#!/bin/bash

query="Between 3400 BCE to 3000 BCE, how does the number of monuments and axes changes with elements of symbolic capital?"

gold_hypo="Between 3400 BCE to 3000 BCE, the number of monuments and axes increases along with symbolic capital"

gold_workflow="Compute correlation between single proxies of forms of capital and demography.

1. Read a CSV file named ""capital.csv"" into a DataFrame called dfX.
2. Exclude the first two rows and remove columns ""group"" and ""color"". Invert the values of the ""BCE"" column.
3. Set the index of the DataFrame to the ""BCE"" column.
4. Select specific columns related to proxies and rename them for clarity.
5. Add two new columns ""Demo14C"" and ""DemoVeg"" initialized with zeros.
6. Define start and end bins for the loop based on BCE time periods.
7. Loop through each time bin and calculate mean values for ""Demo14C"" and ""DemoVeg"" proxies.
8. Normalize the values of ""Demo14C"" and ""DemoVeg"" using z-score normalization.

9. Define start (st) and end (et) dates for time slices.
10. Iterate over each time slice. Calculate correlation coefficients between various proxies within each time slice using Pearson correlation.
11. Compute significance levels for the correlations using Pearson correlation's p-values. Adjust correlations involving ""DemoVeg"" proxy based on whether it contains NaN values or not."

gen_hypo="The observed correlation values are both very close to 1, indicating a near-perfect positive linear relationship between the interpolated 'MonumentCount' and 'AxesCelts' from the 'time_series_data' dataset and the corresponding Z scores ('ZMonument' and 'ZBeil') from the 'capital_data' dataset. This suggests that as the Z scores for monuments and axes increase, which reflects an increase in symbolic capital, the actual interpolated count of monuments and axes also increases.

Based on the analysis, we can form the following hypothesis:

Hypothesis: Between 3400 BCE and 3000 BCE, there is a strong positive relationship between the number of monuments and axes, which are indicators of symbolic capital. Specifically, as the elements of symbolic capital associated with monuments and axes increase, as measured by their Z scores, the actual counts of monuments and axes also increase.

It's important to note that while the correlation is very strong, this does not necessarily imply causation, and further research would be needed to establish any causal relationship."

gen_workflow=":

1. Data Loading: Loaded 'time_series_data.csv' and 'capital.csv' datasets from the provided paths.
2. Data Inspection: Examined the first few rows of the datasets to understand their structure and relevant columns.
3. Data Filtering: Filtered both datasets for the period between 3400 BCE to 3000 BCE based on the CE column in 'time_series_data' and converted BCE to CE in 'capital_data'.
4. Data Cleaning: Converted Z values in 'capital_data' from string to float and interpolated missing values for 'MonumentCount' and 'AxesCelts' in 'time_series_data'.
5. Data Merging: Combined the two datasets on the CE column to analyze the relationship between monuments and axes with elements of symbolic capital.
6. Correlation Analysis: Calculated the correlation between the interpolated 'MonumentCount' and 'AxesCelts' and the Z scores 'ZMonument' and 'ZBeil' to understand their relationships.

FINAL ANSWER:

Between 3400 BCE and 3000 BCE, the number of monuments and axes, which are indicators of symbolic capital, shows a strong positive relationship with elements of symbolic capital. As the Z scores for monuments and axes increase, indicating an increase in symbolic capital, the actual interpolated counts of monuments and axes also increase. This relationship is supported by a correlation near 1 for both monuments and axes with their respective Z scores. However, this analysis does not establish causation, and further research would be required to explore the causal dynamics of this relationship."

metadata_path="/Users/bodhi/projects/datavoyager/DiscoveryBench/archaeology/metadata_23.json"

echo $(python3 ../discovery_eval.py --gold_hypo "$gold_hypo" --pred_hypo "$gen_hypo" --gold_workflow "$gold_workflow" --pred_workflow "$gen_workflow" --metadata_path "$metadata_path" --metadata_type "real" "$query")