schrodinger.application.jaguar.nmr_utils module¶

class schrodinger.application.jaguar.nmr_utils.NMRData(shifts: list[float], couplings: list[Tuple[int, int, float, int]], isotopes: list[str], jnames: list[str], spectrum_type: str, field: float, filename: str, map_to_struct: dict[int, int], table_data: list[dict[str, str | float]], structure_title: str, timestep_npoints: int = 8192, spectrum_npoints: int = 16536, sym_groups: list[str] | None = None, sym_spins: list[list[int]] | None = None, max_subgraph_size: int = 14, debug: bool = False)¶

Bases: object

shifts: list[float]¶

couplings: list[Tuple[int, int, float, int]]¶

isotopes: list[str]¶

jnames: list[str]¶

spectrum_type: str¶

field: float¶

filename: str¶

map_to_struct: dict[int, int]¶

table_data: list[dict[str, str | float]]¶

structure_title: str¶

timestep_npoints: int = 8192¶

spectrum_npoints: int = 16536¶

sym_groups: list[str] | None = None¶

sym_spins: list[list[int]] | None = None¶

max_subgraph_size: int = 14¶

debug: bool = False¶

__init__(shifts: list[float], couplings: list[Tuple[int, int, float, int]], isotopes: list[str], jnames: list[str], spectrum_type: str, field: float, filename: str, map_to_struct: dict[int, int], table_data: list[dict[str, str | float]], structure_title: str, timestep_npoints: int = 8192, spectrum_npoints: int = 16536, sym_groups: list[str] | None = None, sym_spins: list[list[int]] | None = None, max_subgraph_size: int = 14, debug: bool = False) → None¶

schrodinger.application.jaguar.nmr_utils.read_nmr_input(infile_name: str) → schrodinger.application.jaguar.nmr_utils.NMRData¶

Deserialise the JSON file into NMRData object for spectrum calculation.

Parameters: infile_name – Filename containing JSON serialised NMRData object.

class schrodinger.application.jaguar.nmr_utils.PauliMatrices(multiplicity: int)¶

Bases: object

Class building and holding Pauli matrices for a given multiplicity.

The Pauli matrices are stored as COO matrices, these are fairly efficient at building new sparse matrices because they allow the sparsity to change efficiently (unlike CSR/CSC). We aren’t doing matrix algebra with these; we will convert before doing so.

__init__(multiplicity: int)¶

Build the Pauli matrices.

Parameters: multiplicity – Multiplicity of the Pauli matrices to be built.

class schrodinger.application.jaguar.nmr_utils.IsotopeData(multiplicity: int, mg_ratio: float)¶

Bases: object

multiplicity: int¶

mg_ratio: float¶

__init__(multiplicity: int, mg_ratio: float) → None¶

schrodinger.application.jaguar.nmr_utils.negligible(something, tolerance: float) → bool¶

True if something is below tolerance, False otherwise. Wraps and inverts significant() function.

Operates in ‘tensor’, ‘vector’, or ‘scalar’ mode, calling respective max functions for sparse or dense tensors.

Parameters

something – object to check
tolerance – the tolerance level for significance

Returns

True if the object is negligible, False otherwise

schrodinger.application.jaguar.nmr_utils.significant(something, tolerance: float) → bool¶

Determines whether a given object deserves attention given the tolerance specified. Used in the internal decision-making conducted by spin_system kernel functions.

Operates in ‘tensor’, ‘vector’, or ‘scalar’ mode, calling respective max functions for sparse or dense tensors.

Parameters

something – object to check
tolerance – the tolerance level for significance

Returns

True if the object is significant, False otherwise

schrodinger.application.jaguar.nmr_utils.print_proclamation(msg: str)¶

Utility function to print title-formatted message.

Parameters: msg – Message to be printed.

schrodinger.application.jaguar.nmr_utils.where_sorted_array(a, row)¶

Bisection search to find matching row in a lexicographically sorted array. Raises RuntimeError if row not in a.

Significantly accellerated by @njit numba decorator, but this is currently removed due to JAGUAR-11740.

Parameters

a – The array to be searched.
row – The row to locate.

Returns

The index of the row in the array.

schrodinger.application.jaguar.nmr_utils.lexical_ordering(a: numpy.ndarray, b: numpy.ndarray) → int¶

Compare two 1D arrays lexicographically. Returns 1 if a > b, -1 if a < b, or 0 if a == b.

This function is significantly accellerated by @njit numba decorator, but this is currently removed due to JAGUAR-11740

Parameters

a – first array
b – second array

Returns

1 if a > b, -1 if a < b, 0 if a == b

schrodinger.application.jaguar.nmr_utils.remove_vals_below_thresh(mat: scipy.sparse._arrays.csr_array, thresh: float)¶

Floor small values to 0 in sparse matrices so that they are not stored explicitly. Modifies mat in place.

Parameters

mat – matrix possibly with some small values
thresh – threshold for small values to be floored to zeros

schrodinger.application.jaguar.nmr_utils.tesla_to_mhz(magnet: float, isotope: str = '1H') → float¶

schrodinger.application.jaguar.nmr_utils.mhz_to_tesla(magnet: float, isotope: str = '1H') → float¶

schrodinger.application.jaguar.nmr_utils.hz_to_ppm(hz: float, magnet: float, isotope: str = '1H') → float¶

Parameters

hz – Value to convert in Hz (note NOT MHz).
magnet – Magnetic field strength in Tesla.
isotope – The isotope the experiment is probing.

Returns

Value in ppm.

schrodinger.application.jaguar.nmr_utils.ppm_to_hz(ppm: float, magnet: float, isotope: str = '1H') → float¶

Parameters

ppm – Value to convert in ppm.
magnet – Magnetic field strength in Tesla.
isotope – The isotope the experiment is probing.

Returns

Value in Hz (note NOT MHz).

schrodinger.application.jaguar.nmr_utils.convert_to_acs_label(split_lab: str) → str¶: Helper function to convert general splitting label into ACS format. Truncates >5 into “multiplet”.

schrodinger.application.jaguar.nmr_utils.count_protons(st: schrodinger.structure._structure.Structure, excluded_atoms: set[str]) → int¶

Count the number of atoms in a structure that are hydrogen with isotope 1 or 0 (default) and are not part of the excluded set. Exclusion allows us to ignore things like acidic hydrogen which do not show in NMR.

Parameters

st – Structure to be counted.
excluded_atoms – Atom name strings which should be excluded.

Returns

The number of non-excluded protons.

schrodinger.application.jaguar.nmr_utils.json_to_nmrdata(infile_name: pathlib.Path) → schrodinger.application.jaguar.nmr_utils.NMRData¶

Reads a JSON file into an NMRData object. Will raise an exception if the NMR data is not for a 1H spectrum. Performs some additional checks for backward compatibility.

Parameters: infile_name – Path to JSON file.
Returns: NMRData object deserialized from JSON.

schrodinger.application.jaguar.nmr_utils.read_csv(fname: str) → tuple[numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]], numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]]¶

Helper function for parsing nmr.py format CSV into (X, Y) spectrum data.

Parameters: fname – Path to CSV file.
Returns: X data for spectrum (ppm).
Returns: Y data for spectrum (intensity).

schrodinger.application.jaguar.nmr_utils.all_were_assigned(mapping: dict['Signal', list['Signal']]) → int¶

Check if every experimental Signal has had enough theoretical signals assigned to it to exactly fill its expected number of protons and report details of this check.

Parameters: mapping – Dictionary mapping the experimental signals to the theoretical signals.
Returns: True if every experimental signal was exactly filled, false otherwise.
Returns: The number of correctly filled experimental signals.
Returns: The total number of experimental signals.