gfdlvitals package

gfdlvitals - a package for computing global mean metrics

class gfdlvitals.VitalsDataFrame(data=None, index: Axes | None = None, columns: Axes | None = None, dtype: Dtype | None = None, copy: bool | None = None)

Bases: DataFrame

Pandas class extension for holding the Vitals data

Parameters:

pd (pandas.DataFrame) – Input Pandas DataFrame object

Returns:

Class extension object

Return type:

gfdlvitals.VitalsDataFrame

areasum()

Returns the area integrated variable based on the cell_measure method

build_netrad_toa()

Constructs netrad_toa from component terms if available

Parameters:

self (VitalsDataFrame)

Return type:

VitalsDataFrame

detrend(reference=None, order=1, anomaly=True, return_coefs=False)

Detrend VitalsDataFrame object

Parameters:

  • reference (gfdlvitals.DataFrame, optional) – Reference VitalsDataFrame, by default None

  • order (int, optional) – Polynomial order to use for fitting, by default 1

  • anomaly (bool, optional) – Results as anomalies from the fit, by default True

  • return_coefs (bool, optional) – Return polynomial fit coeffiecients, by default False

Returns:

Extended dataset

Return type:

self

extend(maxlen)

Extend VitalsDataFrame to a set length and pad with NaNs

Parameters:

maxlen (int) – New length of the VitalsDataFrame

Returns:

Extended dataset

Return type:

self

smooth(window, extrap=False)

Apply a smoother to the dataset

Parameters:

  • window (int) – Smoothing filter length

  • extrap (bool, optional) – Extrapolate data on the ends, by default False

Returns:

Smoothed dataset

Return type:

self

trend(order=1)

Fits a trend to the VitalsDataFrame object

Parameters:

order (int, optional) – Polynomial order to use for fitting, by default 1

Returns:

Fitted trend dataset

Return type:

self

ttest(df2)

Performs t-test between two instances of VitalsDataFrame

Parameters:

df2 (VitalsDataFrame) – Comparison data set

Returns:

Contains p-values for variables common between the two VitalsDataFrames

Return type:

pandas.DataFrame

gfdlvitals.open_db(dbfile, variables=None, yearshift=0.0, legacy_land=False, start=None, end=None)

Function to read sqlite dbfile

gfdlvitals.plot_timeseries(dsets, var, trend=False, align_times=False, plottype='average', smooth=None, nyears=None, labels=None, legend=True, means=True, title='both')

Standardized function to make a timeseries plot

Parameters:

  • dsets (gfdlvitals.VitalsDataFrame or list) – Dataframe or list of dataframes to plot

  • var (str) – Variable name to plot

  • trend (bool, optional) – Plot linear trend line if True, by default False

  • align_times (bool, optional) – H, by default False

  • plottype (str, optional) – Defines plot type as either “average” or “sum”, by default “average”

  • smooth (int, optional) – Integer number of years to apply smoothing, by default None

  • nyears (int, optional) – Limit the x-axis to nyears number of points, by default None

  • labels (str, optional) – Comma-separated list of dataset labels, by default None

  • legend (bool, optional) – Display a legend for the plot, by default True

  • means (bool, optional) – Add variable means to the legend, by default True

  • title (str, optional) – Specify “varname”, “longname”, “both”, or “none”, by default “both”

Returns:

Matplotlib figure handle and dictionary of axes/dataset mappings

Return type:

matplotlib.pyplot.figure, dict

Subpackages

Submodules