postmd.utils package

postmd.utils.average_replicates(data_arrays)

average the data from replicates.

Parameters:

data_arrays (list or np.ndarray) – a list of data

Returns:

the averaged data

Return type:

np.ndarray

Examples

>>> import numpy as np
>>> from postmd.utils import average_replicates
>>> data_arrays = [
>>>    np.array([4.3, 5.6, 3.8, 5.1, 4.9]),  # First dataset
>>>    np.array([3.2, 4.5, 4.1, 3.7, 4.3]),  # Second dataset
>>>    np.array([5.5, 6.2, 5.9, 6.1, 5.8]),   # Third dataset
>>>    ]
>>> # Calculate the average of each dataset (i.e., each array).
>>> average = average_replicates(data_arrays)
>>> print(f"Averages of replicates: {average}")
Averages of replicates: [4.33333333 5.43333333 4.6        4.96666667 5.        ]

postmd.utils.cal_box_length(num, density=1.0, NA=None)

calculate the length of a cubic water box.

Warning

The built-in Avogadro constant in LAMMPS (units real or metal) is 6.02214129e23, see lammps/src/update.cpp, they write “force->mv2d = 1.0 / 0.602214129” for units real and units metal. However, we defalutly used the Avogadro constant in scipy.constants is 6.022140857e23, which is the international standard.

Parameters:

• num (int) – the number of water molecules.

• density (float, optional) – the density of water, in g/cm^3. Defaults to 1.0.

Returns:

the length of a cubic water box, in Angstrom

Return type:

float

Examples

>>> import postmd.utils as utils
>>> utils.cal_box_length(1000, density=1.0)
The length of a cubic water box for 1000 water molecules and 1.0 g/cm^3 is 31.043047 Angstrom

postmd.utils.create_dir(path, backup=True)

Create a directory at the specified ‘path’. If the directory already exists and ‘backup’ is True, rename the original directory by appending ‘.bkXXX’.

Parameters:

• path (str) – The path of the directory to be created.

• backup (bool, optional) – Whether to back up an existing directory. Default is True.

Examples

>>> import os
>>> import postmd.utils as utils
>>>
>>> print(os.listdir())
['createdir.py']
>>> utils.create_dir("test")
>>> print(os.listdir())      # create a new "test" dir
['createdir.py', 'test']
>>> utils.create_dir("test")
>>> print(os.listdir())      # move orgin "test" dir to "test.bk000" dir
['createdir.py', 'test', 'test.bk000']
>>> utils.create_dir("test")
>>> print(os.listdir())      # move orgin "test" dir to "test.bk001" dir
['createdir.py', 'test', 'test.bk000', 'test.bk001']

postmd.utils.cumave(data)

calculate the cumulative average.

Parameters:

data (1d list) – the data need to do the cumulative average

Returns:

the cumulative average

Return type:

np.ndarray

Examples

>>> import postmd.utils as utils
>>> import numpy as np
>>> array = np.arange(9)
>>> print(array)
[0 1 2 3 4 5 6 7 8]
>>> utils.cumave(array)
array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. ])

postmd.utils.stat_bin(x, y, bins=10, range=None)

statistic the mean and standard deviation(ddof=0) of x and y in each bin. Here we used the [scipy.stats.binned_statistic](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.binned_statistic.html) function.

Parameters:

• x – (N,) array_like. A sequence of values to be binned.

• y – (N,) array_like. The data on which the statistic will be computed. This must be the same shape as x, or a set of sequences - each the same shape as x. If values is a set of sequences, the statistic will be computed on each independently.

• bins (int or sequence of scalars, optional) – If bins is an int, it defines the number of equal-width bins in the given range (10 by default). If bins is a sequence, it defines the bin edges, including the rightmost edge, allowing for non-uniform bin widths. Values in x that are smaller than lowest bin edge are assigned to bin number 0, values beyond the highest bin are assigned to bins[-1]. If the bin edges are specified, the number of bins will be, (nx = len(bins)-1). Defaults to 10.

• range ((float, float) or [(float, float)], optional) – The lower and upper range of the bins. If not provided, range is simply (x.min(), x.max()). Values outside the range are ignored. Defaults to None.

Returns:

(x_mean, x_std, y_mean, y_std)

Return type:

tuple

postmd.utils.judge_file(path)

judge whether the path is a file.

postmd.utils.judge_dir(path)

judge whether the path is a folder.

Submodules

postmd.utils.utils module

postmd.utils.utils.average_replicates(data_arrays)

average the data from replicates.

Parameters:

data_arrays (list or np.ndarray) – a list of data

Returns:

the averaged data

Return type:

np.ndarray

Examples

>>> import numpy as np
>>> from postmd.utils import average_replicates
>>> data_arrays = [
>>>    np.array([4.3, 5.6, 3.8, 5.1, 4.9]),  # First dataset
>>>    np.array([3.2, 4.5, 4.1, 3.7, 4.3]),  # Second dataset
>>>    np.array([5.5, 6.2, 5.9, 6.1, 5.8]),   # Third dataset
>>>    ]
>>> # Calculate the average of each dataset (i.e., each array).
>>> average = average_replicates(data_arrays)
>>> print(f"Averages of replicates: {average}")
Averages of replicates: [4.33333333 5.43333333 4.6        4.96666667 5.        ]

postmd.utils.utils.cal_box_length(num, density=1.0, NA=None)

calculate the length of a cubic water box.

Warning

The built-in Avogadro constant in LAMMPS (units real or metal) is 6.02214129e23, see lammps/src/update.cpp, they write “force->mv2d = 1.0 / 0.602214129” for units real and units metal. However, we defalutly used the Avogadro constant in scipy.constants is 6.022140857e23, which is the international standard.

Parameters:

• num (int) – the number of water molecules.

• density (float, optional) – the density of water, in g/cm^3. Defaults to 1.0.

Returns:

the length of a cubic water box, in Angstrom

Return type:

float

Examples

>>> import postmd.utils as utils
>>> utils.cal_box_length(1000, density=1.0)
The length of a cubic water box for 1000 water molecules and 1.0 g/cm^3 is 31.043047 Angstrom

postmd.utils.utils.create_dir(path, backup=True)

Create a directory at the specified ‘path’. If the directory already exists and ‘backup’ is True, rename the original directory by appending ‘.bkXXX’.

Parameters:

• path (str) – The path of the directory to be created.

• backup (bool, optional) – Whether to back up an existing directory. Default is True.

Examples

>>> import os
>>> import postmd.utils as utils
>>>
>>> print(os.listdir())
['createdir.py']
>>> utils.create_dir("test")
>>> print(os.listdir())      # create a new "test" dir
['createdir.py', 'test']
>>> utils.create_dir("test")
>>> print(os.listdir())      # move orgin "test" dir to "test.bk000" dir
['createdir.py', 'test', 'test.bk000']
>>> utils.create_dir("test")
>>> print(os.listdir())      # move orgin "test" dir to "test.bk001" dir
['createdir.py', 'test', 'test.bk000', 'test.bk001']

postmd.utils.utils.cumave(data)

calculate the cumulative average.

Parameters:

data (1d list) – the data need to do the cumulative average

Returns:

the cumulative average

Return type:

np.ndarray

Examples

>>> import postmd.utils as utils
>>> import numpy as np
>>> array = np.arange(9)
>>> print(array)
[0 1 2 3 4 5 6 7 8]
>>> utils.cumave(array)
array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. ])

postmd.utils.utils.stat_bin(x, y, bins=10, range=None)

statistic the mean and standard deviation(ddof=0) of x and y in each bin. Here we used the [scipy.stats.binned_statistic](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.binned_statistic.html) function.

Parameters:

• x – (N,) array_like. A sequence of values to be binned.

• y – (N,) array_like. The data on which the statistic will be computed. This must be the same shape as x, or a set of sequences - each the same shape as x. If values is a set of sequences, the statistic will be computed on each independently.

• bins (int or sequence of scalars, optional) – If bins is an int, it defines the number of equal-width bins in the given range (10 by default). If bins is a sequence, it defines the bin edges, including the rightmost edge, allowing for non-uniform bin widths. Values in x that are smaller than lowest bin edge are assigned to bin number 0, values beyond the highest bin are assigned to bins[-1]. If the bin edges are specified, the number of bins will be, (nx = len(bins)-1). Defaults to 10.

• range ((float, float) or [(float, float)], optional) – The lower and upper range of the bins. If not provided, range is simply (x.min(), x.max()). Values outside the range are ignored. Defaults to None.

Returns:

(x_mean, x_std, y_mean, y_std)

Return type:

tuple

postmd.utils.utils.judge_file(path)

judge whether the path is a file.

postmd.utils.utils.judge_dir(path)

judge whether the path is a folder.