AI Analysis
Final verdict: SAFE
The package is deemed safe based on the analysis notes, which indicate minimal risks across all categories, with no evidence of malicious behavior or supply-chain attacks.
- Low network and shell execution risks are acceptable given the context.
- No signs of obfuscation, credential misuse, or suspicious metadata.
Per-check LLM notes
- Network: Network calls are expected for packages that require external API interactions.
- Shell: Shell executions may be legitimate for tools that interface with command-line utilities but could indicate potential risks if not properly sanitized.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating secure handling of secrets.
- Metadata: No suspicious elements detected in the metadata.
Heuristic Checks
Outbound Network Calls
score 4.5
Found 3 network call pattern(s)
""" response = requests.post( self._api_url, data=json.dumps(datay: response = requests.post(invokeURL, data=body, timeout=100) except requeipsoot': ipsoot} result = requests.post(api_url, json=myobj, auth=auth, timeout=timeout) if not
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 10.0
Found 6 shell execution pattern(s)
ll_list.extend(exe_args) subprocess.check_call(call_list) # nosec #!/usr/bin/env python """ Remove horizotogether.") calacs_str = subprocess.check_output(['calacs.e', '--version']).split()[0] # nosec # noqa caall_list.append('-bias') subprocess.check_call(call_list) # nosec """ The acscte module contains a functill_list.extend(exe_args) subprocess.check_call(call_list) # nosec """ The acscteforwardmodel module contall_list.extend(exe_args) subprocess.check_call(call_list) # nosec #!/usr/bin/env python """ The ACS Photoll_list.extend(exe_args) subprocess.check_call(call_list) # nosec """ The acssum module contains a functi
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository spacetelescope/acstools appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Matt Davis, Warren Hack, Norman Grogin, Pey Lian Lim, Sara Ogaz, Leonardo Ubeda, Mihai Cara, David Borncamp, Nathan Miles, Tyler Desjardins, Jenna Ryon, David Stark, Gagandeep Anand, Yotam Cohen" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with acstools
Create a Python-based application that leverages the 'acstools' package to analyze and visualize data from the Hubble Space Telescope's Advanced Camera for Surveys (ACS). This application will serve as a tool for astronomers and space enthusiasts to explore ACS data more interactively and visually. Here’s a detailed breakdown of the project requirements: 1. **Data Retrieval**: Implement functionality to download ACS data from the official Hubble archive using the 'acstools' package. Ensure the application can handle different types of ACS datasets (e.g., images, spectra). 2. **Data Preprocessing**: Utilize 'acstools' functions to preprocess the downloaded data. This includes calibrating the data, correcting for any instrumental effects, and preparing it for visualization. 3. **Visualization**: Develop an interactive visualization component where users can view the processed ACS data. Use libraries like Matplotlib or Plotly to create dynamic visualizations such as color-magnitude diagrams, false-color images, etc. 4. **Analysis Tools**: Integrate basic analysis tools within the app. For instance, allow users to measure brightness levels, identify celestial objects, or apply filters to enhance certain features in the images. 5. **User Interface**: Design a simple yet intuitive user interface (UI) using a library like PyQt or Tkinter. The UI should allow users to select datasets, perform preprocessing steps, choose visualization options, and run analyses. 6. **Documentation and Help**: Include comprehensive documentation and help sections within the application to guide users through its functionalities. 7. **Testing and Validation**: Ensure all components of the application work seamlessly together. Validate the preprocessing and analysis outputs against known standards or datasets provided by the ACS team. The goal of this project is not only to demonstrate proficiency in using 'acstools' but also to create a useful, user-friendly tool for exploring Hubble ACS data.