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update get dimensionality method. #361

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update get dimensionality method. #361

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fce167a
update get dimensionality method.
SamTov Oct 22, 2021
0dc0796
Merge branch 'main' into SamTov_update_get_dimensionality
SamTov Feb 11, 2022
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56 changes: 21 additions & 35 deletions mdsuite/utils/meta_functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,7 +70,8 @@ def is_jsonable(x: dict) -> bool:


def join_path(a, b):
"""Join a and b and make sure to use forward slashes
"""
Join a and b and make sure to use forward slashes

Parameters
----------
Expand All @@ -94,12 +95,15 @@ def join_path(a, b):

def get_dimensionality(box: list) -> int:
"""
Calculate the dimensionality of the experiment box
Calculate the dimensionality of the experiment box.
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Run np where to check where in the box a non-zero number occurs. Set these numbers
to 1 and sum over them.

Parameters
----------
box : list
box array of the experiment of the form [x, y, z]
box array of the experiment of the form [x, y, z] shape (N,)

Returns
-------
Expand All @@ -108,25 +112,7 @@ def get_dimensionality(box: list) -> int:
make sense just yet)
"""

# Check if the x, y, or z entries are empty, i.e. 2 dimensions
if box[0] == 0 or box[1] == 0 or box[2] == 0:
if (
box[0] == 0
and box[1] == 0
or box[0] == 0
and box[2] == 0
or box[1] == 0
and box[2] == 0
):
dimensions = 1
else:
dimensions = 2

# Other option is 3 dimensions.
else:
dimensions = 3

return dimensions
return np.where(box != 0, 0, 1, 0).sum()
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Please add a test

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It seems thre has actually been a test for this the whole time. One of the first added.

def test_get_dimensionality(self):
        """
        Test the get_dimensionality method.
        Returns
        -------
        assert that for all choices of dimension array the correct dimension comes out.
        """
        one_d = [1, 0, 0]
        two_d = [1, 1, 0]
        three_d = [1, 1, 1]
        assert get_dimensionality(one_d) == 1
        assert get_dimensionality(two_d) == 2
        assert get_dimensionality(three_d) == 3



def get_machine_properties() -> dict:
Expand Down Expand Up @@ -223,11 +209,11 @@ def optimize_batch_size(
computer_statistics = get_machine_properties() # Get computer statistics
file_size = os.path.getsize(filepath) # Get the size of the file
database_memory = (
0.1 * computer_statistics["memory"]
0.1 * computer_statistics["memory"]
) # We take 10% of the available memory

memory_per_configuration = (
file_size / number_of_configurations
file_size / number_of_configurations
) # get the memory per configuration
initial_batch_number = int(
database_memory / (5 * memory_per_configuration)
Expand Down Expand Up @@ -330,7 +316,7 @@ def wrap(*args, **kw):


def apply_savgol_filter(
data: list, order: int = 2, window_length: int = 17
data: list, order: int = 2, window_length: int = 17
) -> np.ndarray:
"""
Apply a savgol filter for function smoothing
Expand Down Expand Up @@ -380,15 +366,15 @@ def closest_point(data: np.ndarray, value: float):


def golden_section_search(
data: np.array,
a: float,
b: float,
tol: float = 1e-5,
h: float = None,
c: float = None,
d: float = None,
fc: float = None,
fd: float = None,
data: np.array,
a: float,
b: float,
tol: float = 1e-5,
h: float = None,
c: float = None,
d: float = None,
fc: float = None,
fd: float = None,
) -> tuple:
"""
Perform a golden-section search for function minimums
Expand Down Expand Up @@ -492,7 +478,7 @@ def split_array(data: np.array, condition: np.array) -> list:
initial_split = [data[condition], data[~condition]] # attempt to split the array

if (
len(initial_split[1]) == 0
len(initial_split[1]) == 0
): # if the condition is never met, return only the raw tensor_values
return list([data[condition]])
else: # else return the whole array
Expand Down