### Install pangaeapy from GitHub

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/index.md

Install the pangaeapy module directly from its GitHub repository using pip.

```bash
pip install git+https://github.com/pangaea-data-publisher/pangaeapy.git
```

--------------------------------

### Install pangaeapy from PyPI

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/index.md

Install the pangaeapy module from the Python Package Index (PyPI) using pip.

```bash
pip install pangaeapy
```

--------------------------------

### Complete Workflow Example

Source: https://context7.com/pangaea-data-publisher/pangaeapy/llms.txt

Demonstrates a full workflow: searching for datasets, loading a dataset with caching, exploring metadata, analyzing data with pandas, filtering, and downloading.

```python
from pangaeapy import PanQuery, PanDataSet
import pandas as pd

# Step 1: Search for datasets
query = PanQuery("Arctic sea ice thickness", limit=10)
print(f"Found {query.totalcount} datasets")

# Step 2: Load first dataset with caching
doi = query.get_dois()[0] if query.result else None
if doi:
    ds = PanDataSet(doi, enable_cache=True)

    # Step 3: Explore metadata
    print(f"Title: {ds.title}")
    print(f"Citation: {ds.citation}")
    print(f"Parameters: {list(ds.params.keys())}")

    # Step 4: Analyze data with pandas
    df = ds.data
    print(f"Data shape: {df.shape}")
    print(f"Data types:
{df.dtypes}")
    print(f"Summary statistics:
{df.describe()}")

    # Step 5: Filter and process data
    if 'Date/Time' in df.columns:
        df['Date/Time'] = pd.to_datetime(df['Date/Time'])
        recent_data = df[df['Date/Time'] > '2020-01-01']
        print(f"Recent data points: {len(recent_data)}")

    # Step 6: Export results
    ds.download()  # Save as CSV
```

--------------------------------

### Initialize PanDataSet and Import Libraries

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced - Spectral analysis SPECMAP.ipynb

Imports necessary libraries and initializes a PanDataSet object with a given dataset ID. Ensure you have pangaeapy, astropy, scipy, numpy, pandas, and matplotlib installed.

```python
from pangaeapy import PanDataSet
from astropy.timeseries import LombScargle
from scipy.signal import find_peaks, peak_prominences
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

ds=PanDataSet(441706)
```

--------------------------------

### Get Data Summary Statistics

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/basics/Basic 2 - dataframe.ipynb

Generate descriptive statistics for the DataFrame columns using the describe() method. This provides count, mean, std, min, max, and quartile values.

```python
ds.data.describe()
```

--------------------------------

### Get Unique Depth Values

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/ADCP.ipynb

Find all unique values present in the 'Depth water' column.

```python
ds.data['Depth water'].unique()
```

--------------------------------

### Get Column Statistics

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/example.ipynb

Generate descriptive statistics for a specific column (e.g., 'Temp') in the dataset.

```python
ds.data['Temp'].describe()
```

--------------------------------

### Initialize PanDataSet for Binary Data with Authentication

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/user_guide.md

When working with binary data sets, which can be large, you must provide an authentication token ('auth_token') obtained from your PANGAEA user profile. This is required for downloading the complete dataset.

```python
ds = PanDataSet(944070, enable_cache=True,
                cachedir='/path/to/your/storage',
                auth_token='abcdfeghijklmnopqrstuvwxyz')
```

--------------------------------

### Plot PCA Loadings (Initial Attempt)

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced - Principal Component Analysis.ipynb

Generates a scatter plot of the first two principal components' loadings, with elements labeled. This is an initial visualization attempt.

```python
fig = plt.figure(figsize = (6,5))
plt.plot(pcaload['PC1'],pcaload['PC2'], '.',color='k', label=pcaload['Element'],markersize=1)
for i, p in pcaload.iterrows():
        plt.text(p['PC1'], p['PC2'], str(p['Element']),fontsize=10)
```

--------------------------------

### Download All Files from Binary Dataset

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/how_to.md

Download all files from a binary dataset. Requires a PANGAEA user account and a bearer token for authentication. The cache directory must also be specified.

```python
ds = PanDataSet(956151, enable_cache=True,
                cachedir='/your/cache/path',
                auth_token='your_personal_bearer_token')
filenames = ds.download()
```

--------------------------------

### Pagination and Result Limits

Source: https://context7.com/pangaea-data-publisher/pangaeapy/llms.txt

Demonstrates how to control the number of results and implement pagination for large search result sets using PanQuery's limit and offset parameters.

```python
from pangaeapy import PanQuery

# Get first 100 results
query1 = PanQuery("ocean carbon", limit=100, offset=0)
print(f"Total available: {query1.totalcount}")
print(f"Retrieved: {len(query1.result)}")

# Get next 100 results (pagination)
query2 = PanQuery("ocean carbon", limit=100, offset=100)
print(f"Retrieved (page 2): {len(query2.result)}")
```

--------------------------------

### Download Binary and Tabular Data from PANGAEA

Source: https://context7.com/pangaea-data-publisher/pangaeapy/llms.txt

Use the `download()` method to save tabular data as CSV or download binary files. Specify row indices and columns for selective binary downloads. Authentication is required for downloading all binary files.

```python
from pangaeapy import PanDataSet

# Download tabular dataset as CSV
ds = PanDataSet(900388, enable_cache=True, cachedir='/your/cache/path')
filepaths = ds.download()
print(f"Downloaded files: {filepaths}")
# Output: ['/your/cache/path/900388_data.csv']

# Download specific binary files by row indices
ds = PanDataSet(956151, enable_cache=True, cachedir='/your/cache/path')
filepaths = ds.download(indices=[0, 1, 2], columns=['Binary'])
print(f"Downloaded files: {filepaths}")

# Download all binary files (requires auth_token)
ds = PanDataSet(
    956151,
    enable_cache=True,
    cachedir='/your/cache/path',
    auth_token='your_personal_bearer_token'
)
filepaths = ds.download()
print(f"Downloaded files: {filepaths}")
```

--------------------------------

### Initialize PanDataSet

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/ADCP.ipynb

Import necessary libraries and initialize a PanDataSet object with a given DOI.

```python
from pangaeapy.pandataset import PanDataSet
import pandas as pd
import matplotlib.pyplot as plt
ds = PanDataSet('10.1594/PANGAEA.864110')
```

--------------------------------

### View First Rows of Data

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/basics/Basic 2 - dataframe.ipynb

Access the dataset's data as a pandas DataFrame and display the first few rows using the head() method.

```python
ds.data.head()
```

--------------------------------

### Basic Usage of PanDataSet

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/index.md

Demonstrates how to initialize a PanDataSet object with a DOI and access its title and data.

```python
from pangaeapy import PanDataSet

ds = PanDataSet(787140)
print(ds.title)
print(ds.data.head())
```

--------------------------------

### Import pangaeapy Library

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/basics/Basic 3 - plotting with pandas.ipynb

Import the necessary PanDataSet class from the pangaeapy library.

```python
from pangaeapy import PanDataSet
```

--------------------------------

### Perform PCA and Prepare Loadings DataFrame

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced - Principal Component Analysis.ipynb

Performs PCA with 2 components and prepares the loadings for visualization. The component loadings are transposed and column names are cleaned to represent elements.

```python
sklearn_pca = sklearnPCA(n_components=2)
pcaresult = sklearn_pca.fit(X_std).transform(X_std)

pcaload=pd.DataFrame(sklearn_pca.components_).transpose()
pcaload=pcaload.rename(columns={0:'PC1',1:'PC2'})
pcaload['Element']=elementcols
pcaload['Element']=pcaload['Element'].str.extract(r'([A-Z][a-z]?)')
pcaload.head()
```

--------------------------------

### Initialize PanDataSet for Tabular Data

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/user_guide.md

Instantiate PanDataSet with a dataset ID to access tabular data. Enable caching for local storage using pickle. The default cache directory is ~/.pangaeapy_cache/.

```python
from pangaeapy import PanDataSet
ds = PanDataSet(900388, enable_cache=True)
```

--------------------------------

### Download Binary Data Files from PanDataSet

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/user_guide.md

For binary data sets, the download() method returns a list of file paths for the downloaded components. These paths can then be used with appropriate modules (e.g., xarray for netCDF) to process the data.

```python
filepaths = ds.download()
```

--------------------------------

### Load and Access PANGAEA Dataset

Source: https://context7.com/pangaea-data-publisher/pangaeapy/llms.txt

Initialize PanDataSet with a dataset ID or DOI to fetch metadata and tabular data. Access metadata attributes and the data as a pandas DataFrame. Inspect parameter details and print dataset summary information.

```python
from pangaeapy import PanDataSet

# Load a dataset using its numeric ID
ds = PanDataSet(787140)

# Access dataset metadata
print(f"Title: {ds.title}")
print(f"DOI: {ds.doi}")
print(f"Citation: {ds.citation}")
print(f"Year: {ds.year}")
print(f"Abstract: {ds.abstract}")

# Access authors (list of PanAuthor objects)
for author in ds.authors:
    print(f"Author: {author.fullname}, ORCID: {author.ORCID}")

# Access the data as a pandas DataFrame
print(ds.data.head())

# Get parameter information
for param_name, param in ds.params.items():
    print(f"Parameter: {param_name}, Unit: {param.unit}, Type: {param.type}")

# Print dataset summary information
ds.info()
```

--------------------------------

### Load PANGAEA Dataset Metadata Only

Source: https://context7.com/pangaea-data-publisher/pangaeapy/llms.txt

Initialize PanDataSet with `include_data=False` to retrieve only metadata, significantly speeding up initialization by skipping the data table download. Access various metadata fields including title, DOI, authors, keywords, projects, time extent, and license.

```python
from pangaeapy import PanDataSet

# Load only metadata (no data table download)
ds = PanDataSet(787140, include_data=False)

# Access metadata
print(f"Title: {ds.title}")
print(f"DOI: {ds.doi}")
print(f"Authors: {[a.fullname for a in ds.authors]}")
print(f"Keywords: {ds.keywords}")
print(f"Projects: {[(p.label, p.name) for p in ds.projects]}")
print(f"Time extent: {ds.mintimeextent} to {ds.maxtimeextent}")
print(f"License: {ds.licence.name if ds.licence else 'N/A'}")
```

--------------------------------

### Calculate and Prepare Distances

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/SIMMAX.ipynb

Merges sample and analog datasets, calculates distances, handles zero distances by replacing them with a very small value, and sets a multi-level index for further processing.

```python
Distances = pd.merge(sampleDS.data[['Event','Latitude','Longitude', 'Depth']].assign(k=1), analogDS.data[['Event','Latitude','Longitude','Temperature']].assign(k=1), on='k', suffixes=('1', '2')).drop('k', axis=1)
Distances['Event1']=Distances['Event1']+'_'+Distances['Depth'].map(str)
Distances['Distance']=Distances.apply(getDistance, axis=1)
#But we need to avoid to run into a division by zero trap, preperae by the simmean algo which will snap if distance is zero: (sj/dj)
#Therefore we need to replace zero distances by a very, very low distance e.g. 0.000000001 m or so
Distances.loc[Distances['Distance']==0,'Distance']=0.00000000001
Distances.set_index(['Event1', 'Event2'], inplace=True)
```

--------------------------------

### Set Custom Cache Directory

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/how_to.md

Configure a custom directory for caching downloaded data. This is useful for managing storage space and organization.

```python
ds = PanDataSet(956151, enable_cache=True,
                cachedir='/path/to/your/storage')
```

--------------------------------

### Initialize PanDataSet Object

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/basics/Basic 1 - load a PANGAEA dataset.ipynb

Create an instance of PanDataSet using either an integer ID or a DOI. This object represents a specific PANGAEA dataset.

```python
ds=PanDataSet(896621)
```

--------------------------------

### Download Specific File from Binary Dataset

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/how_to.md

Download a specific file from a binary dataset by providing the row index and column name. Ensure you have the dataset's DOI.

```python
ds = PanDataSet(956151, enable_cache=True, cachedir='/your/cache/path')
filenames = ds.download(indices=[3], columns=["Binary"])
```

--------------------------------

### Initialize PanDataSet and Load Data

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/SIMMAX.ipynb

Initializes PanDataSet objects for specific datasets and loads data from a CSV file. This is a prerequisite for further data processing.

```python
from pangaeapy import PanDataSet
import math
import sys
import pandas as pd
analogDS = PanDataSet('10.1594/PANGAEA.77352')
sampleDS=PanDataSet('10.1594/PANGAEA.55156')
#The annual mean
#levitus=pd.read_csv('woa13_decav_t00mn01v2.csv',sep=';')
#summer
levitus=pd.read_csv('woa13_decav_t15mn01.csv',sep=';')

#Compare with:
#http://discovery.ucl.ac.uk/101363/1/2002PA000774.pdf
```

--------------------------------

### Initialize PanDataSet

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/example.ipynb

Initialize a PanDataSet object using a PANGAEA dataset ID. The ID can be a numerical ID or a DOI.

```python
from pangaeapy.pandataset import PanDataSet

#ds = PanDataSet('734969')
#ds = PanDataSet(734969)
ds = PanDataSet('10.1594/PANGAEA.734969')
```

--------------------------------

### PanDataSet Class Initialization

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/api.md

Initializes a PanDataSet object to hold information for a specific PANGAEA dataset.

```APIDOC
## Class pangaeapy.PanDataSet

### Description
Initializes a PANGAEA PanDataSet object which holds necessary information, including data and metadata, to analyze a given PANGAEA dataset.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **id** (str) - The identifier of a PANGAEA dataset. An integer number or a DOI is accepted here.
* **paramlist** (list) - Not described.
* **deleteFlag** (str) - Defines a flag for which data should not be included if quality flags are available. Possible values are listed at https://wiki.pangaea.de/wiki/Quality_flag.
* **enable_cache** (boolean) - If True, PanDataSet objects are cached as pickle files to avoid unnecessary downloads. Defaults to False.
* **cachedir** (str) - The directory for caching pickle files. Will be created if it doesn't exist.
* **include_data** (boolean) - Determines if the data table is downloaded and added to the self.data dataframe. Set to False if only metadata is of interest. Defaults to True.
* **expand_terms** (list or int) - Indicates if found ontology terms for parameters shall be expanded for the given list of terminology ids, i.e., add their hierarchy terms/classification.
* **auth_token** (str) - The PANGAEA authentication token, available at https://www.pangaea.de/user/.
* **cache_expiry_days** (int) - The duration a cached pickle/cache is accepted. After this, pangaeapy will reload the data and ignore the cache. Defaults to 1.

### Attributes
* **id** (str) - The identifier of a PANGAEA dataset.
* **uri** (str) - The PANGAEA DOI (alternative label).
* **doi** (str) - The PANGAEA DOI.
* **title** (str) - The title of the dataset.
* **abstract** (str) - The abstract or summary of the dataset.
* **year** (int) - The publication year of the dataset.
* **authors** (list of PanAuthor) - A list containing the PanAuthor objects (author info) of the dataset.
* **citation** (str) - The full citation of the dataset.
* **params** (list of PanParam) - A list of all PanParam objects (parameters) used in this dataset.
* **parameters** (list of PanParam) - Synonym for self.params.
* **events** (list of PanEvent) - A list of all PanEvent objects (events) used in this dataset.
* **projects** (list of PanProject) - A list containing the PanProjects objects referenced by this dataset.
* **mintimeextent** (str) - A string containing the minimum time extent of the dataset.
* **maxtimeextent** (str) - A string containing the maximum time extent of the dataset.
* **data** (pandas.DataFrame) - A pandas DataFrame holding all the data.
* **loginstatus** (str) - Indicates if the dataset is protected. Default value: ‘unrestricted’.
* **isCollection** (boolean) - Indicates if this dataset is a collection dataset within a collection of child datasets.
* **collection_members** (list) - A list of DOIs of all child datasets if the dataset is a collection dataset.
* **moratorium** (str) - A label providing the date until the dataset is under moratorium.
* **datastatus** (str) - A label providing details about the dataset's status (published, in review, or deleted).
* **registrystatus** (str) - A string indicating the registration status of a dataset.
* **licence** (PanLicence) - A licence object, usually Creative Commons.
* **auth_token** (str) - The PANGAEA authentication token.
* **cache_expiry_days** (int) - The duration a cached pickle/cache is accepted.
* **cachedir** (str) - The full path to the cache directory.
* **keywords** (list[str]) - A list of keyword names.

### Methods
#### check_pickle()
Verifies if a cached pickle file needs to be refreshed (reloaded). Files are checked after 24 hrs earliest but only updated if the metadata indicates changes occurred.
* **Parameters:**
  * **expirydays** - Not described.
* **Returns:**
  * bool - True if the pickle needs refreshing, False otherwise.

#### from_pickle()
Reads a PanDataSet object from a pickle file.

#### to_pickle()
Writes a PanDataSet object to a pickle file.

#### setID(id)
Initializes the ID of a data set if it was not defined in the constructor.
* **Parameters:**
  * **id** (str) - The identifier of a PANGAEA dataset. An integer number or a DOI is accepted here.
```

--------------------------------

### Initialize PanDataSet

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/basics/Basic 3 - plotting with pandas.ipynb

Initialize a PanDataSet object with a specific dataset ID. This loads the dataset for further use.

```python
ds=PanDataSet(900968)
```

--------------------------------

### Enable Caching for PANGAEA Datasets

Source: https://context7.com/pangaea-data-publisher/pangaeapy/llms.txt

Configure PanDataSet to cache datasets locally as pickle files, preventing redundant downloads. Customize the cache directory and set cache expiry duration in days.

```python
from pangaeapy import PanDataSet

# Enable caching with default location (~/.pangaeapy_cache/)
ds = PanDataSet(900388, enable_cache=True)

# Enable caching with custom directory
ds = PanDataSet(900388, enable_cache=True, cachedir='/path/to/your/storage')

# Set cache expiry (default is 1 day)
ds = PanDataSet(900388, enable_cache=True, cache_expiry_days=7)

# Access cached data
print(ds.data.head())
```

--------------------------------

### Access Protected PANGAEA Datasets with Authentication

Source: https://context7.com/pangaea-data-publisher/pangaeapy/llms.txt

Initialize PanDataSet with an authentication token to access protected datasets. Ensure the token is valid and provided during initialization. Check login status and moratorium information.

```python
from pangaeapy import PanDataSet

# Access a protected dataset with authentication token
ds = PanDataSet(
    944070,
    enable_cache=True,
    cachedir='/path/to/your/storage',
    auth_token='your_personal_bearer_token'
)

# Check login status
print(f"Login status: {ds.loginstatus}")
print(f"Moratorium: {ds.moratorium}")

# Access data if authorized
print(ds.data.head())
```

--------------------------------

### Initial Data Plotting

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced  -  Generalized Additive Models (GAM).ipynb

Plots the 'G. ruber δ18O' data against 'Age' from the loaded dataset to visualize the raw observations. Adjusts the figure size for better readability.

```python
ds.data.plot(x='Age',y='G. ruber δ18O', figsize=(12,5), color='k')
```

--------------------------------

### Import Libraries for GAM Analysis

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced  -  Generalized Additive Models (GAM).ipynb

Imports the necessary libraries: PanDataSet from pangaeapy for data handling and pyplot from matplotlib for plotting.

```python
from pangaeapy import PanDataSet
from matplotlib import pyplot as plt
```

--------------------------------

### Import Libraries and Define Helper Function

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced - Principal Component Analysis.ipynb

Imports necessary libraries for data manipulation, plotting, and PCA. Includes a helper function to remove duplicate columns after merging dataframes.

```python
from pangaeapy import PanDataSet
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.decomposition import PCA as sklearnPCA
from sklearn.preprocessing import StandardScaler

#a helper function to delete duplicate columns which are added by the pd.merge actions
def drop_y(df):
    # list comprehension of the cols that end with '_y'
    to_drop = [x for x in df if x.endswith('_y')]
    df.drop(to_drop, axis=1, inplace=True)
```

--------------------------------

### Calculate Similarity Index (SIMMAX)

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/SIMMAX.ipynb

Calculates the square root of the sum of squared percentages for specified foraminifera columns in both sample and analog datasets. It then computes the similarity index and combines it with distance and other relevant data.

```python
#Lets do the SIMMAX
import numpy as np
#sum of squared percentages 
sampleDS.data['SQRTSUM']=np.sqrt((sampleDS.data[foramCols]**2).sum(axis=1))
analogDS.data['SQRTSUM']=np.sqrt((analogDS.data[foramCols]**2).sum(axis=1))
sampleIdx=sampleDS.data[foramCols].div(sampleDS.data['SQRTSUM'], axis=0)
analogIdx=analogDS.data[foramCols].div(analogDS.data['SQRTSUM'], axis=0)
sampleIdx['Event']=sampleDS.data['Event']+'_'+sampleDS.data['Depth'].map(str)
sampleIdx.set_index('Event', inplace=True)
analogIdx['Event']=analogDS.data['Event']
analogIdx.set_index('Event', inplace=True)
SimIndex = pd.DataFrame(
    (analogIdx[foramCols].values * sampleIdx[foramCols].values[:, None]).reshape(-1, analogIdx.shape[1]),
    pd.MultiIndex.from_product([sampleIdx.index, analogIdx.index]),
   sampleIdx.columns
)
Similars=pd.DataFrame(SimIndex.sum(axis=1), columns=['Similarity'])
Similars['Distance']=Distances['Distance']
Similars['Latitude']=Distances['Latitude1']
Similars['Longitude']=Distances['Longitude1']
Similars['Temperature']=Distances['Temperature']
```

--------------------------------

### Import pygam for GAM Fitting

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced  -  Generalized Additive Models (GAM).ipynb

Imports the LinearGAM class from the pygam module, which is required for fitting Generalized Additive Models.

```python
from pygam import LinearGAM
```

--------------------------------

### Inspect Dataset Columns

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/basics/Basic 3 - plotting with pandas.ipynb

View the column names of the loaded dataset. pangaeapy uses short names for data labeling.

```python
ds.data.columns
```

--------------------------------

### Plot Temperature and Salinity Profile

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/example.ipynb

Plot temperature and salinity against water depth for a specific event. Uses a secondary y-axis for salinity.

```python
ds.data.loc['PS04/223-1'].plot(x='Depth water', y=['Temp','Sal'],style='-', secondary_y='Sal')
```

--------------------------------

### Import necessary libraries

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced - Smoothing.ipynb

Imports the required libraries for data manipulation, interpolation, and plotting.

```python
from pangaeapy import PanDataSet
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import interp1d
```

--------------------------------

### Combine Results

Source: https://context7.com/pangaea-data-publisher/pangaeapy/llms.txt

Combines results from multiple queries and prints the total count.

```python
all_results = query1.result + query2.result
print(f"Total retrieved: {len(all_results)}")
```

--------------------------------

### Create DataFrame of Peaks and Sort by Prominence

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced - Spectral analysis SPECMAP.ipynb

Organizes the identified peaks, their prominences, and corresponding frequencies (in ka) into a pandas DataFrame. The DataFrame is then sorted in descending order of prominence to easily identify the most significant frequencies.

```python
powerpeaks=pd.DataFrame({'peak':peaks,'promi':prominences,'ka':1/f[peaks]})
powerpeaks=powerpeaks.sort_values('promi', ascending=False)
powerpeaks.head(3)
```

--------------------------------

### Clean and Convert Data Units

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced - Principal Component Analysis.ipynb

Cleans the dataset by converting values from percentage to mg/kg and recalculating oxide weights to element weights using a provided dictionary. This ensures consistent units for analysis.

```python
# 1) the dataset has data expressed in % as well as in mg/kg, we therefore need to clean the data first
#    in order to have the same units here. We recalculate % to mg/kg with a factor of 10000
# 2) oxides need to be converted to elementt weights
oxide={'SiO2':2.1392, 'TiO2':1.6681, 'Al2O3':1.8895, 'Fe2O3':1.4297, 'MnO':1.2912, 'MgO':1.6582, 'CaO':1.3992,
       'Na2O':1.3480, 'K2O':1.2046, 'P2O5':2.2916, 'SO3':2.4972}
for pshort,param in ds1.params.items():
    if pshort in oxide:
        dsdata[pshort]=dsdata[pshort].divide(oxide[pshort])
    if param.unit=='mg/kg':
        dsdata[pshort]=dsdata[pshort].divide(10000)
```

--------------------------------

### Search PANGAEA Repository

Source: https://context7.com/pangaea-data-publisher/pangaeapy/llms.txt

Perform a basic text search on the PANGAEA repository using PanQuery. Displays total results, individual result details, and extracts DOIs.

```python
from pangaeapy import PanQuery

# Basic text search
query = PanQuery("sediment temperature")
print(f"Total results: {query.totalcount}")

# Access search results
for result in query.result:
    print(f"URI: {result['URI']}")
    print(f"Type: {result['type']}")  # 'member' or 'collection'
    print(f"Position: {result['position']}")

# Get list of DOIs from search results
dois = query.get_dois()
print(f"DOIs found: {dois}")
```

--------------------------------

### Prepare Data for GAM Fitting

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced  -  Generalized Additive Models (GAM).ipynb

Extracts the 'Age' and 'G. ruber δ18O' columns from the dataset and converts them into NumPy arrays, preparing them for the GAM fitting process.

```python
X=ds.data['Age'].values
y=ds.data['G. ruber δ18O'].values
```

--------------------------------

### Download Specific Indices and Columns from PanDataSet

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/user_guide.md

Use this method to download specific files by providing a list of row indices and desired column names. This can be done without a bearer token, simplifying code sharing for tutorials or specific data subsets. Ensure the cachedir is set to your desired storage location.

```python
ds = PanDataSet(956151, enable_cache=True,
                cachedir='/path/to/your/storage')
filepaths = ds.download(indices=[0, 1, 2], columns=['Binary'])
```

--------------------------------

### Import UnivariateSpline

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced - Smoothing.ipynb

Imports the UnivariateSpline function from Scipy for alternative data smoothing.

```python
from scipy.interpolate import UnivariateSpline
```

--------------------------------

### Load Pangaea Dataset

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced - Smoothing.ipynb

Loads a dataset from Pangaea using its unique identifier. Ensure you have the correct dataset ID.

```python
ds=PanDataSet(894182)
```

--------------------------------

### Create Pivot Table for Velocity Data

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/ADCP.ipynb

Create a pivot table from the dataset's data, filtering by date range and using 'Depth water' as index, 'Date/Time' as columns, and 'VC' (velocity component) as values. Missing values are filled with 0.

```python
mf= pd.pivot_table(ds.data.loc['2012-04-01':'2012-06-20'], index='Depth water', columns='Date/Time', values='VC',fill_value=0)
mf.head()
```

--------------------------------

### PanQuery Class

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/api.md

Class for running and analyzing PANGAEA search queries.

```APIDOC
### Class: pangaeapy.PanQuery

#### Description
Runs and analyzes results of PANGAEA search queries.

#### Parameters
- **query** (str) - Required - The query string following the specs at www.pangaea.de.
- **bbox** (tuple of floats, optional) - The bounding box to define geographical search constraints following the GeoJSON specs – (minlon, minlat, maxlon, maxlat).
- **limit** (int, default 10) - The maximum number of results returned (cannot be higher than 500).
- **offset** (int, default 0) - The offset of the search results.

#### Attributes
- **totalcount** (int) - The number of total search results.
- **error** (str) - In case an error occurs, this attribute holds the latest one.

### Example Usage
```python
from pangaeapy import PanQuery

# Search for datasets related to 'ocean temperature' within a bounding box
query = "ocean temperature"
bbox = (10.0, 50.0, 20.0, 60.0) # minlon, minlat, maxlon, maxlat

pq = PanQuery(query, bbox=bbox, limit=50)

print(f"Total results found: {pq.totalcount}")

# Access search results (e.g., dataset IDs)
# for result in pq.results():
#     print(result['id'])

# Check for errors
# if pq.error:
#     print(f"An error occurred: {pq.error}")
```
```

--------------------------------

### Plot Salinity Profile

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/example.ipynb

Plot salinity against water depth for a specific event using a dotted line style.

```python
ds.data.loc['PS04/223-1'].plot(x='Depth water', y=['Sal'],style='.-')
```

--------------------------------

### Export Dataset to Frictionless Data Package

Source: https://context7.com/pangaea-data-publisher/pangaeapy/llms.txt

Export datasets to the Frictionless Data Package format for enhanced interoperability. The function can also return the package object without saving.

```python
from pangaeapy import PanDataSet

ds = PanDataSet(787140, enable_cache=True)

# Export to Frictionless Data Package
ds.to_frictionless(filelocation='/path/to/output')
# Creates: /path/to/output/787140_frictionless/

# Get package object without saving
package = ds.to_frictionless(save=False)
```

--------------------------------

### Filter and Select Top Similarities

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/SIMMAX.ipynb

Filters the Similars DataFrame for similarities >= 0.9, then sorts by similarity in descending order, and finally selects the top 10 most similar analog events for each sample event. The index is then renamed for clarity.

```python
#Original SIMMAX is using those values with similarity index >0.79 only
#mostSimilars=Similars.loc[(Similars['Similarity']>=0.79)]
#The revised SIMMAX is using the 10 top most similars with sim inded >0.9
mostSimilars=Similars[Similars['Similarity']>=0.9].sort_values(by='Similarity',ascending=False).groupby(level=0).head(10).sort_index(level=0,sort_remaining=False)
mostSimilars.index.names=['sampleEvent', 'analogEvent']
```

--------------------------------

### Export Dataset to NetCDF

Source: https://context7.com/pangaea-data-publisher/pangaeapy/llms.txt

Export PANGAEA datasets to NetCDF format, supporting both SeaDataNet (SDN) and PANGAEA styles. Can also retrieve the NetCDF object without saving.

```python
from pangaeapy import PanDataSet

ds = PanDataSet(787140, enable_cache=True)

# Export to SeaDataNet NetCDF format (default)
ds.to_netcdf(filelocation='/path/to/output', type='sdn')

# Export to PANGAEA-style NetCDF format
ds.to_netcdf(filelocation='/path/to/output', type='pan')

# Get NetCDF object without saving to disk
netcdf_obj = ds.to_netcdf(save=False)
```

--------------------------------

### Plot Data with Pandas

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/basics/Basic 3 - plotting with pandas.ipynb

Use the standard pandas plot method to visualize data from the PanDataSet. Ensure the data is loaded into `ds.data`.

```python
ds.data.plot(x='Age', y='CaCO3')
```

--------------------------------

### Handle Quality Flags in PANGAEA Datasets

Source: https://context7.com/pangaea-data-publisher/pangaeapy/llms.txt

Initialize PanDataSet with `deleteFlag` to exclude data points with specific quality control flags. The `qcdata` attribute provides access to quality control information.

```python
from pangaeapy import PanDataSet

# Exclude questionable data (flagged with '?')
ds = PanDataSet(787140, deleteFlag='?')
```

--------------------------------

### Display GAM Summary

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced  -  Generalized Additive Models (GAM).ipynb

Prints a detailed summary of the fitted Linear GAM model, including statistical measures, feature significance, and potential warnings related to model identifiability and p-value accuracy.

```python
gam.summary()
```

--------------------------------

### Extract Temperature Data using getLevitusTemp Function

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/SIMMAX.ipynb

Defines and uses the getLevitusTemp function to extract temperature data from a loaded dataset based on latitude, longitude, and depth. Handles missing values and finds the closest depth.

```python
def getLevitusTemp(lat, lon, lev):
    depth=30
    t=0
    #select the values for the upper water column
    temp=lev[['0','5','10','15','20','25','30','35','40','45','50']][(lev['LATITUDE']== (math.floor(lat)+0.5)) & (lev['LONGITUDE']== (math.floor(lon)+0.5))]
    #delete empty cells
    temp=temp.dropna(axis=1).to_dict(orient='list')
    #cast dict keys from string to int
    temp={int(key): value for key, value in temp.items()} 
    temp_keys=list(temp.keys())
    #find the closest available water depth value
    closest=min(temp_keys, key=lambda x:abs(x-depth))
    if len(temp[closest])>0:
        t=temp[closest][0]
        if t==None:
            print(str(lat)+' x '+str(lon))
            t=0
    else:
        print(str(lat)+' + '+str(lon))
        t=0
    return t
analogDS.data['Temperature']=analogDS.data.apply(lambda x: getLevitusTemp(x['Latitude'],x['Longitude'], levitus), axis=1)
```

--------------------------------

### Visualize Data with Seaborn Heatmap

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/ADCP.ipynb

Use seaborn to create a heatmap visualization of a dataset. Ensure matplotlib and seaborn are imported. This snippet assumes 'mf' is a pre-defined dataset.

```python
#import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import matplotlib.ticker as ticker
import matplotlib.dates as mdates
fig=plt.figure(figsize=(18,4))
ax=sns.heatmap(mf, square=False,cmap="jet")
#ax.xaxis.set_major_locator(ticker.LinearLocator(numticks=6))
plt.show()
```

--------------------------------

### Display DataFrame Columns

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced - Principal Component Analysis.ipynb

Prints the column names of the merged DataFrame to verify the data structure after loading and merging.

```python
dsdata.columns
```

--------------------------------

### Download Tabular Data from PanDataSet

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/user_guide.md

The download() method saves the tabular data set to a CSV file in the configured cache directory. It returns a list containing the path to the downloaded file.

```python
>>> ds.download()
Dataset saved to /path/to/your/storage/900388_data.csv
['/path/to/your/storage/900388_data.csv']
```

--------------------------------

### Print Citations

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/SIMMAX.ipynb

Prints the citation information for the analogDS and sampleDS datasets. This is important for academic and research use to properly attribute data sources.

```python
print(analogDS.citation)
print()
print(sampleDS.citation)
```

--------------------------------

### Access Dataset Title

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/basics/Basic 1 - load a PANGAEA dataset.ipynb

Retrieve the title of the loaded PANGAEA dataset.

```python
ds.title
```

--------------------------------

### Access Topotype

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/example.ipynb

Retrieve the topotype of the dataset, which describes the data's structure (e.g., 'profile series').

```python
ds.topotype
```

--------------------------------

### Iterate Through Events

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/example.ipynb

Loop through the events associated with the dataset and print their labels. This is useful for datasets with multiple series or profiles.

```python
for ev in ds.events:
    print(ev.label)
```

--------------------------------

### Specify Custom Cache Directory for PanDataSet

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/user_guide.md

When initializing PanDataSet, you can specify a custom directory for caching data by providing the 'cachedir' keyword argument.

```python
ds = PanDataSet(900388, enable_cache=True,
                cachedir='/path/to/your/storage')
```

--------------------------------

### Load and Merge PANGAEA Datasets

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced - Principal Component Analysis.ipynb

Loads data from four specified PANGAEA datasets and merges them into a single pandas DataFrame. It uses a helper function to clean up duplicate columns introduced during the merge process.

```python
ds1 = PanDataSet(900972)
ds2 = PanDataSet(900971)
ds3 = PanDataSet(900967)
ds4 = PanDataSet(900968)

#just for convenience, since data is organised in two ways we merge it in two steps
dsC=pd.merge(ds2.data,ds3.data,left_on='Depth',right_on='Depth',suffixes=('', '_y'))
drop_y(dsC)
dsN=pd.merge(ds1.data,ds4.data,left_on='Depth',right_on='Depth',suffixes=('', '_y'))
drop_y(dsN)
#now we concat the frames and drop the index
dsdata=pd.concat([dsN,dsC])
dsdata = dsdata.reset_index(drop=True)
```

--------------------------------

### Load PANGAEA Dataset

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced  -  Generalized Additive Models (GAM).ipynb

Loads a specific PANGAEA dataset using its identifier. This dataset will be used for subsequent analysis and plotting.

```python
ds = PanDataSet('10.1594/PANGAEA.857573')
```

--------------------------------

### Data Retrieval and Processing

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/api.md

Methods for retrieving and processing data from PANGAEA datasets.

```APIDOC
## GET /api/events

### Description
Retrieves all events with their attributes as columns in a DataFrame.

### Method
GET

### Endpoint
/api/events

### Parameters
#### Query Parameters
- **campaign_names** (list) - Required - List of campaign names to retrieve events for.

### Response
#### Success Response (200)
- **dataframe** (DataFrame) - A DataFrame containing all events and their attributes.

### Response Example
{
  "dataframe": "[DataFrame content]"
}
```

```APIDOC
## POST /api/data/set

### Description
Populates the data DataFrame with data from a PANGAEA dataset.

### Method
POST

### Endpoint
/api/data/set

### Parameters
#### Request Body
- **addEventColumns** (boolean) - Optional - If True, adds event-related columns (Latitude, Longitude, Elevation, Date/Time, Event) if they are not present in the dataset. Defaults to True.

### Response
#### Success Response (200)
- **status** (string) - Indicates the success of the operation.

### Response Example
{
  "status": "Data successfully loaded"
}
```

```APIDOC
## GET /api/metadata/set

### Description
Initializes the metadata of the PanDataSet object using the information from a PANGAEA metadata XML file.

### Method
GET

### Endpoint
/api/metadata/set

### Response
#### Success Response (200)
- **status** (string) - Indicates the success of the operation.

### Response Example
{
  "status": "Metadata initialized"
}
```

```APIDOC
## GET /api/geometry

### Description
Retrieves the real geometry (topographic type) of the dataset based on x, y, z, and t information from the data frame content. This method is experimental.

### Method
GET

### Endpoint
/api/geometry

### Response
#### Success Response (200)
- **geometry** (object) - The geometry information of the dataset.

### Response Example
{
  "geometry": {
    "type": "Point",
    "coordinates": [10.0, 20.0, 5.0]
  }
}
```

```APIDOC
## GET /api/paramdict

### Description
Translates the parameter object list into a dictionary.

### Method
GET

### Endpoint
/api/paramdict

### Response
#### Success Response (200)
- **param_dict** (dict) - A dictionary representing the parameter objects.

### Response Example
{
  "param_dict": {
    "parameter1": "value1",
    "parameter2": "value2"
  }
}
```

```APIDOC
## GET /api/info

### Description
Returns a set of basic information about the PANGAEA dataset.

### Method
GET

### Endpoint
/api/info

### Response
#### Success Response (200)
- **info** (object) - Basic information about the dataset.

### Response Example
{
  "info": {
    "dataset_id": "12345",
    "title": "Sample Dataset",
    "creation_date": "2023-01-01"
  }
}
```

--------------------------------

### Calculate and Display Prominent Frequencies

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced - Spectral analysis SPECMAP.ipynb

Calculates the actual frequencies (in ka) corresponding to the identified peaks by taking the reciprocal of the frequencies. This step translates the spectral analysis results into interpretable time scales.

```python
1/f[peaks]
```

--------------------------------

### Plot PCA Components by Age

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/advanced/Advanced - Principal Component Analysis.ipynb

Visualizes the first two principal components (PCA1 and PCA2) colored by 'Age'. This plot helps identify potential age-related clusters in the data.

```python
fig = plt.figure(figsize = (6,5))
plt.scatter(dsdata['PCA1'],dsdata['PCA2'], c=dsdata['Age'])
plt.xlabel('PCA1')
plt.ylabel('PCA2')
plt.show()
```

--------------------------------

### PangaeaPy Query and DOI Retrieval

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/api.md

This section details how to perform queries and retrieve Digital Object Identifiers (DOIs) using the PangaeaPy library.

```APIDOC
## PangaeaPy Query and DOI Retrieval

### Description
This section details how to perform queries and retrieve Digital Object Identifiers (DOIs) using the PangaeaPy library.

### Query Parameters

#### Request Body
- **query** (str) - Required - The query provided by the user.

### Response
#### Success Response (200)
- **result** (list of dictionaries) - A list of retrieved search results.

### Method: get_dois()

#### Description
Get the list of DOIs contained in the search result.

#### Returns
A list of DOIs.

#### Return type
list of str
```

--------------------------------

### Export Dataset to Darwin Core Archive

Source: https://context7.com/pangaea-data-publisher/pangaeapy/llms.txt

Export biodiversity-related datasets to the Darwin Core Archive (DwC-A) format. The function can also return the DwC-A object without saving.

```python
from pangaeapy import PanDataSet

ds = PanDataSet(787140, enable_cache=True)

# Export to Darwin Core Archive
ds.to_dwca()
# Creates: [cachedir]/787140_dwca/

# Get DwC-A object without saving
dwca = ds.to_dwca(save=False)
```

--------------------------------

### Access Tabular Data with PanDataSet

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/docs/user_guide.md

After initializing a PanDataSet object for tabular data, the data can be accessed as a pandas DataFrame via the 'data' attribute. The head() method displays the first few rows.

```python
>>> ds.data.head()
   Time sec  Altitude  ...                        Event           Date/Time
0   38314.0         6  ...  P5_232_HALO_2022_2203100101 2022-03-10 10:10:04
1   38315.0         6  ...  P5_232_HALO_2022_2203100101 2022-03-10 10:10:04
2   38316.0         6  ...  P5_232_HALO_2022_2203100101 2022-03-10 10:10:04
3   38317.0         6  ...  P5_232_HALO_2022_2203100101 2022-03-10 10:10:04
4   38318.0         6  ...  P5_232_HALO_2022_2203100101 2022-03-10 10:10:04
[5 rows x 17 columns]
```

--------------------------------

### Expand Parameter Terms

Source: https://context7.com/pangaea-data-publisher/pangaeapy/llms.txt

Expands ontology terms for parameters in a PanDataSet, useful for semantic analysis. Requires specifying the dataset ID and optionally term IDs for expansion.

```python
from pangaeapy import PanDataSet

# Expand terms for specific terminology IDs
ds = PanDataSet(787140, expand_terms=[18])  # Expand PANGAEA terminology (ID 18)

# Access expanded term information for parameters
for param_name, param in ds.params.items():
    print(f"Parameter: {param_name}")
    for term in param.terms:
        print(f"  Term: {term['name']}")
        print(f"  URI: {term['semantic_uri']}")
        print(f"  Ontology ID: {term['ontology']}")
        if 'classification' in term:
            print(f"  Classification: {term['classification']}")
```

--------------------------------

### Identify Foraminifera Columns

Source: https://github.com/pangaea-data-publisher/pangaeapy/blob/master/examples/SIMMAX.ipynb

Iterates through a predefined list of foraminifera parameters and appends those present in the sample dataset to a list for further analysis.

```python
#Forams used by Pflaumann et. al in SIMMAX28
#G. mentum = Globorotalia cultrata and tumida
foramCols=[]
foramParams=['G. bulloides','G. calida','G. falconensis','G. quinqueloba','G. rubescens','G. digitata','G. aequilateralis',
             'G. conglobatus','G. ruber p','G. ruber w', 'G. tenellus','G. trilobus tril','G. trilobus sac','O. universa',
             'S. dehiscens','G. crassaformis','G. mentum', 'G. hirsuta', 'G. inflata', 'G. scitula','G. truncatulinoides',
             'N. dutertrei','N. pachyderma s','P/D int','G. glutinata','P. obliquiloculata']

for fP in foramParams:
    if fP in sampleDS.data.columns:
        foramCols.append(fP)
```