Data Management¶
Data module for the Experimentalis library.
- experimentalis.data.calculate_uncertainty(raw_data, method='default', indices_range=None, y_range=None, plot=False, graphing_options=None)¶
Calculates the uncertainty \(dy\) in some measurement series. Implicitly assumes that the uncertainty is already unknown (\(dy = \vec{0}\)), so if \(dy \neq \vec{0}\), then this will throw an error.
- experimentalis.data.dataset_apply_selector(dataset, selector)¶
Applies a selector to all dimensions of the dataset simultaneously. These selectors can be filters, sorters, ranges, etc.
import numpy as np from experimentalis.data import Dataset data = Dataset( x = [ 1, 2, 3 ], y = [ 2, 4, 6 ], dx = [ 1/7, 1/6, 1/6 ], dy = [ 1/5, 1/9, 1/5 ] ) last_two_selector = slice(1, 3) is_odd_selector = data.y % 2 != 0 last_two_values = dataset_apply_selector(data, last_two_selector) odd_values = dataset_apply_selector(data, is_odd_selector)
- experimentalis.data.isolate_noise_uncertainty(raw_data)¶
Calculates uncertainty due to noise in a tuple dataset, calculated as the standard deviation over a period where the data should be uniform.
- Returns:
The standard deviation over a uniform distribution.
- Return type:
float
- experimentalis.data.pack_dataset(dataset, packing_factor=100)¶
Downsamples a dataset by averaging consecutive blocks of data (packing).
Returns a new Dataset where consecutive blocks of size p are averaged. The uncertainty in \(y\) is reduced accordingly. Implicitly ignores \(dx\), so the function automatically throws a warning for any dataset where \(dx \neq \vec{0}\).
Mathematically, if our dataset \(D\) is
\[\vec{D} = (x_i, y_i, dx_i, dy_i), \quad i = 1, 2, \dots, N\]and we have packing factor \(p\) (the block size), then for each packed point \(D'_j\) for \(j \in 1, 2, \dots, N/p\),
\[D'_j = (x'_j, y'_j, 0, dy'_j)\]where
\[ \begin{align}\begin{aligned}x'_j = \frac{1}{p} \sum_{k=0}^{p-1} x_{(j-1)p + k},\\y'_j = \frac{1}{p} \sum_{k=0}^{p-1} y_{(j-1)p + k},\end{aligned}\end{align} \]and the y-uncertainty becomes
\[dy'_j = \frac{dy}{\sqrt{p}}.\]- Parameters:
dataset (experimentalis.data.Dataset) – The dataset to be packed.
packing_factor (int) – The “packing factor” or block size
- Returns:
A new dataset of the packed data
- Return type:
experimentalis.data.Dataset
- experimentalis.data.shear_dataset(dataset, n)¶
Autoamtically shears of the last n entries of the dataset.
- Parameters:
dataset (experimentalis.data.Dataset) – Dataset to be sheared.
n (int) – The number of elements to remove.
- Returns:
A new dataset with the last n elements removed.
- Return type:
- experimentalis.data.sort_dataset(dataset)¶
Automatically sorts a dataset along the x-axis.
- experimentalis.data.trim_dataset(dataset, trim_range, graphing_options=None, plot=False)¶
Trims a dataset (optionally with a visual helper) within a selected range. The trim range is the data designated to be kept, not the data to remove.
- class experimentalis.dataset.Dataset(x, y, dx=None, dy=None)¶
Bases:
objectDatasets are tracker objects for two-dimensional coupled measurement, i.e., some independent measurement x and its uncertainty dx alongside a dependent measurement y on x and its uncertainty dy.
For example, a Dataset can typically be used for any one-dimensional time series, like stock prices, temperature, or precipitation chances. For the case of stock prices, x would be the time, dx would be the uncertainty in each clock reading (so, say, the instrumental lag between a measurement and the intended time delta), y would be the stock prices, and dy would be the uncertainty in the stock reading (once again due to lag or other issues).
- dx¶
- dy¶
- size()¶
Returns the size of the dataset
- x¶
- y¶
