org-analyze 0.1.9

Collect data from org-mode/org-roam pages and do some simple analyzing it.

OrgAnalyze

Collect data from org-mode/org-roam pages and do some simple analyzing it

Items Parsed by ParserOrg

The parser processes the following org-mode elements, turning them into specific Python objects:

• Headers: Lines starting with *, **, etc. are parsed as OrgHeader objects.
• Clock Entries: Lines starting with CLOCK: or #+CLK: are parsed as OrgClock objects.
• Tables: Blocks of lines starting with | are parsed into OrgTable objects.
• Source Blocks: Code blocks delimited by #+BEGIN_SRC and #+END_SRC are parsed as OrgSourceBlock.
• Math Blocks: LaTeX math blocks delimited by \[ and \] are parsed as OrgMath.
• Property Drawers: Blocks delimited by :PROPERTIES: and :END: are parsed as OrgProperties.
• File Variables: Lines like #+TITLE: My Document are parsed and stored in the parser's vars dictionary.
• Text and Links: All other lines are treated as plain text (OrgText), with org-mode links like [[...]] being processed.

export_markdown from org2md

Converts an org-mode file into a list of Markdown strings. It handles various org elements like headers, tables, source code blocks, and text.

Conversion Rules

• Headers: * Header becomes # Header, ** Sub-header becomes ## Sub-header, and so on.
• Tables: Org-mode tables are converted to Markdown tables.
• Source Blocks: #+BEGIN_SRC language ... #+END_SRC becomes a fenced code block in Markdown (```language ... ```).
• Math Blocks: \[ ... \] becomes a fenced math block.
• Links: Org-mode links are converted. [[id:some-id][My Link]] becomes [[My Link]] and [[https://example.com][Example]] becomes [Example](https://example.com).
• Text: Plain text lines are preserved.
• Ignored: CLOCK, PROPERTIES blocks are currently ignored in the output.

Example

from org_analyze.org2md import export_markdown

markdown_lines = export_markdown('example.org')
for line in markdown_lines:
    print(line)

read_clockins from clocks

This function parses all *.org files in a given directory. It extracts all clocking information and associates it with its parent header (Feature) and sub-header (Task).

The function returns a tuple containing a list of column names and a list of rows. This structure is ideal for creating a pandas DataFrame.

Example Usage

Let's say you have an org file tasks.org in a directory called my_orgs with the following content:

* Feature A
** Task 1
CLOCK: [2025-10-25 Sat 10:00]--[2025-10-25 Sat 11:30] =>  1:30
** Task 2
CLOCK: [2025-10-25 Sat 12:00]--[2025-10-25 Sat 13:00] =>  1:00

* Feature B
** Task 3
CLOCK: [2025-10-25 Sat 14:00]--[2025-10-25 Sat 14:30] =>  0:30

You can parse this file and analyze the data with pandas like this:

import pandas as pd
from org_analyze.clocks import read_clockins

# 1. Parse the org files in the directory
columns, rows = read_clockins('my_orgs')

# 2. Create a pandas DataFrame
df = pd.DataFrame(rows, columns=columns)

# 3. Analyze the data: Group by feature (head1) and sum the duration
feature_hours = df.groupby('head1')['duration'].sum()

print("Total hours per feature:")
print(feature_hours)

Output:

Total hours per feature:
head1
Feature A    2.5
Feature B    0.5
Name: duration, dtype: float64