'numpy' 검색 결과

Diagnose errors and incorrect inference results in MNN's QNN backend for Qualcomm HTP processors. Compare intermediate calculation data against CPU results to locate the first faulty operator, distinguish bugs from precision limits, and guide operator support and performance fixes.

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• Investigate QNN runtime and model conversion errors
• Find the first operator producing incorrect results
• Distinguish implementation bugs from numerical precision limits

Set up ScenarioRunner, CARLA’s scenario engine, with a branch matched to your simulator version. Install dependencies, configure Python search paths, and verify imports to address setup failures before running scenarios.

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• Install ScenarioRunner for a specific CARLA version
• Switch an existing setup to the UE5 branch
• Fix imports failing because the agents package is missing

Read and display CARLA object bounding boxes, then project them onto a camera image as 2D rectangles. Export an annotated PNG and box coordinates in JSON for object detection annotation work.

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• Adding vehicle boxes to a camera image
• Exporting box coordinates for detection annotations
• Checking vehicle dimensions

Read existing CARLA sensors to view live camera or lidar output, save recordings, or inspect a single reading. Display multiple sensors together and check ROS 2 topic names, delivery settings, and whether publishing is enabled.

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• View a vehicle camera feed live
• Save camera images or lidar data for a fixed duration
• Inspect a GNSS sensor reading

Creates figures for machine learning papers from draft text or experiment data. Extracts components for Gemini-generated diagrams, chooses suitable charts with matplotlib or seaborn, and adjusts colors, fonts, and export formats for publication.

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• Turn a method section into an architecture diagram
• Create comparison charts from experiment results
• Plot training loss and accuracy over time

Build similarity search with FAISS to find nearby matches in large collections of numeric vectors. The skill guides index selection, memory reduction, GPU use, and saving indexes while balancing search speed and accuracy.

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• Find similar items in large vector datasets
• Choose an index for search speed and accuracy
• Reduce memory use for vector search

Transcribe recordings and reshape the text into meeting notes, emails, or social media posts. Combine text from multiple recordings into a continuous document, then prepare the result for copying or sharing while preserving the original meaning.

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• Turn meeting recordings into structured notes
• Draft a formal email from a voice memo
• Shape recorded ideas into social media posts

Analyze individual stocks using current quotes and historical price data. Calculate trend and momentum measures, identify price gaps and potential support or resistance levels, and produce a three-day outlook with trading scenarios and risk notes.

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• Reviewing an individual stock's price trend
• Checking support and resistance around price gaps
• Comparing three-day price scenarios

Coordinate AI agents to create short videos, from copy and storyboards to images, narration, subtitles, and MP4 assembly. The workflow includes quality checks, revision requests, and production data reviews to guide later improvements.

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• Creating a 30-second product introduction video
• Coordinating agents across video production tasks
• Checking images and audio sync before delivery

Generate character images, backgrounds, synthetic speech, and music for product videos featuring 小省导购员, a digital shopping presenter. Coordinate these assets with InfiniteTalk to produce a 25-second vertical video made from five scenes.

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• Producing a 25-second vertical product presentation video
• Generating reference images for the 小省导购员 character
• Creating business setting backgrounds for a video

Continue file reading and data processing when execute_code_sandbox or read_file fails. This skill runs multiline Python through run_shell, with examples for text, JSON, spreadsheets, PDF and Word documents, plus basic summaries and grouped calculations.

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• Reading text or JSON when read_file fails
• Inspecting Excel and CSV data through Python
• Extracting text from PDF and Word documents

Extract edit times from documents, inspect audio for clicks and level issues, and guide production of stems, or separate audio parts. Each stage includes checks, ending with a mixed export verified for duration, format, and audio levels.

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• Editing audio from instructions in DOCX or text documents
• Checking clicks and levels at specified times
• Verifying the duration and sample rate of each stem

Prepare audio stems, the separate parts of a track, for mixing by matching their durations with silence, loops, or crossfades. Verify each generated and processed file before moving to the next production step.

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• Match audio parts with different durations
• Extend drums and melodic parts using suitable methods
• Check audio specifications before mixing

Produce audio in separate stems, meaning individual instrument tracks, and verify each output as you go. Align track lengths with silence, loops, or crossfades, then apply effects, mix a master, and package the files.

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• Align instrument tracks to a reference audio file
• Choose how to extend tracks that are too short
• Verify audio properties after generation and effects

Organize audio production into checked stages, from beat timing to mixing and ZIP packaging. Generate and verify each stem, a separate audio part, then check processed files before combining them into a master track.

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• Generate and verify audio parts one at a time
• Plan song sections from BPM and beat counts
• Package a master track and separate audio parts

Investigate Python failures in a sandbox, an isolated execution environment, by capturing partial logs, testing functions separately, and checking output files one at a time. Use the findings to identify failing steps and rebuild a working script.

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• Investigate failures with missing or incomplete output
• Isolate a failing function in a large script
• Verify multiple generated files one at a time

Produce a master track and separate audio parts through a structured Python workflow. Apply filters and reverb, export WAV files with consistent settings, package them in a ZIP archive, and check that the deliverables meet the requested specifications.

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• Export a master track with separate audio parts
• Apply filters and reverb to audio
• Check duration and format across deliverables

Add burned-in captions to a finished video using Whisper timings from its own audio. Choose paper, bold, or clean styling, and optionally use narration text to preserve intended spelling. Outputs include the captioned video and an SRT file.

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• Adding captions to a finished video
• Restyling captions for social media shorts
• Preserving brand names and numbers from a narration script

Check whether tools for mathematical modeling, numerical analysis, charts, and paper compilation are installed. Get a report separating required and optional dependencies, platform-specific installation commands, and a follow-up check after approved installation.

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• Preparing a computer for mathematical modeling
• Checking missing paper compilation and charting tools
• Installing required dependencies after reviewing the commands

Clean Excel data and group records with similar numerical features. Produce a combined view of indicator trends, value distributions, category shares, and grouped data, then export the charts as an image and cleaned records as Excel.

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• Comparing trends across multiple indicators
• Checking value distributions and category shares
• Grouping records with similar numerical features

Analyze one Excel worksheet by cleaning column names, extracting numbers, and handling gaps from merged cells. Create category counts, percentages, cross-tabulations comparing groups, high-resolution charts, and CSV exports, with Parquet storage for the data.

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• Clean worksheets containing merged cells
• Summarize category counts and percentages
• Compare groups with cross-tabulations

Clean Excel data, summarize groups, and examine relationships between numeric columns. Produce charts such as scatter plots with trend lines, calculate measures of fit and statistical significance, and export cleaned data and statistical summaries.

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• Clean missing group labels and numeric text
• Compare group averages and variation
• Analyze numeric relationships with scatter plots and trend lines

Clean Excel columns by removing unwanted prefixes from numeric values and extracting Chinese characters from text. Add the cleaned results as new columns, then save an Excel file and provide a download link.

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• Cleaning numeric columns with unwanted prefixes
• Extracting Chinese characters from text fields
• Exporting cleaned data as an Excel file

Assess numeric Excel columns using IQR, a measure of the middle half of values, to flag outliers. Summarize counts and limits, create box plots, and examine distribution shape to inform data cleaning.

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• Checking outliers before data analysis
• Comparing outlier rates across numeric columns
• Reviewing distribution shape with box plots