metrics
- class pydaddy.metrics.Metrics(**kwargs)
Bases:
objectHelper/utility module
- _R2(data, op, poly, k, adj=False)
R-square value between the predicted and expected values
- Parameters
data (array) – depended variable values, expected values, data
op (array) – independent variable values
poly (numpy.poly1d) – numpy polynomial fitted object
k (int) – degree of the polynomial poly
adj (bool) – if True, use R2-adjusted method instead of R2
- Returns
R2 – R2 or R2-adjusted depending upon ‘adj’ value
- Return type
float
- _R2_adj(data, op, poly, k)
Get R-squared adjusted parameter between data and fitted polynomial
- Parameters
data (array) – depended variable values, expected values, data
op (array) – independent variable for which the data is defined
poly (numpy.poly1d) – numpy polynomial fitted object
k (int) – degree of polynomial
- Returns
R2-adjusted – R2 adjusted parameter between data and fitted polynomial
- Return type
folat
- _closest_time_scale(time_scale, slider)
Gives closest matching time scale avaiable from the slider keys.
- _combined_data_dict()
Get all drift and diffusion data in dictionary format.
- _csv_header(prefix, file_name)
Generate headers for CSV file.
- _divergence(a, b)
Get the divergence between two timeseries data, the divergence returned here is defined as follows: divergence = 0.5*(KL_divergence(p,q) + KL_divergence(q,p))
The probablity density of a and b input timeseries is calculated before finding the divergence.
- Parameters
a (array) – observed timeseries data
b (array) – simulated timeseries data
- Returns
divergence
- Return type
float
- _fit_plane(x, y, z, order=2)
Fits n-th order plane to data in the form z = f(x,y) where f(x,y) the best fit equation of plane for the data computed using least square method.
- Parameters
x (2D array) – order parameter x
y (2D array) – order parameter y
z (2D array) – derrived drift or diffusion data
order (int) – order of the 2D plane to fit
- Returns
A callable object takes in x and y as inputs and returns z = f(x,y), where f(x,y) is the fitted function of the plane.
- Return type
- _fit_poly(x, y, deg)
Fits polynomial of degree deg
- Parameters
x (array) – independent variable
y (array) – depended variable
deg (int) – degree of the polynomial
- Returns
poly (numpy.poly1d) – polynomial object
x (array) – values of x for where y in defined
Notes
The nan values in the input x and y (if any) will be ignored.
- _fit_poly_sparse(x, y, deg, threshold=0.05, alpha=0, weights=None)
Fit a polynomial using sparse regression using STLSQ (Sequentially thresholded least-squares) :param x: (np.array) Independent and dependent variables :param y: (np.array) Independent and dependent variables :param deg: (int) Maximum degree of the polynomial :param threshold: (float) Threshold for sparse fit.
- _get_data_from_slider(drift_time_scale=None, diff_time_scale=None)
Get drift and diffusion data from slider data dictionary, if key not valid, returns the data corresponding to closest matching one.
- _get_data_range(x)
Get range of the values in x, (min(x), max(x)), rounded to 3 decimal places.
- _get_num_points(drift_time_scale, diff_time_scale)
- _get_stacked_data()
Get a dictionary of all (op_x, op_y, driftX, driftY, diffX, diffY) slider data stacked into numpy arrays.
- _interpolate_missing(y, copy=True)
Interpolate missing data
- Parameters
y (array) – data with missing (nan) values
copy (bool, optional(default=True)) – if True makes a copy of the input array object
- Returns
y – interpolated data
- Return type
array
- _is_valid_slider_timescale_list(slider_list)
Checks if the given slider timescale lists contains valid entries
- Parameters
slider_list (list, tuple) – timescales to include in the slider
- Returns
True if all values are valid, else False
- Return type
bool
- _isnotebook()
- _kl_divergence(p, q)
Calculates KL divergence between two probablity distrubitions p and q
- Parameters
p (array) – distrubution p
q (array) – distrubution q
- Returns
kl_divergence – kl divergence between p and q
- Return type
float
- _make_directory(p, i=1)
Recursively create directorie for a given path
- Parameters
path (str) – destination path
- Returns
path – path of created directory, same as input path.
- Return type
str
- _nan_helper(x)
Helper function used to handle missing data
- Parameters
x (array) – data
- Return type
callable function
- _remove_nan(x, y)
Removes NaN’s by deleting the indices where both x and y have NaN’s
- Parameters
x (array) – first input
y (array) – second input
- Returns
x, y - with all nan’s removed
- Return type
array
- _rms(x)
Calculates root mean square error of x
- Parameters
x (array) – input
- Returns
rms – rms error
- Return type
float
- _save_csv(dir_path, file_name, data, fmt='%.4f', add_headers=True)
Save data to CSV file.
- _stack_slider_data(d, slider_data, index)
Stack data from slider dictionary, corresponding to the given index, into columns of numpy array.
- _zip_dir(dir_path)
Make ZIP file of the exported result.