helikite.plots

Attributes

logger

Functions

plot_scatter_from_variable_list_by_index(...)

generate_grid_plot(→ plotly.graph_objects.Figure)

Generates a 4x3 plot of quicklooks variables from several instruments

generate_particle_heatmap(→ plotly.graph_objects.Figure)

generate_average_bin_concentration_plot(...)

With a given timestamp, generate an average of MSEM bin concentrations

write_plots_to_html(→ None)

generate_altitude_plot(→ plotly.graph_objects.Figure)

generate_altitude_concentration_plot(...)

Generate a plot that sits above MSEMS with selected bins

generate_normalised_colours(→ List[str])

Generate a list of colours for a plot based on index of dataframe

campaign_2023(→ None)

Defines all the plots for the 2023 campaigns

Module Contents

helikite.plots.logger
helikite.plots.plot_scatter_from_variable_list_by_index(df: pandas.DataFrame, title: str, variables: List[str]) → plotly.graph_objects.Figure
helikite.plots.generate_grid_plot(df: pandas.DataFrame, all_instruments: List[helikite.instruments.Instrument], altitude_col: str = 'flight_computer_Altitude', resample_seconds: int | None = None) → plotly.graph_objects.Figure

Generates a 4x3 plot of quicklooks variables from several instruments

Resample values to a time interval in seconds is possible, to decrease the total count of points in the plot. This is useful for large datasets where the plot is too slow to render, or too many points to be useful.

The resampling will not happen on the wind direction due to the nature of the data. The wind direction is a circular variable, and resampling will not work as expected.

Parameters:

  • df (pd.DataFrame) – The dataframe containing the data to plot

  • all_instruments (List[instruments.Instrument]) – A list of all instruments to plot

  • altitude_col (str, optional) – The column name of the altitude variable, by default “flight_computer_Altitude”

  • resample_seconds (int, optional) – The number of seconds to resample the data to, by default None

helikite.plots.generate_particle_heatmap(df: pandas.DataFrame, props_msems_inverted: Dict[str, Any], props_msems_scan: Dict[str, Any]) → plotly.graph_objects.Figure
helikite.plots.generate_average_bin_concentration_plot(df: pandas.DataFrame, title: str, timestamp_start: pandas.Timestamp, timestamp_end: pandas.Timestamp, bin_limit_col_prefix: str = 'msems_inverted_Bin_Lim', bin_concentration_col_prefix: str = 'msems_inverted_Bin_Conc', bin_quantity_col: str = 'msems_inverted_NumBins', y_logscale: bool = False) → plotly.graph_objects.Figure

With a given timestamp, generate an average of MSEM bin concentrations

Parameters:

  • df (pd.DataFrame) – Dataframe containing the data

  • title (str) – Title of plot

  • timestamp_start (str) – Start timestamp of period to average

  • timestamp_end (str) – End timestamp of period to average

  • bin_limit_col_prefix (str, optional) – Prefix of column containing bin limits, default: msems_inverted_Bin_Lim

  • bin_concentration_col_prefix (str, optional) – Prefix of column containing bin concentrations, default ‘msems_inverted_Bin_Conc’

  • bin_quantity_col (str, optional) – Column containing number of bins, default ‘msems_inverted_NumBins’

  • y_logscale (bool, optional) – Whether to use a log scale for the y axis, default False

Returns:

Plotly figure

Return type:

go.Figure

helikite.plots.write_plots_to_html(figures: List[plotly.graph_objects.Figure], filename: str) → None
helikite.plots.generate_altitude_plot(df: pandas.DataFrame, at_ground_level: bool, altitude_col: str = 'flight_computer_Altitude') → plotly.graph_objects.Figure
helikite.plots.generate_altitude_concentration_plot(df: pandas.DataFrame, bins: List[Tuple[str, str, str]], at_ground_level: bool, height: int = 400, altitude_col: str = 'flight_computer_Altitude') → plotly.graph_objects.Figure

Generate a plot that sits above MSEMS with selected bins

The bins represent the periods of time that the averaged plots are calculated from.

helikite.plots.generate_normalised_colours(df: pandas.DataFrame, convert_nan_to: int = 0) → List[str]

Generate a list of colours for a plot based on index of dataframe

helikite.plots.campaign_2023(df: pandas.DataFrame, plot_props: Dict[str, Any], all_instruments: List[helikite.instruments.Instrument], output_path_with_time: str) → None

Defines all the plots for the 2023 campaigns

Due to the plot specifications being bespoke according to plans defined in 2023, this function attemps to isolate these decisions from further plotting requirements in the future

Parameters:

  • df (pd.DataFrame) – Dataframe containing all the merged data to be plotted

  • plot_props (Dict[str, Any]) – Dictionary containing all the properties for the plots originating from the runtime YAML

  • all_instruments (List[instruments.Instrument]) – List of all instruments that are available after merging all the data

  • output_path_with_time (str) – Path to the output directory for the plots, most likely the one generated in helikite.py with the current output time