"""Example processing Bruker ODNP data and calcualting hydration parameters"""

import sys
import os
import copy
import numpy as np

sys.path.append("..")
import dnplab

# directory of base folder containing the numbered folders listed below
directory = "..topspin"

# folder number for p=0 point in ODNP set
folder_p0 = 5

# folder number for T1(0) in ODNP set
folder_T10 = 304

# list of folder numbers for Enhancement(p) points in ODNP set
folders_Enhancements = range(6, 27)

# list of folder numbers for T1(p) points in ODNP set
folders_T1s = range(28, 33)

# give input parameters and set options for dnpHydration
spin_C = 100
field = 348.5
T100 = 2.5
smax_model = "tethered"
t1_interp_method = "second_order"

# array of power readings for Enhancement(p) points
Enhancement_powers = np.array(
    [
        0.0006454923080882520,
        0.004277023425898170,
        0.004719543572446050,
        0.00909714298712173,
        0.01344187403986090,
        0.01896059941058610,
        0.02101937603827090,
        0.022335737104727900,
        0.026029715703921800,
        0.02917012237740640,
        0.0338523245243911,
        0.03820738749745440,
        0.04733370907740660,
        0.05269608016472140,
        0.053790874615060400,
        0.05697639350179900,
        0.06435487925718170,
        0.07909179437004270,
        0.08958910066880800,
        0.1051813598911370,
        0.11617812912435900,
    ]
)

# array of power readings for T1(p) points
T1_powers = np.array(
    [
        0.000589495934876689,
        0.024242327290569100,
        0.054429505156431400,
        0.0862844940360515,
        0.11617812912435900,
    ]
)

proc_params = dict()
proc_params["zero_fill_factor"] = 2
proc_params["window_linewidth"] = 10

int_params = dict()
int_params["integrate_center"] = 0
int_params["integrate_width"] = 20

save_name = "ODNPdata_dnpHydrationResults"


#### Do not change the code below ####
print("Working...")


def workupPhaseOpt(workspace):
    """Return the optimized phase given a workspace.

    Args:
        workspace: Workspace containing an FID.

    Returns:
        float: The best phase.
    """

    curve = workspace["proc"].values

    phases = np.linspace(-np.pi / 2, np.pi / 2, 100).reshape(1, -1)
    rotated_data = (curve.reshape(-1, 1)) * np.exp(-1j * phases)
    success = (np.real(rotated_data) ** 2).sum(axis=0) / (
        (np.imag(rotated_data) ** 2).sum(axis=0)
    )
    bestindex = np.argmax(success)

    return phases[0, bestindex]


def optCenter(workspace):
    """Return the optimized integration center given a workspace.

    Args:
        workspace: Workspace containing an FID.

    Returns:
        int: The best integration center.
    """

    intgrl_array = []
    indx = range(-50, 51)
    int_params = {"integrate_width": 10}
    for k in range(0, len(indx)):
        iterativeopt_workspace = copy.deepcopy(workspace)
        iterativeopt_workspace = dnplab.dnpNMR.integrate(
            iterativeopt_workspace, {"integrate_center": indx[k], "integrate_width": 10}
        )
        if len(iterativeopt_workspace["proc"].values) > 1:
            intgrl_array.append(sum(abs(iterativeopt_workspace["proc"].values)))
        else:
            intgrl_array.append(abs(iterativeopt_workspace["proc"].values[0]))
    cent = np.argmax(intgrl_array)

    return indx[cent]


total_folders = []
total_folders.append(folder_p0)
for e in folders_Enhancements:
    total_folders.append(e)
for t in folders_T1s:
    total_folders.append(t)
total_folders.append(folder_T10)

T1 = []
T1_stdd = []
E = []
for f in range(0, len(total_folders)):

    data = dnplab.dnpImport.topspin.import_topspin(directory, total_folders[f])
    workspace = dnplab.create_workspace("raw", data)
    workspace.copy("raw", "proc")

    dnplab.dnpNMR.remove_offset(workspace, {})
    dnplab.dnpNMR.window(workspace, {"linewidth": proc_params["window_linewidth"]})
    dnplab.dnpNMR.fourier_transform(
        workspace, {"zero_fill_factor": proc_params["zero_fill_factor"]}
    )

    if workspace["proc"].ndim == 2:
        workspace = dnplab.dnpNMR.align(workspace, {})

    phase = workupPhaseOpt(workspace)
    workspace["proc"] *= np.exp(-1j * phase)

    int_params["integrate_center"] = optCenter(workspace)
    workspace = dnplab.dnpNMR.integrate(
        workspace,
        {
            "integrate_center": int_params["integrate_center"],
            "integrate_width": int_params["integrate_width"],
        },
    )

    if total_folders[f] == folder_p0:
        p0 = np.real(workspace["proc"].values[0])
        print("Done with p0 folder...")
    elif total_folders[f] == folder_T10:
        workspace = dnplab.dnpFit.t1Fit(workspace)
        T10 = workspace["fit"].attrs["t1"]
        T10_stdd = workspace["fit"].attrs["t1_stdd"]
        print("Done with T1(0) folder...")
    elif total_folders[f] in folders_T1s:
        workspace = dnplab.dnpFit.t1Fit(workspace)
        T1.append(workspace["fit"].attrs["t1"])
        T1_stdd.append(workspace["fit"].attrs["t1_stdd"])
        print("Done with T1(p) folder " + str(total_folders[f]) + "...")
    elif total_folders[f] in folders_Enhancements:
        E.append(np.real(workspace["proc"].values[0]) / p0)
        print("Done with Enhancement(p) folder " + str(total_folders[f]) + "...")

E = np.array([Enhancement_powers, E])
E = np.transpose(E)
E = E[E[:, 0].argsort()]

T1 = np.array([T1_powers, T1])
T1 = np.transpose(T1)
T1 = T1[T1[:, 0].argsort()]

Enhancement_powers = E[:, 0]
Enhancements = E[:, 1]

T1_powers = T1[:, 0]
T1s = T1[:, 1]

print("Passing to dnpHydration...")

hydration = {
    "E": np.array(Enhancements),
    "E_power": np.array(Enhancement_powers),
    "T1": np.array(T1s),
    "T1_power": np.array(T1_powers),
}

hydration.update(
    {
        "T10": T10,
        "T100": T100,
        "spin_C": spin_C,
        "field": field,
        "smax_model": smax_model,
        "t1_interp_method": t1_interp_method,
    }
)

hydration_workspace = dnplab.create_workspace()
hydration_workspace.add("hydration_inputs", hydration)

hydration_results = dnplab.dnpHydration.hydration(hydration_workspace)

hydration_workspace.add("hydration_results", hydration_results)
hydration_workspace["hydration_results"].update(
    {"T1p_stdd": T1_stdd, "T10_stdd": T10_stdd}
)

svpthnm = directory + save_name
if os.path.isfile(directory + save_name + ".h5"):
    svpthnm = svpthnm + "_COPY"

dnplab.dnpImport.h5.saveh5(hydration_workspace, svpthnm + ".h5")

print("Done with dnpHydration.")
print("T1(0) = " + str(T10) + ", stdd = " + str(T10_stdd))
print("krho = " + str(hydration_results["krho"]))
print(
    "ksigma = "
    + str(hydration_results["ksigma"])
    + ", stdd = "
    + str(hydration_results["ksigma_stdd"])
)
print("coupling factor = " + str(hydration_results["coupling_factor"]))
print("tcorr = " + str(hydration_results["tcorr"]))

"""Example processing Bruker ODNP data and calcualting hydration parameters"""

import sys
import os
import copy
import numpy as np

sys.path.append("..")
import dnplab

# directory of base folder containing the numbered folders listed below
directory = "..topspin"

# folder number for p=0 point in ODNP set
folder_p0 = 5

# folder number for T1(0) in ODNP set
folder_T10 = 304

# list of folder numbers for Enhancement(p) points in ODNP set
folders_Enhancements = range(6, 27)

# list of folder numbers for T1(p) points in ODNP set
folders_T1s = range(28, 33)

# give input parameters and set options for dnpHydration
spin_C = 100
field = 348.5
T100 = 2.5
smax_model = "tethered"
t1_interp_method = "second_order"

# array of power readings for Enhancement(p) points
Enhancement_powers = np.array(
    [
        0.0006454923080882520,
        0.004277023425898170,
        0.004719543572446050,
        0.00909714298712173,
        0.01344187403986090,
        0.01896059941058610,
        0.02101937603827090,
        0.022335737104727900,
        0.026029715703921800,
        0.02917012237740640,
        0.0338523245243911,
        0.03820738749745440,
        0.04733370907740660,
        0.05269608016472140,
        0.053790874615060400,
        0.05697639350179900,
        0.06435487925718170,
        0.07909179437004270,
        0.08958910066880800,
        0.1051813598911370,
        0.11617812912435900,
    ]
)

# array of power readings for T1(p) points
T1_powers = np.array(
    [
        0.000589495934876689,
        0.024242327290569100,
        0.054429505156431400,
        0.0862844940360515,
        0.11617812912435900,
    ]
)

proc_params = dict()
proc_params["zero_fill_factor"] = 2
proc_params["window_linewidth"] = 10

int_params = dict()
int_params["integrate_center"] = 0
int_params["integrate_width"] = 20

save_name = "ODNPdata_dnpHydrationResults"


#### Do not change the code below ####
print("Working...")


def workupPhaseOpt(workspace):
    """Return the optimized phase given a workspace.

    Args:
        workspace: Workspace containing an FID.

    Returns:
        float: The best phase.
    """

    curve = workspace["proc"].values

    phases = np.linspace(-np.pi / 2, np.pi / 2, 100).reshape(1, -1)
    rotated_data = (curve.reshape(-1, 1)) * np.exp(-1j * phases)
    success = (np.real(rotated_data) ** 2).sum(axis=0) / (
        (np.imag(rotated_data) ** 2).sum(axis=0)
    )
    bestindex = np.argmax(success)

    return phases[0, bestindex]


def optCenter(workspace):
    """Return the optimized integration center given a workspace.

    Args:
        workspace: Workspace containing an FID.

    Returns:
        int: The best integration center.
    """

    intgrl_array = []
    indx = range(-50, 51)
    int_params = {"integrate_width": 10}
    for k in range(0, len(indx)):
        iterativeopt_workspace = copy.deepcopy(workspace)
        iterativeopt_workspace = dnplab.dnpNMR.integrate(
            iterativeopt_workspace, {"integrate_center": indx[k], "integrate_width": 10}
        )
        if len(iterativeopt_workspace["proc"].values) > 1:
            intgrl_array.append(sum(abs(iterativeopt_workspace["proc"].values)))
        else:
            intgrl_array.append(abs(iterativeopt_workspace["proc"].values[0]))
    cent = np.argmax(intgrl_array)

    return indx[cent]


total_folders = []
total_folders.append(folder_p0)
for e in folders_Enhancements:
    total_folders.append(e)
for t in folders_T1s:
    total_folders.append(t)
total_folders.append(folder_T10)

T1 = []
T1_stdd = []
E = []
for f in range(0, len(total_folders)):

    data = dnplab.dnpImport.topspin.import_topspin(directory, total_folders[f])
    workspace = dnplab.create_workspace("raw", data)
    workspace.copy("raw", "proc")

    dnplab.dnpNMR.remove_offset(workspace, {})
    dnplab.dnpNMR.window(workspace, {"linewidth": proc_params["window_linewidth"]})
    dnplab.dnpNMR.fourier_transform(
        workspace, {"zero_fill_factor": proc_params["zero_fill_factor"]}
    )

    if workspace["proc"].ndim == 2:
        workspace = dnplab.dnpNMR.align(workspace, {})

    phase = workupPhaseOpt(workspace)
    workspace["proc"] *= np.exp(-1j * phase)

    int_params["integrate_center"] = optCenter(workspace)
    workspace = dnplab.dnpNMR.integrate(
        workspace,
        {
            "integrate_center": int_params["integrate_center"],
            "integrate_width": int_params["integrate_width"],
        },
    )

    if total_folders[f] == folder_p0:
        p0 = np.real(workspace["proc"].values[0])
        print("Done with p0 folder...")
    elif total_folders[f] == folder_T10:
        workspace = dnplab.dnpFit.t1Fit(workspace)
        T10 = workspace["fit"].attrs["t1"]
        T10_stdd = workspace["fit"].attrs["t1_stdd"]
        print("Done with T1(0) folder...")
    elif total_folders[f] in folders_T1s:
        workspace = dnplab.dnpFit.t1Fit(workspace)
        T1.append(workspace["fit"].attrs["t1"])
        T1_stdd.append(workspace["fit"].attrs["t1_stdd"])
        print("Done with T1(p) folder " + str(total_folders[f]) + "...")
    elif total_folders[f] in folders_Enhancements:
        E.append(np.real(workspace["proc"].values[0]) / p0)
        print("Done with Enhancement(p) folder " + str(total_folders[f]) + "...")

E = np.array([Enhancement_powers, E])
E = np.transpose(E)
E = E[E[:, 0].argsort()]

T1 = np.array([T1_powers, T1])
T1 = np.transpose(T1)
T1 = T1[T1[:, 0].argsort()]

Enhancement_powers = E[:, 0]
Enhancements = E[:, 1]

T1_powers = T1[:, 0]
T1s = T1[:, 1]

print("Passing to dnpHydration...")

hydration = {
    "E": np.array(Enhancements),
    "E_power": np.array(Enhancement_powers),
    "T1": np.array(T1s),
    "T1_power": np.array(T1_powers),
}

hydration.update(
    {
        "T10": T10,
        "T100": T100,
        "spin_C": spin_C,
        "field": field,
        "smax_model": smax_model,
        "t1_interp_method": t1_interp_method,
    }
)

hydration_workspace = dnplab.create_workspace()
hydration_workspace.add("hydration_inputs", hydration)

hydration_results = dnplab.dnpHydration.hydration(hydration_workspace)

hydration_workspace.add("hydration_results", hydration_results)
hydration_workspace["hydration_results"].update(
    {"T1p_stdd": T1_stdd, "T10_stdd": T10_stdd}
)

svpthnm = directory + save_name
if os.path.isfile(directory + save_name + ".h5"):
    svpthnm = svpthnm + "_COPY"

dnplab.dnpImport.h5.saveh5(hydration_workspace, svpthnm + ".h5")

print("Done with dnpHydration.")
print("T1(0) = " + str(T10) + ", stdd = " + str(T10_stdd))
print("krho = " + str(hydration_results["krho"]))
print(
    "ksigma = "
    + str(hydration_results["ksigma"])
    + ", stdd = "
    + str(hydration_results["ksigma_stdd"])
)
print("coupling factor = " + str(hydration_results["coupling_factor"]))
print("tcorr = " + str(hydration_results["tcorr"]))

Example processing Bruker ODNP data and calcualting hydration parameters # directory of base folder containing the numbered folders listed below # folder number for p=0 point in ODNP set # folder number for T1(0) in ODNP set # list of folder numbers for Enhancement(p) points in ODNP set # list of folder numbers for T1(p) points in ODNP set # give input parameters and set options for dnpHydration # array of power readings for Enhancement(p) points # array of power readings for T1(p) points #### Do not change the code below #### Return the optimized phase given a workspace.      Args:         workspace: Workspace containing an FID.      Returns:         float: The best phase. Return the optimized integration center given a workspace.      Args:         workspace: Workspace containing an FID.      Returns:         int: The best integration center.