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How to use for_ method in Selene

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value_iteration_6D.py

Source:value_iteration_6D.py

...9# ptsEachDim: the number of grid points in each dimension of the state space10# useNN:      a mode flag (0: use linear interpolation, 1: use nearest neighbour)11def updateVopt(obj, i, j, k, l, m, n, iVals, sVals, actions, Vopt, intermeds, trans, interpV, gamma, bounds, goal, ptsEachDim, useNN):12    p = hcl.scalar(0, "p")13    with hcl.for_(0, actions.shape[0], name="a") as a:14        # set iVals equal to (i,j,k,l,m,n) and sVals equal to the corresponding state values (si,sj,sk,sl,sm,sn)15        updateStateVals(i, j, k, l, m, n, iVals, sVals, bounds, ptsEachDim)16        # call the transition function to obtain the outcome(s) of action a from state (si,sj,sk,sl,sm,sn)17        obj.transition(sVals, actions[a], bounds, trans, goal)18        # initialize the value of the action Q value with the immediate reward of taking that action19        intermeds[a] = obj.reward(sVals, actions[a], bounds, goal, trans)20        # add the value of each possible successor state to the Q value21        with hcl.for_(0, trans.shape[0], name="si") as si:22            p[0]     = trans[si,0]23            sVals[0] = trans[si,1]24            sVals[1] = trans[si,2]25            sVals[2] = trans[si,3]26            sVals[3] = trans[si,4]27            sVals[4] = trans[si,5]28            sVals[5] = trans[si,6]29            # Nearest neighbour30            with hcl.if_(useNN[0] == 1):31                # convert the state values of the successor state (si,sj,sk,sl,sm,sn) into indeces (ia,ja,ka,la,ma,na)32                stateToIndex(sVals, iVals, bounds, ptsEachDim)33                # if (ia,ja,ka,la,ma,na) is within the state space, add its discounted value to the Q value34                with hcl.if_(hcl.and_(iVals[0] < Vopt.shape[0], iVals[1] < Vopt.shape[1], iVals[2] < Vopt.shape[2])):35                    with hcl.if_(hcl.and_(iVals[3] < Vopt.shape[3], iVals[4] < Vopt.shape[4], iVals[5] < Vopt.shape[5])):36                        with hcl.if_(hcl.and_(iVals[0] >= 0, iVals[1] >= 0, iVals[2] >= 0, iVals[3] >= 0, iVals[4] >= 0, iVals[5] >= 0)):37                            intermeds[a] += (gamma[0] * (p[0] * Vopt[iVals[0], iVals[1], iVals[2], iVals[3], iVals[4], iVals[5]]))38        # maximize over each Q value to obtain the optimal value39        Vopt[i,j,k,l,m,n] = -100000040        with hcl.for_(0, intermeds.shape[0], name="r") as r:41            with hcl.if_(Vopt[i,j,k,l,m,n] < intermeds[r]):42                Vopt[i,j,k,l,m,n] = intermeds[r]43# Returns 0 if convergence has been reached44def evaluateConvergence(newV, oldV, epsilon, reSweep):45    delta = hcl.scalar(0, "delta")46    # Calculate the difference, if it's negative, make it positive47    delta[0] = newV[0] - oldV[0]48    with hcl.if_(delta[0] < 0):49        delta[0] = delta[0] * -150    with hcl.if_(delta[0] > epsilon[0]):51        reSweep[0] = 152# Converts state values into indeces using nearest neighbour rounding53def stateToIndex(sVals, iVals, bounds, ptsEachDim):54    iVals[0] = ((sVals[0] - bounds[0,0]) / (bounds[0,1] - bounds[0,0])) *  (ptsEachDim[0] - 1)55    iVals[1] = ((sVals[1] - bounds[1,0]) / (bounds[1,1] - bounds[1,0])) *  (ptsEachDim[1] - 1)56    iVals[2] = ((sVals[2] - bounds[2,0]) / (bounds[2,1] - bounds[2,0])) *  (ptsEachDim[2] - 1)57    iVals[3] = ((sVals[3] - bounds[3,0]) / (bounds[3,1] - bounds[3,0])) *  (ptsEachDim[3] - 1)58    iVals[4] = ((sVals[4] - bounds[4,0]) / (bounds[4,1] - bounds[4,0])) *  (ptsEachDim[4] - 1)59    iVals[5] = ((sVals[5] - bounds[5,0]) / (bounds[5,1] - bounds[5,0])) *  (ptsEachDim[5] - 1)60    # NOTE: add 0.5 to simulate rounding61    iVals[0] = hcl.cast(hcl.Int(), iVals[0] + 0.5)62    iVals[1] = hcl.cast(hcl.Int(), iVals[1] + 0.5)63    iVals[2] = hcl.cast(hcl.Int(), iVals[2] + 0.5)64    iVals[3] = hcl.cast(hcl.Int(), iVals[3] + 0.5)65    iVals[4] = hcl.cast(hcl.Int(), iVals[4] + 0.5)66    iVals[5] = hcl.cast(hcl.Int(), iVals[5] + 0.5)67# Convert indices into state values68def indexToState(iVals, sVals, bounds, ptsEachDim):69    sVals[0] = bounds[0,0] + ( (bounds[0,1] - bounds[0,0]) * (iVals[0] / (ptsEachDim[0]-1)) ) 70    sVals[1] = bounds[1,0] + ( (bounds[1,1] - bounds[1,0]) * (iVals[1] / (ptsEachDim[1]-1)) ) 71    sVals[2] = bounds[2,0] + ( (bounds[2,1] - bounds[2,0]) * (iVals[2] / (ptsEachDim[2]-1)) ) 72    sVals[3] = bounds[3,0] + ( (bounds[3,1] - bounds[3,0]) * (iVals[3] / (ptsEachDim[3]-1)) ) 73    sVals[4] = bounds[4,0] + ( (bounds[4,1] - bounds[4,0]) * (iVals[4] / (ptsEachDim[4]-1)) ) 74    sVals[5] = bounds[5,0] + ( (bounds[5,1] - bounds[5,0]) * (iVals[5] / (ptsEachDim[5]-1)) ) 75# Sets iVals equal to (i,j,k,l,m,n) and sVals equal to the corresponding state values76def updateStateVals(i, j, k, l, m, n, iVals, sVals, bounds, ptsEachDim):77    iVals[0] = i78    iVals[1] = j79    iVals[2] = k80    iVals[3] = l81    iVals[4] = m82    iVals[5] = n83    indexToState(iVals, sVals, bounds, ptsEachDim)84######################################### VALUE ITERATION ##########################################85# Main value iteration algorithm86# reSweep:  a convergence flag (1: continue iterating, 0: convergence reached)87# epsilon:  convergence criteria88# maxIters: maximum number of iterations that can occur without convergence being reached89# count:    the number of iterations that have been performed90def value_iteration_6D(MDP_obj):91    def solve_Vopt(Vopt, actions, intermeds, trans, interpV, gamma, epsilon, iVals, sVals, bounds, goal, ptsEachDim, count, maxIters, useNN):92            reSweep = hcl.scalar(1, "reSweep")93            oldV    = hcl.scalar(0, "oldV")94            newV    = hcl.scalar(0, "newV")95            with hcl.while_(hcl.and_(reSweep[0] == 1, count[0] < maxIters[0])):96                reSweep[0] = 097                # Perform value iteration by sweeping in direction 198                with hcl.Stage("Sweep_1"):99                    with hcl.for_(0, Vopt.shape[0], name="i") as i:100                        with hcl.for_(0, Vopt.shape[1], name="j") as j:101                            with hcl.for_(0, Vopt.shape[2], name="k") as k:102                                with hcl.for_(0, Vopt.shape[3], name="l") as l:103                                    with hcl.for_(0, Vopt.shape[4], name="m") as m:104                                        with hcl.for_(0, Vopt.shape[5], name="n") as n:105                                            oldV[0] = Vopt[i,j,k,l,m,n]106                                            updateVopt(MDP_obj, i, j, k, l, m, n, iVals, sVals, actions, Vopt, intermeds, trans, interpV, gamma, bounds, goal, ptsEachDim, useNN)107                                            newV[0] = Vopt[i,j,k,l,m,n]108                                            evaluateConvergence(newV, oldV, epsilon, reSweep)109                    count[0] += 1110                # Perform value iteration by sweeping in direction 2111                with hcl.Stage("Sweep_2"):112                    with hcl.if_(useNN[0] == 1):113                        with hcl.for_(1, Vopt.shape[0] + 1, name="i") as i:114                            with hcl.for_(1, Vopt.shape[1] + 1, name="j") as j:115                                with hcl.for_(1, Vopt.shape[2] + 1, name="k") as k:116                                    with hcl.for_(0, Vopt.shape[3], name="l") as l:117                                        with hcl.for_(0, Vopt.shape[4], name="m") as m:118                                            with hcl.for_(0, Vopt.shape[5], name="n") as n:119                                                i2 = Vopt.shape[0] - i120                                                j2 = Vopt.shape[1] - j121                                                k2 = Vopt.shape[2] - k122                                                oldV[0] = Vopt[i2,j2,k2,l,m,n]123                                                updateVopt(MDP_obj, i2, j2, k2, l, m, n, iVals, sVals, actions, Vopt, intermeds, trans, interpV, gamma, bounds, goal, ptsEachDim, useNN)124                                                newV[0] = Vopt[i2,j2,k2,l,m,n]125                                                evaluateConvergence(newV, oldV, epsilon, reSweep)126                    count[0] += 1127                # Perform value iteration by sweeping in direction 3128                with hcl.Stage("Sweep_3"):129                    with hcl.if_(useNN[0] == 1):130                        with hcl.for_(1, Vopt.shape[0] + 1, name="i") as i:131                            with hcl.for_(0, Vopt.shape[1], name="j") as j:132                                with hcl.for_(0, Vopt.shape[2], name="k") as k:133                                    with hcl.for_(0, Vopt.shape[3], name="l") as l:134                                        with hcl.for_(0, Vopt.shape[4], name="m") as m:135                                            with hcl.for_(0, Vopt.shape[5], name="n") as n:136                                                i2 = Vopt.shape[0] - i137                                                oldV[0] = Vopt[i2,j,k,l,m,n]138                                                updateVopt(MDP_obj, i2, j, k, l, m, n, iVals, sVals, actions, Vopt, intermeds, trans, interpV, gamma, bounds, goal, ptsEachDim, useNN)139                                                newV[0] = Vopt[i2,j,k,l,m,n]140                                                evaluateConvergence(newV, oldV, epsilon, reSweep)141                    count[0] += 1142                # Perform value iteration by sweeping in direction 4143                with hcl.Stage("Sweep_4"):144                    with hcl.if_(useNN[0] == 1):145                        with hcl.for_(0, Vopt.shape[0], name="i") as i:146                            with hcl.for_(1, Vopt.shape[1] + 1, name="j") as j:147                                with hcl.for_(0, Vopt.shape[2], name="k") as k:148                                    with hcl.for_(0, Vopt.shape[3], name="l") as l:149                                        with hcl.for_(0, Vopt.shape[4], name="m") as m:150                                            with hcl.for_(0, Vopt.shape[5], name="n") as n:151                                                j2 = Vopt.shape[1] - j152                                                oldV[0] = Vopt[i,j2,k,l,m,n]153                                                updateVopt(MDP_obj, i, j2, k, l, m, n, iVals, sVals, actions, Vopt, intermeds, trans, interpV, gamma, bounds, goal, ptsEachDim, useNN)154                                                newV[0] = Vopt[i,j2,k,l,m,n]155                                                evaluateConvergence(newV, oldV, epsilon, reSweep)156                    count[0] += 1157                # Perform value iteration by sweeping in direction 5158                with hcl.Stage("Sweep_5"):159                    with hcl.if_(useNN[0] == 1):160                        with hcl.for_(0, Vopt.shape[0], name="i") as i:161                            with hcl.for_(0, Vopt.shape[1], name="j") as j:162                                with hcl.for_(1, Vopt.shape[2] + 1, name="k") as k:163                                    with hcl.for_(0, Vopt.shape[3], name="l") as l:164                                        with hcl.for_(0, Vopt.shape[4], name="m") as m:165                                            with hcl.for_(0, Vopt.shape[5], name="n") as n:166                                                k2 = Vopt.shape[2] - k167                                                oldV[0] = Vopt[i,j,k2,l,m,n]168                                                updateVopt(MDP_obj, i, j, k2, l, m, n, iVals, sVals, actions, Vopt, intermeds, trans, interpV, gamma, bounds, goal, ptsEachDim, useNN)169                                                newV[0] = Vopt[i,j,k2,l,m,n]170                                                evaluateConvergence(newV, oldV, epsilon, reSweep)171                    count[0] += 1172                # Perform value iteration by sweeping in direction 6173                with hcl.Stage("Sweep_6"):174                    with hcl.if_(useNN[0] == 1):175                        with hcl.for_(1, Vopt.shape[0] + 1, name="i") as i:176                            with hcl.for_(1, Vopt.shape[1] + 1, name="j") as j:177                                with hcl.for_(0, Vopt.shape[2], name="k") as k:178                                    with hcl.for_(0, Vopt.shape[3], name="l") as l:179                                        with hcl.for_(0, Vopt.shape[4], name="m") as m:180                                            with hcl.for_(0, Vopt.shape[5], name="n") as n:181                                                i2 = Vopt.shape[0] - i182                                                j2 = Vopt.shape[1] - j183                                                oldV[0] = Vopt[i2,j2,k,l,m,n]184                                                updateVopt(MDP_obj, i2, j2, k, l, m, n, iVals, sVals, actions, Vopt, intermeds, trans, interpV, gamma, bounds, goal, ptsEachDim, useNN)185                                                newV[0] = Vopt[i2,j2,k,l,m,n]186                                                evaluateConvergence(newV, oldV, epsilon, reSweep)187                    count[0] += 1188                # Perform value iteration by sweeping in direction 7189                with hcl.Stage("Sweep_7"):190                    with hcl.if_(useNN[0] == 1):191                        with hcl.for_(1, Vopt.shape[0] + 1, name="i") as i:192                            with hcl.for_(0, Vopt.shape[1], name="j") as j:193                                with hcl.for_(1, Vopt.shape[2] + 1, name="k") as k:194                                    with hcl.for_(0, Vopt.shape[3], name="l") as l:195                                        with hcl.for_(0, Vopt.shape[4], name="m") as m:196                                            with hcl.for_(0, Vopt.shape[5], name="n") as n:197                                                i2 = Vopt.shape[0] - i198                                                k2 = Vopt.shape[2] - k199                                                oldV[0] = Vopt[i2,j,k2,l,m,n]200                                                updateVopt(MDP_obj, i2, j, k2, l, m, n, iVals, sVals, actions, Vopt, intermeds, trans, interpV, gamma, bounds, goal, ptsEachDim, useNN)201                                                newV[0] = Vopt[i2,j,k2,l,m,n]202                                                evaluateConvergence(newV, oldV, epsilon, reSweep)203                    count[0] += 1204                # Perform value iteration by sweeping in direction 8205                with hcl.Stage("Sweep_8"):206                    with hcl.if_(useNN[0] == 1):207                        with hcl.for_(0, Vopt.shape[0], name="i") as i:208                            with hcl.for_(1, Vopt.shape[1] + 1, name="j") as j:209                                with hcl.for_(1, Vopt.shape[2] + 1, name="k") as k:210                                    with hcl.for_(0, Vopt.shape[3], name="l") as l:211                                        with hcl.for_(0, Vopt.shape[4], name="m") as m:212                                            with hcl.for_(0, Vopt.shape[5], name="n") as n:213                                                j2 = Vopt.shape[1] - j214                                                k2 = Vopt.shape[2] - k215                                                oldV[0] = Vopt[i,j2,k2,l,m,n]216                                                updateVopt(MDP_obj, i, j2, k2, l, m, n, iVals, sVals, actions, Vopt, intermeds, trans, interpV, gamma, bounds, goal, ptsEachDim, useNN)217                                                newV[0] = Vopt[i,j2,k2,l,m,n]218                                                evaluateConvergence(newV, oldV, epsilon, reSweep)219                        count[0] += 1220    ###################################### SETUP PLACEHOLDERS ######################################221    222    # Initialize the HCL environment223    hcl.init()224    hcl.config.init_dtype = hcl.Float()225    # NOTE: trans is a tensor with size = maximum number of transitions226    # NOTE: intermeds must have size  [# possible actions]...

TimeToReach_5D.py

Source:TimeToReach_5D.py

...72    #debugger[i, j, k, l] = phiNew[0]73    phi[i, j, k, l, m] = my_min(phi[i, j, k, l, m], phiNew[0])74def EvalBoundary(phi, g):75    if 0 not in g.pDim:76        with hcl.for_(0, phi.shape[1], name="j") as j:77            with hcl.for_(0, phi.shape[2], name="k") as k:78                with hcl.for_(0, phi.shape[3], name="l") as l:79                    with hcl.for_(0, phi.shape[4], name="m") as m:80                        #debug2[0] = j81                        tmp1 = hcl.scalar(0, "tmp1")82                        tmp1[0] = 2 * phi[1, j, k, l, m] - phi[2, j, k, l, m]83                        tmp1[0] = my_max(tmp1[0], phi[2, j, k, l, m])84                        phi[0, j, k, l, m] = my_min(tmp1[0], phi[0, j, k, l, m])85                        tmp2 = hcl.scalar(0, "tmp2")86                        tmp2[0] = 2 * phi[phi.shape[0] - 2, j, k, l, m] - phi[phi.shape[0] - 3, j, k, l, m]87                        tmp2[0] = my_max(tmp2[0], phi[phi.shape[0] - 3, j, k, l, m])88                        phi[phi.shape[0] - 1, j, k, l, m] = my_min(tmp2[0], phi[phi.shape[0] - 1, j, k, l, m])89    if 1 not in g.pDim:90        with hcl.for_(0, phi.shape[0], name="i") as i:91            with hcl.for_(0, phi.shape[2], name="k") as k:92                with hcl.for_(0, phi.shape[3], name="l") as l:93                    with hcl.for_(0, phi.shape[4], name="m") as m:94                        tmp1 = hcl.scalar(0, "tmp1")95                        tmp1[0] = 2 * phi[i, 1, k, l] - phi[i, 2, k, l, m]96                        tmp1[0] = my_max(tmp1[0], phi[i, 2, k, l, m])97                        phi[i, 0, k, l, m] = my_min(tmp1[0], phi[i, 0, k, l, m])98                        tmp2 = hcl.scalar(0, "tmp2")99                        tmp2[0] = 2 * phi[i, phi.shape[1] - 2, k, l, m] - phi[i, phi.shape[1] - 3, k, l, m]100                        tmp2[0] = my_max(tmp2[0], phi[i, phi.shape[1] - 3, k, l, m])101                        phi[i, phi.shape[1] - 1, k, l, m] = my_min(tmp2[0], phi[i, phi.shape[1] - 1, k, l, m])102    if 2 not in g.pDim:103        with hcl.for_(0, phi.shape[0], name="i") as i:104            with hcl.for_(0, phi.shape[1], name="j") as j:105                with hcl.for_(0, phi.shape[3], name="l") as l:106                    with hcl.for_(0, phi.shape[4], name="m") as m:107                        tmp1 = hcl.scalar(0, "tmp1")108                        tmp1[0] = 2 * phi[i, j, 1, l, m] - phi[i, j, 2, l, m]109                        tmp1[0] = my_max(tmp1[0], phi[i, j, 2, l, m])110                        phi[i, j, 0, l, m] = my_min(tmp1[0], phi[i, j, 0, l, m])111                        tmp2 = hcl.scalar(0, "tmp2")112                        tmp2[0] = 2 * phi[i, j, phi.shape[2] - 2, l, m] - phi[i, j, phi.shape[2] - 3, l, m]113                        tmp2[0] = my_max(tmp2[0], phi[i, j, phi.shape[2] - 3, l, m])114                        phi[i, j, phi.shape[2] - 1, l, m] = my_min(tmp2[0], phi[i, j, phi.shape[2] - 1, l, m])115    if 3 not in g.pDim:116        with hcl.for_(0, phi.shape[0], name="i") as i:117            with hcl.for_(0, phi.shape[1], name="j") as j:118                with hcl.for_(0, phi.shape[2], name="k") as k:119                    with hcl.for_(0, phi.shape[4], name="m") as m:120                        tmp1 = hcl.scalar(0, "tmp1")121                        tmp1[0] = 2 * phi[i, j, k, 1, m] - phi[i, j, k, 2, m]122                        tmp1[0] = my_max(tmp1[0], phi[i, j, k, 2, m])123                        phi[i, j, k, 0, m] = my_min(tmp1[0], phi[i, j, k, 0, m])124                        tmp2 = hcl.scalar(0, "tmp2")125                        tmp2[0] = 2 * phi[i, j, k, phi.shape[2] - 2, m] - phi[i, j, k, phi.shape[2] - 3, m]126                        tmp2[0] = my_max(tmp2[0], phi[i, j, k, phi.shape[2] - 3, m])127                        phi[i, j, k, phi.shape[2] - 1, m] = my_min(tmp2[0], phi[i, j, k, phi.shape[2] - 1, m])128    if 4 not in g.pDim:129        with hcl.for_(0, phi.shape[0], name="i") as i:130            with hcl.for_(0, phi.shape[1], name="j") as j:131                with hcl.for_(0, phi.shape[2], name="k") as k:132                    with hcl.for_(0, phi.shape[3], name="l") as l:133                        tmp1 = hcl.scalar(0, "tmp1")134                        tmp1[0] = 2 * phi[i, j, k, l, 1] - phi[i, j, k, l, 2]135                        tmp1[0] = my_max(tmp1[0], phi[i, j, k, l, 2])136                        phi[i, j, k, l, 0] = my_min(tmp1[0], phi[i, j, k, l, 0])137                        tmp2 = hcl.scalar(0, "tmp2")138                        tmp2[0] = 2 * phi[i, j, k, l, phi.shape[2] - 2] - phi[i, j, k, l, phi.shape[2] - 3]139                        tmp2[0] = my_max(tmp2[0], phi[i, j, k, l, phi.shape[2] - 3])140                        phi[i, j, k, l, phi.shape[2] - 1] = my_min(tmp2[0], phi[i, j, k, l, phi.shape[2] - 1])141######################################### VALUE ITERATION ##########################################142def TTR_5D(my_object, g):143    def solve_phiNew(phi, x1, x2, x3, x4, x5):144        l_i = 0 if 0 in g.pDim else 1145        h_i = phi.shape[0] if 0 in g.pDim else phi.shape[0] - 1146        l_j = 0 if 1 in g.pDim else 1147        h_j = phi.shape[1] if 1 in g.pDim else phi.shape[1] - 1148        l_k = 0 if 2 in g.pDim else 1149        h_k = phi.shape[2] if 2 in g.pDim else phi.shape[2] - 1150        l_l = 0 if 3 in g.pDim else 1151        h_l = phi.shape[3] if 3 in g.pDim else phi.shape[3] - 1152        l_m = 0 if 4 in g.pDim else 1153        h_m = phi.shape[4] if 4 in g.pDim else phi.shape[4] - 1154        # Perform value iteration by sweeping in direction 1155        with hcl.Stage("Sweep_1"):156            with hcl.for_(l_i, h_i, name="i") as i:157                with hcl.for_(l_j, h_j, name="j") as j:158                    with hcl.for_(l_k, h_k, name="k") as k:159                        with hcl.for_(l_l, h_l, name="l") as l:160                            with hcl.for_(l_m, h_m, name="m") as m:161                                updatePhi(i, j, k, l, m, my_object, phi, g, x1, x2, x3, x4, x5)162            EvalBoundary(phi, g)163        # Perform value iteration by sweeping in direction 2164        with hcl.Stage("Sweep_2"):165            with hcl.for_(l_i, h_i, name="i") as i:166                with hcl.for_(l_j, h_j, name="j") as j:167                    with hcl.for_(l_k, h_k, name="k") as k:168                        with hcl.for_(l_l, h_l, name="l") as l:169                            with hcl.for_(l_m, h_m, name="m") as m:170                                i2 = phi.shape[0] - i - 1171                                j2 = phi.shape[1] - j - 1172                                k2 = phi.shape[2] - k - 1173                                l2 = phi.shape[3] - l - 1174                                m2 = phi.shape[4] - m - 1175                                updatePhi(i2, j2, k2, l2, m2, my_object, phi, g, x1, x2, x3, x4, x5)176            EvalBoundary(phi, g)177        # Perform value iteration by sweeping in direction 3178        with hcl.Stage("Sweep_3"):179            with hcl.for_(l_i, h_i, name="i") as i:180                with hcl.for_(l_j, h_j, name="j") as j:181                    with hcl.for_(l_k, h_k, name="k") as k:182                        with hcl.for_(l_l, h_l, name="l") as l:183                            with hcl.for_(l_m, h_m, name="m") as m:184                                i2 = phi.shape[0] - i - 1185                                j2 = phi.shape[1] - j - 1186                                k2 = phi.shape[2] - k - 1187                                l2 = phi.shape[3] - l - 1188                                updatePhi(i2, j2, k2, l2, m, my_object, phi, g, x1, x2, x3, x4, x5)189            EvalBoundary(phi, g)190        # Perform value iteration by sweeping in direction 4191        with hcl.Stage("Sweep_4"):192            with hcl.for_(l_i, h_i, name="i") as i:193                with hcl.for_(l_j, h_j, name="j") as j:194                    with hcl.for_(l_k, h_k, name="k") as k:195                        with hcl.for_(l_l, h_l, name="l") as l:196                            with hcl.for_(l_m, h_m, name="m") as m:197                                i2 = phi.shape[0] - i - 1198                                j2 = phi.shape[1] - j - 1199                                k2 = phi.shape[2] - k - 1200                                updatePhi(i2, j2, k2, l, m, my_object, phi, g, x1, x2, x3, x4, x5)201            EvalBoundary(phi, g)202        # Perform value iteration by sweeping in direction 5203        with hcl.Stage("Sweep_5"):204            with hcl.for_(l_i, h_i, name="i") as i:205                with hcl.for_(l_j, h_j, name="j") as j:206                    with hcl.for_(l_k, h_k, name="k") as k:207                        with hcl.for_(l_l, h_l, name="l") as l:208                            with hcl.for_(l_m, h_m, name="m") as m:209                                i2 = phi.shape[0] - i - 1210                                j2 = phi.shape[1] - j - 1211                                updatePhi(i2, j2, k, l, m, my_object, phi, g, x1, x2, x3, x4, x5)212            EvalBoundary(phi, g)213        # Perform value iteration by sweeping in direction 6214        with hcl.Stage("Sweep_6"):215            with hcl.for_(l_i, h_i, name="i") as i:216                with hcl.for_(l_j, h_j, name="j") as j:217                    with hcl.for_(l_k, h_k, name="k") as k:218                        with hcl.for_(l_l, h_l, name="l") as l:219                            with hcl.for_(l_m, h_m, name="m") as m:220                                i2 = phi.shape[0] - i - 1221                                updatePhi(i2, j, k, l, m, my_object, phi, g, x1, x2, x3, x4, x5)222            EvalBoundary(phi, g)223        # Perform value iteration by sweeping in direction 7224        with hcl.Stage("Sweep_7"):225            with hcl.for_(l_i, h_i, name="i") as i:226                with hcl.for_(l_j, h_j, name="j") as j:227                    with hcl.for_(l_k, h_k, name="k") as k:228                        with hcl.for_(l_l, h_l, name="l") as l:229                            with hcl.for_(l_m, h_m, name="m") as m:230                                j2 = phi.shape[1] - j - 1231                                updatePhi(i, j2, k, l, m, my_object, phi, g, x1, x2, x3, x4, x5)232            EvalBoundary(phi, g)233        # Perform value iteration by sweeping in direction 8234        with hcl.Stage("Sweep_8"):235            with hcl.for_(l_i, h_i, name="i") as i:236                with hcl.for_(l_j, h_j, name="j") as j:237                    with hcl.for_(l_k, h_k, name="k") as k:238                        with hcl.for_(l_l, h_l, name="l") as l:239                            with hcl.for_(l_m, h_m, name="m") as m:240                                j2 = phi.shape[1] - j - 1241                                k2 = phi.shape[2] - k - 1242                                updatePhi(i, j2, k2, l, m, my_object, phi, g, x1, x2, x3, x4, x5)243            EvalBoundary(phi, g)244    ###################################### SETUP PLACEHOLDERS ######################################245        # Positions vector246        x1 = hcl.placeholder((g.pts_each_dim[0],), name="x1", dtype=hcl.Float())247        x2 = hcl.placeholder((g.pts_each_dim[1],), name="x2", dtype=hcl.Float())248        x3 = hcl.placeholder((g.pts_each_dim[2],), name="x3", dtype=hcl.Float())249        x4 = hcl.placeholder((g.pts_each_dim[3],), name="x4", dtype=hcl.Float())250        x5 = hcl.placeholder((g.pts_each_dim[4],), name="x5", dtype=hcl.Float())251        phi = hcl.placeholder(tuple(g.pts_each_dim), name="phi", dtype=hcl.Float())252        debugger = hcl.placeholder(tuple(g.pts_each_dim), name="debugger", dtype=hcl.Float())253        debug2 = hcl.placeholder((0,), "debug2")...

main.py

Source:main.py

1import asyncio2from discord.ext import commands3client = commands.Bot(command_prefix='kh!')4@client.event5async def on_ready():6    print('bot is ready')7@client.command()8@commands.has_permissions(manage_channels=True, administrator=True)9async def delete_channels(ctx, channel_name, *, exceptions='None'):10    deleted_channels = 011    if exceptions == 'None':12        guild = client.get_guild(ctx.guild.id)13        for channel in ctx.guild.text_channels:14            is_exception = False15            if channel.name == channel_name:16                await channel.delete()17                deleted_channels += 118        if deleted_channels == 0:19            await ctx.send("Couldn't find any channels with that name")20        else:21            try:22                await ctx.send(f'Found and deleted {deleted_channels} channels')23            except Exception:  24                pass25    else:26        exceptions_cont = exceptions.split(', ')27        guild = client.get_guild(ctx.guild.id)28        for channel in ctx.guild.text_channels:29            is_exception = False30            if channel.name == channel_name:31                for exception in exceptions_cont:32                    if int(channel.id) == int(exception):33                        is_exception = True34                if is_exception:35                    pass36                else:37                    await channel.delete()38                    deleted_channels += 139        if deleted_channels == 0:40            await ctx.send("Couldn't find any channels with that name")41        else:42            try:43                await ctx.send(f'Found and deleted {deleted_channels} channels')44            except Exception:  45                pass46@client.command()47@commands.has_guild_permissions(manage_roles=True, administrator=True)48async def delete_roles(ctx, role_name, *, exceptions=None):49    deleted_roles = 050    if exceptions == None:51        guild = client.get_guild(ctx.guild.id)52        for role in ctx.guild.roles:53            is_exception = False54            if role.name == role_name:55                await role.delete()56                deleted_roles += 157        if deleted_roles == 0:58            await ctx.send("Couldn't find any roles with that name")59        else:60            await ctx.send(f'Found and deleted {deleted_roles} roles')61    else:62        try:63            exceptions_cont = exceptions.split(' --exc ')64            65            if len(exceptions_cont) > 1:66                role_name = role_name + ' ' + exceptions_cont[0]67                if not exceptions_cont[1] == '':68                    exceptions = exceptions_cont[1].split(', ')69            else:70                exceptions = exceptions.split(', ')71            72        except Exception as e:73            exceptions = exceptions.split(', ')74           75        guild = client.get_guild(ctx.guild.id)76        for role in ctx.guild.roles:77            is_exception = False78            if role.name == '@everyone':79                continue80            if role.name == role_name:81                for exception in exceptions:82                    if int(role.id) == int(exception):83                        is_exception = True84                if is_exception:85                    pass86                else:87                    await role.delete()88                    deleted_roles += 189        if deleted_roles == 0:90            await ctx.send("Couldn't find any roles with that name")91        else:92            await ctx.send(f'Found and deleted {deleted_roles} roles')93@client.command()94@commands.has_guild_permissions(manage_guild=True)95async def delete_messages(ctx, for_, *, message_):96    guild = int(ctx.guild.id)97    if str(for_).endswith('m'):98        for_ = for_[:-1]99        for_ = int(for_) * 60100    elif str(for_).endswith('s'):101        for_ = for_[:-1]102        for_ = int(for_)103    elif str(for_).endswith('h'):104        for_ = for_[:-1]105        for_ = int(for_)106    else:107        for_ = for_[:-1]108        for_ = int(for_)109    @client.listen()110    async def on_message(message):111        if message.guild.id == ctx.guild.id:112            if message.content == message_:113                await message.delete()114    await asyncio.sleep(for_)115    client.remove_listener(func=on_message)...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.