feat: weaponize ASCII sparklines #18
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Contributor
There was a problem hiding this comment. 🟢 The extraction of sparkline generation into a generic script is a great improvement for reusability across the extension. However, consider adding a basic unit test for this new script (e.g., in a |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,115 @@ | ||
| #!/usr/bin/env -S uv run | ||
| # Copyright 2026 Google LLC | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| # /// script | ||
| # requires-python = ">=3.10" | ||
| # dependencies = [ | ||
| # "pandas", | ||
| # "numpy", | ||
| # "python-dateutil" | ||
| # ] | ||
| # /// | ||
|
|
||
| import argparse | ||
| import sys | ||
| import numpy as np | ||
| import pandas as pd | ||
|
|
||
| def generate_sparkline(vals, num_bins=16, wrapper="|"): | ||
| """ | ||
| Generates an ASCII sparkline from a sequence of values. | ||
| Uses ' ' (space) for the lowest/empty bucket to ensure empty spots are visible when framed. | ||
| """ | ||
| if len(vals) == 0: | ||
| return f"{wrapper}{wrapper}" | ||
| if len(vals) < num_bins: | ||
| num_bins = len(vals) | ||
|
|
||
| splits = np.array_split(vals, num_bins) | ||
| binned = np.array([np.mean(s) for s in splits if len(s) > 0]) | ||
|
|
||
| vmin, vmax = np.min(binned), np.max(binned) | ||
| if vmin == vmax: | ||
| shape_str = "▄" * len(binned) | ||
| return f"{wrapper}{shape_str}{wrapper}" | ||
|
|
||
| normalized = np.round((binned - vmin) / (vmax - vmin) * 7).astype(int) | ||
| # Replaced lowest char '_' with ' ' (space) as requested for better framing visibility | ||
| chars = [' ', '▂', '▃', '▄', '▅', '▆', '▇', '█'] | ||
| shape_str = "".join([chars[i] for i in normalized]) | ||
| return f"{wrapper}{shape_str}{wrapper}" | ||
|
|
||
| def main(): | ||
| parser = argparse.ArgumentParser(description="Generates an ASCII sparkline from a CSV file.") | ||
| parser.add_argument("--csv", required=True, help="Path to the input CSV data") | ||
| parser.add_argument("--values-column-name", dest="values_column", required=True, help="Column name containing the numeric values.") | ||
| parser.add_argument("--time-column-name", dest="time_column", required=False, help="(Optional) Time column name. Will try to auto-infer if not provided.") | ||
| parser.add_argument("--bins", type=int, default=16, help="Number of bins (characters) in the sparkline.") | ||
| parser.add_argument("--wrapper", type=str, default="|", help="Wrapper character around the sparkline (default: |).") | ||
| args = parser.parse_args() | ||
|
|
||
| try: | ||
| df = pd.read_csv(args.csv) | ||
| try: | ||
| df = pd.read_csv(args.csv) | ||
| except Exception as e: | ||
|
palladius marked this conversation as resolved.
|
||
| print(f"Error reading CSV '{args.csv}': {e}", file=sys.stderr) | ||
| sys.exit(1) | ||
| print(f"Error reading CSV: {e}", file=sys.stderr) | ||
| sys.exit(1) | ||
|
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||
| if args.values_column not in df.columns: | ||
| print(f"Error: Column '{args.values_column}' not found in CSV. Available columns: {', '.join(df.columns)}", file=sys.stderr) | ||
| sys.exit(1) | ||
|
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| # Autoinfer time column if not provided | ||
| time_col = args.time_column | ||
| if not time_col: | ||
| # Check heuristics for time column | ||
| time_keywords = ['time', 'timestamp', 'date', 'datetime'] | ||
| for col in df.columns: | ||
| if any(k in col.lower() for k in time_keywords): | ||
| time_col = col | ||
| break | ||
|
|
||
| # If still not found, try to see if the first column can be parsed as datetime, or just use the first column | ||
| if not time_col and len(df.columns) > 0: | ||
| potential_col = df.columns[0] | ||
| if potential_col != args.values_column: | ||
| try: | ||
| pd.to_datetime(df[potential_col][:5]) | ||
| time_col = potential_col | ||
| except: | ||
| # just pick the first column if no time column is found, as long as it's not the value! | ||
| time_col = potential_col | ||
|
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||
| if time_col and time_col in df.columns: | ||
| try: | ||
| # Sort by the inferred or explicit time column | ||
| df[time_col] = pd.to_datetime(df[time_col]) | ||
| df = df.sort_values(by=time_col) | ||
| except Exception: | ||
| pass # ignore sorting if it's not actually a time parsing compatible column | ||
|
|
||
| values = df[args.values_column].dropna().values | ||
| if len(values) == 0: | ||
| print(f"Error: No valid numeric data found in column '{args.values_column}'.", file=sys.stderr) | ||
| sys.exit(1) | ||
|
|
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| sparkline = generate_sparkline(values, num_bins=args.bins, wrapper=args.wrapper) | ||
| print(sparkline) | ||
|
|
||
| if __name__ == "__main__": | ||
| main() | ||
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🟢 This change from
_to|pipes, as requested.