{"id":null,"metadata":{"source":"./txt_docs/Advanced - Smoothing.txt"},"page_content":"The content produces visual graphs that display smoothed data using different mathematical techniques. The graphs show relationships between age and a variable called 'Bet', with lines representing the smoothed trends. One graph uses a quadratic curve to fit the data, providing a smooth transition between data points. Another graph employs a method to make the line even smoother using a specific smoothness factor. These visualizations help in understanding patterns and trends within the dataset, making it easier to analyze the data visually. Packages used: pangaeapy, matplotlib, numpy, scipy.","type":"Document"}
{"id":null,"metadata":{"source":"./txt_docs/TS_profile.txt"},"page_content":"The code generates and visualizes synthetic oceanographic data, specifically focusing on vertical profiles of temperature and salinity as they change with water depth. It produces a detailed graph with two lines: one for temperature and one for salinity. The graph is designed to be easy to read, with an inverted axis to show depth increasing downwards, and distinct color-coding for temperature and salinity. This visualization helps in understanding how temperature and salinity vary from the ocean surface to deeper waters, mimicking real-world ocean conditions. The code can also be adapted to real data for similar analysis. Packages used: Matplotlib, Pandas, Numpy.","type":"Document"}
{"id":null,"metadata":{"source":"./txt_docs/Advanced - Spectral analysis SPECMAP.txt"},"page_content":"The code analyzes a scientific data set to identify recurring patterns over time, focusing on specific cycles related to Earth's orbit. It produces a graph called a power spectrum, which shows the strength of various cycles within the data. Important cycles are highlighted with peaks on the graph, depicted as orange circles, with black lines indicating their prominence. The key frequencies identified correspond to known cycles of Earth's climate changes over thousands of years. This visual helps users understand and verify the presence of these natural cycles in the data. Packages used: pangaeapy, astropy, scipy, numpy, pandas, matplotlib.","type":"Document"}
{"id":null,"metadata":{"source":"./txt_docs/Basic 3 - plotting with pandas.txt"},"page_content":"The code generates a plot using data from a dataset identified by the number 900968. It specifically creates a graph that shows the relationship between 'Age' and 'CaCO3' (likely a chemical component) using a line chart. This visual aids in understanding how the CaCO3 levels change over different ages, providing insights into historical chemical composition trends. The plot is created with the pandas library, offering a straightforward visualization of the dataset's columns. The dataset also comes with a citation for reference. \n\nPackages used: pangaeapy, pandas.","type":"Document"}
{"id":null,"metadata":{"source":"./txt_docs/sampling_station_visualization.txt"},"page_content":"The code generates a map visualization showing sampling stations and ocean depth (bathymetry) for a specified region. This map includes red markers indicating the locations of sampling stations, labeled with names. The ocean depths are visually represented using a color gradient, helping to understand the underwater topography. Additional features such as coastlines and land areas are also depicted. The generated plot offers insights into the spatial distribution of sampling stations and the surrounding bathymetric landscape, making it useful for geographical analysis and research purposes. \n\nPackages used: Matplotlib, Cartopy, Pandas, NumPy, Xarray, AdjustText.","type":"Document"}
{"id":null,"metadata":{"source":"./txt_docs/Advanced - Principal Component Analysis.txt"},"page_content":"The code performs a Principal Component Analysis (PCA) on geochemical data to visualize relationships between chemical elements. The main output is a scatter plot that shows the loadings of the first two principal components, highlighting the contributions of various elements. This visualization helps identify patterns and clusters in the data, potentially related to different ages. The code also attempts to reproduce a figure from a scientific paper, adjusting data processing steps to improve accuracy. By analyzing the data without chlorine (Cl), it achieves better results. The final plot includes labeled points for each element and a separate plot showing clusters based on age. \n\nPackages used: pangaeapy, pandas, matplotlib, sklearn.","type":"Document"}
{"id":null,"metadata":{"source":"./txt_docs/ADCP.txt"},"page_content":"The code processes a dataset containing water depth and velocity data over time, then creates a heatmap visualization. This heatmap displays variations in water velocity across different depths and time periods, using color gradients to represent velocity changes. The visual output helps users quickly identify patterns or anomalies in the water movement data, potentially aiding in environmental or oceanographic studies. Packages used: pangaeapy, pandas, matplotlib, seaborn, numpy.","type":"Document"}
{"id":null,"metadata":{"source":"./txt_docs/Advanced  -  Generalized Additive Models (GAM).txt"},"page_content":"The code utilizes advanced statistical modeling to analyze climate-related data, specifically focusing on the relationship between age and a climate indicator called d18O. It produces a graph that plots the observed data points and fits a smooth curve to represent the trend over time using a method called Generalized Additive Models (GAM). A shaded area is also included in the graph to indicate the 95% confidence intervals, which helps to understand the reliability of the predictions. This visualization aids in interpreting historical climate patterns and predicting future changes. Packaged used: pangaeapy, Matplotlib, pygam.","type":"Document"}
{"id":null,"metadata":{"source":"./txt_docs/Basic 1 - load a PANGAEA dataset.txt"},"page_content":"This code demonstrates how to load and interact with a dataset from PANGAEA, a data publisher for Earth and environmental science. The main outcome is accessing the dataset's title and citation information, which can help users understand its content and reference it in their work. The code does not produce any visual outputs like graphs or charts but focuses on retrieving and displaying basic information about a specific dataset. Package used: pangaeapy.","type":"Document"}
{"id":null,"metadata":{"source":"./txt_docs/SIMMAX.txt"},"page_content":"The code processes paleotemperature data using a method called SIMMAX, which involves comparing and analyzing samples from various datasets. The main outcome is a colorful map that visually represents estimated past temperatures at different geographical locations. This map uses a range of colors to indicate temperature variations, providing an intuitive way to understand historical climate patterns. Users can glean insights into how temperatures varied spatially in the past, which can be valuable for climate studies and historical analyses. Packages used: Pangaeapy, Math, Sys, Pandas, Numpy, Cartopy, Matplotlib.","type":"Document"}
{"id":null,"metadata":{"source":"./txt_docs/Basic 2 - dataframe.txt"},"page_content":"The code provides a basic example of working with a dataset using the Pangaeapy library. It allows users to view the first few rows of the data to get an initial sense of its contents and then obtain a summary of basic statistics, such as averages and distribution, helping to understand the data better. Additionally, it offers a citation for the dataset, which is useful for referencing. Packages used: Pangaeapy, Pandas.","type":"Document"}