I just got package codes from ChatGPT. It’s just a package calling internal API to render data. Even open source the packages won’t solve the issue. Since the internal API is unavailable for us.
How did you install it? pip install ace_tools? Then, it is not dangerous at the moment since this package is empty. You can check it by typing “pip show ace_tools”.
Or have you installed the ACE_TOOLS repo from farazpy on Github? Then you have installed some doubtful libraries.
Just happened me as well. Similar sequence to above.
The same thing just happend to me in ChatGPT 4o with canvas. But what’s even more curious about it is that ChatGPT executed the generated code and used ace_tools to show the result in chat.
Got this line when chatGPT was trying to execute some code today.
import ace_tools as tools; tools.display_dataframe_to_user(“LangGraph Code Outputs”, results)
very similar experience just now.
Yup. Just experienced the same. As I didn’t end up downloading the repo, this serves as a hamless, but very valuable reminder of being sceptical when using LLMs for coding.
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It seems like no one from OpenAI monitors this forum, which is frustrating for developers.
Six months have passed since this issue was first reported, with several confirmations, yet the problem still exists (it just happened to me right now).
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This is the nature of OpenAI’s python tool for code interpreter:
## python
When you send a message containing Python code to python, it will be executed in a
stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 60.0
seconds. The drive at '/mnt/data' can be used to save and persist user files. Internet access for this session is disabled. Do not make external web requests or API calls as they will fail.
Use ace_tools.display_dataframe_to_user(name: str, dataframe: pandas.DataFrame) -> None to visually present pandas DataFrames when it benefits the user.
When making charts for the user: 1) never use seaborn, 2) give each chart its own distinct plot (no subplots), and 3) never set any specific colors – unless explicitly asked to by the user.
I REPEAT: when making charts for the user: 1) use matplotlib over seaborn, 2) give each chart its own distinct plot (no subplots), and 3) never, ever, specify colors or matplotlib styles – unless explicitly asked to by the user
The unexpected mid-sentence line breaks are also part of the tool description to add confusion to the AI’s tool use.
The repeated demanding in the text to desperately overcome AI model indifference to tool instructions also makes it more influential in all AI thoughts.
Here’s the simple solution:
If you are having the AI produce Python code for you as its task, and not doing data analysis tasks with the code interpreter sandbox, disable the “code” checkbox in custom instructions, in your account settings. There are checkbox options below the text input boxes for how you want the AI to act (like where you’d tell ChatGPT to focus on advanced Python programming).
This will avoid placement of tool instructions into context, which the AI can confuse as instructions to infuse into your own code output.
My previous recommendation for OpenAI about dataframes output in general still stands. This is solely a module used to display tabular data in the ChatGPT user interface.
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just happened to me as well. OpenAI should fix this before it gets taken advantage of
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Same here. Not an issue for me but I thought OpenAI would like to know the issue persists.
Prompt (after some context):
Can you put together a metric using sin and cosin?
Response (see last line of Python code):
Using trigonometric functions like sine (sin\sinsin) and cosine (cos\coscos) is a great approach to handle circular data, such as dates in a year. This method maps days of the year onto a unit circle, making it easy to compute a similarity metric. Dates close to each other in the year will have similar sine and cosine values, even when wrapping around the end of the year.
Trigonometric Metric for Seasonal Difference
- Map the Date to a Unit Circle:
- Calculate the angle corresponding to the day of the year: Angle=DOY×2π365\text{Angle} = \frac{\text{DOY} \times 2\pi}{365}Angle=365DOY×2π
- Use sin(Angle)\sin(\text{Angle})sin(Angle) and cos(Angle)\cos(\text{Angle})cos(Angle) to represent the date in two dimensions on the unit circle.
- Compute Difference:
- The similarity between two dates can be computed as the Euclidean distance between their sin\sinsin and cos\coscos values.
Code Implementation
python
Copy code
import pandas as pd
import numpy as np
# Define the reference date (e.g., MDD)
reference_date = pd.Timestamp('2024-06-17')
reference_doy = reference_date.day_of_year
# Function to calculate trigonometric difference
def trig_doy_difference(date, reference_doy):
doy = date.day_of_year
angle_date = (doy / 365) * 2 * np.pi
angle_ref = (reference_doy / 365) * 2 * np.pi
# Calculate the Euclidean distance between points on the unit circle
sin_diff = np.sin(angle_date) - np.sin(angle_ref)
cos_diff = np.cos(angle_date) - np.cos(angle_ref)
return np.sqrt(sin_diff**2 + cos_diff**2)
# Apply the trigonometric difference to 'Flow Test Date'
hydrants_1000['Flow Test Date Trig Diff'] = hydrants_1000['Flow Test Date'].apply(
lambda x: trig_doy_difference(x, reference_doy)
)
# Display the updated GeoDataFrame (or use it for plotting)
import ace_tools as tools; tools.display_dataframe_to_user(name="Hydrants with Trigonometric DOY Difference", dataframe=hydrants_1000)
Explanation:
- Angle Calculation:
- Maps the day of the year to an angle on the unit circle.
- Ensures a smooth and cyclical representation of dates.
- Trigonometric Representation:
- sin\sinsin and cos\coscos place the date on a circle.
- Captures both proximity and wrapping around the end of the year.
- Euclidean Distance:
- The distance between two points on the unit circle measures their similarity.
- Dates close in the year (e.g., 2023-06-17 and 2024-06-17) have a small distance.
Benefits:
- Properly handles circular nature of dates.
- Naturally respects seasonal patterns.
- Can be directly used as a feature (e.g., marker size or color in plots).
Let me know if you need further clarifications or refinements!