diff --git a/README.md b/README.md
index 112e885..9fbf1ad 100644
--- a/README.md
+++ b/README.md
@@ -151,9 +151,6 @@ fOVI = pow(10, fOVI)
 print(f"f_OVI = {fOVI:.3e}")
 ```
 
-    f_OVI = 9.463e-02
-
-
 **Note**:
 - Ionization fraction returned by `AstroPlasma` is in **log10** scale.
 - As of now, we **do not** support vectorization of these functions and indivdual values must be passed and **not** arrays. This can lead to errors or un-defined behavior.
@@ -192,9 +189,6 @@ ne = num_dens(nH = nH,
 print(f"Free electron density = {ne:.3e} cm^-3")
 ```
 
-    Free electron density = 1.410e-04 cm^-3
-
-
 In order to get
 - **total particle number density**, use `part_type = "all"`
 - **total ion number density**, use `part_type = "ion"`
@@ -241,11 +235,6 @@ print(f"Total neutral particle density = {nn:.3e} cm^-3")
 print(f"Total HI particle density = {nHI:.3e} cm^-3")
 ```
 
-    Total particle density = 2.714e-04 cm^-3
-    Total ion density = 1.308e-04 cm^-3
-    Total neutral particle density = 1.113e-10 cm^-3
-    Total HI particle density = 1.099e-10 cm^-3
-
 Although it is straightforward to obtain mean particle mass, we provide functions to so for the convenience of the user. We use the following relation for calculating these quantities.
 
 $$\rho = n \mu m_p = n_e \mu_e m_p = n_i \mu_i m_p = n_H m_H X^{-1}$$
@@ -280,11 +269,6 @@ print(f"Mean free electron mass = {mu_e:.2f} mp")
 print(f"Mean ion mass = {mu_i:.2f} mp")
 ```
 
-    Mean particle mass = 0.62 mp
-    Mean free electron mass = 1.19 mp
-    Mean ion mass = 1.28 mp
-
-
 ### Emission spectrum
 
 `AstroPlasma` can be used in determing the emission spectrum emitted from a *one-zone* plasma. Here's the code that does that. This can be used as a starting point for modeling plasma emission from astrophysical objects like the *circumgalactic medium* or *galaxy clusters* by stacking emission from multiple such one-zones.
@@ -390,7 +374,7 @@ All the codes required to generate the `Cloudy` database is in `cloudy-codes` di
   - export `AstroPlasma/cloudy-codes/ionFrac/src` to `LD_LIBRARY_PATH`
   - Inside the `AstroPlasma/cloudy-codes/ionFrac/generateIonFraction-parallel.py` script, change the parameters (`total_size`, `batch_dim`, `nH`, `temperature`, `metallicity`, `redshift`) to desired resolution and range.
   - Now one can run this script in parallel using `mpiexec -n <nproc> python generateIonFraction-parallel.py`. I have tested this using `Python 3.11.6` with `mpi4py`, `numpy`, `h5py` and `colorama` packages installed.
-  **Note for cluster usres**: A sample slurm command that can be copied and executed from the terminal of a cluster is also provided in `slurm-python-job`. However, this neededs tweaking according the specifics of the cluster. Since, this job runs interactively, it is advisable to use something like gnu `screen` ot `tmux` to run this in a detached terminal as using these tools, interruptions in the connection to the cluster won't the kill the job. Also, the user need to make sure that the binaries compiled should be compiled for the compute nodes and not the login nodes if they are of different configuration.
+  **Note for cluster usres**: A sample slurm command that can be copied and executed from the terminal of a cluster is also provided in `slurm-python-job`. However, this neededs tweaking according the specifics of the cluster. Since, this job runs interactively, it is advisable to use something like gnu `screen` ot `tmux` to run this in a detached terminal as using these tools, interruptions in the connection to the cluster won't the kill the job. Also, the user need to make sure that the binaries compiled should be compiled for the compute nodes and not the login nodes if they are of different configuration. One can see the progress of the run in the log files generated by slurm. For example, `tail -F ~/AstroPlasma/cloudy-codes/ionFrac/IonCloudy-2325.log` enables you see the status of the running job with jobID `2325` in this example.
   - Upon successful run, several ascii files with `ionization` in their name will get generated in a directory called `auto` that is created in `AstroPlasma/cloudy-codes/ionFrac/src`. The final `hdf5` files for the database is created in the `data` directory in`AstroPlasma/cloudy-codes/ionFrac/src`. This directory should be copied to `AstroPlasma/astro_plasma/data/` and renamed as `ionization`.
 - Generating *emission* data
   - The steps are similar as above. But in this case both `libcloudy.so` and `cloudy.exe` files need to be copied to `AstroPlasma/cloudy-codes/emmSpec/` from `source/sys_gcc_shared` directory of `Cloudy`.
diff --git a/astro_plasma/core/datasift.py b/astro_plasma/core/datasift.py
index 15c0b24..ebd086e 100644
--- a/astro_plasma/core/datasift.py
+++ b/astro_plasma/core/datasift.py
@@ -9,15 +9,22 @@
 import numpy as np
 from itertools import product
 from pathlib import Path
-from typing import Callable, Optional, Union, Tuple, List, Set
+from typing import Protocol, Callable, Optional, Union, Tuple, List, Set
 import h5py
+import sys
 
 _warn = False
 
 
+class inherited_from_DataSift(Protocol):
+    # This must be compulsorily implemented by any class inheriting from DataSift
+    _check_and_download: Callable
+
+
 class DataSift(ABC):
     def __init__(
         self: "DataSift",
+        child_obj: "inherited_from_DataSift",
         data: h5py.File,
     ) -> None:
         """
@@ -35,6 +42,7 @@ def __init__(
 
         self.batch_size = np.prod(np.array(data["header/batch_dim"]))
         self.total_size = np.prod(np.array(data["header/total_size"]))
+        self._check_and_download = child_obj._check_and_download
 
     def _identify_batch(self: "DataSift", i: int, j: int, k: int, m: int) -> int:
         batch_id = self._get_counter(i, j, k, m) // self.batch_size
@@ -83,13 +91,13 @@ def _determine_multiple_input(
 
         return condition
 
-    def _prepare_multiple_input(
+    def _process_arguments_flags(
         self: "DataSift",
         nH: Union[int, float, List, np.ndarray],
         temperature: Union[int, float, List, np.ndarray],
         metallicity: Union[int, float, List, np.ndarray],
         redshift: Union[int, float, List, np.ndarray],
-    ) -> bool:
+    ) -> Tuple[List[bool], List[bool]]:
         """
         Determine if multiple datapoints are requested.
 
@@ -106,11 +114,129 @@ def _prepare_multiple_input(
 
         Returns
         -------
-        input_tuple : bool
-            processed data for multiple
+        flags tuple : tuple
+            flags.
 
         """
-        pass
+        _argument_type = ["nH", "temperature", "metallicity", "redshift"]
+        _array_argument = [True, True, True, True]  # array to flag which arguments are arrays
+        _array_argument[0] = isinstance(nH, list) or isinstance(nH, np.ndarray)
+        _array_argument[1] = isinstance(temperature, list) or isinstance(temperature, np.ndarray)
+        _array_argument[2] = isinstance(metallicity, list) or isinstance(metallicity, np.ndarray)
+        _array_argument[3] = isinstance(redshift, list) or isinstance(redshift, np.ndarray)
+
+        _dummy_array = [False, False, False, False]  # array to flag which arguments are length 1 array
+        if _array_argument[0]:
+            _dummy_array[0] = np.array(nH).shape[0] == 1
+        if _array_argument[1]:
+            _dummy_array[1] = np.array(temperature).shape[0] == 1
+        if _array_argument[2]:
+            _dummy_array[2] = np.array(metallicity).shape[0] == 1
+        if _array_argument[3]:
+            _dummy_array[3] = np.array(redshift).shape[0] == 1
+
+        return (_array_argument, _dummy_array)
+
+    def _prepare_arguments(
+        self: "DataSift",
+        nH: Union[int, float, List, np.ndarray],
+        temperature: Union[int, float, List, np.ndarray],
+        metallicity: Union[int, float, List, np.ndarray],
+        redshift: Union[int, float, List, np.ndarray],
+        _array_argument: List[bool],
+        _dummy_array: List[bool],
+    ) -> Tuple[Optional[Tuple[int]], List[np.ndarray]]:
+        """
+        Determine if multiple datapoints are requested.
+
+        Parameters
+        ----------
+        nH : float
+            Hydrogen number density (all hydrogen both neutral and ionized.
+        temperature : float, array, list
+            Plasma temperature.
+        metallicity : float, array, list
+            Plasma metallicity with respect to solar.
+        redshift : float, array, list
+            Cosmological redshift of the universe.
+
+        Returns
+        -------
+        input data tuple : tuple
+            prepared input data.
+
+        """
+        _input_shape: Optional[Tuple[int]] = None
+        argument_collection = [np.array(nH), np.array(temperature), np.array(metallicity), np.array(redshift)]
+        for indx, _if_this_is_arr in enumerate(_array_argument):
+            if not (_if_this_is_arr):
+                argument_collection[indx] = np.array(
+                    [
+                        argument_collection[indx],
+                    ]
+                )
+            else:
+                if _input_shape is None:
+                    _input_shape = argument_collection[indx].shape
+                else:
+                    if _input_shape != argument_collection[indx].shape:
+                        print("Error: ", argument_collection[indx].shape, _input_shape)
+                        print("Argument: ", argument_collection[indx])
+                        raise ValueError("Check needed from user: Invalid input arguments which are not in compliance with each other! Code Aborted!")
+            argument_collection[indx] = argument_collection[indx].flatten()
+        if np.sum(_dummy_array) == 4 or np.sum(_array_argument) == 0:
+            _input_shape = (1,)  # default only one datapoint is requested
+        if _input_shape is None:
+            raise ValueError("Check needed from user: Invalid input arguments which are not in compliance with each other! Code Aborted!")
+        return (_input_shape, argument_collection)
+
+    def _find_all_batches(
+        self: "DataSift",
+        nH: Union[int, float, List, np.ndarray],
+        temperature: Union[int, float, List, np.ndarray],
+        metallicity: Union[int, float, List, np.ndarray],
+        redshift: Union[int, float, List, np.ndarray],
+    ) -> Set[int]:
+        """
+        Determine if multiple datapoints are requested.
+
+        Parameters
+        ----------
+        nH : float
+            Hydrogen number density (all hydrogen both neutral and ionized.
+        temperature : float, array, list
+            Plasma temperature.
+        metallicity : float, array, list
+            Plasma metallicity with respect to solar.
+        redshift : float, array, list
+            Cosmological redshift of the universe.
+
+        Returns
+        -------
+        batch_ids : set
+            Set of all the unique batch ids from the entire requested data.
+
+        """
+
+        _array_argument, _dummy_array = self._array_argument, self._dummy_array
+        _input_shape, argument_collection = self._input_shape, self.argument_collection
+
+        if np.sum(_dummy_array) == 4 or np.sum(_array_argument) == 0:
+            _argument = [argument[0] for argument in argument_collection]
+            return self._find_all_batches_single(*_argument)
+
+        _all_batches_all_data: Set[int] = set()
+        for indx in range(np.prod(_input_shape)):
+            _argument = []
+            for arg_pos, _dummy in enumerate(_dummy_array):
+                if _dummy or not (_array_argument[arg_pos]):
+                    _argument.append(argument_collection[arg_pos][0])
+                else:
+                    _argument.append(argument_collection[arg_pos][indx])
+            _all_batches_all_data = _all_batches_all_data.union(self._find_all_batches_single(*_argument))
+        if _all_batches_all_data == set():
+            raise ValueError("Problem identifying batches! Code Aborted!")
+        return _all_batches_all_data
 
     def _transform_edges(self: "DataSift", i: int, j: int, k: int, m: int) -> Tuple[int, int, int, int]:
         # Detect the edge cases
@@ -148,36 +274,17 @@ def _transform_edges(self: "DataSift", i: int, j: int, k: int, m: int) -> Tuple[
             m = m + 1
         return (i, j, k, m)
 
-    def _find_all_batches(
+    def _identify_pos_in_each_dim(
         self: "DataSift",
         nH: Union[int, float],
         temperature: Union[int, float],
         metallicity: Union[int, float],
         redshift: Union[int, float],
-    ) -> Set[int]:
-        """
-        Find the batches needed from the data files.
-
-        Parameters
-        ----------
-        nH : float
-            Hydrogen number density (all hydrogen both neutral and ionized.
-        temperature : float
-            Plasma temperature.
-        metallicity : float
-            Plasma metallicity with respect to solar.
-        redshift : float
-            Cosmological redshift of the universe.
-
-        Returns
-        -------
-        batch_ids : set
-            Set of all the unique batch ids.
-
-        """
+    ) -> Tuple[List[int], List[int], List[int], List[int]]:
+        # positions in each data just around the requested value for any variable
         i_vals, j_vals, k_vals, m_vals = None, None, None, None
         if np.sum(nH == self.nH_data) == 1:
-            eq = np.where(nH == self.nH_data)[0][0]
+            eq = int(np.where(nH == self.nH_data)[0][0])
             i_vals = [eq - 1, eq, eq + 1]
         else:
             i_vals = [
@@ -186,7 +293,7 @@ def _find_all_batches(
             ]
 
         if np.sum(temperature == self.T_data) == 1:
-            eq = np.where(temperature == self.T_data)[0][0]
+            eq = int(np.where(temperature == self.T_data)[0][0])
             j_vals = [eq - 1, eq, eq + 1]
         else:
             j_vals = [
@@ -195,7 +302,7 @@ def _find_all_batches(
             ]
 
         if np.sum(metallicity == self.Z_data) == 1:
-            eq = np.where(metallicity == self.Z_data)[0][0]
+            eq = int(np.where(metallicity == self.Z_data)[0][0])
             k_vals = [eq - 1, eq, eq + 1]
         else:
             k_vals = [
@@ -204,7 +311,7 @@ def _find_all_batches(
             ]
 
         if np.sum(redshift == self.red_data) == 1:
-            eq = np.where(redshift == self.red_data)[0][0]
+            eq = int(np.where(redshift == self.red_data)[0][0])
             m_vals = [eq - 1, eq, eq + 1]
         else:
             m_vals = [
@@ -212,10 +319,36 @@ def _find_all_batches(
                 np.sum(redshift > self.red_data),
             ]
 
-        self.i_vals = i_vals
-        self.j_vals = j_vals
-        self.k_vals = k_vals
-        self.m_vals = m_vals
+        return (i_vals, j_vals, k_vals, m_vals)
+
+    def _find_all_batches_single(
+        self: "DataSift",
+        nH: Union[int, float],
+        temperature: Union[int, float],
+        metallicity: Union[int, float],
+        redshift: Union[int, float],
+    ) -> Set[int]:
+        """
+        Find the batches needed from the data files.
+
+        Parameters
+        ----------
+        nH : float
+            Hydrogen number density (all hydrogen both neutral and ionized.
+        temperature : float
+            Plasma temperature.
+        metallicity : float
+            Plasma metallicity with respect to solar.
+        redshift : float
+            Cosmological redshift of the universe.
+
+        Returns
+        -------
+        batch_ids : set
+            Set of all the unique batch ids.
+
+        """
+        i_vals, j_vals, k_vals, m_vals = self._identify_pos_in_each_dim(nH, temperature, metallicity, redshift)
 
         batch_ids = set()
         # identify unique batches
@@ -223,17 +356,18 @@ def _find_all_batches(
             i, j, k, m = self._transform_edges(i, j, k, m)
             batch_id = self._identify_batch(i, j, k, m)
             batch_ids.add(batch_id)
-        # batch_ids = set(batch_ids)
+
         # print("Batches involved: ", batch_ids)
-        # later use this logic to fetch batches from cloud if not present
 
-        self._fetch_data(batch_ids)
+        # self._fetch_data(batch_ids)
 
         return batch_ids
 
+    """
     @abstractmethod
     def _fetch_data(self: "DataSift", batch_ids: Set[int]) -> None:
         pass
+    """
 
     @abstractmethod
     def _get_file_path(self: "DataSift", batch_id: int) -> Path:
@@ -241,10 +375,10 @@ def _get_file_path(self: "DataSift", batch_id: int) -> Path:
 
     def _interpolate(
         self: "DataSift",
-        nH: Union[int, float],
-        temperature: Union[int, float],
-        metallicity: Union[int, float],
-        redshift: Union[int, float],
+        nH: Union[int, float, list, np.ndarray],
+        temperature: Union[int, float, list, np.ndarray],
+        metallicity: Union[int, float, list, np.ndarray],
+        redshift: Union[int, float, list, np.ndarray],
         mode: str,
         interp_data: str,
         interp_value_shape: Union[Tuple[int], List[int]],
@@ -253,7 +387,7 @@ def _interpolate(
             None,
             None,
         ),
-    ) -> np.ndarray:
+    ) -> Tuple[np.ndarray, bool]:
         """
         Interpolate from pre-computed Cloudy table.
 
@@ -276,10 +410,8 @@ def _interpolate(
             ionization condition
             either CIE (collisional) or PIE (photo).
             The default is 'PIE'.
-        interpField : str
-            ionization condition
-            either CIE (collisional) or PIE (photo).
-            The default is 'PIE'.
+        interp_data : str
+            data location within HDF5 file
         scaling_func : callable function, optional
             function space in which intrepolation is
             carried out.
@@ -289,71 +421,148 @@ def _interpolate(
 
         Returns
         -------
-        interp_value : np.ndarray
+        (interp_value, if_multiple_output) : (np.ndarray, bool)
             The interpolated result.
         """
 
-        if mode != "PIE" and mode != "CIE":
-            print("Problem! Invalid mode: %s." % mode)
-            return None
+        self._array_argument, self._dummy_array = self._process_arguments_flags(nH, temperature, metallicity, redshift)
+        self._input_shape, self.argument_collection = self._prepare_arguments(nH, temperature, metallicity, redshift, self._array_argument, self._dummy_array)
+
+        _array_argument, _dummy_array = self._array_argument, self._dummy_array
+        _input_shape, argument_collection = self._input_shape, self.argument_collection
+
+        if not (mode == "PIE" or mode == "CIE"):
+            raise ValueError("Problem! Invalid mode: %s." % mode)
 
         batch_ids = self._find_all_batches(nH, temperature, metallicity, redshift)
-        i_vals = self.i_vals
-        j_vals = self.j_vals
-        k_vals = self.k_vals
-        m_vals = self.m_vals
+        # Download files on demand if absent locally
+        self._check_and_download(specific_file_ids=batch_ids)
 
+        # Load the data to memory from disk
         data = []
         for batch_id in batch_ids:
-            file = self._get_file_path(batch_id)
-            hdf = h5py.File(file, "r")
+            filename = self._get_file_path(batch_id)
+            hdf = h5py.File(filename, "r")
             data.append({"batch_id": batch_id, "file": hdf})
-
         """
-        The trick is to take the floor value for interpolation only if it is the
-        most frequent value in all the nearest neighbor. If not, then simply ignore
-        the contribution of the floor value in interpolation.
+        if np.sum(_dummy_array) == 4 or np.sum(_array_argument) == 0:
+            _argument = [argument[0] for argument in argument_collection]
+            i_vals, j_vals, k_vals, m_vals = self._identify_pos_in_each_dim(*_argument)
         """
-        epsilon = 1e-15
-        _all_values = []
-        _all_weights = []
-        for i, j, k, m in product(i_vals, j_vals, k_vals, m_vals):
-            i, j, k, m = self._transform_edges(i, j, k, m)
-            # nearest neighbour interpolation
-            d_i = np.abs(scaling_func(self.nH_data[i]) - scaling_func(float(nH)))
-            d_j = np.abs(scaling_func(self.T_data[j]) - scaling_func(float(temperature)))
-            d_k = np.abs(scaling_func(self.Z_data[k]) - scaling_func(float(metallicity)))
-            d_m = np.abs(scaling_func(self.red_data[m]) - scaling_func(float(redshift)))
-            distL2 = np.sqrt(d_i**2 + d_j**2 + d_k**2 + d_m**2)
-            if distL2 <= 0.0:
-                distL2 = epsilon
-            _all_weights.append(1 / distL2)
-
-            batch_id = self._identify_batch(i, j, k, m)
+        interp_value = []
+        for indx in range(np.prod(_input_shape)):
+            _argument = []
+            for arg_pos, _dummy in enumerate(_dummy_array):
+                if _dummy or not (_array_argument[arg_pos]):
+                    _argument.append(argument_collection[arg_pos][0])
+                else:
+                    _argument.append(argument_collection[arg_pos][indx])
+            nH_this, temperature_this, metallicity_this, redshift_this = _argument
+            i_vals, j_vals, k_vals, m_vals = self._identify_pos_in_each_dim(nH_this, temperature_this, metallicity_this, redshift_this)
+
+            """
+            The trick is to take the floor value for interpolation only if it is the
+            most frequent value in all the nearest neighbor. If not, then simply ignore
+            the contribution of the floor value in interpolation.
+            """
+            epsilon = 1e-15
+            _all_values = []
+            _all_weights = []
+            for i, j, k, m in product(i_vals, j_vals, k_vals, m_vals):
+                i, j, k, m = self._transform_edges(i, j, k, m)
+                # nearest neighbour interpolation
+                d_i = np.abs(scaling_func(self.nH_data[i]) - scaling_func(float(_argument[0])))
+                d_j = np.abs(scaling_func(self.T_data[j]) - scaling_func(float(_argument[1])))
+                d_k = np.abs(scaling_func(self.Z_data[k]) - scaling_func(float(_argument[2])))
+                d_m = np.abs(scaling_func(self.red_data[m]) - scaling_func(float(_argument[3])))
+                distL2 = np.sqrt(d_i**2 + d_j**2 + d_k**2 + d_m**2)
+                if distL2 <= 0.0:
+                    distL2 = epsilon
+                _all_weights.append(1 / distL2)
+
+                batch_id = self._identify_batch(i, j, k, m)
+
+                for id_data in data:
+                    if id_data["batch_id"] == batch_id:
+                        hdf = id_data["file"]
+                        local_pos = self._get_counter(i, j, k, m) % self.batch_size - 1
+                try:
+                    value = np.array(hdf[interp_data])[local_pos, :]
+                except UnboundLocalError:
+                    print("Error: HDF5 files not properly loaded/initialized! Code Aborted!")
+                    sys.exit(1)
+
+                if cut[0] is not None:
+                    value = np.piecewise(value, [value <= cut[0]], [cut[0], lambda x: x])
+                if cut[1] is not None:
+                    value = np.piecewise(value, [value >= cut[1]], [cut[1], lambda x: x])
+                _all_values.append(value)
+
+            all_values = np.array(_all_values)
+            all_weights = np.array(_all_weights)
+            # Filter the outliers (deviation from mean across column is large)
+            all_values[(np.abs(all_values - np.mean(all_values, axis=0)) > 2.0 * np.std(all_values, axis=0))] = 0.0
+
+            # _median_value = np.median(_all_values, axis=0)
+            interp_value.append(all_weights.T @ all_values / np.sum(all_weights))
+            # if (len(interp_value.shape)==2):
+            #     interp_value = interp_value[0]
 
-            for id_data in data:
-                if id_data["batch_id"] == batch_id:
-                    hdf = id_data["file"]
-                    local_pos = self._get_counter(i, j, k, m) % self.batch_size - 1
-
-            value = np.array(hdf[interp_data])[local_pos, :]
+        for id_data in data:
+            id_data["file"].close()
+        if np.sum(_dummy_array) == 4 or np.sum(_array_argument) == 0:
+            return (interp_value[0], False)
+        else:
+            _tmp = np.array(interp_value).reshape((*_input_shape, *np.array(interp_value[0]).shape))
+            return (_tmp, True)
 
-            if cut[0] is not None:
-                value = np.piecewise(value, [value <= cut[0]], [cut[0], lambda x: x])
-            if cut[1] is not None:
-                value = np.piecewise(value, [value >= cut[1]], [cut[1], lambda x: x])
-            _all_values.append(value)
+    def _determine_multiple(
+        self: "DataSift",
+        nH: Union[int, float, list, np.ndarray],
+        temperature: Union[int, float, list, np.ndarray],
+        metallicity: Union[int, float, list, np.ndarray],
+        redshift: Union[int, float, list, np.ndarray],
+        mode: str,
+    ) -> bool:
+        """
+        Interpolate from pre-computed Cloudy table.
 
-        all_values = np.array(_all_values)
-        all_weights = np.array(_all_weights)
-        # Filter the outliers (deviation from mean across column is large)
-        all_values[(np.abs(all_values - np.mean(all_values, axis=0)) > 2.0 * np.std(all_values, axis=0))] = 0.0
+        Parameters
+        ----------
+        nH : float, optional
+            Hydrogen number density
+            (all hydrogen both neutral and ionized.
+            The default is 1.2e-4.
+        temperature : float, optional
+            Plasma temperature.
+            The default is 2.7e6.
+        metallicity : float, optional
+            Plasma metallicity with respect to solar.
+            The default is 0.5.
+        redshift : float, optional
+            Cosmological redshift of the universe.
+            The default is 0.2.
+        mode : str, optional
+            ionization condition
+            either CIE (collisional) or PIE (photo).
+            The default is 'PIE'.
+        interp_data : str
+            data location within HDF5 file
+        scaling_func : callable function, optional
+            function space in which intrepolation is
+            carried out.
+            The default is linear. log10 is another popular choice.
+        cut : upper and lower bound on  data
+            The default is (None, None)
 
-        # _median_value = np.median(_all_values, axis=0)
-        interp_value = all_weights.T @ all_values / np.sum(all_weights)
-        # if (len(interp_value.shape)==2):
-        #     interp_value = interp_value[0]
+        Returns
+        -------
+        _multiple_output) : bool
+            If multiple input is passed.
+        """
+        _is_multiple = (isinstance(nH, list) or isinstance(nH, np.ndarray)) and np.array(nH).flatten().shape[0] > 1
+        _is_multiple = _is_multiple or (isinstance(temperature, list) or isinstance(temperature, np.ndarray)) and np.array(temperature).flatten().shape[0] > 1
+        _is_multiple = _is_multiple or (isinstance(metallicity, list) or isinstance(metallicity, np.ndarray)) and np.array(metallicity).flatten().shape[0] > 1
+        _is_multiple = _is_multiple or (isinstance(redshift, list) or isinstance(redshift, np.ndarray)) and np.array(redshift).flatten().shape[0] > 1
 
-        for id_data in data:
-            id_data["file"].close()
-        return interp_value
+        return _is_multiple
diff --git a/astro_plasma/core/download_database.py b/astro_plasma/core/download_database.py
index 1b5d4b0..8564247 100644
--- a/astro_plasma/core/download_database.py
+++ b/astro_plasma/core/download_database.py
@@ -7,6 +7,8 @@
 
 # Built-in imports
 import os
+import sys
+import inspect
 from pathlib import Path
 from typing import Union, Tuple, Optional, List
 
@@ -52,12 +54,19 @@ def check_hashes_and_trim(
     download_location: Path,
     hash_list_url: Optional[str] = None,
 ) -> List[Tuple[str, Path]]:
-    _local_hashlist = True if hash_list_url is None else False
+    _local_hashlist = os.path.isfile(download_location / "hashlist.txt")
     if _local_hashlist:
         with open(download_location / "hashlist.txt", "r") as f:
             hash_list = [line.split("\n")[0] for line in f.readlines()]
     else:
-        hash_list = fetch_list_from_url(hash_list_url)
+        try:
+            hash_list = fetch_list_from_url(hash_list_url)
+        except requests.exceptions.ConnectionError:
+            print("Problem downloading data! Most likely internet connection issue! Code Aborted!")
+            sys.exit(1)
+        with open(download_location / "hashlist.txt", "w") as f:
+            for indx, hash_val in enumerate(hash_list):
+                f.write(hash_val + "\n" if (indx + 1) < len(hash_list) else hash_val)
     links_and_names_trimmed = []
     for indx, (_, filename) in enumerate(links_and_names):
         if not (os.path.isfile(download_location / filename) and checksum(download_location / filename, hash_list[indx])):
@@ -66,14 +75,14 @@ def check_hashes_and_trim(
 
 
 def generate_hashes(links_and_names: List[Tuple[str, Path]], download_location: Path) -> None:
-    hashlist = []
+    hash_list = []
     for _, filename in links_and_names:
         if os.path.isfile(download_location / filename):
             file_hash = blake2bsum(download_location / filename)
-            hashlist.append(file_hash)
+            hash_list.append(file_hash)
     with open(download_location / "hashlist.txt", "w") as f:
-        for indx, hash_val in enumerate(hashlist):
-            f.write(hash_val + "\n" if (indx + 1) < len(hashlist) else hash_val)
+        for indx, hash_val in enumerate(hash_list):
+            f.write(hash_val + "\n" if (indx + 1) < len(hash_list) else hash_val)
 
 
 def download_datafiles(
@@ -82,12 +91,47 @@ def download_datafiles(
     initialize: bool = False,
     hashgen: bool = False,
     hash_list_url: Optional[str] = None,
+    specific_file_ids: Optional[List[int]] = None,
+    force_online: bool = False,
 ) -> None:
-    links = fetch_list_from_url(link_list_url)
-    links = [prepare_onedrive_link(link) for link in links]
-    links_and_names = [(link, Path(get_filename(link))) for link in links]
+    # print("Debug: ", "Inside download datafiles! ", initialize)
+    caller_name = inspect.stack()[1].code_context[0]
+    if initialize:
+        specific_file_ids = [
+            0,
+        ]
+    if specific_file_ids is not None:
+        # This %06d might needs changing according to the specifics of the database
+        if "ionization" in caller_name:
+            names = list(map(lambda id_val: "ionization.b_%06d.h5" % id_val, specific_file_ids))
+        elif "emission" in caller_name:
+            names = list(map(lambda id_val: "emission.b_%06d.h5" % id_val, specific_file_ids))
+        else:
+            print("Invalid call to `download_datafiles`:", end=" ")
+            print("Undefined usage/some recursive call happened! Code Aborted!")
+            sys.exit(1)
+
+    _offline_avail = False not in [os.path.isfile(Path(download_location) / name) for name in names]
+    # print("Debug: Availability: ", _offline_avail, [str(download_location / name) for name in names])
+    if not (_offline_avail):
+        force_online = True
+    try:
+        if force_online or not (_offline_avail):
+            links = fetch_list_from_url(link_list_url)
+            links = [prepare_onedrive_link(link) for link in links]
+            links_and_names = [(link, Path(get_filename(link))) for link in links]
+        else:
+            links_and_names = [("", Path(name)) for name in names]
+    except requests.exceptions.ConnectionError:
+        if not (_offline_avail):
+            print("Problem downloading data! Most likely internet connection issue! Code Aborted!")
+            sys.exit(1)
+
+    # print("Debug: ", links_and_names)
     if initialize:
         links_and_names = [links_and_names[0]]
+    if specific_file_ids is not None and (not (_offline_avail) or force_online):
+        links_and_names = list(map(links_and_names.__getitem__, specific_file_ids))
     if hashgen:
         generate_hashes(
             links_and_names,
@@ -107,6 +151,7 @@ def download_datafiles(
 def download_ionization_data(
     initialize: bool = False,
     hashgen: bool = False,
+    specific_file_ids: Optional[List[int]] = None,
 ) -> None:
     ionization_links_url = prepare_onedrive_link(
         "https://indianinstituteofscience-my.sharepoint.com/:u:/g/personal/alankardutta_iisc_ac_in/EYnSBoTNOmdPqs3GPE0PDW0BDafcR78jbGCUM8tFqW8UAw?e=cCFbse"
@@ -114,12 +159,13 @@ def download_ionization_data(
     ionization_hash_url = prepare_onedrive_link(
         "https://indianinstituteofscience-my.sharepoint.com/:t:/g/personal/alankardutta_iisc_ac_in/EbK_KzUA5lVChnpsJFu2pAcBzVnjX6CEHUQp9e9Yi83-Yw?e=9WAi2b"
     )
-    download_datafiles(ionization_links_url, LOCAL_DATA_PATH / "ionization", initialize, hashgen, ionization_hash_url)
+    download_datafiles(ionization_links_url, LOCAL_DATA_PATH / "ionization", initialize, hashgen, ionization_hash_url, specific_file_ids)
 
 
 def download_emission_data(
     initialize: bool = False,
     hashgen: bool = False,
+    specific_file_ids: Optional[List[int]] = None,
 ) -> None:
     emission_links_url = prepare_onedrive_link(
         "https://indianinstituteofscience-my.sharepoint.com/:u:/g/personal/alankardutta_iisc_ac_in/EWJuOmWHwAVEnEtziAV5kg8BXtFp_44-0smofLpr_f_2Pg?e=7sreMC"
@@ -127,7 +173,7 @@ def download_emission_data(
     emission_hash_url = prepare_onedrive_link(
         "https://indianinstituteofscience-my.sharepoint.com/:t:/g/personal/alankardutta_iisc_ac_in/EZbmBm1BL_hEmKMyYXYsOZIBMIBxr3mJazjGvL53T5ZoAw?e=7bMW2B"
     )
-    download_datafiles(emission_links_url, LOCAL_DATA_PATH / "emission", initialize, hashgen, emission_hash_url)
+    download_datafiles(emission_links_url, LOCAL_DATA_PATH / "emission", initialize, hashgen, emission_hash_url, specific_file_ids)
 
 
 def download_all() -> None:
diff --git a/astro_plasma/core/ionization.py b/astro_plasma/core/ionization.py
index d20f77d..8414bd8 100644
--- a/astro_plasma/core/ionization.py
+++ b/astro_plasma/core/ionization.py
@@ -5,7 +5,7 @@
 
 # Built-in imports
 from pathlib import Path
-from typing import Optional, Union, Set
+from typing import Optional, Union
 
 # Third party imports
 import h5py
@@ -16,6 +16,7 @@
 from .datasift import DataSift
 from .utils import fetch, LOCAL_DATA_PATH, AtmElement, parse_atomic_ion_no
 from .data_dir import set_base_dir
+from .download_database import download_ionization_data
 
 DEFAULT_BASE_DIR = LOCAL_DATA_PATH / "ionization"
 FILE_NAME_TEMPLATE = "ionization.b_{:06d}.h5"
@@ -38,6 +39,7 @@ def __init__(
         None.
 
         """
+        self._check_and_download = download_ionization_data
         self.base_url_template = BASE_URL_TEMPLATE
         self.file_name_template = FILE_NAME_TEMPLATE
         self.base_dir = base_dir
@@ -55,21 +57,9 @@ def base_dir(
 
         fetch(urls=DOWNLOAD_IN_INIT, base_dir=self._base_dir)
         data = h5py.File(self._base_dir / DOWNLOAD_IN_INIT[0][1], "r")
-        super().__init__(data)
+        super().__init__(self, data)
         data.close()
 
-    def _fetch_data(self: "Ionization", batch_ids: Set[int]) -> None:
-        urls = []
-        for batch_id in batch_ids:
-            urls.append(
-                (
-                    self.base_url_template.format(batch_id),
-                    Path(self.file_name_template.format(batch_id)),
-                )
-            )
-
-        fetch(urls=urls, base_dir=self.base_dir)
-
     def _get_file_path(self: "Ionization", batch_id: int) -> Path:
         return self.base_dir / self.file_name_template.format(batch_id)
 
@@ -129,7 +119,7 @@ def _interpolate_ion_frac_all(
         # element = 1: H, 2: He, 3: Li, ... 30: Zn
         # ion = 1 : neutral, 2: +, 3: ++ .... (element+1): (++++... element times)
 
-        fracIon = super()._interpolate(
+        fracIon, self._is_multiple = super()._interpolate(
             nH,
             temperature,
             metallicity,
@@ -164,17 +154,17 @@ def interpolate_ion_frac(
 
         Parameters
         ----------
-        nH : float, optional
+        nH : float, list, np.ndarray, optional
             Hydrogen number density
             (all hydrogen both neutral and ionized.
             The default is 1.2e-4.
-        temperature : float, optional
+        temperature : float, list, np.ndarray, optional
             Plasma temperature.
             The default is 2.7e6.
-        metallicity : float, optional
+        metallicity : float, list, np.ndarray, optional
             Plasma metallicity with respect to solar.
             The default is 0.5.
-        redshift : float, optional
+        redshift : float, list, np.ndarray, optional
             Cosmological redshift of the universe.
             The default is 0.2.
         element : int, optional
@@ -211,10 +201,22 @@ def interpolate_ion_frac(
         # Select only the ions for the requested element
         slice_start = int((element - 1) * (element + 2) / 2)
         slice_stop = int(element * (element + 3) / 2)
-        fracIon = self._interpolate_ion_frac_all(nH, temperature, metallicity, redshift, mode)[slice_start:slice_stop]
 
-        # Array starts from 0 but ion from 1
-        return fracIon[ion - 1]  # This is in log10
+        _is_multiple = self._determine_multiple(nH, temperature, metallicity, redshift, mode)
+
+        if _is_multiple:
+            fracIon = self._interpolate_ion_frac_all(nH, temperature, metallicity, redshift, mode)
+            slices = [slice(None)] * (fracIon.ndim - 1)
+            slices.append(slice(slice_start, slice_stop))
+            fracIon = fracIon[tuple(slices)]
+            # Array starts from 0 but ion from 1
+            slices = slices[:-1]
+            slices.append(slice(ion - 1, ion))
+            return fracIon[tuple(slices)].flatten().reshape(self._input_shape)  # This is in log10
+        else:
+            fracIon = self._interpolate_ion_frac_all(nH, temperature, metallicity, redshift, mode)[slice_start:slice_stop]
+            # Array starts from 0 but ion from 1
+            return fracIon[ion - 1]  # This is in log10
 
     def interpolate_num_dens(
         self: "Ionization",
@@ -274,19 +276,25 @@ def interpolate_num_dens(
             abn = np.array([float(element.split()[-1]) for element in file.readlines()[2:32]])  # Till Zinc
         # element = 1: H, 2: He, 3: Li, ... 30: Zn
         # ion = 1 : neutral, 2: +, 3: ++ .... (element+1): (++++... element times)
-        fracIon = 10.0 ** self._interpolate_ion_frac_all(nH, temperature, metallicity, redshift, mode)
 
+        _is_multiple = self._determine_multiple(nH, temperature, metallicity, redshift, mode)
+        fracIon = np.power(10.0, self._interpolate_ion_frac_all(nH, temperature, metallicity, redshift, mode))
+
+        slices = [slice(None)] * (fracIon.ndim - 1)
         if part_type == "all" and element is None:
             ndens = 0
             ion_count = 0
             for element in range(30):
                 for ion in range(element + 2):
+                    slices.append(slice(ion_count, ion_count + 1))
+                    _ion_fraction = fracIon[tuple(slices)].flatten().reshape(self._input_shape) if _is_multiple else fracIon[ion_count]
+                    slices = slices[:-1]
                     if element + 1 == 1:  # H
-                        ndens += (ion + 1) * (Xp(metallicity) / X_solar) * abn[element] * fracIon[ion_count] * nH
+                        ndens += (ion + 1) * (Xp(metallicity) / X_solar) * abn[element] * _ion_fraction * nH
                     elif element + 1 == 2:  # He
-                        ndens += (ion + 1) * (Yp(metallicity) / Y_solar) * abn[element] * fracIon[ion_count] * nH
+                        ndens += (ion + 1) * (Yp(metallicity) / Y_solar) * abn[element] * _ion_fraction * nH
                     else:
-                        ndens += (ion + 1) * (Zp(metallicity) / Z_solar) * abn[element] * fracIon[ion_count] * nH
+                        ndens += (ion + 1) * (Zp(metallicity) / Z_solar) * abn[element] * _ion_fraction * nH
                     ion_count += 1
             return ndens
 
@@ -295,12 +303,15 @@ def interpolate_num_dens(
             ion_count = 0
             for element in range(30):
                 for ion in range(element + 2):
+                    slices.append(slice(ion_count, ion_count + 1))
+                    _ion_fraction = fracIon[tuple(slices)].flatten().reshape(self._input_shape) if _is_multiple else fracIon[ion_count]
+                    slices = slices[:-1]
                     if element + 1 == 1:  # H
-                        ne += ion * (Xp(metallicity) / X_solar) * nH * abn[element] * fracIon[ion_count]
+                        ne += ion * (Xp(metallicity) / X_solar) * nH * abn[element] * _ion_fraction
                     elif element + 1 == 2:  # He
-                        ne += ion * (Yp(metallicity) / Y_solar) * nH * abn[element] * fracIon[ion_count]
+                        ne += ion * (Yp(metallicity) / Y_solar) * nH * abn[element] * _ion_fraction
                     else:
-                        ne += ion * (Zp(metallicity) / Z_solar) * nH * abn[element] * fracIon[ion_count]
+                        ne += ion * (Zp(metallicity) / Z_solar) * nH * abn[element] * _ion_fraction
                     ion_count += 1
             return ne
 
@@ -309,13 +320,16 @@ def interpolate_num_dens(
             ion_count = 0
             for element in range(30):
                 for ion in range(element + 2):
+                    slices.append(slice(ion_count, ion_count + 1))
+                    _ion_fraction = fracIon[tuple(slices)].flatten().reshape(self._input_shape) if _is_multiple else fracIon[ion_count]
+                    slices = slices[:-1]
                     if ion == 0:
                         if element + 1 == 1:  # H
-                            n_neutral += (Xp(metallicity) / X_solar) * nH * abn[element] * fracIon[ion_count]
+                            n_neutral += (Xp(metallicity) / X_solar) * nH * abn[element] * _ion_fraction
                         elif element + 1 == 2:  # He
-                            n_neutral += (Yp(metallicity) / Y_solar) * nH * abn[element] * fracIon[ion_count]
+                            n_neutral += (Yp(metallicity) / Y_solar) * nH * abn[element] * _ion_fraction
                         else:
-                            n_neutral += (Zp(metallicity) / Z_solar) * nH * abn[element] * fracIon[ion_count]
+                            n_neutral += (Zp(metallicity) / Z_solar) * nH * abn[element] * _ion_fraction
                     ion_count += 1
             return n_neutral
 
@@ -325,12 +339,15 @@ def interpolate_num_dens(
             for element in range(30):
                 ion_count += 1  # neglects the neutral species
                 for ion in range(1, element + 2):
+                    slices.append(slice(ion_count, ion_count + 1))
+                    _ion_fraction = fracIon[tuple(slices)].flatten().reshape(self._input_shape) if _is_multiple else fracIon[ion_count]
+                    slices = slices[:-1]
                     if element + 1 == 1:  # H
-                        nion += (Xp(metallicity) / X_solar) * nH * abn[element] * fracIon[ion_count]
+                        nion += (Xp(metallicity) / X_solar) * nH * abn[element] * _ion_fraction
                     elif element + 1 == 2:  # He
-                        nion += (Yp(metallicity) / Y_solar) * nH * abn[element] * fracIon[ion_count]
+                        nion += (Yp(metallicity) / Y_solar) * nH * abn[element] * _ion_fraction
                     else:
-                        nion += ion * (Zp(metallicity) / Z_solar) * nH * abn[element] * fracIon[ion_count]
+                        nion += ion * (Zp(metallicity) / Z_solar) * nH * abn[element] * _ion_fraction
                     ion_count += 1
             return nion
 
@@ -340,7 +357,7 @@ def interpolate_num_dens(
             raise ValueError(f"Both part_type: {part_type} and element: {element} cannot be specified simultaneously.")
         else:
             _element, _ion = parse_atomic_ion_no(element, ion)
-            fIon = 10.0 ** self.interpolate_ion_frac(nH, temperature, metallicity, redshift, _element, _ion, mode)
+            fIon = np.power(10.0, self.interpolate_ion_frac(nH, temperature, metallicity, redshift, _element, _ion, mode))
             abundance = abn[_element - 1]
             nIon = abundance * (Zp(metallicity) / Z_solar) * fIon * nH
             return nIon
diff --git a/astro_plasma/core/spectrum.py b/astro_plasma/core/spectrum.py
index b213063..1956e5d 100644
--- a/astro_plasma/core/spectrum.py
+++ b/astro_plasma/core/spectrum.py
@@ -7,7 +7,7 @@
 
 # Built-in imports
 from pathlib import Path
-from typing import Union, Optional, Callable, Set
+from typing import Union, Optional, Callable
 
 # Third party imports
 import h5py
@@ -18,6 +18,7 @@
 from .datasift import DataSift
 from .utils import LOCAL_DATA_PATH, fetch
 from .data_dir import set_base_dir
+from .download_database import download_emission_data
 
 DEFAULT_BASE_DIR = LOCAL_DATA_PATH / "emission"
 FILE_NAME_TEMPLATE = "emission.b_{:06d}.h5"
@@ -40,6 +41,7 @@ def __init__(
         None.
 
         """
+        self._check_and_download = download_emission_data
         self.base_url_template = BASE_URL_TEMPLATE
         self.file_name_template = FILE_NAME_TEMPLATE
         self.base_dir = base_dir
@@ -57,22 +59,10 @@ def base_dir(
 
         fetch(urls=DOWNLOAD_IN_INIT, base_dir=self._base_dir)
         data = h5py.File(self._base_dir / DOWNLOAD_IN_INIT[0][1], "r")
-        super().__init__(data)
+        super().__init__(self, data)
         self._energy = np.array(data["output/energy"])
         data.close()
 
-    def _fetch_data(self: "EmissionSpectrum", batch_ids: Set[int]) -> None:
-        urls = []
-        for batch_id in batch_ids:
-            urls.append(
-                (
-                    self.base_url_template.format(batch_id),
-                    Path(self.file_name_template.format(batch_id)),
-                )
-            )
-
-        fetch(urls=urls, base_dir=self.base_dir)
-
     def _get_file_path(self: "EmissionSpectrum", batch_id: int) -> Path:
         return self.base_dir / self.file_name_template.format(batch_id)
 
@@ -126,15 +116,35 @@ def interpolate_spectrum(
         self.spectrum = np.zeros((self._energy.shape[0], 2))
         self.spectrum[:, 0] = self._energy
 
-        self.spectrum[:, 1] = super()._interpolate(
-            nH,
-            temperature,
-            metallicity,
-            redshift,
-            mode,
-            f"output/emission/{mode}/total",
-            (self.spectrum.shape[0],),
-            scaling_func,
-            (None, None),  # threshold cuts
-        )
+        _is_multiple = self._determine_multiple(nH, temperature, metallicity, redshift, mode)
+
+        if _is_multiple:
+            spectrum, _ = super()._interpolate(
+                nH,
+                temperature,
+                metallicity,
+                redshift,
+                mode,
+                f"output/emission/{mode}/total",
+                (self.spectrum.shape[0],),
+                scaling_func,
+                (None, None),  # threshold cuts
+            )
+            spectrum = spectrum.flatten().reshape((*self._input_shape, spectrum.shape[-1]))
+            self.spectrum = np.zeros((*spectrum.shape, 2), dtype=np.float64)
+            for i in range(spectrum.shape[0]):
+                self.spectrum[i, :, 0] = self._energy
+            self.spectrum[:, :, 1] = spectrum
+        else:
+            self.spectrum[:, 1], _ = super()._interpolate(
+                nH,
+                temperature,
+                metallicity,
+                redshift,
+                mode,
+                f"output/emission/{mode}/total",
+                (self.spectrum.shape[0],),
+                scaling_func,
+                (None, None),  # threshold cuts
+            )
         return self.spectrum
diff --git a/example-scripts/ionization_cloudy_compare.py b/example-scripts/ionization_cloudy_compare.py
index 809916f..44bb79a 100644
--- a/example-scripts/ionization_cloudy_compare.py
+++ b/example-scripts/ionization_cloudy_compare.py
@@ -47,7 +47,7 @@
 
 do_isothermal, do_isentropic = True, True
 
-fIon = Ionization().interpolate_ion_frac
+fIon = Ionization.interpolate_ion_frac
 element = 8
 fig = plt.figure()
 ax = fig.add_subplot(111)
@@ -56,19 +56,16 @@
 ion = 5
 p = plt.loglog(
     Temp,
-    10.0
-    ** np.array(
-        [
-            fIon(
-                temperature=T,
-                metallicity=0.99,
-                redshift=0.001,
-                element=element,
-                ion=ion,
-                mode="CIE",
-            )
-            for T in Temp
-        ]
+    np.power(
+        10.0,
+        fIon(
+            temperature=Temp,
+            metallicity=0.99,
+            redshift=0.001,
+            element=element,
+            ion=ion,
+            mode="CIE",
+        ),
     ),
     label=r"$f_{OV}$",
     alpha=0.6,
@@ -85,20 +82,17 @@
 ion = 6
 p = plt.loglog(
     Temp,
-    10.0
-    ** np.array(
-        [
-            fIon(
-                nH=1.2e-4,
-                temperature=T,
-                metallicity=0.99,
-                redshift=0.01,
-                element=element,
-                ion=ion,
-                mode="CIE",
-            )
-            for T in Temp
-        ]
+    np.power(
+        10.0,
+        fIon(
+            nH=1.2e-4,
+            temperature=Temp,
+            metallicity=0.99,
+            redshift=0.01,
+            element=element,
+            ion=ion,
+            mode="CIE",
+        ),
     ),
     label=r"$f_{OVI}$",
     alpha=0.6,
@@ -115,19 +109,16 @@
 ion = 7
 p = plt.loglog(
     Temp,
-    10.0
-    ** np.array(
-        [
-            fIon(
-                temperature=T,
-                metallicity=0.99,
-                redshift=0.001,
-                element=element,
-                ion=ion,
-                mode="CIE",
-            )
-            for T in Temp
-        ]
+    np.power(
+        10.0,
+        fIon(
+            temperature=Temp,
+            metallicity=0.99,
+            redshift=0.001,
+            element=element,
+            ion=ion,
+            mode="CIE",
+        ),
     ),
     label=r"$f_{OVII}$",
     alpha=0.6,
@@ -144,19 +135,16 @@
 ion = 8
 p = plt.loglog(
     Temp,
-    10.0
-    ** np.array(
-        [
-            fIon(
-                temperature=T,
-                metallicity=0.99,
-                redshift=0.001,
-                element=element,
-                ion=ion,
-                mode="CIE",
-            )
-            for T in Temp
-        ]
+    np.power(
+        10.0,
+        fIon(
+            temperature=Temp,
+            metallicity=0.99,
+            redshift=0.001,
+            element=element,
+            ion=ion,
+            mode="CIE",
+        ),
     ),
     label=r"$f_{OVIII}$",
     alpha=0.6,
diff --git a/poetry.lock b/poetry.lock
index ba69c59..04e454f 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2133,4 +2133,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "3ed3fc33b658f7cb1f62809c4b2ccef0761ec685c3a2ad89e163f71869abdd16"
+content-hash = "bee7cc49cd80c2700924d74f557ed9a0f8c93bc8c4174f4c5cbff778dbc3af3d"
diff --git a/pyproject.toml b/pyproject.toml
index 265c065..10a21d5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,6 +21,7 @@ tqdm = "^4.65.0"
 werkzeug = "^3.0.1"
 pytest = "^7.4.3"
 poetry-plugin-export = "^1.6.0"
+colorama = "^0.4.6"
 
 # Packages required for development
 [tool.poetry.group.dev]
diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt
index d6095c3..fbf2a3f 100644
--- a/requirements/requirements-all.txt
+++ b/requirements/requirements-all.txt
@@ -7,7 +7,7 @@ cfgv==3.4.0 ; python_version >= "3.10" and python_version < "4.0"
 charset-normalizer==3.3.2 ; python_version >= "3.10" and python_version < "4.0"
 cleo==2.1.0 ; python_version >= "3.10" and python_version < "4.0"
 click==8.1.7 ; python_version >= "3.10" and python_version < "4.0"
-colorama==0.4.6 ; python_version >= "3.10" and python_version < "4.0" and (platform_system == "Windows" or sys_platform == "win32" or os_name == "nt")
+colorama==0.4.6 ; python_version >= "3.10" and python_version < "4.0"
 contourpy==1.2.0 ; python_version >= "3.10" and python_version < "4.0"
 crashtest==0.4.1 ; python_version >= "3.10" and python_version < "4.0"
 cryptography==41.0.7 ; python_version >= "3.10" and python_version < "4.0" and sys_platform == "linux"
diff --git a/requirements/requirements-dev.txt b/requirements/requirements-dev.txt
index 2baa8a1..d04dd1e 100644
--- a/requirements/requirements-dev.txt
+++ b/requirements/requirements-dev.txt
@@ -7,7 +7,7 @@ cfgv==3.4.0 ; python_version >= "3.10" and python_version < "4.0"
 charset-normalizer==3.3.2 ; python_version >= "3.10" and python_version < "4.0"
 cleo==2.1.0 ; python_version >= "3.10" and python_version < "4.0"
 click==8.1.7 ; python_version >= "3.10" and python_version < "4.0"
-colorama==0.4.6 ; python_version >= "3.10" and python_version < "4.0" and (platform_system == "Windows" or sys_platform == "win32" or os_name == "nt")
+colorama==0.4.6 ; python_version >= "3.10" and python_version < "4.0"
 crashtest==0.4.1 ; python_version >= "3.10" and python_version < "4.0"
 cryptography==41.0.7 ; python_version >= "3.10" and python_version < "4.0" and sys_platform == "linux"
 distlib==0.3.7 ; python_version >= "3.10" and python_version < "4.0"
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 80c3685..b72700a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,7 +4,7 @@ certifi==2023.11.17 ; python_version >= "3.10" and python_version < "4.0"
 cffi==1.16.0 ; python_version >= "3.10" and python_version < "4.0" and (sys_platform == "darwin" or sys_platform == "linux")
 charset-normalizer==3.3.2 ; python_version >= "3.10" and python_version < "4.0"
 cleo==2.1.0 ; python_version >= "3.10" and python_version < "4.0"
-colorama==0.4.6 ; python_version >= "3.10" and python_version < "4.0" and (platform_system == "Windows" or sys_platform == "win32" or os_name == "nt")
+colorama==0.4.6 ; python_version >= "3.10" and python_version < "4.0"
 crashtest==0.4.1 ; python_version >= "3.10" and python_version < "4.0"
 cryptography==41.0.7 ; python_version >= "3.10" and python_version < "4.0" and sys_platform == "linux"
 distlib==0.3.7 ; python_version >= "3.10" and python_version < "4.0"
diff --git a/tests/sample_spectrum.txt b/tests/sample_spectrum.txt
new file mode 100644
index 0000000..4989e6b
--- /dev/null
+++ b/tests/sample_spectrum.txt
@@ -0,0 +1,8228 @@
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diff --git a/tests/test_basic.py b/tests/test_astro_palsma_basic.py
similarity index 59%
rename from tests/test_basic.py
rename to tests/test_astro_palsma_basic.py
index d7710fa..25d0c82 100644
--- a/tests/test_basic.py
+++ b/tests/test_astro_palsma_basic.py
@@ -57,3 +57,61 @@ def test_hash():
     # check the hash of the datafile <filenumber>
     hash_emission_found = astro_plasma.core.utils.blake2bsum(LOCAL_DATA_PATH / "emission" / emission_filename)
     assert hash_emission_found == hash_emission_expect
+
+
+def test_dimension():
+    # Import AstroPlasma Ionization module
+    from astro_plasma import Ionization
+    import numpy as np
+
+    fIon = Ionization.interpolate_ion_frac
+
+    nH = np.power(10.0, np.random.uniform(low=-5.0, high=-2.0))  # Hydrogen number density in cm^-3
+    temperature = np.power(10.0, np.random.uniform(low=3.8, high=6.5))  # Temperature of the plasma in kelvin
+    metallicity = np.random.uniform(low=0.2, high=0.9)  # Metallicity of plasma with respect to solar
+    redshift = np.random.uniform(low=0.1, high=1.2)  # Cosmological redshift
+    mode = "PIE"
+
+    element = np.random.randint(low=1, high=32)
+    np.random.randint(low=1, high=element + 1)
+
+    frac = fIon(
+        nH=nH,
+        temperature=temperature,
+        metallicity=metallicity,
+        redshift=redshift,
+        element=element,
+        mode=mode,
+    )  # This value is in log10
+    assert np.array(frac).ndim == 0
+
+    nH = np.power(10.0, np.random.uniform(low=-5.0, high=-2.0))  # Hydrogen number density in cm^-3
+    temperature = np.power(10.0, np.linspace(3.8, 6.5, 5))  # Temperature of the plasma in kelvin
+    metallicity = np.random.uniform(low=0.2, high=0.9)  # Metallicity of plasma with respect to solar
+    redshift = np.random.uniform(low=0.1, high=1.2)  # Cosmological redshift
+
+    frac = fIon(
+        nH=nH,
+        temperature=temperature,
+        metallicity=metallicity,
+        redshift=redshift,
+        element=element,
+        mode=mode,
+    )  # This value is in log10
+    assert np.array(frac).shape == temperature.shape
+
+    nH = np.logspace(-5.0, -2.0, 2)  # Hydrogen number density in cm^-3
+    temperature = np.logspace(3.8, 6.5, 5)  # Temperature of the plasma in kelvin
+    nH, temperature = np.meshgrid(nH, temperature)
+    metallicity = np.random.uniform(low=0.2, high=0.9)  # Metallicity of plasma with respect to solar
+    redshift = np.random.uniform(low=0.1, high=1.2)  # Cosmological redshift
+
+    frac = fIon(
+        nH=nH,
+        temperature=temperature,
+        metallicity=metallicity,
+        redshift=redshift,
+        element=element,
+        mode=mode,
+    )  # This value is in log10
+    assert np.array(frac).shape == temperature.shape
diff --git a/tests/test_astro_palsma_run.py b/tests/test_astro_palsma_run.py
new file mode 100644
index 0000000..f66b39f
--- /dev/null
+++ b/tests/test_astro_palsma_run.py
@@ -0,0 +1,173 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Dec  9 19:50:11 2023
+
+@author: alankar
+"""
+
+
+def test_ion_frac():
+    # Import AstroPlasma Ionization module
+    from astro_plasma import Ionization
+    from astro_plasma.core.utils import AtmElement  # for element naming using symbols (optional)
+    import numpy as np
+
+    fIon = Ionization.interpolate_ion_frac
+
+    nH = 1.2e-04  # Hydrogen number density in cm^-3
+    temperature = 4.2e05  # Temperature of the plasma in kelvin
+    metallicity = 0.99  # Metallicity of plasma with respect to solar
+    redshift = 0.001  # Cosmological redshift
+    mode = "CIE"
+
+    # Lets get the ionization of OVI
+    element = AtmElement.Oxygen
+    ion = 6
+    fOVI = fIon(
+        nH=nH,
+        temperature=temperature,
+        metallicity=metallicity,
+        redshift=redshift,
+        element=element,
+        ion=ion,
+        mode=mode,
+    )  # This value is in log10
+    fOVI = np.power(10.0, fOVI)
+    fOVI_expected = 8.895256915490418e-02
+    assert np.isclose(fOVI, fOVI_expected)
+
+
+def test_num_dens():
+    # Import AstroPlasma Ionization module
+    from astro_plasma import Ionization
+    import numpy as np
+
+    num_dens = Ionization.interpolate_num_dens
+
+    nH = 1.2e-04  # Hydrogen number density in cm^-3
+    temperature = 4.2e05  # Temperature of the plasma in kelvin
+    metallicity = 0.99  # Metallicity of plasma with respect to solar
+    redshift = 0.001  # Cosmological redshift
+    mode = "CIE"
+
+    ne = num_dens(
+        nH=nH,
+        temperature=temperature,
+        metallicity=metallicity,
+        redshift=redshift,
+        mode=mode,
+        part_type="electron",
+    )
+    ne_expected = 1.4109277149716788e-04
+    assert np.isclose(ne, ne_expected)
+
+    n = num_dens(
+        nH=nH,
+        temperature=temperature,
+        metallicity=metallicity,
+        redshift=redshift,
+        mode=mode,
+        part_type="all",
+    )
+    n_expected = 2.714495226687527e-04
+    assert np.isclose(n, n_expected)
+
+    ni = num_dens(
+        nH=nH,
+        temperature=temperature,
+        metallicity=metallicity,
+        redshift=redshift,
+        mode=mode,
+        part_type="ion",
+    )
+    ni_expected = 1.3087928999569684e-04
+    assert np.isclose(ni, ni_expected)
+
+    nn = num_dens(
+        nH=nH,
+        temperature=temperature,
+        metallicity=metallicity,
+        redshift=redshift,
+        mode=mode,
+        part_type="neutral",
+    )
+    nn_expected = 1.0861042185130847e-10
+    assert np.isclose(nn, nn_expected)
+
+    nHI = num_dens(
+        nH=nH,
+        temperature=temperature,
+        metallicity=metallicity,
+        redshift=redshift,
+        mode=mode,
+        element="HI",
+    )
+    nHI_expected = 1.0731159716825104e-10
+    assert np.isclose(nHI, nHI_expected)
+
+
+def test_mu():
+    # Import AstroPlasma Ionization module
+    from astro_plasma import Ionization
+    import numpy as np
+
+    mean_mass = Ionization.interpolate_mu
+
+    nH = 1.2e-04  # Hydrogen number density in cm^-3
+    temperature = 4.2e05  # Temperature of the plasma in kelvin
+    metallicity = 0.99  # Metallicity of plasma with respect to solar
+    redshift = 0.001  # Cosmological redshift
+    mode = "CIE"
+
+    mu = mean_mass(
+        nH=nH,
+        temperature=temperature,
+        metallicity=metallicity,
+        redshift=redshift,
+        mode=mode,
+        part_type="all",
+    )
+    mu_expected = 0.6181703336141905
+    assert np.isclose(mu, mu_expected)
+
+    mu_e = mean_mass(
+        nH=nH,
+        temperature=temperature,
+        metallicity=metallicity,
+        redshift=redshift,
+        mode=mode,
+        part_type="electron",
+    )
+    mu_e_expected = 1.1893028977102762
+    assert np.isclose(mu_e, mu_e_expected)
+
+    mu_i = mean_mass(
+        nH=nH,
+        temperature=temperature,
+        metallicity=metallicity,
+        redshift=redshift,
+        mode=mode,
+        part_type="ion",
+    )
+    mu_i_expected = 1.2821130218010255
+    assert np.isclose(mu_i, mu_i_expected)
+
+
+def test_spectrum():
+    # Import AstroPlasma EmissionSpectrum module
+    from astro_plasma import EmissionSpectrum
+    import numpy as np
+
+    gen_spectrum = EmissionSpectrum.interpolate_spectrum
+
+    nH = 1.2e-04  # Hydrogen number density in cm^-3
+    temperature = 4.2e05  # Temperature of the plasma in kelvin
+    metallicity = 0.99  # Metallicity of plasma with respect to solar
+    redshift = 0.001  # Cosmological redshift
+    mode = "CIE"
+
+    # Generate spectrum
+    spectrum = gen_spectrum(nH=nH, temperature=temperature, metallicity=metallicity, redshift=redshift, mode=mode)
+
+    spectrum_expected = np.loadtxt("tests/sample_spectrum.txt")
+    assert np.sum(np.abs(spectrum - spectrum_expected)) < 1.0e-06