mooda.WaterFrame()

The WaterFrame object has been designed to contain all the information that can be obtained from a NetCDF file. WaterFrame relies on a pandas.DataFrame to save and manipulate data and two dictionaries to collect metadata.

WaterFrame structure

The following describes the components of a WaterFrame:

data: pandas.DataFrame where are the data to analyze or graph.
metadata: Dictionary with all the related information to the data. Here the general attributes of a NetCDF file are saved.
vocabulary: Here, the attributes of each variable are saved.

Magic methods

Constructor

It creates an empty pandas.DataFrame called WaterFrame.data and two dictionaries called WaterFrame.vocabulary and WaterFrame.metadata.

repr()

It returns a printable representational string of the WaterFrame with the following information:

Consumption of memory of the WaterFrame
Name of parameters
Description of the parameters
Units of the parameters
Minimum value of each parameter
Maximum value of each parameter
Mean value of each parameter

Properties

WaterFrame.parameters

We call parameter to the key of the WaterFrame.data column containing “_QC”. For example, a WaterFrame.data that contains the keys: POSITION, TEMP, TEMP_QC, PSAL, PSAL_QC; the parameters are TEMP and PSAL.

Example

To reproduce the example, download the NetCDF file here and save it as example.nc in the same python script folder.

import mooda as md

path_netcdf = "example.nc"  # Path of the NetCDF file

wf = md.read_nc_emodnet(path_netcdf)
print(", ".join(wf.parameters))

Output:

DEPH, ATMS, CNDC, DRYT, PRES, PSAL, SVEL, TEMP, WDIR, WSPD

WaterFrame.memory_usage

It returns the memory usage of the WaterFrame in bytes.

To reproduce the example, download the NetCDF file here and save it as example.nc in the same python script folder.

import mooda as md

path_netcdf = "example.nc"  # Path of the NetCDF file

wf = md.read_nc_emodnet(path_netcdf)
print(f"{wf.memory_usage} bytes")

Output:

142484054 bytes

Methods

Data analysis

WaterFrame.copy(): Get a copy of the WaterFrame.
WaterFrame.corr(): Compute pairwise correlation of data columns of parameter1 and parameter2, excluding NA/null values.
WaterFrame.drop(parameters, inplace=True): Remove input parameters from WaterFrame.data.
WaterFrame.info_metadata(keys=None): It returns a formatted string with the metadata information.
WaterFrame.info_vocabulary(keys=None): It returns a formatted string with the vocabulary information.
WaterFrame.max_diff(parameter1, parameter2): It calculates the maximum difference between the values of two parameters.
WaterFrame.max(parameter): Get the maximum value of a parameter.
WaterFrame.min(parameter): Get the minimum value of a parameter.
WaterFrame.rename(actual_name, new_name, inplace=True): It renames a parameter.
WaterFrame.resample(rule, method=‘mean’, inplace=True): Convenience method for frequency conversion and sampling of time series of the WaterFrame object.
WaterFrame.time_intervals(parameter, frequency): It returns the index (TIME) of intervals between NaNs.
WaterFrame.use_only(parameters_to_use, inplace=True): It deletes all parameters except the input parameters.
WaterFrame.reduce_memory(inplace=True): It reduces the WaterFrame size in memory by 30% - 50%

Output

WaterFrame.metadata_to_html(): Make a html file with the metadata information.
WaterFrame.to_csv(): Create a CSV file with the WaterFrame data.
WaterFrame.to_es(): Injestion of the WaterFrame into a ElasticSeach DB.
WaterFrame.to_json(): Get a JSON with the WaterFrame information.
WaterFrame.to_nc(path, nc_format=“NETCDF4”): Save the WaterFrame into a NetCDF.
WaterFrame.to_pkl(path): Save the WaterFrame in a Pickle file.

Static plot

WaterFrame.plot_hist(parameters=None, mean_line=False, **kwds): Make a histogram of the WaterFrame’s. A histogram is a representation of the distribution of data.
WaterFrame.plot_timebar(keys, ax=None, time_interval_mean=None): Make a bar plot of the input keys. The bars are positioned at x with date/time. Their dimensions are given by height.
WaterFrame.plot_timeseries(parameters_to_plot=None, qc_flags=None, rolling_window=None, ax=None, average_time=None, secondary_y=None, color=None): Plot the input parameters with time on X and the parameters on Y. It calculates the standar deviation of a rolling window and plot it.
WaterFrame.plot(**kwds): It calls the pandas DataFrame.plot() method.

Interactive plot

WaterFrame.iplot_location(): It creates a Plotly Figure with a map and a spot of the measurement location of the WaterFrame.
WaterFrame.iplot_timeseries(parameters_to_plot=None): It creates a Plotly figure with the time-series of the input parameters.
WaterFrame.iplot_scatter(y, x=‘TIME’, trendline=None, marginal_x=None, marginal_y=‘histogram’, color=‘auto’, symbol=‘DEPTH’, range_y=‘auto’, **kwds): It makes an interactive scatter plot.
WaterFrame.iplot_data_intervals(resample_rule=‘D’, **kwds): It creates a plot to view the time intervals of the parameters.
WaterFrame.iplot_line(y, x=‘TIME’, marginal_x=None, marginal_y=‘histogram’, color=‘auto’, range_y=‘auto’, line_shape=‘lineard’, rangeslider_visible=True, line_group=‘DEPTH’, **kwds): Each data point is represented as a marker point, whose location is given by the x and y columns of self.data.
WaterFrame.iplot_bar_polar(theta, color, r=‘auto’, template=‘xgridoff’, color_continuous_scale=‘auto’, **kwds): In a polar bar plot, each row of ‘color’ is represented as a wedge mark in polar coordinates.

Data Quality Control

WaterFrame.qc_syntax_test(): It checks whether the object data contains all the QC columns required to pass the rest of the tests.
WaterFrame.qc_flat_test(parameters=None, window=3, flag=4, inplace=True): It detects if there are equal consecutive values in the time series.
WaterFrame.qc_range_test(parameters=None, limits=None, flag=4, inplace=True): Check if the values of a parameter are out of range.
WaterFrame.qc_spike_test(parameters=None, window=0, threshold=3, flag=4, inplace=True): It checks if there is any spike in the time series.

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