Map paleo ensemble array

pyleoclim/core/ensemblegeoseries.py

@@ -170,6 +170,7 @@ def from_AgeEnsembleArray(self, geo_series, age_array, value_depth = None, age_d
         .. jupyter-execute::
 
             #Create an ensemble of 100 series with random time axes of length 1000
+
             length = 1000
             age_array = np.array([pyleo.utils.tsmodel.random_time_axis(length) for i in range(100)]).T
 
@@ -186,25 +187,26 @@ def from_AgeEnsembleArray(self, geo_series, age_array, value_depth = None, age_d
 
         .. jupyter-execute::
 
-            #If we have depth vectors for our series and age array, we can pass them to the function
+            #In this example we'll explicitly pass the depth vectors
+
             age_length = 1000
-            age_array = np.array([pyleo.utils.tsmodel.random_time_axis(age_length) for i in range(100)]).T
+            age_array = np.array([pyleo.utils.tsmodel.random_time_axis(length) for i in range(100)]).T
             age_depth = np.arange(age_length)
 
             value_length = 800
-            value = np.random.randn(value_length)
+            value = np.random.normal(0,1,value_length)
             time = pyleo.utils.tsmodel.random_time_axis(value_length)
             lat = np.random.randint(-90,90)
             lon = np.random.randint(-180,180)
             geo_series = pyleo.GeoSeries(time=time, value=value, lat=lat, lon=lon, verbose=False)
             value_depth = np.arange(value_length)
 
-            #Note that the length of the depth vectors must match the length of the corresponding object (number of values or number of rows in age array)
-            ens = pyleo.EnsembleGeoSeries.from_AgeEnsembleArray(geo_series = geo_series,age_array=age_array, value_depth=value_depth, age_depth=age_depth,verbose=False)
+            ens = pyleo.EnsembleGeoSeries.from_AgeEnsembleArray(geo_series = geo_series,age_array=age_array,value_depth=value_depth,age_depth=age_depth,verbose=False)
 
         .. jupyter-execute::
 
             #We can also pass the depth vector through the GeoSeries object itself
+
             age_length = 1000
             age_array = np.array([pyleo.utils.tsmodel.random_time_axis(age_length) for i in range(100)]).T
             age_depth = np.arange(age_length)
@@ -266,7 +268,7 @@ def from_AgeEnsembleArray(self, geo_series, age_array, value_depth = None, age_d
             mapped_age = lipdutils.mapAgeEnsembleToPaleoData(
                 ensembleValues=age_array, 
                 depthEnsemble=age_depth, 
-                depthPaleo=value_depth,
+                depthMapping=value_depth,
                 extrapolate=extrapolate
             )
 
@@ -286,6 +288,164 @@ def from_AgeEnsembleArray(self, geo_series, age_array, value_depth = None, age_d
 
         return EnsembleGeoSeries(series_list)
 
+    @classmethod
+    def from_PaleoEnsembleArray(self, geo_series, paleo_array, paleo_depth = None, age_depth = None, extrapolate=True,verbose=True):
+        '''Function to create an EnsembleGeoSeries object using a paleo ensemble
+
+        Function assumes that the input series and the age array share the same units.
+        If depth vectors are passed, these are also assumed to share the same units
+
+        Parameters
+        ----------
+        geo_series : pyleoclim.core.series.Series
+            A Series object with the values to be mapped
+
+        paleo_array : np.array
+            An array of paleo values to map the values to
+
+        paleo_depth : vector
+            An array of depths corresponding to the paleo values
+
+        age_depth : vector
+            An array of depths corresponding to the time axis of the series object
+
+        extrapolate : bool
+            Whether to extrapolate the age array to the value depth. Default is True
+
+        verbose : bool
+            Whether to print warnings. Default is True
+
+        Returns
+        -------
+        EnsembleSeries : pyleoclim.core.ensembleseries.EnsembleSeries
+            The ensemble created using the time axes from age_array and the values from series.
+
+        Examples
+        --------
+
+        .. jupyter-execute::
+
+            #Create an ensemble of 100 series with random time axes of length 1000
+
+            length = 1000
+            paleo_array = np.array([np.random.normal(0, 1, 1000) for _ in range(100)]).T
+
+            #Create a random geoseries
+            value = np.random.normal(0,1,length)
+            time = pyleo.utils.tsmodel.random_time_axis(length)
+            lat = np.random.randint(-90,90)
+            lon = np.random.randint(-180,180)
+            geo_series = pyleo.GeoSeries(time=time, value=value, lat=lat, lon=lon, verbose=False)
+
+            #Create an ensemble using these objects
+            #Note that the time axis of the series object and the number of rows in the age array must match when depth is not passed
+            ens = pyleo.EnsembleGeoSeries.from_PaleoEnsembleArray(geo_series = geo_series,paleo_array=paleo_array,verbose=False)
+
+        .. jupyter-execute::
+
+            #In this example we'll explicitly pass the depth vectors
+
+            paleo_length = 1000
+            paleo_array = np.array([np.random.normal(0, 1, 1000) for _ in range(100)]).T
+            paleo_depth = np.arange(paleo_length)
+
+            age_length = 800
+            value = np.random.normal(0,1,age_length)
+            time = pyleo.utils.tsmodel.random_time_axis(age_length)
+            lat = np.random.randint(-90,90)
+            lon = np.random.randint(-180,180)
+            geo_series = pyleo.GeoSeries(time=time, value=value, lat=lat, lon=lon, verbose=False)
+            age_depth = np.arange(age_length)
+
+            ens = pyleo.EnsembleGeoSeries.from_PaleoEnsembleArray(geo_series = geo_series,paleo_array=paleo_array,paleo_depth=paleo_depth,age_depth=age_depth,verbose=False)
+
+        .. jupyter-execute::
+
+            #Depth can also be passed via the GeoSeries object
+
+            paleo_length = 1000
+            paleo_array = np.array([np.random.normal(0, 1, 1000) for _ in range(100)]).T
+            paleo_depth = np.arange(paleo_length)
+
+            age_length = 800
+            value = np.random.normal(0,1,age_length)
+            time = pyleo.utils.tsmodel.random_time_axis(age_length)
+            lat = np.random.randint(-90,90)
+            lon = np.random.randint(-180,180)
+            depth = np.arange(age_length)
+            geo_series = pyleo.GeoSeries(time=time, value=value, depth=depth,lat=lat, lon=lon, verbose=False)
+
+            ens = pyleo.EnsembleGeoSeries.from_PaleoEnsembleArray(geo_series = geo_series,paleo_array=paleo_array,paleo_depth=paleo_depth,verbose=False)
+
+        '''
+
+        if not isinstance(geo_series, GeoSeries):
+            raise ValueError('series must be a Series object')
+
+        #squeeze paleoValues into a vector
+        time = np.squeeze(np.array(geo_series.time))
+
+        if paleo_depth is None:
+            if age_depth is None:
+                if len(time) != paleo_array.shape[0]:
+                    raise ValueError("Paleo array and series need to have the same length when paleo_depth is not passed.")
+                else:
+                    mapped_paleo = paleo_array
+                pass
+            else:
+                raise ValueError('Paleo_depth not found. Please pass both a paleo depth array and age depth array if the paleo ensemble and age are not already aligned. Otherwise, pass neither.')
+        else:
+            #Check that both arrays were passed
+            if age_depth is None:
+                if geo_series.depth is None:
+                    raise ValueError('Age_depth not found. Please pass both a value depth array and age depth array if value and age are not already aligned. Otherwise, pass neither.')
+                else:
+                    age_depth = geo_series.depth
+            elif age_depth is not None and geo_series.depth is not None:
+                if verbose:
+                    warnings.warn('Both an age depth array and a series depth array were passed. The age depth array will be used')
+                else:
+                    pass
+
+            #Make sure that numpy arrays were given and try to coerce them into vectors if possible
+            age_depth=np.squeeze(np.array(age_depth))
+            paleo_depth = np.squeeze(np.array(paleo_depth))
+
+            #Check that arrays are vectors for np.interp
+            if age_depth.ndim > 1:
+                raise ValueError('age_depth has more than one dimension, please pass it as a vector')
+            if paleo_depth.ndim > 1:
+                raise ValueError('paleo_depth has more than one dimension, please pass it as a vector')
+            #Check that the shape of the depth arrays matches up with the age array and value vector (separately)
+            if len(paleo_depth)!=paleo_array.shape[0]:
+                raise ValueError("Paleo depth and paleo array need to have the same length")
+            if len(age_depth)!=len(time):
+                raise ValueError("Age depth and series time need to have the same length")
+
+            #Interpolate the age array to the value depth
+            mapped_paleo = lipdutils.mapAgeEnsembleToPaleoData(
+                ensembleValues=paleo_array, 
+                depthEnsemble=paleo_depth, 
+                depthMapping=age_depth,
+                extrapolate=extrapolate
+            )
+
+        series_list = []
+
+        #check that mapped_age and the original time vector are similar, and that the object is not a geoseries object
+        if verbose:
+            if (np.mean(mapped_paleo[-1,:]) > 10*geo_series.value[-1]) or (np.mean(mapped_paleo[-1,:]) < 0.1*geo_series.value[-1]):
+                warnings.warn('The mapped value array is significantly different from the original value vector. You may want to check that the units are appropriate.')
+            elif (np.mean(mapped_paleo[0,:]) > 10*geo_series.value[0]) or (np.mean(mapped_paleo[0,:]) < 0.1*geo_series.value[0]):
+                warnings.warn('The mapped value array is significantly different from the original value vector. You may want to check that the units are appropriate.')
+
+        for s in mapped_paleo.T:
+            series_tmp = geo_series.copy()
+            series_tmp.value = s
+            series_list.append(series_tmp)
+
+        return EnsembleGeoSeries(series_list)
+
     def make_labels(self):
         '''Initialization of labels
 

pyleoclim/core/ensembleseries.py

@@ -42,7 +42,7 @@ def __init__(self, series_list, label=None):
 
     @classmethod
     def from_AgeEnsembleArray(self, series, age_array, value_depth = None, age_depth = None, extrapolate=True,verbose=True):
-        '''Function to create an EnsembleSeries object
+        '''Function to create an EnsembleSeries object using an age ensemble
 
         Function assumes that the input series and the age array share the same units.
         If depth vectors are passed, these are also assumed to share the same units
@@ -150,7 +150,7 @@ def from_AgeEnsembleArray(self, series, age_array, value_depth = None, age_depth
             mapped_age = lipdutils.mapAgeEnsembleToPaleoData(
                 ensembleValues=age_array, 
                 depthEnsemble=age_depth, 
-                depthPaleo=value_depth,
+                depthMapping=value_depth,
                 extrapolate=extrapolate
             )
 
@@ -169,6 +169,142 @@ def from_AgeEnsembleArray(self, series, age_array, value_depth = None, age_depth
             series_list.append(series_tmp)
 
         return EnsembleSeries(series_list)
+
+    @classmethod
+    def from_PaleoEnsembleArray(self, series, paleo_array, paleo_depth = None, age_depth = None, extrapolate=True,verbose=True):
+        '''Function to create an EnsembleSeries object using a paleo ensemble
+
+        Function assumes that the input series and the age array share the same units.
+        If depth vectors are passed, these are also assumed to share the same units
+
+        Parameters
+        ----------
+        series : pyleoclim.core.series.Series
+            A Series object with the values to be mapped
+
+        paleo_array : np.array
+            An array of paleo values to map the values to
+
+        paleo_depth : vector
+            An array of depths corresponding to the paleo values
+
+        age_depth : vector
+            An array of depths corresponding to the time axis of the series object
+
+        extrapolate : bool
+            Whether to extrapolate the age array to the value depth. Default is True
+
+        verbose : bool
+            Whether to print warnings. Default is True
+
+        Returns
+        -------
+        EnsembleSeries : pyleoclim.core.ensembleseries.EnsembleSeries
+            The ensemble created using the time axes from age_array and the values from series.
+
+        Examples
+        --------
+
+        .. jupyter-execute::
+
+            #Create an ensemble of 100 series with random time axes of length 1000
+
+            length = 1000
+            paleo_array = np.array([np.random.normal(0, 1, 1000) for _ in range(100)]).T
+
+            #Create a random series
+            value = np.random.normal(0,1,length)
+            time = pyleo.utils.tsmodel.random_time_axis(length)
+            lat = np.random.randint(-90,90)
+            lon = np.random.randint(-180,180)
+            series = pyleo.Series(time=time, value=value, lat=lat, lon=lon, verbose=False)
+
+            #Create an ensemble using these objects
+            #Note that the time axis of the series object and the number of rows in the age array must match when depth is not passed
+            ens = pyleo.EnsembleSeries.from_PaleoEnsembleArray(series = series,paleo_array=paleo_array,verbose=False)
+
+        .. jupyter-execute::
+
+            #In this example we'll explicitly pass the depth vectors
+
+            paleo_length = 1000
+            paleo_array = np.array([np.random.normal(0, 1, 1000) for _ in range(100)]).T
+            paleo_depth = np.arange(paleo_length)
+
+            age_length = 800
+            value = np.random.normal(0,1,age_length)
+            time = pyleo.utils.tsmodel.random_time_axis(age_length)
+            lat = np.random.randint(-90,90)
+            lon = np.random.randint(-180,180)
+            series = pyleo.Series(time=time, value=value, lat=lat, lon=lon, verbose=False)
+            age_depth = np.arange(age_length)
+
+            ens = pyleo.EnsembleSeries.from_PaleoEnsembleArray(series = series,paleo_array=paleo_array,paleo_depth=paleo_depth,age_depth=age_depth,verbose=False)
+
+        '''
+
+        if not isinstance(series, Series):
+            raise ValueError('series must be a Series object')
+
+        if verbose:
+            if hasattr(series,'lat') or hasattr(series,'lon'):
+                warnings.warn('Passed series object looks like a geoseries object, did you mean to use EnsembleGeoSeries.from_AgeEnsembleArray?')
+
+        #squeeze paleoValues into a vector
+        time = np.squeeze(np.array(series.time))
+
+        if paleo_depth is None:
+            if age_depth is None:
+                if len(time) != paleo_array.shape[0]:
+                    raise ValueError("Paleo array and series need to have the same length when paleo_depth is not passed.")
+                else:
+                    mapped_paleo = paleo_array
+                pass
+            else:
+                raise ValueError('Paleo_depth not found. Please pass both a paleo depth array and age depth array if the paleo ensemble and age are not already aligned. Otherwise, pass neither.')
+        else:
+            #Check that both arrays were passed
+            if age_depth is None:
+                raise ValueError('Value_depth not found. Please pass both a paleo depth array and age depth array if the paleo ensemble and age are not already aligned. Otherwise, pass neither.')
+
+            #Make sure that numpy arrays were given and try to coerce them into vectors if possible
+            age_depth=np.squeeze(np.array(age_depth))
+            paleo_depth = np.squeeze(np.array(paleo_depth))
+
+            #Check that arrays are vectors for np.interp
+            if age_depth.ndim > 1:
+                raise ValueError('age_depth has more than one dimension, please pass it as a vector')
+            if paleo_depth.ndim > 1:
+                raise ValueError('paleo_depth has more than one dimension, please pass it as a vector')
+            #Check that the shape of the depth arrays matches up with the age array and value vector (separately)
+            if len(paleo_depth)!=paleo_array.shape[0]:
+                raise ValueError("Paleo depth and paleo array need to have the same length")
+            if len(age_depth)!=len(time):
+                raise ValueError("Age depth and series time need to have the same length")
+
+            #Interpolate the age array to the value depth
+            mapped_paleo = lipdutils.mapAgeEnsembleToPaleoData(
+                ensembleValues=paleo_array, 
+                depthEnsemble=paleo_depth, 
+                depthMapping=age_depth,
+                extrapolate=extrapolate
+            )
+
+        series_list = []
+
+        #check that mapped_age and the original time vector are similar, and that the object is not a geoseries object
+        if verbose:
+            if (np.mean(mapped_paleo[-1,:]) > 10*series.value[-1]) or (np.mean(mapped_paleo[-1,:]) < 0.1*series.value[-1]):
+                warnings.warn('The mapped value array is significantly different from the original value vector. You may want to check that the units are appropriate.')
+            elif (np.mean(mapped_paleo[0,:]) > 10*series.value[0]) or (np.mean(mapped_paleo[0,:]) < 0.1*series.value[0]):
+                warnings.warn('The mapped value array is significantly different from the original value vector. You may want to check that the units are appropriate.')
+
+        for s in mapped_paleo.T:
+            series_tmp = series.copy()
+            series_tmp.value = s
+            series_list.append(series_tmp)
+
+        return EnsembleSeries(series_list)
 
     def make_labels(self):
         '''Initialization of labels

pyleoclim/core/geoseries.py

@@ -137,6 +137,24 @@ def __init__(self, time, value, lat, lon, elevation = None, time_unit=None, time
                  depth = None, depth_name = None, depth_unit= None,
                  sort_ts = 'ascending', dropna = True,  clean_ts=False, auto_time_params = None):
 
+        # ensure 1D arrays
+        if len(time) > 1:
+            time = np.squeeze(time)
+        if len(value) > 1:
+            value = np.squeeze(value)
+        if depth is not None:
+            if len(depth) >1:
+                depth = np.squeeze(depth)
+
+        #Check that array sizes are equal
+        if depth is not None:
+            if len(depth) != len(time):
+                raise ValueError('Depth and time arrays must be the same length')
+            if len(depth) != len(value):
+                raise ValueError('Depth and value arrays must be the same length')
+        if len(time) != len(value):
+            raise ValueError('Time and value arrays must be the same length')
+
         if auto_time_params is None:
             auto_time_params = True
             if verbose: