API reference

We import the package as follows:

import mooda as md

Read data

• md.read_nc(path): Get a WaterFrame from a generic NetCDF.
• md.read_pkl(path_pkl): Get a WaterFrame from a Pickle file.

WaterFrame

We declare a WaterFrame object as follows:

wf = md.WaterFrame()

• WaterFrame: General WaterFrame object information.

Analyze data

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

Outout

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

Static plot

• wf.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.
• wf.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.
• wf.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.
• wf.plot(**kwds): It calls the pandas DataFrame.plot() method.

Interactive plot

• wf.iplot_location(self): It creates a Plotly Figure with a map and a spot of the measurement location of the WaterFrame.
• wf.iplot_timeseries(parameters_to_plot=None): It creates a Plotly figure with the time-series of the input parameters.
• wf.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.
• wf.iplot_data_intervals(resample_rule=‘D’, **kwds): It creates a plot to view the time intervals of the parameters.
• wf.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 wf.data.
• wf.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

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

Utilities

• md.concat(list_wf): concat does all of the heavy liftings of performing concatenation operations between a list of WaterFrames.
• md.es_create_indexes(delete_previous_indexes=True, **kwargs): Creation of ElasticSearch Indexes to save a WaterFrame object.
• md.md5(file_path, save_dm5=True, md5_path=None): It generates the MD5 code of the input file.

Interactive plot

• md.iplot_location(list_wf): It creates a Plotly Figure with a map and a spot of the measurement location of the input WaterFrames.
• md.iplot_timeseries(list_wf, parameter_to_plot): It creates a Plotly figure with the time-series of the input parameter.

Jupyter Notebook Widgets

• md.widget_qc(wf, parameter, range_test=[-1000, 1000], spike_window=100, spike_threshold=3.5, spike_influence=0.5): It makes a Data QC Widget for Jupyter Notebook.
• md.widget_emso(wf, depth_range=[-10, 10000], user='', password='', token=''): It makes a Jupyter notebook widget to download data from the EMSO API.
• md.widget_emso_qc(wf, depth_range=[-10, 10000], range_test=[-1000, 1000], spike_window=100, spike_threshold=3.5, spike_influence=0.5, user='', password='', token=''): It makes a Jupyter Notebook Widget used to download EMSO data and make data quality control tests.
• md.widget_save(wf): Make a Jupyter notebook widget that allows to save the WaterFrame to a file.

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