Feature/add harvester global mean evaporation

src/score_hv/harvesters/global_bucket_evap_ave.py

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+import os,sys
+
+from collections import namedtuple
+from dataclasses import dataclass
+from dataclasses import field
+import numpy as np
+import netCDF4 as nc 
+import xarray as xr
+import glob
+
+
+from score_hv.config_base import ConfigInterface
+
+HARVESTER_NAME   = 'global_bucket_evap_ave'
+VALID_STATISTICS = ('mean')
+VALID_VARIABLES  = ('lhtfl_ave')
+
+HarvestedData = namedtuple('HarvestedData', ['filenames',
+                                             'statistic',
+                                             'variable',
+                                             'value',
+                                             'units'])
+@dataclass
+class GlobalBucketEvapRateConfig(ConfigInterface):
+
+    config_data: dict = field(default_factory=dict)
+
+    def __post_init__(self):
+        self.set_config()
+
+    def set_config(self):
+        """ function to set configuration variables from given dictionary
+        """
+        self.harvest_filenames = self.config_data.get('filenames')
+        self.set_stats()
+        self.set_variables()
+
+    def set_variables(self):
+        """
+        set the variables specified by the config dict
+        """
+
+        self.variables = self.config_data.get('variable')
+        for var in self.variables:
+            if var not in VALID_VARIABLES:
+                msg = ("'%s' is not a supported global bucket evaporation average "
+                       "variable to harvest from the background forecast data. "
+                       "Please reconfigure the input dictionary using only the "
+                       "following variables: %r" % (var, VALID_VARIABLES))
+                raise KeyError(msg)
+
+    def get_stats(self):
+        ''' return list of all stat types based on harvest_config '''
+        return self.stats
+
+    def set_stats(self):
+        """
+        set the statistics specified by the config dict
+        """
+        self.stats = self.config_data.get('statistic')
+        for stat in self.stats:
+            if stat not in VALID_STATISTICS:
+                msg = ("'%s' is not a supported statistic to harvest for global bucket evaporation. "
+                       "Please reconfigure the input dictionary using only the "
+                       "following statistics: %r" % (stat, VALID_STATISTICS))
+                raise KeyError(msg)
+
+
+
+    def get_variables(self):
+        ''' return list of all variable types based on harvest_config'''
+        return self.variables
+
+@dataclass
+
+
+@dataclass
+class GlobalBucketEvapRateHv(object):
+    """ Harvester dataclass used to parse,lhtfl,evaporation stored in 
+        background forecast data
+
+        Parameters:
+        -----------
+        evaporation config: Config object containing information used to determine
+                which variable to parse the log file
+        Methods:
+        --------
+        get_data: parse descriptive statistics from log files based on input
+                  config file
+
+                  returns a list of tuples containing specific data
+    """
+    config: GlobalBucketEvapRateConfig = field(
+                                default_factory=GlobalBucketEvapRateConfig)
+
+    def get_data(self):
+        """ Harvests lhtfl_ave(Evaporation) from background forecast data
+            returns harvested_data, a list of HarvestData tuples
+        """
+        harvested_data = list()
+
+        mean_values = []
+        filenames   = self.config.harvest_filenames
+        #Open the requested file names
+        for infile in filenames:
+            nc_file = nc.Dataset(infile, 'r')
+        #Open the requested file names
+        for i, variable in enumerate(self.config.get_variables()):
+            """ The first nested loop iterates through each requested variable
+            """
+            for j, statistic in enumerate(self.config.get_stats()):
+                """ The second nested loop iterates through each requested 
+                    statistic
+                """
+                var_name    = variable
+                for infile in filenames:
+                    nc_file            = nc.Dataset(infile, 'r')
+                    requested_var      = nc_file.variables[var_name][:]
+                    units              = nc_file.variables[var_name].units if hasattr(nc_file.variables[var_name], 'units') else None
+                    mean_values.append(np.mean(requested_var)) 
+                    nc_file.close()
+                value = np.mean(mean_values)
+                #print("the value in harvester",value)
+                harvested_data.append(HarvestedData(filenames, statistic, variable,
+                                                   value,units))
+
+
+        return harvested_data

src/score_hv/harvesters/global_bucket_evaporation_ave.py

@@ -0,0 +1,128 @@
+import os,sys
+
+from collections import namedtuple
+from dataclasses import dataclass
+from dataclasses import field
+import numpy as np
+import netCDF4 as nc 
+import xarray as xr
+import glob
+
+
+from score_hv.config_base import ConfigInterface
+
+HARVESTER_NAME   = 'global_bucket_evaporation_ave'
+VALID_STATISTICS = ('mean')
+VALID_VARIABLES  = ('lhtfl_ave')
+
+HarvestedData = namedtuple('HarvestedData', ['filenames',
+                                             'statistic',
+                                             'variable',
+                                             'value',
+                                             'units'])
+@dataclass
+class GlobalBucketEvaporationRateConfig(ConfigInterface):
+
+    config_data: dict = field(default_factory=dict)
+
+    def __post_init__(self):
+        self.set_config()
+
+    def set_config(self):
+        """ function to set configuration variables from given dictionary
+        """
+        self.harvest_filenames = self.config_data.get('filenames')
+        self.set_stats()
+        self.set_variables()
+
+    def set_variables(self):
+        """
+        set the variables specified by the config dict
+        """
+
+        self.variables = self.config_data.get('variable')
+        for var in self.variables:
+            if var not in VALID_VARIABLES:
+                msg = ("'%s' is not a supported global bucket precip average "
+                       "variable to harvest from the background forecast data. "
+                       "Please reconfigure the input dictionary using only the "
+                       "following variables: %r" % (var, VALID_VARIABLES))
+                raise KeyError(msg)
+
+    def get_stats(self):
+        ''' return list of all stat types based on harvest_config '''
+        return self.stats
+
+    def set_stats(self):
+        """
+        set the statistics specified by the config dict
+        """
+        self.stats = self.config_data.get('statistic')
+        for stat in self.stats:
+            if stat not in VALID_STATISTICS:
+                msg = ("'%s' is not a supported statistic to harvest for global bucket precipitation. "
+                       "Please reconfigure the input dictionary using only the "
+                       "following statistics: %r" % (stat, VALID_STATISTICS))
+                raise KeyError(msg)
+
+
+
+    def get_variables(self):
+        ''' return list of all variable types based on harvest_config'''
+        return self.variables
+
+@dataclass
+
+
+@dataclass
+class GlobalBucketEvaporationRateHv(object):
+    """ Harvester dataclass used to parse precip stored in 
+        background forecast data
+
+        Parameters:
+        -----------
+        evaporation config: Config object containing information used to determine
+                which variable to parse the log file
+        Methods:
+        --------
+        get_data: parse descriptive statistics from log files based on input
+                  config file
+
+                  returns a list of tuples containing specific data
+    """
+    config: GlobalBucketEvaporationRateConfig = field(
+                                default_factory=GlobalBucketEvaporationRateConfig)
+
+    def get_data(self):
+        """ Harvests lhtfl_ave(Evaporation) from background forecast data
+            returns harvested_data, a list of HarvestData tuples
+        """
+        harvested_data = list()
+
+        mean_values = []
+        filenames   = self.config.harvest_filenames
+        #Open the requested file names
+        for infile in filenames:
+            nc_file = nc.Dataset(infile, 'r')
+        #Open the requested file names
+        for i, variable in enumerate(self.config.get_variables()):
+            """ The first nested loop iterates through each requested variable
+            """
+            for j, statistic in enumerate(self.config.get_stats()):
+                """ The second nested loop iterates through each requested 
+                    statistic
+                """
+                var_name    = variable
+                for infile in filenames:
+                    nc_file            = nc.Dataset(infile, 'r')
+                    requested_var      = nc_file.variables[var_name][:]
+                    units              = nc_file.variables[var_name].units if hasattr(nc_file.variables[var_name], 'units') else None
+                    mean_values.append(np.mean(requested_var)) 
+                    nc_file.close()
+                value = np.mean(mean_values)
+                print(value)
+                harvested_data.append(HarvestedData(filenames, statistic, variable,
+                                                   value,units))
+
+
+        return harvested_data

src/score_hv/harvesters/global_bucket_precip_ave.py

@@ -0,0 +1,129 @@
+import os,sys
+
+from collections import namedtuple
+from dataclasses import dataclass
+from dataclasses import field
+import numpy as np
+import netCDF4 as nc 
+import xarray as xr
+import glob
+
+
+from score_hv.config_base import ConfigInterface
+
+HARVESTER_NAME   = 'global_bucket_precip_ave'
+VALID_STATISTICS = ('mean')
+VALID_VARIABLES  = ('prateb_ave')
+
+HarvestedData = namedtuple('HarvestedData', ['filenames',
+                                             'statistic',
+                                             'variable',
+                                             'value',
+                                             'units'])
+@dataclass
+class GlobalBucketPrecipRateConfig(ConfigInterface):
+
+    config_data: dict = field(default_factory=dict)
+
+    def __post_init__(self):
+        self.set_config()
+
+    def set_config(self):
+        """ function to set configuration variables from given dictionary
+        """
+        self.harvest_filenames = self.config_data.get('filenames')
+        self.set_stats()
+        self.set_variables()
+
+    def set_variables(self):
+        """
+        set the variables specified by the config dict
+        """
+
+        self.variables = self.config_data.get('variable')
+        for var in self.variables:
+            if var not in VALID_VARIABLES:
+                msg = ("'%s' is not a supported global bucket precip average "
+                       "variable to harvest from the background forecast data. "
+                       "Please reconfigure the input dictionary using only the "
+                       "following variables: %r" % (var, VALID_VARIABLES))
+                raise KeyError(msg)
+
+    def get_stats(self):
+        ''' return list of all stat types based on harvest_config '''
+        return self.stats
+
+    def set_stats(self):
+        """
+        set the statistics specified by the config dict
+        """
+        self.stats = self.config_data.get('statistic')
+        for stat in self.stats:
+            if stat not in VALID_STATISTICS:
+                msg = ("'%s' is not a supported statistic to harvest for global bucket precipitation. "
+                       "Please reconfigure the input dictionary using only the "
+                       "following statistics: %r" % (stat, VALID_STATISTICS))
+                raise KeyError(msg)
+
+
+
+    def get_variables(self):
+        ''' return list of all variable types based on harvest_config'''
+        return self.variables
+
+@dataclass
+
+
+@dataclass
+class GlobalBucketPrecipRateHv(object):
+    """ Harvester dataclass used to parse precip stored in 
+        background forecast data
+
+        Parameters:
+        -----------
+        config: PrecipConfig object containing information used to determine
+                which variable to parse the log file
+        Methods:
+        --------
+        get_data: parse descriptive statistics from log files based on input
+                  config file
+
+                  returns a list of tuples containing specific data
+    """
+    config: GlobalBucketPrecipRateConfig = field(
+                                default_factory=GlobalBucketPrecipRateConfig)
+
+    def get_data(self):
+        """ Harvests prateb_ave from background forecast data
+            returns harvested_data, a list of HarvestData tuples
+        """
+        harvested_data = list()
+
+        mean_values = []
+        filenames   = self.config.harvest_filenames
+        #Open the requested file names
+        for infile in filenames:
+            nc_file = nc.Dataset(infile, 'r')
+        #Open the requested file names
+        for i, variable in enumerate(self.config.get_variables()):
+            """ The first nested loop iterates through each requested variable
+            """
+            for j, statistic in enumerate(self.config.get_stats()):
+                """ The second nested loop iterates through each requested 
+                    statistic
+                """
+                var_name    = variable
+                for infile in filenames:
+                    nc_file            = nc.Dataset(infile, 'r')
+                    requested_var      = nc_file.variables[var_name][:]
+                    units              = nc_file.variables[var_name].units if hasattr(nc_file.variables[var_name], 'units') else None
+                    mean_values.append(np.mean(requested_var)) 
+                    nc_file.close()
+                value = np.mean(mean_values)
+                print(value)
+                harvested_data.append(HarvestedData(filenames, statistic, variable,
+                                                   value,units))
+
+
+        return harvested_data
+