Contenido

turbulence.Sensor
turbulence.Sensor.graph_AllVariables
turbulence.Sensor.backup
turbulence.Sensor.diff_lists
turbulence.Sensor.heatmap_percent

turbulence.Sensor

class turbulence.Sensor(name = None, database = 'Processed', type_data = 'Flux', days = None, all_range = False, all_record = False, temp_resolution = None, type_v = 'rawdata')

Parameters:

name: String type. Name used to identify the sensor in the database. You can use any of the names within the list:


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[IRGASON_Candelaria, IRGASON_Federico_Carrasquilla, IRGASON_SENA,
IRGASON_CASD_10M, IRGASON_ITM, IRGASON_Villa_Niza, IRGASON_CASD_20M,
IRGASON_Jesus_Maria_Valle, IRGASON_Villa_Socorro,IRGASON_CASD_30M,
IRGASON_Altavista, CSAT3B_MED_FISC]

database: Search database. Default is Processed. It can be 'Processed' or 'Raw'. 'Processed' coresponds to databases with procesing by software EasyFlux or EddyPro. 'Raw' corresponds to raw data measured at 0.5s resolution.

type_data: Just functional to database = 'Processed'. Use it to define if you want processed data from EasyFlux with the string 'Flux', which has values every 5 min. On the other hand, you can call processed data from EddyPro, which has values every 30 min, corresponding to flux averaging period. This database has three possibilities:

'stats' to statistical information.
'fluxnet' that is a output file conforming to FLUXNET and


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AmeriFlux's FP-In Standards for labels, units, and format; present 
variables as y intermediate results, statistics on all, high-frequency
variables, covariances between all high-frequency variables, biomet 
mean values and metadata.

'full_output' that is the most comprehensive output file produced


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by EddyPro. It contains many of the intermediate and final variables
calculated during data processing, as turbulence, air propierties, 
Gas densities, concentrations, and time lags.

days: Int, list or None. Default is None. Format to dates in list must be '%Y-%m-%d'. If it is int, a DataFrame is obtained with data from the current day to the number of days back. If you enter a list with dates is obtained a DataFrame with data for the day, or with data ranging from the first date to the second date, or data on each of the dates of interest. If is None, is used to the files corresponding to the backup process.
all_range: Bool. Just used in the case that days contain two dates. Default is False. When two dates are entered, define if you want data from the start date to the end setting as True. If you want only the data for the two dates, set it to False.
all_record: Bool. Default is False. If you want all the data available in the database.
temp_resolution: Temporary resolution of de data as str. Default is None. If is changed, the data is resample to the temp_resolution given, using the average of the data.
type_v: String with type of mesurements. Use it just with database as "Rawdata" or with data from EasyFlux. It can be 'rawdata' or 'anomalies'. Default is 'rawdata'. If you pass 'anomalies', it is calculated as:


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        anomaly = value - average corresponding to 30min

Examples

Calling raw data from last 1 day ago and


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import turbulence as tbl
sensor = tbl.Sensor(name = 'IRGASON_Candelaria', database = 'Raw', days = 1)
sensor.data.head(2)
        
                              RECORD        Ux        Uy        Uz   T_SONIC    diag_sonic  CO2_density  CO2_density_fast_tmpr   H2O_density  diag_irga  T_SONIC_corr  TA_1_1_1 \
TIMESTAMP                                                                      
2021-02-01 00:00:00.000  734805874.0 -4.834607  4.379913  0.158119  21.43830           0.0     560.1277               566.8666      5.880885        0.0      20.53098  18.41907
2021-02-01 00:00:00.050  734805875.0 -4.639253  3.889415  0.359624  21.45966           0.0     559.9778               566.7234      5.883626        0.0      20.54474  18.41907

Calling data in multiples dates


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import turbulence as tbl
sensor = tbl.Sensor(name = 'IRGASON_Candelaria', database = 'Raw', days = ['2021-01-03', '2021-01-08', '2021-01-23'])
sensor.data['2021-01-03'].head(1)
        
                              RECORD        Ux        Uy        Uz   T_SONIC    diag_sonic  CO2_density  CO2_density_fast_tmpr   H2O_density  diag_irga  T_SONIC_corr  TA_1_1_1 \
TIMESTAMP                                                                      
2021-01-03 00:00:00.000  669515869.0 -1.001235 -0.629053  0.192574  20.28459           0.0     583.0040               589.5051      8.501230        0.0      18.97537  16.88681


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sensor.data['2021-01-08'].head(1)
        
                              RECORD        Ux        Uy        Uz   T_SONIC    diag_sonic  CO2_density  CO2_density_fast_tmpr   H2O_density  diag_irga  T_SONIC_corr  TA_1_1_1 \
TIMESTAMP                                                                      
2021-01-08 00:00:00.000  678155867.0 -0.045016  1.057542 -0.215736  19.21257           0.0     601.8800               608.2081      8.742453        0.0      17.88049  15.90817


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sensor.data['2021-01-23'].head(1)
        
                              RECORD        Ux        Uy        Uz   T_SONIC    diag_sonic  CO2_density  CO2_density_fast_tmpr   H2O_density  diag_irga  T_SONIC_corr  TA_1_1_1 \
TIMESTAMP                                                                      
2021-01-23 00:00:00.000  704075845.0 -1.215571 -0.527850  0.243547  20.39452           0.0     590.7277               597.3887      7.602690        0.0      19.21924  17.13992

Calling data in a time range


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import turbulence as tbl
sensor = tbl.Sensor(name = 'IRGASON_Candelaria', database = 'Raw', days = ['2021-01-03', '2021-01-05'], all_range = True)
sensor.data['2021-01-03'].head(1)
        
                              RECORD        Ux        Uy        Uz   T_SONIC    diag_sonic  CO2_density  CO2_density_fast_tmpr   H2O_density  diag_irga  T_SONIC_corr  TA_1_1_1 \
TIMESTAMP                                                                      
2021-01-03 00:00:00.000  669515869.0 -1.001235 -0.629053  0.192574  20.28459           0.0     583.0040               589.5051      8.501230        0.0      18.97537  16.88681


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sensor.data['2021-01-04'].head(1)
        
                              RECORD        Ux        Uy        Uz   T_SONIC    diag_sonic  CO2_density  CO2_density_fast_tmpr   H2O_density  diag_irga  T_SONIC_corr  TA_1_1_1 \
TIMESTAMP                                                                      
2021-02-01 00:00:00.000  671243869.0 -1.239332  0.734344 -0.006824  18.49572           0.0     590.5066               596.6470      6.639220        0.0      17.48788  15.10754


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sensor.data['2021-01-05'].head(1)
        
                              RECORD        Ux        Uy        Uz   T_SONIC    diag_sonic  CO2_density  CO2_density_fast_tmpr   H2O_density  diag_irga  T_SONIC_corr  TA_1_1_1 \
TIMESTAMP                                                                      
2021-02-01 00:00:00.000  672971869.0 -0.747220  0.039915  0.138287  18.86970           0.0     601.1432               607.4532      6.765558        0.0      17.83746  15.92487

Changing the resolution of data


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import turbulence as tbl
sensor = tbl.Sensor(name = 'IRGASON_Candelaria', database = 'Raw', days = ['2021-01-03'], temp_resolution = '30T')
sensor.data['2021-01-03 05'].head(2)
         
                          RECORD        Ux        Uy        Uz    T_SONIC   diag_sonic  CO2_density  CO2_density_fast_tmpr   H2O_density  diag_irga  T_SONIC_corr   TA_1_1_1  \
TIMESTAMP                                                                   
2021-01-03 05:00:00  669533868.5  0.262761  0.748647  0.032138  20.225458          0.0   595.617024             602.176873      9.265051        0.0     18.801197  16.845053
2021-01-03 05:30:00  669569868.5  0.106879  0.111703  0.051869  20.460105          0.0   592.873733             599.503337      9.158846        0.0     19.050288  16.897682

Calculating anomalies with the data


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import turbulence as tbl
sensor = tbl.Sensor(name = 'IRGASON_Candelaria', database = 'Raw', days = ['2021-01-03'], type_v = 'anomalies')
sensor.data['2021-01-03 05'].head(2)
         
                          RECORD        Ux        Uy        Uz   T_SONIC  diag_sonic  CO2_density  CO2_density_fast_tmpr  H2O_density  diag_irga  T_SONIC_corr  TA_1_1_1  
TIMESTAMP                                                                  
2021-01-03 05:00:00.000 -17999.5 -1.263996 -1.377700  0.160436  0.059132         0.0   -12.613024             -12.671773    -0.763821        0.0      0.174173  0.041757
2021-01-03 05:00:00.050 -17998.5 -1.305568 -1.344858  0.156411  0.058482         0.0   -12.378624             -12.435573    -0.761592        0.0      0.175703  0.041757

turbulence.Sensor.graph_AllVariables

turbulence.Sensor.graph_AllVariables(show_figure = False)

Returns a bokeh object of the variables that the sensor measures, this object can be export to HTML. If the database is Flux, it also gives a graph of the turbulent flows

Parameters:

show_figure: Default is False. Option to show or not the figure

Examples

Generating interactive graphs for the variables processed in EddyPro. It is recommended not to use this function with the 'Raw' database since the temporal resolution is too high so the graphing process may hang, if it is necessary to use this database, modify the temporal resolution of the data using temp_resolution or type_v when generating the object Sensor


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import turbulence as tbl
sensor = tbl.Sensor(name = 'IRGASON_Candelaria', database = 'Processed', type_data = 'Flux', days = 1)
sensor.graph_AllVariables(show_figure = True)

turbulence.Sensor.backup

turbulence.Sensor.backup(folder_backup, show_figure = False)

This function is only used in backup periods, for more information here. The files available for backup are analyzed based on:

Has titles
Starts close to 00:00:00
Ends close to 23:59:59
The file name matches the data in the file
Which file has more information, the time real file or the

Parameters:

folder_backup: Path to the folder containing the backup files
show_figure: Default is False. Option to show or not the figure

Examples


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import turbulence as tbl
sensor = tbl.Sensor(name = 'IRGASON_Candelaria', database = 'Raw', days = None) #It's appropriate to make sure to set days as None and necessary set database as 'Raw', to perform a backup process
path_backup = 'Datos_2020_12_02'
sensor.backup(folder_backup = path_backup, show_figure = True)

backup

turbulence.Sensor.diff_lists

turbulence.Sensor.diff_lists(li1, li2)

Evaluate which elements of list 1 are not found in list 2

Parameters:

li1: List object
li2: List object

Returns:

List object

Examples


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import turbulence as tbl
sensor = tbl.Sensor(name = 'IRGASON_Candelaria', database = 'Raw') #It's necessary set database as 'Raw' to generate a Sensor object without problems
l1 = ['java', 1, 2, 'a']
l2 = [2, 5, 1, 'b', 'python']
sensor.diff_lists(l1, l2)
['java', 'a']

turbulence.Sensor.heatmap_percent

turbulence.Sensor.heatmap_percent(data = None, style='Bokeh', fig_size = (14,7), colorbar=False, show_figure = False, save_fig=False)

Create an interactive heatmap using the bokeh library, displaying the sensor's data record, throughout the year.

Parameters:

data: DataFrame with data to calculate record percentage. If is None then the data of the Sensor object will be used
style: String. Can be 'Bokeh' or 'plt' to select the library to use
fig_size: Tuple with ints to set the width and height of the figure
colorbar: Bool. Default is False. Change to True to watch a colorbar next to the graph
show_figure: Default is False. Option to show or not the figure
save_fig: Default is False. Option to save the figure as PDF just if style parameter is 'plt'

Returns:

If style is 'Bokeh' returns a bokeh figure
If style is 'plt' and save_fig is True, save a PDF here¹

Examples


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import turbulence as tbl
sensor = tbl.Sensor(name = 'IRGASON_Candelaria', database = 'Raw', days = 1)
sensor.heatmap_percent(colorbar = True, show_figure = True)

1 If you are a developer you can also find these graphs in this location on the GOMITA server ↩