app.py

import gradio as gr
import os
import matplotlib.pyplot as plt
from scipy.integrate import odeint
import torch
from torch.utils import data
from torch.utils.data import DataLoader, Dataset
from torch import nn, optim
from skimage.transform import rescale, resize
from torch import nn, optim
import torch.nn.functional as F
from torch.utils.data import Subset
from scipy.interpolate import interp1d
import collections
import numpy as np
import random

#for pvloop simulator:
import pandas as pd
from scipy.integrate import odeint 
import torchvision
import echonet
import matplotlib.animation as animation
from functools import partial

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

sequences_all = []
info_data_all = []
path = 'EchoNet-Dynamic'
output_path = ''

class Echo(torchvision.datasets.VisionDataset):
    """EchoNet-Dynamic Dataset.
    Args:
        root (string): Root directory of dataset (defaults to `echonet.config.DATA_DIR`)
        split (string): One of {``train'', ``val'', ``test'', ``all'', or ``external_test''}
        target_type (string or list, optional): Type of target to use,
            ``Filename'', ``EF'', ``EDV'', ``ESV'', ``LargeIndex'',
            ``SmallIndex'', ``LargeFrame'', ``SmallFrame'', ``LargeTrace'',
            or ``SmallTrace''
            Can also be a list to output a tuple with all specified target types.
            The targets represent:
                ``Filename'' (string): filename of video
                ``EF'' (float): ejection fraction
                ``EDV'' (float): end-diastolic volume
                ``ESV'' (float): end-systolic volume
                ``LargeIndex'' (int): index of large (diastolic) frame in video
                ``SmallIndex'' (int): index of small (systolic) frame in video
                ``LargeFrame'' (np.array shape=(3, height, width)): normalized large (diastolic) frame
                ``SmallFrame'' (np.array shape=(3, height, width)): normalized small (systolic) frame
                ``LargeTrace'' (np.array shape=(height, width)): left ventricle large (diastolic) segmentation
                    value of 0 indicates pixel is outside left ventricle
                             1 indicates pixel is inside left ventricle
                ``SmallTrace'' (np.array shape=(height, width)): left ventricle small (systolic) segmentation
                    value of 0 indicates pixel is outside left ventricle
                             1 indicates pixel is inside left ventricle
            Defaults to ``EF''.
        mean (int, float, or np.array shape=(3,), optional): means for all (if scalar) or each (if np.array) channel.
            Used for normalizing the video. Defaults to 0 (video is not shifted).
        std (int, float, or np.array shape=(3,), optional): standard deviation for all (if scalar) or each (if np.array) channel.
            Used for normalizing the video. Defaults to 0 (video is not scaled).
        length (int or None, optional): Number of frames to clip from video. If ``None'', longest possible clip is returned.
            Defaults to 16.
        period (int, optional): Sampling period for taking a clip from the video (i.e. every ``period''-th frame is taken)
            Defaults to 2.
        max_length (int or None, optional): Maximum number of frames to clip from video (main use is for shortening excessively
            long videos when ``length'' is set to None). If ``None'', shortening is not applied to any video.
            Defaults to 250.
        clips (int, optional): Number of clips to sample. Main use is for test-time augmentation with random clips.
            Defaults to 1.
        pad (int or None, optional): Number of pixels to pad all frames on each side (used as augmentation).
            and a window of the original size is taken. If ``None'', no padding occurs.
            Defaults to ``None''.
        noise (float or None, optional): Fraction of pixels to black out as simulated noise. If ``None'', no simulated noise is added.
            Defaults to ``None''.
        target_transform (callable, optional): A function/transform that takes in the target and transforms it.
        external_test_location (string): Path to videos to use for external testing.
    """

    def __init__(self, root=None,
                 split="train", target_type="EF",
                 mean=0., std=1.,
                 length=16, period=2,
                 max_length=250,
                 clips=1,
                 pad=None,
                 noise=None,
                 target_transform=None,
                 external_test_location=None):
        if root is None:
            root = path

        super().__init__(root, target_transform=target_transform)

        self.split = split.upper()
        if not isinstance(target_type, list):
            target_type = [target_type]
        self.target_type = target_type
        self.mean = mean
        self.std = std
        self.length = length
        self.max_length = max_length
        self.period = period
        self.clips = clips
        self.pad = pad
        self.noise = noise
        self.target_transform = target_transform
        self.external_test_location = external_test_location

        self.fnames, self.outcome = [], []

        if self.split == "EXTERNAL_TEST":
            self.fnames = sorted(os.listdir(self.external_test_location))
        else:
            # Load video-level labels
            with open(f"{self.root}/FileList.csv") as f:
                data = pd.read_csv(f)
            data["Split"].map(lambda x: x.upper())

            if self.split != "ALL":
                data = data[data["Split"] == self.split]

            self.header = data.columns.tolist()
            self.fnames = data["FileName"].tolist()
            self.fnames = [fn + ".avi" for fn in self.fnames if os.path.splitext(fn)[1] == ""]  # Assume avi if no suffix
            self.outcome = data.values.tolist()

            # Check that files are present
            """
            missing = set(self.fnames) - set(os.listdir(os.path.join(self.root, "Videos")))
            if len(missing) != 0:
                print("{} videos could not be found in {}:".format(len(missing), os.path.join(self.root, "Videos")))
                for f in sorted(missing):
                    print("\t", f)
                raise FileNotFoundError(os.path.join(self.root, "Videos", sorted(missing)[0]))
            """

            # Load traces
            self.frames = collections.defaultdict(list)
            self.trace = collections.defaultdict(_defaultdict_of_lists)

            with open(f"{self.root}/VolumeTracings.csv") as f:
                header = f.readline().strip().split(",")
                assert header == ["FileName", "X1", "Y1", "X2", "Y2", "Frame"]

                for line in f:
                    filename, x1, y1, x2, y2, frame = line.strip().split(',')
                    x1 = float(x1)
                    y1 = float(y1)
                    x2 = float(x2)
                    y2 = float(y2)
                    frame = int(frame)
                    if frame not in self.trace[filename]:
                        self.frames[filename].append(frame)
                    self.trace[filename][frame].append((x1, y1, x2, y2))
            for filename in self.frames:
                for frame in self.frames[filename]:
                    self.trace[filename][frame] = np.array(self.trace[filename][frame])

            # A small number of videos are missing traces; remove these videos
            keep = [len(self.frames[f]) >= 2 for f in self.fnames]
            self.fnames = [f for (f, k) in zip(self.fnames, keep) if k]
            self.outcome = [f for (f, k) in zip(self.outcome, keep) if k]

    def __getitem__(self, index):
        # Find filename of video
        if self.split == "EXTERNAL_TEST":
            video = os.path.join(self.external_test_location, self.fnames[index])
        elif self.split == "CLINICAL_TEST":
            video = os.path.join(self.root, "ProcessedStrainStudyA4c", self.fnames[index])
        else:
            video = os.path.join(self.root, "Videos", self.fnames[index])

        # Load video into np.array
        video = echonet.utils.loadvideo(video).astype(np.float32)

        # Add simulated noise (black out random pixels)
        # 0 represents black at this point (video has not been normalized yet)
        if self.noise is not None:
            n = video.shape[1] * video.shape[2] * video.shape[3]
            ind = np.random.choice(n, round(self.noise * n), replace=False)
            f = ind % video.shape[1]
            ind //= video.shape[1]
            i = ind % video.shape[2]
            ind //= video.shape[2]
            j = ind
            video[:, f, i, j] = 0

        # Apply normalization
        if isinstance(self.mean, (float, int)):
            video -= self.mean
        else:
            video -= self.mean.reshape(3, 1, 1, 1)

        if isinstance(self.std, (float, int)):
            video /= self.std
        else:
            video /= self.std.reshape(3, 1, 1, 1)

        # Set number of frames
        c, f, h, w = video.shape
        if self.length is None:
            # Take as many frames as possible
            length = f // self.period
        else:
            # Take specified number of frames
            length = self.length

        if self.max_length is not None:
            # Shorten videos to max_length
            length = min(length, self.max_length)

        if f < length * self.period:
            # Pad video with frames filled with zeros if too short
            # 0 represents the mean color (dark grey), since this is after normalization
            video = np.concatenate((video, np.zeros((c, length * self.period - f, h, w), video.dtype)), axis=1)
            c, f, h, w = video.shape  # pylint: disable=E0633

        if self.clips == "all":
            # Take all possible clips of desired length
            start = np.arange(f - (length - 1) * self.period)
        else:
            # Take random clips from video
            start = np.random.choice(f - (length - 1) * self.period, self.clips)

        # Gather targets
        target = []
        for t in self.target_type:
            key = self.fnames[index]
            if t == "Filename":
                target.append(self.fnames[index])
            elif t == "LargeIndex":
                # Traces are sorted by cross-sectional area
                # Largest (diastolic) frame is last
                target.append(int(self.frames[key][-1]))
            elif t == "SmallIndex":
                # Largest (diastolic) frame is first
                target.append(int(self.frames[key][0]))
            elif t == "LargeFrame":
                target.append(video[:, self.frames[key][-1], :, :])
            elif t == "SmallFrame":
                target.append(video[:, self.frames[key][0], :, :])
            elif t in ["LargeTrace", "SmallTrace"]:
                if t == "LargeTrace":
                    t = self.trace[key][self.frames[key][-1]]
                else:
                    t = self.trace[key][self.frames[key][0]]
                x1, y1, x2, y2 = t[:, 0], t[:, 1], t[:, 2], t[:, 3]
                x = np.concatenate((x1[1:], np.flip(x2[1:])))
                y = np.concatenate((y1[1:], np.flip(y2[1:])))

                r, c = skimage.draw.polygon(np.rint(y).astype(np.int), np.rint(x).astype(np.int), (video.shape[2], video.shape[3]))
                mask = np.zeros((video.shape[2], video.shape[3]), np.float32)
                mask[r, c] = 1
                target.append(mask)
            else:
                if self.split == "CLINICAL_TEST" or self.split == "EXTERNAL_TEST":
                    target.append(np.float32(0))
                else:
                    target.append(np.float32(self.outcome[index][self.header.index(t)]))

        if target != []:
            target = tuple(target) if len(target) > 1 else target[0]
            if self.target_transform is not None:
                target = self.target_transform(target)

        # Select clips from video
        video = tuple(video[:, s + self.period * np.arange(length), :, :] for s in start)
        if self.clips == 1:
            video = video[0]
        else:
            video = np.stack(video)

        if self.pad is not None:
            # Add padding of zeros (mean color of videos)
            # Crop of original size is taken out
            # (Used as augmentation)
            c, l, h, w = video.shape
            temp = np.zeros((c, l, h + 2 * self.pad, w + 2 * self.pad), dtype=video.dtype)
            temp[:, :, self.pad:-self.pad, self.pad:-self.pad] = video  # pylint: disable=E1130
            i, j = np.random.randint(0, 2 * self.pad, 2)
            video = temp[:, :, i:(i + h), j:(j + w)]

        return video, target

    def __len__(self):
        return len(self.fnames)

    def extra_repr(self) -> str:
        """Additional information to add at end of __repr__."""
        lines = ["Target type: {target_type}", "Split: {split}"]
        return '\n'.join(lines).format(**self.__dict__)


def _defaultdict_of_lists():
    """Returns a defaultdict of lists.
    This is used to avoid issues with Windows (if this function is anonymous,
    the Echo dataset cannot be used in a dataloader).
    """

    return collections.defaultdict(list)
## 
print("Done loading training data!")
# define normalization layer to make sure output xi in an interval [ai, bi]:
# define normalization layer to make sure output xi in an interval [ai, bi]:


class IntervalNormalizationLayer(torch.nn.Module):
    def __init__(self):
        super().__init__()
        # new_output = [Tc, start_p, Emax, Emin, Rm, Ra, Vd]
        self.a = torch.tensor([0.4, 0., 0.5, 0.02, 0.005, 0.0001, 4.], dtype=torch.float32) #HR in 20-200->Tc in [0.3, 4]
        self.b = torch.tensor([1.7, 280., 3.5, 0.1, 0.1, 0.25, 16.], dtype=torch.float32)
        #taken out (initial conditions): a: 20, 5, 50; b: 400, 20, 100
    def forward(self, inputs):
        sigmoid_output = torch.sigmoid(inputs)
        scaled_output = sigmoid_output * (self.b - self.a) + self.a
        return scaled_output

class NEW3DCNN(nn.Module):
    def __init__(self, num_parameters):
        super(NEW3DCNN, self).__init__()
        
        self.conv1      = nn.Conv3d(3, 8, kernel_size=3, padding=1)
        self.batchnorm1 = nn.BatchNorm3d(8)
        self.conv2      = nn.Conv3d(8, 16, kernel_size=3, padding=1)
        self.batchnorm2 = nn.BatchNorm3d(16)
        self.conv3      = nn.Conv3d(16, 32, kernel_size=3, padding=1)
        self.batchnorm3 = nn.BatchNorm3d(32)
        self.conv4      = nn.Conv3d(32, 64, kernel_size=3, padding=1)
        self.batchnorm4 = nn.BatchNorm3d(64)
        self.conv5      = nn.Conv3d(64, 128, kernel_size=3, padding=1)
        self.batchnorm5 = nn.BatchNorm3d(128)
        self.pool       = nn.AdaptiveAvgPool3d(1)
        self.fc1        = nn.Linear(128, 512)
        self.fc2        = nn.Linear(512, num_parameters)
        self.norm1      = IntervalNormalizationLayer()
        
    def forward(self, x):
        x = F.relu(self.batchnorm1(self.conv1(x)))
        x = F.max_pool3d(x, kernel_size=2, stride=2)
        x = F.relu(self.batchnorm2(self.conv2(x)))
        x = F.max_pool3d(x, kernel_size=2, stride=2)
        x = F.relu(self.batchnorm3(self.conv3(x)))
        x = F.max_pool3d(x, kernel_size=2, stride=2)
        x = F.relu(self.batchnorm4(self.conv4(x)))
        x = F.max_pool3d(x, kernel_size=2, stride=2)
        x = F.relu(self.batchnorm5(self.conv5(x)))
        x = self.pool(x)
        x = x.view(x.size(0), -1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        x = self.norm1(x)

        return x


# Define a neural network with one hidden layer
class Interpolator(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(6, 250).double()
        self.fc2 = nn.Linear(250, 2).double()

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Initialize the neural network
net = Interpolator()
net.load_state_dict(torch.load('final_model_weights/interp6_7param_weight.pt'))
print("Done loading interpolator!")

weights_path = 'final_model_weights/202_full_echonet_7param_Vloss_epoch_200_lr_0.001_weight_best_model.pt'
model = NEW3DCNN(num_parameters = 7)
model.load_state_dict(torch.load(weights_path))
model.to(device)

## PV loops

#returns Plv at time t using Elastance(t) and Vlv(t)-Vd=x1
def Plv(volume, Emax, Emin, t, Tc, Vd):
    return Elastance(Emax,Emin,t, Tc)*(volume - Vd)

#returns Elastance(t)
def Elastance(Emax,Emin, t, Tc):
    t = t-int(t/Tc)*Tc #can remove this if only want 1st ED (and the 1st ES before)
    tn = t/(0.2+0.15*Tc)
    return (Emax-Emin)*1.55*(tn/0.7)**1.9/((tn/0.7)**1.9+1.0)*1.0/((tn/1.17)**21.9+1.0) + Emin

def solve_ODE_for_volume(Rm, Ra, Emax, Emin, Vd, Tc, start_v, t):
    
    # the ODE from Simaan et al 2008
    def heart_ode(y, t, Rs, Rm, Ra, Rc, Ca, Cs, Cr, Ls, Emax, Emin, Tc):
        x1, x2, x3, x4, x5 = y #here y is a vector of 5 values (not functions), at time t, used for getting (dy/dt)(t)
        P_lv = Plv(x1+Vd,Emax,Emin,t,Tc,Vd)
        dydt = [r(x2-P_lv)/Rm-r(P_lv-x4)/Ra, (x3-x2)/(Rs*Cr)-r(x2-P_lv)/(Cr*Rm), (x2-x3)/(Rs*Cs)+x5/Cs, -x5/Ca+r(P_lv-x4)/(Ca*Ra), (x4-x3-Rc*x5)/Ls]
        return dydt

    # RELU for diodes
    def r(u):
        return max(u, 0.)
    
    # Define fixed parameters
    Rs = 1.0
    Rc = 0.0398
    Ca = 0.08
    Cs = 1.33
    Cr = 4.400
    Ls = 0.0005
    startp = 75.

    # Initial conditions
    start_pla = float(start_v*Elastance(Emax, Emin, 0., Tc))
    start_pao = startp
    start_pa = start_pao
    start_qt = 0 #aortic flow is Q_T and is 0 at ED, also see Fig5 in simaan2008dynamical
    y0 = [start_v, start_pla, start_pa, start_pao, start_qt]

    # Solve
    sol = odeint(heart_ode, y0, t, args = (Rs, Rm, Ra, Rc, Ca, Cs, Cr, Ls, Emax, Emin, Tc)) #t: list of values

    # volume is the first state variable plus theoretical zero pressure volume
    volumes = np.array(sol[:, 0]) + Vd
    
    return volumes

def pvloop_simulator(Rm, Ra, Emax, Emin, Vd, Tc, start_v, animate=True):
    

    # Define initial parameters
    init_Emax = Emax # 3.0 # .5 to 3.5
    init_Emin = Emin # 0.04 # .02 to .1
    # init_Tc = Tc # .4 # .4 to 1.7
    init_Vd = Vd # 10.0 # 0 to 25
    
    # DUMMY VOLUME
    # def volume(t, Tc):
        # return 50*np.sin(2 * np.pi * t*(1/Tc))+100
    
    # SOLVE the ODE model for the VOLUME CURVE
    N = 100
    t =  np.linspace(0, Tc*N, int(60000*N)) #np.linspace(1, 100, 1000000)
    volumes = solve_ODE_for_volume(Rm, Ra, Emax, Emin, Vd, Tc, start_v, t)
    
    # FUNCTIONS for PRESSURE CURVE
    vectorized_Elastance = np.vectorize(Elastance)
    vectorized_Plv = np.vectorize(Plv)

    def pressure(t, volume, Emax, Emin, Tc, Vd):
        return vectorized_Plv(volume, Emax, Emin, t, Tc, Vd)

    # calculate PRESSURE
    pressures = pressure(t, volumes, init_Emax, init_Emin, Tc, init_Vd)
    
    # Create the figure and the loop that we will manipulate
    fig, ax = plt.subplots()
    plt.ylim((0,220))
    plt.xlim((0,250))
    start = (N-2)*60000
    end = (N)*60000
    if animate:
        line = ax.plot(volumes[start:(start+1)], pressures[start:(start+1)], lw=1, color='b')
        point = ax.scatter(volumes[start:(start+1)], pressures[start:(start+1)], c="b", s=5)#, label='End Diastole')
        #point = ax.scatter(volumes[start:(start+1)], pressures[start:(start+1)], c="b", s=5, label='End Systole')
    else:
        line = ax.plot(volumes[start:end], pressures[start:end], lw=1, color='b')
    
    plt.title('Predicted PI-SSL LV Pressure Volume Loop', fontsize=16)
    #plt.rcParams['fig.suptitle'] = -2.0
    #ax.set_title(f'Mitral valve circuit resistance (Rm): {Rm} mmHg*s/ml \n Aortic valve circuit resistance (Ra): {Ra} mmHg*s/ml', fontsize=6)
    ax.set_xlabel('LV Volume (ml)')
    ax.set_ylabel('LV Pressure (mmHg)')

    # adjust the main plot to make room for the sliders
    # fig.subplots_adjust(left=0.25, bottom=0.25)

    def update(frame):
        # update to add more of the loop
        end = (N-2)*60000+1000 * frame
        x = volumes[start:end]
        y = pressures[start:end]
        ax.plot(x, y, lw=1, c='b')
        
    if animate:
        anim = animation.FuncAnimation(fig, partial(update), frames=100, interval=30)
        anim.save("prediction.mp4")
        
    return plt, Rm, Ra, Emax, Emin, Vd, Tc, start_v

def pvloop_simulator_plot_only(Rm, Ra, Emax, Emin, Vd, Tc, start_v):
    plot,_,_,_,_,_,_,_ =pvloop_simulator(Rm, Ra, Emax, Emin, Vd, Tc, start_v, animate=False)
    plt.title('Simulated PI-SSL LV Pressure Volume Loop', fontsize=16)
    return plot

#########################################
# LVAD functions
# RELU for diodes
def r(u):
    return max(u, 0.)

def heart_ode0(y, t, Rs, Rm, Ra, Rc, Ca, Cs, Cr, Ls, Emax, Emin, Tc, Vd):
    x1, x2, x3, x4, x5 = y #here y is a vector of 5 values (not functions), at time t, used for getting (dy/dt)(t)
    P_lv = Plv(x1+Vd,Emax,Emin,t,Tc,Vd)
    dydt = [r(x2-P_lv)/Rm-r(P_lv-x4)/Ra, (x3-x2)/(Rs*Cr)-r(x2-P_lv)/(Cr*Rm), (x2-x3)/(Rs*Cs)+x5/Cs, -x5/Ca+r(P_lv-x4)/(Ca*Ra), (x4-x3-Rc*x5)/Ls]
    return dydt

def getslope(y1, y2, y3, x1, x2, x3):
  sum_x = x1 + x2 + x3
  sum_y = y1 + y2 + y3
  sum_xy = x1*y1 + x2*y2 + x3*y3
  sum_xx = x1*x1 + x2*x2 + x3*x3
  # calculate the coefficients of the least-squares line
  n = 3
  slope = (n*sum_xy - sum_x*sum_y) / (n*sum_xx - sum_x*sum_x)
  return slope

### ODE: for each t (here fixed), gives dy/dt as a function of y(t) at that t, so can be used for integrating the vector y over time
#it is run for each t going from 0 to tmax
def lvad_ode(y, t, Rs, Rm, Ra, Rc, Ca, Cs, Cr, Ls, Emax, Emin, Tc, Vd, ratew):
    
    #from simaan2008dynamical:
    Ri = 0.0677
    R0 = 0.0677
    Rk = 0.0
    x1bar = 1.
    alpha = -3.5
    Li = 0.0127
    L0 = 0.0127
    b0 = -0.296
    b1 = -0.027
    b2 = 9.9025e-7
    
    x1, x2, x3, x4, x5, x6, x7 = y #here y is a vector of 5 values (not functions), at time t, used for getting (dy/dt)(t)

    P_lv = Plv(x1+Vd,Emax,Emin,t,Tc,Vd)
    if (P_lv <= x1bar): Rk = alpha * (P_lv - x1bar)
    Lstar = Li + L0 + b1
    Lstar2 = -Li -L0 +b1
    Rstar = Ri + R0 + Rk + b0

    dydt = [-x6 + r(x2-P_lv)/Rm-r(P_lv-x4)/Ra, (x3-x2)/(Rs*Cr)-r(x2-P_lv)/(Cr*Rm), (x2-x3)/(Rs*Cs)+x5/Cs, -x5/Ca+r(P_lv-x4)/(Ca*Ra) + x6/Ca, (x4-x3)/Ls-Rc*x5/Ls, -P_lv / Lstar2 + x4/Lstar2 + (Ri+R0+Rk-b0) / Lstar2 * x6 - b2 / Lstar2 * x7**2, ratew]

    return dydt

#returns pv loop and ef when there is no lvad:
def f_nolvad(Rs, Rm, Ra, Rc, Ca, Cs, Cr, Ls, Emin, Vd, Tc, start_v, Emax, showpvloop):

    N = 20
    
    start_pla = float(start_v*Elastance(Emax, Emin, 0.0, Tc))
    start_pao = 75.
    start_pa = start_pao
    start_qt = 0.0 #aortic flow is Q_T and is 0 at ED, also see Fig5 in simaan2008dynamical

    y0 = [start_v, start_pla, start_pa, start_pao, start_qt]

    t = np.linspace(0, Tc*N, int(60000*N)) #spaced numbers over interval (start, stop, number_of_steps), 60000 time instances for each heart cycle
    #changed to 60000 for having integer positions for Tmax
    #obtain 5D vector solution:
    sol = odeint(heart_ode0, y0, t, args = (Rs, Rm, Ra, Rc, Ca, Cs, Cr, Ls, Emax, Emin, Tc,Vd)) #t: list of values

    result_Vlv = np.array(sol[:, 0]) + Vd
    result_Plv = np.array([Plv(v, Emax, Emin, xi, Tc, Vd) for xi,v in zip(t,sol[:, 0])])

    #if showpvloop: plt.plot(result_Vlv[18*60000:20*60000], result_Plv[18*60000:20*60000], color='black', label='Without LVAD')

    ved = sol[19*60000, 0] + Vd
    ves = sol[200*int(60/Tc)+9000+19*60000, 0] + Vd
    ef = (ved-ves)/ved * 100.
    minv = min(result_Vlv[19*60000:20*60000-1])
    minp = min(result_Plv[19*60000:20*60000-1])

    result_pao = np.array(sol[:, 3])
    pao_ed = min(result_pao[(N-1)*60000:N*60000-1])
    pao_es = max(result_pao[(N-1)*60000:N*60000-1])

    return ef, pao_ed, pao_es, ((ved - ves) * 60/Tc ) / 1000, sol[19*60000, 0], sol[19*60000, 1], sol[19*60000, 2], sol[19*60000, 3], sol[19*60000, 4], result_Vlv[18*60000:20*60000], result_Plv[18*60000:20*60000]

#returns the w at which suction occurs: (i.e. for which the slope of the envelopes of x6 becomes negative)
def get_suctionw(Rs, Rm, Ra, Rc, Ca, Cs, Cr, Ls, Emin, Vd, Tc, start_v, Emax, y00, y01, y02, y03, y04, w0, x60, ratew): #slope is slope0 for w

    N = 70


    start_pla = float(start_v*Elastance(Emax, Emin, 0.0, Tc))
    start_pao = 75.
    start_pa = start_pao
    start_qt = 0 #aortic flow is Q_T and is 0 at ED, also see Fig5 in simaan2008dynamical

    y0 = [start_v, start_pla, start_pa, start_pao, start_qt, x60, w0]
    y0 = [y00, y01, y02, y03, y04, x60, w0]

    ncycle = 20000
    n = N * ncycle
    sol = np.zeros((n, 7))
    t = np.linspace(0., Tc * N, n)
    for j in range(7):
      sol[0][j] = y0[j]

    result_Vlv = []
    result_Plv = []
    result_x6 = []
    result_x7 = []
    envx6 = []
    timesenvx6 = []
    slopes = []
    ws = []

    minx6 = 99999
    tmin = 0
    tlastupdate = 0
    lastw = w0
    update = 1

    #solve the ODE step by step by adding dydt*dt:
    for j in range(0, n-1):
      #update y with dydt * dt
      y = sol[j]
      dydt = lvad_ode(y, t[j], Rs, Rm, Ra, Rc, Ca, Cs, Cr, Ls, Emax, Emin, Tc, Vd, ratew)
      for k in range(7):
        dydt[k] = dydt[k] * (t[j+1] - t[j])
      sol[j+1] = sol[j] + dydt

      #update the min of x6 in the current cylce. also keep the time at which the min is obtained (for getting the slope later)
      if (minx6 > sol[j][5]):
        minx6 = sol[j][5]
        tmin = t[j]

      #add minimum of x6 once each cycle ends: (works). then reset minx6 to 99999 for calculating again the minimum
      if (j%ncycle==0 and j>1):
        envx6.append(minx6)
        timesenvx6.append(tmin)
        minx6 = 99999

      if (len(envx6)>=3):
        slope = getslope(envx6[-1], envx6[-2], envx6[-3], timesenvx6[-1], timesenvx6[-2], timesenvx6[-3])
        slopes.append(slope)
        ws.append(y[6])

    for i in range(n):
      result_x6.append(sol[i, 5])
      result_x7.append(sol[i, 6])

    suction_w = 0
    for i in range(2, len(slopes)):
      if (slopes[i] < 0):
        suction_w = ws[i-1]
        break

    return suction_w

def f_lvad(Rs, Rm, Ra, Rc, Ca, Cs, Cr, Ls, Emin, Vd, Tc, start_v, Emax, c, slope, w0, x60, y00, y01, y02, y03, y04): #slope is slope0 for w

    N = 70

    y0 = [y00, y01, y02, y03, y04, x60, w0]

    ncycle = 10000
    n = N * ncycle
    sol = np.zeros((n, 7))
    t = np.linspace(0., Tc * N, n)
    for j in range(7):
      sol[0][j] = y0[j]

    result_Vlv = []
    result_Plv = []
    result_x6 = []
    result_x7 = []
    envx6 = []
    timesenvx6 = []

    minx6 = 99999
    tmin = 0
    tlastupdate = 0
    lastw = w0
    update = 1
    ratew = 0 #6000/60

    #solve the ODE step by step by adding dydt*dt:
    for j in range(0, n-1):
      #update y with dydt * dt
      y = sol[j]
      dydt = lvad_ode(y, t[j], Rs, Rm, Ra, Rc, Ca, Cs, Cr, Ls, Emax, Emin, Tc, Vd, ratew)
      for k in range(7):
        dydt[k] = dydt[k] * (t[j+1] - t[j])
      sol[j+1] = sol[j] + dydt

      #update the min of x6 in the current cylce. also keep the time at which the min is obtained (for getting the slope later)
      if (minx6 > sol[j][5]):
        minx6 = sol[j][5]
        tmin = t[j]

      #add minimum of x6 once each cycle ends: (works). then reset minx6 to 99999 for calculating again the minimum
      if (j%ncycle==0 and j>1):
        envx6.append(minx6)
        timesenvx6.append(tmin)
        minx6 = 99999

      #update w (if 0.005 s. have passed since the last update):
      if (slope<0):
        update = 0
      if (t[j+1] - tlastupdate > 0.005 and slope>0 and update==1): #abs(slope)>0.0001
        # if there are enough points of envelope: calculate slope:
        if (len(envx6)>=3):
          slope = getslope(envx6[-1], envx6[-2], envx6[-3], timesenvx6[-1], timesenvx6[-2], timesenvx6[-3])
          sol[j+1][6] = lastw + c * slope
        #otherwise: take arbitrary rate (see Fig. 16a in simaan2008dynamical)
        else:
          sol[j+1][6] = lastw + 0.005 * slope
        #save w(k) (see formula (8) simaan2008dynamical) and the last time of update t[j] (will have to wait 0.005 s for next update of w)
        tlastupdate = t[j+1]
        lastw = sol[j+1][6]

    #save functions and print MAP, CO:
    map = 0
    Pao = []

    for i in range(n):
      result_Vlv.append(sol[i, 0] + Vd)
      result_Plv.append(Plv(sol[i, 0]+Vd, Emax, Emin, t[i], Tc, Vd))
      result_x6.append(sol[i, 5])
      result_x7.append(sol[i, 6])
      Pao.append(sol[i, 3])

    colors0=np.zeros((len(result_Vlv[65*ncycle:70*ncycle]), 3))
    for col in colors0:
      col[0]=41/255
      col[1]=128/255
      col[2]=205/255


    #get co and ef:
    ved = max(result_Vlv[50 * ncycle:52 * ncycle])
    ves = min(result_Vlv[50 * ncycle:52 * ncycle])
    #ves = result_Vlv[50 * ncycle + int(ncycle * 0.2 /Tc + 0.15 * ncycle)]
    ef = (ved-ves)/ved*100
    CO = ((ved - ves) * 60/Tc ) / 1000

    #get MAP:
    for i in range(n - 5*ncycle, n):
      map += sol[i, 2]
    map = map/(5*ncycle)

    result_pao = np.array(sol[:, 3])
    pao_ed = min(Pao[50 * ncycle:52 * ncycle])
    pao_es = max(Pao[50 * ncycle:52 * ncycle])

    return ef, pao_ed, pao_es, CO, map, result_Vlv[65*ncycle:70*ncycle], result_Plv[65*ncycle:70*ncycle]

#############################
## Demo functions

def generate_example():
        # get random input
        data_path = 'EchoNet-Dynamic'
        image_data = Echo(root = data_path, split = 'all', target_type=['Filename','LargeIndex','SmallIndex'])
        image_loaded_data = DataLoader(image_data, batch_size=30, shuffle=True)
        val_data = next(iter(image_loaded_data))
        #create_echo_clip(val_data,'test')
        val_seq = val_data[0]
    
        val_tensor = torch.tensor(val_seq, dtype=torch.float32) 
        n=random.randint(0, 27)
        results = model(val_tensor)[n]

        filename = val_data[1][0][n]
        video = f"EchoNet-Dynamic/Videos/{filename}"

        plot, Rm, Ra, Emax, Emin, Vd,Tc, start_v = pvloop_simulator(Rm=round(results[4].item(),2), Ra=round(results[5].item(),2), Emax=round(results[2].item(),2), Emin=round(results[3].item(),2), Vd=round(results[6].item(),2), Tc=round(results[0].item(),2), start_v=round(results[1].item(),2))
        video = video.replace("avi", "mp4")
        animated = "prediction.mp4"
        return video, animated, Rm, Ra, Emax, Emin, Vd, Tc, start_v


def lvad_plots(Rm, Ra, Emax, Emin, Vd, Tc, start_v, gamma):

    ncycle = 10000
    
    Rs = 1.
    Rc = 0.0398
    Ca= 0.08
    Cs= 1.33
    Cr= 4.4
    Ls=0.0005
    
    #get values for periodic loops:
    ef_nolvad, pao_ed, pao_es, co_nolvad, y00, y01, y02, y03, y04, Vlv0, Plv0 = f_nolvad(Rs, Rm, Ra, Rc, Ca, Cs, Cr, Ls, Emin, Vd,Tc, start_v, Emax, 0.0)
    #pao_eds = [pao_ed]
    #pao_ess = [pao_es]
    
    #get suction w: (make w go linearly from w0 to w0 + maxtime * 400, and find w at which suction occurs)
    w0 = 5000.
    ratew = 400.
    x60 = 0.
    suctionw = get_suctionw(Rs, Rm, Ra, Rc, Ca, Cs, Cr, Ls, Emin, Vd, Tc, start_v, Emax, y00, y01, y02, y03, y04, w0, x60, ratew)
    
    #gamma = 1.8
    c = 0.065 #(in simaan2008dynamical: 0.67, but too fast -> 0.061 gives better shape)
    slope0 = 100.
    w0 = suctionw / gamma  #if doesn't work (x6 negative), change gamma down to 1.4 or up to 2.1
    
    #compute new pv loops and ef with lvad added:
    new_ef, pao_ed, pao_es, CO, MAP, Vlvs, Plvs = f_lvad(Rs, Rm, Ra, Rc, Ca, Cs, Cr, Ls, Emin, Vd, Tc, start_v, Emax, c, slope0, w0, x60, y00, y01, y02, y03, y04)
      
    print("\nParameters: Tc, start_v, Emax:", Tc, start_v, Emax)
    print("Suction speed:", suctionw)
    print("EF before LVAD:", ef_nolvad)
    print("CO before LVAD:", co_nolvad)
    print("New EF after LVAD:", new_ef, "New CO:", CO, "New MAP:", MAP, "\n\n")

    fig, ax = plt.subplots()
    ax.plot(Vlv0, Plv0, color='blue', label='No LVAD') #blue
    ax.plot(Vlvs, Plvs, color=(78/255, 192/255, 44/255), label=f"LVAD, ω(0)= {round(w0,2)}r/min") #green
    plt.xlabel('LV volume (ml)')
    plt.ylabel('LV pressure (mmHg)')
    plt.legend(loc='upper left', framealpha=1)
    plt.ylim((0,220))
    plt.xlim((0,250))
    #plt.title('Simulated PI-SSL LV Pressure Volume Loop', fontsize=16)
    
    return plt
    
title = "<h1 style='text-align: center; margin-bottom: 1rem'> Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning </h1>"

description = """
<p style='text-align: center'> Keying Kuang, Frances Dean, Jack B. Jedlicki, David Ouyang, Anthony Philippakis, David Sontag, Ahmed Alaa <br></p>
<p> We develop methodology for predicting digital twins from non-invasive cardiac ultrasound images in <a href='https://arxiv.org/abs/2403.00177'>Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning</a>. Check out our <a href='https://github.com/AlaaLab/CardioPINN' target='_blank'>code.</a> \n \n 
We demonstrate the ability of our model to predict left ventricular pressure-volume loops using image data here. To run example predictions on samples from the <a href='https://echonet.github.io/dynamic/'>EchoNet</a> dataset, click the first button. \n \n 
</p>
"""

title2 = "<h3 style='text-align: center'> Physics-based model simulation</h3>"

description2 = """
\n \n
Our model uses a hydraulic analogy model of cardiac function from <a href='https://ieeexplore.ieee.org/document/4729737/keywords#keywords'>Simaan et al 2008</a>. Below you can input values of predicted parameters and output a simulated pressure-volume loop predicted from the <a href='https://ieeexplore.ieee.org/document/4729737/keywords#keywords'>Simaan et al 2008</a> model, which is an ordinary differential equation. Tune parameters and press 'Run simulation.'
"""

description3 = """
\n\n
This model can incorporate a tunable left-ventricular assistance device (LVAD) for in-silico experimentation. Click to view the effect of adding an LVAD to the simulated PV loop.
"""

gr.Markdown(title)
gr.Markdown(description)

with gr.Blocks() as demo:
        
    # text
    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>" + title + "</h1>")
    gr.Markdown(description)
        
    with gr.Row():
        with gr.Column(scale=1.5, min_width=100):
            
            generate_button = gr.Button("Load sample echocardiogram and generate result")
            with gr.Row():
                video = gr.PlayableVideo(autoplay=True) #format="avi"
                plot = gr.PlayableVideo(autoplay=True)
            
            with gr.Row():
                Rm = gr.Number(label="Mitral valve circuit resistance (Rm) mmHg*s/ml:")
                Ra = gr.Number(label="Aortic valve circuit resistance (Ra) mmHg*s/ml:")
                Emax = gr.Number(label="Maximum elastance (Emax) mmHg/ml:")
                Emin = gr.Number(label="Minimum elastance (Emin) mmHg/ml:")
                Vd = gr.Number(label="Theoretical zero pressure volume (Vd) ml:")
                Tc = gr.Number(label="Cycle duration (Tc) s:")
                start_v = gr.Number(label="Initial volume (start_v) ml:")

            gr.Markdown(title2)
            gr.Markdown(description2)
            
            simulation_button = gr.Button("Run simulation")
            
            
            with gr.Row():
                sl1 = gr.Slider(0.005, 0.1, value=.005, label="Rm (mmHg*s/ml)")
                sl2 = gr.Slider(0.0001, 0.25, value=.0001, label="Ra (mmHg*s/ml)")
                sl3 = gr.Slider(0.5, 3.5, value=.5, label="Emax (mmHg/ml)")
                sl4 = gr.Slider(0.02, 0.1, value= .02, label="Emin (mmHg/ml)")
                sl5 = gr.Slider(4.0, 25.0, value= 4.0, label="Vd (ml)")
                sl6 = gr.Slider(0.4, 1.7, value= 0.4, label="Tc (s)") 
                sl7 = gr.Slider(0.0, 280.0, value= 140., label="start_v (ml)")
            
            with gr.Row():
                simulation = gr.Plot()

            gr.Markdown(description3)

            LVAD_button = gr.Button("Add LVAD")

            with gr.Row():
                gamma = gr.Slider(1.0, 2.0, value= 1.4, label="Pump speed, ω(0)")

            with gr.Row():
                lvad = gr.Plot()
    
    generate_button.click(fn=generate_example, outputs = [video,plot,Rm,Ra,Emax,Emin,Vd,Tc,start_v])
    
    simulation_button.click(fn=pvloop_simulator_plot_only, inputs = [sl1,sl2,sl3,sl4,sl5,sl6,sl7], outputs = [simulation])
    
    LVAD_button.click(fn=lvad_plots, inputs = [sl1,sl2,sl3,sl4,sl5,sl6,sl7,gamma], outputs = [lvad])
    
demo.launch()