Initializing a Dataset with Tabular Data

  1. Initializing a Dataset with Tabular Data:

  • Generate random tabular data for multiple scalars.

  • Initialize a dataset with the tabular data.

  1. Accessing and Manipulating Data in the Dataset:

  • Retrieve and print the dataset and specific samples.

  • Access and display the value of a particular scalar within a sample.

  • Retrieve tabular data from the dataset based on scalar names.

This example demonstrates how to initialize a dataset with tabular data, access specific samples, retrieve scalar values, and extract tabular data based on scalar names.

# Import required libraries
import numpy as np

# Import necessary libraries and functions
from plaid.utils.init_with_tabular import initialize_dataset_with_tabular_data

# Print dict util
def dprint(name: str, dictio: dict):
    print(name, "{")
    for key, value in dictio.items():
        print("    ", key, ":", value)

    print("}")

Section 1: Initializing a Dataset with Tabular Data

# Generate random tabular data for multiple scalars
nb_scalars = 7
nb_samples = 10
names = [f"scalar_{j}" for j in range(nb_scalars)]

tabular_data = {}
for name in names:
    tabular_data[name] = np.random.randn(nb_samples)

dprint("tabular_data", tabular_data)

tabular_data {
     scalar_0 : [-0.7748112   0.58197174 -0.93305148  0.00520075 -0.43612163 -1.38130933
  0.6327033   0.64911131  0.74531583 -0.53086264]
     scalar_1 : [-1.14554866 -1.82872028  0.74539072  0.72077254 -1.90526983 -1.01022042
 -0.57791925 -0.15221116 -1.78209615  1.28911555]
     scalar_2 : [-0.24837586 -0.35781295 -0.55286984 -0.1958495   1.14654056  0.19179581
 -0.65507729 -0.43993229 -1.59230989  0.51258886]
     scalar_3 : [ 0.0250144  -0.30140477  1.08292446  2.13233325  0.05831823 -1.1326039
 -0.11535814  1.1376475   2.97777911 -2.82593091]
     scalar_4 : [-0.5426671   0.74681293  1.41993807  0.39546077 -0.33254456 -0.4891201
  0.17259896  0.38166388  0.36033517 -0.27012217]
     scalar_5 : [-1.46104057  0.13417581 -0.52833346 -0.23916237  0.7163114  -1.0312232
  0.92713797  2.05439065 -0.60179969 -0.38935545]
     scalar_6 : [-0.5539804   1.71416683  0.24040183 -0.84028251  1.1723665  -0.26635766
 -1.00405075 -0.9269535   0.65417661 -0.99195604]
}

# Initialize a dataset with the tabular data
dataset = initialize_dataset_with_tabular_data(tabular_data)
print("Initialized Dataset: ", dataset)

Initialized Dataset:  Dataset(10 samples, 7 scalars, 0 fields)

Section 2: Accessing and Manipulating Data in the Dataset

# Retrieve and print the dataset and specific samples
sample_1 = dataset[1]
print(f"{sample_1 = }")

sample_1 = Sample(7 globals, 1 timestamp, 0 fields)

# Access and display the value of a particular scalar within a sample
scalar_value = sample_1.get_scalar("scalar_0")
print("Scalar 'scalar_0' in Sample 1:", scalar_value)

Scalar 'scalar_0' in Sample 1: 0.5819717437199943

# Retrieve tabular data from the dataset based on scalar names
scalar_names = ["scalar_1", "scalar_3", "scalar_5"]
tabular_data_subset = dataset.get_scalars_to_tabular(scalar_names)
print("Tabular Data Subset for Scalars 1, 3, and 5:")
dprint("tabular_data_subset", tabular_data_subset)

Tabular Data Subset for Scalars 1, 3, and 5:
tabular_data_subset {
     scalar_1 : [-1.14554866 -1.82872028  0.74539072  0.72077254 -1.90526983 -1.01022042
 -0.57791925 -0.15221116 -1.78209615  1.28911555]
     scalar_3 : [ 0.0250144  -0.30140477  1.08292446  2.13233325  0.05831823 -1.1326039
 -0.11535814  1.1376475   2.97777911 -2.82593091]
     scalar_5 : [-1.46104057  0.13417581 -0.52833346 -0.23916237  0.7163114  -1.0312232
  0.92713797  2.05439065 -0.60179969 -0.38935545]
}