816722, 4.137473, 8.574341],\n", + " [16.877482, 4.700744, 8.574341],\n", + " [ 8.326887, 3.423097, 8.988631],\n", + " [17.241067, 5.499748, 8.988631],\n", + " [ 7.006088, 3.756543, 8.200018],\n", + " [17.612695, 4.189178, 8.200018],\n", + " [ 7.475922, 5.165879, 9.57741 ],\n", + " [16.157256, 3.891398, 9.57741 ],\n", + " [16.711042, 3.771316, 10.364074],\n", + " [ 7.095034, 4.746329, 10.364074],\n", + " [15.343941, 4.345349, 9.844505],\n", + " [ 8.275712, 5.643256, 9.844505],\n", + " [ 6.136967, 7.070953, 9.925414],\n", + " [15.176891, 1.779293, 9.925414],\n", + " [ 6.906175, 7.589061, 10.207131],\n", + " [14.343593, 2.186392, 10.207131],\n", + " [ 5.737681, 6.662587, 10.710128],\n", + " [15.730189, 1.637684, 10.710128],\n", + " [ 5.146367, 7.951419, 9.248902],\n", + " [14.909685, 0.481175, 9.248902],\n", + " [ 4.385202, 7.426559, 8.952563],\n", + " [15.744811, 0.084417, 8.952563],\n", + " [ 4.828717, 8.629496, 9.865951],\n", + " [14.481278, -0.132956, 9.865951],\n", + " [ 4.993086, 9.546895, 7.511809],\n", + " [13.604603, -0.449308, 7.511809],\n", + " [ 4.594775, 10.132113, 8.175648],\n", + " [13.296945, -1.086865, 8.175648],\n", + " [ 4.279196, 9.129665, 7.005888],\n", + " [14.32288 , -0.85894 , 7.005888],\n", + " [ 5.848194, 10.327327, 6.612068],\n", + " [12.501175, -0.098979, 6.612068],\n", + " [12.117485, -0.905369, 6.23677 ],\n", + " [ 5.341685, 11.062808, 6.23677 ],\n", + " [11.807999, 0.365102, 7.108242],\n", + " [ 6.596688, 10.695595, 7.108242],\n", + " [11.951289, 1.208003, 4.713158],\n", + " [ 7.255017, 10.150052, 4.713158],\n", + " [11.309653, 1.71788 , 5.232726],\n", + " [ 8.017401, 10.450787, 5.232726],\n", + " [11.486328, 0.45374 , 4.316414],\n", + " [ 6.834286, 10.929851, 4.316414],\n", + " [12.523595, 2.062089, 3.643802],\n", + " [ 7.708524, 9.227378, 3.643802],\n", + " [13.240896, 1.589144, 3.191495],\n", + " [ 6.940291, 8.842649, 3.191495],\n", + " [11.839679, 2.27714 , 2.989712],\n", + " [ 8.236722, 9.71214 , 2.989712],\n", + " [10.06854 , 8.79031 , 8.87068 ],\n", + " [11.722098, 4.324456, 8.87068 ],\n", + " [10.716268, 9.283304, 10.194458],\n", + " [10.971289, 4.638908, 10.194458]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "atoms.get_positions()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ase import Atoms\n", + "from ase.io import read, write\n", + "from ase.spacegroup import crystal\n", + "\n", + "# Define the unit cell parameters\n", + "a = 24.369 # Lattice constant a\n", + "c = 9.748 # Lattice constant c\n", + "cell = [a, a, c, 90, 90, 120] # Rhombohedral cell parameters\n", + "spacegroup = 'R3' # Space group 'R 3'\n", + "\n", + "# Load your initial atomic positions; assuming these are stored in an XYZ file\n", + "atoms = read('path_to_your_file.xyz', format='xyz')\n", + "\n", + "# Create the crystal structure using the symmetry of the space group\n", + "crystal_structure = crystal(symbols=atoms.get_chemical_symbols(),\n", + " basis=atoms.get_positions(),\n", + " spacegroup=spacegroup,\n", + " cellpar=cell)\n", + "\n", + "# Save the complete structure to a new XYZ file\n", + "xyz_file_path = 'complete_structure.xyz'\n", + "write(xyz_file_path, crystal_structure, format='xyz')\n", + "\n", + "print(f\"XYZ file with complete structure saved as {xyz_file_path}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function read in module ase.io.formats:\n", + "\n", + "read(filename: Union[str, pathlib.PurePath, IO], index: Any = None, format: str = None, parallel: bool = True, do_not_split_by_at_sign: bool = False, **kwargs) -> Union[ase.atoms.Atoms, List[ase.atoms.Atoms]]\n", + " Read Atoms object(s) from file.\n", + " \n", + " filename: str or file\n", + " Name of the file to read from or a file descriptor.\n", + " index: int, slice or str\n", + " The last configuration will be returned by default. Examples:\n", + " \n", + " * ``index=0``: first configuration\n", + " * ``index=-2``: second to last\n", + " * ``index=':'`` or ``index=slice(None)``: all\n", + " * ``index='-3:'`` or ``index=slice(-3, None)``: three last\n", + " * ``index='::2'`` or ``index=slice(0, None, 2)``: even\n", + " * ``index='1::2'`` or ``index=slice(1, None, 2)``: odd\n", + " format: str\n", + " Used to specify the file-format. If not given, the\n", + " file-format will be guessed by the *filetype* function.\n", + " parallel: bool\n", + " Default is to read on master and broadcast to slaves. Use\n", + " parallel=False to read on all slaves.\n", + " do_not_split_by_at_sign: bool\n", + " If False (default) ``filename`` is splited by at sign ``@``\n", + " \n", + " Many formats allow on open file-like object to be passed instead\n", + " of ``filename``. In this case the format cannot be auto-decected,\n", + " so the ``format`` argument should be explicitly given.\n", + "\n" + ] + } + ], + "source": [ + "help(ase.io.read)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 0 and 59 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 0 and 60 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 2 and 61 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 2 and 62 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 3 and 63 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 3 and 64 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 4 and 65 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 4 and 66 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 5 and 67 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 5 and 68 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 6 and 69 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 6 and 70 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 7 and 71 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 7 and 72 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 8 and 73 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 8 and 74 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 9 and 75 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 9 and 76 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 10 and 77 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 10 and 78 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 11 and 79 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 11 and 80 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 15 and 81 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 15 and 82 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 16 and 83 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 16 and 84 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 17 and 85 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 17 and 86 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 18 and 87 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 18 and 88 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 19 and 89 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 19 and 90 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 20 and 91 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 20 and 92 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 21 and 93 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 21 and 94 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 22 and 95 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 22 and 96 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 23 and 97 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 23 and 98 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 24 and 99 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 24 and 100 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 25 and 101 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 25 and 102 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 26 and 103 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 26 and 104 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 27 and 105 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 27 and 106 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 28 and 107 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 28 and 108 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 29 and 109 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 29 and 110 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 30 and 111 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 30 and 112 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 31 and 113 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 31 and 114 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 32 and 115 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 32 and 116 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 33 and 117 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 33 and 118 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 34 and 119 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 34 and 120 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 35 and 121 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 35 and 122 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 36 and 123 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 36 and 124 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 37 and 125 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 37 and 126 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 38 and 127 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 38 and 128 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 39 and 129 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 39 and 130 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 40 and 131 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 40 and 132 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 41 and 133 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 41 and 134 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 42 and 135 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 42 and 136 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 43 and 137 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 43 and 138 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 44 and 139 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 44 and 140 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 45 and 141 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 45 and 142 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 46 and 143 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 46 and 144 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 47 and 145 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 47 and 146 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 48 and 147 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 48 and 148 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 49 and 149 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 49 and 150 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 50 and 151 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 50 and 152 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 51 and 153 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 51 and 154 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 52 and 155 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n", + "/opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/spacegroup.py:433: UserWarning: scaled_positions 52 and 156 are equivalent\n", + " warnings.warn('scaled_positions %d and %d '\n" + ] + } + ], + "source": [ + "atoms = ase.io.read(input_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Atom('K', [14.586186795000003, 10.49193963917675, 6.84007412], index=0)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "atoms[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Atom('O', [12.984899805, 0.7426558501511199, 5.554410399999999], index=59)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "atoms[59]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'Atoms' object has no attribute '_get_cell'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [11]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43matoms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_cell\u001b[49m()\n", + "\u001b[0;31mAttributeError\u001b[0m: 'Atoms' object has no attribute '_get_cell'" + ] + } + ], + "source": [ + "atoms._get_cell()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import ase.structure\n", + "\n", + "\n", + "ase.structur" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on package ase.spacegroup in ase:\n", + "\n", + "NAME\n", + " ase.spacegroup\n", + "\n", + "PACKAGE CONTENTS\n", + " crystal_data\n", + " spacegroup\n", + " symmetrize\n", + " utils\n", + " xtal\n", + "\n", + "CLASSES\n", + " builtins.object\n", + " ase.spacegroup.spacegroup.Spacegroup\n", + " \n", + " class Spacegroup(builtins.object)\n", + " | Spacegroup(spacegroup: Union[int, str, ForwardRef('Spacegroup')], setting=1, datafile=None)\n", + " | \n", + " | A space group class.\n", + " | \n", + " | The instances of Spacegroup describes the symmetry operations for\n", + " | the given space group.\n", + " | \n", + " | Example:\n", + " | \n", + " | >>> from ase.spacegroup import Spacegroup\n", + " | >>>\n", + " | >>> sg = Spacegroup(225)\n", + " | >>> print('Space group', sg.no, sg.symbol)\n", + " | Space group 225 F m -3 m\n", + " | >>> sg.scaled_primitive_cell\n", + " | array([[ 0. , 0.5, 0.5],\n", + " | [ 0.5, 0. , 0.5],\n", + " | [ 0.5, 0.5, 0. ]])\n", + " | >>> sites, kinds = sg.equivalent_sites([[0,0,0]])\n", + " | >>> sites\n", + " | array([[ 0. , 0. , 0. ],\n", + " | [ 0. , 0.5, 0.5],\n", + " | [ 0.5, 0. , 0.5],\n", + " | [ 0.5, 0.5, 0. ]])\n", + " | \n", + " | Methods defined here:\n", + " | \n", + " | __eq__(self, other)\n", + " | Return self==value.\n", + " | \n", + " | __ge__(self, other, NotImplemented=NotImplemented)\n", + " | Return a >= b. Computed by @total_ordering from (not a < b).\n", + " | \n", + " | __gt__(self, other, NotImplemented=NotImplemented)\n", + " | Return a > b. Computed by @total_ordering from (not a < b) and (a != b).\n", + " | \n", + " | __index__(self)\n", + " | \n", + " | __init__(self, spacegroup: Union[int, str, ForwardRef('Spacegroup')], setting=1, datafile=None)\n", + " | Returns a new Spacegroup instance.\n", + " | \n", + " | Parameters:\n", + " | \n", + " | spacegroup : int | string | Spacegroup instance\n", + " | The space group number in International Tables of\n", + " | Crystallography or its Hermann-Mauguin symbol. E.g.\n", + " | spacegroup=225 and spacegroup='F m -3 m' are equivalent.\n", + " | setting : 1 | 2\n", + " | Some space groups have more than one setting. `setting`\n", + " | determines Which of these should be used.\n", + " | datafile : None | string\n", + " | Path to database file. If `None`, the the default database\n", + " | will be used.\n", + " | \n", + " | __int__ = __index__(self)\n", + " | \n", + " | __le__(self, other, NotImplemented=NotImplemented)\n", + " | Return a <= b. Computed by @total_ordering from (a < b) or (a == b).\n", + " | \n", + " | __lt__(self, other)\n", + " | Return self>> from ase.spacegroup import Spacegroup\n", + " | >>> sg = Spacegroup(225) # fcc\n", + " | >>> sg.equivalent_lattice_points([[0, 0, 2]])\n", + " | array([[ 0, 0, -2],\n", + " | [ 0, -2, 0],\n", + " | [-2, 0, 0],\n", + " | [ 2, 0, 0],\n", + " | [ 0, 2, 0],\n", + " | [ 0, 0, 2]])\n", + " | \n", + " | equivalent_reflections(self, hkl)\n", + " | Return all equivalent reflections to the list of Miller indices\n", + " | in hkl.\n", + " | \n", + " | Example:\n", + " | \n", + " | >>> from ase.spacegroup import Spacegroup\n", + " | >>> sg = Spacegroup(225) # fcc\n", + " | >>> sg.equivalent_reflections([[0, 0, 2]])\n", + " | array([[ 0, 0, -2],\n", + " | [ 0, -2, 0],\n", + " | [-2, 0, 0],\n", + " | [ 2, 0, 0],\n", + " | [ 0, 2, 0],\n", + " | [ 0, 0, 2]])\n", + " | \n", + " | equivalent_sites(self, scaled_positions, onduplicates='error', symprec=0.001, occupancies=None)\n", + " | Returns the scaled positions and all their equivalent sites.\n", + " | \n", + " | Parameters:\n", + " | \n", + " | scaled_positions: list | array\n", + " | List of non-equivalent sites given in unit cell coordinates.\n", + " | \n", + " | occupancies: list | array, optional (default=None)\n", + " | List of occupancies corresponding to the respective sites.\n", + " | \n", + " | onduplicates : 'keep' | 'replace' | 'warn' | 'error'\n", + " | Action if `scaled_positions` contain symmetry-equivalent\n", + " | positions of full occupancy:\n", + " | \n", + " | 'keep'\n", + " | ignore additional symmetry-equivalent positions\n", + " | 'replace'\n", + " | replace\n", + " | 'warn'\n", + " | like 'keep', but issue an UserWarning\n", + " | 'error'\n", + " | raises a SpacegroupValueError\n", + " | \n", + " | symprec: float\n", + " | Minimum \"distance\" betweed two sites in scaled coordinates\n", + " | before they are counted as the same site.\n", + " | \n", + " | Returns:\n", + " | \n", + " | sites: array\n", + " | A NumPy array of equivalent sites.\n", + " | kinds: list\n", + " | A list of integer indices specifying which input site is\n", + " | equivalent to the corresponding returned site.\n", + " | \n", + " | Example:\n", + " | \n", + " | >>> from ase.spacegroup import Spacegroup\n", + " | >>> sg = Spacegroup(225) # fcc\n", + " | >>> sites, kinds = sg.equivalent_sites([[0, 0, 0], [0.5, 0.0, 0.0]])\n", + " | >>> sites\n", + " | array([[ 0. , 0. , 0. ],\n", + " | [ 0. , 0.5, 0.5],\n", + " | [ 0.5, 0. , 0.5],\n", + " | [ 0.5, 0.5, 0. ],\n", + " | [ 0.5, 0. , 0. ],\n", + " | [ 0. , 0.5, 0. ],\n", + " | [ 0. , 0. , 0.5],\n", + " | [ 0.5, 0.5, 0.5]])\n", + " | >>> kinds\n", + " | [0, 0, 0, 0, 1, 1, 1, 1]\n", + " | \n", + " | get_op(self)\n", + " | Returns all symmetry operations (including inversions and\n", + " | subtranslations), but unlike get_symop(), they are returned as\n", + " | two ndarrays.\n", + " | \n", + " | get_rotations(self)\n", + " | Return all rotations, including inversions for\n", + " | centrosymmetric crystals.\n", + " | \n", + " | get_symop(self)\n", + " | Returns all symmetry operations (including inversions and\n", + " | subtranslations) as a sequence of (rotation, translation)\n", + " | tuples.\n", + " | \n", + " | symmetry_normalised_reflections(self, hkl)\n", + " | Returns an array of same size as *hkl*, containing the\n", + " | corresponding symmetry-equivalent reflections of lowest\n", + " | indices.\n", + " | \n", + " | Example:\n", + " | \n", + " | >>> from ase.spacegroup import Spacegroup\n", + " | >>> sg = Spacegroup(225) # fcc\n", + " | >>> sg.symmetry_normalised_reflections([[2, 0, 0], [0, 2, 0]])\n", + " | array([[ 0, 0, -2],\n", + " | [ 0, 0, -2]])\n", + " | \n", + " | symmetry_normalised_sites(self, scaled_positions, map_to_unitcell=True)\n", + " | Returns an array of same size as *scaled_positions*,\n", + " | containing the corresponding symmetry-equivalent sites of\n", + " | lowest indices.\n", + " | \n", + " | If *map_to_unitcell* is true, the returned positions are all\n", + " | mapped into the unit cell, i.e. lattice translations are\n", + " | included as symmetry operator.\n", + " | \n", + " | Example:\n", + " | \n", + " | >>> from ase.spacegroup import Spacegroup\n", + " | >>> sg = Spacegroup(225) # fcc\n", + " | >>> sg.symmetry_normalised_sites([[0.0, 0.5, 0.5], [1.0, 1.0, 0.0]])\n", + " | array([[ 0., 0., 0.],\n", + " | [ 0., 0., 0.]])\n", + " | \n", + " | tag_sites(self, scaled_positions, symprec=0.001)\n", + " | Returns an integer array of the same length as *scaled_positions*,\n", + " | tagging all equivalent atoms with the same index.\n", + " | \n", + " | Example:\n", + " | \n", + " | >>> from ase.spacegroup import Spacegroup\n", + " | >>> sg = Spacegroup(225) # fcc\n", + " | >>> sg.tag_sites([[0.0, 0.0, 0.0],\n", + " | ... [0.5, 0.5, 0.0],\n", + " | ... [1.0, 0.0, 0.0],\n", + " | ... [0.5, 0.0, 0.0]])\n", + " | array([0, 0, 0, 1])\n", + " | \n", + " | todict(self)\n", + " | \n", + " | unique_reflections(self, hkl)\n", + " | Returns a subset *hkl* containing only the symmetry-unique\n", + " | reflections.\n", + " | \n", + " | Example:\n", + " | \n", + " | >>> from ase.spacegroup import Spacegroup\n", + " | >>> sg = Spacegroup(225) # fcc\n", + " | >>> sg.unique_reflections([[ 2, 0, 0],\n", + " | ... [ 0, -2, 0],\n", + " | ... [ 2, 2, 0],\n", + " | ... [ 0, -2, -2]])\n", + " | array([[2, 0, 0],\n", + " | [2, 2, 0]])\n", + " | \n", + " | unique_sites(self, scaled_positions, symprec=0.001, output_mask=False, map_to_unitcell=True)\n", + " | Returns a subset of *scaled_positions* containing only the\n", + " | symmetry-unique positions. If *output_mask* is True, a boolean\n", + " | array masking the subset is also returned.\n", + " | \n", + " | If *map_to_unitcell* is true, all sites are first mapped into\n", + " | the unit cell making e.g. [0, 0, 0] and [1, 0, 0] equivalent.\n", + " | \n", + " | Example:\n", + " | \n", + " | >>> from ase.spacegroup import Spacegroup\n", + " | >>> sg = Spacegroup(225) # fcc\n", + " | >>> sg.unique_sites([[0.0, 0.0, 0.0],\n", + " | ... [0.5, 0.5, 0.0],\n", + " | ... [1.0, 0.0, 0.0],\n", + " | ... [0.5, 0.0, 0.0]])\n", + " | array([[ 0. , 0. , 0. ],\n", + " | [ 0.5, 0. , 0. ]])\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Readonly properties defined here:\n", + " | \n", + " | centrosymmetric\n", + " | Whether a center of symmetry exists.\n", + " | \n", + " | lattice\n", + " | Lattice type:\n", + " | \n", + " | P primitive\n", + " | I body centering, h+k+l=2n\n", + " | F face centering, h,k,l all odd or even\n", + " | A,B,C single face centering, k+l=2n, h+l=2n, h+k=2n\n", + " | R rhombohedral centering, -h+k+l=3n (obverse); h-k+l=3n (reverse)\n", + " | \n", + " | no\n", + " | Space group number in International Tables of Crystallography.\n", + " | \n", + " | nsubtrans\n", + " | Number of cell-subtranslation vectors.\n", + " | \n", + " | nsymop\n", + " | Total number of symmetry operations.\n", + " | \n", + " | reciprocal_cell\n", + " | Tree Miller indices that span all kinematically non-forbidden reflections as a matrix with the Miller indices along the rows.\n", + " | \n", + " | rotations\n", + " | Symmetry rotation matrices. The invertions are not included for centrosymmetrical crystals.\n", + " | \n", + " | scaled_primitive_cell\n", + " | Primitive cell in scaled coordinates as a matrix with the primitive vectors along the rows.\n", + " | \n", + " | setting\n", + " | Space group setting. Either one or two.\n", + " | \n", + " | subtrans\n", + " | Translations vectors belonging to cell-sub-translations.\n", + " | \n", + " | symbol\n", + " | Hermann-Mauguin (or international) symbol for the space group.\n", + " | \n", + " | translations\n", + " | Symmetry translations. The invertions are not included for centrosymmetrical crystals.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data descriptors defined here:\n", + " | \n", + " | __dict__\n", + " | dictionary for instance variables (if defined)\n", + " | \n", + " | __weakref__\n", + " | list of weak references to the object (if defined)\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data and other attributes defined here:\n", + " | \n", + " | __hash__ = None\n", + "\n", + "FUNCTIONS\n", + " crystal(symbols=None, basis=None, occupancies=None, spacegroup=1, setting=1, cell=None, cellpar=None, ab_normal=(0, 0, 1), a_direction=None, size=(1, 1, 1), onduplicates='warn', symprec=0.001, pbc=True, primitive_cell=False, **kwargs) -> ase.atoms.Atoms\n", + " Create an Atoms instance for a conventional unit cell of a\n", + " space group.\n", + " \n", + " Parameters:\n", + " \n", + " symbols : str | sequence of str | sequence of Atom | Atoms\n", + " Element symbols of the unique sites. Can either be a string\n", + " formula or a sequence of element symbols. E.g. ('Na', 'Cl')\n", + " and 'NaCl' are equivalent. Can also be given as a sequence of\n", + " Atom objects or an Atoms object.\n", + " basis : list of scaled coordinates\n", + " Positions of the unique sites corresponding to symbols given\n", + " either as scaled positions or through an atoms instance. Not\n", + " needed if *symbols* is a sequence of Atom objects or an Atoms\n", + " object.\n", + " occupancies : list of site occupancies\n", + " Occupancies of the unique sites. Defaults to 1.0 and thus no mixed\n", + " occupancies are considered if not explicitly asked for. If occupancies\n", + " are given, the most dominant species will yield the atomic number.\n", + " The occupancies in the atoms.info['occupancy'] dictionary will have\n", + " integers keys converted to strings. The conversion is done in order\n", + " to avoid unexpected conversions when using the JSON serializer.\n", + " spacegroup : int | string | Spacegroup instance\n", + " Space group given either as its number in International Tables\n", + " or as its Hermann-Mauguin symbol.\n", + " setting : 1 | 2\n", + " Space group setting.\n", + " cell : 3x3 matrix\n", + " Unit cell vectors.\n", + " cellpar : [a, b, c, alpha, beta, gamma]\n", + " Cell parameters with angles in degree. Is not used when `cell`\n", + " is given.\n", + " ab_normal : vector\n", + " Is used to define the orientation of the unit cell relative\n", + " to the Cartesian system when `cell` is not given. It is the\n", + " normal vector of the plane spanned by a and b.\n", + " a_direction : vector\n", + " Defines the orientation of the unit cell a vector. a will be\n", + " parallel to the projection of `a_direction` onto the a-b plane.\n", + " size : 3 positive integers\n", + " How many times the conventional unit cell should be repeated\n", + " in each direction.\n", + " onduplicates : 'keep' | 'replace' | 'warn' | 'error'\n", + " Action if `basis` contain symmetry-equivalent positions:\n", + " 'keep' - ignore additional symmetry-equivalent positions\n", + " 'replace' - replace\n", + " 'warn' - like 'keep', but issue an UserWarning\n", + " 'error' - raises a SpacegroupValueError\n", + " symprec : float\n", + " Minimum \"distance\" betweed two sites in scaled coordinates\n", + " before they are counted as the same site.\n", + " pbc : one or three bools\n", + " Periodic boundary conditions flags. Examples: True,\n", + " False, 0, 1, (1, 1, 0), (True, False, False). Default\n", + " is True.\n", + " primitive_cell : bool\n", + " Whether to return the primitive instead of the conventional\n", + " unit cell.\n", + " \n", + " Keyword arguments:\n", + " \n", + " All additional keyword arguments are passed on to the Atoms\n", + " constructor. Currently, probably the most useful additional\n", + " keyword arguments are `info`, `constraint` and `calculator`.\n", + " \n", + " Examples:\n", + " \n", + " Two diamond unit cells (space group number 227)\n", + " \n", + " >>> diamond = crystal('C', [(0,0,0)], spacegroup=227,\n", + " ... cellpar=[3.57, 3.57, 3.57, 90, 90, 90], size=(2,1,1))\n", + " >>> ase.view(diamond) # doctest: +SKIP\n", + " \n", + " A CoSb3 skutterudite unit cell containing 32 atoms\n", + " \n", + " >>> skutterudite = crystal(('Co', 'Sb'),\n", + " ... basis=[(0.25,0.25,0.25), (0.0, 0.335, 0.158)],\n", + " ... spacegroup=204, cellpar=[9.04, 9.04, 9.04, 90, 90, 90])\n", + " >>> len(skutterudite)\n", + " 32\n", + " \n", + " get_basis(atoms: ase.atoms.Atoms, spacegroup: Union[int, str, ForwardRef('Spacegroup')] = None, method: str = 'auto', tol: float = 1e-05) -> numpy.ndarray\n", + " Function for determining a reduced basis of an atoms object.\n", + " Can use either an ASE native algorithm or an spglib based one.\n", + " The native ASE version requires specifying a space group,\n", + " while the (current) spglib version cannot.\n", + " The default behavior is to automatically determine which implementation\n", + " to use, based on the the ``spacegroup`` parameter,\n", + " and whether spglib is installed.\n", + " \n", + " :param atoms: ase Atoms object to get basis from\n", + " :param spacegroup: Optional, ``int``, ``str``\n", + " or :class:`ase.spacegroup.Spacegroup` object.\n", + " If unspecified, the spacegroup can be inferred using spglib,\n", + " if spglib is installed, and ``method`` is set to either\n", + " ``'spglib'`` or ``'auto'``.\n", + " Inferring the spacegroup requires spglib.\n", + " :param method: ``str``, one of: ``'auto'`` | ``'ase'`` | ``'spglib'``.\n", + " Selection of which implementation to use.\n", + " It is recommended to use ``'auto'``, which is also the default.\n", + " :param tol: ``float``, numeric tolerance for positional comparisons\n", + " Default: ``1e-5``\n", + " \n", + " get_bravais_class(sg)\n", + " \n", + " get_point_group(sg)\n", + " \n", + " get_spacegroup(atoms, symprec=1e-05)\n", + " Determine the spacegroup to which belongs the Atoms object.\n", + " \n", + " This requires spglib: https://atztogo.github.io/spglib/ .\n", + " \n", + " Parameters:\n", + " \n", + " atoms: Atoms object\n", + " Types, positions and unit-cell.\n", + " symprec: float\n", + " Symmetry tolerance, i.e. distance tolerance in Cartesian\n", + " coordinates to find crystal symmetry.\n", + " \n", + " The Spacegroup object is returned.\n", + " \n", + " polar_space_group(sg)\n", + "\n", + "DATA\n", + " __all__ = ['Spacegroup', 'crystal', 'get_spacegroup', 'get_bravais_cla...\n", + "\n", + "FILE\n", + " /opt/anaconda3/envs/cell2mol/lib/python3.10/site-packages/ase/spacegroup/__init__.py\n", + "\n", + "\n" + ] + } + ], + "source": [ + "import ase.spacegroup\n", + "\n", + "\n", + "help(ase.spacegroup)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ase.io import read, write\n", + "from ase.spacegroup import crystal\n", + "\n", + "# Path to your CIF file\n", + "cif_file_path = 'path_to_your_cif_file.cif'\n", + "\n", + "# Load the initial structure from CIF file\n", + "initial_structure = read(cif_file_path, format='cif')\n", + "\n", + "# Extract cell parameters and atomic positions from the CIF file\n", + "cell = initial_structure.cell.cellpar()\n", + "symbols = initial_structure.get_chemical_symbols()\n", + "positions = initial_structure.get_positions()\n", + "spacegroup = 'R3' # Space group 'R 3', make sure it matches the CIF space group if different\n", + "\n", + "# Create the crystal structure using the symmetry of the space group\n", + "crystal_structure = crystal(symbols=symbols,\n", + " basis=positions,\n", + " spacegroup=spacegroup,\n", + " cellpar=cell)\n", + "\n", + "# Save the complete structure to an XYZ file\n", + "xyz_file_path = 'complete_structure.xyz'\n", + "write(xyz_file_path, crystal_structure, format='xyz')\n", + "\n", + "print(f\"XYZ file with complete structure saved as {xyz_file_path}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import gemmi" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'gemmi.cif' has no attribute 'parse_symop'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [3]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;66;03m# Process the CIF file\u001b[39;00m\n\u001b[1;32m 55\u001b[0m cell, symmetry_ops \u001b[38;5;241m=\u001b[39m read_cif(cif_path)\n\u001b[0;32m---> 56\u001b[0m cartesian_ops \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_symmetry_to_cartesian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcell\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msymmetry_ops\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;66;03m# Print the converted Cartesian symmetry operations\u001b[39;00m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, op \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(cartesian_ops, \u001b[38;5;241m1\u001b[39m):\n", + "Input \u001b[0;32mIn [3]\u001b[0m, in \u001b[0;36mconvert_symmetry_to_cartesian\u001b[0;34m(cell, symmetry_ops)\u001b[0m\n\u001b[1;32m 36\u001b[0m cartesian_ops \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m op \u001b[38;5;129;01min\u001b[39;00m symmetry_ops:\n\u001b[1;32m 38\u001b[0m \u001b[38;5;66;03m# Parse the operation using gemmi\u001b[39;00m\n\u001b[0;32m---> 39\u001b[0m mat, vec \u001b[38;5;241m=\u001b[39m \u001b[43mgemmi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcif\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_symop\u001b[49m(op)\n\u001b[1;32m 40\u001b[0m rot_matrix_frac \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(mat\u001b[38;5;241m.\u001b[39mmat)\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m3\u001b[39m)\n\u001b[1;32m 41\u001b[0m trans_vector_frac \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(vec)\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'gemmi.cif' has no attribute 'parse_symop'" + ] + } + ], + "source": [ + "import numpy as np\n", + "import gemmi\n", + "import re\n", + "\n", + "def clean_number(string):\n", + " # Remove uncertainty parentheses\n", + " return re.sub(r'\\(.*\\)', '', string)\n", + "\n", + "def read_cif(cif_path):\n", + " doc = gemmi.cif.read_file(cif_path)\n", + " block = doc.sole_block()\n", + "\n", + " # Extract unit cell parameters with uncertainties removed\n", + " cell = gemmi.UnitCell(\n", + " float(clean_number(block.find_value('_cell_length_a'))),\n", + " float(clean_number(block.find_value('_cell_length_b'))),\n", + " float(clean_number(block.find_value('_cell_length_c'))),\n", + " float(clean_number(block.find_value('_cell_angle_alpha'))),\n", + " float(clean_number(block.find_value('_cell_angle_beta'))),\n", + " float(clean_number(block.find_value('_cell_angle_gamma')))\n", + " )\n", + "\n", + " # Extract symmetry operations\n", + " symmetry_ops = []\n", + " sym_loop = block.find_loop('_symmetry_equiv_pos_as_xyz')\n", + " for op in sym_loop:\n", + " symmetry_ops.append(op)\n", + "\n", + " return cell, symmetry_ops\n", + "\n", + "def convert_symmetry_to_cartesian(cell, symmetry_ops):\n", + " # Create transformation matrix from fractional to Cartesian\n", + " to_cartesian = np.array(cell.orthogonalization_matrix).reshape(3, 3)\n", + " to_fractional = np.linalg.inv(to_cartesian)\n", + "\n", + " cartesian_ops = []\n", + " for op in symmetry_ops:\n", + " # Parse the operation using gemmi\n", + " mat, vec = gemmi.cif.parse_symop(op)\n", + " rot_matrix_frac = np.array(mat.mat).reshape(3, 3)\n", + " trans_vector_frac = np.array(vec)\n", + "\n", + " # Convert rotation and translation to Cartesian\n", + " rot_matrix_cart = np.dot(to_cartesian, np.dot(rot_matrix_frac, to_fractional))\n", + " trans_vector_cart = np.dot(to_cartesian, trans_vector_frac)\n", + "\n", + " cartesian_ops.append((rot_matrix_cart, trans_vector_cart))\n", + "\n", + " return cartesian_ops\n", + "\n", + "# Path to the CIF file\n", + "cif_path = \"error_2/BOFFOS/BOFFOS.cif\"\n", + "\n", + "# Process the CIF file\n", + "cell, symmetry_ops = read_cif(cif_path)\n", + "cartesian_ops = convert_symmetry_to_cartesian(cell, symmetry_ops)\n", + "\n", + "# Print the converted Cartesian symmetry operations\n", + "for i, op in enumerate(cartesian_ops, 1):\n", + " print(f\"Operation {i}: Rotation Matrix\\n{op[0]}\\nTranslation Vector\\n{op[1]}\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gemmi" + ] } ], "metadata": { diff --git a/cell2mol/test/error_2/APRCOB.search6.cif b/cell2mol/test/error_2/APRCOB.search6.cif new file mode 100755 index 00000000..a98d684f --- /dev/null +++ b/cell2mol/test/error_2/APRCOB.search6.cif @@ -0,0 +1,112 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_APRCOB +_audit_creation_date 1971-12-31 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD APRCOB +_database_code_depnum_ccdc_archive 'CCDC 1103373' +_chemical_formula_sum 'C6 H14 Br1 Co1 N4 O4' +_chemical_formula_moiety +; +C6 H14 Co1 N4 O4 1+,Br1 1- +; +_journal_coden_Cambridge 9 +_journal_volume 8 +_journal_year 1969 +_journal_page_first 1911 +_journal_name_full 'Inorg.Chem. ' +loop_ +_publ_author_name +"C.F.Liu" +"J.A.Ibers" +_chemical_name_systematic +; +(-)~546~-cis,trans,cis-bis(Diaminopropionato)-cobalt(iii) bromide +; +_cell_volume 520.567 +_exptl_crystal_density_diffrn 2.21 +_diffrn_ambient_temperature ? +_diffrn_special_details +; +The study was carried out at room temperature,in the range 283-303K + +; +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.07 +_refine_ls_wR_factor_gt 0.07 +_symmetry_cell_setting orthorhombic +_symmetry_space_group_name_H-M 'P 21 21 2' +_symmetry_Int_Tables_number 18 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 1/2+x,1/2-y,-z +3 1/2-x,1/2+y,-z +4 -x,-y,z +_cell_length_a 11.76(2) +_cell_length_b 7.49(2) +_cell_length_c 5.91(2) +_cell_angle_alpha 90 +_cell_angle_beta 90 +_cell_angle_gamma 90 +_cell_formula_units_Z 2 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.20 +Br 1.09 +Co 1.26 +N 0.68 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Br1 Br 0.00000 0.50000 0.37350 +Co1 Co 0.50000 0.50000 0.11710 +C1 C 0.32220 0.34650 -0.09200 +C2 C 0.36360 0.23430 0.10410 +C3 C 0.31440 0.30690 0.30690 +H1 H 0.51600 0.18200 0.26000 +H2 H 0.52200 0.18300 -0.02500 +H3 H 0.32100 0.59800 0.32200 +H4 H 0.41000 0.50000 0.50700 +H5 H 0.33600 0.09700 0.08700 +H6 H 0.22300 0.32900 0.28600 +H7 H 0.32900 0.22000 0.45100 +N1 N 0.48530 0.24100 0.11110 +N2 N 0.37610 0.49210 0.34340 +O1 O 0.38710 0.48730 -0.10680 +O2 O 0.23900 0.31410 -0.20470 +N1C N 0.51470 0.75900 0.11110 +N2C N 0.62390 0.50790 0.34340 +O1C O 0.61290 0.51270 -0.10680 +C2C C 0.63640 0.76570 0.10410 +H1C H 0.48400 0.81800 0.26000 +H2C H 0.47800 0.81700 -0.02500 +C3C C 0.68560 0.69310 0.30690 +H3C H 0.67900 0.40200 0.32200 +H4C H 0.59000 0.50000 0.50700 +C1C C 0.67780 0.65350 -0.09200 +H5C H 0.66400 0.90300 0.08700 +H6C H 0.77700 0.67100 0.28600 +H7C H 0.67100 0.78000 0.45100 +O2C O 0.76100 0.68590 -0.20470 +#END diff --git a/cell2mol/test/error_2/BOFFOS.search5.cif b/cell2mol/test/error_2/BOFFOS.search5.cif new file mode 100755 index 00000000..87efae19 --- /dev/null +++ b/cell2mol/test/error_2/BOFFOS.search5.cif @@ -0,0 +1,249 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_BOFFOS +_audit_creation_date 2008-11-25 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD BOFFOS +_database_code_depnum_ccdc_archive 'CCDC 680756' +_chemical_formula_sum 'C42 H90 Fe1 K3 O39' +_chemical_formula_moiety +; +C42 H72 Fe1 K3 O30,9(H2 O1) +; +_journal_coden_Cambridge 1295 +_journal_volume 11 +_journal_year 2008 +_journal_page_first 799 +_journal_name_full 'Inorg.Chem.Commun. ' +loop_ +_publ_author_name +"Wei-Hua Yu" +"Xiao-Zu Wang" +"Yun-Xia Sui" +"Xiao-Ming Ren" +"Qing-Jin Meng" +_chemical_name_systematic +; +tris(\m~2~-oxalato)-tris(1,4,7,10,13,16-hexaoxacyclooctadecane)-iron(iii)-tri- +potassium(i) nonahydrate +; +_cell_volume 5013.275 +_exptl_crystal_colour 'light yellow' +_exptl_crystal_density_diffrn 1.383 +_exptl_special_details +; +isostructural with chromium analogue + +; +_exptl_crystal_description 'prismatic' +_diffrn_ambient_temperature 293 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0281 +_refine_ls_wR_factor_gt 0.0281 +_symmetry_cell_setting rhombohedral +_symmetry_space_group_name_H-M 'R 3' +_symmetry_Int_Tables_number 146 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -y,x-y,z +3 -x+y,-x,z +4 2/3+x,1/3+y,1/3+z +5 2/3-y,1/3+x-y,1/3+z +6 2/3-x+y,1/3-x,1/3+z +7 1/3+x,2/3+y,2/3+z +8 1/3-y,2/3+x-y,2/3+z +9 1/3-x+y,2/3-x,2/3+z +_cell_length_a 24.369(3) +_cell_length_b 24.369(3) +_cell_length_c 9.748(2) +_cell_angle_alpha 90 +_cell_angle_beta 90 +_cell_angle_gamma 120 +_cell_formula_units_Z 3 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Fe 1.34 +K 2.00 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +K1 K 0.84713(2) 0.49715(2) 0.70169(5) +Fe1 Fe 0.66670 0.33330 0.96380(5) +O1 O 0.84564(8) 0.45731(9) 0.43407(18) +O2 O 0.75871(7) 0.49451(7) 0.50598(16) +O3 O 0.76046(8) 0.53529(8) 0.77498(19) +O4 O 0.87556(9) 0.58795(9) 0.92401(17) +O5 O 0.96610(8) 0.56061(10) 0.8361(2) +O6 O 0.96481(8) 0.51525(9) 0.5698(2) +O7 O 0.84884(8) 0.40648(8) 0.8643(2) +O8 O 0.74737(7) 0.37844(7) 0.85468(16) +O9 O 0.72235(8) 0.31431(8) 1.08222(16) +O10 O 0.82236(9) 0.33812(10) 1.1071(2) +O11 O 0.96073(11) 0.41262(15) 0.9709(3) +O12 O 0.68302(19) 0.6317(2) 0.4667(4) +O13 O 0.91917(16) 0.32407(19) 0.2061(4) +C1 C 0.80239(15) 0.45809(15) 0.3380(3) +H1 H 0.79270 0.42620 0.26810 +H2 H 0.82120 0.49920 0.29350 +C2 C 0.74359(13) 0.44512(14) 0.4096(3) +H3 H 0.71280 0.44330 0.34390 +H4 H 0.72540 0.40470 0.45660 +C3 C 0.70399(12) 0.48803(14) 0.5726(3) +H5 H 0.68240 0.44780 0.62070 +H6 H 0.67510 0.48880 0.50520 +C4 C 0.72312(13) 0.54080(14) 0.6713(3) +H7 H 0.74720 0.58110 0.62450 +H8 H 0.68580 0.53900 0.71140 +C5 C 0.77726(14) 0.58121(13) 0.8796(3) +H9 H 0.73940 0.57720 0.92210 +H10 H 0.80150 0.62350 0.84120 +C6 C 0.81561(14) 0.57083(14) 0.9825(3) +H11 H 0.82130 0.59640 1.06320 +H12 H 0.79410 0.52670 1.00990 +C7 C 0.91569(15) 0.58064(17) 1.0182(3) +H13 H 0.89640 0.53680 1.04710 +H14 H 0.92240 0.60670 1.09870 +C8 C 0.97720(15) 0.60043(16) 0.9488(3) +H15 H 0.99600 0.64410 0.91840 +H16 H 1.00630 0.59740 1.01210 +C9 C 1.02129(13) 0.56892(17) 0.7706(4) +H17 H 1.05150 0.57140 0.83870 +H18 H 1.04070 0.60810 0.71870 +C10 C 1.00469(14) 0.51534(16) 0.6783(4) +H19 H 1.04290 0.51870 0.63980 +H20 H 0.98270 0.47590 0.72920 +C11 C 0.94276(14) 0.46181(14) 0.4835(4) +H21 H 0.91860 0.42340 0.53680 +H22 H 0.97850 0.46060 0.44280 +C12 C 0.90229(15) 0.46506(16) 0.3738(3) +H23 H 0.92470 0.50570 0.32740 +H24 H 0.89210 0.43190 0.30670 +C13 C 0.79509(10) 0.37857(10) 0.9100(2) +C14 C 0.78019(11) 0.34031(10) 1.0458(2) +H25 H 0.9259(14) 0.411(2) 0.945(4) +H26 H 0.7016(17) 0.6710(11) 0.435(4) +H27 H 0.8794(11) 0.316(2) 0.195(5) +H28 H 0.9563(16) 0.3911(15) 1.044(3) +H29 H 0.6549(14) 0.6439(18) 0.480(4) +H30 H 0.917(3) 0.322(3) 0.297(2) +K1A K 0.50285(2) 0.34998(2) 0.70169(5) +K1B K 0.65002(2) 0.15287(2) 0.70169(5) +O1A O 0.54269(8) 0.38833(9) 0.43407(18) +O1B O 0.61167(8) 0.15436(9) 0.43407(18) +O2A O 0.50549(7) 0.26420(7) 0.50598(16) +O2B O 0.73580(7) 0.24129(7) 0.50598(16) +O3A O 0.46471(8) 0.22517(8) 0.77498(19) +O3B O 0.77483(8) 0.23954(8) 0.77498(19) +O4A O 0.41205(9) 0.28761(9) 0.92401(17) +O4B O 0.71239(9) 0.12444(9) 0.92401(17) +O5A O 0.43939(8) 0.40549(10) 0.8361(2) +O5B O 0.59451(8) 0.0339(1) 0.8361(2) +O6A O 0.48475(8) 0.44956(9) 0.5698(2) +O6B O 0.55044(8) 0.03519(9) 0.5698(2) +O7A O 0.59352(8) 0.44236(8) 0.8643(2) +O7B O 0.55764(8) 0.15116(8) 0.8643(2) +O8A O 0.62156(7) 0.36893(7) 0.85468(16) +O8B O 0.63107(7) 0.25263(7) 0.85468(16) +O9A O 0.68569(8) 0.40804(8) 1.08222(16) +O9B O 0.59196(8) 0.27765(8) 1.08222(16) +O10B O 0.51576(9) 0.17764(10) 1.1071(2) +O10A O 0.66188(9) 0.48424(10) 1.1071(2) +C1A C 0.54191(15) 0.34430(15) 0.3380(3) +C1B C 0.65570(15) 0.19761(15) 0.3380(3) +H1A H 0.57380 0.36650 0.26810 +H1B H 0.63350 0.20730 0.26810 +H2A H 0.50080 0.32200 0.29350 +H2B H 0.67800 0.17880 0.29350 +C2A C 0.55488(13) 0.29847(14) 0.4096(3) +C2B C 0.70153(13) 0.25641(14) 0.4096(3) +H3B H 0.73050 0.28720 0.34390 +H3A H 0.55670 0.26950 0.34390 +H4B H 0.67930 0.27460 0.45660 +H4A H 0.59530 0.32070 0.45660 +C3A C 0.51197(12) 0.21596(14) 0.5726(3) +C3B C 0.78404(12) 0.29601(14) 0.5726(3) +H5A H 0.55220 0.23460 0.62070 +H5B H 0.76540 0.31760 0.62070 +H6A H 0.51120 0.18630 0.50520 +H6B H 0.81370 0.32490 0.50520 +C4A C 0.45920(13) 0.18232(14) 0.6713(3) +C4B C 0.81768(13) 0.27688(14) 0.6713(3) +H7A H 0.41890 0.16610 0.62450 +H7B H 0.83390 0.25280 0.62450 +H8A H 0.46100 0.14680 0.71140 +H8B H 0.85320 0.31420 0.71140 +C5A C 0.41879(14) 0.19605(13) 0.8796(3) +C5B C 0.80395(14) 0.22274(13) 0.8796(3) +H9A H 0.42280 0.16220 0.92210 +H9B H 0.83780 0.26060 0.92210 +H10A H 0.37650 0.17800 0.84120 +H10B H 0.82200 0.19850 0.84120 +C6A C 0.42917(14) 0.24478(14) 0.9825(3) +C6B C 0.75522(14) 0.18439(14) 0.9825(3) +H11B H 0.77510 0.17870 1.06320 +H11A H 0.40360 0.22490 1.06320 +H12B H 0.73260 0.20590 1.00990 +H12A H 0.47330 0.26740 1.00990 +C7A C 0.41936(15) 0.33505(17) 1.0182(3) +C7B C 0.66495(15) 0.08431(17) 1.0182(3) +H13A H 0.46320 0.35960 1.04710 +H13B H 0.64040 0.10360 1.04710 +H14A H 0.39330 0.31570 1.09870 +H14B H 0.68430 0.07760 1.09870 +C8A C 0.39957(15) 0.37677(16) 0.9488(3) +C8B C 0.62323(15) 0.02280(16) 0.9488(3) +H15A H 0.35590 0.35190 0.91840 +H15B H 0.64810 0.00400 0.91840 +H16A H 0.40260 0.40890 1.01210 +H16B H 0.59110 -0.00630 1.01210 +C9A C 0.43108(13) 0.45237(17) 0.7706(4) +C9B C 0.54763(13) -0.02129(17) 0.7706(4) +H17A H 0.42860 0.48010 0.83870 +H17B H 0.51990 -0.05150 0.83870 +H18A H 0.39190 0.43260 0.71870 +H18B H 0.56740 -0.04070 0.71870 +C10A C 0.48466(14) 0.48935(16) 0.6783(4) +C10B C 0.51065(14) -0.00469(16) 0.6783(4) +H19B H 0.47580 -0.04290 0.63980 +H19A H 0.48130 0.52420 0.63980 +H20B H 0.49320 0.01730 0.72920 +H20A H 0.52410 0.50680 0.72920 +C11B C 0.51905(14) 0.05724(14) 0.4835(4) +C11A C 0.53819(14) 0.48095(14) 0.4835(4) +H21B H 0.50480 0.08140 0.53680 +H21A H 0.57660 0.49520 0.53680 +H22B H 0.48210 0.02150 0.44280 +H22A H 0.53940 0.51790 0.44280 +C12B C 0.56277(15) 0.09771(16) 0.3738(3) +C12A C 0.53494(15) 0.43723(16) 0.3738(3) +H23B H 0.58100 0.07530 0.32740 +H23A H 0.49430 0.41900 0.32740 +H24B H 0.53980 0.10790 0.30670 +H24A H 0.56810 0.46020 0.30670 +C13A C 0.62143(10) 0.41652(10) 0.9100(2) +C13B C 0.58348(10) 0.20491(10) 0.9100(2) +C14A C 0.65969(11) 0.43988(10) 1.0458(2) +C14B C 0.56012(11) 0.21981(10) 1.0458(2) +#END diff --git a/cell2mol/test/error_2/BOXSAK.search5.cif b/cell2mol/test/error_2/BOXSAK.search5.cif new file mode 100755 index 00000000..9b8916d0 --- /dev/null +++ b/cell2mol/test/error_2/BOXSAK.search5.cif @@ -0,0 +1,184 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_BOXSAK +_audit_creation_date 2015-03-06 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD BOXSAK +_database_code_depnum_ccdc_archive 'CCDC 1021433' +_chemical_formula_sum 'C24 H60 Fe1 I2 K1 N2 O6 Si4' +_chemical_formula_moiety +; +C24 H60 Fe1 I2 K1 N2 O6 Si4 +; +_journal_coden_Cambridge 179 +_journal_volume 54 +_journal_year 2015 +_journal_page_first 245 +_journal_name_full 'Angew.Chem.,Int.Ed. ' +loop_ +_publ_author_name +"C.Gunnar Werncke" +"P.C.Bunting" +"C.Duhayon" +"J.R.Long" +"S.Bontemps" +"S.Sabo-Etienne" +_chemical_name_systematic +; +(18-crown-9)-bis(\m-iodo)-bis(1,1,1-trimethyl-N-(trimethylsilyl)silanaminato)- +potassium(i)-iron(iii) +; +_cell_volume 2047.418 +_exptl_crystal_colour 'black' +_exptl_crystal_density_diffrn 1.515 +_exptl_crystal_description 'block' +_diffrn_ambient_temperature 100 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0289 +_refine_ls_wR_factor_gt 0.0289 +_symmetry_cell_setting triclinic +_symmetry_space_group_name_H-M 'P -1' +_symmetry_Int_Tables_number 2 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,-y,-z +_cell_length_a 9.8268(8) +_cell_length_b 12.5116(10) +_cell_length_c 17.0871(14) +_cell_angle_alpha 93.498(2) +_cell_angle_beta 97.872(3) +_cell_angle_gamma 99.113(2) +_cell_formula_units_Z 2 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Fe 1.52 +I 1.40 +K 2.03 +N 0.68 +O 0.68 +Si 1.20 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +O1 O 0.88876(10) 0.66945(9) 0.41206(6) +O2 O 0.98636(10) 0.67529(8) 0.26538(6) +O3 O 0.77823(11) 0.69146(9) 0.13831(6) +O4 O 0.49648(11) 0.61248(9) 0.14116(6) +O5 O 0.41127(10) 0.60859(8) 0.29170(6) +O6 O 0.60965(10) 0.59861(8) 0.42677(6) +C1 C 0.32992(15) 0.02478(16) 0.2172(1) +C2 C 0.41861(16) 0.16282(13) 0.37407(10) +C3 C 0.40706(17) -0.07937(13) 0.36208(11) +C4 C 0.7558(2) 0.01607(15) 0.45578(8) +C5 C 0.71118(15) -0.14919(10) 0.30980(9) +C6 C 0.93388(14) 0.04674(12) 0.33090(9) +C7 C 0.60339(16) 0.00277(12) 0.10027(8) +C8 C 0.8946(2) -0.02078(13) 0.08851(10) +C9 C 0.75799(16) 0.15145(13) -0.00001(8) +C10 C 1.10775(15) 0.29406(14) 0.27254(9) +C11 C 1.10068(16) 0.23262(19) 0.10403(10) +C12 C 0.93971(19) 0.40646(13) 0.14925(14) +C13 C 1.02656(14) 0.65382(12) 0.40378(9) +C14 C 1.07119(14) 0.71861(11) 0.33788(9) +C15 C 1.01093(15) 0.74368(12) 0.20379(10) +C16 C 0.92011(16) 0.69549(13) 0.12822(9) +C17 C 0.68481(19) 0.65000(16) 0.06799(9) +C18 C 0.54171(18) 0.67086(14) 0.07813(9) +C19 C 0.36225(15) 0.62983(13) 0.15452(9) +C20 C 0.32130(14) 0.56440(12) 0.22113(9) +C21 C 0.37981(14) 0.55170(11) 0.35851(9) +C22 C 0.46973(14) 0.61141(11) 0.43130(8) +C23 C 0.70508(15) 0.66211(11) 0.48884(8) +C24 C 0.84806(15) 0.63632(12) 0.48479(8) +Si1 Si 0.45700(4) 0.04744(3) 0.31113(2) +Si2 Si 0.74946(4) -0.00116(3) 0.34513(2) +Si3 Si 0.77841(4) 0.08362(3) 0.094922(19) +Si4 Si 0.98662(4) 0.27342(3) 0.17655(2) +I1 I 0.526219(9) 0.302610(7) 0.165420(5) +I2 I 0.789425(9) 0.346386(6) 0.366494(5) +Fe1 Fe 0.704399(16) 0.195976(12) 0.246199(9) +K1 K 0.69526(3) 0.58896(2) 0.273236(15) +N1 N 0.63081(10) 0.07053(8) 0.29605(6) +N2 N 0.83679(10) 0.17833(7) 0.17613(5) +H1 H 0.34070 -0.03840 0.18640 +H2 H 0.34300 0.08490 0.18530 +H3 H 0.23740 0.01540 0.22920 +H4 H 0.47550 0.17010 0.42490 +H5 H 0.43390 0.22760 0.34890 +H6 H 0.32210 0.14930 0.38260 +H7 H 0.46400 -0.08090 0.41140 +H8 H 0.40890 -0.14230 0.32780 +H9 H 0.31420 -0.07810 0.37090 +H10 H 0.77760 0.09080 0.47260 +H11 H 0.82750 -0.02050 0.48300 +H12 H 0.67030 -0.01380 0.47220 +H13 H 0.71530 -0.15600 0.25470 +H14 H 0.78180 -0.18230 0.33740 +H15 H 0.62190 -0.18220 0.32010 +H16 H 0.94680 0.03970 0.27700 +H17 H 0.99320 0.00330 0.35980 +H18 H 0.96200 0.12130 0.35230 +H19 H 0.53380 0.04750 0.09880 +H20 H 0.60770 -0.03610 0.14720 +H21 H 0.58000 -0.04950 0.05450 +H22 H 0.98950 0.01170 0.08660 +H23 H 0.89200 -0.06220 0.13270 +H24 H 0.86140 -0.06940 0.04130 +H25 H 0.84250 0.19130 -0.00890 +H26 H 0.72590 0.09800 -0.04400 +H27 H 0.69130 0.19860 0.00190 +H28 H 1.06520 0.32420 0.31270 +H29 H 1.19100 0.34190 0.26570 +H30 H 1.13340 0.22550 0.28730 +H31 H 1.05210 0.21520 0.05150 +H32 H 1.17200 0.29250 0.10310 +H33 H 1.14360 0.17060 0.12040 +H34 H 0.88030 0.39870 0.09930 +H35 H 1.02310 0.45670 0.14450 +H36 H 0.89340 0.43680 0.18940 +H37 H 1.08960 0.67980 0.45310 +H38 H 1.02980 0.57570 0.39080 +H39 H 1.16860 0.71650 0.33480 +H40 H 1.06090 0.79470 0.34920 +H41 H 1.10940 0.75220 0.19710 +H42 H 0.98970 0.81430 0.21780 +H43 H 0.94150 0.74120 0.08550 +H44 H 0.93660 0.62290 0.11340 +H45 H 0.71290 0.68190 0.02160 +H46 H 0.68300 0.57270 0.05750 +H47 H 0.54370 0.74810 0.08970 +H48 H 0.48040 0.64700 0.02990 +H49 H 0.36520 0.70730 0.16940 +H50 H 0.29660 0.60670 0.10690 +H51 H 0.22480 0.56810 0.22760 +H52 H 0.33020 0.49010 0.20920 +H53 H 0.28290 0.54840 0.36380 +H54 H 0.39830 0.47910 0.35170 +H55 H 0.46450 0.68850 0.43330 +H56 H 0.44090 0.58230 0.47890 +H57 H 0.70500 0.73910 0.48190 +H58 H 0.67830 0.64570 0.54050 +H59 H 0.91210 0.67340 0.52870 +H60 H 0.84690 0.56080 0.48760 +#END diff --git a/cell2mol/test/error_2/BOXSEO.search5.cif b/cell2mol/test/error_2/BOXSEO.search5.cif new file mode 100755 index 00000000..5cdb2729 --- /dev/null +++ b/cell2mol/test/error_2/BOXSEO.search5.cif @@ -0,0 +1,184 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_BOXSEO +_audit_creation_date 2015-03-06 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD BOXSEO +_database_code_depnum_ccdc_archive 'CCDC 1021434' +_chemical_formula_sum 'C24 H60 Fe1 I1 K1 N2 O6 Si4' +_chemical_formula_moiety +; +C24 H60 Fe1 I1 K1 N2 O6 Si4 +; +_journal_coden_Cambridge 179 +_journal_volume 54 +_journal_year 2015 +_journal_page_first 245 +_journal_name_full 'Angew.Chem.,Int.Ed. ' +loop_ +_publ_author_name +"C.Gunnar Werncke" +"P.C.Bunting" +"C.Duhayon" +"J.R.Long" +"S.Bontemps" +"S.Sabo-Etienne" +_chemical_name_systematic +; +(18-crown-ether)-bis(bis(trimethylsilyl)amido)-(\m-iodo)-iron(ii)-potassium(i) +; +_cell_volume 3973.100 +_exptl_crystal_colour 'pale purple' +_exptl_crystal_density_diffrn 1.349 +_exptl_crystal_description 'block' +_diffrn_ambient_temperature 100 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0169 +_refine_ls_wR_factor_gt 0.0169 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'P 21/c' +_symmetry_Int_Tables_number 14 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,1/2+y,1/2-z +3 -x,-y,-z +4 x,-1/2-y,-1/2+z +_cell_length_a 9.6803(2) +_cell_length_b 18.2801(5) +_cell_length_c 22.4873(6) +_cell_angle_alpha 90 +_cell_angle_beta 93.194(1) +_cell_angle_gamma 90 +_cell_formula_units_Z 4 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Fe 1.52 +I 1.40 +K 2.03 +N 0.68 +O 0.68 +Si 1.20 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Fe1 Fe 0.651378(14) 0.745329(7) 0.415895(6) +I1 I 0.744437(7) 0.610422(4) 0.399766(3) +C1 C 0.71248(11) 0.70544(6) 0.56538(5) +C2 C 0.43687(12) 0.76211(6) 0.59789(5) +C3 C 0.45586(12) 0.61351(6) 0.53964(5) +C4 C 0.19541(12) 0.73610(7) 0.45473(6) +C5 C 0.32875(14) 0.88364(7) 0.48249(6) +C6 C 0.35864(12) 0.81477(7) 0.36351(5) +C7 C 0.75994(16) 0.89182(7) 0.49212(5) +C8 C 0.67721(12) 0.98316(6) 0.38495(5) +C9 C 0.97080(13) 0.92331(7) 0.40388(8) +C10 C 0.7891(2) 0.89376(8) 0.25454(6) +C11 C 0.64705(13) 0.74951(7) 0.25903(5) +C12 C 0.95073(15) 0.75998(10) 0.29929(8) +C13 C 1.19883(12) 0.51164(7) 0.42560(5) +C14 C 1.31017(12) 0.53594(7) 0.38610(5) +C15 C 1.34713(11) 0.59670(7) 0.29620(5) +C16 C 1.28201(11) 0.64762(6) 0.25026(5) +C17 C 1.11108(11) 0.65659(6) 0.17273(5) +C18 C 0.98912(12) 0.61851(6) 0.14242(4) +C19 C 0.76714(11) 0.57148(7) 0.15717(5) +C20 C 0.67953(11) 0.54087(7) 0.20433(5) +C21 C 0.66710(11) 0.44072(6) 0.27033(5) +C22 C 0.75559(12) 0.38566(6) 0.30390(5) +C23 C 0.92099(12) 0.38121(6) 0.38658(5) +C24 C 1.00586(12) 0.43172(6) 0.42663(5) +N1 N 0.50227(8) 0.74965(4) 0.47018(3) +N2 N 0.73484(9) 0.82388(4) 0.37196(4) +O1 O 1.10839(8) 0.46397(4) 0.39186(3) +O2 O 1.24881(7) 0.57924(4) 0.33872(3) +O3 O 1.16859(8) 0.61170(4) 0.21955(3) +O4 O 0.88995(8) 0.60219(4) 0.18500(3) +O5 O 0.75255(7) 0.48174(4) 0.23286(3) +O6 O 0.85092(8) 0.42487(4) 0.34215(3) +K1 K 0.98576(2) 0.543413(12) 0.295073(9) +Si1 Si 0.52527(3) 0.709508(15) 0.538792(11) +Si2 Si 0.35351(3) 0.793416(14) 0.444804(12) +Si3 Si 0.78228(3) 0.901615(15) 0.410426(12) +Si4 Si 0.77908(3) 0.807781(17) 0.300533(13) +H1 H 0.76890 0.68770 0.53470 +H2 H 0.74580 0.75300 0.57740 +H3 H 0.72570 0.67410 0.59890 +H4 H 0.34030 0.76430 0.59040 +H5 H 0.46960 0.81040 0.60000 +H6 H 0.45420 0.74050 0.63560 +H7 H 0.35960 0.61290 0.52740 +H8 H 0.46860 0.59250 0.57800 +H9 H 0.50450 0.58450 0.51290 +H10 H 0.19890 0.69110 0.43120 +H11 H 0.19010 0.72280 0.49560 +H12 H 0.11310 0.76130 0.44180 +H13 H 0.41240 0.91230 0.48290 +H14 H 0.30420 0.87630 0.52260 +H15 H 0.25950 0.91160 0.46250 +H16 H 0.43770 0.84350 0.35530 +H17 H 0.27730 0.84090 0.35060 +H18 H 0.36070 0.77200 0.34110 +H19 H 0.82590 0.85770 0.50850 +H20 H 0.77640 0.93780 0.51100 +H21 H 0.66980 0.87550 0.50010 +H22 H 0.69030 0.99470 0.34470 +H23 H 0.69890 1.02420 0.40780 +H24 H 0.58130 0.97310 0.38800 +H25 H 0.99020 0.94060 0.36480 +H26 H 0.99880 0.96010 0.43190 +H27 H 1.02270 0.88030 0.41220 +H28 H 0.86140 0.92290 0.27100 +H29 H 0.70370 0.92180 0.25400 +H30 H 0.80740 0.88170 0.21470 +H31 H 0.63230 0.70560 0.27960 +H32 H 0.56240 0.77510 0.25300 +H33 H 0.67870 0.73680 0.22150 +H34 H 0.95580 0.71870 0.32660 +H35 H 1.01920 0.79390 0.31110 +H36 H 0.96720 0.74440 0.26090 +H37 H 1.23910 0.48660 0.46030 +H38 H 1.14790 0.55300 0.43980 +H39 H 1.37810 0.56500 0.40890 +H40 H 1.35610 0.49340 0.36950 +H41 H 1.42700 0.62010 0.31590 +H42 H 1.37670 0.55220 0.27770 +H43 H 1.34920 0.66160 0.22220 +H44 H 1.24970 0.69130 0.26940 +H45 H 1.08340 0.70300 0.18920 +H46 H 1.18010 0.66590 0.14330 +H47 H 0.94790 0.65100 0.11210 +H48 H 1.01790 0.57380 0.12410 +H49 H 0.71700 0.61010 0.13530 +H50 H 0.79050 0.53260 0.13000 +H51 H 0.66050 0.57770 0.23380 +H52 H 0.59160 0.52380 0.18540 +H53 H 0.62460 0.47320 0.29850 +H54 H 0.59480 0.41630 0.24650 +H55 H 0.80320 0.35580 0.27670 +H56 H 0.69760 0.35400 0.32740 +H57 H 0.98020 0.34570 0.36790 +H58 H 0.85480 0.35490 0.40960 +H59 H 1.04940 0.40480 0.46030 +H60 H 0.94700 0.46870 0.44150 +#END diff --git a/cell2mol/test/error_2/CIVREF.search2.cif b/cell2mol/test/error_2/CIVREF.search2.cif new file mode 100755 index 00000000..a871ba86 --- /dev/null +++ b/cell2mol/test/error_2/CIVREF.search2.cif @@ -0,0 +1,152 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_CIVREF +_audit_creation_date 2008-04-21 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD CIVREF +_database_code_depnum_ccdc_archive 'CCDC 662469' +_chemical_formula_sum 'C16 H47 Mn1 N2 Na1 Si5' +_chemical_formula_moiety +; +C16 H47 Mn1 N2 Na1 Si5 +; +_journal_coden_Cambridge 182 +_journal_year 2008 +_journal_page_first 308 +_journal_name_full 'Chem.Commun. ' +loop_ +_publ_author_name +"A.R.Kennedy" +"J.Klett" +"R.E.Mulvey" +"S.Newton" +"D.S.Wright" +_chemical_name_systematic +; +bis(\m~2~-bis(trimethylsilyl)amido)-(trimethylsilylmethyl)-manganese(ii)-sodiu +m(i) +; +_cell_volume 1431.781 +_exptl_crystal_colour 'colorless' +_exptl_crystal_density_diffrn 1.127 +_exptl_crystal_description 'large needle' +_diffrn_ambient_temperature 150 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0329 +_refine_ls_wR_factor_gt 0.0329 +_symmetry_cell_setting triclinic +_symmetry_space_group_name_H-M 'P -1' +_symmetry_Int_Tables_number 2 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,-y,-z +_cell_length_a 10.2226(3) +_cell_length_b 11.9296(3) +_cell_length_c 12.0857(3) +_cell_angle_alpha 97.180(1) +_cell_angle_beta 100.700(1) +_cell_angle_gamma 93.377(1) +_cell_formula_units_Z 2 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Mn 1.22 +N 0.68 +Na 1.45 +Si 1.20 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Mn1 Mn 0.78870(3) 0.26076(2) 0.74278(2) +Na1 Na 0.94982(7) 0.04764(6) 0.74658(6) +Si1 Si 0.64175(5) 0.52425(5) 0.75877(5) +Si2 Si 0.83253(5) 0.20390(4) 0.98910(4) +Si3 Si 0.66694(5) 0.02479(4) 0.81375(4) +Si4 Si 0.85872(5) 0.20777(4) 0.50248(4) +Si5 Si 1.09539(5) 0.26915(4) 0.69366(4) +N1 N 0.79057(14) 0.13387(12) 0.85278(12) +N2 N 0.93279(14) 0.21323(12) 0.64298(12) +C1 C 0.6341(2) 0.3710(2) 0.7183(3) +C2 C 0.7848(3) 0.5984(2) 0.7132(3) +H1 H 0.86830 0.56810 0.74570 +H2 H 0.78930 0.67980 0.73990 +H3 H 0.77190 0.58610 0.63000 +C3 C 0.4871(2) 0.5870(2) 0.6923(2) +H4 H 0.47540 0.57190 0.60930 +H5 H 0.49590 0.66900 0.71660 +H6 H 0.40940 0.55270 0.71640 +C4 C 0.6604(3) 0.5633(2) 0.9158(2) +H7 H 0.58760 0.52380 0.94210 +H8 H 0.65710 0.64540 0.93380 +H9 H 0.74630 0.54130 0.95410 +C5 C 0.9334(2) 0.34066(16) 0.99061(17) +H10 H 1.01830 0.32460 0.96790 +H11 H 0.95100 0.38310 1.06740 +H12 H 0.88360 0.38570 0.93730 +C6 C 0.6874(2) 0.2421(2) 1.0558(2) +H13 H 0.63180 0.28930 1.00870 +H14 H 0.72010 0.28430 1.13180 +H15 H 0.63420 0.17290 1.06170 +C7 C 0.9388(2) 0.1217(2) 1.08939(19) +C8 C 0.49735(19) 0.07488(18) 0.77125(19) +H16 H 0.47990 0.13070 0.83230 +H17 H 0.42980 0.01030 0.75750 +H18 H 0.49340 0.10990 0.70170 +C9 C 0.6576(2) -0.06951(19) 0.92555(19) +H19 H 0.74380 -0.10110 0.94610 +H20 H 0.58770 -0.13140 0.89600 +H21 H 0.63640 -0.02540 0.99300 +C10 C 0.7006(2) -0.07157(18) 0.68816(19) +C11 C 0.6764(2) 0.1581(2) 0.47638(18) +H22 H 0.66660 0.07820 0.48710 +H23 H 0.63450 0.16660 0.39840 +H24 H 0.63290 0.20360 0.53010 +C12 C 0.8726(2) 0.34674(18) 0.44780(18) +H25 H 0.83220 0.40360 0.49360 +H26 H 0.82590 0.33860 0.36840 +H27 H 0.96700 0.37110 0.45280 +C13 C 0.9283(2) 0.1026(2) 0.40313(18) +C14 C 1.1075(2) 0.42362(16) 0.74907(18) +H28 H 1.07470 0.46610 0.68660 +H29 H 1.20080 0.44990 0.78200 +H30 H 1.05310 0.43580 0.80760 +C15 C 1.20965(19) 0.2510(2) 0.58936(17) +H31 H 1.21320 0.17000 0.56450 +H32 H 1.29950 0.28470 0.62560 +H33 H 1.17610 0.28860 0.52340 +C16 C 1.1736(2) 0.19521(18) 0.81543(17) +H34 H 0.603(3) 0.359(2) 0.640(3) +H35 H 0.565(3) 0.338(3) 0.747(3) +H36 H 0.887(2) 0.056(2) 1.100(2) +H37 H 0.964(2) 0.171(2) 1.164(2) +H38 H 1.013(3) 0.101(2) 1.061(2) +H39 H 0.719(2) -0.032(2) 0.625(2) +H40 H 0.776(2) -0.1151(17) 0.7104(16) +H41 H 0.631(3) -0.123(2) 0.656(2) +H42 H 0.921(2) 0.029(2) 0.425(2) +H43 H 1.014(3) 0.123(2) 0.402(2) +H44 H 0.879(2) 0.1022(19) 0.324(2) +H45 H 1.124(2) 0.1931(17) 0.8716(18) +H46 H 1.255(2) 0.2357(19) 0.8501(19) +H47 H 1.193(2) 0.1198(19) 0.7901(18) +#END diff --git a/cell2mol/test/error_2/CORLIE.search2.cif b/cell2mol/test/error_2/CORLIE.search2.cif new file mode 100755 index 00000000..dfaf913c --- /dev/null +++ b/cell2mol/test/error_2/CORLIE.search2.cif @@ -0,0 +1,195 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_CORLIE +_audit_creation_date 1985-08-16 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD CORLIE +_chemical_formula_sum 'C32 H72 Li1 Mn1 N1 O2 Si2' +_chemical_formula_moiety +; +C32 H72 Li1 Mn1 N1 O2 Si2 +; +_journal_coden_Cambridge 4 +_journal_volume 106 +_journal_year 1984 +_journal_page_first 7011 +_journal_name_full 'J.Am.Chem.Soc. ' +loop_ +_publ_author_name +"B.D.Murray" +"P.P.Power" +_chemical_name_systematic +; +bis(\m~2~-Tri-t-butylmethoxy)-bis(trimethylsilyl)amino-manganese(ii)-lithium +; +_chemical_melting_point 431.15 +_cell_volume 3695.535 +_exptl_crystal_colour 'pale pink' +_exptl_crystal_density_diffrn 1.12 +_exptl_special_details +; +Melting point range 431.15-433.15K + +; +_diffrn_ambient_temperature 140 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.033 +_refine_ls_wR_factor_gt 0.033 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'C c' +_symmetry_Int_Tables_number 9 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 1/2+x,1/2+y,z +3 x,-y,1/2+z +4 1/2+x,1/2-y,1/2+z +_cell_length_a 21.243(4) +_cell_length_b 11.814(1) +_cell_length_c 20.334(3) +_cell_angle_alpha 90 +_cell_angle_beta 133.60(1) +_cell_angle_gamma 90 +_cell_formula_units_Z 4 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Li 0.74 +Mn 1.35 +N 0.68 +O 0.68 +Si 1.20 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Mn1 Mn 0.9472(1) 0.4099(1) 0.2485(1) +Si1 Si 0.8503(1) 0.6412(1) 0.1437(1) +Si2 Si 1.0252(1) 0.6495(1) 0.3392(1) +O1 O 1.0071(2) 0.2941(2) 0.2344(2) +O2 O 0.9199(2) 0.2826(2) 0.2887(2) +N1 N 0.9392(3) 0.5790(2) 0.2427(3) +C1 C 1.0594(2) 0.2661(3) 0.2171(3) +C2 C 1.1591(3) 0.2819(4) 0.3121(3) +C3 C 1.0318(3) 0.3491(4) 0.1373(3) +C4 C 1.0357(3) 0.1322(4) 0.1830(3) +C5 C 1.1738(3) 0.2438(4) 0.3943(3) +C6 C 1.1865(3) 0.4068(4) 0.3349(3) +C7 C 1.2288(3) 0.2240(5) 0.3193(3) +C8 C 1.0940(3) 0.3504(4) 0.1218(3) +C9 C 1.0210(3) 0.4742(4) 0.1501(3) +C10 C 0.9418(3) 0.3197(4) 0.0444(3) +C11 C 1.0743(4) 0.0474(4) 0.2601(4) +C12 C 1.0642(3) 0.0895(4) 0.1350(3) +C13 C 0.9362(4) 0.1102(4) 0.1162(4) +C14 C 0.8661(3) 0.2421(3) 0.3020(3) +C15 C 0.8276(3) 0.3499(4) 0.3127(3) +C16 C 0.9291(3) 0.1644(4) 0.3931(3) +C17 C 0.7900(3) 0.1672(4) 0.2109(3) +C18 C 0.7577(3) 0.4130(4) 0.2231(4) +C19 C 0.8970(4) 0.4435(4) 0.3735(4) +C20 C 0.7869(4) 0.3253(5) 0.3518(4) +C21 C 0.9900(3) 0.2339(5) 0.4813(3) +C22 C 0.8824(3) 0.0799(4) 0.4046(3) +C23 C 0.9952(3) 0.0963(4) 0.3997(4) +C24 C 0.8205(4) 0.0518(4) 0.2066(4) +C25 C 0.7563(3) 0.2246(4) 0.1227(3) +C26 C 0.7089(3) 0.1416(5) 0.1949(3) +C27 C 0.7865(3) 0.5319(4) 0.0506(3) +C28 C 0.7729(3) 0.7055(4) 0.1483(4) +C29 C 0.8798(3) 0.7573(4) 0.1056(3) +C30 C 0.9914(4) 0.7598(4) 0.3750(4) +C31 C 1.0980(3) 0.5469(4) 0.4366(3) +C32 C 1.0975(4) 0.7197(4) 0.3287(4) +Li1 Li 0.9822(6) 0.1914(6) 0.2796(7) +H1 H 1.16150 0.16440 0.38930 +H2 H 1.13570 0.28550 0.39540 +H3 H 1.23310 0.25780 0.44940 +H4 H 1.19520 0.44370 0.29960 +H5 H 1.24110 0.40030 0.39670 +H6 H 1.14690 0.45050 0.33260 +H7 H 1.23100 0.14370 0.32810 +H8 H 1.28420 0.25680 0.36960 +H9 H 1.21480 0.23880 0.26410 +H10 H 1.14890 0.38140 0.17480 +H11 H 1.06620 0.40140 0.07160 +H12 H 1.10330 0.27880 0.10730 +H13 H 1.07660 0.50920 0.19680 +H14 H 0.98410 0.49010 0.16060 +H15 H 0.99480 0.50360 0.09200 +H16 H 0.93710 0.24960 0.01750 +H17 H 0.92870 0.38140 0.00590 +H18 H 0.90150 0.31980 0.05140 +H19 H 1.03350 0.12100 0.07610 +H20 H 1.05240 0.00980 0.12920 +H21 H 1.12550 0.10110 0.17350 +H22 H 0.91240 0.11820 0.05560 +H23 H 0.91160 0.16650 0.12690 +H24 H 0.92290 0.03620 0.12330 +H25 H 0.70460 0.37140 0.17900 +H26 H 0.77990 0.43530 0.19690 +H27 H 0.74660 0.47910 0.24120 +H28 H 0.93580 0.42270 0.43640 +H29 H 0.86380 0.50860 0.36250 +H30 H 0.93000 0.46110 0.35860 +H31 H 0.83220 0.30180 0.41390 +H32 H 0.74130 0.26990 0.31890 +H33 H 0.76410 0.39690 0.34950 +H34 H 0.96250 0.27490 0.49650 +H35 H 1.02340 0.28510 0.47890 +H36 H 1.02760 0.17710 0.52660 +H37 H 0.84650 0.02860 0.35430 +H38 H 0.84630 0.12760 0.40540 +H39 H 0.92130 0.03770 0.46020 +H40 H 0.84090 0.00510 0.25670 +H41 H 0.86560 0.05980 0.20680 +H42 H 0.77040 0.01740 0.15070 +H43 H 0.72010 0.28870 0.10600 +H44 H 0.72320 0.16870 0.07540 +H45 H 0.80470 0.24810 0.13100 +H46 H 0.67790 0.20690 0.18800 +H47 H 0.72640 0.09620 0.24440 +H48 H 0.67200 0.09790 0.14000 +H49 H 0.73270 0.56720 -0.00040 +H50 H 0.77470 0.46320 0.06560 +H51 H 0.81940 0.51480 0.03540 +H52 H 0.75690 0.66260 0.17510 +H53 H 0.72200 0.72040 0.08570 +H54 H 0.79870 0.77580 0.18030 +H55 H 0.82670 0.78610 0.04900 +H56 H 0.91870 0.73660 0.09860 +H57 H 0.90640 0.81450 0.15150 +H58 H 1.03770 0.79620 0.43180 +H59 H 0.95290 0.72380 0.37790 +H60 H 0.95990 0.81520 0.32720 +H61 H 1.14180 0.59470 0.48720 +H62 H 1.12470 0.49550 0.42530 +H63 H 1.06950 0.50470 0.45020 +H64 H 1.14500 0.75770 0.38410 +H65 H 1.06390 0.77360 0.27990 +H66 H 1.11970 0.66290 0.31540 +H67 H 1.0599(28) -0.0308(38) 0.2354(29) +H68 H 1.0373(27) 0.1377(37) 0.4102(28) +H69 H 0.9725(27) 0.0512(38) 0.3461(29) +H70 H 1.0623(31) 0.0507(40) 0.2963(33) +H71 H 1.1429(37) 0.0420(46) 0.3113(37) +H72 H 1.0245(33) 0.0435(45) 0.4459(35) +#END diff --git a/cell2mol/test/error_2/CSCNCC01.search6.cif b/cell2mol/test/error_2/CSCNCC01.search6.cif new file mode 100755 index 00000000..2089f2e2 --- /dev/null +++ b/cell2mol/test/error_2/CSCNCC01.search6.cif @@ -0,0 +1,302 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_CSCNCC01 +_audit_creation_date 1991-01-07 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD CSCNCC01 +_chemical_formula_sum 'C14 H24 Co1 Cs1 N8' +_chemical_formula_moiety +; +2(C4 H12 N1 1+),C6 Co1 N6 3-,Cs1 1+ +; +_journal_coden_Cambridge 369 +_journal_volume 15 +_journal_year 1990 +_journal_page_first 106 +_journal_name_full 'Transition Met.Chem. ' +loop_ +_publ_author_name +"A.D.Morales" +"R.G.Romero" +"J.D.Rodriguez" +"R.P.Hernandez" +"J.F.Bertran" +_chemical_name_systematic +; +Cesium tetramethylammonium hexacyano-cobalt(iii) +; +_cell_volume 1996.568 +_exptl_crystal_density_diffrn 1.66 +_diffrn_ambient_temperature ? +_diffrn_special_details +; +The study was carried out at room temperature,in the range 283-303K + +; +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.053 +_refine_ls_wR_factor_gt 0.053 +_symmetry_cell_setting cubic +_symmetry_space_group_name_H-M 'F m 3 m' +_symmetry_Int_Tables_number 225 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 x,1/2+y,1/2+z +3 1/2+x,y,1/2+z +4 1/2+x,1/2+y,z +5 z,x,y +6 y,z,x +7 1/2+z,x,1/2+y +8 1/2+y,1/2+z,x +9 1/2+z,1/2+x,y +10 y,1/2+z,1/2+x +11 z,1/2+x,1/2+y +12 1/2+y,z,1/2+x +13 x,y,-z +14 x,1/2+y,1/2-z +15 1/2+x,y,1/2-z +16 1/2+x,1/2+y,-z +17 z,x,-y +18 y,z,-x +19 1/2+z,x,1/2-y +20 1/2+y,1/2+z,-x +21 1/2+z,1/2+x,-y +22 y,1/2+z,1/2-x +23 z,1/2+x,1/2-y +24 1/2+y,z,1/2-x +25 -x,y,z +26 -x,1/2+y,1/2+z +27 1/2-x,y,1/2+z +28 1/2-x,1/2+y,z +29 -z,x,y +30 -y,z,x +31 1/2-z,x,1/2+y +32 1/2-y,1/2+z,x +33 1/2-z,1/2+x,y +34 -y,1/2+z,1/2+x +35 -z,1/2+x,1/2+y +36 1/2-y,z,1/2+x +37 -x,y,-z +38 -x,1/2+y,1/2-z +39 1/2-x,y,1/2-z +40 1/2-x,1/2+y,-z +41 -z,x,-y +42 -y,z,-x +43 1/2-z,x,1/2-y +44 1/2-y,1/2+z,-x +45 1/2-z,1/2+x,-y +46 -y,1/2+z,1/2-x +47 -z,1/2+x,1/2-y +48 1/2-y,z,1/2-x +49 y,x,z +50 1/2+y,x,1/2+z +51 y,1/2+x,1/2+z +52 1/2+y,1/2+x,z +53 x,z,y +54 z,y,x +55 x,1/2+z,1/2+y +56 1/2+z,1/2+y,x +57 1/2+x,1/2+z,y +58 1/2+z,y,1/2+x +59 1/2+x,z,1/2+y +60 z,1/2+y,1/2+x +61 y,x,-z +62 1/2+y,x,1/2-z +63 y,1/2+x,1/2-z +64 1/2+y,1/2+x,-z +65 x,z,-y +66 z,y,-x +67 x,1/2+z,1/2-y +68 1/2+z,1/2+y,-x +69 1/2+x,1/2+z,-y +70 1/2+z,y,1/2-x +71 1/2+x,z,1/2-y +72 z,1/2+y,1/2-x +73 y,-x,z +74 1/2+y,-x,1/2+z +75 y,1/2-x,1/2+z +76 1/2+y,1/2-x,z +77 x,-z,y +78 z,-y,x +79 x,1/2-z,1/2+y +80 1/2+z,1/2-y,x +81 1/2+x,1/2-z,y +82 1/2+z,-y,1/2+x +83 1/2+x,-z,1/2+y +84 z,1/2-y,1/2+x +85 y,-x,-z +86 1/2+y,-x,1/2-z +87 y,1/2-x,1/2-z +88 1/2+y,1/2-x,-z +89 x,-z,-y +90 z,-y,-x +91 x,1/2-z,1/2-y +92 1/2+z,1/2-y,-x +93 1/2+x,1/2-z,-y +94 1/2+z,-y,1/2-x +95 1/2+x,-z,1/2-y +96 z,1/2-y,1/2-x +97 -x,-y,-z +98 -x,-1/2-y,-1/2-z +99 -1/2-x,-y,-1/2-z +100 -1/2-x,-1/2-y,-z +101 -z,-x,-y +102 -y,-z,-x +103 -1/2-z,-x,-1/2-y +104 -1/2-y,-1/2-z,-x +105 -1/2-z,-1/2-x,-y +106 -y,-1/2-z,-1/2-x +107 -z,-1/2-x,-1/2-y +108 -1/2-y,-z,-1/2-x +109 -x,-y,z +110 -x,-1/2-y,-1/2+z +111 -1/2-x,-y,-1/2+z +112 -1/2-x,-1/2-y,z +113 -z,-x,y +114 -y,-z,x +115 -1/2-z,-x,-1/2+y +116 -1/2-y,-1/2-z,x +117 -1/2-z,-1/2-x,y +118 -y,-1/2-z,-1/2+x +119 -z,-1/2-x,-1/2+y +120 -1/2-y,-z,-1/2+x +121 x,-y,-z +122 x,-1/2-y,-1/2-z +123 -1/2+x,-y,-1/2-z +124 -1/2+x,-1/2-y,-z +125 z,-x,-y +126 y,-z,-x +127 -1/2+z,-x,-1/2-y +128 -1/2+y,-1/2-z,-x +129 -1/2+z,-1/2-x,-y +130 y,-1/2-z,-1/2-x +131 z,-1/2-x,-1/2-y +132 -1/2+y,-z,-1/2-x +133 x,-y,z +134 x,-1/2-y,-1/2+z +135 -1/2+x,-y,-1/2+z +136 -1/2+x,-1/2-y,z +137 z,-x,y +138 y,-z,x +139 -1/2+z,-x,-1/2+y +140 -1/2+y,-1/2-z,x +141 -1/2+z,-1/2-x,y +142 y,-1/2-z,-1/2+x +143 z,-1/2-x,-1/2+y +144 -1/2+y,-z,-1/2+x +145 -y,-x,-z +146 -1/2-y,-x,-1/2-z +147 -y,-1/2-x,-1/2-z +148 -1/2-y,-1/2-x,-z +149 -x,-z,-y +150 -z,-y,-x +151 -x,-1/2-z,-1/2-y +152 -1/2-z,-1/2-y,-x +153 -1/2-x,-1/2-z,-y +154 -1/2-z,-y,-1/2-x +155 -1/2-x,-z,-1/2-y +156 -z,-1/2-y,-1/2-x +157 -y,-x,z +158 -1/2-y,-x,-1/2+z +159 -y,-1/2-x,-1/2+z +160 -1/2-y,-1/2-x,z +161 -x,-z,y +162 -z,-y,x +163 -x,-1/2-z,-1/2+y +164 -1/2-z,-1/2-y,x +165 -1/2-x,-1/2-z,y +166 -1/2-z,-y,-1/2+x +167 -1/2-x,-z,-1/2+y +168 -z,-1/2-y,-1/2+x +169 -y,x,-z +170 -1/2-y,x,-1/2-z +171 -y,-1/2+x,-1/2-z +172 -1/2-y,-1/2+x,-z +173 -x,z,-y +174 -z,y,-x +175 -x,-1/2+z,-1/2-y +176 -1/2-z,-1/2+y,-x +177 -1/2-x,-1/2+z,-y +178 -1/2-z,y,-1/2-x +179 -1/2-x,z,-1/2-y +180 -z,-1/2+y,-1/2-x +181 -y,x,z +182 -1/2-y,x,-1/2+z +183 -y,-1/2+x,-1/2+z +184 -1/2-y,-1/2+x,z +185 -x,z,y +186 -z,y,x +187 -x,-1/2+z,-1/2+y +188 -1/2-z,-1/2+y,x +189 -1/2-x,-1/2+z,y +190 -1/2-z,y,-1/2+x +191 -1/2-x,z,-1/2+y +192 -z,-1/2+y,-1/2+x +_cell_length_a 12.592(2) +_cell_length_b 12.592(2) +_cell_length_c 12.592(2) +_cell_angle_alpha 90 +_cell_angle_beta 90 +_cell_angle_gamma 90 +_cell_formula_units_Z 4 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Co 1.33 +Cs 1.67 +N 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Co1 Co 0.00000 0.00000 0.00000 +Cs1 Cs 0.50000 0.50000 0.50000 +N1 N 0.25000 0.25000 0.25000 +C1 C 0.149(1) 0.00000 0.00000 +N2 N 0.242(1) 0.00000 0.00000 +C2 C 0.314(2) 0.314(2) 0.314(2) +H1 H 0.31000 0.31000 0.38000 +C1D C 0.000(1) 0.14900 0.00000 +N2D N 0.000(1) 0.24200 0.00000 +C1E C 0.000(1) 0.00000 0.14900 +N2E N 0.000(1) 0.00000 0.24200 +C1Q C 0.000(1) 0.00000 -0.14900 +N2Q N 0.000(1) 0.00000 -0.24200 +C1X C -0.149(1) 0.00000 0.00000 +N2X N -0.242(1) 0.00000 0.00000 +C1TB C 0.000(1) -0.14900 0.00000 +N2TB N 0.000(1) -0.24200 0.00000 +H1D H 0.38000 0.31000 0.31000 +H1E H 0.31000 0.38000 0.31000 +C2LA C 0.186(2) 0.314(2) 0.186(2) +H1LA H 0.19000 0.31000 0.12000 +H1PA H 0.12000 0.31000 0.19000 +H1UA H 0.19000 0.38000 0.19000 +C2HC C 0.314(2) 0.186(2) 0.186(2) +H1HC H 0.31000 0.19000 0.12000 +H1LC H 0.31000 0.12000 0.19000 +H1QC H 0.38000 0.19000 0.19000 +C2GD C 0.186(2) 0.186(2) 0.314(2) +H1GD H 0.19000 0.19000 0.38000 +H1LD H 0.12000 0.19000 0.31000 +H1KD H 0.19000 0.12000 0.31000 +#END diff --git a/cell2mol/test/error_2/DUGPIF.search5.cif b/cell2mol/test/error_2/DUGPIF.search5.cif new file mode 100755 index 00000000..21b9f6ae --- /dev/null +++ b/cell2mol/test/error_2/DUGPIF.search5.cif @@ -0,0 +1,194 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_DUGPIF +_audit_creation_date 2010-02-16 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD DUGPIF +_database_code_depnum_ccdc_archive 'CCDC 736383' +_chemical_formula_sum 'C31 H52 Fe1 K2 N4 O14' +_chemical_formula_moiety +; +C31 H52 Fe1 K2 N4 O14 +; +_journal_coden_Cambridge 9 +_journal_volume 48 +_journal_year 2009 +_journal_page_first 4462 +_journal_name_full 'Inorg.Chem. ' +loop_ +_publ_author_name +"C.M.Whaley" +"T.B.Rauchfuss" +"S.R.Wilson" +_chemical_name_systematic +; +bis(\m~2~-Cyano)-acetonitrile-cyano-bis(18-crown-6)-dicarbonyl-hydrido-iron(ii +)-di-potassium +; +_cell_volume 2031.491 +_exptl_crystal_colour 'colorless' +_exptl_crystal_density_diffrn 1.371 +_exptl_special_details +; +Absolute configuration + +; +_exptl_crystal_description 'tabular' +_exptl_crystal_preparation 'acetonitrile/ether' +_diffrn_ambient_temperature 193 +_diffrn_special_details +; +racemic twin + +; +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.1002 +_refine_ls_wR_factor_gt 0.1002 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'P 21' +_symmetry_Int_Tables_number 4 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,1/2+y,-z +_cell_length_a 8.7717(10) +_cell_length_b 14.1549(16) +_cell_length_c 16.3775(19) +_cell_angle_alpha 90 +_cell_angle_beta 92.529(7) +_cell_angle_gamma 90 +_cell_formula_units_Z 2 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Fe 1.52 +K 2.03 +N 0.68 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +C1 C 0.6916(11) 0.3727(9) 0.8739(6) +C2 C 0.9196(14) 0.2717(9) 0.8086(8) +C3 C 0.8631(10) 0.3597(8) 0.6634(5) +C4 C 0.6357(16) 0.4492(8) 0.7250(8) +C5 C 0.9215(15) 0.4645(8) 0.8002(7) +Fe1 Fe 0.78395(17) 0.37480(11) 0.77047(8) +H1 H 0.6788(17) 0.2899(9) 0.7471(10) +N1 N 0.6345(13) 0.3719(10) 0.9375(6) +N2 N 0.9983(13) 0.2120(7) 0.8283(7) +N3 N 0.9116(11) 0.3559(8) 0.5982(5) +O1 O 0.5389(10) 0.4923(7) 0.6964(6) +O2 O 1.0143(11) 0.5188(7) 0.8189(6) +C6 C 0.2858(14) 0.1405(8) 1.1080(7) +H2 H 0.22770 0.08980 1.07900 +H3 H 0.24550 0.14820 1.16310 +C7 C 0.4483(16) 0.1179(8) 1.1138(7) +H4 H 0.46220 0.05380 1.13730 +H5 H 0.48860 0.11740 1.05830 +C8 C 0.6912(13) 0.1626(8) 1.1758(7) +H6 H 0.73740 0.15390 1.12220 +H7 H 0.70280 0.10280 1.20670 +C9 C 0.7704(14) 0.2376(8) 1.2204(7) +H8 H 0.72120 0.24890 1.27280 +H9 H 0.87770 0.21900 1.23280 +C10 C 0.8539(13) 0.3956(7) 1.2111(7) +H10 H 0.96350 0.37870 1.21420 +H11 H 0.82060 0.40700 1.26720 +C11 C 0.8285(12) 0.4829(8) 1.1593(7) +H12 H 0.89740 0.53400 1.17940 +H13 H 0.85180 0.46890 1.10190 +C12 C 0.6490(14) 0.6009(8) 1.1225(8) +H14 H 0.67990 0.59630 1.06520 +H15 H 0.71100 0.65080 1.15030 +C13 C 0.4869(14) 0.6251(8) 1.1241(8) +H16 H 0.45420 0.62590 1.18120 +H17 H 0.46910 0.68860 1.10020 +C14 C 0.2431(12) 0.5767(8) 1.0679(7) +H18 H 0.22870 0.64130 1.04570 +H19 H 0.19510 0.57360 1.12140 +C15 C 0.1723(13) 0.5077(9) 1.0116(7) +H20 H 0.06660 0.52700 0.99590 +H21 H 0.23080 0.50310 0.96140 +C16 C 0.1118(13) 0.3460(9) 1.0032(7) +H22 H 0.17130 0.34150 0.95340 +H23 H 0.00500 0.36160 0.98630 +C17 C 0.1164(12) 0.2509(9) 1.0481(9) +H24 H 0.06460 0.25570 1.10040 +H25 H 0.06510 0.20140 1.01410 +K1 K 0.4957(2) 0.36793(18) 1.08265(12) +O3 O 0.2739(9) 0.2289(5) 1.0623(5) +O4 O 0.5327(9) 0.1839(5) 1.1631(5) +O5 O 0.7658(9) 0.3207(5) 1.1732(4) +O6 O 0.6744(8) 0.5124(5) 1.1631(4) +O7 O 0.4013(8) 0.5559(5) 1.0777(5) +O8 O 0.1719(9) 0.4168(6) 1.0534(5) +C18 C 1.3252(14) 0.3128(8) 0.2869(7) +H26 H 1.43450 0.32510 0.27840 +H27 H 1.27300 0.30300 0.23270 +C19 C 1.3093(12) 0.2257(8) 0.3383(8) +H28 H 1.36440 0.17230 0.31400 +H29 H 1.35350 0.23700 0.39410 +C20 C 1.1350(14) 0.1190(8) 0.3889(7) +H30 H 1.17810 0.12850 0.44520 +H31 H 1.18950 0.06600 0.36370 +C21 C 0.9690(14) 0.0984(7) 0.3902(7) +H32 H 0.92740 0.09130 0.33330 +H33 H 0.95390 0.03780 0.41890 +C22 C 0.7343(12) 0.1488(8) 0.4371(7) +H34 H 0.72370 0.08950 0.46830 +H35 H 0.68350 0.13980 0.38240 +C23 C 0.6580(15) 0.2279(9) 0.4805(7) +H36 H 0.54960 0.21190 0.48800 +H37 H 0.70850 0.23750 0.53510 +C24 C 0.6012(12) 0.3893(8) 0.4711(7) +H38 H 0.65240 0.39930 0.52550 +H39 H 0.49230 0.37550 0.47920 +C25 C 0.6141(14) 0.4782(10) 0.4204(8) +H40 H 0.57290 0.46720 0.36400 +H41 H 0.55620 0.53040 0.44470 +C26 C 0.7922(13) 0.5870(8) 0.3718(7) +H42 H 0.75280 0.57710 0.31480 +H43 H 0.73600 0.64030 0.39540 +C27 C 0.9559(15) 0.6075(7) 0.3734(7) +H44 H 0.97260 0.66670 0.34290 +H45 H 0.99280 0.61760 0.43070 +C28 C 1.2025(13) 0.5537(8) 0.3261(7) +H46 H 1.26000 0.55880 0.37930 +H47 H 1.21230 0.61400 0.29620 +C29 C 1.2616(14) 0.4754(9) 0.2782(7) +H48 H 1.19750 0.46650 0.22750 +H49 H 1.36720 0.48920 0.26290 +K2 K 0.9991(3) 0.35629(17) 0.43320(14) +O9 O 1.2593(10) 0.3925(5) 0.3267(4) +O10 O 1.1505(9) 0.2039(5) 0.3415(5) +O11 O 0.8901(9) 0.1683(5) 0.4283(4) +O12 O 0.6681(9) 0.3127(6) 0.4327(4) +O13 O 0.7720(8) 0.5011(5) 0.4202(5) +O14 O 1.0436(9) 0.5332(5) 0.3385(5) +C30 C 1.2756(16) 0.3445(12) 0.7162(8) +H50 H 1.18410 0.37400 0.73710 +H51 H 1.36600 0.38060 0.73460 +H52 H 1.28440 0.27960 0.73680 +C31 C 1.2637(16) 0.3431(11) 0.6228(9) +N4 N 1.2601(15) 0.3377(12) 0.5534(8) +#END diff --git a/cell2mol/test/error_2/EQOQIK.search2.cif b/cell2mol/test/error_2/EQOQIK.search2.cif new file mode 100755 index 00000000..787f86d2 --- /dev/null +++ b/cell2mol/test/error_2/EQOQIK.search2.cif @@ -0,0 +1,114 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_EQOQIK +_audit_creation_date 2004-05-06 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD EQOQIK +_database_code_depnum_ccdc_archive 'CCDC 216140' +_chemical_formula_sum 'C11 H12 Mn1 N1 O3' +_chemical_formula_moiety +; +C11 H12 Mn1 N1 O3 +; +_journal_coden_Cambridge 29 +_journal_volume 629 +_journal_year 2003 +_journal_page_first 2408 +_journal_name_full 'Z.Anorg.Allg.Chem. ' +loop_ +_publ_author_name +"M.Tamm" +"A.Kunst" +"F.E.Hahn" +"T.Pape" +"R.Frohlich" +_chemical_name_systematic +; +(\h^3^-Tropidinyl)-tricarbonyl-manganese(i) +; +_cell_volume 2199.990 +_exptl_crystal_colour 'yellow' +_exptl_crystal_density_diffrn 1.577 +_exptl_crystal_description 'plates' +_exptl_crystal_preparation 'hexanes' +_diffrn_ambient_temperature 123 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0281 +_refine_ls_wR_factor_gt 0.0281 +_symmetry_cell_setting orthorhombic +_symmetry_space_group_name_H-M 'P b c a' +_symmetry_Int_Tables_number 61 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 1/2+x,y,1/2-z +3 x,1/2-y,1/2+z +4 1/2-x,1/2+y,z +5 -x,-y,-z +6 -1/2-x,-y,-1/2+z +7 -x,-1/2+y,-1/2-z +8 -1/2+x,-1/2-y,-z +_cell_length_a 10.8971(5) +_cell_length_b 12.7655(6) +_cell_length_c 15.8151(8) +_cell_angle_alpha 90 +_cell_angle_beta 90 +_cell_angle_gamma 90 +_cell_formula_units_Z 8 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Mn 1.35 +N 0.68 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Mn1 Mn 0.538373(18) 0.232247(16) 0.586929(13) +C1 C 0.50205(13) 0.33619(11) 0.73125(8) +C2 C 0.58490(14) 0.24249(11) 0.72025(9) +C3 C 0.68925(13) 0.25544(12) 0.66931(9) +C4 C 0.69242(13) 0.33675(11) 0.61016(9) +C5 C 0.60753(12) 0.42759(11) 0.62764(9) +C6 C 0.63262(15) 0.48800(12) 0.70980(9) +C7 C 0.55772(14) 0.42929(12) 0.77829(9) +C8 C 0.37965(15) 0.43406(14) 0.62407(11) +N1 N 0.48904(10) 0.37136(9) 0.64184(7) +C9 C 0.39151(14) 0.17087(11) 0.60560(8) +C10 C 0.50880(13) 0.27360(11) 0.48011(9) +C11 C 0.60512(13) 0.11219(12) 0.54997(9) +O1 O 0.29804(10) 0.13313(8) 0.61871(7) +O2 O 0.48724(11) 0.30018(9) 0.41213(7) +O3 O 0.64741(11) 0.03523(8) 0.52586(7) +H1 H 0.4225(12) 0.3172(10) 0.7534(8) +H2 H 0.5808(13) 0.1877(12) 0.7587(9) +H3 H 0.7486(14) 0.2071(12) 0.6678(9) +H4 H 0.7635(13) 0.3455(11) 0.5754(9) +H5 H 0.6005(13) 0.4687(11) 0.5802(9) +H6 H 0.7185(15) 0.4879(12) 0.7238(9) +H7 H 0.6081(14) 0.5626(13) 0.7061(10) +H8 H 0.6059(14) 0.4091(11) 0.8256(10) +H9 H 0.4935(16) 0.4727(13) 0.8022(10) +H10 H 0.3110(15) 0.3964(13) 0.6367(10) +H11 H 0.3766(14) 0.4524(11) 0.5657(11) +H12 H 0.3808(16) 0.4991(14) 0.6548(11) +#END diff --git a/cell2mol/test/error_2/FIYFEB.search5.cif b/cell2mol/test/error_2/FIYFEB.search5.cif new file mode 100755 index 00000000..270d12bf --- /dev/null +++ b/cell2mol/test/error_2/FIYFEB.search5.cif @@ -0,0 +1,153 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_FIYFEB +_audit_creation_date 2019-01-14 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD FIYFEB +_database_code_depnum_ccdc_archive 'CCDC 1858801' +_chemical_formula_sum 'C19 H43 Fe1 K1 N2 Si4' +_chemical_formula_moiety +; +C19 H43 Fe1 K1 N2 Si4 +; +_journal_coden_Cambridge 222 +_journal_volume 48 +_journal_year 2019 +_journal_page_first 1757 +_journal_name_full 'Dalton Trans. ' +loop_ +_publ_author_name +"C.G.Werncke" +"J.Pfeiffer" +"I.Muller" +"L.Vendier" +"S.Sabo-Etienne" +"S.Bontemps" +_chemical_name_systematic +; +bis(bis(trimethylsilyl)amido)-benzyl-iron(ii)-potassium +; +_cell_volume 2872.899 +_exptl_crystal_colour 'light yellow' +_exptl_crystal_density_diffrn 1.172 +_exptl_crystal_description 'block' +_diffrn_ambient_temperature 100 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0273 +_refine_ls_wR_factor_gt 0.0273 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'P 21/n' +_symmetry_Int_Tables_number 14 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 1/2-x,1/2+y,1/2-z +3 -x,-y,-z +4 -1/2+x,-1/2-y,-1/2+z +_cell_length_a 9.1546(5) +_cell_length_b 20.9176(17) +_cell_length_c 15.0072(9) +_cell_angle_alpha 90 +_cell_angle_beta 91.405(5) +_cell_angle_gamma 90 +_cell_formula_units_Z 4 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Fe 1.52 +K 2.03 +N 0.68 +Si 1.20 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Fe1 Fe 0.56380(2) 0.73652(2) 0.33020(2) +K1 K 0.38252(5) 0.68564(2) 0.14658(2) +Si1 Si 0.56065(5) 0.83660(2) 0.16971(3) +Si2 Si 0.31913(5) 0.83855(2) 0.29891(3) +Si3 Si 0.74087(5) 0.61969(2) 0.25985(3) +Si4 Si 0.44799(5) 0.59547(2) 0.34045(3) +N1 N 0.46158(14) 0.79892(6) 0.25076(8) +N2 N 0.57066(14) 0.64564(6) 0.29030(8) +C1 C 0.6656(2) 0.75157(8) 0.45450(11) +H1 H 0.59743 0.73815 0.50116 +H2 H 0.75233 0.72338 0.45922 +C2 C 0.71285(19) 0.81773(8) 0.47381(10) +C3 C 0.62803(19) 0.86007(8) 0.52306(10) +H3 H 0.53623 0.84621 0.54406 +C4 C 0.6750(2) 0.92180(9) 0.54190(11) +H4 H 0.61483 0.94950 0.57523 +C5 C 0.8082(2) 0.94346(9) 0.51276(11) +H5 H 0.84079 0.98551 0.52656 +C6 C 0.8935(2) 0.90265(9) 0.46298(11) +H6 H 0.98465 0.91712 0.44182 +C7 C 0.84713(19) 0.84118(8) 0.44385(10) +H7 H 0.90738 0.81410 0.40965 +C8 C 0.6217(2) 0.77791(11) 0.08339(12) +H8 H 0.66034 0.73950 0.11298 +H9 H 0.69811 0.79738 0.04772 +H10 H 0.53838 0.76626 0.04445 +C9 C 0.7268(2) 0.87688(9) 0.21657(11) +H11 H 0.69786 0.91009 0.25869 +H12 H 0.78114 0.89639 0.16813 +H13 H 0.78881 0.84536 0.24756 +C10 C 0.4548(2) 0.89682(10) 0.10108(12) +H14 H 0.36983 0.87598 0.07264 +H15 H 0.51754 0.91441 0.05515 +H16 H 0.42172 0.93144 0.13971 +C11 C 0.3642(2) 0.92332(8) 0.33086(12) +H17 H 0.44799 0.92364 0.37274 +H18 H 0.27972 0.94286 0.35911 +H19 H 0.38825 0.94766 0.27739 +C12 C 0.2673(2) 0.79953(9) 0.40589(11) +H20 H 0.22661 0.75705 0.39332 +H21 H 0.19409 0.82576 0.43535 +H22 H 0.35411 0.79535 0.44498 +C13 C 0.15085(19) 0.83718(9) 0.22571(12) +H23 H 0.17214 0.85664 0.16807 +H24 H 0.07301 0.86133 0.25419 +H25 H 0.11922 0.79286 0.21653 +C14 C 0.7325(2) 0.57956(10) 0.14823(13) +H26 H 0.66423 0.54353 0.14994 +H27 H 0.82992 0.56390 0.13356 +H28 H 0.69907 0.61026 0.10282 +C15 C 0.8294(2) 0.56309(10) 0.34151(14) +H29 H 0.84339 0.58452 0.39916 +H30 H 0.92437 0.54959 0.31936 +H31 H 0.76671 0.52556 0.34871 +C16 C 0.8742(2) 0.68743(9) 0.25050(15) +H32 H 0.83158 0.72093 0.21232 +H33 H 0.96448 0.67174 0.22441 +H34 H 0.89604 0.70498 0.30989 +C17 C 0.4683(3) 0.59388(11) 0.46488(12) +H35 H 0.45018 0.63671 0.48866 +H36 H 0.56762 0.58036 0.48187 +H37 H 0.39771 0.56374 0.48925 +C18 C 0.4535(2) 0.51138(9) 0.29821(14) +H38 H 0.37551 0.48648 0.32526 +H39 H 0.54839 0.49224 0.31399 +H40 H 0.43957 0.51137 0.23326 +C19 C 0.2550(2) 0.62122(10) 0.31416(14) +H41 H 0.24830 0.66790 0.31784 +H42 H 0.18921 0.60191 0.35711 +H43 H 0.22683 0.60728 0.25381 +#END diff --git a/cell2mol/test/error_2/FURPEO.search5.cif b/cell2mol/test/error_2/FURPEO.search5.cif new file mode 100755 index 00000000..a0aeeb9b --- /dev/null +++ b/cell2mol/test/error_2/FURPEO.search5.cif @@ -0,0 +1,344 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_FURPEO +_audit_creation_date 2010-04-07 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD FURPEO +_database_code_depnum_ccdc_archive 'CCDC 723545' +_chemical_formula_sum 'C106 H120 Cl4 Fe1 K2 N10 O12' +_chemical_formula_moiety +; +2(C18 H36 K1 N2 O6 1+),C46 H28 Fe1 N6 2-,4(C6 H5 Cl1) +; +_journal_coden_Cambridge 179 +_journal_volume 48 +_journal_year 2009 +_journal_page_first 5010 +_journal_name_full 'Angew.Chem.,Int.Ed. ' +loop_ +_publ_author_name +"Jianfeng Li" +"B.C.Noll" +"C.E.Schulz" +"W.R.Scheidt" +_chemical_name_systematic +; +bis((4,7,13,16,21,24-Hexaoxa-1,10-diazabicyclo[8.8.8]hexacosane-N^1^,N^10^,O^4 +^,O^7^,O^13^,O^16^,O^21^,O^24^)-potassium) +bis(cyano)-(5,10,15,20-tetraphenylporphyrinato-N^21^,N^22^,N^23^,N^24^)-iron(i +i) chlorobenzene solvate +; +_chemical_name_common +; +bis((2,2,2-Cryptand)-potassium) +bis(cyano)-(5,10,15,20-tetraphenylporphyrinato-N$21!,N$22!,N$23!,N$24!)-iron(i +i) chlorobenzene solvate +; +_cell_volume 4946.998 +_exptl_crystal_colour 'black' +_exptl_crystal_density_diffrn 1.344 +_exptl_crystal_description 'block' +_diffrn_ambient_temperature 100 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0496 +_refine_ls_wR_factor_gt 0.0496 +_symmetry_cell_setting triclinic +_symmetry_space_group_name_H-M 'P -1' +_symmetry_Int_Tables_number 2 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,-y,-z +_cell_length_a 15.5352(11) +_cell_length_b 15.8391(13) +_cell_length_c 22.0815(16) +_cell_angle_alpha 83.820(4) +_cell_angle_beta 77.934(4) +_cell_angle_gamma 68.686(4) +_cell_formula_units_Z 2 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Cl 0.99 +Fe 1.52 +K 2.03 +N 0.68 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Fe1 Fe 0.26210(3) 0.74701(3) 0.25255(2) +N1 N 0.3586(2) 0.6538(2) 0.19618(13) +N2 N 0.3350(2) 0.6976(2) 0.32049(13) +N3 N 0.1656(2) 0.8412(2) 0.30747(13) +N4 N 0.1886(2) 0.7951(2) 0.18479(13) +N5 N 0.3769(2) 0.8770(2) 0.21859(15) +N6 N 0.1534(2) 0.6141(2) 0.30021(16) +C1 C 0.3554(2) 0.6360(2) 0.13744(16) +C2 C 0.4415(2) 0.5916(2) 0.20877(16) +C3 C 0.4185(2) 0.6267(2) 0.31763(16) +C4 C 0.3099(2) 0.7264(2) 0.38012(15) +C5 C 0.1642(3) 0.8527(3) 0.36862(16) +C6 C 0.0896(2) 0.9122(2) 0.29206(16) +C7 C 0.1095(2) 0.8702(2) 0.18529(16) +C8 C 0.2057(2) 0.7572(2) 0.12806(16) +C9 C 0.4379(3) 0.5625(3) 0.11282(17) +H1 H 0.45170 0.53740 0.07320 +C10 C 0.4921(3) 0.5358(3) 0.15649(16) +H2 H 0.55210 0.48920 0.15340 +C11 C 0.4464(3) 0.6102(3) 0.37752(16) +H3 H 0.50170 0.56490 0.38790 +C12 C 0.3796(2) 0.6712(2) 0.41571(16) +H4 H 0.37840 0.67680 0.45830 +C13 C 0.0865(3) 0.9323(3) 0.39147(17) +H5 H 0.07030 0.95460 0.43210 +C14 C 0.0413(3) 0.9688(3) 0.34390(17) +H6 H -0.01280 1.02230 0.34460 +C15 C 0.0756(3) 0.8793(3) 0.12808(16) +H7 H 0.02150 0.92590 0.11720 +C16 C 0.1343(2) 0.8102(3) 0.09325(17) +H8 H 0.12980 0.79830 0.05300 +C17 C 0.4704(2) 0.5778(3) 0.26587(16) +C18 C 0.2307(2) 0.7984(3) 0.40349(16) +C19 C 0.0631(2) 0.9276(3) 0.23418(16) +C20 C 0.2817(3) 0.6830(3) 0.10575(16) +C21 C 0.5595(3) 0.5026(3) 0.27298(16) +C22 C 0.5568(3) 0.4234(3) 0.30604(18) +H9 H 0.49770 0.41770 0.32340 +C23 C 0.6391(3) 0.3526(3) 0.3141(2) +H10 H 0.63610 0.29930 0.33730 +C24 C 0.7249(3) 0.3597(3) 0.28823(19) +H11 H 0.78120 0.31140 0.29360 +C25 C 0.7288(3) 0.4370(3) 0.25465(19) +H12 H 0.78810 0.44180 0.23660 +C26 C 0.6467(3) 0.5079(3) 0.24701(18) +H13 H 0.65040 0.56090 0.22360 +C27 C 0.2158(3) 0.8187(3) 0.47016(17) +C28 C 0.2775(3) 0.8475(3) 0.49175(18) +H14 H 0.32850 0.85770 0.46370 +C29 C 0.2650(3) 0.8617(3) 0.5545(2) +H15 H 0.30790 0.88130 0.56900 +C30 C 0.1919(3) 0.8478(3) 0.59584(19) +H16 H 0.18430 0.85730 0.63870 +C31 C 0.1293(3) 0.8199(3) 0.5745(2) +H17 H 0.07800 0.81060 0.60260 +C32 C 0.1417(3) 0.8056(3) 0.51174(18) +H18 H 0.09850 0.78640 0.49730 +C33 C -0.0184(3) 1.0094(3) 0.22263(16) +C34 C -0.0039(3) 1.0764(3) 0.17935(18) +H19 H 0.05800 1.06980 0.15840 +C35 C -0.0782(3) 1.1521(3) 0.16668(19) +H20 H -0.06690 1.19690 0.13710 +C36 C -0.1684(3) 1.1633(3) 0.19636(18) +H21 H -0.21940 1.21550 0.18750 +C37 C -0.1843(3) 1.0975(3) 0.23932(18) +H22 H -0.24640 1.10460 0.26010 +C38 C -0.1099(3) 1.0218(3) 0.25192(17) +H23 H -0.12170 0.97710 0.28140 +C39 C 0.2830(3) 0.6539(3) 0.04307(16) +C40 C 0.3398(3) 0.6722(3) -0.00959(18) +H24 H 0.38450 0.69830 -0.00570 +C41 C 0.3336(3) 0.6536(3) -0.06802(19) +H25 H 0.37300 0.66780 -0.10360 +C42 C 0.2700(3) 0.6146(3) -0.07444(19) +H26 H 0.26510 0.60190 -0.11440 +C43 C 0.2138(3) 0.5940(3) -0.02239(18) +H27 H 0.17040 0.56630 -0.02640 +C44 C 0.2202(3) 0.6137(3) 0.03613(18) +H28 H 0.18100 0.59930 0.07170 +C45 C 0.3336(3) 0.8295(3) 0.22862(16) +C46 C 0.1932(3) 0.6635(3) 0.28171(16) +K1 K 0.78502(6) 0.67131(6) 0.48799(4) +N7 N 0.7550(2) 0.5025(2) 0.46732(14) +N8 N 0.8097(2) 0.8438(2) 0.51080(15) +C47 C 0.8338(3) 0.8889(3) 0.45127(19) +H29 H 0.86270 0.93260 0.45820 +H30 H 0.77550 0.92350 0.43520 +C48 C 0.9005(3) 0.8234(3) 0.40341(19) +H31 H 0.91960 0.85720 0.36580 +H32 H 0.95770 0.78570 0.41990 +C49 C 0.9096(3) 0.7156(3) 0.33527(19) +H33 H 0.97290 0.67800 0.34350 +H34 H 0.91710 0.75700 0.29920 +C50 C 0.8615(3) 0.6566(3) 0.32122(18) +H35 H 0.79780 0.69410 0.31380 +H36 H 0.89730 0.62290 0.28330 +C51 C 0.8223(3) 0.5276(3) 0.35705(18) +H37 H 0.86480 0.49510 0.32030 +H38 H 0.75870 0.55740 0.34690 +C52 C 0.8191(3) 0.4614(3) 0.41086(19) +H39 H 0.79960 0.41370 0.39870 +H40 H 0.88320 0.43180 0.42020 +C53 C 0.6577(3) 0.5223(3) 0.4601(2) +H41 H 0.64520 0.46480 0.46390 +H42 H 0.65030 0.54880 0.41800 +C54 C 0.5861(3) 0.5868(3) 0.5070(2) +H43 H 0.52190 0.59380 0.50220 +H44 H 0.59430 0.56230 0.54940 +C55 C 0.5242(3) 0.7385(3) 0.5344(2) +H45 H 0.52410 0.72170 0.57890 +H46 H 0.46310 0.74250 0.52520 +C56 C 0.5364(3) 0.8278(3) 0.5207(2) +H47 H 0.53670 0.84460 0.47620 +H48 H 0.48320 0.87490 0.54510 +C57 C 0.6354(3) 0.9082(3) 0.52507(19) +H49 H 0.57980 0.95600 0.54690 +H50 H 0.64190 0.92410 0.48020 +C58 C 0.7213(3) 0.9037(3) 0.5473(2) +H51 H 0.72340 0.96570 0.54570 +H52 H 0.71680 0.88170 0.59120 +C59 C 0.7747(3) 0.4406(3) 0.52116(19) +H53 H 0.77850 0.37990 0.51080 +H54 H 0.72130 0.46230 0.55590 +C60 C 0.9333(3) 0.5105(3) 0.58878(19) +H55 H 0.99240 0.49560 0.55790 +H56 H 0.94070 0.46150 0.62120 +C61 C 0.9132(3) 0.5982(3) 0.61686(18) +H57 H 0.85370 0.61310 0.64740 +H58 H 0.96430 0.59310 0.63900 +C62 C 0.8640(3) 0.4311(3) 0.54264(19) +H59 H 0.87270 0.38790 0.57870 +H60 H 0.91870 0.40770 0.50900 +C63 C 0.8867(3) 0.8231(3) 0.5452(2) +H61 H 0.88200 0.87960 0.56320 +H62 H 0.94720 0.80220 0.51580 +C64 C 0.8876(3) 0.7526(3) 0.59620(19) +H63 H 0.93680 0.74700 0.62020 +H64 H 0.82610 0.77060 0.62470 +O1 O 0.62263(19) 0.82330(19) 0.53623(13) +O2 O 0.59801(18) 0.67152(19) 0.49800(12) +O3 O 0.85574(18) 0.76720(19) 0.38812(12) +O4 O 0.85502(19) 0.59406(19) 0.37229(12) +O5 O 0.90592(19) 0.66846(19) 0.57040(12) +O6 O 0.85717(18) 0.51798(19) 0.55957(12) +K2 K 0.71400(6) 0.84931(6) 0.01736(4) +N9 N 0.6263(2) 0.7248(2) -0.01246(15) +N10 N 0.8019(2) 0.9753(2) 0.04816(14) +C65 C 0.7443(3) 1.0682(3) 0.0340(2) +H65 H 0.77950 1.10860 0.03570 +H66 H 0.68670 1.08690 0.06610 +C66 C 0.7166(3) 1.0804(3) -0.0288(2) +H67 H 0.68220 1.14530 -0.03740 +H68 H 0.77360 1.05970 -0.06130 +C67 C 0.6397(3) 1.0343(3) -0.09073(18) +H69 H 0.69820 1.00210 -0.11950 +H70 H 0.61610 1.09850 -0.10540 +C68 C 0.5690(3) 0.9926(3) -0.09008(19) +H71 H 0.51220 1.02190 -0.05910 +H72 H 0.55070 1.00150 -0.13130 +C69 C 0.5432(3) 0.8541(3) -0.0780(2) +H73 H 0.52160 0.86900 -0.11820 +H74 H 0.48760 0.87470 -0.04450 +C70 C 0.5923(3) 0.7540(3) -0.0710(2) +H75 H 0.54860 0.72300 -0.07440 +H76 H 0.64640 0.73450 -0.10570 +C71 C 0.5470(3) 0.7273(3) 0.03760(19) +H77 H 0.52470 0.67800 0.03190 +H78 H 0.49500 0.78550 0.03380 +C72 C 0.5678(3) 0.7176(3) 0.10222(19) +H79 H 0.51050 0.72090 0.13300 +H80 H 0.61700 0.65810 0.10790 +C73 C 0.6279(3) 0.8672(3) 0.18562(17) +H81 H 0.57060 0.91990 0.18100 +H82 H 0.63790 0.86450 0.22870 +C74 C 0.6148(3) 0.7828(3) 0.17331(17) +H83 H 0.67150 0.72970 0.17870 +H84 H 0.56050 0.77540 0.20290 +C75 C 0.7210(3) 0.9576(3) 0.15605(19) +H85 H 0.72800 0.95550 0.19980 +H86 H 0.66600 1.01170 0.14930 +C76 C 0.8080(3) 0.9641(3) 0.11396(18) +H87 H 0.82080 1.01620 0.12600 +H88 H 0.86190 0.90860 0.12010 +C77 C 0.9529(3) 0.8551(3) 0.00721(19) +H89 H 1.01750 0.84600 -0.01560 +H90 H 0.95710 0.82840 0.04960 +C78 C 0.8967(3) 0.9539(3) 0.01031(19) +H91 H 0.93130 0.98440 0.02750 +H92 H 0.89100 0.97890 -0.03230 +C79 C 0.9632(3) 0.7172(3) -0.0275(2) +H93 H 0.96550 0.68880 0.01460 +H94 H 1.02830 0.70850 -0.04890 +C80 C 0.9192(3) 0.6730(3) -0.0627(2) +H95 H 0.91290 0.70390 -0.10380 +H96 H 0.95930 0.60860 -0.06900 +C81 C 0.7892(3) 0.6285(3) -0.0561(2) +H97 H 0.83190 0.56450 -0.05940 +H98 H 0.78080 0.65370 -0.09830 +C82 C 0.6954(3) 0.6336(3) -0.0170(2) +H99 H 0.67030 0.59470 -0.03490 +H100 H 0.70490 0.60910 0.02520 +O7 O 0.70665(19) 0.87856(19) 0.14367(12) +O8 O 0.59896(19) 0.7882(2) 0.11132(12) +O9 O 0.65894(19) 1.02944(19) -0.02957(12) +O10 O 0.6068(2) 0.8982(2) -0.07499(13) +O11 O 0.90979(19) 0.8110(2) -0.02326(13) +O12 O 0.8291(2) 0.6788(2) -0.02826(13) +C83 C 0.4296(3) 0.3561(3) 0.21117(19) +H101 H 0.45530 0.36400 0.16890 +C84 C 0.3144(3) 0.4115(3) 0.3021(2) +H102 H 0.26110 0.45800 0.32250 +C85 C 0.3521(3) 0.4247(3) 0.24174(19) +H103 H 0.32550 0.48070 0.22050 +C86 C 0.3538(3) 0.3303(3) 0.3338(2) +H104 H 0.32760 0.32150 0.37580 +C87 C 0.4310(3) 0.2622(3) 0.3043(2) +H105 H 0.45790 0.20630 0.32550 +C88 C 0.4678(3) 0.2767(3) 0.24408(19) +Cl1 Cl 0.56682(8) 0.19235(8) 0.20674(6) +C89 C 0.1025(3) 0.2354(3) 0.24958(18) +C90 C 0.1707(4) 0.2731(4) 0.23540(19) +H106 H 0.15510 0.33680 0.22910 +C91 C 0.2844(4) 0.1238(5) 0.2406(2) +H107 H 0.34820 0.08500 0.23750 +C92 C 0.2629(4) 0.2162(5) 0.2305(2) +H108 H 0.31190 0.24050 0.22010 +C93 C 0.2147(4) 0.0878(4) 0.2551(2) +H109 H 0.23000 0.02410 0.26150 +C94 C 0.1218(3) 0.1444(3) 0.2605(2) +H110 H 0.07250 0.12050 0.27150 +Cl2 Cl -0.01462(9) 0.30614(9) 0.25673(6) +C95 C 0.3700(3) 0.1461(3) 0.64308(18) +H111 H 0.30880 0.16780 0.63330 +C96 C 0.4161(3) 0.2052(3) 0.64598(19) +H112 H 0.38650 0.26830 0.63790 +C97 C 0.5050(3) 0.1731(4) 0.6606(2) +H113 H 0.53570 0.21420 0.66350 +C98 C 0.5043(3) 0.0224(3) 0.66807(19) +H114 H 0.53460 -0.04080 0.67520 +C99 C 0.5485(3) 0.0824(4) 0.6709(2) +H115 H 0.61010 0.06060 0.68010 +C100 C 0.4147(3) 0.0558(3) 0.65460(17) +Cl3 Cl 0.35694(8) -0.01969(8) 0.65316(5) +C101 C 0.9221(3) 0.5850(3) 0.14771(18) +C102 C 1.0006(3) 0.5269(3) 0.16944(19) +H116 H 1.01500 0.46320 0.17060 +C103 C 1.0577(3) 0.5628(4) 0.1893(2) +H117 H 1.11180 0.52340 0.20470 +C104 C 1.0376(3) 0.6554(3) 0.18730(19) +H118 H 1.07790 0.68000 0.20020 +C105 C 0.8989(3) 0.6768(3) 0.14625(18) +H119 H 0.84380 0.71600 0.13190 +C106 C 0.9579(3) 0.7111(3) 0.16623(19) +H120 H 0.94290 0.77490 0.16540 +Cl4 Cl 0.84959(9) 0.53978(10) 0.12211(6) +#END diff --git a/cell2mol/test/error_2/FUYTOK.niquel_all.cif b/cell2mol/test/error_2/FUYTOK.niquel_all.cif new file mode 100755 index 00000000..601364c9 --- /dev/null +++ b/cell2mol/test/error_2/FUYTOK.niquel_all.cif @@ -0,0 +1,234 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_FUYTOK +_audit_creation_date 2016-01-07 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD FUYTOK +_database_code_depnum_ccdc_archive 'CCDC 1421830' +_chemical_formula_sum 'C56 H80 K1 N2 Ni1 O6 S1' +_chemical_formula_moiety +; +C41 H65 K1 N2 Ni1 O6 S1,2.5(C6 H6) +; +_journal_coden_Cambridge 179 +_journal_volume 54 +_journal_year 2015 +_journal_page_first 14956 +_journal_name_full 'Angew.Chem.,Int.Ed. ' +loop_ +_publ_author_name +"N.J.Hartmann" +"Guang Wu" +"T.W.Hayton" +_chemical_name_systematic +; +(\m-Sulfido)-(N,N'-bis(2,6-di-isopropylphenyl)-pentane2,4-di-iminato)-(1,4,7,1 +0,13,16-hexaoxacyclooctadecane)-nickel(ii)-potassium benzene solvate +; +_cell_volume 2821.101 +_exptl_crystal_colour 'green' +_exptl_crystal_density_diffrn 1.186 +_exptl_crystal_description 'Block' +_exptl_crystal_preparation 'Re-crystallisation from solvent' +_diffrn_ambient_temperature 108 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0397 +_refine_ls_wR_factor_gt 0.0397 +_symmetry_cell_setting triclinic +_symmetry_space_group_name_H-M 'P -1' +_symmetry_Int_Tables_number 2 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,-y,-z +_cell_length_a 13.1937(13) +_cell_length_b 13.2978(13) +_cell_length_c 16.6951(16) +_cell_angle_alpha 88.621(2) +_cell_angle_beta 74.520(2) +_cell_angle_gamma 88.213(2) +_cell_formula_units_Z 2 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +K 2.03 +N 0.68 +Ni 1.24 +O 0.68 +S 1.02 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +C1 C 0.83121(14) 0.18249(15) 0.57477(12) +C2 C 0.86760(16) 0.26359(17) 0.52083(12) +C3 C 0.79611(18) 0.32564(18) 0.48063(14) +H1 H 0.72480 0.29590 0.49850 +C4 C 0.7851(3) 0.4337(2) 0.50980(17) +H2 H 0.75610 0.43480 0.57030 +H3 H 0.73790 0.47170 0.48310 +H4 H 0.85440 0.46440 0.49480 +C5 C 0.8323(2) 0.3224(2) 0.38569(16) +H5 H 0.89750 0.35950 0.36570 +H6 H 0.77770 0.35320 0.36250 +H7 H 0.84480 0.25230 0.36810 +C6 C 0.97466(17) 0.28520(18) 0.50371(13) +H8 H 1.00120 0.33940 0.46670 +C7 C 1.04245(16) 0.23051(18) 0.53879(13) +H9 H 1.11460 0.24700 0.52620 +C8 C 1.00461(15) 0.15109(17) 0.59279(13) +H10 H 1.05140 0.11360 0.61730 +C9 C 0.89921(15) 0.12528(15) 0.61170(12) +C10 C 0.85935(16) 0.03921(16) 0.67210(13) +H11 H 0.79020 0.01890 0.66450 +C11 C 0.8406(2) 0.07526(19) 0.76138(14) +H12 H 0.90810 0.09120 0.77160 +H13 H 0.80720 0.02210 0.80030 +H14 H 0.79470 0.13560 0.76950 +C12 C 0.9333(2) -0.05359(19) 0.65847(17) +H15 H 0.94850 -0.07390 0.60030 +H16 H 0.89960 -0.10880 0.69490 +H17 H 0.99900 -0.03740 0.67150 +C13 C 0.69419(15) 0.08417(15) 0.55391(12) +C14 C 0.77595(16) 0.03280(18) 0.48437(14) +H18 H 0.80860 0.08300 0.44220 +H19 H 0.74210 -0.01760 0.45900 +H20 H 0.83000 -0.00020 0.50700 +C15 C 0.59173(15) 0.04784(15) 0.57267(12) +H21 H 0.57940 -0.00280 0.53750 +C16 C 0.50564(15) 0.07800(15) 0.63747(12) +C17 C 0.40753(15) 0.01626(17) 0.65255(13) +H22 H 0.40080 -0.02580 0.70260 +H23 H 0.41270 -0.02670 0.60470 +H24 H 0.34570 0.06140 0.66010 +C18 C 0.41933(14) 0.17057(14) 0.75666(11) +C19 C 0.34065(14) 0.24192(15) 0.75143(12) +C20 C 0.34735(16) 0.30450(17) 0.67270(13) +H25 H 0.42170 0.30010 0.63760 +C21 C 0.3194(3) 0.4148(2) 0.6910(2) +H26 H 0.24500 0.42180 0.72150 +H27 H 0.33190 0.45330 0.63860 +H28 H 0.36310 0.44060 0.72460 +C22 C 0.2775(3) 0.2669(3) 0.6222(2) +H29 H 0.29890 0.19770 0.60500 +H30 H 0.28390 0.31010 0.57290 +H31 H 0.20420 0.26820 0.65600 +C23 C 0.25459(15) 0.25401(16) 0.82100(13) +H32 H 0.19980 0.30100 0.81830 +C24 C 0.24749(16) 0.19924(17) 0.89340(13) +H33 H 0.18820 0.20830 0.93980 +C25 C 0.32728(16) 0.13085(16) 0.89820(12) +H34 H 0.32250 0.09370 0.94830 +C26 C 0.50272(17) 0.04365(16) 0.83881(12) +H35 H 0.55460 0.03930 0.78300 +C27 C 0.41460(15) 0.11589(14) 0.83036(12) +C28 C 0.4637(2) -0.06278(17) 0.86438(16) +H36 H 0.41140 -0.06080 0.91850 +H37 H 0.52320 -0.10690 0.86820 +H38 H 0.43160 -0.08870 0.82270 +C29 C 0.56017(18) 0.08490(18) 0.89916(14) +H39 H 0.58720 0.15140 0.87960 +H40 H 0.61890 0.03910 0.90190 +H41 H 0.51120 0.09050 0.95460 +C30 C 0.8409(2) 0.6506(2) 0.73157(17) +H42 H 0.90410 0.67710 0.74430 +H43 H 0.81880 0.69800 0.69240 +C31 C 0.8650(2) 0.5485(2) 0.69382(15) +H44 H 0.79960 0.51830 0.68840 +H45 H 0.91440 0.55420 0.63780 +C32 C 0.94299(19) 0.3903(2) 0.71027(16) +H46 H 0.99900 0.39780 0.65770 +H47 H 0.88270 0.35730 0.69810 +C33 C 0.98288(18) 0.3287(2) 0.77122(17) +H48 H 1.01480 0.26480 0.74570 +H49 H 1.03750 0.36560 0.78840 +C34 C 0.9267(2) 0.2466(2) 0.90207(18) +H50 H 0.97080 0.28420 0.92990 +H51 H 0.96740 0.18670 0.87580 +C35 C 0.8277(2) 0.21524(18) 0.96397(18) +H52 H 0.78070 0.18290 0.93540 +H53 H 0.84430 0.16630 1.00460 +C36 C 0.6827(2) 0.28167(19) 1.06635(18) +H54 H 0.69470 0.22590 1.10340 +H55 H 0.62840 0.26130 1.03930 +C37 C 0.64678(19) 0.3746(2) 1.11545(15) +H56 H 0.58380 0.36050 1.16160 +H57 H 0.70300 0.39730 1.13950 +C38 C 0.60028(17) 0.54550(18) 1.10348(14) +H58 H 0.66310 0.56700 1.11970 +H59 H 0.54180 0.53850 1.15450 +C39 C 0.57111(17) 0.62226(18) 1.04671(15) +H60 H 0.51010 0.59950 1.02860 +H61 H 0.55100 0.68660 1.07610 +C40 C 0.63704(19) 0.71318(17) 0.92185(15) +H62 H 0.61560 0.77630 0.95290 +H63 H 0.57840 0.69290 0.89970 +C41 C 0.7331(2) 0.73005(17) 0.85173(16) +H64 H 0.71980 0.78580 0.81520 +H65 H 0.79270 0.74820 0.87380 +K1 K 0.73968(3) 0.45098(3) 0.89021(3) +N1 N 0.72156(12) 0.15858(13) 0.59471(10) +N2 N 0.50844(12) 0.15461(12) 0.68670(9) +Ni1 Ni 0.62935(2) 0.23577(2) 0.68414(2) +O1 O 0.75783(13) 0.63988(12) 0.80619(10) +O2 O 0.91069(12) 0.48724(13) 0.74599(10) +O3 O 0.89773(12) 0.30852(13) 0.84123(11) +O4 O 0.77738(12) 0.30281(11) 1.00542(11) +O5 O 0.62225(12) 0.45092(12) 1.06225(9) +O6 O 0.65930(11) 0.63673(11) 0.97567(9) +S1 S 0.64594(4) 0.35185(4) 0.75913(3) +C42 C 0.5286(3) 0.5760(3) 0.5962(3) +H66 H 0.52240 0.50780 0.58290 +C43 C 0.5746(3) 0.5992(3) 0.6586(2) +H67 H 0.59960 0.54690 0.68880 +C44 C 0.5839(3) 0.6965(3) 0.6767(2) +H68 H 0.61540 0.71230 0.71960 +C45 C 0.5484(3) 0.7721(2) 0.6338(2) +H69 H 0.55650 0.84040 0.64590 +C46 C 0.5009(3) 0.7490(3) 0.5729(2) +H70 H 0.47430 0.80180 0.54430 +C47 C 0.4914(3) 0.6534(3) 0.5530(2) +H71 H 0.45970 0.63850 0.51000 +C48 C 0.8950(2) 0.2159(2) 0.17650(17) +H72 H 0.88200 0.28340 0.19460 +C49 C 0.8229(2) 0.1428(2) 0.21079(17) +H73 H 0.76140 0.15970 0.25330 +C50 C 0.8409(2) 0.0457(2) 0.18291(18) +H74 H 0.79100 -0.00450 0.20530 +C51 C 0.9312(2) 0.0214(2) 0.1226(2) +H75 H 0.94350 -0.04570 0.10340 +C52 C 1.0038(2) 0.0932(3) 0.0899(2) +H76 H 1.06670 0.07550 0.04900 +C53 C 0.9853(2) 0.1909(2) 0.11636(17) +H77 H 1.03480 0.24110 0.09310 +C54 C -0.0224(2) 0.53592(19) 0.92792(18) +H78 H -0.03730 0.56040 0.87830 +C55 C 0.07451(19) 0.4906(2) 0.92551(17) +H79 H 0.12600 0.48410 0.87380 +C56 C 0.09688(18) 0.45497(19) 0.99665(18) +H80 H 0.16350 0.42410 0.99410 +C54A C 0.0224(2) 0.46408(19) 1.07208(18) +H78A H 0.03730 0.43960 1.12170 +C55A C -0.07451(19) 0.5094(2) 1.07449(17) +H79A H -0.12600 0.51590 1.12620 +C56A C -0.09688(18) 0.54503(19) 1.00335(18) +H80A H -0.16350 0.57590 1.00590 +#END diff --git a/cell2mol/test/error_2/GAPZEC.search6.cif b/cell2mol/test/error_2/GAPZEC.search6.cif new file mode 100755 index 00000000..cb806b69 --- /dev/null +++ b/cell2mol/test/error_2/GAPZEC.search6.cif @@ -0,0 +1,121 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_GAPZEC +_audit_creation_date 1988-10-10 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD GAPZEC +_chemical_formula_sum 'C6 H22 Cl1 Co1 N4 O4' +_chemical_formula_moiety +; +C6 H18 Co1 N4 O2 1+,Cl1 1-,2(H2 O1) +; +_journal_coden_Cambridge 274 +_journal_volume 14 +_journal_year 1987 +_journal_page_first 55 +_journal_name_full 'Finn.Chem.Lett. ' +loop_ +_publ_author_name +"R.Kivekas" +"A.Pajunen" +_chemical_name_systematic +; +trans-bis(1,3-Diamino-2-propanolato-O,N,N')-cobalt(iii) chloride dihydrate +; +_cell_volume 1263.150 +_exptl_crystal_colour 'purple' +_exptl_crystal_density_diffrn 1.62 +_diffrn_ambient_temperature ? +_diffrn_special_details +; +The study was carried out at room temperature,in the range 283-303K + +; +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.035 +_refine_ls_wR_factor_gt 0.035 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'C 2/c' +_symmetry_Int_Tables_number 15 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 1/2+x,1/2+y,z +3 -x,y,1/2-z +4 1/2-x,1/2+y,1/2-z +5 -x,-y,-z +6 -1/2-x,-1/2-y,-z +7 x,-y,-1/2+z +8 -1/2+x,-1/2-y,-1/2+z +_cell_length_a 12.669(2) +_cell_length_b 11.155(2) +_cell_length_c 9.538(2) +_cell_angle_alpha 90 +_cell_angle_beta 110.43(1) +_cell_angle_gamma 90 +_cell_formula_units_Z 4 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Cl 0.99 +Co 1.33 +N 0.68 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Co1 Co 0.50000 0.5001(1) 0.25000 +Cl1 Cl 0.50000 0.8936(1) 0.25000 +O1 O 0.6120(2) 0.5007(3) 0.4446(2) +O2 O 0.5182(4) 0.1505(3) 0.4198(5) +N1 N 0.5953(3) 0.3810(3) 0.1999(4) +N2 N 0.5961(3) 0.6196(3) 0.2015(4) +C1 C 0.7079(3) 0.3840(4) 0.3203(4) +C2 C 0.7121(2) 0.4982(4) 0.4097(3) +C3 C 0.7109(3) 0.6102(4) 0.3182(4) +H1 H 0.602(3) 0.408(4) 0.111(5) +H2 H 0.567(4) 0.312(4) 0.190(5) +H3 H 0.767(4) 0.378(4) 0.277(5) +H4 H 0.717(4) 0.305(4) 0.388(5) +H5 H 0.776(3) 0.498(4) 0.492(5) +H6 H 0.777(3) 0.610(4) 0.276(4) +H7 H 0.727(3) 0.685(4) 0.385(5) +H8 H 0.602(4) 0.603(4) 0.115(5) +H9 H 0.572(3) 0.696(4) 0.194(5) +H10 H 0.50500 0.06800 0.36200 +H11 H 0.53800 0.12500 0.52400 +O1B O 0.3880(2) 0.5007(3) 0.0554(2) +N1B N 0.4047(3) 0.3810(3) 0.3001(4) +N2B N 0.4039(3) 0.6196(3) 0.2985(4) +C2B C 0.2879(2) 0.4982(4) 0.0903(3) +C1B C 0.2921(3) 0.3840(4) 0.1797(4) +H1B H 0.398(3) 0.408(4) 0.389(5) +H2B H 0.433(4) 0.312(4) 0.310(5) +C3B C 0.2891(3) 0.6102(4) 0.1818(4) +H8B H 0.398(4) 0.603(4) 0.385(5) +H9B H 0.428(3) 0.696(4) 0.306(5) +H5B H 0.224(3) 0.498(4) 0.008(5) +H3B H 0.233(4) 0.378(4) 0.223(5) +H4B H 0.283(4) 0.305(4) 0.112(5) +H6B H 0.223(3) 0.610(4) 0.224(4) +H7B H 0.273(3) 0.685(4) 0.115(5) +#END diff --git a/cell2mol/test/error_2/HAFRAI.search5.cif b/cell2mol/test/error_2/HAFRAI.search5.cif new file mode 100755 index 00000000..3eaf3a7f --- /dev/null +++ b/cell2mol/test/error_2/HAFRAI.search5.cif @@ -0,0 +1,163 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_HAFRAI +_audit_creation_date 2003-08-21 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD HAFRAI +_database_code_depnum_ccdc_archive 'CCDC 210769' +_chemical_formula_sum 'C40 H42 B1 Fe1' +_chemical_formula_moiety +; +C16 H22 Fe1 1+,C24 H20 B1 1- +; +_journal_coden_Cambridge 579 +_journal_volume 22 +_journal_year 2003 +_journal_page_first 1487 +_journal_name_full 'Organometallics ' +loop_ +_publ_author_name +"R.LeSuer" +"R.Basta" +"A.M.Arif" +"W.E.Geiger" +"R.D.Ernst" +_chemical_name_systematic +; +bis(\h^5^-6,6-Dimethylcyclohexa-2,4-dienyl)-iron(iii) tetraphenylborate +; +_cell_volume 1579.359 +_exptl_crystal_colour 'dark green' +_exptl_crystal_density_diffrn 1.239 +_exptl_crystal_description 'plate' +_exptl_crystal_preparation 'acetonitrile' +_diffrn_ambient_temperature 200 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0409 +_refine_ls_wR_factor_gt 0.0409 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'P 21' +_symmetry_Int_Tables_number 4 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,1/2+y,-z +_cell_length_a 9.9006(2) +_cell_length_b 10.4128(2) +_cell_length_c 15.3443(3) +_cell_angle_alpha 90 +_cell_angle_beta 93.2415(15) +_cell_angle_gamma 90 +_cell_formula_units_Z 2 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +B 0.83 +Fe 1.34 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Fe1 Fe 0.74406(3) 1.27160(4) 0.21769(2) +C1 C 0.6317(3) 1.4420(3) 0.1941(2) +H1 H 0.566(4) 1.465(4) 0.220(2) +C2 C 0.7615(4) 1.4707(3) 0.2285(2) +H2 H 0.782(3) 1.512(4) 0.288(2) +C3 C 0.8759(3) 1.4218(3) 0.1886(2) +H3 H 0.961(3) 1.446(3) 0.212(2) +C4 C 0.8572(3) 1.3447(3) 0.1143(2) +H4 H 0.925(3) 1.305(3) 0.0896(17) +C5 C 0.7268(3) 1.3177(3) 0.08162(19) +H5 H 0.717(3) 1.256(4) 0.041(2) +C6 C 0.6106(3) 1.4069(3) 0.09765(17) +C7 C 0.6148(4) 1.5281(3) 0.04015(19) +H6 H 0.60750 1.50330 -0.02150 +H7 H 0.70040 1.57350 0.05270 +H8 H 0.53920 1.58470 0.05270 +C8 C 0.4743(3) 1.3398(3) 0.0793(2) +H9 H 0.47160 1.26090 0.11400 +H10 H 0.46300 1.31860 0.01710 +H11 H 0.40110 1.39720 0.09510 +C9 C 0.5887(3) 1.1716(3) 0.27790(19) +H12 H 0.497(4) 1.183(4) 0.269(2) +C10 C 0.6455(4) 1.0946(3) 0.2161(2) +H13 H 0.602(4) 1.064(5) 0.167(3) +C11 C 0.7886(4) 1.0765(4) 0.2200(3) +H14 H 0.830(3) 1.030(4) 0.173(2) +C12 C 0.8691(3) 1.1339(3) 0.2862(2) +H15 H 0.973(4) 1.139(4) 0.278(3) +C13 C 0.8098(3) 1.2088(3) 0.3472(2) +H16 H 0.860(3) 1.266(4) 0.3846(19) +C14 C 0.6615(3) 1.1911(3) 0.36652(19) +C15 C 0.6442(4) 1.0695(4) 0.4233(2) +H17 H 0.67710 0.99410 0.39260 +H18 H 0.54830 1.05790 0.43400 +H19 H 0.69630 1.07960 0.47910 +C16 C 0.6059(4) 1.3074(4) 0.4135(2) +H20 H 0.50970 1.29420 0.42240 +H21 H 0.61710 1.38460 0.37810 +H22 H 0.65540 1.31800 0.47020 +C17 C 0.2143(2) 0.9485(3) 0.26058(16) +C18 C 0.2356(3) 1.0164(3) 0.33970(18) +H23 H 0.24890 0.96950 0.39260 +C19 C 0.2378(3) 1.1497(3) 0.3428(2) +H24 H 0.25090 1.19190 0.39750 +C20 C 0.2212(3) 1.2216(3) 0.2675(2) +H25 H 0.22180 1.31280 0.27000 +C21 C 0.2038(3) 1.1587(3) 0.1886(2) +H26 H 0.19420 1.20670 0.13590 +C22 C 0.2004(3) 1.0251(3) 0.18578(17) +H27 H 0.18790 0.98420 0.13060 +C23 C 0.3884(2) 0.7658(3) 0.26535(14) +C24 C 0.4599(3) 0.7287(3) 0.34305(18) +H28 H 0.41060 0.70570 0.39210 +C25 C 0.6012(3) 0.7245(3) 0.3504(2) +H29 H 0.64600 0.69970 0.40410 +C26 C 0.6760(3) 0.7558(3) 0.2806(2) +H30 H 0.77210 0.75540 0.28610 +C27 C 0.6088(3) 0.7880(3) 0.20246(19) +H31 H 0.65880 0.80680 0.15300 +C28 C 0.4692(3) 0.7929(3) 0.19572(17) +H32 H 0.42590 0.81580 0.14110 +C29 C 0.1431(3) 0.7195(3) 0.33384(16) +C30 C 0.0421(2) 0.7768(4) 0.38048(15) +H33 H 0.02550 0.86610 0.37310 +C31 C -0.0357(3) 0.7075(4) 0.43785(19) +H34 H -0.10290 0.75020 0.46890 +C32 C -0.0149(3) 0.5771(4) 0.4493(2) +H35 H -0.06750 0.52970 0.48800 +C33 C 0.0828(4) 0.5168(3) 0.4039(2) +H36 H 0.09770 0.42720 0.41100 +C34 C 0.1596(3) 0.5870(3) 0.34756(18) +H37 H 0.22650 0.54320 0.31690 +C35 C 0.1553(3) 0.7295(2) 0.16740(16) +C36 C 0.2096(3) 0.6230(3) 0.12560(17) +H38 H 0.29640 0.59240 0.14540 +C37 C 0.1408(3) 0.5604(3) 0.05600(18) +H39 H 0.18060 0.48800 0.03000 +C38 C 0.0154(3) 0.6031(3) 0.02473(19) +H40 H -0.03120 0.56110 -0.02300 +C39 C -0.0412(3) 0.7079(3) 0.0640(2) +H41 H -0.12760 0.73820 0.04310 +C40 C 0.0269(2) 0.7697(4) 0.13391(15) +H42 H -0.01470 0.84130 0.15990 +B1 B 0.2249(3) 0.7908(3) 0.25735(16) +#END diff --git a/cell2mol/test/error_2/HESMIB.search3.cif b/cell2mol/test/error_2/HESMIB.search3.cif new file mode 100755 index 00000000..244425c9 --- /dev/null +++ b/cell2mol/test/error_2/HESMIB.search3.cif @@ -0,0 +1,113 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_HESMIB +_audit_creation_date 1995-04-30 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD HESMIB +_chemical_formula_sum 'C5 H16 Cr1 I1 N4 O3' +_chemical_formula_moiety +; +C5 H16 Cr1 N4 O3 1+,I1 1- +; +_journal_coden_Cambridge 1094 +_journal_volume 5 +_journal_year 1994 +_journal_page_first 265 +_journal_name_full 'Struct.Chem. ' +loop_ +_publ_author_name +"I.Bernal" +"J.Cai" +"J.Certrullo" +"S.S.Massoud" +_chemical_name_systematic +; +Carbonato-bis(ethylenediamine)-chromium(iii) iodide +; +_cell_volume 1105.753 +_exptl_crystal_density_diffrn 2.157 +_diffrn_ambient_temperature ? +_diffrn_special_details +; +The study was carried out at room temperature,in the range 283-303K + +; +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.1129 +_refine_ls_wR_factor_gt 0.1129 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'P 21/c' +_symmetry_Int_Tables_number 14 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,1/2+y,1/2-z +3 -x,-y,-z +4 x,-1/2-y,-1/2+z +_cell_length_a 7.298(4) +_cell_length_b 8.622(8) +_cell_length_c 17.577(6) +_cell_angle_alpha 90 +_cell_angle_beta 91.22(4) +_cell_angle_gamma 90 +_cell_formula_units_Z 4 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Cr 1.30 +I 1.40 +N 0.68 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +I1 I 0.2039(3) 0.4674(2) 0.38621(9) +Cr1 Cr 0.1256(5) 0.4629(4) 0.1300(2) +O1 O -0.090(2) 0.527(2) 0.0803(7) +O2 O -0.087(2) 0.397(2) 0.1803(8) +O3 O -0.360(2) 0.470(2) 0.1305(8) +N1 N 0.137(3) 0.647(2) 0.189(1) +N2 N 0.299(3) 0.564(2) 0.066(1) +N3 N 0.308(3) 0.364(2) 0.192(1) +N4 N 0.134(3) 0.269(2) 0.068(1) +C1 C 0.296(4) 0.748(3) 0.167(2) +C2 C 0.298(4) 0.730(3) 0.071(1) +C3 C 0.304(4) 0.199(3) 0.178(1) +C4 C 0.287(3) 0.175(3) 0.087(1) +C5 C -0.204(5) 0.465(3) 0.130(1) +H1 H 0.15120 0.61940 0.24110 +H2 H 0.02630 0.70390 0.18140 +H3 H 0.40720 0.71190 0.19020 +H4 H 0.27460 0.85300 0.18190 +H5 H 0.40510 0.77340 0.04990 +H6 H 0.19250 0.77490 0.04690 +H7 H 0.41780 0.52720 0.08020 +H8 H 0.27150 0.53570 0.01480 +H9 H 0.28370 0.38330 0.24440 +H10 H 0.42510 0.40280 0.18060 +H11 H 0.20120 0.15460 0.20210 +H12 H 0.41330 0.15150 0.19730 +H13 H 0.26440 0.06910 0.07460 +H14 H 0.39380 0.20820 0.06250 +H15 H 0.02580 0.21240 0.07580 +H16 H 0.14080 0.29700 0.01570 +#END diff --git a/cell2mol/test/error_2/IFUFIY.search2.cif b/cell2mol/test/error_2/IFUFIY.search2.cif new file mode 100755 index 00000000..f673d406 --- /dev/null +++ b/cell2mol/test/error_2/IFUFIY.search2.cif @@ -0,0 +1,198 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_IFUFIY +_audit_creation_date 2002-09-05 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD IFUFIY +_database_code_depnum_ccdc_archive 'CCDC 167740' +_chemical_formula_sum 'C39 H63 Cs1 Mn1 O12' +_chemical_formula_moiety +; +C24 H48 Cs1 O12 1+,C15 H15 Mn1 1- +; +_journal_coden_Cambridge 1220 +_journal_volume 8 +_journal_year 2002 +_journal_page_first 2526 +_journal_name_full 'Chem.-Eur.J. ' +loop_ +_publ_author_name +"S.Kheradmandan" +"H.W.Schmalle" +"H.Jacobsen" +"O.Blacque" +"T.Fox" +"H.Berke" +"M.Gross" +"S.Decurtins" +_chemical_name_systematic +; +bis(18-Crown-6)-cesium tris(\h^2^-cyclopentadienyl)-manganese(ii) +; +_cell_volume 2166.343 +_exptl_crystal_colour 'pink' +_exptl_crystal_density_diffrn 1.398 +_exptl_crystal_description 'plate' +_diffrn_ambient_temperature 183 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0318 +_refine_ls_wR_factor_gt 0.0318 +_symmetry_cell_setting triclinic +_symmetry_space_group_name_H-M 'P -1' +_symmetry_Int_Tables_number 2 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,-y,-z +_cell_length_a 11.8868(9) +_cell_length_b 13.6526(10) +_cell_length_c 15.6473(12) +_cell_angle_alpha 104.484(8) +_cell_angle_beta 107.242(9) +_cell_angle_gamma 106.417(9) +_cell_formula_units_Z 2 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Cs 2.52 +Mn 1.35 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Cs1 Cs 0.683727(18) 0.897593(18) 0.732917(15) +Mn1 Mn 0.81866(4) 0.54921(4) 1.18630(3) +O1 O 0.62532(17) 1.12637(16) 0.73100(14) +O2 O 0.82555(16) 1.09423(16) 0.67503(13) +O3 O 0.69804(17) 0.88739(16) 0.51794(13) +O4 O 0.54252(17) 0.69360(16) 0.52434(13) +O5 O 0.39377(17) 0.71720(17) 0.63607(13) +O6 O 0.39907(17) 0.93539(17) 0.65902(15) +C1 C 0.7060(3) 1.2083(3) 0.7106(2) +H1 H 0.71770 1.28200 0.75070 +H2 H 0.66690 1.19870 0.64220 +C2 C 0.8326(3) 1.1973(3) 0.7320(2) +H3 H 0.89070 1.25670 0.72140 +H4 H 0.87030 1.20750 0.80070 +C3 C 0.7982(3) 1.0806(3) 0.5767(2) +H5 H 0.86110 1.14240 0.57220 +H6 H 0.71200 1.08000 0.54600 +C4 C 0.8042(3) 0.9752(3) 0.5264(2) +H7 H 0.80200 0.97120 0.46190 +H8 H 0.88470 0.97050 0.56340 +C5 C 0.7009(3) 0.7840(3) 0.4745(2) +H9 H 0.76640 0.77070 0.52100 +H10 H 0.72350 0.78340 0.41840 +C6 C 0.5733(3) 0.6962(3) 0.44307(19) +H11 H 0.50770 0.71010 0.39710 +H12 H 0.57370 0.62430 0.41010 +C7 C 0.4201(3) 0.6138(3) 0.4958(2) +H13 H 0.41660 0.54050 0.46240 +H14 H 0.35560 0.62970 0.45090 +C8 C 0.3915(3) 0.6152(3) 0.5839(2) +H15 H 0.30610 0.55780 0.56390 +H16 H 0.45490 0.59640 0.62690 +C9 C 0.2897(3) 0.7432(3) 0.5892(2) +H17 H 0.20780 0.68320 0.57270 +H18 H 0.29360 0.75170 0.52900 +C10 C 0.2981(3) 0.8472(3) 0.6550(2) +H19 H 0.21670 0.85720 0.63120 +H20 H 0.31400 0.84460 0.72000 +C11 C 0.4183(3) 1.0376(3) 0.7246(2) +H21 H 0.45930 1.04240 0.79140 +H22 H 0.33510 1.04410 0.71590 +C12 C 0.5016(3) 1.1285(3) 0.7068(2) +H23 H 0.46490 1.11930 0.63820 +H24 H 0.50660 1.19990 0.74620 +O7 O 0.89398(16) 1.10468(16) 0.92657(12) +O8 O 1.02191(17) 0.97850(16) 0.84500(13) +O9 O 0.86477(17) 0.77705(17) 0.69300(14) +O10 O 0.68385(17) 0.66266(17) 0.76136(14) +O11 O 0.63279(17) 0.79090(17) 0.90740(14) +O12 O 0.64015(18) 1.00809(17) 0.92241(13) +C13 C 1.0285(2) 1.1328(2) 0.9624(2) +H25 H 1.05300 1.09730 1.00890 +H26 H 1.07380 1.21320 0.99590 +C14 C 1.0652(3) 1.0953(2) 0.8804(2) +H27 H 1.02650 1.11950 0.82850 +H28 H 1.15910 1.12820 0.90200 +C15 C 1.0490(3) 0.9392(3) 0.7637(2) +H29 H 1.14240 0.96930 0.78130 +H30 H 1.00820 0.96320 0.71230 +C16 C 0.9989(2) 0.8168(3) 0.7287(2) +H31 H 1.02660 0.78820 0.67730 +H32 H 1.03220 0.79250 0.78210 +C17 C 0.8057(3) 0.6611(3) 0.6647(2) +H33 H 0.86200 0.62700 0.64550 +H34 H 0.72440 0.63310 0.60810 +C18 C 0.7793(3) 0.6278(3) 0.7443(2) +H35 H 0.75000 0.54710 0.72540 +H36 H 0.85830 0.66190 0.80360 +C19 C 0.6474(3) 0.6248(3) 0.8304(2) +H37 H 0.72460 0.64390 0.88810 +H38 H 0.60250 0.54410 0.80270 +C20 C 0.5623(3) 0.6761(3) 0.8582(2) +H39 H 0.48840 0.66220 0.80010 +H40 H 0.52970 0.64380 0.90040 +C21 C 0.5634(3) 0.8425(3) 0.9486(2) +H41 H 0.54540 0.81270 0.99670 +H42 H 0.48110 0.82870 0.89810 +C22 C 0.6418(3) 0.9628(3) 0.9954(2) +H43 H 0.60630 0.99840 1.03810 +H44 H 0.73070 0.97580 1.03480 +C23 C 0.7156(3) 1.1231(3) 0.9595(2) +H45 H 0.69240 1.16040 1.01050 +H46 H 0.69450 1.15200 0.90730 +C24 C 0.8573(3) 1.1513(3) 1.0000(2) +H47 H 0.90350 1.23190 1.02730 +H48 H 0.88000 1.12270 1.05220 +C25 C 0.8681(3) 0.3974(3) 1.1882(2) +H49 H 0.81480 0.34910 1.12380 +C26 C 0.9873(3) 0.4819(3) 1.2186(3) +H50 H 1.02840 0.50190 1.17830 +C27 C 1.0349(3) 0.5316(3) 1.3184(3) +H51 H 1.11390 0.59150 1.35830 +C28 C 0.9453(3) 0.4772(3) 1.3485(2) +H52 H 0.95450 0.49310 1.41350 +C29 C 0.8416(3) 0.3968(3) 1.2703(3) +H53 H 0.76630 0.34980 1.27130 +C30 C 0.7180(3) 0.6055(3) 1.2872(2) +H54 H 0.68980 0.54010 1.29940 +C31 C 0.8459(3) 0.6840(3) 1.3273(2) +H55 H 0.91820 0.68080 1.37140 +C32 C 0.8453(3) 0.7673(3) 1.2892(2) +H56 H 0.91740 0.83020 1.30280 +C33 C 0.7193(3) 0.7407(3) 1.2278(2) +H57 H 0.69160 0.78310 1.19280 +C34 C 0.6411(3) 0.6420(3) 1.2265(2) +H58 H 0.55150 0.60590 1.19070 +C35 C 0.6745(3) 0.5084(3) 1.0249(2) +H59 H 0.60480 0.51520 1.04020 +C36 C 0.7931(3) 0.5949(3) 1.0520(2) +H60 H 0.81810 0.66990 1.08890 +C37 C 0.8661(3) 0.5478(3) 1.0136(2) +H61 H 0.95010 0.58610 1.01970 +C38 C 0.7961(4) 0.4369(4) 0.9654(2) +H62 H 0.82380 0.38590 0.93290 +C39 C 0.6786(3) 0.4121(3) 0.9722(2) +H63 H 0.61210 0.34140 0.94540 +#END diff --git a/cell2mol/test/error_2/ITEREF.search2.cif b/cell2mol/test/error_2/ITEREF.search2.cif new file mode 100755 index 00000000..65f60f08 --- /dev/null +++ b/cell2mol/test/error_2/ITEREF.search2.cif @@ -0,0 +1,122 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_ITEREF +_audit_creation_date 2011-08-04 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD ITEREF +_database_code_depnum_ccdc_archive 'CCDC 806667' +_chemical_formula_sum 'C14 H16 Mn1 N9 O1' +_chemical_formula_moiety +; +C14 H16 Mn1 N9 O1 +; +_journal_coden_Cambridge 222 +_journal_volume 40 +_journal_year 2011 +_journal_page_first 5762 +_journal_name_full 'Dalton Trans. ' +loop_ +_publ_author_name +"Jong Won Shin" +"S.R.Rowthu" +"Min Young Hyun" +"Young Joo Song" +"Cheal Kim" +"Bong Gon Kim" +"Kil Sik Min" +_chemical_name_systematic +; +Diazido-(2-(bis((pyridin-2-yl)methyl)amino)ethanolato)-manganese(iii) +; +_cell_volume 1569.342 +_exptl_crystal_density_diffrn 1.614 +_diffrn_ambient_temperature 293 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0436 +_refine_ls_wR_factor_gt 0.0436 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'P 21/c' +_symmetry_Int_Tables_number 14 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,1/2+y,1/2-z +3 -x,-y,-z +4 x,-1/2-y,-1/2+z +_cell_length_a 8.6742(8) +_cell_length_b 13.3698(13) +_cell_length_c 13.6701(12) +_cell_angle_alpha 90 +_cell_angle_beta 98.150(2) +_cell_angle_gamma 90 +_cell_formula_units_Z 4 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Mn 1.52 +N 0.68 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Mn1 Mn 0.35081(6) 0.61305(4) 0.21034(4) +C1 C 0.0202(5) 0.5535(3) 0.2749(3) +H1 H 0.00630 0.51680 0.21650 +C2 C -0.0970(4) 0.5530(3) 0.3337(3) +H2 H -0.18730 0.51600 0.31570 +C3 C -0.0768(4) 0.6091(3) 0.4203(3) +H3 H -0.15280 0.60930 0.46200 +C4 C 0.0578(4) 0.6644(3) 0.4435(3) +H4 H 0.07150 0.70510 0.49920 +C5 C 0.1726(4) 0.6586(3) 0.3825(3) +C6 C 0.3286(4) 0.7081(3) 0.4094(3) +H5 H 0.31680 0.76830 0.44720 +H6 H 0.39850 0.66320 0.45010 +C7 C 0.5665(4) 0.7514(3) 0.3426(2) +H7 H 0.60860 0.70620 0.39520 +H8 H 0.58440 0.81920 0.36660 +C8 C 0.6516(4) 0.7355(3) 0.2545(3) +C9 C 0.7864(4) 0.7855(3) 0.2440(3) +H9 H 0.82520 0.83450 0.28910 +C10 C 0.8641(5) 0.7614(3) 0.1648(3) +H10 H 0.95670 0.79340 0.15700 +C11 C 0.8022(4) 0.6896(3) 0.0977(3) +H11 H 0.85280 0.67190 0.04470 +C12 C 0.6633(5) 0.6448(3) 0.1114(3) +H12 H 0.62000 0.59760 0.06560 +C13 C 0.3157(4) 0.8224(3) 0.2692(3) +H13 H 0.36950 0.88330 0.29270 +H14 H 0.20980 0.82590 0.28410 +C14 C 0.3150(5) 0.8118(3) 0.1591(3) +H15 H 0.24740 0.86190 0.12440 +H16 H 0.41940 0.82180 0.14320 +O1 O 0.2615(3) 0.71489(18) 0.12901(17) +N1 N 0.1538(3) 0.6048(2) 0.2988(2) +N2 N 0.3964(3) 0.7341(2) 0.3182(2) +N3 N 0.5882(3) 0.6663(2) 0.1881(2) +N4 N 0.4588(4) 0.5118(2) 0.3030(2) +N5 N 0.4080(4) 0.4756(2) 0.3727(2) +N6 N 0.3660(4) 0.4390(3) 0.4402(3) +N7 N 0.2859(4) 0.5128(2) 0.1028(2) +N8 N 0.2063(4) 0.5418(2) 0.0275(3) +N9 N 0.1313(5) 0.5660(3) -0.0450(3) +#END diff --git a/cell2mol/test/error_2/JEDJAE.search2.cif b/cell2mol/test/error_2/JEDJAE.search2.cif new file mode 100755 index 00000000..e9f2f687 --- /dev/null +++ b/cell2mol/test/error_2/JEDJAE.search2.cif @@ -0,0 +1,126 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_JEDJAE +_audit_creation_date 2006-04-10 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD JEDJAE +_database_code_depnum_ccdc_archive 'CCDC 295910' +_chemical_formula_sum 'C8 H28 Mn1 Na5 O24' +_chemical_formula_moiety +; +5(Na1 1+),C8 H4 Mn1 O12 5-,12(H2 O1) +; +_journal_coden_Cambridge 155 +_journal_volume 359 +_journal_year 2006 +_journal_page_first 374 +_journal_name_full 'Inorg.Chim.Acta ' +loop_ +_publ_author_name +"S.Kaizaki" +"M.Urade" +"A.Fuyuhiro" +"Y.Abe" +_chemical_name_systematic +; +Penta-sodium bis(L-tartrato)-manganese(iii) dodecahydrate +; +_cell_volume 1233.526 +_exptl_crystal_colour 'brown' +_exptl_crystal_density_diffrn 1.826 +_exptl_crystal_description 'column' +_diffrn_ambient_temperature 296.2 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0313 +_refine_ls_wR_factor_gt 0.0313 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'C 2' +_symmetry_Int_Tables_number 5 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,y,-z +3 1/2+x,1/2+y,z +4 1/2-x,1/2+y,-z +_cell_length_a 20.279(2) +_cell_length_b 6.860(2) +_cell_length_c 9.620(2) +_cell_angle_alpha 90 +_cell_angle_beta 112.82(1) +_cell_angle_gamma 90 +_cell_formula_units_Z 2 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Mn 1.35 +Na 0.97 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Mn1 Mn 0.00000 0.00000 0.00000 +O1 O -0.09106(10) 0.0036(5) -0.2383(2) +O2 O -0.2093(1) -0.0003(5) -0.2989(2) +O3 O -0.0630(1) -0.1085(4) 0.3209(2) +O4 O -0.1703(1) 0.0266(5) 0.2153(3) +O5 O -0.0654(1) 0.1848(3) 0.0202(2) +O6 O -0.0565(1) -0.1906(3) 0.0494(2) +C1 C -0.1460(1) 0.0258(4) -0.2075(3) +C2 C -0.1316(1) 0.0833(4) -0.0433(3) +C3 C -0.1208(1) -0.1062(5) 0.0485(3) +C4 C -0.1170(2) -0.0599(5) 0.2090(3) +H1 H -0.16980 0.15850 -0.03760 +H2 H -0.15960 -0.19200 -0.00050 +Na1 Na -0.26202(7) 0.2468(3) -0.5077(2) +Na2 Na 0.02387(7) -0.3146(2) 0.2808(1) +Na3 Na 0.00000 0.1704(3) 0.50000 +O7 O -0.2224(1) 0.4884(5) -0.3233(2) +H3 H -0.17480 0.47020 -0.23350 +H4 H -0.25080 0.52770 -0.26640 +O8 O 0.0825(1) -0.4839(4) 0.1440(2) +H5 H 0.07370 -0.38580 0.08240 +H6 H 0.07510 -0.60800 0.09340 +O9 O 0.1265(1) 0.2193(5) 0.5065(3) +H7 H 0.12190 0.13210 0.43100 +H8 H 0.13900 0.15950 0.59820 +O10 O -0.0405(1) 0.3897(4) 0.2858(3) +H9 H -0.03630 0.29840 0.21420 +H10 H -0.08550 0.42870 0.26130 +O11 O -0.3755(1) 0.1501(5) -0.4940(3) +H11 H -0.37750 0.02750 -0.47050 +H12 H -0.39550 0.25110 -0.43150 +O12 O -0.3118(2) -0.0392(8) -0.1663(4) +H13 H -0.27860 0.01460 -0.20420 +H14 H -0.31340 0.01200 -0.08870 +O1A O 0.09106(10) 0.0036(5) 0.2383(2) +O2A O 0.2093(1) -0.0003(5) 0.2989(2) +O3A O 0.0630(1) -0.1085(4) -0.3209(2) +O4A O 0.1703(1) 0.0266(5) -0.2153(3) +O5A O 0.0654(1) 0.1848(3) -0.0202(2) +O6A O 0.0565(1) -0.1906(3) -0.0494(2) +C1A C 0.1460(1) 0.0258(4) 0.2075(3) +C2A C 0.1316(1) 0.0833(4) 0.0433(3) +C3A C 0.1208(1) -0.1062(5) -0.0485(3) +C4A C 0.1170(2) -0.0599(5) -0.2090(3) +H1A H 0.16980 0.15850 0.03760 +H2A H 0.15960 -0.19200 0.00050 +#END diff --git a/cell2mol/test/error_2/KAGHOS.search6.cif b/cell2mol/test/error_2/KAGHOS.search6.cif new file mode 100755 index 00000000..40787672 --- /dev/null +++ b/cell2mol/test/error_2/KAGHOS.search6.cif @@ -0,0 +1,183 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_KAGHOS +_audit_creation_date 2016-01-20 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD KAGHOS +_database_code_depnum_ccdc_archive 'CCDC 1414086' +_chemical_formula_sum 'C24 H60 Co1 K1 N2 O6 Si4' +_chemical_formula_moiety +; +C12 H36 Co1 N2 Si4 1-,C12 H24 K1 O6 1+ +; +_journal_coden_Cambridge 1220 +_journal_volume 22 +_journal_year 2016 +_journal_page_first 1668 +_journal_name_full 'Chem.-Eur.J. ' +loop_ +_publ_author_name +"C.Gunnar Werncke" +"E.Suturina" +"P.C.Bunting" +"L.Vendier" +"J.R.Long" +"M.Atanasov" +"F.Neese" +"S.Sabo-Etienne" +"S.Bontemps" +_chemical_name_systematic +; +(18-Crown-6)-potassium bis(bis(trimethylsilyl)amide)-cobalt(i) +; +_cell_volume 934.894 +_exptl_crystal_colour 'colorless' +_exptl_crystal_density_diffrn 1.213 +_exptl_crystal_description 'plate' +_diffrn_ambient_temperature 180 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.022 +_refine_ls_wR_factor_gt 0.022 +_symmetry_cell_setting triclinic +_symmetry_space_group_name_H-M 'P -1' +_symmetry_Int_Tables_number 2 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,-y,-z +_cell_length_a 9.6542(4) +_cell_length_b 10.0649(3) +_cell_length_c 10.9839(3) +_cell_angle_alpha 116.594(3) +_cell_angle_beta 94.675(3) +_cell_angle_gamma 97.343(3) +_cell_formula_units_Z 1 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Co 1.26 +K 2.03 +N 0.68 +O 0.68 +Si 1.20 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +C1 C 0.20199(15) 0.64148(17) 0.90343(16) +H1 H 0.14630 0.72280 0.92270 +H2 H 0.13540 0.54410 0.86690 +C2 C 0.29482(16) 0.63676(16) 0.79995(16) +H3 H 0.23680 0.62190 0.71470 +H4 H 0.36260 0.73340 0.83710 +C3 C 0.45456(16) 0.49658(18) 0.66648(14) +H5 H 0.52690 0.58890 0.69720 +H6 H 0.39540 0.47930 0.58090 +C4 C 0.52451(17) 0.36476(18) 0.63962(14) +H7 H 0.45230 0.27330 0.61270 +H8 H 0.57970 0.34580 0.56320 +C5 C 0.69803(16) 0.28218(17) 0.73956(15) +H9 H 0.75430 0.27100 0.66520 +H10 H 0.63520 0.18410 0.71120 +C6 C 0.79399(15) 0.32572(18) 0.86998(16) +H11 H 0.85670 0.25100 0.85450 +H12 H 0.85380 0.42600 0.90060 +O1 O 0.28871(10) 0.66910(11) 1.02738(10) +O2 O 0.36918(10) 0.51536(10) 0.77011(9) +O3 O 0.61565(10) 0.3964(1) 0.76140(9) +K1 K 0.50000 0.50000 1.00000 +C7 C -0.12621(15) -0.36189(15) 0.70332(17) +H13 H -0.04710 -0.41620 0.69240 +H14 H -0.20570 -0.42540 0.62990 +H15 H -0.15550 -0.33780 0.79310 +C8 C -0.0170(2) -0.2465(2) 0.51733(16) +H16 H 0.01300 -0.15800 0.50370 +H17 H -0.09770 -0.31310 0.44680 +H18 H 0.06140 -0.30130 0.50990 +C9 C -0.23238(19) -0.10145(19) 0.68503(18) +H19 H -0.25650 -0.04920 0.77790 +H20 H -0.31110 -0.18290 0.62490 +H21 H -0.21440 -0.02950 0.64820 +C10 C 0.34312(16) 0.1108(2) 0.98209(16) +H22 H 0.31000 0.18470 1.06300 +H23 H 0.43280 0.15740 0.96970 +H24 H 0.35710 0.02320 0.99550 +C11 C 0.3054(2) -0.0472(2) 0.67662(18) +H25 H 0.30790 -0.15070 0.66060 +H26 H 0.40230 0.00920 0.69810 +H27 H 0.25660 -0.04960 0.59370 +C12 C 0.17117(19) 0.22518(19) 0.8230(2) +H28 H 0.09210 0.19950 0.74970 +H29 H 0.25530 0.27580 0.80570 +H30 H 0.14660 0.29280 0.91210 +N1 N 0.05969(11) -0.06166(11) 0.82538(10) +Co1 Co 0.00000 0.00000 1.00000 +Si1 Si -0.06975(4) -0.18321(4) 0.69296(3) +Si2 Si 0.20855(4) 0.04818(4) 0.82559(4) +C1A C 0.79801(15) 0.35852(17) 1.09657(16) +H1A H 0.85370 0.27720 1.07730 +H2A H 0.86460 0.45590 1.13310 +C2A C 0.70518(16) 0.36324(16) 1.20005(16) +H3A H 0.76320 0.37810 1.28530 +H4A H 0.63740 0.26660 1.16290 +C3A C 0.54544(16) 0.50342(18) 1.33352(14) +H5A H 0.47310 0.41110 1.30280 +H6A H 0.60460 0.52070 1.41910 +C4A C 0.47549(17) 0.63524(18) 1.36038(14) +H7A H 0.54770 0.72670 1.38730 +H8A H 0.42030 0.65420 1.43680 +C5A C 0.30197(16) 0.71782(17) 1.26044(15) +H9A H 0.24570 0.72900 1.33480 +H10A H 0.36480 0.81590 1.28880 +C6A C 0.20601(15) 0.67428(18) 1.13002(16) +H11A H 0.14330 0.74900 1.14550 +H12A H 0.14620 0.57400 1.09940 +O1A O 0.71129(10) 0.33090(11) 0.97262(10) +O2A O 0.63082(10) 0.48464(10) 1.22989(9) +O3A O 0.38435(10) 0.6036(1) 1.23860(9) +C7A C 0.12621(15) 0.36189(15) 1.29668(17) +H13A H 0.04710 0.41620 1.30760 +H14A H 0.20570 0.42540 1.37010 +H15A H 0.15550 0.33780 1.20690 +C8A C 0.0170(2) 0.2465(2) 1.48267(16) +H16A H -0.01300 0.15800 1.49630 +H17A H 0.09770 0.31310 1.55320 +H18A H -0.06140 0.30130 1.49010 +C9A C 0.23238(19) 0.10145(19) 1.31497(18) +H19A H 0.25650 0.04920 1.22210 +H20A H 0.31110 0.18290 1.37510 +H21A H 0.21440 0.02950 1.35180 +C10A C -0.34312(16) -0.1108(2) 1.01791(16) +H22A H -0.31000 -0.18470 0.93700 +H23A H -0.43280 -0.15740 1.03030 +H24A H -0.35710 -0.02320 1.00450 +C11A C -0.3054(2) 0.0472(2) 1.32338(18) +H25A H -0.30790 0.15070 1.33940 +H26A H -0.40230 -0.00920 1.30190 +H27A H -0.25660 0.04960 1.40630 +C12A C -0.17117(19) -0.22518(19) 1.1770(2) +H28A H -0.09210 -0.19950 1.25030 +H29A H -0.25530 -0.27580 1.19430 +H30A H -0.14660 -0.29280 1.08790 +N1A N -0.05969(11) 0.06166(11) 1.17462(10) +Si1A Si 0.06975(4) 0.18321(4) 1.30704(3) +Si2A Si -0.20855(4) -0.04818(4) 1.17441(4) +#END diff --git a/cell2mol/test/error_2/KIPLIH.search3.cif b/cell2mol/test/error_2/KIPLIH.search3.cif new file mode 100755 index 00000000..2321e63e --- /dev/null +++ b/cell2mol/test/error_2/KIPLIH.search3.cif @@ -0,0 +1,150 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_KIPLIH +_audit_creation_date 2018-09-15 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD KIPLIH +_database_code_depnum_ccdc_archive 'CCDC 1834043' +_chemical_formula_sum 'C24 H25 Cr1 N1 O8' +_chemical_formula_moiety +; +C24 H25 Cr1 N1 O8 +; +_journal_coden_Cambridge 36 +_journal_volume 875 +_journal_year 2018 +_journal_page_first 59 +_journal_name_full 'J.Organomet.Chem. ' +loop_ +_publ_author_name +"A.Feliciano" +"M.Ines Flores-Conde" +"R.Padilla" +"C.Espinoza-Hicks" +"A.Camacho-Davila" +"M.Renteria" +"Miguel Angel Vazquez" +"J.Tamariz" +"F.Delgado" +_chemical_name_systematic +; +((6S*,7S*)-(6-ethoxy-7-methyl-2-oxo-3-(4-methyl)phenyl-2,3,4,5,6,7-hexahydrobe +nzo[d]oxazole-6-ethoxymethylene))-tetracarbonyl-chromium(0) +; +_chemical_melting_point 427.15 +_cell_volume 1253.181 +_exptl_crystal_colour 'light pink' +_exptl_crystal_density_diffrn 1.345 +_exptl_special_details +; +Air-sensitive,Heat-sensitive,Oxygen-sensitive,Light-sensitive + +; +_exptl_crystal_description 'block' +_exptl_crystal_preparation 'Vapour deposition' +_diffrn_ambient_temperature 292.1 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0503 +_refine_ls_wR_factor_gt 0.0503 +_symmetry_cell_setting triclinic +_symmetry_space_group_name_H-M 'P -1' +_symmetry_Int_Tables_number 2 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,-y,-z +_cell_length_a 8.0844(2) +_cell_length_b 13.4505(4) +_cell_length_c 13.6459(4) +_cell_angle_alpha 119.409(3) +_cell_angle_beta 100.685(2) +_cell_angle_gamma 92.791(2) +_cell_formula_units_Z 2 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Cr 1.39 +N 0.68 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Cr1 Cr 0.02421(4) 0.29864(2) 0.74764(2) +O1 O 0.57681(14) 0.4020(1) 0.52972(10) +O2 O 0.10876(15) 0.44404(9) 0.72765(9) +O3 O 0.25946(16) 0.19114(10) 0.57811(10) +N1 N 0.41910(18) 0.27788(12) 0.35455(12) +O4 O 0.70562(16) 0.33852(13) 0.37922(12) +C1 C -0.0177(3) 0.1630(2) 0.7436(2) +C2 C 0.1832(2) 0.37340(13) 0.62994(13) +C3 C 0.4115(2) 0.37834(14) 0.53797(14) +O5 O -0.2952(2) 0.19369(13) 0.55170(14) +C4 C 0.1293(2) 0.26503(16) 0.40825(14) +H1 H 0.10800 0.18690 0.39400 +H2 H 0.06980 0.26600 0.34020 +C5 C 0.1714(2) 0.26605(13) 0.64073(13) +C6 C 0.0675(2) 0.34936(15) 0.51486(14) +H3 H -0.04710 0.31760 0.50810 +H4 H 0.06250 0.42190 0.51610 +C7 C 0.3706(2) 0.19335(15) 0.23322(14) +C8 C 0.3145(2) 0.30383(14) 0.43364(13) +C9 C 0.3634(2) 0.43474(14) 0.65085(13) +H5 H 0.35440 0.51490 0.67210 +C10 C 0.5795(2) 0.33730(15) 0.41419(15) +O6 O -0.1984(2) 0.40304(19) 0.91830(15) +O7 O 0.3243(3) 0.35283(17) 0.94702(14) +O8 O -0.0466(3) 0.07561(16) 0.73916(19) +C11 C 0.4952(2) 0.43850(17) 0.74927(15) +H6 H 0.51360 0.36130 0.72850 +H7 H 0.45410 0.47230 0.81860 +H8 H 0.60070 0.48430 0.76250 +C12 C -0.1744(3) 0.23835(15) 0.62493(17) +C13 C 0.2495(3) 0.00186(19) 0.0805(2) +H9 H 0.19890 -0.07240 0.05680 +C14 C 0.2991(3) 0.08286(17) 0.19719(18) +H10 H 0.28410 0.06250 0.25140 +C15 C 0.3947(3) 0.22239(17) 0.15286(17) +H11 H 0.44320 0.29720 0.17670 +C16 C 0.2733(3) 0.02869(19) -0.00209(17) +C17 C 0.0283(3) 0.53854(16) 0.72931(18) +H12 H -0.08560 0.50890 0.67880 +H13 H 0.09390 0.57720 0.70130 +C18 C -0.1134(3) 0.3682(2) 0.85445(17) +C19 C 0.2600(3) 0.08237(16) 0.5777(2) +H14 H 0.27180 0.02170 0.50300 +H15 H 0.15250 0.06080 0.58960 +C20 C 0.3464(3) 0.1398(2) 0.03647(18) +H16 H 0.36390 0.15990 -0.01740 +C21 C 0.2142(3) 0.33873(18) 0.87393(18) +C22 C 0.4024(4) 0.0937(2) 0.6704(2) +H17 H 0.50800 0.12090 0.66210 +H18 H 0.40710 0.01980 0.66430 +H19 H 0.38420 0.14790 0.74470 +C23 C 0.0189(4) 0.6212(2) 0.8488(2) +H20 H -0.04360 0.58200 0.87670 +H21 H -0.03790 0.68250 0.84970 +H22 H 0.13220 0.65280 0.89770 +C24 C 0.2193(4) -0.0592(2) -0.1301(2) +H23 H 0.30210 -0.04920 -0.16800 +H24 H 0.11020 -0.04860 -0.16240 +H25 H 0.21120 -0.13570 -0.14140 +#END diff --git a/cell2mol/test/error_2/LAPNIZ.search6.cif b/cell2mol/test/error_2/LAPNIZ.search6.cif new file mode 100755 index 00000000..55664418 --- /dev/null +++ b/cell2mol/test/error_2/LAPNIZ.search6.cif @@ -0,0 +1,144 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_LAPNIZ +_audit_creation_date 1994-02-10 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD LAPNIZ +_chemical_formula_sum 'C25 H28 Co1 N3 O2' +_chemical_formula_moiety +; +C25 H28 Co1 N3 O2 +; +_journal_coden_Cambridge 580 +_journal_volume 12 +_journal_year 1993 +_journal_page_first 1097 +_journal_name_full 'Polyhedron ' +loop_ +_publ_author_name +"Huilan Chen" +"Deyan Han" +"Hong Yan" +"Wenxia Tang" +"Yao Yang" +"Huaqin Wang" +_chemical_name_systematic +; +(N,N-Ethylene-bis(salicylideneiminato))-(iso-butyl)-pyridine-cobalt(iii) +; +_cell_volume 2288.478 +_exptl_crystal_colour 'purple' +_exptl_crystal_density_diffrn 1.339 +_diffrn_ambient_temperature ? +_diffrn_special_details +; +The study was carried out at room temperature,in the range 283-303K + +; +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.038 +_refine_ls_wR_factor_gt 0.038 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'P 21/c' +_symmetry_Int_Tables_number 14 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,1/2+y,1/2-z +3 -x,-y,-z +4 x,-1/2-y,-1/2+z +_cell_length_a 9.476(9) +_cell_length_b 19.621(9) +_cell_length_c 12.837(3) +_cell_angle_alpha 90 +_cell_angle_beta 106.5(4) +_cell_angle_gamma 90 +_cell_formula_units_Z 4 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Co 1.33 +N 0.68 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Co1 Co 0.58302(5) 0.18808(2) 0.90596(3) +O1 O 0.5417(3) 0.1493(1) 0.7642(2) +O2 O 0.7120(2) 0.2496(1) 0.8641(2) +N1 N 0.4579(3) 0.1273(2) 0.9505(2) +N2 N 0.6101(3) 0.2295(2) 1.0422(2) +N3 N 0.4049(3) 0.2601(2) 0.8380(2) +C1 C 0.3893(4) 0.0756(2) 0.8970(3) +C2 C 0.4612(6) 0.1368(3) 1.0645(3) +C3 C 0.7043(4) 0.2748(2) 1.0865(3) +C4 C 0.5026(5) 0.2072(3) 1.0964(3) +C5 C 0.7574(5) 0.1273(2) 0.9648(3) +C6 C 0.4629(4) 0.0956(2) 0.7289(3) +C7 C 0.3868(4) 0.0571(2) 0.7902(3) +C8 C 0.3050(4) -0.0005(2) 0.7426(3) +C9 C 0.2960(5) -0.0212(2) 0.6393(4) +C10 C 0.3680(5) 0.0168(2) 0.5787(3) +C11 C 0.4483(4) 0.0731(2) 0.6219(3) +C12 C 0.8091(4) 0.2882(2) 0.9301(3) +C13 C 0.8086(4) 0.3040(2) 1.0378(3) +C14 C 0.9136(5) 0.3498(2) 1.0997(3) +C15 C 1.0201(5) 0.3771(2) 1.0610(4) +C16 C 1.0234(5) 0.3606(2) 0.9569(4) +C17 C 0.9208(4) 0.3176(2) 0.8932(3) +C18 C 0.4283(4) 0.3270(2) 0.8534(3) +C19 C 0.3266(5) 0.3754(2) 0.8046(3) +C20 C 0.1946(5) 0.3552(2) 0.7354(3) +C21 C 0.1683(4) 0.2865(2) 0.7183(4) +C22 C 0.2754(4) 0.2411(2) 0.7714(3) +C23 C 0.8445(4) 0.1103(2) 0.8858(3) +C24 C 1.0043(5) 0.1267(3) 0.9301(4) +C25 C 0.8233(7) 0.0362(3) 0.8478(5) +H1 H 0.337(4) 0.049(2) 0.934(3) +H2 H 0.259(4) -0.028(2) 0.786(3) +H3 H 0.359(5) 0.002(2) 0.506(3) +H4 H 0.493(4) 0.096(2) 0.587(3) +H5 H 0.914(4) 0.358(2) 1.178(3) +H6 H 1.084(4) 0.405(2) 1.098(3) +H7 H 1.090(5) 0.378(2) 0.929(3) +H8 H 0.921(4) 0.309(2) 0.817(3) +H9 H 0.517(4) 0.340(2) 0.906(3) +H10 H 0.352(4) 0.419(2) 0.822(3) +H11 H 0.123(5) 0.386(2) 0.701(3) +H12 H 0.081(4) 0.271(2) 0.680(3) +H13 H 0.264(3) 0.195(2) 0.757(3) +H14 H 0.381(5) 0.113(2) 1.083(4) +H15 H 0.534(5) 0.220(2) 1.173(3) +H16 H 1.043(4) 0.098(2) 0.987(3) +H17 H 1.058(5) 0.111(3) 0.883(4) +H18 H 1.033(5) 0.171(2) 0.973(4) +H19 H 0.242(4) -0.063(2) 0.613(3) +H20 H 0.895(6) 0.027(3) 0.798(4) +H21 H 0.861(7) 0.008(4) 0.920(5) +H22 H 0.725(9) 0.019(4) 0.812(6) +H23 H 0.816(3) 0.139(2) 0.833(3) +H24 H 0.704(3) 0.289(2) 1.154(2) +H25 H 0.816(4) 0.146(2) 1.032(3) +H26 H 0.416(8) 0.240(4) 1.074(5) +H27 H 0.717(5) 0.091(2) 0.984(3) +H28 H 0.395(5) 0.166(2) 1.039(4) +#END diff --git a/cell2mol/test/error_2/LOJLEE.search2.cif b/cell2mol/test/error_2/LOJLEE.search2.cif new file mode 100755 index 00000000..e0253ef7 --- /dev/null +++ b/cell2mol/test/error_2/LOJLEE.search2.cif @@ -0,0 +1,152 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_LOJLEE +_audit_creation_date 2019-07-03 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD LOJLEE +_database_code_depnum_ccdc_archive 'CCDC 1856615' +_chemical_formula_sum 'C23 H29 K1 Mn1 N1 O12' +_chemical_formula_moiety +; +C23 H29 K1 Mn1 N1 O12 +; +_journal_coden_Cambridge 1441 +_journal_volume 11 +_journal_year 2019 +_journal_page_first 669 +_journal_name_full 'Nature Chemistry ' +loop_ +_publ_author_name +"Hui-Jie Pan" +"Gangfeng Huang" +"M.D.Wodrich" +"F.F.Tirani" +"K.Ataka" +"S.Shima" +"Xile Hu" +_chemical_name_systematic +; +(\m-2-[6-(oxido)pyridin-2-yl]ethyl)-tetrakis(carbonyl)-(18-crown-6)-potassium- +manganese(i) +; +_cell_volume 2752.209 +_exptl_crystal_colour 'intense orange' +_exptl_crystal_density_diffrn 1.461 +_exptl_crystal_description 'prism' +_diffrn_ambient_temperature 140.0 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0341 +_refine_ls_wR_factor_gt 0.0341 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'P 21/n' +_symmetry_Int_Tables_number 14 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 1/2-x,1/2+y,1/2-z +3 -x,-y,-z +4 -1/2+x,-1/2-y,-1/2+z +_cell_length_a 8.01318(10) +_cell_length_b 16.45977(19) +_cell_length_c 21.0415(2) +_cell_angle_alpha 90 +_cell_angle_beta 97.3915(11) +_cell_angle_gamma 90 +_cell_formula_units_Z 4 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +K 2.03 +Mn 1.61 +N 0.68 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Mn1 Mn 0.83122(3) 0.23348(2) 0.53977(2) +K1 K 0.72015(5) 0.44549(2) 0.71106(2) +O1 O 0.8709(2) 0.31297(9) 0.68112(7) +O2 O 0.7792(2) 0.07631(10) 0.47234(8) +O3 O 0.6999(2) 0.26263(10) 0.40452(7) +O4 O 0.8611(3) 0.41193(10) 0.56500(11) +O5 O 1.1785(2) 0.23727(16) 0.50642(9) +O6 O 0.4892(2) 0.20953(17) 0.57827(11) +O7 O 0.99648(17) 0.51501(8) 0.79069(7) +O8 O 0.86642(17) 0.59454(8) 0.67584(6) +O9 O 0.58750(18) 0.53342(8) 0.59704(6) +O10 O 0.35972(18) 0.44101(9) 0.65874(7) +O11 O 0.48208(18) 0.35522(9) 0.77030(7) +O12 O 0.77220(18) 0.41279(8) 0.84379(7) +N1 N 0.91067(17) 0.19164(9) 0.63133(6) +C1 C 0.9155(2) 0.23985(11) 0.68575(9) +C2 C 0.9742(2) 0.20228(13) 0.74611(8) +H1 H 0.98650 0.23447 0.78390 +C3 C 1.0122(2) 0.12252(13) 0.75052(9) +H2 H 1.04870 0.09892 0.79114 +C4 C 0.9977(2) 0.07462(12) 0.69525(9) +H3 H 1.02141 0.01807 0.69770 +C5 C 0.9484(2) 0.1114(1) 0.63734(8) +C6 C 0.9311(3) 0.06463(11) 0.57565(9) +H4 H 0.87148 0.01296 0.58133 +H5 H 1.04444 0.05120 0.56476 +C7 C 0.8346(2) 0.11235(11) 0.52049(9) +C8 C 0.7509(3) 0.25230(12) 0.45764(9) +C9 C 0.8466(3) 0.34288(14) 0.56029(12) +C10 C 1.0457(3) 0.23429(15) 0.52025(9) +C11 C 0.6175(3) 0.21971(15) 0.56204(11) +C12 C 1.0385(3) 0.59593(13) 0.77571(11) +H6 H 1.15158 0.60988 0.79769 +H7 H 0.95598 0.63427 0.79021 +C13 C 1.0364(3) 0.60205(14) 0.70465(11) +H8 H 1.08308 0.65502 0.69330 +H9 H 1.10580 0.55827 0.68922 +C14 C 0.8450(3) 0.60763(12) 0.60888(9) +H10 H 0.89822 0.56305 0.58711 +H11 H 0.89850 0.65948 0.59883 +C15 C 0.6594(3) 0.61042(12) 0.58631(9) +H12 H 0.60565 0.65301 0.60997 +H13 H 0.64020 0.62383 0.54008 +C16 C 0.4126(3) 0.53068(14) 0.57520(9) +H14 H 0.39303 0.53968 0.52831 +H15 H 0.35356 0.57393 0.59623 +C17 C 0.3461(3) 0.44914(15) 0.59106(10) +H16 H 0.22697 0.44392 0.57214 +H17 H 0.41156 0.40575 0.57302 +C18 C 0.2843(3) 0.36726(15) 0.67687(12) +H18 H 0.33788 0.32013 0.65838 +H19 H 0.16290 0.36694 0.66041 +C19 C 0.3077(3) 0.36146(14) 0.74859(12) +H20 H 0.26060 0.41031 0.76719 +H21 H 0.24796 0.31313 0.76227 +C20 C 0.5149(3) 0.34146(13) 0.83708(11) +H22 H 0.46065 0.29036 0.84839 +H23 H 0.46926 0.38667 0.86063 +C21 C 0.7021(3) 0.33583(14) 0.85488(13) +H24 H 0.72907 0.32065 0.90057 +H25 H 0.74921 0.29386 0.82855 +C22 C 0.9454(3) 0.41842(16) 0.86926(12) +H26 H 1.01252 0.37892 0.84773 +H27 H 0.96016 0.40632 0.91574 +C23 C 1.0021(3) 0.50304(15) 0.85793(10) +H28 H 0.92746 0.54261 0.87575 +H29 H 1.11821 0.51128 0.87940 +#END diff --git a/cell2mol/test/error_2/MAKBUW.search5.cif b/cell2mol/test/error_2/MAKBUW.search5.cif new file mode 100755 index 00000000..5e71d6f0 --- /dev/null +++ b/cell2mol/test/error_2/MAKBUW.search5.cif @@ -0,0 +1,195 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_MAKBUW +_audit_creation_date 2005-08-04 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD MAKBUW +_database_code_depnum_ccdc_archive 'CCDC 268752' +_chemical_formula_sum 'C40 H60 Fe1 N4 Na1 O6' +_chemical_formula_moiety +; +C28 H32 Fe1 N4 1-,C12 H28 Na1 O6 1+ +; +_journal_coden_Cambridge 4 +_journal_volume 127 +_journal_year 2005 +_journal_page_first 4730 +_journal_name_full 'J.Am.Chem.Soc. ' +loop_ +_publ_author_name +"J.Bachmann" +"D.G.Nocera" +_chemical_name_systematic +; +bis(diglyme)-sodium +(5,5,10,10,15,15,20,20-octaethylporphyrinogenato)-iron(iii) +; +_cell_volume 8277.151 +_exptl_crystal_colour 'red-black' +_exptl_crystal_density_diffrn 1.239 +_diffrn_ambient_temperature 183 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0962 +_refine_ls_wR_factor_gt 0.0962 +_symmetry_cell_setting orthorhombic +_symmetry_space_group_name_H-M 'P b c a' +_symmetry_Int_Tables_number 61 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 1/2-x,-y,1/2+z +3 -x,1/2+y,1/2-z +4 1/2+x,1/2-y,-z +5 -x,-y,-z +6 -1/2+x,y,-1/2-z +7 x,-1/2-y,-1/2+z +8 -1/2-x,-1/2+y,z +_cell_length_a 15.5367(12) +_cell_length_b 21.1012(16) +_cell_length_c 25.2473(19) +_cell_angle_alpha 90 +_cell_angle_beta 90 +_cell_angle_gamma 90 +_cell_formula_units_Z 8 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Fe 1.34 +N 0.68 +Na 1.37 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Fe1 Fe 0.84576(5) 0.09334(4) 0.61673(3) +Na1 Na 0.96543(16) 0.23499(12) 0.3408(1) +O1 O 0.9724(4) 0.2179(2) 0.2486(2) +O2 O 1.0702(3) 0.3041(2) 0.2985(2) +O3 O 0.9584(3) 0.3366(2) 0.3778(2) +O4 O 0.8181(3) 0.1974(3) 0.3278(2) +O5 O 0.9043(3) 0.1913(3) 0.4197(2) +O6 O 1.0652(3) 0.1651(2) 0.37867(19) +N1 N 0.9237(3) 0.1575(2) 0.59480(19) +N2 N 0.9308(3) 0.0613(2) 0.66396(19) +N3 N 0.7675(3) 0.0303(2) 0.64002(19) +N4 N 0.7614(3) 0.1255(2) 0.56951(18) +C1 C 0.9022(4) 0.2183(3) 0.5802(2) +C2 C 0.9762(4) 0.2516(3) 0.5707(3) +H1 H 0.98000 0.29460 0.55980 +C3 C 1.0464(4) 0.2097(3) 0.5802(3) +H2 H 1.10570 0.21990 0.57730 +C4 C 1.0131(4) 0.1523(3) 0.5941(2) +C5 C 1.0549(4) 0.0892(3) 0.6047(2) +C6 C 1.0406(4) 0.0445(3) 0.5573(2) +H3 H 0.97870 0.03720 0.55260 +H4 H 1.06950 0.00400 0.56400 +H5 H 1.06430 0.06380 0.52520 +C7 C 1.1530(4) 0.0986(3) 0.6113(3) +H6 H 1.17690 0.11650 0.57860 +H7 H 1.18030 0.05770 0.61860 +H8 H 1.16390 0.12770 0.64080 +C8 C 1.0182(3) 0.0602(3) 0.6543(2) +C9 C 1.0572(4) 0.0271(3) 0.6948(2) +H9 H 1.11680 0.01780 0.69790 +C10 C 0.9917(4) 0.0096(3) 0.7310(2) +H10 H 0.99980 -0.01260 0.76340 +C11 C 0.9153(4) 0.0301(3) 0.7114(2) +C12 C 0.8245(4) 0.0267(3) 0.7329(2) +C13 C 0.8224(4) -0.0194(3) 0.7804(3) +H11 H 0.76430 -0.02040 0.79550 +H12 H 0.86330 -0.00510 0.80750 +H13 H 0.83840 -0.06200 0.76850 +C14 C 0.7969(4) 0.0934(3) 0.7531(3) +H14 H 0.79990 0.12390 0.72390 +H15 H 0.83560 0.10690 0.78160 +H16 H 0.73780 0.09140 0.76660 +C15 C 0.7633(4) 0.0045(3) 0.6903(2) +C16 C 0.6959(4) -0.0367(3) 0.6926(3) +H17 H 0.67810 -0.06040 0.72260 +C17 C 0.6571(4) -0.0376(3) 0.6415(3) +H18 H 0.60880 -0.06230 0.63110 +C18 C 0.7019(3) 0.0036(3) 0.6101(2) +C19 C 0.6929(4) 0.0197(3) 0.5519(2) +C20 C 0.7669(4) -0.0110(3) 0.5204(3) +H19 H 0.76210 0.00080 0.48300 +H20 H 0.76350 -0.05720 0.52380 +H21 H 0.82220 0.00380 0.53430 +C21 C 0.6079(4) -0.0060(3) 0.5302(3) +H22 H 0.55980 0.01230 0.55020 +H23 H 0.60690 -0.05220 0.53380 +H24 H 0.60240 0.00550 0.49280 +C22 C 0.6968(4) 0.0902(3) 0.5448(2) +C23 C 0.6481(4) 0.1314(3) 0.5154(3) +H25 H 0.59920 0.12070 0.49470 +C24 C 0.6838(5) 0.1930(3) 0.5214(3) +H26 H 0.66320 0.23070 0.50520 +C25 C 0.7532(4) 0.1884(3) 0.5547(2) +C26 C 0.8089(4) 0.2389(3) 0.5794(3) +C27 C 0.7991(5) 0.3011(3) 0.5483(3) +H27 H 0.81400 0.29400 0.51110 +H28 H 0.83750 0.33330 0.56330 +H29 H 0.73940 0.31590 0.55080 +C28 C 0.7782(4) 0.2510(3) 0.6368(3) +H30 H 0.71670 0.26160 0.63660 +H31 H 0.81080 0.28640 0.65210 +H32 H 0.78750 0.21280 0.65810 +C29 C 0.9648(6) 0.1577(4) 0.2244(4) +H33 H 0.94120 0.16270 0.18870 +H34 H 0.92640 0.13090 0.24550 +H35 H 1.02170 0.13790 0.22220 +C30 C 1.0186(5) 0.2616(3) 0.2188(3) +H36 H 0.98590 0.27250 0.18640 +H37 H 1.07430 0.24290 0.20790 +C31 C 1.0341(6) 0.3193(4) 0.2503(3) +H38 H 1.07320 0.34790 0.23070 +H39 H 0.97900 0.34180 0.25600 +C32 C 1.0875(5) 0.3565(4) 0.3323(3) +H40 H 1.11830 0.38930 0.31160 +H41 H 1.12640 0.34240 0.36100 +C33 C 1.0100(5) 0.3859(3) 0.3566(3) +H42 H 1.02730 0.41560 0.38500 +H43 H 0.97730 0.40990 0.32960 +C34 C 0.8928(7) 0.3602(5) 0.4104(4) +H44 H 0.91830 0.38100 0.44130 +H45 H 0.85600 0.32520 0.42210 +H46 H 0.85820 0.39090 0.39050 +C35 C 0.7634(6) 0.2256(6) 0.2890(5) +H47 H 0.71780 0.19570 0.27930 +H48 H 0.79730 0.23620 0.25740 +H49 H 0.73760 0.26430 0.30350 +C36 C 0.7731(5) 0.1813(5) 0.3747(4) +H50 H 0.72450 0.15270 0.36630 +H51 H 0.74970 0.22000 0.39160 +C37 C 0.8345(6) 0.1494(4) 0.4108(4) +H52 H 0.80590 0.13910 0.44480 +H53 H 0.85540 0.10950 0.39470 +C38 C 0.9713(7) 0.1667(6) 0.4509(4) +H54 H 0.99440 0.20160 0.47300 +H55 H 0.94630 0.13490 0.47540 +C39 C 1.0369(7) 0.1397(9) 0.4259(5) +H56 H 1.02120 0.09500 0.41910 +H57 H 1.08650 0.13930 0.45050 +C40 C 1.1145(6) 0.1205(4) 0.3494(5) +H58 H 1.16790 0.11110 0.36860 +H59 H 1.12850 0.13830 0.31460 +H60 H 1.08130 0.08140 0.34490 +#END diff --git a/cell2mol/test/error_2/QEHBEM.search5.cif b/cell2mol/test/error_2/QEHBEM.search5.cif new file mode 100755 index 00000000..fc7d0329 --- /dev/null +++ b/cell2mol/test/error_2/QEHBEM.search5.cif @@ -0,0 +1,197 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_QEHBEM +_audit_creation_date 2012-12-31 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD QEHBEM +_database_code_depnum_ccdc_archive 'CCDC 864465' +_chemical_formula_sum 'C28 H68 Fe1 N3 Na1 O1 Si6' +_chemical_formula_moiety +; +C22 H62 Fe1 N3 Na1 O1 Si6,C6 H6 +; +_journal_coden_Cambridge 644 +_journal_volume 67 +_journal_year 2012 +_journal_page_first 549 +_journal_name_full 'Z.Naturforsch.,B:Chem.Sci. ' +loop_ +_publ_author_name +"G.Margraf" +"M.Bolte" +"F.Schodel" +"I.Sanger" +"M.Wagner" +"H.-W.Lerner" +_chemical_name_systematic +; +bis(\m~2~-1,1,1-trimethyl-N-(trimethylsilyl)silanamido)-(1,1,1-trimethyl-N-(tr +imethylsilyl)silanamido)-tetrahydrofuran-sodium(i)-iron(ii) benzene solvate +; +_cell_volume 4232.137 +_exptl_crystal_colour 'colorless' +_exptl_crystal_density_diffrn 1.115 +_exptl_crystal_description 'block' +_diffrn_ambient_temperature 173 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0267 +_refine_ls_wR_factor_gt 0.0267 +_symmetry_cell_setting orthorhombic +_symmetry_space_group_name_H-M 'C 2 2 21' +_symmetry_Int_Tables_number 20 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,-y,1/2+z +3 -x,y,1/2-z +4 x,-y,-z +5 1/2+x,1/2+y,z +6 1/2-x,1/2-y,1/2+z +7 1/2-x,1/2+y,1/2-z +8 1/2+x,1/2-y,-z +_cell_length_a 11.8572(4) +_cell_length_b 21.2443(9) +_cell_length_c 16.8010(8) +_cell_angle_alpha 90 +_cell_angle_beta 90 +_cell_angle_gamma 90 +_cell_formula_units_Z 4 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Fe 1.52 +N 0.68 +Na 1.66 +O 0.68 +Si 1.20 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Fe1 Fe 0.50000 0.652672(10) 0.25000 +N1 N 0.50000 0.56042(6) 0.25000 +N2 N 0.61957(9) 0.70806(5) 0.30250(6) +Si1 Si 0.50101(4) 0.520025(15) 0.338753(19) +Si2 Si 0.60996(3) 0.73408(2) 0.40011(2) +Si3 Si 0.75515(3) 0.704125(18) 0.26317(2) +C1 C 0.44660(17) 0.56810(8) 0.42359(9) +H1 H 0.37020 0.58280 0.41140 +H2 H 0.49610 0.60450 0.43200 +H3 H 0.44500 0.54230 0.47190 +C2 C 0.64644(17) 0.49451(12) 0.36676(11) +H4 H 0.67850 0.46890 0.32380 +H5 H 0.64330 0.46960 0.41580 +H6 H 0.69390 0.53170 0.37540 +C3 C 0.4089(2) 0.44798(10) 0.33829(12) +H7 H 0.33170 0.45990 0.32390 +H8 H 0.40900 0.42880 0.39140 +H9 H 0.43790 0.41770 0.29940 +C4 C 0.69197(16) 0.80863(9) 0.41817(11) +H10 H 0.77200 0.80120 0.40710 +H11 H 0.68290 0.82160 0.47380 +H12 H 0.66370 0.84190 0.38300 +C5 C 0.45995(14) 0.75626(9) 0.42402(11) +H13 H 0.43220 0.78620 0.38420 +H14 H 0.45690 0.77570 0.47690 +H15 H 0.41260 0.71840 0.42340 +C6 C 0.65909(17) 0.6768(1) 0.47821(10) +H16 H 0.73710 0.66430 0.46700 +H17 H 0.61050 0.63950 0.47760 +H18 H 0.65530 0.69680 0.53070 +C7 C 0.80283(16) 0.78127(9) 0.22028(12) +H19 H 0.79710 0.81410 0.26100 +H20 H 0.75490 0.79230 0.17480 +H21 H 0.88130 0.77760 0.20260 +C8 C 0.76802(13) 0.64448(9) 0.18191(10) +H22 H 0.74370 0.60330 0.20170 +H23 H 0.84680 0.64200 0.16450 +H24 H 0.72040 0.65690 0.13690 +C9 C 0.86526(13) 0.68020(9) 0.33786(11) +H25 H 0.86380 0.70940 0.38300 +H26 H 0.93990 0.68130 0.31280 +H27 H 0.84950 0.63740 0.35670 +Na1 Na 0.50000 0.79215(4) 0.25000 +O1 O 0.50000 0.89417(9) 0.25000 +C10 C 0.4078(2) 0.93139(14) 0.2765(2) +H28 H 0.39580 0.92590 0.33440 +H29 H 0.33770 0.91960 0.24820 +C11 C 0.4401(3) 0.99794(15) 0.2578(4) +H30 H 0.39820 1.01320 0.21060 +H31 H 0.42260 1.02580 0.30340 +C12 C 0.91053(16) 0.53204(10) 0.49367(15) +H32 H 0.84120 0.55430 0.48990 +C13 C 1.01134(19) 0.56374(9) 0.48654(15) +H33 H 1.01140 0.60770 0.47640 +C14 C 1.11186(17) 0.53203(11) 0.49406(17) +H34 H 1.18120 0.55430 0.49110 +N2B N 0.38043(9) 0.70806(5) 0.19750(6) +Si1B Si 0.49899(4) 0.520025(15) 0.161247(19) +Si2B Si 0.39004(3) 0.73408(2) 0.09989(2) +Si3B Si 0.24485(3) 0.704125(18) 0.23683(2) +C1B C 0.55340(17) 0.56810(8) 0.07641(9) +H1B H 0.62980 0.58280 0.08860 +H2B H 0.50390 0.60450 0.06800 +H3B H 0.55500 0.54230 0.02810 +C2B C 0.35356(17) 0.49451(12) 0.13324(11) +H4B H 0.32150 0.46890 0.17620 +H5B H 0.35670 0.46960 0.08420 +H6B H 0.30610 0.53170 0.12460 +C3B C 0.5911(2) 0.44798(10) 0.16171(12) +H7B H 0.66830 0.45990 0.17610 +H8B H 0.59100 0.42880 0.10860 +H9B H 0.56210 0.41770 0.20060 +C4B C 0.30803(16) 0.80863(9) 0.08183(11) +H10B H 0.22800 0.80120 0.09290 +H11B H 0.31710 0.82160 0.02620 +H12B H 0.33630 0.84190 0.11700 +C5B C 0.54005(14) 0.75626(9) 0.07598(11) +H13B H 0.56780 0.78620 0.11580 +H14B H 0.54310 0.77570 0.02310 +H15B H 0.58740 0.71840 0.07660 +C6B C 0.34091(17) 0.6768(1) 0.02179(10) +H16B H 0.26290 0.66430 0.03300 +H17B H 0.38950 0.63950 0.02240 +H18B H 0.34470 0.69680 -0.03070 +C7B C 0.19717(16) 0.78127(9) 0.27972(12) +H19B H 0.20290 0.81410 0.23900 +H20B H 0.24510 0.79230 0.32520 +H21B H 0.11870 0.77760 0.29740 +C8B C 0.23198(13) 0.64448(9) 0.31809(10) +H22B H 0.25630 0.60330 0.29830 +H23B H 0.15320 0.64200 0.33550 +H24B H 0.27960 0.65690 0.36310 +C9B C 0.13474(13) 0.68020(9) 0.16214(11) +H25B H 0.13620 0.70940 0.11700 +H26B H 0.06010 0.68130 0.18720 +H27B H 0.15050 0.63740 0.14330 +C10B C 0.5922(2) 0.93139(14) 0.2235(2) +H28B H 0.60420 0.92590 0.16560 +H29B H 0.66230 0.91960 0.25180 +C11B C 0.5599(3) 0.99794(15) 0.2422(4) +H30B H 0.60180 1.01320 0.28940 +H31B H 0.57740 1.02580 0.19660 +C12C C 0.91053(16) 0.46796(10) 0.50633(15) +H32C H 0.84120 0.44570 0.51010 +C13C C 1.01134(19) 0.43626(9) 0.51346(15) +H33C H 1.01140 0.39230 0.52360 +C14C C 1.11186(17) 0.46797(11) 0.50594(17) +H34C H 1.18120 0.44570 0.50890 +#END diff --git a/cell2mol/test/error_2/QUSHET.search5.cif b/cell2mol/test/error_2/QUSHET.search5.cif new file mode 100755 index 00000000..5b5aac72 --- /dev/null +++ b/cell2mol/test/error_2/QUSHET.search5.cif @@ -0,0 +1,177 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_QUSHET +_audit_creation_date 2015-11-02 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD QUSHET +_database_code_depnum_ccdc_archive 'CCDC 1412551' +_chemical_formula_sum 'C18 H54 Fe1 N3 Na1 Si6' +_chemical_formula_moiety +; +C18 H54 Fe1 N3 Na1 Si6 +; +_journal_coden_Cambridge 9 +_journal_volume 54 +_journal_year 2015 +_journal_page_first 9201 +_journal_name_full 'Inorg.Chem. ' +loop_ +_publ_author_name +"L.C.H.Maddock" +"T.Cadenbach" +"A.R.Kennedy" +"I.Borilovic" +"G.Aromi" +"Eva Hevia" +_chemical_name_systematic +; +bis(\m~2~-bis(Trimethylsilyl)amide)-(bis(trimethylsilyl)amide)-iron(ii)-sodium +(i) +; +_cell_volume 3304.885 +_exptl_crystal_colour 'green' +_exptl_crystal_density_diffrn 1.126 +_exptl_special_details +; +Air-sensitive, Moisture-sensitive, Oxygen-sensitive + +; +_exptl_crystal_description 'plate' +_diffrn_ambient_temperature 123 +_diffrn_special_details +; +twin + +; +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0456 +_refine_ls_wR_factor_gt 0.0456 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'P 21/c' +_symmetry_Int_Tables_number 14 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,1/2+y,1/2-z +3 -x,-y,-z +4 x,-1/2-y,-1/2+z +_cell_length_a 8.7352(4) +_cell_length_b 19.0049(7) +_cell_length_c 20.6750(9) +_cell_angle_alpha 90 +_cell_angle_beta 105.660(4) +_cell_angle_gamma 90 +_cell_formula_units_Z 4 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Fe 1.52 +N 0.68 +Na 1.66 +Si 1.20 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Fe1 Fe 0.74519(5) 0.25546(2) 0.43002(2) +C1 C 1.0091(4) 0.18922(17) 0.33583(17) +H1 H 1.10860 0.16760 0.33270 +H2 H 1.03270 0.22690 0.36940 +H3 H 0.95250 0.20890 0.29210 +Si1 Si 0.88121(11) 0.12077(4) 0.36137(4) +N1 N 0.7240(3) 0.16054(12) 0.38273(12) +Si2 Si 0.57280(11) 0.10645(4) 0.39407(5) +C2 C 0.8122(5) 0.06232(18) 0.28516(18) +H4 H 0.90480 0.04450 0.27200 +H5 H 0.74440 0.08940 0.24800 +H6 H 0.75180 0.02260 0.29590 +N2 N 0.6947(3) 0.33487(12) 0.36173(12) +Si3 Si 0.84795(11) 0.39356(5) 0.36364(5) +C3 C 1.0162(4) 0.06395(18) 0.42695(19) +H7 H 1.11860 0.05870 0.41660 +H8 H 0.96750 0.01760 0.42730 +H9 H 1.03290 0.08600 0.47120 +N3 N 0.8116(3) 0.26887(12) 0.52783(11) +Si4 Si 0.50299(11) 0.36173(4) 0.32239(4) +C4 C 0.6395(5) 0.01639(15) 0.4277(2) +H10 H 0.72050 0.02080 0.47070 +H11 H 0.68430 -0.00870 0.39560 +H12 H 0.54830 -0.00990 0.43420 +Na1 Na 0.66727(18) 0.23625(7) 0.27998(6) +C5 C 1.1383(4) 0.2168(2) 0.53083(18) +H13 H 1.09110 0.20150 0.48450 +H14 H 1.21580 0.18170 0.55430 +H15 H 1.19170 0.26210 0.53070 +C6 C 0.9269(5) 0.1372(2) 0.5997(2) +H16 H 0.88900 0.10800 0.55950 +H17 H 0.84320 0.14120 0.62290 +H18 H 1.02140 0.11550 0.62980 +C7 C 1.0868(5) 0.2747(2) 0.65333(19) +H19 H 1.12500 0.32000 0.64120 +H20 H 1.17750 0.24640 0.67820 +H21 H 1.01440 0.28270 0.68150 +C8 C 0.6692(5) 0.2841(2) 0.64422(17) +H22 H 0.77220 0.27700 0.67710 +H23 H 0.61380 0.23890 0.63420 +H24 H 0.60480 0.31670 0.66260 +C9 C 0.7943(6) 0.41065(19) 0.5861(3) +H25 H 0.81280 0.43240 0.54590 +H26 H 0.89580 0.40540 0.62040 +H27 H 0.72300 0.44050 0.60350 +Si5 Si 0.97856(11) 0.22648(5) 0.57483(5) +C10 C 0.4174(4) 0.08983(18) 0.31228(19) +H28 H 0.36770 0.13450 0.29420 +H29 H 0.33610 0.05790 0.32010 +H30 H 0.46760 0.06850 0.28000 +C11 C 0.4949(4) 0.3366(2) 0.51071(19) +H31 H 0.49790 0.35110 0.46560 +H32 H 0.44350 0.37340 0.53070 +H33 H 0.43440 0.29270 0.50780 +Si6 Si 0.70179(11) 0.32243(4) 0.56418(4) +C12 C 0.4735(4) 0.14472(18) 0.45577(19) +H34 H 0.55400 0.15650 0.49750 +H35 H 0.39890 0.11030 0.46530 +H36 H 0.41550 0.18740 0.43680 +C13 C 1.0303(4) 0.37525(19) 0.43327(19) +H37 H 1.00270 0.37440 0.47620 +H38 H 1.10930 0.41220 0.43440 +H39 H 1.07450 0.32950 0.42570 +C14 C 0.9106(5) 0.3889(3) 0.2830(2) +H40 H 0.94020 0.34040 0.27560 +H41 H 1.00200 0.41990 0.28630 +H42 H 0.82220 0.40390 0.24530 +C15 C 0.7952(5) 0.48767(19) 0.3722(3) +H43 H 0.76430 0.49390 0.41410 +H44 H 0.70630 0.50100 0.33400 +H45 H 0.88720 0.51750 0.37310 +C16 C 0.3570(4) 0.29062(19) 0.32500(19) +H46 H 0.25100 0.30470 0.29810 +H47 H 0.35490 0.28270 0.37160 +H48 H 0.38860 0.24710 0.30660 +C17 C 0.4758(5) 0.3797(2) 0.23055(17) +H49 H 0.36430 0.39130 0.20930 +H50 H 0.50570 0.33780 0.20910 +H51 H 0.54340 0.41930 0.22540 +C18 C 0.4275(5) 0.44251(19) 0.3554(2) +H52 H 0.31210 0.43890 0.34790 +H53 H 0.45210 0.48400 0.33190 +H54 H 0.47860 0.44700 0.40360 +#END diff --git a/cell2mol/test/error_2/RUKLAK.search3.cif b/cell2mol/test/error_2/RUKLAK.search3.cif new file mode 100755 index 00000000..7972363b --- /dev/null +++ b/cell2mol/test/error_2/RUKLAK.search3.cif @@ -0,0 +1,134 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_RUKLAK +_audit_creation_date 1998-03-04 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD RUKLAK +_database_code_depnum_ccdc_journal 186/371 +_chemical_formula_sum 'C9 H15 Cr1 N3 O3' +_chemical_formula_moiety +; +C9 H15 Cr1 N3 O3 +; +_journal_coden_Cambridge 186 +_journal_year 1997 +_journal_page_first 1363 +_journal_name_full 'J.Chem.Soc.,Dalton Trans. ' +loop_ +_publ_author_name +"M.L.Armanasco" +"M.V.Baker" +"M.R.North" +"B.W.Skelton" +"A.H.White" +_chemical_name_systematic +; +Tricarbonyl-1,3,5-trimethyl-1,3,5-triaza-cyclohexane-chromium(0) +; +_chemical_melting_point 513.15 +_cell_volume 2587.563 +_exptl_crystal_colour 'orange' +_exptl_crystal_density_diffrn 1.362 +_exptl_special_details +; +Melts above 513.15K +Air-sensitive + +; +_diffrn_ambient_temperature ? +_diffrn_special_details +; +The study was carried out at room temperature,in the range 283-303K + +; +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.042 +_refine_ls_wR_factor_gt 0.042 +_symmetry_cell_setting orthorhombic +_symmetry_space_group_name_H-M 'I b a m' +_symmetry_Int_Tables_number 72 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,-y,z +3 x,-y,1/2-z +4 -x,y,1/2-z +5 1/2+x,1/2+y,1/2+z +6 1/2-x,1/2-y,1/2+z +7 1/2+x,1/2-y,-z +8 1/2-x,1/2+y,-z +9 -x,-y,-z +10 x,y,-z +11 -x,y,-1/2+z +12 x,-y,-1/2+z +13 -1/2-x,-1/2-y,-1/2-z +14 -1/2+x,-1/2+y,-1/2-z +15 -1/2-x,-1/2+y,z +16 -1/2+x,-1/2-y,z +_cell_length_a 14.537(5) +_cell_length_b 13.953(3) +_cell_length_c 12.757(3) +_cell_angle_alpha 90 +_cell_angle_beta 90 +_cell_angle_gamma 90 +_cell_formula_units_Z 8 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Cr 1.35 +N 0.68 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Cr1 Cr 0.49191(3) 0.24897(4) 0.50000 +N1 N 0.3695(2) 0.1622(2) 0.50000 +C1 C 0.3600(4) 0.0568(3) 0.50000 +C2 C 0.3330(2) 0.2085(2) 0.5936(2) +N2 N 0.3752(1) 0.3035(1) 0.5893(2) +C3 C 0.3670(3) 0.3572(3) 0.6878(3) +C4 C 0.3388(3) 0.3563(3) 0.50000 +C5 C 0.5710(3) 0.3474(3) 0.50000 +O1 O 0.6256(2) 0.4097(2) 0.50000 +C6 C 0.5634(2) 0.1892(2) 0.5953(2) +O2 O 0.6147(2) 0.1501(2) 0.6513(2) +H1 H 0.300(3) 0.043(3) 0.50000 +H2 H 0.392(2) 0.028(2) 0.558(2) +H3 H 0.356(1) 0.174(1) 0.652(2) +H4 H 0.264(2) 0.208(2) 0.594(2) +H5 H 0.395(2) 0.323(2) 0.744(2) +H6 H 0.291(3) 0.374(2) 0.705(3) +H7 H 0.398(2) 0.419(2) 0.682(3) +H8 H 0.363(2) 0.417(2) 0.50000 +H9 H 0.269(2) 0.355(3) 0.50000 +H2I H 0.392(2) 0.028(2) 0.442(2) +N2I N 0.3752(1) 0.3035(1) 0.4107(2) +C2I C 0.3330(2) 0.2085(2) 0.4064(2) +H3I H 0.356(1) 0.174(1) 0.348(2) +H4I H 0.264(2) 0.208(2) 0.406(2) +C3I C 0.3670(3) 0.3572(3) 0.3122(3) +H5I H 0.395(2) 0.323(2) 0.256(2) +H6I H 0.291(3) 0.374(2) 0.295(3) +H7I H 0.398(2) 0.419(2) 0.318(3) +C6I C 0.5634(2) 0.1892(2) 0.4047(2) +O2I O 0.6147(2) 0.1501(2) 0.3487(2) +#END diff --git a/cell2mol/test/error_2/SEZVEY.search3.cif b/cell2mol/test/error_2/SEZVEY.search3.cif new file mode 100755 index 00000000..1d88da31 --- /dev/null +++ b/cell2mol/test/error_2/SEZVEY.search3.cif @@ -0,0 +1,184 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_SEZVEY +_audit_creation_date 1991-02-22 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD SEZVEY +_chemical_formula_sum 'C36 H58 Cr1 N4 Na2 O3' +_chemical_formula_moiety +; +C24 H32 Cr1 N4 2-,C4 H8 Na1 O1 1+,C8 H18 Na1 O2 1+ +; +_journal_coden_Cambridge 9 +_journal_volume 29 +_journal_year 1990 +_journal_page_first 2147 +_journal_name_full 'Inorg.Chem. ' +loop_ +_publ_author_name +"J.J.H.Edema" +"S.Gambarotta" +"A.Meetsma" +"F.van Bolhuis" +"A.L.Spek" +"W.J.J.Smeets" +_chemical_name_systematic +; +Tetrahydrofuran-sodium tetrahydrofuran-diethyl ether-sodium +tetrakis(2,5-dimethylpyrrolyl)-chromium(ii) +; +_cell_volume 1867.373 +_exptl_crystal_colour 'orange-red' +_exptl_crystal_density_diffrn 1.232 +_diffrn_ambient_temperature 130 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.042 +_refine_ls_wR_factor_gt 0.042 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'P 21' +_symmetry_Int_Tables_number 4 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,1/2+y,-z +_cell_length_a 9.045(1) +_cell_length_b 15.285(1) +_cell_length_c 13.621(1) +_cell_angle_alpha 90 +_cell_angle_beta 97.42(1) +_cell_angle_gamma 90 +_cell_formula_units_Z 2 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Cr 1.35 +N 0.68 +Na 1.31 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Cr1 Cr 0.10015(9) 0.52680 0.19312(6) +N1 N 0.1382(5) 0.4003(3) 0.1474(3) +N2 N 0.1152(5) 0.6608(3) 0.2203(3) +N3 N 0.2007(5) 0.4980(3) 0.3360(3) +N4 N -0.0576(4) 0.5444(3) 0.0689(3) +C1 C 0.2772(6) 0.3671(4) 0.1329(4) +C2 C 0.2617(7) 0.2823(4) 0.1023(4) +C3 C 0.1092(6) 0.2610(4) 0.0968(4) +C4 C 0.0367(6) 0.3344(4) 0.1221(3) +C5 C -0.1263(6) 0.3479(4) 0.1203(4) +C6 C 0.4147(6) 0.4214(4) 0.1486(5) +C7 C 0.1727(7) 0.7167(4) 0.1553(4) +C8 C 0.1519(7) 0.8030(4) 0.1840(5) +C9 C 0.0842(7) 0.7995(4) 0.2702(5) +C10 C 0.0647(7) 0.7122(4) 0.2918(4) +C11 C 0.0104(8) 0.6731(5) 0.3817(5) +C12 C 0.2507(7) 0.6846(4) 0.0734(5) +C13 C 0.3346(5) 0.5264(5) 0.3843(3) +C14 C 0.3582(7) 0.4927(4) 0.4778(4) +C15 C 0.2349(7) 0.4386(4) 0.4885(4) +C16 C 0.1406(6) 0.4454(3) 0.4027(4) +C17 C -0.0113(7) 0.4046(4) 0.3787(5) +C18 C 0.4290(6) 0.5921(4) 0.3395(4) +C19 C -0.0543(5) 0.5249(4) -0.0291(3) +C20 C -0.1939(5) 0.5340(4) -0.0813(4) +C21 C -0.2905(6) 0.5585(3) -0.0133(4) +C22 C -0.2048(6) 0.5653(3) 0.0774(4) +C23 C -0.2580(6) 0.5780(4) 0.1754(4) +C24 C 0.0841(6) 0.4959(4) -0.0693(4) +Na1 Na 0.1179(3) 0.2397(2) -0.0981(2) +O1 O 0.3206(5) 0.2982(3) -0.1626(3) +C25 C 0.4185(7) 0.3639(6) -0.1165(5) +C26 C 0.4491(9) 0.4203(6) -0.2033(7) +C27 C 0.4626(9) 0.3546(6) -0.2839(6) +C28 C 0.3425(9) 0.2935(5) -0.2676(5) +Na2 Na 0.2850(3) 0.3181(2) 0.3187(2) +O2 O 0.1840(4) 0.1897(3) 0.3694(3) +O3 O 0.5152(5) 0.2413(4) 0.3336(3) +C29 C 0.0482(7) 0.1481(5) 0.3267(6) +C30 C 0.0455(8) 0.0578(4) 0.3653(5) +C31 C 0.2040(8) 0.0414(5) 0.4071(6) +C32 C 0.2603(8) 0.1310(5) 0.4400(5) +C33 C 0.6484(9) 0.2437(6) 0.4014(6) +C34 C 0.623(1) 0.3076(6) 0.4757(7) +C35 C 0.5208(9) 0.1606(6) 0.2707(5) +C36 C 0.6237(8) 0.1650(6) 0.1979(6) +H1 H 0.3417(7) 0.2433(4) 0.0870(4) +H2 H 0.0638(6) 0.2041(4) 0.0786(4) +H3 H -0.1689(6) 0.2890(4) 0.1217(4) +H4 H -0.1684(6) 0.3777(4) 0.0593(4) +H5 H -0.1502(6) 0.3812(4) 0.1777(4) +H6 H 0.4946(6) 0.3884(4) 0.1232(5) +H7 H 0.4427(6) 0.4333(4) 0.2192(5) +H8 H 0.3992(6) 0.4769(4) 0.1126(5) +H9 H 0.1796(7) 0.8558(4) 0.1500(5) +H10 H 0.0558(7) 0.8497(4) 0.3085(5) +H11 H -0.0293(8) 0.7187(5) 0.4217(5) +H12 H 0.0989(8) 0.6468(5) 0.4194(5) +H13 H -0.0656(8) 0.6280(5) 0.3649(5) +H14 H 0.2926(7) 0.7370(4) 0.0462(5) +H15 H 0.1830(7) 0.6556(4) 0.0214(5) +H16 H 0.3315(7) 0.6444(4) 0.0980(5) +H17 H 0.4441(7) 0.5040(4) 0.5278(4) +H18 H 0.2204(7) 0.4031(4) 0.5464(4) +H19 H -0.0226(7) 0.3735(4) 0.4402(5) +H20 H -0.0125(7) 0.3624(4) 0.3245(5) +H21 H -0.0936(7) 0.4461(4) 0.3636(5) +H22 H 0.5291(6) 0.5891(4) 0.3767(4) +H23 H 0.3864(6) 0.6501(4) 0.3480(4) +H24 H 0.4349(6) 0.5817(4) 0.2691(4) +H25 H -0.2210(5) 0.5251(4) -0.1526(4) +H26 H -0.3981(6) 0.5687(3) -0.0277(4) +H27 H -0.3614(6) 0.5986(4) 0.1654(4) +H28 H -0.1981(6) 0.6167(4) 0.2222(4) +H29 H -0.2547(6) 0.5184(4) 0.2021(4) +H30 H 0.0565(6) 0.4762(4) -0.1376(4) +H31 H 0.1344(6) 0.4482(4) -0.0300(4) +H32 H 0.1515(6) 0.5462(4) -0.0684(4) +H33 H 0.3704(7) 0.3978(6) -0.0685(5) +H34 H 0.5108(7) 0.3379(6) -0.0834(5) +H35 H 0.5416(9) 0.4536(6) -0.1874(7) +H36 H 0.3662(9) 0.4607(6) -0.2225(7) +H37 H 0.5604(9) 0.3260(6) -0.2754(6) +H38 H 0.4451(9) 0.3816(6) -0.3497(6) +H39 H 0.3707(9) 0.2339(5) -0.2840(5) +H40 H 0.2502(9) 0.3099(5) -0.3094(5) +H41 H -0.0377(7) 0.1809(5) 0.3443(6) +H42 H 0.0445(7) 0.1465(5) 0.2545(6) +H43 H -0.0206(8) 0.0537(4) 0.4167(5) +H44 H 0.0128(8) 0.0162(4) 0.3121(5) +H45 H 0.2097(8) 0.0011(5) 0.4634(6) +H46 H 0.2608(8) 0.0174(5) 0.3567(6) +H47 H 0.3683(8) 0.1348(5) 0.4393(5) +H48 H 0.2368(8) 0.1441(5) 0.5067(5) +H49 H 0.6675(9) 0.1859(6) 0.4315(6) +H50 H 0.7334(9) 0.2610(6) 0.3678(6) +H51 H 0.711(1) 0.3114(6) 0.5250(7) +H52 H 0.537(1) 0.2898(6) 0.5082(7) +H53 H 0.603(1) 0.3649(6) 0.4445(7) +H54 H 0.4205(9) 0.1502(6) 0.2360(5) +H55 H 0.5505(9) 0.1111(6) 0.3146(5) +H56 H 0.6201(8) 0.1101(6) 0.1605(6) +H57 H 0.7252(8) 0.1745(6) 0.2311(6) +H58 H 0.5952(8) 0.2137(6) 0.1525(6) +#END diff --git a/cell2mol/test/error_2/TAVHOO.search5.cif b/cell2mol/test/error_2/TAVHOO.search5.cif new file mode 100755 index 00000000..37167575 --- /dev/null +++ b/cell2mol/test/error_2/TAVHOO.search5.cif @@ -0,0 +1,163 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_TAVHOO +_audit_creation_date 2005-12-15 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD TAVHOO +_database_code_depnum_ccdc_archive 'CCDC 284638' +_chemical_formula_sum 'C26 H45 Fe1 K1 N7 O3' +_chemical_formula_moiety +; +K1 1+,C26 H45 Fe1 N7 O3 1- +; +_journal_coden_Cambridge 4 +_journal_volume 127 +_journal_year 2005 +_journal_page_first 11596 +_journal_name_full 'J.Am.Chem.Soc. ' +loop_ +_publ_author_name +"R.L.Lucas" +"D.R.Powell" +"A.S.Borovik" +_chemical_name_systematic +; +Potassium +(bis((N'-t-butylureayl)-N-ethyl)-(N''-isopropylcarbamoylmethyl)aminato)-(p-tol +ylamido)-iron(iii) +; +_cell_volume 1554.979 +_exptl_crystal_colour 'green' +_exptl_crystal_density_diffrn 1.279 +_exptl_crystal_description 'plate' +_diffrn_ambient_temperature 100 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0642 +_refine_ls_wR_factor_gt 0.0642 +_symmetry_cell_setting triclinic +_symmetry_space_group_name_H-M 'P -1' +_symmetry_Int_Tables_number 2 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,-y,-z +_cell_length_a 10.256(3) +_cell_length_b 11.650(4) +_cell_length_c 14.419(5) +_cell_angle_alpha 108.008(5) +_cell_angle_beta 100.887(6) +_cell_angle_gamma 100.453(6) +_cell_formula_units_Z 2 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Fe 1.34 +K 1.33 +N 0.68 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Fe1 Fe 0.34373(6) 0.22013(6) 0.64141(5) +O1 O 0.6988(4) 0.4625(4) 0.6271(3) +O2 O 0.1915(3) -0.1654(3) 0.5842(2) +O3 O 0.1111(3) 0.4426(3) 0.5662(3) +N1 N 0.2670(3) 0.1904(3) 0.4811(3) +N2 N 0.5091(4) 0.3080(4) 0.6053(3) +N3 N 0.2494(4) 0.0358(3) 0.5862(3) +N4 N 0.2023(3) 0.3216(3) 0.6459(3) +N5 N 0.6070(4) 0.4728(4) 0.7585(3) +H1 H 0.547(7) 0.428(6) 0.778(5) +N6 N 0.2683(5) -0.0062(4) 0.7344(3) +H2 H 0.309(5) 0.066(5) 0.756(4) +N7 N 0.4348(4) 0.2539(4) 0.7835(3) +H3 H 0.356(4) 0.242(3) 0.798(2) +C1 C 0.3825(4) 0.1691(4) 0.4364(3) +H4 H 0.36090 0.16990 0.36670 +H5 H 0.39710 0.08640 0.43310 +C2 C 0.5103(5) 0.2697(5) 0.4997(4) +H6 H 0.59230 0.23820 0.49110 +H7 H 0.51520 0.34240 0.47750 +C3 C 0.6101(5) 0.4147(5) 0.6619(4) +C4 C 0.7171(5) 0.5759(4) 0.8354(4) +C5 C 0.6691(6) 0.6067(5) 0.9322(4) +H8 H 0.58730 0.63830 0.92220 +H9 H 0.74240 0.67040 0.98770 +H10 H 0.64680 0.53110 0.94860 +C6 C 0.7360(7) 0.6903(5) 0.8079(4) +H11 H 0.76340 0.67260 0.74440 +H12 H 0.80750 0.75830 0.86160 +H13 H 0.64940 0.71510 0.79950 +C7 C 0.8506(6) 0.5357(6) 0.8520(5) +H14 H 0.83810 0.46700 0.87780 +H15 H 0.92410 0.60640 0.90110 +H16 H 0.87540 0.50780 0.78780 +C8 C 0.1496(4) 0.0801(4) 0.4390(3) +H17 H 0.12730 0.04940 0.36420 +H18 H 0.06820 0.10240 0.45980 +C9 C 0.1842(5) -0.0208(4) 0.4764(3) +H19 H 0.09970 -0.08660 0.46280 +H20 H 0.24790 -0.05940 0.44090 +C10 C 0.2365(4) -0.0514(4) 0.6321(3) +C11 C 0.2758(5) -0.0799(4) 0.8007(4) +C12 C 0.3141(6) 0.0133(5) 0.9081(4) +H21 H 0.40450 0.07000 0.92230 +H22 H 0.31710 -0.03150 0.95550 +H23 H 0.24540 0.06160 0.91580 +C13 C 0.3852(5) -0.1528(5) 0.7862(4) +H24 H 0.36120 -0.21060 0.71610 +H25 H 0.38970 -0.20010 0.83180 +H26 H 0.47480 -0.09430 0.80140 +C14 C 0.1354(5) -0.1696(5) 0.7784(4) +H27 H 0.06670 -0.12180 0.78920 +H28 H 0.14030 -0.21730 0.82380 +H29 H 0.10920 -0.22710 0.70820 +C15 C 0.2263(5) 0.3043(4) 0.4792(3) +H30 H 0.15300 0.28390 0.41670 +H31 H 0.30610 0.36560 0.47800 +C16 C 0.1749(4) 0.3619(4) 0.5702(3) +C17 C 0.1408(5) 0.3714(4) 0.7303(3) +H32 H 0.05660 0.39420 0.70200 +C18 C 0.0957(5) 0.2707(5) 0.7728(4) +H33 H 0.03870 0.19480 0.71750 +H34 H 0.04240 0.30020 0.82080 +H35 H 0.17690 0.25250 0.80740 +C19 C 0.2368(6) 0.4883(4) 0.8109(4) +H36 H 0.32000 0.46860 0.84080 +H37 H 0.19110 0.52100 0.86350 +H38 H 0.26150 0.55110 0.78070 +C20 C 0.5566(5) 0.2240(4) 0.8212(4) +C21 C 0.6183(5) 0.1495(5) 0.7554(4) +H39 H 0.57980 0.12130 0.68450 +C22 C 0.7355(5) 0.1167(5) 0.7933(4) +H40 H 0.77400 0.06500 0.74690 +C23 C 0.7966(5) 0.1548(5) 0.8925(4) +C24 C 0.7379(5) 0.2321(4) 0.9585(4) +H41 H 0.78070 0.26310 1.02900 +C25 C 0.6219(5) 0.2642(4) 0.9251(4) +H42 H 0.58400 0.31470 0.97270 +C26 C 0.9230(6) 0.1146(5) 0.9322(4) +H43 H 0.91650 0.02900 0.88920 +H44 H 0.92780 0.11810 1.00160 +H45 H 1.00600 0.17090 0.93130 +K1 K 0.84749(11) 0.39119(9) 0.51490(9) +#END diff --git a/cell2mol/test/error_2/URUKIC.search6.cif b/cell2mol/test/error_2/URUKIC.search6.cif new file mode 100755 index 00000000..03550daa --- /dev/null +++ b/cell2mol/test/error_2/URUKIC.search6.cif @@ -0,0 +1,123 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_URUKIC +_audit_creation_date 2011-08-02 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD URUKIC +_database_code_depnum_ccdc_archive 'CCDC 789466' +_chemical_formula_sum 'C15 H19 Co1 N2 O2' +_chemical_formula_moiety +; +C15 H19 Co1 N2 O2 +; +_journal_coden_Cambridge 36 +_journal_volume 696 +_journal_year 2011 +_journal_page_first 1975 +_journal_name_full 'J.Organomet.Chem. ' +loop_ +_publ_author_name +"O.M.El-Kadri" +"M.J.Heeg" +"C.H.Winter" +_chemical_name_systematic +; +(\h^3^-3,5-dimethyl-1-(1,2,3,4-tetramethylcyclobut-2-en-1-yl)-1H-pyrazole)-dic +arbonyl-cobalt(i) +; +_cell_volume 3015.350 +_exptl_crystal_colour 'yellow' +_exptl_crystal_density_diffrn 1.402 +_exptl_crystal_description 'rod' +_diffrn_ambient_temperature 100 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0265 +_refine_ls_wR_factor_gt 0.0265 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'C 2/c' +_symmetry_Int_Tables_number 15 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 -x,y,1/2-z +3 1/2+x,1/2+y,z +4 1/2-x,1/2+y,1/2-z +5 -x,-y,-z +6 x,-y,-1/2+z +7 -1/2-x,-1/2-y,-z +8 -1/2+x,-1/2-y,-1/2+z +_cell_length_a 25.1187(7) +_cell_length_b 9.5441(3) +_cell_length_c 15.5266(4) +_cell_angle_alpha 90 +_cell_angle_beta 125.896(1) +_cell_angle_gamma 90 +_cell_formula_units_Z 8 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Co 1.19 +N 0.68 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Co1 Co 0.152055(7) 0.518345(15) 0.178865(11) +C1 C 0.21965(6) 0.53714(12) 0.31408(9) +O1 O 0.26290(4) 0.55821(10) 0.40115(7) +C2 C 0.09890(6) 0.63974(12) 0.17873(8) +O2 O 0.06556(5) 0.72757(10) 0.17298(7) +N1 N 0.11927(4) 0.31786(10) 0.14919(7) +N2 N 0.11194(5) 0.27777(10) 0.05831(7) +C3 C 0.10314(7) 0.21712(14) 0.27792(10) +C4 C 0.10061(5) 0.20954(12) 0.17965(9) +C5 C 0.08062(6) 0.09864(12) 0.10693(9) +C6 C 0.08827(5) 0.14487(12) 0.03025(9) +C7 C 0.07307(6) 0.06658(13) -0.06524(10) +C8 C 0.13440(5) 0.39017(11) 0.02122(8) +C9 C 0.10641(5) 0.53554(12) 0.01896(9) +C10 C 0.17162(5) 0.59472(12) 0.08008(8) +C11 C 0.19994(5) 0.45796(12) 0.11335(8) +C12 C 0.12969(6) 0.35059(14) -0.07744(10) +C13 C 0.04093(6) 0.59656(13) -0.06519(9) +C14 C 0.19888(6) 0.73613(13) 0.08697(10) +C15 C 0.26893(6) 0.40847(13) 0.16617(10) +H1 H 0.0631(11) 0.190(2) 0.2668(16) +H2 H 0.1359(10) 0.157(2) 0.3310(16) +H3 H 0.113(1) 0.312(2) 0.3069(17) +H4 H 0.0668(9) 0.0133(17) 0.1125(14) +H5 H 0.1122(8) 0.0533(18) -0.0656(13) +H6 H 0.0560(9) -0.0211(17) -0.0630(14) +H7 H 0.0395(8) 0.1147(19) -0.1331(13) +H8 H 0.1448(7) 0.4275(18) -0.0975(12) +H9 H 0.1556(8) 0.2731(18) -0.0676(12) +H10 H 0.0839(8) 0.3294(17) -0.1383(12) +H11 H 0.0065(8) 0.5470(18) -0.0699(13) +H12 H 0.0383(9) 0.694(2) -0.0502(14) +H13 H 0.0336(8) 0.5926(17) -0.1346(13) +H14 H 0.2413(8) 0.7481(17) 0.1542(13) +H15 H 0.2048(8) 0.7507(18) 0.0318(14) +H16 H 0.1696(8) 0.8115(18) 0.0800(13) +H17 H 0.2994(9) 0.4749(18) 0.2181(15) +H18 H 0.2778(8) 0.3209(18) 0.2053(13) +H19 H 0.2787(8) 0.3966(18) 0.1148(13) +#END diff --git a/cell2mol/test/error_2/VEYTAW.search5.cif b/cell2mol/test/error_2/VEYTAW.search5.cif new file mode 100755 index 00000000..01b61137 --- /dev/null +++ b/cell2mol/test/error_2/VEYTAW.search5.cif @@ -0,0 +1,249 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_VEYTAW +_audit_creation_date 2013-05-24 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD VEYTAW +_database_code_depnum_ccdc_archive 'CCDC 941684' +_chemical_formula_sum 'C50 H103 Fe1 K1 O13' +_chemical_formula_moiety +; +C30 H63 Fe1 O3 1-,C20 H40 K1 O10 1+ +; +_journal_coden_Cambridge 9 +_journal_volume 52 +_journal_year 2013 +_journal_page_first 3159 +_journal_name_full 'Inorg.Chem. ' +loop_ +_publ_author_name +"M.B.Chambers" +"S.Groysman" +"D.Villagran" +"D.G.Nocera" +_chemical_name_systematic +; +bis(1,4,7,10,13-pentaoxacyclopentadecane)-potassium +tris(2,2,3,4,4-pentamethylpentan-3-olato)-iron(ii) +; +_cell_volume 5718.555 +_exptl_crystal_colour 'yellow' +_exptl_crystal_density_diffrn 1.17 +_exptl_crystal_description 'Block' +_diffrn_ambient_temperature 100 +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0422 +_refine_ls_wR_factor_gt 0.0422 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'P 21/n' +_symmetry_Int_Tables_number 14 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 1/2-x,1/2+y,1/2-z +3 -x,-y,-z +4 -1/2+x,-1/2-y,-1/2+z +_cell_length_a 11.381(3) +_cell_length_b 17.218(5) +_cell_length_c 29.190(9) +_cell_angle_alpha 90 +_cell_angle_beta 91.295(5) +_cell_angle_gamma 90 +_cell_formula_units_Z 4 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +Fe 1.52 +K 2.03 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +Fe1 Fe 0.85163(3) 0.754865(17) 0.091862(10) +O1 O 0.99747(13) 0.78182(9) 0.06883(6) +C1 C 1.09286(18) 0.74739(13) 0.04721(8) +C2 C 1.0454(2) 0.69305(15) 0.00874(9) +H1 H 0.98310 0.66010 0.02080 +H2 H 1.10940 0.66030 -0.00220 +H3 H 1.01370 0.72440 -0.01670 +C3 C 1.1631(2) 0.69550(14) 0.08386(9) +C4 C 1.2852(2) 0.66953(17) 0.06920(11) +H4 H 1.33830 0.71440 0.06920 +H5 H 1.27990 0.64720 0.03830 +H6 H 1.31580 0.63040 0.09070 +C5 C 1.0932(2) 0.62039(16) 0.09283(11) +H7 H 1.09770 0.58610 0.06610 +H8 H 1.01080 0.63350 0.09820 +H9 H 1.12660 0.59390 0.11980 +C6 C 1.1726(3) 0.73606(19) 0.13051(10) +H10 H 1.21110 0.70130 0.15280 +H11 H 1.09380 0.74920 0.14090 +H12 H 1.21910 0.78370 0.12770 +C7 C 1.1628(2) 0.81742(14) 0.02404(8) +C8 C 1.2508(3) 0.79020(18) -0.01245(10) +H13 H 1.28910 0.83550 -0.02580 +H14 H 1.20850 0.76130 -0.03660 +H15 H 1.31030 0.75650 0.00200 +C9 C 1.0743(2) 0.87096(16) -0.00061(9) +H16 H 1.02100 0.89330 0.02180 +H17 H 1.02880 0.84100 -0.02340 +H18 H 1.11650 0.91280 -0.01600 +C10 C 1.2281(2) 0.86783(16) 0.05906(11) +H19 H 1.25620 0.91500 0.04410 +H20 H 1.29520 0.83890 0.07200 +H21 H 1.17500 0.88200 0.08370 +O2 O 0.80525(13) 0.65074(9) 0.09362(6) +C11 C 0.70351(19) 0.60813(13) 0.10332(8) +C12 C 0.5983(2) 0.66415(16) 0.10711(14) +H22 H 0.60930 0.69690 0.13430 +H23 H 0.52560 0.63410 0.10970 +H24 H 0.59330 0.69700 0.07970 +C13 C 0.7232(2) 0.56818(16) 0.15196(8) +C14 C 0.6113(3) 0.53279(19) 0.17253(10) +H25 H 0.58240 0.49050 0.15280 +H26 H 0.55070 0.57300 0.17460 +H27 H 0.62990 0.51240 0.20320 +C15 C 0.8142(3) 0.5035(2) 0.15154(12) +H28 H 0.88710 0.52320 0.13860 +H29 H 0.78440 0.46020 0.13280 +H30 H 0.82980 0.48550 0.18290 +C16 C 0.7686(3) 0.6301(2) 0.18601(11) +H31 H 0.77510 0.60740 0.21680 +H32 H 0.71370 0.67390 0.18630 +H33 H 0.84600 0.64830 0.17660 +C17 C 0.6817(2) 0.55197(16) 0.06096(9) +C18 C 0.5849(3) 0.4913(2) 0.06621(10) +H34 H 0.56820 0.46660 0.03650 +H35 H 0.51350 0.51650 0.07710 +H36 H 0.61080 0.45180 0.08840 +C19 C 0.6433(4) 0.6010(3) 0.01905(13) +H37 H 0.64220 0.56830 -0.00840 +H38 H 0.69890 0.64390 0.01510 +H39 H 0.56450 0.62210 0.02380 +C20 C 0.7951(3) 0.5132(2) 0.04499(12) +H40 H 0.81800 0.47200 0.06650 +H41 H 0.85790 0.55210 0.04380 +H42 H 0.78180 0.49090 0.01440 +O3 O 0.75530(13) 0.83096(9) 0.11668(6) +C21 C 0.7535(2) 0.91164(13) 0.12152(8) +C22 C 0.8445(2) 0.94873(14) 0.08935(9) +H43 H 0.91960 0.92120 0.09270 +H44 H 0.85570 1.00350 0.09750 +H45 H 0.81580 0.94500 0.05750 +C23 C 0.6251(2) 0.93918(13) 0.10468(8) +C24 C 0.6110(2) 1.02700(15) 0.09772(10) +H46 H 0.53220 1.03810 0.08520 +H47 H 0.66990 1.04550 0.07630 +H48 H 0.62200 1.05360 0.12720 +C25 C 0.5291(2) 0.91014(15) 0.13655(9) +H49 H 0.53140 0.94040 0.16500 +H50 H 0.54250 0.85520 0.14370 +H51 H 0.45200 0.91630 0.12130 +C26 C 0.5957(2) 0.90028(18) 0.05797(9) +H52 H 0.51560 0.91430 0.04810 +H53 H 0.60160 0.84370 0.06110 +H54 H 0.65130 0.91820 0.03510 +C27 C 0.7926(2) 0.93013(14) 0.17326(8) +C28 C 0.7738(3) 1.01508(17) 0.18782(10) +H55 H 0.68950 1.02520 0.19050 +H56 H 0.80640 1.04980 0.16470 +H57 H 0.81360 1.02430 0.21740 +C29 C 0.9244(2) 0.91218(19) 0.17941(11) +H58 H 0.94720 0.91670 0.21190 +H59 H 0.96990 0.94910 0.16150 +H60 H 0.94000 0.85920 0.16880 +C30 C 0.7304(3) 0.87766(17) 0.20811(9) +H61 H 0.73220 0.82370 0.19750 +H62 H 0.64860 0.89440 0.21090 +H63 H 0.77090 0.88150 0.23800 +K1 K 0.24644(4) 1.24847(3) 0.162422(16) +O4 O 0.17213(14) 1.13824(9) 0.09652(5) +O5 O 0.27286(14) 1.08944(9) 0.18450(5) +O6 O 0.42372(14) 1.20754(10) 0.22841(6) +O7 O 0.50868(16) 1.23485(10) 0.14144(6) +O8 O 0.33715(15) 1.25168(10) 0.07047(6) +C31 C 0.1518(2) 1.06573(14) 0.11832(9) +H64 H 0.13240 1.02620 0.09470 +H65 H 0.08330 1.07070 0.13840 +C32 C 0.2557(2) 1.03877(14) 0.14625(9) +H66 H 0.24230 0.98510 0.15710 +H67 H 0.32660 1.03880 0.12720 +C33 C 0.3771(2) 1.07251(15) 0.21055(9) +H68 H 0.44570 1.07230 0.19030 +H69 H 0.37060 1.02060 0.22480 +C34 C 0.3933(2) 1.13345(15) 0.24712(9) +H70 H 0.31960 1.13850 0.26430 +H71 H 0.45610 1.11670 0.26900 +C35 C 0.5465(2) 1.21579(16) 0.22004(9) +H72 H 0.58080 1.16460 0.21260 +H73 H 0.58750 1.23620 0.24780 +C36 C 0.5620(2) 1.27041(16) 0.18098(10) +H74 H 0.52370 1.32080 0.18740 +H75 H 0.64650 1.27980 0.17600 +C37 C 0.5280(2) 1.27739(16) 0.10088(10) +H76 H 0.61270 1.27690 0.09370 +H77 H 0.50320 1.33200 0.10490 +C38 C 0.4584(2) 1.24075(17) 0.06274(9) +H78 H 0.47940 1.26460 0.03320 +H79 H 0.47630 1.18460 0.06120 +C39 C 0.2615(2) 1.21254(15) 0.03922(8) +H80 H 0.29890 1.21030 0.00900 +H81 H 0.18750 1.24240 0.03550 +C40 C 0.2331(2) 1.13163(15) 0.05438(8) +H82 H 0.18300 1.10520 0.03100 +H83 H 0.30610 1.10110 0.05900 +O9 O 0.07887(15) 1.33249(10) 0.11169(6) +O10 O 0.29555(15) 1.40475(10) 0.14265(6) +O11 O 0.21629(16) 1.3802(1) 0.23170(6) +O12 O 0.13191(16) 1.22641(10) 0.24945(6) +O13 O 0.00129(15) 1.20388(10) 0.16654(6) +C41 C 0.1057(2) 1.41243(14) 0.10238(9) +H84 H 0.07430 1.44600 0.12680 +H85 H 0.06910 1.42830 0.07270 +C42 C 0.2362(2) 1.42099(16) 0.10064(9) +H86 H 0.26620 1.38560 0.07680 +H87 H 0.25490 1.47480 0.09130 +C43 C 0.2892(2) 1.46510(15) 0.17645(10) +H88 H 0.21050 1.48960 0.17520 +H89 H 0.34870 1.50560 0.17050 +C44 C 0.3114(2) 1.42977(17) 0.22226(11) +H90 H 0.38570 1.39990 0.22240 +H91 H 0.31820 1.47080 0.24590 +C45 C 0.2258(3) 1.34145(16) 0.27470(9) +H92 H 0.22900 1.37990 0.29990 +H93 H 0.29870 1.31000 0.27620 +C46 C 0.1206(3) 1.29004(16) 0.27935(9) +H94 H 0.11540 1.27150 0.31130 +H95 H 0.04810 1.31940 0.27150 +C47 C 0.0262(2) 1.18165(16) 0.24494(9) +H96 H -0.01410 1.18110 0.27460 +H97 H 0.04650 1.12740 0.23710 +C48 C -0.0556(2) 1.21385(17) 0.20857(9) +H98 H -0.13130 1.18550 0.20830 +H99 H -0.07110 1.26960 0.21420 +C49 C -0.0624(2) 1.23549(15) 0.12789(9) +H100 H -0.14750 1.22700 0.13200 +H101 H -0.03940 1.20740 0.09990 +C50 C -0.0405(2) 1.32043(15) 0.12151(9) +H102 H -0.09070 1.34040 0.09600 +H103 H -0.06060 1.34890 0.14970 +#END diff --git a/cell2mol/test/error_2/WOVMAY.search2.cif b/cell2mol/test/error_2/WOVMAY.search2.cif new file mode 100755 index 00000000..c6d13b00 --- /dev/null +++ b/cell2mol/test/error_2/WOVMAY.search2.cif @@ -0,0 +1,210 @@ + +####################################################################### +# +# Cambridge Crystallographic Data Centre +# CCDC +# +####################################################################### +# +# If this CIF has been generated from an entry in the Cambridge +# Structural Database, then it will include bibliographic, chemical, +# crystal, experimental, refinement or atomic coordinate data resulting +# from the CCDC's data processing and validation procedures. +# +####################################################################### + +data_CSD_CIF_WOVMAY +_audit_creation_date 2019-11-08 +_audit_creation_method CSD-ConQuest-V1 +_database_code_CSD WOVMAY +_database_code_depnum_ccdc_archive 'CCDC 1874629' +_chemical_formula_sum 'C40 H70 K1 Mn1 O8' +_chemical_formula_moiety +; +C20 H40 K1 O8 1+,C20 H30 Mn1 1- +; +_journal_coden_Cambridge 222 +_journal_volume 48 +_journal_year 2019 +_journal_page_first 17078 +_journal_name_full 'Dalton Trans. ' +loop_ +_publ_author_name +"M.Malischewski" +"K.Seppelt" +_chemical_name_systematic +; +(18-crown-6)-bis(tetrahydrofuran)-potassium +bis(pentamethylcyclopentadienyl)-manganese(i) +; +_cell_volume 4315.366 +_exptl_crystal_colour 'brown' +_exptl_crystal_density_diffrn 1.19 +_exptl_special_details +; +Air-sensitive,Moisture-sensitive,Oxygen-sensitive + +; +_exptl_crystal_description 'cube' +_exptl_crystal_preparation 'Re-crystallisation from solvent' +_diffrn_ambient_temperature 100 +_diffrn_special_details +; +twin + +; +#These two values have been output from a single CSD field. +_refine_ls_R_factor_gt 0.0288 +_refine_ls_wR_factor_gt 0.0288 +_symmetry_cell_setting monoclinic +_symmetry_space_group_name_H-M 'C c' +_symmetry_Int_Tables_number 9 +loop_ +_symmetry_equiv_pos_site_id +_symmetry_equiv_pos_as_xyz +1 x,y,z +2 x,-y,1/2+z +3 1/2+x,1/2+y,z +4 1/2+x,1/2-y,1/2+z +_cell_length_a 22.671(2) +_cell_length_b 11.9085(10) +_cell_length_c 17.1759(17) +_cell_angle_alpha 90 +_cell_angle_beta 111.469(3) +_cell_angle_gamma 90 +_cell_formula_units_Z 4 +loop_ +_atom_type_symbol +_atom_type_radius_bond +C 0.68 +H 0.23 +K 2.03 +Mn 1.61 +O 0.68 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +K1 K 0.21716(14) 0.7500(2) 0.08521(19) +O1 O 0.1153(3) 0.7711(5) 0.1358(4) +O2 O 0.3172(3) 0.7288(5) 0.0335(4) +O3 O 0.2777(3) 0.5372(5) 0.0959(4) +C1 C 0.0807(4) 0.8713(7) 0.1169(5) +H1 H 0.05550 0.87830 0.15170 +H2 H 0.05210 0.87050 0.05880 +O4 O 0.2771(3) 0.9461(5) 0.0598(4) +C2 C 0.3548(4) 0.6271(8) 0.0538(6) +H3 H 0.38310 0.62790 0.11200 +H4 H 0.38000 0.62050 0.01900 +C3 C 0.0780(4) 0.6755(8) 0.1239(5) +H5 H 0.05430 0.66490 0.06460 +H6 H 0.04790 0.68490 0.15160 +C4 C 0.3584(4) 0.8273(7) 0.0460(6) +H7 H 0.38810 0.81730 0.01780 +H8 H 0.38210 0.83940 0.10510 +C5 C 0.3094(5) 0.5315(8) 0.0379(6) +H9 H 0.27870 0.53550 -0.01890 +H10 H 0.33210 0.46090 0.04450 +O5 O 0.1530(4) 0.7401(7) -0.0831(4) +O6 O 0.2823(4) 0.7691(6) 0.2531(4) +C6 C 0.3459(5) 0.7201(8) 0.2904(6) +H11 H 0.37750 0.77130 0.28490 +H12 H 0.34830 0.65000 0.26290 +C7 C 0.0910(5) 0.7775(8) -0.1207(7) +H13 H 0.08510 0.84700 -0.09500 +H14 H 0.06170 0.72190 -0.11460 +C8 C 0.2002(4) 0.4616(7) 0.1423(5) +H15 H 0.17700 0.39380 0.14400 +H16 H 0.22930 0.47770 0.19870 +C9 C 0.1972(4) 1.0551(7) 0.0809(6) +H17 H 0.22570 1.06440 0.13860 +H18 H 0.17240 1.12320 0.06340 +C10 C 0.1178(5) 0.5754(8) 0.1582(6) +H19 H 0.14420 0.58800 0.21640 +H20 H 0.09110 0.51070 0.15530 +C11 C 0.2581(5) 0.7906(7) 0.3142(6) +H21 H 0.21230 0.78300 0.29170 +H22 H 0.26880 0.86630 0.33550 +O7 O 0.1563(3) 0.5548(5) 0.1114(4) +C12 C 0.1777(5) 0.7103(8) -0.1473(5) +H23 H 0.17090 0.63140 -0.16180 +H24 H 0.22260 0.72710 -0.12950 +C13 C 0.2355(4) 0.4450(7) 0.0871(6) +H25 H 0.25940 0.37550 0.10130 +H26 H 0.20630 0.43980 0.02950 +C14 C 0.3149(5) 0.9247(8) 0.0092(6) +H27 H 0.33960 0.99080 0.00810 +H28 H 0.28750 0.90750 -0.04780 +C15 C 0.2353(4) 1.0338(7) 0.0254(5) +H29 H 0.20700 1.01430 -0.03070 +H30 H 0.25850 1.10090 0.02220 +C16 C 0.0792(5) 0.7967(9) -0.2146(6) +H31 H 0.04950 0.74230 -0.24970 +H32 H 0.06340 0.87180 -0.23220 +C17 C 0.3569(5) 0.7003(9) 0.3806(7) +H33 H 0.38370 0.75830 0.41560 +H34 H 0.37650 0.62770 0.39880 +C18 C 0.2877(4) 0.7046(5) 0.3839(5) +H35 H 0.26650 0.63240 0.37100 +H36 H 0.28820 0.73100 0.43760 +C19 C 0.1406(4) 0.7814(8) -0.2169(5) +H37 H 0.16130 0.85360 -0.21300 +H38 H 0.13740 0.74650 -0.26940 +O8 O 0.1565(3) 0.9623(5) 0.0744(4) +C20 C 0.1248(4) 0.9685(7) 0.1318(6) +H39 H 0.10130 1.03830 0.12400 +H40 H 0.15540 0.96650 0.18870 +Mn1 Mn 0.46703(10) 0.21887(3) 0.33501(13) +C21 C 0.3958(4) 0.2935(6) 0.3671(5) +C22 C 0.5392(4) 0.2868(7) 0.3042(5) +C23 C 0.5478(3) 0.1740(7) 0.3145(4) +C24 C 0.4777(4) 0.3121(7) 0.2383(5) +C25 C 0.3864(4) 0.1689(6) 0.3569(5) +C26 C 0.4919(4) 0.1178(7) 0.2547(5) +C27 C 0.5865(4) 0.3774(8) 0.3490(6) +H41 H 0.61690 0.38600 0.32270 +H42 H 0.60780 0.35640 0.40650 +H43 H 0.56460 0.44720 0.34630 +C28 C 0.4402(4) 0.1168(7) 0.4142(6) +C29 C 0.4555(4) 0.3078(7) 0.4310(5) +C30 C 0.3897(4) 0.1857(9) 0.1357(5) +H44 H 0.39840 0.18980 0.08520 +H45 H 0.35910 0.24200 0.13460 +H46 H 0.37320 0.11270 0.14020 +C31 C 0.4833(4) 0.2002(7) 0.4620(5) +C32 C 0.4499(4) 0.2053(6) 0.2097(5) +C33 C 0.4834(4) 0.4223(8) 0.4650(6) +H47 H 0.47600 0.43780 0.51560 +H48 H 0.46360 0.47920 0.42430 +H49 H 0.52820 0.42190 0.47670 +C34 C 0.4825(5) -0.0075(8) 0.2409(6) +H50 H 0.43820 -0.02480 0.22330 +H51 H 0.50520 -0.04630 0.29210 +H52 H 0.49810 -0.03070 0.19840 +C35 C 0.4492(4) 0.4226(7) 0.2070(6) +H53 H 0.46360 0.47680 0.25130 +H54 H 0.40380 0.41690 0.18760 +H55 H 0.46160 0.44580 0.16170 +C36 C 0.3285(4) 0.1149(7) 0.2976(5) +H56 H 0.33990 0.04770 0.27560 +H57 H 0.30750 0.16570 0.25250 +H58 H 0.30050 0.09640 0.32630 +C37 C 0.6061(4) 0.1151(7) 0.3759(6) +H59 H 0.63950 0.11600 0.35410 +H60 H 0.59560 0.03880 0.38340 +H61 H 0.61980 0.15350 0.42870 +C38 C 0.5462(4) 0.1820(8) 0.5325(5) +H62 H 0.57320 0.24550 0.53630 +H63 H 0.56580 0.11540 0.52190 +H64 H 0.53940 0.17380 0.58420 +C39 C 0.4499(5) -0.0060(8) 0.4290(6) +H65 H 0.43900 -0.02710 0.47600 +H66 H 0.49350 -0.02420 0.44040 +H67 H 0.42350 -0.04610 0.38020 +C40 C 0.3481(4) 0.3788(7) 0.3219(5) +H68 H 0.31980 0.34780 0.27010 +H69 H 0.36910 0.44330 0.31070 +H70 H 0.32430 0.40070 0.35560 +#END