Data Subset: Depth Profile¶
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depth_profile(table, variable, dt1, dt2, lat1, lat2, lon1, lon2, depth1, depth2)¶ Returns a subset of data according to the specified space-time constraints (dt1, dt2, lat1, lat2, lon1, lon2, depth1, depth2). The returned data subset is aggregated by depth: at each depth level the mean and standard deviation of the variable values within the space-time constraints are computed. The sequence of these values construct the depth profile. The resulting depth profile is returned in the form of a Pandas dataframe ordered by depth.
Parameters: - table: string
Table name (each dataset is stored in a table). A full list of table names can be found in Data Catalog.
- variable: string
Variable short name which directly corresponds to a field name in the table. A subset of this variable is returned by this method according to the spatio-temporal cut parameters (below). 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. Example values: ‘2016-05-25’ or ‘2017-12-10 17:25:00’.
- 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 longitude ranges from -180° to 180°.
- lon2: float
End longitude [degree E]. This parameter sets the upper bound of the zonal cut. Note longitude 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 the surface and increases towards the 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 the surface and increases towards the ocean floor).
Returns: Pandas dataframe.
Example 1:
This example retrieves a depth profile of in-situ chlorophyll concentration measurements by Argo Floats.
#!pip install pycmap -q #uncomment to install pycmap, if necessary
%matplotlib inline
import matplotlib.pyplot as plt
import pycmap
api = pycmap.API(token='<YOUR_API_KEY>')
table, variable = 'tblArgoMerge_REP', 'argo_merge_chl_adj'
df = api.depth_profile(
table=table,
variable=variable,
dt1='2016-04-30',
dt2='2016-04-30',
lat1=20,
lat2=24,
lon1=-170,
lon2=-150,
depth1=0,
depth2=1500
)
Example 2:
This example retrieves a depth profile of modeled chlorophyll concentration estimated by Pisces, a weekly 0.5° resolution BioGeoChemical model. The last few lines of code (lines 22-25) create a simple plot showing the chlorophyll depth profile. The deep chlorophyll maximum (DCM) is approximately at 100 m, closely matching the in-situ observations by ARGO Floats (see Example 1).
#!pip install pycmap -q #uncomment to install pycmap, if necessary
%matplotlib inline
import matplotlib.pyplot as plt
import pycmap
api = pycmap.API(token='<YOUR_API_KEY>')
table, variable = 'tblPisces_NRT', 'CHL'
df = api.depth_profile(
table=table,
variable=variable,
dt1='2016-04-30',
dt2='2016-04-30',
lat1=20,
lat2=24,
lon1=-170,
lon2=-150,
depth1=0,
depth2=1500
)
plt.plot(df['depth'], df[variable])
plt.xlabel('Depth [m]')
plt.ylabel(api.get_var_long_name(table, variable) + api.get_unit(table, variable))
plt.show()
SQL Statement
Here is how to achieve the same results using a direct SQL statement. Please refer to Query for more information.
EXEC uspDepthProfile 'tableName', 'variable', 'dt1', 'dt2', 'lat1', 'lat2', 'lon1', 'lon2', 'depth1', 'depth2'
Example:
EXEC uspDepthProfile 'tblPisces_NRT', 'CHL', '2016-04-30', '2016-04-30', '20', '24', '-170', '-150', '0', '1500'