Run failed due to a directory issue -- [Errno 21] Is a directory: '__MACOSX'

[IanMulvany]

running version version 1.1.6 on OSX 15.1.1 (24B91), running via a pip installed version in a virtual env, with python supplied via anaconda, I am getting a specific error below

2024-12-12 20:16:02.519 python[58043:43262174] +[IMKClient subclass]: chose IMKClient_Modern
2024-12-12 20:16:02.519 python[58043:43262174] +[IMKInputSession subclass]: chose IMKInputSession_Modern
2024-12-12 20:16:05.073 python[58043:43262174] The class 'NSOpenPanel' overrides the method identifier. This method is implemented by class 'NSWindow'
QLayout::addChildLayout: layout QVBoxLayout "" already has a parent
==== Starting conversation ===========================================================================================================================
with_director

CreateConversation(name="with_director", participants=['Director', 'Performer']) -> with_director

==== Starting conversation ===========================================================================================================================
Data Exploration Code

CreateConversation(name="Data Exploration Code", participants=['DataExplorer', 'Performer']) -> Data Exploration Code

[1] ----- SYSTEM casting {Performer} -> "Data Exploration Code" -------------------------------------------------------------------------------------

You are a brilliant data scientist. You are writing a Python code to analyze data.

---

[2] ----- USER {DataExplorer} -> "Data Exploration Code" <background_all_file_descriptions> --------------------------------------------------------

Description of the Dataset

General Description

• Rationale:
  The dataset maps US Congress's Twitter interactions into a directed graph with social interactions (edges) among Congress members (nodes). Each member
  (node) is further characterized by three attributes: Represented State, Political Party, and Chamber, allowing analysis of the adjacency matrix
  structure, graph metrics and likelihood of interactions across these attributes.

• Data Collection and Network Construction:
  Twitter data of members of the 117th US Congress, from both the House and the Senate, were harvested for a 4-month period, February 9 to June 9, 2022
  (using the Twitter API). Members with fewer than 100 tweets were excluded from the network.

• Nodes. Nodes represent Congress members. Each node is designated an integer node ID (0, 1, 2, ...) which corresponds to a row in
  congress_members.csv, providing the member's Represented State, Political Party, and Chamber.

• Edges. A directed edge from node i to node j indicates that member i engaged with member j on Twitter at least once during the 4-month data-
  collection period. An engagement is defined as a tweet by member i that mentions member j's handle, or as retweets, quote tweets, or replies of i to a
  tweet by member j.

• Data analysis guidelines:

• Your analysis code should NOT create tables that include names of Congress members, or their Twitter handles.
• Your analysis code should NOT create tables that include names of States, or their two-letter abbreviations. The code may of course do statistical
  analysis of properties related to States, but should not single out specific states.

Data Files

The dataset consists of 2 data files:

File 1: "congress_members.csv"

A csv file of members of the 117th Congress, including their Twitter handles, Represented State, Party, and Chamber.
Data source: https://pressgallery.house.gov/member-data/members-official-twitter-handles.
Rows are ordered according to the node ID, starting at 0.

Fields:

Handle: Twitter handle (without @)
State: Categorical; Two-letter state abbreviation; including also: "DC", "PR", "VI", "AS", "GU", "MP".
Party: Categorical; Party affiliation ("D", "R", or "I")
Chamber: Categorical; The member's chamber ("House", "Senate")

Here are the first few lines of the file:

Handle,State,Party,Chamber
SenatorBaldwin,WI,D,Senate
SenJohnBarrasso,WY,R,Senate
SenatorBennet,CO,D,Senate

File 2: "congress_edges.dat"

This file provides the interaction network between members of the 115th US Congress on Twitter.
Download and adapted from: https://snap.stanford.edu/data/congress-twitter

Each line contains two integers (i, j), indicating a directed edge from node ID i to node ID j, compatible with nx.read_edgelist('congress_edges.dat',
create_using=nx.DiGraph()). An i->j edge indicates that Congress member i had at least one tweet engaging with Congress member j during the 4-month
collection period.

Here are the first few lines of the file:

0 4
0 12
0 18
0 25

---

[3] ===== SURROGATE {Performer} -> "Data Exploration Code" <background_thanks_all_file_descriptions> ===============================================
Thank you for the Description of the Dataset.

[4] COMMENTER -> "Data Exploration Code" <after_background> : Background messages completed. Requesting "Data Exploration code".

[5] ----- USER {DataExplorer} -> "Data Exploration Code" --------------------------------------------------------------------------------------------
As part of a data-exploration phase, please write a complete short Python code for getting a first sense of the data.

Your code should create an output text file named "data_exploration.txt", which should contain a summary of the data.

The output file should be self-contained; any results you choose to save to this file should be accompanied with a short header.

The output file should be formatted as follows:

Data Size

<Measure of the scale of our data (e.g., number of rows, number of columns)>

Summary Statistics

Categorical Variables

<As applicable, list here categorical values and their most common values>

Missing Values

<Counts of missing, unknown, or undefined values>
<As applicable, counts of special numeric values that stand for unknown/undefined if any (check in the "Description of the Dataset" above for any)>

<title of other summary you deem relevant, if any>

If any of the above sections is not applicable, then write "# Not Applicable" under that section.

If needed, you can use the following packages which are already installed:
('pandas', 'numpy', 'scipy')

Do not provide a sketch or pseudocode; write a complete runnable code.
Do not create any graphics, figures or any plots.
Do not send any presumed output examples.

[6] COMMENTER -> "Data Exploration Code" : Starting to write and debug code. Revision 1/5 (attempt 1/2).

Using GPT4o (max 128000 tokens) for 1042 context tokens and 500 expected tokens.
[7] ===== ASSISTANT{'model_engine': <ModelEngine.GPT4o: 'gpt-4o'>} {Performer} -> "Data Exploration Code" ===========================================

CONTEXT TOTAL (1042 tokens):

0 SYSTEM ( 17 tokens): You are a brilliant data scientist. You are writing a Python code to analyze data.

1 USER ( 704 tokens): # Description of the Dataset ## General Description * Rationale: The dataset [...] 0 4 0 12 0 18 0 25

2 SURROGATE ( 10 tokens): Thank you for the Description of the Dataset.

3 USER ( 311 tokens): As part of a data-exploration phase, please write a complete short Python [...] output examples.

4 ASSISTANT ( 390 tokens): python import pandas as pd # Load the data nodes_df = [...] as specified.

                                                                           {'model_engine': <ModelEngine.GPT4o: 'gpt-4o'>}

import pandas as pd

Load the data

nodes_df = pd.read_csv("congress_members.csv")
edges_df = pd.read_csv("congress_edges.dat", delim_whitespace=True, header=None, names=["Source", "Target"])

Initialize a list to accumulate the lines of the output file

output_lines = []

Data Size

output_lines.append("# Data Size")
output_lines.append(f"Number of nodes (congress members): {len(nodes_df)}")
output_lines.append(f"Number of edges (interactions): {len(edges_df)}\n")

Summary Statistics

output_lines.append("# Summary Statistics")
output_lines.append(nodes_df.describe(include='all').to_string())
output_lines.append("") # Add an extra line for spacing

Categorical Variables

output_lines.append("# Categorical Variables")

Analyzing categorical columns: 'Party', 'Chamber'

categorical_columns = ['State', 'Party', 'Chamber']
for column in categorical_columns:
output_lines.append(f"Column: {column}")
output_lines.append(nodes_df[column].value_counts().to_string())
output_lines.append("") # Add an extra line for spacing

Missing Values

output_lines.append("# Missing Values")
missing_values = nodes_df.isnull().sum()
if missing_values.sum() == 0:
output_lines.append("No missing values.")
else:
output_lines.append(missing_values.to_string())

output_lines.append("") # Add an extra line for spacing

Create and format the output file

with open("data_exploration.txt", "w") as file:
file.write("\n".join(output_lines))

This code performs an initial exploration of the dataset. It reads the data, computes summary statistics, examines categorical variables, and checks
for missing values. The results are written to an output file named data_exploration.txt, formatted with headers as specified.

CodeRunnerWrapper: Running and caching output.
Traceback (most recent call last):
File "/Users/devian/anaconda3/lib/python3.9/site-packages/data_to_paper/base_steps/base_steps_runner.py", line 185, in _run_all_steps
next_stage = self._run_stage(stage)
File "/Users/devian/anaconda3/lib/python3.9/site-packages/data_to_paper/base_steps/base_steps_runner.py", line 275, in _run_stage
return func()
File "/Users/devian/anaconda3/lib/python3.9/site-packages/data_to_paper/research_types/hypothesis_testing/steps_runner.py", line 111, in data_exploration
RequestCodeProducts.from(
File "/Users/devian/anaconda3/lib/python3.9/site-packages/data_to_paper/research_types/hypothesis_testing/coding/after_coding.py", line 239, in get_code_and_output_and_descriptions
code_and_output = self.get_code_and_output()
File "/Users/devian/anaconda3/lib/python3.9/site-packages/data_to_paper/research_types/hypothesis_testing/coding/after_coding.py", line 220, in get_code_and_output
return code_writing.get_code_and_output()
File "/Users/devian/anaconda3/lib/python3.9/site-packages/data_to_paper/base_steps/request_code.py", line 256, in get_code_and_output
code_and_output, debugger = self._run_debugger(code_and_output.code)
File "/Users/devian/anaconda3/lib/python3.9/site-packages/data_to_paper/base_steps/request_code.py", line 309, in _run_debugger
code_and_output = debugger.run_debugging()
File "/Users/devian/anaconda3/lib/python3.9/site-packages/data_to_paper/base_steps/debugger.py", line 608, in run_debugging
code_and_output = self._get_code_and_respond_to_issues(response)
File "/Users/devian/anaconda3/lib/python3.9/site-packages/data_to_paper/interactive/app_interactor.py", line 43, in wrapper
result = func(self, *args, **kwargs)
File "/Users/devian/anaconda3/lib/python3.9/site-packages/data_to_paper/base_steps/debugger.py", line 555, in _get_code_and_respond_to_issues
result, created_files, multi_context, exception = code_runner_wrapper.run()
File "/Users/devian/anaconda3/lib/python3.9/site-packages/data_to_paper/run_gpt_code/code_runner_wrapper.py", line 49, in run
return super().run(*args, **kwargs)
File "/Users/devian/anaconda3/lib/python3.9/site-packages/data_to_paper/run_gpt_code/cache_runs.py", line 169, in run
file_contents = _read_files(created_files)
File "/Users/devian/anaconda3/lib/python3.9/site-packages/data_to_paper/run_gpt_code/cache_runs.py", line 76, in _read_files
file_contents[fname] = _read_file(fname)
File "/Users/devian/anaconda3/lib/python3.9/site-packages/data_to_paper/run_gpt_code/cache_runs.py", line 64, in _read_file
with open(filename, 'rb') as f:
IsADirectoryError: [Errno 21] Is a directory: '__MACOSX'
------ UNEXPECTED ERROR ------

Run failed unexpectedly

data-to-paper failed due to an unexpected error.

---

[Errno 21] Is a directory: '__MACOSX'

---

Please report the exception traceback from the console as a GitHub issue.

You can now:

1. CLOSE the app to terminate the run.

2. RE-TRY by click the reset button of prior stages.

---

qt.qpa.fonts: Populating font family aliases took 59 ms. Replace uses of missing font family "Consolas" with one that exists to avoid this cost.
^Z
zsh: suspended data-to-paper