Results for “numpy”

Calculate seawater carbonate chemistry from two independent measurements using PyCO2SYS. Produce pH, carbonate species, and mineral saturation results, convert laboratory conditions to ocean conditions, and propagate measurement uncertainty while recording calculation assumptions.

• Calculate carbonate chemistry from paired seawater measurements
• Assess aragonite and calcite saturation
• Convert laboratory results to ocean temperature and pressure

Analyze 2D fluorescence microscopy images to detect nuclei and measure their counts, intensity, and area. Run repeatable CellProfiler batches, inspect nucleus outlines, and review measurement tables with recorded pipeline settings and quality checks.

• Count nuclei across microscopy image batches
• Measure fluorescence intensity and area for each nucleus
• Inspect nucleus outlines for segmentation errors

Analyze DepMap cancer model data to rank genes required for cell survival, compare groups defined by biological markers, and explore drug responses. Release tracking and statistical checks help organize evidence for research hypotheses.

• Ranking gene dependencies in cancer models
• Comparing groups defined by biological markers
• Finding genes with similar dependency patterns

Prepare bulk RNA-seq sequencing files or quantification outputs for gene-level comparisons. Check sample information and data quality, configure processing workflows, and assemble validated count tables and metadata for downstream differential expression analysis with PyDESeq2.

• Preparing gene count tables from FASTQ files
• Configuring an nf-core/rnaseq workflow
• Combining Salmon, STAR, or featureCounts outputs

Implement and debug astronomical data analysis with Astropy in Python. Work with physical units, celestial coordinates, observation files, tables, precise time conversions, and cosmological models to prepare data and calculate astronomical quantities.

• Convert celestial coordinates and physical units
• Read and edit FITS observation files
• Match objects across astronomical catalogs

Plan experiments before collecting data. Helps choose study designs, randomly assign subjects or samples, organize treatment combinations, and generate reproducible schedules while checking for confounding and repeated measurements incorrectly counted as independent samples.

• Create random assignments for subjects or samples
• Screen multiple experimental factors
• Balance conditions across batches, days, or plate positions

Simulate a uniform gas mixture in a closed, insulated reactor at constant volume or pressure. Calculate ignition delay from the peak temperature rise rate, export temperature and species histories, and check conservation and numerical stability.

• Calculate ignition delay at constant volume or pressure
• Inspect temperature and species histories
• Compare ignition delays across reaction mechanisms

Process DNA, RNA, and protein sequences with Biopython, convert biological files, and retrieve NCBI records. Supports batch workflows for sequence comparison, searches for similar sequences, protein structure analysis, and evolutionary trees.

• Convert GenBank files to FASTA
• Calculate sequence length and GC content in batches
• Retrieve sequences and publication records from NCBI

Build vector search that considers both semantic similarity and patterns in how items connect. Use graph features to explore structure in embeddings, numerical representations of content, and compare retrieval results with conventional similarity search.

• Investigate structure missed by cosine similarity
• Include contextual roles in RAG retrieval
• Compare graph-based and cosine search results

Measure Python execution time and memory use to locate slow or resource-heavy code. Use profiling results to guide improvements in data structures, caching, input and output operations, algorithms, and parallel processing.

• Finding slow functions in Python applications
• Reducing memory use and investigating leaks
• Improving data processing and database queries

Evaluate LLM applications using automated scores, human reviews, and model-based judging. Build repeatable checks to compare models or prompts, assess answer quality, and detect declines in performance before deployment.

• Comparing models and prompts
• Checking quality after prompt changes
• Detecting performance declines before deployment

Add boxes, arrows, labels, and color-coded highlights to screenshots and diagrams using Pillow. Includes automatic label placement, detection of changed image regions, and guidance for timing annotations in animated GIF demonstrations.

• Highlighting screenshot changes for pull request reviews
• Labeling changes in before-and-after images
• Adding explanations to diagrams and architecture images

Review trading rules across China A-shares, Hong Kong stocks, US stocks, and crypto markets. Use comparison tables and checklists to identify execution limits, short-selling constraints, trading hours, fees, and tax considerations when designing historical trading simulations.

• Adding market rules to historical trading simulations
• Comparing constraints across stock and crypto markets
• Checking execution conditions for cross-market strategies

Analyze the volatility implied by option prices across strike prices and expiration dates. Compare calendar spreads and other volatility strategies, then assess portfolio risk using Greeks, measures of how option values respond to market changes.

• Compare implied volatility across strikes and expiration dates
• Evaluate calendar spread strategies
• Review portfolio risk using Greeks

Generate trading signals from historical volatility, a measure of past price fluctuations. Rank its current level against recent history, signal long positions at low levels, and exit or short at high levels while retaining positions between thresholds.

• Build signals from historical volatility levels
• Adjust calculation windows and percentile thresholds
• Set annualization for cryptocurrency data

Generates buy, sell, or stand-aside signals from calendar patterns using price and volume data. Supports configurable months and weekdays, including combined conditions, for testing seasonal trading ideas against historical data.

• Testing trading rules based on particular months
• Adding weekday conditions to a seasonal strategy
• Combining month and weekday signals

Analyze price and volume data using trend indicators, overbought and oversold measures, and volume changes. Combine these three perspectives into a single long, short, or stand-aside signal, with adjustable calculation periods and thresholds.

• Combine several technical indicators into one signal
• Check trends alongside overbought and oversold conditions
• Adjust indicator periods and thresholds

Build a pair trading strategy for two related instruments using their price ratio. Measure how far the ratio moves from its recent average, generate opposite buying and selling signals, and close positions when the ratio returns near that average.

• Test a pair trade between two stocks in the same industry
• Build a BTC/ETH price ratio strategy
• Set entry and exit thresholds for a pair trade

Apply statistical tests to financial time series, model changing volatility, and diagnose regression problems. Estimate uncertainty in strategy metrics through bootstrap resampling, which repeatedly samples existing data, and organize findings into a statistical testing report.

• Check whether price and return series are statistically stable
• Test long-term relationships between two assets
• Diagnose regression problems in factor research

Rank stocks using several indicators, such as recent returns and trading volume, combined on a common scale. Build equally weighted portfolios from the highest scores, or combine existing factors into strategies with buying and selling positions.

• Rank stocks using several investment indicators
• Build an equally weighted portfolio of top-ranked stocks
• Combine Alpha Zoo factors into a trading strategy

Evaluate investment funds and ETFs using returns, risk, fees, investment style, and manager performance. Detect changes in style, compare how closely ETFs follow their indexes, and plan portfolios that invest in multiple funds.

• Compare funds by returns, risk, and fees
• Detect changes in a fund's investment style
• Evaluate fund managers and ETF index tracking

Recognize classic candlestick patterns using pandas, a Python library for tabular data. Combine bullish and bearish pattern scores into long, short, or stand-aside signals, with adjustable thresholds for candle bodies and shadows.

• Detect classic patterns in candlestick data
• Combine bullish and bearish patterns into one signal
• Adjust thresholds for candle bodies and shadows

Compare exchange-traded funds by fees, tracking accuracy, liquidity, and market-price differences from asset value. Use the China-focused framework and Python examples to screen products, examine sector rotation, and study portfolio allocation.

• Comparing ETFs that track the same index
• Estimating the long-term impact of fees
• Checking cross-border ETF premiums

Analyze bid–ask spreads, order books, and buying and selling imbalances to assess liquidity, or how easily an asset trades. Estimate large-order price impact and transaction costs, with additional guidance on Chinese A-share auction and block trades.

• Estimate transaction costs for large orders
• Assess liquidity using order book depth and spreads
• Study trading imbalances and signs of liquidity stress