physDBD package

Subpackages

Submodules

physDBD.data_desc module

class physDBD.data_desc.DataDesc(no_seeds: int, time_start: float, time_end: float, time_interval: float, species: List[str])

Bases: object

__init__(no_seeds: int, time_start: float, time_end: float, time_interval: float, species: List[str])

Data description

Args:

no_seeds (int): no. seeds time_start (float): time start (real time) time_end (float): time end (real time) time_interval (float): time interval (real time) species (List[str]): list of species

no_seeds: int
no_times: int
species: List[str]
time_end: float
time_interval: float
time_start: float
times: numpy.array

physDBD.diff_tvr module

class physDBD.diff_tvr.DiffTVR(n: int, dx: float)

Bases: object

__init__(n: int, dx: float)

Differentiate with TVR.

Args:

n (int): Number of points in data. dx (float): Spacing of data.

get_deriv_tvr(data: numpy.array, deriv_guess: numpy.array, alpha: float, no_opt_steps: int, return_progress: bool = False, return_interval: int = 1) → Tuple[numpy.array, numpy.array]

Get derivative via TVR over optimization steps

Args:

data (np.array): Data of size N deriv_guess (np.array): Guess for derivative of size N-1 alpha (float): Regularization parameter no_opt_steps (int): No. opt steps to run return_progress (bool, optional): True to return derivative progress during optimization. Defaults to False. return_interval (int, optional): Interval at which to store derivative if returning. Defaults to 1.

Returns:

Tuple[np.array,np.array]: First is the final derivative of size N-1, second is the stored derivatives if return_progress=True of size no_opt_steps+1 x N-1, else [].

get_deriv_tvr_update(data: numpy.array, deriv_curr: numpy.array, alpha: float) → numpy.array

Get the TVR update

Args:

data (np.array): Data of size N deriv_curr (np.array): Current deriv of size N-1 alpha (float): Regularization parameter

Returns:

np.array: Update vector of size N-1

make_en_mat(deriv_curr: numpy.array) → numpy.array

Diffusion matrix

Args:

deriv_curr (np.array): Current derivative of length N-1

Returns:

np.array: N-2 x N-2

make_gn_vec(deriv_curr: numpy.array, data: numpy.array, alpha: float, ln_mat: numpy.array) → numpy.array

Negative right hand side of linear problem

Args:

deriv_curr (np.array): Current derivative of size N-1 data (np.array): Data of size N alpha (float): Regularization parameter ln_mat (np.array): Diffusivity term from make_ln_mat

Returns:

np.array: Vector of length N-1

make_hn_mat(alpha: float, ln_mat: numpy.array) → numpy.array

Matrix in linear problem

Args:

alpha (float): Regularization parameter ln_mat (np.array): Diffusivity term from make_ln_mat

Returns:

np.array: N-1 x N-1

make_ln_mat(en_mat: numpy.array) → numpy.array

Diffusivity term

Args:

en_mat (np.array): Result from make_en_mat

Returns:

np.array: N-1 x N-1

physDBD.helpers module

physDBD.helpers.array_safe_eq(a, b) → bool

Check if a and b are equal, even if they are numpy arrays. Needed for checking equality with @dataclass(eq=False) decorator

physDBD.helpers.convert_np_to_pd(arr_with_times: numpy.array, nv: int, nh: int) → pandas.core.frame.DataFrame

Convert a numpy array of wt, b, sig2, muh, varh_diag to a pandas dataframe with named columns

Args:
arr_with_times (np.array): Array of size TxN where T is the number of timepoints and

N is the size of (wt,b,sig2,muh,varh_diag) = (nv*nh, nv, 1, nh, nh) = nv*nh + nv + 1 + 2*nh

nv (int): No. visible species nh (int): No. hidden species

Returns:

pd.DataFrame: Pandas data frame

physDBD.helpers.dc_eq(dc1, dc2) → bool

checks if two dataclasses which hold numpy arrays are equal. Needed for checking equality with @dataclass(eq=False) decorator

physDBD.helpers.normalize(vec: numpy.array) → numpy.array

Normalize 1D np arr

Args:

vec (np.array): 1D vec to normalize

Returns:

np.array: normalized

physDBD.import_helper module

class physDBD.import_helper.ImportHelper

Bases: object

static create_fnames(data_dir: str, no_seeds: int) → List[str]

Create fnames for different seeds with zero padding 4

Args:

data_dir (str): Data directory no_seeds (int): No seeds

Returns:

List[str]: List of filenames

static import_gillespie_ssa_at_time(fnames: List[str], time: float, species: List[str]) → numpy.array

Import seeds from different files at a single time

Args:

fnames (List[str]): The different files corresponding to the seeds time (float): Time (real time) species (List[str]): Species

Raises:

ValueError: If species not found

Returns:

np.array: Array of size (no_seeds, no_species)

static import_gillespie_ssa_from_data_desc(data_desc: physDBD.data_desc.DataDesc, data_dir: str) → numpy.array

Import Gillespie SSA data

Args:

data_desc (DataDesc): Data description data_dir (str): Data directory

Returns:

np.array: Numpy array of size (no_times, no_seeds, no_species)

static import_gillespie_ssa_from_data_desc_at_time(data_desc: physDBD.data_desc.DataDesc, data_dir: str, time: float) → numpy.array

Import Gillespie SSA data at a single timepoint

Args:

data_desc (DataDesc): Data description data_dir (str): Data directory time (float): Time (real time)

Returns:

np.array: Numpy array of size (no_seeds, no_species)

static import_gillespie_ssa_whole_file(fnames: List[str], times: List[float], species: List[str]) → numpy.array

Import Gillespie SSA whole file and extract multiple times for different seeds

Args:

fnames (List[str]): List of files corresponding to different seeds times (List[float]): List of times (real time) to extract species (List[str]): Species

Raises:

ValueError: If species not found

Returns:

np.array: array of size (no_times, no_seeds, no_species)

Module contents