Time Series Plot

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plot_timeseries(tables, variables, dt1, dt2, lat1, lat2, lon1, lon2, depth1, depth2, exportDataFlag=False, show=True, interval=None)

Creates a timeseries graph for each variable according to the specified space-time constraints (dt1, dt2, lat1, lat2, lon1, lon2, depth1, depth2). By definition, timeseries data points are aggregated by time: at each time interval the mean and standard deviation of the variable values within the space-time constraints are computed. The sequence of these values construct the timeseries. If the interval parameter is set, timeseries can be binned weekly, monthly, quarterly, or annually, (this feature is not applicable to climatological datasets).

Change the `APIs vizEngine`_ parameter if you wish to use a different visualization library. Returns the generated graph objects in form of a python list. One may use the returned objects to modify the graph properties.

Note

This method requires a valid API key. It is not necessary to set the API key every time because the API properties are stored locally after being called the first time.


Parameters:
tables: list of string

Table names (each dataset is stored in a table). A full list of table names can be found in Data Catalog.

variable: list of string

Variable short name which directly corresponds to a field name in the table. A full list of variable short names can be found in Data Catalog.

dt1: string

Start date or datetime. This parameter sets the lower bound of the temporal cut. Example values: ‘2016-05-25’ or ‘2017-12-10 17:25:00’

dt2: string

End date or datetime. This parameter sets the upper bound of the temporal cut.

lat1: float

Start latitude [degree N]. This parameter sets the lower bound of the meridional cut. Note latitude ranges from -90° to 90°.

lat2: float

End latitude [degree N]. This parameter sets the upper bound of the meridional cut. Note latitude ranges from -90° to 90°.

lon1: float

Start longitude [degree E]. This parameter sets the lower bound of the zonal cut. Note latitude ranges from -180° to 180°.

lon2: float

End longitude [degree E]. This parameter sets the upper bound of the zonal cut. Note latitude ranges from -180° to 180°.

depth1: float

Start depth [m]. This parameter sets the lower bound of the vertical cut. Note depth is a positive number (it is 0 at surface and grows towards ocean floor).

depth2: float

End depth [m]. This parameter sets the upper bound of the vertical cut. Note depth is a positive number (it is 0 at surface and grows towards ocean floor).

exportDataFlag: boolean, default: False

If True, the graph data points are stored on the local machine. The export path and file format are set by the APIs parameters.

show: boolean, default: True

If True, the graph object is returned and is displayed. The graph file is saved on the local machine at the figureDir directory. If False, the graph object is returned but not displayed.

interval: None or string, default: None

The timeseries bin size. If None, the native dataset time resolution is used as the bin size. Below is a list of interval values for other binning options:

  • ‘w’ or ‘week’ for weekly timeseries.
  • ‘m’ or ‘month’ for monthly timeseries.
  • ‘s’ or ‘q’ for seasonal/quarterly timeseries.
  • ‘a’ or ‘y’ for annual/yearly timeseries.
Returns:

list of graph objects A list of graph objects. Below are the graph’s properties and methods.

Properties:
data: dataframe

Graph data points to be visualized.

line: boolean, default: True

If True, line plot is superimposed on markers.

color: string

Line and marker colors (e.g. ‘red’, ‘green’, ..) or rgb hex ‘#FF0023’.

msize: int

Marker size.

height: int

Graph’s height in pixels.

width: int

Graph’s width in pixels.

xlabel: str

The graphs’s x-axis label.

ylabel: str

The graphs’s y-axis label.

title: str

The graphs’s title.

Methods:
render()

Displays the plot according to the set properties.

Example:

This example generates two timeseries graphs showing remotly sensed sea level anomaly, and sea surface salinity over a weekly-binned one-year period. The graphs are made using the default visualization library (plotly) which may be changed by: pycmap.API(vizEngine='bokeh')

#!pip install pycmap -q     #uncomment to install pycmap, if necessary
# uncomment the lines below if the API key has not been registered on your machine, previously.
# import pycmap
# pycmap.API(token='YOUR_API_KEY>')


from pycmap.viz import plot_timeseries

go = plot_timeseries(
                    tables=['tblAltimetry_REP', 'tblSSS_NRT'],
                    variables=['sla', 'sss'],
                    dt1='2016-04-30',
                    dt2='2017-04-30',
                    lat1=30,
                    lat2=32,
                    lon1=-160,
                    lon2=-158,
                    depth1=0,
                    depth2=0,
                    exportDataFlag=False,
                    show=True,
                    interval='w'
                    )

# here is how to modify a graph:

go[0].pdf = False
go[0].bins = 20
go[0].xlabel = "new xlabel"
go[0].title= "graph's title"
go[0].width = 600
go[0].height = 600
go[0].render()