= delta)\n\n Stretch the duration of the first ramp to delta while the second ramp remains at\n the same acceleration (hence shorter). After that there are three possible cases.\n \n A: no further correction is needed.\n B: the second ramp becomes shorter than delta and re-interpolating (x0, v0) and (x1, v1)\n using a trajectory with 2 delta-ramps is better\n C: the second ramp becomes shorter than delta and re-interpolating (x0, v0) and (x1, v1)\n using a trajectory with one ramp is better\n \"\"\"\n ramp0 = curve[0]\n ramp1 = curve[-1]\n t0 = ramp0.duration\n t1 = ramp1.duration\n v0 = ramp0.v0\n v1 = ramp1.v1\n a0 = ramp0.a\n a1 = ramp1.a\n d = curve.d\n x0 = ramp0.x0\n vp = ramp0.v1\n deltaInv = 1.0/delta\n\n if FuzzyZero(a1, epsilon):\n # Stretch the first ramp\n ramp0A = Ramp(v0, (vp - v0)*deltaInv, delta, x0)\n dRem = d - ramp0A.d\n if FuzzyZero(vp, epsilon):\n log.warn(\"The peak velocity is zero\")\n return ParabolicCurve()\n t1New = dRem/vp\n\n if (t1New >= delta):\n ramp1A = Ramp(vp, 0.0, t1New)\n curveA = ParabolicCurve([ramp0A, ramp1A])\n # Check before returning\n return curveA\n else:\n k = a1*delta\n vpNew = 0.5*(k - np.sign(a1)*np.sqrt(k**2 + 4*(k*v0 + v1**2) - 8*a1*d))\n t1New = (v1 - vpNew)/a1\n\n if (t1New >= delta):\n # A: no further correction is needed.\n log.debug(\"PP1 A\")\n a0New = (vpNew - v0)*deltaInv\n ramp0A = Ramp(v0, a0New, delta, x0)\n ramp1A = Ramp(vpNew, a1, t1New)\n curveA = ParabolicCurve([ramp0A, ramp1A])\n # Check before returning\n return curveA\n\n # If arrive here, case A does not work. Need some modification.\n # Try case B.\n vpNew = d*deltaInv - 0.5*(v0 + v1)\n a0New = (vpNew - v0)*deltaInv\n a1New = (v1 - vpNew)*deltaInv\n\n if (abs(a0New) <= am + epsilon) and (abs(a1New) <= am + epsilon) and (abs(vpNew) <= vm + epsilon):\n ramp0B = Ramp(v0, a0New, delta, x0)\n ramp1B = Ramp(vpNew, a1New, delta)\n curveB = ParabolicCurve([ramp0B, ramp1B])\n else:\n # Case B does not produce any feasible solution\n curveB = ParabolicCurve()\n\n # Try case C.\n if FuzzyZero(v0 + v1, epsilon):\n # Case C does not produce any feasible solution\n curveC = ParabolicCurve()\n else:\n tNew = 2*d/(v0 + v1)\n aNew = (v1 - v0)/tNew\n if (abs(aNew) <= am + epsilon) and (tNew >= delta):\n ramp0C = Ramp(v0, aNew, tNew, x0)\n curveC = ParabolicCurve([ramp0C])\n else:\n # Case C does not produce any feasible solution\n curveC = ParabolicCurve()\n\n # There has to be at least one valid case\n assert(not (curveB.IsEmpty() and curveC.IsEmpty()))\n\n if curveB.IsEmpty():\n # Check before returning\n return curveC\n elif curveC.IsEmpty():\n # Check before returning\n return curveB\n elif curveB.duration <= curveC.duration:\n # Check before returning\n return curveB\n else:\n # Check before returning\n return curveC\n\n\ndef _PP2(curve, vm, am, delta):\n \"\"\"(t0 >= delta) and (t1 < delta)\n\n Stretch the duration of the second ramp to delta while the first ramp remains at\n the same acceleration (hence shorter). After that there are three possible cases.\n \n A: no further correction is needed.\n B: the first ramp becomes shorter than delta and re-interpolating (x0, v0) and (x1, v1)\n using a trajectory with 2 delta-ramps is better\n C: the first ramp becomes shorter than delta and re-interpolating (x0, v0) and (x1, v1)\n using a trajectory with one ramp is better\n \"\"\"\n ramp0 = curve[0]\n ramp1 = curve[-1]\n t0 = ramp0.duration\n t1 = ramp1.duration\n v0 = ramp0.v0\n v1 = ramp1.v1\n a0 = ramp0.a\n a1 = ramp1.a\n d = curve.d\n x0 = ramp0.x0\n vp = ramp0.v1\n deltaInv = 1.0/delta\n\n if FuzzyZero(a0, epsilon):\n # Stretch the last ramp\n ramp1A = Ramp(vp, (v1 - v0)*deltaInv, delta)\n dRem = d - ramp1A.d\n if FuzzyZero(vp, epsilon):\n log.warn(\"The peak velocity is zero\")\n return ParabolicCurve()\n t0New = dRem/vp\n \n if (t0New >= delta):\n ramp0A = Ramp(v0, 0.0, t0New, x0)\n curveA = ParabolicCurve([ramp0A, ramp1A])\n # Check before returning\n return curveA\n else:\n k = a0*delta\n vpNew = 0.5*( - k + np.sign(a0)*np.sqrt(k**2 - 4*(k*v1 - v0**2) + 8*a0*d))\n t0New = (vpNew - v0)/a0\n \n if (t0New >= delta):\n # A: no further correction is needed.\n log.debug(\"PP2 A\")\n a1New = (v1 - vpNew)*deltaInv\n ramp0A = Ramp(v0, a0, t0New, x0)\n ramp1A = Ramp(vpNew, a1New, delta)\n curveA = ParabolicCurve([ramp0A, ramp1A])\n # Check before returning\n return curveA\n\n # If arrive here, case A does not work. Need some modification.\n # Try case B.\n vpNew = d*deltaInv - 0.5*(v0 + v1)\n a0New = (vpNew - v0)*deltaInv\n a1New = (v1 - vpNew)*deltaInv\n\n if (abs(a0New) <= am + epsilon) and (abs(a1New) <= am + epsilon) and (abs(vpNew) <= vm + epsilon):\n ramp0B = Ramp(v0, a0New, delta, x0)\n ramp1B = Ramp(vpNew, a1New, delta)\n curveB = ParabolicCurve([ramp0B, ramp1B])\n else:\n # Case B does not produce any feasible solution\n curveB = ParabolicCurve()\n\n # Try case C.\n if FuzzyZero(v0 + v1, epsilon):\n # Case C does not produce any feasible solution\n curveC = ParabolicCurve()\n else:\n tNew = 2*d/(v0 + v1)\n aNew = (v1 - v0)/tNew\n if (abs(aNew) <= am + epsilon) and (tNew >= delta):\n ramp0C = Ramp(v0, aNew, tNew, x0)\n curveC = ParabolicCurve([ramp0C])\n else:\n # Case C does not produce any feasible solution\n curveC = ParabolicCurve()\n\n # There has to be at least one valid case\n assert(not (curveB.IsEmpty() and curveC.IsEmpty()))\n\n if curveB.IsEmpty():\n # Check before returning\n return curveC\n elif curveC.IsEmpty():\n # Check before returning\n return curveB\n elif curveB.duration <= curveC.duration:\n # Check before returning\n return curveB\n else:\n # Check before returning\n return curveC\n \n \ndef _PP3(curve, vm, am, delta):\n \"\"\"(t0 < delta) and (t1 < delta)\n\n First we try out two possibilities.\n \n A: re-interpolating (x0, v0) and (x1, v1) using a trajectory with 2 delta-ramps\n B: re-interpolating (x0, v0) and (x1, v1) using a trajectory with one ramp\n\n If the above two cases do not give any feasible trajectory, we try to re-interpolate (x0, v0)\n and (x1, v1) again by using the idea of flipping ramps. Basically if the original trajectory\n goes with +am -> -am, the new trajectory would be -am -> +am.\n \"\"\"\n ramp0 = curve[0]\n ramp1 = curve[-1]\n t0 = ramp0.duration\n t1 = ramp1.duration\n v0 = ramp0.v0\n v1 = ramp1.v1\n a0 = ramp0.a\n a1 = ramp1.a\n d = curve.d\n x0 = ramp0.x0\n vp = ramp0.v1\n deltaInv = 1.0/delta\n\n # Try case A.\n vpNew = d*deltaInv - 0.5*(v0 + v1)\n a0New = (vpNew - v0)*deltaInv\n a1New = (v1 - vpNew)*deltaInv\n if (abs(a0New) <= am + epsilon) and (abs(a1New) <= am + epsilon) and (abs(vpNew) <= vm + epsilon):\n ramp0A = Ramp(v0, a0New, delta, x0)\n ramp1A = Ramp(vpNew, a1New, delta)\n curveA = ParabolicCurve([ramp0A, ramp1A])\n else:\n # Case A does not produce any feasible solution\n curveA = ParabolicCurve()\n\n # Try case B.\n if FuzzyZero(v0 + v1, epsilon):\n # Case C does not produce any feasible solution\n curveB = ParabolicCurve()\n else:\n tNew = 2*d/(v0 + v1)\n aNew = (v1 - v0)/tNew\n if (abs(aNew) <= am + epsilon) and (tNew >= delta):\n ramp0B = Ramp(v0, aNew, tNew, x0)\n curveB = ParabolicCurve([ramp0B])\n else:\n # Case B does not produce any feasible solution\n curveB = ParabolicCurve()\n\n if not (curveA.IsEmpty() and curveB.IsEmpty()):\n # We already have a feasible solution\n if curveA.IsEmpty():\n log.debug(\"PP3 A\")\n return curveB\n elif curveB.IsEmpty():\n log.debug(\"PP3 B\")\n return curveA\n else:\n if curveA.duration < curveB.duration:\n log.debug(\"PP3 A\")\n return curveA\n else:\n log.debug(\"PP3 B\")\n return curveB\n\n log.debug(\"PP3 Flipping\")\n # If arrive here, both cases A and B do not produce any feasible solution.\n # First see if the flipped ramps can go with maximum accel/decel\n if FuzzyZero(a0, epsilon):\n a0New = a1\n a1New = -a1\n elif FuzzyZero(a1, epsilon):\n a0New = -a0\n a1New = a0\n else:\n a0New = -a0\n a1New = -a1\n\n vpSqr = 0.5*(v0*v0 + v1*v1) + a0New*d\n if (vpSqr >= 0) and (not FuzzyZero(a0New, epsilon)) and (not FuzzyZero(a1New, epsilon)):\n # Both ramps can saturate the acceleration bound. Now figure out which value of vpNew to use\n vpNew = Sqrt(vpSqr)\n t0New = (vpNew - v0)/a0New\n t1New = (v1 - vpNew)/a1New\n if (t0New >= delta) and (t1New >= delta):\n ramp0C = Ramp(v0, a0New, t0New, x0)\n ramp1C = Ramp(vpNew, a1New, t1New)\n curveC = ParabolicCurve([ramp0C, ramp1C])\n # Check before returning\n return curveC\n\n # vpNew being positive does not work\n vpNew *= -1.0\n t0New = (vpNew - v0)/a0New\n t1New = (v1 - vpNew)/a1New\n if (t0New >= delta) and (t1New >= delta):\n ramp0C = Ramp(v0, a0New, t0New, x0)\n ramp1C = Ramp(vpNew, a1New, t1New)\n curveC = ParabolicCurve([ramp0C, ramp1C])\n # Check before returning\n return curveC\n\n # Now we know that the flipped velocity profile cannot saturate acceleration bound all the\n # time. We try to modify the (flipped) velocity profile using the idea in PP1 and PP2.\n if (t0 <= t1):\n # When flipped, we would have t0New > t1New. Therefore, we follow the procedure of PP1.\n k = a1New*delta\n discriminant = k**2 + 4*(k*v0 + v1**2) - 8*a1New*d\n if (discriminant < 0):\n # Fail to calculate vpNew following PP1\n return ParabolicCurve()\n\n vpNew = 0.5*(k - np.sign(a1New)*np.sqrt(discriminant))\n t1New = (v1 - vpNew)/a1New\n if (t1New >= delta):\n a0New = (vpNew - v0)*deltaInv\n ramp0A = Ramp(v0, a0New, delta, x0)\n ramp1A = Ramp(vpNew, a1New, t1New)\n curveA = ParabolicCurve([ramp0A, ramp1A])\n # Check before returning\n return curveA\n \n else:\n # (t0 > t1)\n # When flipped, we would have t0New < t1New. Therefore, we follow the procedure of PP2.\n k = a0New*delta\n discriminant = k**2 - 4*(k*v1 - v0**2) + 8*a0New*d\n discriminant = k**2 + 4*(k*v0 + v1**2) - 8*a1New*d\n if (discriminant < 0):\n # Fail to calculate vpNew following PP2\n return ParabolicCurve()\n\n vpNew = 0.5*( - k + np.sign(a0New)*np.sqrt(discriminant))\n t0New = (vpNew - v0)/a0New\n if (t0New >= delta):\n a1New = (v1 - vpNew)*deltaInv\n ramp0A = Ramp(v0, a0New, t0New, x0)\n ramp1A = Ramp(vpNew, a1New, delta)\n curveA = ParabolicCurve([ramp0A, ramp1A])\n # Check before returning\n return curveA\n\n # PP1A (or PP2A) modification does not produce any feasible trajectory.\n # Try case B.\n vpNew = d*deltaInv - 0.5*(v0 + v1)\n a0New = (vpNew - v0)*deltaInv\n a1New = (v1 - vpNew)*deltaInv\n\n if (abs(a0New) <= am + epsilon) and (abs(a1New) <= am + epsilon) and (abs(vpNew) <= vm + epsilon):\n ramp0B = Ramp(v0, a0New, delta, x0)\n ramp1B = Ramp(vpNew, a1New, delta)\n curveB = ParabolicCurve([ramp0B, ramp1B])\n else:\n # Case B does not produce any feasible solution\n curveB = ParabolicCurve()\n\n # Try case C.\n if FuzzyZero(v0 + v1, epsilon):\n # Case C does not produce any feasible solution\n curveC = ParabolicCurve()\n else:\n tNew = 2*d/(v0 + v1)\n aNew = (v1 - v0)/tNew\n if (abs(aNew) <= am + epsilon) and (tNew >= delta):\n ramp0C = Ramp(v0, aNew, tNew, x0)\n curveC = ParabolicCurve([ramp0C])\n else:\n # Case C does not produce any feasible solution\n curveC = ParabolicCurve()\n\n # There has to be at least one valid case\n if curveB.IsEmpty() and curveC.IsEmpty():\n return curveB # return an empty one\n elif curveB.IsEmpty():\n # Check before returning\n return curveC\n elif curveC.IsEmpty():\n # Check before returning\n return curveB\n elif curveB.duration <= curveC.duration:\n # Check before returning\n return curveB\n else:\n # Check before returning\n return curveC \n \n\ndef _FixSwitchTimeThreeRamps(curve, vm, am, delta):\n t0 = curve[0].duration\n t1 = curve[1].duration\n t2 = curve[2].duration\n\n if (t0 >= delta) and (t1 >= delta) and (t2 >= delta):\n return curve\n elif (t0 < delta) and (t1 >= delta) and (t2 >= delta):\n return _PLP1(curve, vm, am, delta)\n elif (t0 >= delta) and (t1 >= delta) and (t2 < delta):\n return _PLP2(curve, vm, am, delta)\n elif (t0 >= delta) and (t1 < delta) and (t2 >= delta):\n return _PLP3(curve, vm, am, delta)\n elif (t0 < delta) and (t1 < delta) and (t2 >= delta):\n return _PLP4(curve, vm, am, delta)\n elif (t0 >= delta) and (t1 < delta) and (t2 < delta):\n return _PLP5(curve, vm, am, delta)\n elif (t0 < delta) and (t1 >= delta) and (t2 < delta):\n return _PLP6(curve, vm, am, delta)\n elif (t0 < delta) and (t1 < delta) and (t2 < delta):\n return _PLP7(curve, vm, am, delta)\n\n\ndef _PLP1(curve, vm, am, delta):\n \"\"\"(t0 < delta) and (t1 >= delta) and (t2 >= delta)\n\n We consider two possibilities.\n\n A: stretch the first ramp's duration to delta. Then use (x1, v1) of the first ramp together with\n the trajectory's (x1, v1) as boundaary conditions to interpolate a sub-trajectory\n B: merge the first two ramps into one\n \"\"\"\n firstRamp = curve[0]\n middleRamp = curve[1]\n lastRamp = curve[2]\n\n t0 = firstRamp.duration\n t1 = middleRamp.duration\n t2 = lastRamp.duration\n v0 = firstRamp.v0\n v1 = lastRamp.v1\n x0 = firstRamp.x0\n d = curve.d\n d0 = firstRamp.d + middleRamp.d\n vp = middleRamp.v0 # the middle has zero acceleration\n deltaInv = 1.0/delta\n\n # Try case A.\n a0New = (vp - v0)*deltaInv\n ramp0A = Ramp(v0, a0New, delta, x0)\n dRem = d - ramp0A.d\n subCurveA = _Compute1DTrajectoryWithDelta(0, dRem, vp, v1, vm, am, delta)\n assert(not subCurveA.IsEmpty()) # this computation should not fail\n if len(subCurveA) == 1:\n curveA = ParabolicCurve([ramp0A, subCurveA[0]])\n else:\n # len(subCurveA) == 2 in this case\n curveA = ParabolicCurve([ramp0A, subCurveA[0], subCurveA[1]])\n \n # Try case B.\n if FuzzyZero(vp + v0, epsilon):\n curveB = ParabolicCurve()\n else:\n t0New = 2*d0/(vp + v0)\n a0New = (vp - v0)/t0New\n ramp0B = Ramp(v0, a0New, t0New, x0)\n curveB = ParabolicCurve([ramp0B, lastRamp])\n\n assert(not (curveA.IsEmpty() and curveB.IsEmpty()))\n if curveA.IsEmpty():\n # Check before returning\n log.debug(\"PLP1 B\")\n return curveB\n elif curveB.IsEmpty():\n # Check before returning\n log.debug(\"PLP1 A\")\n return curveA\n elif curveA.duration < curveB.duration:\n # Check before returning\n log.debug(\"PLP1 A\")\n return curveA\n else:\n # Check before returning\n log.debug(\"PLP1 B\")\n return curveB\n \n\ndef _PLP2(curve, vm, am, delta):\n \"\"\"(t0 >= delta) and (t1 >= delta) and (t2 < delta)\n\n We consider two possibilities.\n\n A: stretch the last ramp's duration to delta. Then use (x0, v0) of the trajectory together with\n (x0, v0) of the last ramp as boundaary conditions to interpolate a sub-trajectory\n B: merge the last two ramps into one\n \"\"\"\n firstRamp = curve[0]\n middleRamp = curve[1]\n lastRamp = curve[2]\n\n t0 = firstRamp.duration\n t1 = middleRamp.duration\n t2 = lastRamp.duration\n v0 = firstRamp.v0\n v1 = lastRamp.v1\n x0 = firstRamp.x0\n d = curve.d\n d1 = middleRamp.d + lastRamp.d\n vp = middleRamp.v0 # the middle has zero acceleration\n deltaInv = 1.0/delta\n\n # Try case A.\n a2New = (v1 - vp)*deltaInv\n ramp2A = Ramp(vp, a2New, delta)\n dRem = d - ramp2A.d\n subCurveA = _Compute1DTrajectoryWithDelta(0, dRem, v0, vp, vm, am, delta)\n assert(not subCurveA.IsEmpty()) # this computation should not fail\n if len(subCurveA) == 1:\n curveA = ParabolicCurve([subCurveA[0], ramp2A])\n else:\n # len(subCurveA) == 2 in this case\n curveA = ParabolicCurve([subCurveA[0], subCurveA[1], ramp2A])\n \n # Try case B.\n if FuzzyZero(vp + v1, epsilon):\n curveB = ParabolicCurve()\n else:\n t1New = 2*d1/(vp + v1)\n a1New = (v1 - vp)/t1New\n ramp1B = Ramp(vp, a1New, t1New)\n curveB = ParabolicCurve([firstRamp, ramp1B])\n\n assert(not (curveA.IsEmpty() and curveB.IsEmpty()))\n if curveA.IsEmpty():\n # Check before returning\n log.debug(\"PLP2 B\")\n return curveB\n elif curveB.IsEmpty():\n # Check before returning\n log.debug(\"PLP2 A\")\n return curveA\n elif curveA.duration < curveB.duration:\n # Check before returning\n log.debug(\"PLP2 A\")\n return curveA\n else:\n # Check before returning\n log.debug(\"PLP2 B\")\n return curveB\n\n\ndef _PLP3(curve, vm, am, delta):\n \"\"\"(t0 >= delta) and (t1 < delta) and (t2 >= delta)\n \n There are three possibilities.\n\n A: stretch the duration of the middle ramp to delta. The acceleration of the middle might not be\n zero after the stretching\n B: merge any two ramps together to obtain a two-ramp velocity profile\n C: the resulting trajectory has 3 delta-ramps\n \"\"\"\n firstRamp = curve[0]\n middleRamp = curve[1]\n lastRamp = curve[2]\n\n t0 = firstRamp.duration\n t1 = middleRamp.duration\n t2 = lastRamp.duration\n v0 = firstRamp.v0\n v1 = lastRamp.v1\n x0 = firstRamp.x0\n d = curve.d\n d1 = middleRamp.d + lastRamp.d\n vp = middleRamp.v0 # the middle has zero acceleration\n deltaInv = 1.0/delta\n \n # Try case A.\n if firstRamp.a > 0:\n am_ = am\n vm_ = am\n else:\n am_ = -am\n vm_ = -vm\n amInv = 1.0/am_\n k = 0.5*delta + v0*amInv\n h = 0.5*delta + v1*amInv\n r2 = amInv*amInv*(v0*v0 + v1*v1 + 2*d*am_ + 0.5*am_*am_*delta*delta)\n\n t0l_0 = max(delta, (v1 - v0))*amInv\n t0u_0 = (vm_ - v0)*amInv\n t2l_0 = -h + Sqrt(r2 - (k + t0u_0)**2)\n t2u_0 = -h + Sqrt(r2 - (k + t0l_0)**2)\n if t2l_0 > t2u_0:\n [t2l_0, t2u_0] = Swap(t2u_0, t2l_0)\n\n t2l_1 = max(delta, (v0 - v1)*amInv)\n t2u_1 = (vm_ - v1)*amInv\n t0l_1 = -k + Sqrt(r2 - (h + t2u_1)**2)\n t0u_1 = -k + Sqrt(r2 - (h + t2l_1)**2)\n if t0l_1 > t0u_1:\n [t0l_1, t0u_1] = Swap(t0u_1, t0l_1)\n\n t0l = max(t0l_0, t0l_1)\n t0u = min(t0u_0, t0u_1)\n t2l = max(t2l_0, t2l_1)\n t2u = min(t2u_0, t2u_1)\n if (t0l <= t0u) and (t2l <= t2u):\n duration0 = t0l + delta + t2u\n duration1 = t0u + delta + t2l\n if duration0 <= duration1:\n t0New = t0l\n t2New = t2u\n else:\n t0New = t0u\n t2New = t2l\n vp0New = v0 + am_*t0New\n vp1New = v1 + am_*t2New\n \n ramp0A = Ramp(v0, am_, t0New, x0)\n ramp1A = Ramp(vp0New, (vp1New - vp0New)*deltaInv, delta)\n ramp2A = Ramp(vp1New, -am_, t2New)\n curveA = ParabolicCurve([ramp0A, ramp1A, ramp2A])\n else:\n curveA = ParabolicCurve()\n\n # Try case B.\n d0 = firstRamp.d + middleRamp.d\n d1 = middleRamp.d + lastRamp.d\n # B1: merge the first two ramps together\n subCurveB1 = _Compute1DTrajectoryWithDelta(0, d0, v0, vp, vm, am, delta)\n if len(subCurveB1) == 1:\n curveB1 = ParabolicCurve([subCurveB1[0], lastRamp])\n else:\n # len(subCurveB1) == 2 in this case\n curveB1 = ParabolicCurve([subCurveB1[0], subCurveB1[1], lastRamp])\n\n # B2: merge the last two ramps together\n subCurveB2 = _Compute1DTrajectoryWithDelta(0, d1, vp, v1, vm, am, delta)\n if len(subCurveB2) == 1:\n curveB2 = ParabolicCurve([firstRamp, subCurveB2[0]])\n else:\n # len(subCurveB2) == 2 in this case\n curveB2 = ParabolicCurve([firstRamp, subCurveB2[0], subCurveB2[1]])\n\n if curveB1.duration <= curveB2.duration:\n curveB = curveB1\n else:\n curveB = curveB2\n\n # Try case C.\n sumVp = d*deltaInv - 0.5*(v0 + v1)\n sumVpL = v0 + v1 - 2*am*delta\n sumVpU = v0 + v1 + 2*am*delta\n if sumVpL <= sumVp <= sumVpU:\n vp0L = v0 - am*delta\n vp0U = v0 + am*delta\n vp1L = max(sumVp - vp0U, v1 - am*delta)\n vp1U = min(sumVp - vp0L, v1 + am*delta)\n if vp1L > vp1U:\n curveC = ParabolicCurve()\n else:\n passed = False\n maxTries = 1000\n # Randomly choose vp1 until the value is feasible\n for it in xrange(maxTries):\n vp1New = _rng.uniform(vp1L, vp1U)\n vp0New = sumVp - vp1New\n if abs(vp1New - vp0New) <= am*delta:\n passed = True\n break\n if not passed:\n curveC = ParabolicCurve()\n else:\n ramp0C = Ramp(v0, (vp0New - v0)*deltaInv, delta, x0)\n ramp1C = Ramp(vp0New, (vp1New - vp0New)*deltaInv, delta)\n ramp2C = Ramp(vp1New, (v1 - vp1New)*deltaInv, delta)\n curveC = ParabolicCurve([ramp0C, ramp1C, ramp2C])\n \n else:\n curveC = ParabolicCurve()\n\n # Compare all the cases and choose the best trajectory\n if not curveC.IsEmpty():\n if not curveB.IsEmpty():\n if curveB.duration <= curveC.duration:\n # Check before returning\n log.debug(\"PLP3 B\")\n return curveB\n else:\n # Check before returning\n log.debug(\"PLP3 C\")\n return curveC\n else:\n # Check before returning\n log.debug(\"PLP3 C\")\n return curveC\n\n if curveA.IsEmpty():\n # Check before returning\n log.debug(\"PLP3 B\")\n return curveB\n else:\n if curveB.IsEmpty():\n # Check before returning\n log.debug(\"PLP3 A\")\n return curveA\n else:\n if curveB.duration <= curveA.duration:\n # Check before returning\n log.debug(\"PLP3 B\")\n return curveB\n else:\n # Check before returning\n log.debug(\"PLP3 A\")\n return curveA\n \n \ndef _PLP4(curve, vm, am, delta):\n \"\"\"(t0 < delta) and (t1 < delta) and (t2 >= delta)\n \n This case is similar to PLP1 and we first follow the procedure of PLP1. However, PLP1A and PLP1B\n can both be infeasible. So we introduct PLP4C where we re-interpolate the trajectory to have\n two-ramp by using PP1 procedure.\n \"\"\"\n firstRamp = curve[0]\n middleRamp = curve[1]\n lastRamp = curve[2]\n\n t0 = firstRamp.duration\n t1 = middleRamp.duration\n t2 = lastRamp.duration\n v0 = firstRamp.v0\n v1 = lastRamp.v1\n x0 = firstRamp.x0\n d = curve.d\n d0 = firstRamp.d + middleRamp.d\n vp = middleRamp.v0 # the middle has zero acceleration\n deltaInv = 1.0/delta\n\n # Try case A.\n a0New = (vp - v0)*deltaInv\n ramp0A = Ramp(v0, a0New, delta, x0)\n dRem = d - ramp0A.d\n subCurveA = _Compute1DTrajectoryWithDelta(0, dRem, vp, v1, vm, am, delta)\n assert(not subCurveA.IsEmpty()) # this computation should not fail\n if len(subCurveA) == 1:\n curveA = ParabolicCurve([ramp0A, subCurveA[0]])\n else:\n # len(subCurveA) == 2 in this case\n curveA = ParabolicCurve([ramp0A, subCurveA[0], subCurveA[1]])\n \n # Try case B.\n if FuzzyZero(vp + v0, epsilon):\n curveB = ParabolicCurve()\n else:\n t0New = 2*d0/(vp + v0)\n passed = False\n if (t0New >= delta):\n a0New = (vp - v0)/t0New\n if abs(a0New) <= am + epsilon:\n passed = True\n ramp0B = Ramp(v0, a0New, t0New, x0)\n curveB = ParabolicCurve([ramp0B, lastRamp])\n if not passed:\n curveB = ParabolicCurve()\n\n # Try case C.\n # Two-ramp velocity profile where the first ramp saturates the minimum-switch-time constraint\n # and the second ramp saturates the acceleration constraint.\n a2 = lastRamp.a\n k = a2*delta\n vpNew = 0.5*(k - np.sign(a2)*np.sqrt(k**2 + 4*(k*v0 + v1**2) - 8*a2*d))\n if abs(vpNew) > vm + epsilon:\n curveC = ParabolicCurve()\n else:\n t1New = (v1 - vpNew)/a2 # a2 is not zero\n if (t1New >= delta):\n # PP1A: no further correction is needed.\n a0New = (vpNew - v0)*deltaInv\n ramp0C_A = Ramp(v0, a0New, delta, x0)\n ramp1C_A = Ramp(vpNew, a2, t1New)\n curveC_A = ParabolicCurve([ramp0C_A, ramp1C_A])\n curveC = curveC_A\n else:\n # Try case PP1B.\n vpNew = d*deltaInv - 0.5*(v0 + v1)\n a0New = (vpNew - v0)*deltaInv\n a1New = (v1 - vpNew)*deltaInv\n\n if (abs(a0New) <= am + epsilon) and (abs(a1New) <= am + epsilon) and (abs(vpNew) <= vm + epsilon):\n ramp0C_B = Ramp(v0, a0New, delta, x0)\n ramp1C_B = Ramp(vpNew, a1New, delta)\n curveC_B = ParabolicCurve([ramp0C_B, ramp1C_B])\n else:\n # Case PP2B does not produce any feasible solution\n curveC_B = ParabolicCurve()\n\n # Try case PP1C.\n if FuzzyZero(v0 + v1, epsilon):\n # Case C does not produce any feasible solution\n curveC_C = ParabolicCurve()\n else:\n tNew = 2*d/(v0 + v1)\n aNew = (v1 - v0)/tNew\n if (abs(aNew) <= am + epsilon) and (tNew >= delta):\n ramp0C_C = Ramp(v0, aNew, tNew, x0)\n curveC_C = ParabolicCurve([ramp0C])\n else:\n # Case C does not produce any feasible solution\n curveC_C = ParabolicCurve()\n\n assert (curveC_B.IsEmpty() and curveC_C.IsEmpty())\n if curveC_B.IsEmpty():\n curveC = curveC_C\n elif curveC_C.IsEmpty():\n curveC = curveC_B\n else:\n curveC = curveC_B if curveC_B.duration < curveC_C.duration else curveC_C\n\n # Now compare PLP1A, PLP1B, and PLP1C\n if curveA.IsEmpty() and curveB.IsEmpty() and curveC.IsEmpty():\n assert False\n newCurve = curveA # empty curve\n elif (not curveA.IsEmpty()) and curveB.IsEmpty() and curveC.IsEmpty():\n log.debug(\"PLP4 A\")\n newCurve = curveA\n elif curveA.IsEmpty() and (not curveB.IsEmpty()) and curveC.IsEmpty():\n log.debug(\"PLP4 B\")\n newCurve = curveB\n elif curveA.IsEmpty() and curveB.IsEmpty() and (not curveC.IsEmpty()):\n log.debug(\"PLP4 C\")\n newCurve = curveC\n elif curveA.IsEmpty():\n if curveB.duration <= curveC.duration:\n log.debug(\"PLP4 B\")\n newCurve = curveB\n else:\n log.debug(\"PLP4 C\")\n newCurve = curveC\n elif curveB.IsEmpty():\n if curveA.duration <= curveC.duration:\n log.debug(\"PLP4 A\")\n newCurve = curveA\n else:\n log.debug(\"PLP4 C\")\n newCurve = curveC\n elif curveC.IsEmpty():\n if curveA.duration <= curveB.duration:\n log.debug(\"PLP4 A\")\n newCurve = curveA\n else:\n log.debug(\"PLP4 B\")\n newCurve = curveB\n else:\n curves = [curveA, curveB, curveC]\n minIndex = min((curve.duration, idx) for (idx, curve) in enumerate(curves))[1]\n newCurve = curves[minIndex]\n if minIndex == 0:\n log.debug(\"PLP4 A\")\n elif minIndex == 1:\n log.debug(\"PLP4 B\")\n else:\n log.debug(\"PLP4 C\")\n \n return newCurve\n \n\ndef _PLP5(curve, vm, am, delta):\n \"\"\"(t0 >= delta) and (t1 < delta) and (t2 < delta)\n \n This case is similar to PLP2 and we first follow the procedure of PLP2. However, PLP2A and PLP2B\n can both be infeasible. So we introduct PLP4C where we re-interpolate the trajectory to have\n two-ramp by using PP2 procedure.\n \"\"\"\n firstRamp = curve[0]\n middleRamp = curve[1]\n lastRamp = curve[2]\n\n t0 = firstRamp.duration\n t1 = middleRamp.duration\n t2 = lastRamp.duration\n v0 = firstRamp.v0\n v1 = lastRamp.v1\n x0 = firstRamp.x0\n d = curve.d\n d1 = middleRamp.d + lastRamp.d\n vp = middleRamp.v0 # the middle has zero acceleration\n deltaInv = 1.0/delta\n\n # Try case A.\n a2New = (v1 - vp)*deltaInv\n ramp2A = Ramp(vp, a2New, delta)\n dRem = d - ramp2A.d\n subCurveA = _Compute1DTrajectoryWithDelta(0, dRem, v0, vp, vm, am, delta)\n assert(not subCurveA.IsEmpty()) # this computation should not fail\n if len(subCurveA) == 1:\n curveA = ParabolicCurve([subCurveA[0], ramp2A])\n else:\n # len(subCurveA) == 2 in this case\n curveA = ParabolicCurve([subCurveA[0], subCurveA[1], ramp2A])\n \n # Try case B.\n if FuzzyZero(vp + v1, epsilon):\n curveB = ParabolicCurve()\n else:\n passed = False\n t1New = 2*d1/(vp + v1)\n if (t1New >= delta):\n a1New = (v1 - vp)/t1New\n if abs(a1New) <= am + epsilon:\n ramp1B = Ramp(vp, a1New, t1New)\n curveB = ParabolicCurve([firstRamp, ramp1B])\n if not passed:\n curveB = ParabolicCurve()\n\n # Try case C.\n # Two-ramp velocity profile where the first ramp saturates the minimum-switch-time constraint\n # and the second ramp saturates the acceleration constraint.\n a0 = lastRamp.a\n k = a0*delta\n vpNew = 0.5*(k - np.sign(a0)*np.sqrt(k**2 + 4*(k*v0 + v1**2) - 8*a0*d))\n if abs(vpNew) > vm + epsilon:\n curveC = ParabolicCurve()\n else:\n t0New = (vpNew - v0)/a0 # a0 is not zero\n if (t0New >= delta):\n # PP2A: no further correction is needed.\n a1New = (v1 - vpNew)*deltaInv\n ramp0C_A = Ramp(v0, a0, t0New, x0)\n ramp1C_A = Ramp(vpNew, a1New, delta)\n curveC_A = ParabolicCurve([ramp0C_A, ramp1C_A])\n curveC = curveC_A\n else:\n # Try case PP1B.\n vpNew = d*deltaInv - 0.5*(v0 + v1)\n a0New = (vpNew - v0)*deltaInv\n a1New = (v1 - vpNew)*deltaInv\n\n if (abs(a0New) <= am + epsilon) and (abs(a1New) <= am + epsilon) and (abs(vpNew) <= vm + epsilon):\n ramp0C_B = Ramp(v0, a0New, delta, x0)\n ramp1C_B = Ramp(vpNew, a1New, delta)\n curveC_B = ParabolicCurve([ramp0C_B, ramp1C_B])\n else:\n # Case PP2B does not produce any feasible solution\n curveC_B = ParabolicCurve()\n\n # Try case PP1C.\n if FuzzyZero(v0 + v1, epsilon):\n # Case C does not produce any feasible solution\n curveC_C = ParabolicCurve()\n else:\n tNew = 2*d/(v0 + v1)\n aNew = (v1 - v0)/tNew\n if (abs(aNew) <= am + epsilon) and (tNew >= delta):\n ramp0C_C = Ramp(v0, aNew, tNew, x0)\n curveC_C = ParabolicCurve([ramp0C])\n else:\n # Case C does not produce any feasible solution\n curveC_C = ParabolicCurve()\n\n assert (curveC_B.IsEmpty() and curveC_C.IsEmpty())\n if curveC_B.IsEmpty():\n curveC = curveC_C\n elif curveC_C.IsEmpty():\n curveC = curveC_B\n else:\n curveC = curveC_B if curveC_B.duration < curveC_C.duration else curveC_C\n \n # Now compare PLP1A, PLP1B, and PLP1C\n if curveA.IsEmpty() and curveB.IsEmpty() and curveC.IsEmpty():\n assert False\n newCurve = curveA # empty curve\n elif (not curveA.IsEmpty()) and curveB.IsEmpty() and curveC.IsEmpty():\n log.debug(\"PLP5 A\")\n newCurve = curveA\n elif curveA.IsEmpty() and (not curveB.IsEmpty()) and curveC.IsEmpty():\n log.debug(\"PLP5 B\")\n newCurve = curveB\n elif curveA.IsEmpty() and curveB.IsEmpty() and (not curveC.IsEmpty()):\n log.debug(\"PLP5 C\")\n newCurve = curveC\n elif curveA.IsEmpty():\n if curveB.duration <= curveC.duration:\n log.debug(\"PLP5 B\")\n newCurve = curveB\n else:\n log.debug(\"PLP5 C\")\n newCurve = curveC\n elif curveB.IsEmpty():\n if curveA.duration <= curveC.duration:\n log.debug(\"PLP5 A\")\n newCurve = curveA\n else:\n log.debug(\"PLP5 C\")\n newCurve = curveC\n elif curveC.IsEmpty():\n if curveA.duration <= curveB.duration:\n log.debug(\"PLP5 A\")\n newCurve = curveA\n else:\n log.debug(\"PLP5 B\")\n newCurve = curveB\n else:\n curves = [curveA, curveB, curveC]\n minIndex = min((curve.duration, idx) for (idx, curve) in enumerate(curves))[1]\n newCurve = curves[minIndex]\n if minIndex == 0:\n log.debug(\"PLP5 A\")\n elif minIndex == 1:\n log.debug(\"PLP5 B\")\n else:\n log.debug(\"PLP5 C\")\n \n return newCurve\n\n\ndef _PLP6(curve, vm, am, delta):\n \"\"\"(t0 < delta) and (t1 >= delta) and (t2 < delta)\n \n We explore four possibilities\n \n A: stretch both the first and the last ramps. \n B: stretch only the first ramp. (The remaining displacement is taken care by\n basically using Compute1DTrajectoryWithDelta.)\n C: stretch only the last ramp, similar to B.\n D: merge everything into one ramp (because sometimes the first and the last ramps\n are both too short).\n \"\"\"\n firstRamp = curve[0]\n middleRamp = curve[1]\n lastRamp = curve[2]\n\n t0 = firstRamp.duration\n t1 = middleRamp.duration\n t2 = lastRamp.duration\n v0 = firstRamp.v0\n v1 = lastRamp.v1\n x0 = firstRamp.x0\n d = curve.d\n d0 = firstRamp.d + middleRamp.d\n vp = middleRamp.v0 # the middle has zero acceleration\n deltaInv = 1.0/delta\n \n newFirstRamp = Ramp(v0, (vp - v0)*deltaInv, delta, x0)\n newLastRamp = Ramp(vp, (v1 - vp)*deltaInv, delta)\n \n # Try case A.\n dRemA = d - (newFirstRamp.d + newLastRamp.d)\n t1New = dRemA/vp\n if (t1New > 0):\n newMiddleRamp = Ramp(vp, 0.0, t1New)\n curveA = ParabolicCurve([newFirstRamp, newMiddleRamp, newLastRamp])\n if (t1New < delta):\n curveA = _FixSwitchTimeThreeRamps(curveA, vm, am, delta) # go to PLP3\n else:\n curveA = ParabolicCurve()\n\n # Try case B.\n # B1\n dRemB = d - newFirstRamp.d\n subCurveB1 = _Compute1DTrajectoryWithDelta(0, dRemB, vp, v1, vm, am, delta)\n if len(subCurveB1) == 1:\n curveB1 = ParabolicCurve([newFirstRamp, subCurveB1[0]])\n else:\n # len(subCurveB) == 2 in this case\n curveB1 = ParabolicCurve([newFirstRamp, subCurveB1[0], subCurveB1[1]])\n\n # B2\n curveB2 = _PP1(curve, vm, am, delta)\n \n if curveB2.IsEmpty() or abs(curveB2[0].v1) > vm:\n curveB = curveB1\n else:\n if curveB1.duration <= curveB2.duration:\n curveB = curveB1\n else:\n curveB = curveB2\n\n # Try case C.\n # C1\n dRemC = d - newLastRamp.d\n subCurveC1 = _Compute1DTrajectoryWithDelta(x0, x0 + dRemC, v0, vp, vm, am, delta)\n if len(subCurveC1) == 1:\n curveC1 = ParabolicCurve([subCurveC1[0], newLastRamp])\n else:\n # len(subCurveC1) == 2 in this case\n curveC1 = ParabolicCurve([subCurveC1[0], subCurveC1[1], newLastRamp])\n\n # C2\n curveC2 = _PP2(curve, vm, am, delta)\n \n if curveC2.IsEmpty() or abs(curveC2[0].v1) > vm:\n curveC = curveC1\n else:\n if curveC1.duration <= curveC2.duration:\n curveC = curveC1\n else:\n curveC = curveC2\n \n # Try case D.\n if FuzzyZero(v0 + v1, epsilon):\n curveD = ParabolicCurve()\n else:\n tNew = 2*d/(v0 + v1)\n if tNew <= 0:\n curveD = ParabolicCurve()\n else:\n aNew = (v1 - v0)/tNew\n if abs(aNew) > am + epsilon:\n curveD = ParabolicCurve()\n else:\n ramp0D = Ramp(v0, aNew, tNew, x0)\n curveD = ParabolicCurve([ramp0D])\n\n # Now compare every case\n if not curveA.IsEmpty():\n if (curveA.duration <= curveB.duration) and (curveA.duration <= curveC.duration) and (curveA[1].duration >= delta):\n if curveD.IsEmpty():\n # Check before returning\n log.debug(\"PLP6 A\")\n return curveA\n else:\n if curveA.duration <= curveD.duration:\n # Check before returning\n log.debug(\"PLP6 A\")\n return curveA\n \n if (curveB.duration <= curveC.duration):\n if curveD.IsEmpty():\n # Check before returning\n log.debug(\"PLP6 B\")\n return curveB\n else:\n if curveB.duration <= curveD.duration:\n # Check before returning\n log.debug(\"PLP6 B\")\n return curveB\n else:\n if curveD.IsEmpty():\n # Check before returning\n log.debug(\"PLP6 C\")\n return curveC\n else:\n if curveC.duration <= curveD.duration:\n # Check before returning\n log.debug(\"PLP6 C\")\n return curveC\n\n # Check before returning\n log.debug(\"PLP6 D\")\n return curveD\n\n\ndef _PLP7(curve, vm, am, delta):\n \"\"\"(t0 < delta) and (t1 < delta) and (t2 < delta) \n \n \"\"\"\n firstRamp = curve[0]\n middleRamp = curve[1]\n lastRamp = curve[2]\n\n t0 = firstRamp.duration\n t1 = middleRamp.duration\n t2 = lastRamp.duration\n v0 = firstRamp.v0\n v1 = lastRamp.v1\n x0 = firstRamp.x0\n d = curve.d\n d0 = firstRamp.d + middleRamp.d\n vp = middleRamp.v0 # the middle has zero acceleration\n deltaInv = 1.0/delta\n \n # A: (delta, delta, delta)\n curveA = Recompute1DTrajectoryThreeRamps(curve, delta, delta, delta, vm, am)\n\n # B: fix using PP3\n curveB = _PP3(curve, vm, am, delta)\n\n # C: exceptional case\n if FuzzyZero(v0, epsilon) or FuzzyZero(v1, epsilon):\n if abs(vp - v0) > abs(vp - v1):\n dNew = 0.5*(vp + v0)*delta\n t1New = (2*d - 2*dNew)/(vp + v1)\n if t1New > 0:\n a0New = (vp - v0)*deltaInv\n a1New = (v1 - vp)/t1New\n ramp0C = Ramp(v0, a0New, delta, x0)\n ramp1C = Ramp(vp, a1New, t1New)\n curveC = ParabolicCurve([ramp0C, ramp1C])\n else:\n curveC = ParabolicCurve()\n else:\n dNew = 0.5*(vp + v1)*delta\n t0New = (2*d - 2*dNew)/(vp + v0)\n if t0New > 0:\n a0New = (vp - v0)/t0New\n a1New = (v1 - vp)*deltaInv\n ramp0C = Ramp(v0, a0New, t0New, x0)\n ramp1C = Ramp(vp, a1New, delta)\n curveC = ParabolicCurve([ramp0C, ramp1C])\n else:\n curveC = ParabolicCurve()\n else:\n curveC = ParabolicCurve()\n \n # Now compare all cases\n if curveA.IsEmpty() and curveB.IsEmpty() and curveC.IsEmpty():\n assert False\n newCurve = curveA # empty curve\n elif (not curveA.IsEmpty()) and curveB.IsEmpty() and curveC.IsEmpty():\n log.debug(\"PLP7 A\")\n newCurve = curveA\n elif curveA.IsEmpty() and (not curveB.IsEmpty()) and curveC.IsEmpty():\n log.debug(\"PLP7 B\")\n newCurve = curveB\n elif curveA.IsEmpty() and curveB.IsEmpty() and (not curveC.IsEmpty()):\n log.debug(\"PLP7 C\")\n newCurve = curveC\n elif curveA.IsEmpty():\n if curveB.duration <= curveC.duration:\n log.debug(\"PLP7 B\")\n newCurve = curveB\n else:\n log.debug(\"PLP7 C\")\n newCurve = curveC\n elif curveB.IsEmpty():\n if curveA.duration <= curveC.duration:\n log.debug(\"PLP7 A\")\n newCurve = curveA\n else:\n log.debug(\"PLP7 C\")\n newCurve = curveC\n elif curveC.IsEmpty():\n if curveA.duration <= curveB.duration:\n log.debug(\"PLP7 A\")\n newCurve = curveA\n else:\n log.debug(\"PLP7 B\")\n newCurve = curveB\n else:\n curves = [curveA, curveB, curveC]\n minIndex = min((curve.duration, idx) for (idx, curve) in enumerate(curves))[1]\n newCurve = curves[minIndex]\n if minIndex == 0:\n log.debug(\"PLP7 A\")\n elif minIndex == 1:\n log.debug(\"PLP7 B\")\n else:\n log.debug(\"PLP7 C\")\n # Check before returning\n return newCurve\n"},{"alpha_fraction":0.6794634461402893,"alphanum_fraction":0.7206290364265442,"avg_line_length":45,"blob_id":"b948f647c07ff059b433b38c1c64d3e3406973a9","content_id":"20966a85a3ef063b5ac8ff6fb065d9915d1bc065","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":2162,"license_type":"no_license","max_line_length":288,"num_lines":46,"path":"/examples/README.md","repo_name":"shengjiangtou/parabint","src_encoding":"UTF-8","text":"# Examples\r\n## Parabint Trajectories\r\n\r\nThere are three types of trajectories:\r\n - `Ramp`: A 1D trajectory segment with constant acceleration. A Ramp is completely determined given four values: `v0` initial velocity, `a` acceleration, `t` duration, and `x0` initial displacement. The initial displacement is an optional argument which is set to `0` when not specified.\r\n - `ParabolicCurve`: A concatenation of `Ramp`s. A ParabolicCurve is then a 1D trajectory with a number of segments of constant accelerations.\r\n - `ParabolicCurvesND`: An n-dimensional trajectory consisting of n `ParabolicCurvesND`s of equal duration.\r\n```python\r\nfrom parabint.trajectory import Ramp, ParabolicCurve, ParabolicCurvesND\r\n\r\nramp1 = Ramp(0, 1, 0.5) # v0 = 0; a = 1; duration = 0.5; x0 = 0 (optional)\r\nramp2 = Ramp(ramp1.v1, -0.7, 0.8)\r\ncurve1 = ParabolicCurve(ramps=[ramp1, ramp2])\r\n\r\nramp3 = Ramp(0.2, -0.66, 1.0)\r\nramp4 = Ramp(ramp3.v1, 0, 0.3)\r\ncurve2 = ParabolicCurve(ramps=[ramp3, ramp4])\r\ncurvesnd = ParabolicCurvesND(curves=[curve1, curve2])\r\n\r\n# curvesnd.PlotVel() # visualize the velocity profile\r\n```\r\n<p align=\"center\">\r\n <img src=\"figures/example_trajectories.png\" width=\"500\"/>\r\n</p>\r\n\r\nThe above figure shows the two velocity profiles in `curvesnd`. Vertical lines represent switch points, i.e., time instants where at least one joint changes its acceleration. When calling `PlotVel`, switch points can be plotted by setting the (optional) argument `includingSW` to `True`.\r\n\r\n## Trajectory Interpolation\r\n\r\nA parabolic trajectory can be generated from a set of boundary conditions `(x0, x1, v0, v1)` subject to max velocity `vm`, max acceleration `am`, and/or minimum-switch-time `delta`.\r\n```python\r\nfrom parabint import interpolator\r\n\r\nx0 = 0; x1 = 0.5; v0 = 0.4; v1 = 0.2\r\nvm = 1.0; am = 0.5\r\ndelta = 0.5\r\n\r\ncurve1 = interpolator.Compute1DTrajectory(x0, x1, v0, v1, vm, am) # without minimum-switch-time\r\ncurve2 = interpolator.Compute1DTrajectory(x0, x1, v0, v1, vm, am, delta) # with minimum-switch-time\r\n\r\n# curve1.PlotVel(fignum=1)\r\n# curve2.PlotVel(fignum=1)\r\n```\r\n<p align=\"center\">\r\n <img src=\"figures/example_reinterpolation.png\" width=\"500\"/>\r\n</p>\r\n"},{"alpha_fraction":0.762499988079071,"alphanum_fraction":0.762499988079071,"avg_line_length":25.66666603088379,"blob_id":"df7e0dbd296494efbd2d0054bd75a859a040d174","content_id":"617c18ed47c65be94034b0d9734c87bf8ceaf742","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":80,"license_type":"no_license","max_line_length":45,"num_lines":3,"path":"/setup.py","repo_name":"shengjiangtou/parabint","src_encoding":"UTF-8","text":"from distutils.core import setup\n\nsetup(name='parabint', packages=['parabint'])\n"},{"alpha_fraction":0.7704160213470459,"alphanum_fraction":0.7704160213470459,"avg_line_length":33.157894134521484,"blob_id":"ec0fe2c8e9f881172f9952af4f586d4a7e226517","content_id":"1fc9bbfe35dbdc9c78f9b3a2829d20e864b2af11","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":649,"license_type":"no_license","max_line_length":235,"num_lines":19,"path":"/README.md","repo_name":"shengjiangtou/parabint","src_encoding":"UTF-8","text":"# parabint\nThis is parabint, a library for time-optimal parabolic interpolation with velocity, acceleration, and minimum-switch-time constraints.\n\n## Dependencies\n\nIn case you want to also use the optimization routines in [optimization.py](./parabint/optimization.py) for comparison, you will need to install [Ipopt](https://projects.coin-or.org/Ipopt) and [pyipopt](https://github.com/xuy/pyipopt).\n\n## Installation\nClone this repository via\n```bash\ngit clone https://github.com/puttichai/parabint\n```\nThen from your parabint directory, run\n```bash\nsudo python setup.py install\n```\n\n## Usage\nSee the [examples](/examples) folder for some examples.\n"},{"alpha_fraction":0.5174774527549744,"alphanum_fraction":0.5639639496803284,"avg_line_length":31.6235294342041,"blob_id":"7df27e4478490290719f2fca5f6d297fef30ca49","content_id":"c477422d9e206748aaf79eec0641e8ccbc2e8fc8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2775,"license_type":"no_license","max_line_length":96,"num_lines":85,"path":"/tests/test_pp_reinterpolation.py","repo_name":"shengjiangtou/parabint","src_encoding":"UTF-8","text":"from parabint import interpolator\nfrom parabint.trajectory import Ramp, ParabolicCurve, ParabolicCurvesND\nfrom matplotlib import pyplot as plt\nimport random\nrng = random.SystemRandom()\n\n_xm = 2.96705973\n_vm = 3.92699082\n_am = 2.0\ndelta = 0.2\n\navailableChoices = dict()\navailableChoices[len(availableChoices.keys())] = \"PP1 : t0 < delta; t1 >= delta\"\navailableChoices[len(availableChoices.keys())] = \"PP2 : t0 >= delta; t1 < delta\"\navailableChoices[len(availableChoices.keys())] = \"PP3 : t0 < delta; t1 < delta\"\navailableChoices[len(availableChoices.keys())] = \"stop\"\nmsg = \"Choose a test case\"\nfor (key, val) in availableChoices.iteritems():\n msg += \"\\n{0} : {1}\".format(key, val)\nmsg += \"\\n\\n>> \"\n\ndef SampleConditions(testcase, delta):\n \"\"\"This function sample boundary conditions (x0, x1, v0, v1) as well\n as the velocity and acceleration bounds vm and am such that the\n time-optimal parabolic trajectory violates the given\n minimum-switch-time constraint in the given case.\n\n \"\"\"\n if testcase == 0:\n t0 = delta + 1\n t1 = 0\n elif testcase == 1:\n t0 = 0\n t1 = delta + 1\n else:\n t0 = delta + 1\n t1 = delta + 1\n\n # Sample a test case\n passed = False\n while not passed:\n x0 = _xm * rng.uniform(-1, 1)\n x1 = _xm * rng.uniform(-1, 1)\n vm = _vm * rng.uniform(0, 1)\n am = _am * rng.uniform(0, 1)\n v0 = vm * rng.uniform(-1, 1)\n v1 = vm * rng.uniform(-1, 1)\n\n curve1 = interpolator.Compute1DTrajectory(x0, x1, v0, v1, vm, am)\n if not (len(curve1) == 2):\n continue\n t0 = curve1[0].duration\n t1 = curve1[1].duration\n if testcase == 0:\n passed = t0 < delta and t1 >= delta\n elif testcase == 1:\n passed = t0 >= delta and t1 < delta\n else:\n passed = t0 < delta and t1 < delta\n \n return x0, x1, v0, v1, vm, am\n\nwhile True:\n try:\n index = int(raw_input(msg))\n if index in availableChoices.keys():\n if index == len(availableChoices.keys()) - 1:\n break\n x0, x1, v0, v1, vm, am = SampleConditions(index, delta)\n\n curve1 = interpolator.Compute1DTrajectory(x0, x1, v0, v1, vm, am)\n curve2 = interpolator.Compute1DTrajectory(x0, x1, v0, v1, vm, am, delta)\n \n # Visualization\n plt.clf()\n curve1.PlotVel(fignum=1, color='r')\n curve2.PlotVel(fignum=1, color='g')\n \n report = \\\n \"x0 = {0};\\nx1 = {1};\\nv0 = {2};\\nv1 = {3};\\nvm = {4};\\nam = {5};\\ndelta = {6};\\n\".\\\n format(x0, x1, v0, v1, vm, am, delta)\n print report\n\n except ValueError:\n print \"Please enter a valie choice\"\n\n\n"},{"alpha_fraction":0.512901782989502,"alphanum_fraction":0.5258350372314453,"avg_line_length":27.228723526000977,"blob_id":"de34e23155dcfae27ffff4ca585877fd449663da","content_id":"4cee73af3a57232fc20a3a9bfa6c7f69c95b2f5f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":31856,"license_type":"no_license","max_line_length":109,"num_lines":1128,"path":"/parabint/trajectory.py","repo_name":"shengjiangtou/parabint","src_encoding":"UTF-8","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom copy import deepcopy\nimport bisect\nfrom pylab import ion\nion()\n\nfrom utilities import epsilon, inf, FuzzyEquals, FuzzyZero\n\n\nclass Ramp(object):\n \"\"\"A Ramp is a constant-acceleration one-dimensional trajectory. When plotting its velocity\n evolution over time, the graph is a `ramp` on the velocity-time plane, hence the name.\n\n Parameters (input)\n ------------------\n v0 : float\n Initial velocity of the trajectory.\n a : float\n Acceleration of the trajectory.\n duration : float\n Duration of the trajectory. It must be non-negative.\n x0 : float, optional\n Initial displacement of the trajectory. If not given, x0 will be set to zero.\n\n Parameters (calculated from inputs)\n -----------------------------------\n v1 : float\n Final velocity of the trajectory.\n d : float\n total displacement made by this Ramp (i.e., independent of x0).\n x1 : float\n Final displacement at the end of the trajectory: x1 = x0 + d.\n\n \"\"\"\n def __init__(self, v0, a, t, x0=0):\n assert(t >= -epsilon)\n\n self.v0 = v0\n self.a = a\n self.duration = t\n self.x0 = x0\n\n self.v1 = v0 + self.a*self.duration\n self.d = self.duration*(self.v0 + 0.5*self.a*self.duration)\n self.x1 = self.x0 + self.d\n\n\n def Initialize(self, v0, a, t, x0=0):\n \"\"\"Initialize (or reinitialize) the Ramp with the given parameters and calculated other parameters\n accordingly.\n\n Parameters (input)\n ------------------\n v0 : float\n Initial velocity of the trajectory.\n a : float\n Acceleration of the trajectory.\n duration : float\n Duration of the trajectory. It must be non-negative.\n x0 : float, optional\n Initial displacement of the trajectory. If not given, x0 will be set to zero.\n\n \"\"\"\n assert(t >= -epsilon)\n\n self.v0 = v0\n self.a = a\n self.duration = t\n self.x0 = x0\n\n self.v1 = v0 + self.a*self.duration\n self.d = self.duration*(self.v0 + 0.5*self.a*self.duration)\n self.x1 = self.x0 + self.d\n\n\n def EvalPos(self, t):\n \"\"\"Evalutaion the position at the given time instant.\n\n Parameters\n ----------\n t : float\n Time instant at which to evaluate the position.\n\n Returns\n -------\n x : float\n Position at time t.\n\n \"\"\"\n if (t <= 0):\n return self.x0\n elif (t >= self.duration):\n return self.x1\n else:\n return self.x0 + t*(self.v0 + 0.5*self.a*t)\n\n\n def EvalVel(self, t):\n \"\"\"Evalutaion the velocity at the given time instant.\n\n Parameters\n ----------\n t : float\n Time instant at which to evaluate the velocity.\n\n Returns\n -------\n v : float\n Velocity at time t.\n\n \"\"\"\n if (t <= 0):\n return self.v0\n elif (t >= self.duration):\n return self.v1\n else:\n return self.v0 + self.a*t\n\n \n def EvalAcc(self):\n \"\"\"Return the acceleration.\n \n Returns\n -------\n a : float\n Acceleration.\n\n \"\"\"\n return self.a\n\n\n def GetPeaks(self):\n \"\"\"Calculate the peaks of positions along the trajectory.\n\n Returns\n -------\n xmin : float\n Minimum position along the trajectory.\n xmax : float\n Maximum position along the trajectory.\n\n \"\"\"\n return self._GetPeaks(0, self.duration)\n\n\n def _GetPeaks(self, ta, tb):\n if (ta > tb):\n return self._GetPeaks(tb, ta)\n\n if (ta < 0):\n ta = 0\n if (tb <= 0):\n return [self.x0, self.x0]\n\n if (tb > self.duration):\n tb = self.duration\n if (ta >= self.duration):\n return [self.x1, self.x1]\n\n curMin = self.EvalPos(ta)\n curMax = self.EvalPos(tb)\n if curMin > curMax:\n [curMin, curMax] = Swap(curMax, curMin)\n \n if FuzzyZero(self.a, epsilon):\n return [curMin, curMax]\n\n tDeflection = -self.v0/self.a\n if (tDeflection <= ta) or (tDeflection >= tb):\n return [curMin, curMax]\n\n xDeflection = self.x0 + 0.5*self.v0*tDeflection\n curMin = min(curMin, xDeflection)\n curMax = max(curMax, xDeflection)\n return [curMin, curMax]\n\n\n def SetInitialValue(self, newX0):\n \"\"\"Set the initial displacement (position) of the trajectory to the given value and recalculate the\n related parameters accordingly.\n\n Parameters\n ----------\n newX0 : float\n The new initial displacement.\n\n \"\"\"\n self.x0 = newX0\n self.x1 = self.x0 + self.d\n\n\n def UpdateDuration(self, newDuration):\n \"\"\"Set the trajectory duration to the given value and recalculate the related parameters accordingly.\n\n Parameters\n ----------\n newDuration : float\n The new duration.\n \n \"\"\"\n assert(newDuration >= -epsilon)\n if (newDuration <= 0):\n self.duration = 0\n self.x1 = self.x0\n self.v1 = self.v0\n self.d = 0\n return\n\n self.duration = newDuration\n self.v1 = self.v0 + self.a*self.duration\n self.d = self.duration*(self.v0 + 0.5*self.a*self.duration)\n self.x1 = self.x0 + self.d\n\n\n def PlotPos(self, t0=0, fignum=None, **kwargs):\n \"\"\"Plot trajectory position vs time.\n\n Parameters\n ----------\n t0 : float\n Position on the horizontal (time) axis to start plotting.\n fignum : int, float, string\n Figure's number/title\n \n \"\"\"\n raise NotImplementedError\n\n\n def PlotVel(self, t0=0, fignum=None, **kwargs):\n \"\"\"Plot trajectory velocity vs time.\n\n Parameters\n ----------\n t0 : float\n Position on the horizontal (time) axis to start plotting.\n fignum : int, float, string\n Figure's number/title\n \n \"\"\"\n if fignum is not None:\n plt.figure(fignum)\n\n line = plt.plot([t0, t0 + self.duration], [self.v0, self.v1], **kwargs)[0]\n plt.show(False)\n return line\n\n\n def PlotAcc(self, t0=0, fignum=None, **kwargs):\n \"\"\"Plot trajectory acceleration vs time.\n\n Parameters\n ----------\n t0 : float\n Position on the horizontal (time) axis to start plotting.\n fignum : int, float, string\n Figure's number/title\n \n \"\"\"\n if fignum is not None:\n plt.figure(fignum)\n\n line = plt.plot([t0, t0 + self.duration], [self.a, self.a], **kwargs)[0]\n plt.show(False)\n return line\n\n\n def Cut(self, t):\n \"\"\"Cut the trajectory into two halves. self will be the left half which is the segment from time 0\n to t. The other half (segment from time t to duration) is returned.\n\n Parameters\n ----------\n t : float\n Time instant at which to cut the trajectory\n\n Returns\n -------\n remRamp : Ramp\n Segment from time t to duration of the original trajectory.\n\n \"\"\"\n if (t <= 0):\n remRamp = Ramp(self.v0, self.a, self.duration, self.x0)\n self.Initialize(self.v0, 0, 0, self.x0)\n return remRamp\n elif (t >= self.duration):\n renRamp = Ramp(self.v1, 0, 0, self.x1)\n return remRamp\n\n remRampDuration = self.duration - t\n self.UpdateDuration(t)\n remRamp = Ramp(self.v1, self.a, remRampDuration, self.x1)\n return remRamp\n\n\n def TrimFront(self, t):\n \"\"\"Trim out the trajectory segment from time 0 to t.\n\n Parameters\n ----------\n t : float\n Time instant at which to trim the trajectory\n \n \"\"\"\n if (t <= 0):\n return\n elif (t >= self.duration):\n self.Initialize(self.v1, 0, 0, self.x1)\n return\n\n remDuration = self.duration - t\n newX0 = self.EvalPos(t)\n newV0 = self.EvalVel(t)\n self.Initialize(newV0, self.a, remDuration, newX0)\n return\n\n\n def TrimBack(self, t):\n \"\"\"Trim out the trajectory segment from time t to duration.\n\n Parameters\n ----------\n t : float\n Time instant at which to trim the trajectory\n \n \"\"\"\n if (t <= 0):\n self.Initialize(self.v0, 0, 0, self.x0)\n return\n elif (t >= self.duration):\n return\n\n self.UpdateDuration(t)\n return\n \n\nclass ParabolicCurve(object):\n \"\"\"A ParabolicCurve is a piecewise-constant-acceleration one-dimensional trajectory. It is a\n concatenation of Ramps.\n\n Parameters (input)\n ------------------\n ramps : list of Ramps, optional\n The list of Ramps to construct the ParabolicCurve with.\n\n Parameters (generated from inputs)\n ----------------------------------\n x0 : float\n Initial displacement of the trajectory.\n x1 : float\n Final displacement of the trajectory.\n v0 : float\n Initial velocity of the trajectory.\n v1 : float\n Final velocity of the trajectory.\n switchpointsList : list of float\n List of switch points, time instants at which the acceleration changes.\n duration : float\n Duration of the trajectory.\n d : float\n Total displacement made by this trajectory.\n\n \"\"\"\n def __init__(self, ramps=[]):\n self.switchpointsList = []\n dur = 0.0\n d = 0.0\n self.ramps = []\n \n if len(ramps) == 0:\n self.isEmpty = True\n self.x0 = 0.0\n self.x1 = 0.0\n self.v0 = 0.0\n self.v1 = 0.0\n self.switchpointsList = []\n self.duration = dur\n self.d = d\n else:\n self.ramps = deepcopy(ramps)\n self.isEmpty = False\n self.v0 = self.ramps[0].v0\n self.v1 = self.ramps[-1].v1\n \n self.switchpointsList.append(dur)\n for ramp in self.ramps:\n dur += ramp.duration\n self.switchpointsList.append(dur)\n d += ramp.d\n self.duration = dur\n self.d = d\n\n self.SetInitialValue(self.ramps[0].x0)\n\n\n def __getitem__(self, index):\n return self.ramps[index]\n\n\n def __len__(self):\n return len(self.ramps)\n\n\n def Initialize(self):\n self.switchpointsList = []\n dur = 0.0\n d = 0.0\n self.ramps = []\n \n if len(ramps) == 0:\n self.x0 = 0.0\n self.x1 = 0.0\n self.v0 = 0.0\n self.v1 = 0.0\n self.switchpointsList = []\n self.duration = dur\n self.d = d\n else:\n self.ramps = deepcopy(ramps)\n self.v0 = self.ramps[0].v0\n self.v1 = self.ramps[-1].v1\n \n self.switchpointsList.append(dur)\n for ramp in self.ramps:\n dur += ramp.duration\n self.switchpointsList.append(dur)\n d += ramp.d\n self.duration = dur\n self.d = d\n\n self.SetInitialValue(self.ramps[0].x0)\n\n\n def IsEmpty(self):\n return len(self) == 0\n\n\n def Append(self, curve):\n \"\"\"Append a ParabolicCurve to this one.\n\n Parameters\n ----------\n curve : ParabolicCurve\n ParabolicCurve to be appended.\n \n \"\"\"\n if len(self) == 0:\n if len(curve) > 0:\n self.ramps = deepcopy(curve.ramps)\n self.x0 = curve.x0\n self.x1 = curve.x1\n self.v0 = curve.v0\n self.v1 = curve.v1\n self.duration = curve.duration\n self.d = curve.d\n self.switchpointsList = deepcopy(curve.switchpointsList)\n else:\n pass\n return\n else:\n dur = self.duration\n d = self.d\n for ramp in curve.ramps:\n self.ramps.append(deepcopy(ramp))\n # Update displacement for the newly appended ramp\n self.ramps[-1].SetInitialValue(self.ramps[-2].x1)\n d += ramp.d\n dur += ramp.duration\n self.switchpointsList.append(dur)\n\n self.v1 = self.ramps[-1].v1\n self.duration = dur\n self.d = d\n self.x1 = self.x0 + self.d\n\n\n def FindRampIndex(self, t):\n \"\"\"Find the index of the ramp in which the given time instant lies.\n\n Parameters\n ----------\n t : float\n Time instant.\n \n Returns\n -------\n i : int\n Ramp index.\n remainder : float\n Time interval between the beginning of the ramp to t.\n \n \"\"\"\n if (t <= epsilon):\n i = 0\n remainder = 0.0\n else:\n i = bisect.bisect_left(self.switchpointsList, t) - 1\n remainder = t - self.switchpointsList[i]\n return [i, remainder]\n\n\n def EvalPos(self, t):\n \"\"\"Evalutaion the position at the given time instant.\n\n Parameters\n ----------\n t : float\n Time instant at which to evaluate the position.\n\n Returns\n -------\n x : float\n Position at time t.\n\n \"\"\"\n assert(t >= -epsilon)\n assert(t <= self.duration + epsilon)\n\n i, remainder = self.FindRampIndex(t)\n return self.ramps[i].EvalPos(remainder)\n\n\n def EvalVel(self, t):\n \"\"\"Evalutaion the velocity at the given time instant.\n\n Parameters\n ----------\n t : float\n Time instant at which to evaluate the velocity.\n\n Returns\n -------\n v : float\n Velocity at time t.\n\n \"\"\"\n assert(t >= -epsilon)\n assert(t <= self.duration + epsilon)\n\n i, remainder = self.FindRampIndex(t)\n return self.ramps[i].EvalVel(remainder)\n\n\n def EvalAcc(self, t):\n \"\"\"Evalutaion the acceleration at the given time instant.\n\n Parameters\n ----------\n t : float\n Time instant at which to evaluate the acceleration.\n\n Returns\n -------\n a : float\n Acceleration at time t.\n\n \"\"\"\n assert(t >= -epsilon)\n assert(t <= self.duration + epsilon)\n\n i, remainder = self.FindRampIndex(t)\n return self.ramps[i].EvalAcc(remainder)\n\n\n def GetPeaks(self):\n \"\"\"Calculate the peaks of positions along the trajectory.\n\n Returns\n -------\n xmin : float\n Minimum position along the trajectory.\n xmax : float\n Maximum position along the trajectory.\n\n \"\"\"\n return self._GetPeaks(0, self.duration)\n\n\n def _GetPeaks(self, ta, tb):\n xmin = inf\n xmax = -inf\n\n for ramp in self.ramps:\n [bmin, bmax] = ramp.GetPeaks()\n if bmin < xmin:\n xmin = bmin\n if bmax > xmax:\n xmax = bmax\n\n return [xmin, xmax]\n\n\n def SetInitialValue(self, x0):\n \"\"\"Set the initial displacement (position) of the trajectory to the given value and recalculate the\n related parameters accordingly.\n\n Parameters\n ----------\n newX0 : float\n The new initial displacement.\n\n \"\"\"\n self.x0 = x0\n newX0 = x0\n for ramp in self.ramps:\n ramp.SetInitialValue(newX0)\n newX0 += ramp.d\n self.x1 = self.x0 + self.d\n\n\n def SetConstant(self, x0, t):\n \"\"\"Set this ParabolicCurve to be a constant trajectory (i.e. zero-velocity and zero-acceleration.\n\n Parameters\n ----------\n x0 : float\n New initial dispalcement of the trajectory\n t : float\n New trajectory duration\n \n \"\"\"\n assert(t >= 0)\n ramp = Ramp(0, 0, t, x0)\n self.Initialize([ramp])\n return\n\n\n def SetSegment(self, x0, x1, v0, v1, t):\n assert(t >= -epsilon)\n if FuzzyZero(t, epsilon):\n a = 0\n else:\n tSqr = t*t\n a = -(v0*tSqr + t*(x0 - x1) + 2*(v0 - v1))/(t*(0.5*tSqr + 2))\n\n ramp = Ramp()\n ramp.x0 = x0\n ramp.x1 = x1\n ramp.v0 = v0\n ramp.v1 = v1\n ramp.duration = t\n ramp.d = x1 - x0\n ramp.a = a\n self.Initialize([ramp])\n return\n\n\n def SetZeroDuration(self, x0, v0):\n ramp = Ramp(v0, 0, 0, x0)\n self.Initialize([ramp])\n return\n \n \n def Cut(self, t):\n \"\"\"Cut the trajectory into two halves. self will be the left half which contains all the ramps from\n time 0 to t. The other half of the trajectory is returned.\n\n Parameters\n ----------\n t : float\n Time instant at which to cut the trajectory\n\n Returns\n -------\n remCurve : ParabolicCurve\n ParabolicCurve containing all the ramps from time t to duration of the original trajectory.\n\n \"\"\"\n if (t <= 0):\n remCurve = ParabolicCurve(self.ramps)\n self.SetZeroDuration(self.x0, self.v0)\n return remCurve\n elif (t >= self.duration):\n remCurve = ParabolicCurve()\n remCurve.SetZeroDuration(self.x1, self.v1)\n return\n\n i, remainder = self.FindRampIndex(t)\n leftHalf = self.ramps[0:i + 1]\n rightHalf = self.ramps[i:]\n\n leftHalf[-1].TrimBack(remainder)\n rightHalf[0].TrimFront(remainder)\n self.Initialize(leftHalf)\n remCurve = ParabolicCurve(rightHalf)\n return remCurve\n\n\n def TrimFront(self, t):\n \"\"\"Trim out the trajectory segment from time 0 to t.\n\n Parameters\n ----------\n t : float\n Time instant at which to trim the trajectory\n \n \"\"\"\n if (t <= 0):\n return\n elif (t >= self.duration):\n self.SetZeroDuration(self.x1, self.v1)\n return\n\n i, remainder = self.FindRampIndex(t)\n rightHalf = self.ramps[i:]\n\n rightHalf[0].TrimFront(remainder)\n self.Initialize(rightHalf)\n return\n\n\n def TrimBack(self, t):\n \"\"\"Trim out the trajectory segment from time t to duration.\n\n Parameters\n ----------\n t : float\n Time instant at which to trim the trajectory\n \n \"\"\"\n if (t <= 0):\n self.SetZeroDuration(self.x0, self.v0)\n return\n elif (t >= self.duration):\n return\n\n i, remainder = self.FindRampIndex(t)\n leftHalf = self.ramps[0:i + 1]\n\n leftHalf[-1].TrimBack(remainder)\n self.Initialize(leftHalf)\n return\n\n\n # Visualization\n def PlotPos(self, fignum=None, color='g', dt=0.01, lw=2, includingSW=False):\n \"\"\"Plot trajectory position vs time.\n\n Parameters\n ----------\n fignum : int, float, string, optional\n Figure's number/title\n color : string, optional\n Matplotlib's color option.\n dt : float, optional\n Time resolution to evaluate the position.\n lw : int, optional\n Linewidth\n includingSW : bool, optional\n If True, draw vertical straight lines at switch points.\n \n \"\"\"\n tVect = np.arange(0, self.duration, dt)\n if tVect[-1] < self.duration:\n tVect = np.append(tVect, self.duration)\n \n xVect = [self.EvalPos(t) for t in tVect]\n if fignum is not None:\n plt.figure(fignum)\n plt.plot(tVect, xVect, color=color, linewidth=lw)\n\n if includingSW:\n ax = plt.gca().axis()\n for s in self.switchpointsList:\n plt.plot([s, s], [ax[2], ax[3]], 'r', linewidth=1)\n \n plt.show(False)\n\n\n def PlotVel(self, fignum=None, color=None, lw=2, includingSW=False, **kwargs):\n \"\"\"Plot trajectory velocity vs time.\n\n Parameters\n ----------\n fignum : int, float, string, optional\n Figure's number/title\n color : string, optional\n Matplotlib's color option.\n lw : int, optional\n Linewidth\n includingSW : bool, optional\n If True, draw vertical straight lines at switch points.\n \n \"\"\"\n if fignum is not None:\n plt.figure(fignum)\n\n t0 = 0.0\n for ramp in self.ramps:\n if color is None:\n line = ramp.PlotVel(t0=t0, fignum=fignum, linewidth=lw, **kwargs)\n color = line.get_color()\n else:\n line = ramp.PlotVel(t0=t0, fignum=fignum, color=color, linewidth=lw, **kwargs)\n t0 += ramp.duration\n\n if includingSW:\n ax = plt.gca().axis()\n for s in self.switchpointsList:\n plt.plot([s, s], [ax[2], ax[3]], 'r', linewidth=1)\n \n plt.show(False)\n return line\n\n\n def PlotAcc(self, fignum=None, color=None, lw=2, **kwargs):\n \"\"\"Plot trajectory velocity vs time.\n\n Parameters\n ----------\n fignum : int, float, string, optional\n Figure's number/title\n color : string, optional\n Matplotlib's color option.\n lw : int, optional\n Linewidth\n \n \"\"\"\n if fignum is not None:\n plt.figure(fignum)\n \n t0 = 0.0\n prevAcc = self.ramps[0].a\n for ramp in self.ramps:\n if color is None:\n line = ramp.PlotAcc(t0=t0, fignum=fignum, linewidth=lw, **kwargs)\n color = line.get_color()\n else:\n line = ramp.PlotAcc(t0=t0, fignum=fignum, color=color, linewidth=lw, **kwargs)\n plt.plot([t0, t0], [prevAcc, ramp.a], color=color, linewidth=lw, **kwargs)\n t0 += ramp.duration\n prevAcc = ramp.a\n plt.show(False)\n return line\n\n\nclass ParabolicCurvesND(object):\n \"\"\"A ParabolicCurvesND is a (parabolic) trajectory of an n-DOF system. A trajectory of each DOF is a\n ParabolicCurve.\n\n \"\"\"\n def __init__(self, curves=[]):\n if len(curves) == 0:\n self.curves = []\n self.x0Vect = None\n self.x1Vect = None\n self.v0Vect = None\n self.v1Vect = None\n self.dVect = None\n self.ndof = 0\n self.switchpointsList = []\n self.duration = 0.0\n else:\n # Check first if the input is valid\n minDur = curves[0].duration\n for curve in curves[1:]:\n assert( FuzzyEquals(curve.duration, minDur, epsilon) )\n minDur = min(curve.duration, minDur)\n\n self.curves = deepcopy(curves)\n self.duration = minDur\n self.ndof = len(self.curves)\n self.x0Vect = np.asarray([curve.x0 for curve in self.curves])\n self.x1Vect = np.asarray([curve.x1 for curve in self.curves])\n self.v0Vect = np.asarray([curve.v0 for curve in self.curves])\n self.v1Vect = np.asarray([curve.v1 for curve in self.curves])\n self.dVect = np.asarray([curve.d for curve in self.curves])\n\n allSwitchPointsList = deepcopy(curves[0].switchpointsList)\n for curve in self.curves[1:]:\n allSwitchPointsList += curve.switchpointsList\n allSwitchPointsList.sort() # a sorted list of all switch points (including deplicate)\n\n self.switchpointsList = [] # a sorted list of all distinct switch points\n if len(allSwitchPointsList) > 0:\n self.switchpointsList.append(allSwitchPointsList[0])\n for sw in allSwitchPointsList[1:]:\n if not FuzzyEquals(sw, self.switchpointsList[-1], epsilon):\n self.switchpointsList.append(sw)\n\n\n def Initialize(self, curves):\n if len(curves) == 0:\n self.curves = []\n self.x0Vect = None\n self.x1Vect = None\n self.v0Vect = None\n self.v1Vect = None\n self.dVect = None\n self.ndof = 0\n self.switchpointsList = []\n self.duration = 0.0\n else:\n # Check first if the input is valid\n minDur = curves[0].duration\n for curve in curves[1:]:\n assert( FuzzyEquals(curve.duration, minDur, epsilon) )\n minDur = min(curve.duration, minDur)\n\n self.curves = deepcopy(curves)\n self.duration = minDur\n self.ndof = len(self.curves)\n self.x0Vect = np.asarray([curve.x0 for curve in self.curves])\n self.x1Vect = np.asarray([curve.x1 for curve in self.curves])\n self.v0Vect = np.asarray([curve.v0 for curve in self.curves])\n self.v1Vect = np.asarray([curve.v1 for curve in self.curves])\n self.dVect = np.asarray([curve.d for curve in self.curves])\n\n allSwitchPointsList = deepcopy(curves[0].switchpointsList)\n for curve in self.curves[1:]:\n allSwitchPointsList += curve.switchpointsList\n allSwitchPointsList.sort() # a sorted list of all switch points (including deplicate)\n\n self.switchpointsList = [] # a sorted list of all distinct switch points\n if len(allSwitchPointsList) > 0:\n self.switchpointsList.append(allSwitchPointsList[0])\n for sw in allSwitchPointsList[1:]:\n if not FuzzyEquals(sw, self.switchpointsList[-1], epsilon):\n self.switchpointsList.append(sw)\n \n \n def __getitem__(self, index):\n return self.curves[index]\n\n\n def __len__(self):\n return len(self.curves)\n\n\n def IsEmpty(self):\n return len(self) == 0\n \n \n def SetInitialValue(self, x0Vect):\n self.x0Vect = np.array(x0Vect) # make a copy\n for (i, curve) in enumerate(self.curves):\n curve.SetInitialValue(self.x0Vect[i])\n self.x1Vect = np.asarray([x0 + d for (x0, d) in zip(self.x0Vect, self.dVect)])\n\n\n def EvalPos(self, t):\n assert(t >= -epsilon)\n assert(t <= self.duration + epsilon)\n \n xVect = [curve.EvalPos(t) for curve in self.curves]\n return np.asarray(xVect)\n\n\n def EvalVel(self, t):\n assert(t >= -epsilon)\n assert(t <= self.duration + epsilon)\n \n return np.asarray([curve.EvalVel(t) for curve in self.curves])\n\n\n def EvalAcc(self, t):\n assert(t >= -epsilon)\n assert(t <= self.duration + epsilon)\n \n return np.asarray([curve.EvalAcc(t) for curve in self.curves])\n\n \n def SetConstant(self, x0Vect, t): \n ndof = len(x0Vect)\n curves = []\n for i in xrange(ndof):\n curve = ParabolicCurve()\n curve.SetConstant(x0Vect[i], t)\n curves.append(curve)\n\n self.Initialize(curves)\n return\n\n\n def SetSegment(self, x0Vect, x1Vect, v0Vect, v1Vect, t):\n assert(t >= 0)\n\n ndof = len(x0Vect)\n curves = []\n for i in xrange(ndof):\n curve = ParabolicCurve()\n curve.SetSegment(x0Vect[i], x1Vect[i], v0Vect[i], v1Vect[i], t)\n curves.append(curve)\n\n self.Initialize(curves)\n return\n\n \n def SetZeroDuration(self, x0Vect, v0Vect):\n ndof = len(x0Vect)\n curves = []\n for i in xrange(ndof):\n curve = ParabolicCurve()\n curve.SetZeroDuration(x0Vect[i], v0Vect[i])\n curves.append(curve)\n\n self.Initialize(curves)\n return\n\n\n def Cut(self, t):\n if (t <= 0):\n remCurvesND = ParabolicCurvesND()\n remCurvesND.SetZeroDuration(self.x0Vect, self.v0Vect)\n return\n elif (t >= self.duration):\n remCurvesND = ParabolicCurvesND()\n remCurvesND.SetZeroDuration(self.x1Vect, self.v1Vect)\n return\n\n leftHalf = self.curves\n rightHalf = []\n for i in xrange(self.ndof):\n rightHalf.append(leftHalf[i].Cut(t))\n\n self.Initialize(leftHalf)\n remCurvesND = ParabolicCurvesND(rightHalf)\n return remCurvesND\n\n\n def TrimFront(self, t):\n if (t <= 0):\n return\n elif (t >= self.duration):\n self.SetZeroDuration(self.x1Vect, self.v1Vect)\n return\n\n newCurves = self.curves\n for i in xrange(self.ndof):\n newCurves[i].TrimFront(t)\n self.Initialize(newCurves)\n return\n\n\n def TrimBack(self, t):\n if (t <= 0):\n self.SetZeroDuration(self.x0Vect, self.v0Vect)\n return\n elif (t >= self.duration):\n return\n\n newCurves = self.curves\n for i in xrange(self.ndof):\n newCurves[i].TrimBack(t)\n self.Initialize(newCurves)\n return\n\n\n # Visualization\n def PlotPos(self, fignum='Displacement Profiles', includingSW=False, dt=0.005):\n \"\"\"Plot trajectory position vs time.\n\n Parameters\n ----------\n fignum : int, float, string, optional\n Figure's number/title\n includingSW : bool, optional\n If True, draw vertical straight lines at switch points.\n dt : float, optional\n Time resolution to evaluate the position.\n \n \"\"\"\n plt.figure(fignum)\n\n tVect = np.arange(0, self.duration, dt)\n if tVect[-1] < self.duration:\n tVect = np.append(tVect, self.duration)\n\n xVect = [self.EvalPos(t) for t in tVect]\n plt.plot(tVect, xVect, linewidth=2)\n handle = ['joint {0}'.format(i + 1) for i in xrange(self.ndof)]\n plt.legend(handle)\n\n if includingSW:\n ax = plt.gca().axis()\n for s in self.switchpointsList:\n plt.plot([s, s], [ax[2], ax[3]], 'r', linewidth=1)\n plt.show(False)\n \n\n def PlotVel(self, fignum='Velocity Profiles', includingSW=False, **kwargs):\n \"\"\"Plot trajectory velocity vs time.\n\n Parameters\n ----------\n fignum : int, float, string, optional\n Figure's number/title\n includingSW : bool, optional\n If True, draw vertical straight lines at switch points.\n \n \"\"\"\n plt.figure(fignum)\n plt.hold(True)\n\n lines = []\n for curve in self.curves:\n lines.append(curve.PlotVel(fignum=fignum, **kwargs))\n\n handles = ['joint {0}'.format(i + 1) for i in xrange(self.ndof)]\n plt.legend(lines, handles)\n\n if includingSW:\n ax = plt.gca().axis()\n for s in self.switchpointsList:\n plt.plot([s, s], [ax[2], ax[3]], 'r', linewidth=1)\n plt.show(False)\n \n\n def PlotAcc(self, fignum='Acceleration Profiles', **kwargs):\n \"\"\"Plot trajectory acceleration vs time.\n\n Parameters\n ----------\n fignum : int, float, string, optional\n Figure's number/title\n \n \"\"\"\n plt.figure(fignum)\n plt.hold(True)\n\n lines = []\n for curve in self.curves:\n lines.append(curve.PlotAcc(fignum=fignum, **kwargs))\n\n handles = ['joint {0}'.format(i + 1) for i in xrange(self.ndof)]\n plt.legend(lines, handles)\n plt.show(False)\n\n \n"},{"alpha_fraction":0.8018018007278442,"alphanum_fraction":0.8018018007278442,"avg_line_length":14.857142448425293,"blob_id":"12687ad6eeadd695dafe916c735d5e719d74d72a","content_id":"386f5b4e1c3918c636a8734a50e8baf643101f23","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":111,"license_type":"no_license","max_line_length":23,"num_lines":7,"path":"/parabint/__init__.py","repo_name":"shengjiangtou/parabint","src_encoding":"UTF-8","text":"import trajectory\nimport interpolator\nimport checker\ntry:\n import optimization\nexcept ImportError:\n pass\n"},{"alpha_fraction":0.36657458543777466,"alphanum_fraction":0.4252762496471405,"avg_line_length":32.9906120300293,"blob_id":"eb1b2fd6d9c703e7d9da7a3062332ed77c6d620a","content_id":"cfb4e6d9ac3256585097b7a63a22537a3cea59f6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7240,"license_type":"no_license","max_line_length":98,"num_lines":213,"path":"/parabint/optimization.py","repo_name":"shengjiangtou/parabint","src_encoding":"UTF-8","text":"import numpy as np\nimport pyipopt\nimport random\nrng = random.SystemRandom()\nfrom .utilities import epsilon\n\npyipopt.set_loglevel(0)\ninf = pyipopt.NLP_UPPER_BOUND_INF\n\ndef SolveTwoRampTrajectory(d, v0, v1, vm, am, delta, max_iter=5000, tol=epsilon, print_level=0):\n \"\"\"Given the boundary conditions for the trajectory, find a fastest\n two-ramp trajectory satisfying velocity, acceleration, and the\n minimum-switch-time constraint.\n\n Optimization variable: x = [t0, t1, vp]\n\n Inequality constarints:\n\n -am <= (vp - v0)/t0 <= am ---(1)\n -am <= (v1 - vp)/t1 <= am ---(2)\n\n Equality constraint:\n\n d = 0.5*(v0 + vp)*t0 + 0.5*(v1 + vp)*t1 ---(3)\n\n \"\"\"\n nvar = 3\n ncon = 3\n nnzj = 9\n nnzh = 6\n\n # Bounds on the variables\n xl = np.array([delta, delta, vm])\n xu = np.array([INF, INF, vm])\n x0 = [2*rng.random() -1 for _ in xrange(nvar)] # randomly select an initial guess\n\n # Bounds on the constraints\n gl = np.array([-am, -am, d])\n gu = np.array([am, am, d])\n\n # Objective function f(x)\n def eval_f(x, user_data=None):\n return x[0] + x[1]\n\n # Gradient of f(x)\n def eval_grad_f(x, user_data=None):\n return np.array([1., 1., 0.])\n\n # Constraint function g(x)\n def eval_g(x, user_data=None):\n return np.array([(x[2] - v0)/x[0],\n (v1 - x[2])/x[0],\n 0.5*(v0 + x[2])*x[0] + 0.5*(v1 + x[2])*x[1]])\n\n # Jacobian of g(x)\n def eval_jacobian_g(x, flag, user_data=None):\n if flag:\n # Return the position of each number in Jacobian\n return (np.array([0, 0, 0, 1, 1, 1, 2, 2, 2]),\n np.array([0, 1, 2, 0, 1, 2, 0, 1, 2]))\n else:\n return np.array([-(x[2] - v0)/(x[0]**2),\n 0.0,\n 1.0/x[0],\n 0.0,\n -(v1 - x[2])/(x[1]**2),\n -1.0/x[1],\n 0.5*(v0 + x[2]),\n 0.5*(v1 + x[2]),\n 0.5*(x[0] + x[1])])\n\n # Hessian of f(x)\n def eval_h(x, lagrange, obj_factor, flag, user_data=None):\n if flag:\n return (np.array([0, 0, 1, 0, 1, 2]),\n np.array([0, 1, 1, 2, 2, 2]))\n else:\n values = np.zeros(nnzh)\n \n values[0] += lagrange[0]*(2*(x[2] - v0)/(x[0]**3))\n values[3] += lagrange[0]*(-1/(x[0]**2))\n \n values[2] += lagrange[1]*(2*(v1 - x[2])/(x[1]**3))\n values[4] += lagrange[1]*(1/(x[1]**2))\n \n values[3] += lagrange[2]*0.5\n values[4] += lagrange[2]*0.5\n\n return values\n\n # Initialize a non-linear program\n nlp = pyipopt.create(nvar, xl, xu, ncon, gl, gu, nnzj, nnzh, \n eval_f, eval_grad_f, eval_g, eval_jacobian_g, eval_h)\n nlp.int_option('max_iter', max_iter)\n nlp.num_option('tol', tol)\n nlp.int_option('print_level', print_level) \n\n x, zl, zu, constraint_multipliers, obj, status = nlp.solve(x0)\n nlp.close()\n \n return x, status\n\n\ndef SolveThreeRampTrajectory(d, v0, v1, vm, am, delta, max_iter=5000, tol=epsilon, print_level=0):\n \"\"\"\n Given the boundary conditions for the trajectory, find a fastest\n two-ramp trajectory satisfying velocity, acceleration, and the\n minimum-switch-time constraint.\n\n Optimization variable: x = [t0, t1, t2, vp0, vp1]\n\n Inequality constarints:\n\n -am <= (vp - v0)/t0 <= am ---(1)\n -am <= (vp1 - vp0)/t1 <= am ---(2)\n -am <= (v1 - vp1)/t2 <= am ---(3)\n\n Equality constraint:\n\n d = 0.5*(v0 + vp0)*t0 + 0.5*(vp0 + vp1)*t1 + 0.5*(vp1 + v1)*t2 ---(4)\n \"\"\"\n nvar = 5\n ncon = 4\n nnzj = 20\n nnzh = 15\n\n # Bounds on the variables\n xl = np.array([delta, delta, delta, -vm, -vm])\n xu = np.array([INF, INF, INF, vm, vm])\n x0 = [2*rng.random() -1 for _ in xrange(nvar)] # randomly select an initial guess\n\n # Bounds on the constraints\n gl = np.array([-am, -am, -am, d])\n gu = np.array([am, am, am, d])\n\n # Objective function f(x)\n def eval_f(x, user_data=None):\n return x[0] + x[1] + x[2]\n\n # Gradient of f(x)\n def eval_grad_f(x, user_data=None):\n return np.array([1., 1., 1., 0., 0.])\n\n # Constraint function g(x)\n def eval_g(x, user_data=None):\n return np.array([(x[3] - v0)/x[0],\n (x[4] - x[3])/x[1],\n (v1 - x[4])/x[2],\n 0.5*(v0 + x[3])*x[0] + 0.5*(x[3] + x[4])*x[1] + 0.5*(x[4] + v1)*x[2]])\n\n # Jacobian of g(x)\n def eval_jacobian_g(x, flag, user_data=None):\n if flag:\n return (np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3]),\n np.array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4]))\n else:\n return np.array([-(x[3] - v0)/(x[0]**2),\n 0.0,\n 0.0,\n 1.0/x[0],\n 0.0,\n 0.0,\n -(x[4] - x[3])/(x[1]**2),\n 0.0,\n -1.0/x[1],\n 1.0/x[1],\n 0.0,\n 0.0,\n -(v1 - x[4])/(x[2]**2),\n 0.0,\n -1.0/x[2],\n 0.5*(v0 + x[3]),\n 0.5*(x[3] + x[4]),\n 0.5*(x[4] + v1),\n 0.5*(x[0] + x[1]),\n 0.5*(x[1] + x[2])])\n\n # Hessian of f(x)\n def eval_h(x, lagrange, obj_factor, flag, user_data=None):\n if flag:\n return (np.array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3, 4]),\n np.array([0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4]))\n else:\n values = np.zeros(nnzh)\n\n values[0] += lagrange[0]*(2.0*(x[3] - v0)/(x[0]**3))\n values[6] += lagrange[0]*(-1.0/(x[0]**2))\n \n values[2] += lagrange[1]*(2.0*(x[4] - x[3])/(x[1]**3))\n values[7] += lagrange[1]*(1.0/(x[1]**2))\n values[11] += lagrange[1]*(-1.0/(x[1]**2))\n \n values[5] += lagrange[2]*(2.0*(v1 - x[4])/(x[3]**2))\n values[12] += lagrange[2]*(1.0/(x[2]**2))\n \n values[6] += lagrange[3]*0.5\n values[7] += lagrange[3]*0.5\n values[11] += lagrange[3]*0.5\n values[12] += lagrange[3]*0.5\n \n return values\n\n # Initialize a non-linear program\n nlp = pyipopt.create(nvar, xl, xu, ncon, gl, gu, nnzj, nnzh, \n eval_f, eval_grad_f, eval_g, eval_jacobian_g, eval_h)\n nlp.int_option('max_iter', max_iter)\n nlp.num_option('tol', tol)\n nlp.int_option('print_level', print_level) \n\n x, zl, zu, constraint_multipliers, obj, status = nlp.solve(x0)\n nlp.close()\n \n return x, status\n"},{"alpha_fraction":0.5377268195152283,"alphanum_fraction":0.5742332339286804,"avg_line_length":35.2423095703125,"blob_id":"e466a0ac4fa1d9f0de7b0990e09817e3138dcd47","content_id":"78cfadf4f812eb7f2743dcadc6b797967f3b9cc2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":9423,"license_type":"no_license","max_line_length":198,"num_lines":260,"path":"/parabint/checker.py","repo_name":"shengjiangtou/parabint","src_encoding":"UTF-8","text":"from .utilities import epsilon, inf, FuzzyEquals, FuzzyZero\nimport logging\nlogging.basicConfig(format='[%(levelname)s] [%(name)s: %(funcName)s] %(message)s', level=logging.DEBUG)\nlog = logging.getLogger(__name__)\n\n# PCR: ParabolicCheckReturn\nPCR_Normal = 0\nPCR_NegativeDuration = 1\nPCR_XBoundViolated = 2\nPCR_VBoundViolated = 3\nPCR_ABoundViolated = 4\nPCR_XDiscrepancy = 5\nPCR_VDiscrepancy = 6\nPCR_DurationDiscrepancy = 7\n\n\ndef _GetPeaks(x0, x1, v0, v1, a, t):\n \"\"\"Calculate the maximum and minimum displacement occuring betweeb time 0 and t given (x0, x1, v0,\n v1, a).\n\n Parameters\n ----------\n x0 : float\n Initial displacement of the segment\n x1 : float\n Final displacement of the segment\n v0 : float\n Initial velocity of the segment\n v1 : float\n Final velocity of the segment\n a : float\n Acceleration of the segment\n t : float\n Duration of the segment\n\n Returns\n -------\n bmin : float\n Minimum displacement along this segment\n bmax : float\n Maximum displacement along this segment\n \n \"\"\"\n if FuzzyZero(a, epsilon):\n if v0 > 0:\n bmin = x0\n bmax = x1\n else:\n bmin = x1\n bmax = x0\n return bmin, bmax\n\n if x0 > x1:\n curMin = x1\n curMax = x0\n else:\n curMin = x0\n curMax = x1\n\n tDeflection = -v0/a # the time when velocity crosses zero\n if (tDeflection <= 0) or (tDeflection >= t):\n bmin = curMin\n bmax = curMax\n return bmin, bmax\n\n xDeflection = x0 + 0.5*v0*tDeflection\n bmin = min(curMin, xDeflection)\n bmax = max(curMax, xDeflection)\n return bmin, bmax\n \n\ndef CheckSegment(x0, x1, v0, v1, a, t, xmin, xmax, vm, am):\n \"\"\"\n Check if a linear trajectory segment connecting (x0, v0) and (x1, v1) is valid.\n \n Parameters\n ----------\n x0 : float\n Initial displacement of the segment\n x1 : float\n Final displacement of the segment\n v0 : float\n Initial velocity of the segment\n v1 : float\n Final velocity of the segment\n a : float\n Acceleration of the segment\n t : float\n Duration of the segment\n xmin : float\n Displacement lower limit\n xmax : float\n Displacement upper limit\n vm : float\n Maximum velocity\n am : float\n Maximum acceleration\n\n Returns\n -------\n ret : one of ParabolicCheckReturn\n \n \"\"\"\n if t < -epsilon:\n log.warn(\"PCR_NegativeDuration: duration = {0}\".format(t))\n return PCR_NegativeDuration\n if not FuzzyEquals(v1, v0 + a*t, epsilon):\n v1_ = v0 + a*t\n log.warn(\"PCR_VDiscrepancy: v1 = {0}; computed v1 = {1}; diff = {2}\".format(v1, v1_, (v1 - v1_)))\n log.warn(\"Info: x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; a = {4}; duration = {5}; xmin = {6}; xmax = {7}; vm = {8}; am = {9}\".format(x0, x1, v0, v1, a, t, xmin, xmax, vm, am))\n return PCR_VDiscrepancy\n if not FuzzyEquals(x1, x0 + t*(v0 + 0.5*a*t), epsilon):\n x1_ = x0 + t*(v0 + 0.5*a*t)\n log.warn(\"PCR_XDiscrepancy: x1 = {0}; computed x1 = {1}; diff = {2}\".format(x1, x1_, (x1 - x1_)))\n log.warn(\"Info: x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; a = {4}; duration = {5}; xmin = {6}; xmax = {7}; vm = {8}; am = {9}\".format(x0, x1, v0, v1, a, t, xmin, xmax, vm, am))\n return PCR_XDiscrepancy\n if xmin == inf and xmax == inf:\n return PCR_Normal\n bmin, bmax = _GetPeaks(x0, x1, v0, v1, a, t)\n if (bmin < xmin - epsilon) or (bmax > xmax + epsilon):\n log.warn(\"PCR_XBoundViolated: xmin = {0}; bmin = {1}; diff@min = {2}; xmax = {3}; bmax = {4}; diff@max = {5}\".format(xmin, bmin, (xmin - bmin), xmax, bmax, (bmax - xmax)))\n log.warn(\"Info: x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; a = {4}; duration = {5}; xmin = {6}; xmax = {7}; vm = {8}; am = {9}\".format(x0, x1, v0, v1, a, t, xmin, xmax, vm, am))\n return PCR_XBoundViolated\n if (abs(v0) > vm + epsilon) or (abs(v1) > vm + epsilon):\n log.warn(\"PCR_VBoundViolated: vm = {0}; v0 = {1}; v1 = {2}; diff@v0 = {3}; diff@v1 = {4}\".format(vm, v0, v1, (abs(v0) - vm), (abs(v1) - vm)))\n log.warn(\"Info: x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; a = {4}; duration = {5}; xmin = {6}; xmax = {7}; vm = {8}; am = {9}\".format(x0, x1, v0, v1, a, t, xmin, xmax, vm, am))\n return PCR_VBoundViolated\n if abs(a) > am + epsilon:\n log.warn(\"PCR_ABoundViolated: am = {0}; a = {1}; diff = {2}\".format(am, a, (abs(a) - am)))\n log.warn(\"Info: x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; a = {4}; duration = {5}; xmin = {6}; xmax = {7}; vm = {8}; am = {9}\".format(x0, x1, v0, v1, a, t, xmin, xmax, vm, am))\n return PCR_ABoundViolated\n return PCR_Normal\n\n\ndef CheckRamp(ramp, xmin, xmax, vm, am):\n \"\"\"\n Check if the given ramp is consistent with (xmin, xmax, vm, am).\n \n Parameters\n ----------\n ramp : Ramp\n xmin : float\n Displacement lower limit\n xmax : float\n Displacement upper limit\n vm : float\n Maximum velocity\n am : float\n Maximum acceleration\n\n Returns\n -------\n ret : one of ParabolicCheckReturn\n \n \"\"\"\n return CheckSegment(ramp.x0, ramp.x1, ramp.v0, ramp.v1, ramp.a, ramp.duration, xmin, xmax, vm, am)\n\n\ndef CheckParabolicCurve(curve, xmin, xmax, vm, am, x0, x1, v0, v1):\n \"\"\"\n Check if the given parabolic curve is consistent with (xmin, xmax, vm, am, x0, x1, v0, v1).\n \n Parameters\n ----------\n curve : ParabolicCurve\n xmin : float\n Displacement lower limit\n xmax : float\n Displacement upper limit\n vm : float\n Maximum velocity\n am : float\n Maximum acceleration\n x0 : float\n Initial displacement\n x1 : float\n Final displacement\n v0 : float\n Initial velocity\n v1 : float\n Final velocity\n\n Returns\n -------\n ret : one of ParabolicCheckReturn\n \n \"\"\"\n # Check the first ramp\n if not FuzzyEquals(curve[0].x0, x0, epsilon):\n log.warn(\"PCR_XDiscrepancy: curve[0].x0 = {0}; x0 = {1}; diff = {2}\".format(curve[0].x0, x0, curve[0].x0 - x0))\n return PCR_XDiscrepancy\n if not FuzzyEquals(curve[0].v0, v0, epsilon):\n log.warn(\"PCR_VDiscrepancy: curve[0].v0 = {0}; v0 = {1}; diff = {2}\".format(curve[0].v0, v0, curve[0].v0 - v0))\n return PCR_VDiscrepancy\n ret = CheckRamp(curve[0], xmin, xmax, vm, am)\n if not (ret == PCR_Normal):\n log.warn(\"curve[0] does not pass CheckRamp\")\n return ret\n \n for iramp in xrange(1, len(curve) - 1):\n if not FuzzyEquals(curve[iramp - 1].x1, curve[iramp].x0, epsilon):\n log.warn(\"PCR_XDiscrepancy: curve[{0}].x1 != curve[{1}].x0; {2} != {3}; diff = {4}\".format(iramp - 1, iramp, curve[iramp - 1].x1, curve[iramp].x0, curve[iramp - 1].x1 - curve[iramp].x0))\n return PCR_XDiscrepancy\n if not FuzzyEquals(curve[iramp - 1].v1, curve[iramp].v0, epsilon):\n log.warn(\"PCR_VDiscrepancy: curve[{0}].v1 != curve[{1}].v0; {2} != {3}; diff = {4}\".format(iramp - 1, iramp, curve[iramp - 1].v1, curve[iramp].v0, curve[iramp - 1].v1 - curve[iramp].v0))\n return PCR_VDiscrepancy\n ret = CheckRamp(curve[iramp], xmin, xmax, vm, am)\n if not (ret == PCR_Normal):\n log.warn(\"curve[{0}] does not pass CheckRamp\".format(iramp))\n return ret\n\n # Check the last ramp\n if not FuzzyEquals(curve[-1].x1, x1, epsilon):\n log.warn(\"PCR_XDiscrepancy: curve[{0}].x1 = {1}; x1 = {2}; diff = {3}\".format(len(curve) - 1, curve[-1].x0, x0, curve[-1].x1 - x1))\n return PCR_XDiscrepancy\n if not FuzzyEquals(curve[-1].v1, v1, epsilon):\n log.warn(\"PCR_VDiscrepancy: curve[{0}].v1 = {1}; v1 = {2}; diff = {3}\".format(len(curve) - 1, curve[-1].v0, v0, curve[-1].v1 - v1))\n return PCR_VDiscrepancy\n return PCR_Normal\n \n\ndef CheckParabolicCurvesND(curvesnd, xminVect, xmaxVect, vmVect, amVect, x0Vect, x1Vect, v0Vect, v1Vect):\n \"\"\"\n Check if the given paraboliccurvesnd is consistent with the given constraints.\n \n Parameters\n ----------\n curvesnd : ParabolicCurvesND\n xminVect : numpy.ndarray\n Displacement lower limit\n xmaxVect : numpy.ndarray\n Displacement upper limit\n vmVect : numpy.ndarray\n Maximum velocity\n amVect : numpy.ndarray\n Maximum acceleration\n x0Vect : numpy.ndarray\n Initial displacement\n x1Vect : numpy.ndarray\n Final displacement\n v0Vect : numpy.ndarray\n Initial velocity\n v1Vect : numpy.ndarray\n Final velocity\n\n Returns\n -------\n ret : one of ParabolicCheckReturn\n \n \"\"\"\n duration = curvesnd.duration\n for (icurve, (curve, xmin, xmax, vm, am, x0, x1, v0, v1)) in enumerate(zip(curvesnd, xminVect, xmaxVect, vmVect, amVect, x0Vect, x1Vect, v0Vect, v1Vect)):\n if not FuzzyEquals(curve.duration, duration, epsilon):\n log.warn(\"PCR_DurationDiscrepancy: curvesnd.duration = {0}; curvesnd[{1}].duration = {2}; diff = {3}\".format(duration, icurve, curve.duration, duration - curve.duration))\n return PCR_DurationDiscrepancy\n ret = CheckParabolicCurve(curve, xmin, xmax, vm, am, x0, x1, v0, v1)\n if not (ret == PCR_Normal):\n log.warn(\"curvsend[{0}] does not psas CheckParabolicCurve\".format(icurve))\n return ret\n return PCR_Normal\n"},{"alpha_fraction":0.5120373964309692,"alphanum_fraction":0.5256916880607605,"avg_line_length":22.584745407104492,"blob_id":"5a393b85f080ac7ed2809b8e9fd2e80d1f830ddb","content_id":"b1b57a50c636fb3e39fed26e96ef1a86dbac11b3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2783,"license_type":"no_license","max_line_length":116,"num_lines":118,"path":"/parabint/utilities.py","repo_name":"shengjiangtou/parabint","src_encoding":"UTF-8","text":"import numpy as np\nepsilon = 1e-8\ninf = 1e300\n\n\ndef FuzzyEquals(a, b, epsilon):\n \"\"\"Check if the given two numbers are equal, up to discrepancy of the given epsilon.\n Return True if abs(a - b) <= epsilon.\n\n Parameters\n ----------\n a : float\n b : float\n epsilon : float\n\n \"\"\"\n return abs(a - b) <= epsilon\n\n\ndef FuzzyZero(a, epsilon):\n \"\"\"Check if the given number is zero, up to discrepancy of the given epsilon.\n Return True if abs(a) <= epsilon.\n\n Parameters\n ----------\n a : float\n b : float\n epsilon : float\n\n \"\"\"\n return abs(a) <= epsilon\n\n\ndef SolveLinearInEq(a, b, epsilon, xmin, xmax):\n \"\"\"This functions safely solves for x \\in [xmin, xmax] such that |ax - b| <= epsilon*max(|a|, |b|).\n\n Return [result, x]\n \"\"\"\n if (a < 0):\n return SolveLinearInEq(-a, -b, epsilon, xmin, xmax)\n epsilonScaled = epsilon*max(a, abs(b))\n\n if (xmin == -inf) and (xmax == inf):\n if (a == 0):\n x = 0.0\n result = abs(b) <= epsilonScaled\n return [result, x]\n\n x = b/a\n return [True, x]\n\n axmin = a*xmin\n axmax = a*xmax\n if not (b + epsilonScaled >= axmin and b - epsilonScaled <= axmax):\n # Ranges do not intersect\n return [False, 0.0]\n\n if not (a == 0):\n x = b/a\n if (xmin <= x) and (x <= xmax):\n return [True, x]\n\n if abs(0.5*(axmin + axmax) - b) <= epsilonScaled:\n x = 0.5*(xmin + xmax)\n return [True, x]\n\n if abs(axmax - b) <= epsilonScaled:\n x = xmax\n return [True, x]\n\n x = xmin\n return [True, x]\n\n\ndef SolveBoundedInEq(a, b, l, u):\n \"\"\"Solve inequalities of the form\n l <= ax + b <= u.\n \n The function returns [solved, xl, xu] such that ax + b \\in [l, u] for all x \\in [xl, xu] if solved.\n \"\"\"\n if l > u:\n return [False, inf, -inf]\n\n if FuzzyZero(a, epsilon):\n if (b >= l) and (b <= u):\n return [True, l, u]\n else:\n return [False, inf, -inf]\n\n l -= b\n u -= b\n aInv = 1.0/a\n if a > 0:\n return [True, l*aInv, u*aInv]\n else:\n return [True, u*aInv, l*aInv]\n\n\ndef BrakeTime(x, v, xbound):\n [solved, t] = SolveLinearInEq(v, 2*(xbound - x), epsilon, 0, inf)\n if not solved:\n log.debug(\"Cannot solve for braking time from the equation: {0}*t - {1} = 0\".format(v, 2*(xbound - x)))\n t = 0\n return t\n\n\ndef BrakeAccel(x, v, xbound):\n coeff0 = 2*(xbound - x)\n coeff1 = v*v\n [solved, a] = SolveLinearInEq(coeff0, -coeff1, epsilon, -inf, inf)\n if not solved:\n log.debug(\"Cannot solve for braking acceleration from the equation: {0}*a + {1} = 0\".format(coeff0, coeff1))\n a = 0\n return a\n \n\ndef Swap(a, b):\n return [b, a]\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5084723830223083,\n \"alphanum_fraction\": 0.5444117188453674,\n \"avg_line_length\": 33.88398742675781,\n \"blob_id\": \"3211a89e54be7b2548af89458a580c339b114ccc\",\n \"content_id\": \"5db17e6b61ed8e0bbbd985d4477759dafa59e562\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 91710,\n \"license_type\": \"no_license\",\n \"max_line_length\": 152,\n \"num_lines\": 2629,\n \"path\": \"/parabint/interpolator.py\",\n \"repo_name\": \"shengjiangtou/parabint\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nimport bisect\\nfrom .trajectory import Ramp, ParabolicCurve, ParabolicCurvesND\\nfrom .utilities import *\\nfrom .checker import *\\nimport random\\n_rng = random.SystemRandom()\\n\\nimport logging\\nlogging.basicConfig(format='[%(levelname)s] [%(name)s: %(funcName)s] %(message)s',\\n level=logging.DEBUG)\\nlog = logging.getLogger(__name__)\\n\\n_defaultGridRes = 50\\n_gridThreshold = 8 # if there are more than _gridThreshold grid lines, try PLP first\\n_defaultMinSwitch = 8e-3\\n\\n# Math operations\\nSqrt = np.sqrt\\n\\n\\n#\\n# ND Trajectory\\n#\\ndef ComputeZeroVelNDTrajjectory(x0Vect, x1Vect, vmVect, amVect, delta=None):\\n ndof = len(x0Vect)\\n assert(ndof == len(x1Vect))\\n assert(ndof == len(vmVect))\\n assert(ndof == len(amVect))\\n\\n dVect = x1Vect - x0Vect\\n\\n vMin = inf\\n aMin = inf\\n for idof in xrange(ndof):\\n if not FuzzyZero(dVect[idof], epsilon):\\n vMin = min(vMin, vmVect[idof]/dVect[idof])\\n aMin = min(aMin, amVect[idof]/dVect[idof])\\n\\n if not ((vMin < inf) and (aMin < inf)):\\n curvesnd = ParabolicCurvesND()\\n curvesnd.SetConstant(x0Vect, 0.0)\\n return curvesnd\\n\\n if delta is None:\\n sdProfile = _Compute1DTrajectoryWithoutDelta(0.0, 1.0, 0.0, 0.0, vMin, aMin)\\n else:\\n sdProfile = ComputeZeroVel1DTrajectoryWithDelta(0.0, 1.0, vMin, aMin, delta)\\n\\n\\n curves = [ParabolicCurve() for _ in xrange(ndof)]\\n for sdRamp in sdProfile:\\n aVect = sdRamp.a * dVect\\n v0Vect = sdRamp.v0 * dVect\\n dur = sdRamp.duration\\n\\n for idof in xrange(ndof):\\n ramp = Ramp(v0Vect[idof], aVect[idof], dur)\\n curve = ParabolicCurve([ramp])\\n curves[idof].Append(curve)\\n\\n for (i, curve) in enumerate(curves):\\n curve.SetInitialValue(x0Vect[i])\\n curvesnd = ParabolicCurvesND(curves)\\n\\n # Check before returning\\n return curvesnd\\n\\n\\ndef ComputeArbitraryVelNDTrajectory(x0Vect, x1Vect, v0Vect, v1Vect, xminVect, xmaxVect,\\n vmVect, amVect, delta=None, tryHarder=False):\\n ndof = len(x0Vect)\\n assert(ndof == len(x1Vect))\\n assert(ndof == len(v0Vect))\\n assert(ndof == len(v1Vect))\\n assert(ndof == len(xminVect))\\n assert(ndof == len(xmaxVect))\\n assert(ndof == len(vmVect))\\n assert(ndof == len(amVect))\\n\\n dVect = x1Vect - x0Vect\\n\\n curves = []\\n maxDuration = 0.0\\n maxIndex = 0\\n for idof in xrange(ndof):\\n if delta is None:\\n curve = _Compute1DTrajectoryWithoutDelta(x0Vect[idof], x1Vect[idof],\\n v0Vect[idof], v1Vect[idof],\\n vmVect[idof], amVect[idof])\\n else:\\n curve = _Compute1DTrajectoryWithDelta(x0Vect[idof], x1Vect[idof],\\n v0Vect[idof], v1Vect[idof],\\n vmVect[idof], amVect[idof], delta)\\n curves.append(curve)\\n if curve.duration > maxDuration:\\n maxDuration = curve.duration\\n maxIndex = idof\\n\\n log.debug(\\\"maxIndex = {0}\\\".format(maxIndex))\\n if delta == 0.0:\\n stretchedCurves = _RecomputeNDTrajectoryFixedDuration(curves, vmVect, amVect,\\n maxIndex, tryHarder)\\n else:\\n stretchedCurves = _RecomputeNDTrajectoryFixedDurationWithDelta(curves, vmVect, amVect,\\n maxIndex, delta, tryHarder)\\n\\n if len(stretchedCurves) == 0:\\n return ParabolicCurvesND()\\n \\n newCurves = []\\n for (i, curve) in enumerate(stretchedCurves):\\n newCurve = ImposeJointLimitFixedDuration(curve, xminVect[i], xmaxVect[i],\\n vmVect[i], amVect[i], delta=0.0)\\n if newCurve.IsEmpty():\\n return ParabolicCurvesND()\\n newCurves.append(newCurve)\\n\\n # Check before returning\\n return ParabolicCurvesND(newCurves)\\n\\n\\ndef _RecomputeNDTrajectoryFixedDuration(curves, vmVect, amVect, maxIndex, tryHarder=False):\\n ndof = len(curves)\\n assert(ndof == len(vmVect))\\n assert(ndof == len(amVect))\\n assert(maxIndex < ndof)\\n\\n newDuration = curve[maxIndex].duration\\n isPrevDurationSafe = True\\n if (tryHarder):\\n for idof in xrange(ndof):\\n tBound = CalculateLeastUpperBoundInoperavtiveTimeInterval\\n (curves[idof].x0, curves[idof].x1, curves[idof].v0, curves[idof].v1,\\n vmVect[idof], amVect[idof])\\n if tBound > newDuration:\\n newDuration = tBound\\n isPrevDurationSafe = False\\n\\n newCurves = []\\n for idof in xrange(ndof):\\n if (isPrevDurationSafe and idof == maxIndex):\\n log.debug(\\\"joint {0} is already the slowest DOF, continue to the next DOF (if any)\\\".format(idof))\\n continue\\n\\n stretchedCurve = _Stretch1DTrajectory(curve, newDuration, vmVect[idof], amVect[idof])\\n if stretchedCurve.IsEmpty():\\n log.debug()\\n return []\\n newCurves.append(stretchedCurve)\\n\\n assert(len(newCurves) == ndof)\\n return newCurves\\n\\n\\ndef ComputeNDTrajectoryFixedDuration(x0Vect, x1Vect, v0Vect, v1Vect, duration,\\n xminVect, xmaxVect, vmVect, amVect):\\n assert(duration > 0)\\n\\n ndof = len(x0Vect)\\n assert(ndof == len(x1Vect))\\n assert(ndof == len(v0Vect))\\n assert(ndof == len(v1Vect))\\n assert(ndof == len(xminVect))\\n assert(ndof == len(xmaxVect))\\n assert(ndof == len(vmVect))\\n assert(ndof == len(amVect))\\n\\n curves = []\\n for idof in xrange(ndof):\\n curve = Compute1DTrajectoryFixedDuration(x0Vect[idof], x1Vect[idof],\\n v0Vect[idof], v1Vect[idof],\\n duration, vmVect[idof], amVect[idof])\\n if curve.IsEmpty():\\n return ParabolicCurvesND()\\n\\n newCurve = ImposeJointLimitFixedDuration(curve, xminVect[idof], xmaxVect[idof],\\n vmVect[idof], amVect[idof])\\n if newCurve.IsEmpty():\\n return ParabolicCurvesND()\\n\\n curves.append(newCurve)\\n\\n # Check before returning\\n return ParabolicCurvesND(curves)\\n\\n\\n#\\n# 1D Trajectory\\n#\\ndef Compute1DTrajectory(x0, x1, v0, v1, vm, am, delta=None):\\n if delta is None:\\n return _Compute1DTrajectoryWithoutDelta(x0, x1, v0, v1, vm, am)\\n assert(delta > 0)\\n return _Compute1DTrajectoryWithDelta(x0, x1, v0, v1, vm, am, delta)\\n\\n\\ndef _Compute1DTrajectoryWithoutDelta(x0, x1, v0, v1, vm, am):\\n d = x1 - x0\\n dv = v1 - v0\\n v0Sqr = v0*v0\\n v1Sqr = v1*v1\\n dvSqr = v1Sqr - v0Sqr\\n\\n # Calculate the displacement caused when maximally accelerate/decelerate from v0 to v1\\n if (dv == 0):\\n if (d == 0):\\n ramp = Ramp(0, 0, 0, x0)\\n return ParabolicCurve([ramp])\\n else:\\n dStraight = 0.0\\n elif (dv > 0):\\n dStraight = 0.5*dvSqr/am\\n else:\\n dStraight = -0.5*dvSqr/am\\n\\n if FuzzyEquals(d, dStraight, epsilon):\\n # v1 can be reached from v0 by the acceleration am or -am\\n a = am if dv > 0 else -am\\n ramp = Ramp(x0, a, dv/a, x0)\\n return ParabolicCurve([ramp])\\n\\n\\n sumVSqr = v0Sqr + v1Sqr\\n noViolation = True\\n if (d > dStraight):\\n # The acceleration of the first ramp is positive\\n a0 = am\\n vp = Sqrt(0.5*sumVSqr + a0*d)\\n if (vp > vm + epsilon):\\n noViolation = False\\n else:\\n # The acceleration of the first ramp is negative\\n a0 = -am\\n vp = -Sqrt(0.5*sumVSqr + a0*d)\\n if (-vp > vm + epsilon):\\n noViolation = False\\n\\n a0Inv = 1.0/a0\\n if noViolation:\\n ramp0 = Ramp(v0, a0, (vp - v0)*a0Inv, x0)\\n ramp1 = Ramp(ramp0.v1, -a0, (vp - v1)*a0Inv)\\n curve = ParabolicCurve([ramp0, ramp1])\\n else:\\n ramps = []\\n h = abs(vp) - vm\\n t = h*abs(a0Inv)\\n if not FuzzyEquals(abs(v0), vm, epsilon):\\n ramp0 = Ramp(v0, a0, (vp - v0)*a0Inv - t, x0)\\n ramps.append(ramp0)\\n nom = h*h\\n denom = abs(a0)*vm\\n newVp = vm if vp > 0 else -vm\\n ramp1 = Ramp(newVp, 0.0, 2*t + nom/denom)\\n ramps.append(ramp1)\\n if not FuzzyEquals(abs(v1), vm, epsilon):\\n ramp2 = Ramp(newVp, -a0, (vp - v1)*a0Inv - t)\\n ramps.append(ramp2)\\n curve = ParabolicCurve(ramps)\\n\\n # Check before returning\\n return curve\\n\\n\\ndef ImposeJointLimitFixedDuration(curve, xmin, xmax, vm, am, delta):\\n [bmin, bmax] = curve.GetPeaks()\\n if (bmin >= xmin - epsilon) and (bmax <= xmax + epsilon):\\n log.debug(\\\"Input curve does not violate joint limits\\\")\\n return curve\\n\\n duration = curve.duration\\n x0 = curve.x0\\n x1 = curve.EvalPos(duration)\\n v0 = curve.v0\\n v1 = curve.v1\\n\\n bt0 = inf\\n bt1 = inf\\n ba0 = inf\\n ba1 = inf\\n bx0 = inf\\n bx1 = inf\\n if (v0 > zero):\\n bt0 = BrakeTime(x0, v0, xmax)\\n bx0 = xmax\\n ba0 = BrakeAccel(x0, v0, xmax)\\n elif (v0 < zero):\\n bt0 = BrakeTime(x0, v0, xmin)\\n bx0 = xmin\\n ba0 = BrakeAccel(x0, v0, xmin)\\n\\n if (v1 < zero):\\n bt1 = BrakeTime(x1, -v1, xmax)\\n bx1 = xmax\\n ba1 = BrakeAccel(x1, -v1, xmax)\\n elif (v1 > zero):\\n bt1 = BrakeTime(x1, -v1, xmin)\\n bx1 = xmin\\n ba1 = BrakeAccel(x1, -v1, xmin)\\n\\n newCurve = ParabolicCurve()\\n if (bt0 < duration) and (abs(ba0) <= am + epsilon):\\n # Case IIa\\n log.debug(\\\"Case IIa\\\")\\n firstRamp = Ramp(v0, ba0, bt0, x0)\\n if (abs(x1 - bx0) < (duration - bt0)*vm):\\n tempCurve1 = Interpolate1D(bx0, x1, 0.0, v1, vm, am)\\n if not tempCurve1.IsEmpty():\\n if (duration - bt0 > tempCurve1.duration):\\n tempCurve2 = _Stretch1DTrajectory(tempCurve1, duration - bt0, vm, am)\\n if not tempCurve2.IsEmpty():\\n tempbmin, tempbmax = tempCurve2.GetPeaks()\\n if not ((tempbmin < xmin - epsilon) or (tempbmax > xmax + epsilon)):\\n log.debug(\\\"Case IIa successful\\\")\\n newCurve = ParabolicCurve([firstRamp] + tempCurve2.ramps)\\n \\n\\n if (bt1 < duration) and (abs(ba1) <= am + epsilon):\\n # Case IIb\\n log.debug(\\\"Case IIb\\\")\\n lastRamp = Ramp(0, ba1, bt1, bx1)\\n if (abs(x0 - bx1) < (duration - bt1)*vm):\\n tempCurve1 = Interpolate1D(x0, bx1, v0, 0.0, vm, am)\\n if not tempCurve1.IsEmpty():\\n if (duration - bt1 >= tempCurve1.duration):\\n tempCurve2 = _Stretch1DTrajectory(tempCurve1, duration - bt1, vm, am)\\n if not tempCurve2.IsEmpty():\\n tempbmin, tempbmax = tempCurve2.GetPeaks()\\n if not ((tempbmin < xmin - epsilon) or (tempbmax > xmax + epsilon)):\\n log.debug(\\\"Case IIb successful\\\")\\n newCurve = ParabolicCurve(tempCurve2.ramps + [lastRamp]) \\n \\n\\n if (bx0 == bx1):\\n # Case III\\n if (bt0 + bt1 < duration) and (max(abs(ba0), abs(ba1)) <= am + epsilon):\\n log.debug(\\\"Case III\\\")\\n ramp0 = Ramp(v0, ba0, bt0, x0)\\n ramp1 = Ramp(0.0, 0.0, duration - (bt0 + bt1))\\n ramp2 = Ramp(0.0, ba1, bt1)\\n newCurve = ParabolicCurve([ramp0, ramp1, ramp2])\\n else:\\n # Case IV\\n if (bt0 + bt1 < duration) and (max(abs(ba0), abs(ba1)) <= am + epsilon):\\n log.debug(\\\"Case IV\\\")\\n firstRamp = Ramp(v0, ba0, bt0, x0)\\n lastRamp = Ramp(0.0, ba1, bt1)\\n if (abs(bx0 - bx1) < (duration - (bt0 + bt1))*vm):\\n tempCurve1 = Interpolate1D(bx0, bx1, 0.0, 0.0, vm, am)\\n if not tempCurve1.IsEmpty():\\n if (duration - (bt0 + bt1) >= tempCurve1.duration):\\n tempCurve2 = _Stretch1DTrajectory(tempCurve1, duration - (bt0 + bt1), vm, am)\\n if not tempCurve2.IsEmpty():\\n tempbmin, tempbmax = tempCurve2.GetPeaks()\\n if not ((tempbmin < xmin - epsilon) or (tempbmax > xmax + epsilon)):\\n log.debug(\\\"Case IV successful\\\")\\n newCurve = ParabolicCurve([firstRamp] + tempCurve2.ramps + [lastRamp])\\n \\n\\n if (newCurve.isEmpty):\\n log.warn(\\\"Cannot solve for a bounded trajectory\\\")\\n log.warn(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; xmin = {4}; xmax = {5}; vm = {6}; am = {7}; duration = {8}\\\".\\\\\\n format(x0, x1, v0, v1, xmin, xmax, vm, am, duration))\\n return newCurve\\n\\n newbmin, newbmax = newCurve.GetPeaks()\\n if (newbmin < xmin + epsilon) or (newbmax > xmax + epsilon):\\n log.warn(\\\"Solving finished but the trajectory still violates the bounds\\\")\\n log.warn(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; xmin = {4}; xmax = {5}; vm = {6}; am = {7}; duration = {8}\\\".\\\\\\n format(x0, x1, v0, v1, xmin, xmax, vm, am, duration))\\n return ParabolicCurve()\\n\\n if CheckParabolicCurve(curve, xmin, xmax, vm, am, x0, x1, v0, v1) == PCR_Normal:\\n log.debug(\\\"Successfully fixed x-bound violation\\\")\\n return newCurve\\n else:\\n log.warn(\\\"Cannot fix x-bound violation\\\")\\n log.warn(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; xmin = {4}; xmax = {5}; vm = {6}; am = {7}; duration = {8}\\\".\\\\\\n format(x0, x1, v0, v1, xmin, xmax, vm, am, duration))\\n return ParabolicCurve()\\n \\n \\ndef _Stretch1DTrajectory(curve, vm, am, duration):\\n return Compute1DTrajectoryFixedDuration(curve.x0, curve.x1, curve.v0, curve.v1, vm, am, duration)\\n\\n\\ndef Compute1DTrajectoryFixedDuration(x0, x1, v0, v1, vm, am, duration):\\n \\\"\\\"\\\"We want to 'stretch' this velocity profile to have a new duration of\\n endTime. First, try re-interpolating this profile to have two ramps. If that\\n doesn't work, try modifying the profile accordingly.\\n\\n Two-ramp case: let t = endTime (the new duration that we want), a0 and a1\\n the new accelerations of the profile, t0 the duration of the new first ramp.\\n\\n Starting from\\n\\n d = (v0*t0 + 0.5*a0*(t0*t0)) + ((v0 + a0*t0)*t1 + 0.5*a1*(t1*t1)),\\n\\n where d is the displacement done by this trajectory, t1 = duration - t0,\\n i.e., the duration of the second ramp. Then we can write a0 and a1 in terms\\n of t0 as\\n\\n a0 = A + B/t0\\n a1 = A - B/t1,\\n\\n where A = (v1 - v0)/t and B = (2d/t) - (v0 + v1). We want to get the\\n velocity profile which has minimal acceleration: set the minimization\\n objective to\\n\\n J(t0) = a0*a0 + a1*a1.\\n\\n We start by calculating feasible ranges of t0 due to various constraints.\\n\\n 1) Acceleration constraints for the first ramp:\\n\\n -amax <= a0 <= amax.\\n\\n From this, we have\\n\\n -amax - A <= B/t0 --- I)\\n B/t0 >= amax - A. --- II)\\n\\n Let sum1 = -amax - A and sum2 = amax - A. We can obtain the feasible ranges\\n of t0 accordingly.\\n\\n 2) Acceleration constraints for the second ramp:\\n\\n -amax <= a1 <= amax.\\n\\n From this, we have\\n\\n -amax - A <= -B/(t - t0) --- III)\\n -B/(t - t0) <= amax - A. --- IV)\\n\\n As before, the feasible ranges of t0 can be computed accordingly.\\n\\n We will obtain an interval iX for each constraint X. Since t0 needs to\\n satisfy all the four constraints plus the initial feasible range [0,\\n endTime], we will obtain only one single feasible range for t0. (Proof\\n sketch: intersection operation is associative and intersection of two\\n intervals gives either an interval or an empty set.)\\n\\n \\\"\\\"\\\"\\n if (duration < -epsilon):\\n return ParabolicCurve()\\n\\n if (duration <= epsilon):\\n if FuzzyEquals(x0, x1, epsilon) and FuzzyEquals(v0, v1, epsilon):\\n ramp0 = Ramp(v0, 0, 0, x0)\\n curve = ParabolicCurve([ramp0])\\n # Check before returning\\n return curve\\n else:\\n log.info(\\\"newDuration is too short for any movement to be made\\\")\\n return ParabolicCurve()\\n\\n # Correct small discrepancies if any\\n if (v0 > vm):\\n if FuzzyEquals(v0, vm, epsilon):\\n v0 = vm\\n else:\\n log.info(\\\"v0 > vm: {0} > {1}\\\".format(v0, vm))\\n return ParabolicCurve()\\n elif (v0 < -vm):\\n if FuzzyEquals(v0, -vm, epsilon):\\n v0 = -vm\\n else:\\n log.info(\\\"v0 < -vm: {0} < {1}\\\".format(v0, -vm))\\n return ParabolicCurve()\\n if (v1 > vm):\\n if FuzzyEquals(v1, vm, epsilon):\\n v1 = vm\\n else:\\n log.info(\\\"v1 > vm: {0} > {1}\\\".format(v1, vm))\\n return ParabolicCurve()\\n elif (v1 < -vm):\\n if FuzzyEquals(v1, -vm, epsilon):\\n v1 = -vm\\n else:\\n log.info(\\\"v1 < -vm: {0} < {1}\\\".format(v1, -vm))\\n return ParabolicCurve()\\n\\n d = x1 - x0\\n durInverse = 1.0/duration\\n A = (v1 - v0)*durInverse\\n B = (2*d*durInverse) - (v0 + v1)\\n\\n # A velocity profile having t = duration connecting (x0, v0) and (x1, v1)\\n # will have one ramp iff \\n # x1 - x0 = dStraight\\n # d = 0.5*(v0 + v1)*duration\\n # The above equation is actually equivalent to\\n # B = 0.\\n # Therefore, if B = 0 we can just interpolate the trajectory right away and return early.\\n if FuzzyZero(B, epsilon):\\n # giving priority to displacement and consistency between acceleration and\\n # displacement\\n a = 2*(x1 - x0 - v0*duration)*durInverse*durInverse; \\n ramp0 = Ramp(v0, a, duration, x0)\\n curve = ParabolicCurve([ramp0])\\n # Check before returning\\n return curve\\n\\n\\n sum1 = -am - A\\n sum2 = am - A\\n C = B/sum1\\n D = B/sum2\\n\\n # Now we need to check a number of feasible intervals of tswitch1 induced by\\n # constraints on the acceleration. Instead of having a class representing an\\n # interval, we use the interval bounds directly. Naming convention: iXl =\\n # lower bound of interval X, iXu = upper bound of interval X.\\n i0l = 0\\n i0u = duration\\n i1l = -inf\\n i1u = inf\\n i2l = -inf\\n i2u = inf\\n i3l = -inf\\n i3u = inf\\n i4l = -inf\\n i4u = inf\\n import IPython; IPython.embed()\\n if (sum1 == 0):\\n if (B == 0):\\n # t0 can be anything\\n pass\\n else:\\n i1l = inf\\n elif (sum1 > 0):\\n log.debug(\\\"sum1 > 0. This implies that duration is too short\\\")\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n else:\\n i1l = C\\n\\n if (sum2 == 0):\\n if (B == 0):\\n pass\\n else:\\n i2l = inf\\n elif (sum2 > 0):\\n i2l = D\\n else:\\n log.debug(\\\"sum2 > 0. This implies that duration is too short\\\")\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n\\n if (i1l > i2u) or (i1u < i2l):\\n log.debug(\\\"Interval 1 and interval 2 do not have any intersection\\\")\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n else:\\n i2l = max(i1l, i2l)\\n i2u = min(i1u, i2u)\\n\\n if (sum1 == 0):\\n if (B == 0):\\n # t0 can be anything\\n pass\\n else:\\n i3l = inf\\n elif (sum1 > 0):\\n log.debug(\\\"sum1 > 0. This implies that duration is too short\\\")\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n else:\\n i3u = duration + C\\n\\n if (sum2 == 0):\\n if (B == 0):\\n pass\\n else:\\n i4l = inf\\n elif (sum2 > 0):\\n i4u = duration + D\\n else:\\n log.debug(\\\"sum2 > 0. This implies that duration is too short\\\")\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n\\n if (i3l > i4u) or (i3u < i4l):\\n log.debug(\\\"Interval 3 and interval 4 do not have any intersection\\\")\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n else:\\n i4l = max(i3l, i4l);\\n i4u = min(i3u, i4u);\\n\\n if FuzzyEquals(i2l, i4u, epsilon) or FuzzyEquals(i2u, i4l, epsilon):\\n log.debug(\\\"Interval 2 and interval 4 intersect at a point, most likely because the given duration is actually its minimum time\\\")\\n curve = _Compute1DTrajectoryWithoutDelta(x0, x1, v0, v1, vm, am)\\n if curve.IsEmpty():\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return curve\\n else:\\n if FuzzyEquals(curve.duration, duration, epsilon):\\n return curve\\n else:\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n elif (i2l > i4u) or (i2u < i4l):\\n log.debug(\\\"Interval 2 and interval 4 do not have any intersection\\\")\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n else:\\n i4l = max(i2l, i4l)\\n i4u = min(i2u, i4u)\\n\\n if (i0l > i4u) or (i0u < i4l):\\n log.debug(\\\"Interval 0 and interval 4 do not have any intersection\\\")\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n else:\\n i4l = max(i0l, i4l)\\n i4u = min(i0u, i4u)\\n\\n # Now we have already obtained a range of feasible values for t0 (the\\n # duration of the first ramp). We choose a value of t0 by selecting the one\\n # which minimize J(t0) := (a0^2 + a1^2). \\n # Let x = t0 for convenience. We can write J(x) as\\n # J(x) = (A + B/x)^2 + (A - B/(t - x))^2.\\n # Then we find x which minimizes J(x) by examining the roots of dJ/dx.\\n [solved, t0] = SolveForT0(A, B, duration, i4l, i4u)\\n if not solved:\\n # Solving dJ/dx = 0 failed. We just choose the midpoint of the feasible interval\\n t0 = 0.5*(i4l + i4u)\\n\\n t1 = duration - t0\\n if (t0 == 0) or (t1 == 0):\\n ramp0 = Ramp(v0, A, duration, x0)\\n curve = ParabolicCurve([ramp0])\\n # Check before returning\\n return curve\\n\\n a0 = A + B/t0\\n a1 = A - B/t1\\n vp = v0 + a0*t0\\n\\n # Consistency checking\\n if not FuzzyEquals(vp, v1 - a1*t1, epsilon):\\n log.warn(\\\"Verification failed.\\\")\\n log.warn(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n\\n # Check velocity bound\\n if (abs(vp) <= vm + epsilon):\\n # The two-ramp profile works.\\n ramp0 = Ramp(v0, a0, t0, x0)\\n ramp1 = Ramp(vp, a1, t1)\\n curve = ParabolicCurve([ramp0, ramp1])\\n # Check before returning\\n return curve\\n \\n else:\\n vmNew = vm if vp > 0 else -vm\\n\\n if FuzzyZero(a0, epsilon) or FuzzyZero(a1, epsilon):\\n log.warn(\\\"Velocity limit is violated but at least one acceleration is zero: a0 = {0}, a1 = {1}\\\".format(a0, a1))\\n log.warn(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n\\n a0inv = 1.0/a0\\n a1inv = 1.0/a1\\n\\n dv1 = vp - vmNew\\n dv2 = vmNew - v0\\n dv3 = vmNew - v1\\n t0Trimmed = dv2*a0inv # from vmaxNew = dx0 + a0*t0Trimmed\\n t1Trimmed = -dv3*a1inv # from dx1 = vmaxNew + a1*t1Trimmed\\n\\n \\\"\\\"\\\"Idea: we cut the excessive area above the velocity limit and paste that on\\n both sides of the velocity profile. We do not divide the area and paste\\n it equally on both sides. Instead, we try to minimize the sum of the new\\n accelerations squared:\\n\\n minimize a0New^2 + a1New^2.\\n\\n Let D2 be the area of the velocity profile above the velocity limit. We have\\n\\n D2 = 0.5*dt1*dv2 + 0.5*dt2*dv3.\\n\\n Using the relations\\n\\n a0New = dv2/(t0Trimmed - dt1) and\\n a1New = -dv3/(t1Trimmed - dt2)\\n\\n we finally arrive at the equation\\n\\n A2/a0New + B2/a1New = C2,\\n\\n where A2 = dv2^2, B2 = -dv3^2, and C2 = t0Trimmed*dv2 + t1Trimmed*dv3 - 2*D2.\\n\\n Let x = a0New and y = a1New for convenience, we can formulate the problem as\\n\\n minimize(x, y) x^2 + y^2\\n subject to A2/x + B2/y = C2.\\n\\n From the above problem, we can see that the objective function is\\n actually a circle while the constraint function is a hyperbola. (The\\n hyperbola is centered at (A2/C2, B2/C2)). Therefore, the minimizer is\\n the point where both curves touch.\\n\\n Let p = (x0, y0) be the point that the two curves touch. Then\\n\\n (slope of the hyperbola at p)*(y0/x0) = -1,\\n\\n i.e., the tangent line of the hyperbola at p and the line connecting the origin and p are\\n perpendicular. Solving the above equation gives us\\n\\n x0 = (A2 + (A2*B2*B2)^(1/3))/C2.\\n\\n \\\"\\\"\\\"\\n A2 = dv2*dv2\\n B2 = -dv3*dv3\\n D2 = 0.5*dv1*(duration - t0Trimmed - t1Trimmed) # area of the velocity profile above the velocity limit.\\n C2 = t0Trimmed*dv2 + t1Trimmed*dv3 - 2*D2\\n root = (A2*B2*B2)**(1./3.)\\n\\n if FuzzyZero(C2, epsilon):\\n # This means the excessive area is too large such that after we\\n # paste it on both sides of the original velocity profile, the whole\\n # profile becomes one-ramp with a = 0 and v = vmNew.\\n log.debug(\\\"C2 == 0. Unable to fix this case.\\\")\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n\\n C2inv = 1.0/C2\\n a0 = (A2 + root)*C2inv\\n if abs(a0) > am:\\n if FuzzyZero(root, epsilon*epsilon):\\n # The computed a0 is exceeding the bound and its corresponding\\n # a1 is zero. Therefore, we cannot fix this case. This is\\n # probably because the given duration is actually less than the\\n # minimum duration that it can get.\\n log.debug(\\\"|a0| > am and a1 == 0: Unable to fix this case since the given duration is too short.\\\")\\n return ParabolicCurve()\\n\\n a0 = am if a0 > 0 else -am\\n # Recalculate the related variable\\n root = C2*a0 - A2\\n\\n # Now compute a1\\n # Special case: a0 == 0. Then this implies vm == dx0. Reevaluate those above equations\\n # leads to a1 = B2/C2\\n if abs(a0) <= epsilon:\\n a0 = 0;\\n a1 = B2/C2;\\n if (abs(a1) > am + epsilon):\\n # The computed a1 is exceeding the bound while a0 being zero. This is similar to\\n # the case above when |a0| > am and a1 == 0.\\n log.debug(\\\"a0 == 0 and |a1| > am: Unable to fix this case since the given duration is too short.\\\")\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n # Recalculate the related variable\\n root = C2*a0 - A2;\\n \\n else:\\n # From the hyperbola equation, we have y = B2*x/(C2*x - A2) = B2*x/root\\n if FuzzyZero(root, epsilon*epsilon):\\n # Special case: a1 == 0. This implies vm == dx1. If we calculate back the value of a0,\\n # we will get a0 = A2/C2 which is actually root = 0.\\n a1 = 0\\n a0 = A2/C2\\n else:\\n a1 = B2*a0/root\\n if abs(a1) > am:\\n # a1 exceeds the bound, try making it stays at the bound.\\n a1 = am if a1 > 0 else -am\\n # Recalculate the related variable\\n if (C2*a1 - B2 == 0):\\n # this case means a0 == 0 which shuold have been catched from above\\n log.debug(\\\"(C2*a1 - B2 == 0) a0 shuold have been zero but a0 = %.15e\\\", a0)\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n a0 = A2*a1/(C2*a1 - B2)\\n\\n # Final check on the accelerations\\n if (abs(a0) > am + epsilon) or (abs(a1) > am + epsilon):\\n log.debug(\\\"Cannot fix acceleration bound violation\\\")\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n\\n if FuzzyZero(a0, epsilon) and FuzzyZero(a1, epsilon):\\n log.debug(\\\"Both accelerations are zero\\\")\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n\\n if FuzzyZero(a0, epsilon):\\n t0 = duration + dv3/a1\\n t1 = duration - t0\\n vp = vmNew\\n\\n ramp0 = Ramp(v0, a0, t0, x0)\\n ramp1 = Ramp(vp, a1, t1)\\n curve = ParabolicCurve([ramp0, ramp1])\\n elif FuzzyZero(a1, epsilon):\\n t0 = dv2/a0\\n t1 = duration - t0\\n vp = vmNew\\n\\n ramp0 = Ramp(v0, a0, t0, x0)\\n ramp1 = Ramp(vp, a1, t1)\\n curve = ParabolicCurve([ramp0, ramp1])\\n else:\\n t0 = dv2/a0\\n if (t0 < 0):\\n log.debug(\\\"t0 < 0. The given duration is not achievable with the given bounds\\\")\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n\\n vp = vmNew\\n tLastRamp = -dv3/a1\\n if (tLastRamp < 0):\\n log.debug(\\\"tLastRamp < 0. The given duration is not achievable with the given bounds\\\");\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n\\n if (t0 + tLastRamp > duration):\\n # Final fix\\n if (A == 0):\\n log.debug(\\\"(Final fix) A == 0. Cannot fix this case\\\")\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n t0 = (dv2 - B)/A\\n if (t0 < 0):\\n log.debug(\\\"(Final fix) t0 < 0. Cannot fix this case\\\")\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n\\n t1 = duration - t0\\n a0 = A + (B/t0)\\n a1 = A - (B/t1)\\n\\n ramp0 = Ramp(v0, a0, t0, x0)\\n ramp1 = Ramp(vp, a1, t1)\\n curve = ParabolicCurve([ramp0, ramp1])\\n else:\\n tMiddle = duration - (t0 + tLastRamp)\\n if FuzzyZero(tMiddle, epsilon):\\n # The middle ramp is too short. If we leave it like this, it may cause errors later on.\\n t0 = (2*d - (v1 + vmNew)*duration)/(v0 - v1)\\n t1 = duration - t0\\n vp = vmNew\\n a0 = dv2/t0\\n a1 = -dv3/t1\\n if (abs(a0) > am + epsilon) or (abs(a1) > am + epsilon):\\n log.debug(\\\"Cannot merge into two-ramp because of acceleration limits\\\");\\n log.debug(\\\"x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; vm = {4}; am = {5}; duration = {6}\\\".format(x0, x1, v0, v1, vm, am, duration))\\n return ParabolicCurve()\\n\\n ramp0 = Ramp(v0, a0, t0, x0);\\n ramp1 = Ramp(vp, a1, t1);\\n curve = ParabolicCurve([ramp0, ramp1])\\n else:\\n # Three-ramp profile really works now\\n ramp0 = Ramp(v0, a0, t0, x0)\\n ramp1 = Ramp(vp, 0, tMiddle)\\n ramp2 = Ramp(vp, a1, tLastRamp)\\n curve = ParabolicCurve([ramp0, ramp1, ramp2])\\n\\n # Check before returning\\n return curve\\n \\n\\n#\\n# Utilities\\n#\\ndef CalculateLeastUpperBoundInoperavtiveTimeInterval(x0, x1, v0, v1, vm, am):\\n \\\"\\\"\\\"Let t be the total duration of the velocity profile, a0 and a1 be the\\n accelerations of both ramps. We write, in the way that has already been\\n described in SolveMinAccel, a0 and a1 in terms of t0 as\\n\\n a0 = A + B/t0 and\\n a1 = A - B/(t - t0).\\n\\n Imposing the acceleration bounds, we have the following inequalities:\\n\\n from -am <= a0 <= am, we have\\n\\n t0*sum1 <= B\\n B <= sum2*t0\\n\\n from -am <= a1 <= am, we have\\n\\n (t - t0)*sum1 <= -B\\n -B <= sum2*(t - t0),\\n\\n where sum1 = -am - A, sum2 = am - A.\\n\\n From those inequalities, we can deduce that a feasible value of t0 must fall\\n in the intersection of\\n\\n [B/sum1, t + B/sum1] and\\n [B/sum2, t + B/sum2].\\n\\n Therefore, the total duration t must satisfy\\n\\n t >= B*(1/sum2 - 1/sum1) and\\n t >= B*(1/sum1 - 1/sum2).\\n\\n By substituting A = (v1 - v0)/t and B = 2*d/t - (v0 + v1) into the above\\n inequalities, we have\\n\\n t >= (2*am*((2*d)/t - (v0 + v1)))/(am*am - ((v1 - v0)/t)**2) and\\n t >= -(2*am*((2*d)/t - (v0 + v1)))/(am*am - ((v1 - v0)/t)**2),\\n\\n (the inequalities are derived using Sympy). Finally, we have two solutions\\n (for the total time) from each inequality. Then we select the maximum one.\\n\\n Important note: position limits are not taken into account here. The\\n calculated upper bound may be invalidated because of position constraints.\\n\\n \\\"\\\"\\\"\\n d = x1 - x0\\n\\n amInv = 1.0/am\\n firstTerm = (v0 + v1)*amInv\\n\\n temp1 = 2*(-am*am)*(2*am*d - v0*v0 - v1*v1)\\n secondTerm1 = Sqrt(temp1)*amInv*amInv\\n if (temp1 < 0):\\n T0 = -1\\n T1 = -1\\n\\n else:\\n T0 = firstTerm + secondTerm1\\n T1 = firstTerm - secondTerm1\\n\\n T1 = max(T0, T1)\\n\\n temp2 = 2*(am*am)*(2*am*d + v0*v0 + v1*v1)\\n secondTerm2 = Sqrt(temp2)*amInv*amInv\\n if (temp2 < 0):\\n T2 = -1\\n T3 = -1\\n\\n else:\\n T2 = -firstTerm + secondTerm2\\n T3 = -firstTerm - secondTerm2\\n\\n T3 = max(T2, T3)\\n\\n t = max(T1, T3)\\n \\n if (t > epsilon):\\n # dStraight is the displacement produced if we were to travel with only\\n # one acceleration from v0 to v1 in t. It is used to determine which\\n # direction we should aceelerate first (posititve or negative\\n # acceleration).\\n dStraight = 0.5*(v0 + v1)*t\\n amNew = am if d - dStraight > 0 else -am\\n vmNew = vm if d - dStraight > 0 else -vm\\n\\n vp = 0.5*(amNew*t + v0 + v1) # the peak velocity\\n if (Abs(vp) > vm):\\n dExcess = (vp - vmNew)*(vp - vmNew)*amInv\\n deltaTime = dExcess/vm\\n t += deltaTime # the time increased from correcting the velocity bound violation\\n \\n # Should be no problem now.\\n t = t * 1.01 # for safety reasons, we don't make t too close to the bound\\n return [True, t]\\n\\n else:\\n if FuzzyEquals(x1, x0, epsilon) and FuzzyZero(v0, epsilon) and FuzzyZero(v1, epsilon):\\n t = 0\\n return [True, t]\\n else:\\n log.debug(\\\"Unable to calculate the least upper bound: T0 = {0}; T1 = {1}; T2 = {2}; T3 = {3}\\\".format(T0, T1, T2, T3))\\n return [False, t]\\n\\n\\ndef SolveForT0(A, B, t, l, u):\\n \\\"\\\"\\\"\\n Let x = t0 for convenience. The two accelerations can be written in terms of x as\\n\\n a0 = A + B/x and\\n a1 = A - B/(t - x),\\n\\n where t is the total duration. We want to solve the following optimization problem:\\n\\n minimize(x) J(x) = a0^2 + a1^2.\\n\\n We find the minimizer by solving dJ/dx = 0. From\\n\\n J(x) = (A + B/x)^2 + (A - B/(t - x))^2,\\n\\n we have\\n\\n dJ/dx = (2*A)*x^4 + (2*B - 4*A*t)*x^3 + (3*A*t^2 - 3*B*t)*x^2 + (A*t^3 + 3*t^2)*x + (B*t^3).\\n \\\"\\\"\\\"\\n if (l < 0):\\n if (u < 0):\\n log.debug(\\\"The given interval is invalid: l = {0}; u = {1}\\\".format(l, u))\\n return [False, -1]\\n log.debug(\\\"Invalid lower bound is given. Reset to zero.\\\")\\n l = 0\\n\\n if FuzzyZero(A, epsilon) and FuzzyZero(B, epsilon):\\n if (l > 0):\\n return [False, -1]\\n else:\\n t0 = 0\\n return [True, t0]\\n\\n tSqr = t*t\\n tCube = tSqr*t\\n if FuzzyZero(A, epsilon):\\n coeffs = np.array([2*B, -3*B*t, 3*B*tSqr, -B*tCube])\\n else:\\n coeffs = np.array([2*A, -4*A*t + 2*B, 3*A*tSqr - 3*B*t, -A*tCube + 3*B*tSqr, -B*tCube])\\n\\n roots = np.roots(coeffs)\\n if len(roots) == 0:\\n return [False, -1]\\n\\n # Find the solution which minimizes the objective function\\n J = inf\\n bestT = -1.0\\n for root in roots:\\n if (root <= u) and (root >= l):\\n if FuzzyZero(root, epsilon):\\n firstTerm = 0\\n else:\\n firstTerm = A + B/root\\n\\n if FuzzyZero(t - root, epsilon):\\n secondTerm = 0\\n else:\\n secondTerm = A - B/(t - root)\\n\\n curObj = firstTerm*firstTerm + secondTerm*secondTerm\\n if (curObj < J):\\n J = curObj\\n bestT = root\\n\\n if bestT < 0:\\n return [False, -1]\\n else:\\n return [True, bestT]\\n\\n\\ndef Recompute1DTrajectoryTwoRamps(curve, t0, t1):\\n \\\"\\\"\\\"Recompute a trajectory interpolating (curve.x0, curve.v0) and (curve.x1,\\n curve.v1) such that the trajectory has two ramps with durations t0 and t1\\n respectively.\\n\\n Given t0 and t1, there is a unique solution to this problem.\\n\\n \\\"\\\"\\\"\\n assert(t0 > 0)\\n assert(t1 > 0)\\n x0 = curve.x0\\n d = curve.d\\n v0 = curve.v0\\n v1 = curve.v1\\n\\n alpha = t0*(0.5*t0 + t1)\\n beta = 0.5*t1*t1\\n gamma = d - v0*(t0 + t1)\\n det = alpha*t1 - beta*t0 # det is provably strictly positive\\n detInv = 1.0/det\\n \\n a0New = (gamma*t1 - beta*(v1 - v0))*detInv\\n a1new = (-gamma*t0 + alpha*(v1 - v0))*detInv\\n\\n ramp0 = Ramp(v0, a0New, t0, x0)\\n ramp1 = Ramp(ramp0.v1, a1New, t1)\\n return ParabolicCurve([ramp0, ramp1])\\n\\n\\ndef Recompute1DTrajectoryThreeRamps(curve, t0, t1, t2, vm, am):\\n \\\"\\\"\\\"Recompute a trajectory interpolating (curve.x0, curve.v0) and (curve.x1,\\n curve.v1) such that the trajectory has three ramps with durations t0, t1,\\n and t2 respectively.\\n\\n \\\"\\\"\\\"\\n assert(t0 > 0)\\n assert(t1 > 0)\\n assert(t2 > 0)\\n x0 = curve.x0\\n d = curve.d\\n v0 = curve.v0\\n v1 = curve.v1\\n \\n alpha = t0*(0.5*t0 + t1 + t2)\\n beta = t1*(0.5*t1 + t2)\\n sigma = 0.5*t2*t2\\n gamma = d - v0*(t0 + t1 + t2)\\n kappa = v1 - v0\\n\\n A = np.array([[alpha, beta, sigma], [t0, t1, t2]])\\n b = np.array([[gamma], [kappa]])\\n AAT = np.dot(A, A.T)\\n pseudoinvA = np.dot(A.T, np.linalg.inv(AAT))\\n xp = np.dot(pseudoinvA, b) # particular solution\\n xh = np.array([[(beta*t2 - sigma*t1)/(alpha*t1 - beta*t0)],\\n [-(alpha*t2 - sigma*t0)/(alpha*t1 - beta*t0)],\\n [1.0]]) # homogenous solution\\n\\n # Solutions to Ax = b are in the form xp + k*xh. Now we need to compute a valid interval of k.\\n \\n l = np.array([[(max((-vm - v0)/t0, -am))], [-am], [max((-vm + v1)/t2, -am)]])\\n u = np.array([[min((vm - v0)/t0, am)], [am], [min((vm + v1)/t2, am)]])\\n\\n [result0, kl0, ku0] = SolveBoundedInEq(xh[0], xp[0], l[0], u[0])\\n [result1, kl1, ku1] = SolveBoundedInEq(xh[1], xp[1], l[1], u[1])\\n [result2, kl2, ku2] = SolveBoundedInEq(xh[2], xp[2], l[2], u[2])\\n assert(result0 and result1 and result2)\\n\\n if (kl0 > ku1) or (ku0 < kl1):\\n return ParabolicCurve()\\n\\n kl0 = max(kl0, kl1)\\n ku0 = min(ku0, ku1)\\n if (kl0 > ku2) or (ku0 < kl2):\\n return ParabolicCurve()\\n\\n kl = max(kl0, kl2)\\n ku = min(ku0, ku2)\\n\\n # Now we can choose any value k \\\\in [kl, ku]. If there is no other\\n # preference, we just randomly choose one.\\n k = _rng.uniform(kl, ku)\\n x = xp + k*xh\\n\\n [a0, a1, a2] = x\\n ramp0 = Ramp(v0, a0, t0, x0)\\n ramp1 = Ramp(ramp0.v1, a1, t1)\\n ramp2 = Ramp(ramp1.v1, a2, t2)\\n return ParabolicCurve([ramp0, ramp1, ramp2])\\n \\n \\n################################################################################\\n\\n#\\n# ND Trajectory with minimum-switch-time constraint\\n#\\ndef _RecomputeNDTrajectoryFixedDurationWithDelta(curves, vmVect, amVect, maxIndex, delta):\\n ndof = len(curves)\\n tmax = curves[maxIndex].duration\\n\\n grid = _ComputeGrid(curves[maxIndex], delta)\\n PLPFirst = len(grid) > _gridThreshold\\n if PLPFirst:\\n first = SnapToGrid_ThreeRamps\\n second = SnapToGrid_TwoRamps\\n else:\\n first = SnapToGrid_TwoRamps\\n second = SnapToGrid_ThreeRamps\\n \\n newcurves = []\\n for j in xrange(ndof):\\n if j == maxIndex:\\n newcurves.append(curves[j])\\n continue\\n\\n newcurve = first(curves[j], vmVect[j], amVect[j], delta, tmax, grid)\\n if len(newcurve) == 0:\\n newcurve = second(curves[j], vmVect[j], amVect[j], delta, tmax, grid)\\n if len(newcurve) == 0:\\n log.debug(\\\"DOF {0} is infeasible.\\\".format(j))\\n return ParabolicCurvesND()\\n newcurves.append(newcurve)\\n\\n return ParabolicCurvesND(newcurves) \\n\\n\\ndef _ComputeGrid(curve, delta):\\n \\\"\\\"\\\"This function compute a grid which devides each ramp of the given curve into\\n intervals of equal duration t, such that t is smallest possible but still greater\\n than delta.\\n\\n Grid resolution at each ramp may be different.\\n\\n \\\"\\\"\\\"\\n totalNumGrid = sum([np.floor(r.duration/delta) for r in curve])\\n if totalNumGrid < _defaultGridRes:\\n grids = np.array([])\\n startTime = 0\\n for ramp in curve:\\n n = np.floor(ramp.duration/delta)\\n endTime = startTime + ramp.duration\\n grid = np.linspace(startTime, endTime, num=n, endpoint=False)\\n grids = np.append(grids, grid)\\n startTime = endTime\\n grids = np.append(grids, curve.duration)\\n else:\\n nums = [np.floor(_defaultGridRes*r.duration/curve.duration) for r in curve]\\n grids = np.array([])\\n startTime = 0\\n for (n, ramp) in zip(nums, curve):\\n endTime = startTime + ramp.duration\\n grid = np.linspace(startTime, endTime, num=n, endpoint=False)\\n grids = np.append(grids, grid)\\n startTime = endTime\\n grids = np.append(grids, curve.duration)\\n\\n return grids\\n\\n\\ndef SnapToGrid_TwoRamps(curve, vm, am, delta, tmax, grid):\\n \\\"\\\"\\\"This function try to generate a two-ramp trajectory which has the switch\\n point lying at one of the grid lines. It iterates through all available grid\\n lines.\\n\\n \\\"\\\"\\\"\\n for g in grid:\\n assert(g > 0)\\n newcurve = Recompute1DTrajectoryTwoRamps(curve, g, tmax - g)\\n if CheckParabolicCurve(curve, xmin, xmax, vm, am, x0, x1, v0, v1) == PCR_Normal:\\n return newcurve\\n return ParabolicCurve()\\n\\n\\ndef SnapToGrid_ThreeRamps(curve, vm, am, delta, tmax, grid):\\n \\\"\\\"\\\"This function try to generate a two-ramp trajectory which has the switch\\n point lying at one of the grid lines.\\n\\n Since there are too many possible combinations of the two switch points, it\\n make an initial guess and then try out four possibilities.\\n\\n \\\"\\\"\\\"\\n # Initial guess\\n t0 = tmax * 0.25\\n t1 = tmax * 0.50\\n\\n index0 = bisect.bisect_left(grid, t0)\\n # i0 is a list of positions of the first switch point\\n if index0 == 0:\\n i0 = [index0]\\n else:\\n i0 = [index0 - 1, index0]\\n\\n index1 = bisect.bisect_left(grid, t1)\\n # i1 is a list of positions of the second switch point\\n if index1 == len(grid):\\n i1 = [index1]\\n else:\\n i1 = [index1 - 1, index1]\\n\\n for index0 in i0:\\n for index1 in i1:\\n if index1 <= index0:\\n continue\\n t0New = grid[index0]\\n t1New = grid[index1] - grid[index0]\\n t2New = tmax - grid[index1]\\n newcurve = Recompute1DTrajectoryThreeRamps(curve, t0New, t1New, t2New, vm, am)\\n if CheckParabolicCurve(curve, xmin, xmax, vm, am, x0, x1, v0, v1) == PCR_Normal:\\n return newcurve\\n return ParabolicCurve()\\n \\n\\n################################################################################\\n\\n#\\n# 1D Trajectory with minimum-switch-time constraint\\n#\\ndef ComputeZeroVel1DTrajectoryWithDelta(x0, x1, vm, am, delta):\\n curve = _Compute1DTrajectoryWithoutDelta(x0, x1, 0.0, 0.0, vm, am)\\n \\n if len(curve) == 1:\\n assert(not (x0 == 0 and x1 == 0))\\n return curve\\n elif len(curve) == 2:\\n return _FixSwitchTimeZeroVelPP(curve, delta)\\n else:\\n # len(curve) == 3 in this case\\n return _FixSwitchTimeZeroVelPLP(curve, vm, am, delta)\\n\\n\\ndef _FixSwitchTimeZeroVelPP(curve, delta):\\n if curve[0].duration >= delta:\\n return curve\\n\\n newVp = curve.d/delta # the new peak velocity\\n a0 = newVp/delta\\n ramp0 = Ramp(0.0, a0, delta, curve.x0)\\n ramp1 = Ramp(newVp, -a0, delta)\\n return ParabolicCurve([ramp0, ramp1])\\n\\n\\ndef _FixSwitchTimeZeroVelPLP(curve, vm, am, delta):\\n if curve[0].duration >= delta and curve[1].duration >= delta:\\n # Note that we do not have to check the last ramp since it has\\n # the same duration as the first ramp\\n return curve\\n\\n d = curve.d\\n x0 = curve.x0\\n\\n if (am*delta <= vm):\\n # two-ramp\\n vp = vm if d > 0 else -vm\\n t0 = d/vp\\n a0 = vp/t0\\n ramp0 = Ramp(0.0, a0, t0, x0)\\n ramp1 = Ramp(vp, -a0, t0)\\n curveA = ParabolicCurve([ramp0, ramp1])\\n \\n # three-ramp\\n a0 = am if d > 0 else -am\\n vp = 0.5*(-a0*delta + np.sign(a0)*np.sqrt((a0*delta)**2 + 4*a0*d))\\n if (vp/a0 < delta):\\n # (delta, delta, delta)\\n vp = 0.5*d/delta\\n ramp0 = Ramp(0.0, vp/delta, delta, x0 = x0)\\n ramp1 = Ramp(vp, 0.0, delta)\\n ramp2 = Ramp(vp, -vp/delta, delta)\\n else:\\n # (> delta, delta, > delta)\\n ramp0 = Ramp(0.0, a0, vp/a0, x0 = x0)\\n ramp1 = Ramp(vp, 0.0, delta)\\n ramp2 = Ramp(vp, -a0, vp/a0)\\n curveB = ParabolicCurve([ramp0, ramp1, ramp2])\\n\\n # Compare the durations\\n if curveA.duration <= curveB.duration:\\n return curveA\\n else:\\n return curveB\\n \\n else:\\n deltaInv = 1.0/delta\\n if (abs(d) <= vm*delta):\\n # two-ramp (delta, delta)\\n vp = d*deltaInv\\n a0 = vp*deltaInv\\n ramp0 = Ramp(0.0, a0, delta, x0)\\n ramp1 = Ramp(vp, -a0, delta)\\n return ParabolicCurve([ramp0, ramp1])\\n elif (abs(d) > 2*vm*delta):\\n # (delta, > delta, delta)\\n vp = np.sign(d)*vm\\n a0 = vp*deltaInv\\n ramp0 = Ramp(0.0, a0, delta, x0)\\n ramp2 = Ramp(vp, -a0, delta)\\n dRem = d - (ramp0.d + ramp2.d) # the remaining distance for the middle ramp\\n ramp1 = Ramp(vp, 0.0, dRem/vp)\\n return ParabolicCurve([ramp0, ramp1, ramp2])\\n elif (abs(d) <= 1.5*vm*delta):\\n # two-ramp (>= delta, >= delta)\\n vp = np.sign(d)*vm\\n t = d/vp\\n a0 = vp/t\\n ramp0 = Ramp(0.0, a0, t, x0)\\n ramp1 = Ramp(vp, -a0, t)\\n return ParabolicCurve([ramp0, ramp1])\\n else:\\n # three-ramp (delta, delta, delta)\\n vp = 0.5*d*deltaInv\\n a0 = vp*deltaInv\\n ramp0 = Ramp(0.0, a0, delta, x0)\\n ramp1 = Ramp(vp, 0.0, delta)\\n ramp2 = Ramp(vp, -a0, delta)\\n return ParabolicCurve([ramp0, ramp1, ramp2])\\n \\n\\ndef _Compute1DTrajectoryWithDelta(x0, x1, v0, v1, vm, am, delta):\\n curve = _Compute1DTrajectoryWithoutDelta(x0, x1, v0, v1, vm, am)\\n\\n if len(curve) == 1:\\n return _FixSwitchTimeOneRamp(curve, vm, am, delta)\\n elif len(curve) == 2:\\n return _FixSwitchTimeTwoRamps(curve, vm, am, delta)\\n else:\\n return _FixSwitchTimeThreeRamps(curve, vm, am, delta)\\n\\n\\ndef _FixSwitchTimeOneRamp(curve, vm, am, delta):\\n # To be determined whether the fix in this case is worth it because the duration\\n # of the modified ParabolicCurve tends to be much greater than the original\\n # duration.\\n return ParabolicCurve()\\n \\n\\ndef _FixSwitchTimeTwoRamps(curve, vm, am, delta):\\n t0 = curve[0].duration\\n t1 = curve[1].duration\\n\\n if (t0 >= delta) and (t1 >= delta):\\n return curve\\n elif (t0 < delta) and (t1 >= delta):\\n return _PP1(curve, vm, am, delta)\\n elif (t0 >= delta) and (t1 < delta):\\n return _PP2(curve, vm, am, delta)\\n else:\\n return _PP3(curve, vm, am, delta)\\n\\n \\ndef _PP1(curve, vm, am, delta):\\n \\\"\\\"\\\"(t0 < delta) and (t1 >= delta)\\n\\n Stretch the duration of the first ramp to delta while the second ramp remains at\\n the same acceleration (hence shorter). After that there are three possible cases.\\n \\n A: no further correction is needed.\\n B: the second ramp becomes shorter than delta and re-interpolating (x0, v0) and (x1, v1)\\n using a trajectory with 2 delta-ramps is better\\n C: the second ramp becomes shorter than delta and re-interpolating (x0, v0) and (x1, v1)\\n using a trajectory with one ramp is better\\n \\\"\\\"\\\"\\n ramp0 = curve[0]\\n ramp1 = curve[-1]\\n t0 = ramp0.duration\\n t1 = ramp1.duration\\n v0 = ramp0.v0\\n v1 = ramp1.v1\\n a0 = ramp0.a\\n a1 = ramp1.a\\n d = curve.d\\n x0 = ramp0.x0\\n vp = ramp0.v1\\n deltaInv = 1.0/delta\\n\\n if FuzzyZero(a1, epsilon):\\n # Stretch the first ramp\\n ramp0A = Ramp(v0, (vp - v0)*deltaInv, delta, x0)\\n dRem = d - ramp0A.d\\n if FuzzyZero(vp, epsilon):\\n log.warn(\\\"The peak velocity is zero\\\")\\n return ParabolicCurve()\\n t1New = dRem/vp\\n\\n if (t1New >= delta):\\n ramp1A = Ramp(vp, 0.0, t1New)\\n curveA = ParabolicCurve([ramp0A, ramp1A])\\n # Check before returning\\n return curveA\\n else:\\n k = a1*delta\\n vpNew = 0.5*(k - np.sign(a1)*np.sqrt(k**2 + 4*(k*v0 + v1**2) - 8*a1*d))\\n t1New = (v1 - vpNew)/a1\\n\\n if (t1New >= delta):\\n # A: no further correction is needed.\\n log.debug(\\\"PP1 A\\\")\\n a0New = (vpNew - v0)*deltaInv\\n ramp0A = Ramp(v0, a0New, delta, x0)\\n ramp1A = Ramp(vpNew, a1, t1New)\\n curveA = ParabolicCurve([ramp0A, ramp1A])\\n # Check before returning\\n return curveA\\n\\n # If arrive here, case A does not work. Need some modification.\\n # Try case B.\\n vpNew = d*deltaInv - 0.5*(v0 + v1)\\n a0New = (vpNew - v0)*deltaInv\\n a1New = (v1 - vpNew)*deltaInv\\n\\n if (abs(a0New) <= am + epsilon) and (abs(a1New) <= am + epsilon) and (abs(vpNew) <= vm + epsilon):\\n ramp0B = Ramp(v0, a0New, delta, x0)\\n ramp1B = Ramp(vpNew, a1New, delta)\\n curveB = ParabolicCurve([ramp0B, ramp1B])\\n else:\\n # Case B does not produce any feasible solution\\n curveB = ParabolicCurve()\\n\\n # Try case C.\\n if FuzzyZero(v0 + v1, epsilon):\\n # Case C does not produce any feasible solution\\n curveC = ParabolicCurve()\\n else:\\n tNew = 2*d/(v0 + v1)\\n aNew = (v1 - v0)/tNew\\n if (abs(aNew) <= am + epsilon) and (tNew >= delta):\\n ramp0C = Ramp(v0, aNew, tNew, x0)\\n curveC = ParabolicCurve([ramp0C])\\n else:\\n # Case C does not produce any feasible solution\\n curveC = ParabolicCurve()\\n\\n # There has to be at least one valid case\\n assert(not (curveB.IsEmpty() and curveC.IsEmpty()))\\n\\n if curveB.IsEmpty():\\n # Check before returning\\n return curveC\\n elif curveC.IsEmpty():\\n # Check before returning\\n return curveB\\n elif curveB.duration <= curveC.duration:\\n # Check before returning\\n return curveB\\n else:\\n # Check before returning\\n return curveC\\n\\n\\ndef _PP2(curve, vm, am, delta):\\n \\\"\\\"\\\"(t0 >= delta) and (t1 < delta)\\n\\n Stretch the duration of the second ramp to delta while the first ramp remains at\\n the same acceleration (hence shorter). After that there are three possible cases.\\n \\n A: no further correction is needed.\\n B: the first ramp becomes shorter than delta and re-interpolating (x0, v0) and (x1, v1)\\n using a trajectory with 2 delta-ramps is better\\n C: the first ramp becomes shorter than delta and re-interpolating (x0, v0) and (x1, v1)\\n using a trajectory with one ramp is better\\n \\\"\\\"\\\"\\n ramp0 = curve[0]\\n ramp1 = curve[-1]\\n t0 = ramp0.duration\\n t1 = ramp1.duration\\n v0 = ramp0.v0\\n v1 = ramp1.v1\\n a0 = ramp0.a\\n a1 = ramp1.a\\n d = curve.d\\n x0 = ramp0.x0\\n vp = ramp0.v1\\n deltaInv = 1.0/delta\\n\\n if FuzzyZero(a0, epsilon):\\n # Stretch the last ramp\\n ramp1A = Ramp(vp, (v1 - v0)*deltaInv, delta)\\n dRem = d - ramp1A.d\\n if FuzzyZero(vp, epsilon):\\n log.warn(\\\"The peak velocity is zero\\\")\\n return ParabolicCurve()\\n t0New = dRem/vp\\n \\n if (t0New >= delta):\\n ramp0A = Ramp(v0, 0.0, t0New, x0)\\n curveA = ParabolicCurve([ramp0A, ramp1A])\\n # Check before returning\\n return curveA\\n else:\\n k = a0*delta\\n vpNew = 0.5*( - k + np.sign(a0)*np.sqrt(k**2 - 4*(k*v1 - v0**2) + 8*a0*d))\\n t0New = (vpNew - v0)/a0\\n \\n if (t0New >= delta):\\n # A: no further correction is needed.\\n log.debug(\\\"PP2 A\\\")\\n a1New = (v1 - vpNew)*deltaInv\\n ramp0A = Ramp(v0, a0, t0New, x0)\\n ramp1A = Ramp(vpNew, a1New, delta)\\n curveA = ParabolicCurve([ramp0A, ramp1A])\\n # Check before returning\\n return curveA\\n\\n # If arrive here, case A does not work. Need some modification.\\n # Try case B.\\n vpNew = d*deltaInv - 0.5*(v0 + v1)\\n a0New = (vpNew - v0)*deltaInv\\n a1New = (v1 - vpNew)*deltaInv\\n\\n if (abs(a0New) <= am + epsilon) and (abs(a1New) <= am + epsilon) and (abs(vpNew) <= vm + epsilon):\\n ramp0B = Ramp(v0, a0New, delta, x0)\\n ramp1B = Ramp(vpNew, a1New, delta)\\n curveB = ParabolicCurve([ramp0B, ramp1B])\\n else:\\n # Case B does not produce any feasible solution\\n curveB = ParabolicCurve()\\n\\n # Try case C.\\n if FuzzyZero(v0 + v1, epsilon):\\n # Case C does not produce any feasible solution\\n curveC = ParabolicCurve()\\n else:\\n tNew = 2*d/(v0 + v1)\\n aNew = (v1 - v0)/tNew\\n if (abs(aNew) <= am + epsilon) and (tNew >= delta):\\n ramp0C = Ramp(v0, aNew, tNew, x0)\\n curveC = ParabolicCurve([ramp0C])\\n else:\\n # Case C does not produce any feasible solution\\n curveC = ParabolicCurve()\\n\\n # There has to be at least one valid case\\n assert(not (curveB.IsEmpty() and curveC.IsEmpty()))\\n\\n if curveB.IsEmpty():\\n # Check before returning\\n return curveC\\n elif curveC.IsEmpty():\\n # Check before returning\\n return curveB\\n elif curveB.duration <= curveC.duration:\\n # Check before returning\\n return curveB\\n else:\\n # Check before returning\\n return curveC\\n \\n \\ndef _PP3(curve, vm, am, delta):\\n \\\"\\\"\\\"(t0 < delta) and (t1 < delta)\\n\\n First we try out two possibilities.\\n \\n A: re-interpolating (x0, v0) and (x1, v1) using a trajectory with 2 delta-ramps\\n B: re-interpolating (x0, v0) and (x1, v1) using a trajectory with one ramp\\n\\n If the above two cases do not give any feasible trajectory, we try to re-interpolate (x0, v0)\\n and (x1, v1) again by using the idea of flipping ramps. Basically if the original trajectory\\n goes with +am -> -am, the new trajectory would be -am -> +am.\\n \\\"\\\"\\\"\\n ramp0 = curve[0]\\n ramp1 = curve[-1]\\n t0 = ramp0.duration\\n t1 = ramp1.duration\\n v0 = ramp0.v0\\n v1 = ramp1.v1\\n a0 = ramp0.a\\n a1 = ramp1.a\\n d = curve.d\\n x0 = ramp0.x0\\n vp = ramp0.v1\\n deltaInv = 1.0/delta\\n\\n # Try case A.\\n vpNew = d*deltaInv - 0.5*(v0 + v1)\\n a0New = (vpNew - v0)*deltaInv\\n a1New = (v1 - vpNew)*deltaInv\\n if (abs(a0New) <= am + epsilon) and (abs(a1New) <= am + epsilon) and (abs(vpNew) <= vm + epsilon):\\n ramp0A = Ramp(v0, a0New, delta, x0)\\n ramp1A = Ramp(vpNew, a1New, delta)\\n curveA = ParabolicCurve([ramp0A, ramp1A])\\n else:\\n # Case A does not produce any feasible solution\\n curveA = ParabolicCurve()\\n\\n # Try case B.\\n if FuzzyZero(v0 + v1, epsilon):\\n # Case C does not produce any feasible solution\\n curveB = ParabolicCurve()\\n else:\\n tNew = 2*d/(v0 + v1)\\n aNew = (v1 - v0)/tNew\\n if (abs(aNew) <= am + epsilon) and (tNew >= delta):\\n ramp0B = Ramp(v0, aNew, tNew, x0)\\n curveB = ParabolicCurve([ramp0B])\\n else:\\n # Case B does not produce any feasible solution\\n curveB = ParabolicCurve()\\n\\n if not (curveA.IsEmpty() and curveB.IsEmpty()):\\n # We already have a feasible solution\\n if curveA.IsEmpty():\\n log.debug(\\\"PP3 A\\\")\\n return curveB\\n elif curveB.IsEmpty():\\n log.debug(\\\"PP3 B\\\")\\n return curveA\\n else:\\n if curveA.duration < curveB.duration:\\n log.debug(\\\"PP3 A\\\")\\n return curveA\\n else:\\n log.debug(\\\"PP3 B\\\")\\n return curveB\\n\\n log.debug(\\\"PP3 Flipping\\\")\\n # If arrive here, both cases A and B do not produce any feasible solution.\\n # First see if the flipped ramps can go with maximum accel/decel\\n if FuzzyZero(a0, epsilon):\\n a0New = a1\\n a1New = -a1\\n elif FuzzyZero(a1, epsilon):\\n a0New = -a0\\n a1New = a0\\n else:\\n a0New = -a0\\n a1New = -a1\\n\\n vpSqr = 0.5*(v0*v0 + v1*v1) + a0New*d\\n if (vpSqr >= 0) and (not FuzzyZero(a0New, epsilon)) and (not FuzzyZero(a1New, epsilon)):\\n # Both ramps can saturate the acceleration bound. Now figure out which value of vpNew to use\\n vpNew = Sqrt(vpSqr)\\n t0New = (vpNew - v0)/a0New\\n t1New = (v1 - vpNew)/a1New\\n if (t0New >= delta) and (t1New >= delta):\\n ramp0C = Ramp(v0, a0New, t0New, x0)\\n ramp1C = Ramp(vpNew, a1New, t1New)\\n curveC = ParabolicCurve([ramp0C, ramp1C])\\n # Check before returning\\n return curveC\\n\\n # vpNew being positive does not work\\n vpNew *= -1.0\\n t0New = (vpNew - v0)/a0New\\n t1New = (v1 - vpNew)/a1New\\n if (t0New >= delta) and (t1New >= delta):\\n ramp0C = Ramp(v0, a0New, t0New, x0)\\n ramp1C = Ramp(vpNew, a1New, t1New)\\n curveC = ParabolicCurve([ramp0C, ramp1C])\\n # Check before returning\\n return curveC\\n\\n # Now we know that the flipped velocity profile cannot saturate acceleration bound all the\\n # time. We try to modify the (flipped) velocity profile using the idea in PP1 and PP2.\\n if (t0 <= t1):\\n # When flipped, we would have t0New > t1New. Therefore, we follow the procedure of PP1.\\n k = a1New*delta\\n discriminant = k**2 + 4*(k*v0 + v1**2) - 8*a1New*d\\n if (discriminant < 0):\\n # Fail to calculate vpNew following PP1\\n return ParabolicCurve()\\n\\n vpNew = 0.5*(k - np.sign(a1New)*np.sqrt(discriminant))\\n t1New = (v1 - vpNew)/a1New\\n if (t1New >= delta):\\n a0New = (vpNew - v0)*deltaInv\\n ramp0A = Ramp(v0, a0New, delta, x0)\\n ramp1A = Ramp(vpNew, a1New, t1New)\\n curveA = ParabolicCurve([ramp0A, ramp1A])\\n # Check before returning\\n return curveA\\n \\n else:\\n # (t0 > t1)\\n # When flipped, we would have t0New < t1New. Therefore, we follow the procedure of PP2.\\n k = a0New*delta\\n discriminant = k**2 - 4*(k*v1 - v0**2) + 8*a0New*d\\n discriminant = k**2 + 4*(k*v0 + v1**2) - 8*a1New*d\\n if (discriminant < 0):\\n # Fail to calculate vpNew following PP2\\n return ParabolicCurve()\\n\\n vpNew = 0.5*( - k + np.sign(a0New)*np.sqrt(discriminant))\\n t0New = (vpNew - v0)/a0New\\n if (t0New >= delta):\\n a1New = (v1 - vpNew)*deltaInv\\n ramp0A = Ramp(v0, a0New, t0New, x0)\\n ramp1A = Ramp(vpNew, a1New, delta)\\n curveA = ParabolicCurve([ramp0A, ramp1A])\\n # Check before returning\\n return curveA\\n\\n # PP1A (or PP2A) modification does not produce any feasible trajectory.\\n # Try case B.\\n vpNew = d*deltaInv - 0.5*(v0 + v1)\\n a0New = (vpNew - v0)*deltaInv\\n a1New = (v1 - vpNew)*deltaInv\\n\\n if (abs(a0New) <= am + epsilon) and (abs(a1New) <= am + epsilon) and (abs(vpNew) <= vm + epsilon):\\n ramp0B = Ramp(v0, a0New, delta, x0)\\n ramp1B = Ramp(vpNew, a1New, delta)\\n curveB = ParabolicCurve([ramp0B, ramp1B])\\n else:\\n # Case B does not produce any feasible solution\\n curveB = ParabolicCurve()\\n\\n # Try case C.\\n if FuzzyZero(v0 + v1, epsilon):\\n # Case C does not produce any feasible solution\\n curveC = ParabolicCurve()\\n else:\\n tNew = 2*d/(v0 + v1)\\n aNew = (v1 - v0)/tNew\\n if (abs(aNew) <= am + epsilon) and (tNew >= delta):\\n ramp0C = Ramp(v0, aNew, tNew, x0)\\n curveC = ParabolicCurve([ramp0C])\\n else:\\n # Case C does not produce any feasible solution\\n curveC = ParabolicCurve()\\n\\n # There has to be at least one valid case\\n if curveB.IsEmpty() and curveC.IsEmpty():\\n return curveB # return an empty one\\n elif curveB.IsEmpty():\\n # Check before returning\\n return curveC\\n elif curveC.IsEmpty():\\n # Check before returning\\n return curveB\\n elif curveB.duration <= curveC.duration:\\n # Check before returning\\n return curveB\\n else:\\n # Check before returning\\n return curveC \\n \\n\\ndef _FixSwitchTimeThreeRamps(curve, vm, am, delta):\\n t0 = curve[0].duration\\n t1 = curve[1].duration\\n t2 = curve[2].duration\\n\\n if (t0 >= delta) and (t1 >= delta) and (t2 >= delta):\\n return curve\\n elif (t0 < delta) and (t1 >= delta) and (t2 >= delta):\\n return _PLP1(curve, vm, am, delta)\\n elif (t0 >= delta) and (t1 >= delta) and (t2 < delta):\\n return _PLP2(curve, vm, am, delta)\\n elif (t0 >= delta) and (t1 < delta) and (t2 >= delta):\\n return _PLP3(curve, vm, am, delta)\\n elif (t0 < delta) and (t1 < delta) and (t2 >= delta):\\n return _PLP4(curve, vm, am, delta)\\n elif (t0 >= delta) and (t1 < delta) and (t2 < delta):\\n return _PLP5(curve, vm, am, delta)\\n elif (t0 < delta) and (t1 >= delta) and (t2 < delta):\\n return _PLP6(curve, vm, am, delta)\\n elif (t0 < delta) and (t1 < delta) and (t2 < delta):\\n return _PLP7(curve, vm, am, delta)\\n\\n\\ndef _PLP1(curve, vm, am, delta):\\n \\\"\\\"\\\"(t0 < delta) and (t1 >= delta) and (t2 >= delta)\\n\\n We consider two possibilities.\\n\\n A: stretch the first ramp's duration to delta. Then use (x1, v1) of the first ramp together with\\n the trajectory's (x1, v1) as boundaary conditions to interpolate a sub-trajectory\\n B: merge the first two ramps into one\\n \\\"\\\"\\\"\\n firstRamp = curve[0]\\n middleRamp = curve[1]\\n lastRamp = curve[2]\\n\\n t0 = firstRamp.duration\\n t1 = middleRamp.duration\\n t2 = lastRamp.duration\\n v0 = firstRamp.v0\\n v1 = lastRamp.v1\\n x0 = firstRamp.x0\\n d = curve.d\\n d0 = firstRamp.d + middleRamp.d\\n vp = middleRamp.v0 # the middle has zero acceleration\\n deltaInv = 1.0/delta\\n\\n # Try case A.\\n a0New = (vp - v0)*deltaInv\\n ramp0A = Ramp(v0, a0New, delta, x0)\\n dRem = d - ramp0A.d\\n subCurveA = _Compute1DTrajectoryWithDelta(0, dRem, vp, v1, vm, am, delta)\\n assert(not subCurveA.IsEmpty()) # this computation should not fail\\n if len(subCurveA) == 1:\\n curveA = ParabolicCurve([ramp0A, subCurveA[0]])\\n else:\\n # len(subCurveA) == 2 in this case\\n curveA = ParabolicCurve([ramp0A, subCurveA[0], subCurveA[1]])\\n \\n # Try case B.\\n if FuzzyZero(vp + v0, epsilon):\\n curveB = ParabolicCurve()\\n else:\\n t0New = 2*d0/(vp + v0)\\n a0New = (vp - v0)/t0New\\n ramp0B = Ramp(v0, a0New, t0New, x0)\\n curveB = ParabolicCurve([ramp0B, lastRamp])\\n\\n assert(not (curveA.IsEmpty() and curveB.IsEmpty()))\\n if curveA.IsEmpty():\\n # Check before returning\\n log.debug(\\\"PLP1 B\\\")\\n return curveB\\n elif curveB.IsEmpty():\\n # Check before returning\\n log.debug(\\\"PLP1 A\\\")\\n return curveA\\n elif curveA.duration < curveB.duration:\\n # Check before returning\\n log.debug(\\\"PLP1 A\\\")\\n return curveA\\n else:\\n # Check before returning\\n log.debug(\\\"PLP1 B\\\")\\n return curveB\\n \\n\\ndef _PLP2(curve, vm, am, delta):\\n \\\"\\\"\\\"(t0 >= delta) and (t1 >= delta) and (t2 < delta)\\n\\n We consider two possibilities.\\n\\n A: stretch the last ramp's duration to delta. Then use (x0, v0) of the trajectory together with\\n (x0, v0) of the last ramp as boundaary conditions to interpolate a sub-trajectory\\n B: merge the last two ramps into one\\n \\\"\\\"\\\"\\n firstRamp = curve[0]\\n middleRamp = curve[1]\\n lastRamp = curve[2]\\n\\n t0 = firstRamp.duration\\n t1 = middleRamp.duration\\n t2 = lastRamp.duration\\n v0 = firstRamp.v0\\n v1 = lastRamp.v1\\n x0 = firstRamp.x0\\n d = curve.d\\n d1 = middleRamp.d + lastRamp.d\\n vp = middleRamp.v0 # the middle has zero acceleration\\n deltaInv = 1.0/delta\\n\\n # Try case A.\\n a2New = (v1 - vp)*deltaInv\\n ramp2A = Ramp(vp, a2New, delta)\\n dRem = d - ramp2A.d\\n subCurveA = _Compute1DTrajectoryWithDelta(0, dRem, v0, vp, vm, am, delta)\\n assert(not subCurveA.IsEmpty()) # this computation should not fail\\n if len(subCurveA) == 1:\\n curveA = ParabolicCurve([subCurveA[0], ramp2A])\\n else:\\n # len(subCurveA) == 2 in this case\\n curveA = ParabolicCurve([subCurveA[0], subCurveA[1], ramp2A])\\n \\n # Try case B.\\n if FuzzyZero(vp + v1, epsilon):\\n curveB = ParabolicCurve()\\n else:\\n t1New = 2*d1/(vp + v1)\\n a1New = (v1 - vp)/t1New\\n ramp1B = Ramp(vp, a1New, t1New)\\n curveB = ParabolicCurve([firstRamp, ramp1B])\\n\\n assert(not (curveA.IsEmpty() and curveB.IsEmpty()))\\n if curveA.IsEmpty():\\n # Check before returning\\n log.debug(\\\"PLP2 B\\\")\\n return curveB\\n elif curveB.IsEmpty():\\n # Check before returning\\n log.debug(\\\"PLP2 A\\\")\\n return curveA\\n elif curveA.duration < curveB.duration:\\n # Check before returning\\n log.debug(\\\"PLP2 A\\\")\\n return curveA\\n else:\\n # Check before returning\\n log.debug(\\\"PLP2 B\\\")\\n return curveB\\n\\n\\ndef _PLP3(curve, vm, am, delta):\\n \\\"\\\"\\\"(t0 >= delta) and (t1 < delta) and (t2 >= delta)\\n \\n There are three possibilities.\\n\\n A: stretch the duration of the middle ramp to delta. The acceleration of the middle might not be\\n zero after the stretching\\n B: merge any two ramps together to obtain a two-ramp velocity profile\\n C: the resulting trajectory has 3 delta-ramps\\n \\\"\\\"\\\"\\n firstRamp = curve[0]\\n middleRamp = curve[1]\\n lastRamp = curve[2]\\n\\n t0 = firstRamp.duration\\n t1 = middleRamp.duration\\n t2 = lastRamp.duration\\n v0 = firstRamp.v0\\n v1 = lastRamp.v1\\n x0 = firstRamp.x0\\n d = curve.d\\n d1 = middleRamp.d + lastRamp.d\\n vp = middleRamp.v0 # the middle has zero acceleration\\n deltaInv = 1.0/delta\\n \\n # Try case A.\\n if firstRamp.a > 0:\\n am_ = am\\n vm_ = am\\n else:\\n am_ = -am\\n vm_ = -vm\\n amInv = 1.0/am_\\n k = 0.5*delta + v0*amInv\\n h = 0.5*delta + v1*amInv\\n r2 = amInv*amInv*(v0*v0 + v1*v1 + 2*d*am_ + 0.5*am_*am_*delta*delta)\\n\\n t0l_0 = max(delta, (v1 - v0))*amInv\\n t0u_0 = (vm_ - v0)*amInv\\n t2l_0 = -h + Sqrt(r2 - (k + t0u_0)**2)\\n t2u_0 = -h + Sqrt(r2 - (k + t0l_0)**2)\\n if t2l_0 > t2u_0:\\n [t2l_0, t2u_0] = Swap(t2u_0, t2l_0)\\n\\n t2l_1 = max(delta, (v0 - v1)*amInv)\\n t2u_1 = (vm_ - v1)*amInv\\n t0l_1 = -k + Sqrt(r2 - (h + t2u_1)**2)\\n t0u_1 = -k + Sqrt(r2 - (h + t2l_1)**2)\\n if t0l_1 > t0u_1:\\n [t0l_1, t0u_1] = Swap(t0u_1, t0l_1)\\n\\n t0l = max(t0l_0, t0l_1)\\n t0u = min(t0u_0, t0u_1)\\n t2l = max(t2l_0, t2l_1)\\n t2u = min(t2u_0, t2u_1)\\n if (t0l <= t0u) and (t2l <= t2u):\\n duration0 = t0l + delta + t2u\\n duration1 = t0u + delta + t2l\\n if duration0 <= duration1:\\n t0New = t0l\\n t2New = t2u\\n else:\\n t0New = t0u\\n t2New = t2l\\n vp0New = v0 + am_*t0New\\n vp1New = v1 + am_*t2New\\n \\n ramp0A = Ramp(v0, am_, t0New, x0)\\n ramp1A = Ramp(vp0New, (vp1New - vp0New)*deltaInv, delta)\\n ramp2A = Ramp(vp1New, -am_, t2New)\\n curveA = ParabolicCurve([ramp0A, ramp1A, ramp2A])\\n else:\\n curveA = ParabolicCurve()\\n\\n # Try case B.\\n d0 = firstRamp.d + middleRamp.d\\n d1 = middleRamp.d + lastRamp.d\\n # B1: merge the first two ramps together\\n subCurveB1 = _Compute1DTrajectoryWithDelta(0, d0, v0, vp, vm, am, delta)\\n if len(subCurveB1) == 1:\\n curveB1 = ParabolicCurve([subCurveB1[0], lastRamp])\\n else:\\n # len(subCurveB1) == 2 in this case\\n curveB1 = ParabolicCurve([subCurveB1[0], subCurveB1[1], lastRamp])\\n\\n # B2: merge the last two ramps together\\n subCurveB2 = _Compute1DTrajectoryWithDelta(0, d1, vp, v1, vm, am, delta)\\n if len(subCurveB2) == 1:\\n curveB2 = ParabolicCurve([firstRamp, subCurveB2[0]])\\n else:\\n # len(subCurveB2) == 2 in this case\\n curveB2 = ParabolicCurve([firstRamp, subCurveB2[0], subCurveB2[1]])\\n\\n if curveB1.duration <= curveB2.duration:\\n curveB = curveB1\\n else:\\n curveB = curveB2\\n\\n # Try case C.\\n sumVp = d*deltaInv - 0.5*(v0 + v1)\\n sumVpL = v0 + v1 - 2*am*delta\\n sumVpU = v0 + v1 + 2*am*delta\\n if sumVpL <= sumVp <= sumVpU:\\n vp0L = v0 - am*delta\\n vp0U = v0 + am*delta\\n vp1L = max(sumVp - vp0U, v1 - am*delta)\\n vp1U = min(sumVp - vp0L, v1 + am*delta)\\n if vp1L > vp1U:\\n curveC = ParabolicCurve()\\n else:\\n passed = False\\n maxTries = 1000\\n # Randomly choose vp1 until the value is feasible\\n for it in xrange(maxTries):\\n vp1New = _rng.uniform(vp1L, vp1U)\\n vp0New = sumVp - vp1New\\n if abs(vp1New - vp0New) <= am*delta:\\n passed = True\\n break\\n if not passed:\\n curveC = ParabolicCurve()\\n else:\\n ramp0C = Ramp(v0, (vp0New - v0)*deltaInv, delta, x0)\\n ramp1C = Ramp(vp0New, (vp1New - vp0New)*deltaInv, delta)\\n ramp2C = Ramp(vp1New, (v1 - vp1New)*deltaInv, delta)\\n curveC = ParabolicCurve([ramp0C, ramp1C, ramp2C])\\n \\n else:\\n curveC = ParabolicCurve()\\n\\n # Compare all the cases and choose the best trajectory\\n if not curveC.IsEmpty():\\n if not curveB.IsEmpty():\\n if curveB.duration <= curveC.duration:\\n # Check before returning\\n log.debug(\\\"PLP3 B\\\")\\n return curveB\\n else:\\n # Check before returning\\n log.debug(\\\"PLP3 C\\\")\\n return curveC\\n else:\\n # Check before returning\\n log.debug(\\\"PLP3 C\\\")\\n return curveC\\n\\n if curveA.IsEmpty():\\n # Check before returning\\n log.debug(\\\"PLP3 B\\\")\\n return curveB\\n else:\\n if curveB.IsEmpty():\\n # Check before returning\\n log.debug(\\\"PLP3 A\\\")\\n return curveA\\n else:\\n if curveB.duration <= curveA.duration:\\n # Check before returning\\n log.debug(\\\"PLP3 B\\\")\\n return curveB\\n else:\\n # Check before returning\\n log.debug(\\\"PLP3 A\\\")\\n return curveA\\n \\n \\ndef _PLP4(curve, vm, am, delta):\\n \\\"\\\"\\\"(t0 < delta) and (t1 < delta) and (t2 >= delta)\\n \\n This case is similar to PLP1 and we first follow the procedure of PLP1. However, PLP1A and PLP1B\\n can both be infeasible. So we introduct PLP4C where we re-interpolate the trajectory to have\\n two-ramp by using PP1 procedure.\\n \\\"\\\"\\\"\\n firstRamp = curve[0]\\n middleRamp = curve[1]\\n lastRamp = curve[2]\\n\\n t0 = firstRamp.duration\\n t1 = middleRamp.duration\\n t2 = lastRamp.duration\\n v0 = firstRamp.v0\\n v1 = lastRamp.v1\\n x0 = firstRamp.x0\\n d = curve.d\\n d0 = firstRamp.d + middleRamp.d\\n vp = middleRamp.v0 # the middle has zero acceleration\\n deltaInv = 1.0/delta\\n\\n # Try case A.\\n a0New = (vp - v0)*deltaInv\\n ramp0A = Ramp(v0, a0New, delta, x0)\\n dRem = d - ramp0A.d\\n subCurveA = _Compute1DTrajectoryWithDelta(0, dRem, vp, v1, vm, am, delta)\\n assert(not subCurveA.IsEmpty()) # this computation should not fail\\n if len(subCurveA) == 1:\\n curveA = ParabolicCurve([ramp0A, subCurveA[0]])\\n else:\\n # len(subCurveA) == 2 in this case\\n curveA = ParabolicCurve([ramp0A, subCurveA[0], subCurveA[1]])\\n \\n # Try case B.\\n if FuzzyZero(vp + v0, epsilon):\\n curveB = ParabolicCurve()\\n else:\\n t0New = 2*d0/(vp + v0)\\n passed = False\\n if (t0New >= delta):\\n a0New = (vp - v0)/t0New\\n if abs(a0New) <= am + epsilon:\\n passed = True\\n ramp0B = Ramp(v0, a0New, t0New, x0)\\n curveB = ParabolicCurve([ramp0B, lastRamp])\\n if not passed:\\n curveB = ParabolicCurve()\\n\\n # Try case C.\\n # Two-ramp velocity profile where the first ramp saturates the minimum-switch-time constraint\\n # and the second ramp saturates the acceleration constraint.\\n a2 = lastRamp.a\\n k = a2*delta\\n vpNew = 0.5*(k - np.sign(a2)*np.sqrt(k**2 + 4*(k*v0 + v1**2) - 8*a2*d))\\n if abs(vpNew) > vm + epsilon:\\n curveC = ParabolicCurve()\\n else:\\n t1New = (v1 - vpNew)/a2 # a2 is not zero\\n if (t1New >= delta):\\n # PP1A: no further correction is needed.\\n a0New = (vpNew - v0)*deltaInv\\n ramp0C_A = Ramp(v0, a0New, delta, x0)\\n ramp1C_A = Ramp(vpNew, a2, t1New)\\n curveC_A = ParabolicCurve([ramp0C_A, ramp1C_A])\\n curveC = curveC_A\\n else:\\n # Try case PP1B.\\n vpNew = d*deltaInv - 0.5*(v0 + v1)\\n a0New = (vpNew - v0)*deltaInv\\n a1New = (v1 - vpNew)*deltaInv\\n\\n if (abs(a0New) <= am + epsilon) and (abs(a1New) <= am + epsilon) and (abs(vpNew) <= vm + epsilon):\\n ramp0C_B = Ramp(v0, a0New, delta, x0)\\n ramp1C_B = Ramp(vpNew, a1New, delta)\\n curveC_B = ParabolicCurve([ramp0C_B, ramp1C_B])\\n else:\\n # Case PP2B does not produce any feasible solution\\n curveC_B = ParabolicCurve()\\n\\n # Try case PP1C.\\n if FuzzyZero(v0 + v1, epsilon):\\n # Case C does not produce any feasible solution\\n curveC_C = ParabolicCurve()\\n else:\\n tNew = 2*d/(v0 + v1)\\n aNew = (v1 - v0)/tNew\\n if (abs(aNew) <= am + epsilon) and (tNew >= delta):\\n ramp0C_C = Ramp(v0, aNew, tNew, x0)\\n curveC_C = ParabolicCurve([ramp0C])\\n else:\\n # Case C does not produce any feasible solution\\n curveC_C = ParabolicCurve()\\n\\n assert (curveC_B.IsEmpty() and curveC_C.IsEmpty())\\n if curveC_B.IsEmpty():\\n curveC = curveC_C\\n elif curveC_C.IsEmpty():\\n curveC = curveC_B\\n else:\\n curveC = curveC_B if curveC_B.duration < curveC_C.duration else curveC_C\\n\\n # Now compare PLP1A, PLP1B, and PLP1C\\n if curveA.IsEmpty() and curveB.IsEmpty() and curveC.IsEmpty():\\n assert False\\n newCurve = curveA # empty curve\\n elif (not curveA.IsEmpty()) and curveB.IsEmpty() and curveC.IsEmpty():\\n log.debug(\\\"PLP4 A\\\")\\n newCurve = curveA\\n elif curveA.IsEmpty() and (not curveB.IsEmpty()) and curveC.IsEmpty():\\n log.debug(\\\"PLP4 B\\\")\\n newCurve = curveB\\n elif curveA.IsEmpty() and curveB.IsEmpty() and (not curveC.IsEmpty()):\\n log.debug(\\\"PLP4 C\\\")\\n newCurve = curveC\\n elif curveA.IsEmpty():\\n if curveB.duration <= curveC.duration:\\n log.debug(\\\"PLP4 B\\\")\\n newCurve = curveB\\n else:\\n log.debug(\\\"PLP4 C\\\")\\n newCurve = curveC\\n elif curveB.IsEmpty():\\n if curveA.duration <= curveC.duration:\\n log.debug(\\\"PLP4 A\\\")\\n newCurve = curveA\\n else:\\n log.debug(\\\"PLP4 C\\\")\\n newCurve = curveC\\n elif curveC.IsEmpty():\\n if curveA.duration <= curveB.duration:\\n log.debug(\\\"PLP4 A\\\")\\n newCurve = curveA\\n else:\\n log.debug(\\\"PLP4 B\\\")\\n newCurve = curveB\\n else:\\n curves = [curveA, curveB, curveC]\\n minIndex = min((curve.duration, idx) for (idx, curve) in enumerate(curves))[1]\\n newCurve = curves[minIndex]\\n if minIndex == 0:\\n log.debug(\\\"PLP4 A\\\")\\n elif minIndex == 1:\\n log.debug(\\\"PLP4 B\\\")\\n else:\\n log.debug(\\\"PLP4 C\\\")\\n \\n return newCurve\\n \\n\\ndef _PLP5(curve, vm, am, delta):\\n \\\"\\\"\\\"(t0 >= delta) and (t1 < delta) and (t2 < delta)\\n \\n This case is similar to PLP2 and we first follow the procedure of PLP2. However, PLP2A and PLP2B\\n can both be infeasible. So we introduct PLP4C where we re-interpolate the trajectory to have\\n two-ramp by using PP2 procedure.\\n \\\"\\\"\\\"\\n firstRamp = curve[0]\\n middleRamp = curve[1]\\n lastRamp = curve[2]\\n\\n t0 = firstRamp.duration\\n t1 = middleRamp.duration\\n t2 = lastRamp.duration\\n v0 = firstRamp.v0\\n v1 = lastRamp.v1\\n x0 = firstRamp.x0\\n d = curve.d\\n d1 = middleRamp.d + lastRamp.d\\n vp = middleRamp.v0 # the middle has zero acceleration\\n deltaInv = 1.0/delta\\n\\n # Try case A.\\n a2New = (v1 - vp)*deltaInv\\n ramp2A = Ramp(vp, a2New, delta)\\n dRem = d - ramp2A.d\\n subCurveA = _Compute1DTrajectoryWithDelta(0, dRem, v0, vp, vm, am, delta)\\n assert(not subCurveA.IsEmpty()) # this computation should not fail\\n if len(subCurveA) == 1:\\n curveA = ParabolicCurve([subCurveA[0], ramp2A])\\n else:\\n # len(subCurveA) == 2 in this case\\n curveA = ParabolicCurve([subCurveA[0], subCurveA[1], ramp2A])\\n \\n # Try case B.\\n if FuzzyZero(vp + v1, epsilon):\\n curveB = ParabolicCurve()\\n else:\\n passed = False\\n t1New = 2*d1/(vp + v1)\\n if (t1New >= delta):\\n a1New = (v1 - vp)/t1New\\n if abs(a1New) <= am + epsilon:\\n ramp1B = Ramp(vp, a1New, t1New)\\n curveB = ParabolicCurve([firstRamp, ramp1B])\\n if not passed:\\n curveB = ParabolicCurve()\\n\\n # Try case C.\\n # Two-ramp velocity profile where the first ramp saturates the minimum-switch-time constraint\\n # and the second ramp saturates the acceleration constraint.\\n a0 = lastRamp.a\\n k = a0*delta\\n vpNew = 0.5*(k - np.sign(a0)*np.sqrt(k**2 + 4*(k*v0 + v1**2) - 8*a0*d))\\n if abs(vpNew) > vm + epsilon:\\n curveC = ParabolicCurve()\\n else:\\n t0New = (vpNew - v0)/a0 # a0 is not zero\\n if (t0New >= delta):\\n # PP2A: no further correction is needed.\\n a1New = (v1 - vpNew)*deltaInv\\n ramp0C_A = Ramp(v0, a0, t0New, x0)\\n ramp1C_A = Ramp(vpNew, a1New, delta)\\n curveC_A = ParabolicCurve([ramp0C_A, ramp1C_A])\\n curveC = curveC_A\\n else:\\n # Try case PP1B.\\n vpNew = d*deltaInv - 0.5*(v0 + v1)\\n a0New = (vpNew - v0)*deltaInv\\n a1New = (v1 - vpNew)*deltaInv\\n\\n if (abs(a0New) <= am + epsilon) and (abs(a1New) <= am + epsilon) and (abs(vpNew) <= vm + epsilon):\\n ramp0C_B = Ramp(v0, a0New, delta, x0)\\n ramp1C_B = Ramp(vpNew, a1New, delta)\\n curveC_B = ParabolicCurve([ramp0C_B, ramp1C_B])\\n else:\\n # Case PP2B does not produce any feasible solution\\n curveC_B = ParabolicCurve()\\n\\n # Try case PP1C.\\n if FuzzyZero(v0 + v1, epsilon):\\n # Case C does not produce any feasible solution\\n curveC_C = ParabolicCurve()\\n else:\\n tNew = 2*d/(v0 + v1)\\n aNew = (v1 - v0)/tNew\\n if (abs(aNew) <= am + epsilon) and (tNew >= delta):\\n ramp0C_C = Ramp(v0, aNew, tNew, x0)\\n curveC_C = ParabolicCurve([ramp0C])\\n else:\\n # Case C does not produce any feasible solution\\n curveC_C = ParabolicCurve()\\n\\n assert (curveC_B.IsEmpty() and curveC_C.IsEmpty())\\n if curveC_B.IsEmpty():\\n curveC = curveC_C\\n elif curveC_C.IsEmpty():\\n curveC = curveC_B\\n else:\\n curveC = curveC_B if curveC_B.duration < curveC_C.duration else curveC_C\\n \\n # Now compare PLP1A, PLP1B, and PLP1C\\n if curveA.IsEmpty() and curveB.IsEmpty() and curveC.IsEmpty():\\n assert False\\n newCurve = curveA # empty curve\\n elif (not curveA.IsEmpty()) and curveB.IsEmpty() and curveC.IsEmpty():\\n log.debug(\\\"PLP5 A\\\")\\n newCurve = curveA\\n elif curveA.IsEmpty() and (not curveB.IsEmpty()) and curveC.IsEmpty():\\n log.debug(\\\"PLP5 B\\\")\\n newCurve = curveB\\n elif curveA.IsEmpty() and curveB.IsEmpty() and (not curveC.IsEmpty()):\\n log.debug(\\\"PLP5 C\\\")\\n newCurve = curveC\\n elif curveA.IsEmpty():\\n if curveB.duration <= curveC.duration:\\n log.debug(\\\"PLP5 B\\\")\\n newCurve = curveB\\n else:\\n log.debug(\\\"PLP5 C\\\")\\n newCurve = curveC\\n elif curveB.IsEmpty():\\n if curveA.duration <= curveC.duration:\\n log.debug(\\\"PLP5 A\\\")\\n newCurve = curveA\\n else:\\n log.debug(\\\"PLP5 C\\\")\\n newCurve = curveC\\n elif curveC.IsEmpty():\\n if curveA.duration <= curveB.duration:\\n log.debug(\\\"PLP5 A\\\")\\n newCurve = curveA\\n else:\\n log.debug(\\\"PLP5 B\\\")\\n newCurve = curveB\\n else:\\n curves = [curveA, curveB, curveC]\\n minIndex = min((curve.duration, idx) for (idx, curve) in enumerate(curves))[1]\\n newCurve = curves[minIndex]\\n if minIndex == 0:\\n log.debug(\\\"PLP5 A\\\")\\n elif minIndex == 1:\\n log.debug(\\\"PLP5 B\\\")\\n else:\\n log.debug(\\\"PLP5 C\\\")\\n \\n return newCurve\\n\\n\\ndef _PLP6(curve, vm, am, delta):\\n \\\"\\\"\\\"(t0 < delta) and (t1 >= delta) and (t2 < delta)\\n \\n We explore four possibilities\\n \\n A: stretch both the first and the last ramps. \\n B: stretch only the first ramp. (The remaining displacement is taken care by\\n basically using Compute1DTrajectoryWithDelta.)\\n C: stretch only the last ramp, similar to B.\\n D: merge everything into one ramp (because sometimes the first and the last ramps\\n are both too short).\\n \\\"\\\"\\\"\\n firstRamp = curve[0]\\n middleRamp = curve[1]\\n lastRamp = curve[2]\\n\\n t0 = firstRamp.duration\\n t1 = middleRamp.duration\\n t2 = lastRamp.duration\\n v0 = firstRamp.v0\\n v1 = lastRamp.v1\\n x0 = firstRamp.x0\\n d = curve.d\\n d0 = firstRamp.d + middleRamp.d\\n vp = middleRamp.v0 # the middle has zero acceleration\\n deltaInv = 1.0/delta\\n \\n newFirstRamp = Ramp(v0, (vp - v0)*deltaInv, delta, x0)\\n newLastRamp = Ramp(vp, (v1 - vp)*deltaInv, delta)\\n \\n # Try case A.\\n dRemA = d - (newFirstRamp.d + newLastRamp.d)\\n t1New = dRemA/vp\\n if (t1New > 0):\\n newMiddleRamp = Ramp(vp, 0.0, t1New)\\n curveA = ParabolicCurve([newFirstRamp, newMiddleRamp, newLastRamp])\\n if (t1New < delta):\\n curveA = _FixSwitchTimeThreeRamps(curveA, vm, am, delta) # go to PLP3\\n else:\\n curveA = ParabolicCurve()\\n\\n # Try case B.\\n # B1\\n dRemB = d - newFirstRamp.d\\n subCurveB1 = _Compute1DTrajectoryWithDelta(0, dRemB, vp, v1, vm, am, delta)\\n if len(subCurveB1) == 1:\\n curveB1 = ParabolicCurve([newFirstRamp, subCurveB1[0]])\\n else:\\n # len(subCurveB) == 2 in this case\\n curveB1 = ParabolicCurve([newFirstRamp, subCurveB1[0], subCurveB1[1]])\\n\\n # B2\\n curveB2 = _PP1(curve, vm, am, delta)\\n \\n if curveB2.IsEmpty() or abs(curveB2[0].v1) > vm:\\n curveB = curveB1\\n else:\\n if curveB1.duration <= curveB2.duration:\\n curveB = curveB1\\n else:\\n curveB = curveB2\\n\\n # Try case C.\\n # C1\\n dRemC = d - newLastRamp.d\\n subCurveC1 = _Compute1DTrajectoryWithDelta(x0, x0 + dRemC, v0, vp, vm, am, delta)\\n if len(subCurveC1) == 1:\\n curveC1 = ParabolicCurve([subCurveC1[0], newLastRamp])\\n else:\\n # len(subCurveC1) == 2 in this case\\n curveC1 = ParabolicCurve([subCurveC1[0], subCurveC1[1], newLastRamp])\\n\\n # C2\\n curveC2 = _PP2(curve, vm, am, delta)\\n \\n if curveC2.IsEmpty() or abs(curveC2[0].v1) > vm:\\n curveC = curveC1\\n else:\\n if curveC1.duration <= curveC2.duration:\\n curveC = curveC1\\n else:\\n curveC = curveC2\\n \\n # Try case D.\\n if FuzzyZero(v0 + v1, epsilon):\\n curveD = ParabolicCurve()\\n else:\\n tNew = 2*d/(v0 + v1)\\n if tNew <= 0:\\n curveD = ParabolicCurve()\\n else:\\n aNew = (v1 - v0)/tNew\\n if abs(aNew) > am + epsilon:\\n curveD = ParabolicCurve()\\n else:\\n ramp0D = Ramp(v0, aNew, tNew, x0)\\n curveD = ParabolicCurve([ramp0D])\\n\\n # Now compare every case\\n if not curveA.IsEmpty():\\n if (curveA.duration <= curveB.duration) and (curveA.duration <= curveC.duration) and (curveA[1].duration >= delta):\\n if curveD.IsEmpty():\\n # Check before returning\\n log.debug(\\\"PLP6 A\\\")\\n return curveA\\n else:\\n if curveA.duration <= curveD.duration:\\n # Check before returning\\n log.debug(\\\"PLP6 A\\\")\\n return curveA\\n \\n if (curveB.duration <= curveC.duration):\\n if curveD.IsEmpty():\\n # Check before returning\\n log.debug(\\\"PLP6 B\\\")\\n return curveB\\n else:\\n if curveB.duration <= curveD.duration:\\n # Check before returning\\n log.debug(\\\"PLP6 B\\\")\\n return curveB\\n else:\\n if curveD.IsEmpty():\\n # Check before returning\\n log.debug(\\\"PLP6 C\\\")\\n return curveC\\n else:\\n if curveC.duration <= curveD.duration:\\n # Check before returning\\n log.debug(\\\"PLP6 C\\\")\\n return curveC\\n\\n # Check before returning\\n log.debug(\\\"PLP6 D\\\")\\n return curveD\\n\\n\\ndef _PLP7(curve, vm, am, delta):\\n \\\"\\\"\\\"(t0 < delta) and (t1 < delta) and (t2 < delta) \\n \\n \\\"\\\"\\\"\\n firstRamp = curve[0]\\n middleRamp = curve[1]\\n lastRamp = curve[2]\\n\\n t0 = firstRamp.duration\\n t1 = middleRamp.duration\\n t2 = lastRamp.duration\\n v0 = firstRamp.v0\\n v1 = lastRamp.v1\\n x0 = firstRamp.x0\\n d = curve.d\\n d0 = firstRamp.d + middleRamp.d\\n vp = middleRamp.v0 # the middle has zero acceleration\\n deltaInv = 1.0/delta\\n \\n # A: (delta, delta, delta)\\n curveA = Recompute1DTrajectoryThreeRamps(curve, delta, delta, delta, vm, am)\\n\\n # B: fix using PP3\\n curveB = _PP3(curve, vm, am, delta)\\n\\n # C: exceptional case\\n if FuzzyZero(v0, epsilon) or FuzzyZero(v1, epsilon):\\n if abs(vp - v0) > abs(vp - v1):\\n dNew = 0.5*(vp + v0)*delta\\n t1New = (2*d - 2*dNew)/(vp + v1)\\n if t1New > 0:\\n a0New = (vp - v0)*deltaInv\\n a1New = (v1 - vp)/t1New\\n ramp0C = Ramp(v0, a0New, delta, x0)\\n ramp1C = Ramp(vp, a1New, t1New)\\n curveC = ParabolicCurve([ramp0C, ramp1C])\\n else:\\n curveC = ParabolicCurve()\\n else:\\n dNew = 0.5*(vp + v1)*delta\\n t0New = (2*d - 2*dNew)/(vp + v0)\\n if t0New > 0:\\n a0New = (vp - v0)/t0New\\n a1New = (v1 - vp)*deltaInv\\n ramp0C = Ramp(v0, a0New, t0New, x0)\\n ramp1C = Ramp(vp, a1New, delta)\\n curveC = ParabolicCurve([ramp0C, ramp1C])\\n else:\\n curveC = ParabolicCurve()\\n else:\\n curveC = ParabolicCurve()\\n \\n # Now compare all cases\\n if curveA.IsEmpty() and curveB.IsEmpty() and curveC.IsEmpty():\\n assert False\\n newCurve = curveA # empty curve\\n elif (not curveA.IsEmpty()) and curveB.IsEmpty() and curveC.IsEmpty():\\n log.debug(\\\"PLP7 A\\\")\\n newCurve = curveA\\n elif curveA.IsEmpty() and (not curveB.IsEmpty()) and curveC.IsEmpty():\\n log.debug(\\\"PLP7 B\\\")\\n newCurve = curveB\\n elif curveA.IsEmpty() and curveB.IsEmpty() and (not curveC.IsEmpty()):\\n log.debug(\\\"PLP7 C\\\")\\n newCurve = curveC\\n elif curveA.IsEmpty():\\n if curveB.duration <= curveC.duration:\\n log.debug(\\\"PLP7 B\\\")\\n newCurve = curveB\\n else:\\n log.debug(\\\"PLP7 C\\\")\\n newCurve = curveC\\n elif curveB.IsEmpty():\\n if curveA.duration <= curveC.duration:\\n log.debug(\\\"PLP7 A\\\")\\n newCurve = curveA\\n else:\\n log.debug(\\\"PLP7 C\\\")\\n newCurve = curveC\\n elif curveC.IsEmpty():\\n if curveA.duration <= curveB.duration:\\n log.debug(\\\"PLP7 A\\\")\\n newCurve = curveA\\n else:\\n log.debug(\\\"PLP7 B\\\")\\n newCurve = curveB\\n else:\\n curves = [curveA, curveB, curveC]\\n minIndex = min((curve.duration, idx) for (idx, curve) in enumerate(curves))[1]\\n newCurve = curves[minIndex]\\n if minIndex == 0:\\n log.debug(\\\"PLP7 A\\\")\\n elif minIndex == 1:\\n log.debug(\\\"PLP7 B\\\")\\n else:\\n log.debug(\\\"PLP7 C\\\")\\n # Check before returning\\n return newCurve\\n\"\n },\n {\n \"alpha_fraction\": 0.6794634461402893,\n \"alphanum_fraction\": 0.7206290364265442,\n \"avg_line_length\": 45,\n \"blob_id\": \"b948f647c07ff059b433b38c1c64d3e3406973a9\",\n \"content_id\": \"20966a85a3ef063b5ac8ff6fb065d9915d1bc065\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 2162,\n \"license_type\": \"no_license\",\n \"max_line_length\": 288,\n \"num_lines\": 46,\n \"path\": \"/examples/README.md\",\n \"repo_name\": \"shengjiangtou/parabint\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Examples\\r\\n## Parabint Trajectories\\r\\n\\r\\nThere are three types of trajectories:\\r\\n - `Ramp`: A 1D trajectory segment with constant acceleration. A Ramp is completely determined given four values: `v0` initial velocity, `a` acceleration, `t` duration, and `x0` initial displacement. The initial displacement is an optional argument which is set to `0` when not specified.\\r\\n - `ParabolicCurve`: A concatenation of `Ramp`s. A ParabolicCurve is then a 1D trajectory with a number of segments of constant accelerations.\\r\\n - `ParabolicCurvesND`: An n-dimensional trajectory consisting of n `ParabolicCurvesND`s of equal duration.\\r\\n```python\\r\\nfrom parabint.trajectory import Ramp, ParabolicCurve, ParabolicCurvesND\\r\\n\\r\\nramp1 = Ramp(0, 1, 0.5) # v0 = 0; a = 1; duration = 0.5; x0 = 0 (optional)\\r\\nramp2 = Ramp(ramp1.v1, -0.7, 0.8)\\r\\ncurve1 = ParabolicCurve(ramps=[ramp1, ramp2])\\r\\n\\r\\nramp3 = Ramp(0.2, -0.66, 1.0)\\r\\nramp4 = Ramp(ramp3.v1, 0, 0.3)\\r\\ncurve2 = ParabolicCurve(ramps=[ramp3, ramp4])\\r\\ncurvesnd = ParabolicCurvesND(curves=[curve1, curve2])\\r\\n\\r\\n# curvesnd.PlotVel() # visualize the velocity profile\\r\\n```\\r\\n<p align=\\\"center\\\">\\r\\n <img src=\\\"figures/example_trajectories.png\\\" width=\\\"500\\\"/>\\r\\n</p>\\r\\n\\r\\nThe above figure shows the two velocity profiles in `curvesnd`. Vertical lines represent switch points, i.e., time instants where at least one joint changes its acceleration. When calling `PlotVel`, switch points can be plotted by setting the (optional) argument `includingSW` to `True`.\\r\\n\\r\\n## Trajectory Interpolation\\r\\n\\r\\nA parabolic trajectory can be generated from a set of boundary conditions `(x0, x1, v0, v1)` subject to max velocity `vm`, max acceleration `am`, and/or minimum-switch-time `delta`.\\r\\n```python\\r\\nfrom parabint import interpolator\\r\\n\\r\\nx0 = 0; x1 = 0.5; v0 = 0.4; v1 = 0.2\\r\\nvm = 1.0; am = 0.5\\r\\ndelta = 0.5\\r\\n\\r\\ncurve1 = interpolator.Compute1DTrajectory(x0, x1, v0, v1, vm, am) # without minimum-switch-time\\r\\ncurve2 = interpolator.Compute1DTrajectory(x0, x1, v0, v1, vm, am, delta) # with minimum-switch-time\\r\\n\\r\\n# curve1.PlotVel(fignum=1)\\r\\n# curve2.PlotVel(fignum=1)\\r\\n```\\r\\n<p align=\\\"center\\\">\\r\\n <img src=\\\"figures/example_reinterpolation.png\\\" width=\\\"500\\\"/>\\r\\n</p>\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.762499988079071,\n \"alphanum_fraction\": 0.762499988079071,\n \"avg_line_length\": 25.66666603088379,\n \"blob_id\": \"df7e0dbd296494efbd2d0054bd75a859a040d174\",\n \"content_id\": \"617c18ed47c65be94034b0d9734c87bf8ceaf742\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 80,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 3,\n \"path\": \"/setup.py\",\n \"repo_name\": \"shengjiangtou/parabint\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from distutils.core import setup\\n\\nsetup(name='parabint', packages=['parabint'])\\n\"\n },\n {\n \"alpha_fraction\": 0.7704160213470459,\n \"alphanum_fraction\": 0.7704160213470459,\n \"avg_line_length\": 33.157894134521484,\n \"blob_id\": \"ec0fe2c8e9f881172f9952af4f586d4a7e226517\",\n \"content_id\": \"1fc9bbfe35dbdc9c78f9b3a2829d20e864b2af11\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 649,\n \"license_type\": \"no_license\",\n \"max_line_length\": 235,\n \"num_lines\": 19,\n \"path\": \"/README.md\",\n \"repo_name\": \"shengjiangtou/parabint\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# parabint\\nThis is parabint, a library for time-optimal parabolic interpolation with velocity, acceleration, and minimum-switch-time constraints.\\n\\n## Dependencies\\n\\nIn case you want to also use the optimization routines in [optimization.py](./parabint/optimization.py) for comparison, you will need to install [Ipopt](https://projects.coin-or.org/Ipopt) and [pyipopt](https://github.com/xuy/pyipopt).\\n\\n## Installation\\nClone this repository via\\n```bash\\ngit clone https://github.com/puttichai/parabint\\n```\\nThen from your parabint directory, run\\n```bash\\nsudo python setup.py install\\n```\\n\\n## Usage\\nSee the [examples](/examples) folder for some examples.\\n\"\n },\n {\n \"alpha_fraction\": 0.5174774527549744,\n \"alphanum_fraction\": 0.5639639496803284,\n \"avg_line_length\": 31.6235294342041,\n \"blob_id\": \"7df27e4478490290719f2fca5f6d297fef30ca49\",\n \"content_id\": \"c477422d9e206748aaf79eec0641e8ccbc2e8fc8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2775,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 85,\n \"path\": \"/tests/test_pp_reinterpolation.py\",\n \"repo_name\": \"shengjiangtou/parabint\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from parabint import interpolator\\nfrom parabint.trajectory import Ramp, ParabolicCurve, ParabolicCurvesND\\nfrom matplotlib import pyplot as plt\\nimport random\\nrng = random.SystemRandom()\\n\\n_xm = 2.96705973\\n_vm = 3.92699082\\n_am = 2.0\\ndelta = 0.2\\n\\navailableChoices = dict()\\navailableChoices[len(availableChoices.keys())] = \\\"PP1 : t0 < delta; t1 >= delta\\\"\\navailableChoices[len(availableChoices.keys())] = \\\"PP2 : t0 >= delta; t1 < delta\\\"\\navailableChoices[len(availableChoices.keys())] = \\\"PP3 : t0 < delta; t1 < delta\\\"\\navailableChoices[len(availableChoices.keys())] = \\\"stop\\\"\\nmsg = \\\"Choose a test case\\\"\\nfor (key, val) in availableChoices.iteritems():\\n msg += \\\"\\\\n{0} : {1}\\\".format(key, val)\\nmsg += \\\"\\\\n\\\\n>> \\\"\\n\\ndef SampleConditions(testcase, delta):\\n \\\"\\\"\\\"This function sample boundary conditions (x0, x1, v0, v1) as well\\n as the velocity and acceleration bounds vm and am such that the\\n time-optimal parabolic trajectory violates the given\\n minimum-switch-time constraint in the given case.\\n\\n \\\"\\\"\\\"\\n if testcase == 0:\\n t0 = delta + 1\\n t1 = 0\\n elif testcase == 1:\\n t0 = 0\\n t1 = delta + 1\\n else:\\n t0 = delta + 1\\n t1 = delta + 1\\n\\n # Sample a test case\\n passed = False\\n while not passed:\\n x0 = _xm * rng.uniform(-1, 1)\\n x1 = _xm * rng.uniform(-1, 1)\\n vm = _vm * rng.uniform(0, 1)\\n am = _am * rng.uniform(0, 1)\\n v0 = vm * rng.uniform(-1, 1)\\n v1 = vm * rng.uniform(-1, 1)\\n\\n curve1 = interpolator.Compute1DTrajectory(x0, x1, v0, v1, vm, am)\\n if not (len(curve1) == 2):\\n continue\\n t0 = curve1[0].duration\\n t1 = curve1[1].duration\\n if testcase == 0:\\n passed = t0 < delta and t1 >= delta\\n elif testcase == 1:\\n passed = t0 >= delta and t1 < delta\\n else:\\n passed = t0 < delta and t1 < delta\\n \\n return x0, x1, v0, v1, vm, am\\n\\nwhile True:\\n try:\\n index = int(raw_input(msg))\\n if index in availableChoices.keys():\\n if index == len(availableChoices.keys()) - 1:\\n break\\n x0, x1, v0, v1, vm, am = SampleConditions(index, delta)\\n\\n curve1 = interpolator.Compute1DTrajectory(x0, x1, v0, v1, vm, am)\\n curve2 = interpolator.Compute1DTrajectory(x0, x1, v0, v1, vm, am, delta)\\n \\n # Visualization\\n plt.clf()\\n curve1.PlotVel(fignum=1, color='r')\\n curve2.PlotVel(fignum=1, color='g')\\n \\n report = \\\\\\n \\\"x0 = {0};\\\\nx1 = {1};\\\\nv0 = {2};\\\\nv1 = {3};\\\\nvm = {4};\\\\nam = {5};\\\\ndelta = {6};\\\\n\\\".\\\\\\n format(x0, x1, v0, v1, vm, am, delta)\\n print report\\n\\n except ValueError:\\n print \\\"Please enter a valie choice\\\"\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.512901782989502,\n \"alphanum_fraction\": 0.5258350372314453,\n \"avg_line_length\": 27.228723526000977,\n \"blob_id\": \"de34e23155dcfae27ffff4ca585877fd449663da\",\n \"content_id\": \"4cee73af3a57232fc20a3a9bfa6c7f69c95b2f5f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 31856,\n \"license_type\": \"no_license\",\n \"max_line_length\": 109,\n \"num_lines\": 1128,\n \"path\": \"/parabint/trajectory.py\",\n \"repo_name\": \"shengjiangtou/parabint\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom copy import deepcopy\\nimport bisect\\nfrom pylab import ion\\nion()\\n\\nfrom utilities import epsilon, inf, FuzzyEquals, FuzzyZero\\n\\n\\nclass Ramp(object):\\n \\\"\\\"\\\"A Ramp is a constant-acceleration one-dimensional trajectory. When plotting its velocity\\n evolution over time, the graph is a `ramp` on the velocity-time plane, hence the name.\\n\\n Parameters (input)\\n ------------------\\n v0 : float\\n Initial velocity of the trajectory.\\n a : float\\n Acceleration of the trajectory.\\n duration : float\\n Duration of the trajectory. It must be non-negative.\\n x0 : float, optional\\n Initial displacement of the trajectory. If not given, x0 will be set to zero.\\n\\n Parameters (calculated from inputs)\\n -----------------------------------\\n v1 : float\\n Final velocity of the trajectory.\\n d : float\\n total displacement made by this Ramp (i.e., independent of x0).\\n x1 : float\\n Final displacement at the end of the trajectory: x1 = x0 + d.\\n\\n \\\"\\\"\\\"\\n def __init__(self, v0, a, t, x0=0):\\n assert(t >= -epsilon)\\n\\n self.v0 = v0\\n self.a = a\\n self.duration = t\\n self.x0 = x0\\n\\n self.v1 = v0 + self.a*self.duration\\n self.d = self.duration*(self.v0 + 0.5*self.a*self.duration)\\n self.x1 = self.x0 + self.d\\n\\n\\n def Initialize(self, v0, a, t, x0=0):\\n \\\"\\\"\\\"Initialize (or reinitialize) the Ramp with the given parameters and calculated other parameters\\n accordingly.\\n\\n Parameters (input)\\n ------------------\\n v0 : float\\n Initial velocity of the trajectory.\\n a : float\\n Acceleration of the trajectory.\\n duration : float\\n Duration of the trajectory. It must be non-negative.\\n x0 : float, optional\\n Initial displacement of the trajectory. If not given, x0 will be set to zero.\\n\\n \\\"\\\"\\\"\\n assert(t >= -epsilon)\\n\\n self.v0 = v0\\n self.a = a\\n self.duration = t\\n self.x0 = x0\\n\\n self.v1 = v0 + self.a*self.duration\\n self.d = self.duration*(self.v0 + 0.5*self.a*self.duration)\\n self.x1 = self.x0 + self.d\\n\\n\\n def EvalPos(self, t):\\n \\\"\\\"\\\"Evalutaion the position at the given time instant.\\n\\n Parameters\\n ----------\\n t : float\\n Time instant at which to evaluate the position.\\n\\n Returns\\n -------\\n x : float\\n Position at time t.\\n\\n \\\"\\\"\\\"\\n if (t <= 0):\\n return self.x0\\n elif (t >= self.duration):\\n return self.x1\\n else:\\n return self.x0 + t*(self.v0 + 0.5*self.a*t)\\n\\n\\n def EvalVel(self, t):\\n \\\"\\\"\\\"Evalutaion the velocity at the given time instant.\\n\\n Parameters\\n ----------\\n t : float\\n Time instant at which to evaluate the velocity.\\n\\n Returns\\n -------\\n v : float\\n Velocity at time t.\\n\\n \\\"\\\"\\\"\\n if (t <= 0):\\n return self.v0\\n elif (t >= self.duration):\\n return self.v1\\n else:\\n return self.v0 + self.a*t\\n\\n \\n def EvalAcc(self):\\n \\\"\\\"\\\"Return the acceleration.\\n \\n Returns\\n -------\\n a : float\\n Acceleration.\\n\\n \\\"\\\"\\\"\\n return self.a\\n\\n\\n def GetPeaks(self):\\n \\\"\\\"\\\"Calculate the peaks of positions along the trajectory.\\n\\n Returns\\n -------\\n xmin : float\\n Minimum position along the trajectory.\\n xmax : float\\n Maximum position along the trajectory.\\n\\n \\\"\\\"\\\"\\n return self._GetPeaks(0, self.duration)\\n\\n\\n def _GetPeaks(self, ta, tb):\\n if (ta > tb):\\n return self._GetPeaks(tb, ta)\\n\\n if (ta < 0):\\n ta = 0\\n if (tb <= 0):\\n return [self.x0, self.x0]\\n\\n if (tb > self.duration):\\n tb = self.duration\\n if (ta >= self.duration):\\n return [self.x1, self.x1]\\n\\n curMin = self.EvalPos(ta)\\n curMax = self.EvalPos(tb)\\n if curMin > curMax:\\n [curMin, curMax] = Swap(curMax, curMin)\\n \\n if FuzzyZero(self.a, epsilon):\\n return [curMin, curMax]\\n\\n tDeflection = -self.v0/self.a\\n if (tDeflection <= ta) or (tDeflection >= tb):\\n return [curMin, curMax]\\n\\n xDeflection = self.x0 + 0.5*self.v0*tDeflection\\n curMin = min(curMin, xDeflection)\\n curMax = max(curMax, xDeflection)\\n return [curMin, curMax]\\n\\n\\n def SetInitialValue(self, newX0):\\n \\\"\\\"\\\"Set the initial displacement (position) of the trajectory to the given value and recalculate the\\n related parameters accordingly.\\n\\n Parameters\\n ----------\\n newX0 : float\\n The new initial displacement.\\n\\n \\\"\\\"\\\"\\n self.x0 = newX0\\n self.x1 = self.x0 + self.d\\n\\n\\n def UpdateDuration(self, newDuration):\\n \\\"\\\"\\\"Set the trajectory duration to the given value and recalculate the related parameters accordingly.\\n\\n Parameters\\n ----------\\n newDuration : float\\n The new duration.\\n \\n \\\"\\\"\\\"\\n assert(newDuration >= -epsilon)\\n if (newDuration <= 0):\\n self.duration = 0\\n self.x1 = self.x0\\n self.v1 = self.v0\\n self.d = 0\\n return\\n\\n self.duration = newDuration\\n self.v1 = self.v0 + self.a*self.duration\\n self.d = self.duration*(self.v0 + 0.5*self.a*self.duration)\\n self.x1 = self.x0 + self.d\\n\\n\\n def PlotPos(self, t0=0, fignum=None, **kwargs):\\n \\\"\\\"\\\"Plot trajectory position vs time.\\n\\n Parameters\\n ----------\\n t0 : float\\n Position on the horizontal (time) axis to start plotting.\\n fignum : int, float, string\\n Figure's number/title\\n \\n \\\"\\\"\\\"\\n raise NotImplementedError\\n\\n\\n def PlotVel(self, t0=0, fignum=None, **kwargs):\\n \\\"\\\"\\\"Plot trajectory velocity vs time.\\n\\n Parameters\\n ----------\\n t0 : float\\n Position on the horizontal (time) axis to start plotting.\\n fignum : int, float, string\\n Figure's number/title\\n \\n \\\"\\\"\\\"\\n if fignum is not None:\\n plt.figure(fignum)\\n\\n line = plt.plot([t0, t0 + self.duration], [self.v0, self.v1], **kwargs)[0]\\n plt.show(False)\\n return line\\n\\n\\n def PlotAcc(self, t0=0, fignum=None, **kwargs):\\n \\\"\\\"\\\"Plot trajectory acceleration vs time.\\n\\n Parameters\\n ----------\\n t0 : float\\n Position on the horizontal (time) axis to start plotting.\\n fignum : int, float, string\\n Figure's number/title\\n \\n \\\"\\\"\\\"\\n if fignum is not None:\\n plt.figure(fignum)\\n\\n line = plt.plot([t0, t0 + self.duration], [self.a, self.a], **kwargs)[0]\\n plt.show(False)\\n return line\\n\\n\\n def Cut(self, t):\\n \\\"\\\"\\\"Cut the trajectory into two halves. self will be the left half which is the segment from time 0\\n to t. The other half (segment from time t to duration) is returned.\\n\\n Parameters\\n ----------\\n t : float\\n Time instant at which to cut the trajectory\\n\\n Returns\\n -------\\n remRamp : Ramp\\n Segment from time t to duration of the original trajectory.\\n\\n \\\"\\\"\\\"\\n if (t <= 0):\\n remRamp = Ramp(self.v0, self.a, self.duration, self.x0)\\n self.Initialize(self.v0, 0, 0, self.x0)\\n return remRamp\\n elif (t >= self.duration):\\n renRamp = Ramp(self.v1, 0, 0, self.x1)\\n return remRamp\\n\\n remRampDuration = self.duration - t\\n self.UpdateDuration(t)\\n remRamp = Ramp(self.v1, self.a, remRampDuration, self.x1)\\n return remRamp\\n\\n\\n def TrimFront(self, t):\\n \\\"\\\"\\\"Trim out the trajectory segment from time 0 to t.\\n\\n Parameters\\n ----------\\n t : float\\n Time instant at which to trim the trajectory\\n \\n \\\"\\\"\\\"\\n if (t <= 0):\\n return\\n elif (t >= self.duration):\\n self.Initialize(self.v1, 0, 0, self.x1)\\n return\\n\\n remDuration = self.duration - t\\n newX0 = self.EvalPos(t)\\n newV0 = self.EvalVel(t)\\n self.Initialize(newV0, self.a, remDuration, newX0)\\n return\\n\\n\\n def TrimBack(self, t):\\n \\\"\\\"\\\"Trim out the trajectory segment from time t to duration.\\n\\n Parameters\\n ----------\\n t : float\\n Time instant at which to trim the trajectory\\n \\n \\\"\\\"\\\"\\n if (t <= 0):\\n self.Initialize(self.v0, 0, 0, self.x0)\\n return\\n elif (t >= self.duration):\\n return\\n\\n self.UpdateDuration(t)\\n return\\n \\n\\nclass ParabolicCurve(object):\\n \\\"\\\"\\\"A ParabolicCurve is a piecewise-constant-acceleration one-dimensional trajectory. It is a\\n concatenation of Ramps.\\n\\n Parameters (input)\\n ------------------\\n ramps : list of Ramps, optional\\n The list of Ramps to construct the ParabolicCurve with.\\n\\n Parameters (generated from inputs)\\n ----------------------------------\\n x0 : float\\n Initial displacement of the trajectory.\\n x1 : float\\n Final displacement of the trajectory.\\n v0 : float\\n Initial velocity of the trajectory.\\n v1 : float\\n Final velocity of the trajectory.\\n switchpointsList : list of float\\n List of switch points, time instants at which the acceleration changes.\\n duration : float\\n Duration of the trajectory.\\n d : float\\n Total displacement made by this trajectory.\\n\\n \\\"\\\"\\\"\\n def __init__(self, ramps=[]):\\n self.switchpointsList = []\\n dur = 0.0\\n d = 0.0\\n self.ramps = []\\n \\n if len(ramps) == 0:\\n self.isEmpty = True\\n self.x0 = 0.0\\n self.x1 = 0.0\\n self.v0 = 0.0\\n self.v1 = 0.0\\n self.switchpointsList = []\\n self.duration = dur\\n self.d = d\\n else:\\n self.ramps = deepcopy(ramps)\\n self.isEmpty = False\\n self.v0 = self.ramps[0].v0\\n self.v1 = self.ramps[-1].v1\\n \\n self.switchpointsList.append(dur)\\n for ramp in self.ramps:\\n dur += ramp.duration\\n self.switchpointsList.append(dur)\\n d += ramp.d\\n self.duration = dur\\n self.d = d\\n\\n self.SetInitialValue(self.ramps[0].x0)\\n\\n\\n def __getitem__(self, index):\\n return self.ramps[index]\\n\\n\\n def __len__(self):\\n return len(self.ramps)\\n\\n\\n def Initialize(self):\\n self.switchpointsList = []\\n dur = 0.0\\n d = 0.0\\n self.ramps = []\\n \\n if len(ramps) == 0:\\n self.x0 = 0.0\\n self.x1 = 0.0\\n self.v0 = 0.0\\n self.v1 = 0.0\\n self.switchpointsList = []\\n self.duration = dur\\n self.d = d\\n else:\\n self.ramps = deepcopy(ramps)\\n self.v0 = self.ramps[0].v0\\n self.v1 = self.ramps[-1].v1\\n \\n self.switchpointsList.append(dur)\\n for ramp in self.ramps:\\n dur += ramp.duration\\n self.switchpointsList.append(dur)\\n d += ramp.d\\n self.duration = dur\\n self.d = d\\n\\n self.SetInitialValue(self.ramps[0].x0)\\n\\n\\n def IsEmpty(self):\\n return len(self) == 0\\n\\n\\n def Append(self, curve):\\n \\\"\\\"\\\"Append a ParabolicCurve to this one.\\n\\n Parameters\\n ----------\\n curve : ParabolicCurve\\n ParabolicCurve to be appended.\\n \\n \\\"\\\"\\\"\\n if len(self) == 0:\\n if len(curve) > 0:\\n self.ramps = deepcopy(curve.ramps)\\n self.x0 = curve.x0\\n self.x1 = curve.x1\\n self.v0 = curve.v0\\n self.v1 = curve.v1\\n self.duration = curve.duration\\n self.d = curve.d\\n self.switchpointsList = deepcopy(curve.switchpointsList)\\n else:\\n pass\\n return\\n else:\\n dur = self.duration\\n d = self.d\\n for ramp in curve.ramps:\\n self.ramps.append(deepcopy(ramp))\\n # Update displacement for the newly appended ramp\\n self.ramps[-1].SetInitialValue(self.ramps[-2].x1)\\n d += ramp.d\\n dur += ramp.duration\\n self.switchpointsList.append(dur)\\n\\n self.v1 = self.ramps[-1].v1\\n self.duration = dur\\n self.d = d\\n self.x1 = self.x0 + self.d\\n\\n\\n def FindRampIndex(self, t):\\n \\\"\\\"\\\"Find the index of the ramp in which the given time instant lies.\\n\\n Parameters\\n ----------\\n t : float\\n Time instant.\\n \\n Returns\\n -------\\n i : int\\n Ramp index.\\n remainder : float\\n Time interval between the beginning of the ramp to t.\\n \\n \\\"\\\"\\\"\\n if (t <= epsilon):\\n i = 0\\n remainder = 0.0\\n else:\\n i = bisect.bisect_left(self.switchpointsList, t) - 1\\n remainder = t - self.switchpointsList[i]\\n return [i, remainder]\\n\\n\\n def EvalPos(self, t):\\n \\\"\\\"\\\"Evalutaion the position at the given time instant.\\n\\n Parameters\\n ----------\\n t : float\\n Time instant at which to evaluate the position.\\n\\n Returns\\n -------\\n x : float\\n Position at time t.\\n\\n \\\"\\\"\\\"\\n assert(t >= -epsilon)\\n assert(t <= self.duration + epsilon)\\n\\n i, remainder = self.FindRampIndex(t)\\n return self.ramps[i].EvalPos(remainder)\\n\\n\\n def EvalVel(self, t):\\n \\\"\\\"\\\"Evalutaion the velocity at the given time instant.\\n\\n Parameters\\n ----------\\n t : float\\n Time instant at which to evaluate the velocity.\\n\\n Returns\\n -------\\n v : float\\n Velocity at time t.\\n\\n \\\"\\\"\\\"\\n assert(t >= -epsilon)\\n assert(t <= self.duration + epsilon)\\n\\n i, remainder = self.FindRampIndex(t)\\n return self.ramps[i].EvalVel(remainder)\\n\\n\\n def EvalAcc(self, t):\\n \\\"\\\"\\\"Evalutaion the acceleration at the given time instant.\\n\\n Parameters\\n ----------\\n t : float\\n Time instant at which to evaluate the acceleration.\\n\\n Returns\\n -------\\n a : float\\n Acceleration at time t.\\n\\n \\\"\\\"\\\"\\n assert(t >= -epsilon)\\n assert(t <= self.duration + epsilon)\\n\\n i, remainder = self.FindRampIndex(t)\\n return self.ramps[i].EvalAcc(remainder)\\n\\n\\n def GetPeaks(self):\\n \\\"\\\"\\\"Calculate the peaks of positions along the trajectory.\\n\\n Returns\\n -------\\n xmin : float\\n Minimum position along the trajectory.\\n xmax : float\\n Maximum position along the trajectory.\\n\\n \\\"\\\"\\\"\\n return self._GetPeaks(0, self.duration)\\n\\n\\n def _GetPeaks(self, ta, tb):\\n xmin = inf\\n xmax = -inf\\n\\n for ramp in self.ramps:\\n [bmin, bmax] = ramp.GetPeaks()\\n if bmin < xmin:\\n xmin = bmin\\n if bmax > xmax:\\n xmax = bmax\\n\\n return [xmin, xmax]\\n\\n\\n def SetInitialValue(self, x0):\\n \\\"\\\"\\\"Set the initial displacement (position) of the trajectory to the given value and recalculate the\\n related parameters accordingly.\\n\\n Parameters\\n ----------\\n newX0 : float\\n The new initial displacement.\\n\\n \\\"\\\"\\\"\\n self.x0 = x0\\n newX0 = x0\\n for ramp in self.ramps:\\n ramp.SetInitialValue(newX0)\\n newX0 += ramp.d\\n self.x1 = self.x0 + self.d\\n\\n\\n def SetConstant(self, x0, t):\\n \\\"\\\"\\\"Set this ParabolicCurve to be a constant trajectory (i.e. zero-velocity and zero-acceleration.\\n\\n Parameters\\n ----------\\n x0 : float\\n New initial dispalcement of the trajectory\\n t : float\\n New trajectory duration\\n \\n \\\"\\\"\\\"\\n assert(t >= 0)\\n ramp = Ramp(0, 0, t, x0)\\n self.Initialize([ramp])\\n return\\n\\n\\n def SetSegment(self, x0, x1, v0, v1, t):\\n assert(t >= -epsilon)\\n if FuzzyZero(t, epsilon):\\n a = 0\\n else:\\n tSqr = t*t\\n a = -(v0*tSqr + t*(x0 - x1) + 2*(v0 - v1))/(t*(0.5*tSqr + 2))\\n\\n ramp = Ramp()\\n ramp.x0 = x0\\n ramp.x1 = x1\\n ramp.v0 = v0\\n ramp.v1 = v1\\n ramp.duration = t\\n ramp.d = x1 - x0\\n ramp.a = a\\n self.Initialize([ramp])\\n return\\n\\n\\n def SetZeroDuration(self, x0, v0):\\n ramp = Ramp(v0, 0, 0, x0)\\n self.Initialize([ramp])\\n return\\n \\n \\n def Cut(self, t):\\n \\\"\\\"\\\"Cut the trajectory into two halves. self will be the left half which contains all the ramps from\\n time 0 to t. The other half of the trajectory is returned.\\n\\n Parameters\\n ----------\\n t : float\\n Time instant at which to cut the trajectory\\n\\n Returns\\n -------\\n remCurve : ParabolicCurve\\n ParabolicCurve containing all the ramps from time t to duration of the original trajectory.\\n\\n \\\"\\\"\\\"\\n if (t <= 0):\\n remCurve = ParabolicCurve(self.ramps)\\n self.SetZeroDuration(self.x0, self.v0)\\n return remCurve\\n elif (t >= self.duration):\\n remCurve = ParabolicCurve()\\n remCurve.SetZeroDuration(self.x1, self.v1)\\n return\\n\\n i, remainder = self.FindRampIndex(t)\\n leftHalf = self.ramps[0:i + 1]\\n rightHalf = self.ramps[i:]\\n\\n leftHalf[-1].TrimBack(remainder)\\n rightHalf[0].TrimFront(remainder)\\n self.Initialize(leftHalf)\\n remCurve = ParabolicCurve(rightHalf)\\n return remCurve\\n\\n\\n def TrimFront(self, t):\\n \\\"\\\"\\\"Trim out the trajectory segment from time 0 to t.\\n\\n Parameters\\n ----------\\n t : float\\n Time instant at which to trim the trajectory\\n \\n \\\"\\\"\\\"\\n if (t <= 0):\\n return\\n elif (t >= self.duration):\\n self.SetZeroDuration(self.x1, self.v1)\\n return\\n\\n i, remainder = self.FindRampIndex(t)\\n rightHalf = self.ramps[i:]\\n\\n rightHalf[0].TrimFront(remainder)\\n self.Initialize(rightHalf)\\n return\\n\\n\\n def TrimBack(self, t):\\n \\\"\\\"\\\"Trim out the trajectory segment from time t to duration.\\n\\n Parameters\\n ----------\\n t : float\\n Time instant at which to trim the trajectory\\n \\n \\\"\\\"\\\"\\n if (t <= 0):\\n self.SetZeroDuration(self.x0, self.v0)\\n return\\n elif (t >= self.duration):\\n return\\n\\n i, remainder = self.FindRampIndex(t)\\n leftHalf = self.ramps[0:i + 1]\\n\\n leftHalf[-1].TrimBack(remainder)\\n self.Initialize(leftHalf)\\n return\\n\\n\\n # Visualization\\n def PlotPos(self, fignum=None, color='g', dt=0.01, lw=2, includingSW=False):\\n \\\"\\\"\\\"Plot trajectory position vs time.\\n\\n Parameters\\n ----------\\n fignum : int, float, string, optional\\n Figure's number/title\\n color : string, optional\\n Matplotlib's color option.\\n dt : float, optional\\n Time resolution to evaluate the position.\\n lw : int, optional\\n Linewidth\\n includingSW : bool, optional\\n If True, draw vertical straight lines at switch points.\\n \\n \\\"\\\"\\\"\\n tVect = np.arange(0, self.duration, dt)\\n if tVect[-1] < self.duration:\\n tVect = np.append(tVect, self.duration)\\n \\n xVect = [self.EvalPos(t) for t in tVect]\\n if fignum is not None:\\n plt.figure(fignum)\\n plt.plot(tVect, xVect, color=color, linewidth=lw)\\n\\n if includingSW:\\n ax = plt.gca().axis()\\n for s in self.switchpointsList:\\n plt.plot([s, s], [ax[2], ax[3]], 'r', linewidth=1)\\n \\n plt.show(False)\\n\\n\\n def PlotVel(self, fignum=None, color=None, lw=2, includingSW=False, **kwargs):\\n \\\"\\\"\\\"Plot trajectory velocity vs time.\\n\\n Parameters\\n ----------\\n fignum : int, float, string, optional\\n Figure's number/title\\n color : string, optional\\n Matplotlib's color option.\\n lw : int, optional\\n Linewidth\\n includingSW : bool, optional\\n If True, draw vertical straight lines at switch points.\\n \\n \\\"\\\"\\\"\\n if fignum is not None:\\n plt.figure(fignum)\\n\\n t0 = 0.0\\n for ramp in self.ramps:\\n if color is None:\\n line = ramp.PlotVel(t0=t0, fignum=fignum, linewidth=lw, **kwargs)\\n color = line.get_color()\\n else:\\n line = ramp.PlotVel(t0=t0, fignum=fignum, color=color, linewidth=lw, **kwargs)\\n t0 += ramp.duration\\n\\n if includingSW:\\n ax = plt.gca().axis()\\n for s in self.switchpointsList:\\n plt.plot([s, s], [ax[2], ax[3]], 'r', linewidth=1)\\n \\n plt.show(False)\\n return line\\n\\n\\n def PlotAcc(self, fignum=None, color=None, lw=2, **kwargs):\\n \\\"\\\"\\\"Plot trajectory velocity vs time.\\n\\n Parameters\\n ----------\\n fignum : int, float, string, optional\\n Figure's number/title\\n color : string, optional\\n Matplotlib's color option.\\n lw : int, optional\\n Linewidth\\n \\n \\\"\\\"\\\"\\n if fignum is not None:\\n plt.figure(fignum)\\n \\n t0 = 0.0\\n prevAcc = self.ramps[0].a\\n for ramp in self.ramps:\\n if color is None:\\n line = ramp.PlotAcc(t0=t0, fignum=fignum, linewidth=lw, **kwargs)\\n color = line.get_color()\\n else:\\n line = ramp.PlotAcc(t0=t0, fignum=fignum, color=color, linewidth=lw, **kwargs)\\n plt.plot([t0, t0], [prevAcc, ramp.a], color=color, linewidth=lw, **kwargs)\\n t0 += ramp.duration\\n prevAcc = ramp.a\\n plt.show(False)\\n return line\\n\\n\\nclass ParabolicCurvesND(object):\\n \\\"\\\"\\\"A ParabolicCurvesND is a (parabolic) trajectory of an n-DOF system. A trajectory of each DOF is a\\n ParabolicCurve.\\n\\n \\\"\\\"\\\"\\n def __init__(self, curves=[]):\\n if len(curves) == 0:\\n self.curves = []\\n self.x0Vect = None\\n self.x1Vect = None\\n self.v0Vect = None\\n self.v1Vect = None\\n self.dVect = None\\n self.ndof = 0\\n self.switchpointsList = []\\n self.duration = 0.0\\n else:\\n # Check first if the input is valid\\n minDur = curves[0].duration\\n for curve in curves[1:]:\\n assert( FuzzyEquals(curve.duration, minDur, epsilon) )\\n minDur = min(curve.duration, minDur)\\n\\n self.curves = deepcopy(curves)\\n self.duration = minDur\\n self.ndof = len(self.curves)\\n self.x0Vect = np.asarray([curve.x0 for curve in self.curves])\\n self.x1Vect = np.asarray([curve.x1 for curve in self.curves])\\n self.v0Vect = np.asarray([curve.v0 for curve in self.curves])\\n self.v1Vect = np.asarray([curve.v1 for curve in self.curves])\\n self.dVect = np.asarray([curve.d for curve in self.curves])\\n\\n allSwitchPointsList = deepcopy(curves[0].switchpointsList)\\n for curve in self.curves[1:]:\\n allSwitchPointsList += curve.switchpointsList\\n allSwitchPointsList.sort() # a sorted list of all switch points (including deplicate)\\n\\n self.switchpointsList = [] # a sorted list of all distinct switch points\\n if len(allSwitchPointsList) > 0:\\n self.switchpointsList.append(allSwitchPointsList[0])\\n for sw in allSwitchPointsList[1:]:\\n if not FuzzyEquals(sw, self.switchpointsList[-1], epsilon):\\n self.switchpointsList.append(sw)\\n\\n\\n def Initialize(self, curves):\\n if len(curves) == 0:\\n self.curves = []\\n self.x0Vect = None\\n self.x1Vect = None\\n self.v0Vect = None\\n self.v1Vect = None\\n self.dVect = None\\n self.ndof = 0\\n self.switchpointsList = []\\n self.duration = 0.0\\n else:\\n # Check first if the input is valid\\n minDur = curves[0].duration\\n for curve in curves[1:]:\\n assert( FuzzyEquals(curve.duration, minDur, epsilon) )\\n minDur = min(curve.duration, minDur)\\n\\n self.curves = deepcopy(curves)\\n self.duration = minDur\\n self.ndof = len(self.curves)\\n self.x0Vect = np.asarray([curve.x0 for curve in self.curves])\\n self.x1Vect = np.asarray([curve.x1 for curve in self.curves])\\n self.v0Vect = np.asarray([curve.v0 for curve in self.curves])\\n self.v1Vect = np.asarray([curve.v1 for curve in self.curves])\\n self.dVect = np.asarray([curve.d for curve in self.curves])\\n\\n allSwitchPointsList = deepcopy(curves[0].switchpointsList)\\n for curve in self.curves[1:]:\\n allSwitchPointsList += curve.switchpointsList\\n allSwitchPointsList.sort() # a sorted list of all switch points (including deplicate)\\n\\n self.switchpointsList = [] # a sorted list of all distinct switch points\\n if len(allSwitchPointsList) > 0:\\n self.switchpointsList.append(allSwitchPointsList[0])\\n for sw in allSwitchPointsList[1:]:\\n if not FuzzyEquals(sw, self.switchpointsList[-1], epsilon):\\n self.switchpointsList.append(sw)\\n \\n \\n def __getitem__(self, index):\\n return self.curves[index]\\n\\n\\n def __len__(self):\\n return len(self.curves)\\n\\n\\n def IsEmpty(self):\\n return len(self) == 0\\n \\n \\n def SetInitialValue(self, x0Vect):\\n self.x0Vect = np.array(x0Vect) # make a copy\\n for (i, curve) in enumerate(self.curves):\\n curve.SetInitialValue(self.x0Vect[i])\\n self.x1Vect = np.asarray([x0 + d for (x0, d) in zip(self.x0Vect, self.dVect)])\\n\\n\\n def EvalPos(self, t):\\n assert(t >= -epsilon)\\n assert(t <= self.duration + epsilon)\\n \\n xVect = [curve.EvalPos(t) for curve in self.curves]\\n return np.asarray(xVect)\\n\\n\\n def EvalVel(self, t):\\n assert(t >= -epsilon)\\n assert(t <= self.duration + epsilon)\\n \\n return np.asarray([curve.EvalVel(t) for curve in self.curves])\\n\\n\\n def EvalAcc(self, t):\\n assert(t >= -epsilon)\\n assert(t <= self.duration + epsilon)\\n \\n return np.asarray([curve.EvalAcc(t) for curve in self.curves])\\n\\n \\n def SetConstant(self, x0Vect, t): \\n ndof = len(x0Vect)\\n curves = []\\n for i in xrange(ndof):\\n curve = ParabolicCurve()\\n curve.SetConstant(x0Vect[i], t)\\n curves.append(curve)\\n\\n self.Initialize(curves)\\n return\\n\\n\\n def SetSegment(self, x0Vect, x1Vect, v0Vect, v1Vect, t):\\n assert(t >= 0)\\n\\n ndof = len(x0Vect)\\n curves = []\\n for i in xrange(ndof):\\n curve = ParabolicCurve()\\n curve.SetSegment(x0Vect[i], x1Vect[i], v0Vect[i], v1Vect[i], t)\\n curves.append(curve)\\n\\n self.Initialize(curves)\\n return\\n\\n \\n def SetZeroDuration(self, x0Vect, v0Vect):\\n ndof = len(x0Vect)\\n curves = []\\n for i in xrange(ndof):\\n curve = ParabolicCurve()\\n curve.SetZeroDuration(x0Vect[i], v0Vect[i])\\n curves.append(curve)\\n\\n self.Initialize(curves)\\n return\\n\\n\\n def Cut(self, t):\\n if (t <= 0):\\n remCurvesND = ParabolicCurvesND()\\n remCurvesND.SetZeroDuration(self.x0Vect, self.v0Vect)\\n return\\n elif (t >= self.duration):\\n remCurvesND = ParabolicCurvesND()\\n remCurvesND.SetZeroDuration(self.x1Vect, self.v1Vect)\\n return\\n\\n leftHalf = self.curves\\n rightHalf = []\\n for i in xrange(self.ndof):\\n rightHalf.append(leftHalf[i].Cut(t))\\n\\n self.Initialize(leftHalf)\\n remCurvesND = ParabolicCurvesND(rightHalf)\\n return remCurvesND\\n\\n\\n def TrimFront(self, t):\\n if (t <= 0):\\n return\\n elif (t >= self.duration):\\n self.SetZeroDuration(self.x1Vect, self.v1Vect)\\n return\\n\\n newCurves = self.curves\\n for i in xrange(self.ndof):\\n newCurves[i].TrimFront(t)\\n self.Initialize(newCurves)\\n return\\n\\n\\n def TrimBack(self, t):\\n if (t <= 0):\\n self.SetZeroDuration(self.x0Vect, self.v0Vect)\\n return\\n elif (t >= self.duration):\\n return\\n\\n newCurves = self.curves\\n for i in xrange(self.ndof):\\n newCurves[i].TrimBack(t)\\n self.Initialize(newCurves)\\n return\\n\\n\\n # Visualization\\n def PlotPos(self, fignum='Displacement Profiles', includingSW=False, dt=0.005):\\n \\\"\\\"\\\"Plot trajectory position vs time.\\n\\n Parameters\\n ----------\\n fignum : int, float, string, optional\\n Figure's number/title\\n includingSW : bool, optional\\n If True, draw vertical straight lines at switch points.\\n dt : float, optional\\n Time resolution to evaluate the position.\\n \\n \\\"\\\"\\\"\\n plt.figure(fignum)\\n\\n tVect = np.arange(0, self.duration, dt)\\n if tVect[-1] < self.duration:\\n tVect = np.append(tVect, self.duration)\\n\\n xVect = [self.EvalPos(t) for t in tVect]\\n plt.plot(tVect, xVect, linewidth=2)\\n handle = ['joint {0}'.format(i + 1) for i in xrange(self.ndof)]\\n plt.legend(handle)\\n\\n if includingSW:\\n ax = plt.gca().axis()\\n for s in self.switchpointsList:\\n plt.plot([s, s], [ax[2], ax[3]], 'r', linewidth=1)\\n plt.show(False)\\n \\n\\n def PlotVel(self, fignum='Velocity Profiles', includingSW=False, **kwargs):\\n \\\"\\\"\\\"Plot trajectory velocity vs time.\\n\\n Parameters\\n ----------\\n fignum : int, float, string, optional\\n Figure's number/title\\n includingSW : bool, optional\\n If True, draw vertical straight lines at switch points.\\n \\n \\\"\\\"\\\"\\n plt.figure(fignum)\\n plt.hold(True)\\n\\n lines = []\\n for curve in self.curves:\\n lines.append(curve.PlotVel(fignum=fignum, **kwargs))\\n\\n handles = ['joint {0}'.format(i + 1) for i in xrange(self.ndof)]\\n plt.legend(lines, handles)\\n\\n if includingSW:\\n ax = plt.gca().axis()\\n for s in self.switchpointsList:\\n plt.plot([s, s], [ax[2], ax[3]], 'r', linewidth=1)\\n plt.show(False)\\n \\n\\n def PlotAcc(self, fignum='Acceleration Profiles', **kwargs):\\n \\\"\\\"\\\"Plot trajectory acceleration vs time.\\n\\n Parameters\\n ----------\\n fignum : int, float, string, optional\\n Figure's number/title\\n \\n \\\"\\\"\\\"\\n plt.figure(fignum)\\n plt.hold(True)\\n\\n lines = []\\n for curve in self.curves:\\n lines.append(curve.PlotAcc(fignum=fignum, **kwargs))\\n\\n handles = ['joint {0}'.format(i + 1) for i in xrange(self.ndof)]\\n plt.legend(lines, handles)\\n plt.show(False)\\n\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.8018018007278442,\n \"alphanum_fraction\": 0.8018018007278442,\n \"avg_line_length\": 14.857142448425293,\n \"blob_id\": \"12687ad6eeadd695dafe916c735d5e719d74d72a\",\n \"content_id\": \"386f5b4e1c3918c636a8734a50e8baf643101f23\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 111,\n \"license_type\": \"no_license\",\n \"max_line_length\": 23,\n \"num_lines\": 7,\n \"path\": \"/parabint/__init__.py\",\n \"repo_name\": \"shengjiangtou/parabint\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import trajectory\\nimport interpolator\\nimport checker\\ntry:\\n import optimization\\nexcept ImportError:\\n pass\\n\"\n },\n {\n \"alpha_fraction\": 0.36657458543777466,\n \"alphanum_fraction\": 0.4252762496471405,\n \"avg_line_length\": 32.9906120300293,\n \"blob_id\": \"eb1b2fd6d9c703e7d9da7a3062332ed77c6d620a\",\n \"content_id\": \"cfb4e6d9ac3256585097b7a63a22537a3cea59f6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7240,\n \"license_type\": \"no_license\",\n \"max_line_length\": 98,\n \"num_lines\": 213,\n \"path\": \"/parabint/optimization.py\",\n \"repo_name\": \"shengjiangtou/parabint\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nimport pyipopt\\nimport random\\nrng = random.SystemRandom()\\nfrom .utilities import epsilon\\n\\npyipopt.set_loglevel(0)\\ninf = pyipopt.NLP_UPPER_BOUND_INF\\n\\ndef SolveTwoRampTrajectory(d, v0, v1, vm, am, delta, max_iter=5000, tol=epsilon, print_level=0):\\n \\\"\\\"\\\"Given the boundary conditions for the trajectory, find a fastest\\n two-ramp trajectory satisfying velocity, acceleration, and the\\n minimum-switch-time constraint.\\n\\n Optimization variable: x = [t0, t1, vp]\\n\\n Inequality constarints:\\n\\n -am <= (vp - v0)/t0 <= am ---(1)\\n -am <= (v1 - vp)/t1 <= am ---(2)\\n\\n Equality constraint:\\n\\n d = 0.5*(v0 + vp)*t0 + 0.5*(v1 + vp)*t1 ---(3)\\n\\n \\\"\\\"\\\"\\n nvar = 3\\n ncon = 3\\n nnzj = 9\\n nnzh = 6\\n\\n # Bounds on the variables\\n xl = np.array([delta, delta, vm])\\n xu = np.array([INF, INF, vm])\\n x0 = [2*rng.random() -1 for _ in xrange(nvar)] # randomly select an initial guess\\n\\n # Bounds on the constraints\\n gl = np.array([-am, -am, d])\\n gu = np.array([am, am, d])\\n\\n # Objective function f(x)\\n def eval_f(x, user_data=None):\\n return x[0] + x[1]\\n\\n # Gradient of f(x)\\n def eval_grad_f(x, user_data=None):\\n return np.array([1., 1., 0.])\\n\\n # Constraint function g(x)\\n def eval_g(x, user_data=None):\\n return np.array([(x[2] - v0)/x[0],\\n (v1 - x[2])/x[0],\\n 0.5*(v0 + x[2])*x[0] + 0.5*(v1 + x[2])*x[1]])\\n\\n # Jacobian of g(x)\\n def eval_jacobian_g(x, flag, user_data=None):\\n if flag:\\n # Return the position of each number in Jacobian\\n return (np.array([0, 0, 0, 1, 1, 1, 2, 2, 2]),\\n np.array([0, 1, 2, 0, 1, 2, 0, 1, 2]))\\n else:\\n return np.array([-(x[2] - v0)/(x[0]**2),\\n 0.0,\\n 1.0/x[0],\\n 0.0,\\n -(v1 - x[2])/(x[1]**2),\\n -1.0/x[1],\\n 0.5*(v0 + x[2]),\\n 0.5*(v1 + x[2]),\\n 0.5*(x[0] + x[1])])\\n\\n # Hessian of f(x)\\n def eval_h(x, lagrange, obj_factor, flag, user_data=None):\\n if flag:\\n return (np.array([0, 0, 1, 0, 1, 2]),\\n np.array([0, 1, 1, 2, 2, 2]))\\n else:\\n values = np.zeros(nnzh)\\n \\n values[0] += lagrange[0]*(2*(x[2] - v0)/(x[0]**3))\\n values[3] += lagrange[0]*(-1/(x[0]**2))\\n \\n values[2] += lagrange[1]*(2*(v1 - x[2])/(x[1]**3))\\n values[4] += lagrange[1]*(1/(x[1]**2))\\n \\n values[3] += lagrange[2]*0.5\\n values[4] += lagrange[2]*0.5\\n\\n return values\\n\\n # Initialize a non-linear program\\n nlp = pyipopt.create(nvar, xl, xu, ncon, gl, gu, nnzj, nnzh, \\n eval_f, eval_grad_f, eval_g, eval_jacobian_g, eval_h)\\n nlp.int_option('max_iter', max_iter)\\n nlp.num_option('tol', tol)\\n nlp.int_option('print_level', print_level) \\n\\n x, zl, zu, constraint_multipliers, obj, status = nlp.solve(x0)\\n nlp.close()\\n \\n return x, status\\n\\n\\ndef SolveThreeRampTrajectory(d, v0, v1, vm, am, delta, max_iter=5000, tol=epsilon, print_level=0):\\n \\\"\\\"\\\"\\n Given the boundary conditions for the trajectory, find a fastest\\n two-ramp trajectory satisfying velocity, acceleration, and the\\n minimum-switch-time constraint.\\n\\n Optimization variable: x = [t0, t1, t2, vp0, vp1]\\n\\n Inequality constarints:\\n\\n -am <= (vp - v0)/t0 <= am ---(1)\\n -am <= (vp1 - vp0)/t1 <= am ---(2)\\n -am <= (v1 - vp1)/t2 <= am ---(3)\\n\\n Equality constraint:\\n\\n d = 0.5*(v0 + vp0)*t0 + 0.5*(vp0 + vp1)*t1 + 0.5*(vp1 + v1)*t2 ---(4)\\n \\\"\\\"\\\"\\n nvar = 5\\n ncon = 4\\n nnzj = 20\\n nnzh = 15\\n\\n # Bounds on the variables\\n xl = np.array([delta, delta, delta, -vm, -vm])\\n xu = np.array([INF, INF, INF, vm, vm])\\n x0 = [2*rng.random() -1 for _ in xrange(nvar)] # randomly select an initial guess\\n\\n # Bounds on the constraints\\n gl = np.array([-am, -am, -am, d])\\n gu = np.array([am, am, am, d])\\n\\n # Objective function f(x)\\n def eval_f(x, user_data=None):\\n return x[0] + x[1] + x[2]\\n\\n # Gradient of f(x)\\n def eval_grad_f(x, user_data=None):\\n return np.array([1., 1., 1., 0., 0.])\\n\\n # Constraint function g(x)\\n def eval_g(x, user_data=None):\\n return np.array([(x[3] - v0)/x[0],\\n (x[4] - x[3])/x[1],\\n (v1 - x[4])/x[2],\\n 0.5*(v0 + x[3])*x[0] + 0.5*(x[3] + x[4])*x[1] + 0.5*(x[4] + v1)*x[2]])\\n\\n # Jacobian of g(x)\\n def eval_jacobian_g(x, flag, user_data=None):\\n if flag:\\n return (np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3]),\\n np.array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4]))\\n else:\\n return np.array([-(x[3] - v0)/(x[0]**2),\\n 0.0,\\n 0.0,\\n 1.0/x[0],\\n 0.0,\\n 0.0,\\n -(x[4] - x[3])/(x[1]**2),\\n 0.0,\\n -1.0/x[1],\\n 1.0/x[1],\\n 0.0,\\n 0.0,\\n -(v1 - x[4])/(x[2]**2),\\n 0.0,\\n -1.0/x[2],\\n 0.5*(v0 + x[3]),\\n 0.5*(x[3] + x[4]),\\n 0.5*(x[4] + v1),\\n 0.5*(x[0] + x[1]),\\n 0.5*(x[1] + x[2])])\\n\\n # Hessian of f(x)\\n def eval_h(x, lagrange, obj_factor, flag, user_data=None):\\n if flag:\\n return (np.array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3, 4]),\\n np.array([0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4]))\\n else:\\n values = np.zeros(nnzh)\\n\\n values[0] += lagrange[0]*(2.0*(x[3] - v0)/(x[0]**3))\\n values[6] += lagrange[0]*(-1.0/(x[0]**2))\\n \\n values[2] += lagrange[1]*(2.0*(x[4] - x[3])/(x[1]**3))\\n values[7] += lagrange[1]*(1.0/(x[1]**2))\\n values[11] += lagrange[1]*(-1.0/(x[1]**2))\\n \\n values[5] += lagrange[2]*(2.0*(v1 - x[4])/(x[3]**2))\\n values[12] += lagrange[2]*(1.0/(x[2]**2))\\n \\n values[6] += lagrange[3]*0.5\\n values[7] += lagrange[3]*0.5\\n values[11] += lagrange[3]*0.5\\n values[12] += lagrange[3]*0.5\\n \\n return values\\n\\n # Initialize a non-linear program\\n nlp = pyipopt.create(nvar, xl, xu, ncon, gl, gu, nnzj, nnzh, \\n eval_f, eval_grad_f, eval_g, eval_jacobian_g, eval_h)\\n nlp.int_option('max_iter', max_iter)\\n nlp.num_option('tol', tol)\\n nlp.int_option('print_level', print_level) \\n\\n x, zl, zu, constraint_multipliers, obj, status = nlp.solve(x0)\\n nlp.close()\\n \\n return x, status\\n\"\n },\n {\n \"alpha_fraction\": 0.5377268195152283,\n \"alphanum_fraction\": 0.5742332339286804,\n \"avg_line_length\": 35.2423095703125,\n \"blob_id\": \"e466a0ac4fa1d9f0de7b0990e09817e3138dcd47\",\n \"content_id\": \"78cfadf4f812eb7f2743dcadc6b797967f3b9cc2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 9423,\n \"license_type\": \"no_license\",\n \"max_line_length\": 198,\n \"num_lines\": 260,\n \"path\": \"/parabint/checker.py\",\n \"repo_name\": \"shengjiangtou/parabint\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from .utilities import epsilon, inf, FuzzyEquals, FuzzyZero\\nimport logging\\nlogging.basicConfig(format='[%(levelname)s] [%(name)s: %(funcName)s] %(message)s', level=logging.DEBUG)\\nlog = logging.getLogger(__name__)\\n\\n# PCR: ParabolicCheckReturn\\nPCR_Normal = 0\\nPCR_NegativeDuration = 1\\nPCR_XBoundViolated = 2\\nPCR_VBoundViolated = 3\\nPCR_ABoundViolated = 4\\nPCR_XDiscrepancy = 5\\nPCR_VDiscrepancy = 6\\nPCR_DurationDiscrepancy = 7\\n\\n\\ndef _GetPeaks(x0, x1, v0, v1, a, t):\\n \\\"\\\"\\\"Calculate the maximum and minimum displacement occuring betweeb time 0 and t given (x0, x1, v0,\\n v1, a).\\n\\n Parameters\\n ----------\\n x0 : float\\n Initial displacement of the segment\\n x1 : float\\n Final displacement of the segment\\n v0 : float\\n Initial velocity of the segment\\n v1 : float\\n Final velocity of the segment\\n a : float\\n Acceleration of the segment\\n t : float\\n Duration of the segment\\n\\n Returns\\n -------\\n bmin : float\\n Minimum displacement along this segment\\n bmax : float\\n Maximum displacement along this segment\\n \\n \\\"\\\"\\\"\\n if FuzzyZero(a, epsilon):\\n if v0 > 0:\\n bmin = x0\\n bmax = x1\\n else:\\n bmin = x1\\n bmax = x0\\n return bmin, bmax\\n\\n if x0 > x1:\\n curMin = x1\\n curMax = x0\\n else:\\n curMin = x0\\n curMax = x1\\n\\n tDeflection = -v0/a # the time when velocity crosses zero\\n if (tDeflection <= 0) or (tDeflection >= t):\\n bmin = curMin\\n bmax = curMax\\n return bmin, bmax\\n\\n xDeflection = x0 + 0.5*v0*tDeflection\\n bmin = min(curMin, xDeflection)\\n bmax = max(curMax, xDeflection)\\n return bmin, bmax\\n \\n\\ndef CheckSegment(x0, x1, v0, v1, a, t, xmin, xmax, vm, am):\\n \\\"\\\"\\\"\\n Check if a linear trajectory segment connecting (x0, v0) and (x1, v1) is valid.\\n \\n Parameters\\n ----------\\n x0 : float\\n Initial displacement of the segment\\n x1 : float\\n Final displacement of the segment\\n v0 : float\\n Initial velocity of the segment\\n v1 : float\\n Final velocity of the segment\\n a : float\\n Acceleration of the segment\\n t : float\\n Duration of the segment\\n xmin : float\\n Displacement lower limit\\n xmax : float\\n Displacement upper limit\\n vm : float\\n Maximum velocity\\n am : float\\n Maximum acceleration\\n\\n Returns\\n -------\\n ret : one of ParabolicCheckReturn\\n \\n \\\"\\\"\\\"\\n if t < -epsilon:\\n log.warn(\\\"PCR_NegativeDuration: duration = {0}\\\".format(t))\\n return PCR_NegativeDuration\\n if not FuzzyEquals(v1, v0 + a*t, epsilon):\\n v1_ = v0 + a*t\\n log.warn(\\\"PCR_VDiscrepancy: v1 = {0}; computed v1 = {1}; diff = {2}\\\".format(v1, v1_, (v1 - v1_)))\\n log.warn(\\\"Info: x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; a = {4}; duration = {5}; xmin = {6}; xmax = {7}; vm = {8}; am = {9}\\\".format(x0, x1, v0, v1, a, t, xmin, xmax, vm, am))\\n return PCR_VDiscrepancy\\n if not FuzzyEquals(x1, x0 + t*(v0 + 0.5*a*t), epsilon):\\n x1_ = x0 + t*(v0 + 0.5*a*t)\\n log.warn(\\\"PCR_XDiscrepancy: x1 = {0}; computed x1 = {1}; diff = {2}\\\".format(x1, x1_, (x1 - x1_)))\\n log.warn(\\\"Info: x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; a = {4}; duration = {5}; xmin = {6}; xmax = {7}; vm = {8}; am = {9}\\\".format(x0, x1, v0, v1, a, t, xmin, xmax, vm, am))\\n return PCR_XDiscrepancy\\n if xmin == inf and xmax == inf:\\n return PCR_Normal\\n bmin, bmax = _GetPeaks(x0, x1, v0, v1, a, t)\\n if (bmin < xmin - epsilon) or (bmax > xmax + epsilon):\\n log.warn(\\\"PCR_XBoundViolated: xmin = {0}; bmin = {1}; diff@min = {2}; xmax = {3}; bmax = {4}; diff@max = {5}\\\".format(xmin, bmin, (xmin - bmin), xmax, bmax, (bmax - xmax)))\\n log.warn(\\\"Info: x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; a = {4}; duration = {5}; xmin = {6}; xmax = {7}; vm = {8}; am = {9}\\\".format(x0, x1, v0, v1, a, t, xmin, xmax, vm, am))\\n return PCR_XBoundViolated\\n if (abs(v0) > vm + epsilon) or (abs(v1) > vm + epsilon):\\n log.warn(\\\"PCR_VBoundViolated: vm = {0}; v0 = {1}; v1 = {2}; diff@v0 = {3}; diff@v1 = {4}\\\".format(vm, v0, v1, (abs(v0) - vm), (abs(v1) - vm)))\\n log.warn(\\\"Info: x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; a = {4}; duration = {5}; xmin = {6}; xmax = {7}; vm = {8}; am = {9}\\\".format(x0, x1, v0, v1, a, t, xmin, xmax, vm, am))\\n return PCR_VBoundViolated\\n if abs(a) > am + epsilon:\\n log.warn(\\\"PCR_ABoundViolated: am = {0}; a = {1}; diff = {2}\\\".format(am, a, (abs(a) - am)))\\n log.warn(\\\"Info: x0 = {0}; x1 = {1}; v0 = {2}; v1 = {3}; a = {4}; duration = {5}; xmin = {6}; xmax = {7}; vm = {8}; am = {9}\\\".format(x0, x1, v0, v1, a, t, xmin, xmax, vm, am))\\n return PCR_ABoundViolated\\n return PCR_Normal\\n\\n\\ndef CheckRamp(ramp, xmin, xmax, vm, am):\\n \\\"\\\"\\\"\\n Check if the given ramp is consistent with (xmin, xmax, vm, am).\\n \\n Parameters\\n ----------\\n ramp : Ramp\\n xmin : float\\n Displacement lower limit\\n xmax : float\\n Displacement upper limit\\n vm : float\\n Maximum velocity\\n am : float\\n Maximum acceleration\\n\\n Returns\\n -------\\n ret : one of ParabolicCheckReturn\\n \\n \\\"\\\"\\\"\\n return CheckSegment(ramp.x0, ramp.x1, ramp.v0, ramp.v1, ramp.a, ramp.duration, xmin, xmax, vm, am)\\n\\n\\ndef CheckParabolicCurve(curve, xmin, xmax, vm, am, x0, x1, v0, v1):\\n \\\"\\\"\\\"\\n Check if the given parabolic curve is consistent with (xmin, xmax, vm, am, x0, x1, v0, v1).\\n \\n Parameters\\n ----------\\n curve : ParabolicCurve\\n xmin : float\\n Displacement lower limit\\n xmax : float\\n Displacement upper limit\\n vm : float\\n Maximum velocity\\n am : float\\n Maximum acceleration\\n x0 : float\\n Initial displacement\\n x1 : float\\n Final displacement\\n v0 : float\\n Initial velocity\\n v1 : float\\n Final velocity\\n\\n Returns\\n -------\\n ret : one of ParabolicCheckReturn\\n \\n \\\"\\\"\\\"\\n # Check the first ramp\\n if not FuzzyEquals(curve[0].x0, x0, epsilon):\\n log.warn(\\\"PCR_XDiscrepancy: curve[0].x0 = {0}; x0 = {1}; diff = {2}\\\".format(curve[0].x0, x0, curve[0].x0 - x0))\\n return PCR_XDiscrepancy\\n if not FuzzyEquals(curve[0].v0, v0, epsilon):\\n log.warn(\\\"PCR_VDiscrepancy: curve[0].v0 = {0}; v0 = {1}; diff = {2}\\\".format(curve[0].v0, v0, curve[0].v0 - v0))\\n return PCR_VDiscrepancy\\n ret = CheckRamp(curve[0], xmin, xmax, vm, am)\\n if not (ret == PCR_Normal):\\n log.warn(\\\"curve[0] does not pass CheckRamp\\\")\\n return ret\\n \\n for iramp in xrange(1, len(curve) - 1):\\n if not FuzzyEquals(curve[iramp - 1].x1, curve[iramp].x0, epsilon):\\n log.warn(\\\"PCR_XDiscrepancy: curve[{0}].x1 != curve[{1}].x0; {2} != {3}; diff = {4}\\\".format(iramp - 1, iramp, curve[iramp - 1].x1, curve[iramp].x0, curve[iramp - 1].x1 - curve[iramp].x0))\\n return PCR_XDiscrepancy\\n if not FuzzyEquals(curve[iramp - 1].v1, curve[iramp].v0, epsilon):\\n log.warn(\\\"PCR_VDiscrepancy: curve[{0}].v1 != curve[{1}].v0; {2} != {3}; diff = {4}\\\".format(iramp - 1, iramp, curve[iramp - 1].v1, curve[iramp].v0, curve[iramp - 1].v1 - curve[iramp].v0))\\n return PCR_VDiscrepancy\\n ret = CheckRamp(curve[iramp], xmin, xmax, vm, am)\\n if not (ret == PCR_Normal):\\n log.warn(\\\"curve[{0}] does not pass CheckRamp\\\".format(iramp))\\n return ret\\n\\n # Check the last ramp\\n if not FuzzyEquals(curve[-1].x1, x1, epsilon):\\n log.warn(\\\"PCR_XDiscrepancy: curve[{0}].x1 = {1}; x1 = {2}; diff = {3}\\\".format(len(curve) - 1, curve[-1].x0, x0, curve[-1].x1 - x1))\\n return PCR_XDiscrepancy\\n if not FuzzyEquals(curve[-1].v1, v1, epsilon):\\n log.warn(\\\"PCR_VDiscrepancy: curve[{0}].v1 = {1}; v1 = {2}; diff = {3}\\\".format(len(curve) - 1, curve[-1].v0, v0, curve[-1].v1 - v1))\\n return PCR_VDiscrepancy\\n return PCR_Normal\\n \\n\\ndef CheckParabolicCurvesND(curvesnd, xminVect, xmaxVect, vmVect, amVect, x0Vect, x1Vect, v0Vect, v1Vect):\\n \\\"\\\"\\\"\\n Check if the given paraboliccurvesnd is consistent with the given constraints.\\n \\n Parameters\\n ----------\\n curvesnd : ParabolicCurvesND\\n xminVect : numpy.ndarray\\n Displacement lower limit\\n xmaxVect : numpy.ndarray\\n Displacement upper limit\\n vmVect : numpy.ndarray\\n Maximum velocity\\n amVect : numpy.ndarray\\n Maximum acceleration\\n x0Vect : numpy.ndarray\\n Initial displacement\\n x1Vect : numpy.ndarray\\n Final displacement\\n v0Vect : numpy.ndarray\\n Initial velocity\\n v1Vect : numpy.ndarray\\n Final velocity\\n\\n Returns\\n -------\\n ret : one of ParabolicCheckReturn\\n \\n \\\"\\\"\\\"\\n duration = curvesnd.duration\\n for (icurve, (curve, xmin, xmax, vm, am, x0, x1, v0, v1)) in enumerate(zip(curvesnd, xminVect, xmaxVect, vmVect, amVect, x0Vect, x1Vect, v0Vect, v1Vect)):\\n if not FuzzyEquals(curve.duration, duration, epsilon):\\n log.warn(\\\"PCR_DurationDiscrepancy: curvesnd.duration = {0}; curvesnd[{1}].duration = {2}; diff = {3}\\\".format(duration, icurve, curve.duration, duration - curve.duration))\\n return PCR_DurationDiscrepancy\\n ret = CheckParabolicCurve(curve, xmin, xmax, vm, am, x0, x1, v0, v1)\\n if not (ret == PCR_Normal):\\n log.warn(\\\"curvsend[{0}] does not psas CheckParabolicCurve\\\".format(icurve))\\n return ret\\n return PCR_Normal\\n\"\n },\n {\n \"alpha_fraction\": 0.5120373964309692,\n \"alphanum_fraction\": 0.5256916880607605,\n \"avg_line_length\": 22.584745407104492,\n \"blob_id\": \"5a393b85f080ac7ed2809b8e9fd2e80d1f830ddb\",\n \"content_id\": \"b1b57a50c636fb3e39fed26e96ef1a86dbac11b3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2783,\n \"license_type\": \"no_license\",\n \"max_line_length\": 116,\n \"num_lines\": 118,\n \"path\": \"/parabint/utilities.py\",\n \"repo_name\": \"shengjiangtou/parabint\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nepsilon = 1e-8\\ninf = 1e300\\n\\n\\ndef FuzzyEquals(a, b, epsilon):\\n \\\"\\\"\\\"Check if the given two numbers are equal, up to discrepancy of the given epsilon.\\n Return True if abs(a - b) <= epsilon.\\n\\n Parameters\\n ----------\\n a : float\\n b : float\\n epsilon : float\\n\\n \\\"\\\"\\\"\\n return abs(a - b) <= epsilon\\n\\n\\ndef FuzzyZero(a, epsilon):\\n \\\"\\\"\\\"Check if the given number is zero, up to discrepancy of the given epsilon.\\n Return True if abs(a) <= epsilon.\\n\\n Parameters\\n ----------\\n a : float\\n b : float\\n epsilon : float\\n\\n \\\"\\\"\\\"\\n return abs(a) <= epsilon\\n\\n\\ndef SolveLinearInEq(a, b, epsilon, xmin, xmax):\\n \\\"\\\"\\\"This functions safely solves for x \\\\in [xmin, xmax] such that |ax - b| <= epsilon*max(|a|, |b|).\\n\\n Return [result, x]\\n \\\"\\\"\\\"\\n if (a < 0):\\n return SolveLinearInEq(-a, -b, epsilon, xmin, xmax)\\n epsilonScaled = epsilon*max(a, abs(b))\\n\\n if (xmin == -inf) and (xmax == inf):\\n if (a == 0):\\n x = 0.0\\n result = abs(b) <= epsilonScaled\\n return [result, x]\\n\\n x = b/a\\n return [True, x]\\n\\n axmin = a*xmin\\n axmax = a*xmax\\n if not (b + epsilonScaled >= axmin and b - epsilonScaled <= axmax):\\n # Ranges do not intersect\\n return [False, 0.0]\\n\\n if not (a == 0):\\n x = b/a\\n if (xmin <= x) and (x <= xmax):\\n return [True, x]\\n\\n if abs(0.5*(axmin + axmax) - b) <= epsilonScaled:\\n x = 0.5*(xmin + xmax)\\n return [True, x]\\n\\n if abs(axmax - b) <= epsilonScaled:\\n x = xmax\\n return [True, x]\\n\\n x = xmin\\n return [True, x]\\n\\n\\ndef SolveBoundedInEq(a, b, l, u):\\n \\\"\\\"\\\"Solve inequalities of the form\\n l <= ax + b <= u.\\n \\n The function returns [solved, xl, xu] such that ax + b \\\\in [l, u] for all x \\\\in [xl, xu] if solved.\\n \\\"\\\"\\\"\\n if l > u:\\n return [False, inf, -inf]\\n\\n if FuzzyZero(a, epsilon):\\n if (b >= l) and (b <= u):\\n return [True, l, u]\\n else:\\n return [False, inf, -inf]\\n\\n l -= b\\n u -= b\\n aInv = 1.0/a\\n if a > 0:\\n return [True, l*aInv, u*aInv]\\n else:\\n return [True, u*aInv, l*aInv]\\n\\n\\ndef BrakeTime(x, v, xbound):\\n [solved, t] = SolveLinearInEq(v, 2*(xbound - x), epsilon, 0, inf)\\n if not solved:\\n log.debug(\\\"Cannot solve for braking time from the equation: {0}*t - {1} = 0\\\".format(v, 2*(xbound - x)))\\n t = 0\\n return t\\n\\n\\ndef BrakeAccel(x, v, xbound):\\n coeff0 = 2*(xbound - x)\\n coeff1 = v*v\\n [solved, a] = SolveLinearInEq(coeff0, -coeff1, epsilon, -inf, inf)\\n if not solved:\\n log.debug(\\\"Cannot solve for braking acceleration from the equation: {0}*a + {1} = 0\\\".format(coeff0, coeff1))\\n a = 0\\n return a\\n \\n\\ndef Swap(a, b):\\n return [b, a]\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":10,"string":"10"}}},{"rowIdx":2983060,"cells":{"repo_name":{"kind":"string","value":"c3iox/leetcode"},"repo_url":{"kind":"string","value":"https://github.com/c3iox/leetcode"},"snapshot_id":{"kind":"string","value":"a67e490b77c5dc71e1a3e5cbbb6b89aa7bfb5a30"},"revision_id":{"kind":"string","value":"fa91193922ff3e34728cac782532cc5fc606a2f6"},"directory_id":{"kind":"string","value":"40a3c54a4fa70c6ae19d560874c0cf61e4041202"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-04-27T09:11:43.407647","string":"2023-04-27T09:11:43.407647"},"revision_date":{"kind":"timestamp","value":"2021-05-22T12:43:05","string":"2021-05-22T12:43:05"},"committer_date":{"kind":"timestamp","value":"2021-05-22T12:43:05","string":"2021-05-22T12:43:05"},"github_id":{"kind":"number","value":369804850,"string":"369,804,850"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6031957268714905,"alphanum_fraction":0.6085219979286194,"avg_line_length":20.457143783569336,"blob_id":"1f5a0f7545bc1005e97f349edbfa64099a62656a","content_id":"251f5a84fb7a6e0dafb56c93a3924b2c79ccaaf7","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1021,"license_type":"permissive","max_line_length":71,"num_lines":35,"path":"/easy/max_profit.py","repo_name":"c3iox/leetcode","src_encoding":"UTF-8","text":"from typing import List\n\nimport typer\nfrom loguru import logger\n\n\napp = typer.Typer()\n\n\n@app.command()\ndef max_profit(prices: List[int]) -> int:\n \"\"\"\n 给定一个数组 prices ，它的第 i 个元素 prices[i] 表示一支给定股票第 i 天的价格。\n\n 你只能选择 某一天 买入这只股票，并选择在 未来的某一个不同的日子 卖出该股票。设计一个算法来计算你所能获取的最大利润。\n\n 返回你可以从这笔交易中获取的最大利润。如果你不能获取任何利润，返回 0 。\n\n 来源：力扣（LeetCode）\n 链接：https://leetcode-cn.com/problems/best-time-to-buy-and-sell-stock\n \"\"\"\n # 什么都不买则是0\n ans = 0\n for buy in range(len(prices)):\n for sell in range(buy + 1, len(prices)):\n profit = prices[sell] - prices[buy]\n logger.debug(f\"{sell=},{buy=},{profit=}\")\n ans = max(profit, ans)\n\n logger.debug(f\"{ans=}\")\n return ans\n\n\nif __name__ == \"__main__\":\n app()\n"},{"alpha_fraction":0.545945942401886,"alphanum_fraction":0.6243243217468262,"avg_line_length":17.5,"blob_id":"429969bf96ed1a776cd17419f722cf80902a8d17","content_id":"f5bf78cdee27c64de59b650789a61ef8785b011a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"TOML","length_bytes":370,"license_type":"permissive","max_line_length":41,"num_lines":20,"path":"/pyproject.toml","repo_name":"c3iox/leetcode","src_encoding":"UTF-8","text":"[tool.poetry]\nname = \"leetcode\"\nversion = \"0.1.0\"\ndescription = \"\"\nauthors = [\"c3io <c3io@outlook.com>\"]\n\n[tool.poetry.dependencies]\npython = \"^3.9\"\ntyper = \"^0.3.2\"\nloguru = \"^0.5.3\"\narrow = \"^1.1.0\"\n\n[tool.poetry.dev-dependencies]\npytest = \"^6.2.4\"\nblack = \"^21.5b1\"\nflake8 = \"^3.9.2\"\n\n[build-system]\nrequires = [\"poetry-core>=1.0.0\"]\nbuild-backend = \"poetry.core.masonry.api\"\n"},{"alpha_fraction":0.5245097875595093,"alphanum_fraction":0.5735294222831726,"avg_line_length":16,"blob_id":"e6c25b4e72832c90a62f123f5b1ca4ea07b7d8df","content_id":"ff56c7d744841e3c8c82594cbc6188b0dc6f7230","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":408,"license_type":"permissive","max_line_length":52,"num_lines":24,"path":"/tests/easy/test_max_profit.py","repo_name":"c3iox/leetcode","src_encoding":"UTF-8","text":"from easy.max_profit import max_profit\n\n\ndef test_empty():\n assert max_profit(list()) == 0\n\n\ndef test_one():\n assert max_profit([1]) == 0\n\n\ndef test_accend():\n stock = [1, 2, 3, 4]\n assert max_profit(stock) == stock[-1] - stock[0]\n\n\ndef test_decent():\n stock = [4, 3, 2, 1]\n assert max_profit(stock) == 0\n\n\ndef test_unsorted():\n stock = [4, 3, 2, 4, 5]\n assert max_profit(stock) == 3\n"},{"alpha_fraction":0.6662303805351257,"alphanum_fraction":0.6701570749282837,"avg_line_length":21.47058868408203,"blob_id":"2f62a4ee1ec9cc4900d0498cf370aeb38c7da2aa","content_id":"0e40e4995b5359ca663a97b9e2d335bd83c6b89d","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":764,"license_type":"permissive","max_line_length":75,"num_lines":34,"path":"/tests/other/test_longest_diff_sub_string.py","repo_name":"c3iox/leetcode","src_encoding":"UTF-8","text":"import pytest\nfrom loguru import logger\n\nfrom other.find_longest_diff_sub_string import find_longest_diff_sub_string\n\n\n@pytest.mark.parametrize(\"not_str\", [None, True, 6, 7.0])\ndef test_bad_class_input(not_str):\n logger.debug(f\"{not_str=}\")\n assert find_longest_diff_sub_string(not_str) == \"\"\n\n\ndef test_empty():\n assert find_longest_diff_sub_string(\"\") == \"\"\n\n\ndef test_whole():\n assert find_longest_diff_sub_string(\"abc\") == \"abc\"\n\n\ndef test_begin():\n assert find_longest_diff_sub_string(\"abcdb\") == \"abcd\"\n\n\ndef test_mid():\n assert find_longest_diff_sub_string(\"abcdaa\") == \"bcda\"\n\n\ndef test_end():\n assert find_longest_diff_sub_string(\"abcdab\") == \"cdab\"\n\n\ndef test_mult():\n assert find_longest_diff_sub_string(\"abcabdabmab\") == \"dabm\"\n"},{"alpha_fraction":0.5702160596847534,"alphanum_fraction":0.5779321193695068,"avg_line_length":29.85714340209961,"blob_id":"f1a7392fb1e732d43ff190d9c7bb0aecb0f4d4f7","content_id":"0cedfc086116e5c0418b91d91d4d17bb2da997ca","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1350,"license_type":"permissive","max_line_length":70,"num_lines":42,"path":"/other/find_longest_diff_sub_string.py","repo_name":"c3iox/leetcode","src_encoding":"UTF-8","text":"import typer\nfrom loguru import logger\n\napp = typer.Typer()\n\n\n@app.command()\ndef find_longest_diff_sub_string(content: str) -> str:\n \"\"\"多个相同的最长 保留最后一个\"\"\"\n if not isinstance(content, str):\n return \"\"\n\n longest_start, longest_end = 0, 0\n current_start, current_end = 0, 0\n\n for current_start, start_value in enumerate(content):\n logger.debug(f\"{current_start=},{start_value=}\")\n current_end_start = current_start + 1\n for current_end, end_value in enumerate(\n content[current_end_start:], current_end_start\n ):\n logger.debug(f\"{current_end=},{end_value=}\")\n # 遍历从start开始，到有重复元素终止\n if len(content[current_start : current_end + 1]) > len(\n set(content[current_start : current_end + 1])\n ):\n break\n else:\n current_end += 1\n current_end -= 1\n logger.debug(f\"x -> {current_end=},{current_start=}\")\n if current_end - current_start >= longest_end - longest_start:\n longest_start, longest_end = current_start, current_end\n logger.debug(f\"r -> {longest_end=},{longest_start=}\")\n ret = content[longest_start : longest_end + 1]\n logger.debug(f\"longest:{ret}\")\n\n return ret\n\n\nif __name__ == \"__main__\":\n app()\n"},{"alpha_fraction":0.774193525314331,"alphanum_fraction":0.774193525314331,"avg_line_length":9.333333015441895,"blob_id":"c105842044aa7566f4ccdcbb95eb4c453e3b0739","content_id":"8e740ebf5b16e10304e1ae2b45f73252dfe0ecf4","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":31,"license_type":"permissive","max_line_length":18,"num_lines":3,"path":"/README.md","repo_name":"c3iox/leetcode","src_encoding":"UTF-8","text":"# leetcode\n\nMy Leetcode Answer\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6031957268714905,\n \"alphanum_fraction\": 0.6085219979286194,\n \"avg_line_length\": 20.457143783569336,\n \"blob_id\": \"1f5a0f7545bc1005e97f349edbfa64099a62656a\",\n \"content_id\": \"251f5a84fb7a6e0dafb56c93a3924b2c79ccaaf7\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1021,\n \"license_type\": \"permissive\",\n \"max_line_length\": 71,\n \"num_lines\": 35,\n \"path\": \"/easy/max_profit.py\",\n \"repo_name\": \"c3iox/leetcode\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from typing import List\\n\\nimport typer\\nfrom loguru import logger\\n\\n\\napp = typer.Typer()\\n\\n\\n@app.command()\\ndef max_profit(prices: List[int]) -> int:\\n \\\"\\\"\\\"\\n 给定一个数组 prices ，它的第 i 个元素 prices[i] 表示一支给定股票第 i 天的价格。\\n\\n 你只能选择 某一天 买入这只股票，并选择在 未来的某一个不同的日子 卖出该股票。设计一个算法来计算你所能获取的最大利润。\\n\\n 返回你可以从这笔交易中获取的最大利润。如果你不能获取任何利润，返回 0 。\\n\\n 来源：力扣（LeetCode）\\n 链接：https://leetcode-cn.com/problems/best-time-to-buy-and-sell-stock\\n \\\"\\\"\\\"\\n # 什么都不买则是0\\n ans = 0\\n for buy in range(len(prices)):\\n for sell in range(buy + 1, len(prices)):\\n profit = prices[sell] - prices[buy]\\n logger.debug(f\\\"{sell=},{buy=},{profit=}\\\")\\n ans = max(profit, ans)\\n\\n logger.debug(f\\\"{ans=}\\\")\\n return ans\\n\\n\\nif __name__ == \\\"__main__\\\":\\n app()\\n\"\n },\n {\n \"alpha_fraction\": 0.545945942401886,\n \"alphanum_fraction\": 0.6243243217468262,\n \"avg_line_length\": 17.5,\n \"blob_id\": \"429969bf96ed1a776cd17419f722cf80902a8d17\",\n \"content_id\": \"f5bf78cdee27c64de59b650789a61ef8785b011a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"TOML\",\n \"length_bytes\": 370,\n \"license_type\": \"permissive\",\n \"max_line_length\": 41,\n \"num_lines\": 20,\n \"path\": \"/pyproject.toml\",\n \"repo_name\": \"c3iox/leetcode\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"[tool.poetry]\\nname = \\\"leetcode\\\"\\nversion = \\\"0.1.0\\\"\\ndescription = \\\"\\\"\\nauthors = [\\\"c3io <c3io@outlook.com>\\\"]\\n\\n[tool.poetry.dependencies]\\npython = \\\"^3.9\\\"\\ntyper = \\\"^0.3.2\\\"\\nloguru = \\\"^0.5.3\\\"\\narrow = \\\"^1.1.0\\\"\\n\\n[tool.poetry.dev-dependencies]\\npytest = \\\"^6.2.4\\\"\\nblack = \\\"^21.5b1\\\"\\nflake8 = \\\"^3.9.2\\\"\\n\\n[build-system]\\nrequires = [\\\"poetry-core>=1.0.0\\\"]\\nbuild-backend = \\\"poetry.core.masonry.api\\\"\\n\"\n },\n {\n \"alpha_fraction\": 0.5245097875595093,\n \"alphanum_fraction\": 0.5735294222831726,\n \"avg_line_length\": 16,\n \"blob_id\": \"e6c25b4e72832c90a62f123f5b1ca4ea07b7d8df\",\n \"content_id\": \"ff56c7d744841e3c8c82594cbc6188b0dc6f7230\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 408,\n \"license_type\": \"permissive\",\n \"max_line_length\": 52,\n \"num_lines\": 24,\n \"path\": \"/tests/easy/test_max_profit.py\",\n \"repo_name\": \"c3iox/leetcode\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from easy.max_profit import max_profit\\n\\n\\ndef test_empty():\\n assert max_profit(list()) == 0\\n\\n\\ndef test_one():\\n assert max_profit([1]) == 0\\n\\n\\ndef test_accend():\\n stock = [1, 2, 3, 4]\\n assert max_profit(stock) == stock[-1] - stock[0]\\n\\n\\ndef test_decent():\\n stock = [4, 3, 2, 1]\\n assert max_profit(stock) == 0\\n\\n\\ndef test_unsorted():\\n stock = [4, 3, 2, 4, 5]\\n assert max_profit(stock) == 3\\n\"\n },\n {\n \"alpha_fraction\": 0.6662303805351257,\n \"alphanum_fraction\": 0.6701570749282837,\n \"avg_line_length\": 21.47058868408203,\n \"blob_id\": \"2f62a4ee1ec9cc4900d0498cf370aeb38c7da2aa\",\n \"content_id\": \"0e40e4995b5359ca663a97b9e2d335bd83c6b89d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 764,\n \"license_type\": \"permissive\",\n \"max_line_length\": 75,\n \"num_lines\": 34,\n \"path\": \"/tests/other/test_longest_diff_sub_string.py\",\n \"repo_name\": \"c3iox/leetcode\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import pytest\\nfrom loguru import logger\\n\\nfrom other.find_longest_diff_sub_string import find_longest_diff_sub_string\\n\\n\\n@pytest.mark.parametrize(\\\"not_str\\\", [None, True, 6, 7.0])\\ndef test_bad_class_input(not_str):\\n logger.debug(f\\\"{not_str=}\\\")\\n assert find_longest_diff_sub_string(not_str) == \\\"\\\"\\n\\n\\ndef test_empty():\\n assert find_longest_diff_sub_string(\\\"\\\") == \\\"\\\"\\n\\n\\ndef test_whole():\\n assert find_longest_diff_sub_string(\\\"abc\\\") == \\\"abc\\\"\\n\\n\\ndef test_begin():\\n assert find_longest_diff_sub_string(\\\"abcdb\\\") == \\\"abcd\\\"\\n\\n\\ndef test_mid():\\n assert find_longest_diff_sub_string(\\\"abcdaa\\\") == \\\"bcda\\\"\\n\\n\\ndef test_end():\\n assert find_longest_diff_sub_string(\\\"abcdab\\\") == \\\"cdab\\\"\\n\\n\\ndef test_mult():\\n assert find_longest_diff_sub_string(\\\"abcabdabmab\\\") == \\\"dabm\\\"\\n\"\n },\n {\n \"alpha_fraction\": 0.5702160596847534,\n \"alphanum_fraction\": 0.5779321193695068,\n \"avg_line_length\": 29.85714340209961,\n \"blob_id\": \"f1a7392fb1e732d43ff190d9c7bb0aecb0f4d4f7\",\n \"content_id\": \"0cedfc086116e5c0418b91d91d4d17bb2da997ca\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1350,\n \"license_type\": \"permissive\",\n \"max_line_length\": 70,\n \"num_lines\": 42,\n \"path\": \"/other/find_longest_diff_sub_string.py\",\n \"repo_name\": \"c3iox/leetcode\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import typer\\nfrom loguru import logger\\n\\napp = typer.Typer()\\n\\n\\n@app.command()\\ndef find_longest_diff_sub_string(content: str) -> str:\\n \\\"\\\"\\\"多个相同的最长 保留最后一个\\\"\\\"\\\"\\n if not isinstance(content, str):\\n return \\\"\\\"\\n\\n longest_start, longest_end = 0, 0\\n current_start, current_end = 0, 0\\n\\n for current_start, start_value in enumerate(content):\\n logger.debug(f\\\"{current_start=},{start_value=}\\\")\\n current_end_start = current_start + 1\\n for current_end, end_value in enumerate(\\n content[current_end_start:], current_end_start\\n ):\\n logger.debug(f\\\"{current_end=},{end_value=}\\\")\\n # 遍历从start开始，到有重复元素终止\\n if len(content[current_start : current_end + 1]) > len(\\n set(content[current_start : current_end + 1])\\n ):\\n break\\n else:\\n current_end += 1\\n current_end -= 1\\n logger.debug(f\\\"x -> {current_end=},{current_start=}\\\")\\n if current_end - current_start >= longest_end - longest_start:\\n longest_start, longest_end = current_start, current_end\\n logger.debug(f\\\"r -> {longest_end=},{longest_start=}\\\")\\n ret = content[longest_start : longest_end + 1]\\n logger.debug(f\\\"longest:{ret}\\\")\\n\\n return ret\\n\\n\\nif __name__ == \\\"__main__\\\":\\n app()\\n\"\n },\n {\n \"alpha_fraction\": 0.774193525314331,\n \"alphanum_fraction\": 0.774193525314331,\n \"avg_line_length\": 9.333333015441895,\n \"blob_id\": \"c105842044aa7566f4ccdcbb95eb4c453e3b0739\",\n \"content_id\": \"8e740ebf5b16e10304e1ae2b45f73252dfe0ecf4\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 31,\n \"license_type\": \"permissive\",\n \"max_line_length\": 18,\n \"num_lines\": 3,\n \"path\": \"/README.md\",\n \"repo_name\": \"c3iox/leetcode\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# leetcode\\n\\nMy Leetcode Answer\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":6,"string":"6"}}},{"rowIdx":2983061,"cells":{"repo_name":{"kind":"string","value":"denizurgun/graduation-project"},"repo_url":{"kind":"string","value":"https://github.com/denizurgun/graduation-project"},"snapshot_id":{"kind":"string","value":"41b6dcc5ec8b3172716f680ff31dc2bd973839f1"},"revision_id":{"kind":"string","value":"fa0ef22454050d9de207daef38862c5ec702d7c7"},"directory_id":{"kind":"string","value":"93286557f5baeb8e7d9c841247ddf9b41b6d7d43"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-05-30T16:49:20.381955","string":"2023-05-30T16:49:20.381955"},"revision_date":{"kind":"timestamp","value":"2021-06-20T16:04:27","string":"2021-06-20T16:04:27"},"committer_date":{"kind":"timestamp","value":"2021-06-20T16:04:27","string":"2021-06-20T16:04:27"},"github_id":{"kind":"number","value":378682986,"string":"378,682,986"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5966183543205261,"alphanum_fraction":0.6268116235733032,"avg_line_length":31.687074661254883,"blob_id":"13fdd01aa1879ce193731f1b043437594ad1f648","content_id":"8d986ebb8e2185b4fbdae9cd8b778a1808a91ed0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4981,"license_type":"no_license","max_line_length":192,"num_lines":147,"path":"/Det_Net_model.py","repo_name":"denizurgun/graduation-project","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri May 14 22:04:36 2021\r\n\r\n@author: \r\n\r\nGRADUATION PROJECT : DEEP LEARNING BASED DECODER IN MASSIVE MIMO CHANNELS\r\n\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport time as tm\r\nimport math\r\nimport sys\r\nimport pickle as pkl\r\nimport tensorflow.compat.v1 as tf\r\nfrom generate_data import get_data\r\nfrom Models import Model,__init__\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\nsess = tf.InteractiveSession()\r\ntf.disable_v2_behavior() \r\n#parameters\r\nNt = 20 # 2\r\nNr = 30 # 3\r\nSNRdB_low = 7.0\r\nSNRdB_high = 14.0\r\nSNR_low = 10.0 ** (SNRdB_low/10.0)\r\nSNR_high = 10.0 ** (SNRdB_high/10.0)\r\nL=30\r\nv_size = 2*Nt #4\r\nhidden_size = 8*Nt #16\r\nLearningRate = 0.0001\r\ndecay_rate = 0.97\r\ndecay_size = 1000\r\ntraining_iteration = 10000\r\ntrain_batch_size = 5000\r\ntest_iteration= 10000\r\ntest_batch_size = 1000\r\nLOG_LOSS = 1\r\nresidual_ft_alpha=0.9\r\nsnr_no = 14\r\nSNRdB_low_test=1.0\r\nSNRdB_high_test=14.0\r\n\r\n\r\nmodel = Model(Nt,v_size)\r\nloss = model.Det_Net(Nt, v_size, hidden_size, LOG_LOSS,L,residual_ft_alpha)\r\n# loss = model.Det_Net(Nt, v_size, hidden_size, LOG_LOSS,L)\r\nTOTAL_LOSS=tf.add_n(loss)\r\n\r\nglobal_step = tf.Variable(0, trainable=False)\r\ndecayLR= tf.train.exponential_decay(LearningRate, global_step, decay_size, decay_rate, staircase=True) #Learning rate en başta yüksek verilip giderek azaltılmıştır.\r\ntrain_step = tf.train.AdamOptimizer(decayLR).minimize(TOTAL_LOSS) #Train aşaması\r\n\r\n\r\n\r\ninit_op=tf.initialize_all_variables()\r\nsess.run(init_op) #tensorflow session'ı başlatılır\r\n\r\nloss_array = []\r\nloss_array1 = []\r\nloss_array2 = []\r\n\r\n#Training DetNet\r\nfor i in range(training_iteration): #num of train iter\r\n batchY, batchH, batchHY, batchHH, batchX , SNR1= get_data(train_batch_size,Nt,Nr,SNR_low,SNR_high)\r\n train_step.run(feed_dict={model.HY: batchHY, model.HH: batchHH, model.X: batchX}) #training \r\n if i % 100 == 0 :\r\n batchY, batchH, batchHY, batchHH, batchX ,SNR1= get_data(train_batch_size,Nt,Nr,SNR_low,SNR_high) #update values -- perform mini batch update her 100 iterasyonda bir \r\n results = sess.run([model.loss_LS, model.LOSS[L-1], model.ber_LS, model.BER[L-1]], {model.HY: batchHY, model.HH: batchHH, model.X: batchX}) # parametreleri güncelle.\r\n loss_array.append(results[0])\r\n loss_array1.append(results[1]) #LOSS List\r\n loss_array2.append(results[3]) #Ber list for each layer\r\n \r\n\r\n\r\nXr_ = np.zeros([snr_no,test_iteration,test_batch_size,Nt])\r\nXt_ = np.zeros([snr_no,test_iteration,test_batch_size,Nt])\r\n\r\n#Testing the trained model\r\nSNRdB_list = np.linspace(SNRdB_low_test,SNRdB_high_test,snr_no)\r\nSNR_list = 10.0 ** (SNRdB_list/10.0)\r\nBERS = np.zeros((1,snr_no))\r\nTIME = np.zeros((1,snr_no))\r\ntemp_bers= np.zeros((1,test_iteration))\r\ntemp_times = np.zeros((1,test_iteration))\r\nfor j in range(snr_no):\r\n for jj in range(test_iteration):\r\n batchY, batchH, batchHY, batchHH, batchX ,SNR1= get_data(test_batch_size , Nt,Nr,SNR_list[j],SNR_list[j])\r\n Xt_[j,jj,:,:] = batchX\r\n tic = tm.time()\r\n [temp_bers[:,jj], Xr_[j,jj,:,:]] = np.array(sess.run([model.BER[L-1],model.S[L-1]], {model.HY: batchHY, model.HH: batchHH, model.X: batchX})) \r\n toc = tm.time()\r\n temp_times[0][jj] = toc - tic\r\n BERS[0][j] = np.mean(temp_bers,1)\r\n TIME[0][j] = np.mean(temp_times[0])/test_batch_size\r\n\r\nprint('SNRdB_list')\r\nprint(SNRdB_list)\r\nprint('BERS')\r\nprint(BERS)\r\nprint('TIME')\r\nprint(TIME) \r\n \r\n#%% Plot Graph\r\n\r\nplt.plot(np.arange(len(loss_array1)), loss_array1)\r\n# plt.plot(np.arange(len(loss_array2)), loss_array2)\r\n\r\n#%% Save the data\r\nimport csv\r\n\r\nwith open('XT.csv', 'w', newline='') as file:\r\n for j in range(snr_no):\r\n for jj in range(test_iteration):\r\n for k in range(test_batch_size):\r\n mywriter = csv.writer(file, delimiter=',')\r\n mywriter.writerow(Xt_[j,jj,k,:])\r\n \r\nwith open('XR.csv', 'w', newline='') as file:\r\n for j in range(snr_no):\r\n for jj in range(test_iteration):\r\n for k in range(test_batch_size):\r\n mywriter1 = csv.writer(file, delimiter=',')\r\n mywriter1.writerow(Xr_[j,jj,k,:])\r\n \r\nwith open('loss_20x30.csv', 'w', newline='') as file:\r\n mywriter = csv.writer(file, delimiter=',')\r\n mywriter.writerow(loss_array1)\r\n \r\nwith open('ber_list_layers_20x30.csv', 'w', newline='') as file:\r\n mywriter = csv.writer(file, delimiter=',')\r\n mywriter.writerow(loss_array2)\r\n \r\nwith open('snrdblist_20x30.csv', 'w', newline='') as file:\r\n mywriter = csv.writer(file, delimiter=',')\r\n mywriter.writerow(SNRdB_list) \r\n\r\nwith open('bers_20x30.csv', 'w', newline='') as file:\r\n mywriter = csv.writer(file, delimiter=',')\r\n mywriter.writerow(BERS) \r\n \r\nwith open('times_20x30.csv', 'w', newline='') as file:\r\n mywriter = csv.writer(file, delimiter=',')\r\n mywriter.writerow(TIME) \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},{"alpha_fraction":0.5704736709594727,"alphanum_fraction":0.5925602912902832,"avg_line_length":43.19736862182617,"blob_id":"523f82841e2df530b1d2203f68e78238ec8aa7bb","content_id":"ec1420e1fffe1fa453ba31ea09281438e8c3692b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3456,"license_type":"no_license","max_line_length":233,"num_lines":76,"path":"/Models.py","repo_name":"denizurgun/graduation-project","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed May 12 22:06:10 2021\r\n\r\n@author: \r\n\r\nGRADUATION PROJECT : DEEP LEARNING BASED DECODER IN MASSIVE MIMO CHANNELS\r\n\r\n\"\"\"\r\n\r\n\r\nimport tensorflow.compat.v1 as tf\r\nimport numpy as np\r\n\r\n\r\ndef piecewise_linear_soft_sign(x):\r\n t = tf.Variable(0.1)\r\n psi = -1+tf.nn.relu(x+t)/(tf.abs(t)+0.00001)-tf.nn.relu(x-t)/(tf.abs(t)+0.00001)\r\n return psi\r\n\r\ndef ara_katman(x,input_size,output_size,Layer_num):\r\n W = tf.Variable(tf.random_normal([input_size, output_size], stddev=0.01))\r\n b = tf.Variable(tf.random_normal([1, output_size], stddev=0.01))\r\n y = tf.matmul(x, W)+b\r\n return y\r\n\r\ndef relu_katmani(x,input_size,output_size,Layer_num):\r\n y = tf.nn.relu(ara_katman(x,input_size,output_size,Layer_num))\r\n return y\r\n\r\ndef sign_katmani(x,input_size,output_size,Layer_num):\r\n y = piecewise_linear_soft_sign(ara_katman(x,input_size,output_size,Layer_num))\r\n return y\r\n\r\n\r\nclass Model:\r\n def __init__(self,Nt,v_size):\r\n \r\n self.HY = tf.placeholder(tf.float32,shape=[None,Nt])\r\n self.X = tf.placeholder(tf.float32,shape=[None,Nt])\r\n self.HH = tf.placeholder(tf.float32,shape=[None, Nt , Nt])\r\n \r\n self.batch_size = tf.shape(self.HY)[0] #equal to B parameter.\r\n self.X_LS = tf.matmul(tf.expand_dims(self.HY,1),tf.matrix_inverse(self.HH)) #Matris çarpımı yapılabilmesi için HY'nin boyutu 1 arttırıldı, 1 eksen eklendi. X_LS = HY*(HH')\r\n self.X_LS= tf.squeeze(self.X_LS,1) #1 eksen kaldırır.\r\n self.loss_LS = tf.reduce_mean(tf.square(self.X - self.X_LS)) #LOSS Function Hesaplandı.\r\n self.ber_LS = tf.reduce_mean(tf.cast(tf.not_equal(self.X,tf.sign(self.X_LS)), tf.float32))\r\n \r\n self.S=[]\r\n self.S.append(tf.zeros([self.batch_size,Nt])) #Xk+1\r\n self.V=[]\r\n self.V.append(tf.zeros([self.batch_size,v_size])) #Vk+1\r\n self.LOSS=[]\r\n self.LOSS.append(tf.zeros([]))\r\n self.BER=[]\r\n self.BER.append(tf.zeros([]))\r\n\r\n \r\n def Det_Net(self,Nt, v_size, hidden_size, LOG_LOSS,L,residual_ft_alpha): #\r\n \r\n for i in range(1,L):\r\n temp_s = tf.matmul(tf.expand_dims(self.S[-1],1),self.HH) \r\n temp_s= tf.squeeze(temp_s,1)\r\n Z = tf.concat([self.HY,self.S[-1],temp_s,self.V[-1]],1)\r\n ZZ = relu_katmani(Z,3*Nt + v_size , hidden_size,'relu'+str(i))\r\n self.S.append(sign_katmani(ZZ , hidden_size , Nt,'sign'+str(i)))\r\n self. S[i]=(1-residual_ft_alpha)*self.S[i]+residual_ft_alpha*self.S[i-1] #residual feature\r\n self.V.append(ara_katman(ZZ , hidden_size , v_size,'aff'+str(i)))\r\n self.V[i]=(1-residual_ft_alpha)*self.V[i]+residual_ft_alpha*self.V[i-1] #residual feature\r\n if LOG_LOSS == 1:\r\n \tself.LOSS.append(np.log(i)*tf.reduce_mean(tf.reduce_mean(tf.square(self.X - self.S[-1]),1)/tf.reduce_mean(tf.square(self.X - self.X_LS),1))) #X - Xk+1 -- (İletilen - tahmin edilen)^2 \r\n else:\r\n self.LOSS.append(tf.reduce_mean(tf.reduce_mean(tf.square(self.X - self.S[-1]),1)/tf.reduce_mean(tf.square(self.X - self.X_LS),1))) # X_LS : the standard decorrelator decoder., S: Her adımda tahmin edilen xk\r\n self.BER.append(tf.reduce_mean(tf.cast(tf.not_equal(self.X,tf.sign(self.S[-1])), tf.float32))) #X in xk'ya eşit olmadığı yerlerde hata sayar.\r\n \r\n return self.LOSS\r\n\r\n\r\n\r\n"},{"alpha_fraction":0.5128424763679504,"alphanum_fraction":0.5299657583236694,"avg_line_length":37,"blob_id":"bf3022b8870ca823b5ca6b1506990a5ce6d0f7e9","content_id":"26c5c89c31cfb6d94b4abe2c91ca8a0f2e45f5cb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1181,"license_type":"no_license","max_line_length":102,"num_lines":30,"path":"/generate_data.py","repo_name":"denizurgun/graduation-project","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed May 12 23:08:11 2021\r\n\r\n@author: \r\n\r\nGRADUATION PROJECT : DEEP LEARNING BASED DECODER IN MASSIVE MIMO CHANNELS\r\n\r\n\"\"\"\r\nimport numpy as np\r\n\r\ndef get_data(B,Nt,Nr,snr_low,snr_high):\r\n H_=np.random.randn(B,Nr,Nt)\r\n x_=np.sign(np.random.rand(B,Nt)-0.5) #transmitted BPSK signal \r\n y_=np.zeros([B,Nr])\r\n n=np.random.randn(B,Nr) # gürültü\r\n Hy_=x_*0 #0'a initiliaze\r\n HH_=np.zeros([B,Nt,Nt]) #0'a initiliaze\r\n SNR_= np.zeros([B]) #0'a initiliaze\r\n for i in range(B): #Batchsize boyutunda data oluşturulur\r\n SNR = np.random.uniform(low=snr_low,high=snr_high) #sınır SNR değerlerinde random SNR seçilir.\r\n H=H_[i,:,:] #H(1,N,Nt) boyutunda her bir adımda alınarak işleme sokulur.\r\n snr_temp=(H.T.dot(H)).trace()/Nt\r\n H=H/np.sqrt(snr_temp)*np.sqrt(SNR)\r\n H_[i,:,:]=H\r\n y_[i,:]=(H.dot(x_[i,:])+n[i,:]) # y = H*x + n*(normalize güç) *np.sqrt(snr_temp)/np.sqrt(SNR)\r\n Hy_[i,:]=H.T.dot(y_[i,:])\r\n HH_[i,:,:]=H.T.dot( H_[i,:,:])\r\n SNR_[i] = SNR\r\n return y_,H_,Hy_,HH_,x_,SNR_"},{"alpha_fraction":0.8266666531562805,"alphanum_fraction":0.8266666531562805,"avg_line_length":24,"blob_id":"985c3423fa635018dafe4da7528f715dd44c06f5","content_id":"2a738348c49a2ae15545a6f0421014d1c6e0ef10","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":75,"license_type":"no_license","max_line_length":52,"num_lines":3,"path":"/README.md","repo_name":"denizurgun/graduation-project","src_encoding":"UTF-8","text":"# graduation-project\n\nDEEP LEARNING BASED DECODER IN MASSIVE MIMO CHANNELS\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5966183543205261,\n \"alphanum_fraction\": 0.6268116235733032,\n \"avg_line_length\": 31.687074661254883,\n \"blob_id\": \"13fdd01aa1879ce193731f1b043437594ad1f648\",\n \"content_id\": \"8d986ebb8e2185b4fbdae9cd8b778a1808a91ed0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4981,\n \"license_type\": \"no_license\",\n \"max_line_length\": 192,\n \"num_lines\": 147,\n \"path\": \"/Det_Net_model.py\",\n \"repo_name\": \"denizurgun/graduation-project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\r\\n\\\"\\\"\\\"\\r\\nCreated on Fri May 14 22:04:36 2021\\r\\n\\r\\n@author: \\r\\n\\r\\nGRADUATION PROJECT : DEEP LEARNING BASED DECODER IN MASSIVE MIMO CHANNELS\\r\\n\\r\\n\\\"\\\"\\\"\\r\\n\\r\\nimport numpy as np\\r\\nimport time as tm\\r\\nimport math\\r\\nimport sys\\r\\nimport pickle as pkl\\r\\nimport tensorflow.compat.v1 as tf\\r\\nfrom generate_data import get_data\\r\\nfrom Models import Model,__init__\\r\\nimport matplotlib.pyplot as plt\\r\\n\\r\\n\\r\\nsess = tf.InteractiveSession()\\r\\ntf.disable_v2_behavior() \\r\\n#parameters\\r\\nNt = 20 # 2\\r\\nNr = 30 # 3\\r\\nSNRdB_low = 7.0\\r\\nSNRdB_high = 14.0\\r\\nSNR_low = 10.0 ** (SNRdB_low/10.0)\\r\\nSNR_high = 10.0 ** (SNRdB_high/10.0)\\r\\nL=30\\r\\nv_size = 2*Nt #4\\r\\nhidden_size = 8*Nt #16\\r\\nLearningRate = 0.0001\\r\\ndecay_rate = 0.97\\r\\ndecay_size = 1000\\r\\ntraining_iteration = 10000\\r\\ntrain_batch_size = 5000\\r\\ntest_iteration= 10000\\r\\ntest_batch_size = 1000\\r\\nLOG_LOSS = 1\\r\\nresidual_ft_alpha=0.9\\r\\nsnr_no = 14\\r\\nSNRdB_low_test=1.0\\r\\nSNRdB_high_test=14.0\\r\\n\\r\\n\\r\\nmodel = Model(Nt,v_size)\\r\\nloss = model.Det_Net(Nt, v_size, hidden_size, LOG_LOSS,L,residual_ft_alpha)\\r\\n# loss = model.Det_Net(Nt, v_size, hidden_size, LOG_LOSS,L)\\r\\nTOTAL_LOSS=tf.add_n(loss)\\r\\n\\r\\nglobal_step = tf.Variable(0, trainable=False)\\r\\ndecayLR= tf.train.exponential_decay(LearningRate, global_step, decay_size, decay_rate, staircase=True) #Learning rate en başta yüksek verilip giderek azaltılmıştır.\\r\\ntrain_step = tf.train.AdamOptimizer(decayLR).minimize(TOTAL_LOSS) #Train aşaması\\r\\n\\r\\n\\r\\n\\r\\ninit_op=tf.initialize_all_variables()\\r\\nsess.run(init_op) #tensorflow session'ı başlatılır\\r\\n\\r\\nloss_array = []\\r\\nloss_array1 = []\\r\\nloss_array2 = []\\r\\n\\r\\n#Training DetNet\\r\\nfor i in range(training_iteration): #num of train iter\\r\\n batchY, batchH, batchHY, batchHH, batchX , SNR1= get_data(train_batch_size,Nt,Nr,SNR_low,SNR_high)\\r\\n train_step.run(feed_dict={model.HY: batchHY, model.HH: batchHH, model.X: batchX}) #training \\r\\n if i % 100 == 0 :\\r\\n batchY, batchH, batchHY, batchHH, batchX ,SNR1= get_data(train_batch_size,Nt,Nr,SNR_low,SNR_high) #update values -- perform mini batch update her 100 iterasyonda bir \\r\\n results = sess.run([model.loss_LS, model.LOSS[L-1], model.ber_LS, model.BER[L-1]], {model.HY: batchHY, model.HH: batchHH, model.X: batchX}) # parametreleri güncelle.\\r\\n loss_array.append(results[0])\\r\\n loss_array1.append(results[1]) #LOSS List\\r\\n loss_array2.append(results[3]) #Ber list for each layer\\r\\n \\r\\n\\r\\n\\r\\nXr_ = np.zeros([snr_no,test_iteration,test_batch_size,Nt])\\r\\nXt_ = np.zeros([snr_no,test_iteration,test_batch_size,Nt])\\r\\n\\r\\n#Testing the trained model\\r\\nSNRdB_list = np.linspace(SNRdB_low_test,SNRdB_high_test,snr_no)\\r\\nSNR_list = 10.0 ** (SNRdB_list/10.0)\\r\\nBERS = np.zeros((1,snr_no))\\r\\nTIME = np.zeros((1,snr_no))\\r\\ntemp_bers= np.zeros((1,test_iteration))\\r\\ntemp_times = np.zeros((1,test_iteration))\\r\\nfor j in range(snr_no):\\r\\n for jj in range(test_iteration):\\r\\n batchY, batchH, batchHY, batchHH, batchX ,SNR1= get_data(test_batch_size , Nt,Nr,SNR_list[j],SNR_list[j])\\r\\n Xt_[j,jj,:,:] = batchX\\r\\n tic = tm.time()\\r\\n [temp_bers[:,jj], Xr_[j,jj,:,:]] = np.array(sess.run([model.BER[L-1],model.S[L-1]], {model.HY: batchHY, model.HH: batchHH, model.X: batchX})) \\r\\n toc = tm.time()\\r\\n temp_times[0][jj] = toc - tic\\r\\n BERS[0][j] = np.mean(temp_bers,1)\\r\\n TIME[0][j] = np.mean(temp_times[0])/test_batch_size\\r\\n\\r\\nprint('SNRdB_list')\\r\\nprint(SNRdB_list)\\r\\nprint('BERS')\\r\\nprint(BERS)\\r\\nprint('TIME')\\r\\nprint(TIME) \\r\\n \\r\\n#%% Plot Graph\\r\\n\\r\\nplt.plot(np.arange(len(loss_array1)), loss_array1)\\r\\n# plt.plot(np.arange(len(loss_array2)), loss_array2)\\r\\n\\r\\n#%% Save the data\\r\\nimport csv\\r\\n\\r\\nwith open('XT.csv', 'w', newline='') as file:\\r\\n for j in range(snr_no):\\r\\n for jj in range(test_iteration):\\r\\n for k in range(test_batch_size):\\r\\n mywriter = csv.writer(file, delimiter=',')\\r\\n mywriter.writerow(Xt_[j,jj,k,:])\\r\\n \\r\\nwith open('XR.csv', 'w', newline='') as file:\\r\\n for j in range(snr_no):\\r\\n for jj in range(test_iteration):\\r\\n for k in range(test_batch_size):\\r\\n mywriter1 = csv.writer(file, delimiter=',')\\r\\n mywriter1.writerow(Xr_[j,jj,k,:])\\r\\n \\r\\nwith open('loss_20x30.csv', 'w', newline='') as file:\\r\\n mywriter = csv.writer(file, delimiter=',')\\r\\n mywriter.writerow(loss_array1)\\r\\n \\r\\nwith open('ber_list_layers_20x30.csv', 'w', newline='') as file:\\r\\n mywriter = csv.writer(file, delimiter=',')\\r\\n mywriter.writerow(loss_array2)\\r\\n \\r\\nwith open('snrdblist_20x30.csv', 'w', newline='') as file:\\r\\n mywriter = csv.writer(file, delimiter=',')\\r\\n mywriter.writerow(SNRdB_list) \\r\\n\\r\\nwith open('bers_20x30.csv', 'w', newline='') as file:\\r\\n mywriter = csv.writer(file, delimiter=',')\\r\\n mywriter.writerow(BERS) \\r\\n \\r\\nwith open('times_20x30.csv', 'w', newline='') as file:\\r\\n mywriter = csv.writer(file, delimiter=',')\\r\\n mywriter.writerow(TIME) \\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5704736709594727,\n \"alphanum_fraction\": 0.5925602912902832,\n \"avg_line_length\": 43.19736862182617,\n \"blob_id\": \"523f82841e2df530b1d2203f68e78238ec8aa7bb\",\n \"content_id\": \"ec1420e1fffe1fa453ba31ea09281438e8c3692b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3456,\n \"license_type\": \"no_license\",\n \"max_line_length\": 233,\n \"num_lines\": 76,\n \"path\": \"/Models.py\",\n \"repo_name\": \"denizurgun/graduation-project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\r\\n\\\"\\\"\\\"\\r\\nCreated on Wed May 12 22:06:10 2021\\r\\n\\r\\n@author: \\r\\n\\r\\nGRADUATION PROJECT : DEEP LEARNING BASED DECODER IN MASSIVE MIMO CHANNELS\\r\\n\\r\\n\\\"\\\"\\\"\\r\\n\\r\\n\\r\\nimport tensorflow.compat.v1 as tf\\r\\nimport numpy as np\\r\\n\\r\\n\\r\\ndef piecewise_linear_soft_sign(x):\\r\\n t = tf.Variable(0.1)\\r\\n psi = -1+tf.nn.relu(x+t)/(tf.abs(t)+0.00001)-tf.nn.relu(x-t)/(tf.abs(t)+0.00001)\\r\\n return psi\\r\\n\\r\\ndef ara_katman(x,input_size,output_size,Layer_num):\\r\\n W = tf.Variable(tf.random_normal([input_size, output_size], stddev=0.01))\\r\\n b = tf.Variable(tf.random_normal([1, output_size], stddev=0.01))\\r\\n y = tf.matmul(x, W)+b\\r\\n return y\\r\\n\\r\\ndef relu_katmani(x,input_size,output_size,Layer_num):\\r\\n y = tf.nn.relu(ara_katman(x,input_size,output_size,Layer_num))\\r\\n return y\\r\\n\\r\\ndef sign_katmani(x,input_size,output_size,Layer_num):\\r\\n y = piecewise_linear_soft_sign(ara_katman(x,input_size,output_size,Layer_num))\\r\\n return y\\r\\n\\r\\n\\r\\nclass Model:\\r\\n def __init__(self,Nt,v_size):\\r\\n \\r\\n self.HY = tf.placeholder(tf.float32,shape=[None,Nt])\\r\\n self.X = tf.placeholder(tf.float32,shape=[None,Nt])\\r\\n self.HH = tf.placeholder(tf.float32,shape=[None, Nt , Nt])\\r\\n \\r\\n self.batch_size = tf.shape(self.HY)[0] #equal to B parameter.\\r\\n self.X_LS = tf.matmul(tf.expand_dims(self.HY,1),tf.matrix_inverse(self.HH)) #Matris çarpımı yapılabilmesi için HY'nin boyutu 1 arttırıldı, 1 eksen eklendi. X_LS = HY*(HH')\\r\\n self.X_LS= tf.squeeze(self.X_LS,1) #1 eksen kaldırır.\\r\\n self.loss_LS = tf.reduce_mean(tf.square(self.X - self.X_LS)) #LOSS Function Hesaplandı.\\r\\n self.ber_LS = tf.reduce_mean(tf.cast(tf.not_equal(self.X,tf.sign(self.X_LS)), tf.float32))\\r\\n \\r\\n self.S=[]\\r\\n self.S.append(tf.zeros([self.batch_size,Nt])) #Xk+1\\r\\n self.V=[]\\r\\n self.V.append(tf.zeros([self.batch_size,v_size])) #Vk+1\\r\\n self.LOSS=[]\\r\\n self.LOSS.append(tf.zeros([]))\\r\\n self.BER=[]\\r\\n self.BER.append(tf.zeros([]))\\r\\n\\r\\n \\r\\n def Det_Net(self,Nt, v_size, hidden_size, LOG_LOSS,L,residual_ft_alpha): #\\r\\n \\r\\n for i in range(1,L):\\r\\n temp_s = tf.matmul(tf.expand_dims(self.S[-1],1),self.HH) \\r\\n temp_s= tf.squeeze(temp_s,1)\\r\\n Z = tf.concat([self.HY,self.S[-1],temp_s,self.V[-1]],1)\\r\\n ZZ = relu_katmani(Z,3*Nt + v_size , hidden_size,'relu'+str(i))\\r\\n self.S.append(sign_katmani(ZZ , hidden_size , Nt,'sign'+str(i)))\\r\\n self. S[i]=(1-residual_ft_alpha)*self.S[i]+residual_ft_alpha*self.S[i-1] #residual feature\\r\\n self.V.append(ara_katman(ZZ , hidden_size , v_size,'aff'+str(i)))\\r\\n self.V[i]=(1-residual_ft_alpha)*self.V[i]+residual_ft_alpha*self.V[i-1] #residual feature\\r\\n if LOG_LOSS == 1:\\r\\n \\tself.LOSS.append(np.log(i)*tf.reduce_mean(tf.reduce_mean(tf.square(self.X - self.S[-1]),1)/tf.reduce_mean(tf.square(self.X - self.X_LS),1))) #X - Xk+1 -- (İletilen - tahmin edilen)^2 \\r\\n else:\\r\\n self.LOSS.append(tf.reduce_mean(tf.reduce_mean(tf.square(self.X - self.S[-1]),1)/tf.reduce_mean(tf.square(self.X - self.X_LS),1))) # X_LS : the standard decorrelator decoder., S: Her adımda tahmin edilen xk\\r\\n self.BER.append(tf.reduce_mean(tf.cast(tf.not_equal(self.X,tf.sign(self.S[-1])), tf.float32))) #X in xk'ya eşit olmadığı yerlerde hata sayar.\\r\\n \\r\\n return self.LOSS\\r\\n\\r\\n\\r\\n\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5128424763679504,\n \"alphanum_fraction\": 0.5299657583236694,\n \"avg_line_length\": 37,\n \"blob_id\": \"bf3022b8870ca823b5ca6b1506990a5ce6d0f7e9\",\n \"content_id\": \"26c5c89c31cfb6d94b4abe2c91ca8a0f2e45f5cb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1181,\n \"license_type\": \"no_license\",\n \"max_line_length\": 102,\n \"num_lines\": 30,\n \"path\": \"/generate_data.py\",\n \"repo_name\": \"denizurgun/graduation-project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\r\\n\\\"\\\"\\\"\\r\\nCreated on Wed May 12 23:08:11 2021\\r\\n\\r\\n@author: \\r\\n\\r\\nGRADUATION PROJECT : DEEP LEARNING BASED DECODER IN MASSIVE MIMO CHANNELS\\r\\n\\r\\n\\\"\\\"\\\"\\r\\nimport numpy as np\\r\\n\\r\\ndef get_data(B,Nt,Nr,snr_low,snr_high):\\r\\n H_=np.random.randn(B,Nr,Nt)\\r\\n x_=np.sign(np.random.rand(B,Nt)-0.5) #transmitted BPSK signal \\r\\n y_=np.zeros([B,Nr])\\r\\n n=np.random.randn(B,Nr) # gürültü\\r\\n Hy_=x_*0 #0'a initiliaze\\r\\n HH_=np.zeros([B,Nt,Nt]) #0'a initiliaze\\r\\n SNR_= np.zeros([B]) #0'a initiliaze\\r\\n for i in range(B): #Batchsize boyutunda data oluşturulur\\r\\n SNR = np.random.uniform(low=snr_low,high=snr_high) #sınır SNR değerlerinde random SNR seçilir.\\r\\n H=H_[i,:,:] #H(1,N,Nt) boyutunda her bir adımda alınarak işleme sokulur.\\r\\n snr_temp=(H.T.dot(H)).trace()/Nt\\r\\n H=H/np.sqrt(snr_temp)*np.sqrt(SNR)\\r\\n H_[i,:,:]=H\\r\\n y_[i,:]=(H.dot(x_[i,:])+n[i,:]) # y = H*x + n*(normalize güç) *np.sqrt(snr_temp)/np.sqrt(SNR)\\r\\n Hy_[i,:]=H.T.dot(y_[i,:])\\r\\n HH_[i,:,:]=H.T.dot( H_[i,:,:])\\r\\n SNR_[i] = SNR\\r\\n return y_,H_,Hy_,HH_,x_,SNR_\"\n },\n {\n \"alpha_fraction\": 0.8266666531562805,\n \"alphanum_fraction\": 0.8266666531562805,\n \"avg_line_length\": 24,\n \"blob_id\": \"985c3423fa635018dafe4da7528f715dd44c06f5\",\n \"content_id\": \"2a738348c49a2ae15545a6f0421014d1c6e0ef10\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 75,\n \"license_type\": \"no_license\",\n \"max_line_length\": 52,\n \"num_lines\": 3,\n \"path\": \"/README.md\",\n \"repo_name\": \"denizurgun/graduation-project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# graduation-project\\n\\nDEEP LEARNING BASED DECODER IN MASSIVE MIMO CHANNELS\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":4,"string":"4"}}},{"rowIdx":2983062,"cells":{"repo_name":{"kind":"string","value":"GarvinChanderia/WindowsNotificationTrigger"},"repo_url":{"kind":"string","value":"https://github.com/GarvinChanderia/WindowsNotificationTrigger"},"snapshot_id":{"kind":"string","value":"a6ff4caeaa8e9725966cf420b3f0f3ee46933106"},"revision_id":{"kind":"string","value":"9ae5093987749abd05098ff7025f1a8f2a71b479"},"directory_id":{"kind":"string","value":"c2d127675b94b7e6294e38eab1d9bde559b710af"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-07-14T23:32:54.706309","string":"2023-07-14T23:32:54.706309"},"revision_date":{"kind":"timestamp","value":"2021-09-06T18:22:33","string":"2021-09-06T18:22:33"},"committer_date":{"kind":"timestamp","value":"2021-09-06T18:22:33","string":"2021-09-06T18:22:33"},"github_id":{"kind":"number","value":403720695,"string":"403,720,695"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5707376003265381,"alphanum_fraction":0.5840386748313904,"avg_line_length":31.15999984741211,"blob_id":"c241b488a741092b7971500149c30ec4e3c36398","content_id":"f0b3fc6e1ac7f3397654d13bb84ba16b94c80ba7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":827,"license_type":"no_license","max_line_length":139,"num_lines":25,"path":"/main.py","repo_name":"GarvinChanderia/WindowsNotificationTrigger","src_encoding":"UTF-8","text":"import datetime\r\nimport time\r\nimport requests\r\nfrom plyer import notification\r\n\r\ncovidData = None\r\ntry:\r\n covidData= requests.get(\"https://corona-rest-api.herokuapp.com/Api/india\")\r\nexcept:\r\n print(\"Error!\")\r\n\r\nif(covidData!= None):\r\n data = covidData.json()[\"Success\"]\r\n while(True):\r\n notification.notify(\r\n title= \"Covid19 Stats on {}\".format(datetime.date.today()),\r\n message = \"Total Cases: {totalcases}\\nToday's Cases: {todaycases}\\nToday Deaths: {todaydeaths}\\nTotal Active: {active}\".format(\r\n totalcases=data['cases'],\r\n todaycases=data['todayCases'],\r\n todaydeaths=data[\"todayDeaths\"],\r\n active=data['active']),\r\n app_icon=\"icon.ico\",\r\n timeout=50\r\n )\r\n time.sleep(60*60*24*7)"}],"string":"[\n {\n \"alpha_fraction\": 0.5707376003265381,\n \"alphanum_fraction\": 0.5840386748313904,\n \"avg_line_length\": 31.15999984741211,\n \"blob_id\": \"c241b488a741092b7971500149c30ec4e3c36398\",\n \"content_id\": \"f0b3fc6e1ac7f3397654d13bb84ba16b94c80ba7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 827,\n \"license_type\": \"no_license\",\n \"max_line_length\": 139,\n \"num_lines\": 25,\n \"path\": \"/main.py\",\n \"repo_name\": \"GarvinChanderia/WindowsNotificationTrigger\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import datetime\\r\\nimport time\\r\\nimport requests\\r\\nfrom plyer import notification\\r\\n\\r\\ncovidData = None\\r\\ntry:\\r\\n covidData= requests.get(\\\"https://corona-rest-api.herokuapp.com/Api/india\\\")\\r\\nexcept:\\r\\n print(\\\"Error!\\\")\\r\\n\\r\\nif(covidData!= None):\\r\\n data = covidData.json()[\\\"Success\\\"]\\r\\n while(True):\\r\\n notification.notify(\\r\\n title= \\\"Covid19 Stats on {}\\\".format(datetime.date.today()),\\r\\n message = \\\"Total Cases: {totalcases}\\\\nToday's Cases: {todaycases}\\\\nToday Deaths: {todaydeaths}\\\\nTotal Active: {active}\\\".format(\\r\\n totalcases=data['cases'],\\r\\n todaycases=data['todayCases'],\\r\\n todaydeaths=data[\\\"todayDeaths\\\"],\\r\\n active=data['active']),\\r\\n app_icon=\\\"icon.ico\\\",\\r\\n timeout=50\\r\\n )\\r\\n time.sleep(60*60*24*7)\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2983063,"cells":{"repo_name":{"kind":"string","value":"biteapple777/RPG-in-Python"},"repo_url":{"kind":"string","value":"https://github.com/biteapple777/RPG-in-Python"},"snapshot_id":{"kind":"string","value":"e1e459bba61fa3a953df4fb04c702533cc3a3b2c"},"revision_id":{"kind":"string","value":"a875210fd63e7cb69bf4d411992667c93e02a5f0"},"directory_id":{"kind":"string","value":"714123b28257fec46ae8ed0cb75fa6049071cc3d"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-02-27T04:19:39.976636","string":"2020-02-27T04:19:39.976636"},"revision_date":{"kind":"timestamp","value":"2016-06-12T08:36:10","string":"2016-06-12T08:36:10"},"committer_date":{"kind":"timestamp","value":"2016-06-12T08:36:10","string":"2016-06-12T08:36:10"},"github_id":{"kind":"number","value":56909019,"string":"56,909,019"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"timestamp","value":"2016-04-23T08:32:32","string":"2016-04-23T08:32:32"},"gha_updated_at":{"kind":"timestamp","value":"2016-04-23T08:32:32","string":"2016-04-23T08:32:32"},"gha_pushed_at":{"kind":"timestamp","value":"2016-06-12T08:36:10","string":"2016-06-12T08:36:10"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7333333492279053,"alphanum_fraction":0.7333333492279053,"avg_line_length":14,"blob_id":"cebf30f1b5e13ff3ec7f6a69ca9489b1a01684cf","content_id":"b9d418470cebc35e3c1871c1f9a340e609c0678d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":30,"license_type":"no_license","max_line_length":15,"num_lines":2,"path":"/README.md","repo_name":"biteapple777/RPG-in-Python","src_encoding":"UTF-8","text":"# RPG-in-Python\nRPG in Python\n"},{"alpha_fraction":0.47112560272216797,"alphanum_fraction":0.4786296784877777,"avg_line_length":20.43356704711914,"blob_id":"822368cf99bbc71694c3fb9beaef8e0fc3cb2189","content_id":"43dda8dc0843d52283f5ac2cda6ecc11e924b194","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3065,"license_type":"no_license","max_line_length":69,"num_lines":143,"path":"/main.py","repo_name":"biteapple777/RPG-in-Python","src_encoding":"UTF-8","text":"import pickle\nimport random\n\nxPos = 0\nyPos = 0\ntileBackup = '.'\n\nclass entity():\n \n def __init__(self, name, hp, xp, atk):\n self.name = name\n self.hp = hp\n self.xp = xp\n self.atk = atk\n \n def printStats(self):\n stats = vars(self)\n print ('-----')\n print ('Name: {}'.format(self.name))\n print ('HP: {}'.format(self.hp))\n print ('XP: {}'.format(self.xp))\n print ('Attack: {}'.format(self.atk))\n #print ('\\n'.join(\"%s: %s\" % item for item in stats.items()))\n print ('-----')\n \nclass player(entity):\n def __init__(self, name, hp, xp, atk):\n entity.__init__(self, name, hp, xp, atk)\n\nclass mob(entity):\n def __init__(self, name, hp, xp, atk):\n entity.__init__(self, name, hp, xp, atk)\n\n#=====GAMESL=====\n\ndef saveGame():\n pickle.dump([plr, wld],open('RPGData','wb'))\n\ndef loadGame():\n global plr\n global wld\n try:\n plr = pickle.load(open('RPGData','rb'))[0]\n wld = pickle.load(open('RPGData','rb'))[1]\n except:\n print('Some error occured.')\n print('Creating new character.')\n createPlr()\n return False\n\n#=====PLAYER=====\n\ndef createPlr():\n global plr\n plrName = input('Enter your name: ')\n plr = entity(plrName, 100, 0, 15)\n\n\n\n#=====WORLD=====\n\nworldLayout = {\n '.':5,\n '#':3,\n '@':2\n }\n\ndef genWorld():\n global wld\n wld = [\n [choose(worldLayout) for _ in range(6)]\n for _ in range(6)\n ]\n\ndef printWorld():\n print ('\\n'.join(' '.join(row) for row in wld))\n\ndef choose(d):\n choiceList = []\n for i in range(len(d)):\n for _ in range(d[list(d.keys())[i]]):\n choiceList.append(list(d.keys())[i])\n return random.choice(choiceList)\n\n#=====GAME=====\n\ndef game():\n global xPos\n global yPos\n global tileBackup\n wld[yPos][xPos] = 'O'\n plr.printStats()\n printWorld()\n cmd = input('Game Command: ')\n if cmd == 's':\n if yPos != 5:\n wld[yPos][xPos] = tileBackup\n yPos += 1\n tileBackup = wld[yPos][xPos]\n wld[yPos][xPos] = 'O'\n if cmd == 'w':\n if yPos != 0:\n wld[yPos][xPos] = tileBackup\n yPos -= 1\n tileBackup = wld[yPos][xPos]\n wld[yPos][xPos] = 'O'\n if cmd == 'a':\n if xPos != 0:\n wld[yPos][xPos] = tileBackup\n xPos -= 1\n tileBackup = wld[yPos][xPos]\n wld[yPos][xPos] = 'O'\n if cmd == 'd':\n if xPos != 5:\n wld[yPos][xPos] = tileBackup\n xPos += 1\n tileBackup = wld[yPos][xPos]\n wld[yPos][xPos] = 'O'\n game()\n\ndef battle():\n \n\n#=====MAIN=====\n\ndef main():\n cmd = input('Main Command: ')\n if cmd == 'start':\n print('Loading characters...')\n loadGame()\n game()\n if cmd == 'new':\n createPlr()\n genWorld()\n saveGame()\n game()\n if cmd == 'save':\n saveGame()\n if cmd == 'exit':\n exit()\n return main()\n\nmain()\n"}],"string":"[\n {\n \"alpha_fraction\": 0.7333333492279053,\n \"alphanum_fraction\": 0.7333333492279053,\n \"avg_line_length\": 14,\n \"blob_id\": \"cebf30f1b5e13ff3ec7f6a69ca9489b1a01684cf\",\n \"content_id\": \"b9d418470cebc35e3c1871c1f9a340e609c0678d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 30,\n \"license_type\": \"no_license\",\n \"max_line_length\": 15,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"biteapple777/RPG-in-Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# RPG-in-Python\\nRPG in Python\\n\"\n },\n {\n \"alpha_fraction\": 0.47112560272216797,\n \"alphanum_fraction\": 0.4786296784877777,\n \"avg_line_length\": 20.43356704711914,\n \"blob_id\": \"822368cf99bbc71694c3fb9beaef8e0fc3cb2189\",\n \"content_id\": \"43dda8dc0843d52283f5ac2cda6ecc11e924b194\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3065,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 143,\n \"path\": \"/main.py\",\n \"repo_name\": \"biteapple777/RPG-in-Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import pickle\\nimport random\\n\\nxPos = 0\\nyPos = 0\\ntileBackup = '.'\\n\\nclass entity():\\n \\n def __init__(self, name, hp, xp, atk):\\n self.name = name\\n self.hp = hp\\n self.xp = xp\\n self.atk = atk\\n \\n def printStats(self):\\n stats = vars(self)\\n print ('-----')\\n print ('Name: {}'.format(self.name))\\n print ('HP: {}'.format(self.hp))\\n print ('XP: {}'.format(self.xp))\\n print ('Attack: {}'.format(self.atk))\\n #print ('\\\\n'.join(\\\"%s: %s\\\" % item for item in stats.items()))\\n print ('-----')\\n \\nclass player(entity):\\n def __init__(self, name, hp, xp, atk):\\n entity.__init__(self, name, hp, xp, atk)\\n\\nclass mob(entity):\\n def __init__(self, name, hp, xp, atk):\\n entity.__init__(self, name, hp, xp, atk)\\n\\n#=====GAMESL=====\\n\\ndef saveGame():\\n pickle.dump([plr, wld],open('RPGData','wb'))\\n\\ndef loadGame():\\n global plr\\n global wld\\n try:\\n plr = pickle.load(open('RPGData','rb'))[0]\\n wld = pickle.load(open('RPGData','rb'))[1]\\n except:\\n print('Some error occured.')\\n print('Creating new character.')\\n createPlr()\\n return False\\n\\n#=====PLAYER=====\\n\\ndef createPlr():\\n global plr\\n plrName = input('Enter your name: ')\\n plr = entity(plrName, 100, 0, 15)\\n\\n\\n\\n#=====WORLD=====\\n\\nworldLayout = {\\n '.':5,\\n '#':3,\\n '@':2\\n }\\n\\ndef genWorld():\\n global wld\\n wld = [\\n [choose(worldLayout) for _ in range(6)]\\n for _ in range(6)\\n ]\\n\\ndef printWorld():\\n print ('\\\\n'.join(' '.join(row) for row in wld))\\n\\ndef choose(d):\\n choiceList = []\\n for i in range(len(d)):\\n for _ in range(d[list(d.keys())[i]]):\\n choiceList.append(list(d.keys())[i])\\n return random.choice(choiceList)\\n\\n#=====GAME=====\\n\\ndef game():\\n global xPos\\n global yPos\\n global tileBackup\\n wld[yPos][xPos] = 'O'\\n plr.printStats()\\n printWorld()\\n cmd = input('Game Command: ')\\n if cmd == 's':\\n if yPos != 5:\\n wld[yPos][xPos] = tileBackup\\n yPos += 1\\n tileBackup = wld[yPos][xPos]\\n wld[yPos][xPos] = 'O'\\n if cmd == 'w':\\n if yPos != 0:\\n wld[yPos][xPos] = tileBackup\\n yPos -= 1\\n tileBackup = wld[yPos][xPos]\\n wld[yPos][xPos] = 'O'\\n if cmd == 'a':\\n if xPos != 0:\\n wld[yPos][xPos] = tileBackup\\n xPos -= 1\\n tileBackup = wld[yPos][xPos]\\n wld[yPos][xPos] = 'O'\\n if cmd == 'd':\\n if xPos != 5:\\n wld[yPos][xPos] = tileBackup\\n xPos += 1\\n tileBackup = wld[yPos][xPos]\\n wld[yPos][xPos] = 'O'\\n game()\\n\\ndef battle():\\n \\n\\n#=====MAIN=====\\n\\ndef main():\\n cmd = input('Main Command: ')\\n if cmd == 'start':\\n print('Loading characters...')\\n loadGame()\\n game()\\n if cmd == 'new':\\n createPlr()\\n genWorld()\\n saveGame()\\n game()\\n if cmd == 'save':\\n saveGame()\\n if cmd == 'exit':\\n exit()\\n return main()\\n\\nmain()\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2983064,"cells":{"repo_name":{"kind":"string","value":"Waightman/pyerz"},"repo_url":{"kind":"string","value":"https://github.com/Waightman/pyerz"},"snapshot_id":{"kind":"string","value":"da13741885aee9cc90694f8d10e908d6d184ec78"},"revision_id":{"kind":"string","value":"03311ce81dda7cba69f2eb0f0bf9c2c5a282a297"},"directory_id":{"kind":"string","value":"b20f9602b61cc05e20ecd4a7123284b083377dee"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-03-24T09:31:52.528388","string":"2023-03-24T09:31:52.528388"},"revision_date":{"kind":"timestamp","value":"2021-01-10T01:43:00","string":"2021-01-10T01:43:00"},"committer_date":{"kind":"timestamp","value":"2021-01-10T01:43:00","string":"2021-01-10T01:43:00"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5603086352348328,"alphanum_fraction":0.5669419169425964,"avg_line_length":24.828670501708984,"blob_id":"bbb5190a2b7ccfd7b9303c75d53ca46f7aba5b67","content_id":"428a5bd5e210636cf95548784447c21e62d88f15","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8343,"license_type":"permissive","max_line_length":86,"num_lines":286,"path":"/pyerz/pyerz.py","repo_name":"Waightman/pyerz","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nimport codecs\nimport logging\nimport pkg_resources\nfrom os.path import abspath\ntry:\n from os import scandir\nexcept ImportError:\n from scandir import scandir\n\nimport click\nfrom docx import Document\nfrom docx.shared import Pt\nfrom docx.enum.text import WD_PARAGRAPH_ALIGNMENT\n\n\nlogger = logging.getLogger(__name__)\n\n\n# 默认源代码文件目录\nDEFAULT_INDIRS = ['.']\n# 默认支持的代码格式\nDEFAULT_EXTS = ['py']\n# 默认的注释前缀\nDEFAULT_COMMENT_CHARS = (\n '#', '//'\n)\n\n\ndef del_slash(dirs):\n \"\"\"\n 删除文件夹最后一位的/\n\n Args:\n dirs: 文件夹列表\n Returns:\n 删除之后的文件夹\n \"\"\"\n no_slash_dirs = []\n for dir_ in dirs:\n if dir_[-1] == '/':\n no_slash_dirs.append(dir_[: -1])\n else:\n no_slash_dirs.append(dir_)\n return no_slash_dirs\n\n\nclass CodeFinder(object):\n \"\"\"\n 给定一个目录，和若干个后缀名，\n 递归地遍历该目录，找到该目录下\n 所有以这些后缀结束的文件\n \"\"\"\n def __init__(self, exts=None):\n \"\"\"\n Args:\n exts: 后缀名，默认为以py结尾\n \"\"\"\n self.exts = exts if exts else ['py']\n\n def is_code(self, file):\n for ext in self.exts:\n if file.endswith(ext):\n return True\n return False\n\n @staticmethod\n def is_hidden_file(file):\n \"\"\"\n 是否是隐藏文件\n \"\"\"\n return file[0] == '.'\n\n @staticmethod\n def should_be_excluded(file, excludes=None):\n \"\"\"\n 是否需要略过此文件\n \"\"\"\n if not excludes:\n return False\n if not isinstance(excludes, list):\n excludes = [excludes]\n should_be_excluded = False\n for exclude in excludes:\n if file.startswith(exclude):\n should_be_excluded = True\n break\n return should_be_excluded\n\n def find(self, indir, excludes=None):\n \"\"\"\n 给定一个文件夹查找这个文件夹下所有的代码\n\n Args:\n indir: 需要查到代码的目录\n excludes: 排除文件或目录\n Returns:\n 代码文件列表\n \"\"\"\n files = []\n for entry in scandir(indir):\n # 防止根目录有一些含有非常多文件的隐藏文件夹\n # 例如，.git文件，如果不排除，此程序很难运行\n entry_name = entry.name\n entry_path = abspath(entry.path)\n if self.is_hidden_file(entry_name):\n continue\n if self.should_be_excluded(entry_path, excludes):\n continue\n if entry.is_file():\n if self.is_code(entry_name):\n files.append(entry_path)\n continue\n for file in self.find(entry_path, excludes=excludes):\n files.append(file)\n logger.debug('在%s目录下找到%d个代码文件.', indir, len(files))\n return files\n\n\nclass CodeWriter(object):\n def __init__(\n self, font_name='宋体',\n font_size=10.5, space_before=0.0,\n space_after=2.3, line_spacing=10.5,\n command_chars=None, document=None\n ):\n self.font_name = font_name\n self.font_size = font_size\n self.space_before = space_before\n self.space_after = space_after\n self.line_spacing = line_spacing\n self.command_chars = command_chars if command_chars else DEFAULT_COMMENT_CHARS\n self.document = Document(pkg_resources.resource_filename(\n 'pyerz', 'template.docx'\n )) if not document else document\n\n @staticmethod\n def is_blank_line(line):\n \"\"\"\n 判断是否是空行\n \"\"\"\n return not bool(line)\n\n def is_comment_line(self, line):\n line = line.lstrip() # 去除左侧缩进\n is_comment = False\n for comment_char in self.command_chars:\n if line.startswith(comment_char):\n is_comment = True\n break\n return is_comment\n\n def write_header(self, title):\n \"\"\"\n 写入页眉\n \"\"\"\n paragraph = self.document.sections[0].header.paragraphs[0]\n paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER\n run = paragraph.add_run(title)\n run.font.name = self.font_name\n run.font.size = Pt(self.font_size)\n return self\n\n def write_file(self, file):\n \"\"\"\n 把单个文件添加到程序文档里面\n \"\"\"\n with codecs.open(file, encoding='utf-8') as fp:\n for line in fp:\n line = line.rstrip()\n if self.is_blank_line(line):\n continue\n if self.is_comment_line(line):\n continue\n paragraph = self.document.add_paragraph()\n paragraph.paragraph_format.space_before = Pt(self.space_before)\n paragraph.paragraph_format.space_after = Pt(self.space_after)\n paragraph.paragraph_format.line_spacing = Pt(self.line_spacing)\n run = paragraph.add_run(line)\n run.font.name = self.font_name\n run.font.size = Pt(self.font_size)\n return self\n\n def save(self, file):\n self.document.save(file)\n\n\n@click.command(name='pyerz')\n@click.option(\n '-t', '--title', default='软件著作权程序鉴别材料生成器V1.0',\n help='软件名称+版本号，默认为软件著作权程序鉴别材料生成器V1.0，此名称用于生成页眉'\n)\n@click.option(\n '-i', '--indir', 'indirs',\n multiple=True, type=click.Path(exists=True),\n help='源码所在文件夹，可以指定多个，默认为当前目录'\n)\n@click.option(\n '-e', '--ext', 'exts',\n multiple=True, help='源代码后缀，可以指定多个，默认为Python源代码'\n)\n@click.option(\n '-c', '--comment-char', 'comment_chars',\n multiple=True, help='注释字符串，可以指定多个，默认为#、//'\n)\n@click.option(\n '--font-name', default='宋体',\n help='字体，默认为宋体'\n)\n@click.option(\n '--font-size', default=10.5,\n type=click.FloatRange(min=1.0),\n help='字号，默认为五号，即10.5号'\n)\n@click.option(\n '--space-before', default=0.0,\n type=click.FloatRange(min=0.0),\n help='段前间距，默认为0'\n)\n@click.option(\n '--space-after', default=2.3,\n type=click.FloatRange(min=0.0),\n help='段后间距，默认为2.3'\n)\n@click.option(\n '--line-spacing', default=10.5,\n type=click.FloatRange(min=0.0),\n help='行距，默认为固定值10.5'\n)\n@click.option(\n '--exclude', 'excludes',\n multiple=True, type=click.Path(exists=True),\n help='需要排除的文件或路径，可以指定多个'\n)\n@click.option(\n '-o', '--outfile', default='code.docx',\n type=click.Path(exists=False),\n help='输出文件（docx格式），默认为当前目录的code.docx'\n)\n@click.option('-v', '--verbose', is_flag=True, help='打印调试信息')\ndef main(\n title, indirs, exts,\n comment_chars, font_name,\n font_size, space_before,\n space_after, line_spacing,\n excludes, outfile, verbose\n):\n if not indirs:\n indirs = DEFAULT_INDIRS\n if not exts:\n exts = DEFAULT_EXTS\n if not comment_chars:\n comment_chars = DEFAULT_COMMENT_CHARS\n if verbose:\n logging.basicConfig(level=logging.DEBUG)\n\n # 第零步，把所有的路径都转换为绝对路径\n indirs = [abspath(indir) for indir in indirs]\n excludes = del_slash(\n [abspath(exclude) for exclude in excludes] if excludes else []\n )\n\n # 第一步，查找代码文件\n finder = CodeFinder(exts)\n files = [file for indir in indirs for file in finder.find(\n indir, excludes=excludes\n )]\n\n # 第二步，逐个把代码文件写入到docx中\n writer = CodeWriter(\n command_chars=comment_chars,\n font_name=font_name,\n font_size=font_size,\n space_before=space_before,\n space_after=space_after,\n line_spacing=line_spacing\n )\n writer.write_header(title)\n for file in files:\n writer.write_file(file)\n writer.save(outfile)\n return 0\n\n\nif __name__ == '__main__':\n main()\n"},{"alpha_fraction":0.6737979054450989,"alphanum_fraction":0.6900978088378906,"avg_line_length":24.304122924804688,"blob_id":"415650b79ed16aa4daf6fec871f1c6571a5054ee","content_id":"c8b91b48e9502b6460a52c079f95514bda5dafc2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":7406,"license_type":"permissive","max_line_length":115,"num_lines":194,"path":"/README.md","repo_name":"Waightman/pyerz","src_encoding":"UTF-8","text":"# pyerz：计算机软件著作权程序鉴别材料（即源代码）生成器\n\n目录\n=================\n\n * [安装](#安装)\n * [背景](#背景)\n * [使用](#使用)\n * [程序鉴别材料要求](#程序鉴别材料要求)\n * [如何实现每页50行](#如何实现每页50行)\n * [参数](#参数)\n * [示例](#示例)\n * [克隆代码](#克隆代码)\n * [生成文档](#生成文档)\n * [常见问题](#常见问题)\n * [如何指定页眉？](#如何指定页眉？)\n * [如何添加其他格式的代码？](#如何添加其他格式的代码？)\n * [如何排除指定文件或文件夹？](#如何排除指定文件或文件夹？)\n * [如何调整默认的注释风格？](#如何调整默认的注释风格？)\n * [如何调整字体？](#如何调整字体？)\n * [虽然我知道默认的字体、字号、段前间距、段后间距、行间距可以实现每页50行，但是我还是想调整，怎么办？](#虽然我知道默认的字体、字号、段前间距、段后间距、行间距可以实现每页50行，但是我还是想调整，怎么办？)\n * [能不能输出查找文件的详细过程呢？](#能不能输出查找文件的详细过程呢？)\n\n## 安装\n\n```shell script\npip install pyerz\n```\n\n## 背景\n\n工作中需要申请软件著作权，软件著作权需要提供以下材料：\n\n1. 申请表：可以在官网通过网页生成\n2. 身份证明：企业的话一般就是营业执照\n3. 程序鉴别材料：一般就是源代码整理出的PDF文件\n4. 文档鉴别材料：一般就是该软件的操作手册\n\n申请表身份证明比较好准备，文档鉴别材料则必须手写，`pyerz`则用于生成程序鉴别材料。目前支持如下功能：\n\n1. 指定多个源代码目录\n2. 指定多中注释风格\n3. 指定字体、字号、段前间距、段后间距、行距\n4. 排除特定文件、文件夹\n\n## 使用\n\n### 程序鉴别材料要求\n\n1. 每页至少50行\n2. 不能含有注释、空行\n3. 页眉部分必须包含软件名称、版本号、页码（软件名+版本号居中，页码右侧对齐）\n\n### 如何实现每页50行\n\n上述3点，第2、3两点比较好实现，第1点我通过测试发现，当：\n\n1. 字号为10.5pt\n2. 行间距为10.5pt\n3. 段前间距为0\n4. 段后间距为2.3pt\n\n时，刚好实现每页50行。\n\n### 参数\n\n```\nUsage: pyerz [OPTIONS]\n\nOptions:\n -t, --title TEXT 软件名称+版本号，默认为软件著作权程序鉴别材料生成器V1.0，此名称用于生成页眉\n -i, --indir PATH 源码所在文件夹，可以指定多个，默认为当前目录\n -e, --ext TEXT 源代码后缀，可以指定多个，默认为Python源代码\n -c, --comment-char TEXT 注释字符串，可以指定多个，默认为#、//\n --font-name TEXT 字体，默认为宋体\n --font-size FLOAT RANGE 字号，默认为五号，即10.5号\n --space-before FLOAT RANGE 段前间距，默认为0\n --space-after FLOAT RANGE 段后间距，默认为2.3\n --line-spacing FLOAT RANGE 行距，默认为固定值10.5\n --exclude PATH 需要排除的文件或路径，可以指定多个\n -o, --outfile PATH 输出文件（docx格式），默认为当前目录的code.docx\n -v, --verbose 打印调试信息\n --help Show this message and exit.\n```\n\n### 示例\n\n下面以[django-guardian项目](https://github.com/django-guardian/django-guardian)为例来说明`pyerz`的用法。\n\n#### 克隆代码\n\n```shell script\ngit clone git@github.com:django-guardian/django-guardian.git\n```\n\n#### 生成文档\n\n```shell script\npyerz -i django-guardian -o django-guardian.docx\n```\n\n### 常见问题\n\n#### 如何指定页眉？\n\n```shell script\npyerz -i django-guardian -t django-guardian -o django-guardian.docx\n```\n\n#### 如何添加其他格式的代码？\n\n上述方法只能识别Python源码，如果需要识别html、css、js代码，可以指定`-e`参数。\n\n```shell script\npyerz -i django-guardian \\\n -t django-guardian \\\n -e py -e html -e js \\\n -o django-guardian.docx\n```\n\n#### 如何排除指定文件或文件夹？\n\n```shell script\npyerz -i django-guardian \\\n -t django-guardian \\\n --exclude django-guardian/contrib/ \\\n --exclude django-guardian/docs/ \\\n --exclude django-guardian/benchmarks/ \\\n --exclude django-guardian/example_project/ \\\n -o django-guardian.docx\n```\n\n#### 如何调整默认的注释风格？\n\n默认情况下，`pyerz`把以`#`、`//`开头的行作为注释行删除，例如我想删除以`\"\"\"`开头的行（Python另一种注释风格）：\n\n```shell script\npyerz -i django-guardian \\\n -t django-guardian \\\n -c '#' -c '//' -c '\"\"\"' \\\n -o django-guardian.docx\n```\n\n注意，`pyerz`目前不支持删除多行注释。\n\n#### 如何调整字体？\n\n`pyerz`默认使用宋体，如果需要调整可以使用`--font-name`参数。\n\n```shell script\npyerz -i django-guardian \\\n -t django-guardian \\\n --font-name menlo \\\n -o django-guardian.docx\n```\n\n#### 虽然我知道默认的字体、字号、段前间距、段后间距、行间距可以实现每页50行，但是我还是想调整，怎么办？\n\n```shell script\npyerz -i django-guardian \\\n -t django-guardian \\\n --font-name menlo \\\n --font-size 12 \\\n --space-before 1 \\\n --space-after 5 \\\n --line-spacing 12 \\\n -o django-guardian.docx\n```\n\n#### 能不能输出查找文件的详细过程呢？\n\n```shell script\npyerz -i django-guardian -o django-guardian.docx -v\n```\n\n```\n...\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/posts/templates/posts目录下找到0个代码文件.\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/posts/templates目录下找到0个代码文件.\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/posts目录下找到8个代码文件.\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/core/migrations目录下找到3个代码文件.\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/core目录下找到7个代码文件.\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/articles/migrations目录下找到3个代码文件.\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/articles/templates/articles目录下找到0个代码文件.\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/articles/templates目录下找到0个代码文件.\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/articles目录下找到10个代码文件.\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/static/css目录下找到0个代码文件.\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/static/js目录下找到0个代码文件.\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/static/img目录下找到0个代码文件.\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/static目录下找到0个代码文件.\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/templates目录下找到0个代码文件.\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project目录下找到29个代码文件.\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian目录下找到94个代码文件.\n```"}],"string":"[\n {\n \"alpha_fraction\": 0.5603086352348328,\n \"alphanum_fraction\": 0.5669419169425964,\n \"avg_line_length\": 24.828670501708984,\n \"blob_id\": \"bbb5190a2b7ccfd7b9303c75d53ca46f7aba5b67\",\n \"content_id\": \"428a5bd5e210636cf95548784447c21e62d88f15\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8343,\n \"license_type\": \"permissive\",\n \"max_line_length\": 86,\n \"num_lines\": 286,\n \"path\": \"/pyerz/pyerz.py\",\n \"repo_name\": \"Waightman/pyerz\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nimport codecs\\nimport logging\\nimport pkg_resources\\nfrom os.path import abspath\\ntry:\\n from os import scandir\\nexcept ImportError:\\n from scandir import scandir\\n\\nimport click\\nfrom docx import Document\\nfrom docx.shared import Pt\\nfrom docx.enum.text import WD_PARAGRAPH_ALIGNMENT\\n\\n\\nlogger = logging.getLogger(__name__)\\n\\n\\n# 默认源代码文件目录\\nDEFAULT_INDIRS = ['.']\\n# 默认支持的代码格式\\nDEFAULT_EXTS = ['py']\\n# 默认的注释前缀\\nDEFAULT_COMMENT_CHARS = (\\n '#', '//'\\n)\\n\\n\\ndef del_slash(dirs):\\n \\\"\\\"\\\"\\n 删除文件夹最后一位的/\\n\\n Args:\\n dirs: 文件夹列表\\n Returns:\\n 删除之后的文件夹\\n \\\"\\\"\\\"\\n no_slash_dirs = []\\n for dir_ in dirs:\\n if dir_[-1] == '/':\\n no_slash_dirs.append(dir_[: -1])\\n else:\\n no_slash_dirs.append(dir_)\\n return no_slash_dirs\\n\\n\\nclass CodeFinder(object):\\n \\\"\\\"\\\"\\n 给定一个目录，和若干个后缀名，\\n 递归地遍历该目录，找到该目录下\\n 所有以这些后缀结束的文件\\n \\\"\\\"\\\"\\n def __init__(self, exts=None):\\n \\\"\\\"\\\"\\n Args:\\n exts: 后缀名，默认为以py结尾\\n \\\"\\\"\\\"\\n self.exts = exts if exts else ['py']\\n\\n def is_code(self, file):\\n for ext in self.exts:\\n if file.endswith(ext):\\n return True\\n return False\\n\\n @staticmethod\\n def is_hidden_file(file):\\n \\\"\\\"\\\"\\n 是否是隐藏文件\\n \\\"\\\"\\\"\\n return file[0] == '.'\\n\\n @staticmethod\\n def should_be_excluded(file, excludes=None):\\n \\\"\\\"\\\"\\n 是否需要略过此文件\\n \\\"\\\"\\\"\\n if not excludes:\\n return False\\n if not isinstance(excludes, list):\\n excludes = [excludes]\\n should_be_excluded = False\\n for exclude in excludes:\\n if file.startswith(exclude):\\n should_be_excluded = True\\n break\\n return should_be_excluded\\n\\n def find(self, indir, excludes=None):\\n \\\"\\\"\\\"\\n 给定一个文件夹查找这个文件夹下所有的代码\\n\\n Args:\\n indir: 需要查到代码的目录\\n excludes: 排除文件或目录\\n Returns:\\n 代码文件列表\\n \\\"\\\"\\\"\\n files = []\\n for entry in scandir(indir):\\n # 防止根目录有一些含有非常多文件的隐藏文件夹\\n # 例如，.git文件，如果不排除，此程序很难运行\\n entry_name = entry.name\\n entry_path = abspath(entry.path)\\n if self.is_hidden_file(entry_name):\\n continue\\n if self.should_be_excluded(entry_path, excludes):\\n continue\\n if entry.is_file():\\n if self.is_code(entry_name):\\n files.append(entry_path)\\n continue\\n for file in self.find(entry_path, excludes=excludes):\\n files.append(file)\\n logger.debug('在%s目录下找到%d个代码文件.', indir, len(files))\\n return files\\n\\n\\nclass CodeWriter(object):\\n def __init__(\\n self, font_name='宋体',\\n font_size=10.5, space_before=0.0,\\n space_after=2.3, line_spacing=10.5,\\n command_chars=None, document=None\\n ):\\n self.font_name = font_name\\n self.font_size = font_size\\n self.space_before = space_before\\n self.space_after = space_after\\n self.line_spacing = line_spacing\\n self.command_chars = command_chars if command_chars else DEFAULT_COMMENT_CHARS\\n self.document = Document(pkg_resources.resource_filename(\\n 'pyerz', 'template.docx'\\n )) if not document else document\\n\\n @staticmethod\\n def is_blank_line(line):\\n \\\"\\\"\\\"\\n 判断是否是空行\\n \\\"\\\"\\\"\\n return not bool(line)\\n\\n def is_comment_line(self, line):\\n line = line.lstrip() # 去除左侧缩进\\n is_comment = False\\n for comment_char in self.command_chars:\\n if line.startswith(comment_char):\\n is_comment = True\\n break\\n return is_comment\\n\\n def write_header(self, title):\\n \\\"\\\"\\\"\\n 写入页眉\\n \\\"\\\"\\\"\\n paragraph = self.document.sections[0].header.paragraphs[0]\\n paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER\\n run = paragraph.add_run(title)\\n run.font.name = self.font_name\\n run.font.size = Pt(self.font_size)\\n return self\\n\\n def write_file(self, file):\\n \\\"\\\"\\\"\\n 把单个文件添加到程序文档里面\\n \\\"\\\"\\\"\\n with codecs.open(file, encoding='utf-8') as fp:\\n for line in fp:\\n line = line.rstrip()\\n if self.is_blank_line(line):\\n continue\\n if self.is_comment_line(line):\\n continue\\n paragraph = self.document.add_paragraph()\\n paragraph.paragraph_format.space_before = Pt(self.space_before)\\n paragraph.paragraph_format.space_after = Pt(self.space_after)\\n paragraph.paragraph_format.line_spacing = Pt(self.line_spacing)\\n run = paragraph.add_run(line)\\n run.font.name = self.font_name\\n run.font.size = Pt(self.font_size)\\n return self\\n\\n def save(self, file):\\n self.document.save(file)\\n\\n\\n@click.command(name='pyerz')\\n@click.option(\\n '-t', '--title', default='软件著作权程序鉴别材料生成器V1.0',\\n help='软件名称+版本号，默认为软件著作权程序鉴别材料生成器V1.0，此名称用于生成页眉'\\n)\\n@click.option(\\n '-i', '--indir', 'indirs',\\n multiple=True, type=click.Path(exists=True),\\n help='源码所在文件夹，可以指定多个，默认为当前目录'\\n)\\n@click.option(\\n '-e', '--ext', 'exts',\\n multiple=True, help='源代码后缀，可以指定多个，默认为Python源代码'\\n)\\n@click.option(\\n '-c', '--comment-char', 'comment_chars',\\n multiple=True, help='注释字符串，可以指定多个，默认为#、//'\\n)\\n@click.option(\\n '--font-name', default='宋体',\\n help='字体，默认为宋体'\\n)\\n@click.option(\\n '--font-size', default=10.5,\\n type=click.FloatRange(min=1.0),\\n help='字号，默认为五号，即10.5号'\\n)\\n@click.option(\\n '--space-before', default=0.0,\\n type=click.FloatRange(min=0.0),\\n help='段前间距，默认为0'\\n)\\n@click.option(\\n '--space-after', default=2.3,\\n type=click.FloatRange(min=0.0),\\n help='段后间距，默认为2.3'\\n)\\n@click.option(\\n '--line-spacing', default=10.5,\\n type=click.FloatRange(min=0.0),\\n help='行距，默认为固定值10.5'\\n)\\n@click.option(\\n '--exclude', 'excludes',\\n multiple=True, type=click.Path(exists=True),\\n help='需要排除的文件或路径，可以指定多个'\\n)\\n@click.option(\\n '-o', '--outfile', default='code.docx',\\n type=click.Path(exists=False),\\n help='输出文件（docx格式），默认为当前目录的code.docx'\\n)\\n@click.option('-v', '--verbose', is_flag=True, help='打印调试信息')\\ndef main(\\n title, indirs, exts,\\n comment_chars, font_name,\\n font_size, space_before,\\n space_after, line_spacing,\\n excludes, outfile, verbose\\n):\\n if not indirs:\\n indirs = DEFAULT_INDIRS\\n if not exts:\\n exts = DEFAULT_EXTS\\n if not comment_chars:\\n comment_chars = DEFAULT_COMMENT_CHARS\\n if verbose:\\n logging.basicConfig(level=logging.DEBUG)\\n\\n # 第零步，把所有的路径都转换为绝对路径\\n indirs = [abspath(indir) for indir in indirs]\\n excludes = del_slash(\\n [abspath(exclude) for exclude in excludes] if excludes else []\\n )\\n\\n # 第一步，查找代码文件\\n finder = CodeFinder(exts)\\n files = [file for indir in indirs for file in finder.find(\\n indir, excludes=excludes\\n )]\\n\\n # 第二步，逐个把代码文件写入到docx中\\n writer = CodeWriter(\\n command_chars=comment_chars,\\n font_name=font_name,\\n font_size=font_size,\\n space_before=space_before,\\n space_after=space_after,\\n line_spacing=line_spacing\\n )\\n writer.write_header(title)\\n for file in files:\\n writer.write_file(file)\\n writer.save(outfile)\\n return 0\\n\\n\\nif __name__ == '__main__':\\n main()\\n\"\n },\n {\n \"alpha_fraction\": 0.6737979054450989,\n \"alphanum_fraction\": 0.6900978088378906,\n \"avg_line_length\": 24.304122924804688,\n \"blob_id\": \"415650b79ed16aa4daf6fec871f1c6571a5054ee\",\n \"content_id\": \"c8b91b48e9502b6460a52c079f95514bda5dafc2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 7406,\n \"license_type\": \"permissive\",\n \"max_line_length\": 115,\n \"num_lines\": 194,\n \"path\": \"/README.md\",\n \"repo_name\": \"Waightman/pyerz\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# pyerz：计算机软件著作权程序鉴别材料（即源代码）生成器\\n\\n目录\\n=================\\n\\n * [安装](#安装)\\n * [背景](#背景)\\n * [使用](#使用)\\n * [程序鉴别材料要求](#程序鉴别材料要求)\\n * [如何实现每页50行](#如何实现每页50行)\\n * [参数](#参数)\\n * [示例](#示例)\\n * [克隆代码](#克隆代码)\\n * [生成文档](#生成文档)\\n * [常见问题](#常见问题)\\n * [如何指定页眉？](#如何指定页眉？)\\n * [如何添加其他格式的代码？](#如何添加其他格式的代码？)\\n * [如何排除指定文件或文件夹？](#如何排除指定文件或文件夹？)\\n * [如何调整默认的注释风格？](#如何调整默认的注释风格？)\\n * [如何调整字体？](#如何调整字体？)\\n * [虽然我知道默认的字体、字号、段前间距、段后间距、行间距可以实现每页50行，但是我还是想调整，怎么办？](#虽然我知道默认的字体、字号、段前间距、段后间距、行间距可以实现每页50行，但是我还是想调整，怎么办？)\\n * [能不能输出查找文件的详细过程呢？](#能不能输出查找文件的详细过程呢？)\\n\\n## 安装\\n\\n```shell script\\npip install pyerz\\n```\\n\\n## 背景\\n\\n工作中需要申请软件著作权，软件著作权需要提供以下材料：\\n\\n1. 申请表：可以在官网通过网页生成\\n2. 身份证明：企业的话一般就是营业执照\\n3. 程序鉴别材料：一般就是源代码整理出的PDF文件\\n4. 文档鉴别材料：一般就是该软件的操作手册\\n\\n申请表身份证明比较好准备，文档鉴别材料则必须手写，`pyerz`则用于生成程序鉴别材料。目前支持如下功能：\\n\\n1. 指定多个源代码目录\\n2. 指定多中注释风格\\n3. 指定字体、字号、段前间距、段后间距、行距\\n4. 排除特定文件、文件夹\\n\\n## 使用\\n\\n### 程序鉴别材料要求\\n\\n1. 每页至少50行\\n2. 不能含有注释、空行\\n3. 页眉部分必须包含软件名称、版本号、页码（软件名+版本号居中，页码右侧对齐）\\n\\n### 如何实现每页50行\\n\\n上述3点，第2、3两点比较好实现，第1点我通过测试发现，当：\\n\\n1. 字号为10.5pt\\n2. 行间距为10.5pt\\n3. 段前间距为0\\n4. 段后间距为2.3pt\\n\\n时，刚好实现每页50行。\\n\\n### 参数\\n\\n```\\nUsage: pyerz [OPTIONS]\\n\\nOptions:\\n -t, --title TEXT 软件名称+版本号，默认为软件著作权程序鉴别材料生成器V1.0，此名称用于生成页眉\\n -i, --indir PATH 源码所在文件夹，可以指定多个，默认为当前目录\\n -e, --ext TEXT 源代码后缀，可以指定多个，默认为Python源代码\\n -c, --comment-char TEXT 注释字符串，可以指定多个，默认为#、//\\n --font-name TEXT 字体，默认为宋体\\n --font-size FLOAT RANGE 字号，默认为五号，即10.5号\\n --space-before FLOAT RANGE 段前间距，默认为0\\n --space-after FLOAT RANGE 段后间距，默认为2.3\\n --line-spacing FLOAT RANGE 行距，默认为固定值10.5\\n --exclude PATH 需要排除的文件或路径，可以指定多个\\n -o, --outfile PATH 输出文件（docx格式），默认为当前目录的code.docx\\n -v, --verbose 打印调试信息\\n --help Show this message and exit.\\n```\\n\\n### 示例\\n\\n下面以[django-guardian项目](https://github.com/django-guardian/django-guardian)为例来说明`pyerz`的用法。\\n\\n#### 克隆代码\\n\\n```shell script\\ngit clone git@github.com:django-guardian/django-guardian.git\\n```\\n\\n#### 生成文档\\n\\n```shell script\\npyerz -i django-guardian -o django-guardian.docx\\n```\\n\\n### 常见问题\\n\\n#### 如何指定页眉？\\n\\n```shell script\\npyerz -i django-guardian -t django-guardian -o django-guardian.docx\\n```\\n\\n#### 如何添加其他格式的代码？\\n\\n上述方法只能识别Python源码，如果需要识别html、css、js代码，可以指定`-e`参数。\\n\\n```shell script\\npyerz -i django-guardian \\\\\\n -t django-guardian \\\\\\n -e py -e html -e js \\\\\\n -o django-guardian.docx\\n```\\n\\n#### 如何排除指定文件或文件夹？\\n\\n```shell script\\npyerz -i django-guardian \\\\\\n -t django-guardian \\\\\\n --exclude django-guardian/contrib/ \\\\\\n --exclude django-guardian/docs/ \\\\\\n --exclude django-guardian/benchmarks/ \\\\\\n --exclude django-guardian/example_project/ \\\\\\n -o django-guardian.docx\\n```\\n\\n#### 如何调整默认的注释风格？\\n\\n默认情况下，`pyerz`把以`#`、`//`开头的行作为注释行删除，例如我想删除以`\\\"\\\"\\\"`开头的行（Python另一种注释风格）：\\n\\n```shell script\\npyerz -i django-guardian \\\\\\n -t django-guardian \\\\\\n -c '#' -c '//' -c '\\\"\\\"\\\"' \\\\\\n -o django-guardian.docx\\n```\\n\\n注意，`pyerz`目前不支持删除多行注释。\\n\\n#### 如何调整字体？\\n\\n`pyerz`默认使用宋体，如果需要调整可以使用`--font-name`参数。\\n\\n```shell script\\npyerz -i django-guardian \\\\\\n -t django-guardian \\\\\\n --font-name menlo \\\\\\n -o django-guardian.docx\\n```\\n\\n#### 虽然我知道默认的字体、字号、段前间距、段后间距、行间距可以实现每页50行，但是我还是想调整，怎么办？\\n\\n```shell script\\npyerz -i django-guardian \\\\\\n -t django-guardian \\\\\\n --font-name menlo \\\\\\n --font-size 12 \\\\\\n --space-before 1 \\\\\\n --space-after 5 \\\\\\n --line-spacing 12 \\\\\\n -o django-guardian.docx\\n```\\n\\n#### 能不能输出查找文件的详细过程呢？\\n\\n```shell script\\npyerz -i django-guardian -o django-guardian.docx -v\\n```\\n\\n```\\n...\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/posts/templates/posts目录下找到0个代码文件.\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/posts/templates目录下找到0个代码文件.\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/posts目录下找到8个代码文件.\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/core/migrations目录下找到3个代码文件.\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/core目录下找到7个代码文件.\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/articles/migrations目录下找到3个代码文件.\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/articles/templates/articles目录下找到0个代码文件.\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/articles/templates目录下找到0个代码文件.\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/articles目录下找到10个代码文件.\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/static/css目录下找到0个代码文件.\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/static/js目录下找到0个代码文件.\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/static/img目录下找到0个代码文件.\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/static目录下找到0个代码文件.\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project/templates目录下找到0个代码文件.\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian/example_project目录下找到29个代码文件.\\nDEBUG:pyerz.pyerz:在/Users/dev/Temp/django-guardian目录下找到94个代码文件.\\n```\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2983065,"cells":{"repo_name":{"kind":"string","value":"Panako14/mi_primer_programa_2"},"repo_url":{"kind":"string","value":"https://github.com/Panako14/mi_primer_programa_2"},"snapshot_id":{"kind":"string","value":"5e399b229e66b863c110d3b167e6f16abd4e8c85"},"revision_id":{"kind":"string","value":"4f470a3a50506f0721f039c0547e5aacb2fe2e8e"},"directory_id":{"kind":"string","value":"8cc063b282947812664face15ae94a6a1884c6fc"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-03-13T08:39:48.902653","string":"2020-03-13T08:39:48.902653"},"revision_date":{"kind":"timestamp","value":"2018-06-22T14:27:27","string":"2018-06-22T14:27:27"},"committer_date":{"kind":"timestamp","value":"2018-06-22T14:27:27","string":"2018-06-22T14:27:27"},"github_id":{"kind":"number","value":131047982,"string":"131,047,982"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.703839123249054,"alphanum_fraction":0.703839123249054,"avg_line_length":31.235294342041016,"blob_id":"0876e275ffdd7d4adacc51c4176bfc27866db340","content_id":"acef0f6d6669c36b48b6ba87a3a0a2a6ccc683ae","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":551,"license_type":"no_license","max_line_length":96,"num_lines":17,"path":"/calculadora.py","repo_name":"Panako14/mi_primer_programa_2","src_encoding":"UTF-8","text":"operacion = input('¿Que operación quieres realizar (multiplicar / dividir / sumar / restar)?: ')\nprimer_numero = int(input('Dime un número: '))\nsegundo_numero = int(input('Dime otro número: '))\n\nif operacion == 'multiplicar':\n resultado = primer_numero * segundo_numero\n\nelif operacion == 'dividir':\n resultado = primer_numero / segundo_numero\n\nelif operacion == 'sumar':\n resultado = primer_numero + segundo_numero\n\nelif operacion == 'restar':\n resultado = primer_numero - segundo_numero\n\nprint('El resultado es {}'.format(resultado))"},{"alpha_fraction":0.7014217972755432,"alphanum_fraction":0.7014217972755432,"avg_line_length":29.14285659790039,"blob_id":"0589866dbd4987ef34d253c2f211c71fd26b8cb3","content_id":"6eb6afd0c8bbd58c828bf7dd1073562cf28c8e7b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":214,"license_type":"no_license","max_line_length":49,"num_lines":7,"path":"/adivina_un_numero_2.py","repo_name":"Panako14/mi_primer_programa_2","src_encoding":"UTF-8","text":"numero_a_adivinar = input('Elige un número: ')\nnumero_elegido = input('Adivina el número: ')\n\nwhile numero_a_adivinar != numero_elegido:\n numero_elegido = input('Adivina el número: ')\n\nprint('Has acertado.')\n"},{"alpha_fraction":0.6878306865692139,"alphanum_fraction":0.7089946866035461,"avg_line_length":30.66666603088379,"blob_id":"ba7c66d564aa4cc5379d0390f8a37a2e7ddc5a51","content_id":"d47b9ea78e56a52b9185a9d457367aaa5aab63c0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":189,"license_type":"no_license","max_line_length":56,"num_lines":6,"path":"/conversor_farenheit_a_celsius.py","repo_name":"Panako14/mi_primer_programa_2","src_encoding":"UTF-8","text":"print('-Conversor de grados Farenheit a Celsius-')\nfarenheit = int(input('Dime los grados en Farenheit: '))\n\ncelsius = (farenheit - 32) /1.8\n\nprint('Son {} grados Celsius.'.format(celsius))"},{"alpha_fraction":0.533957839012146,"alphanum_fraction":0.5351288318634033,"avg_line_length":30.66666603088379,"blob_id":"b54bb3da2aa9eaa074cb1f1f546b53d9bcbf5429","content_id":"5522adfbce08d89026713a1beb04b36535d52183","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":859,"license_type":"no_license","max_line_length":79,"num_lines":27,"path":"/adivina_un_numero.py","repo_name":"Panako14/mi_primer_programa_2","src_encoding":"UTF-8","text":"number_to_guess = 2\nuser_number = int(input(\"Adivina un número: \"))\n\nif user_number == number_to_guess:\n print(\"Has ganado\")\nelse:\n user_number = int(input(\"No es correcto, elige otro número: \"))\n\n if user_number == number_to_guess:\n print(\"Has ganado\")\n else:\n user_number = int(input(\"No es correcto, elige otro número: \"))\n\n if user_number == number_to_guess:\n print(\"Has ganado\")\n else:\n user_number = int(input(\"No es correcto, elige otro número: \"))\n\n if user_number == number_to_guess:\n print(\"Has ganado\")\n else:\n user_number = int(input(\"No es correcto, elige otro número: \"))\n\n if user_number == number_to_guess:\n print(\"Has ganado\")\n else:\n print(\"Has perdido\")"},{"alpha_fraction":0.6097345352172852,"alphanum_fraction":0.6336283087730408,"avg_line_length":26.512195587158203,"blob_id":"f63fc2c0e5a6eececa268ae373fb897954b96046","content_id":"fdabec80ba2ee6435c48b1135a0dc7edc6583c27","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1140,"license_type":"no_license","max_line_length":134,"num_lines":41,"path":"/combate_pokemon.py","repo_name":"Panako14/mi_primer_programa_2","src_encoding":"UTF-8","text":"pokemon_elegido = input('Elige un enemigo (Bulbasaur / Charmander / Squirtle): ')\n\nvida_pikachu = 100\nvida_enemigo = 0\nataque_enemigo = 0\n\nif pokemon_elegido == 'Bulbasaur':\n vida_enemigo = 100\n ataque_enemigo = 25\n\nelif pokemon_elegido == 'Charmander':\n vida_enemigo = 80\n ataque_enemigo = 40\n\nelif pokemon_elegido == 'Squirtle':\n vida_enemigo = 90\n ataque_enemigo = 30\n\n\nwhile vida_enemigo > 0 and vida_pikachu > 0:\n\n ataque_pikachu = input('Elige un ataque( Chispazo / Rayo ): ')\n daño = 0\n if ataque_pikachu == 'Chispazo':\n daño = 30\n vida_enemigo -= daño\n\n elif ataque_pikachu == 'Rayo':\n daño = 40\n vida_enemigo -= daño\n\n print('Pikachu usa {} y causa un daño de {}, la vida de {} es de {}.'.format(ataque_pikachu, daño, pokemon_elegido, vida_enemigo))\n vida_pikachu -= ataque_enemigo\n print('{} usa ataque y causa un daño de {}, la vida de Pikachu es de {}'.format(pokemon_elegido, ataque_enemigo, vida_pikachu))\n\nif vida_pikachu <= 0:\n print('¡Has perdido!')\nif vida_enemigo <= 0:\n print('!Has ganado¡')\n\nprint('El combate ha terminado.')\n\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.703839123249054,\n \"alphanum_fraction\": 0.703839123249054,\n \"avg_line_length\": 31.235294342041016,\n \"blob_id\": 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[],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 859,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 27,\n \"path\": \"/adivina_un_numero.py\",\n \"repo_name\": \"Panako14/mi_primer_programa_2\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"number_to_guess = 2\\nuser_number = int(input(\\\"Adivina un número: \\\"))\\n\\nif user_number == number_to_guess:\\n print(\\\"Has ganado\\\")\\nelse:\\n user_number = int(input(\\\"No es correcto, elige otro número: \\\"))\\n\\n if user_number == number_to_guess:\\n print(\\\"Has ganado\\\")\\n else:\\n user_number = int(input(\\\"No es correcto, elige otro número: \\\"))\\n\\n if user_number == number_to_guess:\\n print(\\\"Has ganado\\\")\\n else:\\n user_number = int(input(\\\"No es correcto, elige otro número: \\\"))\\n\\n if user_number == number_to_guess:\\n print(\\\"Has ganado\\\")\\n else:\\n user_number = int(input(\\\"No es correcto, elige otro número: \\\"))\\n\\n if user_number == number_to_guess:\\n print(\\\"Has ganado\\\")\\n else:\\n print(\\\"Has perdido\\\")\"\n },\n {\n \"alpha_fraction\": 0.6097345352172852,\n \"alphanum_fraction\": 0.6336283087730408,\n \"avg_line_length\": 26.512195587158203,\n \"blob_id\": \"f63fc2c0e5a6eececa268ae373fb897954b96046\",\n \"content_id\": \"fdabec80ba2ee6435c48b1135a0dc7edc6583c27\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1140,\n \"license_type\": \"no_license\",\n \"max_line_length\": 134,\n \"num_lines\": 41,\n \"path\": \"/combate_pokemon.py\",\n \"repo_name\": \"Panako14/mi_primer_programa_2\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"pokemon_elegido = input('Elige un enemigo (Bulbasaur / Charmander / Squirtle): ')\\n\\nvida_pikachu = 100\\nvida_enemigo = 0\\nataque_enemigo = 0\\n\\nif pokemon_elegido == 'Bulbasaur':\\n vida_enemigo = 100\\n ataque_enemigo = 25\\n\\nelif pokemon_elegido == 'Charmander':\\n vida_enemigo = 80\\n ataque_enemigo = 40\\n\\nelif pokemon_elegido == 'Squirtle':\\n vida_enemigo = 90\\n ataque_enemigo = 30\\n\\n\\nwhile vida_enemigo > 0 and vida_pikachu > 0:\\n\\n ataque_pikachu = input('Elige un ataque( Chispazo / Rayo ): ')\\n daño = 0\\n if ataque_pikachu == 'Chispazo':\\n daño = 30\\n vida_enemigo -= daño\\n\\n elif ataque_pikachu == 'Rayo':\\n daño = 40\\n vida_enemigo -= daño\\n\\n print('Pikachu usa {} y causa un daño de {}, la vida de {} es de {}.'.format(ataque_pikachu, daño, pokemon_elegido, vida_enemigo))\\n vida_pikachu -= ataque_enemigo\\n print('{} usa ataque y causa un daño de {}, la vida de Pikachu es de {}'.format(pokemon_elegido, ataque_enemigo, vida_pikachu))\\n\\nif vida_pikachu <= 0:\\n print('¡Has perdido!')\\nif vida_enemigo <= 0:\\n print('!Has ganado¡')\\n\\nprint('El combate ha terminado.')\\n\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":5,"string":"5"}}},{"rowIdx":2983066,"cells":{"repo_name":{"kind":"string","value":"C00kiie/youtubeSearch_archive"},"repo_url":{"kind":"string","value":"https://github.com/C00kiie/youtubeSearch_archive"},"snapshot_id":{"kind":"string","value":"de75c36aeec59ee619f36246908631ef81a76881"},"revision_id":{"kind":"string","value":"857ed3cf13ab72f5a28b01c813bbd4d412c58efd"},"directory_id":{"kind":"string","value":"61579a3a27dd293e67e26393781d0723fccd08fd"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-04-19T10:46:59.223462","string":"2022-04-19T10:46:59.223462"},"revision_date":{"kind":"timestamp","value":"2020-04-19T06:15:47","string":"2020-04-19T06:15:47"},"committer_date":{"kind":"timestamp","value":"2020-04-19T06:15:47","string":"2020-04-19T06:15:47"},"github_id":{"kind":"number","value":256924096,"string":"256,924,096"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7673130035400391,"alphanum_fraction":0.7673130035400391,"avg_line_length":31.81818199157715,"blob_id":"ebd6792ed78e3d4690073a1225e05fd21c282da6","content_id":"42741c313921a4c2142c28efd9ae7425538101f7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":361,"license_type":"no_license","max_line_length":101,"num_lines":11,"path":"/README.md","repo_name":"C00kiie/youtubeSearch_archive","src_encoding":"UTF-8","text":"# Youtube Search Archiver\nA simple script to archive youtube searches content through youtube API\n\n# usage\n* put the script in an empty folder\n* make a file called input.txt, add to it the searches you want to do\n* open the script and add your youtube API key (https://console.developers.google.com/apis/dashboard)\n* Done!\n\n#To-do\nAdd a timestamp on each file\n"},{"alpha_fraction":0.613545835018158,"alphanum_fraction":0.6205765008926392,"avg_line_length":32.598426818847656,"blob_id":"e2f995a3c8c8f79fd52874eda59a51da92315356","content_id":"122b3a162a0e2f30825d4723e84181598d10ba9c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4267,"license_type":"no_license","max_line_length":169,"num_lines":127,"path":"/main.py","repo_name":"C00kiie/youtubeSearch_archive","src_encoding":"UTF-8","text":"#!/usr/bin/python\n\n\nimport argparse\nimport re\nimport json2html\nimport json\nimport youtube_dl as handler_aux\nfrom googleapiclient.discovery import build\nfrom googleapiclient.errors import HttpError\n\n\n# Set DEVELOPER_KEY to the API key value from the APIs & auth > Registered apps\n# tab of\n# https://cloud.google.com/console\n# Please ensure that you have enabled the YouTube Data API for your project.\nDEVELOPER_KEY = '#REPLACE_ME'\nYOUTUBE_API_SERVICE_NAME = 'youtube'\nYOUTUBE_API_VERSION = 'v3'\n\n\nincludes = \"\"\"\n<link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css\" integrity=\"sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T\" crossorigin=\"anonymous\">\n\"\"\"\nopts = {\n # used to output less verbose information\n 'no_warnings':True,\n 'quiet':True,\n # audios filter\n 'format': 'bestaudio/best',\n 'postprocessors': [{\n 'key': 'FFmpegExtractAudio',\n 'preferredcodec': 'mp3',\n 'preferredquality': '192',\n }]\n}\n\ndef number_readable(number):\n number = str(number)\n orig = number\n new = re.sub(\"^(-?\\d+)(\\d{3})\", '\\g<1>,\\g<2>', number)\n if orig == new:\n return new\n else:\n return number_readable(new)\n\ndef\tget_stats(link):\n statistics = {}\n with handler_aux.YoutubeDL(opts) as ydl:\n # video information for the given video\n info = ydl.extract_info(link, download=False)\n statistics['title'] = info.get('title', None)\n statistics['video_duration'] = info.get(\"duration\", None)\n statistics['like_count'] = number_readable (info.get(\"like_count\", None))\n statistics['dislike_count'] = number_readable (info.get(\"dislike_count\", None))\n statistics['view_count'] = number_readable ( info.get(\"view_count\", None))\n m, s = divmod(statistics['video_duration'], 60)\n h, m = divmod(m, 60)\n if h > 0:\n statistics['video_duration'] = '{}:{}:{}'.format(h, m, s)\n else:\n statistics['video_duration'] = '{}:{}'.format(m, s)\n # checking if video size exceeds the normal limit [50 MB]\n return statistics\n\ndef youtube_search(search_term,max_results):\n\n youtube = build(YOUTUBE_API_SERVICE_NAME, YOUTUBE_API_VERSION,\n developerKey=DEVELOPER_KEY)\n\n # Call the search.list method to retrieve results matching the specified\n # query term.\n search_response = youtube.search().list(\n q=search_term,\n part='id,snippet',\n maxResults=max_results\n ).execute()\n\n video_arr = []\n # Add each result to the appropriate list, and then display the lists of\n # matching videos, channels, and playlists.\n for search_result in search_response.get('items', []):\n if search_result['id']['kind'] == 'youtube#video':\n videoId = search_result['id']['videoId']\n snippet = search_result['snippet']\n link = 'http://www.youtube.com/watch?v='+videoId\n snippet['link'] = link\n url = snippet['thumbnails']['high']['url']\n snippet.pop('thumbnails')\n snippet['thumbnail'] = '<img src=\"{0}\">'.format(url)\n snippet.pop('liveBroadcastContent')\n statistics = get_stats(link)\n for i in statistics.keys():\n snippet[i] = statistics[i]\n pub = snippet['publishedAt']\n snippet.pop('publishedAt')\n snippet['publishedAt'] = pub\n snippet.pop('channelId')\n video_arr.append(snippet)\n return video_arr\ndef json2html_output(search_term,maxResults=10):\n items = youtube_search(search_term,maxResults)\n \n for i in items:\n i['link'] = '<a href=\"{0}\">link</a>'.format(i['link'])\n f = open(search_term,'w')\n html = includes + json2html.json2html.convert(escape = False,json = json.dumps(items), table_attributes=\"id=\\\"info-table\\\" class=\\\"table table-bordered table-hover\\\"\")\n f.write(html)\n f.close()\ndef get_keywords(file='input.txt'):\n try:\n f = open('input.txt','r')\n content = f.readlines()\n f.close()\n c = 0\n for i in content:\n content[c] = content[c].replace('\\n','')\n c+=1 \n return content\n except Exception as e:\n print('input.txt not found!')\n exit(-1)\n\nif __name__ == '__main__':\n keywords = get_keywords()\n for keyword in keywords:\n \tjson2html_output(keyword);\n"}],"string":"[\n {\n \"alpha_fraction\": 0.7673130035400391,\n \"alphanum_fraction\": 0.7673130035400391,\n \"avg_line_length\": 31.81818199157715,\n \"blob_id\": \"ebd6792ed78e3d4690073a1225e05fd21c282da6\",\n \"content_id\": \"42741c313921a4c2142c28efd9ae7425538101f7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 361,\n \"license_type\": \"no_license\",\n \"max_line_length\": 101,\n \"num_lines\": 11,\n \"path\": \"/README.md\",\n \"repo_name\": \"C00kiie/youtubeSearch_archive\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Youtube Search Archiver\\nA simple script to archive youtube searches content through youtube API\\n\\n# usage\\n* put the script in an empty folder\\n* make a file called input.txt, add to it the searches you want to do\\n* open the script and add your youtube API key (https://console.developers.google.com/apis/dashboard)\\n* Done!\\n\\n#To-do\\nAdd a timestamp on each file\\n\"\n },\n {\n \"alpha_fraction\": 0.613545835018158,\n \"alphanum_fraction\": 0.6205765008926392,\n \"avg_line_length\": 32.598426818847656,\n \"blob_id\": \"e2f995a3c8c8f79fd52874eda59a51da92315356\",\n \"content_id\": \"122b3a162a0e2f30825d4723e84181598d10ba9c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4267,\n \"license_type\": \"no_license\",\n \"max_line_length\": 169,\n \"num_lines\": 127,\n \"path\": \"/main.py\",\n \"repo_name\": \"C00kiie/youtubeSearch_archive\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n\\n\\nimport argparse\\nimport re\\nimport json2html\\nimport json\\nimport youtube_dl as handler_aux\\nfrom googleapiclient.discovery import build\\nfrom googleapiclient.errors import HttpError\\n\\n\\n# Set DEVELOPER_KEY to the API key value from the APIs & auth > Registered apps\\n# tab of\\n# https://cloud.google.com/console\\n# Please ensure that you have enabled the YouTube Data API for your project.\\nDEVELOPER_KEY = '#REPLACE_ME'\\nYOUTUBE_API_SERVICE_NAME = 'youtube'\\nYOUTUBE_API_VERSION = 'v3'\\n\\n\\nincludes = \\\"\\\"\\\"\\n<link rel=\\\"stylesheet\\\" href=\\\"https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css\\\" integrity=\\\"sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T\\\" crossorigin=\\\"anonymous\\\">\\n\\\"\\\"\\\"\\nopts = {\\n # used to output less verbose information\\n 'no_warnings':True,\\n 'quiet':True,\\n # audios filter\\n 'format': 'bestaudio/best',\\n 'postprocessors': [{\\n 'key': 'FFmpegExtractAudio',\\n 'preferredcodec': 'mp3',\\n 'preferredquality': '192',\\n }]\\n}\\n\\ndef number_readable(number):\\n number = str(number)\\n orig = number\\n new = re.sub(\\\"^(-?\\\\d+)(\\\\d{3})\\\", '\\\\g<1>,\\\\g<2>', number)\\n if orig == new:\\n return new\\n else:\\n return number_readable(new)\\n\\ndef\\tget_stats(link):\\n statistics = {}\\n with handler_aux.YoutubeDL(opts) as ydl:\\n # video information for the given video\\n info = ydl.extract_info(link, download=False)\\n statistics['title'] = info.get('title', None)\\n statistics['video_duration'] = info.get(\\\"duration\\\", None)\\n statistics['like_count'] = number_readable (info.get(\\\"like_count\\\", None))\\n statistics['dislike_count'] = number_readable (info.get(\\\"dislike_count\\\", None))\\n statistics['view_count'] = number_readable ( info.get(\\\"view_count\\\", None))\\n m, s = divmod(statistics['video_duration'], 60)\\n h, m = divmod(m, 60)\\n if h > 0:\\n statistics['video_duration'] = '{}:{}:{}'.format(h, m, s)\\n else:\\n statistics['video_duration'] = '{}:{}'.format(m, s)\\n # checking if video size exceeds the normal limit [50 MB]\\n return statistics\\n\\ndef youtube_search(search_term,max_results):\\n\\n youtube = build(YOUTUBE_API_SERVICE_NAME, YOUTUBE_API_VERSION,\\n developerKey=DEVELOPER_KEY)\\n\\n # Call the search.list method to retrieve results matching the specified\\n # query term.\\n search_response = youtube.search().list(\\n q=search_term,\\n part='id,snippet',\\n maxResults=max_results\\n ).execute()\\n\\n video_arr = []\\n # Add each result to the appropriate list, and then display the lists of\\n # matching videos, channels, and playlists.\\n for search_result in search_response.get('items', []):\\n if search_result['id']['kind'] == 'youtube#video':\\n videoId = search_result['id']['videoId']\\n snippet = search_result['snippet']\\n link = 'http://www.youtube.com/watch?v='+videoId\\n snippet['link'] = link\\n url = snippet['thumbnails']['high']['url']\\n snippet.pop('thumbnails')\\n snippet['thumbnail'] = '<img src=\\\"{0}\\\">'.format(url)\\n snippet.pop('liveBroadcastContent')\\n statistics = get_stats(link)\\n for i in statistics.keys():\\n snippet[i] = statistics[i]\\n pub = snippet['publishedAt']\\n snippet.pop('publishedAt')\\n snippet['publishedAt'] = pub\\n snippet.pop('channelId')\\n video_arr.append(snippet)\\n return video_arr\\ndef json2html_output(search_term,maxResults=10):\\n items = youtube_search(search_term,maxResults)\\n \\n for i in items:\\n i['link'] = '<a href=\\\"{0}\\\">link</a>'.format(i['link'])\\n f = open(search_term,'w')\\n html = includes + json2html.json2html.convert(escape = False,json = json.dumps(items), table_attributes=\\\"id=\\\\\\\"info-table\\\\\\\" class=\\\\\\\"table table-bordered table-hover\\\\\\\"\\\")\\n f.write(html)\\n f.close()\\ndef get_keywords(file='input.txt'):\\n try:\\n f = open('input.txt','r')\\n content = f.readlines()\\n f.close()\\n c = 0\\n for i in content:\\n content[c] = content[c].replace('\\\\n','')\\n c+=1 \\n return content\\n except Exception as e:\\n print('input.txt not found!')\\n exit(-1)\\n\\nif __name__ == '__main__':\\n keywords = get_keywords()\\n for keyword in keywords:\\n \\tjson2html_output(keyword);\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2983067,"cells":{"repo_name":{"kind":"string","value":"Valentin2771/SimpleBanking"},"repo_url":{"kind":"string","value":"https://github.com/Valentin2771/SimpleBanking"},"snapshot_id":{"kind":"string","value":"6299b19ec4095ef511231f51fdf7d90e5d2a26a9"},"revision_id":{"kind":"string","value":"f32fed95d6699acef29f1a144235ad80b2676cdf"},"directory_id":{"kind":"string","value":"7ed59604095126d6fbae2edaab42593b5f8c69d4"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2022-12-19T17:42:42.905374","string":"2022-12-19T17:42:42.905374"},"revision_date":{"kind":"timestamp","value":"2020-10-24T03:54:36","string":"2020-10-24T03:54:36"},"committer_date":{"kind":"timestamp","value":"2020-10-24T03:54:36","string":"2020-10-24T03:54:36"},"github_id":{"kind":"number","value":306798227,"string":"306,798,227"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.8080000281333923,"alphanum_fraction":0.8080000281333923,"avg_line_length":124,"blob_id":"27e6f4e25022f64a5ecdf2a6b71d79b3456d7bf2","content_id":"3f25eaa10587886509b105d892f8d7c04cb7f83e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":125,"license_type":"no_license","max_line_length":124,"num_lines":1,"path":"/README.md","repo_name":"Valentin2771/SimpleBanking","src_encoding":"UTF-8","text":"# A Simple Banking System, allowing for account creation, log into account, transfer between accounts, account deletion, etc\n"},{"alpha_fraction":0.5725259780883789,"alphanum_fraction":0.5815634727478027,"avg_line_length":32.589664459228516,"blob_id":"e0af52277e8f4cb8e9e0188223139bfb9581a11e","content_id":"1bf0a9f7a6c1600872905e8dda741cab8d395a22","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":11065,"license_type":"no_license","max_line_length":235,"num_lines":329,"path":"/banking_system_stage4.py","repo_name":"Valentin2771/SimpleBanking","src_encoding":"UTF-8","text":"# banking system stage # 4\n\nimport random\nimport sqlite3\n\nconn = sqlite3.connect('card.s3db') # single file database creation\n\ncur = conn.cursor() # we're using this object's method 'execute' to perform queries\n\nmy_table_string = 'CREATE TABLE IF NOT EXISTS card (`id` INTEGER, `number` TEXT, `pin` TEXT, balance INTEGER DEFAULT 0);'\n\ncur.execute(my_table_string)\n\nconn.commit()\n\nrandom.seed()\n\ncard_to_check = list() # Global variable. We're using it for the purpose of temporarily storing the account no. and the pin no. for the card to check. This list will count as a static property when switching to Object Oriented approach\n\naccount_to_transfer = 0\n\npossible_options = (\"1\", \"2\", \"0\")\n\npossible_balance_options = (\"1\", \"2\", \"3\", \"4\", \"5\", \"0\") # Global variable. It will count as a static property when switching to Object Oriented approach\n\n# account_balance = 0 # Global variable. It will count as a static property when switching to Object Oriented approach\n\nCREATE = True # Global variable. We're using it for diverting the execution flow to the \"create\" or \"log into\" or \"exit\" branch. This variable will amount to a static property when switching to Object Oriented approach\n\nLOGIN = False # Global variable. See the explanation above\n\nBALANCE = False # Global variable. See the explanation above\n\nADD = False # Global variable. See the explanation above\n\nCHK_TRANSFER = False # Global variable. See the explanation above\n\n# Auxiliary & printing functions\n\ndef get_user_input():\n return input().strip()\n\ndef print_exit_statement():\n print(\"\\nBye!\")\n\t\t\ndef print_first_menu():\n print(\"1. Create an account\")\n print(\"2. Log into account\")\n print(\"0. Exit\")\n\t\ndef login_successfully_message():\n print(\"\\nYou have successfully logged in!\")\n \ndef logout_successfully_message():\n print(\"\\nYou have successfully logged out!\\n\")\n\t\ndef print_balance_menu():\n print(\"\\n1. Balance\\n2. Add income\\n3. Do transfer\\n4. Close account\\n5. Log out\\n0. Exit\")\n\n# CREATE True functions \n\ndef Luhn_generator():\n '''\n It generates the pin no. and an associated, Luhn compliant, card number\n '''\n pin = ''\n for num in range (4):\n pin += str(random.randint(0, 9)) # this is the pin no.\n \n account_no = str(400000) # first 6 digits are constant\n last_digits = list() # a container for the remaining 10 digits\n original_digits = '' # The string corresponding to the digits' number\n \n digit_sum = 8\n for digit in range(9): # implementing the Luhn algorithm\n current_digit = random.randint(0, 9)\n last_digits.append(current_digit)\n original_digits += str(current_digit)\n \n for index in range(len(last_digits)):\n if index % 2 == 0:\n last_digits[index] *= 2\n if last_digits[index] > 9:\n last_digits[index] -= 9\n digit_sum += last_digits[index]\n \n check_digit = 0\n for chk in range(10):\n if (chk + digit_sum) % 10 == 0:\n check_digit = chk\n break\n original_digits += str(check_digit)\n \n account_no += original_digits\n \n return (account_no, pin)\n\ndef card_exists(card_number):\n placeholder = (card_number, )\n unique_check = \"SELECT * FROM card WHERE number = ?\"\n cur.execute(unique_check, placeholder)\n return cur.fetchone() \n \n\ndef card_generator(card_data):\n temp_res1 = card_exists(card_data[0])\n \n if temp_res1 is None: # if this number hasn't been previously assigned\n no_pin = (card_data[0], card_data[1])\n insert_unique = \"INSERT INTO card (number, pin) VALUES(?, ?)\"\n cur.execute(insert_unique, no_pin)\n conn.commit()\n print(str.format(\"\\nYour card has been created\\nYour card number:\\n{}\\nYour card PIN:\\n{}\\n\", card_data[0], card_data[1]))\n return True\n else:\n return False\n \n# LOGIN True functions\n\ndef print_login_menu():\n if len(card_to_check) == 0:\n print(\"\\nEnter your card number:\")\n else:\n print(\"Enter your PIN:\")\n\ndef login_checker(account, pin):\n # Data from table 'card' used here\n check_login_query = \"SELECT number, pin FROM card WHERE number = ? AND pin = ?\"\n placeholder = (account, pin)\n cur.execute(check_login_query, placeholder)\n temp_res = cur.fetchall()\n if len(temp_res) == 1:\n login_successfully_message()\n return True\n else:\n print(\"\\nWrong card number or PIN!\\n\") \n \n# BALANCE True functions\n\ndef get_balance(account_number):\n # Data from table 'card' MUST be used here\n placeholder = (account_number,)\n balance_query = \"SELECT balance FROM card WHERE number = ?\"\n cur.execute(balance_query, placeholder)\n return cur.fetchone()[0] # this is the balance\n \n\ndef display_balance():\n temp_res3 = get_balance(card_to_check[0])\n print(str.format(\"\\nBalance: {}\", temp_res3))\n\ndef Luhn_checker(digit_string):\n digit_list = list(digit_string)\n if len(digit_list) != 16:\n return False\n try:\n for index in range(len(digit_list)):\n digit_list[index] = int(digit_list[index])\n except:\n return False\n \n # Luhn checker starts here\n digit_sum = digit_list[-1]\n for index in range(len(digit_list) - 1):\n if index % 2 == 0:\n digit_list[index] *= 2\n if digit_list[index] > 9:\n digit_list[index] -= 9\n digit_sum += digit_list[index]\n \n if digit_sum % 10 == 0:\n return True\n else:\n return False\n\t\t\ndef close_account():\n delete_query = \"DELETE FROM card WHERE number = ?\"\n placeholder = (card_to_check[0],) # because we're only allowed to close the account we're logged in\n cur.execute(delete_query, placeholder)\n conn.commit()\n\n# ADD True\n\ndef add_income(amount):\n current_balance = get_balance(card_to_check[0])\n current_balance += int(amount)\n update_balance = \"UPDATE card SET balance = ? WHERE number = ?\"\n cur.execute(update_balance, (current_balance, card_to_check[0]))\n conn.commit()\n print(\"Income was added!\")\n print_balance_menu()\n\n# CHK_TRANSFER True\n\ndef check_transfer_acc(card_number):\n # print(\"You've just entered card number \" + str(card_number))\n global CHK_TRANSFER\n global BALANCE\n CHK_TRANSFER = False\n BALANCE = True \n if card_number == card_to_check[0]:\n print(\"You can't transfer money to the same account!\")\n print_balance_menu()\n elif not Luhn_checker(card_number):\n print(\"Probably you made a mistake in the card number. Please try again!\")\n print_balance_menu()\n elif card_exists(card_number) is None:\n print(\"Such a card does not exist.\")\n print_balance_menu()\n else:\n BALANCE = False\n global account_to_transfer\n account_to_transfer = card_number\n print(\"Enter how much money you want to transfer:\")\n\n# All state constants on False\n\ndef perform_transfer(amount):\n current_balance_grantor = get_balance(card_to_check[0])\n if current_balance_grantor < int(amount):\n print(\"Not enough money!\")\n else:\n current_balance_receiver = get_balance(account_to_transfer)\n placeholder1 = (current_balance_grantor - int(amount), card_to_check[0])\n placeholder2 = (current_balance_receiver + int(amount), account_to_transfer)\n update_query = \"UPDATE card SET balance = ? WHERE number = ?\"\n cur.execute(update_query, placeholder1)\n conn.commit()\n cur.execute(update_query, placeholder2)\n conn.commit()\n print(\"Success!\")\n \n \n#-------------------------------------------------------------------------------------------------------------------------------\n#End of function definitions section; start of the function calls section\n\nprint_first_menu() # first call; first user input; default state is CREATE = True\n\nwhile(True):\n usr_inp = get_user_input()\n \n if CREATE:\n if usr_inp in possible_options:\n if usr_inp == \"1\":\n while(not card_generator(Luhn_generator())):\n pass\n print_first_menu()\n elif usr_inp == \"2\":\n cur.execute(\"SELECT * FROM card\") # check if any cards have already been created\n temp_res2 = cur.fetchall()\n if 0 != len(temp_res2): # SELECT query returns a list of tuples, if list is empty, then there's no card created\n CREATE = False\n LOGIN = True\n \n print_login_menu()\n else:\n print(\"\\nNo account created yet!\")\n print_first_menu()\n else:\n print_exit_statement()\n exit()\n else:\n print(\"\\nInvalid option\")\n print_first_menu()\n \n elif LOGIN: \n card_to_check.append(usr_inp) # account no. comes first, pin comes afterwards\n if len(card_to_check) != 2:\n print_login_menu()\n else:\n login_result = login_checker(card_to_check[0], card_to_check[1])\n \n if login_result:\n \n LOGIN = False\n BALANCE = True\n print_balance_menu()\n else:\n CREATE = True\n LOGIN = False\n \n card_to_check.clear() # Because we may want to try a new login\n print_first_menu() \n \n elif BALANCE:\n if usr_inp in possible_balance_options:\n if usr_inp == \"1\":\n display_balance() \n print_balance_menu()\n elif usr_inp == \"2\":\n BALANCE = False\n ADD = True\n print(\"\\nEnter income:\")\n elif usr_inp == \"3\":\n print(\"\\nTransfer\\nEnter card number:\") \n BALANCE = False\n CHK_TRANSFER = True\n elif usr_inp == \"4\":\n close_account()\n print(\"\\nThe account has been closed!\\n\") \n CREATE = True\n BALANCE = False # Leaving this branch and returning to the first menu\n card_to_check.clear() # Because the account no longer exists\n print_first_menu()\n elif usr_inp == \"5\":\n CREATE = True\n BALANCE = False # Leaving this branch and returning to the first menu\n card_to_check.clear() # Because we're logging out\n logout_successfully_message()\n print_first_menu()\n else: # exit branch\n print_exit_statement()\n exit() \n else:\n print(\"\\nInvalid option!\")\n print_balance_menu()\n \n elif ADD:\n add_income(usr_inp)\n BALANCE = True\n ADD = False\n \n elif CHK_TRANSFER:\n check_transfer_acc(usr_inp)\n \n else:\n perform_transfer(usr_inp)\n BALANCE = True\n print_balance_menu()\n "}],"string":"[\n {\n \"alpha_fraction\": 0.8080000281333923,\n \"alphanum_fraction\": 0.8080000281333923,\n \"avg_line_length\": 124,\n \"blob_id\": \"27e6f4e25022f64a5ecdf2a6b71d79b3456d7bf2\",\n \"content_id\": \"3f25eaa10587886509b105d892f8d7c04cb7f83e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 125,\n \"license_type\": \"no_license\",\n \"max_line_length\": 124,\n \"num_lines\": 1,\n \"path\": \"/README.md\",\n \"repo_name\": \"Valentin2771/SimpleBanking\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# A Simple Banking System, allowing for account creation, log into account, transfer between accounts, account deletion, etc\\n\"\n },\n {\n \"alpha_fraction\": 0.5725259780883789,\n \"alphanum_fraction\": 0.5815634727478027,\n \"avg_line_length\": 32.589664459228516,\n \"blob_id\": \"e0af52277e8f4cb8e9e0188223139bfb9581a11e\",\n \"content_id\": \"1bf0a9f7a6c1600872905e8dda741cab8d395a22\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 11065,\n \"license_type\": \"no_license\",\n \"max_line_length\": 235,\n \"num_lines\": 329,\n \"path\": \"/banking_system_stage4.py\",\n \"repo_name\": \"Valentin2771/SimpleBanking\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# banking system stage # 4\\n\\nimport random\\nimport sqlite3\\n\\nconn = sqlite3.connect('card.s3db') # single file database creation\\n\\ncur = conn.cursor() # we're using this object's method 'execute' to perform queries\\n\\nmy_table_string = 'CREATE TABLE IF NOT EXISTS card (`id` INTEGER, `number` TEXT, `pin` TEXT, balance INTEGER DEFAULT 0);'\\n\\ncur.execute(my_table_string)\\n\\nconn.commit()\\n\\nrandom.seed()\\n\\ncard_to_check = list() # Global variable. We're using it for the purpose of temporarily storing the account no. and the pin no. for the card to check. This list will count as a static property when switching to Object Oriented approach\\n\\naccount_to_transfer = 0\\n\\npossible_options = (\\\"1\\\", \\\"2\\\", \\\"0\\\")\\n\\npossible_balance_options = (\\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\", \\\"5\\\", \\\"0\\\") # Global variable. It will count as a static property when switching to Object Oriented approach\\n\\n# account_balance = 0 # Global variable. It will count as a static property when switching to Object Oriented approach\\n\\nCREATE = True # Global variable. We're using it for diverting the execution flow to the \\\"create\\\" or \\\"log into\\\" or \\\"exit\\\" branch. This variable will amount to a static property when switching to Object Oriented approach\\n\\nLOGIN = False # Global variable. See the explanation above\\n\\nBALANCE = False # Global variable. See the explanation above\\n\\nADD = False # Global variable. See the explanation above\\n\\nCHK_TRANSFER = False # Global variable. See the explanation above\\n\\n# Auxiliary & printing functions\\n\\ndef get_user_input():\\n return input().strip()\\n\\ndef print_exit_statement():\\n print(\\\"\\\\nBye!\\\")\\n\\t\\t\\ndef print_first_menu():\\n print(\\\"1. Create an account\\\")\\n print(\\\"2. Log into account\\\")\\n print(\\\"0. Exit\\\")\\n\\t\\ndef login_successfully_message():\\n print(\\\"\\\\nYou have successfully logged in!\\\")\\n \\ndef logout_successfully_message():\\n print(\\\"\\\\nYou have successfully logged out!\\\\n\\\")\\n\\t\\ndef print_balance_menu():\\n print(\\\"\\\\n1. Balance\\\\n2. Add income\\\\n3. Do transfer\\\\n4. Close account\\\\n5. Log out\\\\n0. Exit\\\")\\n\\n# CREATE True functions \\n\\ndef Luhn_generator():\\n '''\\n It generates the pin no. and an associated, Luhn compliant, card number\\n '''\\n pin = ''\\n for num in range (4):\\n pin += str(random.randint(0, 9)) # this is the pin no.\\n \\n account_no = str(400000) # first 6 digits are constant\\n last_digits = list() # a container for the remaining 10 digits\\n original_digits = '' # The string corresponding to the digits' number\\n \\n digit_sum = 8\\n for digit in range(9): # implementing the Luhn algorithm\\n current_digit = random.randint(0, 9)\\n last_digits.append(current_digit)\\n original_digits += str(current_digit)\\n \\n for index in range(len(last_digits)):\\n if index % 2 == 0:\\n last_digits[index] *= 2\\n if last_digits[index] > 9:\\n last_digits[index] -= 9\\n digit_sum += last_digits[index]\\n \\n check_digit = 0\\n for chk in range(10):\\n if (chk + digit_sum) % 10 == 0:\\n check_digit = chk\\n break\\n original_digits += str(check_digit)\\n \\n account_no += original_digits\\n \\n return (account_no, pin)\\n\\ndef card_exists(card_number):\\n placeholder = (card_number, )\\n unique_check = \\\"SELECT * FROM card WHERE number = ?\\\"\\n cur.execute(unique_check, placeholder)\\n return cur.fetchone() \\n \\n\\ndef card_generator(card_data):\\n temp_res1 = card_exists(card_data[0])\\n \\n if temp_res1 is None: # if this number hasn't been previously assigned\\n no_pin = (card_data[0], card_data[1])\\n insert_unique = \\\"INSERT INTO card (number, pin) VALUES(?, ?)\\\"\\n cur.execute(insert_unique, no_pin)\\n conn.commit()\\n print(str.format(\\\"\\\\nYour card has been created\\\\nYour card number:\\\\n{}\\\\nYour card PIN:\\\\n{}\\\\n\\\", card_data[0], card_data[1]))\\n return True\\n else:\\n return False\\n \\n# LOGIN True functions\\n\\ndef print_login_menu():\\n if len(card_to_check) == 0:\\n print(\\\"\\\\nEnter your card number:\\\")\\n else:\\n print(\\\"Enter your PIN:\\\")\\n\\ndef login_checker(account, pin):\\n # Data from table 'card' used here\\n check_login_query = \\\"SELECT number, pin FROM card WHERE number = ? AND pin = ?\\\"\\n placeholder = (account, pin)\\n cur.execute(check_login_query, placeholder)\\n temp_res = cur.fetchall()\\n if len(temp_res) == 1:\\n login_successfully_message()\\n return True\\n else:\\n print(\\\"\\\\nWrong card number or PIN!\\\\n\\\") \\n \\n# BALANCE True functions\\n\\ndef get_balance(account_number):\\n # Data from table 'card' MUST be used here\\n placeholder = (account_number,)\\n balance_query = \\\"SELECT balance FROM card WHERE number = ?\\\"\\n cur.execute(balance_query, placeholder)\\n return cur.fetchone()[0] # this is the balance\\n \\n\\ndef display_balance():\\n temp_res3 = get_balance(card_to_check[0])\\n print(str.format(\\\"\\\\nBalance: {}\\\", temp_res3))\\n\\ndef Luhn_checker(digit_string):\\n digit_list = list(digit_string)\\n if len(digit_list) != 16:\\n return False\\n try:\\n for index in range(len(digit_list)):\\n digit_list[index] = int(digit_list[index])\\n except:\\n return False\\n \\n # Luhn checker starts here\\n digit_sum = digit_list[-1]\\n for index in range(len(digit_list) - 1):\\n if index % 2 == 0:\\n digit_list[index] *= 2\\n if digit_list[index] > 9:\\n digit_list[index] -= 9\\n digit_sum += digit_list[index]\\n \\n if digit_sum % 10 == 0:\\n return True\\n else:\\n return False\\n\\t\\t\\ndef close_account():\\n delete_query = \\\"DELETE FROM card WHERE number = ?\\\"\\n placeholder = (card_to_check[0],) # because we're only allowed to close the account we're logged in\\n cur.execute(delete_query, placeholder)\\n conn.commit()\\n\\n# ADD True\\n\\ndef add_income(amount):\\n current_balance = get_balance(card_to_check[0])\\n current_balance += int(amount)\\n update_balance = \\\"UPDATE card SET balance = ? WHERE number = ?\\\"\\n cur.execute(update_balance, (current_balance, card_to_check[0]))\\n conn.commit()\\n print(\\\"Income was added!\\\")\\n print_balance_menu()\\n\\n# CHK_TRANSFER True\\n\\ndef check_transfer_acc(card_number):\\n # print(\\\"You've just entered card number \\\" + str(card_number))\\n global CHK_TRANSFER\\n global BALANCE\\n CHK_TRANSFER = False\\n BALANCE = True \\n if card_number == card_to_check[0]:\\n print(\\\"You can't transfer money to the same account!\\\")\\n print_balance_menu()\\n elif not Luhn_checker(card_number):\\n print(\\\"Probably you made a mistake in the card number. Please try again!\\\")\\n print_balance_menu()\\n elif card_exists(card_number) is None:\\n print(\\\"Such a card does not exist.\\\")\\n print_balance_menu()\\n else:\\n BALANCE = False\\n global account_to_transfer\\n account_to_transfer = card_number\\n print(\\\"Enter how much money you want to transfer:\\\")\\n\\n# All state constants on False\\n\\ndef perform_transfer(amount):\\n current_balance_grantor = get_balance(card_to_check[0])\\n if current_balance_grantor < int(amount):\\n print(\\\"Not enough money!\\\")\\n else:\\n current_balance_receiver = get_balance(account_to_transfer)\\n placeholder1 = (current_balance_grantor - int(amount), card_to_check[0])\\n placeholder2 = (current_balance_receiver + int(amount), account_to_transfer)\\n update_query = \\\"UPDATE card SET balance = ? WHERE number = ?\\\"\\n cur.execute(update_query, placeholder1)\\n conn.commit()\\n cur.execute(update_query, placeholder2)\\n conn.commit()\\n print(\\\"Success!\\\")\\n \\n \\n#-------------------------------------------------------------------------------------------------------------------------------\\n#End of function definitions section; start of the function calls section\\n\\nprint_first_menu() # first call; first user input; default state is CREATE = True\\n\\nwhile(True):\\n usr_inp = get_user_input()\\n \\n if CREATE:\\n if usr_inp in possible_options:\\n if usr_inp == \\\"1\\\":\\n while(not card_generator(Luhn_generator())):\\n pass\\n print_first_menu()\\n elif usr_inp == \\\"2\\\":\\n cur.execute(\\\"SELECT * FROM card\\\") # check if any cards have already been created\\n temp_res2 = cur.fetchall()\\n if 0 != len(temp_res2): # SELECT query returns a list of tuples, if list is empty, then there's no card created\\n CREATE = False\\n LOGIN = True\\n \\n print_login_menu()\\n else:\\n print(\\\"\\\\nNo account created yet!\\\")\\n print_first_menu()\\n else:\\n print_exit_statement()\\n exit()\\n else:\\n print(\\\"\\\\nInvalid option\\\")\\n print_first_menu()\\n \\n elif LOGIN: \\n card_to_check.append(usr_inp) # account no. comes first, pin comes afterwards\\n if len(card_to_check) != 2:\\n print_login_menu()\\n else:\\n login_result = login_checker(card_to_check[0], card_to_check[1])\\n \\n if login_result:\\n \\n LOGIN = False\\n BALANCE = True\\n print_balance_menu()\\n else:\\n CREATE = True\\n LOGIN = False\\n \\n card_to_check.clear() # Because we may want to try a new login\\n print_first_menu() \\n \\n elif BALANCE:\\n if usr_inp in possible_balance_options:\\n if usr_inp == \\\"1\\\":\\n display_balance() \\n print_balance_menu()\\n elif usr_inp == \\\"2\\\":\\n BALANCE = False\\n ADD = True\\n print(\\\"\\\\nEnter income:\\\")\\n elif usr_inp == \\\"3\\\":\\n print(\\\"\\\\nTransfer\\\\nEnter card number:\\\") \\n BALANCE = False\\n CHK_TRANSFER = True\\n elif usr_inp == \\\"4\\\":\\n close_account()\\n print(\\\"\\\\nThe account has been closed!\\\\n\\\") \\n CREATE = True\\n BALANCE = False # Leaving this branch and returning to the first menu\\n card_to_check.clear() # Because the account no longer exists\\n print_first_menu()\\n elif usr_inp == \\\"5\\\":\\n CREATE = True\\n BALANCE = False # Leaving this branch and returning to the first menu\\n card_to_check.clear() # Because we're logging out\\n logout_successfully_message()\\n print_first_menu()\\n else: # exit branch\\n print_exit_statement()\\n exit() \\n else:\\n print(\\\"\\\\nInvalid option!\\\")\\n print_balance_menu()\\n \\n elif ADD:\\n add_income(usr_inp)\\n BALANCE = True\\n ADD = False\\n \\n elif CHK_TRANSFER:\\n check_transfer_acc(usr_inp)\\n \\n else:\\n perform_transfer(usr_inp)\\n BALANCE = True\\n print_balance_menu()\\n \"\n 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like","value":[{"alpha_fraction":0.5482378005981445,"alphanum_fraction":0.5699537396430969,"avg_line_length":28.26041603088379,"blob_id":"0cc3e9a49035d4f3a9c9f9f186da72c053b062e6","content_id":"2720ae5640220f826e7dd68b8f268df48a4499ab","detected_licenses":["BSD-3-Clause","LicenseRef-scancode-unknown-license-reference"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2809,"license_type":"permissive","max_line_length":128,"num_lines":96,"path":"/infer.py","repo_name":"1447964530/CVPR2021_PFNet","src_encoding":"UTF-8","text":"\"\"\"\n @Time : 2021/7/6 14:36\n @Author : Haiyang Mei\n @E-mail : mhy666@mail.dlut.edu.cn\n \n @Project : CVPR2021_PFNet\n @File : infer.py\n @Function: Inference\n \n\"\"\"\nimport time\nimport datetime\n\nimport torch\nfrom PIL import Image\nfrom torch.autograd import Variable\nfrom torchvision import transforms\nfrom collections import OrderedDict\nfrom numpy import mean\n\nfrom config import *\nfrom misc import *\nfrom PFNet import PFNet\n\ntorch.manual_seed(2021)\ndevice_ids = [1]\ntorch.cuda.set_device(device_ids[0])\n\nresults_path = './results'\ncheck_mkdir(results_path)\nexp_name = 'PFNet'\nargs = {\n 'scale': 416,\n 'save_results': True\n}\n\nprint(torch.__version__)\n\nimg_transform = transforms.Compose([\n transforms.Resize((args['scale'], args['scale'])),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n])\n\nto_pil = transforms.ToPILImage()\n\nto_test = OrderedDict([\n ('CHAMELEON', chameleon_path),\n ('CAMO', camo_path),\n ('COD10K', cod10k_path),\n ('NC4K', nc4k_path)\n ])\n\nresults = OrderedDict()\n\ndef main():\n net = PFNet(backbone_path).cuda(device_ids[0])\n\n net.load_state_dict(torch.load('PFNet.pth'))\n print('Load {} succeed!'.format('PFNet.pth'))\n\n net.eval()\n with torch.no_grad():\n start = time.time()\n for name, root in to_test.items():\n time_list = []\n image_path = os.path.join(root, 'image')\n\n if args['save_results']:\n check_mkdir(os.path.join(results_path, exp_name, name))\n\n img_list = [os.path.splitext(f)[0] for f in os.listdir(image_path) if f.endswith('jpg')]\n for idx, img_name in enumerate(img_list):\n img = Image.open(os.path.join(image_path, img_name + '.jpg')).convert('RGB')\n\n w, h = img.size\n img_var = Variable(img_transform(img).unsqueeze(0)).cuda(device_ids[0])\n\n start_each = time.time()\n _, _, _, prediction = net(img_var)\n time_each = time.time() - start_each\n time_list.append(time_each)\n\n prediction = np.array(transforms.Resize((h, w))(to_pil(prediction.data.squeeze(0).cpu())))\n\n if args['save_results']:\n Image.fromarray(prediction).convert('L').save(os.path.join(results_path, exp_name, name, img_name + '.png'))\n print(('{}'.format(exp_name)))\n print(\"{}'s average Time Is : {:.3f} s\".format(name, mean(time_list)))\n print(\"{}'s average Time Is : {:.1f} fps\".format(name, 1 / mean(time_list)))\n\n end = time.time()\n print(\"Total Testing Time: {}\".format(str(datetime.timedelta(seconds=int(end - start)))))\n\nif __name__ == '__main__':\n main()\n"},{"alpha_fraction":0.4209612011909485,"alphanum_fraction":0.4418066143989563,"avg_line_length":32.21154022216797,"blob_id":"ac81211225e0bafa9eccdae9dc32f4dfd8009036","content_id":"0ff64bb86e8ac8537ece178ca65e23bf2032c32e","detected_licenses":["BSD-3-Clause","LicenseRef-scancode-unknown-license-reference"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1727,"license_type":"permissive","max_line_length":97,"num_lines":52,"path":"/loss.py","repo_name":"1447964530/CVPR2021_PFNet","src_encoding":"UTF-8","text":"\"\"\"\n @Time : 2021/7/6 14:31\n @Author : Haiyang Mei\n @E-mail : mhy666@mail.dlut.edu.cn\n \n @Project : CVPR2021_PFNet\n @File : loss.py\n @Function: Loss\n \n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n###################################################################\n# ########################## iou loss #############################\n###################################################################\nclass IOU(torch.nn.Module):\n def __init__(self):\n super(IOU, self).__init__()\n\n def _iou(self, pred, target):\n pred = torch.sigmoid(pred)\n inter = (pred * target).sum(dim=(2, 3))\n union = (pred + target).sum(dim=(2, 3)) - inter\n iou = 1 - (inter / union)\n\n return iou.mean()\n\n def forward(self, pred, target):\n return self._iou(pred, target)\n\n###################################################################\n# #################### structure loss #############################\n###################################################################\nclass structure_loss(torch.nn.Module):\n def __init__(self):\n super(structure_loss, self).__init__()\n\n def _structure_loss(self, pred, mask):\n weit = 1 + 5 * torch.abs(F.avg_pool2d(mask, kernel_size=31, stride=1, padding=15) - mask)\n wbce = F.binary_cross_entropy_with_logits(pred, mask, reduce='none')\n wbce = (weit * wbce).sum(dim=(2, 3)) / weit.sum(dim=(2, 3))\n\n pred = torch.sigmoid(pred)\n inter = ((pred * mask) * weit).sum(dim=(2, 3))\n union = ((pred + mask) * weit).sum(dim=(2, 3))\n wiou = 1 - (inter) / (union - inter)\n return (wbce + wiou).mean()\n\n def forward(self, pred, mask):\n return self._structure_loss(pred, mask)\n"},{"alpha_fraction":0.6301115155220032,"alphanum_fraction":0.6821561455726624,"avg_line_length":23.454545974731445,"blob_id":"3311eb0610e20572abd1227fb45a90f882fe57a6","content_id":"856af78e0e8e94e5d7e283865725cd4cd1cea0ff","detected_licenses":["BSD-3-Clause","LicenseRef-scancode-unknown-license-reference"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":538,"license_type":"permissive","max_line_length":62,"num_lines":22,"path":"/config.py","repo_name":"1447964530/CVPR2021_PFNet","src_encoding":"UTF-8","text":"\"\"\"\n @Time : 2021/7/6 09:46\n @Author : Haiyang Mei\n @E-mail : mhy666@mail.dlut.edu.cn\n \n @Project : CVPR2021_PFNet\n @File : config.py\n @Function: Configuration\n \n\"\"\"\nimport os\n\nbackbone_path = './backbone/resnet/resnet50-19c8e357.pth'\n\ndatasets_root = '../data/NEW'\n\ncod_training_root = os.path.join(datasets_root, 'train')\n\nchameleon_path = os.path.join(datasets_root, 'test/CHAMELEON')\ncamo_path = os.path.join(datasets_root, 'test/CAMO')\ncod10k_path = os.path.join(datasets_root, 'test/COD10K')\nnc4k_path = os.path.join(datasets_root, 'test/NC4K')\n"},{"alpha_fraction":0.7304936051368713,"alphanum_fraction":0.7571656107902527,"avg_line_length":60.26829147338867,"blob_id":"0073f04eefeabaf58f5c829f06a0bf9c88902cff","content_id":"02869664693c25b44c37b846d8458717fbe981e8","detected_licenses":["BSD-3-Clause","LicenseRef-scancode-unknown-license-reference"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":2512,"license_type":"permissive","max_line_length":1287,"num_lines":41,"path":"/README.md","repo_name":"1447964530/CVPR2021_PFNet","src_encoding":"UTF-8","text":"# CVPR2021_PFNet\n\n## Camouflaged Object Segmentation With Distraction Mining\n[Haiyang Mei](https://mhaiyang.github.io/), Ge-Peng Ji, Ziqi Wei, Xin Yang, Xiaopeng Wei, [Deng-Ping Fan](http://dpfan.net/)\n\n[[Paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Mei_Camouflaged_Object_Segmentation_With_Distraction_Mining_CVPR_2021_paper.pdf)] [[Project Page](https://mhaiyang.github.io/CVPR2021_PFNet/index.html)]\n\n### Abstract\nCamouflaged object segmentation (COS) aims to identify objects that are \"perfectly\" assimilate into their surroundings, which has a wide range of valuable applications. The key challenge of COS is that there exist high intrinsic similarities between the candidate objects and noise background. In this paper, we strive to embrace challenges towards effective and efficient COS. To this end, we develop a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature. Specifically, our PFNet contains two key modules, i.e., the positioning module (PM) and the focus module (FM). The PM is designed to mimic the detection process in predation for positioning the potential target objects from a global perspective and the FM is then used to perform the identification process in predation for progressively refining the coarse prediction via focusing on the ambiguous regions. Notably, in the FM, we develop a novel distraction mining strategy for the distraction region discovery and removal, to benefit the performance of estimation. Extensive experiments demonstrate that our PFNet runs in real-time (72 FPS) and significantly outperforms 18 cutting-edge models on three challenging benchmark datasets under four standard metrics.\n\n### Citation\nIf you use this code, please cite:\n\n```\n@InProceedings{Mei_2021_CVPR,\n author = {Mei, Haiyang and Ji, Ge-Peng and Wei, Ziqi and Yang, Xin and Wei, Xiaopeng and Fan, Deng-Ping},\n title = {Camouflaged Object Segmentation With Distraction Mining},\n booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},\n month = {June},\n year = {2021},\n pages = {8772-8781}\n}\n```\n\n### Requirements\n* PyTorch == 1.0.0\n* TorchVision == 0.2.1\n* CUDA 10.0 cudnn 7.2\n\n### Train\nDownload 'resnet50-19c8e357.pth' at [here](https://download.pytorch.org/models/resnet50-19c8e357.pth), then run `train.py`.\n\n\n### Test\nDownload trained model 'PFNet.pth' at [here](https://mhaiyang.github.io/CVPR2021_PFNet/index.html), then run `infer.py`.\n\n### License\nPlease see `license.txt`\n\n### Contact\nE-Mail: mhy666@mail.dlut.edu.cn\n"},{"alpha_fraction":0.5068702101707458,"alphanum_fraction":0.5389313101768494,"avg_line_length":17.714284896850586,"blob_id":"4e12eb1706349eb5217223325398c1abbc02b176","content_id":"9cc9524493a9674d2a31a8eafde3e3a3d5099749","detected_licenses":["BSD-3-Clause","LicenseRef-scancode-unknown-license-reference"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":655,"license_type":"permissive","max_line_length":40,"num_lines":35,"path":"/misc.py","repo_name":"1447964530/CVPR2021_PFNet","src_encoding":"UTF-8","text":"\"\"\"\n @Time : 2021/7/6 11:21\n @Author : Haiyang Mei\n @E-mail : mhy666@mail.dlut.edu.cn\n \n @Project : CVPR2021_PFNet\n @File : misc.py\n @Function: Useful functions\n \n\"\"\"\nimport numpy as np\nimport os\n\nclass AvgMeter(object):\n def __init__(self):\n self.reset()\n\n def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0\n\n def update(self, val, n=1):\n self.val = val\n self.sum += val * n\n self.count += n\n self.avg = self.sum / self.count\n\ndef check_mkdir(dir_name):\n if not os.path.exists(dir_name):\n os.makedirs(dir_name)\n\ndef _sigmoid(x):\n return 1 / (1 + np.exp(-x))\n"},{"alpha_fraction":0.49757280945777893,"alphanum_fraction":0.5354542136192322,"avg_line_length":39.05555725097656,"blob_id":"3feeb51c585d7f679aa1a02b7734db2848d8b7ed","content_id":"43a4d7d6f69fc91a417ffdb6f200907e46445838","detected_licenses":["BSD-3-Clause","LicenseRef-scancode-unknown-license-reference"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":11536,"license_type":"permissive","max_line_length":114,"num_lines":288,"path":"/PFNet.py","repo_name":"1447964530/CVPR2021_PFNet","src_encoding":"UTF-8","text":"\"\"\"\n @Time : 2021/7/6 14:23\n @Author : Haiyang Mei\n @E-mail : mhy666@mail.dlut.edu.cn\n \n @Project : CVPR2021_PFNet\n @File : PFNet.py\n @Function: Focus and Exploration Network\n \n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport backbone.resnet.resnet as resnet\n\n###################################################################\n# ################## Channel Attention Block ######################\n###################################################################\nclass CA_Block(nn.Module):\n def __init__(self, in_dim):\n super(CA_Block, self).__init__()\n self.chanel_in = in_dim\n self.gamma = nn.Parameter(torch.ones(1))\n self.softmax = nn.Softmax(dim=-1)\n\n def forward(self, x):\n \"\"\"\n inputs :\n x : input feature maps (B X C X H X W)\n returns :\n out : channel attentive features\n \"\"\"\n m_batchsize, C, height, width = x.size()\n proj_query = x.view(m_batchsize, C, -1)\n proj_key = x.view(m_batchsize, C, -1).permute(0, 2, 1)\n energy = torch.bmm(proj_query, proj_key)\n attention = self.softmax(energy)\n proj_value = x.view(m_batchsize, C, -1)\n\n out = torch.bmm(attention, proj_value)\n out = out.view(m_batchsize, C, height, width)\n\n out = self.gamma * out + x\n return out\n\n###################################################################\n# ################## Spatial Attention Block ######################\n###################################################################\nclass SA_Block(nn.Module):\n def __init__(self, in_dim):\n super(SA_Block, self).__init__()\n self.chanel_in = in_dim\n self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)\n self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)\n self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)\n self.gamma = nn.Parameter(torch.ones(1))\n self.softmax = nn.Softmax(dim=-1)\n\n def forward(self, x):\n \"\"\"\n inputs :\n x : input feature maps (B X C X H X W)\n returns :\n out : spatial attentive features\n \"\"\"\n m_batchsize, C, height, width = x.size()\n proj_query = self.query_conv(x).view(m_batchsize, -1, width * height).permute(0, 2, 1)\n proj_key = self.key_conv(x).view(m_batchsize, -1, width * height)\n energy = torch.bmm(proj_query, proj_key)\n attention = self.softmax(energy)\n proj_value = self.value_conv(x).view(m_batchsize, -1, width * height)\n\n out = torch.bmm(proj_value, attention.permute(0, 2, 1))\n out = out.view(m_batchsize, C, height, width)\n\n out = self.gamma * out + x\n return out\n\n###################################################################\n# ################## Context Exploration Block ####################\n###################################################################\nclass Context_Exploration_Block(nn.Module):\n def __init__(self, input_channels):\n super(Context_Exploration_Block, self).__init__()\n self.input_channels = input_channels\n self.channels_single = int(input_channels / 4)\n\n self.p1_channel_reduction = nn.Sequential(\n nn.Conv2d(self.input_channels, self.channels_single, 1, 1, 0),\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\n self.p2_channel_reduction = nn.Sequential(\n nn.Conv2d(self.input_channels, self.channels_single, 1, 1, 0),\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\n self.p3_channel_reduction = nn.Sequential(\n nn.Conv2d(self.input_channels, self.channels_single, 1, 1, 0),\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\n self.p4_channel_reduction = nn.Sequential(\n nn.Conv2d(self.input_channels, self.channels_single, 1, 1, 0),\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\n\n self.p1 = nn.Sequential(\n nn.Conv2d(self.channels_single, self.channels_single, 1, 1, 0),\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\n self.p1_dc = nn.Sequential(\n nn.Conv2d(self.channels_single, self.channels_single, kernel_size=3, stride=1, padding=1, dilation=1),\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\n\n self.p2 = nn.Sequential(\n nn.Conv2d(self.channels_single, self.channels_single, 3, 1, 1),\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\n self.p2_dc = nn.Sequential(\n nn.Conv2d(self.channels_single, self.channels_single, kernel_size=3, stride=1, padding=2, dilation=2),\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\n\n self.p3 = nn.Sequential(\n nn.Conv2d(self.channels_single, self.channels_single, 5, 1, 2),\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\n self.p3_dc = nn.Sequential(\n nn.Conv2d(self.channels_single, self.channels_single, kernel_size=3, stride=1, padding=4, dilation=4),\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\n\n self.p4 = nn.Sequential(\n nn.Conv2d(self.channels_single, self.channels_single, 7, 1, 3),\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\n self.p4_dc = nn.Sequential(\n nn.Conv2d(self.channels_single, self.channels_single, kernel_size=3, stride=1, padding=8, dilation=8),\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\n\n self.fusion = nn.Sequential(nn.Conv2d(self.input_channels, self.input_channels, 1, 1, 0),\n nn.BatchNorm2d(self.input_channels), nn.ReLU())\n\n def forward(self, x):\n p1_input = self.p1_channel_reduction(x)\n p1 = self.p1(p1_input)\n p1_dc = self.p1_dc(p1)\n\n p2_input = self.p2_channel_reduction(x) + p1_dc\n p2 = self.p2(p2_input)\n p2_dc = self.p2_dc(p2)\n\n p3_input = self.p3_channel_reduction(x) + p2_dc\n p3 = self.p3(p3_input)\n p3_dc = self.p3_dc(p3)\n\n p4_input = self.p4_channel_reduction(x) + p3_dc\n p4 = self.p4(p4_input)\n p4_dc = self.p4_dc(p4)\n\n ce = self.fusion(torch.cat((p1_dc, p2_dc, p3_dc, p4_dc), 1))\n\n return ce\n\n###################################################################\n# ##################### Positioning Module ########################\n###################################################################\nclass Positioning(nn.Module):\n def __init__(self, channel):\n super(Positioning, self).__init__()\n self.channel = channel\n self.cab = CA_Block(self.channel)\n self.sab = SA_Block(self.channel)\n self.map = nn.Conv2d(self.channel, 1, 7, 1, 3)\n\n def forward(self, x):\n cab = self.cab(x)\n sab = self.sab(cab)\n map = self.map(sab)\n\n return sab, map\n\n###################################################################\n# ######################## Focus Module ###########################\n###################################################################\nclass Focus(nn.Module):\n def __init__(self, channel1, channel2):\n super(Focus, self).__init__()\n self.channel1 = channel1\n self.channel2 = channel2\n\n self.up = nn.Sequential(nn.Conv2d(self.channel2, self.channel1, 7, 1, 3),\n nn.BatchNorm2d(self.channel1), nn.ReLU(), nn.UpsamplingBilinear2d(scale_factor=2))\n\n self.input_map = nn.Sequential(nn.UpsamplingBilinear2d(scale_factor=2), nn.Sigmoid())\n self.output_map = nn.Conv2d(self.channel1, 1, 7, 1, 3)\n\n self.fp = Context_Exploration_Block(self.channel1)\n self.fn = Context_Exploration_Block(self.channel1)\n self.alpha = nn.Parameter(torch.ones(1))\n self.beta = nn.Parameter(torch.ones(1))\n self.bn1 = nn.BatchNorm2d(self.channel1)\n self.relu1 = nn.ReLU()\n self.bn2 = nn.BatchNorm2d(self.channel1)\n self.relu2 = nn.ReLU()\n\n def forward(self, x, y, in_map):\n # x; current-level features\n # y: higher-level features\n # in_map: higher-level prediction\n\n up = self.up(y)\n\n input_map = self.input_map(in_map)\n f_feature = x * input_map\n b_feature = x * (1 - input_map)\n\n fp = self.fp(f_feature)\n fn = self.fn(b_feature)\n\n refine1 = up - (self.alpha * fp)\n refine1 = self.bn1(refine1)\n refine1 = self.relu1(refine1)\n\n refine2 = refine1 + (self.beta * fn)\n refine2 = self.bn2(refine2)\n refine2 = self.relu2(refine2)\n\n output_map = self.output_map(refine2)\n\n return refine2, output_map\n\n###################################################################\n# ########################## NETWORK ##############################\n###################################################################\nclass PFNet(nn.Module):\n def __init__(self, backbone_path=None):\n super(PFNet, self).__init__()\n # params\n\n # backbone\n resnet50 = resnet.resnet50(backbone_path)\n self.layer0 = nn.Sequential(resnet50.conv1, resnet50.bn1, resnet50.relu)\n self.layer1 = nn.Sequential(resnet50.maxpool, resnet50.layer1)\n self.layer2 = resnet50.layer2\n self.layer3 = resnet50.layer3\n self.layer4 = resnet50.layer4\n\n # channel reduction\n self.cr4 = nn.Sequential(nn.Conv2d(2048, 512, 3, 1, 1), nn.BatchNorm2d(512), nn.ReLU())\n self.cr3 = nn.Sequential(nn.Conv2d(1024, 256, 3, 1, 1), nn.BatchNorm2d(256), nn.ReLU())\n self.cr2 = nn.Sequential(nn.Conv2d(512, 128, 3, 1, 1), nn.BatchNorm2d(128), nn.ReLU())\n self.cr1 = nn.Sequential(nn.Conv2d(256, 64, 3, 1, 1), nn.BatchNorm2d(64), nn.ReLU())\n\n # positioning\n self.positioning = Positioning(512)\n\n # focus\n self.focus3 = Focus(256, 512)\n self.focus2 = Focus(128, 256)\n self.focus1 = Focus(64, 128)\n\n for m in self.modules():\n if isinstance(m, nn.ReLU):\n m.inplace = True\n\n def forward(self, x):\n # x: [batch_size, channel=3, h, w]\n layer0 = self.layer0(x) # [-1, 64, h/2, w/2]\n layer1 = self.layer1(layer0) # [-1, 256, h/4, w/4]\n layer2 = self.layer2(layer1) # [-1, 512, h/8, w/8]\n layer3 = self.layer3(layer2) # [-1, 1024, h/16, w/16]\n layer4 = self.layer4(layer3) # [-1, 2048, h/32, w/32]\n\n # channel reduction\n cr4 = self.cr4(layer4)\n cr3 = self.cr3(layer3)\n cr2 = self.cr2(layer2)\n cr1 = self.cr1(layer1)\n\n # positioning\n positioning, predict4 = self.positioning(cr4)\n\n # focus\n focus3, predict3 = self.focus3(cr3, positioning, predict4)\n focus2, predict2 = self.focus2(cr2, focus3, predict3)\n focus1, predict1 = self.focus1(cr1, focus2, predict2)\n\n # rescale\n predict4 = F.interpolate(predict4, size=x.size()[2:], mode='bilinear', align_corners=True)\n predict3 = F.interpolate(predict3, size=x.size()[2:], mode='bilinear', align_corners=True)\n predict2 = F.interpolate(predict2, size=x.size()[2:], mode='bilinear', align_corners=True)\n predict1 = F.interpolate(predict1, size=x.size()[2:], mode='bilinear', align_corners=True)\n\n if self.training:\n return predict4, predict3, predict2, predict1\n\n return torch.sigmoid(predict4), torch.sigmoid(predict3), torch.sigmoid(predict2), torch.sigmoid(\n predict1)\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5482378005981445,\n \"alphanum_fraction\": 0.5699537396430969,\n \"avg_line_length\": 28.26041603088379,\n \"blob_id\": \"0cc3e9a49035d4f3a9c9f9f186da72c053b062e6\",\n \"content_id\": \"2720ae5640220f826e7dd68b8f268df48a4499ab\",\n \"detected_licenses\": [\n \"BSD-3-Clause\",\n \"LicenseRef-scancode-unknown-license-reference\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2809,\n \"license_type\": \"permissive\",\n \"max_line_length\": 128,\n \"num_lines\": 96,\n \"path\": \"/infer.py\",\n \"repo_name\": \"1447964530/CVPR2021_PFNet\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n @Time : 2021/7/6 14:36\\n @Author : Haiyang Mei\\n @E-mail : mhy666@mail.dlut.edu.cn\\n \\n @Project : CVPR2021_PFNet\\n @File : infer.py\\n @Function: Inference\\n \\n\\\"\\\"\\\"\\nimport time\\nimport datetime\\n\\nimport torch\\nfrom PIL import Image\\nfrom torch.autograd import Variable\\nfrom torchvision import transforms\\nfrom collections import OrderedDict\\nfrom numpy import mean\\n\\nfrom config import *\\nfrom misc import *\\nfrom PFNet import PFNet\\n\\ntorch.manual_seed(2021)\\ndevice_ids = [1]\\ntorch.cuda.set_device(device_ids[0])\\n\\nresults_path = './results'\\ncheck_mkdir(results_path)\\nexp_name = 'PFNet'\\nargs = {\\n 'scale': 416,\\n 'save_results': True\\n}\\n\\nprint(torch.__version__)\\n\\nimg_transform = transforms.Compose([\\n transforms.Resize((args['scale'], args['scale'])),\\n transforms.ToTensor(),\\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\\n])\\n\\nto_pil = transforms.ToPILImage()\\n\\nto_test = OrderedDict([\\n ('CHAMELEON', chameleon_path),\\n ('CAMO', camo_path),\\n ('COD10K', cod10k_path),\\n ('NC4K', nc4k_path)\\n ])\\n\\nresults = OrderedDict()\\n\\ndef main():\\n net = PFNet(backbone_path).cuda(device_ids[0])\\n\\n net.load_state_dict(torch.load('PFNet.pth'))\\n print('Load {} succeed!'.format('PFNet.pth'))\\n\\n net.eval()\\n with torch.no_grad():\\n start = time.time()\\n for name, root in to_test.items():\\n time_list = []\\n image_path = os.path.join(root, 'image')\\n\\n if args['save_results']:\\n check_mkdir(os.path.join(results_path, exp_name, name))\\n\\n img_list = [os.path.splitext(f)[0] for f in os.listdir(image_path) if f.endswith('jpg')]\\n for idx, img_name in enumerate(img_list):\\n img = Image.open(os.path.join(image_path, img_name + '.jpg')).convert('RGB')\\n\\n w, h = img.size\\n img_var = Variable(img_transform(img).unsqueeze(0)).cuda(device_ids[0])\\n\\n start_each = time.time()\\n _, _, _, prediction = net(img_var)\\n time_each = time.time() - start_each\\n time_list.append(time_each)\\n\\n prediction = np.array(transforms.Resize((h, w))(to_pil(prediction.data.squeeze(0).cpu())))\\n\\n if args['save_results']:\\n Image.fromarray(prediction).convert('L').save(os.path.join(results_path, exp_name, name, img_name + '.png'))\\n print(('{}'.format(exp_name)))\\n print(\\\"{}'s average Time Is : {:.3f} s\\\".format(name, mean(time_list)))\\n print(\\\"{}'s average Time Is : {:.1f} fps\\\".format(name, 1 / mean(time_list)))\\n\\n end = time.time()\\n print(\\\"Total Testing Time: {}\\\".format(str(datetime.timedelta(seconds=int(end - start)))))\\n\\nif __name__ == '__main__':\\n main()\\n\"\n },\n {\n \"alpha_fraction\": 0.4209612011909485,\n \"alphanum_fraction\": 0.4418066143989563,\n \"avg_line_length\": 32.21154022216797,\n \"blob_id\": \"ac81211225e0bafa9eccdae9dc32f4dfd8009036\",\n \"content_id\": \"0ff64bb86e8ac8537ece178ca65e23bf2032c32e\",\n \"detected_licenses\": [\n \"BSD-3-Clause\",\n \"LicenseRef-scancode-unknown-license-reference\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1727,\n \"license_type\": \"permissive\",\n \"max_line_length\": 97,\n \"num_lines\": 52,\n \"path\": \"/loss.py\",\n \"repo_name\": \"1447964530/CVPR2021_PFNet\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n @Time : 2021/7/6 14:31\\n @Author : Haiyang Mei\\n @E-mail : mhy666@mail.dlut.edu.cn\\n \\n @Project : CVPR2021_PFNet\\n @File : loss.py\\n @Function: Loss\\n \\n\\\"\\\"\\\"\\nimport torch\\nimport torch.nn as nn\\nimport torch.nn.functional as F\\n\\n###################################################################\\n# ########################## iou loss #############################\\n###################################################################\\nclass IOU(torch.nn.Module):\\n def __init__(self):\\n super(IOU, self).__init__()\\n\\n def _iou(self, pred, target):\\n pred = torch.sigmoid(pred)\\n inter = (pred * target).sum(dim=(2, 3))\\n union = (pred + target).sum(dim=(2, 3)) - inter\\n iou = 1 - (inter / union)\\n\\n return iou.mean()\\n\\n def forward(self, pred, target):\\n return self._iou(pred, target)\\n\\n###################################################################\\n# #################### structure loss #############################\\n###################################################################\\nclass structure_loss(torch.nn.Module):\\n def __init__(self):\\n super(structure_loss, self).__init__()\\n\\n def _structure_loss(self, pred, mask):\\n weit = 1 + 5 * torch.abs(F.avg_pool2d(mask, kernel_size=31, stride=1, padding=15) - mask)\\n wbce = F.binary_cross_entropy_with_logits(pred, mask, reduce='none')\\n wbce = (weit * wbce).sum(dim=(2, 3)) / weit.sum(dim=(2, 3))\\n\\n pred = torch.sigmoid(pred)\\n inter = ((pred * mask) * weit).sum(dim=(2, 3))\\n union = ((pred + mask) * weit).sum(dim=(2, 3))\\n wiou = 1 - (inter) / (union - inter)\\n return (wbce + wiou).mean()\\n\\n def forward(self, pred, mask):\\n return self._structure_loss(pred, mask)\\n\"\n },\n {\n \"alpha_fraction\": 0.6301115155220032,\n \"alphanum_fraction\": 0.6821561455726624,\n \"avg_line_length\": 23.454545974731445,\n \"blob_id\": \"3311eb0610e20572abd1227fb45a90f882fe57a6\",\n \"content_id\": \"856af78e0e8e94e5d7e283865725cd4cd1cea0ff\",\n \"detected_licenses\": [\n \"BSD-3-Clause\",\n \"LicenseRef-scancode-unknown-license-reference\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 538,\n \"license_type\": \"permissive\",\n \"max_line_length\": 62,\n \"num_lines\": 22,\n \"path\": \"/config.py\",\n \"repo_name\": \"1447964530/CVPR2021_PFNet\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n @Time : 2021/7/6 09:46\\n @Author : Haiyang Mei\\n @E-mail : mhy666@mail.dlut.edu.cn\\n \\n @Project : CVPR2021_PFNet\\n @File : config.py\\n @Function: Configuration\\n \\n\\\"\\\"\\\"\\nimport os\\n\\nbackbone_path = './backbone/resnet/resnet50-19c8e357.pth'\\n\\ndatasets_root = '../data/NEW'\\n\\ncod_training_root = os.path.join(datasets_root, 'train')\\n\\nchameleon_path = os.path.join(datasets_root, 'test/CHAMELEON')\\ncamo_path = os.path.join(datasets_root, 'test/CAMO')\\ncod10k_path = os.path.join(datasets_root, 'test/COD10K')\\nnc4k_path = os.path.join(datasets_root, 'test/NC4K')\\n\"\n },\n {\n \"alpha_fraction\": 0.7304936051368713,\n \"alphanum_fraction\": 0.7571656107902527,\n \"avg_line_length\": 60.26829147338867,\n \"blob_id\": \"0073f04eefeabaf58f5c829f06a0bf9c88902cff\",\n \"content_id\": \"02869664693c25b44c37b846d8458717fbe981e8\",\n \"detected_licenses\": [\n \"BSD-3-Clause\",\n \"LicenseRef-scancode-unknown-license-reference\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 2512,\n \"license_type\": \"permissive\",\n \"max_line_length\": 1287,\n \"num_lines\": 41,\n \"path\": \"/README.md\",\n \"repo_name\": \"1447964530/CVPR2021_PFNet\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# CVPR2021_PFNet\\n\\n## Camouflaged Object Segmentation With Distraction Mining\\n[Haiyang Mei](https://mhaiyang.github.io/), Ge-Peng Ji, Ziqi Wei, Xin Yang, Xiaopeng Wei, [Deng-Ping Fan](http://dpfan.net/)\\n\\n[[Paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Mei_Camouflaged_Object_Segmentation_With_Distraction_Mining_CVPR_2021_paper.pdf)] [[Project Page](https://mhaiyang.github.io/CVPR2021_PFNet/index.html)]\\n\\n### Abstract\\nCamouflaged object segmentation (COS) aims to identify objects that are \\\"perfectly\\\" assimilate into their surroundings, which has a wide range of valuable applications. The key challenge of COS is that there exist high intrinsic similarities between the candidate objects and noise background. In this paper, we strive to embrace challenges towards effective and efficient COS. To this end, we develop a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature. Specifically, our PFNet contains two key modules, i.e., the positioning module (PM) and the focus module (FM). The PM is designed to mimic the detection process in predation for positioning the potential target objects from a global perspective and the FM is then used to perform the identification process in predation for progressively refining the coarse prediction via focusing on the ambiguous regions. Notably, in the FM, we develop a novel distraction mining strategy for the distraction region discovery and removal, to benefit the performance of estimation. Extensive experiments demonstrate that our PFNet runs in real-time (72 FPS) and significantly outperforms 18 cutting-edge models on three challenging benchmark datasets under four standard metrics.\\n\\n### Citation\\nIf you use this code, please cite:\\n\\n```\\n@InProceedings{Mei_2021_CVPR,\\n author = {Mei, Haiyang and Ji, Ge-Peng and Wei, Ziqi and Yang, Xin and Wei, Xiaopeng and Fan, Deng-Ping},\\n title = {Camouflaged Object Segmentation With Distraction Mining},\\n booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},\\n month = {June},\\n year = {2021},\\n pages = {8772-8781}\\n}\\n```\\n\\n### Requirements\\n* PyTorch == 1.0.0\\n* TorchVision == 0.2.1\\n* CUDA 10.0 cudnn 7.2\\n\\n### Train\\nDownload 'resnet50-19c8e357.pth' at [here](https://download.pytorch.org/models/resnet50-19c8e357.pth), then run `train.py`.\\n\\n\\n### Test\\nDownload trained model 'PFNet.pth' at [here](https://mhaiyang.github.io/CVPR2021_PFNet/index.html), then run `infer.py`.\\n\\n### License\\nPlease see `license.txt`\\n\\n### Contact\\nE-Mail: mhy666@mail.dlut.edu.cn\\n\"\n },\n {\n \"alpha_fraction\": 0.5068702101707458,\n \"alphanum_fraction\": 0.5389313101768494,\n \"avg_line_length\": 17.714284896850586,\n \"blob_id\": \"4e12eb1706349eb5217223325398c1abbc02b176\",\n \"content_id\": \"9cc9524493a9674d2a31a8eafde3e3a3d5099749\",\n \"detected_licenses\": [\n \"BSD-3-Clause\",\n \"LicenseRef-scancode-unknown-license-reference\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 655,\n \"license_type\": \"permissive\",\n \"max_line_length\": 40,\n \"num_lines\": 35,\n \"path\": \"/misc.py\",\n \"repo_name\": \"1447964530/CVPR2021_PFNet\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n @Time : 2021/7/6 11:21\\n @Author : Haiyang Mei\\n @E-mail : mhy666@mail.dlut.edu.cn\\n \\n @Project : CVPR2021_PFNet\\n @File : misc.py\\n @Function: Useful functions\\n \\n\\\"\\\"\\\"\\nimport numpy as np\\nimport os\\n\\nclass AvgMeter(object):\\n def __init__(self):\\n self.reset()\\n\\n def reset(self):\\n self.val = 0\\n self.avg = 0\\n self.sum = 0\\n self.count = 0\\n\\n def update(self, val, n=1):\\n self.val = val\\n self.sum += val * n\\n self.count += n\\n self.avg = self.sum / self.count\\n\\ndef check_mkdir(dir_name):\\n if not os.path.exists(dir_name):\\n os.makedirs(dir_name)\\n\\ndef _sigmoid(x):\\n return 1 / (1 + np.exp(-x))\\n\"\n },\n {\n \"alpha_fraction\": 0.49757280945777893,\n \"alphanum_fraction\": 0.5354542136192322,\n \"avg_line_length\": 39.05555725097656,\n \"blob_id\": \"3feeb51c585d7f679aa1a02b7734db2848d8b7ed\",\n \"content_id\": \"43a4d7d6f69fc91a417ffdb6f200907e46445838\",\n \"detected_licenses\": [\n \"BSD-3-Clause\",\n \"LicenseRef-scancode-unknown-license-reference\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 11536,\n \"license_type\": \"permissive\",\n \"max_line_length\": 114,\n \"num_lines\": 288,\n \"path\": \"/PFNet.py\",\n \"repo_name\": \"1447964530/CVPR2021_PFNet\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n @Time : 2021/7/6 14:23\\n @Author : Haiyang Mei\\n @E-mail : mhy666@mail.dlut.edu.cn\\n \\n @Project : CVPR2021_PFNet\\n @File : PFNet.py\\n @Function: Focus and Exploration Network\\n \\n\\\"\\\"\\\"\\nimport torch\\nimport torch.nn as nn\\nimport torch.nn.functional as F\\n\\nimport backbone.resnet.resnet as resnet\\n\\n###################################################################\\n# ################## Channel Attention Block ######################\\n###################################################################\\nclass CA_Block(nn.Module):\\n def __init__(self, in_dim):\\n super(CA_Block, self).__init__()\\n self.chanel_in = in_dim\\n self.gamma = nn.Parameter(torch.ones(1))\\n self.softmax = nn.Softmax(dim=-1)\\n\\n def forward(self, x):\\n \\\"\\\"\\\"\\n inputs :\\n x : input feature maps (B X C X H X W)\\n returns :\\n out : channel attentive features\\n \\\"\\\"\\\"\\n m_batchsize, C, height, width = x.size()\\n proj_query = x.view(m_batchsize, C, -1)\\n proj_key = x.view(m_batchsize, C, -1).permute(0, 2, 1)\\n energy = torch.bmm(proj_query, proj_key)\\n attention = self.softmax(energy)\\n proj_value = x.view(m_batchsize, C, -1)\\n\\n out = torch.bmm(attention, proj_value)\\n out = out.view(m_batchsize, C, height, width)\\n\\n out = self.gamma * out + x\\n return out\\n\\n###################################################################\\n# ################## Spatial Attention Block ######################\\n###################################################################\\nclass SA_Block(nn.Module):\\n def __init__(self, in_dim):\\n super(SA_Block, self).__init__()\\n self.chanel_in = in_dim\\n self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)\\n self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)\\n self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)\\n self.gamma = nn.Parameter(torch.ones(1))\\n self.softmax = nn.Softmax(dim=-1)\\n\\n def forward(self, x):\\n \\\"\\\"\\\"\\n inputs :\\n x : input feature maps (B X C X H X W)\\n returns :\\n out : spatial attentive features\\n \\\"\\\"\\\"\\n m_batchsize, C, height, width = x.size()\\n proj_query = self.query_conv(x).view(m_batchsize, -1, width * height).permute(0, 2, 1)\\n proj_key = self.key_conv(x).view(m_batchsize, -1, width * height)\\n energy = torch.bmm(proj_query, proj_key)\\n attention = self.softmax(energy)\\n proj_value = self.value_conv(x).view(m_batchsize, -1, width * height)\\n\\n out = torch.bmm(proj_value, attention.permute(0, 2, 1))\\n out = out.view(m_batchsize, C, height, width)\\n\\n out = self.gamma * out + x\\n return out\\n\\n###################################################################\\n# ################## Context Exploration Block ####################\\n###################################################################\\nclass Context_Exploration_Block(nn.Module):\\n def __init__(self, input_channels):\\n super(Context_Exploration_Block, self).__init__()\\n self.input_channels = input_channels\\n self.channels_single = int(input_channels / 4)\\n\\n self.p1_channel_reduction = nn.Sequential(\\n nn.Conv2d(self.input_channels, self.channels_single, 1, 1, 0),\\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\\n self.p2_channel_reduction = nn.Sequential(\\n nn.Conv2d(self.input_channels, self.channels_single, 1, 1, 0),\\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\\n self.p3_channel_reduction = nn.Sequential(\\n nn.Conv2d(self.input_channels, self.channels_single, 1, 1, 0),\\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\\n self.p4_channel_reduction = nn.Sequential(\\n nn.Conv2d(self.input_channels, self.channels_single, 1, 1, 0),\\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\\n\\n self.p1 = nn.Sequential(\\n nn.Conv2d(self.channels_single, self.channels_single, 1, 1, 0),\\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\\n self.p1_dc = nn.Sequential(\\n nn.Conv2d(self.channels_single, self.channels_single, kernel_size=3, stride=1, padding=1, dilation=1),\\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\\n\\n self.p2 = nn.Sequential(\\n nn.Conv2d(self.channels_single, self.channels_single, 3, 1, 1),\\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\\n self.p2_dc = nn.Sequential(\\n nn.Conv2d(self.channels_single, self.channels_single, kernel_size=3, stride=1, padding=2, dilation=2),\\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\\n\\n self.p3 = nn.Sequential(\\n nn.Conv2d(self.channels_single, self.channels_single, 5, 1, 2),\\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\\n self.p3_dc = nn.Sequential(\\n nn.Conv2d(self.channels_single, self.channels_single, kernel_size=3, stride=1, padding=4, dilation=4),\\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\\n\\n self.p4 = nn.Sequential(\\n nn.Conv2d(self.channels_single, self.channels_single, 7, 1, 3),\\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\\n self.p4_dc = nn.Sequential(\\n nn.Conv2d(self.channels_single, self.channels_single, kernel_size=3, stride=1, padding=8, dilation=8),\\n nn.BatchNorm2d(self.channels_single), nn.ReLU())\\n\\n self.fusion = nn.Sequential(nn.Conv2d(self.input_channels, self.input_channels, 1, 1, 0),\\n nn.BatchNorm2d(self.input_channels), nn.ReLU())\\n\\n def forward(self, x):\\n p1_input = self.p1_channel_reduction(x)\\n p1 = self.p1(p1_input)\\n p1_dc = self.p1_dc(p1)\\n\\n p2_input = self.p2_channel_reduction(x) + p1_dc\\n p2 = self.p2(p2_input)\\n p2_dc = self.p2_dc(p2)\\n\\n p3_input = self.p3_channel_reduction(x) + p2_dc\\n p3 = self.p3(p3_input)\\n p3_dc = self.p3_dc(p3)\\n\\n p4_input = self.p4_channel_reduction(x) + p3_dc\\n p4 = self.p4(p4_input)\\n p4_dc = self.p4_dc(p4)\\n\\n ce = self.fusion(torch.cat((p1_dc, p2_dc, p3_dc, p4_dc), 1))\\n\\n return ce\\n\\n###################################################################\\n# ##################### Positioning Module ########################\\n###################################################################\\nclass Positioning(nn.Module):\\n def __init__(self, channel):\\n super(Positioning, self).__init__()\\n self.channel = channel\\n self.cab = CA_Block(self.channel)\\n self.sab = SA_Block(self.channel)\\n self.map = nn.Conv2d(self.channel, 1, 7, 1, 3)\\n\\n def forward(self, x):\\n cab = self.cab(x)\\n sab = self.sab(cab)\\n map = self.map(sab)\\n\\n return sab, map\\n\\n###################################################################\\n# ######################## Focus Module ###########################\\n###################################################################\\nclass Focus(nn.Module):\\n def __init__(self, channel1, channel2):\\n super(Focus, self).__init__()\\n self.channel1 = channel1\\n self.channel2 = channel2\\n\\n self.up = nn.Sequential(nn.Conv2d(self.channel2, self.channel1, 7, 1, 3),\\n nn.BatchNorm2d(self.channel1), nn.ReLU(), nn.UpsamplingBilinear2d(scale_factor=2))\\n\\n self.input_map = nn.Sequential(nn.UpsamplingBilinear2d(scale_factor=2), nn.Sigmoid())\\n self.output_map = nn.Conv2d(self.channel1, 1, 7, 1, 3)\\n\\n self.fp = Context_Exploration_Block(self.channel1)\\n self.fn = Context_Exploration_Block(self.channel1)\\n self.alpha = nn.Parameter(torch.ones(1))\\n self.beta = nn.Parameter(torch.ones(1))\\n self.bn1 = nn.BatchNorm2d(self.channel1)\\n self.relu1 = nn.ReLU()\\n self.bn2 = nn.BatchNorm2d(self.channel1)\\n self.relu2 = nn.ReLU()\\n\\n def forward(self, x, y, in_map):\\n # x; current-level features\\n # y: higher-level features\\n # in_map: higher-level prediction\\n\\n up = self.up(y)\\n\\n input_map = self.input_map(in_map)\\n f_feature = x * input_map\\n b_feature = x * (1 - input_map)\\n\\n fp = self.fp(f_feature)\\n fn = self.fn(b_feature)\\n\\n refine1 = up - (self.alpha * fp)\\n refine1 = self.bn1(refine1)\\n refine1 = self.relu1(refine1)\\n\\n refine2 = refine1 + (self.beta * fn)\\n refine2 = self.bn2(refine2)\\n refine2 = self.relu2(refine2)\\n\\n output_map = self.output_map(refine2)\\n\\n return refine2, output_map\\n\\n###################################################################\\n# ########################## NETWORK ##############################\\n###################################################################\\nclass PFNet(nn.Module):\\n def __init__(self, backbone_path=None):\\n super(PFNet, self).__init__()\\n # params\\n\\n # backbone\\n resnet50 = resnet.resnet50(backbone_path)\\n self.layer0 = nn.Sequential(resnet50.conv1, resnet50.bn1, resnet50.relu)\\n self.layer1 = nn.Sequential(resnet50.maxpool, resnet50.layer1)\\n self.layer2 = resnet50.layer2\\n self.layer3 = resnet50.layer3\\n self.layer4 = resnet50.layer4\\n\\n # channel reduction\\n self.cr4 = nn.Sequential(nn.Conv2d(2048, 512, 3, 1, 1), nn.BatchNorm2d(512), nn.ReLU())\\n self.cr3 = nn.Sequential(nn.Conv2d(1024, 256, 3, 1, 1), nn.BatchNorm2d(256), nn.ReLU())\\n self.cr2 = nn.Sequential(nn.Conv2d(512, 128, 3, 1, 1), nn.BatchNorm2d(128), nn.ReLU())\\n self.cr1 = nn.Sequential(nn.Conv2d(256, 64, 3, 1, 1), nn.BatchNorm2d(64), nn.ReLU())\\n\\n # positioning\\n self.positioning = Positioning(512)\\n\\n # focus\\n self.focus3 = Focus(256, 512)\\n self.focus2 = Focus(128, 256)\\n self.focus1 = Focus(64, 128)\\n\\n for m in self.modules():\\n if isinstance(m, nn.ReLU):\\n m.inplace = True\\n\\n def forward(self, x):\\n # x: [batch_size, channel=3, h, w]\\n layer0 = self.layer0(x) # [-1, 64, h/2, w/2]\\n layer1 = self.layer1(layer0) # [-1, 256, h/4, w/4]\\n layer2 = self.layer2(layer1) # [-1, 512, h/8, w/8]\\n layer3 = self.layer3(layer2) # [-1, 1024, h/16, w/16]\\n layer4 = self.layer4(layer3) # [-1, 2048, h/32, w/32]\\n\\n # channel reduction\\n cr4 = self.cr4(layer4)\\n cr3 = self.cr3(layer3)\\n cr2 = self.cr2(layer2)\\n cr1 = self.cr1(layer1)\\n\\n # positioning\\n positioning, predict4 = self.positioning(cr4)\\n\\n # focus\\n focus3, predict3 = self.focus3(cr3, positioning, predict4)\\n focus2, predict2 = self.focus2(cr2, focus3, predict3)\\n focus1, predict1 = self.focus1(cr1, focus2, predict2)\\n\\n # rescale\\n predict4 = F.interpolate(predict4, size=x.size()[2:], mode='bilinear', align_corners=True)\\n predict3 = F.interpolate(predict3, size=x.size()[2:], mode='bilinear', align_corners=True)\\n predict2 = F.interpolate(predict2, size=x.size()[2:], mode='bilinear', align_corners=True)\\n predict1 = F.interpolate(predict1, size=x.size()[2:], mode='bilinear', align_corners=True)\\n\\n if self.training:\\n return predict4, predict3, predict2, predict1\\n\\n return torch.sigmoid(predict4), torch.sigmoid(predict3), torch.sigmoid(predict2), torch.sigmoid(\\n predict1)\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":6,"string":"6"}}},{"rowIdx":2983069,"cells":{"repo_name":{"kind":"string","value":"murtraja/django-sensor-server"},"repo_url":{"kind":"string","value":"https://github.com/murtraja/django-sensor-server"},"snapshot_id":{"kind":"string","value":"72fd09141ff84eee2a2e75db474b0ecc208070a0"},"revision_id":{"kind":"string","value":"3ed329af2d38ed0d1809cd89e254b93e7fcc4daf"},"directory_id":{"kind":"string","value":"8441383d17d70fdc57ff2d54352f336d0f666c50"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-10T06:51:51.676488","string":"2021-01-10T06:51:51.676488"},"revision_date":{"kind":"timestamp","value":"2015-09-24T11:25:59","string":"2015-09-24T11:25:59"},"committer_date":{"kind":"timestamp","value":"2015-09-24T11:25:59","string":"2015-09-24T11:25:59"},"github_id":{"kind":"number","value":43062338,"string":"43,062,338"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6692014932632446,"alphanum_fraction":0.6692014932632446,"avg_line_length":36.71428680419922,"blob_id":"a7df2c22e0ac51a158b695a52c8a438652dd552a","content_id":"6a4928f13bbf1da93f86c6435978850acbab4388","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":263,"license_type":"no_license","max_line_length":62,"num_lines":7,"path":"/post/urls.py","repo_name":"murtraja/django-sensor-server","src_encoding":"UTF-8","text":"from django.conf.urls import patterns, url\nfrom post import views\n\nurlpatterns = patterns('',\n url(r'^$', views.postData, name= 'post'),\n url(r'^monitor/$', views.monitorData, name = 'monitor'),\n url(r'^download/$', views.downloadAPK, name = 'download'))"},{"alpha_fraction":0.6848428845405579,"alphanum_fraction":0.6866912841796875,"avg_line_length":29.94285774230957,"blob_id":"516b4cff777dc8f63abb6375de971e3638687a68","content_id":"b15d1d24b037dba242aa4d455cf193968f856947","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1082,"license_type":"no_license","max_line_length":80,"num_lines":35,"path":"/post/views.py","repo_name":"murtraja/django-sensor-server","src_encoding":"UTF-8","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom ws4redis.publisher import RedisPublisher\nfrom ws4redis.redis_store import RedisMessage\n\nimport json\n\n# Create your views here.\n@csrf_exempt\ndef postData(request):\n print 'inside postData'\n if request.method == 'POST':\n print 'inside post'\n name = request.POST['name']\n print name\n data = request.POST['data']\n print data\n mydict = {'name':name, 'data':data}\n print mydict\n redis_publisher = RedisPublisher(facility = 'sensor', broadcast = True)\n message = RedisMessage(json.dumps(mydict))\n redis_publisher.publish_message(message)\n return HttpResponse(\"OK\")\n else:\n print 'get request'\n #return render(request,'post/postdata.html')\n return downloadAPK(request)\n\ndef monitorData(request):\n return render(request, 'post/monitor.html')\n\ndef downloadAPK(request):\n return HttpResponse(\"<a href = /static/Sensorization.apk>Download now!</a>\")"}],"string":"[\n {\n \"alpha_fraction\": 0.6692014932632446,\n \"alphanum_fraction\": 0.6692014932632446,\n \"avg_line_length\": 36.71428680419922,\n \"blob_id\": \"a7df2c22e0ac51a158b695a52c8a438652dd552a\",\n \"content_id\": \"6a4928f13bbf1da93f86c6435978850acbab4388\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 263,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 7,\n \"path\": \"/post/urls.py\",\n \"repo_name\": \"murtraja/django-sensor-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.conf.urls import patterns, url\\nfrom post import views\\n\\nurlpatterns = patterns('',\\n url(r'^$', views.postData, name= 'post'),\\n url(r'^monitor/$', views.monitorData, name = 'monitor'),\\n url(r'^download/$', views.downloadAPK, name = 'download'))\"\n },\n {\n \"alpha_fraction\": 0.6848428845405579,\n \"alphanum_fraction\": 0.6866912841796875,\n \"avg_line_length\": 29.94285774230957,\n \"blob_id\": \"516b4cff777dc8f63abb6375de971e3638687a68\",\n \"content_id\": \"b15d1d24b037dba242aa4d455cf193968f856947\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1082,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 35,\n \"path\": \"/post/views.py\",\n \"repo_name\": \"murtraja/django-sensor-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.shortcuts import render\\nfrom django.http import HttpResponse\\nfrom django.views.decorators.csrf import csrf_exempt\\n\\nfrom ws4redis.publisher import RedisPublisher\\nfrom ws4redis.redis_store import RedisMessage\\n\\nimport json\\n\\n# Create your views here.\\n@csrf_exempt\\ndef postData(request):\\n print 'inside postData'\\n if request.method == 'POST':\\n print 'inside post'\\n name = request.POST['name']\\n print name\\n data = request.POST['data']\\n print data\\n mydict = {'name':name, 'data':data}\\n print mydict\\n redis_publisher = RedisPublisher(facility = 'sensor', broadcast = True)\\n message = RedisMessage(json.dumps(mydict))\\n redis_publisher.publish_message(message)\\n return HttpResponse(\\\"OK\\\")\\n else:\\n print 'get request'\\n #return render(request,'post/postdata.html')\\n return downloadAPK(request)\\n\\ndef monitorData(request):\\n return render(request, 'post/monitor.html')\\n\\ndef downloadAPK(request):\\n return HttpResponse(\\\"<a href = /static/Sensorization.apk>Download now!</a>\\\")\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2983070,"cells":{"repo_name":{"kind":"string","value":"rrifaldiu/line-bot-medik"},"repo_url":{"kind":"string","value":"https://github.com/rrifaldiu/line-bot-medik"},"snapshot_id":{"kind":"string","value":"02b19ac07291575926c23a93d22f6761cd11e961"},"revision_id":{"kind":"string","value":"bfcf6570e62d841a6b3fe13adf55ac9779ffad62"},"directory_id":{"kind":"string","value":"df6fa4fa52d4708ae51ea2b47d5d1fa9f7b6d83e"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-09-02T05:00:05.729420","string":"2021-09-02T05:00:05.729420"},"revision_date":{"kind":"timestamp","value":"2017-12-30T15:09:20","string":"2017-12-30T15:09:20"},"committer_date":{"kind":"timestamp","value":"2017-12-30T15:09:20","string":"2017-12-30T15:09:20"},"github_id":{"kind":"number","value":115486950,"string":"115,486,950"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.4998168349266052,"alphanum_fraction":0.5082221031188965,"avg_line_length":52.46681213378906,"blob_id":"459cb386bd70e2ffc87ed64b665121405470060b","content_id":"9171239448e7e5ccb7aa500afe9b7bbe87deca47","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":49136,"license_type":"no_license","max_line_length":366,"num_lines":919,"path":"/app.py","repo_name":"rrifaldiu/line-bot-medik","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom __future__ import unicode_literals\n\nimport errno\nimport os\nimport sys\nimport tempfile\nfrom argparse import ArgumentParser\n\nfrom flask import Flask, request, abort\n\nfrom linebot import (\n LineBotApi, WebhookHandler\n)\nfrom linebot.exceptions import (\n InvalidSignatureError\n)\nfrom linebot.models import (\n MessageEvent, TextMessage, TextSendMessage,\n SourceUser, SourceGroup, SourceRoom,\n TemplateSendMessage, ConfirmTemplate, MessageTemplateAction,\n ButtonsTemplate, ImageCarouselTemplate, ImageCarouselColumn, URITemplateAction,\n PostbackTemplateAction, DatetimePickerTemplateAction,\n CarouselTemplate, CarouselColumn, PostbackEvent,\n StickerMessage, StickerSendMessage, LocationMessage, LocationSendMessage,\n ImageMessage, VideoMessage, AudioMessage, FileMessage,\n UnfollowEvent, FollowEvent, JoinEvent, LeaveEvent, BeaconEvent,\n ImageSendMessage\n)\n\napp = Flask(__name__)\n\n# get channel_secret and channel_access_token from your environment variable\n# channel_secret = os.getenv('LINE_CHANNEL_SECRET', None)\n# channel_access_token = os.getenv('LINE_CHANNEL_ACCESS_TOKEN', None)\n# if channel_secret is None:\n# print('Specify LINE_CHANNEL_SECRET as environment variable.')\n# sys.exit(1)\n# if channel_access_token is None:\n# print('Specify LINE_CHANNEL_ACCESS_TOKEN as environment variable.')\n# sys.exit(1)\n\nline_bot_api = LineBotApi('yCLqDYJLIiRo5xSfnc/9tmKbtKL9oEcBhzG8Mczc0WH+8B+/Q9PT0ifkMT+frqRsgAKYqB1/CVhwx7vWPLKP1nyZszn6gGdqQ0H1CgfLUxFhX2Wgreq3cFZw1D1km/IXBljdJyY4fOw5kyvSQpcu6gdB04t89/1O/w1cDnyilFU=')\nhandler = WebhookHandler('62826fb39d7f3cb6a27f8573bc5e9b29')\n\n#SEGALA INISIALISASI ADA DI SINI HEHEHE\n\n#INISIALISASI NAMA\nnama_admin = 'faldi'\nnama_tandu_1 = 'ilham'\nnama_tandu_2 = 'sarah'\nnama_obat_1 = 'kahfi'\nnama_obat_2 = 'rany'\nnama_tft_1 = 'ivannsa'\nnama_tft_2 = 'fathin'\nnama_humas_1 = 'atsila'\nnama_humas_2 = 'benito'\n\n#INISIALISASI USER ID\nuser_id_admin = 'U36bb6303be7a1563a7d27d0ee2234ea5' #faldi\nuser_id_tandu_1 = 'Ua84e82b214cc4cf6783aa167d9ec3ce8' #ilham\nuser_id_tandu_2 = 'tandu2' #sarah\nuser_id_obat_1 = 'Ufa385bd386f3634b04f5ca57360b0a8a' #kahfi\nuser_id_obat_2 = 'obat2' #rani\nuser_id_tft_1 = 'Uc21abb39fe1b5c1ab376ba9b5209ccce' #ivansa\nuser_id_tft_2 = 'tft2' #fathin ?????\nuser_id_humas_1 = 'humas1' #atsila\nuser_id_humas_2 = 'humas2' #benito ?????\n\n#RESOURCE - IMAGE\nimgurl_tandu = 'https://raw.githubusercontent.com/rrifaldiu/line-bot-medik/master/static/res/jpg/tandu/tandu.jpg'\nimgurl_obat = 'https://raw.githubusercontent.com/rrifaldiu/line-bot-medik/master/static/res/jpg/obat/obat.jpg'\nimgurl_obat_base = 'https://raw.githubusercontent.com/rrifaldiu/line-bot-medik/master/static/res/jpg/obat/obat_base.jpg'\nimgurl_obat_pj = 'https://raw.githubusercontent.com/rrifaldiu/line-bot-medik/master/static/res/jpg/obat/obat_pj.jpg'\nimgurl_obat_satuan = 'https://raw.githubusercontent.com/rrifaldiu/line-bot-medik/master/static/res/jpg/obat/obat_pilih.jpg'\nimgurl_tft = 'https://raw.githubusercontent.com/rrifaldiu/line-bot-medik/master/static/res/jpg/tft/tft.jpg'\nimgurl_humas = 'https://raw.githubusercontent.com/rrifaldiu/line-bot-medik/master/static/res/jpg/humas/humas.jpg'\nimgurl_dp = 'https://image.ibb.co/bJPgVR/240240.jpg'\n\nform_template = ('\\n' +\n 'Nama : \\n' +\n 'Jurusan : \\n' +\n 'ID Line : \\n' +\n 'Lembaga : \\n' +\n 'Tujuan peminjaman : \\n' +\n 'Tanggal peminjaman : \\n' +\n 'Tanggal pengembalian : ')\n\nstatic_tmp_path = os.path.join(os.path.dirname(__file__), 'static', 'tmp')\n\n\n# function for create tmp dir for download content\ndef make_static_tmp_dir():\n try:\n os.makedirs(static_tmp_path)\n except OSError as exc:\n if exc.errno == errno.EEXIST and os.path.isdir(static_tmp_path):\n pass\n else:\n raise\n\n\n@app.route(\"/callback\", methods=['POST'])\ndef callback():\n # get X-Line-Signature header value\n signature = request.headers['X-Line-Signature']\n\n # get request body as text\n body = request.get_data(as_text=True)\n app.logger.info(\"Request body: \" + body)\n\n # handle webhook body\n try:\n handler.handle(body, signature)\n except InvalidSignatureError:\n abort(400)\n\n return 'OK'\n\n\n@handler.add(MessageEvent, message=TextMessage)\ndef handle_text_message(event):\n text = event.message.text\n profile = line_bot_api.get_profile(event.source.user_id)\n if text.lower() == 'menu':\n image_carousel_template = ImageCarouselTemplate(columns=[\n ImageCarouselColumn(image_url=imgurl_tandu,\n action=PostbackTemplateAction(\n label='Pinjam Tandu',\n data='tandu')),\n ImageCarouselColumn(image_url=imgurl_obat,\n action=PostbackTemplateAction(\n label='Pinjam Obat',\n data='obat')),\n ImageCarouselColumn(image_url=imgurl_tft,\n action=PostbackTemplateAction(\n label='TFT Medik',\n data='tft')),\n ImageCarouselColumn(image_url=imgurl_humas,\n action=PostbackTemplateAction(\n label='Hubungi Kami',\n data='form_humas'))\n ])\n template_message = TemplateSendMessage(\n alt_text='Silahkan pilih menu yang diinginkan', template=image_carousel_template)\n line_bot_api.reply_message(\n event.reply_token, [\n StickerSendMessage(\n package_id='3',\n sticker_id='242'\n ),\n TextSendMessage(text='Halo! Selamat datang di OA Medik OSKM!\\n\\n Silahkan pilih menu di bawah ini'),\n template_message\n ]\n )\n\n elif text.lower().startswith('balas-'):\n profile = line_bot_api.get_profile(event.source.user_id)\n if text.lower().startswith('balas-' + nama_admin):\n line_bot_api.push_message(user_id_admin, [\n TextSendMessage(text='Menerima balasan dari:\\nNama LINE: ' + profile.display_name + '\\nFoto: ' + profile.picture_url),\n TextSendMessage(text=text[(6+len(nama_admin)) : ]),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\n<pesan Anda>')\n ])\n line_bot_api.reply_message(\n event.reply_token, \n TextSendMessage(text='Pesan telah terkirim')\n )\n elif text.lower().startswith('balas-' + nama_tandu_1):\n line_bot_api.multicast([user_id_admin, user_id_tandu_1], [\n TextSendMessage(text=nama_tandu_1.title() + ' menerima balasan dari:\\nNama LINE: ' + profile.display_name + '\\nFoto: ' + profile.picture_url),\n TextSendMessage(text=text[(6+len(nama_tandu_1)) : ]),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\n<pesan Anda>')\n ])\n line_bot_api.reply_message(\n event.reply_token, \n TextSendMessage(text='Pesan telah terkirim')\n )\n # elif text.lower().startswith('balas-' + nama_tandu_2):\n # line_bot_api.multicast([user_id_admin, user_id_tandu_2], [\n # TextSendMessage(text=nama_tandu_2.title() + ' menerima balasan dari:\\nNama LINE: ' + profile.display_name + '\\nFoto: ' + profile.picture_url),\n # TextSendMessage(text=text[(6+len(nama_tandu_2)) : ]),\n # TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\n # TextSendMessage(text='balas_admin-' + profile.user_id + ':\\n<pesan Anda>')\n # ])\n # line_bot_api.reply_message(\n # event.reply_token, \n # TextSendMessage(text='Pesan telah terkirim')\n # )\n elif text.lower().startswith('balas-' + nama_obat_1):\n line_bot_api.multicast([user_id_admin, user_id_obat_1], [\n TextSendMessage(text=nama_obat_1.title() + ' menerima balasan dari:\\nNama LINE: ' + profile.display_name + '\\nFoto: ' + profile.picture_url),\n TextSendMessage(text=text[(6+len(nama_obat_1)) : ]),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\n<pesan Anda>')\n ])\n line_bot_api.reply_message(\n event.reply_token, \n TextSendMessage(text='Pesan telah terkirim')\n )\n # elif text.lower().startswith('balas-' + nama_obat_2):\n # line_bot_api.multicast([user_id_admin, user_id_obat_2], [\n # TextSendMessage(text=nama_obat_2.title() + ' menerima balasan dari:\\nNama LINE: ' + profile.display_name + '\\nFoto: ' + profile.picture_url),\n # TextSendMessage(text=text[(6+len(nama_obat_2)) : ]),\n # TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\n # TextSendMessage(text='balas_admin-' + profile.user_id + ':\\n<pesan Anda>')\n # ])\n # line_bot_api.reply_message(\n # event.reply_token, \n # TextSendMessage(text='Pesan telah terkirim')\n # )\n elif text.lower().startswith('balas-' + nama_tft_1):\n line_bot_api.multicast([user_id_admin, user_id_tft_1], [\n TextSendMessage(text=nama_tft_1.title() + ' menerima balasan dari:\\nNama LINE: ' + profile.display_name + '\\nFoto: ' + profile.picture_url),\n TextSendMessage(text=text[(6+len(nama_tft_1)) : ]),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\n<pesan Anda>')\n ])\n line_bot_api.reply_message(\n event.reply_token, \n TextSendMessage(text='Pesan telah terkirim')\n )\n # elif text.lower().startswith('balas-' + nama_tft_2):\n # line_bot_api.multicast([user_id_admin, user_id_tft_2], [\n # TextSendMessage(text=nama_tft_2.title() + ' menerima balasan dari:\\nNama LINE: ' + profile.display_name + '\\nFoto: ' + profile.picture_url),\n # TextSendMessage(text=text[(6+len(nama_tft_2)) : ]),\n # TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\n # TextSendMessage(text='balas_admin-' + profile.user_id + ':\\n<pesan Anda>')\n # ])\n # line_bot_api.reply_message(\n # event.reply_token, \n # TextSendMessage(text='Pesan telah terkirim')\n # )\n # elif text.lower().startswith('balas-' + nama_humas_1):\n # line_bot_api.multicast([user_id_admin, user_id_humas_1], [\n # TextSendMessage(text=nama_humas_1.title() + ' menerima balasan dari:\\nNama LINE: ' + profile.display_name + '\\nFoto: ' + profile.picture_url),\n # TextSendMessage(text=text[(6+len(nama_humas_1)) : ]),\n # TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\n # TextSendMessage(text='balas_admin-' + profile.user_id + ':\\n<pesan Anda>')\n # ])\n # line_bot_api.reply_message(\n # event.reply_token, \n # TextSendMessage(text='Pesan telah terkirim')\n # )\n # elif text.lower().startswith('balas-' + nama_humas_2):\n # line_bot_api.multicast([user_id_admin, user_id_humas_2], [\n # TextSendMessage(text=nama_humas_2.title() + ' menerima balasan dari:\\nNama LINE: ' + profile.display_name + '\\nFoto: ' + profile.picture_url),\n # TextSendMessage(text=text[(6+len(nama_humas_2)) : ]),\n # TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\n # TextSendMessage(text='balas_admin-' + profile.user_id + ':\\n<pesan Anda>')\n # ])\n # line_bot_api.reply_message(\n # event.reply_token, \n # TextSendMessage(text='Pesan telah terkirim')\n # )\n else:\n line_bot_api.reply_message(\n event.reply_token, \n TextSendMessage(text='Nama admin tidak ditemukan\\nPastikan nama admin tidak dituliskan dengan kapital')\n )\n\n elif text.lower().startswith('balas_admin-'):\n user_id_balas = text[(text.find('-') + 1) : (text.find(':'))]\n balasan = text[(text.find(':')+2) : ]\n if (event.source.user_id == user_id_admin):\n line_bot_api.push_message(user_id_balas, [\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_admin.title()),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\u0027balas-' + nama_admin +'\\u0027 diikuti dengan pesan Anda'),\n ])\n line_bot_api.reply_message(\n event.reply_token, \n TextSendMessage(text='Pesan telah terkirim')\n )\n elif (event.source.user_id == user_id_tandu_1):\n line_bot_api.push_message(user_id_balas, [\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_tandu_1.title()),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\u0027balas-' + nama_tandu_1 +'\\u0027 diikuti dengan pesan Anda'),\n ])\n if (user_id_admin != user_id_balas):\n line_bot_api.push_message(user_id_admin, [\n TextSendMessage(text=nama_tandu_1 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_tandu_1.title())\n ])\n line_bot_api.reply_message(\n event.reply_token, \n TextSendMessage(text='Pesan telah terkirim')\n )\n elif (event.source.user_id == user_id_tandu_2):\n line_bot_api.push_message(user_id_balas, [\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_tandu_2.title()),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\u0027balas-' + nama_tandu_2 +'\\u0027 diikuti dengan pesan Anda'),\n ])\n if (user_id_admin != user_id_balas):\n line_bot_api.push_message(user_id_admin, [\n TextSendMessage(text=nama_tandu_2 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_tandu_2.title())\n ])\n line_bot_api.reply_message(\n event.reply_token, \n TextSendMessage(text='Pesan telah terkirim')\n )\n elif (event.source.user_id == user_id_obat_1):\n line_bot_api.push_message(user_id_balas, [\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_obat_1.title()),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\u0027balas-' + nama_obat_1 +'\\u0027 diikuti dengan pesan Anda'),\n ])\n if (user_id_admin != user_id_balas):\n line_bot_api.push_message(user_id_admin, [\n TextSendMessage(text=nama_obat_1 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_obat_1.title())\n ])\n \n\n line_bot_api.reply_message(\n event.reply_token, \n TextSendMessage(text='Pesan telah terkirim')\n )\n elif (event.source.user_id == user_id_obat_2):\n line_bot_api.push_message(user_id_balas, [\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_obat_2.title()),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\u0027balas-' + nama_obat_2 +'\\u0027 diikuti dengan pesan Anda'),\n ])\n if (user_id_admin != user_id_balas):\n line_bot_api.push_message(user_id_admin, [\n TextSendMessage(text=nama_obat_2 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_obat_2.title())\n ])\n line_bot_api.reply_message(\n event.reply_token, \n TextSendMessage(text='Pesan telah terkirim')\n )\n elif (event.source.user_id == user_id_tft_1):\n line_bot_api.push_message(user_id_balas, [\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_tft_1.title()),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\u0027balas-' + nama_tft_1 +'\\u0027 diikuti dengan pesan Anda'),\n ])\n if (user_id_admin != user_id_balas):\n line_bot_api.push_message(user_id_admin, [\n TextSendMessage(text=nama_tft_1 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_tft_1.title())\n ])\n line_bot_api.reply_message(\n event.reply_token, \n TextSendMessage(text='Pesan telah terkirim')\n )\n elif (event.source.user_id == user_id_tft_2):\n line_bot_api.push_message(user_id_balas, [\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_tft_2.title()),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\u0027balas-' + nama_tft_2 +'\\u0027 diikuti dengan pesan Anda'),\n ])\n if (user_id_admin != user_id_balas):\n line_bot_api.push_message(user_id_admin, [\n TextSendMessage(text=nama_tft_2 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_tft_2.title())\n ])\n line_bot_api.reply_message(\n event.reply_token, \n TextSendMessage(text='Pesan telah terkirim')\n )\n elif (event.source.user_id == user_id_humas_1):\n line_bot_api.push_message(user_id_balas, [\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_humas_1.title()),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\u0027balas-' + nama_humas_1 +'\\u0027 diikuti dengan pesan Anda'),\n ])\n if (user_id_admin != user_id_balas):\n line_bot_api.push_message(user_id_admin, [\n TextSendMessage(text=nama_humas_1 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_humas_1.title())\n ])\n line_bot_api.reply_message(\n event.reply_token, \n TextSendMessage(text='Pesan telah terkirim')\n )\n elif (event.source.user_id == user_id_humas_2):\n line_bot_api.push_message(user_id_balas, [\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_humas_2.title()),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\u0027balas-' + nama_humas_2 +'\\u0027 diikuti dengan pesan Anda'),\n ])\n if (user_id_admin != user_id_balas):\n line_bot_api.push_message(user_id_admin, [\n TextSendMessage(text=nama_humas_2 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\n TextSendMessage(text=balasan + '\\n\\nSalam, ' + nama_humas_2.title())\n ])\n line_bot_api.reply_message(\n event.reply_token, \n TextSendMessage(text='Pesan telah terkirim')\n )\n else:\n line_bot_api.reply_message(\n event.reply_token, \n TextSendMessage(text='Anda bukan admin')\n )\n\n elif text.startswith('[Form Peminjaman Tandu]'):\n line_bot_api.multicast([user_id_admin\n #, user_id_tandu_1, user_id_tandu_2\n ], [\n TextSendMessage(\n text=text\n ),\n TextSendMessage(\n text='Nama LINE: ' + profile.display_name + '\\nFoto: ' + profile.picture_url\n ),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\n<pesan Anda>')\n ])\n line_bot_api.reply_message(\n event.reply_token, TextSendMessage(\n text='Form telah dikirimkan\\n\\nKami akan memproses secepatnya. Harap bersabar dan menunggu'\n )\n )\n elif text.startswith('[Form Peminjaman Obat '):\n line_bot_api.multicast([user_id_admin\n , user_id_obat_1\n #, user_id_obat_2\n ], [\n TextSendMessage(\n text=text\n ),\n TextSendMessage(\n text='Nama LINE: ' + profile.display_name + '\\nFoto: ' + profile.picture_url\n ),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\n<pesan Anda>')\n ])\n line_bot_api.reply_message(\n event.reply_token, TextSendMessage(\n text='Form telah dikirimkan\\n\\nKami akan memproses secepatnya. Harap bersabar dan menunggu'\n )\n )\n elif text.startswith('[Form TFT Medik]'):\n line_bot_api.multicast([user_id_admin\n , user_id_tft_1\n #, user_id_tft_2\n ], [\n TextSendMessage(\n text=text\n ),\n TextSendMessage(\n text='Nama LINE: ' + profile.display_name + '\\nFoto: ' + profile.picture_url\n ),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\n<pesan Anda>')\n ])\n line_bot_api.reply_message(\n event.reply_token, TextSendMessage(\n text='Form telah dikirimkan\\n\\nKami akan memproses secepatnya. Harap bersabar dan menunggu'\n )\n )\n elif text.startswith('[Form Kontak]'):\n line_bot_api.multicast([user_id_admin\n #, user_id_humas_1, user_id_humas_2\n ], [\n TextSendMessage(\n text=text\n ),\n TextSendMessage(\n text='Nama LINE: ' + profile.display_name + '\\nFoto: ' + profile.picture_url\n ),\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\n<pesan Anda>')\n ])\n line_bot_api.reply_message(\n event.reply_token, TextSendMessage(\n text='Form telah dikirimkan\\n\\nKami akan memproses secepatnya. Harap bersabar dan menunggu'\n )\n )\n elif text.lower() == 'profile':\n if isinstance(event.source, SourceUser):\n line_bot_api.reply_message(\n event.reply_token, [\n TextSendMessage(\n text='Display name: ' + profile.display_name\n ),\n TextSendMessage(\n text='Status message: ' + profile.status_message\n ),\n TextSendMessage(\n text='User ID: ' + event.source.user_id\n )\n ]\n )\n line_bot_api.push_message(user_id_admin, [\n TextSendMessage(\n text='Display name: ' + profile.display_name\n ),\n TextSendMessage(\n text='Status message: ' + profile.status_message\n ),\n TextSendMessage(\n text='User ID: ' + event.source.user_id\n )\n ])\n else:\n line_bot_api.reply_message(\n event.reply_token,\n TextMessage(text=\"Bot can't use profile API without user ID\"))\n \n else:\n line_bot_api.reply_message(\n event.reply_token, TextSendMessage(text='Maaf, perintah yang Anda masukkan salah. Ketik \"menu\" untuk melihat menu medik'))\n\n\n@handler.add(MessageEvent, message=LocationMessage)\ndef handle_location_message(event):\n line_bot_api.reply_message(\n event.reply_token,\n LocationSendMessage(\n title=event.message.title, address=event.message.address,\n latitude=event.message.latitude, longitude=event.message.longitude\n )\n )\n\n\n@handler.add(MessageEvent, message=StickerMessage)\ndef handle_sticker_message(event):\n line_bot_api.reply_message(\n event.reply_token,\n StickerSendMessage(\n package_id=event.message.package_id,\n sticker_id=event.message.sticker_id)\n )\n\n\n# Other Message Type\n@handler.add(MessageEvent, message=(ImageMessage, VideoMessage, AudioMessage))\ndef handle_content_message(event):\n if isinstance(event.message, ImageMessage):\n ext = 'jpg'\n elif isinstance(event.message, VideoMessage):\n ext = 'mp4'\n elif isinstance(event.message, AudioMessage):\n ext = 'm4a'\n else:\n return\n\n message_content = line_bot_api.get_message_content(event.message.id)\n with tempfile.NamedTemporaryFile(dir=static_tmp_path, prefix=ext + '-', delete=False) as tf:\n for chunk in message_content.iter_content():\n tf.write(chunk)\n tempfile_path = tf.name\n\n dist_path = tempfile_path + '.' + ext\n dist_name = os.path.basename(dist_path)\n os.rename(tempfile_path, dist_path)\n\n line_bot_api.reply_message(\n event.reply_token, [\n TextSendMessage(text='Save content.'),\n TextSendMessage(text=request.host_url + os.path.join('static', 'tmp', dist_name))\n ])\n\n\n@handler.add(MessageEvent, message=FileMessage)\ndef handle_file_message(event):\n message_content = line_bot_api.get_message_content(event.message.id)\n with tempfile.NamedTemporaryFile(dir=static_tmp_path, prefix='file-', delete=False) as tf:\n for chunk in message_content.iter_content():\n tf.write(chunk)\n tempfile_path = tf.name\n\n dist_path = tempfile_path + '-' + event.message.file_name\n dist_name = os.path.basename(dist_path)\n os.rename(tempfile_path, dist_path)\n\n line_bot_api.reply_message(\n event.reply_token, [\n TextSendMessage(text='Save file.'),\n TextSendMessage(text=request.host_url + os.path.join('static', 'tmp', dist_name))\n ])\n\n\n@handler.add(FollowEvent)\ndef handle_follow(event):\n line_bot_api.reply_message(\n event.reply_token, TextSendMessage(text='Got follow event'))\n\n\n@handler.add(UnfollowEvent)\ndef handle_unfollow():\n app.logger.info(\"Got Unfollow event\")\n\n\n@handler.add(JoinEvent)\ndef handle_join(event):\n line_bot_api.reply_message(\n event.reply_token,\n TextSendMessage(text='Joined this ' + event.source.type))\n\n\n@handler.add(LeaveEvent)\ndef handle_leave():\n app.logger.info(\"Got leave event\")\n\n@handler.add(PostbackEvent)\ndef handle_postback(event):\n if event.postback.data == 'tandu':\n buttons_template = ButtonsTemplate(\n title='Pastikan Anda telah memahami SOP di atas', text='Klik tombol di bawah untuk melanjutkan', actions= [\n PostbackTemplateAction(\n label='Oke', data='form_tandu')\n ]\n )\n template_message = TemplateSendMessage(\n alt_text='[SOP Peminjaman Tandu]', template=buttons_template)\n line_bot_api.reply_message(\n event.reply_token, [TextSendMessage(text='[SOP Peminjaman Tandu]\\n' +\n '\\n' +\n '1. Peminjaman dilakukan dengan terlebih dahulu menghubungi OA line Medik 2017 lalu mengikuti format yang telah disediakan.\\n' +\n '\\n' +\n '2. Peminjam lalu akan dihubungi untuk konfirmasi peminjaman jika peminjaman dapat dilakukan.\\n' +\n '\\n' +\n '3. Peminjam dan perwakilan dari medik kemudian akan bertemu untuk pengambilan tandu.\\n' +\n 'Untuk pengembalian, peminjam dan perwakilan medik akan bertemu kembali untuk penyerahan tandu.\\n' +\n '\\n' +\n '4. Setiap tandu dapat dipinjam oleh massa kampus selama tandu masih tersedia.\\n' +\n '\\n' +\n '5. Untuk peminjaman, peminjam diminta untuk menitipkan KTM sebagai jaminan.\\n' +\n '\\n' +\n '6. Peminjaman tandu gratis, tidak dipungut biaya.\\n' +\n '\\n' +\n '7. Peminjam wajib melakukan penggantian apabila tandu hilang, atau terjadi kerusakan pada tandu yang bukan diakibatkan oleh penggunaan.\\n' +\n '\\n' +\n '8. Peminjam diminta untuk mencuci mitela yang terdapat pada tandu apabila kotor setelah penggunaan.\\n' +\n '\\n' +\n '9. Selama durasi peminjaman, peminjam melakukan sendiri pengencangan dan perawatan terhadap tandu. Saat peminjaman, peminjam dapat mengutus perwakilan untuk diberi pengarahan tentang cara pengencangan dan perawatan tandu.'\n ),\n template_message\n ])\n elif event.postback.data == 'obat':\n buttons_template = ButtonsTemplate(\n title='Pastikan Anda telah memahami SOP di atas', text='Klik tombol di bawah untuk melanjutkan', actions= [\n PostbackTemplateAction(\n label='Oke', data='pilih_obat')\n ]\n )\n template_message = TemplateSendMessage(\n alt_text='[SOP Peminjaman Obat]', template=buttons_template)\n line_bot_api.reply_message(\n event.reply_token, [TextSendMessage(text='[SOP Peminjaman Obat]\\n' +\n '\\n' +\n '1. Menghubungi OA Medik OSKM 2017 maksimal 3 hari sebelum peminjaman.\\n' +\n '\\n' +\n '2. Memperkenalkan diri serta menyampaikan tujuan peminjaman obat.\\n' +\n '\\n' +\n '3. Menyebutkan obat apa saja yang akan dipinjam.\\n' +\n '\\n' +\n '4. Peminjam menentukan lama waktu peminjaman obat. Peminjaman obat maksimal 14 hari dan jika terjadi keterlambatan dalam pengembalian obat, peminjam akan dikenakan sanksi yang akan diberitahukan lebih lanjut.(jika peminjam ingin meminjam lebih dari 14 hari dapat memperpanjang dengan menghubungi ke OA Medik)\\n' +\n '\\n' +\n '5. Peminjam memberikan jaminan peminjaman berupa KTM/KTP.\\n' +\n '\\n' +\n '6. Peminjam diwajibkan untuk menuliskan data penggunaan obat dengan format yang sudah ditentukan oleh Medik.\\n' +\n '\\n' +\n '7. Pengembalian obat dilakukan dengan menghubungi OA Medik sesuai dengan waktu yang telah ditentukan diawal peminjaman.\\n' +\n '\\n' +\n '8. Apabila obat yang dikembalikan rusak ataupun hilang, peminjam diharuskan mengganti obat tersebut'\n ),\n TextSendMessage(text='9. Apabila obat yang dikembalikan habis dikarenakan pemakaian, kemasan obat tersebut tetap harus disertakan saat pengembalian obat. Apabila kemasan hilang, peminjam dianggap menghilangkan obat.'), \n template_message\n ])\n elif event.postback.data == 'pilih_obat':\n carousel_template = CarouselTemplate(columns=[\n CarouselColumn(title='Obat Base', text='Obat yang digunakan di Base saat OSKM',\n thumbnail_image_url=imgurl_obat_base, actions=[\n PostbackTemplateAction(label='List Obat', data='list_obat_base'),\n PostbackTemplateAction(label='Pinjam', data='form_obat_base')\n ]),\n CarouselColumn(title='Obat PJ Obat', text='Obat yang dibawa oleh PJ Obat saat OSKM',\n thumbnail_image_url=imgurl_obat_pj, actions=[\n PostbackTemplateAction(label='List Obat', data='list_obat_pj'),\n PostbackTemplateAction(label='Pinjam', data='form_obat_pj')\n ]),\n CarouselColumn(title='Obat Satuan', text='Pilih obat-obatan tertentu yang Anda butuhkan',\n thumbnail_image_url=imgurl_obat_satuan, actions=[\n PostbackTemplateAction(label='List Obat', data='list_obat_satuan'),\n PostbackTemplateAction(label='Pinjam', data='form_obat_satuan')\n ])\n ])\n template_message = TemplateSendMessage(\n alt_text='Silahkan pilih jenis obat', template=carousel_template)\n line_bot_api.reply_message(\n event.reply_token, [TextSendMessage(text='Silahkan pilih jenis obat yang ingin Anda pinjam\\n'),\n template_message\n ]\n )\n elif event.postback.data == 'list_obat_base':\n buttons_template = ButtonsTemplate(\n text='Kembali ke pemilihan obat', actions= [\n PostbackTemplateAction(\n label='Kembali', data='pilih_obat')\n ]\n )\n template_message = TemplateSendMessage(\n alt_text='Kembali ke pemilihan obat', template=buttons_template)\n line_bot_api.reply_message(\n event.reply_token, [TextSendMessage(text='[List Obat Base]\\n' +\n '\\n' +\n '1. Bioplacenton\\n' +\n '2. Thrombophob\\n' +\n '3. Counterpain Patch Hot\\n' +\n '4. Counterpain Cool\\n' +\n '5. Ethyl Chloride\\n' +\n '6. Rivanol\\n' +\n '7. Oxycan\\n' +\n '8. Imboost\\n' +\n '9. Sangobion\\n' +\n '10. Mylanta Cair\\n' +\n '11. Ranitidin Cair\\n' +\n '12. Fludane\\n' +\n '13. Spasminal\\n' +\n '14. Parasetamol\\n' +\n '15. Feminax\\n' +\n '16. Ibuprofen\\n' +\n '17. Oralit\\n' +\n '18. Sanadryl\\n' +\n '19. Komix\\n' +\n '20. Salbutamol Syrup\\n' +\n '\\n' +\n 'Perlu diingat bahwa obat-obat tersebut mungkin tidak tersedia karena habis atau sedang dipinjam. Info lebih lanjut akan dihubungi setelah pengisian form peminjaman.'\n ),\n template_message\n ])\n elif event.postback.data == 'list_obat_pj':\n buttons_template = ButtonsTemplate(\n text='Kembali ke pemilihan obat', actions= [\n PostbackTemplateAction(\n label='Kembali', data='pilih_obat')\n ]\n )\n template_message = TemplateSendMessage(\n alt_text='Kembali ke pemilihan obat', template=buttons_template)\n line_bot_api.reply_message(\n event.reply_token, [TextSendMessage(text='[List Obat PJ Obat]\\n' +\n '\\n' +\n '1. Bioplacenton\\n' +\n '2. Thrombophob\\n' +\n '3. Counterpain\\n' +\n '4. Ethyl Chloride\\n' +\n '5. Rivanol\\n' +\n '6. Oxycan\\n' +\n '7. Sangobion\\n' +\n '8. Mylanta Cair\\n' +\n '9. Ranitidin Cair\\n' +\n '10. Komix\\n' +\n '\\n' +\n 'Perlu diingat bahwa obat-obat tersebut mungkin tidak tersedia karena habis atau sedang dipinjam. Info lebih lanjut akan dihubungi setelah pengisian form peminjaman.'\n ),\n template_message\n ])\n elif event.postback.data == 'list_obat_satuan':\n buttons_template = ButtonsTemplate(\n text='Kembali ke pemilihan obat', actions= [\n PostbackTemplateAction(\n label='Kembali', data='pilih_obat')\n ]\n )\n template_message = TemplateSendMessage(\n alt_text='Kembali ke pemilihan obat', template=buttons_template)\n line_bot_api.reply_message(\n event.reply_token, [TextSendMessage(text='[List Obat Satuan]\\n' +\n '\\n' +\n '1. Bioplacenton\\n' +\n '2. Thrombophob\\n' +\n '3. Counterpain Patch Hot\\n' +\n '4. Counterpain Cool\\n' +\n '5. Ethyl Chloride\\n' +\n '6. Rivanol\\n' +\n '7. Oxycan\\n' +\n '8. Imboost\\n' +\n '9. Sangobion\\n' +\n '10. Mylanta Cair\\n' +\n '11. Ranitidin Cair\\n' +\n '12. Fludane\\n' +\n '13. Spasminal\\n' +\n '14. Parasetamol\\n' +\n '15. Feminax\\n' +\n '16. Ibuprofen\\n' +\n '17. Oralit\\n' +\n '18. Sanadryl\\n' +\n '19. Komix\\n' +\n '20. Salbutamol Syrup\\n' +\n '\\n' +\n 'Perlu diingat bahwa obat-obat tersebut mungkin tidak tersedia karena habis atau sedang dipinjam. Info lebih lanjut akan dihubungi setelah pengisian form peminjaman.'\n ),\n template_message\n ])\n elif event.postback.data == 'tft':\n buttons_template = ButtonsTemplate(\n title='Pastikan Anda telah memahami SOP di atas', text='Klik tombol di bawah untuk melanjutkan', actions= [\n PostbackTemplateAction(\n label='Oke', data='form_tft')\n ]\n )\n template_message = TemplateSendMessage(\n alt_text='[SOP TFT Medik]', template=buttons_template)\n line_bot_api.reply_message(\n event.reply_token, [TextSendMessage(text='[SOP TFT Medik]\\n' +\n '\\n' +\n '1. Maksimal permintaan TFT medik adalah H-5 dari hari pemberian TFT medik.\\n' +\n '\\n' +\n '2. Mengisi form yang telah disediakan.'\n ),\n template_message\n ])\n elif event.postback.data == 'form_tandu':\n line_bot_api.reply_message(\n event.reply_token, [TextSendMessage(text= '[Form Peminjaman Tandu]\\n' + form_template\n ),\n TextSendMessage(text= 'Mohon isi form di atas dan pastikan form yang Anda isikan sudah benar\\n' +\n 'Form yang dikirim akan langsung dimasukkan ke dalam sistem'\n )\n ])\n elif event.postback.data == 'form_obat_base':\n line_bot_api.reply_message(\n event.reply_token, [TextSendMessage(text= '[Form Peminjaman Obat Base]\\n' + form_template\n ),\n TextSendMessage(text= 'Mohon isi form di atas dan pastikan form yang Anda isikan sudah benar\\n' +\n 'Form yang dikirim akan langsung dimasukkan ke dalam sistem'\n )\n ])\n elif event.postback.data == 'form_obat_pj':\n line_bot_api.reply_message(\n event.reply_token, [TextSendMessage(text= '[Form Peminjaman Obat PJ Obat]\\n' + form_template\n ),\n TextSendMessage(text= 'Mohon isi form di atas dan pastikan form yang Anda isikan sudah benar\\n' +\n 'Form yang dikirim akan langsung dimasukkan ke dalam sistem'\n )\n ])\n elif event.postback.data == 'form_obat_satuan':\n line_bot_api.reply_message(\n event.reply_token, [TextSendMessage(text= '[Form Peminjaman Obat Satuan]\\n' + form_template + '\\nObat yang ingin dipinjam : '\n ),\n TextSendMessage(text= 'Mohon isi form di atas dan pastikan form yang Anda isikan sudah benar\\n' +\n 'Form yang dikirim akan langsung dimasukkan ke dalam sistem'\n )\n ])\n elif event.postback.data == 'form_tft':\n line_bot_api.reply_message(\n event.reply_token, [TextSendMessage(text= '[Form TFT Medik]\\n' +\n '\\n' +\n 'Nama : \\n' +\n 'Jurusan : \\n' +\n 'ID Line : \\n' +\n 'Lembaga : \\n' +\n 'Gambaran keberjalanan acara : \\n' +\n 'List obat yang telah disediakan : \\n' +\n 'Sasaran peserta TFT : \\n' +\n 'Estimasi jumlah peserta TFT : \\n' +\n 'Alokasi waktu TFT : \\n' +\n 'Sarana dan prasarana yang telah disediakan : \\n' +\n 'Lokasi TFT : '\n ),\n TextSendMessage(text= 'Mohon isi form di atas dan pastikan form yang Anda isikan sudah benar\\n' +\n 'Form yang dikirim akan langsung dimasukkan ke dalam sistem'\n )\n ])\n elif event.postback.data == 'form_humas':\n line_bot_api.reply_message(\n event.reply_token, [TextSendMessage(text= '[Form Kontak]\\n' +\n '\\n' +\n 'Nama : \\n' +\n 'Jurusan : \\n' +\n 'ID Line : \\n' +\n 'Pesan : '\n ),\n TextSendMessage(text= 'Mohon isi form di atas dan pastikan form yang Anda isikan sudah benar\\n' +\n 'Form yang dikirim akan langsung dimasukkan ke dalam sistem'\n )\n ])\n elif event.postback.data == 'ping':\n line_bot_api.reply_message(\n event.reply_token, [TextSendMessage(text='pong'),TextSendMessage(text='pong2')])\n elif event.postback.data == 'datetime_postback':\n line_bot_api.reply_message(\n event.reply_token, TextSendMessage(text=event.postback.params['datetime']))\n elif event.postback.data == 'date_postback':\n line_bot_api.reply_message(\n event.reply_token, TextSendMessage(text=event.postback.params['date']))\n elif event.postback.data == 'time_postback':\n line_bot_api.reply_message(\n event.reply_token, TextSendMessage(text=event.postback.params['time']))\n\n\n@handler.add(BeaconEvent)\ndef handle_beacon(event):\n line_bot_api.reply_message(\n event.reply_token,\n TextSendMessage(\n text='Got beacon event. hwid={}, device_message(hex string)={}'.format(\n event.beacon.hwid, event.beacon.dm)))\n\n\nif __name__ == \"__main__\":\n arg_parser = ArgumentParser(\n usage='Usage: python ' + __file__ + ' [--port <port>] [--help]'\n )\n arg_parser.add_argument('-p', '--port', default=8000, help='port')\n arg_parser.add_argument('-d', '--debug', default=False, help='debug')\n options = arg_parser.parse_args()\n\n # create tmp dir for download content\n make_static_tmp_dir()\n\n app.run(debug=options.debug, port=options.port)\n"},{"alpha_fraction":0.505464494228363,"alphanum_fraction":0.7131147384643555,"avg_line_length":14.913043022155762,"blob_id":"a127fc3648c90103e3770347b52e81a416635751","content_id":"d31bd957c8504c71d3fe04f1a2160e0819bfddda","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":366,"license_type":"no_license","max_line_length":26,"num_lines":23,"path":"/requirements.txt","repo_name":"rrifaldiu/line-bot-medik","src_encoding":"UTF-8","text":"line-bot-sdk\naltgraph==0.14\nclick==6.7\nCython==0.27.3\nflask==0.12.2\nfuture==0.16.0\nglfw==1.4.0\ngunicorn==19.7.1\nitsdangerous==0.24\nJinja2==2.10\nmacholib==1.8\nMarkupSafe==1.0\nnumpy==1.13.3\nolefile==0.44\npefile==2017.11.5\nPillow==4.3.0\nPyDispatcher==2.0.5\nPyInstaller==3.3\nPyOpenGL==3.1.0\nPyOpenGL-accelerate==3.1.0\nPyVRML97==2.3.0b1\nvirtualenv==15.1.0\nWerkzeug==0.13\n"}],"string":"[\n {\n \"alpha_fraction\": 0.4998168349266052,\n \"alphanum_fraction\": 0.5082221031188965,\n \"avg_line_length\": 52.46681213378906,\n \"blob_id\": \"459cb386bd70e2ffc87ed64b665121405470060b\",\n \"content_id\": \"9171239448e7e5ccb7aa500afe9b7bbe87deca47\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 49136,\n \"license_type\": \"no_license\",\n \"max_line_length\": 366,\n \"num_lines\": 919,\n \"path\": \"/app.py\",\n \"repo_name\": \"rrifaldiu/line-bot-medik\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\n# Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); you may\\n# not use this file except in compliance with the License. You may obtain\\n# a copy of the License at\\n#\\n# http://www.apache.org/licenses/LICENSE-2.0\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT\\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\\n# License for the specific language governing permissions and limitations\\n# under the License.\\n\\nfrom __future__ import unicode_literals\\n\\nimport errno\\nimport os\\nimport sys\\nimport tempfile\\nfrom argparse import ArgumentParser\\n\\nfrom flask import Flask, request, abort\\n\\nfrom linebot import (\\n LineBotApi, WebhookHandler\\n)\\nfrom linebot.exceptions import (\\n InvalidSignatureError\\n)\\nfrom linebot.models import (\\n MessageEvent, TextMessage, TextSendMessage,\\n SourceUser, SourceGroup, SourceRoom,\\n TemplateSendMessage, ConfirmTemplate, MessageTemplateAction,\\n ButtonsTemplate, ImageCarouselTemplate, ImageCarouselColumn, URITemplateAction,\\n PostbackTemplateAction, DatetimePickerTemplateAction,\\n CarouselTemplate, CarouselColumn, PostbackEvent,\\n StickerMessage, StickerSendMessage, LocationMessage, LocationSendMessage,\\n ImageMessage, VideoMessage, AudioMessage, FileMessage,\\n UnfollowEvent, FollowEvent, JoinEvent, LeaveEvent, BeaconEvent,\\n ImageSendMessage\\n)\\n\\napp = Flask(__name__)\\n\\n# get channel_secret and channel_access_token from your environment variable\\n# channel_secret = os.getenv('LINE_CHANNEL_SECRET', None)\\n# channel_access_token = os.getenv('LINE_CHANNEL_ACCESS_TOKEN', None)\\n# if channel_secret is None:\\n# print('Specify LINE_CHANNEL_SECRET as environment variable.')\\n# sys.exit(1)\\n# if channel_access_token is None:\\n# print('Specify LINE_CHANNEL_ACCESS_TOKEN as environment variable.')\\n# sys.exit(1)\\n\\nline_bot_api = LineBotApi('yCLqDYJLIiRo5xSfnc/9tmKbtKL9oEcBhzG8Mczc0WH+8B+/Q9PT0ifkMT+frqRsgAKYqB1/CVhwx7vWPLKP1nyZszn6gGdqQ0H1CgfLUxFhX2Wgreq3cFZw1D1km/IXBljdJyY4fOw5kyvSQpcu6gdB04t89/1O/w1cDnyilFU=')\\nhandler = WebhookHandler('62826fb39d7f3cb6a27f8573bc5e9b29')\\n\\n#SEGALA INISIALISASI ADA DI SINI HEHEHE\\n\\n#INISIALISASI NAMA\\nnama_admin = 'faldi'\\nnama_tandu_1 = 'ilham'\\nnama_tandu_2 = 'sarah'\\nnama_obat_1 = 'kahfi'\\nnama_obat_2 = 'rany'\\nnama_tft_1 = 'ivannsa'\\nnama_tft_2 = 'fathin'\\nnama_humas_1 = 'atsila'\\nnama_humas_2 = 'benito'\\n\\n#INISIALISASI USER ID\\nuser_id_admin = 'U36bb6303be7a1563a7d27d0ee2234ea5' #faldi\\nuser_id_tandu_1 = 'Ua84e82b214cc4cf6783aa167d9ec3ce8' #ilham\\nuser_id_tandu_2 = 'tandu2' #sarah\\nuser_id_obat_1 = 'Ufa385bd386f3634b04f5ca57360b0a8a' #kahfi\\nuser_id_obat_2 = 'obat2' #rani\\nuser_id_tft_1 = 'Uc21abb39fe1b5c1ab376ba9b5209ccce' #ivansa\\nuser_id_tft_2 = 'tft2' #fathin ?????\\nuser_id_humas_1 = 'humas1' #atsila\\nuser_id_humas_2 = 'humas2' #benito ?????\\n\\n#RESOURCE - IMAGE\\nimgurl_tandu = 'https://raw.githubusercontent.com/rrifaldiu/line-bot-medik/master/static/res/jpg/tandu/tandu.jpg'\\nimgurl_obat = 'https://raw.githubusercontent.com/rrifaldiu/line-bot-medik/master/static/res/jpg/obat/obat.jpg'\\nimgurl_obat_base = 'https://raw.githubusercontent.com/rrifaldiu/line-bot-medik/master/static/res/jpg/obat/obat_base.jpg'\\nimgurl_obat_pj = 'https://raw.githubusercontent.com/rrifaldiu/line-bot-medik/master/static/res/jpg/obat/obat_pj.jpg'\\nimgurl_obat_satuan = 'https://raw.githubusercontent.com/rrifaldiu/line-bot-medik/master/static/res/jpg/obat/obat_pilih.jpg'\\nimgurl_tft = 'https://raw.githubusercontent.com/rrifaldiu/line-bot-medik/master/static/res/jpg/tft/tft.jpg'\\nimgurl_humas = 'https://raw.githubusercontent.com/rrifaldiu/line-bot-medik/master/static/res/jpg/humas/humas.jpg'\\nimgurl_dp = 'https://image.ibb.co/bJPgVR/240240.jpg'\\n\\nform_template = ('\\\\n' +\\n 'Nama : \\\\n' +\\n 'Jurusan : \\\\n' +\\n 'ID Line : \\\\n' +\\n 'Lembaga : \\\\n' +\\n 'Tujuan peminjaman : \\\\n' +\\n 'Tanggal peminjaman : \\\\n' +\\n 'Tanggal pengembalian : ')\\n\\nstatic_tmp_path = os.path.join(os.path.dirname(__file__), 'static', 'tmp')\\n\\n\\n# function for create tmp dir for download content\\ndef make_static_tmp_dir():\\n try:\\n os.makedirs(static_tmp_path)\\n except OSError as exc:\\n if exc.errno == errno.EEXIST and os.path.isdir(static_tmp_path):\\n pass\\n else:\\n raise\\n\\n\\n@app.route(\\\"/callback\\\", methods=['POST'])\\ndef callback():\\n # get X-Line-Signature header value\\n signature = request.headers['X-Line-Signature']\\n\\n # get request body as text\\n body = request.get_data(as_text=True)\\n app.logger.info(\\\"Request body: \\\" + body)\\n\\n # handle webhook body\\n try:\\n handler.handle(body, signature)\\n except InvalidSignatureError:\\n abort(400)\\n\\n return 'OK'\\n\\n\\n@handler.add(MessageEvent, message=TextMessage)\\ndef handle_text_message(event):\\n text = event.message.text\\n profile = line_bot_api.get_profile(event.source.user_id)\\n if text.lower() == 'menu':\\n image_carousel_template = ImageCarouselTemplate(columns=[\\n ImageCarouselColumn(image_url=imgurl_tandu,\\n action=PostbackTemplateAction(\\n label='Pinjam Tandu',\\n data='tandu')),\\n ImageCarouselColumn(image_url=imgurl_obat,\\n action=PostbackTemplateAction(\\n label='Pinjam Obat',\\n data='obat')),\\n ImageCarouselColumn(image_url=imgurl_tft,\\n action=PostbackTemplateAction(\\n label='TFT Medik',\\n data='tft')),\\n ImageCarouselColumn(image_url=imgurl_humas,\\n action=PostbackTemplateAction(\\n label='Hubungi Kami',\\n data='form_humas'))\\n ])\\n template_message = TemplateSendMessage(\\n alt_text='Silahkan pilih menu yang diinginkan', template=image_carousel_template)\\n line_bot_api.reply_message(\\n event.reply_token, [\\n StickerSendMessage(\\n package_id='3',\\n sticker_id='242'\\n ),\\n TextSendMessage(text='Halo! Selamat datang di OA Medik OSKM!\\\\n\\\\n Silahkan pilih menu di bawah ini'),\\n template_message\\n ]\\n )\\n\\n elif text.lower().startswith('balas-'):\\n profile = line_bot_api.get_profile(event.source.user_id)\\n if text.lower().startswith('balas-' + nama_admin):\\n line_bot_api.push_message(user_id_admin, [\\n TextSendMessage(text='Menerima balasan dari:\\\\nNama LINE: ' + profile.display_name + '\\\\nFoto: ' + profile.picture_url),\\n TextSendMessage(text=text[(6+len(nama_admin)) : ]),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\\\n<pesan Anda>')\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, \\n TextSendMessage(text='Pesan telah terkirim')\\n )\\n elif text.lower().startswith('balas-' + nama_tandu_1):\\n line_bot_api.multicast([user_id_admin, user_id_tandu_1], [\\n TextSendMessage(text=nama_tandu_1.title() + ' menerima balasan dari:\\\\nNama LINE: ' + profile.display_name + '\\\\nFoto: ' + profile.picture_url),\\n TextSendMessage(text=text[(6+len(nama_tandu_1)) : ]),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\\\n<pesan Anda>')\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, \\n TextSendMessage(text='Pesan telah terkirim')\\n )\\n # elif text.lower().startswith('balas-' + nama_tandu_2):\\n # line_bot_api.multicast([user_id_admin, user_id_tandu_2], [\\n # TextSendMessage(text=nama_tandu_2.title() + ' menerima balasan dari:\\\\nNama LINE: ' + profile.display_name + '\\\\nFoto: ' + profile.picture_url),\\n # TextSendMessage(text=text[(6+len(nama_tandu_2)) : ]),\\n # TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\\n # TextSendMessage(text='balas_admin-' + profile.user_id + ':\\\\n<pesan Anda>')\\n # ])\\n # line_bot_api.reply_message(\\n # event.reply_token, \\n # TextSendMessage(text='Pesan telah terkirim')\\n # )\\n elif text.lower().startswith('balas-' + nama_obat_1):\\n line_bot_api.multicast([user_id_admin, user_id_obat_1], [\\n TextSendMessage(text=nama_obat_1.title() + ' menerima balasan dari:\\\\nNama LINE: ' + profile.display_name + '\\\\nFoto: ' + profile.picture_url),\\n TextSendMessage(text=text[(6+len(nama_obat_1)) : ]),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\\\n<pesan Anda>')\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, \\n TextSendMessage(text='Pesan telah terkirim')\\n )\\n # elif text.lower().startswith('balas-' + nama_obat_2):\\n # line_bot_api.multicast([user_id_admin, user_id_obat_2], [\\n # TextSendMessage(text=nama_obat_2.title() + ' menerima balasan dari:\\\\nNama LINE: ' + profile.display_name + '\\\\nFoto: ' + profile.picture_url),\\n # TextSendMessage(text=text[(6+len(nama_obat_2)) : ]),\\n # TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\\n # TextSendMessage(text='balas_admin-' + profile.user_id + ':\\\\n<pesan Anda>')\\n # ])\\n # line_bot_api.reply_message(\\n # event.reply_token, \\n # TextSendMessage(text='Pesan telah terkirim')\\n # )\\n elif text.lower().startswith('balas-' + nama_tft_1):\\n line_bot_api.multicast([user_id_admin, user_id_tft_1], [\\n TextSendMessage(text=nama_tft_1.title() + ' menerima balasan dari:\\\\nNama LINE: ' + profile.display_name + '\\\\nFoto: ' + profile.picture_url),\\n TextSendMessage(text=text[(6+len(nama_tft_1)) : ]),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\\\n<pesan Anda>')\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, \\n TextSendMessage(text='Pesan telah terkirim')\\n )\\n # elif text.lower().startswith('balas-' + nama_tft_2):\\n # line_bot_api.multicast([user_id_admin, user_id_tft_2], [\\n # TextSendMessage(text=nama_tft_2.title() + ' menerima balasan dari:\\\\nNama LINE: ' + profile.display_name + '\\\\nFoto: ' + profile.picture_url),\\n # TextSendMessage(text=text[(6+len(nama_tft_2)) : ]),\\n # TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\\n # TextSendMessage(text='balas_admin-' + profile.user_id + ':\\\\n<pesan Anda>')\\n # ])\\n # line_bot_api.reply_message(\\n # event.reply_token, \\n # TextSendMessage(text='Pesan telah terkirim')\\n # )\\n # elif text.lower().startswith('balas-' + nama_humas_1):\\n # line_bot_api.multicast([user_id_admin, user_id_humas_1], [\\n # TextSendMessage(text=nama_humas_1.title() + ' menerima balasan dari:\\\\nNama LINE: ' + profile.display_name + '\\\\nFoto: ' + profile.picture_url),\\n # TextSendMessage(text=text[(6+len(nama_humas_1)) : ]),\\n # TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\\n # TextSendMessage(text='balas_admin-' + profile.user_id + ':\\\\n<pesan Anda>')\\n # ])\\n # line_bot_api.reply_message(\\n # event.reply_token, \\n # TextSendMessage(text='Pesan telah terkirim')\\n # )\\n # elif text.lower().startswith('balas-' + nama_humas_2):\\n # line_bot_api.multicast([user_id_admin, user_id_humas_2], [\\n # TextSendMessage(text=nama_humas_2.title() + ' menerima balasan dari:\\\\nNama LINE: ' + profile.display_name + '\\\\nFoto: ' + profile.picture_url),\\n # TextSendMessage(text=text[(6+len(nama_humas_2)) : ]),\\n # TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\\n # TextSendMessage(text='balas_admin-' + profile.user_id + ':\\\\n<pesan Anda>')\\n # ])\\n # line_bot_api.reply_message(\\n # event.reply_token, \\n # TextSendMessage(text='Pesan telah terkirim')\\n # )\\n else:\\n line_bot_api.reply_message(\\n event.reply_token, \\n TextSendMessage(text='Nama admin tidak ditemukan\\\\nPastikan nama admin tidak dituliskan dengan kapital')\\n )\\n\\n elif text.lower().startswith('balas_admin-'):\\n user_id_balas = text[(text.find('-') + 1) : (text.find(':'))]\\n balasan = text[(text.find(':')+2) : ]\\n if (event.source.user_id == user_id_admin):\\n line_bot_api.push_message(user_id_balas, [\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_admin.title()),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\\\u0027balas-' + nama_admin +'\\\\u0027 diikuti dengan pesan Anda'),\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, \\n TextSendMessage(text='Pesan telah terkirim')\\n )\\n elif (event.source.user_id == user_id_tandu_1):\\n line_bot_api.push_message(user_id_balas, [\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_tandu_1.title()),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\\\u0027balas-' + nama_tandu_1 +'\\\\u0027 diikuti dengan pesan Anda'),\\n ])\\n if (user_id_admin != user_id_balas):\\n line_bot_api.push_message(user_id_admin, [\\n TextSendMessage(text=nama_tandu_1 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_tandu_1.title())\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, \\n TextSendMessage(text='Pesan telah terkirim')\\n )\\n elif (event.source.user_id == user_id_tandu_2):\\n line_bot_api.push_message(user_id_balas, [\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_tandu_2.title()),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\\\u0027balas-' + nama_tandu_2 +'\\\\u0027 diikuti dengan pesan Anda'),\\n ])\\n if (user_id_admin != user_id_balas):\\n line_bot_api.push_message(user_id_admin, [\\n TextSendMessage(text=nama_tandu_2 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_tandu_2.title())\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, \\n TextSendMessage(text='Pesan telah terkirim')\\n )\\n elif (event.source.user_id == user_id_obat_1):\\n line_bot_api.push_message(user_id_balas, [\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_obat_1.title()),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\\\u0027balas-' + nama_obat_1 +'\\\\u0027 diikuti dengan pesan Anda'),\\n ])\\n if (user_id_admin != user_id_balas):\\n line_bot_api.push_message(user_id_admin, [\\n TextSendMessage(text=nama_obat_1 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_obat_1.title())\\n ])\\n \\n\\n line_bot_api.reply_message(\\n event.reply_token, \\n TextSendMessage(text='Pesan telah terkirim')\\n )\\n elif (event.source.user_id == user_id_obat_2):\\n line_bot_api.push_message(user_id_balas, [\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_obat_2.title()),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\\\u0027balas-' + nama_obat_2 +'\\\\u0027 diikuti dengan pesan Anda'),\\n ])\\n if (user_id_admin != user_id_balas):\\n line_bot_api.push_message(user_id_admin, [\\n TextSendMessage(text=nama_obat_2 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_obat_2.title())\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, \\n TextSendMessage(text='Pesan telah terkirim')\\n )\\n elif (event.source.user_id == user_id_tft_1):\\n line_bot_api.push_message(user_id_balas, [\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_tft_1.title()),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\\\u0027balas-' + nama_tft_1 +'\\\\u0027 diikuti dengan pesan Anda'),\\n ])\\n if (user_id_admin != user_id_balas):\\n line_bot_api.push_message(user_id_admin, [\\n TextSendMessage(text=nama_tft_1 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_tft_1.title())\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, \\n TextSendMessage(text='Pesan telah terkirim')\\n )\\n elif (event.source.user_id == user_id_tft_2):\\n line_bot_api.push_message(user_id_balas, [\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_tft_2.title()),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\\\u0027balas-' + nama_tft_2 +'\\\\u0027 diikuti dengan pesan Anda'),\\n ])\\n if (user_id_admin != user_id_balas):\\n line_bot_api.push_message(user_id_admin, [\\n TextSendMessage(text=nama_tft_2 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_tft_2.title())\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, \\n TextSendMessage(text='Pesan telah terkirim')\\n )\\n elif (event.source.user_id == user_id_humas_1):\\n line_bot_api.push_message(user_id_balas, [\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_humas_1.title()),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\\\u0027balas-' + nama_humas_1 +'\\\\u0027 diikuti dengan pesan Anda'),\\n ])\\n if (user_id_admin != user_id_balas):\\n line_bot_api.push_message(user_id_admin, [\\n TextSendMessage(text=nama_humas_1 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_humas_1.title())\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, \\n TextSendMessage(text='Pesan telah terkirim')\\n )\\n elif (event.source.user_id == user_id_humas_2):\\n line_bot_api.push_message(user_id_balas, [\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_humas_2.title()),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan ketik \\\\u0027balas-' + nama_humas_2 +'\\\\u0027 diikuti dengan pesan Anda'),\\n ])\\n if (user_id_admin != user_id_balas):\\n line_bot_api.push_message(user_id_admin, [\\n TextSendMessage(text=nama_humas_2 + ' membalas untuk ' + profile.display_name + ' (User ID = ' + profile.user_id + ')'),\\n TextSendMessage(text=balasan + '\\\\n\\\\nSalam, ' + nama_humas_2.title())\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, \\n TextSendMessage(text='Pesan telah terkirim')\\n )\\n else:\\n line_bot_api.reply_message(\\n event.reply_token, \\n TextSendMessage(text='Anda bukan admin')\\n )\\n\\n elif text.startswith('[Form Peminjaman Tandu]'):\\n line_bot_api.multicast([user_id_admin\\n #, user_id_tandu_1, user_id_tandu_2\\n ], [\\n TextSendMessage(\\n text=text\\n ),\\n TextSendMessage(\\n text='Nama LINE: ' + profile.display_name + '\\\\nFoto: ' + profile.picture_url\\n ),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\\\n<pesan Anda>')\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, TextSendMessage(\\n text='Form telah dikirimkan\\\\n\\\\nKami akan memproses secepatnya. Harap bersabar dan menunggu'\\n )\\n )\\n elif text.startswith('[Form Peminjaman Obat '):\\n line_bot_api.multicast([user_id_admin\\n , user_id_obat_1\\n #, user_id_obat_2\\n ], [\\n TextSendMessage(\\n text=text\\n ),\\n TextSendMessage(\\n text='Nama LINE: ' + profile.display_name + '\\\\nFoto: ' + profile.picture_url\\n ),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\\\n<pesan Anda>')\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, TextSendMessage(\\n text='Form telah dikirimkan\\\\n\\\\nKami akan memproses secepatnya. Harap bersabar dan menunggu'\\n )\\n )\\n elif text.startswith('[Form TFT Medik]'):\\n line_bot_api.multicast([user_id_admin\\n , user_id_tft_1\\n #, user_id_tft_2\\n ], [\\n TextSendMessage(\\n text=text\\n ),\\n TextSendMessage(\\n text='Nama LINE: ' + profile.display_name + '\\\\nFoto: ' + profile.picture_url\\n ),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\\\n<pesan Anda>')\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, TextSendMessage(\\n text='Form telah dikirimkan\\\\n\\\\nKami akan memproses secepatnya. Harap bersabar dan menunggu'\\n )\\n )\\n elif text.startswith('[Form Kontak]'):\\n line_bot_api.multicast([user_id_admin\\n #, user_id_humas_1, user_id_humas_2\\n ], [\\n TextSendMessage(\\n text=text\\n ),\\n TextSendMessage(\\n text='Nama LINE: ' + profile.display_name + '\\\\nFoto: ' + profile.picture_url\\n ),\\n TextSendMessage(text='Untuk membalas pesan di atas, silahkan gunakan format di bawah:'),\\n TextSendMessage(text='balas_admin-' + profile.user_id + ':\\\\n<pesan Anda>')\\n ])\\n line_bot_api.reply_message(\\n event.reply_token, TextSendMessage(\\n text='Form telah dikirimkan\\\\n\\\\nKami akan memproses secepatnya. Harap bersabar dan menunggu'\\n )\\n )\\n elif text.lower() == 'profile':\\n if isinstance(event.source, SourceUser):\\n line_bot_api.reply_message(\\n event.reply_token, [\\n TextSendMessage(\\n text='Display name: ' + profile.display_name\\n ),\\n TextSendMessage(\\n text='Status message: ' + profile.status_message\\n ),\\n TextSendMessage(\\n text='User ID: ' + event.source.user_id\\n )\\n ]\\n )\\n line_bot_api.push_message(user_id_admin, [\\n TextSendMessage(\\n text='Display name: ' + profile.display_name\\n ),\\n TextSendMessage(\\n text='Status message: ' + profile.status_message\\n ),\\n TextSendMessage(\\n text='User ID: ' + event.source.user_id\\n )\\n ])\\n else:\\n line_bot_api.reply_message(\\n event.reply_token,\\n TextMessage(text=\\\"Bot can't use profile API without user ID\\\"))\\n \\n else:\\n line_bot_api.reply_message(\\n event.reply_token, TextSendMessage(text='Maaf, perintah yang Anda masukkan salah. Ketik \\\"menu\\\" untuk melihat menu medik'))\\n\\n\\n@handler.add(MessageEvent, message=LocationMessage)\\ndef handle_location_message(event):\\n line_bot_api.reply_message(\\n event.reply_token,\\n LocationSendMessage(\\n title=event.message.title, address=event.message.address,\\n latitude=event.message.latitude, longitude=event.message.longitude\\n )\\n )\\n\\n\\n@handler.add(MessageEvent, message=StickerMessage)\\ndef handle_sticker_message(event):\\n line_bot_api.reply_message(\\n event.reply_token,\\n StickerSendMessage(\\n package_id=event.message.package_id,\\n sticker_id=event.message.sticker_id)\\n )\\n\\n\\n# Other Message Type\\n@handler.add(MessageEvent, message=(ImageMessage, VideoMessage, AudioMessage))\\ndef handle_content_message(event):\\n if isinstance(event.message, ImageMessage):\\n ext = 'jpg'\\n elif isinstance(event.message, VideoMessage):\\n ext = 'mp4'\\n elif isinstance(event.message, AudioMessage):\\n ext = 'm4a'\\n else:\\n return\\n\\n message_content = line_bot_api.get_message_content(event.message.id)\\n with tempfile.NamedTemporaryFile(dir=static_tmp_path, prefix=ext + '-', delete=False) as tf:\\n for chunk in message_content.iter_content():\\n tf.write(chunk)\\n tempfile_path = tf.name\\n\\n dist_path = tempfile_path + '.' + ext\\n dist_name = os.path.basename(dist_path)\\n os.rename(tempfile_path, dist_path)\\n\\n line_bot_api.reply_message(\\n event.reply_token, [\\n TextSendMessage(text='Save content.'),\\n TextSendMessage(text=request.host_url + os.path.join('static', 'tmp', dist_name))\\n ])\\n\\n\\n@handler.add(MessageEvent, message=FileMessage)\\ndef handle_file_message(event):\\n message_content = line_bot_api.get_message_content(event.message.id)\\n with tempfile.NamedTemporaryFile(dir=static_tmp_path, prefix='file-', delete=False) as tf:\\n for chunk in message_content.iter_content():\\n tf.write(chunk)\\n tempfile_path = tf.name\\n\\n dist_path = tempfile_path + '-' + event.message.file_name\\n dist_name = os.path.basename(dist_path)\\n os.rename(tempfile_path, dist_path)\\n\\n line_bot_api.reply_message(\\n event.reply_token, [\\n TextSendMessage(text='Save file.'),\\n TextSendMessage(text=request.host_url + os.path.join('static', 'tmp', dist_name))\\n ])\\n\\n\\n@handler.add(FollowEvent)\\ndef handle_follow(event):\\n line_bot_api.reply_message(\\n event.reply_token, TextSendMessage(text='Got follow event'))\\n\\n\\n@handler.add(UnfollowEvent)\\ndef handle_unfollow():\\n app.logger.info(\\\"Got Unfollow event\\\")\\n\\n\\n@handler.add(JoinEvent)\\ndef handle_join(event):\\n line_bot_api.reply_message(\\n event.reply_token,\\n TextSendMessage(text='Joined this ' + event.source.type))\\n\\n\\n@handler.add(LeaveEvent)\\ndef handle_leave():\\n app.logger.info(\\\"Got leave event\\\")\\n\\n@handler.add(PostbackEvent)\\ndef handle_postback(event):\\n if event.postback.data == 'tandu':\\n buttons_template = ButtonsTemplate(\\n title='Pastikan Anda telah memahami SOP di atas', text='Klik tombol di bawah untuk melanjutkan', actions= [\\n PostbackTemplateAction(\\n label='Oke', data='form_tandu')\\n ]\\n )\\n template_message = TemplateSendMessage(\\n alt_text='[SOP Peminjaman Tandu]', template=buttons_template)\\n line_bot_api.reply_message(\\n event.reply_token, [TextSendMessage(text='[SOP Peminjaman Tandu]\\\\n' +\\n '\\\\n' +\\n '1. Peminjaman dilakukan dengan terlebih dahulu menghubungi OA line Medik 2017 lalu mengikuti format yang telah disediakan.\\\\n' +\\n '\\\\n' +\\n '2. Peminjam lalu akan dihubungi untuk konfirmasi peminjaman jika peminjaman dapat dilakukan.\\\\n' +\\n '\\\\n' +\\n '3. Peminjam dan perwakilan dari medik kemudian akan bertemu untuk pengambilan tandu.\\\\n' +\\n 'Untuk pengembalian, peminjam dan perwakilan medik akan bertemu kembali untuk penyerahan tandu.\\\\n' +\\n '\\\\n' +\\n '4. Setiap tandu dapat dipinjam oleh massa kampus selama tandu masih tersedia.\\\\n' +\\n '\\\\n' +\\n '5. Untuk peminjaman, peminjam diminta untuk menitipkan KTM sebagai jaminan.\\\\n' +\\n '\\\\n' +\\n '6. Peminjaman tandu gratis, tidak dipungut biaya.\\\\n' +\\n '\\\\n' +\\n '7. Peminjam wajib melakukan penggantian apabila tandu hilang, atau terjadi kerusakan pada tandu yang bukan diakibatkan oleh penggunaan.\\\\n' +\\n '\\\\n' +\\n '8. Peminjam diminta untuk mencuci mitela yang terdapat pada tandu apabila kotor setelah penggunaan.\\\\n' +\\n '\\\\n' +\\n '9. Selama durasi peminjaman, peminjam melakukan sendiri pengencangan dan perawatan terhadap tandu. Saat peminjaman, peminjam dapat mengutus perwakilan untuk diberi pengarahan tentang cara pengencangan dan perawatan tandu.'\\n ),\\n template_message\\n ])\\n elif event.postback.data == 'obat':\\n buttons_template = ButtonsTemplate(\\n title='Pastikan Anda telah memahami SOP di atas', text='Klik tombol di bawah untuk melanjutkan', actions= [\\n PostbackTemplateAction(\\n label='Oke', data='pilih_obat')\\n ]\\n )\\n template_message = TemplateSendMessage(\\n alt_text='[SOP Peminjaman Obat]', template=buttons_template)\\n line_bot_api.reply_message(\\n event.reply_token, [TextSendMessage(text='[SOP Peminjaman Obat]\\\\n' +\\n '\\\\n' +\\n '1. Menghubungi OA Medik OSKM 2017 maksimal 3 hari sebelum peminjaman.\\\\n' +\\n '\\\\n' +\\n '2. Memperkenalkan diri serta menyampaikan tujuan peminjaman obat.\\\\n' +\\n '\\\\n' +\\n '3. Menyebutkan obat apa saja yang akan dipinjam.\\\\n' +\\n '\\\\n' +\\n '4. Peminjam menentukan lama waktu peminjaman obat. Peminjaman obat maksimal 14 hari dan jika terjadi keterlambatan dalam pengembalian obat, peminjam akan dikenakan sanksi yang akan diberitahukan lebih lanjut.(jika peminjam ingin meminjam lebih dari 14 hari dapat memperpanjang dengan menghubungi ke OA Medik)\\\\n' +\\n '\\\\n' +\\n '5. Peminjam memberikan jaminan peminjaman berupa KTM/KTP.\\\\n' +\\n '\\\\n' +\\n '6. Peminjam diwajibkan untuk menuliskan data penggunaan obat dengan format yang sudah ditentukan oleh Medik.\\\\n' +\\n '\\\\n' +\\n '7. Pengembalian obat dilakukan dengan menghubungi OA Medik sesuai dengan waktu yang telah ditentukan diawal peminjaman.\\\\n' +\\n '\\\\n' +\\n '8. Apabila obat yang dikembalikan rusak ataupun hilang, peminjam diharuskan mengganti obat tersebut'\\n ),\\n TextSendMessage(text='9. Apabila obat yang dikembalikan habis dikarenakan pemakaian, kemasan obat tersebut tetap harus disertakan saat pengembalian obat. Apabila kemasan hilang, peminjam dianggap menghilangkan obat.'), \\n template_message\\n ])\\n elif event.postback.data == 'pilih_obat':\\n carousel_template = CarouselTemplate(columns=[\\n CarouselColumn(title='Obat Base', text='Obat yang digunakan di Base saat OSKM',\\n thumbnail_image_url=imgurl_obat_base, actions=[\\n PostbackTemplateAction(label='List Obat', data='list_obat_base'),\\n PostbackTemplateAction(label='Pinjam', data='form_obat_base')\\n ]),\\n CarouselColumn(title='Obat PJ Obat', text='Obat yang dibawa oleh PJ Obat saat OSKM',\\n thumbnail_image_url=imgurl_obat_pj, actions=[\\n PostbackTemplateAction(label='List Obat', data='list_obat_pj'),\\n PostbackTemplateAction(label='Pinjam', data='form_obat_pj')\\n ]),\\n CarouselColumn(title='Obat Satuan', text='Pilih obat-obatan tertentu yang Anda butuhkan',\\n thumbnail_image_url=imgurl_obat_satuan, actions=[\\n PostbackTemplateAction(label='List Obat', data='list_obat_satuan'),\\n PostbackTemplateAction(label='Pinjam', data='form_obat_satuan')\\n ])\\n ])\\n template_message = TemplateSendMessage(\\n alt_text='Silahkan pilih jenis obat', template=carousel_template)\\n line_bot_api.reply_message(\\n event.reply_token, [TextSendMessage(text='Silahkan pilih jenis obat yang ingin Anda pinjam\\\\n'),\\n template_message\\n ]\\n )\\n elif event.postback.data == 'list_obat_base':\\n buttons_template = ButtonsTemplate(\\n text='Kembali ke pemilihan obat', actions= [\\n PostbackTemplateAction(\\n label='Kembali', data='pilih_obat')\\n ]\\n )\\n template_message = TemplateSendMessage(\\n alt_text='Kembali ke pemilihan obat', template=buttons_template)\\n line_bot_api.reply_message(\\n event.reply_token, [TextSendMessage(text='[List Obat Base]\\\\n' +\\n '\\\\n' +\\n '1. Bioplacenton\\\\n' +\\n '2. Thrombophob\\\\n' +\\n '3. Counterpain Patch Hot\\\\n' +\\n '4. Counterpain Cool\\\\n' +\\n '5. Ethyl Chloride\\\\n' +\\n '6. Rivanol\\\\n' +\\n '7. Oxycan\\\\n' +\\n '8. Imboost\\\\n' +\\n '9. Sangobion\\\\n' +\\n '10. Mylanta Cair\\\\n' +\\n '11. Ranitidin Cair\\\\n' +\\n '12. Fludane\\\\n' +\\n '13. Spasminal\\\\n' +\\n '14. Parasetamol\\\\n' +\\n '15. Feminax\\\\n' +\\n '16. Ibuprofen\\\\n' +\\n '17. Oralit\\\\n' +\\n '18. Sanadryl\\\\n' +\\n '19. Komix\\\\n' +\\n '20. Salbutamol Syrup\\\\n' +\\n '\\\\n' +\\n 'Perlu diingat bahwa obat-obat tersebut mungkin tidak tersedia karena habis atau sedang dipinjam. Info lebih lanjut akan dihubungi setelah pengisian form peminjaman.'\\n ),\\n template_message\\n ])\\n elif event.postback.data == 'list_obat_pj':\\n buttons_template = ButtonsTemplate(\\n text='Kembali ke pemilihan obat', actions= [\\n PostbackTemplateAction(\\n label='Kembali', data='pilih_obat')\\n ]\\n )\\n template_message = TemplateSendMessage(\\n alt_text='Kembali ke pemilihan obat', template=buttons_template)\\n line_bot_api.reply_message(\\n event.reply_token, [TextSendMessage(text='[List Obat PJ Obat]\\\\n' +\\n '\\\\n' +\\n '1. Bioplacenton\\\\n' +\\n '2. Thrombophob\\\\n' +\\n '3. Counterpain\\\\n' +\\n '4. Ethyl Chloride\\\\n' +\\n '5. Rivanol\\\\n' +\\n '6. Oxycan\\\\n' +\\n '7. Sangobion\\\\n' +\\n '8. Mylanta Cair\\\\n' +\\n '9. Ranitidin Cair\\\\n' +\\n '10. Komix\\\\n' +\\n '\\\\n' +\\n 'Perlu diingat bahwa obat-obat tersebut mungkin tidak tersedia karena habis atau sedang dipinjam. Info lebih lanjut akan dihubungi setelah pengisian form peminjaman.'\\n ),\\n template_message\\n ])\\n elif event.postback.data == 'list_obat_satuan':\\n buttons_template = ButtonsTemplate(\\n text='Kembali ke pemilihan obat', actions= [\\n PostbackTemplateAction(\\n label='Kembali', data='pilih_obat')\\n ]\\n )\\n template_message = TemplateSendMessage(\\n alt_text='Kembali ke pemilihan obat', template=buttons_template)\\n line_bot_api.reply_message(\\n event.reply_token, [TextSendMessage(text='[List Obat Satuan]\\\\n' +\\n '\\\\n' +\\n '1. Bioplacenton\\\\n' +\\n '2. Thrombophob\\\\n' +\\n '3. Counterpain Patch Hot\\\\n' +\\n '4. Counterpain Cool\\\\n' +\\n '5. Ethyl Chloride\\\\n' +\\n '6. Rivanol\\\\n' +\\n '7. Oxycan\\\\n' +\\n '8. Imboost\\\\n' +\\n '9. Sangobion\\\\n' +\\n '10. Mylanta Cair\\\\n' +\\n '11. Ranitidin Cair\\\\n' +\\n '12. Fludane\\\\n' +\\n '13. Spasminal\\\\n' +\\n '14. Parasetamol\\\\n' +\\n '15. Feminax\\\\n' +\\n '16. Ibuprofen\\\\n' +\\n '17. Oralit\\\\n' +\\n '18. Sanadryl\\\\n' +\\n '19. Komix\\\\n' +\\n '20. Salbutamol Syrup\\\\n' +\\n '\\\\n' +\\n 'Perlu diingat bahwa obat-obat tersebut mungkin tidak tersedia karena habis atau sedang dipinjam. Info lebih lanjut akan dihubungi setelah pengisian form peminjaman.'\\n ),\\n template_message\\n ])\\n elif event.postback.data == 'tft':\\n buttons_template = ButtonsTemplate(\\n title='Pastikan Anda telah memahami SOP di atas', text='Klik tombol di bawah untuk melanjutkan', actions= [\\n PostbackTemplateAction(\\n label='Oke', data='form_tft')\\n ]\\n )\\n template_message = TemplateSendMessage(\\n alt_text='[SOP TFT Medik]', template=buttons_template)\\n line_bot_api.reply_message(\\n event.reply_token, [TextSendMessage(text='[SOP TFT Medik]\\\\n' +\\n '\\\\n' +\\n '1. Maksimal permintaan TFT medik adalah H-5 dari hari pemberian TFT medik.\\\\n' +\\n '\\\\n' +\\n '2. Mengisi form yang telah disediakan.'\\n ),\\n template_message\\n ])\\n elif event.postback.data == 'form_tandu':\\n line_bot_api.reply_message(\\n event.reply_token, [TextSendMessage(text= '[Form Peminjaman Tandu]\\\\n' + form_template\\n ),\\n TextSendMessage(text= 'Mohon isi form di atas dan pastikan form yang Anda isikan sudah benar\\\\n' +\\n 'Form yang dikirim akan langsung dimasukkan ke dalam sistem'\\n )\\n ])\\n elif event.postback.data == 'form_obat_base':\\n line_bot_api.reply_message(\\n event.reply_token, [TextSendMessage(text= '[Form Peminjaman Obat Base]\\\\n' + form_template\\n ),\\n TextSendMessage(text= 'Mohon isi form di atas dan pastikan form yang Anda isikan sudah benar\\\\n' +\\n 'Form yang dikirim akan langsung dimasukkan ke dalam sistem'\\n )\\n ])\\n elif event.postback.data == 'form_obat_pj':\\n line_bot_api.reply_message(\\n event.reply_token, [TextSendMessage(text= '[Form Peminjaman Obat PJ Obat]\\\\n' + form_template\\n ),\\n TextSendMessage(text= 'Mohon isi form di atas dan pastikan form yang Anda isikan sudah benar\\\\n' +\\n 'Form yang dikirim akan langsung dimasukkan ke dalam sistem'\\n )\\n ])\\n elif event.postback.data == 'form_obat_satuan':\\n line_bot_api.reply_message(\\n event.reply_token, [TextSendMessage(text= '[Form Peminjaman Obat Satuan]\\\\n' + form_template + '\\\\nObat yang ingin dipinjam : '\\n ),\\n TextSendMessage(text= 'Mohon isi form di atas dan pastikan form yang Anda isikan sudah benar\\\\n' +\\n 'Form yang dikirim akan langsung dimasukkan ke dalam sistem'\\n )\\n ])\\n elif event.postback.data == 'form_tft':\\n line_bot_api.reply_message(\\n event.reply_token, [TextSendMessage(text= '[Form TFT Medik]\\\\n' +\\n '\\\\n' +\\n 'Nama : \\\\n' +\\n 'Jurusan : \\\\n' +\\n 'ID Line : \\\\n' +\\n 'Lembaga : \\\\n' +\\n 'Gambaran keberjalanan acara : \\\\n' +\\n 'List obat yang telah disediakan : \\\\n' +\\n 'Sasaran peserta TFT : \\\\n' +\\n 'Estimasi jumlah peserta TFT : \\\\n' +\\n 'Alokasi waktu TFT : \\\\n' +\\n 'Sarana dan prasarana yang telah disediakan : \\\\n' +\\n 'Lokasi TFT : '\\n ),\\n TextSendMessage(text= 'Mohon isi form di atas dan pastikan form yang Anda isikan sudah benar\\\\n' +\\n 'Form yang dikirim akan langsung dimasukkan ke dalam sistem'\\n )\\n ])\\n elif event.postback.data == 'form_humas':\\n line_bot_api.reply_message(\\n event.reply_token, [TextSendMessage(text= '[Form Kontak]\\\\n' +\\n '\\\\n' +\\n 'Nama : \\\\n' +\\n 'Jurusan : \\\\n' +\\n 'ID Line : \\\\n' +\\n 'Pesan : '\\n ),\\n TextSendMessage(text= 'Mohon isi form di atas dan pastikan form yang Anda isikan sudah benar\\\\n' +\\n 'Form yang dikirim akan langsung dimasukkan ke dalam sistem'\\n )\\n ])\\n elif event.postback.data == 'ping':\\n line_bot_api.reply_message(\\n event.reply_token, [TextSendMessage(text='pong'),TextSendMessage(text='pong2')])\\n elif event.postback.data == 'datetime_postback':\\n line_bot_api.reply_message(\\n event.reply_token, TextSendMessage(text=event.postback.params['datetime']))\\n elif event.postback.data == 'date_postback':\\n line_bot_api.reply_message(\\n event.reply_token, TextSendMessage(text=event.postback.params['date']))\\n elif event.postback.data == 'time_postback':\\n line_bot_api.reply_message(\\n event.reply_token, TextSendMessage(text=event.postback.params['time']))\\n\\n\\n@handler.add(BeaconEvent)\\ndef handle_beacon(event):\\n line_bot_api.reply_message(\\n event.reply_token,\\n TextSendMessage(\\n text='Got beacon event. hwid={}, device_message(hex string)={}'.format(\\n event.beacon.hwid, event.beacon.dm)))\\n\\n\\nif __name__ == \\\"__main__\\\":\\n arg_parser = ArgumentParser(\\n usage='Usage: python ' + __file__ + ' [--port <port>] [--help]'\\n )\\n arg_parser.add_argument('-p', '--port', default=8000, help='port')\\n arg_parser.add_argument('-d', '--debug', default=False, help='debug')\\n options = arg_parser.parse_args()\\n\\n # create tmp dir for download content\\n make_static_tmp_dir()\\n\\n app.run(debug=options.debug, port=options.port)\\n\"\n },\n {\n \"alpha_fraction\": 0.505464494228363,\n \"alphanum_fraction\": 0.7131147384643555,\n \"avg_line_length\": 14.913043022155762,\n \"blob_id\": \"a127fc3648c90103e3770347b52e81a416635751\",\n \"content_id\": \"d31bd957c8504c71d3fe04f1a2160e0819bfddda\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 366,\n \"license_type\": \"no_license\",\n \"max_line_length\": 26,\n \"num_lines\": 23,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"rrifaldiu/line-bot-medik\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"line-bot-sdk\\naltgraph==0.14\\nclick==6.7\\nCython==0.27.3\\nflask==0.12.2\\nfuture==0.16.0\\nglfw==1.4.0\\ngunicorn==19.7.1\\nitsdangerous==0.24\\nJinja2==2.10\\nmacholib==1.8\\nMarkupSafe==1.0\\nnumpy==1.13.3\\nolefile==0.44\\npefile==2017.11.5\\nPillow==4.3.0\\nPyDispatcher==2.0.5\\nPyInstaller==3.3\\nPyOpenGL==3.1.0\\nPyOpenGL-accelerate==3.1.0\\nPyVRML97==2.3.0b1\\nvirtualenv==15.1.0\\nWerkzeug==0.13\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2983071,"cells":{"repo_name":{"kind":"string","value":"michjk/Question_Classifier_Pytorch"},"repo_url":{"kind":"string","value":"https://github.com/michjk/Question_Classifier_Pytorch"},"snapshot_id":{"kind":"string","value":"fd6930316acdb29189f0e29e9666802d9f669ef6"},"revision_id":{"kind":"string","value":"dead77242f069f37c04e2013e526130d8cdc93c3"},"directory_id":{"kind":"string","value":"ab9bf463f061780d76d813e74a072dde299787d0"},"branch_name":{"kind":"string","value":"refs/heads/refactor"},"visit_date":{"kind":"timestamp","value":"2023-02-23T22:02:40.101352","string":"2023-02-23T22:02:40.101352"},"revision_date":{"kind":"timestamp","value":"2022-05-26T05:48:19","string":"2022-05-26T05:48:19"},"committer_date":{"kind":"timestamp","value":"2022-05-26T05:48:19","string":"2022-05-26T05:48:19"},"github_id":{"kind":"number","value":116722798,"string":"116,722,798"},"star_events_count":{"kind":"number","value":4,"string":"4"},"fork_events_count":{"kind":"number","value":2,"string":"2"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"timestamp","value":"2018-01-08T20:10:55","string":"2018-01-08T20:10:55"},"gha_updated_at":{"kind":"timestamp","value":"2022-05-26T05:48:23","string":"2022-05-26T05:48:23"},"gha_pushed_at":{"kind":"timestamp","value":"2023-02-15T18:35:34","string":"2023-02-15T18:35:34"},"gha_language":{"kind":"string","value":"Python"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6177293658256531,"alphanum_fraction":0.6194401383399963,"avg_line_length":38.19512176513672,"blob_id":"562d448bdc6fe29933c6d7ef9fc6d6d7eba6164b","content_id":"711d570fe04419d9bfe6d8543c8876bc682da7de","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6430,"license_type":"no_license","max_line_length":221,"num_lines":164,"path":"/data_module/data_writer.py","repo_name":"michjk/Question_Classifier_Pytorch","src_encoding":"UTF-8","text":"import os\nimport shutil\n\nimport torch\n\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport numpy as np\nimport pandas as pd\nfrom pandas_ml import ConfusionMatrix\n\nimport sklearn\n\nfrom tensorboard_logger import Logger\n\nimport dill as pickle\n\nclass PreprocessingPipelineWriter:\n '''\n Class for saving data.Field for production.\n\n Inputs:\n - result_folder_path (string): folder for saving data\n - saved_text_pipeline_file_path: file path relative to result_folder_path for saving data.Field for text\n - saved_label_pipeline_file_path: file path relative to result_folder_path for saving data.Field for label\n '''\n def __init__(self, result_folder_path = \"runs\", saved_text_pipeline_file_path = \"text_pipeline.pkl\", saved_label_pipeline_file_path = \"label_pipeline.pkl\"):\n self.result_folder_path = result_folder_path\n self.saved_text_pipeline_file_path = os.path.join(result_folder_path, saved_text_pipeline_file_path)\n self.saved_label_pipeline_file_path = os.path.join(result_folder_path, saved_label_pipeline_file_path)\n \n def save_pipeline(self, data_pipeline, label=False):\n '''\n Save data.Field\n\n Inputs:\n - data_piepline: data.Field to be saved\n '''\n os.makedirs(self.result_folder_path, exist_ok=True)\n if not label:\n dirname = os.path.dirname(self.saved_text_pipeline_file_path)\n os.makedirs(dirname, exist_ok=True)\n pickle.dump(data_pipeline, open(self.saved_text_pipeline_file_path, 'wb'))\n else:\n dirname = os.path.dirname(self.saved_label_pipeline_file_path)\n os.makedirs(dirname, exist_ok=True)\n pickle.dump(data_pipeline, open(self.saved_label_pipeline_file_path, 'wb')) \n \nclass PlotWriter:\n '''\n Save plot that can be openned by tensorboard\n\n Inputs:\n path (string): path to the directory for saving plot\n '''\n def __init__(self, path):\n self.plot_instance = Logger(path)\n \n def log_value(self, name, value, step):\n '''\n write plot file\n \n Inputs:\n - name (string): label of plot\n - value (double): value to be written (y-axis)\n - step (double): epoch (x-axis)\n '''\n\n self.plot_instance.log_value(name, value, step)\n\nclass LearningWriter:\n '''\n Class writter train & test plot (accuracy and loss) and model.\n\n Inputs:\n - label_map (dict): map index to topic string\n - result_folder_path (str): path to folder for saving result\n - saved_model_file_path (str): path to file relative to result_folder_path for trained model\n - train_log_folder_path (str): path to folder relative to result_folder_path for saving train plot\n - dev_log_folder_path (str): path to folder relative to result_folder_path for saving test plot\n - confusion_matrix_folder_path (str): path to folder relative to result_folder_path for saving confusion matrix\n \n '''\n def __init__(self, label_map, result_folder_path = \"runs\", saved_model_file_path = \"saved_model.model\", train_log_folder_path = \"train\", dev_log_folder_path = \"dev\", confusion_matrix_folder_path = \"confusion_matrix\"):\n self.result_path = result_folder_path\n self.saved_model_file_path = os.path.join(result_folder_path, saved_model_file_path)\n self.train_log_folder_path = os.path.join(result_folder_path, train_log_folder_path)\n self.dev_log_folder_path = os.path.join(result_folder_path, dev_log_folder_path)\n self.confusion_matrix_file_path = os.path.join(result_folder_path, confusion_matrix_folder_path, 'confusion_matrix.png')\n self.label_map = label_map\n\n def initialize(self):\n '''\n Initialize folder creation\n '''\n os.makedirs(self.result_path, exist_ok=True)\n \n self.train_logger = PlotWriter(self.train_log_folder_path)\n self.dev_logger = PlotWriter(self.dev_log_folder_path)\n \n dirname = os.path.dirname(self.saved_model_file_path)\n os.makedirs(dirname, exist_ok=True)\n\n dirname = os.path.dirname(self.confusion_matrix_file_path)\n os.makedirs(dirname, exist_ok=True)\n\n def train_log_value(self, name, value, step):\n '''\n Write train plot\n\n Inputs:\n - name (str): label/tag of the plot\n - value (double): value to be written (y-axis)\n - step (double): current epoch (x-axis)\n '''\n self.train_logger.log_value(name, value, step)\n \n def dev_log_value(self, name, value, step):\n '''\n Write test plot\n\n Inputs:\n - name (str): label/tag of the plot\n - value (double): value to be written (y-axis)\n - step (double): current epoch (x-axis)\n '''\n self.dev_logger.log_value(name, value, step)\n \n def save_model(self, model):\n '''\n Save model inside result folder\n \n Inputs:\n - model (torch.nn): Pytorch model to be saved\n '''\n torch.save(model, self.saved_model_file_path)\n print(\"model saved at\", self.saved_model_file_path)\n\n def save_confusion_matrix(self, truth_res, pred_res):\n '''\n Save confusion matrix in result folder and print Recall, Precision, and F1 score in console.\n\n Inputs:\n - truth_res (list): list of truth label\n - pred_res (list): list of predicted label\n '''\n # create confusion matrix data structure\n s = sklearn.metrics.confusion_matrix(truth_res, pred_res)\n df_cm = pd.DataFrame(data = s, columns=self.label_map, index=self.label_map)\n plt.figure(dpi=100)\n \n # create heatmap png\n heatmap = sns.heatmap(df_cm, annot=True, fmt='d')\n heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=70, ha='right', fontsize=5)\n heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=20, ha='right', fontsize=5)\n\n plt.savefig(self.confusion_matrix_file_path)\n\n # print recall, precission, F1 score\n confusion_matrix = ConfusionMatrix(truth_res, pred_res)\n confusion_matrix.print_stats()\n\n\n"},{"alpha_fraction":0.531874418258667,"alphanum_fraction":0.5341233611106873,"avg_line_length":35.70158767700195,"blob_id":"f018771ca35655550b4ee0c6b95a05f0a8c0f876","content_id":"18e19ed01a6e4bd23b1262218f334aa5554f096a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":11561,"license_type":"no_license","max_line_length":137,"num_lines":315,"path":"/model_module/model_runner.py","repo_name":"michjk/Question_Classifier_Pytorch","src_encoding":"UTF-8","text":"import os\nfrom data_module.data_writer import * \nimport shutil\nimport torch\nfrom torchtext import data\nfrom sklearn.model_selection import KFold\nimport numpy as np\nimport copy\nimport torch.autograd as autograd\n\ndef sort_key(ex):\n '''\n Function for sorting dataset based on length. It is applicable for sorting test dataset.\n\n Inputs:\n - ex (data.Example): one record of dataset\n '''\n return len(ex.text)\n\nclass ModelRunner:\n '''\n This is class for run training model and also cross validation model\n '''\n def __init__(self, model_factory, loss_factory, optimizer_factory, epochs, batch_size, learning_logger, use_gpu = True):\n '''\n Inputs:\n - model_factory (FactoryClass): a factory class for creating a model.\n - loss_factory (FactoryClass): a factory class for creating loss function.\n - optimizer_factory (FactoryClass): a factory class for creating optimizer function\n - epochs (int): number of epochs or rounds of training\n - batch_size (int): batch size of dataset to be passed to model in one foward pass\n - learning_logger: Class for writing logs and trained model\n - use_gpu (boolean): use gpu for training\n '''\n self.model_factory = model_factory\n self.loss_factory = loss_factory\n self.optimizer_factory = optimizer_factory\n self.epochs = epochs\n self.learning_logger = learning_logger\n self.use_gpu = use_gpu\n self.batch_size = batch_size\n \n def get_iterator(self, dataset, batch_size, train=True, shuffle=True, repeat=False, sort_key = sort_key):\n '''\n Generate iterator from torchtext.data.Dataset\n\n Inputs:\n - dataset (data.Dataset): dataset instance\n - batch_size (int)\n - train (boolean): Is it train dataset or test dataset\n - shuffle (boolean): shuffle dataset or not\n - repeat (boolean): repeat iteration or not\n - sort_key (boolean): repeat dataset iteration or not\n '''\n device = -1\n if self.use_gpu:\n device = None\n \n if train:\n sort_key = None\n\n dataset_iter = data.Iterator(\n dataset, batch_size=batch_size, device=device,\n train=train, shuffle=shuffle, repeat=repeat, sort_key=sort_key\n )\n\n return dataset_iter\n\n def get_dataset_cv(self, train_data, n_folds = 10):\n '''\n Generate n_folds set iterator from torchtext.data.Dataset for cross validation\n\n Inputs:\n - train_data (data.Dataset): train dataset instance\n - n_folds (int): number of folds\n '''\n train_examples = train_data.examples\n train_fields = train_data.fields\n \n # create folds\n def iter_folds():\n train_examples_np = np.array(train_examples)\n kf = KFold(n_splits=n_folds)\n for train_idx, val_idx in kf.split(train_examples_np):\n yield (\n data.Dataset(train_examples_np[train_idx], train_fields),\n data.Dataset(train_examples_np[val_idx], train_fields)\n )\n \n return iter_folds()\n \n def learn(self, train_data, dev_data):\n '''\n Start train model\n\n Inputs:\n - train_data (data.Dataset): training dataset\n - dev_data (data.Dataset): test dataset\n '''\n best_dev_acc = 0\n best_dev_loss = None\n best_truth_res = []\n best_pred_res = []\n \n # create model instance\n self.model = self.model_factory.create_class()\n \n # create optmizer instance\n update_parameter = filter(lambda p: p.requires_grad, self.model.parameters()) # only update parameter that indicates need updates\n self.optimizer = self.optimizer_factory.create_class({'params':update_parameter})\n \n # create loss function\n self.loss_function = self.loss_factory.create_class()\n\n # generate iterator\n train_iter = self.get_iterator(train_data, self.batch_size)\n dev_iter = self.get_iterator(dev_data, len(dev_data), train=False)\n\n # initialize folder\n self.learning_logger.initialize()\n\n # start training\n for i in range(self.epochs):\n # train\n print('epoch: %d start!' % i)\n self.learn_epoch(train_iter, i)\n \n # test\n print('now best dev acc:',best_dev_acc)\n dev_acc, dev_loss, truth_res, pred_res = self.evaluate(dev_iter, i)\n \n # save best model up to current epoch\n if best_dev_loss is None or dev_loss < best_dev_loss:\n best_dev_acc = dev_acc\n best_dev_loss = dev_loss\n print('New Best Dev!!!')\n self.learning_logger.save_model(self.model)\n best_truth_res = truth_res\n best_pred_res = pred_res\n \n print(\"best model accuracy: \", best_dev_acc)\n print(\"best model error: \", best_dev_loss)\n self.learning_logger.save_confusion_matrix(best_truth_res, best_pred_res)\n \n def learn_cv(self, train_data, n_folds):\n '''\n Start cross validate model\n\n Inputs:\n - train_data (data.Dataset): training dataset\n - n_folds (int): number of folds\n '''\n best_val_acc_list = []\n best_val_loss_list = []\n\n # genereate iterators for each folds\n train_val_generator = self.get_dataset_cv(train_data, n_folds=n_folds)\n \n # start cross validation\n for fold, (train_data_fold, val_data_fold) in enumerate(train_val_generator):\n \n # create model\n self.model = self.model_factory.create_class()\n \n # create optimizer\n update_parameter = filter(lambda p: p.requires_grad, self.model.parameters())\n self.optimizer = self.optimizer_factory.create_class({'params':update_parameter})\n \n # create loss function\n self.loss_function = self.loss_factory.create_class()\n \n print(\"Fold: \", fold)\n best_val_acc = 0.0\n best_val_loss = None\n \n best_truth_res = []\n best_pred_res = []\n\n # create data iterator\n train_iter = self.get_iterator(train_data_fold, self.batch_size)\n val_iter = self.get_iterator(val_data_fold, len(val_data_fold), train=False)\n for i in range(self.epochs):\n # train\n print('epoch: %d start!' % i)\n self.learn_epoch(train_iter, i, cv=True)\n \n # test\n print('now best dev acc:',best_val_acc)\n val_acc, val_loss, truth_res, pred_res = self.evaluate(val_iter, i, cv=True)\n \n # best model from entire epoch\n if best_val_loss is None or val_loss < best_val_loss:\n best_val_acc = val_acc\n best_val_loss = val_loss\n print('New Best Dev!!!')\n best_truth_res = truth_res\n best_pred_res = pred_res\n print(\"best val acc: \", best_val_acc)\n print(\"best val loss: \", best_val_loss)\n best_val_acc_list.append(best_val_acc)\n best_val_loss_list.append(best_val_loss)\n \n # output result\n print(\"All cross validation accuracy: \", best_val_acc_list)\n print(\"Avg cross validation accuracy: \", np.average(best_val_acc_list))\n\n print(\"All cross validation loss: \", best_val_loss_list)\n print(\"Avg cross validation loss: \", np.average(best_val_loss_list))\n \n \n def learn_epoch(self, train_iter, i, cv = False):\n '''\n Train in one epoch\n\n Inputs:\n - train_iter (iterator): Iterator train dataset\n - i (int): i-th epoch\n - cv (boolean): cross validation process or not\n \n '''\n self.model.train()\n \n avg_loss = 0.0\n count = 0\n truth_res = []\n pred_res = []\n \n # pass each batch to model\n for batch in train_iter:\n sent, label = batch.text, batch.label\n truth_res += [int(x) for x in label.data]\n pred = self.model(sent)\n pred_label = pred.data.max(1)[1]\n pred_res += [int(x) for x in pred_label]\n self.model.zero_grad()\n loss = self.loss_function(pred, label)\n avg_loss += float(loss.data[0])\n count += 1\n \n if count % 100 == 0:\n print('epoch: %d iterations: %d loss :%g' % (i, count*model.batch_size, loss.data[0]))\n \n loss.backward()\n self.optimizer.step()\n \n # calculate result\n avg_loss /= len(train_iter)\n acc = self.get_accuracy(truth_res,pred_res)\n print('epoch: %d done!\\ntrain avg_loss:%g , acc:%g'%(i, avg_loss, acc))\n\n # not support saving accuracy & loss for cross validation \n if not cv:\n self.learning_logger.train_log_value(\"accuracy\", acc, i)\n self.learning_logger.train_log_value(\"loss\", avg_loss, i)\n\n def evaluate(self, eval_iter, i, cv=False):\n '''\n Evaluate model in one epoch\n \n Inputs:\n - eval_iter (iterator): test/eval dataset iterator\n - i (int): i-th epoch\n - cv : cross validation or not\n \n Outputs:\n - acc (double): accuracy\n - avg_loss (double): average loss of current trained model\n - truth_res (list): truth label\n - pred_res (list): predicted label\n '''\n self.model.eval()\n \n avg_loss = 0.0\n truth_res = []\n pred_res = []\n \n # pass each batch to model\n for batch in eval_iter:\n sent, label = batch.text, batch.label\n truth_res += [int(x) for x in label.data]\n pred = self.model(sent)\n pred_label = pred.data.max(1)[1]\n pred_res += [int(x) for x in pred_label]\n loss = self.loss_function(pred, label)\n avg_loss += float(loss.data[0])\n \n #calculate result\n avg_loss /= len(eval_iter)\n acc = self.get_accuracy(truth_res, pred_res)\n print('dev avg_loss:%g train acc:%g' % (avg_loss, acc))\n \n # not support saving accuracy & loss for cross validation\n if not cv:\n self.learning_logger.dev_log_value(\"accuracy\", acc, i)\n self.learning_logger.dev_log_value(\"loss\", avg_loss, i)\n\n return acc, avg_loss, truth_res, pred_res\n \n def get_accuracy(self, truth, pred):\n '''\n Calculate percentage accuracy\n\n Inputs:\n - truth (list): list of truth label\n - pre (list): list of predicted label\n \n Outputs:\n - accuracy in percentage\n '''\n assert len(truth)==len(pred)\n right = 0\n for i in range(len(truth)):\n if truth[i]==pred[i]:\n right += 1.0\n return right/len(truth)\n"},{"alpha_fraction":0.7665607333183289,"alphanum_fraction":0.7829381823539734,"avg_line_length":50.131248474121094,"blob_id":"20f6725933f1b9bd7948c67b9e6bfbdc695a2f84","content_id":"60bae58e9df6894671e4c198e221ac400a38e33d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":8182,"license_type":"no_license","max_line_length":405,"num_lines":160,"path":"/README.md","repo_name":"michjk/Question_Classifier_Pytorch","src_encoding":"UTF-8","text":"# Question_Classifier_Pytorch\nPytorch Implementation for question classification model\n\n## Requirements\nThe application is written in Python 3 and runs on Ubuntu 16.04 server with dedicated GPU. The following Ubuntu 16.04 packages should be installed:\n1. Python 3\n2. virtualenv\n3. Git\n4. CUDA 9/8\n5. cuDNN 6\n6. Nginx\n7. uWSGI\n\nBefore running Python application or installing Python dependencies, please create a virtualenv and activate the respective virtualenv.\nTo create virtualenv:\n```\nvirtualenv -p python3 virtualenv_name\n```\nTo activate the created virtualenv:\n```\nsource virtualenv_name/bin/activate\n```\nTo install python depencies library for this appliaction:\n```\npip install http://download.pytorch.org/whl/cu90/torch-0.4.0-cp35-cp35m-linux_x86_64.whl # for CUDA 9 or pip install torch for CUDA 8 \npip install cupy-cuda90 # or cupy-cuda80\npip install pynvrtc git+https://github.com/salesforce/pytorch-qrnn # for installing QRNN\npip install spacy\npython -m spacy download en #for downloading English model for spaCy tokenizer\npip install cffi # probably not needed by just try\npip install -r requirements.txt\n```\nIf it fails, please look at requirements.txt and install one by one.\n\n## Dataset\nThe dataset should be seperated into training file and test file in CSV format.\nWhen preparing CSV file, the dataset should not use indexing.\nIf you want to save CSV file using Pandas, use Python command:\n```\npandas.to_csv('file_name', index=False)\n```\n\n## Project Structure\nThis project is basically my own framework for developing question classification. It is mostly built with Pytorch and also torchtext for Pytorch specific NLP preprocessor.\nThe framework suggest user to load module from 3 different folder.\n1. data_module: The folder consist of function for loading & preprocessing dataset and writing results.\n2. model_module: The folder consist of several classification models and a module for running training.\n\nThere is utils.py file which contains utility to load parameters from json file.\nYou can learn how to extend the framework from the current implementation.\n\n## Models\nCurrently 3 different models are provided for question classification:\n1. CNN (Convolutional Neural Network) \n\nEven though CNN is created for image related problem. It can be used for text classifiction. Some papers related to the CNN model are [\nConvolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1408.5882) and [\nA Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1510.03820).\n\n2. LSTM (Long Short Term Memory)\n\nLSTM is RNN based neural network architecture that solves vanishing gradient problem and long-term dependencies problem. It is a natural way to learn text pattern in sequential manner. The theory behind LSTM can be found in [Understanding LSTMs](http://colah.github.io/posts/2015-08-Understanding-LSTMs/). With additional fully connected layer and softmax layer, LSTM can be used for text classification. \n\n3. QRNN (Quasi Recurrent Neural Networks) \n\nQRNN is RNN based neural network architecture that try to solve disadvantages of LSTM and CNN. LSTM is good for long-term dependency, but it is slow since it learn in sequential manner. Also, CNN can use parallelism with convolution function, but it cannot learn long-term dependencies. The theory behind QRNN is published in [Quasi Recurrent Neural Networks](https://arxiv.org/abs/1611.01576).\n\n## Run training to save model and get test accuracy and loss\nExample Python file are provided as example for training. For every training, please add new git commit if you indicate use_git = True in parameter json file to ensure there is no clash in saving result.\n\nQRNN\n```\npython train_qrnn.py --path train_rnn_parameter.json\n```\n\nLSTM\n```\npython train_lstm.py --path train_rnn_parameter.json\n```\n\nCNN\n```\npython train_cnn.py --path train_cnn_parameter.json\n```\nThe result are saved under result_folder_path. For accuracy & loss plot, use tensorboard to visualize them (will be explained).\n\n## k-fold Cross Validation of model\nQRNN\n```\npython train_cv_qrnn.py --path train_rnn_parameter.json\n```\n\nLSTM\n```\npython train_cv_lstm.py --path train_rnn_parameter.json\n```\n\nCNN\n```\npython train_cv_cnn.py --path train_cnn_parameter.json\n```\nNote: currently the cross validation result are printed to console, not saved \n\n## JSON file for training model\nThe JSON file are already prepared with appropriate setting. You can have parameter name different from example, but it is best to follow the example.Most of the setting are for model hyperparameter, loss function parameter, and optimizer parameter that has same parameter name as the model, loss function, and optimizer itselves.\nBesides that, another parameter that important:\n1. \"epoch\": number of epochs\n2. \"batch_size\": batch size of training data\n3. \"max_text_length\": max lenght of a sentence (must be indicated for CNN)\n4. \"train_dataset_path\": train data path\n5. \"dev_dataset_path\": test data path\n6. \"result_folder_path\": where to save result such as best model, test accuracy (not available for cross validation)\n7. \"use_git\": whether to use current commit information for better result versioning. if true result_folder_path become result_folder_path/branch_name_commit_date\n8. \"n_folds\": number of k-fold for cross validation\n9. \"saved_model_file_path\": file path relative to result_folder_path to save trained model\n10. \"saved_text_pipeline_file_path\": file path relative to result_folder_path to save text preprocessing data\n11. \"saved_label_pipeline_file_path\": file path relative to result_folder_path to save label preprocessing data\n12. \"train_log_folder_path\": file path relative to result_folder_path to save train plot for tensorboard\n13. \"dev_log_folder_path\": file path relative to result_folder_path to save test/evaluation plot for tensorboard.\n14. \"confusion_matrix_folder_path\": folder path relative to result_folder_path to save confusion matrix of test result.\n15. \"pretrained_word_embedding_name\": name of pretrained word embedding vectors. Currently support word2vec and GloVe (glove.6B.300d)\n16. \"pretrained_word_embedding_path\": path to word embedding vectors file (support word2vec only example: \"../dataset/GoogleNews-vectors-negative300.bin\")\n\nPlease look at the json file and try to run training first to understand more\n\n## View train & test accuracy and loss plot with tensorboard\nPlease run\n```\ntensorboard --logdir=[result_folder_path] --host 0.0.0.0\n```\nTo view graph of test loss and accuracy.\n\n## REST API server\nTo run in debug mode please run\n```\npython rest_api_server.py --path rest_api_param.json\n```\nThe json file contain important parameter that need to be updated\n1. \"saved_model_file_path\": path where the trained model saved\n2. \"debug_log_file_path\": file path to debug log\n3. \"error_log_file_path\": file path to error log\n4. \"saved_text_pipeline_file_path\": data.Field preprocessing information from questions in dataset (it should be in current directory after training)\n5. \"saved_label_pipeline_file_path\": data.Field preprocessing information from labels in dataset (it should be in current directory after training)\n\nTo run for production, we use uwsgi as server and nginx for receiving simultaneous\nplease look at [A Guide to Scalling Machine Learning Models in Productions](https://hackernoon.com/a-guide-to-scaling-machine-learning-models-in-production-aa8831163846).\nThe .ini file is already prepared as uwsgi.ini\n\nCurrently, the endpoint to get prediction:\n```\nGET IP_ADDRESS/predict?question='question to be predicted'\n```\n\n## Telegram bot for trying model\nThere is available python file for running Telegram bot as UI for trying the classification model. Please look for tutorial how to make Telegram bot first. After that, run:\n```\npython telegram_bot.py --token TELEGRAM_TOKEN --ip http://0.0.0.0:port\n```\n--token is found after createing telegram bot. --ip is the target url to REST API model.\nAlso, google sheet can used for logging prediction and user suggestion, but it is not used by default. You can try to look how to setup google sheet api and use google_sheet_api.py as example. "},{"alpha_fraction":0.49659863114356995,"alphanum_fraction":0.6991103887557983,"avg_line_length":15.911504745483398,"blob_id":"ec3a2502c45f72af33185fb36202d83c398a08e3","content_id":"077b6f4c397c123a3584353c94ee8496b9287795","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":1911,"license_type":"no_license","max_line_length":32,"num_lines":113,"path":"/requirements.txt","repo_name":"michjk/Question_Classifier_Pytorch","src_encoding":"UTF-8","text":"absl-py==0.1.13\nastor==0.6.2\nbleach==3.1.4\ncertifi==2018.4.16\nchainer==3.5.0\nchardet==3.0.4\nclick==6.7\ncycler==0.10.0\ncymem==1.31.2\ncytoolz==0.8.2\ndecorator==4.2.1\ndill==0.2.7.1\nentrypoints==0.2.3\nenum34==1.1.6\nfastrlock==0.3\nfilelock==3.0.4\nFlask==0.12.2\nftfy==4.4.3\nfuture==0.16.0\ngast==0.2.0\ngitdb2==2.0.3\nGitPython==2.1.8\ngoogle-api-python-client==1.6.5\ngrpcio==1.10.0\nhtml5lib==0.9999999\nhttplib2==0.18.0\nidna==2.6\nipykernel==4.8.2\nipython==6.2.1\nipython-genutils==0.2.0\nipywidgets==7.1.2\nitsdangerous==0.24\njedi==0.11.1\nJinja2==2.10\njsonschema==2.6.0\njupyter==1.0.0\njupyter-client==5.2.3\njupyter-console==5.2.0\njupyter-core==4.4.0\nMarkdown==2.6.11\nMarkupSafe==1.0\nmatplotlib==2.1.2\nmistune==0.8.3\nmsgpack-numpy==0.4.1\nmsgpack-python==0.5.4\nmurmurhash==0.28.0\nnbconvert==5.3.1\nnbformat==4.4.0\nnotebook==5.7.8\nnumpy==1.14.2\noauth2client==4.1.2\nonnx==1.1.2\npandas==0.22.0\npandas-ml==0.5.0\npandocfilters==1.4.2\nparso==0.1.1\npathlib==1.0.1\npexpect==4.4.0\npickleshare==0.7.4\nPillow==5.1.0\nplac==0.9.6\npreshed==1.0.0\nprompt-toolkit==1.0.15\nprotobuf==3.5.2.post1\nptyprocess==0.5.2\npyasn1==0.4.2\npyasn1-modules==0.2.1\npycparser==2.18\nPygments==2.2.0\npyparsing==2.2.0\npython-dateutil==2.6.1\npython-telegram-bot==9.0.0\nPyTorch-QRNN==0.2.1\npytz==2017.3\nPyYAML==3.12\npyzmq==17.0.0\nqtconsole==4.3.1\nregex==2017.4.5\nrequests==2.18.4\nrsa==3.4.2\nscikit-learn==0.19.1\nscipy==1.0.0\nseaborn==0.8.1\nSend2Trash==1.5.0\nsimplegeneric==0.8.1\nsimplejson==3.13.2\nsix==1.11.0\nsmmap2==2.0.3\ntensorboard==1.7.0\ntensorboard-logger==0.0.4\ntensorboardX==1.2\ntensorflow==1.7.0\ntensorflow-gpu==1.4.1\ntensorflow-tensorboard==0.4.0rc3\ntermcolor==1.1.0\nterminado==0.8.1\ntestpath==0.3.1\nthinc==6.10.2\ntoolz==0.9.0\ntorchtext==0.2.3\ntorchwordemb==0.0.8\ntornado==5.0.1\ntqdm==4.23.0\ntraitlets==4.3.2\nujson==5.2.0\nuritemplate==3.0.0\nurllib3==1.22\nuWSGI==2.0.17\nuwsgitop==0.10\nwcwidth==0.1.7\nWerkzeug==0.14.1\nwidgetsnbextension==3.1.4\nwrapt==1.10.11\n"},{"alpha_fraction":0.6178725957870483,"alphanum_fraction":0.6178725957870483,"avg_line_length":30.23958396911621,"blob_id":"e868ad0b1fd386705b6fbe11f470b07edb7e13ab","content_id":"9ab3d10418733b25314e4d085fde5f3b80a7b17a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2999,"license_type":"no_license","max_line_length":127,"num_lines":96,"path":"/utils.py","repo_name":"michjk/Question_Classifier_Pytorch","src_encoding":"UTF-8","text":"import json\nimport git\nimport os\nimport time\n\nclass DotDict(dict):\n '''\n dot.notation access to dictionary attributes\n\n Inputs: dict: dict\n - dict: a non-empty dict\n '''\n __getattr__ = dict.get\n __setattr__ = dict.__setitem__\n __delattr__ = dict.__delattr__\n\ndef filter_dotdict_class_propoperty(dotdict_object, class_blueprint):\n '''\n Generate new dotdict with properties exist in class_blueprint\n \n Inputs: dotdict_object (dotdict), class_blueprint (Object)\n - dotdict_object: an instance of dotdict\n - class_blueprint: a class where its properties act as filter for dotdict_object\n\n Outputs: dotdict_object: dotdict\n\n '''\n varnames = class_blueprint.__init__.__code__.co_varnames\n new_dotdict = DotDict({k:v for (k, v) in dotdict_object.items() if k in varnames})\n return new_dotdict\n\ndef load_training_parameter_from_json(path):\n '''\n Load parameters from json file to DotDict.\n\n Inputs: path (str)\n - path: path of the json file\n \n Outputs: dotdict_object (DotDict)\n - dotdict_object: loaded parameter\n '''\n\n json_object = json.load(open(path, \"r\"))\n dotdict_object = DotDict(json_object)\n\n # use git versioning\n if dotdict_object.use_git:\n repo = git.Repo(os.getcwd())\n headcommit = repo.head.commit\n current_branch = repo.active_branch.name\n\n #get current branch + time stamp\n version = current_branch + \"_commited_at_\" + time.strftime(\"%a_%d_%b_%Y_%H_%M\", time.gmtime(headcommit.committed_date))\n \n # new result folder path\n dotdict_object.result_folder_path = os.path.join(dotdict_object.result_folder_path, version)\n\n return dotdict_object\n\ndef load_rest_api_parameter_from_json(path):\n '''\n Load parameters from json file to DotDict.\n\n Inputs: path (str)\n - path: path of the json file\n \n Outputs: dotdict_object (DotDict)\n - dotdict_object: loaded parameter\n '''\n json_object = json.load(open(path, \"r\"))\n dotdict_object = DotDict(json_object)\n\n return dotdict_object\n\nclass FactoryClass:\n '''\n Class for generating an intance of a class\n\n Inputs: class_constructor (Class), param_dict (dict)\n - class_constructor: a class that will be used to create instance\n - param_dict: a dict contains parameter for the class_constructor\n '''\n def __init__(self, class_contructor, param_dict = {}):\n\n self.class_contructor = class_contructor\n self.param_dict = param_dict\n \n def create_class(self, new_param_dict={}):\n '''\n Create new instance of class constructor\n\n Inputs: new_param_dict (dict)\n - new_param_dict : dict of new parameter that is not available at param_dict\n '''\n new_object = self.class_contructor(**self.param_dict, **new_param_dict)\n return new_object\n"},{"alpha_fraction":0.7992957830429077,"alphanum_fraction":0.8010563254356384,"avg_line_length":36.24590301513672,"blob_id":"2fb998ddc2b98a16c96712a570ca2cb885a55df6","content_id":"161ff381c0357757bfa8ce5a4a4d81569164816b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2272,"license_type":"no_license","max_line_length":190,"num_lines":61,"path":"/train_qrnn_cv.py","repo_name":"michjk/Question_Classifier_Pytorch","src_encoding":"UTF-8","text":"import time\n\nimport numpy as np\n\nimport torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport torch.optim as optim\n\nfrom model_module.qrnn_classifier import QRNNClassifier\n\nfrom data_module.data_preprocessor import *\n\nimport os\nimport random\n\nimport datetime\n\nfrom model_module.model_runner import ModelRunner\n\nfrom utils import load_training_parameter_from_json, filter_dotdict_class_propoperty, FactoryClass\n\nfrom data_module.data_writer import LearningWriter, PreprocessingPipelineWriter\n\nimport argparse\n\nnp.random.seed(1)\ntorch.manual_seed(1)\ntorch.cuda.manual_seed_all(1)\nrandom.seed(1)\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--path\", help=\"path parameter json file\")\nparam_json_path = parser.parse_args().path\n\nparam = load_training_parameter_from_json(param_json_path)\nqrnn_parameter = filter_dotdict_class_propoperty(param, QRNNClassifier)\n\npreprocessing_pipeline_writer = PreprocessingPipelineWriter(param.result_folder_path, param.saved_text_pipeline_file_path, param.saved_label_pipeline_file_path)\n\ntrain_data, dev_data, vocab_size, label_size, label_map, pretrained_embedding_weight = load_dataset(\n param.train_dataset_path, param.dev_dataset_path, param.max_text_length, preprocessing_pipeline_writer,\n pretrained_word_embedding_name = param.pretrained_word_embedding_name, pretrained_word_embedding_path = param.pretrained_word_embedding_path\n)\n\nqrnn_parameter.vocab_size = vocab_size\nqrnn_parameter.label_size = label_size\nqrnn_parameter.pretrained_embedding_weight = pretrained_embedding_weight\n\nmodel_factory = FactoryClass(QRNNClassifier, qrnn_parameter)\n\nloss_factory = FactoryClass(nn.NLLLoss)\n\noptimizer_param_dict = filter_dotdict_class_propoperty(param, optim.Adam)\noptimizer_factory = FactoryClass(optim.Adam, optimizer_param_dict)\n\nlearning_logger = LearningWriter(label_map, param.result_folder_path, param.saved_model_file_path, param.train_log_folder_path, param.dev_log_folder_path, param.confusion_matrix_folder_path)\nmodel_runner = ModelRunner(model_factory, loss_factory, optimizer_factory, param.epoch, param.batch_size, learning_logger, param.use_gpu)\nstart_time = time.time()\nmodel_runner.learn_cv(train_data, param.n_folds)\nprint(\"Overall time elapsed {} sec\".format(time.time() - start_time))\n"},{"alpha_fraction":0.7928483486175537,"alphanum_fraction":0.7981915473937988,"avg_line_length":37.015625,"blob_id":"daeced4d69344fc6b905a26a4c4def21911fba86","content_id":"89db8c15a9c3bed615bb4e26eca591a5dcaad3c1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2433,"license_type":"no_license","max_line_length":190,"num_lines":64,"path":"/train_lstm.py","repo_name":"michjk/Question_Classifier_Pytorch","src_encoding":"UTF-8","text":"import time\n\nimport numpy as np\n\nimport torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport torch.optim as optim\n\nfrom model_module.lstm_classifier import LSTMClassifier\n\nfrom data_module.data_preprocessor import *\n\nimport os\nimport random\n\nimport datetime\n\nfrom model_module.model_runner import ModelRunner\n\nfrom utils import load_training_parameter_from_json, filter_dotdict_class_propoperty, FactoryClass\n\nfrom data_module.data_writer import LearningWriter, PreprocessingPipelineWriter\n\nimport argparse\n\nnp.random.seed(1)\ntorch.manual_seed(1)\ntorch.cuda.manual_seed_all(1)\nrandom.seed(1)\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--path\", help=\"path parameter json file\")\nparam_json_path = parser.parse_args().path\n\nparam = load_training_parameter_from_json(param_json_path)\nlstm_parameter = filter_dotdict_class_propoperty(param, LSTMClassifier)\n\npreprocessing_pipeline_writer = PreprocessingPipelineWriter(param.result_folder_path, param.saved_text_pipeline_file_path, param.saved_label_pipeline_file_path)\n\ntrain_data, dev_data, vocab_size, label_size, label_map, pretrained_embedding_weight = load_dataset(\n param.train_dataset_path, param.dev_dataset_path, param.max_text_length, preprocessing_pipeline_writer,\n pretrained_word_embedding_name = param.pretrained_word_embedding_name, pretrained_word_embedding_path = param.pretrained_word_embedding_path\n)\n\nlstm_parameter.vocab_size = vocab_size\nlstm_parameter.label_size = label_size\nlstm_parameter.pretrained_embedding_weight = pretrained_embedding_weight\n\nmodel_factory = FactoryClass(LSTMClassifier, lstm_parameter)\n\nloss_factory = FactoryClass(nn.NLLLoss)\n\noptimizer_param_dict = filter_dotdict_class_propoperty(param, optim.Adam)\noptimizer_factory = FactoryClass(optim.Adam, optimizer_param_dict)\n\n#optimizer = optim.Adagrad(update_parameter, lr=1e-3)\n#optimizer = optim.RMSprop(update_parameter, lr=parameter.learning_rate, alpha=0.99, eps=1e-8, weight_decay=5e-4)\n\nlearning_logger = LearningWriter(label_map, param.result_folder_path, param.saved_model_file_path, param.train_log_folder_path, param.dev_log_folder_path, param.confusion_matrix_folder_path)\nmodel_runner = ModelRunner(model_factory, loss_factory, optimizer_factory, param.epoch, param.batch_size, learning_logger, param.use_gpu)\nstart_time = time.time()\nmodel_runner.learn(train_data, dev_data)\nprint(\"Overall time elapsed {} sec\".format(time.time() - start_time))\n"},{"alpha_fraction":0.6185395121574402,"alphanum_fraction":0.6202067136764526,"avg_line_length":39.5,"blob_id":"07e5750f56ba5fb3ab0b19dd203d5fc904b73035","content_id":"a967bc44152b4dc0801035ca45aacf8cd524b02f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2999,"license_type":"no_license","max_line_length":184,"num_lines":74,"path":"/model_module/lstm_classifier.py","repo_name":"michjk/Question_Classifier_Pytorch","src_encoding":"UTF-8","text":"import torch\nimport torch.nn as nn\nimport torch.autograd as autograd\nimport torch.nn.functional as F\n\nclass LSTMClassifier(nn.Module):\n '''\n LSTMClassifier is classification module based on LSTM\n Args:\n embedding_dim (int): The size of word vector.\n vocab_size (int): The number of words/tokens in vocabulary.\n label_size (int): The number of possible labels.\n hidden_dim: The number of features in the hidden state h of QRNN\n pretrained_embedding_weight (torch.Tensor): The pretrained word vectors (optional).\n train_embedding_layer (bool): Whether to train embedding layer or let embedding layer to be fixed.\n num_layers (int): The number of layers of QRNN.\n dropout (float): The probability of dropout in QRNN and dropout layer.\n use_gpu (bool): Whether to use GPU or not\n \n Inputs: x:\n - x (seq_len, batch, input_size): tensor containing the features of the input sequence.\n \n Output: logsoftmax\n - logsoftmax (batch, label_size) : tensor result of log softmax\n '''\n def __init__(self, embedding_dim, hidden_dim, vocab_size, label_size, pretrained_embedding_weight = None, train_embedding_layer = True,num_layers = 1, dropout = 0, use_gpu = True):\n super().__init__()\n\n #initialize properties\n self.hidden_dim = hidden_dim\n self.num_layers = num_layers\n self.use_gpu = use_gpu\n \n ## create nn module\n self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)\n if not (pretrained_embedding_weight is None): #use pretrained word vectors\n self.word_embeddings.weight.data = pretrained_embedding_weight\n self.word_embeddings.weight.requires_grad = train_embedding_layer\n self.lstm = nn.LSTM(embedding_dim, hidden_dim, dropout=dropout, num_layers=num_layers)\n self.dropout = nn.Dropout(dropout)\n self.hidden_to_label = nn.Linear(hidden_dim, label_size)\n\n #use gpu\n if use_gpu:\n self.cuda()\n \n def init_hidden(self):\n \"\"\"\n Initialize weight of hidden\n \"\"\"\n # the first is the hidden h\n # the second is the cell c\n h = torch.zeros(self.num_layers, self.batch_size, self.hidden_dim)\n c = torch.zeros(self.num_layers, self.batch_size, self.hidden_dim)\n\n if self.use_gpu:\n h = h.cuda()\n c = c.cuda()\n \n self.hidden = (autograd.Variable(h),\n autograd.Variable(c))\n \n def forward(self, sentence):\n #get batch size and init hidden\n self.batch_size = sentence.data.shape[1]\n self.init_hidden()\n \n embeds = self.word_embeddings(sentence) # (N,W,D)\n x = embeds.view(len(sentence), self.batch_size, -1)\n out, self.hidden = self.lstm(x, self.hidden)\n out = self.dropout(out)\n y = self.hidden_to_label(out[-1])\n log_probs = F.log_softmax(y)\n return log_probs\n\n\n"},{"alpha_fraction":0.6330029964447021,"alphanum_fraction":0.6364637613296509,"avg_line_length":36.122806549072266,"blob_id":"21bdd974cfe560fcf67bee764eb3043d6a556881","content_id":"8dc561f9344dc23858948841d6c86473faf075de","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6357,"license_type":"no_license","max_line_length":220,"num_lines":171,"path":"/data_module/data_preprocessor.py","repo_name":"michjk/Question_Classifier_Pytorch","src_encoding":"UTF-8","text":"import numpy as np\nimport re\nimport itertools\nfrom collections import Counter\nimport json\nfrom torchtext import data\nimport codecs\nfrom sklearn.model_selection import KFold\nimport torchwordemb\nimport dill as pickle\nimport spacy\n\n# load English model\nspacy_nlp = spacy.load('en')\n\ndef clean_str(string):\n '''\n Tokenization/string cleaning for all datasets except for SST.\n Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py\n '''\n string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`@-]\", \" \", string)\n string = re.sub(r\"\\'s\", \" \\'s\", string)\n string = re.sub(r\"\\'ve\", \" \\'ve\", string)\n string = re.sub(r\"n\\'t\", \" n\\'t\", string)\n string = re.sub(r\"\\'re\", \" \\'re\", string)\n string = re.sub(r\"\\'d\", \" \\'d\", string)\n string = re.sub(r\"\\'ll\", \" \\'ll\", string)\n string = re.sub(r\",\", \" , \", string)\n string = re.sub(r\"!\", \" ! \", string)\n string = re.sub(r\"\\(\", \" ( \", string)\n string = re.sub(r\"\\)\", \" ) \", string)\n string = re.sub(r\"\\?\", \" ? \", string)\n string = re.sub(r\"\\s{2,}\", \" \", string)\n return string.strip().lower()\n\nclass QuestionWrapper(data.Dataset):\n '''\n A class wrapper for createing torchtext.data.Dataset instance for preprocessing question in production\n\n Inputs: text_field (data.Field), question (string)\n - text_field : loaded data.Field for preprocessing question\n - question : question sent by user\n '''\n def __init__(self, text_field, question, **kwargs):\n fields = [('text', text_field)]\n question = clean_str(question)\n examples = []\n examples.append(data.Example.fromlist([question], fields))\n\n super().__init__(examples, fields, **kwargs)\n\ndef preprocess_question(question, text_field, use_gpu = False):\n '''\n Preprocess question sent by user\n\n Inputs: question (string), text_field (data.Field), use_gpu (boolean)\n - question : question sent by user\n - text_field : loaded data.Field for preprocessing question\n - use_gpu : use gpu or not\n\n Outputs: text (Iterator)\n - text : Iterator of preprocessed question\n '''\n device = -1\n if use_gpu:\n device = None\n question_data = QuestionWrapper(text_field, question)\n _, question_iter = data.Iterator.splits(\n (question_data, question_data), batch_size=len(question_data),\n repeat=False, device = device\n )\n\n for batch in question_iter:\n text = batch.text\n\n return text\n\ndef get_label(label_tensor, label_field):\n '''\n Get label string from tensor result\n\n Inputs: label_tensor (tensor), label_field (data.Field)\n - label_tensor: tensor result of classification\n - label_field: loaded data.Field for label preprocessing\n '''\n pred_index = label_tensor.data.max(1)[1]\n pred_index = pred_index.cpu().numpy()[0]\n label_string = label_field.vocab.itos[pred_index]\n\n return label_string\n\ndef tokenizer(text):\n '''\n Tokenizer, includes clear string and lemmatization\n\n Inputs:\n - text (string): string to be tokenized\n \n Outputs:\n - tokens (list): list of tokens\n\n '''\n\n #clean string\n text = clean_str(text)\n\n #lemmatized\n lemmatized = spacy_nlp(text)\n text = ' '.join([token.lemma_ for token in lemmatized])\n \n #tokenizer from tensorflow.preprocessing library\n tokenizer_re = re.compile(r\"[A-Z]{2,}(?![a-z])|[A-Z][a-z]+(?=[A-Z])|[\\'\\w\\-]+\", re.UNICODE) \n return tokenizer_re.findall(text)\n\ndef load_dataset(train_path, dev_path, max_text_length, preprocessing_pipeline_writer, tokenizer = tokenizer, embedding_dim = 300, pretrained_word_embedding_name = \"glove.6B.300d\", pretrained_word_embedding_path = None):\n '''\n Load and preprocess dataset.\n\n Inputs:\n - train_path (string): Path to train dataset json\n - dev_path (string): Path to test/dev dataset json\n - max_text_length (integer): Max length of sentence. It pads dummy token up to max length\n - preprocessing_pipeline_writer (object): class for saving data.Field data\n - tokenizer (function): Tokenizer function\n - Embedding_dim (integer): size of word embedding dim\n - pretrained_word_embedding_name (string): name of pretrained word embedding. It can be glove.6B.300d or word2vec\n - pretrained_word_embedding_path (string): path to word embedding file. No need for glove.\n\n Outputs:\n - train_data (data.Dataset): train dataset\n - test_data (data.Dataset): test dataset\n - vocab_size (int): vocabulary size\n - label_size (int): size of possible label\n - label_vocab (dict): map from topic index number to topic label string\n - vectors : word embedding vectors\n '''\n\n # data.Field for preprocessing pipeline data\n text_field = data.Field(lower=True, tokenize=tokenizer, fix_length=max_text_length)\n label_field = data.LabelField()\n\n # load data\n print('loading data')\n train_data = data.TabularDataset(path=train_path, format='csv', skip_header=True, fields=[(\"text\", text_field), ('label', label_field)])\n dev_data = data.TabularDataset(path=dev_path, format='csv', skip_header=True, fields=[(\"text\", text_field), ('label', label_field)])\n \n # build vocabulary\n print('building vocab')\n text_field.build_vocab(train_data, dev_data)\n label_field.build_vocab(train_data, dev_data)\n\n vectors = None\n\n # load word embedding vectors\n if pretrained_word_embedding_name == \"word2vec\":\n vocab, vec = torchwordemb.load_word2vec_bin(pretrained_word_embedding_path)\n text_field.vocab.set_vectors(vocab, vec, embedding_dim)\n vectors = text_field.vocab.vectors\n elif \"glove\" in pretrained_word_embedding_name:\n text_field.vocab.load_vectors(pretrained_word_embedding_name)\n vectors = text_field.vocab.vectors\n \n # save data.Field\n preprocessing_pipeline_writer.save_pipeline(text_field, False)\n preprocessing_pipeline_writer.save_pipeline(label_field, True)\n \n vocab_size = len(text_field.vocab)\n print(\"vocab size \", vocab_size)\n label_size = len(label_field.vocab)\n\n return train_data, dev_data, vocab_size, label_size, label_field.vocab.itos, vectors\n\n\n\n\n \n"},{"alpha_fraction":0.3065875768661499,"alphanum_fraction":0.3090852200984955,"avg_line_length":51.86666488647461,"blob_id":"1d5bf16ef492dd408f3890f1508a5b399ff94a01","content_id":"f1785a7dd5ec15127806fee716d6bcfb5b326d9d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"INI","length_bytes":3203,"license_type":"no_license","max_line_length":204,"num_lines":60,"path":"/uwsgi.ini","repo_name":"michjk/Question_Classifier_Pytorch","src_encoding":"UTF-8","text":"[uwsgi]\n# placeholders that you have to change\nmy_app_folder = /home/michael/Question_Classifier_Pytorch\nmy_user = michael\n\nsocket = %(my_app_folder)/production_ml.sock\nchdir = %(my_app_folder)\nfile = rest_api_server.py\ncallable = app\n\n# environment variables\nenv = PYTHONPATH=%(my_app_folder):$PATH\nenv = LD_LIBRARY_PATH=$LD_LIBRARY_PATH\nenv = CUDA_HOME=$CUDA_HOME\n\nmaster = true\nprocesses = 1\n# allows nginx (and all users) to read and write on this socket\nchmod-socket = 666\n# remove the socket when the process stops\nvacuum = true\n\n# loads your application one time per worker\n# will very probably consume more memory,\n# but will run in a more consistent and clean environment.\nlazy-apps = true\n\nuid = %(my_user)\ngid = %(my_user)\n\n# uWSGI will kill the process instead of reloading it\ndie-on-term = true\n# socket file for getting stats about the workers\nstats = %(my_app_folder)/stats.production_ml.sock\n\n# Scaling the server with the Cheaper subsystem\n\n# set cheaper algorithm to use, if not set default will be used\ncheaper-algo = spare\n# minimum number of workers to keep at all times\ncheaper = 0\n# number of workers to spawn at startup\ncheaper-initial = 1\n# maximum number of workers that can be spawned\nworkers = 1\n# how many workers should be spawned at a time\nheaper-step = 1\n\npyargv = --path rest_api_param.json\n\n~ \n~ \n~ \n~ \n~ \n~ \n~ \n~ \n~ \n~ \n"},{"alpha_fraction":0.702724039554596,"alphanum_fraction":0.7058823704719543,"avg_line_length":26.846153259277344,"blob_id":"982aac6dce2f4416d004a24092722b233e6e0791","content_id":"d16b0417d3a58840162a4370b176ee7032afef2e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2533,"license_type":"no_license","max_line_length":99,"num_lines":91,"path":"/rest_api_server.py","repo_name":"michjk/Question_Classifier_Pytorch","src_encoding":"UTF-8","text":"from flask import Flask\nfrom flask import request\n\nimport sys\n\nimport torch\nfrom torchtext import data\n\nimport time\n\nfrom data_module.data_preprocessor import get_label, preprocess_question\n\nimport os\n\nfrom flask import jsonify\n\nimport logging\n\nimport dill as pickle\n\nimport argparse\n\nfrom utils import *\n\napp = Flask(__name__)\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--path\", help=\"path parameter json file\")\nparam_json_path = parser.parse_args().path\n\n# load parameter\nparam = load_rest_api_parameter_from_json(param_json_path)\n\n# load model\nmodel = None\nif param.use_gpu:\n model = torch.load(param.saved_model_file_path)\nelse:\n model = torch.load(param.saved_model_file_path, map_location=lambda storage, location: storage)\n\n# Create the Logger\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\n \n# Create the Handler for logging data to a file\nlogger_handler_debug = logging.FileHandler(param.debug_log_file_path)\nlogger_handler_debug.setLevel(logging.DEBUG)\n\n# Create the Handler for logging data to a file\nlogger_handler_error = logging.FileHandler(param.error_log_file_path)\nlogger_handler_error.setLevel(logging.ERROR)\n\n# Create a Formatter for formatting the log messages\nlogger_formatter = logging.Formatter('%(asctime)s - %(message)s')\n \n# Add the Formatter to the Handler\nlogger_handler_debug.setFormatter(logger_formatter)\nlogger_handler_error.setFormatter(logger_formatter)\n \n# Add the Handler to the Logger\nlogger.addHandler(logger_handler_debug)\nlogger.addHandler(logger_handler_error)\nlogger.info('Completed configuring logger()!')\n\n# load data.Field\ntext_field = pickle.load(open(param.saved_text_pipeline_file_path, \"rb\"))\nlabel_field = pickle.load(open(param.saved_label_pipeline_file_path, \"rb\"))\n\n@app.route('/predict', methods=['GET'])\ndef prediction():\n try:\n question = request.args.get('question')\n logger.info(\"Question: \" + question)\n x = preprocess_question(question, text_field, use_gpu=param.use_gpu)\n model.eval()\n t = time.time()\n y = model(x)\n dur = time.time() - t\n label_string = get_label(y, label_field)\n logger.info(\"Result: \" + str(label_string))\n logger.info(\"Duration: \" + str(dur))\n return jsonify({'result': str(label_string)})\n except:\n e = sys.exc_info()[0]\n logger.error(\"error \", str(e))\n response = jsonify({'error': str(e)})\n response.status_code = 400\n return response\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0')"},{"alpha_fraction":0.6227173209190369,"alphanum_fraction":0.6300219297409058,"avg_line_length":47.05263137817383,"blob_id":"995f3067cc0ef03abc6497b351648184e3b7bf5e","content_id":"d33408e55fd78200a80406e063fff0827b94da37","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2738,"license_type":"no_license","max_line_length":183,"num_lines":57,"path":"/model_module/cnn_classifier.py","repo_name":"michjk/Question_Classifier_Pytorch","src_encoding":"UTF-8","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass CNNClassifier(nn.Module):\n\n '''\n CNNClassifier is Question Classifier base on CNN\n\n Args:\n embedding_dim (int): The size of word vector.\n vocab_size (int): The number of words/tokens in vocabulary.\n label_size (int): The number of possible labels.\n kernel_num (int): The number of kernels/filters for each kernels/filters size\n kernel_sizes (str/list): The list of kernel sizes. It can be in string such as \"3, 4, 5\" or in list such as [3, 4, 5]\n pretrained_embedding_weight (torch.Tensor): The pretrained word vectors (optional).\n train_embedding_layer (bool): Whether to train embedding layer or let embedding layer to be fixed.\n dropout (float): The probability of dropout's action.\n use_gpu (bool): Whether to use GPU or not\n layers: List of preconstructed QRNN layers to use for the QRNN module (optional).\n\n Inputs: x\n - x (seq_len, batch, input_size): tensor containing the features of the input sequence.\n Output: logsoftmax\n - logsoftmax (batch, label_size) : tensor result of log softmax\n '''\n def __init__(self, embedding_dim, vocab_size, label_size, kernel_num, kernel_sizes, pretrained_embedding_weight = None, train_embedding_layer = True, dropout = 0, use_gpu = True):\n super().__init__()\n \n #check if kernel sizes in str\n if isinstance(kernel_sizes, str):\n kernel_sizes = [int(i) for i in kernel_sizes.split(',')]\n\n #build network\n self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)\n if not (pretrained_embedding_weight is None): #check if pretrain word vectors are used\n self.word_embeddings.weight.data = pretrained_embedding_weight\n self.word_embeddings.weight.requires_grad = train_embedding_layer #need training or static\n self.convs1 = nn.ModuleList([nn.Conv2d(1, kernel_num, (K, embedding_dim)) for K in kernel_sizes])\n self.dropout = nn.Dropout(dropout)\n self.fc1 = nn.Linear(len(kernel_sizes)*kernel_num, label_size)\n\n #use gpu\n if use_gpu:\n self.cuda()\n \n def forward(self, x):\n x.t_() # transpose to match Conv2d input\n x = self.word_embeddings(x) # (N,W,D)\n x = x.unsqueeze(1) # (N,Ci,W,D)\n x = [F.relu(conv(x)).squeeze(3) for conv in self.convs1] # [(N,Co,W), ...]*len(Ks)\n x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x] # [(N,Co), ...]*len(Ks)\n x = torch.cat(x, 1)\n x = self.dropout(x) # (N,len(Ks)*Co)\n logit = self.fc1(x) # (N,C)\n log_probs = F.log_softmax(logit) # log of softmax\n return log_probs"},{"alpha_fraction":0.6423264741897583,"alphanum_fraction":0.643812358379364,"avg_line_length":33.639705657958984,"blob_id":"ef52a7ccf28667e1725916eac0b8e4b286e1cfe4","content_id":"18cb177a1e8bcd67275719cab9af174c79958fd7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4711,"license_type":"no_license","max_line_length":134,"num_lines":136,"path":"/telegram_bot.py","repo_name":"michjk/Question_Classifier_Pytorch","src_encoding":"UTF-8","text":"from telegram import InlineKeyboardButton, InlineKeyboardMarkup\nfrom telegram.ext import Updater, CommandHandler, CallbackQueryHandler, ConversationHandler, MessageHandler, Filters\n\nimport requests\nimport logging\nimport argparse\nimport traceback\n\n#from google_sheet_api import send_log\n\nparser = argparse.ArgumentParser(description=\"This is for establishing telegram bot\")\nparser.add_argument('-t', '--token', help=\"telegram token\", required=True)\nparser.add_argument('--ip', help=\"IP address\", required = True)\n\nargs = parser.parse_args()\n\nTOKEN = args.token\nURL = args.ip\n\n# http request logging\nlogging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\n\n#Next stage conversation\nFEEDBACK = 0\nRECOMMENDED = 1\ncache_data = {}\n\ndef predict(bot, update):\n try:\n predict_msg = {'question': update.message.text}\n logger.debug(\"Message: \" + update.message.text)\n resp = requests.get(URL+\"/predict\", params=predict_msg)\n logger.debug(\"Predicted: \" + resp.json()['result'])\n update.message.reply_text(\"Possible topic: \" + resp.json()['result'])\n\n question = predict_msg\n predicted = resp.json()['result']\n \n cache_data[update.message.chat_id] = []\n cache_data[update.message.chat_id].append(update.message.text)\n cache_data[update.message.chat_id].append(predicted)\n \n keyboard = [\n [\n InlineKeyboardButton(u\"Yes\", callback_data=\"yes\"),\n InlineKeyboardButton(u\"No\", callback_data=\"no\")\n ]\n ]\n\n reply_markup = InlineKeyboardMarkup(keyboard)\n update.message.reply_text(\"Do you satisfied with the answer?\", reply_markup = reply_markup)\n \n return FEEDBACK\n except:\n logger.debug(traceback.format_exc())\n update.message.reply_text(\"Error, please repeat again!\")\n\ndef feedback(bot, update):\n try:\n query = update.callback_query\n logger.debug(\"Satisfied: \" + query.data)\n if (query.data == 'yes'):\n cache_data[query.message.chat_id].append(\"none\")\n #send_log(cache_data[query.message.chat_id])\n query.message.reply_text(\"Thanks for responding\")\n return\n keyboard = [\n [InlineKeyboardButton(u\"Academic\", callback_data=\"Academic\")],\n [InlineKeyboardButton(u\"Admission & Financial Services\", callback_data=\"Admission & Financial Services\")],\n [InlineKeyboardButton(u\"Campus Life & Accommodation\", callback_data=\"Campus Life & Accommodation\")],\n [InlineKeyboardButton(u\"Outreach & Exchange\", callback_data=\"Outreach & Exchange\")],\n [InlineKeyboardButton(u\"Other services\", callback_data=\"Other services\")]\n \n ]\n\n reply_markup = InlineKeyboardMarkup(keyboard)\n query.message.reply_text(\"Choose your recommendation?\", reply_markup = reply_markup) \n\n return RECOMMENDED\n except:\n logger.debug(traceback.format_exc())\n query.message.reply_text(\"Error, please repeat again!\")\n\ndef recommended(bot, update):\n query = update.callback_query\n logger.debug(\"Recommended: \" + query.data)\n \n cache_data[query.message.chat_id].append(query.data)\n #send_log(cache_data[query.message.chat_id])\n\n query.message.reply_text(\"Thanks for responding\")\n return\n\ndef help_command(bot, update):\n try:\n update.message.reply_text(\"This is NTU FAQ topic classifier. Send a message and you get the predicted topic. The topic are:\" \\\n \"\\n1. Academic\" \\\n \"\\n2. Admission & Financial Services\" \\\n \"\\n3. Campus Life & Accommodation\" \\\n \"\\n4. Outreach & Exchange\" \\\n \"\\n5. Other services\" \\\n )\n except:\n logger.debug(traceback.format_exc())\n\ndef main():\n # Create Updater object and attach dispatcher to it\n updater = Updater(TOKEN)\n dispatcher = updater.dispatcher\n \n # Add command handler to dispatcher\n\n conv_handler = ConversationHandler(\n entry_points=[MessageHandler(Filters.text, predict)],\n states={\n FEEDBACK: [CallbackQueryHandler(feedback)],\n RECOMMENDED: [CallbackQueryHandler(recommended)]\n },\n fallbacks=[MessageHandler(Filters.text, predict)]\n )\n help_handler = CommandHandler('help', help_command)\n \n dispatcher.add_handler(conv_handler)\n dispatcher.add_handler(help_handler)\n\n # Start the bot\n updater.start_polling()\n logger.debug(\"Bot started!\")\n\n # Run the bot until you press Ctrl-C\n updater.idle()\n\nif __name__ == '__main__':\n main()\n"},{"alpha_fraction":0.6585691571235657,"alphanum_fraction":0.6615055799484253,"avg_line_length":48.25,"blob_id":"d27c635a71dd1bef7b93b2e015e32a80f32b5065","content_id":"a8f99408e054998aba747ccd4d4758e579575218","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3746,"license_type":"no_license","max_line_length":286,"num_lines":76,"path":"/model_module/qrnn_classifier.py","repo_name":"michjk/Question_Classifier_Pytorch","src_encoding":"UTF-8","text":"import torch\nimport torch.nn as nn\nimport torch.autograd as autograd\nimport torch.nn.functional as F\n\nimport torchqrnn.forget_mult\nfrom torchqrnn import QRNN\n\nclass QRNNClassifier(nn.Module):\n '''\n QRNNClassifier is classification module based on QRNN\n Args:\n embedding_dim (int): The size of word vector.\n vocab_size (int): The number of words/tokens in vocabulary.\n label_size (int): The number of possible labels.\n hidden_dim: The number of features in the hidden state h of QRNN\n pretrained_embedding_weight (torch.Tensor): The pretrained word vectors (optional).\n train_embedding_layer (bool): Whether to train embedding layer or let embedding layer to be fixed.\n num_layers (int): The number of layers of QRNN.\n save_prev_x (bool): Whether to store previous inputs for use in future convolutional windows of QRNN (i.e. for a continuing sequence such as in language modeling). If true, you must call reset to remove cached previous values of x (reset is not yet implemented). Default: False.\n window (int): Defines the size of the convolutional window (how many previous tokens to look when computing the QRNN values). Supports 1 and 2. Default: 1.\n dropout (float): The probability of dropout in QRNN and dropout layer.\n zoneout (float): Whether to apply zoneout of QRNN (i.e. failing to update elements in the hidden state) to the hidden state updates. Default: 0.\n use_gpu (bool): Whether to use GPU or not\n \n Inputs: x\n - x (seq_len, batch, input_size): tensor containing the features of the input sequence.\n \n Output: logsoftmax\n - logsoftmax (batch, label_size) : tensor result of log softmax\n '''\n def __init__(self, embedding_dim, hidden_dim, vocab_size, label_size, pretrained_embedding_weight = None, train_embedding_layer = True, num_layers = 1, dropout = 0, zoneout = 0, window = 1, save_prev_x = False, use_gpu=True):\n super().__init__()\n \n #initialize properties\n self.hidden_dim = hidden_dim\n self.num_layers = num_layers\n self.use_gpu = use_gpu\n self.use_pretrained_word_embedding = not (pretrained_embedding_weight is None)\n self.train_embedding_layer = train_embedding_layer\n self.pretrained_embedding_weight = pretrained_embedding_weight\n\n #create nn module\n self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)\n if self.use_pretrained_word_embedding:\n self.word_embeddings.weight.data = pretrained_embedding_weight\n self.word_embeddings.weight.requires_grad = train_embedding_layer\n self.qrnn = QRNN(embedding_dim, hidden_dim, dropout=dropout, zoneout=zoneout, window = window, save_prev_x = save_prev_x, num_layers=num_layers, use_cuda = use_gpu)\n self.dropout = nn.Dropout(dropout)\n self.hidden_to_label = nn.Linear(hidden_dim, label_size)\n \n #use gpu or not\n if use_gpu:\n self.cuda()\n \n def init_hidden(self):\n '''\n Initialize weight of hidden state\n '''\n self.hidden = autograd.Variable(torch.zeros(self.num_layers, self.batch_size, self.hidden_dim))\n if self.use_gpu:\n self.hidden.cuda()\n \n def forward(self, sentence):\n #get batch size and init hidden\n self.batch_size = sentence.data.shape[1]\n self.init_hidden()\n \n embeds = self.word_embeddings(sentence) # (N,W,D)\n x = embeds.view(len(sentence), self.batch_size, -1)\n out, self.hidden = self.qrnn(x, self.hidden)\n out = self.dropout(out)\n y = self.hidden_to_label(out[-1])\n log_probs = F.log_softmax(y)\n \n return log_probs\n\n\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6177293658256531,\n \"alphanum_fraction\": 0.6194401383399963,\n \"avg_line_length\": 38.19512176513672,\n \"blob_id\": \"562d448bdc6fe29933c6d7ef9fc6d6d7eba6164b\",\n \"content_id\": \"711d570fe04419d9bfe6d8543c8876bc682da7de\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6430,\n \"license_type\": \"no_license\",\n \"max_line_length\": 221,\n \"num_lines\": 164,\n \"path\": \"/data_module/data_writer.py\",\n \"repo_name\": \"michjk/Question_Classifier_Pytorch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nimport shutil\\n\\nimport torch\\n\\nimport matplotlib\\nmatplotlib.use('Agg')\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\n\\nimport numpy as np\\nimport pandas as pd\\nfrom pandas_ml import ConfusionMatrix\\n\\nimport sklearn\\n\\nfrom tensorboard_logger import Logger\\n\\nimport dill as pickle\\n\\nclass PreprocessingPipelineWriter:\\n '''\\n Class for saving data.Field for production.\\n\\n Inputs:\\n - result_folder_path (string): folder for saving data\\n - saved_text_pipeline_file_path: file path relative to result_folder_path for saving data.Field for text\\n - saved_label_pipeline_file_path: file path relative to result_folder_path for saving data.Field for label\\n '''\\n def __init__(self, result_folder_path = \\\"runs\\\", saved_text_pipeline_file_path = \\\"text_pipeline.pkl\\\", saved_label_pipeline_file_path = \\\"label_pipeline.pkl\\\"):\\n self.result_folder_path = result_folder_path\\n self.saved_text_pipeline_file_path = os.path.join(result_folder_path, saved_text_pipeline_file_path)\\n self.saved_label_pipeline_file_path = os.path.join(result_folder_path, saved_label_pipeline_file_path)\\n \\n def save_pipeline(self, data_pipeline, label=False):\\n '''\\n Save data.Field\\n\\n Inputs:\\n - data_piepline: data.Field to be saved\\n '''\\n os.makedirs(self.result_folder_path, exist_ok=True)\\n if not label:\\n dirname = os.path.dirname(self.saved_text_pipeline_file_path)\\n os.makedirs(dirname, exist_ok=True)\\n pickle.dump(data_pipeline, open(self.saved_text_pipeline_file_path, 'wb'))\\n else:\\n dirname = os.path.dirname(self.saved_label_pipeline_file_path)\\n os.makedirs(dirname, exist_ok=True)\\n pickle.dump(data_pipeline, open(self.saved_label_pipeline_file_path, 'wb')) \\n \\nclass PlotWriter:\\n '''\\n Save plot that can be openned by tensorboard\\n\\n Inputs:\\n path (string): path to the directory for saving plot\\n '''\\n def __init__(self, path):\\n self.plot_instance = Logger(path)\\n \\n def log_value(self, name, value, step):\\n '''\\n write plot file\\n \\n Inputs:\\n - name (string): label of plot\\n - value (double): value to be written (y-axis)\\n - step (double): epoch (x-axis)\\n '''\\n\\n self.plot_instance.log_value(name, value, step)\\n\\nclass LearningWriter:\\n '''\\n Class writter train & test plot (accuracy and loss) and model.\\n\\n Inputs:\\n - label_map (dict): map index to topic string\\n - result_folder_path (str): path to folder for saving result\\n - saved_model_file_path (str): path to file relative to result_folder_path for trained model\\n - train_log_folder_path (str): path to folder relative to result_folder_path for saving train plot\\n - dev_log_folder_path (str): path to folder relative to result_folder_path for saving test plot\\n - confusion_matrix_folder_path (str): path to folder relative to result_folder_path for saving confusion matrix\\n \\n '''\\n def __init__(self, label_map, result_folder_path = \\\"runs\\\", saved_model_file_path = \\\"saved_model.model\\\", train_log_folder_path = \\\"train\\\", dev_log_folder_path = \\\"dev\\\", confusion_matrix_folder_path = \\\"confusion_matrix\\\"):\\n self.result_path = result_folder_path\\n self.saved_model_file_path = os.path.join(result_folder_path, saved_model_file_path)\\n self.train_log_folder_path = os.path.join(result_folder_path, train_log_folder_path)\\n self.dev_log_folder_path = os.path.join(result_folder_path, dev_log_folder_path)\\n self.confusion_matrix_file_path = os.path.join(result_folder_path, confusion_matrix_folder_path, 'confusion_matrix.png')\\n self.label_map = label_map\\n\\n def initialize(self):\\n '''\\n Initialize folder creation\\n '''\\n os.makedirs(self.result_path, exist_ok=True)\\n \\n self.train_logger = PlotWriter(self.train_log_folder_path)\\n self.dev_logger = PlotWriter(self.dev_log_folder_path)\\n \\n dirname = os.path.dirname(self.saved_model_file_path)\\n os.makedirs(dirname, exist_ok=True)\\n\\n dirname = os.path.dirname(self.confusion_matrix_file_path)\\n os.makedirs(dirname, exist_ok=True)\\n\\n def train_log_value(self, name, value, step):\\n '''\\n Write train plot\\n\\n Inputs:\\n - name (str): label/tag of the plot\\n - value (double): value to be written (y-axis)\\n - step (double): current epoch (x-axis)\\n '''\\n self.train_logger.log_value(name, value, step)\\n \\n def dev_log_value(self, name, value, step):\\n '''\\n Write test plot\\n\\n Inputs:\\n - name (str): label/tag of the plot\\n - value (double): value to be written (y-axis)\\n - step (double): current epoch (x-axis)\\n '''\\n self.dev_logger.log_value(name, value, step)\\n \\n def save_model(self, model):\\n '''\\n Save model inside result folder\\n \\n Inputs:\\n - model (torch.nn): Pytorch model to be saved\\n '''\\n torch.save(model, self.saved_model_file_path)\\n print(\\\"model saved at\\\", self.saved_model_file_path)\\n\\n def save_confusion_matrix(self, truth_res, pred_res):\\n '''\\n Save confusion matrix in result folder and print Recall, Precision, and F1 score in console.\\n\\n Inputs:\\n - truth_res (list): list of truth label\\n - pred_res (list): list of predicted label\\n '''\\n # create confusion matrix data structure\\n s = sklearn.metrics.confusion_matrix(truth_res, pred_res)\\n df_cm = pd.DataFrame(data = s, columns=self.label_map, index=self.label_map)\\n plt.figure(dpi=100)\\n \\n # create heatmap png\\n heatmap = sns.heatmap(df_cm, annot=True, fmt='d')\\n heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=70, ha='right', fontsize=5)\\n heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=20, ha='right', fontsize=5)\\n\\n plt.savefig(self.confusion_matrix_file_path)\\n\\n # print recall, precission, F1 score\\n confusion_matrix = ConfusionMatrix(truth_res, pred_res)\\n confusion_matrix.print_stats()\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.531874418258667,\n \"alphanum_fraction\": 0.5341233611106873,\n \"avg_line_length\": 35.70158767700195,\n \"blob_id\": \"f018771ca35655550b4ee0c6b95a05f0a8c0f876\",\n \"content_id\": \"18e19ed01a6e4bd23b1262218f334aa5554f096a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 11561,\n \"license_type\": \"no_license\",\n \"max_line_length\": 137,\n \"num_lines\": 315,\n \"path\": \"/model_module/model_runner.py\",\n \"repo_name\": \"michjk/Question_Classifier_Pytorch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nfrom data_module.data_writer import * \\nimport shutil\\nimport torch\\nfrom torchtext import data\\nfrom sklearn.model_selection import KFold\\nimport numpy as np\\nimport copy\\nimport torch.autograd as autograd\\n\\ndef sort_key(ex):\\n '''\\n Function for sorting dataset based on length. It is applicable for sorting test dataset.\\n\\n Inputs:\\n - ex (data.Example): one record of dataset\\n '''\\n return len(ex.text)\\n\\nclass ModelRunner:\\n '''\\n This is class for run training model and also cross validation model\\n '''\\n def __init__(self, model_factory, loss_factory, optimizer_factory, epochs, batch_size, learning_logger, use_gpu = True):\\n '''\\n Inputs:\\n - model_factory (FactoryClass): a factory class for creating a model.\\n - loss_factory (FactoryClass): a factory class for creating loss function.\\n - optimizer_factory (FactoryClass): a factory class for creating optimizer function\\n - epochs (int): number of epochs or rounds of training\\n - batch_size (int): batch size of dataset to be passed to model in one foward pass\\n - learning_logger: Class for writing logs and trained model\\n - use_gpu (boolean): use gpu for training\\n '''\\n self.model_factory = model_factory\\n self.loss_factory = loss_factory\\n self.optimizer_factory = optimizer_factory\\n self.epochs = epochs\\n self.learning_logger = learning_logger\\n self.use_gpu = use_gpu\\n self.batch_size = batch_size\\n \\n def get_iterator(self, dataset, batch_size, train=True, shuffle=True, repeat=False, sort_key = sort_key):\\n '''\\n Generate iterator from torchtext.data.Dataset\\n\\n Inputs:\\n - dataset (data.Dataset): dataset instance\\n - batch_size (int)\\n - train (boolean): Is it train dataset or test dataset\\n - shuffle (boolean): shuffle dataset or not\\n - repeat (boolean): repeat iteration or not\\n - sort_key (boolean): repeat dataset iteration or not\\n '''\\n device = -1\\n if self.use_gpu:\\n device = None\\n \\n if train:\\n sort_key = None\\n\\n dataset_iter = data.Iterator(\\n dataset, batch_size=batch_size, device=device,\\n train=train, shuffle=shuffle, repeat=repeat, sort_key=sort_key\\n )\\n\\n return dataset_iter\\n\\n def get_dataset_cv(self, train_data, n_folds = 10):\\n '''\\n Generate n_folds set iterator from torchtext.data.Dataset for cross validation\\n\\n Inputs:\\n - train_data (data.Dataset): train dataset instance\\n - n_folds (int): number of folds\\n '''\\n train_examples = train_data.examples\\n train_fields = train_data.fields\\n \\n # create folds\\n def iter_folds():\\n train_examples_np = np.array(train_examples)\\n kf = KFold(n_splits=n_folds)\\n for train_idx, val_idx in kf.split(train_examples_np):\\n yield (\\n data.Dataset(train_examples_np[train_idx], train_fields),\\n data.Dataset(train_examples_np[val_idx], train_fields)\\n )\\n \\n return iter_folds()\\n \\n def learn(self, train_data, dev_data):\\n '''\\n Start train model\\n\\n Inputs:\\n - train_data (data.Dataset): training dataset\\n - dev_data (data.Dataset): test dataset\\n '''\\n best_dev_acc = 0\\n best_dev_loss = None\\n best_truth_res = []\\n best_pred_res = []\\n \\n # create model instance\\n self.model = self.model_factory.create_class()\\n \\n # create optmizer instance\\n update_parameter = filter(lambda p: p.requires_grad, self.model.parameters()) # only update parameter that indicates need updates\\n self.optimizer = self.optimizer_factory.create_class({'params':update_parameter})\\n \\n # create loss function\\n self.loss_function = self.loss_factory.create_class()\\n\\n # generate iterator\\n train_iter = self.get_iterator(train_data, self.batch_size)\\n dev_iter = self.get_iterator(dev_data, len(dev_data), train=False)\\n\\n # initialize folder\\n self.learning_logger.initialize()\\n\\n # start training\\n for i in range(self.epochs):\\n # train\\n print('epoch: %d start!' % i)\\n self.learn_epoch(train_iter, i)\\n \\n # test\\n print('now best dev acc:',best_dev_acc)\\n dev_acc, dev_loss, truth_res, pred_res = self.evaluate(dev_iter, i)\\n \\n # save best model up to current epoch\\n if best_dev_loss is None or dev_loss < best_dev_loss:\\n best_dev_acc = dev_acc\\n best_dev_loss = dev_loss\\n print('New Best Dev!!!')\\n self.learning_logger.save_model(self.model)\\n best_truth_res = truth_res\\n best_pred_res = pred_res\\n \\n print(\\\"best model accuracy: \\\", best_dev_acc)\\n print(\\\"best model error: \\\", best_dev_loss)\\n self.learning_logger.save_confusion_matrix(best_truth_res, best_pred_res)\\n \\n def learn_cv(self, train_data, n_folds):\\n '''\\n Start cross validate model\\n\\n Inputs:\\n - train_data (data.Dataset): training dataset\\n - n_folds (int): number of folds\\n '''\\n best_val_acc_list = []\\n best_val_loss_list = []\\n\\n # genereate iterators for each folds\\n train_val_generator = self.get_dataset_cv(train_data, n_folds=n_folds)\\n \\n # start cross validation\\n for fold, (train_data_fold, val_data_fold) in enumerate(train_val_generator):\\n \\n # create model\\n self.model = self.model_factory.create_class()\\n \\n # create optimizer\\n update_parameter = filter(lambda p: p.requires_grad, self.model.parameters())\\n self.optimizer = self.optimizer_factory.create_class({'params':update_parameter})\\n \\n # create loss function\\n self.loss_function = self.loss_factory.create_class()\\n \\n print(\\\"Fold: \\\", fold)\\n best_val_acc = 0.0\\n best_val_loss = None\\n \\n best_truth_res = []\\n best_pred_res = []\\n\\n # create data iterator\\n train_iter = self.get_iterator(train_data_fold, self.batch_size)\\n val_iter = self.get_iterator(val_data_fold, len(val_data_fold), train=False)\\n for i in range(self.epochs):\\n # train\\n print('epoch: %d start!' % i)\\n self.learn_epoch(train_iter, i, cv=True)\\n \\n # test\\n print('now best dev acc:',best_val_acc)\\n val_acc, val_loss, truth_res, pred_res = self.evaluate(val_iter, i, cv=True)\\n \\n # best model from entire epoch\\n if best_val_loss is None or val_loss < best_val_loss:\\n best_val_acc = val_acc\\n best_val_loss = val_loss\\n print('New Best Dev!!!')\\n best_truth_res = truth_res\\n best_pred_res = pred_res\\n print(\\\"best val acc: \\\", best_val_acc)\\n print(\\\"best val loss: \\\", best_val_loss)\\n best_val_acc_list.append(best_val_acc)\\n best_val_loss_list.append(best_val_loss)\\n \\n # output result\\n print(\\\"All cross validation accuracy: \\\", best_val_acc_list)\\n print(\\\"Avg cross validation accuracy: \\\", np.average(best_val_acc_list))\\n\\n print(\\\"All cross validation loss: \\\", best_val_loss_list)\\n print(\\\"Avg cross validation loss: \\\", np.average(best_val_loss_list))\\n \\n \\n def learn_epoch(self, train_iter, i, cv = False):\\n '''\\n Train in one epoch\\n\\n Inputs:\\n - train_iter (iterator): Iterator train dataset\\n - i (int): i-th epoch\\n - cv (boolean): cross validation process or not\\n \\n '''\\n self.model.train()\\n \\n avg_loss = 0.0\\n count = 0\\n truth_res = []\\n pred_res = []\\n \\n # pass each batch to model\\n for batch in train_iter:\\n sent, label = batch.text, batch.label\\n truth_res += [int(x) for x in label.data]\\n pred = self.model(sent)\\n pred_label = pred.data.max(1)[1]\\n pred_res += [int(x) for x in pred_label]\\n self.model.zero_grad()\\n loss = self.loss_function(pred, label)\\n avg_loss += float(loss.data[0])\\n count += 1\\n \\n if count % 100 == 0:\\n print('epoch: %d iterations: %d loss :%g' % (i, count*model.batch_size, loss.data[0]))\\n \\n loss.backward()\\n self.optimizer.step()\\n \\n # calculate result\\n avg_loss /= len(train_iter)\\n acc = self.get_accuracy(truth_res,pred_res)\\n print('epoch: %d done!\\\\ntrain avg_loss:%g , acc:%g'%(i, avg_loss, acc))\\n\\n # not support saving accuracy & loss for cross validation \\n if not cv:\\n self.learning_logger.train_log_value(\\\"accuracy\\\", acc, i)\\n self.learning_logger.train_log_value(\\\"loss\\\", avg_loss, i)\\n\\n def evaluate(self, eval_iter, i, cv=False):\\n '''\\n Evaluate model in one epoch\\n \\n Inputs:\\n - eval_iter (iterator): test/eval dataset iterator\\n - i (int): i-th epoch\\n - cv : cross validation or not\\n \\n Outputs:\\n - acc (double): accuracy\\n - avg_loss (double): average loss of current trained model\\n - truth_res (list): truth label\\n - pred_res (list): predicted label\\n '''\\n self.model.eval()\\n \\n avg_loss = 0.0\\n truth_res = []\\n pred_res = []\\n \\n # pass each batch to model\\n for batch in eval_iter:\\n sent, label = batch.text, batch.label\\n truth_res += [int(x) for x in label.data]\\n pred = self.model(sent)\\n pred_label = pred.data.max(1)[1]\\n pred_res += [int(x) for x in pred_label]\\n loss = self.loss_function(pred, label)\\n avg_loss += float(loss.data[0])\\n \\n #calculate result\\n avg_loss /= len(eval_iter)\\n acc = self.get_accuracy(truth_res, pred_res)\\n print('dev avg_loss:%g train acc:%g' % (avg_loss, acc))\\n \\n # not support saving accuracy & loss for cross validation\\n if not cv:\\n self.learning_logger.dev_log_value(\\\"accuracy\\\", acc, i)\\n self.learning_logger.dev_log_value(\\\"loss\\\", avg_loss, i)\\n\\n return acc, avg_loss, truth_res, pred_res\\n \\n def get_accuracy(self, truth, pred):\\n '''\\n Calculate percentage accuracy\\n\\n Inputs:\\n - truth (list): list of truth label\\n - pre (list): list of predicted label\\n \\n Outputs:\\n - accuracy in percentage\\n '''\\n assert len(truth)==len(pred)\\n right = 0\\n for i in range(len(truth)):\\n if truth[i]==pred[i]:\\n right += 1.0\\n return right/len(truth)\\n\"\n },\n {\n \"alpha_fraction\": 0.7665607333183289,\n \"alphanum_fraction\": 0.7829381823539734,\n \"avg_line_length\": 50.131248474121094,\n \"blob_id\": \"20f6725933f1b9bd7948c67b9e6bfbdc695a2f84\",\n \"content_id\": \"60bae58e9df6894671e4c198e221ac400a38e33d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 8182,\n \"license_type\": \"no_license\",\n \"max_line_length\": 405,\n \"num_lines\": 160,\n \"path\": \"/README.md\",\n \"repo_name\": \"michjk/Question_Classifier_Pytorch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Question_Classifier_Pytorch\\nPytorch Implementation for question classification model\\n\\n## Requirements\\nThe application is written in Python 3 and runs on Ubuntu 16.04 server with dedicated GPU. The following Ubuntu 16.04 packages should be installed:\\n1. Python 3\\n2. virtualenv\\n3. Git\\n4. CUDA 9/8\\n5. cuDNN 6\\n6. Nginx\\n7. uWSGI\\n\\nBefore running Python application or installing Python dependencies, please create a virtualenv and activate the respective virtualenv.\\nTo create virtualenv:\\n```\\nvirtualenv -p python3 virtualenv_name\\n```\\nTo activate the created virtualenv:\\n```\\nsource virtualenv_name/bin/activate\\n```\\nTo install python depencies library for this appliaction:\\n```\\npip install http://download.pytorch.org/whl/cu90/torch-0.4.0-cp35-cp35m-linux_x86_64.whl # for CUDA 9 or pip install torch for CUDA 8 \\npip install cupy-cuda90 # or cupy-cuda80\\npip install pynvrtc git+https://github.com/salesforce/pytorch-qrnn # for installing QRNN\\npip install spacy\\npython -m spacy download en #for downloading English model for spaCy tokenizer\\npip install cffi # probably not needed by just try\\npip install -r requirements.txt\\n```\\nIf it fails, please look at requirements.txt and install one by one.\\n\\n## Dataset\\nThe dataset should be seperated into training file and test file in CSV format.\\nWhen preparing CSV file, the dataset should not use indexing.\\nIf you want to save CSV file using Pandas, use Python command:\\n```\\npandas.to_csv('file_name', index=False)\\n```\\n\\n## Project Structure\\nThis project is basically my own framework for developing question classification. It is mostly built with Pytorch and also torchtext for Pytorch specific NLP preprocessor.\\nThe framework suggest user to load module from 3 different folder.\\n1. data_module: The folder consist of function for loading & preprocessing dataset and writing results.\\n2. model_module: The folder consist of several classification models and a module for running training.\\n\\nThere is utils.py file which contains utility to load parameters from json file.\\nYou can learn how to extend the framework from the current implementation.\\n\\n## Models\\nCurrently 3 different models are provided for question classification:\\n1. CNN (Convolutional Neural Network) \\n\\nEven though CNN is created for image related problem. It can be used for text classifiction. Some papers related to the CNN model are [\\nConvolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1408.5882) and [\\nA Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1510.03820).\\n\\n2. LSTM (Long Short Term Memory)\\n\\nLSTM is RNN based neural network architecture that solves vanishing gradient problem and long-term dependencies problem. It is a natural way to learn text pattern in sequential manner. The theory behind LSTM can be found in [Understanding LSTMs](http://colah.github.io/posts/2015-08-Understanding-LSTMs/). With additional fully connected layer and softmax layer, LSTM can be used for text classification. \\n\\n3. QRNN (Quasi Recurrent Neural Networks) \\n\\nQRNN is RNN based neural network architecture that try to solve disadvantages of LSTM and CNN. LSTM is good for long-term dependency, but it is slow since it learn in sequential manner. Also, CNN can use parallelism with convolution function, but it cannot learn long-term dependencies. The theory behind QRNN is published in [Quasi Recurrent Neural Networks](https://arxiv.org/abs/1611.01576).\\n\\n## Run training to save model and get test accuracy and loss\\nExample Python file are provided as example for training. For every training, please add new git commit if you indicate use_git = True in parameter json file to ensure there is no clash in saving result.\\n\\nQRNN\\n```\\npython train_qrnn.py --path train_rnn_parameter.json\\n```\\n\\nLSTM\\n```\\npython train_lstm.py --path train_rnn_parameter.json\\n```\\n\\nCNN\\n```\\npython train_cnn.py --path train_cnn_parameter.json\\n```\\nThe result are saved under result_folder_path. For accuracy & loss plot, use tensorboard to visualize them (will be explained).\\n\\n## k-fold Cross Validation of model\\nQRNN\\n```\\npython train_cv_qrnn.py --path train_rnn_parameter.json\\n```\\n\\nLSTM\\n```\\npython train_cv_lstm.py --path train_rnn_parameter.json\\n```\\n\\nCNN\\n```\\npython train_cv_cnn.py --path train_cnn_parameter.json\\n```\\nNote: currently the cross validation result are printed to console, not saved \\n\\n## JSON file for training model\\nThe JSON file are already prepared with appropriate setting. You can have parameter name different from example, but it is best to follow the example.Most of the setting are for model hyperparameter, loss function parameter, and optimizer parameter that has same parameter name as the model, loss function, and optimizer itselves.\\nBesides that, another parameter that important:\\n1. \\\"epoch\\\": number of epochs\\n2. \\\"batch_size\\\": batch size of training data\\n3. \\\"max_text_length\\\": max lenght of a sentence (must be indicated for CNN)\\n4. \\\"train_dataset_path\\\": train data path\\n5. \\\"dev_dataset_path\\\": test data path\\n6. \\\"result_folder_path\\\": where to save result such as best model, test accuracy (not available for cross validation)\\n7. \\\"use_git\\\": whether to use current commit information for better result versioning. if true result_folder_path become result_folder_path/branch_name_commit_date\\n8. \\\"n_folds\\\": number of k-fold for cross validation\\n9. \\\"saved_model_file_path\\\": file path relative to result_folder_path to save trained model\\n10. \\\"saved_text_pipeline_file_path\\\": file path relative to result_folder_path to save text preprocessing data\\n11. \\\"saved_label_pipeline_file_path\\\": file path relative to result_folder_path to save label preprocessing data\\n12. \\\"train_log_folder_path\\\": file path relative to result_folder_path to save train plot for tensorboard\\n13. \\\"dev_log_folder_path\\\": file path relative to result_folder_path to save test/evaluation plot for tensorboard.\\n14. \\\"confusion_matrix_folder_path\\\": folder path relative to result_folder_path to save confusion matrix of test result.\\n15. \\\"pretrained_word_embedding_name\\\": name of pretrained word embedding vectors. Currently support word2vec and GloVe (glove.6B.300d)\\n16. \\\"pretrained_word_embedding_path\\\": path to word embedding vectors file (support word2vec only example: \\\"../dataset/GoogleNews-vectors-negative300.bin\\\")\\n\\nPlease look at the json file and try to run training first to understand more\\n\\n## View train & test accuracy and loss plot with tensorboard\\nPlease run\\n```\\ntensorboard --logdir=[result_folder_path] --host 0.0.0.0\\n```\\nTo view graph of test loss and accuracy.\\n\\n## REST API server\\nTo run in debug mode please run\\n```\\npython rest_api_server.py --path rest_api_param.json\\n```\\nThe json file contain important parameter that need to be updated\\n1. \\\"saved_model_file_path\\\": path where the trained model saved\\n2. \\\"debug_log_file_path\\\": file path to debug log\\n3. \\\"error_log_file_path\\\": file path to error log\\n4. \\\"saved_text_pipeline_file_path\\\": data.Field preprocessing information from questions in dataset (it should be in current directory after training)\\n5. \\\"saved_label_pipeline_file_path\\\": data.Field preprocessing information from labels in dataset (it should be in current directory after training)\\n\\nTo run for production, we use uwsgi as server and nginx for receiving simultaneous\\nplease look at [A Guide to Scalling Machine Learning Models in Productions](https://hackernoon.com/a-guide-to-scaling-machine-learning-models-in-production-aa8831163846).\\nThe .ini file is already prepared as uwsgi.ini\\n\\nCurrently, the endpoint to get prediction:\\n```\\nGET IP_ADDRESS/predict?question='question to be predicted'\\n```\\n\\n## Telegram bot for trying model\\nThere is available python file for running Telegram bot as UI for trying the classification model. Please look for tutorial how to make Telegram bot first. After that, run:\\n```\\npython telegram_bot.py --token TELEGRAM_TOKEN --ip http://0.0.0.0:port\\n```\\n--token is found after createing telegram bot. --ip is the target url to REST API model.\\nAlso, google sheet can used for logging prediction and user suggestion, but it is not used by default. You can try to look how to setup google sheet api and use google_sheet_api.py as example. \"\n },\n {\n \"alpha_fraction\": 0.49659863114356995,\n \"alphanum_fraction\": 0.6991103887557983,\n \"avg_line_length\": 15.911504745483398,\n \"blob_id\": \"ec3a2502c45f72af33185fb36202d83c398a08e3\",\n \"content_id\": \"077b6f4c397c123a3584353c94ee8496b9287795\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 1911,\n \"license_type\": \"no_license\",\n \"max_line_length\": 32,\n \"num_lines\": 113,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"michjk/Question_Classifier_Pytorch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"absl-py==0.1.13\\nastor==0.6.2\\nbleach==3.1.4\\ncertifi==2018.4.16\\nchainer==3.5.0\\nchardet==3.0.4\\nclick==6.7\\ncycler==0.10.0\\ncymem==1.31.2\\ncytoolz==0.8.2\\ndecorator==4.2.1\\ndill==0.2.7.1\\nentrypoints==0.2.3\\nenum34==1.1.6\\nfastrlock==0.3\\nfilelock==3.0.4\\nFlask==0.12.2\\nftfy==4.4.3\\nfuture==0.16.0\\ngast==0.2.0\\ngitdb2==2.0.3\\nGitPython==2.1.8\\ngoogle-api-python-client==1.6.5\\ngrpcio==1.10.0\\nhtml5lib==0.9999999\\nhttplib2==0.18.0\\nidna==2.6\\nipykernel==4.8.2\\nipython==6.2.1\\nipython-genutils==0.2.0\\nipywidgets==7.1.2\\nitsdangerous==0.24\\njedi==0.11.1\\nJinja2==2.10\\njsonschema==2.6.0\\njupyter==1.0.0\\njupyter-client==5.2.3\\njupyter-console==5.2.0\\njupyter-core==4.4.0\\nMarkdown==2.6.11\\nMarkupSafe==1.0\\nmatplotlib==2.1.2\\nmistune==0.8.3\\nmsgpack-numpy==0.4.1\\nmsgpack-python==0.5.4\\nmurmurhash==0.28.0\\nnbconvert==5.3.1\\nnbformat==4.4.0\\nnotebook==5.7.8\\nnumpy==1.14.2\\noauth2client==4.1.2\\nonnx==1.1.2\\npandas==0.22.0\\npandas-ml==0.5.0\\npandocfilters==1.4.2\\nparso==0.1.1\\npathlib==1.0.1\\npexpect==4.4.0\\npickleshare==0.7.4\\nPillow==5.1.0\\nplac==0.9.6\\npreshed==1.0.0\\nprompt-toolkit==1.0.15\\nprotobuf==3.5.2.post1\\nptyprocess==0.5.2\\npyasn1==0.4.2\\npyasn1-modules==0.2.1\\npycparser==2.18\\nPygments==2.2.0\\npyparsing==2.2.0\\npython-dateutil==2.6.1\\npython-telegram-bot==9.0.0\\nPyTorch-QRNN==0.2.1\\npytz==2017.3\\nPyYAML==3.12\\npyzmq==17.0.0\\nqtconsole==4.3.1\\nregex==2017.4.5\\nrequests==2.18.4\\nrsa==3.4.2\\nscikit-learn==0.19.1\\nscipy==1.0.0\\nseaborn==0.8.1\\nSend2Trash==1.5.0\\nsimplegeneric==0.8.1\\nsimplejson==3.13.2\\nsix==1.11.0\\nsmmap2==2.0.3\\ntensorboard==1.7.0\\ntensorboard-logger==0.0.4\\ntensorboardX==1.2\\ntensorflow==1.7.0\\ntensorflow-gpu==1.4.1\\ntensorflow-tensorboard==0.4.0rc3\\ntermcolor==1.1.0\\nterminado==0.8.1\\ntestpath==0.3.1\\nthinc==6.10.2\\ntoolz==0.9.0\\ntorchtext==0.2.3\\ntorchwordemb==0.0.8\\ntornado==5.0.1\\ntqdm==4.23.0\\ntraitlets==4.3.2\\nujson==5.2.0\\nuritemplate==3.0.0\\nurllib3==1.22\\nuWSGI==2.0.17\\nuwsgitop==0.10\\nwcwidth==0.1.7\\nWerkzeug==0.14.1\\nwidgetsnbextension==3.1.4\\nwrapt==1.10.11\\n\"\n },\n {\n \"alpha_fraction\": 0.6178725957870483,\n \"alphanum_fraction\": 0.6178725957870483,\n \"avg_line_length\": 30.23958396911621,\n \"blob_id\": \"e868ad0b1fd386705b6fbe11f470b07edb7e13ab\",\n \"content_id\": \"9ab3d10418733b25314e4d085fde5f3b80a7b17a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2999,\n \"license_type\": \"no_license\",\n \"max_line_length\": 127,\n \"num_lines\": 96,\n \"path\": \"/utils.py\",\n \"repo_name\": \"michjk/Question_Classifier_Pytorch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import json\\nimport git\\nimport os\\nimport time\\n\\nclass DotDict(dict):\\n '''\\n dot.notation access to dictionary attributes\\n\\n Inputs: dict: dict\\n - dict: a non-empty dict\\n '''\\n __getattr__ = dict.get\\n __setattr__ = dict.__setitem__\\n __delattr__ = dict.__delattr__\\n\\ndef filter_dotdict_class_propoperty(dotdict_object, class_blueprint):\\n '''\\n Generate new dotdict with properties exist in class_blueprint\\n \\n Inputs: dotdict_object (dotdict), class_blueprint (Object)\\n - dotdict_object: an instance of dotdict\\n - class_blueprint: a class where its properties act as filter for dotdict_object\\n\\n Outputs: dotdict_object: dotdict\\n\\n '''\\n varnames = class_blueprint.__init__.__code__.co_varnames\\n new_dotdict = DotDict({k:v for (k, v) in dotdict_object.items() if k in varnames})\\n return new_dotdict\\n\\ndef load_training_parameter_from_json(path):\\n '''\\n Load parameters from json file to DotDict.\\n\\n Inputs: path (str)\\n - path: path of the json file\\n \\n Outputs: dotdict_object (DotDict)\\n - dotdict_object: loaded parameter\\n '''\\n\\n json_object = json.load(open(path, \\\"r\\\"))\\n dotdict_object = DotDict(json_object)\\n\\n # use git versioning\\n if dotdict_object.use_git:\\n repo = git.Repo(os.getcwd())\\n headcommit = repo.head.commit\\n current_branch = repo.active_branch.name\\n\\n #get current branch + time stamp\\n version = current_branch + \\\"_commited_at_\\\" + time.strftime(\\\"%a_%d_%b_%Y_%H_%M\\\", time.gmtime(headcommit.committed_date))\\n \\n # new result folder path\\n dotdict_object.result_folder_path = os.path.join(dotdict_object.result_folder_path, version)\\n\\n return dotdict_object\\n\\ndef load_rest_api_parameter_from_json(path):\\n '''\\n Load parameters from json file to DotDict.\\n\\n Inputs: path (str)\\n - path: path of the json file\\n \\n Outputs: dotdict_object (DotDict)\\n - dotdict_object: loaded parameter\\n '''\\n json_object = json.load(open(path, \\\"r\\\"))\\n dotdict_object = DotDict(json_object)\\n\\n return dotdict_object\\n\\nclass FactoryClass:\\n '''\\n Class for generating an intance of a class\\n\\n Inputs: class_constructor (Class), param_dict (dict)\\n - class_constructor: a class that will be used to create instance\\n - param_dict: a dict contains parameter for the class_constructor\\n '''\\n def __init__(self, class_contructor, param_dict = {}):\\n\\n self.class_contructor = class_contructor\\n self.param_dict = param_dict\\n \\n def create_class(self, new_param_dict={}):\\n '''\\n Create new instance of class constructor\\n\\n Inputs: new_param_dict (dict)\\n - new_param_dict : dict of new parameter that is not available at param_dict\\n '''\\n new_object = self.class_contructor(**self.param_dict, **new_param_dict)\\n return new_object\\n\"\n },\n {\n \"alpha_fraction\": 0.7992957830429077,\n \"alphanum_fraction\": 0.8010563254356384,\n \"avg_line_length\": 36.24590301513672,\n \"blob_id\": \"2fb998ddc2b98a16c96712a570ca2cb885a55df6\",\n \"content_id\": \"161ff381c0357757bfa8ce5a4a4d81569164816b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2272,\n \"license_type\": \"no_license\",\n \"max_line_length\": 190,\n \"num_lines\": 61,\n \"path\": \"/train_qrnn_cv.py\",\n \"repo_name\": \"michjk/Question_Classifier_Pytorch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import time\\n\\nimport numpy as np\\n\\nimport torch\\nimport torch.autograd as autograd\\nimport torch.nn as nn\\nimport torch.optim as optim\\n\\nfrom model_module.qrnn_classifier import QRNNClassifier\\n\\nfrom data_module.data_preprocessor import *\\n\\nimport os\\nimport random\\n\\nimport datetime\\n\\nfrom model_module.model_runner import ModelRunner\\n\\nfrom utils import load_training_parameter_from_json, filter_dotdict_class_propoperty, FactoryClass\\n\\nfrom data_module.data_writer import LearningWriter, PreprocessingPipelineWriter\\n\\nimport argparse\\n\\nnp.random.seed(1)\\ntorch.manual_seed(1)\\ntorch.cuda.manual_seed_all(1)\\nrandom.seed(1)\\n\\nparser = argparse.ArgumentParser()\\nparser.add_argument(\\\"--path\\\", help=\\\"path parameter json file\\\")\\nparam_json_path = parser.parse_args().path\\n\\nparam = load_training_parameter_from_json(param_json_path)\\nqrnn_parameter = filter_dotdict_class_propoperty(param, QRNNClassifier)\\n\\npreprocessing_pipeline_writer = PreprocessingPipelineWriter(param.result_folder_path, param.saved_text_pipeline_file_path, param.saved_label_pipeline_file_path)\\n\\ntrain_data, dev_data, vocab_size, label_size, label_map, pretrained_embedding_weight = load_dataset(\\n param.train_dataset_path, param.dev_dataset_path, param.max_text_length, preprocessing_pipeline_writer,\\n pretrained_word_embedding_name = param.pretrained_word_embedding_name, pretrained_word_embedding_path = param.pretrained_word_embedding_path\\n)\\n\\nqrnn_parameter.vocab_size = vocab_size\\nqrnn_parameter.label_size = label_size\\nqrnn_parameter.pretrained_embedding_weight = pretrained_embedding_weight\\n\\nmodel_factory = FactoryClass(QRNNClassifier, qrnn_parameter)\\n\\nloss_factory = FactoryClass(nn.NLLLoss)\\n\\noptimizer_param_dict = filter_dotdict_class_propoperty(param, optim.Adam)\\noptimizer_factory = FactoryClass(optim.Adam, optimizer_param_dict)\\n\\nlearning_logger = LearningWriter(label_map, param.result_folder_path, param.saved_model_file_path, param.train_log_folder_path, param.dev_log_folder_path, param.confusion_matrix_folder_path)\\nmodel_runner = ModelRunner(model_factory, loss_factory, optimizer_factory, param.epoch, param.batch_size, learning_logger, param.use_gpu)\\nstart_time = time.time()\\nmodel_runner.learn_cv(train_data, param.n_folds)\\nprint(\\\"Overall time elapsed {} sec\\\".format(time.time() - start_time))\\n\"\n },\n {\n \"alpha_fraction\": 0.7928483486175537,\n \"alphanum_fraction\": 0.7981915473937988,\n \"avg_line_length\": 37.015625,\n \"blob_id\": \"daeced4d69344fc6b905a26a4c4def21911fba86\",\n \"content_id\": \"89db8c15a9c3bed615bb4e26eca591a5dcaad3c1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2433,\n \"license_type\": \"no_license\",\n \"max_line_length\": 190,\n \"num_lines\": 64,\n \"path\": \"/train_lstm.py\",\n \"repo_name\": \"michjk/Question_Classifier_Pytorch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import time\\n\\nimport numpy as np\\n\\nimport torch\\nimport torch.autograd as autograd\\nimport torch.nn as nn\\nimport torch.optim as optim\\n\\nfrom model_module.lstm_classifier import LSTMClassifier\\n\\nfrom data_module.data_preprocessor import *\\n\\nimport os\\nimport random\\n\\nimport datetime\\n\\nfrom model_module.model_runner import ModelRunner\\n\\nfrom utils import load_training_parameter_from_json, filter_dotdict_class_propoperty, FactoryClass\\n\\nfrom data_module.data_writer import LearningWriter, PreprocessingPipelineWriter\\n\\nimport argparse\\n\\nnp.random.seed(1)\\ntorch.manual_seed(1)\\ntorch.cuda.manual_seed_all(1)\\nrandom.seed(1)\\n\\nparser = argparse.ArgumentParser()\\nparser.add_argument(\\\"--path\\\", help=\\\"path parameter json file\\\")\\nparam_json_path = parser.parse_args().path\\n\\nparam = load_training_parameter_from_json(param_json_path)\\nlstm_parameter = filter_dotdict_class_propoperty(param, LSTMClassifier)\\n\\npreprocessing_pipeline_writer = PreprocessingPipelineWriter(param.result_folder_path, param.saved_text_pipeline_file_path, param.saved_label_pipeline_file_path)\\n\\ntrain_data, dev_data, vocab_size, label_size, label_map, pretrained_embedding_weight = load_dataset(\\n param.train_dataset_path, param.dev_dataset_path, param.max_text_length, preprocessing_pipeline_writer,\\n pretrained_word_embedding_name = param.pretrained_word_embedding_name, pretrained_word_embedding_path = param.pretrained_word_embedding_path\\n)\\n\\nlstm_parameter.vocab_size = vocab_size\\nlstm_parameter.label_size = label_size\\nlstm_parameter.pretrained_embedding_weight = pretrained_embedding_weight\\n\\nmodel_factory = FactoryClass(LSTMClassifier, lstm_parameter)\\n\\nloss_factory = FactoryClass(nn.NLLLoss)\\n\\noptimizer_param_dict = filter_dotdict_class_propoperty(param, optim.Adam)\\noptimizer_factory = FactoryClass(optim.Adam, optimizer_param_dict)\\n\\n#optimizer = optim.Adagrad(update_parameter, lr=1e-3)\\n#optimizer = optim.RMSprop(update_parameter, lr=parameter.learning_rate, alpha=0.99, eps=1e-8, weight_decay=5e-4)\\n\\nlearning_logger = LearningWriter(label_map, param.result_folder_path, param.saved_model_file_path, param.train_log_folder_path, param.dev_log_folder_path, param.confusion_matrix_folder_path)\\nmodel_runner = ModelRunner(model_factory, loss_factory, optimizer_factory, param.epoch, param.batch_size, learning_logger, param.use_gpu)\\nstart_time = time.time()\\nmodel_runner.learn(train_data, dev_data)\\nprint(\\\"Overall time elapsed {} sec\\\".format(time.time() - start_time))\\n\"\n },\n {\n \"alpha_fraction\": 0.6185395121574402,\n \"alphanum_fraction\": 0.6202067136764526,\n \"avg_line_length\": 39.5,\n \"blob_id\": \"07e5750f56ba5fb3ab0b19dd203d5fc904b73035\",\n \"content_id\": \"a967bc44152b4dc0801035ca45aacf8cd524b02f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2999,\n \"license_type\": \"no_license\",\n \"max_line_length\": 184,\n \"num_lines\": 74,\n \"path\": \"/model_module/lstm_classifier.py\",\n \"repo_name\": \"michjk/Question_Classifier_Pytorch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch\\nimport torch.nn as nn\\nimport torch.autograd as autograd\\nimport torch.nn.functional as F\\n\\nclass LSTMClassifier(nn.Module):\\n '''\\n LSTMClassifier is classification module based on LSTM\\n Args:\\n embedding_dim (int): The size of word vector.\\n vocab_size (int): The number of words/tokens in vocabulary.\\n label_size (int): The number of possible labels.\\n hidden_dim: The number of features in the hidden state h of QRNN\\n pretrained_embedding_weight (torch.Tensor): The pretrained word vectors (optional).\\n train_embedding_layer (bool): Whether to train embedding layer or let embedding layer to be fixed.\\n num_layers (int): The number of layers of QRNN.\\n dropout (float): The probability of dropout in QRNN and dropout layer.\\n use_gpu (bool): Whether to use GPU or not\\n \\n Inputs: x:\\n - x (seq_len, batch, input_size): tensor containing the features of the input sequence.\\n \\n Output: logsoftmax\\n - logsoftmax (batch, label_size) : tensor result of log softmax\\n '''\\n def __init__(self, embedding_dim, hidden_dim, vocab_size, label_size, pretrained_embedding_weight = None, train_embedding_layer = True,num_layers = 1, dropout = 0, use_gpu = True):\\n super().__init__()\\n\\n #initialize properties\\n self.hidden_dim = hidden_dim\\n self.num_layers = num_layers\\n self.use_gpu = use_gpu\\n \\n ## create nn module\\n self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)\\n if not (pretrained_embedding_weight is None): #use pretrained word vectors\\n self.word_embeddings.weight.data = pretrained_embedding_weight\\n self.word_embeddings.weight.requires_grad = train_embedding_layer\\n self.lstm = nn.LSTM(embedding_dim, hidden_dim, dropout=dropout, num_layers=num_layers)\\n self.dropout = nn.Dropout(dropout)\\n self.hidden_to_label = nn.Linear(hidden_dim, label_size)\\n\\n #use gpu\\n if use_gpu:\\n self.cuda()\\n \\n def init_hidden(self):\\n \\\"\\\"\\\"\\n Initialize weight of hidden\\n \\\"\\\"\\\"\\n # the first is the hidden h\\n # the second is the cell c\\n h = torch.zeros(self.num_layers, self.batch_size, self.hidden_dim)\\n c = torch.zeros(self.num_layers, self.batch_size, self.hidden_dim)\\n\\n if self.use_gpu:\\n h = h.cuda()\\n c = c.cuda()\\n \\n self.hidden = (autograd.Variable(h),\\n autograd.Variable(c))\\n \\n def forward(self, sentence):\\n #get batch size and init hidden\\n self.batch_size = sentence.data.shape[1]\\n self.init_hidden()\\n \\n embeds = self.word_embeddings(sentence) # (N,W,D)\\n x = embeds.view(len(sentence), self.batch_size, -1)\\n out, self.hidden = self.lstm(x, self.hidden)\\n out = self.dropout(out)\\n y = self.hidden_to_label(out[-1])\\n log_probs = F.log_softmax(y)\\n return log_probs\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6330029964447021,\n \"alphanum_fraction\": 0.6364637613296509,\n \"avg_line_length\": 36.122806549072266,\n \"blob_id\": \"21bdd974cfe560fcf67bee764eb3043d6a556881\",\n \"content_id\": \"8dc561f9344dc23858948841d6c86473faf075de\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6357,\n \"license_type\": \"no_license\",\n \"max_line_length\": 220,\n \"num_lines\": 171,\n \"path\": \"/data_module/data_preprocessor.py\",\n \"repo_name\": \"michjk/Question_Classifier_Pytorch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nimport re\\nimport itertools\\nfrom collections import Counter\\nimport json\\nfrom torchtext import data\\nimport codecs\\nfrom sklearn.model_selection import KFold\\nimport torchwordemb\\nimport dill as pickle\\nimport spacy\\n\\n# load English model\\nspacy_nlp = spacy.load('en')\\n\\ndef clean_str(string):\\n '''\\n Tokenization/string cleaning for all datasets except for SST.\\n Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py\\n '''\\n string = re.sub(r\\\"[^A-Za-z0-9(),!?\\\\'\\\\`@-]\\\", \\\" \\\", string)\\n string = re.sub(r\\\"\\\\'s\\\", \\\" \\\\'s\\\", string)\\n string = re.sub(r\\\"\\\\'ve\\\", \\\" \\\\'ve\\\", string)\\n string = re.sub(r\\\"n\\\\'t\\\", \\\" n\\\\'t\\\", string)\\n string = re.sub(r\\\"\\\\'re\\\", \\\" \\\\'re\\\", string)\\n string = re.sub(r\\\"\\\\'d\\\", \\\" \\\\'d\\\", string)\\n string = re.sub(r\\\"\\\\'ll\\\", \\\" \\\\'ll\\\", string)\\n string = re.sub(r\\\",\\\", \\\" , \\\", string)\\n string = re.sub(r\\\"!\\\", \\\" ! \\\", string)\\n string = re.sub(r\\\"\\\\(\\\", \\\" ( \\\", string)\\n string = re.sub(r\\\"\\\\)\\\", \\\" ) \\\", string)\\n string = re.sub(r\\\"\\\\?\\\", \\\" ? \\\", string)\\n string = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string)\\n return string.strip().lower()\\n\\nclass QuestionWrapper(data.Dataset):\\n '''\\n A class wrapper for createing torchtext.data.Dataset instance for preprocessing question in production\\n\\n Inputs: text_field (data.Field), question (string)\\n - text_field : loaded data.Field for preprocessing question\\n - question : question sent by user\\n '''\\n def __init__(self, text_field, question, **kwargs):\\n fields = [('text', text_field)]\\n question = clean_str(question)\\n examples = []\\n examples.append(data.Example.fromlist([question], fields))\\n\\n super().__init__(examples, fields, **kwargs)\\n\\ndef preprocess_question(question, text_field, use_gpu = False):\\n '''\\n Preprocess question sent by user\\n\\n Inputs: question (string), text_field (data.Field), use_gpu (boolean)\\n - question : question sent by user\\n - text_field : loaded data.Field for preprocessing question\\n - use_gpu : use gpu or not\\n\\n Outputs: text (Iterator)\\n - text : Iterator of preprocessed question\\n '''\\n device = -1\\n if use_gpu:\\n device = None\\n question_data = QuestionWrapper(text_field, question)\\n _, question_iter = data.Iterator.splits(\\n (question_data, question_data), batch_size=len(question_data),\\n repeat=False, device = device\\n )\\n\\n for batch in question_iter:\\n text = batch.text\\n\\n return text\\n\\ndef get_label(label_tensor, label_field):\\n '''\\n Get label string from tensor result\\n\\n Inputs: label_tensor (tensor), label_field (data.Field)\\n - label_tensor: tensor result of classification\\n - label_field: loaded data.Field for label preprocessing\\n '''\\n pred_index = label_tensor.data.max(1)[1]\\n pred_index = pred_index.cpu().numpy()[0]\\n label_string = label_field.vocab.itos[pred_index]\\n\\n return label_string\\n\\ndef tokenizer(text):\\n '''\\n Tokenizer, includes clear string and lemmatization\\n\\n Inputs:\\n - text (string): string to be tokenized\\n \\n Outputs:\\n - tokens (list): list of tokens\\n\\n '''\\n\\n #clean string\\n text = clean_str(text)\\n\\n #lemmatized\\n lemmatized = spacy_nlp(text)\\n text = ' '.join([token.lemma_ for token in lemmatized])\\n \\n #tokenizer from tensorflow.preprocessing library\\n tokenizer_re = re.compile(r\\\"[A-Z]{2,}(?![a-z])|[A-Z][a-z]+(?=[A-Z])|[\\\\'\\\\w\\\\-]+\\\", re.UNICODE) \\n return tokenizer_re.findall(text)\\n\\ndef load_dataset(train_path, dev_path, max_text_length, preprocessing_pipeline_writer, tokenizer = tokenizer, embedding_dim = 300, pretrained_word_embedding_name = \\\"glove.6B.300d\\\", pretrained_word_embedding_path = None):\\n '''\\n Load and preprocess dataset.\\n\\n Inputs:\\n - train_path (string): Path to train dataset json\\n - dev_path (string): Path to test/dev dataset json\\n - max_text_length (integer): Max length of sentence. It pads dummy token up to max length\\n - preprocessing_pipeline_writer (object): class for saving data.Field data\\n - tokenizer (function): Tokenizer function\\n - Embedding_dim (integer): size of word embedding dim\\n - pretrained_word_embedding_name (string): name of pretrained word embedding. It can be glove.6B.300d or word2vec\\n - pretrained_word_embedding_path (string): path to word embedding file. No need for glove.\\n\\n Outputs:\\n - train_data (data.Dataset): train dataset\\n - test_data (data.Dataset): test dataset\\n - vocab_size (int): vocabulary size\\n - label_size (int): size of possible label\\n - label_vocab (dict): map from topic index number to topic label string\\n - vectors : word embedding vectors\\n '''\\n\\n # data.Field for preprocessing pipeline data\\n text_field = data.Field(lower=True, tokenize=tokenizer, fix_length=max_text_length)\\n label_field = data.LabelField()\\n\\n # load data\\n print('loading data')\\n train_data = data.TabularDataset(path=train_path, format='csv', skip_header=True, fields=[(\\\"text\\\", text_field), ('label', label_field)])\\n dev_data = data.TabularDataset(path=dev_path, format='csv', skip_header=True, fields=[(\\\"text\\\", text_field), ('label', label_field)])\\n \\n # build vocabulary\\n print('building vocab')\\n text_field.build_vocab(train_data, dev_data)\\n label_field.build_vocab(train_data, dev_data)\\n\\n vectors = None\\n\\n # load word embedding vectors\\n if pretrained_word_embedding_name == \\\"word2vec\\\":\\n vocab, vec = torchwordemb.load_word2vec_bin(pretrained_word_embedding_path)\\n text_field.vocab.set_vectors(vocab, vec, embedding_dim)\\n vectors = text_field.vocab.vectors\\n elif \\\"glove\\\" in pretrained_word_embedding_name:\\n text_field.vocab.load_vectors(pretrained_word_embedding_name)\\n vectors = text_field.vocab.vectors\\n \\n # save data.Field\\n preprocessing_pipeline_writer.save_pipeline(text_field, False)\\n preprocessing_pipeline_writer.save_pipeline(label_field, True)\\n \\n vocab_size = len(text_field.vocab)\\n print(\\\"vocab size \\\", vocab_size)\\n label_size = len(label_field.vocab)\\n\\n return train_data, dev_data, vocab_size, label_size, label_field.vocab.itos, vectors\\n\\n\\n\\n\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.3065875768661499,\n \"alphanum_fraction\": 0.3090852200984955,\n \"avg_line_length\": 51.86666488647461,\n \"blob_id\": \"1d5bf16ef492dd408f3890f1508a5b399ff94a01\",\n \"content_id\": \"f1785a7dd5ec15127806fee716d6bcfb5b326d9d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"INI\",\n \"length_bytes\": 3203,\n \"license_type\": \"no_license\",\n \"max_line_length\": 204,\n \"num_lines\": 60,\n \"path\": \"/uwsgi.ini\",\n \"repo_name\": \"michjk/Question_Classifier_Pytorch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"[uwsgi]\\n# placeholders that you have to change\\nmy_app_folder = /home/michael/Question_Classifier_Pytorch\\nmy_user = michael\\n\\nsocket = %(my_app_folder)/production_ml.sock\\nchdir = %(my_app_folder)\\nfile = rest_api_server.py\\ncallable = app\\n\\n# environment variables\\nenv = PYTHONPATH=%(my_app_folder):$PATH\\nenv = LD_LIBRARY_PATH=$LD_LIBRARY_PATH\\nenv = CUDA_HOME=$CUDA_HOME\\n\\nmaster = true\\nprocesses = 1\\n# allows nginx (and all users) to read and write on this socket\\nchmod-socket = 666\\n# remove the socket when the process stops\\nvacuum = true\\n\\n# loads your application one time per worker\\n# will very probably consume more memory,\\n# but will run in a more consistent and clean environment.\\nlazy-apps = true\\n\\nuid = %(my_user)\\ngid = %(my_user)\\n\\n# uWSGI will kill the process instead of reloading it\\ndie-on-term = true\\n# socket file for getting stats about the workers\\nstats = %(my_app_folder)/stats.production_ml.sock\\n\\n# Scaling the server with the Cheaper subsystem\\n\\n# set cheaper algorithm to use, if not set default will be used\\ncheaper-algo = spare\\n# minimum number of workers to keep at all times\\ncheaper = 0\\n# number of workers to spawn at startup\\ncheaper-initial = 1\\n# maximum number of workers that can be spawned\\nworkers = 1\\n# how many workers should be spawned at a time\\nheaper-step = 1\\n\\npyargv = --path rest_api_param.json\\n\\n~ \\n~ \\n~ \\n~ \\n~ \\n~ \\n~ \\n~ \\n~ \\n~ \\n\"\n },\n {\n \"alpha_fraction\": 0.702724039554596,\n \"alphanum_fraction\": 0.7058823704719543,\n \"avg_line_length\": 26.846153259277344,\n \"blob_id\": \"982aac6dce2f4416d004a24092722b233e6e0791\",\n \"content_id\": \"d16b0417d3a58840162a4370b176ee7032afef2e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2533,\n \"license_type\": \"no_license\",\n \"max_line_length\": 99,\n \"num_lines\": 91,\n \"path\": \"/rest_api_server.py\",\n \"repo_name\": \"michjk/Question_Classifier_Pytorch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import Flask\\nfrom flask import request\\n\\nimport sys\\n\\nimport torch\\nfrom torchtext import data\\n\\nimport time\\n\\nfrom data_module.data_preprocessor import get_label, preprocess_question\\n\\nimport os\\n\\nfrom flask import jsonify\\n\\nimport logging\\n\\nimport dill as pickle\\n\\nimport argparse\\n\\nfrom utils import *\\n\\napp = Flask(__name__)\\n\\nparser = argparse.ArgumentParser()\\nparser.add_argument(\\\"--path\\\", help=\\\"path parameter json file\\\")\\nparam_json_path = parser.parse_args().path\\n\\n# load parameter\\nparam = load_rest_api_parameter_from_json(param_json_path)\\n\\n# load model\\nmodel = None\\nif param.use_gpu:\\n model = torch.load(param.saved_model_file_path)\\nelse:\\n model = torch.load(param.saved_model_file_path, map_location=lambda storage, location: storage)\\n\\n# Create the Logger\\nlogger = logging.getLogger(__name__)\\nlogger.setLevel(logging.DEBUG)\\n \\n# Create the Handler for logging data to a file\\nlogger_handler_debug = logging.FileHandler(param.debug_log_file_path)\\nlogger_handler_debug.setLevel(logging.DEBUG)\\n\\n# Create the Handler for logging data to a file\\nlogger_handler_error = logging.FileHandler(param.error_log_file_path)\\nlogger_handler_error.setLevel(logging.ERROR)\\n\\n# Create a Formatter for formatting the log messages\\nlogger_formatter = logging.Formatter('%(asctime)s - %(message)s')\\n \\n# Add the Formatter to the Handler\\nlogger_handler_debug.setFormatter(logger_formatter)\\nlogger_handler_error.setFormatter(logger_formatter)\\n \\n# Add the Handler to the Logger\\nlogger.addHandler(logger_handler_debug)\\nlogger.addHandler(logger_handler_error)\\nlogger.info('Completed configuring logger()!')\\n\\n# load data.Field\\ntext_field = pickle.load(open(param.saved_text_pipeline_file_path, \\\"rb\\\"))\\nlabel_field = pickle.load(open(param.saved_label_pipeline_file_path, \\\"rb\\\"))\\n\\n@app.route('/predict', methods=['GET'])\\ndef prediction():\\n try:\\n question = request.args.get('question')\\n logger.info(\\\"Question: \\\" + question)\\n x = preprocess_question(question, text_field, use_gpu=param.use_gpu)\\n model.eval()\\n t = time.time()\\n y = model(x)\\n dur = time.time() - t\\n label_string = get_label(y, label_field)\\n logger.info(\\\"Result: \\\" + str(label_string))\\n logger.info(\\\"Duration: \\\" + str(dur))\\n return jsonify({'result': str(label_string)})\\n except:\\n e = sys.exc_info()[0]\\n logger.error(\\\"error \\\", str(e))\\n response = jsonify({'error': str(e)})\\n response.status_code = 400\\n return response\\n\\nif __name__ == '__main__':\\n app.run(host='0.0.0.0')\"\n },\n {\n \"alpha_fraction\": 0.6227173209190369,\n \"alphanum_fraction\": 0.6300219297409058,\n \"avg_line_length\": 47.05263137817383,\n \"blob_id\": \"995f3067cc0ef03abc6497b351648184e3b7bf5e\",\n \"content_id\": \"d33408e55fd78200a80406e063fff0827b94da37\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2738,\n \"license_type\": \"no_license\",\n \"max_line_length\": 183,\n \"num_lines\": 57,\n \"path\": \"/model_module/cnn_classifier.py\",\n \"repo_name\": \"michjk/Question_Classifier_Pytorch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch\\nimport torch.nn as nn\\nimport torch.nn.functional as F\\n\\nclass CNNClassifier(nn.Module):\\n\\n '''\\n CNNClassifier is Question Classifier base on CNN\\n\\n Args:\\n embedding_dim (int): The size of word vector.\\n vocab_size (int): The number of words/tokens in vocabulary.\\n label_size (int): The number of possible labels.\\n kernel_num (int): The number of kernels/filters for each kernels/filters size\\n kernel_sizes (str/list): The list of kernel sizes. It can be in string such as \\\"3, 4, 5\\\" or in list such as [3, 4, 5]\\n pretrained_embedding_weight (torch.Tensor): The pretrained word vectors (optional).\\n train_embedding_layer (bool): Whether to train embedding layer or let embedding layer to be fixed.\\n dropout (float): The probability of dropout's action.\\n use_gpu (bool): Whether to use GPU or not\\n layers: List of preconstructed QRNN layers to use for the QRNN module (optional).\\n\\n Inputs: x\\n - x (seq_len, batch, input_size): tensor containing the features of the input sequence.\\n Output: logsoftmax\\n - logsoftmax (batch, label_size) : tensor result of log softmax\\n '''\\n def __init__(self, embedding_dim, vocab_size, label_size, kernel_num, kernel_sizes, pretrained_embedding_weight = None, train_embedding_layer = True, dropout = 0, use_gpu = True):\\n super().__init__()\\n \\n #check if kernel sizes in str\\n if isinstance(kernel_sizes, str):\\n kernel_sizes = [int(i) for i in kernel_sizes.split(',')]\\n\\n #build network\\n self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)\\n if not (pretrained_embedding_weight is None): #check if pretrain word vectors are used\\n self.word_embeddings.weight.data = pretrained_embedding_weight\\n self.word_embeddings.weight.requires_grad = train_embedding_layer #need training or static\\n self.convs1 = nn.ModuleList([nn.Conv2d(1, kernel_num, (K, embedding_dim)) for K in kernel_sizes])\\n self.dropout = nn.Dropout(dropout)\\n self.fc1 = nn.Linear(len(kernel_sizes)*kernel_num, label_size)\\n\\n #use gpu\\n if use_gpu:\\n self.cuda()\\n \\n def forward(self, x):\\n x.t_() # transpose to match Conv2d input\\n x = self.word_embeddings(x) # (N,W,D)\\n x = x.unsqueeze(1) # (N,Ci,W,D)\\n x = [F.relu(conv(x)).squeeze(3) for conv in self.convs1] # [(N,Co,W), ...]*len(Ks)\\n x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x] # [(N,Co), ...]*len(Ks)\\n x = torch.cat(x, 1)\\n x = self.dropout(x) # (N,len(Ks)*Co)\\n logit = self.fc1(x) # (N,C)\\n log_probs = F.log_softmax(logit) # log of softmax\\n return log_probs\"\n },\n {\n \"alpha_fraction\": 0.6423264741897583,\n \"alphanum_fraction\": 0.643812358379364,\n \"avg_line_length\": 33.639705657958984,\n \"blob_id\": \"ef52a7ccf28667e1725916eac0b8e4b286e1cfe4\",\n \"content_id\": \"18cb177a1e8bcd67275719cab9af174c79958fd7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4711,\n \"license_type\": \"no_license\",\n \"max_line_length\": 134,\n \"num_lines\": 136,\n \"path\": \"/telegram_bot.py\",\n \"repo_name\": \"michjk/Question_Classifier_Pytorch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from telegram import InlineKeyboardButton, InlineKeyboardMarkup\\nfrom telegram.ext import Updater, CommandHandler, CallbackQueryHandler, ConversationHandler, MessageHandler, Filters\\n\\nimport requests\\nimport logging\\nimport argparse\\nimport traceback\\n\\n#from google_sheet_api import send_log\\n\\nparser = argparse.ArgumentParser(description=\\\"This is for establishing telegram bot\\\")\\nparser.add_argument('-t', '--token', help=\\\"telegram token\\\", required=True)\\nparser.add_argument('--ip', help=\\\"IP address\\\", required = True)\\n\\nargs = parser.parse_args()\\n\\nTOKEN = args.token\\nURL = args.ip\\n\\n# http request logging\\nlogging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\\nlogger = logging.getLogger(__name__)\\nlogger.setLevel(logging.DEBUG)\\n\\n#Next stage conversation\\nFEEDBACK = 0\\nRECOMMENDED = 1\\ncache_data = {}\\n\\ndef predict(bot, update):\\n try:\\n predict_msg = {'question': update.message.text}\\n logger.debug(\\\"Message: \\\" + update.message.text)\\n resp = requests.get(URL+\\\"/predict\\\", params=predict_msg)\\n logger.debug(\\\"Predicted: \\\" + resp.json()['result'])\\n update.message.reply_text(\\\"Possible topic: \\\" + resp.json()['result'])\\n\\n question = predict_msg\\n predicted = resp.json()['result']\\n \\n cache_data[update.message.chat_id] = []\\n cache_data[update.message.chat_id].append(update.message.text)\\n cache_data[update.message.chat_id].append(predicted)\\n \\n keyboard = [\\n [\\n InlineKeyboardButton(u\\\"Yes\\\", callback_data=\\\"yes\\\"),\\n InlineKeyboardButton(u\\\"No\\\", callback_data=\\\"no\\\")\\n ]\\n ]\\n\\n reply_markup = InlineKeyboardMarkup(keyboard)\\n update.message.reply_text(\\\"Do you satisfied with the answer?\\\", reply_markup = reply_markup)\\n \\n return FEEDBACK\\n except:\\n logger.debug(traceback.format_exc())\\n update.message.reply_text(\\\"Error, please repeat again!\\\")\\n\\ndef feedback(bot, update):\\n try:\\n query = update.callback_query\\n logger.debug(\\\"Satisfied: \\\" + query.data)\\n if (query.data == 'yes'):\\n cache_data[query.message.chat_id].append(\\\"none\\\")\\n #send_log(cache_data[query.message.chat_id])\\n query.message.reply_text(\\\"Thanks for responding\\\")\\n return\\n keyboard = [\\n [InlineKeyboardButton(u\\\"Academic\\\", callback_data=\\\"Academic\\\")],\\n [InlineKeyboardButton(u\\\"Admission & Financial Services\\\", callback_data=\\\"Admission & Financial Services\\\")],\\n [InlineKeyboardButton(u\\\"Campus Life & Accommodation\\\", callback_data=\\\"Campus Life & Accommodation\\\")],\\n [InlineKeyboardButton(u\\\"Outreach & Exchange\\\", callback_data=\\\"Outreach & Exchange\\\")],\\n [InlineKeyboardButton(u\\\"Other services\\\", callback_data=\\\"Other services\\\")]\\n \\n ]\\n\\n reply_markup = InlineKeyboardMarkup(keyboard)\\n query.message.reply_text(\\\"Choose your recommendation?\\\", reply_markup = reply_markup) \\n\\n return RECOMMENDED\\n except:\\n logger.debug(traceback.format_exc())\\n query.message.reply_text(\\\"Error, please repeat again!\\\")\\n\\ndef recommended(bot, update):\\n query = update.callback_query\\n logger.debug(\\\"Recommended: \\\" + query.data)\\n \\n cache_data[query.message.chat_id].append(query.data)\\n #send_log(cache_data[query.message.chat_id])\\n\\n query.message.reply_text(\\\"Thanks for responding\\\")\\n return\\n\\ndef help_command(bot, update):\\n try:\\n update.message.reply_text(\\\"This is NTU FAQ topic classifier. Send a message and you get the predicted topic. The topic are:\\\" \\\\\\n \\\"\\\\n1. Academic\\\" \\\\\\n \\\"\\\\n2. Admission & Financial Services\\\" \\\\\\n \\\"\\\\n3. Campus Life & Accommodation\\\" \\\\\\n \\\"\\\\n4. Outreach & Exchange\\\" \\\\\\n \\\"\\\\n5. Other services\\\" \\\\\\n )\\n except:\\n logger.debug(traceback.format_exc())\\n\\ndef main():\\n # Create Updater object and attach dispatcher to it\\n updater = Updater(TOKEN)\\n dispatcher = updater.dispatcher\\n \\n # Add command handler to dispatcher\\n\\n conv_handler = ConversationHandler(\\n entry_points=[MessageHandler(Filters.text, predict)],\\n states={\\n FEEDBACK: [CallbackQueryHandler(feedback)],\\n RECOMMENDED: [CallbackQueryHandler(recommended)]\\n },\\n fallbacks=[MessageHandler(Filters.text, predict)]\\n )\\n help_handler = CommandHandler('help', help_command)\\n \\n dispatcher.add_handler(conv_handler)\\n dispatcher.add_handler(help_handler)\\n\\n # Start the bot\\n updater.start_polling()\\n logger.debug(\\\"Bot started!\\\")\\n\\n # Run the bot until you press Ctrl-C\\n updater.idle()\\n\\nif __name__ == '__main__':\\n main()\\n\"\n },\n {\n \"alpha_fraction\": 0.6585691571235657,\n \"alphanum_fraction\": 0.6615055799484253,\n \"avg_line_length\": 48.25,\n \"blob_id\": \"d27c635a71dd1bef7b93b2e015e32a80f32b5065\",\n \"content_id\": \"a8f99408e054998aba747ccd4d4758e579575218\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3746,\n \"license_type\": \"no_license\",\n \"max_line_length\": 286,\n \"num_lines\": 76,\n \"path\": \"/model_module/qrnn_classifier.py\",\n \"repo_name\": \"michjk/Question_Classifier_Pytorch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch\\nimport torch.nn as nn\\nimport torch.autograd as autograd\\nimport torch.nn.functional as F\\n\\nimport torchqrnn.forget_mult\\nfrom torchqrnn import QRNN\\n\\nclass QRNNClassifier(nn.Module):\\n '''\\n QRNNClassifier is classification module based on QRNN\\n Args:\\n embedding_dim (int): The size of word vector.\\n vocab_size (int): The number of words/tokens in vocabulary.\\n label_size (int): The number of possible labels.\\n hidden_dim: The number of features in the hidden state h of QRNN\\n pretrained_embedding_weight (torch.Tensor): The pretrained word vectors (optional).\\n train_embedding_layer (bool): Whether to train embedding layer or let embedding layer to be fixed.\\n num_layers (int): The number of layers of QRNN.\\n save_prev_x (bool): Whether to store previous inputs for use in future convolutional windows of QRNN (i.e. for a continuing sequence such as in language modeling). If true, you must call reset to remove cached previous values of x (reset is not yet implemented). Default: False.\\n window (int): Defines the size of the convolutional window (how many previous tokens to look when computing the QRNN values). Supports 1 and 2. Default: 1.\\n dropout (float): The probability of dropout in QRNN and dropout layer.\\n zoneout (float): Whether to apply zoneout of QRNN (i.e. failing to update elements in the hidden state) to the hidden state updates. Default: 0.\\n use_gpu (bool): Whether to use GPU or not\\n \\n Inputs: x\\n - x (seq_len, batch, input_size): tensor containing the features of the input sequence.\\n \\n Output: logsoftmax\\n - logsoftmax (batch, label_size) : tensor result of log softmax\\n '''\\n def __init__(self, embedding_dim, hidden_dim, vocab_size, label_size, pretrained_embedding_weight = None, train_embedding_layer = True, num_layers = 1, dropout = 0, zoneout = 0, window = 1, save_prev_x = False, use_gpu=True):\\n super().__init__()\\n \\n #initialize properties\\n self.hidden_dim = hidden_dim\\n self.num_layers = num_layers\\n self.use_gpu = use_gpu\\n self.use_pretrained_word_embedding = not (pretrained_embedding_weight is None)\\n self.train_embedding_layer = train_embedding_layer\\n self.pretrained_embedding_weight = pretrained_embedding_weight\\n\\n #create nn module\\n self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)\\n if self.use_pretrained_word_embedding:\\n self.word_embeddings.weight.data = pretrained_embedding_weight\\n self.word_embeddings.weight.requires_grad = train_embedding_layer\\n self.qrnn = QRNN(embedding_dim, hidden_dim, dropout=dropout, zoneout=zoneout, window = window, save_prev_x = save_prev_x, num_layers=num_layers, use_cuda = use_gpu)\\n self.dropout = nn.Dropout(dropout)\\n self.hidden_to_label = nn.Linear(hidden_dim, label_size)\\n \\n #use gpu or not\\n if use_gpu:\\n self.cuda()\\n \\n def init_hidden(self):\\n '''\\n Initialize weight of hidden state\\n '''\\n self.hidden = autograd.Variable(torch.zeros(self.num_layers, self.batch_size, self.hidden_dim))\\n if self.use_gpu:\\n self.hidden.cuda()\\n \\n def forward(self, sentence):\\n #get batch size and init hidden\\n self.batch_size = sentence.data.shape[1]\\n self.init_hidden()\\n \\n embeds = self.word_embeddings(sentence) # (N,W,D)\\n x = embeds.view(len(sentence), self.batch_size, -1)\\n out, self.hidden = self.qrnn(x, self.hidden)\\n out = self.dropout(out)\\n y = self.hidden_to_label(out[-1])\\n log_probs = F.log_softmax(y)\\n \\n return log_probs\\n\\n\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":14,"string":"14"}}},{"rowIdx":2983072,"cells":{"repo_name":{"kind":"string","value":"CORRUPTOR2037/last-word-project"},"repo_url":{"kind":"string","value":"https://github.com/CORRUPTOR2037/last-word-project"},"snapshot_id":{"kind":"string","value":"ff0c1637ebef753cecc33ad20365790cb705f2ee"},"revision_id":{"kind":"string","value":"7a4b890fcae02ac5e534d90a2581db11b66ae2fa"},"directory_id":{"kind":"string","value":"695f4b5af5498e9516671fa8f649b6af4a652c69"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-09-25T18:08:13.093550","string":"2020-09-25T18:08:13.093550"},"revision_date":{"kind":"timestamp","value":"2019-12-30T16:45:16","string":"2019-12-30T16:45:16"},"committer_date":{"kind":"timestamp","value":"2019-12-30T16:45:16","string":"2019-12-30T16:45:16"},"github_id":{"kind":"number","value":226060616,"string":"226,060,616"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.8230769038200378,"alphanum_fraction":0.8230769038200378,"avg_line_length":73.28571319580078,"blob_id":"6aa2cdb9ecaff6d1762af59f8f0dac95bbf37fee","content_id":"96078e6e944344bc20fc6f6a2f3bfa18c077c01e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":904,"license_type":"no_license","max_line_length":260,"num_lines":7,"path":"/README.md","repo_name":"CORRUPTOR2037/last-word-project","src_encoding":"UTF-8","text":"# last-word-project\nСайт с последними словами\n\nДемонстрация: http://lastword.pythonanywhere.com\n\nСобрано практически всё, что я смог найти за пару дней усердного поиска (кроме некоторых лиц). Исключение: последнее слово Белова (Поткина) пока что не дописано.\nВ дальнейшем поддерживать проект желания не имею, сутью было именно собрать базу и концепт-проект, чтобы его ведением заинтересовалось какое-нибудь СМИ или правозащитный проект на отдельном разделе своего сайта. Буду благодарен, если жюри этому поспособствуют.\n"},{"alpha_fraction":0.5409702658653259,"alphanum_fraction":0.5491235256195068,"avg_line_length":30.05063247680664,"blob_id":"5f92b6c051fdfb842e0222902d853bc3d227d13f","content_id":"b24b692c3845348a51fc3500cdcd32198ba046f9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2495,"license_type":"no_license","max_line_length":97,"num_lines":79,"path":"/server.py","repo_name":"CORRUPTOR2037/last-word-project","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nfrom http.server import BaseHTTPRequestHandler, HTTPServer\nimport logging\n\nfrom os import listdir\nfrom os.path import isfile, join\nimport urllib.parse\n\nmypath = 'records/'\ndef load_index():\n result = \"\"\n onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n for file in onlyfiles:\n result += \"<a href='./\" + file[0:-4].replace(' ', '_') + \"'>\" + file[0:-4] + \"</a>\"\n \n return result\n\ndef load(filename):\n filename = urllib.parse.unquote(filename).replace(\"_\", \" \")\n print(filename)\n with open(filename.replace(\"_\", \" \")) as f:\n lines = f.readlines()\n \n result = '<p><span>Кто:</span>' + lines[0] + '</p>\\n'\n result += '<p><span>Когда:</span>' + lines[1] + '</p>\\n'\n result += '<p><span>По какой статье:</span>' + lines[2] + '</p>\\n'\n result += '<p><span>За что:</span>' + lines[3] + '</p>\\n'\n result += '<p><span>Что получил:</span>' + lines[4] + '</p>\\n'\n \n result += '<div class=\"text\"><p>' + '</p><p>'.join(lines[6:]) + '</p></div>'\n \n return result\n \nclass S(BaseHTTPRequestHandler):\n def _set_response(self):\n self.send_response(200)\n self.send_header('Content-type', 'text/html')\n self.end_headers()\n\n def do_GET(self):\n logging.info(\"GET request,\\nPath: %s\\nHeaders:\\n%s\\n\", str(self.path), str(self.headers))\n self._set_response()\n if self.path == '/':\n self.print_page(load_index())\n try:\n self.print_page(load('records' + self.path + '.txt'))\n except Exception as e:\n self.print_page('Ошибка: ' + self.path[1:])\n print(e)\n\n def do_POST(self):\n pass\n \n def print_page(self, content):\n with open('header.html', 'rb') as f:\n self.wfile.write(f.read())\n \n self.wfile.write(content.encode('utf-8'))\n \n with open('footer.html', 'rb') as f:\n self.wfile.write(f.read())\n\n\ndef run(server_class=HTTPServer, handler_class=S, port=8080):\n logging.basicConfig(level=logging.INFO)\n server_address = ('', port)\n httpd = server_class(server_address, handler_class)\n logging.info('Starting httpd...\\n')\n try:\n httpd.serve_forever()\n except KeyboardInterrupt:\n pass\n httpd.server_close()\n logging.info('Stopping httpd...\\n')\n\nif __name__ == '__main__':\n from sys import argv\n\n run()\n"}],"string":"[\n {\n \"alpha_fraction\": 0.8230769038200378,\n \"alphanum_fraction\": 0.8230769038200378,\n \"avg_line_length\": 73.28571319580078,\n \"blob_id\": \"6aa2cdb9ecaff6d1762af59f8f0dac95bbf37fee\",\n \"content_id\": \"96078e6e944344bc20fc6f6a2f3bfa18c077c01e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 904,\n \"license_type\": \"no_license\",\n \"max_line_length\": 260,\n \"num_lines\": 7,\n \"path\": \"/README.md\",\n \"repo_name\": \"CORRUPTOR2037/last-word-project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# last-word-project\\nСайт с последними словами\\n\\nДемонстрация: http://lastword.pythonanywhere.com\\n\\nСобрано практически всё, что я смог найти за пару дней усердного поиска (кроме некоторых лиц). Исключение: последнее слово Белова (Поткина) пока что не дописано.\\nВ дальнейшем поддерживать проект желания не имею, сутью было именно собрать базу и концепт-проект, чтобы его ведением заинтересовалось какое-нибудь СМИ или правозащитный проект на отдельном разделе своего сайта. Буду благодарен, если жюри этому поспособствуют.\\n\"\n },\n {\n \"alpha_fraction\": 0.5409702658653259,\n \"alphanum_fraction\": 0.5491235256195068,\n \"avg_line_length\": 30.05063247680664,\n \"blob_id\": \"5f92b6c051fdfb842e0222902d853bc3d227d13f\",\n \"content_id\": \"b24b692c3845348a51fc3500cdcd32198ba046f9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2495,\n \"license_type\": \"no_license\",\n \"max_line_length\": 97,\n \"num_lines\": 79,\n \"path\": \"/server.py\",\n \"repo_name\": \"CORRUPTOR2037/last-word-project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nfrom http.server import BaseHTTPRequestHandler, HTTPServer\\nimport logging\\n\\nfrom os import listdir\\nfrom os.path import isfile, join\\nimport urllib.parse\\n\\nmypath = 'records/'\\ndef load_index():\\n result = \\\"\\\"\\n onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]\\n for file in onlyfiles:\\n result += \\\"<a href='./\\\" + file[0:-4].replace(' ', '_') + \\\"'>\\\" + file[0:-4] + \\\"</a>\\\"\\n \\n return result\\n\\ndef load(filename):\\n filename = urllib.parse.unquote(filename).replace(\\\"_\\\", \\\" \\\")\\n print(filename)\\n with open(filename.replace(\\\"_\\\", \\\" \\\")) as f:\\n lines = f.readlines()\\n \\n result = '<p><span>Кто:</span>' + lines[0] + '</p>\\\\n'\\n result += '<p><span>Когда:</span>' + lines[1] + '</p>\\\\n'\\n result += '<p><span>По какой статье:</span>' + lines[2] + '</p>\\\\n'\\n result += '<p><span>За что:</span>' + lines[3] + '</p>\\\\n'\\n result += '<p><span>Что получил:</span>' + lines[4] + '</p>\\\\n'\\n \\n result += '<div class=\\\"text\\\"><p>' + '</p><p>'.join(lines[6:]) + '</p></div>'\\n \\n return result\\n \\nclass S(BaseHTTPRequestHandler):\\n def _set_response(self):\\n self.send_response(200)\\n self.send_header('Content-type', 'text/html')\\n self.end_headers()\\n\\n def do_GET(self):\\n logging.info(\\\"GET request,\\\\nPath: %s\\\\nHeaders:\\\\n%s\\\\n\\\", str(self.path), str(self.headers))\\n self._set_response()\\n if self.path == '/':\\n self.print_page(load_index())\\n try:\\n self.print_page(load('records' + self.path + '.txt'))\\n except Exception as e:\\n self.print_page('Ошибка: ' + self.path[1:])\\n print(e)\\n\\n def do_POST(self):\\n pass\\n \\n def print_page(self, content):\\n with open('header.html', 'rb') as f:\\n self.wfile.write(f.read())\\n \\n self.wfile.write(content.encode('utf-8'))\\n \\n with open('footer.html', 'rb') as f:\\n self.wfile.write(f.read())\\n\\n\\ndef run(server_class=HTTPServer, handler_class=S, port=8080):\\n logging.basicConfig(level=logging.INFO)\\n server_address = ('', port)\\n httpd = server_class(server_address, handler_class)\\n logging.info('Starting httpd...\\\\n')\\n try:\\n httpd.serve_forever()\\n except KeyboardInterrupt:\\n pass\\n httpd.server_close()\\n logging.info('Stopping httpd...\\\\n')\\n\\nif __name__ == '__main__':\\n from sys import argv\\n\\n run()\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2983073,"cells":{"repo_name":{"kind":"string","value":"zhangquanit/fastapp"},"repo_url":{"kind":"string","value":"https://github.com/zhangquanit/fastapp"},"snapshot_id":{"kind":"string","value":"01449ad8c42d9866ea5e20dcc55ffc42c3fbde62"},"revision_id":{"kind":"string","value":"10f3906790b33736aabf0d59324a5e30cddbae84"},"directory_id":{"kind":"string","value":"d2444eeb13c2c4693d4a5c37c16bc18532e9c229"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-07-27T10:41:48.018353","string":"2023-07-27T10:41:48.018353"},"revision_date":{"kind":"timestamp","value":"2021-09-18T09:21:38","string":"2021-09-18T09:21:38"},"committer_date":{"kind":"timestamp","value":"2021-09-18T09:21:38","string":"2021-09-18T09:21:38"},"github_id":{"kind":"number","value":277781324,"string":"277,781,324"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5808632373809814,"alphanum_fraction":0.5824232697486877,"avg_line_length":29.046875,"blob_id":"8ada2152f3f00251206dd0e2da7e022cf5f86161","content_id":"6799bb8c8b2fdadb80d826138b82e802074d6cfb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1965,"license_type":"no_license","max_line_length":92,"num_lines":64,"path":"/librarys/CommonWidget/src/main/java/common/widget/viewpager/AutoScrollViewPager.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package common.widget.viewpager;\n\nimport android.content.Context;\nimport android.util.AttributeSet;\nimport android.view.MotionEvent;\n\nimport androidx.core.view.MotionEventCompat;\n\n\npublic class AutoScrollViewPager extends common.widget.viewpager.ViewPager {\n private float touchX = 0f, downX = 0f;\n // 滑动距离及坐标\n private float xDistance, yDistance, xLast, yLast;\n private BannerViewPager mBannerViewPager;\n\n public AutoScrollViewPager(Context context) {\n super(context);\n }\n\n public AutoScrollViewPager(Context context, AttributeSet attrs) {\n super(context, attrs);\n }\n\n public void setBanner(BannerViewPager banner) {\n mBannerViewPager = banner;\n }\n\n @Override\n public boolean dispatchTouchEvent(MotionEvent ev) {\n int action = MotionEventCompat.getActionMasked(ev);\n touchX = ev.getX();\n\n if ((action == MotionEvent.ACTION_DOWN)) {\n // 暂停滚动\n downX = touchX;\n mBannerViewPager.pauseAutoScroll();\n } else if (action == MotionEvent.ACTION_UP || action == MotionEvent.ACTION_CANCEL) {\n // 继续滚动\n mBannerViewPager.resumeAutoScroll();\n }\n\n // -----------是否拦截手势\n switch (ev.getAction()) {\n case MotionEvent.ACTION_DOWN:\n xDistance = yDistance = 0f;\n xLast = ev.getX();\n yLast = ev.getY();\n break;\n case MotionEvent.ACTION_MOVE:\n final float curX = ev.getX();\n final float curY = ev.getY();\n\n xDistance += Math.abs(curX - xLast);\n yDistance += Math.abs(curY - yLast);\n xLast = curX;\n yLast = curY;\n\n if (xDistance > yDistance) {\n getParent().requestDisallowInterceptTouchEvent(true);\n }\n }\n return super.dispatchTouchEvent(ev);\n }\n}\n"},{"alpha_fraction":0.5906040072441101,"alphanum_fraction":0.5922818779945374,"avg_line_length":24.645160675048828,"blob_id":"4984af87bab44d586784b7c2661da7644b55ac2c","content_id":"2bc511d12746eaf66b5886994480caf1e5738de4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Kotlin","length_bytes":2416,"license_type":"no_license","max_line_length":83,"num_lines":93,"path":"/app/src/main/java/com/android/base/ui/splash/SplashActivity.kt","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.ui.splash\n\nimport android.Manifest\nimport android.os.Bundle\nimport android.os.Handler\nimport androidx.appcompat.app.AppCompatActivity\nimport com.android.base.App\nimport com.android.util.common.CommonCallBack\nimport com.android.util.permission.Permission\nimport com.android.util.permission.PermissionListener\nimport com.android.util.permission.PermissionUtil\nimport com.fastapp.MainActivity\nimport net.medlinker.android.splash.SplashUtil\n\n/**\n * @author zhangquan\n */\nclass SplashActivity : AppCompatActivity() {\n private var mHandler = Handler()\n private val mDelay = 1000L\n override fun onCreate(savedInstanceState: Bundle?) {\n super.onCreate(savedInstanceState)\n checkShowDialog()\n }\n\n override fun onDestroy() {\n super.onDestroy()\n mHandler.removeCallbacks(mRunnable)\n }\n\n private fun checkShowDialog() {\n if (!SplashUtil.isPrivacyGranted()) {\n showPrivacyDialog()\n } else {\n grantPermission()\n }\n }\n\n /**\n * 隐私政策弹框\n */\n private fun showPrivacyDialog() {\n val dialogFragment = PrivacyDialog()\n dialogFragment.callback(CommonCallBack { granted ->\n if (granted) {\n grantPermission()\n App.initAfterPrivacy()\n } else {\n finish()\n }\n })\n dialogFragment.show(supportFragmentManager, \"PrivacyProtocol\")\n }\n\n /**\n * 权限申请\n */\n private fun grantPermission() {\n PermissionUtil(this).requestPermissions(\n arrayOf(Manifest.permission.WRITE_EXTERNAL_STORAGE),\n object : PermissionListener {\n override fun onGranted() {\n doNextStep()\n }\n\n override fun onDenied(deniedPermission: MutableList<Permission>?) {\n doNextStep()\n }\n })\n }\n\n private fun doNextStep() {\n if (App.coldStart) { //冷启动\n doNext()\n } else {\n mHandler.postDelayed(mRunnable, mDelay) //热启动\n }\n App.coldStart = false\n }\n\n private fun doNext() {\n if (SplashUtil.hasShowGuide()) {\n MainActivity.start(this)\n } else {\n GuideActivity.start(this)\n }\n finish()\n }\n\n private var mRunnable = Runnable {\n doNext()\n }\n}"},{"alpha_fraction":0.6291578412055969,"alphanum_fraction":0.6447275280952454,"avg_line_length":31.482759475708008,"blob_id":"25ad9ea7d147e1299b525fda35425fb539ea2367","content_id":"7b68974cb8a07ecb1eaccc2216cb8e97e70349aa","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":2964,"license_type":"no_license","max_line_length":89,"num_lines":87,"path":"/librarys/CommonUtil/src/main/java/com/android/util/encode/Des3Encoder.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.util.encode;\n\nimport android.util.Base64;\n\nimport java.security.Key;\n\nimport javax.crypto.Cipher;\nimport javax.crypto.SecretKeyFactory;\nimport javax.crypto.spec.DESedeKeySpec;\nimport javax.crypto.spec.IvParameterSpec;\n\n/**\n * 3DES加密\n */\npublic class Des3Encoder {\n private static final String KEY = \"sndo@agriculture@analytics\"; //3DES的加密密钥长度要求是24个字节\n private final static String IV = \"01234567\"; // 向量\n private final static String ENCODING = \"UTF-8\"; // 加解密统一使用的编码方式\n\n public static void main(String[] args) throws Exception {\n test(\"12345678\");\n }\n\n public static void test(String src) throws Exception {\n //UTF8编码->DES3加密->DES3解密->UTF8解码\n System.out.println(\"key.length=\" + KEY.length());\n byte[] encryptBytes = Des3Encoder.encrypt(KEY, src.getBytes(\"UTF-8\"));\n String data = Base64.encodeToString(encryptBytes, Base64.DEFAULT);\n System.out.println(\"加密后的数据=\" + data);\n byte[] decrypt = Des3Encoder.decrypt(KEY, Base64.decode(data, Base64.DEFAULT));\n data = new String(decrypt, \"UTF-8\");\n System.out.println(\"解密后的数据=\" + data);\n }\n\n /**\n * 加密\n *\n * @param key 密钥\n * @param srcBytes 原始数据\n * @return\n * @throws Exception\n */\n public static byte[] encrypt(String key, byte[] srcBytes) throws Exception {\n//\t\tif (TextUtils.isEmpty(key) || null == srcBytes || srcBytes.length == 0) {\n//\t\t\treturn srcBytes;\n//\t\t}\n Key deskey = null;\n DESedeKeySpec spec = new DESedeKeySpec(key.getBytes());\n SecretKeyFactory keyfactory = SecretKeyFactory.getInstance(\"desede\");\n deskey = keyfactory.generateSecret(spec);\n\n Cipher cipher = Cipher.getInstance(\"desede/CBC/PKCS5Padding\");\n IvParameterSpec ips = new IvParameterSpec(IV.getBytes());\n cipher.init(Cipher.ENCRYPT_MODE, deskey, ips);\n byte[] encryptData = cipher.doFinal(srcBytes);\n\n return encryptData;\n }\n\n /**\n * 解密\n *\n * @param key 解密密钥\n * @param encodedBytes 加密后的字节\n * @return\n * @throws Exception\n */\n public static byte[] decrypt(String key, byte[] encodedBytes)\n throws Exception {\n//\t\tif (TextUtils.isEmpty(key) || null == encodedBytes\n//\t\t\t\t|| encodedBytes.length == 0) {\n//\t\t\treturn encodedBytes;\n//\t\t}\n\n Key deskey = null;\n DESedeKeySpec spec = new DESedeKeySpec(key.getBytes());\n SecretKeyFactory keyfactory = SecretKeyFactory.getInstance(\"desede\");\n deskey = keyfactory.generateSecret(spec);\n Cipher cipher = Cipher.getInstance(\"desede/CBC/PKCS5Padding\");\n IvParameterSpec ips = new IvParameterSpec(IV.getBytes());\n cipher.init(Cipher.DECRYPT_MODE, deskey, ips);\n\n byte[] decryptData = cipher.doFinal(encodedBytes);\n\n return decryptData;\n }\n}\n"},{"alpha_fraction":0.6921886801719666,"alphanum_fraction":0.6921886801719666,"avg_line_length":27.10869598388672,"blob_id":"80785b741e5a57bf51b692d6d8eee7adbe9371dd","content_id":"38ec538c444fcced75a1e62df634947fd1255621","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1309,"license_type":"no_license","max_line_length":96,"num_lines":46,"path":"/app/src/main/java/com/fastapp/TestFrag.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.fastapp;\n\nimport android.content.Context;\nimport android.os.Bundle;\nimport android.view.View;\nimport android.widget.TextView;\n\nimport com.android.base.Constant;\nimport com.android.base.event.EventMsg;\nimport com.android.base.ui.SimpleFrag;\nimport com.android.base.ui.SimpleFragAct;\nimport com.android.base.widget.StatusBar;\n\nimport org.greenrobot.eventbus.EventBus;\n\n\n/**\n * @author 张全\n */\npublic class TestFrag extends SimpleFrag {\n public static void start(Context ctx) {\n SimpleFragAct.start(ctx, new SimpleFragAct.SimpleFragParam(\"TestFrag\", TestFrag.class));\n }\n\n @Override\n protected int getLayoutId() {\n return R.layout.activity_main;\n }\n\n @Override\n protected void init(Bundle savedInstanceState) {\n StatusBar.setStatusBar(mContext, true, getTitleBar());\n\n findViewById(R.id.showDialog).setVisibility(View.GONE);\n findViewById(R.id.http).setVisibility(View.GONE);\n TextView textView = findViewById(R.id.testFrag);\n textView.setText(\"发送Event\");\n textView.setOnClickListener(new View.OnClickListener() {\n @Override\n public void onClick(View v) {\n EventBus.getDefault().post(new EventMsg(Constant.Event.LOGIN_SUCCESS,\"登录成功\"));\n }\n });\n\n }\n}\n"},{"alpha_fraction":0.7093495726585388,"alphanum_fraction":0.7093495726585388,"avg_line_length":17.923076629638672,"blob_id":"4c157199f78b4f9931302e10088e1142e6376856","content_id":"fe4f1f6709f1abfcce74133baa173668aca94bfe","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":530,"license_type":"no_license","max_line_length":55,"num_lines":26,"path":"/app/src/main/java/com/android/base/util/pay/OrderPayResponse.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.util.pay;\n\nimport com.google.gson.annotations.SerializedName;\n\nimport java.io.Serializable;\n\n/**\n * 支付信息\n *\n * @author 张全\n */\npublic class OrderPayResponse implements Serializable {\n //微信支付信息\n public String appid;\n public String partnerid;\n public String prepayid;\n @SerializedName(\"package\")\n public String packageStr;\n public String timestamp;\n public String noncestr;\n public String sign;\n\n //支付宝支付信息.\n public String orderInfo;\n\n}\n"},{"alpha_fraction":0.5889744162559509,"alphanum_fraction":0.5950997471809387,"avg_line_length":39.38266372680664,"blob_id":"a31245df7d9b3d4de588f56780d33b430b98d918","content_id":"4defb3015aea0cb8994fd14194c7a70835ad7a2f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":19125,"license_type":"no_license","max_line_length":131,"num_lines":473,"path":"/librarys/CommonWidget/src/main/java/common/widget/shape/ShapeAttrParser.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package common.widget.shape;\n\nimport android.content.Context;\nimport android.content.res.ColorStateList;\nimport android.content.res.Resources;\nimport android.content.res.TypedArray;\nimport android.content.res.XmlResourceParser;\nimport android.graphics.BlendMode;\nimport android.graphics.drawable.GradientDrawable;\nimport android.os.Build;\nimport android.text.TextUtils;\nimport android.util.ArrayMap;\nimport android.util.AttributeSet;\nimport android.util.TypedValue;\nimport android.view.View;\n\nimport androidx.annotation.RequiresApi;\n\nimport java.lang.reflect.Constructor;\nimport java.util.Map;\n\nimport common.widget.R;\n\n\n/**\n * 属性解析\n *\n * @author zhangquan\n */\npublic class ShapeAttrParser {\n private static final int RADIUS_TYPE_PIXELS = 0;\n private static final int RADIUS_TYPE_FRACTION = 1;\n private static final int RADIUS_TYPE_FRACTION_PARENT = 2;\n\n private static final float DEFAULT_INNER_RADIUS_RATIO = 3.0f;\n private static final float DEFAULT_THICKNESS_RATIO = 9.0f;\n\n private static final Class<?>[] sConstructorSignature = new Class[]{Context.class, AttributeSet.class};\n private static final Object[] mConstructorArgs = new Object[2];\n private static final Map<String, Constructor<? extends View>> sConstructorMap = new ArrayMap<>();\n\n private static final String sViewClassPrefix = \"android.view.\";\n private static final String[] sClassPrefixList = {\n \"android.widget.\",\n \"android.webkit.\",\n \"android.app.\"\n };\n\n\n public static View parseAttr(Context context, AttributeSet attrs, View view) {\n return parseAttr(context, attrs, view, null);\n }\n\n public static View parseAttr(Context context, AttributeSet attrs, View view, String viewName) {\n if (null == attrs || Build.VERSION.SDK_INT <= 19) {\n return view;\n }\n TypedArray typedArray = context.obtainStyledAttributes(attrs, R.styleable.ShapeBg);\n if (typedArray.getIndexCount() == 0) {\n return view;\n }\n if (null == view) {\n view = createViewFromName(context, viewName, attrs);\n }\n if (null != view) {\n try {\n GradientDrawable gradientDrawable = new GradientDrawable();\n //<shape>\n updateStateFromTypedArray(typedArray, gradientDrawable);\n //<gradient>\n updateGradientDrawableGradient(context, typedArray, gradientDrawable);\n //<solid>\n updateGradientDrawableSolid(typedArray, gradientDrawable);\n //<stroke>\n updateGradientDrawableStroke(typedArray, gradientDrawable);\n //<corners>\n updateDrawableCorners(typedArray, gradientDrawable);\n //<padding>\n updateGradientDrawablePadding(typedArray, gradientDrawable);\n //<size>\n updateGradientDrawableSize(typedArray, gradientDrawable);\n\n view.setBackground(gradientDrawable);\n } finally {\n typedArray.recycle();\n }\n }\n return view;\n }\n\n\n /**\n * <shape xmlns:android=\"http://schemas.android.com/apk/res/android\"\n * android:shape=\"rectangle\"\n * android:useLevel=\"false\"\n * android:dither=\"true\"\n * android:innerRadius=\"10dp\"\n * android:innerRadiusRatio=\"1.2\"\n * android:tint=\"#fff\"\n * android:tintMode=\"src_in\"\n * android:visible=\"true\"\n * android:thickness=\"10dp\"\n * android:thicknessRatio=\"1.2\"\n * >\n */\n @RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)\n private static void updateStateFromTypedArray(TypedArray a, GradientDrawable gradientDrawable) {\n int shape = a.getInt(R.styleable.ShapeBg_sp_shape, GradientDrawable.RECTANGLE);\n gradientDrawable.setShape(shape);\n\n boolean dither = a.getBoolean(R.styleable.ShapeBg_sp_dither, false);\n gradientDrawable.setDither(dither);\n\n if (shape == GradientDrawable.RING) {\n if (android.os.Build.VERSION.SDK_INT >= android.os.Build.VERSION_CODES.Q) {\n int mInnerRadius = a.getDimensionPixelSize(\n R.styleable.ShapeBg_sp_innerRadius, -1);\n gradientDrawable.setInnerRadius(mInnerRadius);\n if (mInnerRadius == -1) {\n float mInnerRadiusRatio = a.getFloat(\n R.styleable.ShapeBg_sp_innerRadiusRatio, DEFAULT_INNER_RADIUS_RATIO);\n gradientDrawable.setInnerRadiusRatio(mInnerRadiusRatio);\n }\n\n int mThickness = a.getDimensionPixelSize(\n R.styleable.ShapeBg_sp_thickness, -1);\n gradientDrawable.setThickness(mThickness);\n if (mThickness == -1) {\n float mThicknessRatio = a.getFloat(\n R.styleable.ShapeBg_sp_thicknessRatio, DEFAULT_THICKNESS_RATIO);\n gradientDrawable.setThicknessRatio(mThicknessRatio);\n }\n }\n\n boolean mUseLevelForShape = a.getBoolean(\n R.styleable.ShapeBg_sp_useLevel, true);\n gradientDrawable.setUseLevel(mUseLevelForShape);\n }\n\n if (android.os.Build.VERSION.SDK_INT >= android.os.Build.VERSION_CODES.Q) {\n final int tintMode = a.getInt(R.styleable.ShapeBg_sp_tintMode, -1);\n if (tintMode != -1) {\n BlendMode blendMode = parseBlendMode(tintMode, BlendMode.SRC_IN);\n gradientDrawable.setTintBlendMode(blendMode);\n }\n }\n\n final ColorStateList tint = a.getColorStateList(R.styleable.ShapeBg_sp_tint);\n if (tint != null) {\n gradientDrawable.setTintList(tint);\n }\n\n// Insets mOpticalInsets = Insets.NONE;\n// final int insetLeft = a.getDimensionPixelSize(\n// R.styleable.ShapeLayout_opticalInsetLeft, mOpticalInsets.left);\n// final int insetTop = a.getDimensionPixelSize(\n// R.styleable.ShapeLayout_opticalInsetTop, mOpticalInsets.top);\n// final int insetRight = a.getDimensionPixelSize(\n// R.styleable.ShapeLayout_opticalInsetRight, mOpticalInsets.right);\n// final int insetBottom = a.getDimensionPixelSize(\n// R.styleable.ShapeLayout_opticalInsetBottom, mOpticalInsets.bottom);\n// mOpticalInsets = Insets.of(insetLeft, insetTop, insetRight, insetBottom);\n\n }\n\n /**\n * <size android:width=\"\" android:height=\"\"/>\n */\n private static void updateGradientDrawableSize(TypedArray typedArray, GradientDrawable gradientDrawable) {\n if (typedArray.hasValue(R.styleable.ShapeBg_sp_width)\n || typedArray.hasValue(R.styleable.ShapeBg_sp_height)) {\n int width = typedArray.getDimensionPixelSize(\n R.styleable.ShapeBg_sp_width, -1);\n int height = typedArray.getDimensionPixelSize(\n R.styleable.ShapeBg_sp_height, -1);\n gradientDrawable.setSize(width, height);\n }\n }\n\n /**\n * <gradient\n * android:angle=\"\"\n * android:centerColor=\"\"\n * android:centerX=\"\"\n * android:centerY=\"\"\n * android:endColor=\"\"\n * android:startColor=\"\"\n * android:type=\"\"\n * android:gradientRadius=\"\"\n * android:useLevel=\"\"\n * />\n */\n private static void updateGradientDrawableGradient(Context context, TypedArray typedArray, GradientDrawable gradientDrawable) {\n\n float mCenterX = 0.5f;\n float mCenterY = 0.5f;\n float mGradientRadius = 0.5f;\n int mGradientRadiusType = RADIUS_TYPE_PIXELS;\n boolean mUseLevel = false;\n int gradientType = GradientDrawable.LINEAR_GRADIENT;\n\n mUseLevel = typedArray.getBoolean(R.styleable.ShapeBg_sp_gradientUseLevel, mUseLevel);\n gradientDrawable.setUseLevel(mUseLevel);\n\n gradientType = typedArray.getInt(R.styleable.ShapeBg_sp_gradientType, gradientType);\n gradientDrawable.setGradientType(gradientType);\n\n mCenterX = getFloatOrFraction(typedArray, R.styleable.ShapeBg_sp_gradientCenterX, mCenterX);\n mCenterY = getFloatOrFraction(typedArray, R.styleable.ShapeBg_sp_gradientCenterY, mCenterY);\n\n int startColor = typedArray.getColor(R.styleable.ShapeBg_sp_gradientStartColor, 0);\n int centerColor = typedArray.getColor(R.styleable.ShapeBg_sp_gradientCenterColor, 0);\n boolean hasCenterColor = typedArray.hasValue(R.styleable.ShapeBg_sp_gradientCenterColor);\n int endColor = typedArray.getColor(R.styleable.ShapeBg_sp_gradientEndColor, 0);\n if (hasCenterColor && Build.VERSION.SDK_INT >= Build.VERSION_CODES.Q) {\n int[] colors = new int[]{startColor, centerColor, endColor};\n float[] positions = new float[]{0.0f, mCenterX != 0.5f ? mCenterX : mCenterY, 1f};\n gradientDrawable.setColors(colors, positions);\n } else {\n int[] colors = new int[]{startColor, endColor};\n gradientDrawable.setColors(colors);\n }\n\n GradientDrawable.Orientation mOrientation = GradientDrawable.Orientation.TOP_BOTTOM;\n int angle = (int) typedArray.getFloat(R.styleable.ShapeBg_sp_gradientAngle, 0);\n angle = ((angle % 360) + 360) % 360;\n if (angle >= 0) {\n switch (angle) {\n case 0:\n mOrientation = GradientDrawable.Orientation.LEFT_RIGHT;\n break;\n case 45:\n mOrientation = GradientDrawable.Orientation.BL_TR;\n break;\n case 90:\n mOrientation = GradientDrawable.Orientation.BOTTOM_TOP;\n break;\n case 135:\n mOrientation = GradientDrawable.Orientation.BR_TL;\n break;\n case 180:\n mOrientation = GradientDrawable.Orientation.RIGHT_LEFT;\n break;\n case 225:\n mOrientation = GradientDrawable.Orientation.TR_BL;\n break;\n case 270:\n mOrientation = GradientDrawable.Orientation.TOP_BOTTOM;\n break;\n case 315:\n mOrientation = GradientDrawable.Orientation.TL_BR;\n break;\n }\n } else {\n mOrientation = GradientDrawable.Orientation.TOP_BOTTOM;\n }\n gradientDrawable.setOrientation(mOrientation);\n\n\n final TypedValue tv = typedArray.peekValue(R.styleable.ShapeBg_sp_gradientRadius);\n if (tv != null) {\n if (tv.type == TypedValue.TYPE_FRACTION) {\n mGradientRadius = tv.getFraction(1.0f, 1.0f);\n\n final int unit = (tv.data >> TypedValue.COMPLEX_UNIT_SHIFT)\n & TypedValue.COMPLEX_UNIT_MASK;\n if (unit == TypedValue.COMPLEX_UNIT_FRACTION_PARENT) {\n mGradientRadiusType = RADIUS_TYPE_FRACTION_PARENT;\n } else {\n mGradientRadiusType = RADIUS_TYPE_FRACTION;\n }\n } else if (tv.type == TypedValue.TYPE_DIMENSION) {\n mGradientRadius = tv.getDimension(context.getResources().getDisplayMetrics());\n mGradientRadiusType = RADIUS_TYPE_PIXELS;\n } else {\n mGradientRadius = tv.getFloat();\n mGradientRadiusType = RADIUS_TYPE_PIXELS;\n }\n gradientDrawable.setGradientType(mGradientRadiusType);\n gradientDrawable.setGradientRadius(mGradientRadius);\n }\n\n }\n\n /**\n * <solid android:color=\"\" />\n */\n @RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)\n private static void updateGradientDrawableSolid(TypedArray typedArray, GradientDrawable gradientDrawable) {\n final ColorStateList solidColor = typedArray.getColorStateList(\n R.styleable.ShapeBg_sp_solid);\n if (solidColor != null) {\n gradientDrawable.setColor(solidColor);\n }\n\n }\n\n /**\n * <stroke\n * android:width=\"\"\n * android:color=\"\"\n * android:dashWidth=\"\"\n * android:dashGap=\"\" />\n */\n @RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)\n private static void updateGradientDrawableStroke(TypedArray typedArray, GradientDrawable gradientDrawable) {\n if (!typedArray.hasValue(R.styleable.ShapeBg_sp_strokeWidth)) {\n return;\n }\n\n int strokeWidth = typedArray.getDimensionPixelSize(R.styleable.ShapeBg_sp_strokeWidth, 0);\n float dashWidth = typedArray.getDimension(R.styleable.ShapeBg_sp_strokeDashWidth, 0);\n ColorStateList strokeColor = typedArray.getColorStateList(\n R.styleable.ShapeBg_sp_strokeColor);\n if (dashWidth != 0.0f) {\n float dashGap = typedArray.getDimension(R.styleable.ShapeBg_sp_strokeDashGap, 0);\n gradientDrawable.setStroke(strokeWidth, strokeColor, dashWidth, dashGap);\n } else {\n gradientDrawable.setStroke(strokeWidth, strokeColor);\n }\n }\n\n /**\n * <corners\n * android:bottomLeftRadius=\"\"\n * android:bottomRightRadius=\"\"\n * android:radius=\"\"\n * android:topLeftRadius=\"\"\n * android:topRightRadius=\"\" />\n */\n private static void updateDrawableCorners(TypedArray typedArray, GradientDrawable gradientDrawable) {\n\n int radius = typedArray.getDimensionPixelSize(R.styleable.ShapeBg_sp_radius, 0);\n final int topLeftRadius = typedArray.getDimensionPixelSize(\n R.styleable.ShapeBg_sp_topLeftRadius, radius);\n final int topRightRadius = typedArray.getDimensionPixelSize(\n R.styleable.ShapeBg_sp_topRightRadius, radius);\n final int bottomLeftRadius = typedArray.getDimensionPixelSize(\n R.styleable.ShapeBg_sp_bottomLeftRadius, radius);\n final int bottomRightRadius = typedArray.getDimensionPixelSize(\n R.styleable.ShapeBg_sp_bottomRightRadius, radius);\n if (topLeftRadius != radius || topRightRadius != radius ||\n bottomLeftRadius != radius || bottomRightRadius != radius) {\n gradientDrawable.setCornerRadii(new float[]{\n topLeftRadius, topLeftRadius,\n topRightRadius, topRightRadius,\n bottomRightRadius, bottomRightRadius,\n bottomLeftRadius, bottomLeftRadius\n });\n } else {\n gradientDrawable.setCornerRadius(radius);\n }\n }\n\n /**\n * <padding\n * android:bottom=\"\"\n * android:left=\"\"\n * android:right=\"\"\n * android:top=\"\" />\n * <p>\n * 注意会覆盖View的android:padding属性\n */\n private static void updateGradientDrawablePadding(TypedArray typedArray, GradientDrawable gradientDrawable) {\n if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.Q) {\n int padding = typedArray.getDimensionPixelOffset(R.styleable.ShapeBg_sp_padding, 0);\n if (padding > 0) {\n gradientDrawable.setPadding(padding, padding, padding, padding);\n } else {\n int paddingLeft = typedArray.getDimensionPixelOffset(R.styleable.ShapeBg_sp_paddingLeft, 0);\n int paddingTop = typedArray.getDimensionPixelOffset(R.styleable.ShapeBg_sp_paddingTop, 0);\n int paddingRight = typedArray.getDimensionPixelOffset(R.styleable.ShapeBg_sp_paddingRight, 0);\n int paddingBottom = typedArray.getDimensionPixelOffset(R.styleable.ShapeBg_sp_paddingBottom, 0);\n if (paddingLeft > 0 || paddingTop > 0 || paddingRight > 0 || paddingBottom > 0) {\n gradientDrawable.setPadding(paddingLeft,\n paddingTop,\n paddingRight,\n paddingBottom);\n }\n }\n }\n }\n\n private static float getFloatOrFraction(TypedArray a, int index, float defaultValue) {\n TypedValue tv = a.peekValue(index);\n float v = defaultValue;\n if (tv != null) {\n boolean vIsFraction = tv.type == TypedValue.TYPE_FRACTION;\n v = vIsFraction ? tv.getFraction(1.0f, 1.0f) : tv.getFloat();\n }\n return v;\n }\n\n public static BlendMode parseBlendMode(int value, BlendMode defaultMode) {\n if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.Q) {\n switch (value) {\n case 3:\n return BlendMode.SRC_OVER;\n case 5:\n return BlendMode.SRC_IN;\n case 9:\n return BlendMode.SRC_ATOP;\n // b/73224934 PorterDuff Multiply maps to Skia Modulate so actually\n // return BlendMode.MODULATE here\n case 14:\n return BlendMode.MODULATE;\n case 15:\n return BlendMode.SCREEN;\n case 16:\n return BlendMode.PLUS;\n default:\n return defaultMode;\n }\n }\n return defaultMode;\n }\n\n\n private static View createViewFromName(Context context, String name, AttributeSet attrs) {\n if (TextUtils.isEmpty(name)) {\n return null;\n }\n if (name.equals(\"view\")) {\n name = attrs.getAttributeValue(null, \"class\");\n }\n\n View view = null;\n try {\n mConstructorArgs[0] = context;\n mConstructorArgs[1] = attrs;\n\n if (!name.contains(\".\")) {\n if (TextUtils.equals(name, \"View\")) {\n view = createView(context, name, sViewClassPrefix);\n }\n\n if (view == null) {\n for (String prefix : sClassPrefixList) {\n try {\n view = createView(context, name, prefix);\n if (view != null) {\n break;\n }\n } catch (Exception e) {\n }\n }\n }\n } else {\n view = createView(context, name, null);\n }\n } finally {\n mConstructorArgs[0] = null;\n mConstructorArgs[1] = null;\n }\n return view;\n }\n\n private static View createView(Context context, String name, String prefix) {\n Constructor<? extends View> constructor = sConstructorMap.get(name);\n try {\n if (constructor == null) {\n Class<? extends View> clazz = Class.forName(prefix != null ? (prefix + name) : name, false,\n context.getClassLoader()).asSubclass(View.class);\n constructor = clazz.getConstructor(sConstructorSignature);\n sConstructorMap.put(name, constructor);\n }\n constructor.setAccessible(true);\n return constructor.newInstance(mConstructorArgs);\n } catch (Exception e) {\n return null;\n }\n }\n}\n"},{"alpha_fraction":0.5490729808807373,"alphanum_fraction":0.5499974489212036,"avg_line_length":34.79227828979492,"blob_id":"3119a1d9518fdf106f2cba92f21b7ad0d6bd7bf7","content_id":"87317fa64655d482a567365542d220e165e65879","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":40618,"license_type":"no_license","max_line_length":127,"num_lines":1088,"path":"/app/src/main/java/com/android/base/ui/WebViewFrag.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.ui;\n\nimport android.app.Activity;\nimport android.content.ClipData;\nimport android.content.ClipboardManager;\nimport android.content.Context;\nimport android.content.Intent;\nimport android.graphics.Bitmap;\nimport android.graphics.Color;\nimport android.net.Uri;\nimport android.net.http.SslError;\nimport android.os.Bundle;\nimport android.text.TextUtils;\nimport android.view.View;\nimport android.view.ViewGroup;\nimport android.view.WindowManager;\nimport android.webkit.DownloadListener;\nimport android.webkit.JsPromptResult;\nimport android.webkit.JsResult;\nimport android.webkit.SslErrorHandler;\nimport android.webkit.ValueCallback;\nimport android.webkit.WebChromeClient;\nimport android.webkit.WebSettings;\nimport android.webkit.WebView;\nimport android.webkit.WebViewClient;\nimport android.widget.FrameLayout;\nimport android.widget.ProgressBar;\nimport android.widget.RelativeLayout;\n\nimport com.android.util.LContext;\nimport com.android.util.ext.ToastUtil;\nimport com.android.util.log.LogUtil;\nimport com.android.base.Constant;\nimport com.android.base.event.EventMsg;\nimport com.android.base.util.CommonUtil;\nimport com.android.base.util.DispatchUtil;\nimport com.android.base.util.NotificationPageHelper;\nimport com.android.base.util.filedownloader.FileDownloadDialogView;\nimport com.android.base.util.filedownloader.FileDownloader;\nimport com.android.base.util.share.ShareDialog;\nimport com.android.base.util.share.ShareInfo;\nimport com.android.base.widget.AlertDialogView;\nimport com.android.base.widget.StatusBar;\nimport com.android.base.widget.TitleBarView;\nimport com.blankj.utilcode.util.BarUtils;\nimport com.blankj.utilcode.util.NetworkUtils;\nimport com.blankj.utilcode.util.SizeUtils;\nimport com.fastapp.R;\n\nimport org.greenrobot.eventbus.Subscribe;\nimport org.greenrobot.eventbus.ThreadMode;\nimport org.json.JSONArray;\nimport org.json.JSONException;\nimport org.json.JSONObject;\n\nimport java.io.File;\nimport java.io.Serializable;\nimport java.lang.reflect.Method;\nimport java.util.ArrayList;\nimport java.util.Arrays;\nimport java.util.List;\nimport java.util.Set;\n\nimport common.widget.LoadingBar;\nimport common.widget.dialog.EffectDialogBuilder;\nimport common.widget.dialog.LoadingDialog;\n\n/**\n * WebView页面\n *\n * @author zhangquan\n */\npublic class WebViewFrag extends SimpleFrag {\n public static final String PARAM = \"PARAM\";\n private WebView webView;\n private ProgressBar progressBar;\n private LoadingBar loadingBar;\n private WebViewParam webViewParam;\n private TitleBarView titleBarView;\n private FrameLayout fullScreenView;\n private RelativeLayout webContainer;\n private View myView;\n private static final String TAG = \"WebViewFrag\";\n private LoadingDialog loadingDialog;\n private String original_Url; //进入页面的url，没有添加公共参数\n private boolean isInit = false;\n\n public static class WebViewParam implements Serializable {\n //标题\n public String title;\n //是否需要重置title 显示webview的标题\n public boolean shouldResetTitle = true;\n //链接URL url目前支持needLogin(先登录)、hideTitlebar(隐藏标题栏)、lightModel(透明状态栏、黑色文字)\n public String url;\n public String titleBarColor; //标题栏color\n\n public ShareInfo shareInfo; //分享信息\n public boolean isShowShare; // 是否显示右上角分享按钮\n public boolean checkNetwork = true; //入口检查网络\n public boolean sensorOriention; //自动旋转屏幕方向\n }\n\n public static Bundle getParamBundle(WebViewParam param) {\n Bundle bundle = new Bundle();\n bundle.putSerializable(PARAM, param);\n return bundle;\n }\n\n public static SimpleFragAct.SimpleFragParam getStartParam(WebViewParam param) {\n return new SimpleFragAct.SimpleFragParam(param.title,\n WebViewFrag.class, WebViewFrag.getParamBundle(param));\n }\n\n public static void start(Context ctx, SimpleFragAct.SimpleFragParam param) {\n SimpleFragAct.start(ctx, param);\n }\n\n public static void start(Context ctx, WebViewParam param) {\n try {\n// String url = param.url;\n// String needLogin = Uri.parse(url).getQueryParameter(\"needLogin\");\n// if (TextUtils.equals(needLogin, \"1\") && UserClient.getUser() == null) { //首先登录\n// LoginFragment.start(ctx);\n// } else {\n// SimpleFragAct.SimpleFragParam startParam = getStartParam(param);\n// startParam.mutliPage = true;\n// SimpleFragAct.start(ctx, startParam,param.sensorOriention);\n// }\n } catch (Exception e) {\n e.printStackTrace();\n }\n }\n\n @Override\n public int getLayoutId() {\n return R.layout.common_webview;\n }\n\n @Override\n public void init(Bundle savedInstanceState) {\n\n if (null != getArguments()) {\n webViewParam = (WebViewParam) getArguments().getSerializable(PARAM);\n }\n\n if (null == webViewParam) {\n close();\n return;\n }\n\n // 网络无连接\n if (webViewParam.checkNetwork && !NetworkUtils.isAvailable()) {\n showToastShort(R.string.net_noconnection);\n close();\n return;\n }\n\n //请求无链接\n if (TextUtils.isEmpty(webViewParam.url)) {\n showToastShort(\"请求无链接\");\n close();\n return;\n }\n\n LogUtil.d(TAG, webViewParam.url);\n\n addAction(Constant.Event.LOGIN_SUCCESS);\n init();\n }\n\n\n @Override\n public void onResume() {\n super.onResume();\n try {\n Method onResume = webView.getClass().getMethod(\"onResume\");\n if (null != onResume) onResume.invoke(webView, (Object[]) null);\n } catch (Exception e) {\n e.printStackTrace();\n }\n if (isInit) {\n viewWillAppear();\n }\n isInit = true;\n }\n\n @Override\n public void onPause() {\n super.onPause();\n try {\n Method onPause = webView.getClass().getMethod(\"onPause\");\n if (null != onPause) onPause.invoke(webView, (Object[]) null);\n } catch (Exception e) {\n e.printStackTrace();\n }\n }\n\n private boolean handleBackEvent() {\n if (null != webView && webView.canGoBack()) {\n webView.goBack();\n return true;\n }\n return false;\n }\n\n protected void init() {\n webView = findViewById(R.id.webview);\n handleLongClick(); //长按保存图片\n fullScreenView = findViewById(R.id.video_fullscreen); //webview视频全屏播放\n webContainer = findViewById(R.id.web_parent);\n progressBar = findViewById(R.id.pb);\n loadingBar = findViewById(R.id.loadingBar);\n loadingBar.setOnClickListener(new View.OnClickListener() {\n @Override\n public void onClick(View v) {\n if (!loadingBar.canLoading()) {\n return;\n }\n if (!NetworkUtils.isAvailable()) {\n ToastUtil.show(R.string.net_noconnection);\n return;\n }\n webView.reload();\n }\n });\n\n titleBarView = getTitleBar();\n\n if (null != titleBarView) {\n View.OnClickListener backListener = new View.OnClickListener() {\n @Override\n public void onClick(View v) {\n if (handleBackEvent()) {\n return;\n }\n close();\n }\n };\n titleBarView.setOnLeftTxtClickListener(backListener);\n titleBarView.setOnLeftBtnClickListener(backListener);\n\n\n //刷新按钮\n titleBarView.setRightBtnDrawable(R.drawable.icon_refresh);\n titleBarView.mRightButton.setOnClickListener(new View.OnClickListener() {\n @Override\n public void onClick(View v) {\n if (!NetworkUtils.isAvailable()) {\n ToastUtil.show(R.string.net_noconnection);\n return;\n }\n webView.reload();\n }\n });\n\n //分享\n if (null != webViewParam.shareInfo) {\n titleBarView.setRightBtnDrawable(R.drawable.icon_share);\n titleBarView.mRightButton.setOnClickListener(new View.OnClickListener() {\n @Override\n public void onClick(View v) {\n ShareDialog.share(getActivity(), webViewParam.shareInfo);\n }\n });\n }\n\n if (webViewParam.isShowShare) {\n titleBarView.findViewById(R.id.title_icon_share).setVisibility(View.VISIBLE);\n titleBarView.findViewById(R.id.title_icon_share).setOnClickListener(new View.OnClickListener() {\n @Override\n public void onClick(View v) {\n\n }\n });\n } else {\n titleBarView.findViewById(R.id.title_icon_share).setVisibility(View.GONE);\n }\n }\n\n\n WebSettings webSettings = webView.getSettings();\n webSettings.setJavaScriptEnabled(true);\n webSettings.setAllowFileAccess(true);\n webSettings.setLoadWithOverviewMode(true);\n webSettings.setDomStorageEnabled(true);\n webSettings.setUseWideViewPort(true);\n webSettings.setLoadWithOverviewMode(true);\n webSettings.setMediaPlaybackRequiresUserGesture(false);//自动播放视频\n webSettings.setAllowContentAccess(true); // 是否可访问Content Provider的资源，默认值 true\n webSettings.setAllowFileAccess(true); // 是否可访问本地文件，默认值 true\n // 是否允许通过file url加载的Javascript读取本地文件，默认值 false\n webSettings.setAllowFileAccessFromFileURLs(false);\n // 是否允许通过file url加载的Javascript读取全部资源(包括文件,http,https)，默认值 false\n webSettings.setAllowUniversalAccessFromFileURLs(false);\n\n\n //设置缓存\n// webSettings.setCacheMode(WebSettings.LOAD_CACHE_ELSE_NETWORK);\n webSettings.setCacheMode(WebSettings.LOAD_NO_CACHE);\n\n int statusBarHeight = 0;\n try {\n statusBarHeight = BarUtils.getStatusBarHeight();\n statusBarHeight = SizeUtils.px2dp(statusBarHeight);\n } catch (Exception e) {\n statusBarHeight = 25;\n }\n LogUtil.d(TAG, \"statusBarHeight=\" + statusBarHeight);\n\n original_Url = webViewParam.url;\n\n //拼接参数\n Uri uri = Uri.parse(webViewParam.url);\n Uri.Builder builder = uri.buildUpon();\n Set<String> parameterNames = uri.getQueryParameterNames();\n if (!parameterNames.contains(\"appVersion\")) {\n builder.appendQueryParameter(\"appVersion\", LContext.versionName);\n }\n if (!parameterNames.contains(\"isApp\")) {\n builder.appendQueryParameter(\"isApp\", \"1\");\n }\n\n if (!parameterNames.contains(\"statusBarH\")) { //状态栏高度\n builder.appendQueryParameter(\"statusBarH\", statusBarHeight + \"\");\n }\n\n webViewParam.url = builder.build().toString();\n\n //标题栏背景\n if (webViewParam.titleBarColor != null) {\n getTitleBar().setBackgroundColor(Color.parseColor(webViewParam.titleBarColor));\n getTitleBar().setTitleTextColor(R.color.white);\n getTitleBar().setLeftBtnWhiteColor();\n } else {\n titleBarView.setBackgroundColor(Color.WHITE);\n }\n\n uri = Uri.parse(webViewParam.url);\n //隐藏标题栏\n String hideTitlebar = uri.getQueryParameter(\"hideTitlebar\");\n if (TextUtils.equals(hideTitlebar, \"1\")) {\n titleBarView.setVisibility(View.GONE);\n String lightModel = uri.getQueryParameter(\"lightModel\");\n if (TextUtils.equals(lightModel, \"1\")) {\n StatusBar.setStatusBar(mContext, true, titleBarView);\n } else {\n StatusBar.setStatusBar(mContext, false, titleBarView);\n }\n } else {\n StatusBar.setStatusBar(mContext, true, titleBarView);\n }\n\n\n LogUtil.d(TAG, \"加载 url=\" + webViewParam.url);\n webView.loadUrl(webViewParam.url);\n webView.setWebChromeClient(new DWebChromeClient());\n webView.setWebViewClient(new DWebViewClient());\n webView.setDownloadListener(new DWebViewDownLoadListener());\n\n }\n\n /**\n * 辅助WebView处理Javascript的对话框、网站图标、网站title、加载进度等\n */\n private final class DWebChromeClient extends WebChromeClient {\n\n /**\n * 全屏模式\n *\n * @param view 需要展示的view内容\n * @param callback\n */\n @Override\n public void onShowCustomView(View view, CustomViewCallback callback) {\n super.onShowCustomView(view, callback);\n LogUtil.d(TAG, \"onShowCustomView view=\" + view);\n try {\n ViewGroup parent = (ViewGroup) webView.getParent();\n parent.removeView(webView);\n\n fullScreenView.addView(view);\n fullScreenView.setVisibility(View.VISIBLE);\n getTitleBar().setVisibility(View.GONE);\n myView = view;\n setFullScreen();\n } catch (Exception e) {\n e.printStackTrace();\n }\n }\n\n @Override\n public void onHideCustomView() {\n super.onHideCustomView();\n LogUtil.d(TAG, \"onHideCustomView \");\n try {\n if (myView != null) {\n fullScreenView.removeAllViews();\n webContainer.addView(webView);\n fullScreenView.setVisibility(View.GONE);\n\n getTitleBar().setVisibility(View.VISIBLE);\n myView = null;\n quitFullScreen();\n }\n } catch (Exception e) {\n e.printStackTrace();\n }\n }\n\n @Override\n public boolean onJsAlert(WebView view, String url, String message, final JsResult result) {\n LogUtil.d(TAG, \"onJsAlert,url=\" + url + \",message=\" + message);\n AlertDialogView dialogView = new AlertDialogView(webView.getContext())\n .setTitle(\"提示\")\n .setContent(message)//\n .setSingleBtn(\"确定\", new View.OnClickListener() {\n @Override\n public void onClick(View v) {\n result.confirm();\n }\n });\n\n new EffectDialogBuilder(webView.getContext())\n .setCancelable(false)\n .setCancelableOnTouchOutside(false)\n .setContentView(dialogView).show();\n\n// return super.onJsAlert(view, url, message, result);\n return true;\n }\n\n @Override\n public boolean onJsBeforeUnload(WebView view, String url, String message, JsResult result) {\n return super.onJsBeforeUnload(view, url, message, result);\n }\n\n @Override\n public boolean onJsConfirm(WebView view, String url, String message, final JsResult result) {\n LogUtil.d(TAG, \"onJsConfirm,url=\" + url + \",message=\" + message);\n AlertDialogView dialogView = new AlertDialogView(webView.getContext())\n .setContent(message)//\n .setRightBtn(\"确定\", new View.OnClickListener() {\n @Override\n public void onClick(View v) {\n result.confirm();\n }\n })\n .setLeftBtn(\"取消\", new View.OnClickListener() {\n @Override\n public void onClick(View v) {\n result.cancel();\n }\n });\n new EffectDialogBuilder(webView.getContext())\n .setCancelable(false)\n .setCancelableOnTouchOutside(false)\n .setContentView(dialogView).show();\n// return super.onJsConfirm(view, url, message, result);\n return true;\n }\n\n @Override\n public boolean onJsPrompt(WebView view, String url, String message, String defaultValue, JsPromptResult result) {\n LogUtil.d(TAG, \"onJsPrompt,url=\" + url + \",message=\" + message);\n return super.onJsPrompt(view, url, message, defaultValue, result);\n }\n\n\n @Override\n public void onReceivedTitle(WebView view, String title) {\n super.onReceivedTitle(view, title);\n LogUtil.d(TAG, \"onReceivedTitle,url=\" + view.getUrl());\n LogUtil.d(TAG, \"onReceivedTitle,title=\" + title);\n if (webViewParam.shouldResetTitle && !TextUtils.isEmpty(title) && !title.startsWith(\"http\")) {\n if (null != titleBarView) {\n titleBarView.setTitleText(title);\n }\n if (!\"登录\".equals(title)) {\n webViewParam.title = title;\n }\n }\n\n }\n\n /**\n * 上传文件\n *\n * @param webView\n * @param filePathCallback\n * @param fileChooserParams\n * @return\n */\n @Override\n public boolean onShowFileChooser(WebView webView,\n ValueCallback<Uri[]> filePathCallback,\n FileChooserParams fileChooserParams) {\n fileCallback = filePathCallback;\n openFileChooser(null != fileChooserParams ? fileChooserParams.getAcceptTypes() : null);\n return true;\n }\n }\n\n /**\n * 设置全屏\n */\n private void setFullScreen() {\n // 设置全屏的相关属性，获取当前的屏幕状态，然后设置全屏\n try {\n getActivity().getWindow().setFlags(WindowManager.LayoutParams.FLAG_FULLSCREEN,\n WindowManager.LayoutParams.FLAG_FULLSCREEN);\n } catch (Exception e) {\n e.printStackTrace();\n }\n }\n\n /**\n * 退出全屏\n */\n private void quitFullScreen() {\n // 声明当前屏幕状态的参数并获取\n try {\n final WindowManager.LayoutParams attrs = getActivity().getWindow().getAttributes();\n attrs.flags &= (~WindowManager.LayoutParams.FLAG_FULLSCREEN);\n getActivity().getWindow().setAttributes(attrs);\n getActivity().getWindow().clearFlags(WindowManager.LayoutParams.FLAG_LAYOUT_NO_LIMITS);\n } catch (Exception e) {\n e.printStackTrace();\n }\n }\n\n private ValueCallback<Uri[]> fileCallback;\n private static final int FILE_CHOOSER_RESULT_CODE = 11;\n\n\n /**\n * webview选择上传图片或视频\n *\n * @param types\n */\n private void openFileChooser(String[] types) {\n LogUtil.d(TAG, \"onShowFileChooser 请求type=\" + Arrays.toString(types));\n String type = \"\";\n if (null != types && types.length > 0) {\n type = types[0];\n }\n if (TextUtils.isEmpty(type)) {\n type = \"image/*;video/*\";\n }\n\n if (type.contains(\"jpg\") || type.contains(\"png\")) {\n type = \"image/*\";\n } else if (type.contains(\"mp4\")) {\n type = \"video/*\";\n }\n LogUtil.d(TAG, \"onShowFileChooser 执行type=\" + type);\n\n try {\n Intent i = new Intent(Intent.ACTION_GET_CONTENT);\n i.addCategory(Intent.CATEGORY_OPENABLE);\n i.putExtra(Intent.EXTRA_LOCAL_ONLY, true);\n i.setType(type);\n startActivityForResult(Intent.createChooser(i, \"选择图片/视频\"), FILE_CHOOSER_RESULT_CODE);\n } catch (Exception e) {\n e.printStackTrace();\n ToastUtil.show(\"无法选择文件\");\n fileCallback.onReceiveValue(null);\n fileCallback = null;\n }\n }\n\n /**\n * 长按保存图片\n */\n private void handleLongClick() {\n View.OnLongClickListener onLongClickListener = new View.OnLongClickListener() {\n @Override\n public boolean onLongClick(View v) {\n final WebView.HitTestResult hitTestResult = webView.getHitTestResult();\n if (null == hitTestResult) return false;\n int type = hitTestResult.getType();\n LogUtil.d(TAG, \"onLongClick type=\" + type);\n // 如果是图片类型或者是带有图片链接的类型\n if (type == WebView.HitTestResult.IMAGE_TYPE ||\n type == WebView.HitTestResult.SRC_IMAGE_ANCHOR_TYPE) {\n AlertDialogView dialogView = new AlertDialogView(webView.getContext())\n .setContent(\"保存到相册\")//\n .setRightBtn(\"确定\", new View.OnClickListener() {\n @Override\n public void onClick(View v) {\n String pic = hitTestResult.getExtra();//获取图片\n LogUtil.d(TAG, \"onLongClick 图片url=\" + pic);\n if (TextUtils.isEmpty(pic)) {\n return;\n }\n new FileDownloader(mContext)\n .downloadFile(pic, new FileDownloader.DownloadCallback() {\n @Override\n public void success(File file, String url) {\n ToastUtil.show(\"保存成功\");\n }\n\n @Override\n public void fail(String url) {\n ToastUtil.show(\"保存失败\");\n }\n });\n }\n })\n .setLeftBtn(\"取消\", new View.OnClickListener() {\n @Override\n public void onClick(View v) {\n\n }\n });\n new EffectDialogBuilder(webView.getContext())\n .setCancelable(false)\n .setCancelableOnTouchOutside(false)\n .setContentView(dialogView).show();\n\n return true;\n }\n return false; //保持长按复制文字\n }\n };\n webView.setOnLongClickListener(onLongClickListener);\n }\n\n @Override\n public void onActivityResult(int requestCode, int resultCode, Intent data) {\n super.onActivityResult(requestCode, resultCode, data);\n //上传图片或视频\n if (requestCode == FILE_CHOOSER_RESULT_CODE) {\n if (null == fileCallback) return;\n try {\n Uri[] results = null;\n if (resultCode == Activity.RESULT_OK && data != null) {\n String dataString = data.getDataString();\n ClipData clipData = data.getClipData();\n LogUtil.d(TAG, \"onShowFileChooser,dataString=\" + dataString + \",clipData=\" + clipData);\n if (!TextUtils.isEmpty(dataString)) {\n results = new Uri[]{Uri.parse(dataString)};\n } else if (clipData != null && clipData.getItemCount() > 0) {\n results = new Uri[clipData.getItemCount()];\n for (int i = 0; i < clipData.getItemCount(); i++) {\n ClipData.Item item = clipData.getItemAt(i);\n results[i] = item.getUri();\n }\n }\n }\n\n\n LogUtil.d(TAG, \"onShowFileChooser,results=\" + results);\n fileCallback.onReceiveValue(results);\n fileCallback = null;\n } catch (Exception e) {\n e.printStackTrace();\n ToastUtil.show(\"选择文件失败\");\n fileCallback.onReceiveValue(null);\n fileCallback = null;\n }\n }\n }\n\n\n /**\n * 主要帮助WebView处理各种通知、请求事件的\n */\n private final int LOAD_START = 1;\n private final int LOAD_ERROR = 2;\n private final int LOAD_FINISHED = 3;\n private int loadStatus = LOAD_FINISHED;\n\n private class DWebViewClient extends WebViewClient {\n\n @Override\n public void onLoadResource(WebView view, String url) {\n progressBar.setProgress(view.getProgress());\n super.onLoadResource(view, url);\n }\n\n @Override\n public void onPageStarted(WebView view, String url, Bitmap favicon) {\n loadStatus = LOAD_START;\n progressBar.setProgress(1);\n progressBar.setVisibility(View.VISIBLE);\n super.onPageStarted(view, url, favicon);\n }\n\n @Override\n public void onPageFinished(WebView view, String url) {\n progressBar.setProgress(100);\n progressBar.setVisibility(View.GONE);\n if (loadStatus != LOAD_ERROR) {\n loadingBar.setLoadingStatus(LoadingBar.LoadingStatus.SUCCESS);\n }\n loadStatus = LOAD_FINISHED;\n super.onPageFinished(view, url);\n }\n\n @Override\n public void onReceivedError(WebView view, int errorCode,\n String description, String failingUrl) {\n super.onReceivedError(view, errorCode, description, failingUrl);\n loadStatus = LOAD_ERROR;\n //显示上层的错误页面\n if (!NetworkUtils.isAvailable()) {\n loadingBar.setLoadingStatus(LoadingBar.LoadingStatus.NOCONNECTION, R.drawable.icon_fail);\n } else {\n loadingBar.setLoadingStatus(LoadingBar.LoadingStatus.RELOAD, R.drawable.icon_fail);\n }\n }\n\n @Override\n public void onReceivedSslError(WebView view, SslErrorHandler handler,\n SslError error) {\n handler.proceed();\n }\n\n @Override\n public boolean shouldOverrideUrlLoading(WebView view, String url) {\n return handleUrl(url);\n }\n }\n\n /**\n * 下载\n *\n * @author zhangquan\n */\n private class DWebViewDownLoadListener implements DownloadListener {\n\n @Override\n public void onDownloadStart(String url, String userAgent,\n String contentDisposition, String mimetype, long contentLength) {\n try {\n Uri uri = Uri.parse(url);\n Intent intent = new Intent(Intent.ACTION_VIEW, uri);\n startActivity(intent);\n } catch (Exception e) {\n e.printStackTrace();\n }\n }\n }\n\n @Override\n public void close() {\n finish();\n }\n\n @Override\n public boolean onBackPressedSupport() {\n return handleBackEvent();\n }\n\n @Override\n public void onDestroy() {\n super.onDestroy();\n releaseWebView();\n }\n\n private void releaseWebView() {\n try {\n if (null != webView) {\n webView.setVisibility(View.GONE);\n webView.removeAllViews();\n webView.destroy();\n }\n } catch (Exception e) {\n e.printStackTrace();\n }\n }\n\n //------------------------------url handle\n @Subscribe(threadMode = ThreadMode.MAIN)\n public void onEvent(EventMsg eventMsg) {\n if (TextUtils.equals(eventMsg.getAction(), Constant.Event.LOGIN_SUCCESS)) {\n loginCallback();\n }\n }\n\n private static final String URL_SHARE = \"xkd://app/share?param=\"; //分享\n private static final String URL_GETTOKEN = \"xkd://user/getToken\";//获取token\n private static final String URL_USERINFO = \"xkd://user/info\";//获取用户信息\n private static final String URL_LOGIN = \"xkd://user/login\"; //登录\n private static final String URL_OPENPAGE = \"xkd://app/appPage?param=\";//打开原生页面\n private static final String URL_CLOSEPAGE = \"xkd://app/closepage\";//关闭页面\n private static final String URL_OPENURL = \"xkd://app/openURL\";//通过浏览器打开\n private static final String URL_CLIPBOARD = \"xkd://app/clipboard\"; //复制到剪贴板\n private static final String URL_CLIPBOARD_READ = \"xkd://app/readclipboard\"; //读取剪贴板\n private static final String URL_NOTIFICATION_ENABLED = \"xkd://app/notification/status\";//获取通知状态 1开启 0关闭\n private static final String URL_NOTIFICATION_OPEN = \"xkd://app/notification/setting\";//打开通知设置界面\n private static final String URL_SAVE_PIC = \"xkd://app/storage/imgs\"; //保存图片\n private static final String URL_STATUSBAR = \"xkd://app/statusbar\"; //修改状态栏\n private static final String URL_H5_ADJUMP = \"xkd://app/nativeAdJump\";//h5广告跳转\n private static final String URL_SAVE_PHOTOS = \"xkd://savedPhotosAlbum\";\n\n\n private static final String URL_WEIXIN = \"weixin://\"; //打开微信\n private static final String URL_OPEN_ALIPAY = \"alipays://\"; //打开支付宝\n private static final String URL_VPAGE = \"vipshop://\";//唯品会\n private static final String URL_TPOPEN = \"tbopen://\";//淘宝t\n private static final String URL_TAOBAO = \"taobao://\";//淘宝\n private static final String URL_PDD = \"pinduoduo://\";//拼多多\n private static final String URL_BDISK = \"bdnetdisk://\";//百度网盘\n private static final String URL_TIANMAO = \"tmall://\";//天猫\n private static final String URL_MEITUAN = \"imeituan://\";//美团\n private static final String URL_QQ = \"wtloginmqq://\";//QQ\n private static final String URL_SUNING = \"suning://\";//苏宁\n\n\n private boolean handleUrl(String path) {\n LogUtil.d(TAG, \"shouldOverrideUrlLoading ,url=\" + path);\n\n //拼多多\n if (path.startsWith(URL_PDD)) {\n openUrl(path, \"打开失败，检查是否安装拼多多APP \");\n return true;\n }\n\n //唯品会\n if (path.startsWith(URL_VPAGE)) {\n openUrl(path, \"打开失败，检查是否安装唯品会APP\");\n return true;\n }\n\n //打开淘宝\n if (path.startsWith(URL_TPOPEN)) {\n openUrl(path, \"打开失败，检查是否安装淘宝APP\");\n return true;\n }\n\n if (path.startsWith(URL_TAOBAO)) {\n openUrl(path, \"打开失败，检查是否安装淘宝APP\");\n return true;\n }\n\n //打开苏宁\n if (path.startsWith(URL_SUNING)) {\n openUrl(path, \"\");\n return true;\n }\n\n\n //拨打电话\n if (path.startsWith(\"tel\")) {\n try {\n startActivity(new Intent(Intent.ACTION_DIAL, Uri.parse(path)));\n } catch (Exception e) {\n e.printStackTrace();\n }\n return true;\n }\n\n\n //打开支付宝\n if (path.startsWith(URL_OPEN_ALIPAY)) {\n openUrl(path, \"打开失败，检查是否安装支付宝App\");\n return true;\n }\n\n\n //跳转到微信\n if (path.startsWith(URL_WEIXIN)) {\n openUrl(path, \"打开失败，检查是否安装微信App\");\n return true;\n }\n\n //百度网盘\n if (path.startsWith(URL_BDISK)) {\n openUrl(path, \"打开失败，检查是否安装百度网盘APP\");\n return true;\n }\n\n if (path.startsWith(URL_MEITUAN)) {\n openUrl(path, \"打开失败，检查是否安装美团APP (非美团外卖APP) \");\n return true;\n }\n if (path.startsWith(URL_QQ)) {\n openUrl(path, \"打开失败\");\n return true;\n }\n if (path.startsWith(URL_SUNING)) {\n openUrl(path, \"\");\n return true;\n }\n\n //自定义协议\n if (!path.startsWith(LContext.getString(R.string.app_schema))) {\n return false;\n }\n try {\n String param = Uri.parse(path).getQueryParameter(\"param\");\n if (path.startsWith(URL_SHARE)) {\n share(param);\n } else if (path.startsWith(URL_GETTOKEN)) {\n loginCallback();\n } else if (path.startsWith(URL_USERINFO)) {\n userInfoCallback();\n } else if (path.startsWith(URL_LOGIN)) {\n login();\n } else if (path.startsWith(URL_OPENURL)) { //打开浏览器\n openUrl(param, \"打开失败\");\n } else if (path.startsWith(URL_OPENPAGE)) { //打开APP页面\n DispatchUtil.goToPage(mContext, param);\n if (!TextUtils.isEmpty(param)) {\n JSONObject jsonObject = new JSONObject(param);\n int closepage = jsonObject.optInt(\"closepage\");\n if (closepage == 1) {\n close();\n }\n }\n } else if (path.startsWith(URL_CLOSEPAGE)) { //关闭页面\n close();\n } else if (path.startsWith(URL_CLIPBOARD)) { //复制到剪贴板\n try {\n ClipboardManager cm = (ClipboardManager) LContext.getContext().getSystemService(Context.CLIPBOARD_SERVICE);\n cm.setText(param);\n ToastUtil.show(\"复制成功\");\n } catch (Exception e) {\n e.printStackTrace();\n ToastUtil.show(\"复制失败，请长按文字复制\");\n }\n } else if (path.startsWith(URL_CLIPBOARD_READ)) { //读取剪贴板\n readClipboard();\n } else if (path.startsWith(URL_NOTIFICATION_ENABLED)) { //app是否开启通知权限\n notificationCallback();\n } else if (path.startsWith(URL_NOTIFICATION_OPEN)) { //跳转到app通知权限页面\n NotificationPageHelper.openNotificationSetting(mContext);\n } else if (path.startsWith(URL_SAVE_PIC)) { //保存图片到相册\n if (!TextUtils.isEmpty(param)) {\n LoadingDialog loadingDialog = LoadingDialog.showCancelableDialog(mContext, \"图片保存中\");\n new FileDownloader(mContext).downloadFile(param, new FileDownloader.DownloadCallback() {\n @Override\n public void success(File file, String url) {\n loadingDialog.dismiss();\n ToastUtil.show(\"保存成功\");\n }\n\n @Override\n public void fail(String url) {\n loadingDialog.dismiss();\n ToastUtil.show(\"保存失败\");\n }\n });\n } else {\n ToastUtil.show(\"图片地址为空\");\n }\n } else if (path.startsWith(URL_STATUSBAR)) { //修改状态栏\n boolean lightModel = TextUtils.equals(param, \"1\");\n StatusBar.setStatusBar(mContext, lightModel, titleBarView);\n } else if (path.startsWith(URL_SAVE_PHOTOS)) {\n savePics(param);\n }\n\n } catch (Exception e) {\n e.printStackTrace();\n }\n return true;\n }\n\n /**\n * 保存图片或视频到相册\n *\n * @param param\n */\n private void savePics(String param) {\n JSONObject jsonObject = null;\n try {\n List<String> saveFiles = new ArrayList<>();\n\n jsonObject = new JSONObject(param);\n\n JSONArray jsonElements = jsonObject.optJSONArray(\"videos\");\n if (jsonElements != null && jsonElements.length() > 0) {\n for (int i = 0; i < jsonElements.length(); i++) {\n saveFiles.add(jsonElements.get(i).toString());\n }\n }\n jsonElements = jsonObject.optJSONArray(\"images\");\n if (jsonElements != null && jsonElements.length() > 0) {\n for (int i = 0; i < jsonElements.length(); i++) {\n saveFiles.add(jsonElements.get(i).toString());\n }\n }\n\n if (!saveFiles.isEmpty()) {\n FileDownloadDialogView dialogView = new FileDownloadDialogView(mContext, saveFiles, \"素材下载\");\n new EffectDialogBuilder(mContext)\n .setContentView(dialogView)\n .setCancelable(true)\n .show();\n }\n\n } catch (JSONException e) {\n e.printStackTrace();\n }\n }\n\n private void openUrl(String param, String msg) {\n try {\n Intent intent = new Intent(Intent.ACTION_VIEW);\n intent.addCategory(Intent.CATEGORY_BROWSABLE);\n intent.setData(Uri.parse(param));\n mContext.startActivity(intent);\n LogUtil.e(\"url=\" + param + \",\" + intent.resolveActivity(getActivity().getPackageManager()));\n } catch (Exception e) {\n e.printStackTrace();\n if (!TextUtils.isEmpty(msg)) {\n ToastUtil.show(msg);\n }\n }\n }\n\n /**\n * 登录\n */\n public void login() {\n// LoginFragment.Companion.start(mContext);\n }\n\n /**\n * 登录回调\n */\n private void loginCallback() {\n// String token = UserClient.getToken();\n// webView.loadUrl(\"javascript:userLoginStatus('\" + token + \"')\");\n }\n\n /**\n * 获取用户信息回调\n */\n private void userInfoCallback() {\n// UserEntity user = UserClient.getUser();\n// String userInfo = \"\";\n// if (null != user) {\n// userInfo = new Gson().toJson(user);\n// }\n// webView.loadUrl(\"javascript:userInfoCallback('\" + userInfo + \"')\");\n }\n\n private void viewWillAppear() {\n webView.loadUrl(\"javascript:viewWillAppearEvent()\");\n }\n\n private void notificationCallback() {\n int status = NotificationPageHelper.areNotificationsEnabled(mContext) ? 1 : 0;\n webView.loadUrl(\"javascript:notificationEnabled('\" + status + \"')\");\n }\n\n /**\n * 读取剪贴板\n */\n private void readClipboard() {\n ClipboardManager cm = (ClipboardManager) mContext.getSystemService(Context.CLIPBOARD_SERVICE);\n CharSequence text = \"\";\n ClipData clipData = cm.getPrimaryClip();\n if (clipData != null && clipData.getItemCount() > 0) {\n text = clipData.getItemAt(0).getText();\n }\n if (!TextUtils.isEmpty(text)) {\n String content = text.toString();\n String[] test = content.split(\"\\n\");\n StringBuffer sb = new StringBuffer();\n for (String line : test) {\n sb.append(line).append(\"\\\\n\");\n }\n webView.loadUrl(\"javascript:readClipboard('\" + sb.toString() + \"')\");\n CommonUtil.addToClipboard(null);\n }\n }\n\n /**\n * 分享\n */\n public void share(String json) throws Exception {\n if (TextUtils.isEmpty(json)) return;\n //{\"title\":\"分享标题\",\"content\":\"分享内容\",\"url\":\"分享链接\",\"pic_url\":\"分享图片链接\"}\n\n ShareInfo shareInfo = new ShareInfo();\n JSONObject jsonObject = new JSONObject(json);\n shareInfo.title = jsonObject.optString(\"title\");\n shareInfo.content = jsonObject.optString(\"content\");\n shareInfo.url = jsonObject.optString(\"url\");\n shareInfo.pic_url = jsonObject.optString(\"pic_url\");\n\n ShareDialog.share(getActivity(), shareInfo);\n }\n}\n"},{"alpha_fraction":0.5748160481452942,"alphanum_fraction":0.5748160481452942,"avg_line_length":26.177778244018555,"blob_id":"d12308eb60fbed0cd208a0ba88e3ccafe4c93b5a","content_id":"10c96b48392553b4a04516664c0c8f6b1e0ab42d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1247,"license_type":"no_license","max_line_length":91,"num_lines":45,"path":"/app/src/main/java/com/android/base/util/DispatchUtil.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.util;\n\n\nimport android.app.Activity;\nimport android.text.TextUtils;\n\nimport com.android.util.log.LogUtil;\nimport com.android.base.ui.WebViewFrag;\nimport com.fastapp.MainActivity;\n\nimport org.json.JSONObject;\n\n/**\n * @author 张全\n */\npublic class DispatchUtil {\n\n /**\n * 跳转页面\n */\n public static void goToPage(Activity ctx, String json) throws Exception {\n LogUtil.d(\"DispatchUtil\", \"请求参数 json=\" + json);\n JSONObject jsonObject = new JSONObject(json);\n String page = jsonObject.optString(\"page\");\n JSONObject dataJson = jsonObject.optJSONObject(\"data\");\n\n// UserEntity user = UserClient.getUser();\n switch (page) {\n case \"HomePage\": //首页\n MainActivity.start(ctx);\n break;\n case \"WebViewPage\":\n String url = dataJson.optString(\"url\");\n if (!TextUtils.isEmpty(url)) {\n WebViewFrag.WebViewParam webViewParam = new WebViewFrag.WebViewParam();\n webViewParam.url = url;\n WebViewFrag.start(ctx, webViewParam);\n }\n break;\n default:\n break;\n }\n }\n\n}\n"},{"alpha_fraction":0.6855496764183044,"alphanum_fraction":0.6855496764183044,"avg_line_length":30.074626922607422,"blob_id":"543a20503f98c2973d69d501d80167df4cdb73e2","content_id":"f022e85ebc128604613c40aea9e14d36554524c4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Kotlin","length_bytes":2095,"license_type":"no_license","max_line_length":95,"num_lines":67,"path":"/app/src/main/java/com/android/base/ui/splash/PrivacyDialog.kt","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.ui.splash\n\nimport android.annotation.SuppressLint\nimport android.graphics.Color\nimport android.graphics.drawable.ColorDrawable\nimport android.os.Bundle\nimport android.view.KeyEvent\nimport android.view.LayoutInflater\nimport android.view.View\nimport android.view.ViewGroup\nimport androidx.fragment.app.DialogFragment\nimport com.android.base.util.ext.onClick\nimport com.android.util.common.CommonCallBack\nimport com.fastapp.R\nimport kotlinx.android.synthetic.main.privacy_dialog.*\nimport net.medlinker.android.splash.SplashUtil\n\n\n/**\n * 隐私政策弹窗\n */\nclass PrivacyDialog : DialogFragment() {\n override fun onCreate(savedInstanceState: Bundle?) {\n super.onCreate(savedInstanceState)\n setStyle(STYLE_NO_FRAME, R.style.update_dialog_style)\n }\n\n\n override fun onCreateView(\n inflater: LayoutInflater,\n container: ViewGroup?,\n savedInstanceState: Bundle?\n ): View? = inflater.inflate(R.layout.privacy_dialog, container)\n\n @SuppressLint(\"CheckResult\")\n override fun onViewCreated(view: View, savedInstanceState: Bundle?) {\n super.onViewCreated(view, savedInstanceState)\n val window = dialog?.window\n window?.apply {\n setLayout(ViewGroup.LayoutParams.MATCH_PARENT, ViewGroup.LayoutParams.MATCH_PARENT)\n setBackgroundDrawable(ColorDrawable(Color.TRANSPARENT))\n }\n dialog?.setCancelable(false)\n dialog?.setCanceledOnTouchOutside(false)\n dialog?.setOnKeyListener { _, keyCode, _ ->\n if (keyCode == KeyEvent.KEYCODE_BACK) {\n return@setOnKeyListener true\n }\n return@setOnKeyListener false\n }\n\n tv_cancel.onClick {\n mCallBack?.onCallBack(false)\n dismiss()\n }\n tv_know.onClick {\n SplashUtil.setPrivacyGranted(true)\n mCallBack?.onCallBack(true)\n dismiss()\n }\n }\n\n private var mCallBack: CommonCallBack<Boolean>? = null\n fun callback(callBack: CommonCallBack<Boolean>) {\n mCallBack = callBack\n }\n}\n\n"},{"alpha_fraction":0.5235849022865295,"alphanum_fraction":0.5235849022865295,"avg_line_length":18.272727966308594,"blob_id":"336a96a217e949fe1e41cf4cb9a2da59b47f3cbc","content_id":"7bd6b7b4acdf83ed1f9ec8af23bec0658ec28ade","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":430,"license_type":"no_license","max_line_length":57,"num_lines":22,"path":"/app/src/main/java/com/android/base/data/ResponseDataObject.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.data;\n\nimport androidx.annotation.Keep;\n\n/**\n * data为JsonObject\n *\n * @author 张全\n */\n@Keep\npublic class ResponseDataObject<T> extends ResponseData {\n public T data;\n\n @Override\n public String toString() {\n return \"ResponseDataObject{\" +\n \"data=\" + data +\n \", code=\" + code +\n \", message='\" + message + '\\'' +\n '}';\n }\n}\n"},{"alpha_fraction":0.678787887096405,"alphanum_fraction":0.678787887096405,"avg_line_length":11.692307472229004,"blob_id":"f74d8c44e044f41fa8f0a7f3ba5c12d141ed3166","content_id":"451a304285a9b45ef6b02ba9712b1f737fa71441","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":169,"license_type":"no_license","max_line_length":32,"num_lines":13,"path":"/app/src/main/java/com/android/base/data/ListData.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.data;\n\nimport androidx.annotation.Keep;\n\nimport java.util.List;\n\n/**\n * @author 张全\n */\n@Keep\npublic class ListData<T> {\n List<T> list;\n}\n"},{"alpha_fraction":0.6570892930030823,"alphanum_fraction":0.6570892930030823,"avg_line_length":19.278480529785156,"blob_id":"cfce2d9a014cb4dd1d1cc20e94051aa592671a42","content_id":"8f86b32d108da15c5d470a3b9d906b26e254e69a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1611,"license_type":"no_license","max_line_length":108,"num_lines":79,"path":"/librarys/CommonWidget/src/main/java/common/widget/adapter/RefreshFragmentAdapter.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package common.widget.adapter;\n\nimport androidx.fragment.app.Fragment;\nimport androidx.fragment.app.FragmentManager;\nimport androidx.fragment.app.FragmentPagerAdapter;\nimport androidx.fragment.app.FragmentTransaction;\n\nimport java.util.List;\n\n/**\n * 支持刷新的FragmentPagerAdapter\n */\npublic class RefreshFragmentAdapter extends FragmentPagerAdapter {\n\n private List<String> mTitleList;\n\n private List<Fragment> mFragmentList;\n\n private FragmentManager fm;\n\n public RefreshFragmentAdapter(FragmentManager fm, List<String> titleList, List<Fragment> fragmentList) {\n\n super(fm);\n\n this.fm = fm;\n this.mTitleList = titleList;\n this.mFragmentList = fragmentList;\n\n }\n\n @Override\n\n public Fragment getItem(int position) {\n return mFragmentList.get(position);\n\n }\n\n @Override\n\n public int getCount() {\n return mFragmentList.size();\n\n }\n\n public void setFragments(List<String> titleList, List<Fragment> fragments) {\n\n if (this.mFragmentList != null) {\n\n FragmentTransaction ft = fm.beginTransaction();\n\n for (Fragment f : this.mFragmentList) {\n ft.remove(f);\n }\n\n ft.commit();\n fm.executePendingTransactions();\n }\n\n this.mTitleList = titleList;\n this.mFragmentList = fragments;\n\n notifyDataSetChanged();\n\n }\n\n @Override\n\n public int getItemPosition(Object object) {\n\n return POSITION_NONE;\n\n }\n\n @Override\n public CharSequence getPageTitle(int position) {\n return mTitleList.get(position);\n }\n\n}"},{"alpha_fraction":0.4943999946117401,"alphanum_fraction":0.498879998922348,"avg_line_length":32.42245864868164,"blob_id":"e461282b1b1b8483f09e064898d41f0f27a82d5a","content_id":"41c6ec6dc68847a43e41914731b201c035bcdbd1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":6310,"license_type":"no_license","max_line_length":84,"num_lines":187,"path":"/app/src/main/java/com/android/base/version/UpdateDialogView.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.version;\n\nimport android.content.Context;\nimport android.os.Handler;\nimport android.os.Looper;\nimport android.os.Message;\nimport android.view.View;\nimport android.view.View.OnClickListener;\nimport android.widget.ProgressBar;\nimport android.widget.TextView;\n\nimport com.android.util.ext.ToastUtil;\nimport com.android.util.log.LogUtil;\nimport com.android.base.data.RestClient;\nimport com.fastapp.R;\n\nimport java.io.File;\nimport java.io.FileOutputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\n\nimport common.widget.dialog.DialogView;\nimport component.update.AppDownloadClient;\nimport component.update.AppVersion;\nimport okhttp3.Call;\nimport okhttp3.Callback;\nimport okhttp3.Request;\nimport okhttp3.Response;\n\n/**\n * 强制升級框\n *\n * @author 张全\n */\npublic class UpdateDialogView extends DialogView {\n private static final String TAG = \"VersionUpdate\";\n AppVersion appVersion;\n ProgressBar progressBar;\n TextView tv_progress;\n View reload;\n public static long fileSize = -1;\n\n\n public UpdateDialogView(Context ctx, AppVersion appVersion) {\n super(ctx);\n this.appVersion = appVersion;\n }\n\n @Override\n protected void initView(View view) {\n progressBar = findViewById(R.id.pb);\n tv_progress = findViewById(R.id.progress);\n reload = findViewById(R.id.reload);\n reload.setOnClickListener(new OnClickListener() {\n @Override\n public void onClick(View v) {\n startDownload();\n }\n });\n startDownload();\n }\n\n private void startDownload() {\n reload.setVisibility(View.GONE);\n try {\n File updateFile = AppDownloadClient.getUpdateFile();\n downLoadFile(appVersion.downloadUrl, updateFile);\n } catch (Exception e) {\n e.printStackTrace();\n ToastUtil.show(\"下载失败\");\n dismiss();\n }\n }\n\n @Override\n protected int getLayoutId() {\n return R.layout.update_dialog;\n }\n\n\n private class DownloadCallback extends Handler {\n private static final int msg_success = 1;\n private static final int msg_fail = 2;\n private static final int msg_progress = 3;\n\n public DownloadCallback() {\n super(Looper.getMainLooper());\n }\n\n @Override\n public void handleMessage(Message msg) {\n switch (msg.what) {\n case msg_progress:\n int progress = (int) msg.obj;\n progressBar.setProgress(progress);\n tv_progress.setText(progress + \"%\");\n break;\n case msg_success:\n LogUtil.e(TAG, \"下载成功\");\n fileSize = AppDownloadClient.getUpdateFile().length();\n AppDownloadClient.installAPK();\n dismiss();\n break;\n case msg_fail:\n LogUtil.e(TAG, \"下载失败\");\n ToastUtil.show(\"下载失败,请重试\");\n reload.setVisibility(View.VISIBLE);\n break;\n }\n }\n\n public void sendMsg(int what) {\n sendEmptyMessage(what);\n }\n\n public void updateProgress(int progress) {\n sendMessage(obtainMessage(msg_progress, progress));\n }\n }\n\n /**\n * 下载文件\n */\n private void downLoadFile(String url, File file) {\n\n final Request request = new Request.Builder().url(url).build();\n final Call call = RestClient.getDownloadClient().newCall(request);\n DownloadCallback downloadCallback = new DownloadCallback();\n call.enqueue(new Callback() {\n @Override\n public void onFailure(Call call, IOException e) {\n downloadCallback.sendMsg(DownloadCallback.msg_fail);\n }\n\n @Override\n public void onResponse(Call call, Response response) {\n int lastProgress = 0;\n if (response.isSuccessful()) {\n InputStream is = null;\n byte[] buf = new byte[1024];\n int len;\n FileOutputStream fos = null;\n try {\n long total = response.body().contentLength();\n LogUtil.e(TAG, \"total------>\" + total);\n long current = 0;\n is = response.body().byteStream();\n fos = new FileOutputStream(file);\n while ((len = is.read(buf)) != -1) {\n current += len;\n fos.write(buf, 0, len);\n LogUtil.e(TAG, \"current------>\" + current);\n if (total == -1) {\n downloadCallback.updateProgress(-1);\n } else {\n int progress = (int) (current * 1.0f / total * 100);\n progress = progress >= 100 ? 99 : progress;\n if (lastProgress != progress) {\n downloadCallback.updateProgress(progress);\n }\n lastProgress = progress;\n }\n }\n fos.flush();\n downloadCallback.sendMsg(DownloadCallback.msg_success);\n } catch (Exception e) {\n LogUtil.e(TAG, e.toString());\n downloadCallback.sendMsg(DownloadCallback.msg_fail);\n } finally {\n try {\n if (is != null) {\n is.close();\n }\n if (fos != null) {\n fos.close();\n }\n } catch (Exception e) {\n LogUtil.e(TAG, e.toString());\n }\n }\n } else {\n downloadCallback.sendMsg(DownloadCallback.msg_fail);\n }\n }\n });\n }\n}\n"},{"alpha_fraction":0.5645310878753662,"alphanum_fraction":0.5676261186599731,"avg_line_length":38.40243911743164,"blob_id":"211857d2a05817335c2672d9802091cddeed7a3e","content_id":"d255c6d78d62ae4734bcfcf324e020cc725936de","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":3593,"license_type":"no_license","max_line_length":125,"num_lines":82,"path":"/app/src/main/java/com/android/base/util/RecycleViewScrollToTop.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.util;\n\nimport android.view.View;\nimport android.widget.ImageView;\n\nimport androidx.recyclerview.widget.LinearLayoutManager;\nimport androidx.recyclerview.widget.RecyclerView;\nimport androidx.recyclerview.widget.StaggeredGridLayoutManager;\n\n/**\n * @author 张全\n */\npublic class RecycleViewScrollToTop {\n\n\n public static void addScroolToTop(RecyclerView recyclerView, ImageView ivTop, StaggeredGridLayoutManager layoutManager) {\n recyclerView.addOnScrollListener(new RecyclerView.OnScrollListener() {\n @Override\n public void onScrollStateChanged(RecyclerView recyclerView, int newState) {\n super.onScrollStateChanged(recyclerView, newState);\n //获得recyclerView的线性布局管理器\n //获取到第一个item的显示的下标 不等于0表示第一个item处于不可见状态 说明列表没有滑动到顶部 显示回到顶部按钮\n int[] pos = new int[10];\n layoutManager.findFirstVisibleItemPositions(pos);\n // 当不滚动时\n if (newState == RecyclerView.SCROLL_STATE_IDLE) {\n // 判断是否滚动超过一屏\n if (pos[0] == 0) {\n ivTop.setVisibility(View.GONE);\n } else {\n //显示回到顶部按钮\n ivTop.setVisibility(View.VISIBLE);\n }\n //获取RecyclerView滑动时候的状态\n }\n else if (newState == RecyclerView.SCROLL_STATE_DRAGGING) {//拖动中\n ivTop.setVisibility(View.GONE);\n }\n }\n });\n ivTop.setOnClickListener(new View.OnClickListener() {\n @Override\n public void onClick(View v) {\n recyclerView.scrollToPosition(0);\n ivTop.setVisibility(View.GONE);\n }\n });\n }\n\n\n public static void addScroolToTop(RecyclerView recyclerView, ImageView ivTop) {\n recyclerView.addOnScrollListener(new RecyclerView.OnScrollListener() {\n @Override\n public void onScrollStateChanged(RecyclerView recyclerView, int newState) {\n super.onScrollStateChanged(recyclerView, newState);\n //获得recyclerView的线性布局管理器\n LinearLayoutManager manager = (LinearLayoutManager) recyclerView.getLayoutManager();\n //获取到第一个item的显示的下标 不等于0表示第一个item处于不可见状态 说明列表没有滑动到顶部 显示回到顶部按钮\n int firstVisibleItemPosition = manager.findFirstVisibleItemPosition();\n // 当不滚动时\n if (newState == RecyclerView.SCROLL_STATE_IDLE) {\n // 判断是否滚动超过一屏\n if (firstVisibleItemPosition == 0) {\n ivTop.setVisibility(View.GONE);\n } else {\n //显示回到顶部按钮\n ivTop.setVisibility(View.VISIBLE);\n }\n //获取RecyclerView滑动时候的状态\n }\n }\n });\n ivTop.setOnClickListener(new View.OnClickListener() {\n @Override\n public void onClick(View v) {\n recyclerView.scrollToPosition(0);\n// recyclerView.smoothScrollToPosition(0);\n ivTop.setVisibility(View.GONE);\n }\n });\n }\n}\n"},{"alpha_fraction":0.5681528449058533,"alphanum_fraction":0.5732483863830566,"avg_line_length":18.14634132385254,"blob_id":"1a6131e678444dbc1a89f9d6b28a395bbabe9b43","content_id":"096b38c8bb4d241ff855ff50a20d084d3de01b1b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":803,"license_type":"no_license","max_line_length":56,"num_lines":41,"path":"/app/src/main/java/com/android/base/data/ResponseDataArray.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.data;\n\nimport androidx.annotation.Keep;\n\nimport java.util.ArrayList;\nimport java.util.List;\n\n/**\n * data为JsonArray\n *\n * @author 张全\n */\n@Keep\npublic class ResponseDataArray<T> extends ResponseData {\n public List<T> data;\n\n public int count;\n public int page;\n public int pageSize;\n public int pageCount;\n\n\n public List<T> getDataList() {\n return null == data ? new ArrayList<>() : data;\n }\n\n /**\n * 是否还有更多\n *\n * @return\n */\n public boolean hasMore() {\n// int dataSize = getDataList().size();\n// if (dataSize == 0) {\n// return false;\n// }\n// pageSize = pageSize == 0 ? 10 : pageSize;\n// return dataSize >= pageSize;\n return !getDataList().isEmpty();\n }\n}\n"},{"alpha_fraction":0.5504032373428345,"alphanum_fraction":0.5544354915618896,"avg_line_length":25.078947067260742,"blob_id":"2d3c94de28c00a153f3602baa74ca5256d8cfa8b","content_id":"540be6216ec619310c2f349f8e064f92a0e96283","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1092,"license_type":"no_license","max_line_length":60,"num_lines":38,"path":"/scripts/tinkerPatch.py","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"import os\nimport platform\nimport sys, getopt\n\nif __name__ == '__main__':\n buildEnv = 'qa'\n argv = sys.argv[1:]\n print (argv)\n try:\n # 这里的 h 就表示该选项无参数，冒号(:)表示该选项必须有附加的参数，不带冒号表示该选项不附加参数。\n opts, args = getopt.getopt(argv, \"e:\",[ \"env=\"])\n except getopt.GetoptError:\n print (getUsageStr())\n sys.exit(2)\n\n for opt, arg in opts:\n if opt in (\"-e\", \"--env\"):\n buildEnv = arg\n print('-e arg =%s'%arg)\n\n print('开始打tinker补丁包')\n sysstr = platform.system()\n cmdPrefix = './gradlew'\n if(sysstr ==\"Windows\"):\n print (\"Call Windows tasks\")\n cmdPrefix = 'gradlew'\n elif(sysstr == \"Linux\"):\n print (\"Call Linux tasks\")\n else:\n print (\"Other System tasks\")\n\n suffix = '-PhostType=3'\n if buildEnv == 'qa':\n suffix = '-PhostType=4'\n assemble = 'buildTinkerPatchOnlineRelease'\n cmdStr = '%s %s %s' %(cmdPrefix, assemble, suffix)\n print(cmdStr)\n os.system(cmdStr)\n\n"},{"alpha_fraction":0.5660540461540222,"alphanum_fraction":0.5711711645126343,"avg_line_length":27.08704376220703,"blob_id":"e606e1cdebc1990717f7b98eb5faaf634072a42d","content_id":"389a0eea5573e99fb5fdadf45281cf2d7dc0e61b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":14247,"license_type":"no_license","max_line_length":117,"num_lines":494,"path":"/librarys/CommonWidget/src/main/java/common/widget/viewpager/BannerViewPager.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package common.widget.viewpager;\n\n\nimport android.annotation.SuppressLint;\nimport android.content.Context;\nimport android.os.Handler;\nimport android.os.Looper;\nimport android.os.Message;\nimport android.util.AttributeSet;\nimport android.util.TypedValue;\nimport android.view.LayoutInflater;\nimport android.view.View;\nimport android.view.ViewGroup;\nimport android.widget.AdapterView;\nimport android.widget.ImageView;\nimport android.widget.LinearLayout;\nimport android.widget.RelativeLayout;\n\nimport androidx.annotation.Nullable;\nimport androidx.viewpager.widget.PagerAdapter;\n\nimport java.util.ArrayList;\nimport java.util.List;\n\nimport common.widget.R;\n\n/**\n * 一、功能\n * <ol>\n * <li>支持无限滚动，调用startAutoScroll开始滚动</li>\n * <li>支持无限左右滑动切换页面</li>\n * </ol>\n * 二、使用\n *\n * <pre>\n * BannerViewPager mViewPager = (BannerViewPager) findViewById(R.id.viewPager);\n * mViewPager.setImageLoader(loader);\n * mViewPager.setmOnItemClickListener(new AdapterView.OnItemClickListener() {\n * @Override\n * public void onItemClick(AdapterView<?> parent, View view, int position, long id) {\n * }\n * });\n * mViewPager.setInterval(3 * 1000);// 设置滚动间隔时长\n * mViewPager.setScrollDurationFactor(4.0f);// 设置自动滚动的Scroller时长因子\n * mViewPager.startAutoScroll(4 * 1000);// 延迟4秒开始自动滚动\n * </pre>\n * <p>\n * 三、注意\n * <ol>\n * <li>为避免内存浪费，请在Activity或Fragment的onRause中暂停滚动，onResume中继续滚动</li>\n * </ol>\n *\n * @author zhangquan\n */\npublic class BannerViewPager<T> extends RelativeLayout {\n public String tag = BannerViewPager.class.getSimpleName();\n private AutoScrollViewPager viewPager;\n private LinearLayout indicator;\n private int mIndicatorSize, mIndicatorMargin;\n private BannerPagerAdapter adapter;\n private List<T> dataList;\n private List<View> views = new ArrayList<>();\n private List<ImageView> indicatorImages = new ArrayList<>();\n private int count = 0;\n private int currentItem = 1;\n private int lastPosition;\n private ViewPager.OnPageChangeListener mOnPageChangeListener;\n private AdapterView.OnItemClickListener mOnItemClickListener;\n private BannerImageLoader imageLoader;\n\n public static final int DEFAULT_INTERVAL = 1500;\n private boolean started;\n private TimerHandler timerHandler;\n private CustomDurationScroller mScroller = null;\n private long interval = DEFAULT_INTERVAL;\n private int imageHeight;\n\n\n public BannerViewPager(Context context) {\n super(context);\n init();\n }\n\n public BannerViewPager(Context context, AttributeSet attrs) {\n super(context, attrs);\n init();\n }\n\n public BannerViewPager(Context context, AttributeSet attrs, int defStyleAttr) {\n super(context, attrs, defStyleAttr);\n init();\n }\n\n private void init() {\n LayoutInflater.from(getContext()).inflate(R.layout.banner_layout, this);\n viewPager = findViewById(R.id.bannerViewPager);\n viewPager.setBanner(this);\n mScroller = viewPager.mScroller;\n indicator = findViewById(R.id.circleIndicator);\n mIndicatorSize = dip2px(getContext(), 5);\n mIndicatorMargin = dip2px(getContext(), 5);\n\n timerHandler = new TimerHandler();\n\n }\n\n public static int dip2px(Context context, float dip) {\n return (int) TypedValue.applyDimension(TypedValue.COMPLEX_UNIT_DIP, dip,\n context.getResources().getDisplayMetrics());\n }\n\n\n public void setDataList(List<T> list) {\n dataList = list;\n count = dataList.size();\n\n //指示器\n createIndicator();\n //banner\n setImageList();\n setData();\n }\n\n private void setImageList() {\n if (dataList == null || dataList.size() <= 0) {\n return;\n }\n if (imageLoader == null) {\n throw new RuntimeException(\"imageLoader不能为null\");\n }\n viewPager.setPagingEnabled(count > 1);\n views.clear();\n if (count == 1) { //只有一条数据\n createItemView(0);\n } else {\n for (int i = 0; i <= count + 1; i++) {\n createItemView(i);\n }\n }\n }\n\n private void createItemView(int index) {\n View view = null;\n if (imageLoader != null) {\n view = (View) imageLoader.createView(getContext());\n }\n if (view == null) {\n view = new ImageView(getContext());\n }\n\n T t;\n if (index == 0) {\n t = dataList.get(count - 1);\n imageLoader.displayView(getContext(), t, view, count - 1, count);\n } else if (index == count + 1) {\n t = dataList.get(0);\n imageLoader.displayView(getContext(), t, view, 0, count);\n } else {\n t = dataList.get(index - 1);\n imageLoader.displayView(getContext(), t, view, index - 1, count);\n }\n views.add(view);\n }\n\n private void createIndicator() {\n int visibility = count > 1 ? View.VISIBLE : View.GONE;\n indicator.setVisibility(visibility);\n indicatorImages.clear();\n indicator.removeAllViews();\n if (count <= 1) {\n return;\n }\n for (int i = 0; i < count; i++) {\n ImageView imageView = new ImageView(getContext());\n imageView.setScaleType(ImageView.ScaleType.CENTER_CROP);\n LinearLayout.LayoutParams params = new LinearLayout.LayoutParams(mIndicatorSize, mIndicatorSize);\n params.leftMargin = mIndicatorMargin;\n params.rightMargin = mIndicatorMargin;\n imageView.setImageResource(R.drawable.indicator_selector);\n if (i == 0) {\n imageView.setSelected(true);\n }\n indicatorImages.add(imageView);\n indicator.addView(imageView, params);\n }\n }\n\n\n private void setData() {\n currentItem = 1;\n// if (adapter == null) {\n adapter = new BannerPagerAdapter();\n viewPager.addOnPageChangeListener(pageChangeListener);\n// }\n viewPager.setAdapter(adapter);\n viewPager.setFocusable(true);\n viewPager.setCurrentItem(1);\n\n }\n\n public List<T> getDataList() {\n return dataList;\n }\n\n\n /**\n * 返回真实的位置\n *\n * @param position\n * @return 下标从0开始\n */\n public int toRealPosition(int position) {\n if (count == 0) return 0;\n int realPosition = (position - 1) % count;\n if (realPosition < 0)\n realPosition += count;\n return realPosition;\n }\n\n class BannerPagerAdapter extends PagerAdapter {\n\n @Override\n public int getCount() {\n return views.size();\n }\n\n @Override\n public boolean isViewFromObject(View view, Object object) {\n return view == object;\n }\n\n @Override\n public Object instantiateItem(ViewGroup container, final int position) {\n View itemView = views.get(position);\n container.addView(itemView);\n View view = itemView;\n if (mOnItemClickListener != null) {\n view.setOnClickListener(new OnClickListener() {\n @Override\n public void onClick(View v) {\n mOnItemClickListener.onItemClick(null, v, toRealPosition(position), 1);\n }\n });\n }\n// int height = itemView.getHeight();\n// if (height != imageHeight) {\n// itemView.setMinimumHeight(imageHeight);\n// }\n return view;\n }\n\n @Override\n public void destroyItem(ViewGroup container, int position, Object object) {\n container.removeView((View) object);\n }\n\n }\n\n ViewPager.OnPageChangeListener pageChangeListener = new ViewPager.OnPageChangeListener() {\n @Override\n public void onPageScrollStateChanged(int state) {\n if (mOnPageChangeListener != null) {\n mOnPageChangeListener.onPageScrollStateChanged(state);\n }\n switch (state) {\n case 0://No operation\n if (currentItem == 0) {\n viewPager.setCurrentItem(count, false);\n } else if (currentItem == count + 1) {\n viewPager.setCurrentItem(1, false);\n }\n break;\n case 1://start Sliding\n if (currentItem == count + 1) {\n viewPager.setCurrentItem(1, false);\n } else if (currentItem == 0) {\n viewPager.setCurrentItem(count, false);\n }\n break;\n case 2://end Sliding\n break;\n }\n }\n\n\n @Override\n public void onPageScrolled(int position, float positionOffset, int positionOffsetPixels) {\n if (mOnPageChangeListener != null) {\n mOnPageChangeListener.onPageScrolled(toRealPosition(position), positionOffset, positionOffsetPixels);\n }\n }\n\n @Override\n public void onPageSelected(int position) {\n currentItem = position;\n if (mOnPageChangeListener != null) {\n mOnPageChangeListener.onPageSelected(toRealPosition(position));\n }\n\n if (indicatorImages.isEmpty()) {\n return;\n }\n indicatorImages.get((lastPosition - 1 + count) % count).setSelected(false);\n indicatorImages.get((position - 1 + count) % count).setSelected(true);\n lastPosition = position;\n }\n };\n//------------------------------------------\n\n /**\n * 开始自动滚动\n */\n public void startAutoScroll() {\n startAutoScroll(0);\n }\n\n /**\n * 延迟delayTimeInMills秒后开始自动滚动\n *\n * @param delayTime\n */\n public void startAutoScroll(long delayTime) {\n started = true;\n timerHandler.sendScrollMsg(delayTime);\n }\n\n /**\n * 停止滚动\n */\n public void stopAutoScroll() {\n started = false;\n timerHandler.pause();\n }\n\n /**\n * 暂停滚动\n */\n public void pauseAutoScroll() {\n if (isStarted()) {\n timerHandler.pause();\n }\n if (null != mScroller) mScroller.resetDurationFactor();\n }\n\n /**\n * 继续滚动\n */\n public void resumeAutoScroll() {\n if (isStarted()) {\n timerHandler.resume();\n }\n }\n\n /**\n * 是否已开始滚动\n *\n * @return\n */\n public boolean isStarted() {\n return started;\n }\n\n /**\n * 两次滚动之间的时间间隔\n *\n * @return\n */\n public long getInterval() {\n return interval;\n }\n\n /**\n * 设置两次滚动之间的时间间隔\n *\n * @param interval\n */\n public void setInterval(long interval) {\n this.interval = interval;\n }\n\n /**\n * 定时器\n *\n * @author zhangquan\n */\n @SuppressLint(\"HandlerLeak\")\n private class TimerHandler extends Handler {\n private final int msg_start = 1;\n private boolean paused;// 是否处于暂停\n\n public TimerHandler() {\n super(Looper.getMainLooper());\n }\n\n @Override\n public void handleMessage(Message msg) {\n if (paused) {\n return;\n }\n\n if (count > 1) {\n if (null != mScroller) {\n mScroller.setScrollDurationFactor(mScroller.getScrollDuraionFactor());\n }\n currentItem = currentItem % (count + 1) + 1;\n if (currentItem == 1) {\n viewPager.setCurrentItem(currentItem, false);\n sendScrollMsg(0);\n } else {\n viewPager.setCurrentItem(currentItem);\n sendScrollMsg(interval + 500);\n }\n }\n }\n\n /**\n * 继续\n */\n public synchronized void resume() {\n paused = false;\n sendScrollMsg(interval);\n }\n\n /**\n * 暂停\n */\n public synchronized void pause() {\n paused = true;\n if (hasMessages(msg_start)) {\n removeMessages(msg_start);\n }\n }\n\n public void sendScrollMsg(long delayTime) {\n if (hasMessages(msg_start)) {\n removeMessages(msg_start);\n }\n sendMessageDelayed(obtainMessage(msg_start), delayTime);\n }\n }\n\n\n public void setOnPageChangeListener(ViewPager.OnPageChangeListener onPageChangeListener) {\n mOnPageChangeListener = onPageChangeListener;\n }\n\n public void setmOnItemClickListener(AdapterView.OnItemClickListener listener) {\n mOnItemClickListener = listener;\n }\n\n public void setImageLoader(BannerImageLoader loader) {\n imageLoader = loader;\n }\n\n// public void setImageHeight(int height) {\n// this.imageHeight = height;\n// for (View view : views) {\n// view.setMinimumHeight(imageHeight);\n// }\n// }\n\n /**\n * ---------------------- ViewPager支持 api\n */\n public void setPageMargin(int marginPixels) {\n viewPager.setPageMargin(marginPixels);\n }\n\n public void setPageTransformer(boolean reverseDrawingOrder, @Nullable ViewPager.PageTransformer transformer) {\n viewPager.setPageTransformer(reverseDrawingOrder, transformer);\n }\n\n public void setOffscreenPageLimit(int limit) {\n viewPager.setOffscreenPageLimit(limit);\n }\n\n public void setScrollDurationFactor(float scrollFactor) {\n viewPager.setScrollDurationFactor(scrollFactor);\n }\n\n public ViewPager getViewPager() {\n return viewPager;\n }\n\n public PagerAdapter getAdapter() {\n return viewPager.getAdapter();\n }\n\n public int getCurrentItem() {\n return toRealPosition(currentItem);\n }\n\n\n}\n"},{"alpha_fraction":0.5432960987091064,"alphanum_fraction":0.5984637141227722,"avg_line_length":33.95121765136719,"blob_id":"b3a9181a3bdf267a1f1d5541622576285ba94730","content_id":"99788792739cb57df5ba83b22c4f0baf96a4ec64","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":1686,"license_type":"no_license","max_line_length":147,"num_lines":41,"path":"/scripts/checkIfBehind.sh","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"#!/bin/bash\n\ngit-log-flat-colored() {\n git --no-pager log --format=\"%C(yellow)%h%Creset %C(cyan)%cd%Creset %s %Cgreen%an%Creset\" --date=short \"$@\"\n}\n\nCURRENT_FEATURE=`git symbolic-ref --short -q HEAD`\nCURRENT_PATH=`git rev-parse --show-toplevel`\nREFERENCE_BRANCH=\"origin/master\"\n\necho -e \"\\033[0;34m >>-----当前核查仓库路径${CURRENT_PATH}-----<< \\033[0m\"\n\necho\n\n# 检查 “origin/master” 分支是否存在，如果存在，更新远端记录并比较是否存在落后提交\ngit rev-parse --verify ${REFERENCE_BRANCH} > /dev/null 2>&1\nif [ \"$?\" == \"0\" ]; then\n\n echo -e \"\\033[0;34m >>-----${CURRENT_FEATURE}-----<< 更新远端提交记录... \\033[0;34m\"\n git fetch origin\n\n echo -e \"\\033[0;35m >>-----${CURRENT_FEATURE}-----<< 检查当前分支是否包含 \\\"${REFERENCE_BRANCH}\\\" 分支所有提交记录 \\033[0m\"\n NB_COMMITS_BEHIND=$(git rev-list --left-right --count ${REFERENCE_BRANCH}...@ | cut -f1)\n \n if [ \"${NB_COMMITS_BEHIND}\" -gt \"0\" ]; then\n echo -e \"\\033[0;31m >>-----${CURRENT_FEATURE}-----<< 当前分支有 ${NB_COMMITS_BEHIND} 个提交落后于 \\\"${REFERENCE_BRANCH}\\\", 请合并最新的 master 分支代码\\n \\033[0m\"\n git-log-flat-colored ${REFERENCE_BRANCH} | head -\"${NB_COMMITS_BEHIND}\"\n echo -e \"\\033[0;33m \\n >>-----${CURRENT_FEATURE}-----<< 你可以通过 \\\"git merge origin/master\\\" 来合并代码 \\033[0m\"\n exit 2\n else\n echo -e \"\\033[0;32m >>-----${CURRENT_FEATURE}-----<< 完美，当前分支包含 ${REFERENCE_BRANCH} 分支上的所有提交记录 \\033[0m\"\n fi\n\n else\n\n echo -e \"\\033[0;31m >>-----不能比较，检测${REFERENCE_BRANCH}不存在-----<< \\033[0m\"\n exit 2\n\nfi\n\necho"},{"alpha_fraction":0.641970694065094,"alphanum_fraction":0.6434749960899353,"avg_line_length":34,"blob_id":"244741b926a55d2e6259c3fe9aa84dbaf5d0c109","content_id":"0210a1e65a31933ad1e1b3ffc6d7af4865f83ad1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Kotlin","length_bytes":2673,"license_type":"no_license","max_line_length":177,"num_lines":76,"path":"/app/src/main/java/com/android/base/version/UpDateDialogFragment.kt","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.version\n\nimport android.graphics.Color\nimport android.graphics.drawable.ColorDrawable\nimport android.os.Bundle\nimport android.os.Process\nimport android.view.LayoutInflater\nimport android.view.View\nimport android.view.ViewGroup\nimport androidx.fragment.app.DialogFragment\nimport com.android.base.util.ext.onClick\nimport com.android.base.version.UpdateDialogView.fileSize\nimport com.fastapp.R\nimport common.widget.dialog.EffectDialogBuilder\nimport component.update.AppDownloadClient\nimport component.update.AppVersion\nimport kotlinx.android.synthetic.main.update_dialog_fragment.*\n\n/**\n * App升级弹框\n */\nclass UpDateDialogFragment : DialogFragment() {\n private val appVersion by lazy {\n arguments?.getSerializable(\"appversion\") as AppVersion?\n }\n\n override fun onCreate(savedInstanceState: Bundle?) {\n super.onCreate(savedInstanceState)\n setStyle(STYLE_NO_FRAME, R.style.update_dialog_style)\n }\n\n override fun onCreateView(inflater: LayoutInflater, container: ViewGroup?, savedInstanceState: Bundle?): View? = inflater.inflate(R.layout.update_dialog_fragment, container)\n\n override fun onViewCreated(view: View, savedInstanceState: Bundle?) {\n super.onViewCreated(view, savedInstanceState)\n val window = dialog?.window\n window?.apply {\n setLayout(ViewGroup.LayoutParams.MATCH_PARENT, ViewGroup.LayoutParams.MATCH_PARENT)\n setBackgroundDrawable(ColorDrawable(Color.TRANSPARENT))\n }\n\n tv_content.text = appVersion?.desc\n tv_version.text = \"V\" + appVersion?.versionName\n\n tv_cancel.onClick {\n dismiss()\n if (appVersion?.forceUpdate == 1) {\n Process.killProcess(Process.myPid())\n }\n }\n if (appVersion?.forceUpdate == 1) {\n tv_cancel.visibility = View.GONE\n }\n tv_ensure.onClick {\n if (appVersion?.forceUpdate == 1) {\n val updateFile = AppDownloadClient.getUpdateFile()\n if (null != updateFile && fileSize != -1L && fileSize == updateFile.length()) { //已下载\n AppDownloadClient.installAPK()\n return@onClick\n }\n val dialogView = UpdateDialogView(context, appVersion)\n EffectDialogBuilder(context)\n .setContentView(dialogView)\n .setCancelable(false)\n .setCancelableOnTouchOutside(false)\n .show()\n } else {\n dismiss()\n UpdateService.update(context, appVersion)\n }\n }\n\n }\n\n\n}"},{"alpha_fraction":0.6930860280990601,"alphanum_fraction":0.6981450319290161,"avg_line_length":27.261905670166016,"blob_id":"457b16944ede500b3c4873b05de3f7573acf161a","content_id":"857bcfcce527e8005e4a70cce2da39aaed226fce","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1278,"license_type":"no_license","max_line_length":104,"num_lines":42,"path":"/librarys/CommonWidget/src/main/java/common/widget/listview/GridSpaceItemDecoration.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package common.widget.listview;\n\nimport android.graphics.Rect;\nimport android.view.View;\n\nimport androidx.recyclerview.widget.RecyclerView;\n\npublic class GridSpaceItemDecoration extends RecyclerView.ItemDecoration {\n private int headerCount;\n private int horizontalSpace;\n private int verticalSpace;\n private int spanCount = 2;\n\n\n public GridSpaceItemDecoration(int space) {\n this(space, space);\n }\n\n public GridSpaceItemDecoration(int horizontalSpace, int verticalSpace) {\n this.horizontalSpace = horizontalSpace;\n this.verticalSpace = verticalSpace;\n }\n\n public void setHeaderCount(int headerCount) {\n this.headerCount = headerCount;\n }\n\n public void setSpanCount(int spanCount) {\n this.spanCount = spanCount;\n }\n\n @Override\n public void getItemOffsets(Rect outRect, View view, RecyclerView parent, RecyclerView.State state) {\n //不是第一个的格子都设一个左边和底部的间距\n outRect.left = horizontalSpace;\n outRect.bottom = verticalSpace;\n //由于每行都只有2个，所以第一个都是2的倍数，把左边距设为0\n if ((parent.getChildLayoutPosition(view)-headerCount) % spanCount == 0) {\n outRect.left = 0;\n }\n }\n}"},{"alpha_fraction":0.5393980145454407,"alphanum_fraction":0.5448089241981506,"avg_line_length":36.910255432128906,"blob_id":"cb6826f3f92665f594ec1164978fe47611050d18","content_id":"d21faab5a52c3d4e0f668adfcfbdbf205c3e1910","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":2989,"license_type":"no_license","max_line_length":119,"num_lines":78,"path":"/librarys/CommonWidget/src/main/java/common/widget/shape/ShapeInflaterInject.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package common.widget.shape;\n\nimport android.content.Context;\nimport android.util.AttributeSet;\nimport android.view.LayoutInflater;\nimport android.view.View;\n\nimport androidx.annotation.Nullable;\nimport androidx.appcompat.app.AppCompatActivity;\nimport androidx.appcompat.app.AppCompatDelegate;\n\nimport java.lang.reflect.Field;\n\n/**\n * 注入自定义LayoutInflater.Factory2\n * 保证自定义属性得到解析\n *\n * @author zhangquan\n */\npublic class ShapeInflaterInject {\n\n public static void inject(Context ctx) {\n LayoutInflater inflater = LayoutInflater.from(ctx);\n LayoutInflater.Factory factory = inflater.getFactory();\n LayoutInflater.Factory2 factory2 = inflater.getFactory2();\n if (null == factory2) {\n ShapeInflaterFactory shapeInflater = new ShapeInflaterFactory();\n if (ctx instanceof AppCompatActivity) {\n final AppCompatDelegate delegate = ((AppCompatActivity) ctx).getDelegate();\n shapeInflater.setImplFactory2(new LayoutInflater.Factory2() {\n @Nullable\n @Override\n public View onCreateView(@Nullable View parent, String name, Context context, AttributeSet attrs) {\n return delegate.createView(parent, name, context, attrs);\n }\n\n @Nullable\n @Override\n public View onCreateView(String name, Context context, AttributeSet attrs) {\n return onCreateView(null, name, context, attrs);\n }\n });\n }\n\n inflater.setFactory2(shapeInflater);\n return;\n }\n\n if (!(factory2 instanceof ShapeInflaterFactory)) {\n try {\n Class<? extends LayoutInflater> inflaterClass = inflater.getClass();\n ShapeInflaterFactory shapeFactory = new ShapeInflaterFactory();\n\n Class<?> cls = inflaterClass;\n while (cls != null && cls != Object.class) {\n try {\n Field mFactory = cls.getDeclaredField(\"mFactory\");\n mFactory.setAccessible(true);\n Field mFactory2 = cls.getDeclaredField(\"mFactory2\");\n mFactory2.setAccessible(true);\n if (factory2 != null) {\n shapeFactory.setImplFactory2(factory2);\n } else if (factory != null) {\n shapeFactory.setImplFactory(factory);\n }\n mFactory2.set(inflater, shapeFactory);\n mFactory.set(inflater, shapeFactory);\n break;\n } catch (Exception e) {\n cls = cls.getSuperclass();\n }\n }\n } catch (Exception e) {\n e.printStackTrace();\n }\n }\n }\n}\n"},{"alpha_fraction":0.660646378993988,"alphanum_fraction":0.6625475287437439,"avg_line_length":30.909090042114258,"blob_id":"c3b388ba4fceacb6d4f538f9ec293cc64acadedc","content_id":"823fb0ea60dbb56a476a2da30daaa49c7dbe4017","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Kotlin","length_bytes":1052,"license_type":"no_license","max_line_length":90,"num_lines":33,"path":"/app/src/main/java/com/fastapp/MyTask.kt","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.fastapp\n\nimport android.content.Context\nimport androidx.work.WorkerParameters\nimport com.android.base.task.BackgroundTask\nimport java.io.InputStreamReader\nimport java.net.URL\n\n/**\n *\n * @author zhangquan\n */\nclass MyTask(context: Context, param: WorkerParameters) : BackgroundTask(context, param) {\n override fun doTask(parameters: WorkerParameters) {\n val inputData = parameters.inputData\n val value = inputData?.getString(\"key\")\n val value2 = inputData?.getBoolean(\"key2\", false)\n println(\"doTask thread=${Thread.currentThread().name},data=$inputData\")\n\n var url = URL(\"http://www.baidu.com\");\n var urlConnection = url.openConnection();\n try {\n val inputStream = urlConnection.getInputStream()\n val inputStreamReader = InputStreamReader(inputStream)\n val readLines = inputStreamReader.readLines()\n// println(\"data=$readLines\")\n inputStream.close()\n\n } catch (e: Exception) {\n e.printStackTrace()\n }\n }\n}"},{"alpha_fraction":0.6235138773918152,"alphanum_fraction":0.6235138773918152,"avg_line_length":25.10344886779785,"blob_id":"f5f527cb4195bc7cb0de5a032282804235273a31","content_id":"85afeeb02d117b0acb7ddb74c9f80a041b3dfe6b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":757,"license_type":"no_license","max_line_length":83,"num_lines":29,"path":"/app/src/main/java/com/fastapp/ActivityTwo.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.fastapp;\n\nimport android.app.Activity;\nimport android.content.Intent;\nimport android.os.Bundle;\nimport android.view.View;\n\nimport com.android.base.ui.BaseActivity;\n\npublic class ActivityTwo extends BaseActivity {\n @Override\n public int getLayoutId() {\n return R.layout.activity_main;\n }\n\n @Override\n public void init(Bundle savedInstanceState) {\n findViewById(R.id.testFrag).setOnClickListener(new View.OnClickListener() {\n @Override\n public void onClick(View v) {\n Intent intent = new Intent();\n intent.putExtra(\"param\", \"data from ActivityTwo\");\n setResult(Activity.RESULT_OK, intent);\n finish();\n }\n });\n }\n\n}\n"},{"alpha_fraction":0.5845588445663452,"alphanum_fraction":0.5868983864784241,"avg_line_length":26.200000762939453,"blob_id":"5c2ec666f83eaff973b4660578910415fdbb7498","content_id":"e19ba116aa1870ef3bacd99c6526ac2fd15e5830","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":3060,"license_type":"no_license","max_line_length":92,"num_lines":110,"path":"/app/src/main/java/com/android/base/util/filedownloader/FileDownloadDialogView.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.util.filedownloader;\n\nimport android.content.Context;\nimport android.text.TextUtils;\nimport android.view.View;\nimport android.view.View.OnClickListener;\nimport android.widget.ProgressBar;\nimport android.widget.TextView;\n\nimport com.fastapp.R;\n\nimport java.io.File;\nimport java.util.ArrayList;\nimport java.util.List;\n\nimport common.widget.dialog.DialogView;\n\n/**\n * 文件下载弹框\n *\n * @author 张全\n */\npublic class FileDownloadDialogView extends DialogView {\n private String title;\n private List<String> downloadSources;\n private List<File> files = new ArrayList<>();\n private FileDownloader fileDownloader;\n private TextView item_label2;\n private TextView btn;\n private ProgressBar progressBar;\n private int total;\n private int progress;\n\n\n public FileDownloadDialogView(Context ctx, List<String> downloadSources, String title) {\n super(ctx);\n this.fileDownloader = new FileDownloader(ctx);\n this.downloadSources = downloadSources;\n this.title = title;\n this.total = downloadSources.size();\n }\n\n @Override\n protected int getLayoutId() {\n return R.layout.file_download_dialog;\n }\n\n @Override\n public void initView(View view) {\n\n item_label2 = findViewById(R.id.item_label2);\n progressBar = findViewById(R.id.progressBar);\n\n btn = findViewById(R.id.btn);\n btn.setOnClickListener(new OnClickListener() {\n @Override\n public void onClick(View v) {\n if (TextUtils.equals(btn.getText().toString(), \"下载失败\")) { //重新下载\n startDownload();\n } else {\n fileDownloader.stop();\n dismiss();\n }\n }\n });\n\n if (!TextUtils.isEmpty(title) && !\"sharegoods\".equals(title)) {\n TextView textView = findViewById(R.id.item_label1);\n textView.setText(title);\n }\n\n startDownload();\n }\n\n private void startDownload() {\n progress = 0;\n files.clear();\n btn.setText(\"取消下载\");\n\n progressBar.setMax(total);\n progressBar.setProgress(progress);\n item_label2.setText(\"已下载 \" + progress + \"/\" + total);\n\n fileDownloader.downloadFile(downloadSources, new FileDownloader.DownloadCallback() {\n @Override\n public void success(File file, String url) {\n files.add(file);\n updateUI(file, url);\n }\n\n @Override\n public void fail(String url) {\n updateUI(null, url);\n }\n });\n }\n\n private void updateUI(File file, String url) {\n progress++;\n progressBar.setProgress(progress);\n item_label2.setText(\"已下载 \" + progress + \"/\" + total);\n if (progress == total) {\n if (files.isEmpty()) {\n btn.setText(\"下载失败\");\n } else {\n btn.setText(\"下载完成\");\n }\n }\n }\n}\n"},{"alpha_fraction":0.543448805809021,"alphanum_fraction":0.5438358783721924,"avg_line_length":28.357954025268555,"blob_id":"7264d7bacd9421cfe740803f5a65d2573928868f","content_id":"f247d77af6715a68f191c6a477eb3d178e05b197","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":5181,"license_type":"no_license","max_line_length":90,"num_lines":176,"path":"/librarys/CommonUtil/src/main/java/com/android/util/db/DBSession.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.util.db;\n\nimport android.text.TextUtils;\n\nimport com.google.gson.Gson;\n\nimport java.lang.reflect.Field;\nimport java.util.ArrayList;\nimport java.util.HashMap;\nimport java.util.Iterator;\nimport java.util.List;\nimport java.util.Map;\n\nimport io.reactivex.Observable;\nimport io.reactivex.ObservableEmitter;\nimport io.reactivex.ObservableOnSubscribe;\n\n/**\n * @author 张全\n */\npublic class DBSession<T> {\n private Class<T> cls;\n private Field uidField;\n private Field keyField;\n private String modleName;\n private String uid;\n private static Map<String, List<ObservableEmitter>> observerableMap = new HashMap<>();\n private static final String DEF_UID = \"-1\";\n\n\n public DBSession(Class<T> cls) {\n this(cls, null);\n }\n\n public DBSession(Class<T> cls, String uid) {\n this.cls = cls;\n this.uid = uid;\n modleName = cls.getSimpleName();\n\n Field[] fields = cls.getDeclaredFields();\n for (Field field : fields) {\n if (field.isAnnotationPresent(Uid.class)) {\n uidField = field;\n uidField.setAccessible(true);\n }\n if (field.isAnnotationPresent(Key.class)) {\n keyField = field;\n keyField.setAccessible(true);\n }\n }\n }\n\n public List<T> query() {\n List<String> values = ModelDao.getUserModels(getUid(), modleName);\n List<T> list = new ArrayList<>();\n for (String value : values) {\n list.add(new Gson().fromJson(value, cls));\n }\n return list;\n }\n\n public Observable<List<T>> queryWithObserverable() {\n return Observable.create(new ObservableOnSubscribe<List<T>>() {\n @Override\n public void subscribe(ObservableEmitter<List<T>> emitter) throws Exception {\n List<ObservableEmitter> weakReferences = observerableMap.get(modleName);\n if (null == weakReferences) {\n weakReferences = new ArrayList<>();\n observerableMap.put(modleName, weakReferences);\n }\n weakReferences.add(emitter);\n\n //先发射一次\n push();\n }\n });\n }\n\n public void delete() {\n ModelDao.deleteUserModels(getUid(), modleName);\n push();\n }\n\n public void delete(T value) {\n if (null == value) return;\n List<T> values = new ArrayList<>();\n values.add(value);\n delete(values);\n }\n\n public void delete(List<T> values) {\n if (null == values | values.isEmpty()) return;\n String uid = getUid(values.get(0));\n List<String> keys = new ArrayList<>();\n for (T value : values) {\n String key = getKey(value);\n keys.add(key);\n }\n ModelDao.delete(uid, modleName, keys);\n push();\n }\n\n public void insert(T value) {\n if (null == value) return;\n List<T> values = new ArrayList<>();\n values.add(value);\n insert(values);\n }\n\n public void insert(List<T> values) {\n if (null == values | values.isEmpty()) return;\n List<DBModel> models = new ArrayList<>();\n for (T value : values) {\n DBModel dbModel = new DBModel();\n dbModel.uid = getUid(value);\n dbModel.name = modleName;\n dbModel.key = getKey(value);\n dbModel.value = new Gson().toJson(value);\n models.add(dbModel);\n }\n ModelDao.save(models);\n push();\n }\n\n private String getUid() {\n String userId = uid;\n if (TextUtils.isEmpty(userId)) {\n userId = DEF_UID;\n }\n return userId;\n }\n\n private String getUid(T value) {\n String userId = uid;\n if (TextUtils.isEmpty(userId) && null != uidField && null != value) {\n try {\n Object id = uidField.get(value);\n if (null != id) userId = id.toString();\n } catch (Exception e) {\n e.printStackTrace();\n }\n }\n if (TextUtils.isEmpty(userId)) {\n userId = DEF_UID;\n }\n return userId;\n }\n\n private void push() {\n if (observerableMap.containsKey(modleName)) {\n List<ObservableEmitter> emitterWeakReference = observerableMap.get(modleName);\n if (null == emitterWeakReference) return;\n Iterator<ObservableEmitter> iterator = emitterWeakReference.iterator();\n while (iterator.hasNext()) {\n ObservableEmitter emitter = iterator.next();\n if (null != emitter && !emitter.isDisposed()) {\n emitter.onNext(query());\n } else {\n iterator.remove();\n }\n }\n }\n }\n\n private String getKey(T value) {\n String key = null;\n if (null == value || null == keyField) return null;\n try {\n Object keyValue = keyField.get(value);\n if (null != keyValue) key = keyValue.toString();\n } catch (Exception e) {\n e.printStackTrace();\n }\n return key;\n }\n}\n"},{"alpha_fraction":0.672245442867279,"alphanum_fraction":0.672245442867279,"avg_line_length":21.4375,"blob_id":"e931543f04b9f226b1177d9944207807aa50119a","content_id":"20f9cd486b64e8c99b20cc8ec29dc6e58594af70","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Kotlin","length_bytes":717,"license_type":"no_license","max_line_length":79,"num_lines":32,"path":"/app/src/main/java/com/android/base/ui/splash/SplashUtil.kt","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package net.medlinker.android.splash\n\nimport com.android.base.util.KVUtil\n\n/**\n *\n * @author zhangquan\n */\nobject SplashUtil {\n private const val KEY_PRIVACY_PROTOCOL_STATE = \"KEY_PRIVACY_PROTOCOL_STATE\"\n private const val KEY_SHOW_GUIDE = \"KEY_SHOW_GUIDE\"\n\n @JvmStatic\n fun isPrivacyGranted(): Boolean {\n return KVUtil.getBoolean(KEY_PRIVACY_PROTOCOL_STATE)\n }\n\n @JvmStatic\n fun setPrivacyGranted(granted: Boolean) {\n KVUtil.set(KEY_PRIVACY_PROTOCOL_STATE, granted)\n }\n\n @JvmStatic\n fun hasShowGuide(): Boolean {\n return KVUtil.getBoolean(KEY_SHOW_GUIDE)\n }\n\n @JvmStatic\n fun setShowGuide(shown: Boolean) {\n KVUtil.set(KEY_SHOW_GUIDE, shown)\n }\n}"},{"alpha_fraction":0.7338129281997681,"alphanum_fraction":0.7338129281997681,"avg_line_length":14.44444465637207,"blob_id":"f3eebc0a4330401a248a3007b87bdce65ad90d98","content_id":"a2c1a93200c46d45e107dd85d49f50427e29d172","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":163,"license_type":"no_license","max_line_length":59,"num_lines":9,"path":"/app/src/main/java/com/android/base/ui/SimpleOrientionFragAct.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.ui;\n\n/**\n * 可以旋转屏幕方向Activity容器\n *\n * @author 张全\n */\npublic class SimpleOrientionFragAct extends SimpleFragAct {\n}\n"},{"alpha_fraction":0.665730357170105,"alphanum_fraction":0.6811797618865967,"avg_line_length":32.92856979370117,"blob_id":"cf8fda25e48e179e65ffb5a1b8fb0890b264c2af","content_id":"4cb08c417a938cba1f67d15ab5e335de0cebd4dd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Kotlin","length_bytes":1454,"license_type":"no_license","max_line_length":106,"num_lines":42,"path":"/app/src/main/java/com/android/base/util/CornerTransform.kt","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.util\n\nimport android.graphics.*\nimport com.bumptech.glide.load.engine.bitmap_recycle.BitmapPool\nimport com.bumptech.glide.load.resource.bitmap.BitmapTransformation\nimport java.lang.Math.min\nimport java.security.MessageDigest\n\n\n/**\n * desc:\n * time: 2019/11/21\n * @author 银进\n */\nclass CornerTransform constructor(private val radius: Float) : BitmapTransformation() {\n override fun transform(pool: BitmapPool, toTransform: Bitmap, outWidth: Int, outHeight: Int): Bitmap {\n //得到图片最小边\n val size = min(toTransform.width, toTransform.height)\n //计算图片起点\n val x = (toTransform.width - size) / 2\n val y = (toTransform.height - size) / 2\n\n val circleBitmap = Bitmap.createBitmap(x,\n y, Bitmap.Config.ARGB_8888)\n val circle = pool.get(size, size, Bitmap.Config.ARGB_8888)\n val canvas = Canvas(circle)\n val paint = Paint()\n paint.isAntiAlias = true\n val rectF = RectF(0f, 0f, circleBitmap.width * 1f, circleBitmap.height * 1f)\n paint.shader = BitmapShader(circleBitmap, Shader.TileMode.CLAMP, Shader.TileMode.CLAMP)\n canvas.drawRoundRect(rectF, radius, radius, paint)\n return circle\n }\n\n override fun updateDiskCacheKey(messageDigest: MessageDigest) {\n messageDigest.update(TAG.toByte())\n }\n companion object{\n private val TAG = CornerTransform::class.java.name\n }\n\n}"},{"alpha_fraction":0.5806451439857483,"alphanum_fraction":0.5831681489944458,"avg_line_length":25.174528121948242,"blob_id":"945b09c43d3f969be18925df554235a32c0d00d3","content_id":"f8d6fa532f59326a6ff5a75b5250acb7fda5a872","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":5777,"license_type":"no_license","max_line_length":112,"num_lines":212,"path":"/librarys/CommonWidget/src/main/java/common/widget/LoadingBar.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package common.widget;\n\nimport android.content.Context;\nimport android.text.TextUtils;\nimport android.util.AttributeSet;\nimport android.view.View;\nimport android.view.animation.Animation;\nimport android.view.animation.LinearInterpolator;\nimport android.view.animation.RotateAnimation;\nimport android.widget.ImageView;\nimport android.widget.ProgressBar;\nimport android.widget.RelativeLayout;\nimport android.widget.TextView;\n\n/**\n * 加载进度条\n *\n * @author 张全\n */\npublic class LoadingBar extends RelativeLayout {\n private ImageView iv_loading;\n private ProgressBar mProgressBar;\n private TextView tv_loading;\n private LoadingStatus status;\n\n public LoadingBar(Context context, AttributeSet attrs, int defStyle) {\n super(context, attrs, defStyle);\n init();\n }\n\n public LoadingBar(Context context, AttributeSet attrs) {\n super(context, attrs);\n init();\n }\n\n public LoadingBar(Context context) {\n super(context);\n init();\n }\n\n private void init() {\n View.inflate(getContext(), R.layout.common_loadingbar, this);\n iv_loading = (ImageView) findViewById(R.id.loading_img);\n tv_loading = (TextView) findViewById(R.id.loading_text);\n mProgressBar = (ProgressBar) findViewById(R.id.loading_progressbar);\n }\n\n Animation getAnim() {\n RotateAnimation anim = new RotateAnimation(0, 360,\n Animation.RELATIVE_TO_SELF, 0.5f, Animation.RELATIVE_TO_SELF,\n 0.5f);\n anim.setDuration(500);\n anim.setInterpolator(new LinearInterpolator());\n anim.setRepeatCount(Animation.INFINITE);\n anim.setRepeatMode(Animation.RESTART);\n return anim;\n }\n\n /**\n * 设置加载状态\n *\n * @param status 加载状态\n */\n public void setLoadingStatus(LoadingStatus status) {\n setLoadingStatus(status, -1, null);\n }\n\n /**\n * 设置加载状态\n *\n * @param status 加载状态\n * @param imgRes 显示的图片\n */\n public void setLoadingStatus(LoadingStatus status, int imgRes) {\n setLoadingStatus(status, imgRes, null);\n }\n\n /**\n * 设置加载状态\n *\n * @param status 加载状态\n * @param text 显示的文字\n */\n public void setLoadingStatus(LoadingStatus status, String text) {\n setLoadingStatus(status, -1, text);\n }\n\n /**\n * 设置加载状态\n *\n * @param status 加载状态\n * @param imgRes 显示的图片\n * @param text 显示的文字\n */\n public void setLoadingStatus(LoadingStatus status, int imgRes, String text) {\n\n if (null != this.status && this.status == status) {\n return;\n }\n\n if (status == LoadingStatus.SUCCESS) {\n loadSuccess();\n return;\n }\n\n this.status = status;\n setVisibility(View.VISIBLE);\n tv_loading.setVisibility(View.GONE);\n mProgressBar.setVisibility(View.GONE);\n//\t\tiv_loading.setVisibility(View.GONE);\n//\t\tiv_loading.clearAnimation();\n\n // 设置文字\n String loadingText = null == text ? getContext().getString(status.text) : text;\n tv_loading.setText(loadingText);\n if (!TextUtils.isEmpty(loadingText)) {\n tv_loading.setVisibility(View.VISIBLE);\n }\n\n // 设置图片\n if (imgRes > 0) {\n iv_loading.setImageResource(imgRes);\n iv_loading.setVisibility(View.VISIBLE);\n } else {\n//\t\t\tiv_loading.setImageBitmap(null);\n }\n\n switch (status) {\n case START:// 加载中...\n mProgressBar.setVisibility(View.VISIBLE);\n iv_loading.setVisibility(View.GONE);\n break;\n case RELOAD:// 重新加载\n iv_loading.setVisibility(View.VISIBLE);\n break;\n case NOCONNECTION:// 无网络连接\n iv_loading.setVisibility(View.VISIBLE);\n break;\n case SUCCESS:// 加载成功\n setVisibility(View.GONE);\n break;\n case EMPTY: // 无数据\n iv_loading.setVisibility(View.VISIBLE);\n break;\n }\n }\n\n public LoadingStatus getLoadingStatus() {\n return this.status;\n }\n\n /**\n * 是否正在加载\n *\n * @return\n */\n public boolean isLoading() {\n return getVisibility() == View.VISIBLE && null != status\n && (status == LoadingStatus.START);\n }\n\n /**\n * 加载成功\n */\n public void loadSuccess() {\n this.status = LoadingStatus.SUCCESS;\n setVisibility(View.GONE);\n }\n\n /**\n * 是否能够加载\n *\n * @return\n */\n public boolean canLoading() {\n if (null == status) return false;\n if (status == LoadingStatus.START || status == LoadingStatus.EMPTY || status == LoadingStatus.SUCCESS) {\n return false;\n }\n return true;\n }\n\n /**\n * 加载状态\n *\n * @author zhangquan\n */\n public enum LoadingStatus {\n START(R.string.loadingbar_start), SUCCESS(R.string.loadingbar_success), NOCONNECTION(\n R.string.loadingbar_noconnection), RELOAD(\n R.string.loadingbar_reload), EMPTY(R.string.loadingbar_empty);\n\n public int text;\n\n private LoadingStatus(int text) {\n this.text = text;\n }\n }\n\n // --------------------------------\n public void setTextView(TextView textView) {\n this.tv_loading = textView;\n }\n\n public void setImageView(ImageView imageView) {\n this.iv_loading = imageView;\n }\n\n public void setProgressBar(ProgressBar progressBar) {\n this.mProgressBar = progressBar;\n }\n}\n"},{"alpha_fraction":0.5897009968757629,"alphanum_fraction":0.5897009968757629,"avg_line_length":24.08333396911621,"blob_id":"326619ebb66677162ab6092db4b293c0f9957e3c","content_id":"1ef64e6d4f9ec4059f0ab91d02c1466f64189562","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1912,"license_type":"no_license","max_line_length":90,"num_lines":72,"path":"/app/src/main/java/com/android/base/data/BaseResponseObserver.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.data;\n\n\nimport java.net.ConnectException;\nimport java.net.SocketTimeoutException;\nimport java.net.UnknownHostException;\n\nimport io.reactivex.observers.DisposableObserver;\n\n\npublic abstract class BaseResponseObserver<T> extends DisposableObserver<T> {\n\n private static final String TAG = \"BaseResponseObserver\";\n\n @Override\n public final void onError(Throwable e) {\n if (e != null) {\n e.printStackTrace();\n }\n HttpResponseException errorCause;\n if (e instanceof UnknownHostException) {\n errorCause = new HttpResponseException(\"无网络连接, 请重试\", e);\n } else if (e instanceof SocketTimeoutException || e instanceof ConnectException) {\n errorCause = new HttpResponseException(\"网络连接超时, 请重试\", e);\n } else if (e instanceof HttpResponseException) { //服务器错误\n errorCause = (HttpResponseException) e;\n } else {\n errorCause = new HttpResponseException(\"网络异常\", e);\n }\n onError(errorCause);\n onEnd();\n }\n\n @Override\n public void onNext(T value) {\n if (value instanceof ResponseData) {\n ResponseData data = (ResponseData) value;\n if (!data.isSuccessful()) {\n onError(new HttpResponseException(data));\n } else {\n onSuccess(value);\n }\n } else {\n onSuccess(value);\n }\n }\n\n @Override\n public void onComplete() {\n onEnd();\n }\n\n /**\n * 请求成功\n *\n * @param value\n */\n public abstract void onSuccess(T value);\n\n /**\n * 请求失败\n *\n * @param e\n */\n public abstract void onError(HttpResponseException e);\n\n /**\n * 请求结束\n * <p>不管请求成功还是失败，都会回调onEnd</p>\n */\n public abstract void onEnd();\n}\n"},{"alpha_fraction":0.5630878806114197,"alphanum_fraction":0.566980242729187,"avg_line_length":30.783504486083984,"blob_id":"0f8c96cdba9702cf9c6346d781021d728fa626d7","content_id":"dca512cf7fa8eb773640e79992bf2575fe774082","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":3181,"license_type":"no_license","max_line_length":120,"num_lines":97,"path":"/app/src/main/java/com/android/base/util/CommonUtil.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.util;\n\nimport android.content.ClipData;\nimport android.content.ClipboardManager;\nimport android.content.Context;\nimport android.content.Intent;\nimport android.media.MediaScannerConnection;\nimport android.net.Uri;\nimport android.provider.MediaStore;\nimport android.text.TextUtils;\n\nimport com.android.util.LContext;\nimport com.android.util.log.LogUtil;\n\nimport java.io.File;\n\n/**\n * @author 张全\n */\npublic class CommonUtil {\n\n /**\n * 保存文件到相册\n *\n * @param ctx\n * @param file\n */\n public static void saveFileToGallery(Context ctx, File file) {\n if (null == file) return;\n try {\n MediaStore.Images.Media.insertImage(ctx.getContentResolver(),\n file.getAbsolutePath(), file.getName(), null);\n LogUtil.d(\"gallery\", \"保存相册成功\");\n } catch (Exception e) {\n e.printStackTrace();\n LogUtil.d(\"gallery\", \"保存相册失败 e=\" + e.getMessage());\n }\n\n // 通知图库更新11\n try {\n ctx.sendBroadcast(new Intent(Intent.ACTION_MEDIA_SCANNER_SCAN_FILE, Uri.fromFile(file)));\n } catch (Exception e) {\n e.printStackTrace();\n LogUtil.d(\"gallery\", \"通知图库更新失败 111 e=\" + e.getMessage());\n }\n\n // 通知图库更新22\n try {\n String[] paths = new String[]{file.getAbsolutePath()};\n MediaScannerConnection.scanFile(ctx, paths, null, new MediaScannerConnection.OnScanCompletedListener() {\n @Override\n public void onScanCompleted(String path, Uri uri) {\n Intent mediaScanIntent = new Intent(Intent.ACTION_MEDIA_SCANNER_SCAN_FILE);\n mediaScanIntent.setData(uri);\n ctx.sendBroadcast(mediaScanIntent);\n }\n });\n\n } catch (Exception e) {\n e.printStackTrace();\n LogUtil.d(\"gallery\", \"通知图库更新失败 222 e=\" + e.getMessage());\n }\n }\n\n public static void clearClipboard() {\n ClipboardManager manager = (ClipboardManager) LContext.getContext().getSystemService(Context.CLIPBOARD_SERVICE);\n if (manager != null) {\n try {\n manager.setPrimaryClip(ClipData.newPlainText(null, null));\n } catch (Exception e) {\n e.printStackTrace();\n }\n }\n }\n\n public static void addToClipboard(String text) {\n try {\n ClipboardManager cm = (ClipboardManager) LContext.getContext().getSystemService(Context.CLIPBOARD_SERVICE);\n cm.setText(text);\n } catch (Exception e) {\n e.printStackTrace();\n }\n }\n\n public static String checkUrl(String url) {\n if (!TextUtils.isEmpty(url) && !url.contains(\"http\")) {\n if (url.startsWith(\"//\")) {\n url = \"https:\" + url.subSequence(1, url.length());\n } else if (url.startsWith(\"/\")) {\n url = \"https:/\" + url.subSequence(1, url.length());\n } else {\n url = \"https://\" + url;\n }\n }\n return url;\n }\n}\n"},{"alpha_fraction":0.5980066657066345,"alphanum_fraction":0.5986710786819458,"avg_line_length":22.515625,"blob_id":"3a24a2e98403cbebc5ca872c28848664a7890707","content_id":"e0abccd94992ef1515cff3325bf9d045f839a791","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1509,"license_type":"no_license","max_line_length":71,"num_lines":64,"path":"/app/src/main/java/com/android/base/ui/SimpleFrag.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.ui;\n\nimport android.view.View;\n\nimport androidx.core.app.ActivityCompat;\n\nimport com.android.base.widget.TitleBarView;\n\nimport me.yokeyword.fragmentation.ISupportFragment;\n\n/**\n * @author 张全\n */\npublic abstract class SimpleFrag extends BaseFragment {\n private TitleBarView mToolbar;\n\n public void setToolbar(TitleBarView toolbar) {\n this.mToolbar = toolbar;\n toolbar.setOnLeftBtnClickListener(v -> {\n assert getFragmentManager() != null;\n if (getFragmentManager().getBackStackEntryCount() > 1) {\n pop();\n } else {\n ActivityCompat.finishAfterTransition(_mActivity);\n }\n });\n }\n\n protected void hideToolbar() {\n TitleBarView titleBar = getTitleBar();\n if (null != titleBar) {\n titleBar.setVisibility(View.GONE);\n }\n }\n\n protected TitleBarView getTitleBar() {\n if (null != mToolbar) {\n return mToolbar;\n }\n ISupportFragment preFragment = getPreFragment();\n if (null != preFragment && preFragment instanceof SimpleFrag) {\n SimpleFrag simpleFrag = (SimpleFrag) preFragment;\n mToolbar = simpleFrag.getTitleBar();\n }\n return mToolbar;\n }\n\n @Override\n public void onResume() {\n super.onResume();\n }\n\n @Override\n public void onPause() {\n super.onPause();\n }\n\n @Override\n public void onStop() {\n super.onStop();\n }\n\n\n}\n"},{"alpha_fraction":0.6463306546211243,"alphanum_fraction":0.6480990052223206,"avg_line_length":27.299999237060547,"blob_id":"c2e972c62097b3fe326568162a121b08a7462dcb","content_id":"47ed69e8b866c21e628b304fd5146c5ba7c8b1b6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Kotlin","length_bytes":1163,"license_type":"no_license","max_line_length":111,"num_lines":40,"path":"/app/src/main/java/com/android/base/task/BackgroundTask.kt","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.task\n\nimport android.content.Context\nimport androidx.work.*\nimport com.blankj.utilcode.util.Utils\n\n/**\n * 后台任务\n * 1、继承BackgroundTask 重写doTask()\n * 2、调用BackgroundTask.execute()执行任务\n * @author zhangquan\n */\nabstract class BackgroundTask(context: Context, wokerParams: WorkerParameters) : Worker(context, wokerParams) {\n private val params = wokerParams\n override fun doWork(): Result {\n doTask(params)\n return Result.success()\n }\n\n abstract fun doTask(parameters: WorkerParameters)\n\n\n companion object {\n @JvmStatic\n fun execute(workerClass: Class<out ListenableWorker>) {\n val request = OneTimeWorkRequest.Builder(workerClass)\n .build()\n WorkManager.getInstance(Utils.getApp()).enqueue(request)\n }\n\n @JvmStatic\n fun execute(workerClass: Class<out ListenableWorker>, data: Data) {\n val request = OneTimeWorkRequest.Builder(workerClass)\n .setInputData(data)\n .build()\n WorkManager.getInstance(Utils.getApp()).enqueue(request)\n }\n }\n\n}"},{"alpha_fraction":0.6032915115356445,"alphanum_fraction":0.6111174821853638,"avg_line_length":34.90495681762695,"blob_id":"752190c225d74041e037461b4fbe56d7fb654c40","content_id":"cd58fbf39ca2aca758f459e275dbeeb482a4678f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":8823,"license_type":"no_license","max_line_length":118,"num_lines":242,"path":"/app/src/main/java/com/android/base/data/RestClient.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.data;\n\n\nimport com.android.base.util.log.LogClient;\n\nimport java.io.IOException;\nimport java.nio.charset.Charset;\nimport java.security.SecureRandom;\nimport java.security.cert.CertificateException;\nimport java.security.cert.X509Certificate;\nimport java.util.concurrent.TimeUnit;\n\nimport javax.net.ssl.HostnameVerifier;\nimport javax.net.ssl.SSLContext;\nimport javax.net.ssl.SSLSession;\nimport javax.net.ssl.SSLSocketFactory;\nimport javax.net.ssl.TrustManager;\nimport javax.net.ssl.X509TrustManager;\n\nimport okhttp3.Call;\nimport okhttp3.Headers;\nimport okhttp3.Interceptor;\nimport okhttp3.MediaType;\nimport okhttp3.OkHttpClient;\nimport okhttp3.Request;\nimport okhttp3.Response;\nimport okhttp3.ResponseBody;\nimport okhttp3.logging.HttpLoggingInterceptor;\nimport okio.Buffer;\nimport okio.BufferedSource;\nimport okio.GzipSource;\nimport retrofit2.Retrofit;\nimport retrofit2.adapter.rxjava2.RxJava2CallAdapterFactory;\nimport retrofit2.converter.gson.GsonConverterFactory;\nimport retrofit2.converter.scalars.ScalarsConverterFactory;\n\n/**\n * OkHttp请求\n *\n * @author 张全\n */\npublic final class RestClient {\n private static final String TAG = \"RestClient\";\n public static final int TIMEOUT_CONNECTION = 20; //连接超时\n public static final int TIMEOUT_READ = 20; //读取超时\n public static final int TIMEOUT_WRITE = 20; //写入超时\n\n private static final Charset UTF8 = Charset.forName(\"UTF-8\");\n public static final String REST_API_URL = DataConfig.API_HOST;\n private static Retrofit s_retrofit;\n private static OkHttpClient fileDownloadClient;\n private static OkHttpClient imgDownloadClient;\n\n\n static {\n OkHttpClient.Builder builder = new OkHttpClient.Builder()\n// .proxy(Proxy.NO_PROXY)\n .connectTimeout(TIMEOUT_CONNECTION, TimeUnit.SECONDS)\n .readTimeout(TIMEOUT_READ, TimeUnit.SECONDS)\n .writeTimeout(TIMEOUT_WRITE, TimeUnit.SECONDS)\n .addInterceptor(new HeaderIntercepter());\n\n if (DataConfig.DEBUG) {\n HttpLoggingInterceptor loggingInterceptor = new HttpLoggingInterceptor();\n loggingInterceptor.setLevel(HttpLoggingInterceptor.Level.BODY);\n builder.addInterceptor(loggingInterceptor);\n }\n getUnsafeOkHttpClient(builder);\n OkHttpClient client = builder.build();\n\n s_retrofit = new Retrofit.Builder()\n .baseUrl(REST_API_URL)\n .addConverterFactory(ScalarsConverterFactory.create())\n .addConverterFactory(GsonConverterFactory.create())\n .addCallAdapterFactory(RxJava2CallAdapterFactory.create())\n .client(client)\n .build();\n }\n\n /**\n * 文件下载\n *\n * @return\n */\n public static OkHttpClient getDownloadClient() {\n if (null == fileDownloadClient) {\n OkHttpClient.Builder builder = new OkHttpClient.Builder()\n .connectTimeout(TIMEOUT_CONNECTION, TimeUnit.SECONDS)\n .readTimeout(TIMEOUT_READ, TimeUnit.SECONDS)\n .writeTimeout(TIMEOUT_WRITE, TimeUnit.SECONDS);\n getUnsafeOkHttpClient(builder);\n fileDownloadClient = builder.build();\n }\n return fileDownloadClient;\n }\n\n /**\n * 图片下载\n *\n * @return\n */\n public static OkHttpClient getImgDownloadClient() {\n if (null == imgDownloadClient) {\n OkHttpClient.Builder builder = new OkHttpClient.Builder()\n .connectTimeout(TIMEOUT_CONNECTION, TimeUnit.SECONDS)\n .readTimeout(50, TimeUnit.SECONDS)\n .writeTimeout(50, TimeUnit.SECONDS);\n getUnsafeOkHttpClient(builder);\n imgDownloadClient = builder.build();\n }\n return imgDownloadClient;\n }\n\n\n public static void getUnsafeOkHttpClient(OkHttpClient.Builder builder) {\n try {\n final X509TrustManager trustManager = new X509TrustManager() {\n @Override\n public void checkClientTrusted(X509Certificate[] chain, String authType) throws CertificateException {\n\n }\n\n @Override\n public void checkServerTrusted(X509Certificate[] chain, String authType) throws CertificateException {\n\n }\n\n @Override\n public X509Certificate[] getAcceptedIssuers() {\n return new X509Certificate[0];\n }\n };\n\n SSLContext sslContext = SSLContext.getInstance(\"SSL\");\n sslContext.init(null, new TrustManager[]{trustManager}, new SecureRandom());\n SSLSocketFactory sslSocketFactory = sslContext.getSocketFactory();\n builder.sslSocketFactory(sslSocketFactory,trustManager);\n builder.hostnameVerifier(new HostnameVerifier() {\n @Override\n public boolean verify(String hostname, SSLSession session) {\n return true;\n }\n });\n\n } catch (Exception e) {\n e.printStackTrace();\n }\n }\n\n static class HeaderIntercepter implements Interceptor {\n\n @Override\n public Response intercept(Chain chain) throws IOException {\n\n Request request = chain.request();\n Request.Builder builder = request.newBuilder();\n\n// String token = UserClient.getToken();\n// builder.addHeader(\"x-app-source\", APP_SOURCE); // 1 星口袋 2 星乐桃\n// builder.addHeader(\"x-sign\", sign); //签名\n// builder.addHeader(\"x-timestamp\", \"\" + timestamp);\n// builder.addHeader(\"x-appid\", APPID);\n// builder.addHeader(\"x-m\", AnalysisUtil.getUniqueId());//设备id\n//\n//\n// if (!TextUtils.isEmpty(token)) {\n// builder.addHeader(\"Authorization\", token);\n// }\n//\n// builder.addHeader(\"User-Agent\", AnalysisUtil.getUA());\n// builder.addHeader(\"Cache-Control\", CacheControl.FORCE_NETWORK.toString());\n// builder.addHeader(\"sgn\", DeviceUtil.getSignature()); //签名校验\n// builder.addHeader(\"pkg\", DeviceUtil.getPkgName()); //包名校验\n// builder.addHeader(\"appVersion\", LContext.versionName); //版本号\n// builder.addHeader(\"client-v\", LContext.versionName); //版本号\n// builder.addHeader(\"channel\", LContext.channel);//渠道\n// builder.addHeader(\"check-enable\", \"1\");\n// builder.addHeader(\"dev-type\", \"1\");// 1 安卓 , 2 IOS , 3 其他\n// builder.addHeader(\"client-type\", \"2\");//1,微信小程序 , 2 APP\n\n\n request = builder.build();\n Response response = chain.proceed(request);\n checkResponse(request.url().toString(), response);\n return response;\n }\n\n }\n\n private static void checkResponse(String url, Response response) {\n try {\n ResponseBody body = response.body();\n BufferedSource source = body.source();\n Headers headers = response.headers();\n source.request(Long.MAX_VALUE); // Buffer the entire body.\n Buffer buffer = source.buffer();\n if (\"gzip\".equalsIgnoreCase(headers.get(\"Content-Encoding\"))) {\n GzipSource gzippedResponseBody = null;\n try {\n gzippedResponseBody = new GzipSource(buffer.clone());\n buffer = new Buffer();\n buffer.writeAll(gzippedResponseBody);\n } finally {\n if (gzippedResponseBody != null) {\n gzippedResponseBody.close();\n }\n }\n }\n Charset charset = UTF8;\n MediaType contentType = body.contentType();\n if (contentType != null) {\n charset = contentType.charset(UTF8);\n }\n\n String responseStr = buffer.clone().readString(charset);\n //本地日志\n LogClient.logResponse(url, responseStr);\n\n //登录失效\n// int code = new JSONObject(responseStr).optInt(\"code\");\n// if (code == 201) {\n// UserClient.loginOut();\n// EventBus.getDefault().post(new EventMsg(Constant.Event.LOGIN_OUT));\n// EventBus.getDefault().post(new EventMsg(Constant.Event.LOGIN_SESSION_INVALIDATE));\n// }\n\n } catch (Exception e) {\n }\n }\n\n\n public static <T> T getService(Class<T> serviceClass) {\n return s_retrofit.create(serviceClass);\n }\n\n public static OkHttpClient getHttpClient() {\n Call.Factory factory = s_retrofit.callFactory();\n return (OkHttpClient) factory;\n }\n\n\n}\n"},{"alpha_fraction":0.5146363377571106,"alphanum_fraction":0.5174453258514404,"avg_line_length":28.03004264831543,"blob_id":"f222f44fc085c30d28369539878e9cd5313dc749","content_id":"c7420524ff48b1d0115e3a53ace63fa52e6653b0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":6842,"license_type":"no_license","max_line_length":87,"num_lines":233,"path":"/app/src/main/java/com/android/base/util/filedownloader/FileDownloader.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.util.filedownloader;\n\nimport android.content.Context;\nimport android.os.Environment;\nimport android.os.Handler;\nimport android.os.Looper;\nimport android.os.Message;\nimport android.text.TextUtils;\n\nimport com.android.util.encode.MD5;\nimport com.android.util.log.LogUtil;\nimport com.android.base.data.RestClient;\nimport com.android.base.util.CommonUtil;\n\nimport java.io.File;\nimport java.io.FileOutputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.Serializable;\nimport java.util.ArrayList;\nimport java.util.List;\n\nimport okhttp3.Call;\nimport okhttp3.Callback;\nimport okhttp3.Request;\nimport okhttp3.Response;\n\n/**\n * 文件下载器\n *\n * @author 张全\n */\npublic class FileDownloader {\n private static final String TAG = \"FileDownloader\";\n private static final String DIR=\"files\";\n private DownloadHandler downloadHandler = new DownloadHandler();\n private DownloadCallback callback;\n private Context ctx;\n private boolean exit;\n private boolean saveToGallery; //自动保存到相册\n\n public FileDownloader(Context ctx) {\n this(ctx, true);\n }\n\n public FileDownloader(Context ctx, boolean saveToGallery) {\n this.ctx = ctx;\n this.saveToGallery = saveToGallery;\n }\n\n private File getImgDir() {\n File fileDir = new File(Environment.getExternalStorageDirectory(), DIR);\n if (!fileDir.exists()) {\n fileDir.mkdirs();\n }\n return fileDir;\n }\n\n /**\n * 下载文件\n *\n * @param url\n * @param callback\n */\n public void downloadFile(String url, DownloadCallback callback) {\n if (TextUtils.isEmpty(url)) {\n return;\n }\n List<String> urls = new ArrayList<>();\n urls.add(url);\n downloadFile(urls, callback);\n }\n\n /**\n * 下载文件\n *\n * @param urls\n * @param callback\n */\n public void downloadFile(List<String> urls, DownloadCallback callback) {\n if (null == urls || urls.isEmpty()) return;\n this.callback = callback;\n for (String url : urls) {\n File imgDir = getImgDir();\n String fileName = MD5.MD5Encode(url);\n if (url.contains(\".mp4\")) {\n fileName += \".mp4\";\n } else {\n fileName += \".png\";\n }\n File file = new File(imgDir, fileName);\n if (file.exists() && file.length() > 0) { //已下载完成的\n success(url, file);\n } else {\n if (url.startsWith(\"http\")) {\n downLoadFile(url, file);\n } else {\n fail(url);\n }\n }\n }\n }\n\n /**\n * 结束\n */\n public void stop() {\n this.exit = true;\n this.ctx = null;\n callback = null;\n downloadHandler.removeAllMsg();\n }\n\n private boolean isExit() {\n return exit || null == callback;\n }\n\n private void success(String url, File file) {\n if (saveToGallery && null != file && null != ctx) {\n CommonUtil.saveFileToGallery(ctx, file);\n }\n if (isExit()) return;\n callback.success(file, url);\n }\n\n private void fail(String url) {\n if (isExit()) return;\n callback.fail(url);\n }\n\n private void downLoadFile(String url, File file) {\n final Request request = new Request.Builder().url(url).build();\n final Call call = RestClient.getHttpClient().newCall(request);\n\n\n call.enqueue(new Callback() {\n @Override\n public void onFailure(Call call, IOException e) {\n if (isExit()) return;\n downloadHandler.sendFail(url, file);\n }\n\n @Override\n public void onResponse(Call call, Response response) {\n if (isExit()) return;\n if (response.isSuccessful()) {\n InputStream is = null;\n byte[] buf = new byte[2048];\n int len;\n FileOutputStream fos = null;\n try {\n is = response.body().byteStream();\n fos = new FileOutputStream(file);\n while ((len = is.read(buf)) != -1) {\n fos.write(buf, 0, len);\n }\n fos.flush();\n downloadHandler.sendSuccess(url, file);\n } catch (Exception e) {\n LogUtil.e(TAG, e.toString());\n downloadHandler.sendFail(url, file);\n } finally {\n try {\n if (is != null) {\n is.close();\n }\n if (fos != null) {\n fos.close();\n }\n } catch (Exception e) {\n LogUtil.e(TAG, e.toString());\n }\n }\n } else {\n downloadHandler.sendFail(url, file);\n }\n }\n });\n }\n\n private class DownloadHandler extends Handler {\n private int msg_success = 1;\n private int msg_fail = 2;\n\n public DownloadHandler() {\n super(Looper.getMainLooper());\n }\n\n public void sendSuccess(String url, File file) {\n Message message = obtainMessage(msg_success);\n message.obj = new FileDownloader.FileEntity(url, file);\n sendMessage(message);\n }\n\n public void sendFail(String url, File file) {\n if (null != file && file.length() > 0) file.delete(); //删除下载失败的文件\n Message message = obtainMessage(msg_fail);\n message.obj = new FileDownloader.FileEntity(url, null);\n sendMessage(message);\n }\n\n @Override\n public void handleMessage(Message msg) {\n FileDownloader.FileEntity fileEntity = (FileDownloader.FileEntity) msg.obj;\n if (msg.what == msg_success) {\n success(fileEntity.url, fileEntity.file);\n } else {\n fail(fileEntity.url);\n }\n }\n\n public void removeAllMsg() {\n removeMessages(msg_fail);\n removeMessages(msg_success);\n }\n }\n\n private static class FileEntity implements Serializable {\n public String url;\n public File file;\n\n public FileEntity(String url, File file) {\n this.url = url;\n this.file = file;\n }\n }\n\n public static interface DownloadCallback {\n void success(File file, String url);\n\n void fail(String url);\n }\n}\n"},{"alpha_fraction":0.5860113501548767,"alphanum_fraction":0.5904221534729004,"avg_line_length":32.410526275634766,"blob_id":"05b2b2bb312f03bcac09d5dc89a0dc8cff52e4ae","content_id":"92d3f0db9797c15da13639f59ae6e319229f83e6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":3350,"license_type":"no_license","max_line_length":114,"num_lines":95,"path":"/app/src/main/java/com/android/base/util/pay/OrderPay.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.util.pay;\n\nimport android.annotation.SuppressLint;\nimport android.app.Activity;\nimport android.content.ComponentName;\nimport android.content.Context;\nimport android.content.Intent;\nimport android.net.Uri;\nimport android.os.Handler;\nimport android.os.Message;\nimport android.text.TextUtils;\n\nimport com.alipay.sdk.app.PayTask;\nimport com.android.base.event.EventMsg;\nimport com.android.util.ext.ToastUtil;\nimport com.android.base.Constant;\n\nimport org.greenrobot.eventbus.EventBus;\n\nimport java.util.Map;\n\n/**\n * 支付\n *\n * @author 张全\n */\npublic class OrderPay {\n private static final int SDK_PAY_FLAG = 1;\n\n public void wxPay(Context ctx, OrderPayResponse orderPayResponse) {\n WXHelper wxHelper = new WXHelper(ctx);\n wxHelper.pay(orderPayResponse);\n }\n\n public void alipay(Activity ctx, OrderPayResponse orderPayResponse) {\n if (!checkAliPayInstalled(ctx)) { //未安装支付宝\n ToastUtil.show(\"未安装支付宝\");\n return;\n }\n String orderInfo = orderPayResponse.orderInfo;\n final Runnable payRunnable = new Runnable() {\n\n @Override\n public void run() {\n PayTask alipay = new PayTask(ctx);\n Map<String, String> result = alipay.payV2(orderInfo, true);\n\n Message msg = new Message();\n msg.what = SDK_PAY_FLAG;\n msg.obj = result;\n mHandler.sendMessage(msg);\n }\n };\n\n // 必须异步调用\n Thread payThread = new Thread(payRunnable);\n payThread.start();\n }\n\n @SuppressLint(\"HandlerLeak\")\n private Handler mHandler = new Handler() {\n @Override\n public void handleMessage(Message msg) {\n switch (msg.what) {\n case SDK_PAY_FLAG: {\n PayResult payResult = new PayResult((Map<String, String>) msg.obj);\n /**\n * 对于支付结果，请商户依赖服务端的异步通知结果。同步通知结果，仅作为支付结束的通知。\n */\n String resultInfo = payResult.getResult();// 同步返回需要验证的信息\n String resultStatus = payResult.getResultStatus();\n // 判断resultStatus 为9000则代表支付成功\n if (TextUtils.equals(resultStatus, \"9000\")) {\n EventBus.getDefault().post(new EventMsg(Constant.Event.ORDER_BUY_SUCCESS, PayWay.ZHIBAO));\n } else if (TextUtils.equals(resultStatus, \"6001\")) {\n// ToastUtil.show(\"取消支付\");\n EventBus.getDefault().post(new EventMsg(Constant.Event.ORDER_BUY_CANCEL, PayWay.ZHIBAO));\n } else {\n EventBus.getDefault().post(new EventMsg(Constant.Event.ORDER_BUY_FAIL, PayWay.ZHIBAO));\n }\n break;\n }\n }\n\n }\n };\n\n public boolean checkAliPayInstalled(Context context) {\n\n Uri uri = Uri.parse(\"alipays://platformapi/startApp\");\n Intent intent = new Intent(Intent.ACTION_VIEW, uri);\n ComponentName componentName = intent.resolveActivity(context.getPackageManager());\n return componentName != null;\n }\n}\n"},{"alpha_fraction":0.5242528915405273,"alphanum_fraction":0.5282562375068665,"avg_line_length":38.63468551635742,"blob_id":"ace8617efc37278a0eabe61f8c405ad8ce0a1515","content_id":"451aaaaed139a447f54a47cbb5b9d2e8491974ba","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":10917,"license_type":"no_license","max_line_length":108,"num_lines":271,"path":"/app/src/main/java/com/android/base/version/UpdateService.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.version;\n\nimport android.app.Notification;\nimport android.app.NotificationChannel;\nimport android.app.NotificationManager;\nimport android.app.PendingIntent;\nimport android.app.Service;\nimport android.content.Context;\nimport android.content.Intent;\nimport android.graphics.Color;\nimport android.net.Uri;\nimport android.os.Build;\nimport android.os.Handler;\nimport android.os.IBinder;\nimport android.os.Looper;\nimport android.os.Message;\n\nimport androidx.annotation.Nullable;\nimport androidx.core.app.NotificationCompat;\nimport androidx.core.content.FileProvider;\n\nimport com.android.util.ext.ToastUtil;\nimport com.android.util.log.LogUtil;\nimport com.android.base.data.RestClient;\nimport com.fastapp.R;\n\nimport java.io.File;\nimport java.io.FileOutputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\n\nimport component.update.AppDownloadClient;\nimport component.update.AppVersion;\nimport okhttp3.Call;\nimport okhttp3.Callback;\nimport okhttp3.Request;\nimport okhttp3.Response;\n\n/**\n * App后台下载服务\n *\n * @author 张全\n */\npublic class UpdateService extends Service {\n private static final String TAG = \"VersionUpdate\";\n private static final String APP_VERSION = \"APP_VERSION\";\n private boolean isDownload;\n\n public static void update(Context ctx, AppVersion appVersion) {\n Intent intent = new Intent(ctx, UpdateService.class);\n intent.putExtra(APP_VERSION, appVersion);\n ctx.startService(intent);\n\n }\n\n @Nullable\n @Override\n public IBinder onBind(Intent intent) {\n return null;\n }\n\n @Override\n public int onStartCommand(Intent intent, int flags, int startId) {\n if (null == intent || !intent.hasExtra(APP_VERSION))\n return super.onStartCommand(intent, flags, startId);\n\n ToastUtil.show(\"后台下载中,请稍候..\");\n\n if (isDownload) return super.onStartCommand(intent, flags, startId);\n try {\n AppVersion appVersion = (AppVersion) intent.getSerializableExtra(APP_VERSION);\n File updateFile = AppDownloadClient.getUpdateFile();\n downLoadFile(appVersion.downloadUrl, updateFile);\n } catch (Exception e) {\n e.printStackTrace();\n isDownload = false;\n }\n return super.onStartCommand(intent, flags, startId);\n }\n\n private static class DownloadCallback extends Handler {\n private static final int msg_success = 1;\n private static final int msg_fail = 2;\n private static final int msg_progress = 3;\n private int notificationId;\n final String channelId = \"1\";\n private NotificationManager notificationManager;\n private NotificationCompat.Builder mBuilder;\n private Context ctx;\n\n public DownloadCallback(Context ctx, String url) {\n super(Looper.getMainLooper());\n this.ctx = ctx;\n notificationId = url.hashCode();\n notificationManager = (NotificationManager) ctx.getSystemService(Context.NOTIFICATION_SERVICE);\n\n if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.O) {\n NotificationChannel channel = new NotificationChannel(channelId,\n \"Channel1\", NotificationManager.IMPORTANCE_DEFAULT);\n channel.enableLights(false);\n channel.enableVibration(false);\n channel.setVibrationPattern(new long[]{0});\n channel.setSound(null, null);\n channel.setLightColor(Color.RED); //小红点颜色\n channel.setLockscreenVisibility(Notification.VISIBILITY_PRIVATE);\n channel.setShowBadge(true); //是否在久按桌面图标时显示此渠道的通知\n mBuilder = new NotificationCompat.Builder(ctx, channelId);\n notificationManager.createNotificationChannel(channel);\n } else {\n mBuilder = new NotificationCompat.Builder(ctx);\n mBuilder.setDefaults(NotificationCompat.FLAG_ONLY_ALERT_ONCE)\n .setVibrate(new long[]{0})\n .setSound(null);\n }\n }\n\n @Override\n public void handleMessage(Message msg) {\n switch (msg.what) {\n case msg_progress:\n int progress = (int) msg.obj;\n sendNotification(progress);\n break;\n case msg_success:\n LogUtil.e(TAG, \"下载成功\");\n notificationManager.cancel(notificationId);\n AppDownloadClient.installAPK();\n break;\n case msg_fail:\n LogUtil.e(TAG, \"下载失败\");\n notificationManager.cancel(notificationId);\n ToastUtil.show(\"下载失败,请重试\");\n break;\n }\n }\n\n public void sendMsg(int what) {\n sendEmptyMessage(what);\n }\n\n public void updateProgress(int progress) {\n sendMessage(obtainMessage(msg_progress, progress));\n }\n\n public void sendNotification(int progress) {\n LogUtil.e(TAG, \"progress=\" + progress);\n if (progress < 100) {\n Intent localIntent = new Intent(ctx, UpdateService.class);\n PendingIntent localPendingIntent = PendingIntent.getService(ctx, 0,\n localIntent, PendingIntent.FLAG_UPDATE_CURRENT);\n if (progress <= 0) {\n mBuilder.setContentTitle(\"星乐桃\")\n .setContentText(\"版本下载中\");\n } else {\n mBuilder.setContentTitle(\"版本下载\")\n .setContentText(\"当前进度：\" + progress + \"%\")\n .setProgress(100, progress, false);\n }\n mBuilder\n .setContentIntent(localPendingIntent)\n .setTicker(\"版本下载中...\")\n .setWhen(System.currentTimeMillis())\n// .setDefaults(Notification.DEFAULT_ALL)\n .setSmallIcon(R.mipmap.ic_launcher)\n .setAutoCancel(true);\n\n\n } else {\n File updateFile = AppDownloadClient.getUpdateFile();\n Intent intent = new Intent(Intent.ACTION_VIEW);\n intent.setFlags(Intent.FLAG_ACTIVITY_NEW_TASK);\n Uri uri = null;\n if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.N) {\n String authority = AppDownloadClient.getAuthority();\n uri = FileProvider.getUriForFile(AppDownloadClient.getContext(), authority, updateFile);\n intent.addFlags(Intent.FLAG_GRANT_READ_URI_PERMISSION);\n } else {\n uri = Uri.fromFile(updateFile);\n }\n intent.setDataAndType(uri, \"application/vnd.android.package-archive\");\n PendingIntent pendingIntent = PendingIntent.getActivity(ctx, 0, intent,\n 0);\n mBuilder.setContentText(\"下载完毕点击安装\")\n .setWhen(System.currentTimeMillis())\n .setDefaults(Notification.DEFAULT_ALL)\n .setSmallIcon(R.mipmap.ic_launcher)\n .setContentIntent(pendingIntent)\n .setAutoCancel(true);\n }\n\n Notification notify = mBuilder.build();\n notificationManager.notify(notificationId, notify);\n }\n }\n\n\n /**\n * 下载文件\n */\n private void downLoadFile(String url, File file) {\n// url=\"https://www.9ben.cn/download/andriod/9benfresh.apk\";\n isDownload = true;\n final Request request = new Request.Builder().url(url).build();\n final Call call = RestClient.getDownloadClient().newCall(request);\n DownloadCallback downloadCallback = new DownloadCallback(this, url);\n call.enqueue(new Callback() {\n @Override\n public void onFailure(Call call, IOException e) {\n isDownload = false;\n downloadCallback.sendMsg(DownloadCallback.msg_fail);\n }\n\n @Override\n public void onResponse(Call call, Response response) {\n int lastProgress = 0;\n if (response.isSuccessful()) {\n InputStream is = null;\n byte[] buf = new byte[2048];\n int len;\n FileOutputStream fos = null;\n try {\n long total = response.body().contentLength();\n LogUtil.e(TAG, \"total------>\" + total);\n long current = 0;\n is = response.body().byteStream();\n fos = new FileOutputStream(file);\n while ((len = is.read(buf)) != -1) {\n current += len;\n fos.write(buf, 0, len);\n LogUtil.e(TAG, \"current------>\" + current);\n if (total == -1) {\n downloadCallback.updateProgress(-1);\n } else {\n int progress = (int) (current * 1.0f / total * 100);\n progress = progress >= 100 ? 99 : progress;\n if (lastProgress != progress) {\n downloadCallback.updateProgress(progress);\n }\n lastProgress = progress;\n }\n }\n fos.flush();\n try {\n fos.close();\n } catch (Exception e) {\n e.printStackTrace();\n }\n downloadCallback.sendMsg(DownloadCallback.msg_success);\n } catch (Exception e) {\n LogUtil.e(TAG, e.toString());\n downloadCallback.sendMsg(DownloadCallback.msg_fail);\n } finally {\n try {\n if (is != null) {\n is.close();\n }\n if (fos != null) {\n fos.close();\n }\n } catch (Exception e) {\n LogUtil.e(TAG, e.toString());\n }\n }\n } else {\n downloadCallback.sendMsg(DownloadCallback.msg_fail);\n }\n isDownload = false;\n }\n });\n }\n}\n"},{"alpha_fraction":0.5586017966270447,"alphanum_fraction":0.5606579780578613,"avg_line_length":33.761905670166016,"blob_id":"c0b087763810562ea460c7a1fa316d9de16e9ba0","content_id":"b99d88c37e2e4b560ea107bfd2ab5d8ce2c5254d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1489,"license_type":"no_license","max_line_length":171,"num_lines":42,"path":"/scripts/jiaguModule.py","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"#coding:utf8\nimport os\nimport sys\n\nclass JiaGuUtil:\n def startJiaGu(self, apkPath):\n print(\"apkPath = \" + apkPath)\n SecretId = 'AKIDoZ9vY2w1Um06ILi3JOODyv7RocnawW4U'\n SecretKey = 'tlMNZ4Ti1Ma8XC5CXM8116A5b4Pdj6LO'\n\n (filepath, tempfilename) = os.path.split(apkPath)\n (filename, extension) = os.path.splitext(tempfilename)\n print('apkFileName = ' + filename)\n apkResignerForWallePath = 'ToolMakeChannelApk'\n downloadPath = apkResignerForWallePath + '/apk-jiagu'\n\n cmdLeguStr = (\n ' rm -rf ' + downloadPath + '/*.apk'\n + '\\n rm -rf ' + downloadPath\n + '\\n mkdir ' + downloadPath\n + '\\n java -Dfile.encoding=utf-8 -jar scripts/lib/ms-shield.jar -sid %s -skey %s -uploadPath %s -downloadPath %s' %(SecretId, SecretKey, apkPath, downloadPath)\n )\n print(cmdLeguStr)\n print('开始上传，并加固，请耐心等待～')\n os.system(cmdLeguStr)\n\n leguAppPath = ''\n appVersionName = ''\n for fpath, dirname, fnames in os.walk(downloadPath):\n print(fnames)\n print(fpath)\n fname = fnames[0]\n leguApkPath = fpath\n leguAppPath = leguApkPath + '/' + fname\n\n newName = '%s-legu.apk' %(filename)\n oldPath = os.path.join(fpath, fname)\n newPath = os.path.join(fpath, newName)\n os.rename(oldPath, newPath)\n leguAppPath = newPath\n print('leguAppPath = ' + leguAppPath)\n break"},{"alpha_fraction":0.6793145537376404,"alphanum_fraction":0.6793145537376404,"avg_line_length":28.214284896850586,"blob_id":"1b362dc7632707557c5c0def63a9bcf80ed248ca","content_id":"8e02b202101246240893c2503a64c0cbbabef964","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Kotlin","length_bytes":829,"license_type":"no_license","max_line_length":94,"num_lines":28,"path":"/app/src/main/java/com/android/base/task/UpdateTask.kt","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.task\n\nimport android.content.Context\nimport androidx.work.WorkerParameters\nimport com.blankj.utilcode.util.NetworkUtils\nimport component.update.AppDownloadClient\nimport component.update.AppVersion\nimport component.update.VersionUpdateListener\n\n\n/**\n * 升级下载任务\n * @author zhangquan\n */\nclass UpdateTask(context: Context, param: WorkerParameters) : BackgroundTask(context, param) {\n override fun doTask(parameters: WorkerParameters) {\n if (NetworkUtils.isAvailable()) {\n AppDownloadClient.doCheckVersion(object : VersionUpdateListener {\n override fun onNoVersionReturned() {\n }\n\n override fun fail() {}\n override fun onNewVersionReturned(appVersion: AppVersion) {\n }\n })\n }\n }\n}"},{"alpha_fraction":0.7230769395828247,"alphanum_fraction":0.7230769395828247,"avg_line_length":13.55555534362793,"blob_id":"d8760f436b1f782a89bf8832c1de061d2db63ebd","content_id":"d4eb6989c714236b8d261497ee21cca288fe3979","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Kotlin","length_bytes":130,"license_type":"no_license","max_line_length":60,"num_lines":9,"path":"/app/src/main/java/com/android/base/util/crash/MainQuitException.kt","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.example.crash_handler\n\n/**\n *\n * @author zhangquan\n */\nclass MainQuitException(str:String) :RuntimeException(str) {\n\n}"},{"alpha_fraction":0.5966101884841919,"alphanum_fraction":0.5966101884841919,"avg_line_length":18.66666603088379,"blob_id":"92b84492f7ae9b1ed65f385c2df134a7d35e6335","content_id":"6f558665a7616f93b1461418a2263827bbdd46e9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":299,"license_type":"no_license","max_line_length":67,"num_lines":15,"path":"/librarys/CommonUtil/src/main/java/com/android/util/db/EasyDB.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.util.db;\n\n/**\n * @author 张全\n */\npublic class EasyDB {\n\n public static <T> DBSession<T> with(Class<T> cls) {\n return new DBSession<T>(cls);\n }\n\n public static <T> DBSession<T> with(Class<T> cls, String uid) {\n return new DBSession<T>(cls, uid);\n }\n}\n"},{"alpha_fraction":0.7047532200813293,"alphanum_fraction":0.7084094882011414,"avg_line_length":25.682926177978516,"blob_id":"9fc6b3afd69005cc55fc89f9ad8401729bd132c0","content_id":"b0e2965d30b6552c6b52abdffc2fd5a99be74619","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1132,"license_type":"no_license","max_line_length":71,"num_lines":41,"path":"/app/src/main/java/com/android/base/widget/BottomInAllDialog.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.widget;\n\nimport android.app.Dialog;\nimport android.content.Context;\nimport android.os.Bundle;\nimport android.view.Gravity;\nimport android.view.Window;\nimport android.view.WindowManager;\n\nimport androidx.annotation.NonNull;\n\nimport com.fastapp.R;\n\n\n/**\n * 底部弹框\n *\n * @author 张全\n */\npublic class BottomInAllDialog extends Dialog {\n public BottomInAllDialog(@NonNull Context context) {\n super(context);\n }\n\n @Override\n protected void onCreate(Bundle savedInstanceState) {\n super.onCreate(savedInstanceState);\n\n\n Window win = this.getWindow();\n win.getDecorView().setPadding(0, 0, 0, 0);\n WindowManager.LayoutParams lp = win.getAttributes();\n lp.width = WindowManager.LayoutParams.MATCH_PARENT;\n lp.height = WindowManager.LayoutParams.MATCH_PARENT;\n lp.windowAnimations = R.style.AnimBottom;\n lp.gravity = Gravity.BOTTOM;\n win.setAttributes(lp);\n win.setBackgroundDrawableResource(android.R.color.transparent);\n setCanceledOnTouchOutside(false); // 点击屏幕Dialog以外的地方是否消失\n }\n}\n"},{"alpha_fraction":0.6342329382896423,"alphanum_fraction":0.6342329382896423,"avg_line_length":22.46666717529297,"blob_id":"68e3608807fb68a6e4be8f16727ad54fc1a65f19","content_id":"8028c9ad0a789bbcf9fac6bf3bab9097363315f7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1412,"license_type":"no_license","max_line_length":67,"num_lines":60,"path":"/librarys/CommonWidget/src/main/java/common/widget/dialog/LoadingLoadingView.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package common.widget.dialog;\n\nimport android.animation.ValueAnimator;\nimport android.content.Context;\nimport android.text.TextUtils;\nimport android.view.View;\nimport android.widget.ImageView;\nimport android.widget.TextView;\nimport common.widget.R;\n\n/**\n * @author 张全\n */\npublic class LoadingLoadingView extends DialogView {\n private ImageView mLoadingView;\n private TextView mLoadingTxt;\n private String loadingTxt;\n private ValueAnimator anim;\n\n public LoadingLoadingView(Context ctx) {\n super(ctx);\n }\n\n public LoadingLoadingView(Context ctx, String loadingTxt) {\n super(ctx);\n this.loadingTxt = loadingTxt;\n }\n\n @Override\n protected void initView(View view) {\n mLoadingView = (ImageView) findViewById(R.id.loadingbar);\n mLoadingTxt = (TextView) findViewById(R.id.loadingbar_txt);\n if (!TextUtils.isEmpty(loadingTxt)) {\n mLoadingTxt.setText(loadingTxt);\n }\n }\n\n public void startAnimal() {\n if (mLoadingView != null && anim != null) {\n anim.start();\n }\n }\n\n public void stop() {\n if (mLoadingView != null && anim != null) {\n anim.cancel();\n anim = null;\n }\n }\n\n public void setText(String text) {\n mLoadingTxt.setText(text);\n }\n\n @Override\n protected int getLayoutId() {\n return R.layout.dialog_loading;\n }\n\n}\n"},{"alpha_fraction":0.6191219687461853,"alphanum_fraction":0.6557348370552063,"avg_line_length":39.02777862548828,"blob_id":"e342f397eb8f6ffde4302a89eb1666171bebb4b3","content_id":"41cee00ad5c279bd3f312d51891abaf5b6d71ee1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Gradle","length_bytes":6085,"license_type":"no_license","max_line_length":105,"num_lines":144,"path":"/buildsystem/dependencies.gradle","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"ext {\n\n //################################# 版本号 #################################\n app = [\n versionCode : 10000,\n versionName : '1.0.0', //versionName: a.b.c -> versionCode = a*10000+b*100+c*1 如1.2.3->10203\n applicationId: 'com.fastapp'\n ]\n\n //Android\n buildToolsVersion = \"30.0.2\"\n minSdkVersion = 21\n targetSdkVersion = 28\n compileSdkVersion = 30\n\n javaVersion = JavaVersion.VERSION_1_8\n\n //Libraries\n supportVersion = '1.0.0'\n retrofit2Version = '2.9.0'\n okHttpVersion = '4.7.2'\n glideVersion = '4.11.0'\n roomVersion = '2.2.5'\n autoDisposeVersion = '2.0.0'\n work_version = '2.4.0'\n //调试\n leakCanaryVersion = '1.6.3'\n blockCanaryVersion = '1.5.0'\n\n //################################# 依赖库 #################################\n //kotlin\n kotlin = \"org.jetbrains.kotlin:kotlin-stdlib-jdk8:1.3.72\"\n kotlin_core = \"androidx.core:core-ktx:1.3.2\"\n kotlin_coroutines = \"org.jetbrains.kotlinx:kotlinx-coroutines-core:1.5.0\"\n //Android Support\n supportV4 = \"androidx.legacy:legacy-support-v4:${supportVersion}\"\n appCompat = \"androidx.appcompat:appcompat:1.3.0-alpha02\"\n materialDesign = \"com.google.android.material:material:1.2.1\"\n recyclerView = \"androidx.recyclerview:recyclerview:1.2.0\"\n percentlayout = \"androidx.percentlayout:percentlayout:${supportVersion}\"\n cardView = \"androidx.cardview:cardview:1.0.0\"\n\n constraint = \"androidx.constraintlayout:constraintlayout:2.0.4\"\n lifecycleExtensions = \"androidx.lifecycle:lifecycle-extensions:2.2.0\" //ViewModelProviders\n multidex = 'androidx.multidex:multidex:2.0.1'\n\n //Rx系列\n rxJava = \"io.reactivex.rxjava2:rxjava:2.2.6\"\n rxAndroid = \"io.reactivex.rxjava2:rxandroid:2.1.1\"\n rxPermissions = \"com.github.tbruyelle:rxpermissions:0.10.2\"\n rxBinding = \"com.jakewharton.rxbinding4:rxbinding:4.0.0\"\n\n //DI系列\n butterKnife = \"com.jakewharton:butterknife:10.2.1\"\n butterKnifeCompiler = \"com.jakewharton:butterknife-compiler:10.2.1\"\n arouter = \"com.alibaba:arouter-api:1.5.1\"\n arouterCompiler = \"com.alibaba:arouter-compiler:1.5.1\"\n\n //网络依赖库\n retrofit2 = \"com.squareup.retrofit2:retrofit:${retrofit2Version}\"\n retrofit2AdapterRxjava = \"com.squareup.retrofit2:adapter-rxjava2:${retrofit2Version}\"\n retrofit2ConvertGson = \"com.squareup.retrofit2:converter-gson:${retrofit2Version}\"\n retrofit2Scalars = \"com.squareup.retrofit2:converter-scalars:2.0.0\"\n okHttp = \"com.squareup.okhttp3:okhttp:${okHttpVersion}\"\n loggingInterceptor = \"com.squareup.okhttp3:logging-interceptor:${okHttpVersion}\"\n gson = \"com.google.code.gson:gson:2.8.6\"\n\n //事件通知\n eventBus = \"org.greenrobot:eventbus:3.2.0\"\n //google工具类聚合\n guava = \"com.google.guava:guava:29.0-android\"\n //Android工具库\n utilCode = 'com.blankj:utilcodex:1.29.0'\n //第三方RecycleView adapter\n recycleViewAdapter = \"com.github.CymChad:BaseRecyclerViewAdapterHelper:3.0.4\"\n //Fragment容器\n fragmentation = \"me.yokeyword:fragmentationx:1.0.2\"\n\n //下拉刷新\n smartRefresh = 'com.scwang.smartrefresh:SmartRefreshLayout:1.1.0-x'\n smartRefreshHeader = 'com.scwang.smartrefresh:SmartRefreshHeader:1.1.0-x' //没有使用特殊Header，可以不加这行\n\n //Glide图片加载\n glide = \"com.github.bumptech.glide:glide:${glideVersion}\"\n glideCompiler = \"com.github.bumptech.glide:compiler:${glideVersion}\"\n glideOkHttp = \"com.github.bumptech.glide:okhttp3-integration:${glideVersion}\" //使用okhttp加载图片\n glideWebp = \"com.zlc.glide:webpdecoder:2.0.${glideVersion}\"\n glideTransform = \"jp.wasabeef:glide-transformations:4.3.0\" //图片处理(圆角、颜色、Blur、Mask)\n\n //拍照和选择照片 https://github.com/yanzhenjie/Album\n album = 'com.yanzhenjie:album:2.1.3'\n //图片压缩 https://github.com/Curzibn/Luban\n luban = 'top.zibin:Luban:1.1.8'\n //圆角图片 适合加载静态图片和非gif的网络图片 如果是网络圆角图片 建议使用Glide\n roundedImageView = 'com.makeramen:roundedimageview:2.3.0'\n\n\n //*************************** START *****************************\n\n //Room数据库\n roomRuntime = \"androidx.room:room-runtime:$roomVersion\"\n roomProcessor = \"androidx.room:room-compiler:$roomVersion\"\n roomRxjava2 = \"androidx.room:room-rxjava2:$roomVersion\"\n roomGuava = \"androidx.room:room-guava:$roomVersion\"\n roomKotlin = \"androidx.room:room-ktx:$roomVersion\"\n\n //WorkManager\n // (Java only)\n workManager = \"androidx.work:work-runtime:$work_version\"\n //Kotlin + coroutines\n workManager_kotlin = \"androidx.work:work-runtime-ktx:$work_version\"\n // optional - RxJava2 support\n workManager_rxjava2 = \"androidx.work:work-rxjava2:$work_version\"\n\n //微信K-V组件\n MMKV = \"com.tencent:mmkv-static:1.2.2\"\n\n\n //获取时间防止修改手机时间\n truetime = 'com.github.instacart.truetime-android:library-extension-rx:3.4'\n //时间或条件选择控件 https://github.com/Bigkoo/Android-PickerView\n pickerView = \"com.contrarywind:Android-PickerView:4.1.9\"\n //二维码扫描zxing\n zxing = 'cn.bingoogolapple:bga-qrcode-zxing:1.3.7'\n //标签流式布局\n flowlayout = \"com.hyman:flowlayout-lib:1.1.2\"\n //apk防护\n easyProtector = \"com.lahm.library:easy-protector-release:1.1.1\"\n\n //*************************** END *****************************\n\n\n //################################# 开发调试 #################################\n //https://github.com/markzhai/AndroidPerformanceMonitor\n //https://github.com/square/leakcanary\n debugDependencies = [\n leakCanary : \"com.squareup.leakcanary:leakcanary-android:${leakCanaryVersion}\",\n blockCanary: \"com.github.markzhai:blockcanary-android:${blockCanaryVersion}\"\n ]\n releaseDependencies = [\n leakCanary : \"com.squareup.leakcanary:leakcanary-android-no-op:${leakCanaryVersion}\",\n blockCanary: \"com.github.markzhai:blockcanary-no-op:${blockCanaryVersion}\"\n ]\n}"},{"alpha_fraction":0.6019451022148132,"alphanum_fraction":0.6047238707542419,"avg_line_length":31.34831428527832,"blob_id":"9f2e4819cb26c3689f9dd8273f303b456f52424b","content_id":"5445e340b441ed6ec0d76926210267ad842b5f77","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":2885,"license_type":"no_license","max_line_length":108,"num_lines":89,"path":"/app/src/main/java/com/android/base/ui/splash/GuideActivity.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.ui.splash;\n\nimport android.content.Context;\nimport android.content.Intent;\nimport android.os.Bundle;\nimport android.view.LayoutInflater;\nimport android.view.View;\nimport android.view.ViewGroup;\nimport android.widget.ImageView;\n\nimport androidx.annotation.NonNull;\nimport androidx.viewpager.widget.PagerAdapter;\nimport androidx.viewpager.widget.ViewPager;\n\nimport com.android.base.ui.BaseActivity;\nimport com.fastapp.MainActivity;\nimport com.fastapp.R;\n\nimport net.medlinker.android.splash.SplashUtil;\n\nimport java.util.ArrayList;\n\n/**\n * 引导页\n */\npublic class GuideActivity extends BaseActivity {\n private static final int[] imgs = new int[]{R.drawable.guide01, R.drawable.guide02, R.drawable.guide03};\n private ViewPager viewPager;\n\n\n public static void start(Context ctx) {\n Intent intent = new Intent(ctx, GuideActivity.class);\n ctx.startActivity(intent);\n }\n\n @Override\n public int getLayoutId() {\n return R.layout.guide_layout;\n }\n\n @Override\n public void init(Bundle savedInstanceState) {\n ArrayList<View> viewList = new ArrayList<>();\n for (int i = 0; i < imgs.length; i++) {\n View view = LayoutInflater.from(this).inflate(R.layout.guide_item, null);\n ImageView imageView = view.findViewById(R.id.guide_img);\n imageView.setImageResource(imgs[i]);\n viewList.add(view);\n if (i == imgs.length - 1) {\n View btn = view.findViewById(R.id.tv_go_login);\n btn.setVisibility(View.VISIBLE);\n btn.setOnClickListener(new View.OnClickListener() {\n @Override\n public void onClick(View v) {\n SplashUtil.setShowGuide(true);\n MainActivity.start(GuideActivity.this);\n finish();\n }\n });\n }\n }\n viewPager = findViewById(R.id.viewpager);\n viewPager.setAdapter(new PagerAdapter() {\n @Override\n public int getCount() {\n return viewList.size();\n }\n\n @NonNull\n @Override\n public Object instantiateItem(@NonNull ViewGroup container, int position) {\n container.addView(viewList.get(position), ViewGroup.LayoutParams.MATCH_PARENT,\n ViewGroup.LayoutParams.MATCH_PARENT);\n return viewList.get(position);\n }\n\n @Override\n public void destroyItem(@NonNull ViewGroup container, int position, @NonNull Object object) {\n container.removeView(viewList.get(position));\n }\n\n @Override\n public boolean isViewFromObject(@NonNull View view, @NonNull Object object) {\n return view == object;\n }\n });\n }\n\n}\n"},{"alpha_fraction":0.46669667959213257,"alphanum_fraction":0.46804681420326233,"avg_line_length":31.691177368164062,"blob_id":"5c8446a3e182d4c9ee1579366982a0d877651b08","content_id":"b6e7920fde2c3bfab13ed0a72f55408b24a04197","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Kotlin","length_bytes":2270,"license_type":"no_license","max_line_length":93,"num_lines":68,"path":"/app/src/main/java/com/fastapp/wxapi/WXEntryActivity.kt","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.fastapp.wxapi\n\nimport android.content.ClipData\nimport android.content.ClipboardManager\nimport android.content.Context\nimport com.android.base.Constant\nimport com.android.base.event.EventMsg\nimport com.fastapp.BuildConfig\nimport com.tencent.mm.opensdk.modelbase.BaseResp\nimport com.tencent.mm.opensdk.modelmsg.SendAuth\nimport com.umeng.socialize.weixin.view.WXCallbackActivity\nimport org.greenrobot.eventbus.EventBus\n\n/**\n * 微信登录回调\n */\nclass WXEntryActivity : WXCallbackActivity() {\n override fun onResp(authResp: BaseResp) {\n if (authResp is SendAuth.Resp) { // 授权\n when (authResp.errCode) {\n BaseResp.ErrCode.ERR_OK -> { // 用户同意\n addToClip(authResp.code)\n EventBus.getDefault().post(\n EventMsg(\n Constant.Event.WX_CODE,\n authResp.code\n )\n )\n }\n BaseResp.ErrCode.ERR_AUTH_DENIED -> { // 用户拒绝授权\n EventBus.getDefault().post(\n EventMsg(\n Constant.Event.WX_CODE,\n \"0\"\n )\n )\n }\n BaseResp.ErrCode.ERR_USER_CANCEL -> { // 取消授权\n EventBus.getDefault().post(\n EventMsg(\n Constant.Event.WX_CODE,\n \"1\"\n )\n )\n }\n else -> {\n EventBus.getDefault().post(\n EventMsg(\n Constant.Event.WX_CODE,\n \"1\"\n )\n )\n }\n }\n finish()\n } else { // 分享\n super.onResp(authResp)\n }\n }\n\n private fun addToClip(code: String) {\n if (BuildConfig.DEBUG) {\n val myClipboard = getSystemService(Context.CLIPBOARD_SERVICE) as ClipboardManager\n val myClip = ClipData.newPlainText(\"levelText\", code)\n myClipboard.setPrimaryClip(myClip)\n }\n }\n}"},{"alpha_fraction":0.6269261837005615,"alphanum_fraction":0.6382806301116943,"avg_line_length":28.380952835083008,"blob_id":"afac9966217d058b274726bd3aa41a75a0560206","content_id":"4141aeb7de4cdab9faf25fdf9f683d05975fc161","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1233,"license_type":"no_license","max_line_length":114,"num_lines":42,"path":"/librarys/CommonWidget/src/main/java/common/widget/listview/LinearSpaceItemDecoration.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package common.widget.listview;\n\nimport android.graphics.Rect;\nimport android.view.View;\n\nimport androidx.recyclerview.widget.RecyclerView;\n\npublic class LinearSpaceItemDecoration extends RecyclerView.ItemDecoration {\n private int headerCount;\n private int mOrientation;\n private int space;\n\n public LinearSpaceItemDecoration(int space,int oriention) {\n this.space = space;\n setOrientation(oriention);\n }\n\n public void setOrientation(int orientation) {\n if (orientation != 0 && orientation != 1) {\n throw new IllegalArgumentException(\"Invalid orientation. It should be either HORIZONTAL or VERTICAL\");\n } else {\n this.mOrientation = orientation;\n }\n }\n\n public void setHeaderCount(int headerCount) {\n this.headerCount = headerCount;\n }\n\n\n @Override\n public void getItemOffsets(Rect outRect, View view, RecyclerView parent, RecyclerView.State state) {\n outRect.set(0, 0, 0, 0);\n if ((parent.getChildLayoutPosition(view)-headerCount)>=0) {\n if (this.mOrientation == 1) {\n outRect.set(0, 0, 0, space);\n } else {\n outRect.set(0, 0, space, 0);\n }\n }\n }\n}"},{"alpha_fraction":0.8002322912216187,"alphanum_fraction":0.8025551438331604,"avg_line_length":30.925926208496094,"blob_id":"3c1ddbfa902aeb51936c26721da2bc1630b4e0eb","content_id":"0c0644bf4f4d231bf104328b94955b291f12c2bb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":865,"license_type":"no_license","max_line_length":112,"num_lines":27,"path":"/app/src/main/java/com/android/base/util/OkHttpAppGlideModule.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.util;\n\nimport android.content.Context;\n\nimport androidx.annotation.NonNull;\n\nimport com.android.base.data.RestClient;\nimport com.bumptech.glide.Glide;\nimport com.bumptech.glide.Registry;\nimport com.bumptech.glide.annotation.GlideModule;\nimport com.bumptech.glide.integration.okhttp3.OkHttpUrlLoader;\nimport com.bumptech.glide.load.model.GlideUrl;\nimport com.bumptech.glide.module.AppGlideModule;\n\nimport java.io.InputStream;\n\nimport okhttp3.OkHttpClient;\n\n@GlideModule\npublic class OkHttpAppGlideModule extends AppGlideModule {\n\n @Override\n public void registerComponents(@NonNull Context context, @NonNull Glide glide, @NonNull Registry registry) {\n OkHttpClient client = RestClient.getImgDownloadClient();\n registry.replace(GlideUrl.class, InputStream.class, new OkHttpUrlLoader.Factory(client)); //https支持\n }\n}"},{"alpha_fraction":0.7425742745399475,"alphanum_fraction":0.7524752616882324,"avg_line_length":19.399999618530273,"blob_id":"608ba2c93700cee012ce9748d4c9c08d836e53c3","content_id":"837ade3a58145304873eda057b0dd3d2163d1e65","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":101,"license_type":"no_license","max_line_length":36,"num_lines":5,"path":"/librarys/CommonUtil/src/main/java/com/android/util/common/CommonCallBack.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.util.common;\n\npublic interface CommonCallBack<T> {\n void onCallBack(T var1);\n}"},{"alpha_fraction":0.7556886076927185,"alphanum_fraction":0.7736526727676392,"avg_line_length":32.400001525878906,"blob_id":"853b4ab7590855f416110f564881ee9e1617d933","content_id":"05fbc0ebb78066fa5ee2555d79c92f3d8c42981f","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1089,"license_type":"permissive","max_line_length":78,"num_lines":25,"path":"/scripts_channels/config.py","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"#!/usr/bin/python \n#-*-coding:utf-8-*-\n\n#keystore信息\n#Windows 下路径分割线请注意使用\\\\转义\nkeystorePath = \"../medlinker.keystore\"\nkeyAlias = \"medlinker\"\nkeystorePassword = \"medlinker\"\nkeyPassword = \"medlinker\"\n\n#加固后的源文件名（未重签名）\nprotectedSourceApkName = \"\"\n#加固后的源文件所在文件夹路径(...path),注意结尾不要带分隔符，默认在此文件夹根目录\nprotectedSourceApkDirPath = \"apk-jiagu\"\n#渠道包输出路径，默认在此文件夹channels目录下\nchannelsOutputFilePath = \"channels\"\n#渠道名配置文件路径，默认在此文件夹根目录\nchannelFilePath = \"\"\n#额外信息配置文件（绝对路径，例如/Users/mac/Desktop/walle360/config.json）\n#配置信息示例参看https://github.com/Meituan-Dianping/walle/blob/master/app/config.json\nextraChannelFilePath = \"\"\n#Android SDK buidtools path , please use above 25.0+\n# sdkBuildToolPath = \"/Users/macbook/Library/Android/sdk/build-tools/26.0.2\"\n# sdkBuildToolPath = \"/Users/macbook/workspace/android-sdk/build-tools/26.0.2\"\nsdkBuildToolPath = \"lib\"\n"},{"alpha_fraction":0.6151078939437866,"alphanum_fraction":0.6187050342559814,"avg_line_length":26.83333396911621,"blob_id":"9856fedaec1e1cca9aadf144fce36a16a7ba15d0","content_id":"fca52780b50f03b1a94b033e7848c234bec79df0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Kotlin","length_bytes":864,"license_type":"no_license","max_line_length":92,"num_lines":30,"path":"/app/src/main/java/com/android/base/util/AppInstallUtil.kt","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.util\n\nimport android.content.Context\nimport com.blankj.utilcode.util.AppUtils\n\n/**\n *\n */\nobject AppInstallUtil {\n\n @JvmStatic\n fun isAppInstalled(context: Context?, packageName: String): Boolean {\n return AppUtils.isAppInstalled(packageName) || isAppInstalled1(context, packageName)\n }\n\n\n fun isAppInstalled1(context: Context?, packageName: String): Boolean {\n val packageManager = context?.packageManager// 获取packagemanager\n val pinfo = packageManager?.getInstalledPackages(0)// 获取所有已安装程序的包信息\n if (pinfo != null) {\n for (i in pinfo.indices) {\n val pn = pinfo[i].packageName\n if (pn.equals(packageName, ignoreCase = true)) {\n return true\n }\n }\n }\n return false\n }\n}"},{"alpha_fraction":0.7616580128669739,"alphanum_fraction":0.7616580128669739,"avg_line_length":21.705883026123047,"blob_id":"1828623f381314ab63e5a180689e9e4e468971f9","content_id":"53177db5316325618327c3ef878bd4afd3146f75","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":390,"license_type":"no_license","max_line_length":90,"num_lines":17,"path":"/app/src/main/java/com/android/base/version/VersionClient.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.android.base.version;\n\nimport com.android.base.data.ResponseDataObject;\nimport com.android.base.data.RestClient;\n\nimport io.reactivex.Observable;\n\n/**\n * @author 张全\n */\npublic class VersionClient {\n\n public static Observable<ResponseDataObject<VersionEntity>> doUpdate(String channel) {\n return RestClient.getService(VersionApi.class).appUpdate(channel);\n }\n\n}\n"},{"alpha_fraction":0.7046413421630859,"alphanum_fraction":0.7046413421630859,"avg_line_length":18.75,"blob_id":"f682c6d5d6e3247c9000e23c4b839291f5e1e165","content_id":"2819572e0e7216136b690aa22faa4d28d75bf063","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":241,"license_type":"no_license","max_line_length":70,"num_lines":12,"path":"/librarys/CommonWidget/src/main/java/common/widget/viewpager/BannerImageLoader.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package common.widget.viewpager;\n\nimport android.content.Context;\n\n/**\n * @author 张全\n */\npublic interface BannerImageLoader<V, T> {\n void displayView(Context ctx, T data, V view, int pos, int count);\n\n V createView(Context ctx);\n}\n"},{"alpha_fraction":0.5055791735649109,"alphanum_fraction":0.5063762068748474,"avg_line_length":32.61606979370117,"blob_id":"69f9d2f0e15efa7b381e2dae361f57e121702b9a","content_id":"c54044ee6fa804b5c279f5666c42af45a8a2f3bd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Kotlin","length_bytes":3946,"license_type":"no_license","max_line_length":83,"num_lines":112,"path":"/app/src/main/java/com/fastapp/wxapi/WXPayEntryActivity.kt","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.fastapp.wxapi\n\nimport android.app.Activity\nimport android.content.Intent\nimport android.os.Bundle\nimport com.android.util.LContext\nimport com.android.util.ext.ToastUtil\nimport com.android.util.log.LogUtil\nimport com.android.base.Constant\nimport com.android.base.event.EventMsg\nimport com.android.base.util.pay.PayWay\nimport com.fastapp.R\nimport com.tencent.mm.opensdk.constants.ConstantsAPI\nimport com.tencent.mm.opensdk.modelbase.BaseReq\nimport com.tencent.mm.opensdk.modelbase.BaseResp\nimport com.tencent.mm.opensdk.openapi.IWXAPI\nimport com.tencent.mm.opensdk.openapi.IWXAPIEventHandler\nimport com.tencent.mm.opensdk.openapi.WXAPIFactory\nimport org.greenrobot.eventbus.EventBus\n\n/**\n * 微信支付回调页面\n *\n * @author 张全\n */\nclass WXPayEntryActivity : Activity(), IWXAPIEventHandler {\n private var api: IWXAPI? = null\n public override fun onCreate(savedInstanceState: Bundle?) {\n super.onCreate(savedInstanceState)\n setContentView(R.layout.wx_pay_result)\n api = WXAPIFactory.createWXAPI(this, LContext.getString(R.string.wx_appid))\n api?.handleIntent(intent, this)\n LogUtil.d(TAG, \"WXPayEntryActivity............onCreate\")\n }\n\n override fun onNewIntent(intent: Intent) {\n super.onNewIntent(intent)\n setIntent(intent)\n api?.handleIntent(intent, this)\n LogUtil.d(TAG, \"WXPayEntryActivity............onNewIntent\")\n }\n\n override fun onReq(req: BaseReq) {}\n override fun onResp(resp: BaseResp) {\n if (null == resp) {\n finish()\n return\n }\n printInfo(resp)\n LogUtil.d(TAG, \"WXPayEntryActivity,onResp...onPayFinish, errCode = \"\n + resp.errCode + \",type=\" + resp.type)\n if (resp.type == ConstantsAPI.COMMAND_PAY_BY_WX) {\n when (resp.errCode) {\n 0 -> {\n EventBus.getDefault().post(\n EventMsg(\n Constant.Event.ORDER_BUY_SUCCESS,\n PayWay.WX\n )\n )\n finish()\n }\n -1 -> {\n /*\n * 可能的原因：签名错误、未注册APPID、项目设置APPID不正确、注册的APPID与设置的不匹配、其他异常等。\n */EventBus.getDefault().post(\n EventMsg(\n Constant.Event.ORDER_BUY_FAIL,\n PayWay.WX\n )\n )\n ToastUtil.show(\"支付失败\")\n finish()\n }\n -2 -> {\n /*\n * 无需处理。发生场景：用户不支付了，点击取消，返回APP。\n */EventBus.getDefault().post(\n EventMsg(\n Constant.Event.ORDER_BUY_CANCEL,\n PayWay.WX\n )\n )\n ToastUtil.show(\"取消支付\")\n finish()\n }\n else -> finish()\n }\n }\n }\n\n companion object {\n private const val TAG = \"WXPayEntryActivity\"\n fun printInfo(baseResp: BaseResp?) {\n if (!LContext.isDebug || null == baseResp) {\n return\n }\n LogUtil.d(TAG, \"---------微信支付--------start\")\n val fields = baseResp.javaClass.declaredFields\n for (field in fields) {\n field.isAccessible = true\n try {\n val value = field[baseResp]\n LogUtil.d(TAG, field.name + \"=\" + value)\n } catch (e: Exception) {\n e.printStackTrace()\n }\n }\n LogUtil.d(TAG, \"---------微信支付--------end\")\n }\n }\n}"},{"alpha_fraction":0.5960000157356262,"alphanum_fraction":0.5960000157356262,"avg_line_length":26.77777862548828,"blob_id":"1365250e31ffb9fd87b07c41f747635ba3c1c13d","content_id":"ac7ed01c0b225fa5036f9818024edad66b7f5b50","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Gradle","length_bytes":250,"license_type":"no_license","max_line_length":33,"num_lines":9,"path":"/settings.gradle","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"include ':ApkChannel'\nrootProject.name = 'fastapp'\ninclude ':app',\n ':librarys:ApkChannel',\n ':librarys:Logger',\n ':librarys:CommonUtil',\n ':librarys:AppUpdate',\n ':librarys:CommonWidget',\n ':librarys:umeng'\n"},{"alpha_fraction":0.6818181872367859,"alphanum_fraction":0.6818181872367859,"avg_line_length":15.5,"blob_id":"6f1a8d826b61d2ea2dc78f92b6bb3d37625f3e1a","content_id":"3f9a21e9d39ad4d1e9e48b89985af09cffae391a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":330,"license_type":"no_license","max_line_length":49,"num_lines":20,"path":"/app/src/main/java/com/fastapp/LoginActivity.java","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"package com.fastapp;\n\nimport android.os.Bundle;\n\nimport com.android.base.ui.BaseActivity;\n\n/**\n * @author zhangquan\n */\npublic class LoginActivity extends BaseActivity {\n @Override\n public int getLayoutId() {\n return R.layout.login_act;\n }\n\n @Override\n public void init(Bundle savedInstanceState) {\n\n }\n}\n"},{"alpha_fraction":0.6086329221725464,"alphanum_fraction":0.6136828064918518,"avg_line_length":30.744274139404297,"blob_id":"205b416c36cf6cdaf813bfe3dcce918563fe8773","content_id":"0de57c39e7d50be215a66e5108b92c231a873102","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Gradle","length_bytes":8649,"license_type":"no_license","max_line_length":133,"num_lines":262,"path":"/app/build.gradle","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"apply plugin: 'com.android.application'\napply plugin: 'kotlin-android'\napply plugin: 'kotlin-android-extensions'\napply plugin: 'kotlin-kapt'\n\ndef config = rootProject.ext\nandroid {\n\n compileSdkVersion config.compileSdkVersion\n buildToolsVersion config.buildToolsVersion\n\n defaultConfig {\n minSdkVersion config.minSdkVersion\n targetSdkVersion config.targetSdkVersion\n\n applicationId config.app.applicationId\n versionCode config.app.versionCode\n versionName config.app.versionName\n\n\n //矢量图\n vectorDrawables.useSupportLibrary = true\n\n manifestPlaceholders = [UMENG_CHANNEL_VALUE: \"official\"] // 默认渠道\n\n ndk {\n // 设置支持的 SO 库构架\n abiFilters 'armeabi-v7a' //'x86'\n }\n\n resConfigs \"zh\"\n\n multiDexEnabled true\n\n //arouter\n javaCompileOptions {\n annotationProcessorOptions {\n arguments = [AROUTER_MODULE_NAME: project.getName()]\n }\n }\n }\n\n aaptOptions {\n cruncherEnabled = false //禁止AS对png图片进行校验\n useNewCruncher = false\n }\n\n //----------------------签名配置----------------------\n signingConfigs {\n debug {\n //为了方便测试分享或第三方登录 使用正式签名\n storeFile file('../buildsystem/keystore.jks')\n storePassword 'snqu_123456_agent'\n keyAlias 'key0'\n keyPassword 'snqu_123456_agent'\n v2SigningEnabled true\n }\n release {\n storeFile file('../buildsystem/keystore.jks')\n storePassword 'snqu_123456_agent'\n keyAlias 'key0'\n keyPassword 'snqu_123456_agent'\n v2SigningEnabled false\n }\n }\n sourceSets {\n main {\n jniLibs.srcDirs = ['libs']\n }\n }\n buildTypes {\n debug {\n minifyEnabled false\n shrinkResources false\n manifestPlaceholders = [DEBUGABLE: true]\n proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro'\n signingConfig signingConfigs.debug\n }\n release {\n minifyEnabled true\n zipAlignEnabled true\n shrinkResources true\n proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro'\n manifestPlaceholders = [DEBUGABLE: false]\n signingConfig signingConfigs.release\n }\n }\n\n flavorDimensions \"channel\"\n productFlavors {\n //生产测试\n \"prod\" {}\n\n //应用商店\n// \"offical\" {} //官方\n// \"xiaomi\" {} //小米\n// \"vivo\" {} //vivo商店\n// \"oppo\" {} //oppo商店\n// \"huawei\" {} //华为\n// \"yyb\" {} //应用宝\n// \"p360\" {} //360\n// \"ali\" {} //阿里\n// \"baidu\" {}//百度\n\n// kuan {} //酷安\n// anzhi {} //安智\n// samsung {} //三星\n// meizu {}//魅族\n// sogou {}//搜狗\n// liqu {}//历趣\n// mumayi {}//木蚂蚁\n// yingyognhui {}//应用汇\n// jifeng {}//机锋\n// mm {}//mm应用商店\n// lenovo {}//联想\n }\n productFlavors.all {\n flavor ->\n //合作渠道\n buildConfigField(\"String\", \"cooperChannels\", '\"xx\"')\n\n if (name == \"prod\") {\n ndk {\n abiFilters \"armeabi-v7a\", 'x86'\n }\n buildConfigField \"String\", \"API_HOST\", '\"https://api-t.xin1.cn/\" ' //测试服务器\n buildConfigField \"String\", \"H5_HOST\", '\"https://m-xlt-t.xin1.cn/\" '\n buildConfigField \"String\", \"LOG_HOST\", '\"https://report-t.xin1.cn/\"'\n manifestPlaceholders = [APP_NAME: \"@string/app_name_test\", DEV_ENV: true, UMENG_CHANNEL_VALUE: name]\n } else {\n ndk {\n abiFilters \"armeabi-v7a\"\n }\n buildConfigField \"String\", \"API_HOST\", '\"https://api.xinletao.vip/\" ' //线上服务器\n buildConfigField \"String\", \"H5_HOST\", '\"https://m.xinletao.vip/\" '\n buildConfigField \"String\", \"LOG_HOST\", '\"https://report.xin1.cn/\" '\n flavor.manifestPlaceholders = [APP_NAME: \"@string/app_name\", DEV_ENV: false, UMENG_CHANNEL_VALUE: name]\n }\n }\n\n applicationVariants.all { variant ->\n if (variant.buildType.name == \"release\" && variant.flavorName != \"prod\") {\n variant.getPackageApplicationProvider().get().outputDirectory = new File(project.rootDir.absolutePath + \"/apk\")\n variant.getPackageApplicationProvider().get().outputScope.apkDatas.forEach { apkData ->\n\n apkData.outputFileName = \"xlt_\" + variant.flavorName + \".apk\"\n// if (apkData.getFullName() == variant.flavorName + \"UniversalRelease\") {\n// variant.getPackageApplicationProvider().get().outputDirectory = new File(project.rootDir.absolutePath + \"/apk\")\n// apkData.outputFileName = variant.flavorName + \".apk\"\n// } else {\n// apkData.outputFileName = apkData.getFullName() + \".apk\"\n// }\n }\n }\n }\n\n compileOptions {\n sourceCompatibility JavaVersion.VERSION_1_8\n targetCompatibility JavaVersion.VERSION_1_8\n }\n\n packagingOptions {\n exclude 'LICENSE.txt'\n exclude 'META-INF/DEPENDENCIES'\n exclude 'META-INF/ASL2.0'\n exclude 'META-INF/NOTICE'\n exclude 'META-INF/LICENSE'\n exclude 'META-INF/proguard/androidx-annotations.pro'\n }\n\n lintOptions {\n quiet true\n abortOnError false\n ignoreWarnings true\n disable 'InvalidPackage', 'OldTargetApi', 'IconDensities', 'IconMissingDensityFolder'\n }\n\n useLibrary 'org.apache.http.legacy'\n}\nrepositories {\n flatDir {\n dirs 'libs'\n }\n}\nkapt {\n arguments {\n arg(\"AROUTER_MODULE_NAME\", project.getName())\n }\n}\ndependencies {\n implementation fileTree(dir: 'libs', include: ['*.jar', '*.aar'])\n api project(':librarys:AppUpdate')\n api project(':librarys:CommonUtil')\n api project(':librarys:Logger')\n api project(':librarys:CommonWidget')\n api project(':librarys:umeng')\n\n implementation config.kotlin\n implementation config.kotlin_core\n implementation config.appCompat\n implementation config.supportV4\n implementation config.recyclerView\n implementation config.cardView\n implementation config.multidex\n implementation config.constraint //约束布局\n implementation config.materialDesign //material\n implementation config.lifecycleExtensions //ViewModelProviders\n implementation config.fragmentation //页面容器\n implementation config.rxPermissions //rxpermission\n// implementation config.rxBinding //RxBinding组件 依赖Rxjava3 RxAndroid3\n\n //数据\n implementation config.retrofit2\n implementation config.retrofit2AdapterRxjava\n implementation config.retrofit2ConvertGson\n implementation config.retrofit2Scalars\n implementation config.okHttp\n implementation config.loggingInterceptor\n implementation config.rxJava\n implementation config.rxAndroid\n implementation config.gson\n\n //图片加载\n implementation config.glide\n annotationProcessor config.glideCompiler\n implementation(config.glideOkHttp) {\n exclude group: \"com.android.support\"\n }\n implementation config.glideWebp //glide支持webp\n implementation config.glideTransform //glide图片处理\n implementation config.album //图片选择\n implementation config.luban //图片压缩\n implementation config.roundedImageView //圆角图片\n implementation config.recycleViewAdapter //recycleView Adapter\n\n //workmanager\n implementation config.workManager_kotlin\n\n //注入\n implementation config.butterKnife //UI\n annotationProcessor config.butterKnifeCompiler\n implementation config.eventBus //事件通知\n implementation config.arouter\n annotationProcessor config.arouterCompiler\n\n //工具箱\n implementation config.guava\n implementation config.utilCode\n\n //K-V\n implementation config.MMKV\n\n //UI控件\n implementation config.smartRefresh //下拉刷新\n implementation config.smartRefreshHeader\n\n //调试\n debugImplementation config.debugDependencies.leakCanary\n debugImplementation config.debugDependencies.blockCanary\n releaseImplementation config.releaseDependencies.leakCanary\n releaseImplementation config.releaseDependencies.blockCanary\n}\n"},{"alpha_fraction":0.6076124310493469,"alphanum_fraction":0.6179930567741394,"avg_line_length":21.5625,"blob_id":"2de5943d20e99a8e7842a08836432628ee134f67","content_id":"790cef9231f62998eca8bd795ccc4dd1d510e3ab","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Gradle","length_bytes":1465,"license_type":"no_license","max_line_length":91,"num_lines":64,"path":"/build.gradle","repo_name":"zhangquanit/fastapp","src_encoding":"UTF-8","text":"apply from: 'buildsystem/dependencies.gradle'\n\nbuildscript {\n ext {\n kotlin_version = '1.3.72'\n }\n repositories {\n maven{ url 'https://maven.aliyun.com/repository/public/'}\n google()\n jcenter()\n\n }\n dependencies {\n classpath \"com.android.tools.build:gradle:4.0.1\"\n classpath \"org.jetbrains.kotlin:kotlin-gradle-plugin:1.3.21\"\n }\n}\n\nallprojects {\n repositories {\n maven{ url 'https://maven.aliyun.com/repository/public/'}\n google()\n jcenter()\n maven { url 'https://jitpack.io' }\n }\n\n gradle.taskGraph.whenReady {\n tasks.each { task ->\n\n }\n }\n}\n\nsubprojects {\n project.afterEvaluate {\n project.plugins.withId('com.android.library') {\n project.android.compileSdkVersion rootProject.ext.compileSdkVersion\n project.android.buildToolsVersion rootProject.ext.buildToolsVersion\n project.android.defaultConfig.minSdkVersion rootProject.ext.minSdkVersion\n project.android.defaultConfig.targetSdkVersion rootProject.ext.targetSdkVersion\n }\n\n }\n\n // 强制使用某个版本的库\n// configurations.all {\n// resolutionStrategy{\n// force 'com.xx.network:retrofit:1.0.1'\n// }\n// }\n}\n\nrepositories {\n flatDir {\n dirs 'libs'\n }\n}\n\ntask clean(type: Delete) {\n delete rootProject.buildDir\n}\ntasks.withType(JavaCompile) {\n options.encoding = \"UTF-8\"\n}\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5808632373809814,\n \"alphanum_fraction\": 0.5824232697486877,\n \"avg_line_length\": 29.046875,\n \"blob_id\": \"8ada2152f3f00251206dd0e2da7e022cf5f86161\",\n \"content_id\": \"6799bb8c8b2fdadb80d826138b82e802074d6cfb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1965,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 64,\n \"path\": \"/librarys/CommonWidget/src/main/java/common/widget/viewpager/AutoScrollViewPager.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package common.widget.viewpager;\\n\\nimport android.content.Context;\\nimport android.util.AttributeSet;\\nimport android.view.MotionEvent;\\n\\nimport androidx.core.view.MotionEventCompat;\\n\\n\\npublic class AutoScrollViewPager extends common.widget.viewpager.ViewPager {\\n private float touchX = 0f, downX = 0f;\\n // 滑动距离及坐标\\n private float xDistance, yDistance, xLast, yLast;\\n private BannerViewPager mBannerViewPager;\\n\\n public AutoScrollViewPager(Context context) {\\n super(context);\\n }\\n\\n public AutoScrollViewPager(Context context, AttributeSet attrs) {\\n super(context, attrs);\\n }\\n\\n public void setBanner(BannerViewPager banner) {\\n mBannerViewPager = banner;\\n }\\n\\n @Override\\n public boolean dispatchTouchEvent(MotionEvent ev) {\\n int action = MotionEventCompat.getActionMasked(ev);\\n touchX = ev.getX();\\n\\n if ((action == MotionEvent.ACTION_DOWN)) {\\n // 暂停滚动\\n downX = touchX;\\n mBannerViewPager.pauseAutoScroll();\\n } else if (action == MotionEvent.ACTION_UP || action == MotionEvent.ACTION_CANCEL) {\\n // 继续滚动\\n mBannerViewPager.resumeAutoScroll();\\n }\\n\\n // -----------是否拦截手势\\n switch (ev.getAction()) {\\n case MotionEvent.ACTION_DOWN:\\n xDistance = yDistance = 0f;\\n xLast = ev.getX();\\n yLast = ev.getY();\\n break;\\n case MotionEvent.ACTION_MOVE:\\n final float curX = ev.getX();\\n final float curY = ev.getY();\\n\\n xDistance += Math.abs(curX - xLast);\\n yDistance += Math.abs(curY - yLast);\\n xLast = curX;\\n yLast = curY;\\n\\n if (xDistance > yDistance) {\\n getParent().requestDisallowInterceptTouchEvent(true);\\n }\\n }\\n return super.dispatchTouchEvent(ev);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5906040072441101,\n \"alphanum_fraction\": 0.5922818779945374,\n \"avg_line_length\": 24.645160675048828,\n \"blob_id\": \"4984af87bab44d586784b7c2661da7644b55ac2c\",\n \"content_id\": \"2bc511d12746eaf66b5886994480caf1e5738de4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Kotlin\",\n \"length_bytes\": 2416,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 93,\n \"path\": \"/app/src/main/java/com/android/base/ui/splash/SplashActivity.kt\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.ui.splash\\n\\nimport android.Manifest\\nimport android.os.Bundle\\nimport android.os.Handler\\nimport androidx.appcompat.app.AppCompatActivity\\nimport com.android.base.App\\nimport com.android.util.common.CommonCallBack\\nimport com.android.util.permission.Permission\\nimport com.android.util.permission.PermissionListener\\nimport com.android.util.permission.PermissionUtil\\nimport com.fastapp.MainActivity\\nimport net.medlinker.android.splash.SplashUtil\\n\\n/**\\n * @author zhangquan\\n */\\nclass SplashActivity : AppCompatActivity() {\\n private var mHandler = Handler()\\n private val mDelay = 1000L\\n override fun onCreate(savedInstanceState: Bundle?) {\\n super.onCreate(savedInstanceState)\\n checkShowDialog()\\n }\\n\\n override fun onDestroy() {\\n super.onDestroy()\\n mHandler.removeCallbacks(mRunnable)\\n }\\n\\n private fun checkShowDialog() {\\n if (!SplashUtil.isPrivacyGranted()) {\\n showPrivacyDialog()\\n } else {\\n grantPermission()\\n }\\n }\\n\\n /**\\n * 隐私政策弹框\\n */\\n private fun showPrivacyDialog() {\\n val dialogFragment = PrivacyDialog()\\n dialogFragment.callback(CommonCallBack { granted ->\\n if (granted) {\\n grantPermission()\\n App.initAfterPrivacy()\\n } else {\\n finish()\\n }\\n })\\n dialogFragment.show(supportFragmentManager, \\\"PrivacyProtocol\\\")\\n }\\n\\n /**\\n * 权限申请\\n */\\n private fun grantPermission() {\\n PermissionUtil(this).requestPermissions(\\n arrayOf(Manifest.permission.WRITE_EXTERNAL_STORAGE),\\n object : PermissionListener {\\n override fun onGranted() {\\n doNextStep()\\n }\\n\\n override fun onDenied(deniedPermission: MutableList<Permission>?) {\\n doNextStep()\\n }\\n })\\n }\\n\\n private fun doNextStep() {\\n if (App.coldStart) { //冷启动\\n doNext()\\n } else {\\n mHandler.postDelayed(mRunnable, mDelay) //热启动\\n }\\n App.coldStart = false\\n }\\n\\n private fun doNext() {\\n if (SplashUtil.hasShowGuide()) {\\n MainActivity.start(this)\\n } else {\\n GuideActivity.start(this)\\n }\\n finish()\\n }\\n\\n private var mRunnable = Runnable {\\n doNext()\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6291578412055969,\n \"alphanum_fraction\": 0.6447275280952454,\n \"avg_line_length\": 31.482759475708008,\n \"blob_id\": \"25ad9ea7d147e1299b525fda35425fb539ea2367\",\n \"content_id\": \"7b68974cb8a07ecb1eaccc2216cb8e97e70349aa\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 2964,\n \"license_type\": \"no_license\",\n \"max_line_length\": 89,\n \"num_lines\": 87,\n \"path\": \"/librarys/CommonUtil/src/main/java/com/android/util/encode/Des3Encoder.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.util.encode;\\n\\nimport android.util.Base64;\\n\\nimport java.security.Key;\\n\\nimport javax.crypto.Cipher;\\nimport javax.crypto.SecretKeyFactory;\\nimport javax.crypto.spec.DESedeKeySpec;\\nimport javax.crypto.spec.IvParameterSpec;\\n\\n/**\\n * 3DES加密\\n */\\npublic class Des3Encoder {\\n private static final String KEY = \\\"sndo@agriculture@analytics\\\"; //3DES的加密密钥长度要求是24个字节\\n private final static String IV = \\\"01234567\\\"; // 向量\\n private final static String ENCODING = \\\"UTF-8\\\"; // 加解密统一使用的编码方式\\n\\n public static void main(String[] args) throws Exception {\\n test(\\\"12345678\\\");\\n }\\n\\n public static void test(String src) throws Exception {\\n //UTF8编码->DES3加密->DES3解密->UTF8解码\\n System.out.println(\\\"key.length=\\\" + KEY.length());\\n byte[] encryptBytes = Des3Encoder.encrypt(KEY, src.getBytes(\\\"UTF-8\\\"));\\n String data = Base64.encodeToString(encryptBytes, Base64.DEFAULT);\\n System.out.println(\\\"加密后的数据=\\\" + data);\\n byte[] decrypt = Des3Encoder.decrypt(KEY, Base64.decode(data, Base64.DEFAULT));\\n data = new String(decrypt, \\\"UTF-8\\\");\\n System.out.println(\\\"解密后的数据=\\\" + data);\\n }\\n\\n /**\\n * 加密\\n *\\n * @param key 密钥\\n * @param srcBytes 原始数据\\n * @return\\n * @throws Exception\\n */\\n public static byte[] encrypt(String key, byte[] srcBytes) throws Exception {\\n//\\t\\tif (TextUtils.isEmpty(key) || null == srcBytes || srcBytes.length == 0) {\\n//\\t\\t\\treturn srcBytes;\\n//\\t\\t}\\n Key deskey = null;\\n DESedeKeySpec spec = new DESedeKeySpec(key.getBytes());\\n SecretKeyFactory keyfactory = SecretKeyFactory.getInstance(\\\"desede\\\");\\n deskey = keyfactory.generateSecret(spec);\\n\\n Cipher cipher = Cipher.getInstance(\\\"desede/CBC/PKCS5Padding\\\");\\n IvParameterSpec ips = new IvParameterSpec(IV.getBytes());\\n cipher.init(Cipher.ENCRYPT_MODE, deskey, ips);\\n byte[] encryptData = cipher.doFinal(srcBytes);\\n\\n return encryptData;\\n }\\n\\n /**\\n * 解密\\n *\\n * @param key 解密密钥\\n * @param encodedBytes 加密后的字节\\n * @return\\n * @throws Exception\\n */\\n public static byte[] decrypt(String key, byte[] encodedBytes)\\n throws Exception {\\n//\\t\\tif (TextUtils.isEmpty(key) || null == encodedBytes\\n//\\t\\t\\t\\t|| encodedBytes.length == 0) {\\n//\\t\\t\\treturn encodedBytes;\\n//\\t\\t}\\n\\n Key deskey = null;\\n DESedeKeySpec spec = new DESedeKeySpec(key.getBytes());\\n SecretKeyFactory keyfactory = SecretKeyFactory.getInstance(\\\"desede\\\");\\n deskey = keyfactory.generateSecret(spec);\\n Cipher cipher = Cipher.getInstance(\\\"desede/CBC/PKCS5Padding\\\");\\n IvParameterSpec ips = new IvParameterSpec(IV.getBytes());\\n cipher.init(Cipher.DECRYPT_MODE, deskey, ips);\\n\\n byte[] decryptData = cipher.doFinal(encodedBytes);\\n\\n return decryptData;\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6921886801719666,\n \"alphanum_fraction\": 0.6921886801719666,\n \"avg_line_length\": 27.10869598388672,\n \"blob_id\": \"80785b741e5a57bf51b692d6d8eee7adbe9371dd\",\n \"content_id\": \"38ec538c444fcced75a1e62df634947fd1255621\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1309,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 46,\n \"path\": \"/app/src/main/java/com/fastapp/TestFrag.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.fastapp;\\n\\nimport android.content.Context;\\nimport android.os.Bundle;\\nimport android.view.View;\\nimport android.widget.TextView;\\n\\nimport com.android.base.Constant;\\nimport com.android.base.event.EventMsg;\\nimport com.android.base.ui.SimpleFrag;\\nimport com.android.base.ui.SimpleFragAct;\\nimport com.android.base.widget.StatusBar;\\n\\nimport org.greenrobot.eventbus.EventBus;\\n\\n\\n/**\\n * @author 张全\\n */\\npublic class TestFrag extends SimpleFrag {\\n public static void start(Context ctx) {\\n SimpleFragAct.start(ctx, new SimpleFragAct.SimpleFragParam(\\\"TestFrag\\\", TestFrag.class));\\n }\\n\\n @Override\\n protected int getLayoutId() {\\n return R.layout.activity_main;\\n }\\n\\n @Override\\n protected void init(Bundle savedInstanceState) {\\n StatusBar.setStatusBar(mContext, true, getTitleBar());\\n\\n findViewById(R.id.showDialog).setVisibility(View.GONE);\\n findViewById(R.id.http).setVisibility(View.GONE);\\n TextView textView = findViewById(R.id.testFrag);\\n textView.setText(\\\"发送Event\\\");\\n textView.setOnClickListener(new View.OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n EventBus.getDefault().post(new EventMsg(Constant.Event.LOGIN_SUCCESS,\\\"登录成功\\\"));\\n }\\n });\\n\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7093495726585388,\n \"alphanum_fraction\": 0.7093495726585388,\n \"avg_line_length\": 17.923076629638672,\n \"blob_id\": \"4c157199f78b4f9931302e10088e1142e6376856\",\n \"content_id\": \"fe4f1f6709f1abfcce74133baa173668aca94bfe\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 530,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 26,\n \"path\": \"/app/src/main/java/com/android/base/util/pay/OrderPayResponse.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.util.pay;\\n\\nimport com.google.gson.annotations.SerializedName;\\n\\nimport java.io.Serializable;\\n\\n/**\\n * 支付信息\\n *\\n * @author 张全\\n */\\npublic class OrderPayResponse implements Serializable {\\n //微信支付信息\\n public String appid;\\n public String partnerid;\\n public String prepayid;\\n @SerializedName(\\\"package\\\")\\n public String packageStr;\\n public String timestamp;\\n public String noncestr;\\n public String sign;\\n\\n //支付宝支付信息.\\n public String orderInfo;\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5889744162559509,\n \"alphanum_fraction\": 0.5950997471809387,\n \"avg_line_length\": 39.38266372680664,\n \"blob_id\": \"a31245df7d9b3d4de588f56780d33b430b98d918\",\n \"content_id\": \"4defb3015aea0cb8994fd14194c7a70835ad7a2f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 19125,\n \"license_type\": \"no_license\",\n \"max_line_length\": 131,\n \"num_lines\": 473,\n \"path\": \"/librarys/CommonWidget/src/main/java/common/widget/shape/ShapeAttrParser.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package common.widget.shape;\\n\\nimport android.content.Context;\\nimport android.content.res.ColorStateList;\\nimport android.content.res.Resources;\\nimport android.content.res.TypedArray;\\nimport android.content.res.XmlResourceParser;\\nimport android.graphics.BlendMode;\\nimport android.graphics.drawable.GradientDrawable;\\nimport android.os.Build;\\nimport android.text.TextUtils;\\nimport android.util.ArrayMap;\\nimport android.util.AttributeSet;\\nimport android.util.TypedValue;\\nimport android.view.View;\\n\\nimport androidx.annotation.RequiresApi;\\n\\nimport java.lang.reflect.Constructor;\\nimport java.util.Map;\\n\\nimport common.widget.R;\\n\\n\\n/**\\n * 属性解析\\n *\\n * @author zhangquan\\n */\\npublic class ShapeAttrParser {\\n private static final int RADIUS_TYPE_PIXELS = 0;\\n private static final int RADIUS_TYPE_FRACTION = 1;\\n private static final int RADIUS_TYPE_FRACTION_PARENT = 2;\\n\\n private static final float DEFAULT_INNER_RADIUS_RATIO = 3.0f;\\n private static final float DEFAULT_THICKNESS_RATIO = 9.0f;\\n\\n private static final Class<?>[] sConstructorSignature = new Class[]{Context.class, AttributeSet.class};\\n private static final Object[] mConstructorArgs = new Object[2];\\n private static final Map<String, Constructor<? extends View>> sConstructorMap = new ArrayMap<>();\\n\\n private static final String sViewClassPrefix = \\\"android.view.\\\";\\n private static final String[] sClassPrefixList = {\\n \\\"android.widget.\\\",\\n \\\"android.webkit.\\\",\\n \\\"android.app.\\\"\\n };\\n\\n\\n public static View parseAttr(Context context, AttributeSet attrs, View view) {\\n return parseAttr(context, attrs, view, null);\\n }\\n\\n public static View parseAttr(Context context, AttributeSet attrs, View view, String viewName) {\\n if (null == attrs || Build.VERSION.SDK_INT <= 19) {\\n return view;\\n }\\n TypedArray typedArray = context.obtainStyledAttributes(attrs, R.styleable.ShapeBg);\\n if (typedArray.getIndexCount() == 0) {\\n return view;\\n }\\n if (null == view) {\\n view = createViewFromName(context, viewName, attrs);\\n }\\n if (null != view) {\\n try {\\n GradientDrawable gradientDrawable = new GradientDrawable();\\n //<shape>\\n updateStateFromTypedArray(typedArray, gradientDrawable);\\n //<gradient>\\n updateGradientDrawableGradient(context, typedArray, gradientDrawable);\\n //<solid>\\n updateGradientDrawableSolid(typedArray, gradientDrawable);\\n //<stroke>\\n updateGradientDrawableStroke(typedArray, gradientDrawable);\\n //<corners>\\n updateDrawableCorners(typedArray, gradientDrawable);\\n //<padding>\\n updateGradientDrawablePadding(typedArray, gradientDrawable);\\n //<size>\\n updateGradientDrawableSize(typedArray, gradientDrawable);\\n\\n view.setBackground(gradientDrawable);\\n } finally {\\n typedArray.recycle();\\n }\\n }\\n return view;\\n }\\n\\n\\n /**\\n * <shape xmlns:android=\\\"http://schemas.android.com/apk/res/android\\\"\\n * android:shape=\\\"rectangle\\\"\\n * android:useLevel=\\\"false\\\"\\n * android:dither=\\\"true\\\"\\n * android:innerRadius=\\\"10dp\\\"\\n * android:innerRadiusRatio=\\\"1.2\\\"\\n * android:tint=\\\"#fff\\\"\\n * android:tintMode=\\\"src_in\\\"\\n * android:visible=\\\"true\\\"\\n * android:thickness=\\\"10dp\\\"\\n * android:thicknessRatio=\\\"1.2\\\"\\n * >\\n */\\n @RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)\\n private static void updateStateFromTypedArray(TypedArray a, GradientDrawable gradientDrawable) {\\n int shape = a.getInt(R.styleable.ShapeBg_sp_shape, GradientDrawable.RECTANGLE);\\n gradientDrawable.setShape(shape);\\n\\n boolean dither = a.getBoolean(R.styleable.ShapeBg_sp_dither, false);\\n gradientDrawable.setDither(dither);\\n\\n if (shape == GradientDrawable.RING) {\\n if (android.os.Build.VERSION.SDK_INT >= android.os.Build.VERSION_CODES.Q) {\\n int mInnerRadius = a.getDimensionPixelSize(\\n R.styleable.ShapeBg_sp_innerRadius, -1);\\n gradientDrawable.setInnerRadius(mInnerRadius);\\n if (mInnerRadius == -1) {\\n float mInnerRadiusRatio = a.getFloat(\\n R.styleable.ShapeBg_sp_innerRadiusRatio, DEFAULT_INNER_RADIUS_RATIO);\\n gradientDrawable.setInnerRadiusRatio(mInnerRadiusRatio);\\n }\\n\\n int mThickness = a.getDimensionPixelSize(\\n R.styleable.ShapeBg_sp_thickness, -1);\\n gradientDrawable.setThickness(mThickness);\\n if (mThickness == -1) {\\n float mThicknessRatio = a.getFloat(\\n R.styleable.ShapeBg_sp_thicknessRatio, DEFAULT_THICKNESS_RATIO);\\n gradientDrawable.setThicknessRatio(mThicknessRatio);\\n }\\n }\\n\\n boolean mUseLevelForShape = a.getBoolean(\\n R.styleable.ShapeBg_sp_useLevel, true);\\n gradientDrawable.setUseLevel(mUseLevelForShape);\\n }\\n\\n if (android.os.Build.VERSION.SDK_INT >= android.os.Build.VERSION_CODES.Q) {\\n final int tintMode = a.getInt(R.styleable.ShapeBg_sp_tintMode, -1);\\n if (tintMode != -1) {\\n BlendMode blendMode = parseBlendMode(tintMode, BlendMode.SRC_IN);\\n gradientDrawable.setTintBlendMode(blendMode);\\n }\\n }\\n\\n final ColorStateList tint = a.getColorStateList(R.styleable.ShapeBg_sp_tint);\\n if (tint != null) {\\n gradientDrawable.setTintList(tint);\\n }\\n\\n// Insets mOpticalInsets = Insets.NONE;\\n// final int insetLeft = a.getDimensionPixelSize(\\n// R.styleable.ShapeLayout_opticalInsetLeft, mOpticalInsets.left);\\n// final int insetTop = a.getDimensionPixelSize(\\n// R.styleable.ShapeLayout_opticalInsetTop, mOpticalInsets.top);\\n// final int insetRight = a.getDimensionPixelSize(\\n// R.styleable.ShapeLayout_opticalInsetRight, mOpticalInsets.right);\\n// final int insetBottom = a.getDimensionPixelSize(\\n// R.styleable.ShapeLayout_opticalInsetBottom, mOpticalInsets.bottom);\\n// mOpticalInsets = Insets.of(insetLeft, insetTop, insetRight, insetBottom);\\n\\n }\\n\\n /**\\n * <size android:width=\\\"\\\" android:height=\\\"\\\"/>\\n */\\n private static void updateGradientDrawableSize(TypedArray typedArray, GradientDrawable gradientDrawable) {\\n if (typedArray.hasValue(R.styleable.ShapeBg_sp_width)\\n || typedArray.hasValue(R.styleable.ShapeBg_sp_height)) {\\n int width = typedArray.getDimensionPixelSize(\\n R.styleable.ShapeBg_sp_width, -1);\\n int height = typedArray.getDimensionPixelSize(\\n R.styleable.ShapeBg_sp_height, -1);\\n gradientDrawable.setSize(width, height);\\n }\\n }\\n\\n /**\\n * <gradient\\n * android:angle=\\\"\\\"\\n * android:centerColor=\\\"\\\"\\n * android:centerX=\\\"\\\"\\n * android:centerY=\\\"\\\"\\n * android:endColor=\\\"\\\"\\n * android:startColor=\\\"\\\"\\n * android:type=\\\"\\\"\\n * android:gradientRadius=\\\"\\\"\\n * android:useLevel=\\\"\\\"\\n * />\\n */\\n private static void updateGradientDrawableGradient(Context context, TypedArray typedArray, GradientDrawable gradientDrawable) {\\n\\n float mCenterX = 0.5f;\\n float mCenterY = 0.5f;\\n float mGradientRadius = 0.5f;\\n int mGradientRadiusType = RADIUS_TYPE_PIXELS;\\n boolean mUseLevel = false;\\n int gradientType = GradientDrawable.LINEAR_GRADIENT;\\n\\n mUseLevel = typedArray.getBoolean(R.styleable.ShapeBg_sp_gradientUseLevel, mUseLevel);\\n gradientDrawable.setUseLevel(mUseLevel);\\n\\n gradientType = typedArray.getInt(R.styleable.ShapeBg_sp_gradientType, gradientType);\\n gradientDrawable.setGradientType(gradientType);\\n\\n mCenterX = getFloatOrFraction(typedArray, R.styleable.ShapeBg_sp_gradientCenterX, mCenterX);\\n mCenterY = getFloatOrFraction(typedArray, R.styleable.ShapeBg_sp_gradientCenterY, mCenterY);\\n\\n int startColor = typedArray.getColor(R.styleable.ShapeBg_sp_gradientStartColor, 0);\\n int centerColor = typedArray.getColor(R.styleable.ShapeBg_sp_gradientCenterColor, 0);\\n boolean hasCenterColor = typedArray.hasValue(R.styleable.ShapeBg_sp_gradientCenterColor);\\n int endColor = typedArray.getColor(R.styleable.ShapeBg_sp_gradientEndColor, 0);\\n if (hasCenterColor && Build.VERSION.SDK_INT >= Build.VERSION_CODES.Q) {\\n int[] colors = new int[]{startColor, centerColor, endColor};\\n float[] positions = new float[]{0.0f, mCenterX != 0.5f ? mCenterX : mCenterY, 1f};\\n gradientDrawable.setColors(colors, positions);\\n } else {\\n int[] colors = new int[]{startColor, endColor};\\n gradientDrawable.setColors(colors);\\n }\\n\\n GradientDrawable.Orientation mOrientation = GradientDrawable.Orientation.TOP_BOTTOM;\\n int angle = (int) typedArray.getFloat(R.styleable.ShapeBg_sp_gradientAngle, 0);\\n angle = ((angle % 360) + 360) % 360;\\n if (angle >= 0) {\\n switch (angle) {\\n case 0:\\n mOrientation = GradientDrawable.Orientation.LEFT_RIGHT;\\n break;\\n case 45:\\n mOrientation = GradientDrawable.Orientation.BL_TR;\\n break;\\n case 90:\\n mOrientation = GradientDrawable.Orientation.BOTTOM_TOP;\\n break;\\n case 135:\\n mOrientation = GradientDrawable.Orientation.BR_TL;\\n break;\\n case 180:\\n mOrientation = GradientDrawable.Orientation.RIGHT_LEFT;\\n break;\\n case 225:\\n mOrientation = GradientDrawable.Orientation.TR_BL;\\n break;\\n case 270:\\n mOrientation = GradientDrawable.Orientation.TOP_BOTTOM;\\n break;\\n case 315:\\n mOrientation = GradientDrawable.Orientation.TL_BR;\\n break;\\n }\\n } else {\\n mOrientation = GradientDrawable.Orientation.TOP_BOTTOM;\\n }\\n gradientDrawable.setOrientation(mOrientation);\\n\\n\\n final TypedValue tv = typedArray.peekValue(R.styleable.ShapeBg_sp_gradientRadius);\\n if (tv != null) {\\n if (tv.type == TypedValue.TYPE_FRACTION) {\\n mGradientRadius = tv.getFraction(1.0f, 1.0f);\\n\\n final int unit = (tv.data >> TypedValue.COMPLEX_UNIT_SHIFT)\\n & TypedValue.COMPLEX_UNIT_MASK;\\n if (unit == TypedValue.COMPLEX_UNIT_FRACTION_PARENT) {\\n mGradientRadiusType = RADIUS_TYPE_FRACTION_PARENT;\\n } else {\\n mGradientRadiusType = RADIUS_TYPE_FRACTION;\\n }\\n } else if (tv.type == TypedValue.TYPE_DIMENSION) {\\n mGradientRadius = tv.getDimension(context.getResources().getDisplayMetrics());\\n mGradientRadiusType = RADIUS_TYPE_PIXELS;\\n } else {\\n mGradientRadius = tv.getFloat();\\n mGradientRadiusType = RADIUS_TYPE_PIXELS;\\n }\\n gradientDrawable.setGradientType(mGradientRadiusType);\\n gradientDrawable.setGradientRadius(mGradientRadius);\\n }\\n\\n }\\n\\n /**\\n * <solid android:color=\\\"\\\" />\\n */\\n @RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)\\n private static void updateGradientDrawableSolid(TypedArray typedArray, GradientDrawable gradientDrawable) {\\n final ColorStateList solidColor = typedArray.getColorStateList(\\n R.styleable.ShapeBg_sp_solid);\\n if (solidColor != null) {\\n gradientDrawable.setColor(solidColor);\\n }\\n\\n }\\n\\n /**\\n * <stroke\\n * android:width=\\\"\\\"\\n * android:color=\\\"\\\"\\n * android:dashWidth=\\\"\\\"\\n * android:dashGap=\\\"\\\" />\\n */\\n @RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)\\n private static void updateGradientDrawableStroke(TypedArray typedArray, GradientDrawable gradientDrawable) {\\n if (!typedArray.hasValue(R.styleable.ShapeBg_sp_strokeWidth)) {\\n return;\\n }\\n\\n int strokeWidth = typedArray.getDimensionPixelSize(R.styleable.ShapeBg_sp_strokeWidth, 0);\\n float dashWidth = typedArray.getDimension(R.styleable.ShapeBg_sp_strokeDashWidth, 0);\\n ColorStateList strokeColor = typedArray.getColorStateList(\\n R.styleable.ShapeBg_sp_strokeColor);\\n if (dashWidth != 0.0f) {\\n float dashGap = typedArray.getDimension(R.styleable.ShapeBg_sp_strokeDashGap, 0);\\n gradientDrawable.setStroke(strokeWidth, strokeColor, dashWidth, dashGap);\\n } else {\\n gradientDrawable.setStroke(strokeWidth, strokeColor);\\n }\\n }\\n\\n /**\\n * <corners\\n * android:bottomLeftRadius=\\\"\\\"\\n * android:bottomRightRadius=\\\"\\\"\\n * android:radius=\\\"\\\"\\n * android:topLeftRadius=\\\"\\\"\\n * android:topRightRadius=\\\"\\\" />\\n */\\n private static void updateDrawableCorners(TypedArray typedArray, GradientDrawable gradientDrawable) {\\n\\n int radius = typedArray.getDimensionPixelSize(R.styleable.ShapeBg_sp_radius, 0);\\n final int topLeftRadius = typedArray.getDimensionPixelSize(\\n R.styleable.ShapeBg_sp_topLeftRadius, radius);\\n final int topRightRadius = typedArray.getDimensionPixelSize(\\n R.styleable.ShapeBg_sp_topRightRadius, radius);\\n final int bottomLeftRadius = typedArray.getDimensionPixelSize(\\n R.styleable.ShapeBg_sp_bottomLeftRadius, radius);\\n final int bottomRightRadius = typedArray.getDimensionPixelSize(\\n R.styleable.ShapeBg_sp_bottomRightRadius, radius);\\n if (topLeftRadius != radius || topRightRadius != radius ||\\n bottomLeftRadius != radius || bottomRightRadius != radius) {\\n gradientDrawable.setCornerRadii(new float[]{\\n topLeftRadius, topLeftRadius,\\n topRightRadius, topRightRadius,\\n bottomRightRadius, bottomRightRadius,\\n bottomLeftRadius, bottomLeftRadius\\n });\\n } else {\\n gradientDrawable.setCornerRadius(radius);\\n }\\n }\\n\\n /**\\n * <padding\\n * android:bottom=\\\"\\\"\\n * android:left=\\\"\\\"\\n * android:right=\\\"\\\"\\n * android:top=\\\"\\\" />\\n * <p>\\n * 注意会覆盖View的android:padding属性\\n */\\n private static void updateGradientDrawablePadding(TypedArray typedArray, GradientDrawable gradientDrawable) {\\n if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.Q) {\\n int padding = typedArray.getDimensionPixelOffset(R.styleable.ShapeBg_sp_padding, 0);\\n if (padding > 0) {\\n gradientDrawable.setPadding(padding, padding, padding, padding);\\n } else {\\n int paddingLeft = typedArray.getDimensionPixelOffset(R.styleable.ShapeBg_sp_paddingLeft, 0);\\n int paddingTop = typedArray.getDimensionPixelOffset(R.styleable.ShapeBg_sp_paddingTop, 0);\\n int paddingRight = typedArray.getDimensionPixelOffset(R.styleable.ShapeBg_sp_paddingRight, 0);\\n int paddingBottom = typedArray.getDimensionPixelOffset(R.styleable.ShapeBg_sp_paddingBottom, 0);\\n if (paddingLeft > 0 || paddingTop > 0 || paddingRight > 0 || paddingBottom > 0) {\\n gradientDrawable.setPadding(paddingLeft,\\n paddingTop,\\n paddingRight,\\n paddingBottom);\\n }\\n }\\n }\\n }\\n\\n private static float getFloatOrFraction(TypedArray a, int index, float defaultValue) {\\n TypedValue tv = a.peekValue(index);\\n float v = defaultValue;\\n if (tv != null) {\\n boolean vIsFraction = tv.type == TypedValue.TYPE_FRACTION;\\n v = vIsFraction ? tv.getFraction(1.0f, 1.0f) : tv.getFloat();\\n }\\n return v;\\n }\\n\\n public static BlendMode parseBlendMode(int value, BlendMode defaultMode) {\\n if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.Q) {\\n switch (value) {\\n case 3:\\n return BlendMode.SRC_OVER;\\n case 5:\\n return BlendMode.SRC_IN;\\n case 9:\\n return BlendMode.SRC_ATOP;\\n // b/73224934 PorterDuff Multiply maps to Skia Modulate so actually\\n // return BlendMode.MODULATE here\\n case 14:\\n return BlendMode.MODULATE;\\n case 15:\\n return BlendMode.SCREEN;\\n case 16:\\n return BlendMode.PLUS;\\n default:\\n return defaultMode;\\n }\\n }\\n return defaultMode;\\n }\\n\\n\\n private static View createViewFromName(Context context, String name, AttributeSet attrs) {\\n if (TextUtils.isEmpty(name)) {\\n return null;\\n }\\n if (name.equals(\\\"view\\\")) {\\n name = attrs.getAttributeValue(null, \\\"class\\\");\\n }\\n\\n View view = null;\\n try {\\n mConstructorArgs[0] = context;\\n mConstructorArgs[1] = attrs;\\n\\n if (!name.contains(\\\".\\\")) {\\n if (TextUtils.equals(name, \\\"View\\\")) {\\n view = createView(context, name, sViewClassPrefix);\\n }\\n\\n if (view == null) {\\n for (String prefix : sClassPrefixList) {\\n try {\\n view = createView(context, name, prefix);\\n if (view != null) {\\n break;\\n }\\n } catch (Exception e) {\\n }\\n }\\n }\\n } else {\\n view = createView(context, name, null);\\n }\\n } finally {\\n mConstructorArgs[0] = null;\\n mConstructorArgs[1] = null;\\n }\\n return view;\\n }\\n\\n private static View createView(Context context, String name, String prefix) {\\n Constructor<? extends View> constructor = sConstructorMap.get(name);\\n try {\\n if (constructor == null) {\\n Class<? extends View> clazz = Class.forName(prefix != null ? (prefix + name) : name, false,\\n context.getClassLoader()).asSubclass(View.class);\\n constructor = clazz.getConstructor(sConstructorSignature);\\n sConstructorMap.put(name, constructor);\\n }\\n constructor.setAccessible(true);\\n return constructor.newInstance(mConstructorArgs);\\n } catch (Exception e) {\\n return null;\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5490729808807373,\n \"alphanum_fraction\": 0.5499974489212036,\n \"avg_line_length\": 34.79227828979492,\n \"blob_id\": \"3119a1d9518fdf106f2cba92f21b7ad0d6bd7bf7\",\n \"content_id\": \"87317fa64655d482a567365542d220e165e65879\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 40618,\n \"license_type\": \"no_license\",\n \"max_line_length\": 127,\n \"num_lines\": 1088,\n \"path\": \"/app/src/main/java/com/android/base/ui/WebViewFrag.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.ui;\\n\\nimport android.app.Activity;\\nimport android.content.ClipData;\\nimport android.content.ClipboardManager;\\nimport android.content.Context;\\nimport android.content.Intent;\\nimport android.graphics.Bitmap;\\nimport android.graphics.Color;\\nimport android.net.Uri;\\nimport android.net.http.SslError;\\nimport android.os.Bundle;\\nimport android.text.TextUtils;\\nimport android.view.View;\\nimport android.view.ViewGroup;\\nimport android.view.WindowManager;\\nimport android.webkit.DownloadListener;\\nimport android.webkit.JsPromptResult;\\nimport android.webkit.JsResult;\\nimport android.webkit.SslErrorHandler;\\nimport android.webkit.ValueCallback;\\nimport android.webkit.WebChromeClient;\\nimport android.webkit.WebSettings;\\nimport android.webkit.WebView;\\nimport android.webkit.WebViewClient;\\nimport android.widget.FrameLayout;\\nimport android.widget.ProgressBar;\\nimport android.widget.RelativeLayout;\\n\\nimport com.android.util.LContext;\\nimport com.android.util.ext.ToastUtil;\\nimport com.android.util.log.LogUtil;\\nimport com.android.base.Constant;\\nimport com.android.base.event.EventMsg;\\nimport com.android.base.util.CommonUtil;\\nimport com.android.base.util.DispatchUtil;\\nimport com.android.base.util.NotificationPageHelper;\\nimport com.android.base.util.filedownloader.FileDownloadDialogView;\\nimport com.android.base.util.filedownloader.FileDownloader;\\nimport com.android.base.util.share.ShareDialog;\\nimport com.android.base.util.share.ShareInfo;\\nimport com.android.base.widget.AlertDialogView;\\nimport com.android.base.widget.StatusBar;\\nimport com.android.base.widget.TitleBarView;\\nimport com.blankj.utilcode.util.BarUtils;\\nimport com.blankj.utilcode.util.NetworkUtils;\\nimport com.blankj.utilcode.util.SizeUtils;\\nimport com.fastapp.R;\\n\\nimport org.greenrobot.eventbus.Subscribe;\\nimport org.greenrobot.eventbus.ThreadMode;\\nimport org.json.JSONArray;\\nimport org.json.JSONException;\\nimport org.json.JSONObject;\\n\\nimport java.io.File;\\nimport java.io.Serializable;\\nimport java.lang.reflect.Method;\\nimport java.util.ArrayList;\\nimport java.util.Arrays;\\nimport java.util.List;\\nimport java.util.Set;\\n\\nimport common.widget.LoadingBar;\\nimport common.widget.dialog.EffectDialogBuilder;\\nimport common.widget.dialog.LoadingDialog;\\n\\n/**\\n * WebView页面\\n *\\n * @author zhangquan\\n */\\npublic class WebViewFrag extends SimpleFrag {\\n public static final String PARAM = \\\"PARAM\\\";\\n private WebView webView;\\n private ProgressBar progressBar;\\n private LoadingBar loadingBar;\\n private WebViewParam webViewParam;\\n private TitleBarView titleBarView;\\n private FrameLayout fullScreenView;\\n private RelativeLayout webContainer;\\n private View myView;\\n private static final String TAG = \\\"WebViewFrag\\\";\\n private LoadingDialog loadingDialog;\\n private String original_Url; //进入页面的url，没有添加公共参数\\n private boolean isInit = false;\\n\\n public static class WebViewParam implements Serializable {\\n //标题\\n public String title;\\n //是否需要重置title 显示webview的标题\\n public boolean shouldResetTitle = true;\\n //链接URL url目前支持needLogin(先登录)、hideTitlebar(隐藏标题栏)、lightModel(透明状态栏、黑色文字)\\n public String url;\\n public String titleBarColor; //标题栏color\\n\\n public ShareInfo shareInfo; //分享信息\\n public boolean isShowShare; // 是否显示右上角分享按钮\\n public boolean checkNetwork = true; //入口检查网络\\n public boolean sensorOriention; //自动旋转屏幕方向\\n }\\n\\n public static Bundle getParamBundle(WebViewParam param) {\\n Bundle bundle = new Bundle();\\n bundle.putSerializable(PARAM, param);\\n return bundle;\\n }\\n\\n public static SimpleFragAct.SimpleFragParam getStartParam(WebViewParam param) {\\n return new SimpleFragAct.SimpleFragParam(param.title,\\n WebViewFrag.class, WebViewFrag.getParamBundle(param));\\n }\\n\\n public static void start(Context ctx, SimpleFragAct.SimpleFragParam param) {\\n SimpleFragAct.start(ctx, param);\\n }\\n\\n public static void start(Context ctx, WebViewParam param) {\\n try {\\n// String url = param.url;\\n// String needLogin = Uri.parse(url).getQueryParameter(\\\"needLogin\\\");\\n// if (TextUtils.equals(needLogin, \\\"1\\\") && UserClient.getUser() == null) { //首先登录\\n// LoginFragment.start(ctx);\\n// } else {\\n// SimpleFragAct.SimpleFragParam startParam = getStartParam(param);\\n// startParam.mutliPage = true;\\n// SimpleFragAct.start(ctx, startParam,param.sensorOriention);\\n// }\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n }\\n\\n @Override\\n public int getLayoutId() {\\n return R.layout.common_webview;\\n }\\n\\n @Override\\n public void init(Bundle savedInstanceState) {\\n\\n if (null != getArguments()) {\\n webViewParam = (WebViewParam) getArguments().getSerializable(PARAM);\\n }\\n\\n if (null == webViewParam) {\\n close();\\n return;\\n }\\n\\n // 网络无连接\\n if (webViewParam.checkNetwork && !NetworkUtils.isAvailable()) {\\n showToastShort(R.string.net_noconnection);\\n close();\\n return;\\n }\\n\\n //请求无链接\\n if (TextUtils.isEmpty(webViewParam.url)) {\\n showToastShort(\\\"请求无链接\\\");\\n close();\\n return;\\n }\\n\\n LogUtil.d(TAG, webViewParam.url);\\n\\n addAction(Constant.Event.LOGIN_SUCCESS);\\n init();\\n }\\n\\n\\n @Override\\n public void onResume() {\\n super.onResume();\\n try {\\n Method onResume = webView.getClass().getMethod(\\\"onResume\\\");\\n if (null != onResume) onResume.invoke(webView, (Object[]) null);\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n if (isInit) {\\n viewWillAppear();\\n }\\n isInit = true;\\n }\\n\\n @Override\\n public void onPause() {\\n super.onPause();\\n try {\\n Method onPause = webView.getClass().getMethod(\\\"onPause\\\");\\n if (null != onPause) onPause.invoke(webView, (Object[]) null);\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n }\\n\\n private boolean handleBackEvent() {\\n if (null != webView && webView.canGoBack()) {\\n webView.goBack();\\n return true;\\n }\\n return false;\\n }\\n\\n protected void init() {\\n webView = findViewById(R.id.webview);\\n handleLongClick(); //长按保存图片\\n fullScreenView = findViewById(R.id.video_fullscreen); //webview视频全屏播放\\n webContainer = findViewById(R.id.web_parent);\\n progressBar = findViewById(R.id.pb);\\n loadingBar = findViewById(R.id.loadingBar);\\n loadingBar.setOnClickListener(new View.OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n if (!loadingBar.canLoading()) {\\n return;\\n }\\n if (!NetworkUtils.isAvailable()) {\\n ToastUtil.show(R.string.net_noconnection);\\n return;\\n }\\n webView.reload();\\n }\\n });\\n\\n titleBarView = getTitleBar();\\n\\n if (null != titleBarView) {\\n View.OnClickListener backListener = new View.OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n if (handleBackEvent()) {\\n return;\\n }\\n close();\\n }\\n };\\n titleBarView.setOnLeftTxtClickListener(backListener);\\n titleBarView.setOnLeftBtnClickListener(backListener);\\n\\n\\n //刷新按钮\\n titleBarView.setRightBtnDrawable(R.drawable.icon_refresh);\\n titleBarView.mRightButton.setOnClickListener(new View.OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n if (!NetworkUtils.isAvailable()) {\\n ToastUtil.show(R.string.net_noconnection);\\n return;\\n }\\n webView.reload();\\n }\\n });\\n\\n //分享\\n if (null != webViewParam.shareInfo) {\\n titleBarView.setRightBtnDrawable(R.drawable.icon_share);\\n titleBarView.mRightButton.setOnClickListener(new View.OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n ShareDialog.share(getActivity(), webViewParam.shareInfo);\\n }\\n });\\n }\\n\\n if (webViewParam.isShowShare) {\\n titleBarView.findViewById(R.id.title_icon_share).setVisibility(View.VISIBLE);\\n titleBarView.findViewById(R.id.title_icon_share).setOnClickListener(new View.OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n\\n }\\n });\\n } else {\\n titleBarView.findViewById(R.id.title_icon_share).setVisibility(View.GONE);\\n }\\n }\\n\\n\\n WebSettings webSettings = webView.getSettings();\\n webSettings.setJavaScriptEnabled(true);\\n webSettings.setAllowFileAccess(true);\\n webSettings.setLoadWithOverviewMode(true);\\n webSettings.setDomStorageEnabled(true);\\n webSettings.setUseWideViewPort(true);\\n webSettings.setLoadWithOverviewMode(true);\\n webSettings.setMediaPlaybackRequiresUserGesture(false);//自动播放视频\\n webSettings.setAllowContentAccess(true); // 是否可访问Content Provider的资源，默认值 true\\n webSettings.setAllowFileAccess(true); // 是否可访问本地文件，默认值 true\\n // 是否允许通过file url加载的Javascript读取本地文件，默认值 false\\n webSettings.setAllowFileAccessFromFileURLs(false);\\n // 是否允许通过file url加载的Javascript读取全部资源(包括文件,http,https)，默认值 false\\n webSettings.setAllowUniversalAccessFromFileURLs(false);\\n\\n\\n //设置缓存\\n// webSettings.setCacheMode(WebSettings.LOAD_CACHE_ELSE_NETWORK);\\n webSettings.setCacheMode(WebSettings.LOAD_NO_CACHE);\\n\\n int statusBarHeight = 0;\\n try {\\n statusBarHeight = BarUtils.getStatusBarHeight();\\n statusBarHeight = SizeUtils.px2dp(statusBarHeight);\\n } catch (Exception e) {\\n statusBarHeight = 25;\\n }\\n LogUtil.d(TAG, \\\"statusBarHeight=\\\" + statusBarHeight);\\n\\n original_Url = webViewParam.url;\\n\\n //拼接参数\\n Uri uri = Uri.parse(webViewParam.url);\\n Uri.Builder builder = uri.buildUpon();\\n Set<String> parameterNames = uri.getQueryParameterNames();\\n if (!parameterNames.contains(\\\"appVersion\\\")) {\\n builder.appendQueryParameter(\\\"appVersion\\\", LContext.versionName);\\n }\\n if (!parameterNames.contains(\\\"isApp\\\")) {\\n builder.appendQueryParameter(\\\"isApp\\\", \\\"1\\\");\\n }\\n\\n if (!parameterNames.contains(\\\"statusBarH\\\")) { //状态栏高度\\n builder.appendQueryParameter(\\\"statusBarH\\\", statusBarHeight + \\\"\\\");\\n }\\n\\n webViewParam.url = builder.build().toString();\\n\\n //标题栏背景\\n if (webViewParam.titleBarColor != null) {\\n getTitleBar().setBackgroundColor(Color.parseColor(webViewParam.titleBarColor));\\n getTitleBar().setTitleTextColor(R.color.white);\\n getTitleBar().setLeftBtnWhiteColor();\\n } else {\\n titleBarView.setBackgroundColor(Color.WHITE);\\n }\\n\\n uri = Uri.parse(webViewParam.url);\\n //隐藏标题栏\\n String hideTitlebar = uri.getQueryParameter(\\\"hideTitlebar\\\");\\n if (TextUtils.equals(hideTitlebar, \\\"1\\\")) {\\n titleBarView.setVisibility(View.GONE);\\n String lightModel = uri.getQueryParameter(\\\"lightModel\\\");\\n if (TextUtils.equals(lightModel, \\\"1\\\")) {\\n StatusBar.setStatusBar(mContext, true, titleBarView);\\n } else {\\n StatusBar.setStatusBar(mContext, false, titleBarView);\\n }\\n } else {\\n StatusBar.setStatusBar(mContext, true, titleBarView);\\n }\\n\\n\\n LogUtil.d(TAG, \\\"加载 url=\\\" + webViewParam.url);\\n webView.loadUrl(webViewParam.url);\\n webView.setWebChromeClient(new DWebChromeClient());\\n webView.setWebViewClient(new DWebViewClient());\\n webView.setDownloadListener(new DWebViewDownLoadListener());\\n\\n }\\n\\n /**\\n * 辅助WebView处理Javascript的对话框、网站图标、网站title、加载进度等\\n */\\n private final class DWebChromeClient extends WebChromeClient {\\n\\n /**\\n * 全屏模式\\n *\\n * @param view 需要展示的view内容\\n * @param callback\\n */\\n @Override\\n public void onShowCustomView(View view, CustomViewCallback callback) {\\n super.onShowCustomView(view, callback);\\n LogUtil.d(TAG, \\\"onShowCustomView view=\\\" + view);\\n try {\\n ViewGroup parent = (ViewGroup) webView.getParent();\\n parent.removeView(webView);\\n\\n fullScreenView.addView(view);\\n fullScreenView.setVisibility(View.VISIBLE);\\n getTitleBar().setVisibility(View.GONE);\\n myView = view;\\n setFullScreen();\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n }\\n\\n @Override\\n public void onHideCustomView() {\\n super.onHideCustomView();\\n LogUtil.d(TAG, \\\"onHideCustomView \\\");\\n try {\\n if (myView != null) {\\n fullScreenView.removeAllViews();\\n webContainer.addView(webView);\\n fullScreenView.setVisibility(View.GONE);\\n\\n getTitleBar().setVisibility(View.VISIBLE);\\n myView = null;\\n quitFullScreen();\\n }\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n }\\n\\n @Override\\n public boolean onJsAlert(WebView view, String url, String message, final JsResult result) {\\n LogUtil.d(TAG, \\\"onJsAlert,url=\\\" + url + \\\",message=\\\" + message);\\n AlertDialogView dialogView = new AlertDialogView(webView.getContext())\\n .setTitle(\\\"提示\\\")\\n .setContent(message)//\\n .setSingleBtn(\\\"确定\\\", new View.OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n result.confirm();\\n }\\n });\\n\\n new EffectDialogBuilder(webView.getContext())\\n .setCancelable(false)\\n .setCancelableOnTouchOutside(false)\\n .setContentView(dialogView).show();\\n\\n// return super.onJsAlert(view, url, message, result);\\n return true;\\n }\\n\\n @Override\\n public boolean onJsBeforeUnload(WebView view, String url, String message, JsResult result) {\\n return super.onJsBeforeUnload(view, url, message, result);\\n }\\n\\n @Override\\n public boolean onJsConfirm(WebView view, String url, String message, final JsResult result) {\\n LogUtil.d(TAG, \\\"onJsConfirm,url=\\\" + url + \\\",message=\\\" + message);\\n AlertDialogView dialogView = new AlertDialogView(webView.getContext())\\n .setContent(message)//\\n .setRightBtn(\\\"确定\\\", new View.OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n result.confirm();\\n }\\n })\\n .setLeftBtn(\\\"取消\\\", new View.OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n result.cancel();\\n }\\n });\\n new EffectDialogBuilder(webView.getContext())\\n .setCancelable(false)\\n .setCancelableOnTouchOutside(false)\\n .setContentView(dialogView).show();\\n// return super.onJsConfirm(view, url, message, result);\\n return true;\\n }\\n\\n @Override\\n public boolean onJsPrompt(WebView view, String url, String message, String defaultValue, JsPromptResult result) {\\n LogUtil.d(TAG, \\\"onJsPrompt,url=\\\" + url + \\\",message=\\\" + message);\\n return super.onJsPrompt(view, url, message, defaultValue, result);\\n }\\n\\n\\n @Override\\n public void onReceivedTitle(WebView view, String title) {\\n super.onReceivedTitle(view, title);\\n LogUtil.d(TAG, \\\"onReceivedTitle,url=\\\" + view.getUrl());\\n LogUtil.d(TAG, \\\"onReceivedTitle,title=\\\" + title);\\n if (webViewParam.shouldResetTitle && !TextUtils.isEmpty(title) && !title.startsWith(\\\"http\\\")) {\\n if (null != titleBarView) {\\n titleBarView.setTitleText(title);\\n }\\n if (!\\\"登录\\\".equals(title)) {\\n webViewParam.title = title;\\n }\\n }\\n\\n }\\n\\n /**\\n * 上传文件\\n *\\n * @param webView\\n * @param filePathCallback\\n * @param fileChooserParams\\n * @return\\n */\\n @Override\\n public boolean onShowFileChooser(WebView webView,\\n ValueCallback<Uri[]> filePathCallback,\\n FileChooserParams fileChooserParams) {\\n fileCallback = filePathCallback;\\n openFileChooser(null != fileChooserParams ? fileChooserParams.getAcceptTypes() : null);\\n return true;\\n }\\n }\\n\\n /**\\n * 设置全屏\\n */\\n private void setFullScreen() {\\n // 设置全屏的相关属性，获取当前的屏幕状态，然后设置全屏\\n try {\\n getActivity().getWindow().setFlags(WindowManager.LayoutParams.FLAG_FULLSCREEN,\\n WindowManager.LayoutParams.FLAG_FULLSCREEN);\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n }\\n\\n /**\\n * 退出全屏\\n */\\n private void quitFullScreen() {\\n // 声明当前屏幕状态的参数并获取\\n try {\\n final WindowManager.LayoutParams attrs = getActivity().getWindow().getAttributes();\\n attrs.flags &= (~WindowManager.LayoutParams.FLAG_FULLSCREEN);\\n getActivity().getWindow().setAttributes(attrs);\\n getActivity().getWindow().clearFlags(WindowManager.LayoutParams.FLAG_LAYOUT_NO_LIMITS);\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n }\\n\\n private ValueCallback<Uri[]> fileCallback;\\n private static final int FILE_CHOOSER_RESULT_CODE = 11;\\n\\n\\n /**\\n * webview选择上传图片或视频\\n *\\n * @param types\\n */\\n private void openFileChooser(String[] types) {\\n LogUtil.d(TAG, \\\"onShowFileChooser 请求type=\\\" + Arrays.toString(types));\\n String type = \\\"\\\";\\n if (null != types && types.length > 0) {\\n type = types[0];\\n }\\n if (TextUtils.isEmpty(type)) {\\n type = \\\"image/*;video/*\\\";\\n }\\n\\n if (type.contains(\\\"jpg\\\") || type.contains(\\\"png\\\")) {\\n type = \\\"image/*\\\";\\n } else if (type.contains(\\\"mp4\\\")) {\\n type = \\\"video/*\\\";\\n }\\n LogUtil.d(TAG, \\\"onShowFileChooser 执行type=\\\" + type);\\n\\n try {\\n Intent i = new Intent(Intent.ACTION_GET_CONTENT);\\n i.addCategory(Intent.CATEGORY_OPENABLE);\\n i.putExtra(Intent.EXTRA_LOCAL_ONLY, true);\\n i.setType(type);\\n startActivityForResult(Intent.createChooser(i, \\\"选择图片/视频\\\"), FILE_CHOOSER_RESULT_CODE);\\n } catch (Exception e) {\\n e.printStackTrace();\\n ToastUtil.show(\\\"无法选择文件\\\");\\n fileCallback.onReceiveValue(null);\\n fileCallback = null;\\n }\\n }\\n\\n /**\\n * 长按保存图片\\n */\\n private void handleLongClick() {\\n View.OnLongClickListener onLongClickListener = new View.OnLongClickListener() {\\n @Override\\n public boolean onLongClick(View v) {\\n final WebView.HitTestResult hitTestResult = webView.getHitTestResult();\\n if (null == hitTestResult) return false;\\n int type = hitTestResult.getType();\\n LogUtil.d(TAG, \\\"onLongClick type=\\\" + type);\\n // 如果是图片类型或者是带有图片链接的类型\\n if (type == WebView.HitTestResult.IMAGE_TYPE ||\\n type == WebView.HitTestResult.SRC_IMAGE_ANCHOR_TYPE) {\\n AlertDialogView dialogView = new AlertDialogView(webView.getContext())\\n .setContent(\\\"保存到相册\\\")//\\n .setRightBtn(\\\"确定\\\", new View.OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n String pic = hitTestResult.getExtra();//获取图片\\n LogUtil.d(TAG, \\\"onLongClick 图片url=\\\" + pic);\\n if (TextUtils.isEmpty(pic)) {\\n return;\\n }\\n new FileDownloader(mContext)\\n .downloadFile(pic, new FileDownloader.DownloadCallback() {\\n @Override\\n public void success(File file, String url) {\\n ToastUtil.show(\\\"保存成功\\\");\\n }\\n\\n @Override\\n public void fail(String url) {\\n ToastUtil.show(\\\"保存失败\\\");\\n }\\n });\\n }\\n })\\n .setLeftBtn(\\\"取消\\\", new View.OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n\\n }\\n });\\n new EffectDialogBuilder(webView.getContext())\\n .setCancelable(false)\\n .setCancelableOnTouchOutside(false)\\n .setContentView(dialogView).show();\\n\\n return true;\\n }\\n return false; //保持长按复制文字\\n }\\n };\\n webView.setOnLongClickListener(onLongClickListener);\\n }\\n\\n @Override\\n public void onActivityResult(int requestCode, int resultCode, Intent data) {\\n super.onActivityResult(requestCode, resultCode, data);\\n //上传图片或视频\\n if (requestCode == FILE_CHOOSER_RESULT_CODE) {\\n if (null == fileCallback) return;\\n try {\\n Uri[] results = null;\\n if (resultCode == Activity.RESULT_OK && data != null) {\\n String dataString = data.getDataString();\\n ClipData clipData = data.getClipData();\\n LogUtil.d(TAG, \\\"onShowFileChooser,dataString=\\\" + dataString + \\\",clipData=\\\" + clipData);\\n if (!TextUtils.isEmpty(dataString)) {\\n results = new Uri[]{Uri.parse(dataString)};\\n } else if (clipData != null && clipData.getItemCount() > 0) {\\n results = new Uri[clipData.getItemCount()];\\n for (int i = 0; i < clipData.getItemCount(); i++) {\\n ClipData.Item item = clipData.getItemAt(i);\\n results[i] = item.getUri();\\n }\\n }\\n }\\n\\n\\n LogUtil.d(TAG, \\\"onShowFileChooser,results=\\\" + results);\\n fileCallback.onReceiveValue(results);\\n fileCallback = null;\\n } catch (Exception e) {\\n e.printStackTrace();\\n ToastUtil.show(\\\"选择文件失败\\\");\\n fileCallback.onReceiveValue(null);\\n fileCallback = null;\\n }\\n }\\n }\\n\\n\\n /**\\n * 主要帮助WebView处理各种通知、请求事件的\\n */\\n private final int LOAD_START = 1;\\n private final int LOAD_ERROR = 2;\\n private final int LOAD_FINISHED = 3;\\n private int loadStatus = LOAD_FINISHED;\\n\\n private class DWebViewClient extends WebViewClient {\\n\\n @Override\\n public void onLoadResource(WebView view, String url) {\\n progressBar.setProgress(view.getProgress());\\n super.onLoadResource(view, url);\\n }\\n\\n @Override\\n public void onPageStarted(WebView view, String url, Bitmap favicon) {\\n loadStatus = LOAD_START;\\n progressBar.setProgress(1);\\n progressBar.setVisibility(View.VISIBLE);\\n super.onPageStarted(view, url, favicon);\\n }\\n\\n @Override\\n public void onPageFinished(WebView view, String url) {\\n progressBar.setProgress(100);\\n progressBar.setVisibility(View.GONE);\\n if (loadStatus != LOAD_ERROR) {\\n loadingBar.setLoadingStatus(LoadingBar.LoadingStatus.SUCCESS);\\n }\\n loadStatus = LOAD_FINISHED;\\n super.onPageFinished(view, url);\\n }\\n\\n @Override\\n public void onReceivedError(WebView view, int errorCode,\\n String description, String failingUrl) {\\n super.onReceivedError(view, errorCode, description, failingUrl);\\n loadStatus = LOAD_ERROR;\\n //显示上层的错误页面\\n if (!NetworkUtils.isAvailable()) {\\n loadingBar.setLoadingStatus(LoadingBar.LoadingStatus.NOCONNECTION, R.drawable.icon_fail);\\n } else {\\n loadingBar.setLoadingStatus(LoadingBar.LoadingStatus.RELOAD, R.drawable.icon_fail);\\n }\\n }\\n\\n @Override\\n public void onReceivedSslError(WebView view, SslErrorHandler handler,\\n SslError error) {\\n handler.proceed();\\n }\\n\\n @Override\\n public boolean shouldOverrideUrlLoading(WebView view, String url) {\\n return handleUrl(url);\\n }\\n }\\n\\n /**\\n * 下载\\n *\\n * @author zhangquan\\n */\\n private class DWebViewDownLoadListener implements DownloadListener {\\n\\n @Override\\n public void onDownloadStart(String url, String userAgent,\\n String contentDisposition, String mimetype, long contentLength) {\\n try {\\n Uri uri = Uri.parse(url);\\n Intent intent = new Intent(Intent.ACTION_VIEW, uri);\\n startActivity(intent);\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n }\\n }\\n\\n @Override\\n public void close() {\\n finish();\\n }\\n\\n @Override\\n public boolean onBackPressedSupport() {\\n return handleBackEvent();\\n }\\n\\n @Override\\n public void onDestroy() {\\n super.onDestroy();\\n releaseWebView();\\n }\\n\\n private void releaseWebView() {\\n try {\\n if (null != webView) {\\n webView.setVisibility(View.GONE);\\n webView.removeAllViews();\\n webView.destroy();\\n }\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n }\\n\\n //------------------------------url handle\\n @Subscribe(threadMode = ThreadMode.MAIN)\\n public void onEvent(EventMsg eventMsg) {\\n if (TextUtils.equals(eventMsg.getAction(), Constant.Event.LOGIN_SUCCESS)) {\\n loginCallback();\\n }\\n }\\n\\n private static final String URL_SHARE = \\\"xkd://app/share?param=\\\"; //分享\\n private static final String URL_GETTOKEN = \\\"xkd://user/getToken\\\";//获取token\\n private static final String URL_USERINFO = \\\"xkd://user/info\\\";//获取用户信息\\n private static final String URL_LOGIN = \\\"xkd://user/login\\\"; //登录\\n private static final String URL_OPENPAGE = \\\"xkd://app/appPage?param=\\\";//打开原生页面\\n private static final String URL_CLOSEPAGE = \\\"xkd://app/closepage\\\";//关闭页面\\n private static final String URL_OPENURL = \\\"xkd://app/openURL\\\";//通过浏览器打开\\n private static final String URL_CLIPBOARD = \\\"xkd://app/clipboard\\\"; //复制到剪贴板\\n private static final String URL_CLIPBOARD_READ = \\\"xkd://app/readclipboard\\\"; //读取剪贴板\\n private static final String URL_NOTIFICATION_ENABLED = \\\"xkd://app/notification/status\\\";//获取通知状态 1开启 0关闭\\n private static final String URL_NOTIFICATION_OPEN = \\\"xkd://app/notification/setting\\\";//打开通知设置界面\\n private static final String URL_SAVE_PIC = \\\"xkd://app/storage/imgs\\\"; //保存图片\\n private static final String URL_STATUSBAR = \\\"xkd://app/statusbar\\\"; //修改状态栏\\n private static final String URL_H5_ADJUMP = \\\"xkd://app/nativeAdJump\\\";//h5广告跳转\\n private static final String URL_SAVE_PHOTOS = \\\"xkd://savedPhotosAlbum\\\";\\n\\n\\n private static final String URL_WEIXIN = \\\"weixin://\\\"; //打开微信\\n private static final String URL_OPEN_ALIPAY = \\\"alipays://\\\"; //打开支付宝\\n private static final String URL_VPAGE = \\\"vipshop://\\\";//唯品会\\n private static final String URL_TPOPEN = \\\"tbopen://\\\";//淘宝t\\n private static final String URL_TAOBAO = \\\"taobao://\\\";//淘宝\\n private static final String URL_PDD = \\\"pinduoduo://\\\";//拼多多\\n private static final String URL_BDISK = \\\"bdnetdisk://\\\";//百度网盘\\n private static final String URL_TIANMAO = \\\"tmall://\\\";//天猫\\n private static final String URL_MEITUAN = \\\"imeituan://\\\";//美团\\n private static final String URL_QQ = \\\"wtloginmqq://\\\";//QQ\\n private static final String URL_SUNING = \\\"suning://\\\";//苏宁\\n\\n\\n private boolean handleUrl(String path) {\\n LogUtil.d(TAG, \\\"shouldOverrideUrlLoading ,url=\\\" + path);\\n\\n //拼多多\\n if (path.startsWith(URL_PDD)) {\\n openUrl(path, \\\"打开失败，检查是否安装拼多多APP \\\");\\n return true;\\n }\\n\\n //唯品会\\n if (path.startsWith(URL_VPAGE)) {\\n openUrl(path, \\\"打开失败，检查是否安装唯品会APP\\\");\\n return true;\\n }\\n\\n //打开淘宝\\n if (path.startsWith(URL_TPOPEN)) {\\n openUrl(path, \\\"打开失败，检查是否安装淘宝APP\\\");\\n return true;\\n }\\n\\n if (path.startsWith(URL_TAOBAO)) {\\n openUrl(path, \\\"打开失败，检查是否安装淘宝APP\\\");\\n return true;\\n }\\n\\n //打开苏宁\\n if (path.startsWith(URL_SUNING)) {\\n openUrl(path, \\\"\\\");\\n return true;\\n }\\n\\n\\n //拨打电话\\n if (path.startsWith(\\\"tel\\\")) {\\n try {\\n startActivity(new Intent(Intent.ACTION_DIAL, Uri.parse(path)));\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n return true;\\n }\\n\\n\\n //打开支付宝\\n if (path.startsWith(URL_OPEN_ALIPAY)) {\\n openUrl(path, \\\"打开失败，检查是否安装支付宝App\\\");\\n return true;\\n }\\n\\n\\n //跳转到微信\\n if (path.startsWith(URL_WEIXIN)) {\\n openUrl(path, \\\"打开失败，检查是否安装微信App\\\");\\n return true;\\n }\\n\\n //百度网盘\\n if (path.startsWith(URL_BDISK)) {\\n openUrl(path, \\\"打开失败，检查是否安装百度网盘APP\\\");\\n return true;\\n }\\n\\n if (path.startsWith(URL_MEITUAN)) {\\n openUrl(path, \\\"打开失败，检查是否安装美团APP (非美团外卖APP) \\\");\\n return true;\\n }\\n if (path.startsWith(URL_QQ)) {\\n openUrl(path, \\\"打开失败\\\");\\n return true;\\n }\\n if (path.startsWith(URL_SUNING)) {\\n openUrl(path, \\\"\\\");\\n return true;\\n }\\n\\n //自定义协议\\n if (!path.startsWith(LContext.getString(R.string.app_schema))) {\\n return false;\\n }\\n try {\\n String param = Uri.parse(path).getQueryParameter(\\\"param\\\");\\n if (path.startsWith(URL_SHARE)) {\\n share(param);\\n } else if (path.startsWith(URL_GETTOKEN)) {\\n loginCallback();\\n } else if (path.startsWith(URL_USERINFO)) {\\n userInfoCallback();\\n } else if (path.startsWith(URL_LOGIN)) {\\n login();\\n } else if (path.startsWith(URL_OPENURL)) { //打开浏览器\\n openUrl(param, \\\"打开失败\\\");\\n } else if (path.startsWith(URL_OPENPAGE)) { //打开APP页面\\n DispatchUtil.goToPage(mContext, param);\\n if (!TextUtils.isEmpty(param)) {\\n JSONObject jsonObject = new JSONObject(param);\\n int closepage = jsonObject.optInt(\\\"closepage\\\");\\n if (closepage == 1) {\\n close();\\n }\\n }\\n } else if (path.startsWith(URL_CLOSEPAGE)) { //关闭页面\\n close();\\n } else if (path.startsWith(URL_CLIPBOARD)) { //复制到剪贴板\\n try {\\n ClipboardManager cm = (ClipboardManager) LContext.getContext().getSystemService(Context.CLIPBOARD_SERVICE);\\n cm.setText(param);\\n ToastUtil.show(\\\"复制成功\\\");\\n } catch (Exception e) {\\n e.printStackTrace();\\n ToastUtil.show(\\\"复制失败，请长按文字复制\\\");\\n }\\n } else if (path.startsWith(URL_CLIPBOARD_READ)) { //读取剪贴板\\n readClipboard();\\n } else if (path.startsWith(URL_NOTIFICATION_ENABLED)) { //app是否开启通知权限\\n notificationCallback();\\n } else if (path.startsWith(URL_NOTIFICATION_OPEN)) { //跳转到app通知权限页面\\n NotificationPageHelper.openNotificationSetting(mContext);\\n } else if (path.startsWith(URL_SAVE_PIC)) { //保存图片到相册\\n if (!TextUtils.isEmpty(param)) {\\n LoadingDialog loadingDialog = LoadingDialog.showCancelableDialog(mContext, \\\"图片保存中\\\");\\n new FileDownloader(mContext).downloadFile(param, new FileDownloader.DownloadCallback() {\\n @Override\\n public void success(File file, String url) {\\n loadingDialog.dismiss();\\n ToastUtil.show(\\\"保存成功\\\");\\n }\\n\\n @Override\\n public void fail(String url) {\\n loadingDialog.dismiss();\\n ToastUtil.show(\\\"保存失败\\\");\\n }\\n });\\n } else {\\n ToastUtil.show(\\\"图片地址为空\\\");\\n }\\n } else if (path.startsWith(URL_STATUSBAR)) { //修改状态栏\\n boolean lightModel = TextUtils.equals(param, \\\"1\\\");\\n StatusBar.setStatusBar(mContext, lightModel, titleBarView);\\n } else if (path.startsWith(URL_SAVE_PHOTOS)) {\\n savePics(param);\\n }\\n\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n return true;\\n }\\n\\n /**\\n * 保存图片或视频到相册\\n *\\n * @param param\\n */\\n private void savePics(String param) {\\n JSONObject jsonObject = null;\\n try {\\n List<String> saveFiles = new ArrayList<>();\\n\\n jsonObject = new JSONObject(param);\\n\\n JSONArray jsonElements = jsonObject.optJSONArray(\\\"videos\\\");\\n if (jsonElements != null && jsonElements.length() > 0) {\\n for (int i = 0; i < jsonElements.length(); i++) {\\n saveFiles.add(jsonElements.get(i).toString());\\n }\\n }\\n jsonElements = jsonObject.optJSONArray(\\\"images\\\");\\n if (jsonElements != null && jsonElements.length() > 0) {\\n for (int i = 0; i < jsonElements.length(); i++) {\\n saveFiles.add(jsonElements.get(i).toString());\\n }\\n }\\n\\n if (!saveFiles.isEmpty()) {\\n FileDownloadDialogView dialogView = new FileDownloadDialogView(mContext, saveFiles, \\\"素材下载\\\");\\n new EffectDialogBuilder(mContext)\\n .setContentView(dialogView)\\n .setCancelable(true)\\n .show();\\n }\\n\\n } catch (JSONException e) {\\n e.printStackTrace();\\n }\\n }\\n\\n private void openUrl(String param, String msg) {\\n try {\\n Intent intent = new Intent(Intent.ACTION_VIEW);\\n intent.addCategory(Intent.CATEGORY_BROWSABLE);\\n intent.setData(Uri.parse(param));\\n mContext.startActivity(intent);\\n LogUtil.e(\\\"url=\\\" + param + \\\",\\\" + intent.resolveActivity(getActivity().getPackageManager()));\\n } catch (Exception e) {\\n e.printStackTrace();\\n if (!TextUtils.isEmpty(msg)) {\\n ToastUtil.show(msg);\\n }\\n }\\n }\\n\\n /**\\n * 登录\\n */\\n public void login() {\\n// LoginFragment.Companion.start(mContext);\\n }\\n\\n /**\\n * 登录回调\\n */\\n private void loginCallback() {\\n// String token = UserClient.getToken();\\n// webView.loadUrl(\\\"javascript:userLoginStatus('\\\" + token + \\\"')\\\");\\n }\\n\\n /**\\n * 获取用户信息回调\\n */\\n private void userInfoCallback() {\\n// UserEntity user = UserClient.getUser();\\n// String userInfo = \\\"\\\";\\n// if (null != user) {\\n// userInfo = new Gson().toJson(user);\\n// }\\n// webView.loadUrl(\\\"javascript:userInfoCallback('\\\" + userInfo + \\\"')\\\");\\n }\\n\\n private void viewWillAppear() {\\n webView.loadUrl(\\\"javascript:viewWillAppearEvent()\\\");\\n }\\n\\n private void notificationCallback() {\\n int status = NotificationPageHelper.areNotificationsEnabled(mContext) ? 1 : 0;\\n webView.loadUrl(\\\"javascript:notificationEnabled('\\\" + status + \\\"')\\\");\\n }\\n\\n /**\\n * 读取剪贴板\\n */\\n private void readClipboard() {\\n ClipboardManager cm = (ClipboardManager) mContext.getSystemService(Context.CLIPBOARD_SERVICE);\\n CharSequence text = \\\"\\\";\\n ClipData clipData = cm.getPrimaryClip();\\n if (clipData != null && clipData.getItemCount() > 0) {\\n text = clipData.getItemAt(0).getText();\\n }\\n if (!TextUtils.isEmpty(text)) {\\n String content = text.toString();\\n String[] test = content.split(\\\"\\\\n\\\");\\n StringBuffer sb = new StringBuffer();\\n for (String line : test) {\\n sb.append(line).append(\\\"\\\\\\\\n\\\");\\n }\\n webView.loadUrl(\\\"javascript:readClipboard('\\\" + sb.toString() + \\\"')\\\");\\n CommonUtil.addToClipboard(null);\\n }\\n }\\n\\n /**\\n * 分享\\n */\\n public void share(String json) throws Exception {\\n if (TextUtils.isEmpty(json)) return;\\n //{\\\"title\\\":\\\"分享标题\\\",\\\"content\\\":\\\"分享内容\\\",\\\"url\\\":\\\"分享链接\\\",\\\"pic_url\\\":\\\"分享图片链接\\\"}\\n\\n ShareInfo shareInfo = new ShareInfo();\\n JSONObject jsonObject = new JSONObject(json);\\n shareInfo.title = jsonObject.optString(\\\"title\\\");\\n shareInfo.content = jsonObject.optString(\\\"content\\\");\\n shareInfo.url = jsonObject.optString(\\\"url\\\");\\n shareInfo.pic_url = jsonObject.optString(\\\"pic_url\\\");\\n\\n ShareDialog.share(getActivity(), shareInfo);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5748160481452942,\n \"alphanum_fraction\": 0.5748160481452942,\n \"avg_line_length\": 26.177778244018555,\n \"blob_id\": \"d12308eb60fbed0cd208a0ba88e3ccafe4c93b5a\",\n \"content_id\": \"10c96b48392553b4a04516664c0c8f6b1e0ab42d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1247,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 45,\n \"path\": \"/app/src/main/java/com/android/base/util/DispatchUtil.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.util;\\n\\n\\nimport android.app.Activity;\\nimport android.text.TextUtils;\\n\\nimport com.android.util.log.LogUtil;\\nimport com.android.base.ui.WebViewFrag;\\nimport com.fastapp.MainActivity;\\n\\nimport org.json.JSONObject;\\n\\n/**\\n * @author 张全\\n */\\npublic class DispatchUtil {\\n\\n /**\\n * 跳转页面\\n */\\n public static void goToPage(Activity ctx, String json) throws Exception {\\n LogUtil.d(\\\"DispatchUtil\\\", \\\"请求参数 json=\\\" + json);\\n JSONObject jsonObject = new JSONObject(json);\\n String page = jsonObject.optString(\\\"page\\\");\\n JSONObject dataJson = jsonObject.optJSONObject(\\\"data\\\");\\n\\n// UserEntity user = UserClient.getUser();\\n switch (page) {\\n case \\\"HomePage\\\": //首页\\n MainActivity.start(ctx);\\n break;\\n case \\\"WebViewPage\\\":\\n String url = dataJson.optString(\\\"url\\\");\\n if (!TextUtils.isEmpty(url)) {\\n WebViewFrag.WebViewParam webViewParam = new WebViewFrag.WebViewParam();\\n webViewParam.url = url;\\n WebViewFrag.start(ctx, webViewParam);\\n }\\n break;\\n default:\\n break;\\n }\\n }\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6855496764183044,\n \"alphanum_fraction\": 0.6855496764183044,\n \"avg_line_length\": 30.074626922607422,\n \"blob_id\": \"543a20503f98c2973d69d501d80167df4cdb73e2\",\n \"content_id\": \"f022e85ebc128604613c40aea9e14d36554524c4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Kotlin\",\n \"length_bytes\": 2095,\n \"license_type\": \"no_license\",\n \"max_line_length\": 95,\n \"num_lines\": 67,\n \"path\": \"/app/src/main/java/com/android/base/ui/splash/PrivacyDialog.kt\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.ui.splash\\n\\nimport android.annotation.SuppressLint\\nimport android.graphics.Color\\nimport android.graphics.drawable.ColorDrawable\\nimport android.os.Bundle\\nimport android.view.KeyEvent\\nimport android.view.LayoutInflater\\nimport android.view.View\\nimport android.view.ViewGroup\\nimport androidx.fragment.app.DialogFragment\\nimport com.android.base.util.ext.onClick\\nimport com.android.util.common.CommonCallBack\\nimport com.fastapp.R\\nimport kotlinx.android.synthetic.main.privacy_dialog.*\\nimport net.medlinker.android.splash.SplashUtil\\n\\n\\n/**\\n * 隐私政策弹窗\\n */\\nclass PrivacyDialog : DialogFragment() {\\n override fun onCreate(savedInstanceState: Bundle?) {\\n super.onCreate(savedInstanceState)\\n setStyle(STYLE_NO_FRAME, R.style.update_dialog_style)\\n }\\n\\n\\n override fun onCreateView(\\n inflater: LayoutInflater,\\n container: ViewGroup?,\\n savedInstanceState: Bundle?\\n ): View? = inflater.inflate(R.layout.privacy_dialog, container)\\n\\n @SuppressLint(\\\"CheckResult\\\")\\n override fun onViewCreated(view: View, savedInstanceState: Bundle?) {\\n super.onViewCreated(view, savedInstanceState)\\n val window = dialog?.window\\n window?.apply {\\n setLayout(ViewGroup.LayoutParams.MATCH_PARENT, ViewGroup.LayoutParams.MATCH_PARENT)\\n setBackgroundDrawable(ColorDrawable(Color.TRANSPARENT))\\n }\\n dialog?.setCancelable(false)\\n dialog?.setCanceledOnTouchOutside(false)\\n dialog?.setOnKeyListener { _, keyCode, _ ->\\n if (keyCode == KeyEvent.KEYCODE_BACK) {\\n return@setOnKeyListener true\\n }\\n return@setOnKeyListener false\\n }\\n\\n tv_cancel.onClick {\\n mCallBack?.onCallBack(false)\\n dismiss()\\n }\\n tv_know.onClick {\\n SplashUtil.setPrivacyGranted(true)\\n mCallBack?.onCallBack(true)\\n dismiss()\\n }\\n }\\n\\n private var mCallBack: CommonCallBack<Boolean>? = null\\n fun callback(callBack: CommonCallBack<Boolean>) {\\n mCallBack = callBack\\n }\\n}\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5235849022865295,\n \"alphanum_fraction\": 0.5235849022865295,\n \"avg_line_length\": 18.272727966308594,\n \"blob_id\": \"336a96a217e949fe1e41cf4cb9a2da59b47f3cbc\",\n \"content_id\": \"7bd6b7b4acdf83ed1f9ec8af23bec0658ec28ade\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 430,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 22,\n \"path\": \"/app/src/main/java/com/android/base/data/ResponseDataObject.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.data;\\n\\nimport androidx.annotation.Keep;\\n\\n/**\\n * data为JsonObject\\n *\\n * @author 张全\\n */\\n@Keep\\npublic class ResponseDataObject<T> extends ResponseData {\\n public T data;\\n\\n @Override\\n public String toString() {\\n return \\\"ResponseDataObject{\\\" +\\n \\\"data=\\\" + data +\\n \\\", code=\\\" + code +\\n \\\", message='\\\" + message + '\\\\'' +\\n '}';\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.678787887096405,\n \"alphanum_fraction\": 0.678787887096405,\n \"avg_line_length\": 11.692307472229004,\n \"blob_id\": \"f74d8c44e044f41fa8f0a7f3ba5c12d141ed3166\",\n \"content_id\": \"451a304285a9b45ef6b02ba9712b1f737fa71441\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 169,\n \"license_type\": \"no_license\",\n \"max_line_length\": 32,\n \"num_lines\": 13,\n \"path\": \"/app/src/main/java/com/android/base/data/ListData.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.data;\\n\\nimport androidx.annotation.Keep;\\n\\nimport java.util.List;\\n\\n/**\\n * @author 张全\\n */\\n@Keep\\npublic class ListData<T> {\\n List<T> list;\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6570892930030823,\n \"alphanum_fraction\": 0.6570892930030823,\n \"avg_line_length\": 19.278480529785156,\n \"blob_id\": \"cfce2d9a014cb4dd1d1cc20e94051aa592671a42\",\n \"content_id\": \"8f86b32d108da15c5d470a3b9d906b26e254e69a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1611,\n \"license_type\": \"no_license\",\n \"max_line_length\": 108,\n \"num_lines\": 79,\n \"path\": \"/librarys/CommonWidget/src/main/java/common/widget/adapter/RefreshFragmentAdapter.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package common.widget.adapter;\\n\\nimport androidx.fragment.app.Fragment;\\nimport androidx.fragment.app.FragmentManager;\\nimport androidx.fragment.app.FragmentPagerAdapter;\\nimport androidx.fragment.app.FragmentTransaction;\\n\\nimport java.util.List;\\n\\n/**\\n * 支持刷新的FragmentPagerAdapter\\n */\\npublic class RefreshFragmentAdapter extends FragmentPagerAdapter {\\n\\n private List<String> mTitleList;\\n\\n private List<Fragment> mFragmentList;\\n\\n private FragmentManager fm;\\n\\n public RefreshFragmentAdapter(FragmentManager fm, List<String> titleList, List<Fragment> fragmentList) {\\n\\n super(fm);\\n\\n this.fm = fm;\\n this.mTitleList = titleList;\\n this.mFragmentList = fragmentList;\\n\\n }\\n\\n @Override\\n\\n public Fragment getItem(int position) {\\n return mFragmentList.get(position);\\n\\n }\\n\\n @Override\\n\\n public int getCount() {\\n return mFragmentList.size();\\n\\n }\\n\\n public void setFragments(List<String> titleList, List<Fragment> fragments) {\\n\\n if (this.mFragmentList != null) {\\n\\n FragmentTransaction ft = fm.beginTransaction();\\n\\n for (Fragment f : this.mFragmentList) {\\n ft.remove(f);\\n }\\n\\n ft.commit();\\n fm.executePendingTransactions();\\n }\\n\\n this.mTitleList = titleList;\\n this.mFragmentList = fragments;\\n\\n notifyDataSetChanged();\\n\\n }\\n\\n @Override\\n\\n public int getItemPosition(Object object) {\\n\\n return POSITION_NONE;\\n\\n }\\n\\n @Override\\n public CharSequence getPageTitle(int position) {\\n return mTitleList.get(position);\\n }\\n\\n}\"\n },\n {\n \"alpha_fraction\": 0.4943999946117401,\n \"alphanum_fraction\": 0.498879998922348,\n \"avg_line_length\": 32.42245864868164,\n \"blob_id\": \"e461282b1b1b8483f09e064898d41f0f27a82d5a\",\n \"content_id\": \"41c6ec6dc68847a43e41914731b201c035bcdbd1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 6310,\n \"license_type\": \"no_license\",\n \"max_line_length\": 84,\n \"num_lines\": 187,\n \"path\": \"/app/src/main/java/com/android/base/version/UpdateDialogView.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.version;\\n\\nimport android.content.Context;\\nimport android.os.Handler;\\nimport android.os.Looper;\\nimport android.os.Message;\\nimport android.view.View;\\nimport android.view.View.OnClickListener;\\nimport android.widget.ProgressBar;\\nimport android.widget.TextView;\\n\\nimport com.android.util.ext.ToastUtil;\\nimport com.android.util.log.LogUtil;\\nimport com.android.base.data.RestClient;\\nimport com.fastapp.R;\\n\\nimport java.io.File;\\nimport java.io.FileOutputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\n\\nimport common.widget.dialog.DialogView;\\nimport component.update.AppDownloadClient;\\nimport component.update.AppVersion;\\nimport okhttp3.Call;\\nimport okhttp3.Callback;\\nimport okhttp3.Request;\\nimport okhttp3.Response;\\n\\n/**\\n * 强制升級框\\n *\\n * @author 张全\\n */\\npublic class UpdateDialogView extends DialogView {\\n private static final String TAG = \\\"VersionUpdate\\\";\\n AppVersion appVersion;\\n ProgressBar progressBar;\\n TextView tv_progress;\\n View reload;\\n public static long fileSize = -1;\\n\\n\\n public UpdateDialogView(Context ctx, AppVersion appVersion) {\\n super(ctx);\\n this.appVersion = appVersion;\\n }\\n\\n @Override\\n protected void initView(View view) {\\n progressBar = findViewById(R.id.pb);\\n tv_progress = findViewById(R.id.progress);\\n reload = findViewById(R.id.reload);\\n reload.setOnClickListener(new OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n startDownload();\\n }\\n });\\n startDownload();\\n }\\n\\n private void startDownload() {\\n reload.setVisibility(View.GONE);\\n try {\\n File updateFile = AppDownloadClient.getUpdateFile();\\n downLoadFile(appVersion.downloadUrl, updateFile);\\n } catch (Exception e) {\\n e.printStackTrace();\\n ToastUtil.show(\\\"下载失败\\\");\\n dismiss();\\n }\\n }\\n\\n @Override\\n protected int getLayoutId() {\\n return R.layout.update_dialog;\\n }\\n\\n\\n private class DownloadCallback extends Handler {\\n private static final int msg_success = 1;\\n private static final int msg_fail = 2;\\n private static final int msg_progress = 3;\\n\\n public DownloadCallback() {\\n super(Looper.getMainLooper());\\n }\\n\\n @Override\\n public void handleMessage(Message msg) {\\n switch (msg.what) {\\n case msg_progress:\\n int progress = (int) msg.obj;\\n progressBar.setProgress(progress);\\n tv_progress.setText(progress + \\\"%\\\");\\n break;\\n case msg_success:\\n LogUtil.e(TAG, \\\"下载成功\\\");\\n fileSize = AppDownloadClient.getUpdateFile().length();\\n AppDownloadClient.installAPK();\\n dismiss();\\n break;\\n case msg_fail:\\n LogUtil.e(TAG, \\\"下载失败\\\");\\n ToastUtil.show(\\\"下载失败,请重试\\\");\\n reload.setVisibility(View.VISIBLE);\\n break;\\n }\\n }\\n\\n public void sendMsg(int what) {\\n sendEmptyMessage(what);\\n }\\n\\n public void updateProgress(int progress) {\\n sendMessage(obtainMessage(msg_progress, progress));\\n }\\n }\\n\\n /**\\n * 下载文件\\n */\\n private void downLoadFile(String url, File file) {\\n\\n final Request request = new Request.Builder().url(url).build();\\n final Call call = RestClient.getDownloadClient().newCall(request);\\n DownloadCallback downloadCallback = new DownloadCallback();\\n call.enqueue(new Callback() {\\n @Override\\n public void onFailure(Call call, IOException e) {\\n downloadCallback.sendMsg(DownloadCallback.msg_fail);\\n }\\n\\n @Override\\n public void onResponse(Call call, Response response) {\\n int lastProgress = 0;\\n if (response.isSuccessful()) {\\n InputStream is = null;\\n byte[] buf = new byte[1024];\\n int len;\\n FileOutputStream fos = null;\\n try {\\n long total = response.body().contentLength();\\n LogUtil.e(TAG, \\\"total------>\\\" + total);\\n long current = 0;\\n is = response.body().byteStream();\\n fos = new FileOutputStream(file);\\n while ((len = is.read(buf)) != -1) {\\n current += len;\\n fos.write(buf, 0, len);\\n LogUtil.e(TAG, \\\"current------>\\\" + current);\\n if (total == -1) {\\n downloadCallback.updateProgress(-1);\\n } else {\\n int progress = (int) (current * 1.0f / total * 100);\\n progress = progress >= 100 ? 99 : progress;\\n if (lastProgress != progress) {\\n downloadCallback.updateProgress(progress);\\n }\\n lastProgress = progress;\\n }\\n }\\n fos.flush();\\n downloadCallback.sendMsg(DownloadCallback.msg_success);\\n } catch (Exception e) {\\n LogUtil.e(TAG, e.toString());\\n downloadCallback.sendMsg(DownloadCallback.msg_fail);\\n } finally {\\n try {\\n if (is != null) {\\n is.close();\\n }\\n if (fos != null) {\\n fos.close();\\n }\\n } catch (Exception e) {\\n LogUtil.e(TAG, e.toString());\\n }\\n }\\n } else {\\n downloadCallback.sendMsg(DownloadCallback.msg_fail);\\n }\\n }\\n });\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5645310878753662,\n \"alphanum_fraction\": 0.5676261186599731,\n \"avg_line_length\": 38.40243911743164,\n \"blob_id\": \"211857d2a05817335c2672d9802091cddeed7a3e\",\n \"content_id\": \"d255c6d78d62ae4734bcfcf324e020cc725936de\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 3593,\n \"license_type\": \"no_license\",\n \"max_line_length\": 125,\n \"num_lines\": 82,\n \"path\": \"/app/src/main/java/com/android/base/util/RecycleViewScrollToTop.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.util;\\n\\nimport android.view.View;\\nimport android.widget.ImageView;\\n\\nimport androidx.recyclerview.widget.LinearLayoutManager;\\nimport androidx.recyclerview.widget.RecyclerView;\\nimport androidx.recyclerview.widget.StaggeredGridLayoutManager;\\n\\n/**\\n * @author 张全\\n */\\npublic class RecycleViewScrollToTop {\\n\\n\\n public static void addScroolToTop(RecyclerView recyclerView, ImageView ivTop, StaggeredGridLayoutManager layoutManager) {\\n recyclerView.addOnScrollListener(new RecyclerView.OnScrollListener() {\\n @Override\\n public void onScrollStateChanged(RecyclerView recyclerView, int newState) {\\n super.onScrollStateChanged(recyclerView, newState);\\n //获得recyclerView的线性布局管理器\\n //获取到第一个item的显示的下标 不等于0表示第一个item处于不可见状态 说明列表没有滑动到顶部 显示回到顶部按钮\\n int[] pos = new int[10];\\n layoutManager.findFirstVisibleItemPositions(pos);\\n // 当不滚动时\\n if (newState == RecyclerView.SCROLL_STATE_IDLE) {\\n // 判断是否滚动超过一屏\\n if (pos[0] == 0) {\\n ivTop.setVisibility(View.GONE);\\n } else {\\n //显示回到顶部按钮\\n ivTop.setVisibility(View.VISIBLE);\\n }\\n //获取RecyclerView滑动时候的状态\\n }\\n else if (newState == RecyclerView.SCROLL_STATE_DRAGGING) {//拖动中\\n ivTop.setVisibility(View.GONE);\\n }\\n }\\n });\\n ivTop.setOnClickListener(new View.OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n recyclerView.scrollToPosition(0);\\n ivTop.setVisibility(View.GONE);\\n }\\n });\\n }\\n\\n\\n public static void addScroolToTop(RecyclerView recyclerView, ImageView ivTop) {\\n recyclerView.addOnScrollListener(new RecyclerView.OnScrollListener() {\\n @Override\\n public void onScrollStateChanged(RecyclerView recyclerView, int newState) {\\n super.onScrollStateChanged(recyclerView, newState);\\n //获得recyclerView的线性布局管理器\\n LinearLayoutManager manager = (LinearLayoutManager) recyclerView.getLayoutManager();\\n //获取到第一个item的显示的下标 不等于0表示第一个item处于不可见状态 说明列表没有滑动到顶部 显示回到顶部按钮\\n int firstVisibleItemPosition = manager.findFirstVisibleItemPosition();\\n // 当不滚动时\\n if (newState == RecyclerView.SCROLL_STATE_IDLE) {\\n // 判断是否滚动超过一屏\\n if (firstVisibleItemPosition == 0) {\\n ivTop.setVisibility(View.GONE);\\n } else {\\n //显示回到顶部按钮\\n ivTop.setVisibility(View.VISIBLE);\\n }\\n //获取RecyclerView滑动时候的状态\\n }\\n }\\n });\\n ivTop.setOnClickListener(new View.OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n recyclerView.scrollToPosition(0);\\n// recyclerView.smoothScrollToPosition(0);\\n ivTop.setVisibility(View.GONE);\\n }\\n });\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5681528449058533,\n \"alphanum_fraction\": 0.5732483863830566,\n \"avg_line_length\": 18.14634132385254,\n \"blob_id\": \"1a6131e678444dbc1a89f9d6b28a395bbabe9b43\",\n \"content_id\": \"096b38c8bb4d241ff855ff50a20d084d3de01b1b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 803,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 41,\n \"path\": \"/app/src/main/java/com/android/base/data/ResponseDataArray.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.data;\\n\\nimport androidx.annotation.Keep;\\n\\nimport java.util.ArrayList;\\nimport java.util.List;\\n\\n/**\\n * data为JsonArray\\n *\\n * @author 张全\\n */\\n@Keep\\npublic class ResponseDataArray<T> extends ResponseData {\\n public List<T> data;\\n\\n public int count;\\n public int page;\\n public int pageSize;\\n public int pageCount;\\n\\n\\n public List<T> getDataList() {\\n return null == data ? new ArrayList<>() : data;\\n }\\n\\n /**\\n * 是否还有更多\\n *\\n * @return\\n */\\n public boolean hasMore() {\\n// int dataSize = getDataList().size();\\n// if (dataSize == 0) {\\n// return false;\\n// }\\n// pageSize = pageSize == 0 ? 10 : pageSize;\\n// return dataSize >= pageSize;\\n return !getDataList().isEmpty();\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5504032373428345,\n \"alphanum_fraction\": 0.5544354915618896,\n \"avg_line_length\": 25.078947067260742,\n \"blob_id\": \"2d3c94de28c00a153f3602baa74ca5256d8cfa8b\",\n \"content_id\": \"540be6216ec619310c2f349f8e064f92a0e96283\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1092,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 38,\n \"path\": \"/scripts/tinkerPatch.py\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nimport platform\\nimport sys, getopt\\n\\nif __name__ == '__main__':\\n buildEnv = 'qa'\\n argv = sys.argv[1:]\\n print (argv)\\n try:\\n # 这里的 h 就表示该选项无参数，冒号(:)表示该选项必须有附加的参数，不带冒号表示该选项不附加参数。\\n opts, args = getopt.getopt(argv, \\\"e:\\\",[ \\\"env=\\\"])\\n except getopt.GetoptError:\\n print (getUsageStr())\\n sys.exit(2)\\n\\n for opt, arg in opts:\\n if opt in (\\\"-e\\\", \\\"--env\\\"):\\n buildEnv = arg\\n print('-e arg =%s'%arg)\\n\\n print('开始打tinker补丁包')\\n sysstr = platform.system()\\n cmdPrefix = './gradlew'\\n if(sysstr ==\\\"Windows\\\"):\\n print (\\\"Call Windows tasks\\\")\\n cmdPrefix = 'gradlew'\\n elif(sysstr == \\\"Linux\\\"):\\n print (\\\"Call Linux tasks\\\")\\n else:\\n print (\\\"Other System tasks\\\")\\n\\n suffix = '-PhostType=3'\\n if buildEnv == 'qa':\\n suffix = '-PhostType=4'\\n assemble = 'buildTinkerPatchOnlineRelease'\\n cmdStr = '%s %s %s' %(cmdPrefix, assemble, suffix)\\n print(cmdStr)\\n os.system(cmdStr)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5660540461540222,\n \"alphanum_fraction\": 0.5711711645126343,\n \"avg_line_length\": 27.08704376220703,\n \"blob_id\": \"e606e1cdebc1990717f7b98eb5faaf634072a42d\",\n \"content_id\": \"389a0eea5573e99fb5fdadf45281cf2d7dc0e61b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 14247,\n \"license_type\": \"no_license\",\n \"max_line_length\": 117,\n \"num_lines\": 494,\n \"path\": \"/librarys/CommonWidget/src/main/java/common/widget/viewpager/BannerViewPager.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package common.widget.viewpager;\\n\\n\\nimport android.annotation.SuppressLint;\\nimport android.content.Context;\\nimport android.os.Handler;\\nimport android.os.Looper;\\nimport android.os.Message;\\nimport android.util.AttributeSet;\\nimport android.util.TypedValue;\\nimport android.view.LayoutInflater;\\nimport android.view.View;\\nimport android.view.ViewGroup;\\nimport android.widget.AdapterView;\\nimport android.widget.ImageView;\\nimport android.widget.LinearLayout;\\nimport android.widget.RelativeLayout;\\n\\nimport androidx.annotation.Nullable;\\nimport androidx.viewpager.widget.PagerAdapter;\\n\\nimport java.util.ArrayList;\\nimport java.util.List;\\n\\nimport common.widget.R;\\n\\n/**\\n * 一、功能\\n * <ol>\\n * <li>支持无限滚动，调用startAutoScroll开始滚动</li>\\n * <li>支持无限左右滑动切换页面</li>\\n * </ol>\\n * 二、使用\\n *\\n * <pre>\\n * BannerViewPager mViewPager = (BannerViewPager) findViewById(R.id.viewPager);\\n * mViewPager.setImageLoader(loader);\\n * mViewPager.setmOnItemClickListener(new AdapterView.OnItemClickListener() {\\n * @Override\\n * public void onItemClick(AdapterView<?> parent, View view, int position, long id) {\\n * }\\n * });\\n * mViewPager.setInterval(3 * 1000);// 设置滚动间隔时长\\n * mViewPager.setScrollDurationFactor(4.0f);// 设置自动滚动的Scroller时长因子\\n * mViewPager.startAutoScroll(4 * 1000);// 延迟4秒开始自动滚动\\n * </pre>\\n * <p>\\n * 三、注意\\n * <ol>\\n * <li>为避免内存浪费，请在Activity或Fragment的onRause中暂停滚动，onResume中继续滚动</li>\\n * </ol>\\n *\\n * @author zhangquan\\n */\\npublic class BannerViewPager<T> extends RelativeLayout {\\n public String tag = BannerViewPager.class.getSimpleName();\\n private AutoScrollViewPager viewPager;\\n private LinearLayout indicator;\\n private int mIndicatorSize, mIndicatorMargin;\\n private BannerPagerAdapter adapter;\\n private List<T> dataList;\\n private List<View> views = new ArrayList<>();\\n private List<ImageView> indicatorImages = new ArrayList<>();\\n private int count = 0;\\n private int currentItem = 1;\\n private int lastPosition;\\n private ViewPager.OnPageChangeListener mOnPageChangeListener;\\n private AdapterView.OnItemClickListener mOnItemClickListener;\\n private BannerImageLoader imageLoader;\\n\\n public static final int DEFAULT_INTERVAL = 1500;\\n private boolean started;\\n private TimerHandler timerHandler;\\n private CustomDurationScroller mScroller = null;\\n private long interval = DEFAULT_INTERVAL;\\n private int imageHeight;\\n\\n\\n public BannerViewPager(Context context) {\\n super(context);\\n init();\\n }\\n\\n public BannerViewPager(Context context, AttributeSet attrs) {\\n super(context, attrs);\\n init();\\n }\\n\\n public BannerViewPager(Context context, AttributeSet attrs, int defStyleAttr) {\\n super(context, attrs, defStyleAttr);\\n init();\\n }\\n\\n private void init() {\\n LayoutInflater.from(getContext()).inflate(R.layout.banner_layout, this);\\n viewPager = findViewById(R.id.bannerViewPager);\\n viewPager.setBanner(this);\\n mScroller = viewPager.mScroller;\\n indicator = findViewById(R.id.circleIndicator);\\n mIndicatorSize = dip2px(getContext(), 5);\\n mIndicatorMargin = dip2px(getContext(), 5);\\n\\n timerHandler = new TimerHandler();\\n\\n }\\n\\n public static int dip2px(Context context, float dip) {\\n return (int) TypedValue.applyDimension(TypedValue.COMPLEX_UNIT_DIP, dip,\\n context.getResources().getDisplayMetrics());\\n }\\n\\n\\n public void setDataList(List<T> list) {\\n dataList = list;\\n count = dataList.size();\\n\\n //指示器\\n createIndicator();\\n //banner\\n setImageList();\\n setData();\\n }\\n\\n private void setImageList() {\\n if (dataList == null || dataList.size() <= 0) {\\n return;\\n }\\n if (imageLoader == null) {\\n throw new RuntimeException(\\\"imageLoader不能为null\\\");\\n }\\n viewPager.setPagingEnabled(count > 1);\\n views.clear();\\n if (count == 1) { //只有一条数据\\n createItemView(0);\\n } else {\\n for (int i = 0; i <= count + 1; i++) {\\n createItemView(i);\\n }\\n }\\n }\\n\\n private void createItemView(int index) {\\n View view = null;\\n if (imageLoader != null) {\\n view = (View) imageLoader.createView(getContext());\\n }\\n if (view == null) {\\n view = new ImageView(getContext());\\n }\\n\\n T t;\\n if (index == 0) {\\n t = dataList.get(count - 1);\\n imageLoader.displayView(getContext(), t, view, count - 1, count);\\n } else if (index == count + 1) {\\n t = dataList.get(0);\\n imageLoader.displayView(getContext(), t, view, 0, count);\\n } else {\\n t = dataList.get(index - 1);\\n imageLoader.displayView(getContext(), t, view, index - 1, count);\\n }\\n views.add(view);\\n }\\n\\n private void createIndicator() {\\n int visibility = count > 1 ? View.VISIBLE : View.GONE;\\n indicator.setVisibility(visibility);\\n indicatorImages.clear();\\n indicator.removeAllViews();\\n if (count <= 1) {\\n return;\\n }\\n for (int i = 0; i < count; i++) {\\n ImageView imageView = new ImageView(getContext());\\n imageView.setScaleType(ImageView.ScaleType.CENTER_CROP);\\n LinearLayout.LayoutParams params = new LinearLayout.LayoutParams(mIndicatorSize, mIndicatorSize);\\n params.leftMargin = mIndicatorMargin;\\n params.rightMargin = mIndicatorMargin;\\n imageView.setImageResource(R.drawable.indicator_selector);\\n if (i == 0) {\\n imageView.setSelected(true);\\n }\\n indicatorImages.add(imageView);\\n indicator.addView(imageView, params);\\n }\\n }\\n\\n\\n private void setData() {\\n currentItem = 1;\\n// if (adapter == null) {\\n adapter = new BannerPagerAdapter();\\n viewPager.addOnPageChangeListener(pageChangeListener);\\n// }\\n viewPager.setAdapter(adapter);\\n viewPager.setFocusable(true);\\n viewPager.setCurrentItem(1);\\n\\n }\\n\\n public List<T> getDataList() {\\n return dataList;\\n }\\n\\n\\n /**\\n * 返回真实的位置\\n *\\n * @param position\\n * @return 下标从0开始\\n */\\n public int toRealPosition(int position) {\\n if (count == 0) return 0;\\n int realPosition = (position - 1) % count;\\n if (realPosition < 0)\\n realPosition += count;\\n return realPosition;\\n }\\n\\n class BannerPagerAdapter extends PagerAdapter {\\n\\n @Override\\n public int getCount() {\\n return views.size();\\n }\\n\\n @Override\\n public boolean isViewFromObject(View view, Object object) {\\n return view == object;\\n }\\n\\n @Override\\n public Object instantiateItem(ViewGroup container, final int position) {\\n View itemView = views.get(position);\\n container.addView(itemView);\\n View view = itemView;\\n if (mOnItemClickListener != null) {\\n view.setOnClickListener(new OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n mOnItemClickListener.onItemClick(null, v, toRealPosition(position), 1);\\n }\\n });\\n }\\n// int height = itemView.getHeight();\\n// if (height != imageHeight) {\\n// itemView.setMinimumHeight(imageHeight);\\n// }\\n return view;\\n }\\n\\n @Override\\n public void destroyItem(ViewGroup container, int position, Object object) {\\n container.removeView((View) object);\\n }\\n\\n }\\n\\n ViewPager.OnPageChangeListener pageChangeListener = new ViewPager.OnPageChangeListener() {\\n @Override\\n public void onPageScrollStateChanged(int state) {\\n if (mOnPageChangeListener != null) {\\n mOnPageChangeListener.onPageScrollStateChanged(state);\\n }\\n switch (state) {\\n case 0://No operation\\n if (currentItem == 0) {\\n viewPager.setCurrentItem(count, false);\\n } else if (currentItem == count + 1) {\\n viewPager.setCurrentItem(1, false);\\n }\\n break;\\n case 1://start Sliding\\n if (currentItem == count + 1) {\\n viewPager.setCurrentItem(1, false);\\n } else if (currentItem == 0) {\\n viewPager.setCurrentItem(count, false);\\n }\\n break;\\n case 2://end Sliding\\n break;\\n }\\n }\\n\\n\\n @Override\\n public void onPageScrolled(int position, float positionOffset, int positionOffsetPixels) {\\n if (mOnPageChangeListener != null) {\\n mOnPageChangeListener.onPageScrolled(toRealPosition(position), positionOffset, positionOffsetPixels);\\n }\\n }\\n\\n @Override\\n public void onPageSelected(int position) {\\n currentItem = position;\\n if (mOnPageChangeListener != null) {\\n mOnPageChangeListener.onPageSelected(toRealPosition(position));\\n }\\n\\n if (indicatorImages.isEmpty()) {\\n return;\\n }\\n indicatorImages.get((lastPosition - 1 + count) % count).setSelected(false);\\n indicatorImages.get((position - 1 + count) % count).setSelected(true);\\n lastPosition = position;\\n }\\n };\\n//------------------------------------------\\n\\n /**\\n * 开始自动滚动\\n */\\n public void startAutoScroll() {\\n startAutoScroll(0);\\n }\\n\\n /**\\n * 延迟delayTimeInMills秒后开始自动滚动\\n *\\n * @param delayTime\\n */\\n public void startAutoScroll(long delayTime) {\\n started = true;\\n timerHandler.sendScrollMsg(delayTime);\\n }\\n\\n /**\\n * 停止滚动\\n */\\n public void stopAutoScroll() {\\n started = false;\\n timerHandler.pause();\\n }\\n\\n /**\\n * 暂停滚动\\n */\\n public void pauseAutoScroll() {\\n if (isStarted()) {\\n timerHandler.pause();\\n }\\n if (null != mScroller) mScroller.resetDurationFactor();\\n }\\n\\n /**\\n * 继续滚动\\n */\\n public void resumeAutoScroll() {\\n if (isStarted()) {\\n timerHandler.resume();\\n }\\n }\\n\\n /**\\n * 是否已开始滚动\\n *\\n * @return\\n */\\n public boolean isStarted() {\\n return started;\\n }\\n\\n /**\\n * 两次滚动之间的时间间隔\\n *\\n * @return\\n */\\n public long getInterval() {\\n return interval;\\n }\\n\\n /**\\n * 设置两次滚动之间的时间间隔\\n *\\n * @param interval\\n */\\n public void setInterval(long interval) {\\n this.interval = interval;\\n }\\n\\n /**\\n * 定时器\\n *\\n * @author zhangquan\\n */\\n @SuppressLint(\\\"HandlerLeak\\\")\\n private class TimerHandler extends Handler {\\n private final int msg_start = 1;\\n private boolean paused;// 是否处于暂停\\n\\n public TimerHandler() {\\n super(Looper.getMainLooper());\\n }\\n\\n @Override\\n public void handleMessage(Message msg) {\\n if (paused) {\\n return;\\n }\\n\\n if (count > 1) {\\n if (null != mScroller) {\\n mScroller.setScrollDurationFactor(mScroller.getScrollDuraionFactor());\\n }\\n currentItem = currentItem % (count + 1) + 1;\\n if (currentItem == 1) {\\n viewPager.setCurrentItem(currentItem, false);\\n sendScrollMsg(0);\\n } else {\\n viewPager.setCurrentItem(currentItem);\\n sendScrollMsg(interval + 500);\\n }\\n }\\n }\\n\\n /**\\n * 继续\\n */\\n public synchronized void resume() {\\n paused = false;\\n sendScrollMsg(interval);\\n }\\n\\n /**\\n * 暂停\\n */\\n public synchronized void pause() {\\n paused = true;\\n if (hasMessages(msg_start)) {\\n removeMessages(msg_start);\\n }\\n }\\n\\n public void sendScrollMsg(long delayTime) {\\n if (hasMessages(msg_start)) {\\n removeMessages(msg_start);\\n }\\n sendMessageDelayed(obtainMessage(msg_start), delayTime);\\n }\\n }\\n\\n\\n public void setOnPageChangeListener(ViewPager.OnPageChangeListener onPageChangeListener) {\\n mOnPageChangeListener = onPageChangeListener;\\n }\\n\\n public void setmOnItemClickListener(AdapterView.OnItemClickListener listener) {\\n mOnItemClickListener = listener;\\n }\\n\\n public void setImageLoader(BannerImageLoader loader) {\\n imageLoader = loader;\\n }\\n\\n// public void setImageHeight(int height) {\\n// this.imageHeight = height;\\n// for (View view : views) {\\n// view.setMinimumHeight(imageHeight);\\n// }\\n// }\\n\\n /**\\n * ---------------------- ViewPager支持 api\\n */\\n public void setPageMargin(int marginPixels) {\\n viewPager.setPageMargin(marginPixels);\\n }\\n\\n public void setPageTransformer(boolean reverseDrawingOrder, @Nullable ViewPager.PageTransformer transformer) {\\n viewPager.setPageTransformer(reverseDrawingOrder, transformer);\\n }\\n\\n public void setOffscreenPageLimit(int limit) {\\n viewPager.setOffscreenPageLimit(limit);\\n }\\n\\n public void setScrollDurationFactor(float scrollFactor) {\\n viewPager.setScrollDurationFactor(scrollFactor);\\n }\\n\\n public ViewPager getViewPager() {\\n return viewPager;\\n }\\n\\n public PagerAdapter getAdapter() {\\n return viewPager.getAdapter();\\n }\\n\\n public int getCurrentItem() {\\n return toRealPosition(currentItem);\\n }\\n\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5432960987091064,\n \"alphanum_fraction\": 0.5984637141227722,\n \"avg_line_length\": 33.95121765136719,\n \"blob_id\": \"b3a9181a3bdf267a1f1d5541622576285ba94730\",\n \"content_id\": \"99788792739cb57df5ba83b22c4f0baf96a4ec64\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 1686,\n \"license_type\": \"no_license\",\n \"max_line_length\": 147,\n \"num_lines\": 41,\n \"path\": \"/scripts/checkIfBehind.sh\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n\\ngit-log-flat-colored() {\\n git --no-pager log --format=\\\"%C(yellow)%h%Creset %C(cyan)%cd%Creset %s %Cgreen%an%Creset\\\" --date=short \\\"$@\\\"\\n}\\n\\nCURRENT_FEATURE=`git symbolic-ref --short -q HEAD`\\nCURRENT_PATH=`git rev-parse --show-toplevel`\\nREFERENCE_BRANCH=\\\"origin/master\\\"\\n\\necho -e \\\"\\\\033[0;34m >>-----当前核查仓库路径${CURRENT_PATH}-----<< \\\\033[0m\\\"\\n\\necho\\n\\n# 检查 “origin/master” 分支是否存在，如果存在，更新远端记录并比较是否存在落后提交\\ngit rev-parse --verify ${REFERENCE_BRANCH} > /dev/null 2>&1\\nif [ \\\"$?\\\" == \\\"0\\\" ]; then\\n\\n echo -e \\\"\\\\033[0;34m >>-----${CURRENT_FEATURE}-----<< 更新远端提交记录... \\\\033[0;34m\\\"\\n git fetch origin\\n\\n echo -e \\\"\\\\033[0;35m >>-----${CURRENT_FEATURE}-----<< 检查当前分支是否包含 \\\\\\\"${REFERENCE_BRANCH}\\\\\\\" 分支所有提交记录 \\\\033[0m\\\"\\n NB_COMMITS_BEHIND=$(git rev-list --left-right --count ${REFERENCE_BRANCH}...@ | cut -f1)\\n \\n if [ \\\"${NB_COMMITS_BEHIND}\\\" -gt \\\"0\\\" ]; then\\n echo -e \\\"\\\\033[0;31m >>-----${CURRENT_FEATURE}-----<< 当前分支有 ${NB_COMMITS_BEHIND} 个提交落后于 \\\\\\\"${REFERENCE_BRANCH}\\\\\\\", 请合并最新的 master 分支代码\\\\n \\\\033[0m\\\"\\n git-log-flat-colored ${REFERENCE_BRANCH} | head -\\\"${NB_COMMITS_BEHIND}\\\"\\n echo -e \\\"\\\\033[0;33m \\\\n >>-----${CURRENT_FEATURE}-----<< 你可以通过 \\\\\\\"git merge origin/master\\\\\\\" 来合并代码 \\\\033[0m\\\"\\n exit 2\\n else\\n echo -e \\\"\\\\033[0;32m >>-----${CURRENT_FEATURE}-----<< 完美，当前分支包含 ${REFERENCE_BRANCH} 分支上的所有提交记录 \\\\033[0m\\\"\\n fi\\n\\n else\\n\\n echo -e \\\"\\\\033[0;31m >>-----不能比较，检测${REFERENCE_BRANCH}不存在-----<< \\\\033[0m\\\"\\n exit 2\\n\\nfi\\n\\necho\"\n },\n {\n \"alpha_fraction\": 0.641970694065094,\n \"alphanum_fraction\": 0.6434749960899353,\n \"avg_line_length\": 34,\n \"blob_id\": \"244741b926a55d2e6259c3fe9aa84dbaf5d0c109\",\n \"content_id\": \"0210a1e65a31933ad1e1b3ffc6d7af4865f83ad1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Kotlin\",\n \"length_bytes\": 2673,\n \"license_type\": \"no_license\",\n \"max_line_length\": 177,\n \"num_lines\": 76,\n \"path\": \"/app/src/main/java/com/android/base/version/UpDateDialogFragment.kt\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.version\\n\\nimport android.graphics.Color\\nimport android.graphics.drawable.ColorDrawable\\nimport android.os.Bundle\\nimport android.os.Process\\nimport android.view.LayoutInflater\\nimport android.view.View\\nimport android.view.ViewGroup\\nimport androidx.fragment.app.DialogFragment\\nimport com.android.base.util.ext.onClick\\nimport com.android.base.version.UpdateDialogView.fileSize\\nimport com.fastapp.R\\nimport common.widget.dialog.EffectDialogBuilder\\nimport component.update.AppDownloadClient\\nimport component.update.AppVersion\\nimport kotlinx.android.synthetic.main.update_dialog_fragment.*\\n\\n/**\\n * App升级弹框\\n */\\nclass UpDateDialogFragment : DialogFragment() {\\n private val appVersion by lazy {\\n arguments?.getSerializable(\\\"appversion\\\") as AppVersion?\\n }\\n\\n override fun onCreate(savedInstanceState: Bundle?) {\\n super.onCreate(savedInstanceState)\\n setStyle(STYLE_NO_FRAME, R.style.update_dialog_style)\\n }\\n\\n override fun onCreateView(inflater: LayoutInflater, container: ViewGroup?, savedInstanceState: Bundle?): View? = inflater.inflate(R.layout.update_dialog_fragment, container)\\n\\n override fun onViewCreated(view: View, savedInstanceState: Bundle?) {\\n super.onViewCreated(view, savedInstanceState)\\n val window = dialog?.window\\n window?.apply {\\n setLayout(ViewGroup.LayoutParams.MATCH_PARENT, ViewGroup.LayoutParams.MATCH_PARENT)\\n setBackgroundDrawable(ColorDrawable(Color.TRANSPARENT))\\n }\\n\\n tv_content.text = appVersion?.desc\\n tv_version.text = \\\"V\\\" + appVersion?.versionName\\n\\n tv_cancel.onClick {\\n dismiss()\\n if (appVersion?.forceUpdate == 1) {\\n Process.killProcess(Process.myPid())\\n }\\n }\\n if (appVersion?.forceUpdate == 1) {\\n tv_cancel.visibility = View.GONE\\n }\\n tv_ensure.onClick {\\n if (appVersion?.forceUpdate == 1) {\\n val updateFile = AppDownloadClient.getUpdateFile()\\n if (null != updateFile && fileSize != -1L && fileSize == updateFile.length()) { //已下载\\n AppDownloadClient.installAPK()\\n return@onClick\\n }\\n val dialogView = UpdateDialogView(context, appVersion)\\n EffectDialogBuilder(context)\\n .setContentView(dialogView)\\n .setCancelable(false)\\n .setCancelableOnTouchOutside(false)\\n .show()\\n } else {\\n dismiss()\\n UpdateService.update(context, appVersion)\\n }\\n }\\n\\n }\\n\\n\\n}\"\n },\n {\n \"alpha_fraction\": 0.6930860280990601,\n \"alphanum_fraction\": 0.6981450319290161,\n \"avg_line_length\": 27.261905670166016,\n \"blob_id\": \"457b16944ede500b3c4873b05de3f7573acf161a\",\n \"content_id\": \"857bcfcce527e8005e4a70cce2da39aaed226fce\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1278,\n \"license_type\": \"no_license\",\n \"max_line_length\": 104,\n \"num_lines\": 42,\n \"path\": \"/librarys/CommonWidget/src/main/java/common/widget/listview/GridSpaceItemDecoration.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package common.widget.listview;\\n\\nimport android.graphics.Rect;\\nimport android.view.View;\\n\\nimport androidx.recyclerview.widget.RecyclerView;\\n\\npublic class GridSpaceItemDecoration extends RecyclerView.ItemDecoration {\\n private int headerCount;\\n private int horizontalSpace;\\n private int verticalSpace;\\n private int spanCount = 2;\\n\\n\\n public GridSpaceItemDecoration(int space) {\\n this(space, space);\\n }\\n\\n public GridSpaceItemDecoration(int horizontalSpace, int verticalSpace) {\\n this.horizontalSpace = horizontalSpace;\\n this.verticalSpace = verticalSpace;\\n }\\n\\n public void setHeaderCount(int headerCount) {\\n this.headerCount = headerCount;\\n }\\n\\n public void setSpanCount(int spanCount) {\\n this.spanCount = spanCount;\\n }\\n\\n @Override\\n public void getItemOffsets(Rect outRect, View view, RecyclerView parent, RecyclerView.State state) {\\n //不是第一个的格子都设一个左边和底部的间距\\n outRect.left = horizontalSpace;\\n outRect.bottom = verticalSpace;\\n //由于每行都只有2个，所以第一个都是2的倍数，把左边距设为0\\n if ((parent.getChildLayoutPosition(view)-headerCount) % spanCount == 0) {\\n outRect.left = 0;\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5393980145454407,\n \"alphanum_fraction\": 0.5448089241981506,\n \"avg_line_length\": 36.910255432128906,\n \"blob_id\": \"cb6826f3f92665f594ec1164978fe47611050d18\",\n \"content_id\": \"d21faab5a52c3d4e0f668adfcfbdbf205c3e1910\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 2989,\n \"license_type\": \"no_license\",\n \"max_line_length\": 119,\n \"num_lines\": 78,\n \"path\": \"/librarys/CommonWidget/src/main/java/common/widget/shape/ShapeInflaterInject.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package common.widget.shape;\\n\\nimport android.content.Context;\\nimport android.util.AttributeSet;\\nimport android.view.LayoutInflater;\\nimport android.view.View;\\n\\nimport androidx.annotation.Nullable;\\nimport androidx.appcompat.app.AppCompatActivity;\\nimport androidx.appcompat.app.AppCompatDelegate;\\n\\nimport java.lang.reflect.Field;\\n\\n/**\\n * 注入自定义LayoutInflater.Factory2\\n * 保证自定义属性得到解析\\n *\\n * @author zhangquan\\n */\\npublic class ShapeInflaterInject {\\n\\n public static void inject(Context ctx) {\\n LayoutInflater inflater = LayoutInflater.from(ctx);\\n LayoutInflater.Factory factory = inflater.getFactory();\\n LayoutInflater.Factory2 factory2 = inflater.getFactory2();\\n if (null == factory2) {\\n ShapeInflaterFactory shapeInflater = new ShapeInflaterFactory();\\n if (ctx instanceof AppCompatActivity) {\\n final AppCompatDelegate delegate = ((AppCompatActivity) ctx).getDelegate();\\n shapeInflater.setImplFactory2(new LayoutInflater.Factory2() {\\n @Nullable\\n @Override\\n public View onCreateView(@Nullable View parent, String name, Context context, AttributeSet attrs) {\\n return delegate.createView(parent, name, context, attrs);\\n }\\n\\n @Nullable\\n @Override\\n public View onCreateView(String name, Context context, AttributeSet attrs) {\\n return onCreateView(null, name, context, attrs);\\n }\\n });\\n }\\n\\n inflater.setFactory2(shapeInflater);\\n return;\\n }\\n\\n if (!(factory2 instanceof ShapeInflaterFactory)) {\\n try {\\n Class<? extends LayoutInflater> inflaterClass = inflater.getClass();\\n ShapeInflaterFactory shapeFactory = new ShapeInflaterFactory();\\n\\n Class<?> cls = inflaterClass;\\n while (cls != null && cls != Object.class) {\\n try {\\n Field mFactory = cls.getDeclaredField(\\\"mFactory\\\");\\n mFactory.setAccessible(true);\\n Field mFactory2 = cls.getDeclaredField(\\\"mFactory2\\\");\\n mFactory2.setAccessible(true);\\n if (factory2 != null) {\\n shapeFactory.setImplFactory2(factory2);\\n } else if (factory != null) {\\n shapeFactory.setImplFactory(factory);\\n }\\n mFactory2.set(inflater, shapeFactory);\\n mFactory.set(inflater, shapeFactory);\\n break;\\n } catch (Exception e) {\\n cls = cls.getSuperclass();\\n }\\n }\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.660646378993988,\n \"alphanum_fraction\": 0.6625475287437439,\n \"avg_line_length\": 30.909090042114258,\n \"blob_id\": \"c3b388ba4fceacb6d4f538f9ec293cc64acadedc\",\n \"content_id\": \"823fb0ea60dbb56a476a2da30daaa49c7dbe4017\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Kotlin\",\n \"length_bytes\": 1052,\n \"license_type\": \"no_license\",\n \"max_line_length\": 90,\n \"num_lines\": 33,\n \"path\": \"/app/src/main/java/com/fastapp/MyTask.kt\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.fastapp\\n\\nimport android.content.Context\\nimport androidx.work.WorkerParameters\\nimport com.android.base.task.BackgroundTask\\nimport java.io.InputStreamReader\\nimport java.net.URL\\n\\n/**\\n *\\n * @author zhangquan\\n */\\nclass MyTask(context: Context, param: WorkerParameters) : BackgroundTask(context, param) {\\n override fun doTask(parameters: WorkerParameters) {\\n val inputData = parameters.inputData\\n val value = inputData?.getString(\\\"key\\\")\\n val value2 = inputData?.getBoolean(\\\"key2\\\", false)\\n println(\\\"doTask thread=${Thread.currentThread().name},data=$inputData\\\")\\n\\n var url = URL(\\\"http://www.baidu.com\\\");\\n var urlConnection = url.openConnection();\\n try {\\n val inputStream = urlConnection.getInputStream()\\n val inputStreamReader = InputStreamReader(inputStream)\\n val readLines = inputStreamReader.readLines()\\n// println(\\\"data=$readLines\\\")\\n inputStream.close()\\n\\n } catch (e: Exception) {\\n e.printStackTrace()\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6235138773918152,\n \"alphanum_fraction\": 0.6235138773918152,\n \"avg_line_length\": 25.10344886779785,\n \"blob_id\": \"f5f527cb4195bc7cb0de5a032282804235273a31\",\n \"content_id\": \"85afeeb02d117b0acb7ddb74c9f80a041b3dfe6b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 757,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 29,\n \"path\": \"/app/src/main/java/com/fastapp/ActivityTwo.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.fastapp;\\n\\nimport android.app.Activity;\\nimport android.content.Intent;\\nimport android.os.Bundle;\\nimport android.view.View;\\n\\nimport com.android.base.ui.BaseActivity;\\n\\npublic class ActivityTwo extends BaseActivity {\\n @Override\\n public int getLayoutId() {\\n return R.layout.activity_main;\\n }\\n\\n @Override\\n public void init(Bundle savedInstanceState) {\\n findViewById(R.id.testFrag).setOnClickListener(new View.OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n Intent intent = new Intent();\\n intent.putExtra(\\\"param\\\", \\\"data from ActivityTwo\\\");\\n setResult(Activity.RESULT_OK, intent);\\n finish();\\n }\\n });\\n }\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5845588445663452,\n \"alphanum_fraction\": 0.5868983864784241,\n \"avg_line_length\": 26.200000762939453,\n \"blob_id\": \"5c2ec666f83eaff973b4660578910415fdbb7498\",\n \"content_id\": \"e19ba116aa1870ef3bacd99c6526ac2fd15e5830\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 3060,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 110,\n \"path\": \"/app/src/main/java/com/android/base/util/filedownloader/FileDownloadDialogView.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.util.filedownloader;\\n\\nimport android.content.Context;\\nimport android.text.TextUtils;\\nimport android.view.View;\\nimport android.view.View.OnClickListener;\\nimport android.widget.ProgressBar;\\nimport android.widget.TextView;\\n\\nimport com.fastapp.R;\\n\\nimport java.io.File;\\nimport java.util.ArrayList;\\nimport java.util.List;\\n\\nimport common.widget.dialog.DialogView;\\n\\n/**\\n * 文件下载弹框\\n *\\n * @author 张全\\n */\\npublic class FileDownloadDialogView extends DialogView {\\n private String title;\\n private List<String> downloadSources;\\n private List<File> files = new ArrayList<>();\\n private FileDownloader fileDownloader;\\n private TextView item_label2;\\n private TextView btn;\\n private ProgressBar progressBar;\\n private int total;\\n private int progress;\\n\\n\\n public FileDownloadDialogView(Context ctx, List<String> downloadSources, String title) {\\n super(ctx);\\n this.fileDownloader = new FileDownloader(ctx);\\n this.downloadSources = downloadSources;\\n this.title = title;\\n this.total = downloadSources.size();\\n }\\n\\n @Override\\n protected int getLayoutId() {\\n return R.layout.file_download_dialog;\\n }\\n\\n @Override\\n public void initView(View view) {\\n\\n item_label2 = findViewById(R.id.item_label2);\\n progressBar = findViewById(R.id.progressBar);\\n\\n btn = findViewById(R.id.btn);\\n btn.setOnClickListener(new OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n if (TextUtils.equals(btn.getText().toString(), \\\"下载失败\\\")) { //重新下载\\n startDownload();\\n } else {\\n fileDownloader.stop();\\n dismiss();\\n }\\n }\\n });\\n\\n if (!TextUtils.isEmpty(title) && !\\\"sharegoods\\\".equals(title)) {\\n TextView textView = findViewById(R.id.item_label1);\\n textView.setText(title);\\n }\\n\\n startDownload();\\n }\\n\\n private void startDownload() {\\n progress = 0;\\n files.clear();\\n btn.setText(\\\"取消下载\\\");\\n\\n progressBar.setMax(total);\\n progressBar.setProgress(progress);\\n item_label2.setText(\\\"已下载 \\\" + progress + \\\"/\\\" + total);\\n\\n fileDownloader.downloadFile(downloadSources, new FileDownloader.DownloadCallback() {\\n @Override\\n public void success(File file, String url) {\\n files.add(file);\\n updateUI(file, url);\\n }\\n\\n @Override\\n public void fail(String url) {\\n updateUI(null, url);\\n }\\n });\\n }\\n\\n private void updateUI(File file, String url) {\\n progress++;\\n progressBar.setProgress(progress);\\n item_label2.setText(\\\"已下载 \\\" + progress + \\\"/\\\" + total);\\n if (progress == total) {\\n if (files.isEmpty()) {\\n btn.setText(\\\"下载失败\\\");\\n } else {\\n btn.setText(\\\"下载完成\\\");\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.543448805809021,\n \"alphanum_fraction\": 0.5438358783721924,\n \"avg_line_length\": 28.357954025268555,\n \"blob_id\": \"7264d7bacd9421cfe740803f5a65d2573928868f\",\n \"content_id\": \"f247d77af6715a68f191c6a477eb3d178e05b197\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 5181,\n \"license_type\": \"no_license\",\n \"max_line_length\": 90,\n \"num_lines\": 176,\n \"path\": \"/librarys/CommonUtil/src/main/java/com/android/util/db/DBSession.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.util.db;\\n\\nimport android.text.TextUtils;\\n\\nimport com.google.gson.Gson;\\n\\nimport java.lang.reflect.Field;\\nimport java.util.ArrayList;\\nimport java.util.HashMap;\\nimport java.util.Iterator;\\nimport java.util.List;\\nimport java.util.Map;\\n\\nimport io.reactivex.Observable;\\nimport io.reactivex.ObservableEmitter;\\nimport io.reactivex.ObservableOnSubscribe;\\n\\n/**\\n * @author 张全\\n */\\npublic class DBSession<T> {\\n private Class<T> cls;\\n private Field uidField;\\n private Field keyField;\\n private String modleName;\\n private String uid;\\n private static Map<String, List<ObservableEmitter>> observerableMap = new HashMap<>();\\n private static final String DEF_UID = \\\"-1\\\";\\n\\n\\n public DBSession(Class<T> cls) {\\n this(cls, null);\\n }\\n\\n public DBSession(Class<T> cls, String uid) {\\n this.cls = cls;\\n this.uid = uid;\\n modleName = cls.getSimpleName();\\n\\n Field[] fields = cls.getDeclaredFields();\\n for (Field field : fields) {\\n if (field.isAnnotationPresent(Uid.class)) {\\n uidField = field;\\n uidField.setAccessible(true);\\n }\\n if (field.isAnnotationPresent(Key.class)) {\\n keyField = field;\\n keyField.setAccessible(true);\\n }\\n }\\n }\\n\\n public List<T> query() {\\n List<String> values = ModelDao.getUserModels(getUid(), modleName);\\n List<T> list = new ArrayList<>();\\n for (String value : values) {\\n list.add(new Gson().fromJson(value, cls));\\n }\\n return list;\\n }\\n\\n public Observable<List<T>> queryWithObserverable() {\\n return Observable.create(new ObservableOnSubscribe<List<T>>() {\\n @Override\\n public void subscribe(ObservableEmitter<List<T>> emitter) throws Exception {\\n List<ObservableEmitter> weakReferences = observerableMap.get(modleName);\\n if (null == weakReferences) {\\n weakReferences = new ArrayList<>();\\n observerableMap.put(modleName, weakReferences);\\n }\\n weakReferences.add(emitter);\\n\\n //先发射一次\\n push();\\n }\\n });\\n }\\n\\n public void delete() {\\n ModelDao.deleteUserModels(getUid(), modleName);\\n push();\\n }\\n\\n public void delete(T value) {\\n if (null == value) return;\\n List<T> values = new ArrayList<>();\\n values.add(value);\\n delete(values);\\n }\\n\\n public void delete(List<T> values) {\\n if (null == values | values.isEmpty()) return;\\n String uid = getUid(values.get(0));\\n List<String> keys = new ArrayList<>();\\n for (T value : values) {\\n String key = getKey(value);\\n keys.add(key);\\n }\\n ModelDao.delete(uid, modleName, keys);\\n push();\\n }\\n\\n public void insert(T value) {\\n if (null == value) return;\\n List<T> values = new ArrayList<>();\\n values.add(value);\\n insert(values);\\n }\\n\\n public void insert(List<T> values) {\\n if (null == values | values.isEmpty()) return;\\n List<DBModel> models = new ArrayList<>();\\n for (T value : values) {\\n DBModel dbModel = new DBModel();\\n dbModel.uid = getUid(value);\\n dbModel.name = modleName;\\n dbModel.key = getKey(value);\\n dbModel.value = new Gson().toJson(value);\\n models.add(dbModel);\\n }\\n ModelDao.save(models);\\n push();\\n }\\n\\n private String getUid() {\\n String userId = uid;\\n if (TextUtils.isEmpty(userId)) {\\n userId = DEF_UID;\\n }\\n return userId;\\n }\\n\\n private String getUid(T value) {\\n String userId = uid;\\n if (TextUtils.isEmpty(userId) && null != uidField && null != value) {\\n try {\\n Object id = uidField.get(value);\\n if (null != id) userId = id.toString();\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n }\\n if (TextUtils.isEmpty(userId)) {\\n userId = DEF_UID;\\n }\\n return userId;\\n }\\n\\n private void push() {\\n if (observerableMap.containsKey(modleName)) {\\n List<ObservableEmitter> emitterWeakReference = observerableMap.get(modleName);\\n if (null == emitterWeakReference) return;\\n Iterator<ObservableEmitter> iterator = emitterWeakReference.iterator();\\n while (iterator.hasNext()) {\\n ObservableEmitter emitter = iterator.next();\\n if (null != emitter && !emitter.isDisposed()) {\\n emitter.onNext(query());\\n } else {\\n iterator.remove();\\n }\\n }\\n }\\n }\\n\\n private String getKey(T value) {\\n String key = null;\\n if (null == value || null == keyField) return null;\\n try {\\n Object keyValue = keyField.get(value);\\n if (null != keyValue) key = keyValue.toString();\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n return key;\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.672245442867279,\n \"alphanum_fraction\": 0.672245442867279,\n \"avg_line_length\": 21.4375,\n \"blob_id\": \"e931543f04b9f226b1177d9944207807aa50119a\",\n \"content_id\": \"20f9cd486b64e8c99b20cc8ec29dc6e58594af70\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Kotlin\",\n \"length_bytes\": 717,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 32,\n \"path\": \"/app/src/main/java/com/android/base/ui/splash/SplashUtil.kt\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package net.medlinker.android.splash\\n\\nimport com.android.base.util.KVUtil\\n\\n/**\\n *\\n * @author zhangquan\\n */\\nobject SplashUtil {\\n private const val KEY_PRIVACY_PROTOCOL_STATE = \\\"KEY_PRIVACY_PROTOCOL_STATE\\\"\\n private const val KEY_SHOW_GUIDE = \\\"KEY_SHOW_GUIDE\\\"\\n\\n @JvmStatic\\n fun isPrivacyGranted(): Boolean {\\n return KVUtil.getBoolean(KEY_PRIVACY_PROTOCOL_STATE)\\n }\\n\\n @JvmStatic\\n fun setPrivacyGranted(granted: Boolean) {\\n KVUtil.set(KEY_PRIVACY_PROTOCOL_STATE, granted)\\n }\\n\\n @JvmStatic\\n fun hasShowGuide(): Boolean {\\n return KVUtil.getBoolean(KEY_SHOW_GUIDE)\\n }\\n\\n @JvmStatic\\n fun setShowGuide(shown: Boolean) {\\n KVUtil.set(KEY_SHOW_GUIDE, shown)\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.7338129281997681,\n \"alphanum_fraction\": 0.7338129281997681,\n \"avg_line_length\": 14.44444465637207,\n \"blob_id\": \"f3eebc0a4330401a248a3007b87bdce65ad90d98\",\n \"content_id\": \"a2c1a93200c46d45e107dd85d49f50427e29d172\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 163,\n \"license_type\": \"no_license\",\n \"max_line_length\": 59,\n \"num_lines\": 9,\n \"path\": \"/app/src/main/java/com/android/base/ui/SimpleOrientionFragAct.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.ui;\\n\\n/**\\n * 可以旋转屏幕方向Activity容器\\n *\\n * @author 张全\\n */\\npublic class SimpleOrientionFragAct extends SimpleFragAct {\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.665730357170105,\n \"alphanum_fraction\": 0.6811797618865967,\n \"avg_line_length\": 32.92856979370117,\n \"blob_id\": \"cf8fda25e48e179e65ffb5a1b8fb0890b264c2af\",\n \"content_id\": \"4cb08c417a938cba1f67d15ab5e335de0cebd4dd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Kotlin\",\n \"length_bytes\": 1454,\n \"license_type\": \"no_license\",\n \"max_line_length\": 106,\n \"num_lines\": 42,\n \"path\": \"/app/src/main/java/com/android/base/util/CornerTransform.kt\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.util\\n\\nimport android.graphics.*\\nimport com.bumptech.glide.load.engine.bitmap_recycle.BitmapPool\\nimport com.bumptech.glide.load.resource.bitmap.BitmapTransformation\\nimport java.lang.Math.min\\nimport java.security.MessageDigest\\n\\n\\n/**\\n * desc:\\n * time: 2019/11/21\\n * @author 银进\\n */\\nclass CornerTransform constructor(private val radius: Float) : BitmapTransformation() {\\n override fun transform(pool: BitmapPool, toTransform: Bitmap, outWidth: Int, outHeight: Int): Bitmap {\\n //得到图片最小边\\n val size = min(toTransform.width, toTransform.height)\\n //计算图片起点\\n val x = (toTransform.width - size) / 2\\n val y = (toTransform.height - size) / 2\\n\\n val circleBitmap = Bitmap.createBitmap(x,\\n y, Bitmap.Config.ARGB_8888)\\n val circle = pool.get(size, size, Bitmap.Config.ARGB_8888)\\n val canvas = Canvas(circle)\\n val paint = Paint()\\n paint.isAntiAlias = true\\n val rectF = RectF(0f, 0f, circleBitmap.width * 1f, circleBitmap.height * 1f)\\n paint.shader = BitmapShader(circleBitmap, Shader.TileMode.CLAMP, Shader.TileMode.CLAMP)\\n canvas.drawRoundRect(rectF, radius, radius, paint)\\n return circle\\n }\\n\\n override fun updateDiskCacheKey(messageDigest: MessageDigest) {\\n messageDigest.update(TAG.toByte())\\n }\\n companion object{\\n private val TAG = CornerTransform::class.java.name\\n }\\n\\n}\"\n },\n {\n \"alpha_fraction\": 0.5806451439857483,\n \"alphanum_fraction\": 0.5831681489944458,\n \"avg_line_length\": 25.174528121948242,\n \"blob_id\": \"945b09c43d3f969be18925df554235a32c0d00d3\",\n \"content_id\": \"f8d6fa532f59326a6ff5a75b5250acb7fda5a872\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 5777,\n \"license_type\": \"no_license\",\n \"max_line_length\": 112,\n \"num_lines\": 212,\n \"path\": \"/librarys/CommonWidget/src/main/java/common/widget/LoadingBar.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package common.widget;\\n\\nimport android.content.Context;\\nimport android.text.TextUtils;\\nimport android.util.AttributeSet;\\nimport android.view.View;\\nimport android.view.animation.Animation;\\nimport android.view.animation.LinearInterpolator;\\nimport android.view.animation.RotateAnimation;\\nimport android.widget.ImageView;\\nimport android.widget.ProgressBar;\\nimport android.widget.RelativeLayout;\\nimport android.widget.TextView;\\n\\n/**\\n * 加载进度条\\n *\\n * @author 张全\\n */\\npublic class LoadingBar extends RelativeLayout {\\n private ImageView iv_loading;\\n private ProgressBar mProgressBar;\\n private TextView tv_loading;\\n private LoadingStatus status;\\n\\n public LoadingBar(Context context, AttributeSet attrs, int defStyle) {\\n super(context, attrs, defStyle);\\n init();\\n }\\n\\n public LoadingBar(Context context, AttributeSet attrs) {\\n super(context, attrs);\\n init();\\n }\\n\\n public LoadingBar(Context context) {\\n super(context);\\n init();\\n }\\n\\n private void init() {\\n View.inflate(getContext(), R.layout.common_loadingbar, this);\\n iv_loading = (ImageView) findViewById(R.id.loading_img);\\n tv_loading = (TextView) findViewById(R.id.loading_text);\\n mProgressBar = (ProgressBar) findViewById(R.id.loading_progressbar);\\n }\\n\\n Animation getAnim() {\\n RotateAnimation anim = new RotateAnimation(0, 360,\\n Animation.RELATIVE_TO_SELF, 0.5f, Animation.RELATIVE_TO_SELF,\\n 0.5f);\\n anim.setDuration(500);\\n anim.setInterpolator(new LinearInterpolator());\\n anim.setRepeatCount(Animation.INFINITE);\\n anim.setRepeatMode(Animation.RESTART);\\n return anim;\\n }\\n\\n /**\\n * 设置加载状态\\n *\\n * @param status 加载状态\\n */\\n public void setLoadingStatus(LoadingStatus status) {\\n setLoadingStatus(status, -1, null);\\n }\\n\\n /**\\n * 设置加载状态\\n *\\n * @param status 加载状态\\n * @param imgRes 显示的图片\\n */\\n public void setLoadingStatus(LoadingStatus status, int imgRes) {\\n setLoadingStatus(status, imgRes, null);\\n }\\n\\n /**\\n * 设置加载状态\\n *\\n * @param status 加载状态\\n * @param text 显示的文字\\n */\\n public void setLoadingStatus(LoadingStatus status, String text) {\\n setLoadingStatus(status, -1, text);\\n }\\n\\n /**\\n * 设置加载状态\\n *\\n * @param status 加载状态\\n * @param imgRes 显示的图片\\n * @param text 显示的文字\\n */\\n public void setLoadingStatus(LoadingStatus status, int imgRes, String text) {\\n\\n if (null != this.status && this.status == status) {\\n return;\\n }\\n\\n if (status == LoadingStatus.SUCCESS) {\\n loadSuccess();\\n return;\\n }\\n\\n this.status = status;\\n setVisibility(View.VISIBLE);\\n tv_loading.setVisibility(View.GONE);\\n mProgressBar.setVisibility(View.GONE);\\n//\\t\\tiv_loading.setVisibility(View.GONE);\\n//\\t\\tiv_loading.clearAnimation();\\n\\n // 设置文字\\n String loadingText = null == text ? getContext().getString(status.text) : text;\\n tv_loading.setText(loadingText);\\n if (!TextUtils.isEmpty(loadingText)) {\\n tv_loading.setVisibility(View.VISIBLE);\\n }\\n\\n // 设置图片\\n if (imgRes > 0) {\\n iv_loading.setImageResource(imgRes);\\n iv_loading.setVisibility(View.VISIBLE);\\n } else {\\n//\\t\\t\\tiv_loading.setImageBitmap(null);\\n }\\n\\n switch (status) {\\n case START:// 加载中...\\n mProgressBar.setVisibility(View.VISIBLE);\\n iv_loading.setVisibility(View.GONE);\\n break;\\n case RELOAD:// 重新加载\\n iv_loading.setVisibility(View.VISIBLE);\\n break;\\n case NOCONNECTION:// 无网络连接\\n iv_loading.setVisibility(View.VISIBLE);\\n break;\\n case SUCCESS:// 加载成功\\n setVisibility(View.GONE);\\n break;\\n case EMPTY: // 无数据\\n iv_loading.setVisibility(View.VISIBLE);\\n break;\\n }\\n }\\n\\n public LoadingStatus getLoadingStatus() {\\n return this.status;\\n }\\n\\n /**\\n * 是否正在加载\\n *\\n * @return\\n */\\n public boolean isLoading() {\\n return getVisibility() == View.VISIBLE && null != status\\n && (status == LoadingStatus.START);\\n }\\n\\n /**\\n * 加载成功\\n */\\n public void loadSuccess() {\\n this.status = LoadingStatus.SUCCESS;\\n setVisibility(View.GONE);\\n }\\n\\n /**\\n * 是否能够加载\\n *\\n * @return\\n */\\n public boolean canLoading() {\\n if (null == status) return false;\\n if (status == LoadingStatus.START || status == LoadingStatus.EMPTY || status == LoadingStatus.SUCCESS) {\\n return false;\\n }\\n return true;\\n }\\n\\n /**\\n * 加载状态\\n *\\n * @author zhangquan\\n */\\n public enum LoadingStatus {\\n START(R.string.loadingbar_start), SUCCESS(R.string.loadingbar_success), NOCONNECTION(\\n R.string.loadingbar_noconnection), RELOAD(\\n R.string.loadingbar_reload), EMPTY(R.string.loadingbar_empty);\\n\\n public int text;\\n\\n private LoadingStatus(int text) {\\n this.text = text;\\n }\\n }\\n\\n // --------------------------------\\n public void setTextView(TextView textView) {\\n this.tv_loading = textView;\\n }\\n\\n public void setImageView(ImageView imageView) {\\n this.iv_loading = imageView;\\n }\\n\\n public void setProgressBar(ProgressBar progressBar) {\\n this.mProgressBar = progressBar;\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5897009968757629,\n \"alphanum_fraction\": 0.5897009968757629,\n \"avg_line_length\": 24.08333396911621,\n \"blob_id\": \"326619ebb66677162ab6092db4b293c0f9957e3c\",\n \"content_id\": \"1ef64e6d4f9ec4059f0ab91d02c1466f64189562\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1912,\n \"license_type\": \"no_license\",\n \"max_line_length\": 90,\n \"num_lines\": 72,\n \"path\": \"/app/src/main/java/com/android/base/data/BaseResponseObserver.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.data;\\n\\n\\nimport java.net.ConnectException;\\nimport java.net.SocketTimeoutException;\\nimport java.net.UnknownHostException;\\n\\nimport io.reactivex.observers.DisposableObserver;\\n\\n\\npublic abstract class BaseResponseObserver<T> extends DisposableObserver<T> {\\n\\n private static final String TAG = \\\"BaseResponseObserver\\\";\\n\\n @Override\\n public final void onError(Throwable e) {\\n if (e != null) {\\n e.printStackTrace();\\n }\\n HttpResponseException errorCause;\\n if (e instanceof UnknownHostException) {\\n errorCause = new HttpResponseException(\\\"无网络连接, 请重试\\\", e);\\n } else if (e instanceof SocketTimeoutException || e instanceof ConnectException) {\\n errorCause = new HttpResponseException(\\\"网络连接超时, 请重试\\\", e);\\n } else if (e instanceof HttpResponseException) { //服务器错误\\n errorCause = (HttpResponseException) e;\\n } else {\\n errorCause = new HttpResponseException(\\\"网络异常\\\", e);\\n }\\n onError(errorCause);\\n onEnd();\\n }\\n\\n @Override\\n public void onNext(T value) {\\n if (value instanceof ResponseData) {\\n ResponseData data = (ResponseData) value;\\n if (!data.isSuccessful()) {\\n onError(new HttpResponseException(data));\\n } else {\\n onSuccess(value);\\n }\\n } else {\\n onSuccess(value);\\n }\\n }\\n\\n @Override\\n public void onComplete() {\\n onEnd();\\n }\\n\\n /**\\n * 请求成功\\n *\\n * @param value\\n */\\n public abstract void onSuccess(T value);\\n\\n /**\\n * 请求失败\\n *\\n * @param e\\n */\\n public abstract void onError(HttpResponseException e);\\n\\n /**\\n * 请求结束\\n * <p>不管请求成功还是失败，都会回调onEnd</p>\\n */\\n public abstract void onEnd();\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5630878806114197,\n \"alphanum_fraction\": 0.566980242729187,\n \"avg_line_length\": 30.783504486083984,\n \"blob_id\": \"0f8c96cdba9702cf9c6346d781021d728fa626d7\",\n \"content_id\": \"dca512cf7fa8eb773640e79992bf2575fe774082\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 3181,\n \"license_type\": \"no_license\",\n \"max_line_length\": 120,\n \"num_lines\": 97,\n \"path\": \"/app/src/main/java/com/android/base/util/CommonUtil.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.util;\\n\\nimport android.content.ClipData;\\nimport android.content.ClipboardManager;\\nimport android.content.Context;\\nimport android.content.Intent;\\nimport android.media.MediaScannerConnection;\\nimport android.net.Uri;\\nimport android.provider.MediaStore;\\nimport android.text.TextUtils;\\n\\nimport com.android.util.LContext;\\nimport com.android.util.log.LogUtil;\\n\\nimport java.io.File;\\n\\n/**\\n * @author 张全\\n */\\npublic class CommonUtil {\\n\\n /**\\n * 保存文件到相册\\n *\\n * @param ctx\\n * @param file\\n */\\n public static void saveFileToGallery(Context ctx, File file) {\\n if (null == file) return;\\n try {\\n MediaStore.Images.Media.insertImage(ctx.getContentResolver(),\\n file.getAbsolutePath(), file.getName(), null);\\n LogUtil.d(\\\"gallery\\\", \\\"保存相册成功\\\");\\n } catch (Exception e) {\\n e.printStackTrace();\\n LogUtil.d(\\\"gallery\\\", \\\"保存相册失败 e=\\\" + e.getMessage());\\n }\\n\\n // 通知图库更新11\\n try {\\n ctx.sendBroadcast(new Intent(Intent.ACTION_MEDIA_SCANNER_SCAN_FILE, Uri.fromFile(file)));\\n } catch (Exception e) {\\n e.printStackTrace();\\n LogUtil.d(\\\"gallery\\\", \\\"通知图库更新失败 111 e=\\\" + e.getMessage());\\n }\\n\\n // 通知图库更新22\\n try {\\n String[] paths = new String[]{file.getAbsolutePath()};\\n MediaScannerConnection.scanFile(ctx, paths, null, new MediaScannerConnection.OnScanCompletedListener() {\\n @Override\\n public void onScanCompleted(String path, Uri uri) {\\n Intent mediaScanIntent = new Intent(Intent.ACTION_MEDIA_SCANNER_SCAN_FILE);\\n mediaScanIntent.setData(uri);\\n ctx.sendBroadcast(mediaScanIntent);\\n }\\n });\\n\\n } catch (Exception e) {\\n e.printStackTrace();\\n LogUtil.d(\\\"gallery\\\", \\\"通知图库更新失败 222 e=\\\" + e.getMessage());\\n }\\n }\\n\\n public static void clearClipboard() {\\n ClipboardManager manager = (ClipboardManager) LContext.getContext().getSystemService(Context.CLIPBOARD_SERVICE);\\n if (manager != null) {\\n try {\\n manager.setPrimaryClip(ClipData.newPlainText(null, null));\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n }\\n }\\n\\n public static void addToClipboard(String text) {\\n try {\\n ClipboardManager cm = (ClipboardManager) LContext.getContext().getSystemService(Context.CLIPBOARD_SERVICE);\\n cm.setText(text);\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n }\\n\\n public static String checkUrl(String url) {\\n if (!TextUtils.isEmpty(url) && !url.contains(\\\"http\\\")) {\\n if (url.startsWith(\\\"//\\\")) {\\n url = \\\"https:\\\" + url.subSequence(1, url.length());\\n } else if (url.startsWith(\\\"/\\\")) {\\n url = \\\"https:/\\\" + url.subSequence(1, url.length());\\n } else {\\n url = \\\"https://\\\" + url;\\n }\\n }\\n return url;\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5980066657066345,\n \"alphanum_fraction\": 0.5986710786819458,\n \"avg_line_length\": 22.515625,\n \"blob_id\": \"3a24a2e98403cbebc5ca872c28848664a7890707\",\n \"content_id\": \"e0abccd94992ef1515cff3325bf9d045f839a791\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1509,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 64,\n \"path\": \"/app/src/main/java/com/android/base/ui/SimpleFrag.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.ui;\\n\\nimport android.view.View;\\n\\nimport androidx.core.app.ActivityCompat;\\n\\nimport com.android.base.widget.TitleBarView;\\n\\nimport me.yokeyword.fragmentation.ISupportFragment;\\n\\n/**\\n * @author 张全\\n */\\npublic abstract class SimpleFrag extends BaseFragment {\\n private TitleBarView mToolbar;\\n\\n public void setToolbar(TitleBarView toolbar) {\\n this.mToolbar = toolbar;\\n toolbar.setOnLeftBtnClickListener(v -> {\\n assert getFragmentManager() != null;\\n if (getFragmentManager().getBackStackEntryCount() > 1) {\\n pop();\\n } else {\\n ActivityCompat.finishAfterTransition(_mActivity);\\n }\\n });\\n }\\n\\n protected void hideToolbar() {\\n TitleBarView titleBar = getTitleBar();\\n if (null != titleBar) {\\n titleBar.setVisibility(View.GONE);\\n }\\n }\\n\\n protected TitleBarView getTitleBar() {\\n if (null != mToolbar) {\\n return mToolbar;\\n }\\n ISupportFragment preFragment = getPreFragment();\\n if (null != preFragment && preFragment instanceof SimpleFrag) {\\n SimpleFrag simpleFrag = (SimpleFrag) preFragment;\\n mToolbar = simpleFrag.getTitleBar();\\n }\\n return mToolbar;\\n }\\n\\n @Override\\n public void onResume() {\\n super.onResume();\\n }\\n\\n @Override\\n public void onPause() {\\n super.onPause();\\n }\\n\\n @Override\\n public void onStop() {\\n super.onStop();\\n }\\n\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6463306546211243,\n \"alphanum_fraction\": 0.6480990052223206,\n \"avg_line_length\": 27.299999237060547,\n \"blob_id\": \"c2e972c62097b3fe326568162a121b08a7462dcb\",\n \"content_id\": \"47ed69e8b866c21e628b304fd5146c5ba7c8b1b6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Kotlin\",\n \"length_bytes\": 1163,\n \"license_type\": \"no_license\",\n \"max_line_length\": 111,\n \"num_lines\": 40,\n \"path\": \"/app/src/main/java/com/android/base/task/BackgroundTask.kt\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.task\\n\\nimport android.content.Context\\nimport androidx.work.*\\nimport com.blankj.utilcode.util.Utils\\n\\n/**\\n * 后台任务\\n * 1、继承BackgroundTask 重写doTask()\\n * 2、调用BackgroundTask.execute()执行任务\\n * @author zhangquan\\n */\\nabstract class BackgroundTask(context: Context, wokerParams: WorkerParameters) : Worker(context, wokerParams) {\\n private val params = wokerParams\\n override fun doWork(): Result {\\n doTask(params)\\n return Result.success()\\n }\\n\\n abstract fun doTask(parameters: WorkerParameters)\\n\\n\\n companion object {\\n @JvmStatic\\n fun execute(workerClass: Class<out ListenableWorker>) {\\n val request = OneTimeWorkRequest.Builder(workerClass)\\n .build()\\n WorkManager.getInstance(Utils.getApp()).enqueue(request)\\n }\\n\\n @JvmStatic\\n fun execute(workerClass: Class<out ListenableWorker>, data: Data) {\\n val request = OneTimeWorkRequest.Builder(workerClass)\\n .setInputData(data)\\n .build()\\n WorkManager.getInstance(Utils.getApp()).enqueue(request)\\n }\\n }\\n\\n}\"\n },\n {\n \"alpha_fraction\": 0.6032915115356445,\n \"alphanum_fraction\": 0.6111174821853638,\n \"avg_line_length\": 34.90495681762695,\n \"blob_id\": \"752190c225d74041e037461b4fbe56d7fb654c40\",\n \"content_id\": \"cd58fbf39ca2aca758f459e275dbeeb482a4678f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 8823,\n \"license_type\": \"no_license\",\n \"max_line_length\": 118,\n \"num_lines\": 242,\n \"path\": \"/app/src/main/java/com/android/base/data/RestClient.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.data;\\n\\n\\nimport com.android.base.util.log.LogClient;\\n\\nimport java.io.IOException;\\nimport java.nio.charset.Charset;\\nimport java.security.SecureRandom;\\nimport java.security.cert.CertificateException;\\nimport java.security.cert.X509Certificate;\\nimport java.util.concurrent.TimeUnit;\\n\\nimport javax.net.ssl.HostnameVerifier;\\nimport javax.net.ssl.SSLContext;\\nimport javax.net.ssl.SSLSession;\\nimport javax.net.ssl.SSLSocketFactory;\\nimport javax.net.ssl.TrustManager;\\nimport javax.net.ssl.X509TrustManager;\\n\\nimport okhttp3.Call;\\nimport okhttp3.Headers;\\nimport okhttp3.Interceptor;\\nimport okhttp3.MediaType;\\nimport okhttp3.OkHttpClient;\\nimport okhttp3.Request;\\nimport okhttp3.Response;\\nimport okhttp3.ResponseBody;\\nimport okhttp3.logging.HttpLoggingInterceptor;\\nimport okio.Buffer;\\nimport okio.BufferedSource;\\nimport okio.GzipSource;\\nimport retrofit2.Retrofit;\\nimport retrofit2.adapter.rxjava2.RxJava2CallAdapterFactory;\\nimport retrofit2.converter.gson.GsonConverterFactory;\\nimport retrofit2.converter.scalars.ScalarsConverterFactory;\\n\\n/**\\n * OkHttp请求\\n *\\n * @author 张全\\n */\\npublic final class RestClient {\\n private static final String TAG = \\\"RestClient\\\";\\n public static final int TIMEOUT_CONNECTION = 20; //连接超时\\n public static final int TIMEOUT_READ = 20; //读取超时\\n public static final int TIMEOUT_WRITE = 20; //写入超时\\n\\n private static final Charset UTF8 = Charset.forName(\\\"UTF-8\\\");\\n public static final String REST_API_URL = DataConfig.API_HOST;\\n private static Retrofit s_retrofit;\\n private static OkHttpClient fileDownloadClient;\\n private static OkHttpClient imgDownloadClient;\\n\\n\\n static {\\n OkHttpClient.Builder builder = new OkHttpClient.Builder()\\n// .proxy(Proxy.NO_PROXY)\\n .connectTimeout(TIMEOUT_CONNECTION, TimeUnit.SECONDS)\\n .readTimeout(TIMEOUT_READ, TimeUnit.SECONDS)\\n .writeTimeout(TIMEOUT_WRITE, TimeUnit.SECONDS)\\n .addInterceptor(new HeaderIntercepter());\\n\\n if (DataConfig.DEBUG) {\\n HttpLoggingInterceptor loggingInterceptor = new HttpLoggingInterceptor();\\n loggingInterceptor.setLevel(HttpLoggingInterceptor.Level.BODY);\\n builder.addInterceptor(loggingInterceptor);\\n }\\n getUnsafeOkHttpClient(builder);\\n OkHttpClient client = builder.build();\\n\\n s_retrofit = new Retrofit.Builder()\\n .baseUrl(REST_API_URL)\\n .addConverterFactory(ScalarsConverterFactory.create())\\n .addConverterFactory(GsonConverterFactory.create())\\n .addCallAdapterFactory(RxJava2CallAdapterFactory.create())\\n .client(client)\\n .build();\\n }\\n\\n /**\\n * 文件下载\\n *\\n * @return\\n */\\n public static OkHttpClient getDownloadClient() {\\n if (null == fileDownloadClient) {\\n OkHttpClient.Builder builder = new OkHttpClient.Builder()\\n .connectTimeout(TIMEOUT_CONNECTION, TimeUnit.SECONDS)\\n .readTimeout(TIMEOUT_READ, TimeUnit.SECONDS)\\n .writeTimeout(TIMEOUT_WRITE, TimeUnit.SECONDS);\\n getUnsafeOkHttpClient(builder);\\n fileDownloadClient = builder.build();\\n }\\n return fileDownloadClient;\\n }\\n\\n /**\\n * 图片下载\\n *\\n * @return\\n */\\n public static OkHttpClient getImgDownloadClient() {\\n if (null == imgDownloadClient) {\\n OkHttpClient.Builder builder = new OkHttpClient.Builder()\\n .connectTimeout(TIMEOUT_CONNECTION, TimeUnit.SECONDS)\\n .readTimeout(50, TimeUnit.SECONDS)\\n .writeTimeout(50, TimeUnit.SECONDS);\\n getUnsafeOkHttpClient(builder);\\n imgDownloadClient = builder.build();\\n }\\n return imgDownloadClient;\\n }\\n\\n\\n public static void getUnsafeOkHttpClient(OkHttpClient.Builder builder) {\\n try {\\n final X509TrustManager trustManager = new X509TrustManager() {\\n @Override\\n public void checkClientTrusted(X509Certificate[] chain, String authType) throws CertificateException {\\n\\n }\\n\\n @Override\\n public void checkServerTrusted(X509Certificate[] chain, String authType) throws CertificateException {\\n\\n }\\n\\n @Override\\n public X509Certificate[] getAcceptedIssuers() {\\n return new X509Certificate[0];\\n }\\n };\\n\\n SSLContext sslContext = SSLContext.getInstance(\\\"SSL\\\");\\n sslContext.init(null, new TrustManager[]{trustManager}, new SecureRandom());\\n SSLSocketFactory sslSocketFactory = sslContext.getSocketFactory();\\n builder.sslSocketFactory(sslSocketFactory,trustManager);\\n builder.hostnameVerifier(new HostnameVerifier() {\\n @Override\\n public boolean verify(String hostname, SSLSession session) {\\n return true;\\n }\\n });\\n\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n }\\n\\n static class HeaderIntercepter implements Interceptor {\\n\\n @Override\\n public Response intercept(Chain chain) throws IOException {\\n\\n Request request = chain.request();\\n Request.Builder builder = request.newBuilder();\\n\\n// String token = UserClient.getToken();\\n// builder.addHeader(\\\"x-app-source\\\", APP_SOURCE); // 1 星口袋 2 星乐桃\\n// builder.addHeader(\\\"x-sign\\\", sign); //签名\\n// builder.addHeader(\\\"x-timestamp\\\", \\\"\\\" + timestamp);\\n// builder.addHeader(\\\"x-appid\\\", APPID);\\n// builder.addHeader(\\\"x-m\\\", AnalysisUtil.getUniqueId());//设备id\\n//\\n//\\n// if (!TextUtils.isEmpty(token)) {\\n// builder.addHeader(\\\"Authorization\\\", token);\\n// }\\n//\\n// builder.addHeader(\\\"User-Agent\\\", AnalysisUtil.getUA());\\n// builder.addHeader(\\\"Cache-Control\\\", CacheControl.FORCE_NETWORK.toString());\\n// builder.addHeader(\\\"sgn\\\", DeviceUtil.getSignature()); //签名校验\\n// builder.addHeader(\\\"pkg\\\", DeviceUtil.getPkgName()); //包名校验\\n// builder.addHeader(\\\"appVersion\\\", LContext.versionName); //版本号\\n// builder.addHeader(\\\"client-v\\\", LContext.versionName); //版本号\\n// builder.addHeader(\\\"channel\\\", LContext.channel);//渠道\\n// builder.addHeader(\\\"check-enable\\\", \\\"1\\\");\\n// builder.addHeader(\\\"dev-type\\\", \\\"1\\\");// 1 安卓 , 2 IOS , 3 其他\\n// builder.addHeader(\\\"client-type\\\", \\\"2\\\");//1,微信小程序 , 2 APP\\n\\n\\n request = builder.build();\\n Response response = chain.proceed(request);\\n checkResponse(request.url().toString(), response);\\n return response;\\n }\\n\\n }\\n\\n private static void checkResponse(String url, Response response) {\\n try {\\n ResponseBody body = response.body();\\n BufferedSource source = body.source();\\n Headers headers = response.headers();\\n source.request(Long.MAX_VALUE); // Buffer the entire body.\\n Buffer buffer = source.buffer();\\n if (\\\"gzip\\\".equalsIgnoreCase(headers.get(\\\"Content-Encoding\\\"))) {\\n GzipSource gzippedResponseBody = null;\\n try {\\n gzippedResponseBody = new GzipSource(buffer.clone());\\n buffer = new Buffer();\\n buffer.writeAll(gzippedResponseBody);\\n } finally {\\n if (gzippedResponseBody != null) {\\n gzippedResponseBody.close();\\n }\\n }\\n }\\n Charset charset = UTF8;\\n MediaType contentType = body.contentType();\\n if (contentType != null) {\\n charset = contentType.charset(UTF8);\\n }\\n\\n String responseStr = buffer.clone().readString(charset);\\n //本地日志\\n LogClient.logResponse(url, responseStr);\\n\\n //登录失效\\n// int code = new JSONObject(responseStr).optInt(\\\"code\\\");\\n// if (code == 201) {\\n// UserClient.loginOut();\\n// EventBus.getDefault().post(new EventMsg(Constant.Event.LOGIN_OUT));\\n// EventBus.getDefault().post(new EventMsg(Constant.Event.LOGIN_SESSION_INVALIDATE));\\n// }\\n\\n } catch (Exception e) {\\n }\\n }\\n\\n\\n public static <T> T getService(Class<T> serviceClass) {\\n return s_retrofit.create(serviceClass);\\n }\\n\\n public static OkHttpClient getHttpClient() {\\n Call.Factory factory = s_retrofit.callFactory();\\n return (OkHttpClient) factory;\\n }\\n\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5146363377571106,\n \"alphanum_fraction\": 0.5174453258514404,\n \"avg_line_length\": 28.03004264831543,\n \"blob_id\": \"f222f44fc085c30d28369539878e9cd5313dc749\",\n \"content_id\": \"c7420524ff48b1d0115e3a53ace63fa52e6653b0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 6842,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 233,\n \"path\": \"/app/src/main/java/com/android/base/util/filedownloader/FileDownloader.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.util.filedownloader;\\n\\nimport android.content.Context;\\nimport android.os.Environment;\\nimport android.os.Handler;\\nimport android.os.Looper;\\nimport android.os.Message;\\nimport android.text.TextUtils;\\n\\nimport com.android.util.encode.MD5;\\nimport com.android.util.log.LogUtil;\\nimport com.android.base.data.RestClient;\\nimport com.android.base.util.CommonUtil;\\n\\nimport java.io.File;\\nimport java.io.FileOutputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.Serializable;\\nimport java.util.ArrayList;\\nimport java.util.List;\\n\\nimport okhttp3.Call;\\nimport okhttp3.Callback;\\nimport okhttp3.Request;\\nimport okhttp3.Response;\\n\\n/**\\n * 文件下载器\\n *\\n * @author 张全\\n */\\npublic class FileDownloader {\\n private static final String TAG = \\\"FileDownloader\\\";\\n private static final String DIR=\\\"files\\\";\\n private DownloadHandler downloadHandler = new DownloadHandler();\\n private DownloadCallback callback;\\n private Context ctx;\\n private boolean exit;\\n private boolean saveToGallery; //自动保存到相册\\n\\n public FileDownloader(Context ctx) {\\n this(ctx, true);\\n }\\n\\n public FileDownloader(Context ctx, boolean saveToGallery) {\\n this.ctx = ctx;\\n this.saveToGallery = saveToGallery;\\n }\\n\\n private File getImgDir() {\\n File fileDir = new File(Environment.getExternalStorageDirectory(), DIR);\\n if (!fileDir.exists()) {\\n fileDir.mkdirs();\\n }\\n return fileDir;\\n }\\n\\n /**\\n * 下载文件\\n *\\n * @param url\\n * @param callback\\n */\\n public void downloadFile(String url, DownloadCallback callback) {\\n if (TextUtils.isEmpty(url)) {\\n return;\\n }\\n List<String> urls = new ArrayList<>();\\n urls.add(url);\\n downloadFile(urls, callback);\\n }\\n\\n /**\\n * 下载文件\\n *\\n * @param urls\\n * @param callback\\n */\\n public void downloadFile(List<String> urls, DownloadCallback callback) {\\n if (null == urls || urls.isEmpty()) return;\\n this.callback = callback;\\n for (String url : urls) {\\n File imgDir = getImgDir();\\n String fileName = MD5.MD5Encode(url);\\n if (url.contains(\\\".mp4\\\")) {\\n fileName += \\\".mp4\\\";\\n } else {\\n fileName += \\\".png\\\";\\n }\\n File file = new File(imgDir, fileName);\\n if (file.exists() && file.length() > 0) { //已下载完成的\\n success(url, file);\\n } else {\\n if (url.startsWith(\\\"http\\\")) {\\n downLoadFile(url, file);\\n } else {\\n fail(url);\\n }\\n }\\n }\\n }\\n\\n /**\\n * 结束\\n */\\n public void stop() {\\n this.exit = true;\\n this.ctx = null;\\n callback = null;\\n downloadHandler.removeAllMsg();\\n }\\n\\n private boolean isExit() {\\n return exit || null == callback;\\n }\\n\\n private void success(String url, File file) {\\n if (saveToGallery && null != file && null != ctx) {\\n CommonUtil.saveFileToGallery(ctx, file);\\n }\\n if (isExit()) return;\\n callback.success(file, url);\\n }\\n\\n private void fail(String url) {\\n if (isExit()) return;\\n callback.fail(url);\\n }\\n\\n private void downLoadFile(String url, File file) {\\n final Request request = new Request.Builder().url(url).build();\\n final Call call = RestClient.getHttpClient().newCall(request);\\n\\n\\n call.enqueue(new Callback() {\\n @Override\\n public void onFailure(Call call, IOException e) {\\n if (isExit()) return;\\n downloadHandler.sendFail(url, file);\\n }\\n\\n @Override\\n public void onResponse(Call call, Response response) {\\n if (isExit()) return;\\n if (response.isSuccessful()) {\\n InputStream is = null;\\n byte[] buf = new byte[2048];\\n int len;\\n FileOutputStream fos = null;\\n try {\\n is = response.body().byteStream();\\n fos = new FileOutputStream(file);\\n while ((len = is.read(buf)) != -1) {\\n fos.write(buf, 0, len);\\n }\\n fos.flush();\\n downloadHandler.sendSuccess(url, file);\\n } catch (Exception e) {\\n LogUtil.e(TAG, e.toString());\\n downloadHandler.sendFail(url, file);\\n } finally {\\n try {\\n if (is != null) {\\n is.close();\\n }\\n if (fos != null) {\\n fos.close();\\n }\\n } catch (Exception e) {\\n LogUtil.e(TAG, e.toString());\\n }\\n }\\n } else {\\n downloadHandler.sendFail(url, file);\\n }\\n }\\n });\\n }\\n\\n private class DownloadHandler extends Handler {\\n private int msg_success = 1;\\n private int msg_fail = 2;\\n\\n public DownloadHandler() {\\n super(Looper.getMainLooper());\\n }\\n\\n public void sendSuccess(String url, File file) {\\n Message message = obtainMessage(msg_success);\\n message.obj = new FileDownloader.FileEntity(url, file);\\n sendMessage(message);\\n }\\n\\n public void sendFail(String url, File file) {\\n if (null != file && file.length() > 0) file.delete(); //删除下载失败的文件\\n Message message = obtainMessage(msg_fail);\\n message.obj = new FileDownloader.FileEntity(url, null);\\n sendMessage(message);\\n }\\n\\n @Override\\n public void handleMessage(Message msg) {\\n FileDownloader.FileEntity fileEntity = (FileDownloader.FileEntity) msg.obj;\\n if (msg.what == msg_success) {\\n success(fileEntity.url, fileEntity.file);\\n } else {\\n fail(fileEntity.url);\\n }\\n }\\n\\n public void removeAllMsg() {\\n removeMessages(msg_fail);\\n removeMessages(msg_success);\\n }\\n }\\n\\n private static class FileEntity implements Serializable {\\n public String url;\\n public File file;\\n\\n public FileEntity(String url, File file) {\\n this.url = url;\\n this.file = file;\\n }\\n }\\n\\n public static interface DownloadCallback {\\n void success(File file, String url);\\n\\n void fail(String url);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5860113501548767,\n \"alphanum_fraction\": 0.5904221534729004,\n \"avg_line_length\": 32.410526275634766,\n \"blob_id\": \"05b2b2bb312f03bcac09d5dc89a0dc8cff52e4ae\",\n \"content_id\": \"92d3f0db9797c15da13639f59ae6e319229f83e6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 3350,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 95,\n \"path\": \"/app/src/main/java/com/android/base/util/pay/OrderPay.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.util.pay;\\n\\nimport android.annotation.SuppressLint;\\nimport android.app.Activity;\\nimport android.content.ComponentName;\\nimport android.content.Context;\\nimport android.content.Intent;\\nimport android.net.Uri;\\nimport android.os.Handler;\\nimport android.os.Message;\\nimport android.text.TextUtils;\\n\\nimport com.alipay.sdk.app.PayTask;\\nimport com.android.base.event.EventMsg;\\nimport com.android.util.ext.ToastUtil;\\nimport com.android.base.Constant;\\n\\nimport org.greenrobot.eventbus.EventBus;\\n\\nimport java.util.Map;\\n\\n/**\\n * 支付\\n *\\n * @author 张全\\n */\\npublic class OrderPay {\\n private static final int SDK_PAY_FLAG = 1;\\n\\n public void wxPay(Context ctx, OrderPayResponse orderPayResponse) {\\n WXHelper wxHelper = new WXHelper(ctx);\\n wxHelper.pay(orderPayResponse);\\n }\\n\\n public void alipay(Activity ctx, OrderPayResponse orderPayResponse) {\\n if (!checkAliPayInstalled(ctx)) { //未安装支付宝\\n ToastUtil.show(\\\"未安装支付宝\\\");\\n return;\\n }\\n String orderInfo = orderPayResponse.orderInfo;\\n final Runnable payRunnable = new Runnable() {\\n\\n @Override\\n public void run() {\\n PayTask alipay = new PayTask(ctx);\\n Map<String, String> result = alipay.payV2(orderInfo, true);\\n\\n Message msg = new Message();\\n msg.what = SDK_PAY_FLAG;\\n msg.obj = result;\\n mHandler.sendMessage(msg);\\n }\\n };\\n\\n // 必须异步调用\\n Thread payThread = new Thread(payRunnable);\\n payThread.start();\\n }\\n\\n @SuppressLint(\\\"HandlerLeak\\\")\\n private Handler mHandler = new Handler() {\\n @Override\\n public void handleMessage(Message msg) {\\n switch (msg.what) {\\n case SDK_PAY_FLAG: {\\n PayResult payResult = new PayResult((Map<String, String>) msg.obj);\\n /**\\n * 对于支付结果，请商户依赖服务端的异步通知结果。同步通知结果，仅作为支付结束的通知。\\n */\\n String resultInfo = payResult.getResult();// 同步返回需要验证的信息\\n String resultStatus = payResult.getResultStatus();\\n // 判断resultStatus 为9000则代表支付成功\\n if (TextUtils.equals(resultStatus, \\\"9000\\\")) {\\n EventBus.getDefault().post(new EventMsg(Constant.Event.ORDER_BUY_SUCCESS, PayWay.ZHIBAO));\\n } else if (TextUtils.equals(resultStatus, \\\"6001\\\")) {\\n// ToastUtil.show(\\\"取消支付\\\");\\n EventBus.getDefault().post(new EventMsg(Constant.Event.ORDER_BUY_CANCEL, PayWay.ZHIBAO));\\n } else {\\n EventBus.getDefault().post(new EventMsg(Constant.Event.ORDER_BUY_FAIL, PayWay.ZHIBAO));\\n }\\n break;\\n }\\n }\\n\\n }\\n };\\n\\n public boolean checkAliPayInstalled(Context context) {\\n\\n Uri uri = Uri.parse(\\\"alipays://platformapi/startApp\\\");\\n Intent intent = new Intent(Intent.ACTION_VIEW, uri);\\n ComponentName componentName = intent.resolveActivity(context.getPackageManager());\\n return componentName != null;\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5242528915405273,\n \"alphanum_fraction\": 0.5282562375068665,\n \"avg_line_length\": 38.63468551635742,\n \"blob_id\": \"ace8617efc37278a0eabe61f8c405ad8ce0a1515\",\n \"content_id\": \"451aaaaed139a447f54a47cbb5b9d2e8491974ba\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 10917,\n \"license_type\": \"no_license\",\n \"max_line_length\": 108,\n \"num_lines\": 271,\n \"path\": \"/app/src/main/java/com/android/base/version/UpdateService.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.version;\\n\\nimport android.app.Notification;\\nimport android.app.NotificationChannel;\\nimport android.app.NotificationManager;\\nimport android.app.PendingIntent;\\nimport android.app.Service;\\nimport android.content.Context;\\nimport android.content.Intent;\\nimport android.graphics.Color;\\nimport android.net.Uri;\\nimport android.os.Build;\\nimport android.os.Handler;\\nimport android.os.IBinder;\\nimport android.os.Looper;\\nimport android.os.Message;\\n\\nimport androidx.annotation.Nullable;\\nimport androidx.core.app.NotificationCompat;\\nimport androidx.core.content.FileProvider;\\n\\nimport com.android.util.ext.ToastUtil;\\nimport com.android.util.log.LogUtil;\\nimport com.android.base.data.RestClient;\\nimport com.fastapp.R;\\n\\nimport java.io.File;\\nimport java.io.FileOutputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\n\\nimport component.update.AppDownloadClient;\\nimport component.update.AppVersion;\\nimport okhttp3.Call;\\nimport okhttp3.Callback;\\nimport okhttp3.Request;\\nimport okhttp3.Response;\\n\\n/**\\n * App后台下载服务\\n *\\n * @author 张全\\n */\\npublic class UpdateService extends Service {\\n private static final String TAG = \\\"VersionUpdate\\\";\\n private static final String APP_VERSION = \\\"APP_VERSION\\\";\\n private boolean isDownload;\\n\\n public static void update(Context ctx, AppVersion appVersion) {\\n Intent intent = new Intent(ctx, UpdateService.class);\\n intent.putExtra(APP_VERSION, appVersion);\\n ctx.startService(intent);\\n\\n }\\n\\n @Nullable\\n @Override\\n public IBinder onBind(Intent intent) {\\n return null;\\n }\\n\\n @Override\\n public int onStartCommand(Intent intent, int flags, int startId) {\\n if (null == intent || !intent.hasExtra(APP_VERSION))\\n return super.onStartCommand(intent, flags, startId);\\n\\n ToastUtil.show(\\\"后台下载中,请稍候..\\\");\\n\\n if (isDownload) return super.onStartCommand(intent, flags, startId);\\n try {\\n AppVersion appVersion = (AppVersion) intent.getSerializableExtra(APP_VERSION);\\n File updateFile = AppDownloadClient.getUpdateFile();\\n downLoadFile(appVersion.downloadUrl, updateFile);\\n } catch (Exception e) {\\n e.printStackTrace();\\n isDownload = false;\\n }\\n return super.onStartCommand(intent, flags, startId);\\n }\\n\\n private static class DownloadCallback extends Handler {\\n private static final int msg_success = 1;\\n private static final int msg_fail = 2;\\n private static final int msg_progress = 3;\\n private int notificationId;\\n final String channelId = \\\"1\\\";\\n private NotificationManager notificationManager;\\n private NotificationCompat.Builder mBuilder;\\n private Context ctx;\\n\\n public DownloadCallback(Context ctx, String url) {\\n super(Looper.getMainLooper());\\n this.ctx = ctx;\\n notificationId = url.hashCode();\\n notificationManager = (NotificationManager) ctx.getSystemService(Context.NOTIFICATION_SERVICE);\\n\\n if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.O) {\\n NotificationChannel channel = new NotificationChannel(channelId,\\n \\\"Channel1\\\", NotificationManager.IMPORTANCE_DEFAULT);\\n channel.enableLights(false);\\n channel.enableVibration(false);\\n channel.setVibrationPattern(new long[]{0});\\n channel.setSound(null, null);\\n channel.setLightColor(Color.RED); //小红点颜色\\n channel.setLockscreenVisibility(Notification.VISIBILITY_PRIVATE);\\n channel.setShowBadge(true); //是否在久按桌面图标时显示此渠道的通知\\n mBuilder = new NotificationCompat.Builder(ctx, channelId);\\n notificationManager.createNotificationChannel(channel);\\n } else {\\n mBuilder = new NotificationCompat.Builder(ctx);\\n mBuilder.setDefaults(NotificationCompat.FLAG_ONLY_ALERT_ONCE)\\n .setVibrate(new long[]{0})\\n .setSound(null);\\n }\\n }\\n\\n @Override\\n public void handleMessage(Message msg) {\\n switch (msg.what) {\\n case msg_progress:\\n int progress = (int) msg.obj;\\n sendNotification(progress);\\n break;\\n case msg_success:\\n LogUtil.e(TAG, \\\"下载成功\\\");\\n notificationManager.cancel(notificationId);\\n AppDownloadClient.installAPK();\\n break;\\n case msg_fail:\\n LogUtil.e(TAG, \\\"下载失败\\\");\\n notificationManager.cancel(notificationId);\\n ToastUtil.show(\\\"下载失败,请重试\\\");\\n break;\\n }\\n }\\n\\n public void sendMsg(int what) {\\n sendEmptyMessage(what);\\n }\\n\\n public void updateProgress(int progress) {\\n sendMessage(obtainMessage(msg_progress, progress));\\n }\\n\\n public void sendNotification(int progress) {\\n LogUtil.e(TAG, \\\"progress=\\\" + progress);\\n if (progress < 100) {\\n Intent localIntent = new Intent(ctx, UpdateService.class);\\n PendingIntent localPendingIntent = PendingIntent.getService(ctx, 0,\\n localIntent, PendingIntent.FLAG_UPDATE_CURRENT);\\n if (progress <= 0) {\\n mBuilder.setContentTitle(\\\"星乐桃\\\")\\n .setContentText(\\\"版本下载中\\\");\\n } else {\\n mBuilder.setContentTitle(\\\"版本下载\\\")\\n .setContentText(\\\"当前进度：\\\" + progress + \\\"%\\\")\\n .setProgress(100, progress, false);\\n }\\n mBuilder\\n .setContentIntent(localPendingIntent)\\n .setTicker(\\\"版本下载中...\\\")\\n .setWhen(System.currentTimeMillis())\\n// .setDefaults(Notification.DEFAULT_ALL)\\n .setSmallIcon(R.mipmap.ic_launcher)\\n .setAutoCancel(true);\\n\\n\\n } else {\\n File updateFile = AppDownloadClient.getUpdateFile();\\n Intent intent = new Intent(Intent.ACTION_VIEW);\\n intent.setFlags(Intent.FLAG_ACTIVITY_NEW_TASK);\\n Uri uri = null;\\n if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.N) {\\n String authority = AppDownloadClient.getAuthority();\\n uri = FileProvider.getUriForFile(AppDownloadClient.getContext(), authority, updateFile);\\n intent.addFlags(Intent.FLAG_GRANT_READ_URI_PERMISSION);\\n } else {\\n uri = Uri.fromFile(updateFile);\\n }\\n intent.setDataAndType(uri, \\\"application/vnd.android.package-archive\\\");\\n PendingIntent pendingIntent = PendingIntent.getActivity(ctx, 0, intent,\\n 0);\\n mBuilder.setContentText(\\\"下载完毕点击安装\\\")\\n .setWhen(System.currentTimeMillis())\\n .setDefaults(Notification.DEFAULT_ALL)\\n .setSmallIcon(R.mipmap.ic_launcher)\\n .setContentIntent(pendingIntent)\\n .setAutoCancel(true);\\n }\\n\\n Notification notify = mBuilder.build();\\n notificationManager.notify(notificationId, notify);\\n }\\n }\\n\\n\\n /**\\n * 下载文件\\n */\\n private void downLoadFile(String url, File file) {\\n// url=\\\"https://www.9ben.cn/download/andriod/9benfresh.apk\\\";\\n isDownload = true;\\n final Request request = new Request.Builder().url(url).build();\\n final Call call = RestClient.getDownloadClient().newCall(request);\\n DownloadCallback downloadCallback = new DownloadCallback(this, url);\\n call.enqueue(new Callback() {\\n @Override\\n public void onFailure(Call call, IOException e) {\\n isDownload = false;\\n downloadCallback.sendMsg(DownloadCallback.msg_fail);\\n }\\n\\n @Override\\n public void onResponse(Call call, Response response) {\\n int lastProgress = 0;\\n if (response.isSuccessful()) {\\n InputStream is = null;\\n byte[] buf = new byte[2048];\\n int len;\\n FileOutputStream fos = null;\\n try {\\n long total = response.body().contentLength();\\n LogUtil.e(TAG, \\\"total------>\\\" + total);\\n long current = 0;\\n is = response.body().byteStream();\\n fos = new FileOutputStream(file);\\n while ((len = is.read(buf)) != -1) {\\n current += len;\\n fos.write(buf, 0, len);\\n LogUtil.e(TAG, \\\"current------>\\\" + current);\\n if (total == -1) {\\n downloadCallback.updateProgress(-1);\\n } else {\\n int progress = (int) (current * 1.0f / total * 100);\\n progress = progress >= 100 ? 99 : progress;\\n if (lastProgress != progress) {\\n downloadCallback.updateProgress(progress);\\n }\\n lastProgress = progress;\\n }\\n }\\n fos.flush();\\n try {\\n fos.close();\\n } catch (Exception e) {\\n e.printStackTrace();\\n }\\n downloadCallback.sendMsg(DownloadCallback.msg_success);\\n } catch (Exception e) {\\n LogUtil.e(TAG, e.toString());\\n downloadCallback.sendMsg(DownloadCallback.msg_fail);\\n } finally {\\n try {\\n if (is != null) {\\n is.close();\\n }\\n if (fos != null) {\\n fos.close();\\n }\\n } catch (Exception e) {\\n LogUtil.e(TAG, e.toString());\\n }\\n }\\n } else {\\n downloadCallback.sendMsg(DownloadCallback.msg_fail);\\n }\\n isDownload = false;\\n }\\n });\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5586017966270447,\n \"alphanum_fraction\": 0.5606579780578613,\n \"avg_line_length\": 33.761905670166016,\n \"blob_id\": \"c0b087763810562ea460c7a1fa316d9de16e9ba0\",\n \"content_id\": \"b99d88c37e2e4b560ea107bfd2ab5d8ce2c5254d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1489,\n \"license_type\": \"no_license\",\n \"max_line_length\": 171,\n \"num_lines\": 42,\n \"path\": \"/scripts/jiaguModule.py\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#coding:utf8\\nimport os\\nimport sys\\n\\nclass JiaGuUtil:\\n def startJiaGu(self, apkPath):\\n print(\\\"apkPath = \\\" + apkPath)\\n SecretId = 'AKIDoZ9vY2w1Um06ILi3JOODyv7RocnawW4U'\\n SecretKey = 'tlMNZ4Ti1Ma8XC5CXM8116A5b4Pdj6LO'\\n\\n (filepath, tempfilename) = os.path.split(apkPath)\\n (filename, extension) = os.path.splitext(tempfilename)\\n print('apkFileName = ' + filename)\\n apkResignerForWallePath = 'ToolMakeChannelApk'\\n downloadPath = apkResignerForWallePath + '/apk-jiagu'\\n\\n cmdLeguStr = (\\n ' rm -rf ' + downloadPath + '/*.apk'\\n + '\\\\n rm -rf ' + downloadPath\\n + '\\\\n mkdir ' + downloadPath\\n + '\\\\n java -Dfile.encoding=utf-8 -jar scripts/lib/ms-shield.jar -sid %s -skey %s -uploadPath %s -downloadPath %s' %(SecretId, SecretKey, apkPath, downloadPath)\\n )\\n print(cmdLeguStr)\\n print('开始上传，并加固，请耐心等待～')\\n os.system(cmdLeguStr)\\n\\n leguAppPath = ''\\n appVersionName = ''\\n for fpath, dirname, fnames in os.walk(downloadPath):\\n print(fnames)\\n print(fpath)\\n fname = fnames[0]\\n leguApkPath = fpath\\n leguAppPath = leguApkPath + '/' + fname\\n\\n newName = '%s-legu.apk' %(filename)\\n oldPath = os.path.join(fpath, fname)\\n newPath = os.path.join(fpath, newName)\\n os.rename(oldPath, newPath)\\n leguAppPath = newPath\\n print('leguAppPath = ' + leguAppPath)\\n break\"\n },\n {\n \"alpha_fraction\": 0.6793145537376404,\n \"alphanum_fraction\": 0.6793145537376404,\n \"avg_line_length\": 28.214284896850586,\n \"blob_id\": \"1b362dc7632707557c5c0def63a9bcf80ed248ca\",\n \"content_id\": \"8e02b202101246240893c2503a64c0cbbabef964\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Kotlin\",\n \"length_bytes\": 829,\n \"license_type\": \"no_license\",\n \"max_line_length\": 94,\n \"num_lines\": 28,\n \"path\": \"/app/src/main/java/com/android/base/task/UpdateTask.kt\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.task\\n\\nimport android.content.Context\\nimport androidx.work.WorkerParameters\\nimport com.blankj.utilcode.util.NetworkUtils\\nimport component.update.AppDownloadClient\\nimport component.update.AppVersion\\nimport component.update.VersionUpdateListener\\n\\n\\n/**\\n * 升级下载任务\\n * @author zhangquan\\n */\\nclass UpdateTask(context: Context, param: WorkerParameters) : BackgroundTask(context, param) {\\n override fun doTask(parameters: WorkerParameters) {\\n if (NetworkUtils.isAvailable()) {\\n AppDownloadClient.doCheckVersion(object : VersionUpdateListener {\\n override fun onNoVersionReturned() {\\n }\\n\\n override fun fail() {}\\n override fun onNewVersionReturned(appVersion: AppVersion) {\\n }\\n })\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.7230769395828247,\n \"alphanum_fraction\": 0.7230769395828247,\n \"avg_line_length\": 13.55555534362793,\n \"blob_id\": \"d8760f436b1f782a89bf8832c1de061d2db63ebd\",\n \"content_id\": \"d4eb6989c714236b8d261497ee21cca288fe3979\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Kotlin\",\n \"length_bytes\": 130,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 9,\n \"path\": \"/app/src/main/java/com/android/base/util/crash/MainQuitException.kt\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.example.crash_handler\\n\\n/**\\n *\\n * @author zhangquan\\n */\\nclass MainQuitException(str:String) :RuntimeException(str) {\\n\\n}\"\n },\n {\n \"alpha_fraction\": 0.5966101884841919,\n \"alphanum_fraction\": 0.5966101884841919,\n \"avg_line_length\": 18.66666603088379,\n \"blob_id\": \"92b84492f7ae9b1ed65f385c2df134a7d35e6335\",\n \"content_id\": \"6f558665a7616f93b1461418a2263827bbdd46e9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 299,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 15,\n \"path\": \"/librarys/CommonUtil/src/main/java/com/android/util/db/EasyDB.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.util.db;\\n\\n/**\\n * @author 张全\\n */\\npublic class EasyDB {\\n\\n public static <T> DBSession<T> with(Class<T> cls) {\\n return new DBSession<T>(cls);\\n }\\n\\n public static <T> DBSession<T> with(Class<T> cls, String uid) {\\n return new DBSession<T>(cls, uid);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7047532200813293,\n \"alphanum_fraction\": 0.7084094882011414,\n \"avg_line_length\": 25.682926177978516,\n \"blob_id\": \"9fc6b3afd69005cc55fc89f9ad8401729bd132c0\",\n \"content_id\": \"b0e2965d30b6552c6b52abdffc2fd5a99be74619\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1132,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 41,\n \"path\": \"/app/src/main/java/com/android/base/widget/BottomInAllDialog.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.widget;\\n\\nimport android.app.Dialog;\\nimport android.content.Context;\\nimport android.os.Bundle;\\nimport android.view.Gravity;\\nimport android.view.Window;\\nimport android.view.WindowManager;\\n\\nimport androidx.annotation.NonNull;\\n\\nimport com.fastapp.R;\\n\\n\\n/**\\n * 底部弹框\\n *\\n * @author 张全\\n */\\npublic class BottomInAllDialog extends Dialog {\\n public BottomInAllDialog(@NonNull Context context) {\\n super(context);\\n }\\n\\n @Override\\n protected void onCreate(Bundle savedInstanceState) {\\n super.onCreate(savedInstanceState);\\n\\n\\n Window win = this.getWindow();\\n win.getDecorView().setPadding(0, 0, 0, 0);\\n WindowManager.LayoutParams lp = win.getAttributes();\\n lp.width = WindowManager.LayoutParams.MATCH_PARENT;\\n lp.height = WindowManager.LayoutParams.MATCH_PARENT;\\n lp.windowAnimations = R.style.AnimBottom;\\n lp.gravity = Gravity.BOTTOM;\\n win.setAttributes(lp);\\n win.setBackgroundDrawableResource(android.R.color.transparent);\\n setCanceledOnTouchOutside(false); // 点击屏幕Dialog以外的地方是否消失\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6342329382896423,\n \"alphanum_fraction\": 0.6342329382896423,\n \"avg_line_length\": 22.46666717529297,\n \"blob_id\": \"68e3608807fb68a6e4be8f16727ad54fc1a65f19\",\n \"content_id\": \"8028c9ad0a789bbcf9fac6bf3bab9097363315f7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1412,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 60,\n \"path\": \"/librarys/CommonWidget/src/main/java/common/widget/dialog/LoadingLoadingView.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package common.widget.dialog;\\n\\nimport android.animation.ValueAnimator;\\nimport android.content.Context;\\nimport android.text.TextUtils;\\nimport android.view.View;\\nimport android.widget.ImageView;\\nimport android.widget.TextView;\\nimport common.widget.R;\\n\\n/**\\n * @author 张全\\n */\\npublic class LoadingLoadingView extends DialogView {\\n private ImageView mLoadingView;\\n private TextView mLoadingTxt;\\n private String loadingTxt;\\n private ValueAnimator anim;\\n\\n public LoadingLoadingView(Context ctx) {\\n super(ctx);\\n }\\n\\n public LoadingLoadingView(Context ctx, String loadingTxt) {\\n super(ctx);\\n this.loadingTxt = loadingTxt;\\n }\\n\\n @Override\\n protected void initView(View view) {\\n mLoadingView = (ImageView) findViewById(R.id.loadingbar);\\n mLoadingTxt = (TextView) findViewById(R.id.loadingbar_txt);\\n if (!TextUtils.isEmpty(loadingTxt)) {\\n mLoadingTxt.setText(loadingTxt);\\n }\\n }\\n\\n public void startAnimal() {\\n if (mLoadingView != null && anim != null) {\\n anim.start();\\n }\\n }\\n\\n public void stop() {\\n if (mLoadingView != null && anim != null) {\\n anim.cancel();\\n anim = null;\\n }\\n }\\n\\n public void setText(String text) {\\n mLoadingTxt.setText(text);\\n }\\n\\n @Override\\n protected int getLayoutId() {\\n return R.layout.dialog_loading;\\n }\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6191219687461853,\n \"alphanum_fraction\": 0.6557348370552063,\n \"avg_line_length\": 39.02777862548828,\n \"blob_id\": \"e342f397eb8f6ffde4302a89eb1666171bebb4b3\",\n \"content_id\": \"41cee00ad5c279bd3f312d51891abaf5b6d71ee1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Gradle\",\n \"length_bytes\": 6085,\n \"license_type\": \"no_license\",\n \"max_line_length\": 105,\n \"num_lines\": 144,\n \"path\": \"/buildsystem/dependencies.gradle\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"ext {\\n\\n //################################# 版本号 #################################\\n app = [\\n versionCode : 10000,\\n versionName : '1.0.0', //versionName: a.b.c -> versionCode = a*10000+b*100+c*1 如1.2.3->10203\\n applicationId: 'com.fastapp'\\n ]\\n\\n //Android\\n buildToolsVersion = \\\"30.0.2\\\"\\n minSdkVersion = 21\\n targetSdkVersion = 28\\n compileSdkVersion = 30\\n\\n javaVersion = JavaVersion.VERSION_1_8\\n\\n //Libraries\\n supportVersion = '1.0.0'\\n retrofit2Version = '2.9.0'\\n okHttpVersion = '4.7.2'\\n glideVersion = '4.11.0'\\n roomVersion = '2.2.5'\\n autoDisposeVersion = '2.0.0'\\n work_version = '2.4.0'\\n //调试\\n leakCanaryVersion = '1.6.3'\\n blockCanaryVersion = '1.5.0'\\n\\n //################################# 依赖库 #################################\\n //kotlin\\n kotlin = \\\"org.jetbrains.kotlin:kotlin-stdlib-jdk8:1.3.72\\\"\\n kotlin_core = \\\"androidx.core:core-ktx:1.3.2\\\"\\n kotlin_coroutines = \\\"org.jetbrains.kotlinx:kotlinx-coroutines-core:1.5.0\\\"\\n //Android Support\\n supportV4 = \\\"androidx.legacy:legacy-support-v4:${supportVersion}\\\"\\n appCompat = \\\"androidx.appcompat:appcompat:1.3.0-alpha02\\\"\\n materialDesign = \\\"com.google.android.material:material:1.2.1\\\"\\n recyclerView = \\\"androidx.recyclerview:recyclerview:1.2.0\\\"\\n percentlayout = \\\"androidx.percentlayout:percentlayout:${supportVersion}\\\"\\n cardView = \\\"androidx.cardview:cardview:1.0.0\\\"\\n\\n constraint = \\\"androidx.constraintlayout:constraintlayout:2.0.4\\\"\\n lifecycleExtensions = \\\"androidx.lifecycle:lifecycle-extensions:2.2.0\\\" //ViewModelProviders\\n multidex = 'androidx.multidex:multidex:2.0.1'\\n\\n //Rx系列\\n rxJava = \\\"io.reactivex.rxjava2:rxjava:2.2.6\\\"\\n rxAndroid = \\\"io.reactivex.rxjava2:rxandroid:2.1.1\\\"\\n rxPermissions = \\\"com.github.tbruyelle:rxpermissions:0.10.2\\\"\\n rxBinding = \\\"com.jakewharton.rxbinding4:rxbinding:4.0.0\\\"\\n\\n //DI系列\\n butterKnife = \\\"com.jakewharton:butterknife:10.2.1\\\"\\n butterKnifeCompiler = \\\"com.jakewharton:butterknife-compiler:10.2.1\\\"\\n arouter = \\\"com.alibaba:arouter-api:1.5.1\\\"\\n arouterCompiler = \\\"com.alibaba:arouter-compiler:1.5.1\\\"\\n\\n //网络依赖库\\n retrofit2 = \\\"com.squareup.retrofit2:retrofit:${retrofit2Version}\\\"\\n retrofit2AdapterRxjava = \\\"com.squareup.retrofit2:adapter-rxjava2:${retrofit2Version}\\\"\\n retrofit2ConvertGson = \\\"com.squareup.retrofit2:converter-gson:${retrofit2Version}\\\"\\n retrofit2Scalars = \\\"com.squareup.retrofit2:converter-scalars:2.0.0\\\"\\n okHttp = \\\"com.squareup.okhttp3:okhttp:${okHttpVersion}\\\"\\n loggingInterceptor = \\\"com.squareup.okhttp3:logging-interceptor:${okHttpVersion}\\\"\\n gson = \\\"com.google.code.gson:gson:2.8.6\\\"\\n\\n //事件通知\\n eventBus = \\\"org.greenrobot:eventbus:3.2.0\\\"\\n //google工具类聚合\\n guava = \\\"com.google.guava:guava:29.0-android\\\"\\n //Android工具库\\n utilCode = 'com.blankj:utilcodex:1.29.0'\\n //第三方RecycleView adapter\\n recycleViewAdapter = \\\"com.github.CymChad:BaseRecyclerViewAdapterHelper:3.0.4\\\"\\n //Fragment容器\\n fragmentation = \\\"me.yokeyword:fragmentationx:1.0.2\\\"\\n\\n //下拉刷新\\n smartRefresh = 'com.scwang.smartrefresh:SmartRefreshLayout:1.1.0-x'\\n smartRefreshHeader = 'com.scwang.smartrefresh:SmartRefreshHeader:1.1.0-x' //没有使用特殊Header，可以不加这行\\n\\n //Glide图片加载\\n glide = \\\"com.github.bumptech.glide:glide:${glideVersion}\\\"\\n glideCompiler = \\\"com.github.bumptech.glide:compiler:${glideVersion}\\\"\\n glideOkHttp = \\\"com.github.bumptech.glide:okhttp3-integration:${glideVersion}\\\" //使用okhttp加载图片\\n glideWebp = \\\"com.zlc.glide:webpdecoder:2.0.${glideVersion}\\\"\\n glideTransform = \\\"jp.wasabeef:glide-transformations:4.3.0\\\" //图片处理(圆角、颜色、Blur、Mask)\\n\\n //拍照和选择照片 https://github.com/yanzhenjie/Album\\n album = 'com.yanzhenjie:album:2.1.3'\\n //图片压缩 https://github.com/Curzibn/Luban\\n luban = 'top.zibin:Luban:1.1.8'\\n //圆角图片 适合加载静态图片和非gif的网络图片 如果是网络圆角图片 建议使用Glide\\n roundedImageView = 'com.makeramen:roundedimageview:2.3.0'\\n\\n\\n //*************************** START *****************************\\n\\n //Room数据库\\n roomRuntime = \\\"androidx.room:room-runtime:$roomVersion\\\"\\n roomProcessor = \\\"androidx.room:room-compiler:$roomVersion\\\"\\n roomRxjava2 = \\\"androidx.room:room-rxjava2:$roomVersion\\\"\\n roomGuava = \\\"androidx.room:room-guava:$roomVersion\\\"\\n roomKotlin = \\\"androidx.room:room-ktx:$roomVersion\\\"\\n\\n //WorkManager\\n // (Java only)\\n workManager = \\\"androidx.work:work-runtime:$work_version\\\"\\n //Kotlin + coroutines\\n workManager_kotlin = \\\"androidx.work:work-runtime-ktx:$work_version\\\"\\n // optional - RxJava2 support\\n workManager_rxjava2 = \\\"androidx.work:work-rxjava2:$work_version\\\"\\n\\n //微信K-V组件\\n MMKV = \\\"com.tencent:mmkv-static:1.2.2\\\"\\n\\n\\n //获取时间防止修改手机时间\\n truetime = 'com.github.instacart.truetime-android:library-extension-rx:3.4'\\n //时间或条件选择控件 https://github.com/Bigkoo/Android-PickerView\\n pickerView = \\\"com.contrarywind:Android-PickerView:4.1.9\\\"\\n //二维码扫描zxing\\n zxing = 'cn.bingoogolapple:bga-qrcode-zxing:1.3.7'\\n //标签流式布局\\n flowlayout = \\\"com.hyman:flowlayout-lib:1.1.2\\\"\\n //apk防护\\n easyProtector = \\\"com.lahm.library:easy-protector-release:1.1.1\\\"\\n\\n //*************************** END *****************************\\n\\n\\n //################################# 开发调试 #################################\\n //https://github.com/markzhai/AndroidPerformanceMonitor\\n //https://github.com/square/leakcanary\\n debugDependencies = [\\n leakCanary : \\\"com.squareup.leakcanary:leakcanary-android:${leakCanaryVersion}\\\",\\n blockCanary: \\\"com.github.markzhai:blockcanary-android:${blockCanaryVersion}\\\"\\n ]\\n releaseDependencies = [\\n leakCanary : \\\"com.squareup.leakcanary:leakcanary-android-no-op:${leakCanaryVersion}\\\",\\n blockCanary: \\\"com.github.markzhai:blockcanary-no-op:${blockCanaryVersion}\\\"\\n ]\\n}\"\n },\n {\n \"alpha_fraction\": 0.6019451022148132,\n \"alphanum_fraction\": 0.6047238707542419,\n \"avg_line_length\": 31.34831428527832,\n \"blob_id\": \"9f2e4819cb26c3689f9dd8273f303b456f52424b\",\n \"content_id\": \"5445e340b441ed6ec0d76926210267ad842b5f77\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 2885,\n \"license_type\": \"no_license\",\n \"max_line_length\": 108,\n \"num_lines\": 89,\n \"path\": \"/app/src/main/java/com/android/base/ui/splash/GuideActivity.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.ui.splash;\\n\\nimport android.content.Context;\\nimport android.content.Intent;\\nimport android.os.Bundle;\\nimport android.view.LayoutInflater;\\nimport android.view.View;\\nimport android.view.ViewGroup;\\nimport android.widget.ImageView;\\n\\nimport androidx.annotation.NonNull;\\nimport androidx.viewpager.widget.PagerAdapter;\\nimport androidx.viewpager.widget.ViewPager;\\n\\nimport com.android.base.ui.BaseActivity;\\nimport com.fastapp.MainActivity;\\nimport com.fastapp.R;\\n\\nimport net.medlinker.android.splash.SplashUtil;\\n\\nimport java.util.ArrayList;\\n\\n/**\\n * 引导页\\n */\\npublic class GuideActivity extends BaseActivity {\\n private static final int[] imgs = new int[]{R.drawable.guide01, R.drawable.guide02, R.drawable.guide03};\\n private ViewPager viewPager;\\n\\n\\n public static void start(Context ctx) {\\n Intent intent = new Intent(ctx, GuideActivity.class);\\n ctx.startActivity(intent);\\n }\\n\\n @Override\\n public int getLayoutId() {\\n return R.layout.guide_layout;\\n }\\n\\n @Override\\n public void init(Bundle savedInstanceState) {\\n ArrayList<View> viewList = new ArrayList<>();\\n for (int i = 0; i < imgs.length; i++) {\\n View view = LayoutInflater.from(this).inflate(R.layout.guide_item, null);\\n ImageView imageView = view.findViewById(R.id.guide_img);\\n imageView.setImageResource(imgs[i]);\\n viewList.add(view);\\n if (i == imgs.length - 1) {\\n View btn = view.findViewById(R.id.tv_go_login);\\n btn.setVisibility(View.VISIBLE);\\n btn.setOnClickListener(new View.OnClickListener() {\\n @Override\\n public void onClick(View v) {\\n SplashUtil.setShowGuide(true);\\n MainActivity.start(GuideActivity.this);\\n finish();\\n }\\n });\\n }\\n }\\n viewPager = findViewById(R.id.viewpager);\\n viewPager.setAdapter(new PagerAdapter() {\\n @Override\\n public int getCount() {\\n return viewList.size();\\n }\\n\\n @NonNull\\n @Override\\n public Object instantiateItem(@NonNull ViewGroup container, int position) {\\n container.addView(viewList.get(position), ViewGroup.LayoutParams.MATCH_PARENT,\\n ViewGroup.LayoutParams.MATCH_PARENT);\\n return viewList.get(position);\\n }\\n\\n @Override\\n public void destroyItem(@NonNull ViewGroup container, int position, @NonNull Object object) {\\n container.removeView(viewList.get(position));\\n }\\n\\n @Override\\n public boolean isViewFromObject(@NonNull View view, @NonNull Object object) {\\n return view == object;\\n }\\n });\\n }\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.46669667959213257,\n \"alphanum_fraction\": 0.46804681420326233,\n \"avg_line_length\": 31.691177368164062,\n \"blob_id\": \"5c8446a3e182d4c9ee1579366982a0d877651b08\",\n \"content_id\": \"b6e7920fde2c3bfab13ed0a72f55408b24a04197\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Kotlin\",\n \"length_bytes\": 2270,\n \"license_type\": \"no_license\",\n \"max_line_length\": 93,\n \"num_lines\": 68,\n \"path\": \"/app/src/main/java/com/fastapp/wxapi/WXEntryActivity.kt\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.fastapp.wxapi\\n\\nimport android.content.ClipData\\nimport android.content.ClipboardManager\\nimport android.content.Context\\nimport com.android.base.Constant\\nimport com.android.base.event.EventMsg\\nimport com.fastapp.BuildConfig\\nimport com.tencent.mm.opensdk.modelbase.BaseResp\\nimport com.tencent.mm.opensdk.modelmsg.SendAuth\\nimport com.umeng.socialize.weixin.view.WXCallbackActivity\\nimport org.greenrobot.eventbus.EventBus\\n\\n/**\\n * 微信登录回调\\n */\\nclass WXEntryActivity : WXCallbackActivity() {\\n override fun onResp(authResp: BaseResp) {\\n if (authResp is SendAuth.Resp) { // 授权\\n when (authResp.errCode) {\\n BaseResp.ErrCode.ERR_OK -> { // 用户同意\\n addToClip(authResp.code)\\n EventBus.getDefault().post(\\n EventMsg(\\n Constant.Event.WX_CODE,\\n authResp.code\\n )\\n )\\n }\\n BaseResp.ErrCode.ERR_AUTH_DENIED -> { // 用户拒绝授权\\n EventBus.getDefault().post(\\n EventMsg(\\n Constant.Event.WX_CODE,\\n \\\"0\\\"\\n )\\n )\\n }\\n BaseResp.ErrCode.ERR_USER_CANCEL -> { // 取消授权\\n EventBus.getDefault().post(\\n EventMsg(\\n Constant.Event.WX_CODE,\\n \\\"1\\\"\\n )\\n )\\n }\\n else -> {\\n EventBus.getDefault().post(\\n EventMsg(\\n Constant.Event.WX_CODE,\\n \\\"1\\\"\\n )\\n )\\n }\\n }\\n finish()\\n } else { // 分享\\n super.onResp(authResp)\\n }\\n }\\n\\n private fun addToClip(code: String) {\\n if (BuildConfig.DEBUG) {\\n val myClipboard = getSystemService(Context.CLIPBOARD_SERVICE) as ClipboardManager\\n val myClip = ClipData.newPlainText(\\\"levelText\\\", code)\\n myClipboard.setPrimaryClip(myClip)\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6269261837005615,\n \"alphanum_fraction\": 0.6382806301116943,\n \"avg_line_length\": 28.380952835083008,\n \"blob_id\": \"afac9966217d058b274726bd3aa41a75a0560206\",\n \"content_id\": \"4141aeb7de4cdab9faf25fdf9f683d05975fc161\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1233,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 42,\n \"path\": \"/librarys/CommonWidget/src/main/java/common/widget/listview/LinearSpaceItemDecoration.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package common.widget.listview;\\n\\nimport android.graphics.Rect;\\nimport android.view.View;\\n\\nimport androidx.recyclerview.widget.RecyclerView;\\n\\npublic class LinearSpaceItemDecoration extends RecyclerView.ItemDecoration {\\n private int headerCount;\\n private int mOrientation;\\n private int space;\\n\\n public LinearSpaceItemDecoration(int space,int oriention) {\\n this.space = space;\\n setOrientation(oriention);\\n }\\n\\n public void setOrientation(int orientation) {\\n if (orientation != 0 && orientation != 1) {\\n throw new IllegalArgumentException(\\\"Invalid orientation. It should be either HORIZONTAL or VERTICAL\\\");\\n } else {\\n this.mOrientation = orientation;\\n }\\n }\\n\\n public void setHeaderCount(int headerCount) {\\n this.headerCount = headerCount;\\n }\\n\\n\\n @Override\\n public void getItemOffsets(Rect outRect, View view, RecyclerView parent, RecyclerView.State state) {\\n outRect.set(0, 0, 0, 0);\\n if ((parent.getChildLayoutPosition(view)-headerCount)>=0) {\\n if (this.mOrientation == 1) {\\n outRect.set(0, 0, 0, space);\\n } else {\\n outRect.set(0, 0, space, 0);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.8002322912216187,\n \"alphanum_fraction\": 0.8025551438331604,\n \"avg_line_length\": 30.925926208496094,\n \"blob_id\": \"3c1ddbfa902aeb51936c26721da2bc1630b4e0eb\",\n \"content_id\": \"0c0644bf4f4d231bf104328b94955b291f12c2bb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 865,\n \"license_type\": \"no_license\",\n \"max_line_length\": 112,\n \"num_lines\": 27,\n \"path\": \"/app/src/main/java/com/android/base/util/OkHttpAppGlideModule.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.util;\\n\\nimport android.content.Context;\\n\\nimport androidx.annotation.NonNull;\\n\\nimport com.android.base.data.RestClient;\\nimport com.bumptech.glide.Glide;\\nimport com.bumptech.glide.Registry;\\nimport com.bumptech.glide.annotation.GlideModule;\\nimport com.bumptech.glide.integration.okhttp3.OkHttpUrlLoader;\\nimport com.bumptech.glide.load.model.GlideUrl;\\nimport com.bumptech.glide.module.AppGlideModule;\\n\\nimport java.io.InputStream;\\n\\nimport okhttp3.OkHttpClient;\\n\\n@GlideModule\\npublic class OkHttpAppGlideModule extends AppGlideModule {\\n\\n @Override\\n public void registerComponents(@NonNull Context context, @NonNull Glide glide, @NonNull Registry registry) {\\n OkHttpClient client = RestClient.getImgDownloadClient();\\n registry.replace(GlideUrl.class, InputStream.class, new OkHttpUrlLoader.Factory(client)); //https支持\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.7425742745399475,\n \"alphanum_fraction\": 0.7524752616882324,\n \"avg_line_length\": 19.399999618530273,\n \"blob_id\": \"608ba2c93700cee012ce9748d4c9c08d836e53c3\",\n \"content_id\": \"837ade3a58145304873eda057b0dd3d2163d1e65\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 101,\n \"license_type\": \"no_license\",\n \"max_line_length\": 36,\n \"num_lines\": 5,\n \"path\": \"/librarys/CommonUtil/src/main/java/com/android/util/common/CommonCallBack.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.util.common;\\n\\npublic interface CommonCallBack<T> {\\n void onCallBack(T var1);\\n}\"\n },\n {\n \"alpha_fraction\": 0.7556886076927185,\n \"alphanum_fraction\": 0.7736526727676392,\n \"avg_line_length\": 32.400001525878906,\n \"blob_id\": \"853b4ab7590855f416110f564881ee9e1617d933\",\n \"content_id\": \"05fbc0ebb78066fa5ee2555d79c92f3d8c42981f\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1089,\n \"license_type\": \"permissive\",\n \"max_line_length\": 78,\n \"num_lines\": 25,\n \"path\": \"/scripts_channels/config.py\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python \\n#-*-coding:utf-8-*-\\n\\n#keystore信息\\n#Windows 下路径分割线请注意使用\\\\\\\\转义\\nkeystorePath = \\\"../medlinker.keystore\\\"\\nkeyAlias = \\\"medlinker\\\"\\nkeystorePassword = \\\"medlinker\\\"\\nkeyPassword = \\\"medlinker\\\"\\n\\n#加固后的源文件名（未重签名）\\nprotectedSourceApkName = \\\"\\\"\\n#加固后的源文件所在文件夹路径(...path),注意结尾不要带分隔符，默认在此文件夹根目录\\nprotectedSourceApkDirPath = \\\"apk-jiagu\\\"\\n#渠道包输出路径，默认在此文件夹channels目录下\\nchannelsOutputFilePath = \\\"channels\\\"\\n#渠道名配置文件路径，默认在此文件夹根目录\\nchannelFilePath = \\\"\\\"\\n#额外信息配置文件（绝对路径，例如/Users/mac/Desktop/walle360/config.json）\\n#配置信息示例参看https://github.com/Meituan-Dianping/walle/blob/master/app/config.json\\nextraChannelFilePath = \\\"\\\"\\n#Android SDK buidtools path , please use above 25.0+\\n# sdkBuildToolPath = \\\"/Users/macbook/Library/Android/sdk/build-tools/26.0.2\\\"\\n# sdkBuildToolPath = \\\"/Users/macbook/workspace/android-sdk/build-tools/26.0.2\\\"\\nsdkBuildToolPath = \\\"lib\\\"\\n\"\n },\n {\n \"alpha_fraction\": 0.6151078939437866,\n \"alphanum_fraction\": 0.6187050342559814,\n \"avg_line_length\": 26.83333396911621,\n \"blob_id\": \"9856fedaec1e1cca9aadf144fce36a16a7ba15d0\",\n \"content_id\": \"fca52780b50f03b1a94b033e7848c234bec79df0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Kotlin\",\n \"length_bytes\": 864,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 30,\n \"path\": \"/app/src/main/java/com/android/base/util/AppInstallUtil.kt\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.util\\n\\nimport android.content.Context\\nimport com.blankj.utilcode.util.AppUtils\\n\\n/**\\n *\\n */\\nobject AppInstallUtil {\\n\\n @JvmStatic\\n fun isAppInstalled(context: Context?, packageName: String): Boolean {\\n return AppUtils.isAppInstalled(packageName) || isAppInstalled1(context, packageName)\\n }\\n\\n\\n fun isAppInstalled1(context: Context?, packageName: String): Boolean {\\n val packageManager = context?.packageManager// 获取packagemanager\\n val pinfo = packageManager?.getInstalledPackages(0)// 获取所有已安装程序的包信息\\n if (pinfo != null) {\\n for (i in pinfo.indices) {\\n val pn = pinfo[i].packageName\\n if (pn.equals(packageName, ignoreCase = true)) {\\n return true\\n }\\n }\\n }\\n return false\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.7616580128669739,\n \"alphanum_fraction\": 0.7616580128669739,\n \"avg_line_length\": 21.705883026123047,\n \"blob_id\": \"1828623f381314ab63e5a180689e9e4e468971f9\",\n \"content_id\": \"53177db5316325618327c3ef878bd4afd3146f75\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 390,\n \"license_type\": \"no_license\",\n \"max_line_length\": 90,\n \"num_lines\": 17,\n \"path\": \"/app/src/main/java/com/android/base/version/VersionClient.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.android.base.version;\\n\\nimport com.android.base.data.ResponseDataObject;\\nimport com.android.base.data.RestClient;\\n\\nimport io.reactivex.Observable;\\n\\n/**\\n * @author 张全\\n */\\npublic class VersionClient {\\n\\n public static Observable<ResponseDataObject<VersionEntity>> doUpdate(String channel) {\\n return RestClient.getService(VersionApi.class).appUpdate(channel);\\n }\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7046413421630859,\n \"alphanum_fraction\": 0.7046413421630859,\n \"avg_line_length\": 18.75,\n \"blob_id\": \"f682c6d5d6e3247c9000e23c4b839291f5e1e165\",\n \"content_id\": \"2819572e0e7216136b690aa22faa4d28d75bf063\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 241,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 12,\n \"path\": \"/librarys/CommonWidget/src/main/java/common/widget/viewpager/BannerImageLoader.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package common.widget.viewpager;\\n\\nimport android.content.Context;\\n\\n/**\\n * @author 张全\\n */\\npublic interface BannerImageLoader<V, T> {\\n void displayView(Context ctx, T data, V view, int pos, int count);\\n\\n V createView(Context ctx);\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5055791735649109,\n \"alphanum_fraction\": 0.5063762068748474,\n \"avg_line_length\": 32.61606979370117,\n \"blob_id\": \"69f9d2f0e15efa7b381e2dae361f57e121702b9a\",\n \"content_id\": \"c54044ee6fa804b5c279f5666c42af45a8a2f3bd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Kotlin\",\n \"length_bytes\": 3946,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 112,\n \"path\": \"/app/src/main/java/com/fastapp/wxapi/WXPayEntryActivity.kt\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.fastapp.wxapi\\n\\nimport android.app.Activity\\nimport android.content.Intent\\nimport android.os.Bundle\\nimport com.android.util.LContext\\nimport com.android.util.ext.ToastUtil\\nimport com.android.util.log.LogUtil\\nimport com.android.base.Constant\\nimport com.android.base.event.EventMsg\\nimport com.android.base.util.pay.PayWay\\nimport com.fastapp.R\\nimport com.tencent.mm.opensdk.constants.ConstantsAPI\\nimport com.tencent.mm.opensdk.modelbase.BaseReq\\nimport com.tencent.mm.opensdk.modelbase.BaseResp\\nimport com.tencent.mm.opensdk.openapi.IWXAPI\\nimport com.tencent.mm.opensdk.openapi.IWXAPIEventHandler\\nimport com.tencent.mm.opensdk.openapi.WXAPIFactory\\nimport org.greenrobot.eventbus.EventBus\\n\\n/**\\n * 微信支付回调页面\\n *\\n * @author 张全\\n */\\nclass WXPayEntryActivity : Activity(), IWXAPIEventHandler {\\n private var api: IWXAPI? = null\\n public override fun onCreate(savedInstanceState: Bundle?) {\\n super.onCreate(savedInstanceState)\\n setContentView(R.layout.wx_pay_result)\\n api = WXAPIFactory.createWXAPI(this, LContext.getString(R.string.wx_appid))\\n api?.handleIntent(intent, this)\\n LogUtil.d(TAG, \\\"WXPayEntryActivity............onCreate\\\")\\n }\\n\\n override fun onNewIntent(intent: Intent) {\\n super.onNewIntent(intent)\\n setIntent(intent)\\n api?.handleIntent(intent, this)\\n LogUtil.d(TAG, \\\"WXPayEntryActivity............onNewIntent\\\")\\n }\\n\\n override fun onReq(req: BaseReq) {}\\n override fun onResp(resp: BaseResp) {\\n if (null == resp) {\\n finish()\\n return\\n }\\n printInfo(resp)\\n LogUtil.d(TAG, \\\"WXPayEntryActivity,onResp...onPayFinish, errCode = \\\"\\n + resp.errCode + \\\",type=\\\" + resp.type)\\n if (resp.type == ConstantsAPI.COMMAND_PAY_BY_WX) {\\n when (resp.errCode) {\\n 0 -> {\\n EventBus.getDefault().post(\\n EventMsg(\\n Constant.Event.ORDER_BUY_SUCCESS,\\n PayWay.WX\\n )\\n )\\n finish()\\n }\\n -1 -> {\\n /*\\n * 可能的原因：签名错误、未注册APPID、项目设置APPID不正确、注册的APPID与设置的不匹配、其他异常等。\\n */EventBus.getDefault().post(\\n EventMsg(\\n Constant.Event.ORDER_BUY_FAIL,\\n PayWay.WX\\n )\\n )\\n ToastUtil.show(\\\"支付失败\\\")\\n finish()\\n }\\n -2 -> {\\n /*\\n * 无需处理。发生场景：用户不支付了，点击取消，返回APP。\\n */EventBus.getDefault().post(\\n EventMsg(\\n Constant.Event.ORDER_BUY_CANCEL,\\n PayWay.WX\\n )\\n )\\n ToastUtil.show(\\\"取消支付\\\")\\n finish()\\n }\\n else -> finish()\\n }\\n }\\n }\\n\\n companion object {\\n private const val TAG = \\\"WXPayEntryActivity\\\"\\n fun printInfo(baseResp: BaseResp?) {\\n if (!LContext.isDebug || null == baseResp) {\\n return\\n }\\n LogUtil.d(TAG, \\\"---------微信支付--------start\\\")\\n val fields = baseResp.javaClass.declaredFields\\n for (field in fields) {\\n field.isAccessible = true\\n try {\\n val value = field[baseResp]\\n LogUtil.d(TAG, field.name + \\\"=\\\" + value)\\n } catch (e: Exception) {\\n e.printStackTrace()\\n }\\n }\\n LogUtil.d(TAG, \\\"---------微信支付--------end\\\")\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5960000157356262,\n \"alphanum_fraction\": 0.5960000157356262,\n \"avg_line_length\": 26.77777862548828,\n \"blob_id\": \"1365250e31ffb9fd87b07c41f747635ba3c1c13d\",\n \"content_id\": \"ac7ed01c0b225fa5036f9818024edad66b7f5b50\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Gradle\",\n \"length_bytes\": 250,\n \"license_type\": \"no_license\",\n \"max_line_length\": 33,\n \"num_lines\": 9,\n \"path\": \"/settings.gradle\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"include ':ApkChannel'\\nrootProject.name = 'fastapp'\\ninclude ':app',\\n ':librarys:ApkChannel',\\n ':librarys:Logger',\\n ':librarys:CommonUtil',\\n ':librarys:AppUpdate',\\n ':librarys:CommonWidget',\\n ':librarys:umeng'\\n\"\n },\n {\n \"alpha_fraction\": 0.6818181872367859,\n \"alphanum_fraction\": 0.6818181872367859,\n \"avg_line_length\": 15.5,\n \"blob_id\": \"6f1a8d826b61d2ea2dc78f92b6bb3d37625f3e1a\",\n \"content_id\": \"3f9a21e9d39ad4d1e9e48b89985af09cffae391a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 330,\n \"license_type\": \"no_license\",\n \"max_line_length\": 49,\n \"num_lines\": 20,\n \"path\": \"/app/src/main/java/com/fastapp/LoginActivity.java\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"package com.fastapp;\\n\\nimport android.os.Bundle;\\n\\nimport com.android.base.ui.BaseActivity;\\n\\n/**\\n * @author zhangquan\\n */\\npublic class LoginActivity extends BaseActivity {\\n @Override\\n public int getLayoutId() {\\n return R.layout.login_act;\\n }\\n\\n @Override\\n public void init(Bundle savedInstanceState) {\\n\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6086329221725464,\n \"alphanum_fraction\": 0.6136828064918518,\n \"avg_line_length\": 30.744274139404297,\n \"blob_id\": \"205b416c36cf6cdaf813bfe3dcce918563fe8773\",\n \"content_id\": \"0de57c39e7d50be215a66e5108b92c231a873102\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Gradle\",\n \"length_bytes\": 8649,\n \"license_type\": \"no_license\",\n \"max_line_length\": 133,\n \"num_lines\": 262,\n \"path\": \"/app/build.gradle\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"apply plugin: 'com.android.application'\\napply plugin: 'kotlin-android'\\napply plugin: 'kotlin-android-extensions'\\napply plugin: 'kotlin-kapt'\\n\\ndef config = rootProject.ext\\nandroid {\\n\\n compileSdkVersion config.compileSdkVersion\\n buildToolsVersion config.buildToolsVersion\\n\\n defaultConfig {\\n minSdkVersion config.minSdkVersion\\n targetSdkVersion config.targetSdkVersion\\n\\n applicationId config.app.applicationId\\n versionCode config.app.versionCode\\n versionName config.app.versionName\\n\\n\\n //矢量图\\n vectorDrawables.useSupportLibrary = true\\n\\n manifestPlaceholders = [UMENG_CHANNEL_VALUE: \\\"official\\\"] // 默认渠道\\n\\n ndk {\\n // 设置支持的 SO 库构架\\n abiFilters 'armeabi-v7a' //'x86'\\n }\\n\\n resConfigs \\\"zh\\\"\\n\\n multiDexEnabled true\\n\\n //arouter\\n javaCompileOptions {\\n annotationProcessorOptions {\\n arguments = [AROUTER_MODULE_NAME: project.getName()]\\n }\\n }\\n }\\n\\n aaptOptions {\\n cruncherEnabled = false //禁止AS对png图片进行校验\\n useNewCruncher = false\\n }\\n\\n //----------------------签名配置----------------------\\n signingConfigs {\\n debug {\\n //为了方便测试分享或第三方登录 使用正式签名\\n storeFile file('../buildsystem/keystore.jks')\\n storePassword 'snqu_123456_agent'\\n keyAlias 'key0'\\n keyPassword 'snqu_123456_agent'\\n v2SigningEnabled true\\n }\\n release {\\n storeFile file('../buildsystem/keystore.jks')\\n storePassword 'snqu_123456_agent'\\n keyAlias 'key0'\\n keyPassword 'snqu_123456_agent'\\n v2SigningEnabled false\\n }\\n }\\n sourceSets {\\n main {\\n jniLibs.srcDirs = ['libs']\\n }\\n }\\n buildTypes {\\n debug {\\n minifyEnabled false\\n shrinkResources false\\n manifestPlaceholders = [DEBUGABLE: true]\\n proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro'\\n signingConfig signingConfigs.debug\\n }\\n release {\\n minifyEnabled true\\n zipAlignEnabled true\\n shrinkResources true\\n proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro'\\n manifestPlaceholders = [DEBUGABLE: false]\\n signingConfig signingConfigs.release\\n }\\n }\\n\\n flavorDimensions \\\"channel\\\"\\n productFlavors {\\n //生产测试\\n \\\"prod\\\" {}\\n\\n //应用商店\\n// \\\"offical\\\" {} //官方\\n// \\\"xiaomi\\\" {} //小米\\n// \\\"vivo\\\" {} //vivo商店\\n// \\\"oppo\\\" {} //oppo商店\\n// \\\"huawei\\\" {} //华为\\n// \\\"yyb\\\" {} //应用宝\\n// \\\"p360\\\" {} //360\\n// \\\"ali\\\" {} //阿里\\n// \\\"baidu\\\" {}//百度\\n\\n// kuan {} //酷安\\n// anzhi {} //安智\\n// samsung {} //三星\\n// meizu {}//魅族\\n// sogou {}//搜狗\\n// liqu {}//历趣\\n// mumayi {}//木蚂蚁\\n// yingyognhui {}//应用汇\\n// jifeng {}//机锋\\n// mm {}//mm应用商店\\n// lenovo {}//联想\\n }\\n productFlavors.all {\\n flavor ->\\n //合作渠道\\n buildConfigField(\\\"String\\\", \\\"cooperChannels\\\", '\\\"xx\\\"')\\n\\n if (name == \\\"prod\\\") {\\n ndk {\\n abiFilters \\\"armeabi-v7a\\\", 'x86'\\n }\\n buildConfigField \\\"String\\\", \\\"API_HOST\\\", '\\\"https://api-t.xin1.cn/\\\" ' //测试服务器\\n buildConfigField \\\"String\\\", \\\"H5_HOST\\\", '\\\"https://m-xlt-t.xin1.cn/\\\" '\\n buildConfigField \\\"String\\\", \\\"LOG_HOST\\\", '\\\"https://report-t.xin1.cn/\\\"'\\n manifestPlaceholders = [APP_NAME: \\\"@string/app_name_test\\\", DEV_ENV: true, UMENG_CHANNEL_VALUE: name]\\n } else {\\n ndk {\\n abiFilters \\\"armeabi-v7a\\\"\\n }\\n buildConfigField \\\"String\\\", \\\"API_HOST\\\", '\\\"https://api.xinletao.vip/\\\" ' //线上服务器\\n buildConfigField \\\"String\\\", \\\"H5_HOST\\\", '\\\"https://m.xinletao.vip/\\\" '\\n buildConfigField \\\"String\\\", \\\"LOG_HOST\\\", '\\\"https://report.xin1.cn/\\\" '\\n flavor.manifestPlaceholders = [APP_NAME: \\\"@string/app_name\\\", DEV_ENV: false, UMENG_CHANNEL_VALUE: name]\\n }\\n }\\n\\n applicationVariants.all { variant ->\\n if (variant.buildType.name == \\\"release\\\" && variant.flavorName != \\\"prod\\\") {\\n variant.getPackageApplicationProvider().get().outputDirectory = new File(project.rootDir.absolutePath + \\\"/apk\\\")\\n variant.getPackageApplicationProvider().get().outputScope.apkDatas.forEach { apkData ->\\n\\n apkData.outputFileName = \\\"xlt_\\\" + variant.flavorName + \\\".apk\\\"\\n// if (apkData.getFullName() == variant.flavorName + \\\"UniversalRelease\\\") {\\n// variant.getPackageApplicationProvider().get().outputDirectory = new File(project.rootDir.absolutePath + \\\"/apk\\\")\\n// apkData.outputFileName = variant.flavorName + \\\".apk\\\"\\n// } else {\\n// apkData.outputFileName = apkData.getFullName() + \\\".apk\\\"\\n// }\\n }\\n }\\n }\\n\\n compileOptions {\\n sourceCompatibility JavaVersion.VERSION_1_8\\n targetCompatibility JavaVersion.VERSION_1_8\\n }\\n\\n packagingOptions {\\n exclude 'LICENSE.txt'\\n exclude 'META-INF/DEPENDENCIES'\\n exclude 'META-INF/ASL2.0'\\n exclude 'META-INF/NOTICE'\\n exclude 'META-INF/LICENSE'\\n exclude 'META-INF/proguard/androidx-annotations.pro'\\n }\\n\\n lintOptions {\\n quiet true\\n abortOnError false\\n ignoreWarnings true\\n disable 'InvalidPackage', 'OldTargetApi', 'IconDensities', 'IconMissingDensityFolder'\\n }\\n\\n useLibrary 'org.apache.http.legacy'\\n}\\nrepositories {\\n flatDir {\\n dirs 'libs'\\n }\\n}\\nkapt {\\n arguments {\\n arg(\\\"AROUTER_MODULE_NAME\\\", project.getName())\\n }\\n}\\ndependencies {\\n implementation fileTree(dir: 'libs', include: ['*.jar', '*.aar'])\\n api project(':librarys:AppUpdate')\\n api project(':librarys:CommonUtil')\\n api project(':librarys:Logger')\\n api project(':librarys:CommonWidget')\\n api project(':librarys:umeng')\\n\\n implementation config.kotlin\\n implementation config.kotlin_core\\n implementation config.appCompat\\n implementation config.supportV4\\n implementation config.recyclerView\\n implementation config.cardView\\n implementation config.multidex\\n implementation config.constraint //约束布局\\n implementation config.materialDesign //material\\n implementation config.lifecycleExtensions //ViewModelProviders\\n implementation config.fragmentation //页面容器\\n implementation config.rxPermissions //rxpermission\\n// implementation config.rxBinding //RxBinding组件 依赖Rxjava3 RxAndroid3\\n\\n //数据\\n implementation config.retrofit2\\n implementation config.retrofit2AdapterRxjava\\n implementation config.retrofit2ConvertGson\\n implementation config.retrofit2Scalars\\n implementation config.okHttp\\n implementation config.loggingInterceptor\\n implementation config.rxJava\\n implementation config.rxAndroid\\n implementation config.gson\\n\\n //图片加载\\n implementation config.glide\\n annotationProcessor config.glideCompiler\\n implementation(config.glideOkHttp) {\\n exclude group: \\\"com.android.support\\\"\\n }\\n implementation config.glideWebp //glide支持webp\\n implementation config.glideTransform //glide图片处理\\n implementation config.album //图片选择\\n implementation config.luban //图片压缩\\n implementation config.roundedImageView //圆角图片\\n implementation config.recycleViewAdapter //recycleView Adapter\\n\\n //workmanager\\n implementation config.workManager_kotlin\\n\\n //注入\\n implementation config.butterKnife //UI\\n annotationProcessor config.butterKnifeCompiler\\n implementation config.eventBus //事件通知\\n implementation config.arouter\\n annotationProcessor config.arouterCompiler\\n\\n //工具箱\\n implementation config.guava\\n implementation config.utilCode\\n\\n //K-V\\n implementation config.MMKV\\n\\n //UI控件\\n implementation config.smartRefresh //下拉刷新\\n implementation config.smartRefreshHeader\\n\\n //调试\\n debugImplementation config.debugDependencies.leakCanary\\n debugImplementation config.debugDependencies.blockCanary\\n releaseImplementation config.releaseDependencies.leakCanary\\n releaseImplementation config.releaseDependencies.blockCanary\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6076124310493469,\n \"alphanum_fraction\": 0.6179930567741394,\n \"avg_line_length\": 21.5625,\n \"blob_id\": \"2de5943d20e99a8e7842a08836432628ee134f67\",\n \"content_id\": \"790cef9231f62998eca8bd795ccc4dd1d510e3ab\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Gradle\",\n \"length_bytes\": 1465,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 64,\n \"path\": \"/build.gradle\",\n \"repo_name\": \"zhangquanit/fastapp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"apply from: 'buildsystem/dependencies.gradle'\\n\\nbuildscript {\\n ext {\\n kotlin_version = '1.3.72'\\n }\\n repositories {\\n maven{ url 'https://maven.aliyun.com/repository/public/'}\\n google()\\n jcenter()\\n\\n }\\n dependencies {\\n classpath \\\"com.android.tools.build:gradle:4.0.1\\\"\\n classpath \\\"org.jetbrains.kotlin:kotlin-gradle-plugin:1.3.21\\\"\\n }\\n}\\n\\nallprojects {\\n repositories {\\n maven{ url 'https://maven.aliyun.com/repository/public/'}\\n google()\\n jcenter()\\n maven { url 'https://jitpack.io' }\\n }\\n\\n gradle.taskGraph.whenReady {\\n tasks.each { task ->\\n\\n }\\n }\\n}\\n\\nsubprojects {\\n project.afterEvaluate {\\n project.plugins.withId('com.android.library') {\\n project.android.compileSdkVersion rootProject.ext.compileSdkVersion\\n project.android.buildToolsVersion rootProject.ext.buildToolsVersion\\n project.android.defaultConfig.minSdkVersion rootProject.ext.minSdkVersion\\n project.android.defaultConfig.targetSdkVersion rootProject.ext.targetSdkVersion\\n }\\n\\n }\\n\\n // 强制使用某个版本的库\\n// configurations.all {\\n// resolutionStrategy{\\n// force 'com.xx.network:retrofit:1.0.1'\\n// }\\n// }\\n}\\n\\nrepositories {\\n flatDir {\\n dirs 'libs'\\n }\\n}\\n\\ntask clean(type: Delete) {\\n delete rootProject.buildDir\\n}\\ntasks.withType(JavaCompile) {\\n options.encoding = \\\"UTF-8\\\"\\n}\\n\\n\"\n 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like","value":[{"alpha_fraction":0.4403669834136963,"alphanum_fraction":0.44954127073287964,"avg_line_length":12.75,"blob_id":"6ad5dc2c3d56ef29b5a61f4c1d8a1af476c63821","content_id":"724870027464b282723becc5e67a5d4a9313109c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":109,"license_type":"no_license","max_line_length":32,"num_lines":8,"path":"/test/pathtest.py","repo_name":"davidfang/dolphin","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\nprint(\"__file__: %s\" % __file__)\nprint(\"__name__: %s\" % __name__)\n\n\n\n# test windows"},{"alpha_fraction":0.5970149040222168,"alphanum_fraction":0.611940324306488,"avg_line_length":12.399999618530273,"blob_id":"3b0f832326aa1cd14e2ae227369a56022269bc73","content_id":"9bf2c8e98128a2fd3f5bf5d5f993d849e1474d6b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":67,"license_type":"no_license","max_line_length":31,"num_lines":5,"path":"/utils/exceptions.py","repo_name":"davidfang/dolphin","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\n\nclass TestException(Exception):\n pass\n"},{"alpha_fraction":0.5312756299972534,"alphanum_fraction":0.5585905313491821,"avg_line_length":29.773109436035156,"blob_id":"ba5c93e8920b7e356564db23b7b6b0ccb8879f29","content_id":"3721b3b080a7462abd3c5a93dff417ca70e94918","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3955,"license_type":"no_license","max_line_length":129,"num_lines":119,"path":"/utils/webfont.py","repo_name":"davidfang/dolphin","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nimport os\nfrom utils.log import logging\nfrom utils.requestshelper import RequestsHelper\nfrom fontTools.ttLib import TTFont\nfrom PIL import Image, ImageEnhance\nfrom pytesseract import *\n\nlogger = logging.getLogger(__name__)\n\n\nclass WebFont(object):\n def __init__(self, plat_name):\n self.url = 'https://vfile.meituan.net/colorstone/2c8d9a8f5031f26f4e9fe924263e31ce2076.woff'\n self.plat_name = plat_name\n self.font_dict = dict()\n self.manual_dict = {\n 'uniF3C5': 8,\n 'uniEDEE': 6,\n 'uniF38E': 2,\n 'uniE824': 3,\n 'uniE829': 5,\n 'uniE851': 0,\n 'uniEBCF': 1,\n 'uniEE5A': 9,\n 'uniEFFE': 4,\n 'uniF35D': 7,\n }\n self.__init_font_map()\n\n def __init_font_map(self):\n \"\"\"\n 初始化猫眼的字符集模版,只在倒入模块时有构造方法调用一次\n \"\"\"\n font_file = self.__save_font_file(self.url)\n font = TTFont(font_file)\n glyph_set = font.getGlyphSet()\n glyph_dict = glyph_set._glyphs.glyphs\n for k, v in self.manual_dict.items():\n self.font_dict[glyph_dict[k].data] = v\n\n def __save_font_file(self, url):\n filename = url.split('/')[-1]\n font_dir = \"%s/%s\" % (os.path.dirname(__file__), '../opt/fonts')\n # 判断文件是否存在\n if not os.path.exists(\"%s/%s\" % (font_dir, filename)):\n if not os.path.exists(font_dir):\n os.mkdir(font_dir)\n try:\n response = RequestsHelper.get(url)\n except Exception:\n raise Exception()\n with open(\"%s/%s\" % (font_dir, filename), 'wb') as fw:\n fw.write(response.content)\n return \"%s/%s\" % (font_dir, filename)\n\n def convert_to_num(self, series, url):\n \"\"\"\n 获取unicode的对应的数字\n :param series: int\n :param url: 字符集文件的地址\n :return: int，series对应数字\n \"\"\"\n font_file = self.__save_font_file(url)\n font = TTFont(font_file)\n cmap = font.getBestCmap()\n num = cmap.get(series)\n glyph_set = font.getGlyphSet()\n\n return self.font_dict[glyph_set._glyphs.glyphs[num].data]\n\n\nclass FontManager(object):\n def __init__(self):\n self.fonts = dict()\n\n def add_font(self, plat_name):\n \"\"\"\n 倒入该模块时调用此方法初始化字符集模版\n :param plat_name: str 'maoyan'/\n \"\"\"\n if plat_name == 'maoyan':\n self.fonts['maoyan'] = WebFont(plat_name)\n elif plat_name == 'douyin':\n pass\n else:\n raise Exception('平台：%s 暂不支持' % plat_name)\n\n def get_font(self, plat_name):\n try:\n return self.fonts[plat_name]\n except KeyError:\n raise Exception('请先调用get_font()来添加平台%s的字符集' % plat_name)\n\n def convert_to_num_ocr(self, fp):\n \"\"\"\n 获取unicode的对应的数字\n :param fp: 图片文件的路径或文件对象(必须byte方式打开)\n :return: 图片对应的数字, 如果不符合数字格式，则返回图片上的文本\n \"\"\"\n im = Image.open(fp)\n enhancer = ImageEnhance.Contrast(im)\n image_enhancer = enhancer.enhance(4)\n im_orig = image_enhancer.resize((image_enhancer.size[0]*2, image_enhancer.size[1]*2), Image.BILINEAR)\n\n text = image_to_string(im_orig)\n try:\n return int(text)\n except ValueError:\n return text\n\n\nfm = FontManager()\nfm.add_font('maoyan')\nmaoyan_font = fm.get_font('maoyan')\nlogger.info(maoyan_font.convert_to_num(0xf8c3, 'https://vfile.meituan.net/colorstone/7986a5279399aeee3ef19fe37989a00d2088.woff'))\n\nfp = open('/Users/apple/test/dolphin/opt/WX20180607-172930@2x.png', 'rb')\nprint(fm.convert_to_num_ocr(fp))"},{"alpha_fraction":0.6398058533668518,"alphanum_fraction":0.6407766938209534,"avg_line_length":26.83783721923828,"blob_id":"0e32f065277d58683f05ff1b2ae2111cf0a3e2c8","content_id":"3c1d187abe8b6dc47765854704503105e7f06db8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1030,"license_type":"no_license","max_line_length":82,"num_lines":37,"path":"/utils/requestshelper.py","repo_name":"davidfang/dolphin","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\nimport requests\nfrom settings import PROXIES\n\n\nclass RequestsHelper(object):\n def __init__(self):\n pass\n\n @classmethod\n def get(cls, url, params=None, **kwargs):\n return requests.get(url, params, proxies=PROXIES, **kwargs)\n\n @classmethod\n def options(cls, url, **kwargs):\n return requests.options(url, proxies=PROXIES, **kwargs)\n\n @classmethod\n def head(cls, url, **kwargs):\n return requests.head(url, **kwargs)\n\n @classmethod\n def post(cls, url, data=None, json=None, **kwargs):\n return requests.post(url, data=data, json=json, proxies=PROXIES, **kwargs)\n\n @classmethod\n def put(cls, url, data=None, **kwargs):\n return requests.put(url, data=data, proxies=PROXIES, **kwargs)\n\n @classmethod\n def patch(cls, url, data=None, **kwargs):\n return requests.patch(url, data=data, proxies=PROXIES, **kwargs)\n\n @classmethod\n def delete(cls, url, **kwargs):\n return requests.delete(url, proxies=PROXIES, **kwargs)\n"},{"alpha_fraction":0.6233974099159241,"alphanum_fraction":0.6538461446762085,"avg_line_length":23.959999084472656,"blob_id":"fd18b7afed88c2080f2595e52cafbce1cd308154","content_id":"c7a5bfbc22247f94040255cfcc4d46ef10d72565","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":624,"license_type":"no_license","max_line_length":74,"num_lines":25,"path":"/test/main.py","repo_name":"davidfang/dolphin","src_encoding":"UTF-8","text":"#coding=utf-8\nfrom appium import webdriver\n\n\nimport os\n\n# Returns abs path relative to this file and not cwd\nPATH = lambda p: os.path.abspath(\n os.path.join(os.path.dirname(__file__), p)\n)\n\n\ndef get_desired_capabilities(app):\n desired_caps = {\n 'platformName': 'Android',\n 'platformVersion': '5.1',\n 'deviceName': 'Android Emulator',\n 'app': PATH('../opt/' + app),\n 'newCommandTimeout': 240\n }\n\n return desired_caps\ndesired_caps = get_desired_capabilities(u'aweme_aweGW_v1.8.3_61b8304.apk')\ndriver = webdriver.Remote('http://localhost:4723/wd/hub', desired_caps)\ndriver.quit()\n"},{"alpha_fraction":0.7857142686843872,"alphanum_fraction":0.7857142686843872,"avg_line_length":13,"blob_id":"90cf89b1626280af7f6e11f85d1e18dc81a38c63","content_id":"25d65ce72a3540b4371c3f0928a41be86ac15a40","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":28,"license_type":"no_license","max_line_length":17,"num_lines":2,"path":"/README.md","repo_name":"davidfang/dolphin","src_encoding":"UTF-8","text":"# dolphin\nscrapy for maoyan\n"},{"alpha_fraction":0.7416666746139526,"alphanum_fraction":0.75,"avg_line_length":18.83333396911621,"blob_id":"437b34a385b7ac76539ce82b4563440d66ef135a","content_id":"66ab94ed8fb2d85305c35ca9e777312c45bbe698","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":120,"license_type":"no_license","max_line_length":41,"num_lines":6,"path":"/utils/log.py","repo_name":"davidfang/dolphin","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nimport logging\nfrom settings import logging_config\n\n\nlogging.config.dictConfig(logging_config)\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.4403669834136963,\n \"alphanum_fraction\": 0.44954127073287964,\n \"avg_line_length\": 12.75,\n \"blob_id\": \"6ad5dc2c3d56ef29b5a61f4c1d8a1af476c63821\",\n \"content_id\": \"724870027464b282723becc5e67a5d4a9313109c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 109,\n \"license_type\": \"no_license\",\n \"max_line_length\": 32,\n \"num_lines\": 8,\n \"path\": \"/test/pathtest.py\",\n \"repo_name\": \"davidfang/dolphin\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\nprint(\\\"__file__: %s\\\" % __file__)\\nprint(\\\"__name__: %s\\\" % __name__)\\n\\n\\n\\n# test windows\"\n },\n {\n \"alpha_fraction\": 0.5970149040222168,\n \"alphanum_fraction\": 0.611940324306488,\n \"avg_line_length\": 12.399999618530273,\n \"blob_id\": \"3b0f832326aa1cd14e2ae227369a56022269bc73\",\n \"content_id\": \"9bf2c8e98128a2fd3f5bf5d5f993d849e1474d6b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 67,\n \"license_type\": \"no_license\",\n \"max_line_length\": 31,\n \"num_lines\": 5,\n \"path\": \"/utils/exceptions.py\",\n \"repo_name\": \"davidfang/dolphin\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\n\\nclass TestException(Exception):\\n pass\\n\"\n },\n {\n \"alpha_fraction\": 0.5312756299972534,\n \"alphanum_fraction\": 0.5585905313491821,\n \"avg_line_length\": 29.773109436035156,\n \"blob_id\": \"ba5c93e8920b7e356564db23b7b6b0ccb8879f29\",\n \"content_id\": \"3721b3b080a7462abd3c5a93dff417ca70e94918\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3955,\n \"license_type\": \"no_license\",\n \"max_line_length\": 129,\n \"num_lines\": 119,\n \"path\": \"/utils/webfont.py\",\n \"repo_name\": \"davidfang/dolphin\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nimport os\\nfrom utils.log import logging\\nfrom utils.requestshelper import RequestsHelper\\nfrom fontTools.ttLib import TTFont\\nfrom PIL import Image, ImageEnhance\\nfrom pytesseract import *\\n\\nlogger = logging.getLogger(__name__)\\n\\n\\nclass WebFont(object):\\n def __init__(self, plat_name):\\n self.url = 'https://vfile.meituan.net/colorstone/2c8d9a8f5031f26f4e9fe924263e31ce2076.woff'\\n self.plat_name = plat_name\\n self.font_dict = dict()\\n self.manual_dict = {\\n 'uniF3C5': 8,\\n 'uniEDEE': 6,\\n 'uniF38E': 2,\\n 'uniE824': 3,\\n 'uniE829': 5,\\n 'uniE851': 0,\\n 'uniEBCF': 1,\\n 'uniEE5A': 9,\\n 'uniEFFE': 4,\\n 'uniF35D': 7,\\n }\\n self.__init_font_map()\\n\\n def __init_font_map(self):\\n \\\"\\\"\\\"\\n 初始化猫眼的字符集模版,只在倒入模块时有构造方法调用一次\\n \\\"\\\"\\\"\\n font_file = self.__save_font_file(self.url)\\n font = TTFont(font_file)\\n glyph_set = font.getGlyphSet()\\n glyph_dict = glyph_set._glyphs.glyphs\\n for k, v in self.manual_dict.items():\\n self.font_dict[glyph_dict[k].data] = v\\n\\n def __save_font_file(self, url):\\n filename = url.split('/')[-1]\\n font_dir = \\\"%s/%s\\\" % (os.path.dirname(__file__), '../opt/fonts')\\n # 判断文件是否存在\\n if not os.path.exists(\\\"%s/%s\\\" % (font_dir, filename)):\\n if not os.path.exists(font_dir):\\n os.mkdir(font_dir)\\n try:\\n response = RequestsHelper.get(url)\\n except Exception:\\n raise Exception()\\n with open(\\\"%s/%s\\\" % (font_dir, filename), 'wb') as fw:\\n fw.write(response.content)\\n return \\\"%s/%s\\\" % (font_dir, filename)\\n\\n def convert_to_num(self, series, url):\\n \\\"\\\"\\\"\\n 获取unicode的对应的数字\\n :param series: int\\n :param url: 字符集文件的地址\\n :return: int，series对应数字\\n \\\"\\\"\\\"\\n font_file = self.__save_font_file(url)\\n font = TTFont(font_file)\\n cmap = font.getBestCmap()\\n num = cmap.get(series)\\n glyph_set = font.getGlyphSet()\\n\\n return self.font_dict[glyph_set._glyphs.glyphs[num].data]\\n\\n\\nclass FontManager(object):\\n def __init__(self):\\n self.fonts = dict()\\n\\n def add_font(self, plat_name):\\n \\\"\\\"\\\"\\n 倒入该模块时调用此方法初始化字符集模版\\n :param plat_name: str 'maoyan'/\\n \\\"\\\"\\\"\\n if plat_name == 'maoyan':\\n self.fonts['maoyan'] = WebFont(plat_name)\\n elif plat_name == 'douyin':\\n pass\\n else:\\n raise Exception('平台：%s 暂不支持' % plat_name)\\n\\n def get_font(self, plat_name):\\n try:\\n return self.fonts[plat_name]\\n except KeyError:\\n raise Exception('请先调用get_font()来添加平台%s的字符集' % plat_name)\\n\\n def convert_to_num_ocr(self, fp):\\n \\\"\\\"\\\"\\n 获取unicode的对应的数字\\n :param fp: 图片文件的路径或文件对象(必须byte方式打开)\\n :return: 图片对应的数字, 如果不符合数字格式，则返回图片上的文本\\n \\\"\\\"\\\"\\n im = Image.open(fp)\\n enhancer = ImageEnhance.Contrast(im)\\n image_enhancer = enhancer.enhance(4)\\n im_orig = image_enhancer.resize((image_enhancer.size[0]*2, image_enhancer.size[1]*2), Image.BILINEAR)\\n\\n text = image_to_string(im_orig)\\n try:\\n return int(text)\\n except ValueError:\\n return text\\n\\n\\nfm = FontManager()\\nfm.add_font('maoyan')\\nmaoyan_font = fm.get_font('maoyan')\\nlogger.info(maoyan_font.convert_to_num(0xf8c3, 'https://vfile.meituan.net/colorstone/7986a5279399aeee3ef19fe37989a00d2088.woff'))\\n\\nfp = open('/Users/apple/test/dolphin/opt/WX20180607-172930@2x.png', 'rb')\\nprint(fm.convert_to_num_ocr(fp))\"\n },\n {\n \"alpha_fraction\": 0.6398058533668518,\n \"alphanum_fraction\": 0.6407766938209534,\n \"avg_line_length\": 26.83783721923828,\n \"blob_id\": \"0e32f065277d58683f05ff1b2ae2111cf0a3e2c8\",\n \"content_id\": \"3c1d187abe8b6dc47765854704503105e7f06db8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1030,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 37,\n \"path\": \"/utils/requestshelper.py\",\n \"repo_name\": \"davidfang/dolphin\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\nimport requests\\nfrom settings import PROXIES\\n\\n\\nclass RequestsHelper(object):\\n def __init__(self):\\n pass\\n\\n @classmethod\\n def get(cls, url, params=None, **kwargs):\\n return requests.get(url, params, proxies=PROXIES, **kwargs)\\n\\n @classmethod\\n def options(cls, url, **kwargs):\\n return requests.options(url, proxies=PROXIES, **kwargs)\\n\\n @classmethod\\n def head(cls, url, **kwargs):\\n return requests.head(url, **kwargs)\\n\\n @classmethod\\n def post(cls, url, data=None, json=None, **kwargs):\\n return requests.post(url, data=data, json=json, proxies=PROXIES, **kwargs)\\n\\n @classmethod\\n def put(cls, url, data=None, **kwargs):\\n return requests.put(url, data=data, proxies=PROXIES, **kwargs)\\n\\n @classmethod\\n def patch(cls, url, data=None, **kwargs):\\n return requests.patch(url, data=data, proxies=PROXIES, **kwargs)\\n\\n @classmethod\\n def delete(cls, url, **kwargs):\\n return requests.delete(url, proxies=PROXIES, **kwargs)\\n\"\n },\n {\n \"alpha_fraction\": 0.6233974099159241,\n \"alphanum_fraction\": 0.6538461446762085,\n \"avg_line_length\": 23.959999084472656,\n \"blob_id\": \"fd18b7afed88c2080f2595e52cafbce1cd308154\",\n \"content_id\": \"c7a5bfbc22247f94040255cfcc4d46ef10d72565\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 624,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 25,\n \"path\": \"/test/main.py\",\n \"repo_name\": \"davidfang/dolphin\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#coding=utf-8\\nfrom appium import webdriver\\n\\n\\nimport os\\n\\n# Returns abs path relative to this file and not cwd\\nPATH = lambda p: os.path.abspath(\\n os.path.join(os.path.dirname(__file__), p)\\n)\\n\\n\\ndef get_desired_capabilities(app):\\n desired_caps = {\\n 'platformName': 'Android',\\n 'platformVersion': '5.1',\\n 'deviceName': 'Android Emulator',\\n 'app': PATH('../opt/' + app),\\n 'newCommandTimeout': 240\\n }\\n\\n return desired_caps\\ndesired_caps = get_desired_capabilities(u'aweme_aweGW_v1.8.3_61b8304.apk')\\ndriver = webdriver.Remote('http://localhost:4723/wd/hub', desired_caps)\\ndriver.quit()\\n\"\n },\n {\n \"alpha_fraction\": 0.7857142686843872,\n \"alphanum_fraction\": 0.7857142686843872,\n \"avg_line_length\": 13,\n \"blob_id\": \"90cf89b1626280af7f6e11f85d1e18dc81a38c63\",\n \"content_id\": \"25d65ce72a3540b4371c3f0928a41be86ac15a40\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 28,\n \"license_type\": \"no_license\",\n \"max_line_length\": 17,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"davidfang/dolphin\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# dolphin\\nscrapy for maoyan\\n\"\n },\n {\n \"alpha_fraction\": 0.7416666746139526,\n \"alphanum_fraction\": 0.75,\n \"avg_line_length\": 18.83333396911621,\n \"blob_id\": \"437b34a385b7ac76539ce82b4563440d66ef135a\",\n \"content_id\": \"66ab94ed8fb2d85305c35ca9e777312c45bbe698\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 120,\n \"license_type\": \"no_license\",\n \"max_line_length\": 41,\n \"num_lines\": 6,\n \"path\": \"/utils/log.py\",\n \"repo_name\": \"davidfang/dolphin\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nimport logging\\nfrom settings import logging_config\\n\\n\\nlogging.config.dictConfig(logging_config)\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":7,"string":"7"}}},{"rowIdx":2983075,"cells":{"repo_name":{"kind":"string","value":"shanirub/blinkandtouch"},"repo_url":{"kind":"string","value":"https://github.com/shanirub/blinkandtouch"},"snapshot_id":{"kind":"string","value":"58b91871fe0322665ee04e634658497be4dc9e59"},"revision_id":{"kind":"string","value":"f4abc830c85e0e4c1b5e8afda1bfd73e114e08e7"},"directory_id":{"kind":"string","value":"858a3e88eee1d0f06d3d338ce7754a100d4f032a"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-05-21T05:20:43.744635","string":"2021-05-21T05:20:43.744635"},"revision_date":{"kind":"timestamp","value":"2020-05-24T14:41:30","string":"2020-05-24T14:41:30"},"committer_date":{"kind":"timestamp","value":"2020-05-24T14:41:30","string":"2020-05-24T14:41:30"},"github_id":{"kind":"number","value":252563457,"string":"252,563,457"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.8208954930305481,"alphanum_fraction":0.8208954930305481,"avg_line_length":32.5,"blob_id":"f16b28a6678657c1d6a529a9dd7e6d1a72d6067a","content_id":"a31bc611c4c3090156d41be1a2eec9f5f495eefb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":67,"license_type":"no_license","max_line_length":50,"num_lines":2,"path":"/README.md","repo_name":"shanirub/blinkandtouch","src_encoding":"UTF-8","text":"# blinkandtouch\na snake game with accel control instead of buttons\n"},{"alpha_fraction":0.5047528743743896,"alphanum_fraction":0.5280418395996094,"avg_line_length":37.96296310424805,"blob_id":"03cc034aef68e86573f3065ad4b5290af77a5c99","content_id":"265108c97e5a161c9cf332f564e0aa27cc91c9bd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2104,"license_type":"no_license","max_line_length":103,"num_lines":54,"path":"/main.py","repo_name":"shanirub/blinkandtouch","src_encoding":"UTF-8","text":"import pyb\nfrom random import randint\nimport lcd160cr\nlcd = lcd160cr.LCD160CR('X')\naccel = pyb.Accel()\n\nSENSITIVITY = 2 # minimal sensitivity for the accel\nWIDTH = 12 # food and moving rect width and height\n\nbg = lcd.rgb(255, 255, 255) # background color = white\nrect_fill_color = lcd.rgb(255, 0, 127) # moving rect fill color = pink\nrect_line_color = lcd.rgb(255, 0, 0) # moving rect line color\n\nrect_x = lcd.w / 2 # initial x value for rect\nrect_y = lcd.h / 2 # initial y value for rect\n\nfood_x = randint(0, lcd.w - WIDTH) # initial x value for first food\nfood_y = randint(0, lcd.h - WIDTH) # initial y value for first food\nif food_x % 2 != 0:\n food_x += 1\nif food_y % 2 != 0:\n food_y += 1\n\nfood_color = lcd.rgb(0, 0 ,0) # food color = black (to be used for both line and fill colors)\n\ndraw_food = True # do we need to draw the food?\n\nwhile True:\n lcd.set_pen(rect_line_color, bg) \n lcd.erase() # backgroud is set to white\n lcd.set_pen(rect_line_color, rect_fill_color)\n lcd.rect(int(rect_x), int(rect_y), WIDTH, WIDTH) # rect is drawn with its colots and x,y values\n\n x = accel.x() # accel objects\n y = accel.y()\n\n if abs(x) > SENSITIVITY: # identifing movement in x axis\n rect_x += x \n\n if abs(y) > SENSITIVITY: # identifing movement in y axis\n rect_y -= y\n\n if draw_food: # drawing the food\n lcd.set_pen(food_color, food_color)\n lcd.rect(int(food_x), int(food_y), WIDTH, WIDTH)\n\n # if (rect_x >= food_x and rect_x <= food_x + WIDTH and rect_y >= food_y and rect_y <= food_y):\n if abs(rect_x - food_x) < WIDTH and abs(rect_y - food_y) < WIDTH:\n lcd.set_pen(rect_line_color, rect_fill_color)\n lcd.erase()\n lcd.write(\"very good\")\n break\n\n pyb.delay(200)\n"}],"string":"[\n {\n \"alpha_fraction\": 0.8208954930305481,\n \"alphanum_fraction\": 0.8208954930305481,\n \"avg_line_length\": 32.5,\n \"blob_id\": \"f16b28a6678657c1d6a529a9dd7e6d1a72d6067a\",\n \"content_id\": \"a31bc611c4c3090156d41be1a2eec9f5f495eefb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 67,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"shanirub/blinkandtouch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# blinkandtouch\\na snake game with accel control instead of buttons\\n\"\n },\n {\n \"alpha_fraction\": 0.5047528743743896,\n \"alphanum_fraction\": 0.5280418395996094,\n \"avg_line_length\": 37.96296310424805,\n \"blob_id\": \"03cc034aef68e86573f3065ad4b5290af77a5c99\",\n \"content_id\": \"265108c97e5a161c9cf332f564e0aa27cc91c9bd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2104,\n \"license_type\": \"no_license\",\n \"max_line_length\": 103,\n \"num_lines\": 54,\n \"path\": \"/main.py\",\n \"repo_name\": \"shanirub/blinkandtouch\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import pyb\\nfrom random import randint\\nimport lcd160cr\\nlcd = lcd160cr.LCD160CR('X')\\naccel = pyb.Accel()\\n\\nSENSITIVITY = 2 # minimal sensitivity for the accel\\nWIDTH = 12 # food and moving rect width and height\\n\\nbg = lcd.rgb(255, 255, 255) # background color = white\\nrect_fill_color = lcd.rgb(255, 0, 127) # moving rect fill color = pink\\nrect_line_color = lcd.rgb(255, 0, 0) # moving rect line color\\n\\nrect_x = lcd.w / 2 # initial x value for rect\\nrect_y = lcd.h / 2 # initial y value for rect\\n\\nfood_x = randint(0, lcd.w - WIDTH) # initial x value for first food\\nfood_y = randint(0, lcd.h - WIDTH) # initial y value for first food\\nif food_x % 2 != 0:\\n food_x += 1\\nif food_y % 2 != 0:\\n food_y += 1\\n\\nfood_color = lcd.rgb(0, 0 ,0) # food color = black (to be used for both line and fill colors)\\n\\ndraw_food = True # do we need to draw the food?\\n\\nwhile True:\\n lcd.set_pen(rect_line_color, bg) \\n lcd.erase() # backgroud is set to white\\n lcd.set_pen(rect_line_color, rect_fill_color)\\n lcd.rect(int(rect_x), int(rect_y), WIDTH, WIDTH) # rect is drawn with its colots and x,y values\\n\\n x = accel.x() # accel objects\\n y = accel.y()\\n\\n if abs(x) > SENSITIVITY: # identifing movement in x axis\\n rect_x += x \\n\\n if abs(y) > SENSITIVITY: # identifing movement in y axis\\n rect_y -= y\\n\\n if draw_food: # drawing the food\\n lcd.set_pen(food_color, food_color)\\n lcd.rect(int(food_x), int(food_y), WIDTH, WIDTH)\\n\\n # if (rect_x >= food_x and rect_x <= food_x + WIDTH and rect_y >= food_y and rect_y <= food_y):\\n if abs(rect_x - food_x) < WIDTH and abs(rect_y - food_y) < WIDTH:\\n lcd.set_pen(rect_line_color, rect_fill_color)\\n lcd.erase()\\n lcd.write(\\\"very good\\\")\\n break\\n\\n pyb.delay(200)\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2983076,"cells":{"repo_name":{"kind":"string","value":"gusbacos/UAVPreparer"},"repo_url":{"kind":"string","value":"https://github.com/gusbacos/UAVPreparer"},"snapshot_id":{"kind":"string","value":"97a7a95496db66fb17fcf30a8e4d74e1b52a9adc"},"revision_id":{"kind":"string","value":"171b08af2aae2e19f467bf5efbefa679eabeb612"},"directory_id":{"kind":"string","value":"0a3a2e133dd26b45dfede937d54d24cf700dc82e"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-04-23T11:47:51.048575","string":"2023-04-23T11:47:51.048575"},"revision_date":{"kind":"timestamp","value":"2021-05-06T08:13:43","string":"2021-05-06T08:13:43"},"committer_date":{"kind":"timestamp","value":"2021-05-06T08:13:43","string":"2021-05-06T08:13:43"},"github_id":{"kind":"number","value":364549559,"string":"364,549,559"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5849704742431641,"alphanum_fraction":0.5889348983764648,"avg_line_length":35.03491973876953,"blob_id":"b2805123683c814abc728074f19fbaaf5f0ee02d","content_id":"45e32d55a6f6f0d6f5ffdff95b95dc1febb1e2cf","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":11351,"license_type":"no_license","max_line_length":118,"num_lines":315,"path":"/uav_preparer/uav_preparer.py","repo_name":"gusbacos/UAVPreparer","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\"\"\"\n/***************************************************************************\n UAVPreparer\n A QGIS plugin\n this plugin examines height properties on a DSM\n Generated by Plugin Builder: http://g-sherman.github.io/Qgis-Plugin-Builder/\n -------------------\n begin : 2021-05-05\n git sha : $Format:%H$\n copyright : (C) 2021 by oskar bäcklin Gu\n email : oskar.backlin@gu.se\n ***************************************************************************/\n\n/***************************************************************************\n * *\n * This program is free software; you can redistribute it and/or modify *\n * it under the terms of the GNU General Public License as published by *\n * the Free Software Foundation; either version 2 of the License, or *\n * (at your option) any later version. *\n * *\n ***************************************************************************/\n\"\"\"\nfrom qgis.PyQt.QtCore import QSettings, QTranslator, QCoreApplication\nfrom qgis.PyQt.QtGui import QIcon\nfrom qgis.PyQt.QtWidgets import QFileDialog, QAction, QMessageBox\nfrom qgis.gui import QgsMapLayerComboBox, QgsFieldComboBox, QgsMessageBar\nfrom qgis.core import QgsVectorLayer, QgsMapLayerProxyModel, QgsFieldProxyModel, Qgis\nimport numpy as np\nfrom osgeo import gdal\n# Initialize Qt resources from file resources.py\nfrom .resources import *\n# Import the code for the dialog\nfrom .uav_preparer_dialog import UAVPreparerDialog\nimport os.path\nimport webbrowser\n\n\nclass UAVPreparer:\n \"\"\"QGIS Plugin Implementation.\"\"\"\n\n def __init__(self, iface):\n \"\"\"Constructor.\n\n :param iface: An interface instance that will be passed to this class\n which provides the hook by which you can manipulate the QGIS\n application at run time.\n :type iface: QgsInterface\n \"\"\"\n # Save reference to the QGIS interface\n self.iface = iface\n # initialize plugin directory\n self.plugin_dir = os.path.dirname(__file__)\n # initialize locale\n locale = QSettings().value('locale/userLocale')[0:2]\n locale_path = os.path.join(\n self.plugin_dir,\n 'i18n',\n 'UAVPreparer_{}.qm'.format(locale))\n\n if os.path.exists(locale_path):\n self.translator = QTranslator()\n self.translator.load(locale_path)\n QCoreApplication.installTranslator(self.translator)\n\n # Declare instance attributes\n self.actions = []\n self.menu = self.tr(u'&UAV Preparer')\n\n # Check if plugin was started the first time in current QGIS session\n # Must be set in initGui() to survive plugin reloads\n self.first_start = None\n\n # Declare Variables\n self.outputfile = None\n\n # noinspection PyMethodMayBeStatic\n def tr(self, message):\n \"\"\"Get the translation for a string using Qt translation API.\n\n We implement this ourselves since we do not inherit QObject.\n\n :param message: String for translation.\n :type message: str, QString\n\n :returns: Translated version of message.\n :rtype: QString\n \"\"\"\n # noinspection PyTypeChecker,PyArgumentList,PyCallByClass\n return QCoreApplication.translate('UAVPreparer', message)\n\n\n def add_action(\n self,\n icon_path,\n text,\n callback,\n enabled_flag=True,\n add_to_menu=True,\n add_to_toolbar=True,\n status_tip=None,\n whats_this=None,\n parent=None):\n \"\"\"Add a toolbar icon to the toolbar.\n\n :param icon_path: Path to the icon for this action. Can be a resource\n path (e.g. ':/plugins/foo/bar.png') or a normal file system path.\n :type icon_path: str\n\n :param text: Text that should be shown in menu items for this action.\n :type text: str\n\n :param callback: Function to be called when the action is triggered.\n :type callback: function\n\n :param enabled_flag: A flag indicating if the action should be enabled\n by default. Defaults to True.\n :type enabled_flag: bool\n\n :param add_to_menu: Flag indicating whether the action should also\n be added to the menu. Defaults to True.\n :type add_to_menu: bool\n\n :param add_to_toolbar: Flag indicating whether the action should also\n be added to the toolbar. Defaults to True.\n :type add_to_toolbar: bool\n\n :param status_tip: Optional text to show in a popup when mouse pointer\n hovers over the action.\n :type status_tip: str\n\n :param parent: Parent widget for the new action. Defaults None.\n :type parent: QWidget\n\n :param whats_this: Optional text to show in the status bar when the\n mouse pointer hovers over the action.\n\n :returns: The action that was created. Note that the action is also\n added to self.actions list.\n :rtype: QAction\n \"\"\"\n\n icon = QIcon(icon_path)\n action = QAction(icon, text, parent)\n action.triggered.connect(callback)\n action.setEnabled(enabled_flag)\n\n if status_tip is not None:\n action.setStatusTip(status_tip)\n\n if whats_this is not None:\n action.setWhatsThis(whats_this)\n\n if add_to_toolbar:\n # Adds plugin icon to Plugins toolbar\n self.iface.addToolBarIcon(action)\n\n if add_to_menu:\n self.iface.addPluginToMenu(\n self.menu,\n action)\n\n self.actions.append(action)\n\n return action\n\n def initGui(self):\n \"\"\"Create the menu entries and toolbar icons inside the QGIS GUI.\"\"\"\n\n icon_path = ':/plugins/uav_preparer/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u''),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True\n\n\n def unload(self):\n \"\"\"Removes the plugin menu item and icon from QGIS GUI.\"\"\"\n for action in self.actions:\n self.iface.removePluginMenu(\n self.tr(u'&UAV Preparer'),\n action)\n self.iface.removeToolBarIcon(action)\n\n\n def run(self):\n \"\"\"Run method that performs all the real work\"\"\"\n\n # Create the dialog with elements (after translation) and keep reference\n # Only create GUI ONCE in callback, so that it will only load when the plugin is started\n if self.first_start == True:\n self.first_start = False\n self.dlg = UAVPreparerDialog()\n\n # Access the raster layer\n self.layerComboManagerDSM = QgsMapLayerComboBox(self.dlg.widgetDSM)\n self.layerComboManagerDSM.setFilters(QgsMapLayerProxyModel.RasterLayer)\n self.layerComboManagerDSM.setFixedWidth(175)\n self.layerComboManagerDSM.setCurrentIndex(-1)\n\n # Access the vector layer and an attribute field\n self.layerComboManagerPoint = QgsMapLayerComboBox(self.dlg.widgetPointLayer)\n self.layerComboManagerPoint.setCurrentIndex(-1)\n self.layerComboManagerPoint.setFilters(QgsMapLayerProxyModel.PointLayer)\n self.layerComboManagerPoint.setFixedWidth(175)\n self.layerComboManagerPointField = QgsFieldComboBox(self.dlg.widgetField)\n self.layerComboManagerPointField.setFilters(QgsFieldProxyModel.Numeric)\n self.layerComboManagerPoint.layerChanged.connect(self.layerComboManagerPointField.setLayer)\n\n # Set up of file save dialog\n self.fileDialog = QFileDialog()\n self.dlg.pushButtonSave.clicked.connect(self.savefile)\n\n # Set up for the Help button\n self.dlg.helpButton.clicked.connect(self.help)\n\n # Set up for the run button\n self.dlg.runButton.clicked.connect(self.start_progress)\n\n # Show the dialog\n self.dlg.show()\n\n #run the dialog event loop\n result = self.dlg.exec_()\n\n # See if OK was pressed\n if result:\n # Do something useful here - delete the line containng pass and \n # substitute with your code.\n pass\n\n # show the dialog\n self.dlg.show()\n # Run the dialog event loop\n result = self.dlg.exec_()\n # See if OK was pressed\n if result:\n # Do something useful here - delete the line containing pass and\n # substitute with your code.\n pass\n \n def savefile(self):\n self.outputfile = self.fileDialog.getSaveFileName(None, \"Save File As:\", None, \"Text Files (*.txt)\")\n self.dlg.textOutput.setText(self.outputfile[0])\n\n def help(self):\n url = \"https://github.com/gusbacos/UAVPreparer\"\n webbrowser.open_new_tab(url)\n\n def start_progress(self):\n if not self.outputfile:\n QMessageBox.critical(None, \"error allt e hemskt\")\n return\n\n # Aquire geodata anm attributes \n dsm_layer = self.layerComboManagerDSM.currentLayer()\n if dsm_layer is None: \n QMessageBox.critical(None, \"Error\",'No valid raster layer is selected')\n return\n else:\n provider = dsm_layer.dataProvider()\n filepath_dsm = str(provider.dataSourceUri())\n\n point_layer = self.layerComboManagerPoint.currentLayer()\n if point_layer is None: \n QMessageBox.critical(None, \"Error\",'No valid vector layer is selected')\n return\n else:\n vlayer = QgsVectorLayer(point_layer.source(), \"polygon\", \"ogr\")\n\n point_field = self.layerComboManagerPointField.currentField()\n idx = vlayer.fields().indexFromName(point_field)\n if idx == -1:\n QMessageBox.critical(None, \"Errop\", \"An attribute with unique fields must be selected\")\n return\n\n r = 100\n\n numfeat = vlayer.featureCount()\n result = np.zeros([numfeat, 4])\n\n bigraster = gdal.Open(filepath_dsm)\n filepath_tempdsm = self.plugin_dir + \"/clipdsm.tif\"\n\n self.dlg.progressBar.setRange(0, numfeat)\n i = 0\n\n for f in vlayer.getFeatures():\n self.dlg.progressBar.setValue(i + 1)\n\n y = f.geometry().centroid().asPoint().y()\n x = f.geometry().centroid().asPoint().x()\n\n bbox = (x - r, y + r, x + r, y - r)\n gdal.Translate(filepath_tempdsm, bigraster, projWin = bbox)\n data = gdal.Open(filepath_tempdsm)\n mat = np.array(data.ReadAsArray())\n\n result[i, 0] = int(f.attributes()[idx])\n result[i, 1] = np.mean(mat)\n result[i, 2] = np.max(mat)\n result[i, 3] = np.min(mat)\n\n i = i + 1\n\n # Saving to file\n numformat = '%d ' + '%6.2f ' * 3\n headertext = 'id mean max min'\n np.savetxt(self.outputfile[0], result, fmt = numformat, delimiter = ' ', header = headertext) \n\n self.iface.messageBar().pushMessage('UAV Preparer. Operation successful!', level = Qgis.Success, duration =5 )\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5849704742431641,\n \"alphanum_fraction\": 0.5889348983764648,\n \"avg_line_length\": 35.03491973876953,\n \"blob_id\": \"b2805123683c814abc728074f19fbaaf5f0ee02d\",\n \"content_id\": \"45e32d55a6f6f0d6f5ffdff95b95dc1febb1e2cf\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 11351,\n \"license_type\": \"no_license\",\n \"max_line_length\": 118,\n \"num_lines\": 315,\n \"path\": \"/uav_preparer/uav_preparer.py\",\n \"repo_name\": \"gusbacos/UAVPreparer\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\\"\\\"\\\"\\n/***************************************************************************\\n UAVPreparer\\n A QGIS plugin\\n this plugin examines height properties on a DSM\\n Generated by Plugin Builder: http://g-sherman.github.io/Qgis-Plugin-Builder/\\n -------------------\\n begin : 2021-05-05\\n git sha : $Format:%H$\\n copyright : (C) 2021 by oskar bäcklin Gu\\n email : oskar.backlin@gu.se\\n ***************************************************************************/\\n\\n/***************************************************************************\\n * *\\n * This program is free software; you can redistribute it and/or modify *\\n * it under the terms of the GNU General Public License as published by *\\n * the Free Software Foundation; either version 2 of the License, or *\\n * (at your option) any later version. *\\n * *\\n ***************************************************************************/\\n\\\"\\\"\\\"\\nfrom qgis.PyQt.QtCore import QSettings, QTranslator, QCoreApplication\\nfrom qgis.PyQt.QtGui import QIcon\\nfrom qgis.PyQt.QtWidgets import QFileDialog, QAction, QMessageBox\\nfrom qgis.gui import QgsMapLayerComboBox, QgsFieldComboBox, QgsMessageBar\\nfrom qgis.core import QgsVectorLayer, QgsMapLayerProxyModel, QgsFieldProxyModel, Qgis\\nimport numpy as np\\nfrom osgeo import gdal\\n# Initialize Qt resources from file resources.py\\nfrom .resources import *\\n# Import the code for the dialog\\nfrom .uav_preparer_dialog import UAVPreparerDialog\\nimport os.path\\nimport webbrowser\\n\\n\\nclass UAVPreparer:\\n \\\"\\\"\\\"QGIS Plugin Implementation.\\\"\\\"\\\"\\n\\n def __init__(self, iface):\\n \\\"\\\"\\\"Constructor.\\n\\n :param iface: An interface instance that will be passed to this class\\n which provides the hook by which you can manipulate the QGIS\\n application at run time.\\n :type iface: QgsInterface\\n \\\"\\\"\\\"\\n # Save reference to the QGIS interface\\n self.iface = iface\\n # initialize plugin directory\\n self.plugin_dir = os.path.dirname(__file__)\\n # initialize locale\\n locale = QSettings().value('locale/userLocale')[0:2]\\n locale_path = os.path.join(\\n self.plugin_dir,\\n 'i18n',\\n 'UAVPreparer_{}.qm'.format(locale))\\n\\n if os.path.exists(locale_path):\\n self.translator = QTranslator()\\n self.translator.load(locale_path)\\n QCoreApplication.installTranslator(self.translator)\\n\\n # Declare instance attributes\\n self.actions = []\\n self.menu = self.tr(u'&UAV Preparer')\\n\\n # Check if plugin was started the first time in current QGIS session\\n # Must be set in initGui() to survive plugin reloads\\n self.first_start = None\\n\\n # Declare Variables\\n self.outputfile = None\\n\\n # noinspection PyMethodMayBeStatic\\n def tr(self, message):\\n \\\"\\\"\\\"Get the translation for a string using Qt translation API.\\n\\n We implement this ourselves since we do not inherit QObject.\\n\\n :param message: String for translation.\\n :type message: str, QString\\n\\n :returns: Translated version of message.\\n :rtype: QString\\n \\\"\\\"\\\"\\n # noinspection PyTypeChecker,PyArgumentList,PyCallByClass\\n return QCoreApplication.translate('UAVPreparer', message)\\n\\n\\n def add_action(\\n self,\\n icon_path,\\n text,\\n callback,\\n enabled_flag=True,\\n add_to_menu=True,\\n add_to_toolbar=True,\\n status_tip=None,\\n whats_this=None,\\n parent=None):\\n \\\"\\\"\\\"Add a toolbar icon to the toolbar.\\n\\n :param icon_path: Path to the icon for this action. Can be a resource\\n path (e.g. ':/plugins/foo/bar.png') or a normal file system path.\\n :type icon_path: str\\n\\n :param text: Text that should be shown in menu items for this action.\\n :type text: str\\n\\n :param callback: Function to be called when the action is triggered.\\n :type callback: function\\n\\n :param enabled_flag: A flag indicating if the action should be enabled\\n by default. Defaults to True.\\n :type enabled_flag: bool\\n\\n :param add_to_menu: Flag indicating whether the action should also\\n be added to the menu. Defaults to True.\\n :type add_to_menu: bool\\n\\n :param add_to_toolbar: Flag indicating whether the action should also\\n be added to the toolbar. Defaults to True.\\n :type add_to_toolbar: bool\\n\\n :param status_tip: Optional text to show in a popup when mouse pointer\\n hovers over the action.\\n :type status_tip: str\\n\\n :param parent: Parent widget for the new action. Defaults None.\\n :type parent: QWidget\\n\\n :param whats_this: Optional text to show in the status bar when the\\n mouse pointer hovers over the action.\\n\\n :returns: The action that was created. Note that the action is also\\n added to self.actions list.\\n :rtype: QAction\\n \\\"\\\"\\\"\\n\\n icon = QIcon(icon_path)\\n action = QAction(icon, text, parent)\\n action.triggered.connect(callback)\\n action.setEnabled(enabled_flag)\\n\\n if status_tip is not None:\\n action.setStatusTip(status_tip)\\n\\n if whats_this is not None:\\n action.setWhatsThis(whats_this)\\n\\n if add_to_toolbar:\\n # Adds plugin icon to Plugins toolbar\\n self.iface.addToolBarIcon(action)\\n\\n if add_to_menu:\\n self.iface.addPluginToMenu(\\n self.menu,\\n action)\\n\\n self.actions.append(action)\\n\\n return action\\n\\n def initGui(self):\\n \\\"\\\"\\\"Create the menu entries and toolbar icons inside the QGIS GUI.\\\"\\\"\\\"\\n\\n icon_path = ':/plugins/uav_preparer/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u''),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\\n\\n\\n def unload(self):\\n \\\"\\\"\\\"Removes the plugin menu item and icon from QGIS GUI.\\\"\\\"\\\"\\n for action in self.actions:\\n self.iface.removePluginMenu(\\n self.tr(u'&UAV Preparer'),\\n action)\\n self.iface.removeToolBarIcon(action)\\n\\n\\n def run(self):\\n \\\"\\\"\\\"Run method that performs all the real work\\\"\\\"\\\"\\n\\n # Create the dialog with elements (after translation) and keep reference\\n # Only create GUI ONCE in callback, so that it will only load when the plugin is started\\n if self.first_start == True:\\n self.first_start = False\\n self.dlg = UAVPreparerDialog()\\n\\n # Access the raster layer\\n self.layerComboManagerDSM = QgsMapLayerComboBox(self.dlg.widgetDSM)\\n self.layerComboManagerDSM.setFilters(QgsMapLayerProxyModel.RasterLayer)\\n self.layerComboManagerDSM.setFixedWidth(175)\\n self.layerComboManagerDSM.setCurrentIndex(-1)\\n\\n # Access the vector layer and an attribute field\\n self.layerComboManagerPoint = QgsMapLayerComboBox(self.dlg.widgetPointLayer)\\n self.layerComboManagerPoint.setCurrentIndex(-1)\\n self.layerComboManagerPoint.setFilters(QgsMapLayerProxyModel.PointLayer)\\n self.layerComboManagerPoint.setFixedWidth(175)\\n self.layerComboManagerPointField = QgsFieldComboBox(self.dlg.widgetField)\\n self.layerComboManagerPointField.setFilters(QgsFieldProxyModel.Numeric)\\n self.layerComboManagerPoint.layerChanged.connect(self.layerComboManagerPointField.setLayer)\\n\\n # Set up of file save dialog\\n self.fileDialog = QFileDialog()\\n self.dlg.pushButtonSave.clicked.connect(self.savefile)\\n\\n # Set up for the Help button\\n self.dlg.helpButton.clicked.connect(self.help)\\n\\n # Set up for the run button\\n self.dlg.runButton.clicked.connect(self.start_progress)\\n\\n # Show the dialog\\n self.dlg.show()\\n\\n #run the dialog event loop\\n result = self.dlg.exec_()\\n\\n # See if OK was pressed\\n if result:\\n # Do something useful here - delete the line containng pass and \\n # substitute with your code.\\n pass\\n\\n # show the dialog\\n self.dlg.show()\\n # Run the dialog event loop\\n result = self.dlg.exec_()\\n # See if OK was pressed\\n if result:\\n # Do something useful here - delete the line containing pass and\\n # substitute with your code.\\n pass\\n \\n def savefile(self):\\n self.outputfile = self.fileDialog.getSaveFileName(None, \\\"Save File As:\\\", None, \\\"Text Files (*.txt)\\\")\\n self.dlg.textOutput.setText(self.outputfile[0])\\n\\n def help(self):\\n url = \\\"https://github.com/gusbacos/UAVPreparer\\\"\\n webbrowser.open_new_tab(url)\\n\\n def start_progress(self):\\n if not self.outputfile:\\n QMessageBox.critical(None, \\\"error allt e hemskt\\\")\\n return\\n\\n # Aquire geodata anm attributes \\n dsm_layer = self.layerComboManagerDSM.currentLayer()\\n if dsm_layer is None: \\n QMessageBox.critical(None, \\\"Error\\\",'No valid raster layer is selected')\\n return\\n else:\\n provider = dsm_layer.dataProvider()\\n filepath_dsm = str(provider.dataSourceUri())\\n\\n point_layer = self.layerComboManagerPoint.currentLayer()\\n if point_layer is None: \\n QMessageBox.critical(None, \\\"Error\\\",'No valid vector layer is selected')\\n return\\n else:\\n vlayer = QgsVectorLayer(point_layer.source(), \\\"polygon\\\", \\\"ogr\\\")\\n\\n point_field = self.layerComboManagerPointField.currentField()\\n idx = vlayer.fields().indexFromName(point_field)\\n if idx == -1:\\n QMessageBox.critical(None, \\\"Errop\\\", \\\"An attribute with unique fields must be selected\\\")\\n return\\n\\n r = 100\\n\\n numfeat = vlayer.featureCount()\\n result = np.zeros([numfeat, 4])\\n\\n bigraster = gdal.Open(filepath_dsm)\\n filepath_tempdsm = self.plugin_dir + \\\"/clipdsm.tif\\\"\\n\\n self.dlg.progressBar.setRange(0, numfeat)\\n i = 0\\n\\n for f in vlayer.getFeatures():\\n self.dlg.progressBar.setValue(i + 1)\\n\\n y = f.geometry().centroid().asPoint().y()\\n x = f.geometry().centroid().asPoint().x()\\n\\n bbox = (x - r, y + r, x + r, y - r)\\n gdal.Translate(filepath_tempdsm, bigraster, projWin = bbox)\\n data = gdal.Open(filepath_tempdsm)\\n mat = np.array(data.ReadAsArray())\\n\\n result[i, 0] = int(f.attributes()[idx])\\n result[i, 1] = np.mean(mat)\\n result[i, 2] = np.max(mat)\\n result[i, 3] = np.min(mat)\\n\\n i = i + 1\\n\\n # Saving to file\\n numformat = '%d ' + '%6.2f ' * 3\\n headertext = 'id mean max min'\\n np.savetxt(self.outputfile[0], result, fmt = numformat, delimiter = ' ', header = headertext) \\n\\n self.iface.messageBar().pushMessage('UAV Preparer. Operation successful!', level = Qgis.Success, duration =5 )\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2983077,"cells":{"repo_name":{"kind":"string","value":"ChristianSchuessler/Robot-Project"},"repo_url":{"kind":"string","value":"https://github.com/ChristianSchuessler/Robot-Project"},"snapshot_id":{"kind":"string","value":"666644a0b5391665360fde14c33180f600c66b79"},"revision_id":{"kind":"string","value":"1abf05f5f8c73ee5f549591428d5ef3b1fdd5e6a"},"directory_id":{"kind":"string","value":"e7763867e5324bec9d8785cef3d2897ee33456c6"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-03-22T14:11:10.812389","string":"2020-03-22T14:11:10.812389"},"revision_date":{"kind":"timestamp","value":"2018-08-19T14:21:56","string":"2018-08-19T14:21:56"},"committer_date":{"kind":"timestamp","value":"2018-08-19T14:21:56","string":"2018-08-19T14:21:56"},"github_id":{"kind":"number","value":140159326,"string":"140,159,326"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7431372404098511,"alphanum_fraction":0.7686274647712708,"avg_line_length":21.909090042114258,"blob_id":"90db75c8dfc4c1fd222a6ce624f93a075471e75f","content_id":"d3e3e7735bdbf4168b819bf586225e2e15fdb874","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":517,"license_type":"no_license","max_line_length":88,"num_lines":22,"path":"/doc/ToDo.txt","repo_name":"ChristianSchuessler/Robot-Project","src_encoding":"UTF-8","text":"Nächstes treffen 5. August 2018\n\nBis dahin, versucht Chris das Zeug soweit \"zum laufen\" zu bringen.\nStefan bringt sich Python bei\n\nMaterial:\n- Lötkolben\n- Multimeter\n\n\n2018-08-05\n==========\n\nnächster Meilenstein: \n- Roboter soll autark fahren können. Also mit Akku/batterie ohne externen Stromanschluss\n- Abstandssensor kann angesteuert und ausgewertet werden\n\nToDos:\n- Platine für Treiber-IC löten\n- Rpi übers Netzwerk steuern\n- Rpi mit Akku bzw Batterien mit Strom vesorgen\n- Daten von Sensor auslesen\n\n\n\n\n\n\n"},{"alpha_fraction":0.8063063025474548,"alphanum_fraction":0.8108108043670654,"avg_line_length":39.3636360168457,"blob_id":"c6ca04b369c2219d1d94129da727c4c5973efb59","content_id":"5c5cf0d56575c089dc09a4041db37c21071da7c4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":446,"license_type":"no_license","max_line_length":105,"num_lines":11,"path":"/Readme.md","repo_name":"ChristianSchuessler/Robot-Project","src_encoding":"UTF-8","text":"# Robot-Project\n\nWir(mein Bruder Stefan und ich) wollen zusammen einen Roboter bauen. Der im ersten Schritt nur \"rumfahren\" kann.\nAm Anfang wollen wir dieses Tutorial:\nhttps://tutorials-raspberrypi.de/raspberry-pi-roboter-bausatz-zusammenbau/\numsetzen um erstmal reinzukommen.\n\nAnschließend wollen wir uns weitere Dinge überlegen.\n\n## Weitere Quellen\nhttps://tutorials-raspberrypi.de/raspberry-pi-sensoren-uebersicht-die-50-wichtigsten-module/#motion\n"},{"alpha_fraction":0.5912408828735352,"alphanum_fraction":0.6751824617385864,"avg_line_length":14.222222328186035,"blob_id":"8911a21cf020806140c34717a4a88f006812166a","content_id":"560080989cc74acecb4d953ca9d7e07f4b946de3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":274,"license_type":"no_license","max_line_length":29,"num_lines":18,"path":"/src/robot-run.py","repo_name":"ChristianSchuessler/Robot-Project","src_encoding":"UTF-8","text":"\nimport RPi.GPIO as GPIO\nimport time\nfrom l293d import L293D\n \nGPIO.setmode(GPIO.BCM)\nGPIO.setwarnings(False)\n\nprint(\"robot will run...\\n\");\n \nl = L293D(17,27,23,24)\nl.stop()\n \nfor i in range(20):\n l.forward()\n time.sleep(0.5)\n l.forwardRight()\n time.sleep(0.5)\nl.stop()"}],"string":"[\n {\n \"alpha_fraction\": 0.7431372404098511,\n \"alphanum_fraction\": 0.7686274647712708,\n \"avg_line_length\": 21.909090042114258,\n \"blob_id\": \"90db75c8dfc4c1fd222a6ce624f93a075471e75f\",\n \"content_id\": \"d3e3e7735bdbf4168b819bf586225e2e15fdb874\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 517,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 22,\n \"path\": \"/doc/ToDo.txt\",\n \"repo_name\": \"ChristianSchuessler/Robot-Project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Nächstes treffen 5. August 2018\\n\\nBis dahin, versucht Chris das Zeug soweit \\\"zum laufen\\\" zu bringen.\\nStefan bringt sich Python bei\\n\\nMaterial:\\n- Lötkolben\\n- Multimeter\\n\\n\\n2018-08-05\\n==========\\n\\nnächster Meilenstein: \\n- Roboter soll autark fahren können. Also mit Akku/batterie ohne externen Stromanschluss\\n- Abstandssensor kann angesteuert und ausgewertet werden\\n\\nToDos:\\n- Platine für Treiber-IC löten\\n- Rpi übers Netzwerk steuern\\n- Rpi mit Akku bzw Batterien mit Strom vesorgen\\n- Daten von Sensor auslesen\\n\\n\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.8063063025474548,\n \"alphanum_fraction\": 0.8108108043670654,\n \"avg_line_length\": 39.3636360168457,\n \"blob_id\": \"c6ca04b369c2219d1d94129da727c4c5973efb59\",\n \"content_id\": \"5c5cf0d56575c089dc09a4041db37c21071da7c4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 446,\n \"license_type\": \"no_license\",\n \"max_line_length\": 105,\n \"num_lines\": 11,\n \"path\": \"/Readme.md\",\n \"repo_name\": \"ChristianSchuessler/Robot-Project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Robot-Project\\n\\nWir(mein Bruder Stefan und ich) wollen zusammen einen Roboter bauen. Der im ersten Schritt nur \\\"rumfahren\\\" kann.\\nAm Anfang wollen wir dieses Tutorial:\\nhttps://tutorials-raspberrypi.de/raspberry-pi-roboter-bausatz-zusammenbau/\\numsetzen um erstmal reinzukommen.\\n\\nAnschließend wollen wir uns weitere Dinge überlegen.\\n\\n## Weitere Quellen\\nhttps://tutorials-raspberrypi.de/raspberry-pi-sensoren-uebersicht-die-50-wichtigsten-module/#motion\\n\"\n },\n {\n \"alpha_fraction\": 0.5912408828735352,\n \"alphanum_fraction\": 0.6751824617385864,\n \"avg_line_length\": 14.222222328186035,\n \"blob_id\": \"8911a21cf020806140c34717a4a88f006812166a\",\n \"content_id\": \"560080989cc74acecb4d953ca9d7e07f4b946de3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 274,\n \"license_type\": \"no_license\",\n \"max_line_length\": 29,\n \"num_lines\": 18,\n \"path\": \"/src/robot-run.py\",\n \"repo_name\": \"ChristianSchuessler/Robot-Project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nimport RPi.GPIO as GPIO\\nimport time\\nfrom l293d import L293D\\n \\nGPIO.setmode(GPIO.BCM)\\nGPIO.setwarnings(False)\\n\\nprint(\\\"robot will run...\\\\n\\\");\\n \\nl = L293D(17,27,23,24)\\nl.stop()\\n \\nfor i in range(20):\\n l.forward()\\n time.sleep(0.5)\\n l.forwardRight()\\n time.sleep(0.5)\\nl.stop()\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2983078,"cells":{"repo_name":{"kind":"string","value":"Creature-03/Reddit-Bot-Customizable"},"repo_url":{"kind":"string","value":"https://github.com/Creature-03/Reddit-Bot-Customizable"},"snapshot_id":{"kind":"string","value":"107a5f6a9d25071692e9811834551c7e4407768c"},"revision_id":{"kind":"string","value":"66252f0b3b243abebd1058db0576b648c03f7ce9"},"directory_id":{"kind":"string","value":"829de733045f6caaa93fd5eb3156e40d439b53fd"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-02-19T19:29:23.679992","string":"2023-02-19T19:29:23.679992"},"revision_date":{"kind":"timestamp","value":"2021-01-16T05:04:33","string":"2021-01-16T05:04:33"},"committer_date":{"kind":"timestamp","value":"2021-01-16T05:04:33","string":"2021-01-16T05:04:33"},"github_id":{"kind":"number","value":330088821,"string":"330,088,821"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.594629168510437,"alphanum_fraction":0.5991048812866211,"avg_line_length":39.128204345703125,"blob_id":"495755434c4e390fc1e6f378cd237eb5c8360cf7","content_id":"c4ea800fd0de39c5cac3353ff3f94c064747f8c1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1564,"license_type":"no_license","max_line_length":189,"num_lines":39,"path":"/main.py","repo_name":"Creature-03/Reddit-Bot-Customizable","src_encoding":"UTF-8","text":"#praw = Python Reddit API Wrapper\nimport praw\nimport random\nimport time\n\n#create an instance of reddit, fill in the credentials according to your Reddit API registration and OAuth verification(For info on how to obtain your refresh token see PRAW documentation.)\nreddit = praw.Reddit(client_id = '', \n client_secret = '', \n user_agent = '', \n redirect_uri = '', \n refresh_token = '', \n username = '', \n password = '')\n\n#define what subreddit/s to search\nsub = reddit.subreddit('')\n\n#list of possible replies\nreplies = ['']\n\n#define which posts to the subreddit to pull(remember to set a limit unless you want to pull all of reddit history)\nfor post in sub.rising(limit = 10):\n print(post.title)\n\n #loop through the comments on the post and search for a trigger phrase\n for comment in post.comments:\n if hasattr(comment, 'body'):\n comment_lower = comment.body.lower()\n #define the trigger phrase\n trigger = ''\n \n #if the trigger phrase was found, pull a random reply from replies list\n if trigger in comment_lower:\n random_index = random.randint(0, len(replies) - 1)\n \n #post a reply to the comment containing the trigger phrase using a random object from replies list\n comment.reply(replies[random_index])\n #sleep for a bit so you dont get banned for spam\n time.sleep(600)"}],"string":"[\n {\n \"alpha_fraction\": 0.594629168510437,\n \"alphanum_fraction\": 0.5991048812866211,\n \"avg_line_length\": 39.128204345703125,\n \"blob_id\": \"495755434c4e390fc1e6f378cd237eb5c8360cf7\",\n \"content_id\": \"c4ea800fd0de39c5cac3353ff3f94c064747f8c1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1564,\n \"license_type\": \"no_license\",\n \"max_line_length\": 189,\n \"num_lines\": 39,\n \"path\": \"/main.py\",\n \"repo_name\": \"Creature-03/Reddit-Bot-Customizable\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#praw = Python Reddit API Wrapper\\nimport praw\\nimport random\\nimport time\\n\\n#create an instance of reddit, fill in the credentials according to your Reddit API registration and OAuth verification(For info on how to obtain your refresh token see PRAW documentation.)\\nreddit = praw.Reddit(client_id = '', \\n client_secret = '', \\n user_agent = '', \\n redirect_uri = '', \\n refresh_token = '', \\n username = '', \\n password = '')\\n\\n#define what subreddit/s to search\\nsub = reddit.subreddit('')\\n\\n#list of possible replies\\nreplies = ['']\\n\\n#define which posts to the subreddit to pull(remember to set a limit unless you want to pull all of reddit history)\\nfor post in sub.rising(limit = 10):\\n print(post.title)\\n\\n #loop through the comments on the post and search for a trigger phrase\\n for comment in post.comments:\\n if hasattr(comment, 'body'):\\n comment_lower = comment.body.lower()\\n #define the trigger phrase\\n trigger = ''\\n \\n #if the trigger phrase was found, pull a random reply from replies list\\n if trigger in comment_lower:\\n random_index = random.randint(0, len(replies) - 1)\\n \\n #post a reply to the comment containing the trigger phrase using a random object from replies list\\n comment.reply(replies[random_index])\\n #sleep for a bit so you dont get banned for spam\\n time.sleep(600)\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2983079,"cells":{"repo_name":{"kind":"string","value":"niuwan1/PU-gbdt"},"repo_url":{"kind":"string","value":"https://github.com/niuwan1/PU-gbdt"},"snapshot_id":{"kind":"string","value":"4b8f680c392279e370ad8c9fa9507830c3037730"},"revision_id":{"kind":"string","value":"e4d3f3783a3db0a0cdc5b88469525f85fc9e7e99"},"directory_id":{"kind":"string","value":"df7bb92372ac22ca0ac51695552f42429e23d5f8"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-09-19T20:58:54.005220","string":"2022-09-19T20:58:54.005220"},"revision_date":{"kind":"timestamp","value":"2020-06-06T00:31:27","string":"2020-06-06T00:31:27"},"committer_date":{"kind":"timestamp","value":"2020-06-06T00:31:27","string":"2020-06-06T00:31:27"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6359077095985413,"alphanum_fraction":0.6419257521629333,"avg_line_length":27.514286041259766,"blob_id":"f5b539546d03d1ca8ec87cd4f1018c40607c2b8f","content_id":"0aefd64877a3423742e2342629a4cc469b1c7df4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":997,"license_type":"no_license","max_line_length":76,"num_lines":35,"path":"/PU-gbdt-code/script/RF.py","repo_name":"niuwan1/PU-gbdt","src_encoding":"UTF-8","text":"from sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score, accuracy_score\nfrom sklearn.ensemble import RandomForestClassifier\nimport pandas as pd\ndef main():\n\n print (\"-- Gradient Boosting Classification --\")\n\n path = '/Users/junowang/Desktop/PU-gbdt-code/data/test_new.csv'\n data = pd.read_csv(path, header=0, encoding = \"utf-8\")\n print(data.info())\n y = data['label']\n data = data.drop('label',axis = 1)\n X = data\n\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)\n clf = RandomForestClassifier()\n clf.fit(X_train, y_train)\n y_pred = clf.predict(X_test)\n print(y_pred)\n accuracy = accuracy_score(y_test, y_pred)\n auc = roc_auc_score(y_test,y_pred)\n\n print (\"Accuracy:\", accuracy)\n print('AUC', auc)\n\n # Plot().plot_in_2d(X_test, y_pred,\n #title=\"Gradient Boosting\",\n #accuracy=accuracy,\n #legend_labels=data.target_names)\n\n\n\nif __name__ == \"__main__\":\n main()"},{"alpha_fraction":0.5937731862068176,"alphanum_fraction":0.6056337952613831,"avg_line_length":26,"blob_id":"c29fffc68d0db89ad2f82d1e8c5827cee0333b2d","content_id":"122e68e62415499bfb14b1a6fda4935c8e7bdc32","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1349,"license_type":"no_license","max_line_length":76,"num_lines":50,"path":"/PU-gbdt-code/script/gbdt-supervised.py","repo_name":"niuwan1/PU-gbdt","src_encoding":"UTF-8","text":"from sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score, accuracy_score\nfrom sklearn.ensemble import GradientBoostingClassifier\nimport pandas as pd\ndef main():\n\n print (\"-- Gradient Boosting Classification --\")\n\n path = '/Users/junowang/Desktop/PU-gbdt-code/data/test_new.csv'\n data = pd.read_csv(path, header=0, encoding = \"utf-8\")\n print(data.info())\n y = data['label']\n data = data.drop('label',axis = 1)\n X = data\n\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)\n gbdt = GradientBoostingClassifier(\n loss='deviance'\n , learning_rate=0.1\n , n_estimators=100\n , subsample=1\n , min_samples_split=2\n , min_samples_leaf=1\n , max_depth=3\n , init=None\n , random_state=None\n , max_features=None\n , verbose=0\n , max_leaf_nodes=None\n , warm_start=False\n )\n gbdt.fit(X_train, y_train)\n y_pred = gbdt.predict(X_test)\n print(y_pred)\n accuracy = accuracy_score(y_test, y_pred)\n auc = roc_auc_score(y_test,y_pred)\n\n print (\"Accuracy:\", accuracy)\n print('AUC', auc)\n\n\n # Plot().plot_in_2d(X_test, y_pred,\n #title=\"Gradient Boosting\",\n #accuracy=accuracy,\n #legend_labels=data.target_names)\n\n\n\nif __name__ == \"__main__\":\n main()"},{"alpha_fraction":0.5476190447807312,"alphanum_fraction":0.625,"avg_line_length":39.75757598876953,"blob_id":"3489bd63adcf257649390ce6414b75088693fc21","content_id":"5523072b32efd94076b9b8b5e671bda96c1a4beb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1362,"license_type":"no_license","max_line_length":103,"num_lines":33,"path":"/PU-gbdt-code/script/fig_testloss.py","repo_name":"niuwan1/PU-gbdt","src_encoding":"UTF-8","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\n\n# 数据集\npath = '/Users/junowang/Desktop/gbdt-code/output/testloss.xls'\ndata1 = pd.read_excel(path, sheet_name='step1')\ndata2 = pd.read_excel(path, sheet_name= 'step2')\ndata1[['t1','t2','t3','t4','t5']] = np.exp(data1[['t1','t2','t3','t4','t5']]*100)\ndata2[['t1','t2','t3','t4','t5']] = np.exp(data2[['t1','t2','t3','t4','t5']]*100)\n# 绘画折线图：\nf, axes = plt.subplots(2, 1, figsize=(15, 10))\nfor ylabel in ['t1','t2','t3','t4','t5']:\n #axes[0].set_xscale(\"log\")data1[['t1','t2','t3','t4','t5']] = data1[['t1','t2','t3','t4','t5']]*100\n #axes[0].set_yscale(\"log\")\n sns.regplot(\"itera\", ylabel, data1, ax=axes[0], scatter_kws={\"s\": 35}, label = ylabel)\nfor ylabel in ['t1', 't2', 't3', 't4', 't5']:\n #axes[0].set_yscale(\"log\")\n sns.regplot(\"itera\", ylabel, data2, ax=axes[1], scatter_kws={\"s\": 35}, label = ylabel)\naxes[0].legend(loc='right')\naxes[1].legend(loc='right')\naxes[0].set_title('step = 0.01')\naxes[1].set_title('step = 0.03')\naxes[0].xaxis.set_ticks_position('bottom')\naxes[1].xaxis.set_ticks_position('bottom')\naxes[1].set_xlabel('Iteration')\naxes[1].set_ylabel('exp(testloss)')\naxes[0].set_xlabel(' ')\naxes[0].set_ylabel('exp(testloss)')\naxes[0].set_xticks(np.linspace(1,20,20))\naxes[1].set_xticks(np.linspace(1,20,20))\nplt.show()"},{"alpha_fraction":0.6299694180488586,"alphanum_fraction":0.6472986936569214,"avg_line_length":43.6363639831543,"blob_id":"81332e6b3b5d3fa79f39d64fa64247138f452972","content_id":"eb8f22848aa156915f4b57fefdc2e663c646c522","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":981,"license_type":"no_license","max_line_length":107,"num_lines":22,"path":"/PU-gbdt-code/script/preprocess.py","repo_name":"niuwan1/PU-gbdt","src_encoding":"UTF-8","text":"import pandas as pd\npath = '/Users/junowang/Desktop/gbdt-code/data/test.csv'\ndf = pd.read_csv(path, header=0, encoding = \"utf-8\")\n#df_raw[~pos_index]['label'] = -1\n#df_raw.to_csv('/Users/junowang/Desktop/gbdt-code/data/data_for_tongji.csv',index=False,encoding = \"utf-8\")\nfor id in df.index:\n if df.loc[id, 'label'] == 0.0:\n df.loc[id, 'label'] = 1\n else:\n df.loc[id, 'label'] = -1\ndf.to_csv('/Users/junowang/Desktop/gbdt-code/data/data_for_tongji.csv',index=False,encoding = \"utf-8\")\n\npath = '/Users/junowang/Desktop/gbdt-code/data/test.csv'\ndf = pd.read_csv(path, header=0, encoding = \"utf-8\")\n#df_raw[~pos_index]['label'] = -1\n#df_raw.to_csv('/Users/junowang/Desktop/gbdt-code/data/data_for_tongji.csv',index=False,encoding = \"utf-8\")\nfor id in df.index:\n if df.loc[id, 'label'] == 0:\n df.loc[id, 'label'] = 1\n else:\n df.loc[id, 'label'] = -1\ndf.to_csv('/Users/junowang/Desktop/gbdt-code/data/test_new.csv',index=False,encoding = \"utf-8\")"},{"alpha_fraction":0.8062015771865845,"alphanum_fraction":0.8062015771865845,"avg_line_length":63.5,"blob_id":"831d0b40f6eb1f16f2b15c8035ccbc163f180ac2","content_id":"1d9205fe94b304e211ea0c7365f8629ecd97bc36","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":129,"license_type":"no_license","max_line_length":118,"num_lines":2,"path":"/README.md","repo_name":"niuwan1/PU-gbdt","src_encoding":"UTF-8","text":"# PU-gbdt\nConvert the loss function in gbdt to PU-Learning loss function and use the classifier to predict a bank credit dataset\n"},{"alpha_fraction":0.6112600564956665,"alphanum_fraction":0.622654139995575,"avg_line_length":25.192981719970703,"blob_id":"6ecbcd53885f2a31729092638447481dd2972541","content_id":"a4819e17d5c67bebe34bf7a6e21e4c8842f5ef3f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1492,"license_type":"no_license","max_line_length":76,"num_lines":57,"path":"/PU-gbdt-code/Machine-Learning-From-Scratch-master/gradient_boosting_decision_tree/gbdt_classifier_example.py","repo_name":"niuwan1/PU-gbdt","src_encoding":"UTF-8","text":"from __future__ import division, print_function\nimport numpy as np\nfrom sklearn import datasets\nimport matplotlib.pyplot as plt\n\n# Import helper functions\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score, accuracy_score\nfrom sklearn.ensemble import GradientBoostingRegressor\nimport pandas as pd\ndef main():\n\n print (\"-- Gradient Boosting Classification --\")\n\n path = '/Users/junowang/Desktop/gbdt-code/data/test_new.csv'\n data = pd.read_csv(path, header=0, encoding = \"utf-8\")\n print(data.head())\n print(data.head)\n y = data['label']\n X = data.drop('label')\n\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)\n gbdt = GradientBoostingRegressor(\n loss='ls'\n , learning_rate=0.1\n , n_estimators=100\n , subsample=1\n , min_samples_split=2\n , min_samples_leaf=1\n , max_depth=3\n , init=None\n , random_state=None\n , max_features=None\n , alpha=0.9\n , verbose=0\n , max_leaf_nodes=None\n , warm_start=False\n )\n gbdt.fit(X_train, y_train)\n y_pred = gbdt.predict(X_test)\n\n accuracy = accuracy_score(y_test, y_pred)\n auc = roc_auc_score(y_test,y_pred)\n\n print (\"Accuracy:\", accuracy)\n print('AUC', auc)\n\n\n # Plot().plot_in_2d(X_test, y_pred,\n #title=\"Gradient Boosting\",\n #accuracy=accuracy,\n #legend_labels=data.target_names)\n\n\n\nif __name__ == \"__main__\":\n main()"}],"string":"[\n {\n \"alpha_fraction\": 0.6359077095985413,\n \"alphanum_fraction\": 0.6419257521629333,\n \"avg_line_length\": 27.514286041259766,\n \"blob_id\": \"f5b539546d03d1ca8ec87cd4f1018c40607c2b8f\",\n \"content_id\": \"0aefd64877a3423742e2342629a4cc469b1c7df4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 997,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 35,\n \"path\": \"/PU-gbdt-code/script/RF.py\",\n \"repo_name\": \"niuwan1/PU-gbdt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from sklearn.model_selection import train_test_split\\nfrom sklearn.metrics import roc_auc_score, accuracy_score\\nfrom sklearn.ensemble import RandomForestClassifier\\nimport pandas as pd\\ndef main():\\n\\n print (\\\"-- Gradient Boosting Classification --\\\")\\n\\n path = '/Users/junowang/Desktop/PU-gbdt-code/data/test_new.csv'\\n data = pd.read_csv(path, header=0, encoding = \\\"utf-8\\\")\\n print(data.info())\\n y = data['label']\\n data = data.drop('label',axis = 1)\\n X = data\\n\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)\\n clf = RandomForestClassifier()\\n clf.fit(X_train, y_train)\\n y_pred = clf.predict(X_test)\\n print(y_pred)\\n accuracy = accuracy_score(y_test, y_pred)\\n auc = roc_auc_score(y_test,y_pred)\\n\\n print (\\\"Accuracy:\\\", accuracy)\\n print('AUC', auc)\\n\\n # Plot().plot_in_2d(X_test, y_pred,\\n #title=\\\"Gradient Boosting\\\",\\n #accuracy=accuracy,\\n #legend_labels=data.target_names)\\n\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\"\n },\n {\n \"alpha_fraction\": 0.5937731862068176,\n \"alphanum_fraction\": 0.6056337952613831,\n \"avg_line_length\": 26,\n \"blob_id\": \"c29fffc68d0db89ad2f82d1e8c5827cee0333b2d\",\n \"content_id\": \"122e68e62415499bfb14b1a6fda4935c8e7bdc32\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1349,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 50,\n \"path\": \"/PU-gbdt-code/script/gbdt-supervised.py\",\n \"repo_name\": \"niuwan1/PU-gbdt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from sklearn.model_selection import train_test_split\\nfrom sklearn.metrics import roc_auc_score, accuracy_score\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport pandas as pd\\ndef main():\\n\\n print (\\\"-- Gradient Boosting Classification --\\\")\\n\\n path = '/Users/junowang/Desktop/PU-gbdt-code/data/test_new.csv'\\n data = pd.read_csv(path, header=0, encoding = \\\"utf-8\\\")\\n print(data.info())\\n y = data['label']\\n data = data.drop('label',axis = 1)\\n X = data\\n\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)\\n gbdt = GradientBoostingClassifier(\\n loss='deviance'\\n , learning_rate=0.1\\n , n_estimators=100\\n , subsample=1\\n , min_samples_split=2\\n , min_samples_leaf=1\\n , max_depth=3\\n , init=None\\n , random_state=None\\n , max_features=None\\n , verbose=0\\n , max_leaf_nodes=None\\n , warm_start=False\\n )\\n gbdt.fit(X_train, y_train)\\n y_pred = gbdt.predict(X_test)\\n print(y_pred)\\n accuracy = accuracy_score(y_test, y_pred)\\n auc = roc_auc_score(y_test,y_pred)\\n\\n print (\\\"Accuracy:\\\", accuracy)\\n print('AUC', auc)\\n\\n\\n # Plot().plot_in_2d(X_test, y_pred,\\n #title=\\\"Gradient Boosting\\\",\\n #accuracy=accuracy,\\n #legend_labels=data.target_names)\\n\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\"\n },\n {\n \"alpha_fraction\": 0.5476190447807312,\n \"alphanum_fraction\": 0.625,\n \"avg_line_length\": 39.75757598876953,\n \"blob_id\": \"3489bd63adcf257649390ce6414b75088693fc21\",\n \"content_id\": \"5523072b32efd94076b9b8b5e671bda96c1a4beb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1362,\n \"license_type\": \"no_license\",\n \"max_line_length\": 103,\n \"num_lines\": 33,\n \"path\": \"/PU-gbdt-code/script/fig_testloss.py\",\n \"repo_name\": \"niuwan1/PU-gbdt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\nimport pandas as pd\\n\\n# 数据集\\npath = '/Users/junowang/Desktop/gbdt-code/output/testloss.xls'\\ndata1 = pd.read_excel(path, sheet_name='step1')\\ndata2 = pd.read_excel(path, sheet_name= 'step2')\\ndata1[['t1','t2','t3','t4','t5']] = np.exp(data1[['t1','t2','t3','t4','t5']]*100)\\ndata2[['t1','t2','t3','t4','t5']] = np.exp(data2[['t1','t2','t3','t4','t5']]*100)\\n# 绘画折线图：\\nf, axes = plt.subplots(2, 1, figsize=(15, 10))\\nfor ylabel in ['t1','t2','t3','t4','t5']:\\n #axes[0].set_xscale(\\\"log\\\")data1[['t1','t2','t3','t4','t5']] = data1[['t1','t2','t3','t4','t5']]*100\\n #axes[0].set_yscale(\\\"log\\\")\\n sns.regplot(\\\"itera\\\", ylabel, data1, ax=axes[0], scatter_kws={\\\"s\\\": 35}, label = ylabel)\\nfor ylabel in ['t1', 't2', 't3', 't4', 't5']:\\n #axes[0].set_yscale(\\\"log\\\")\\n sns.regplot(\\\"itera\\\", ylabel, data2, ax=axes[1], scatter_kws={\\\"s\\\": 35}, label = ylabel)\\naxes[0].legend(loc='right')\\naxes[1].legend(loc='right')\\naxes[0].set_title('step = 0.01')\\naxes[1].set_title('step = 0.03')\\naxes[0].xaxis.set_ticks_position('bottom')\\naxes[1].xaxis.set_ticks_position('bottom')\\naxes[1].set_xlabel('Iteration')\\naxes[1].set_ylabel('exp(testloss)')\\naxes[0].set_xlabel(' ')\\naxes[0].set_ylabel('exp(testloss)')\\naxes[0].set_xticks(np.linspace(1,20,20))\\naxes[1].set_xticks(np.linspace(1,20,20))\\nplt.show()\"\n },\n {\n \"alpha_fraction\": 0.6299694180488586,\n \"alphanum_fraction\": 0.6472986936569214,\n \"avg_line_length\": 43.6363639831543,\n \"blob_id\": \"81332e6b3b5d3fa79f39d64fa64247138f452972\",\n \"content_id\": \"eb8f22848aa156915f4b57fefdc2e663c646c522\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 981,\n \"license_type\": \"no_license\",\n \"max_line_length\": 107,\n \"num_lines\": 22,\n \"path\": \"/PU-gbdt-code/script/preprocess.py\",\n \"repo_name\": \"niuwan1/PU-gbdt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import pandas as pd\\npath = '/Users/junowang/Desktop/gbdt-code/data/test.csv'\\ndf = pd.read_csv(path, header=0, encoding = \\\"utf-8\\\")\\n#df_raw[~pos_index]['label'] = -1\\n#df_raw.to_csv('/Users/junowang/Desktop/gbdt-code/data/data_for_tongji.csv',index=False,encoding = \\\"utf-8\\\")\\nfor id in df.index:\\n if df.loc[id, 'label'] == 0.0:\\n df.loc[id, 'label'] = 1\\n else:\\n df.loc[id, 'label'] = -1\\ndf.to_csv('/Users/junowang/Desktop/gbdt-code/data/data_for_tongji.csv',index=False,encoding = \\\"utf-8\\\")\\n\\npath = '/Users/junowang/Desktop/gbdt-code/data/test.csv'\\ndf = pd.read_csv(path, header=0, encoding = \\\"utf-8\\\")\\n#df_raw[~pos_index]['label'] = -1\\n#df_raw.to_csv('/Users/junowang/Desktop/gbdt-code/data/data_for_tongji.csv',index=False,encoding = \\\"utf-8\\\")\\nfor id in df.index:\\n if df.loc[id, 'label'] == 0:\\n df.loc[id, 'label'] = 1\\n else:\\n df.loc[id, 'label'] = -1\\ndf.to_csv('/Users/junowang/Desktop/gbdt-code/data/test_new.csv',index=False,encoding = \\\"utf-8\\\")\"\n },\n {\n \"alpha_fraction\": 0.8062015771865845,\n \"alphanum_fraction\": 0.8062015771865845,\n \"avg_line_length\": 63.5,\n \"blob_id\": \"831d0b40f6eb1f16f2b15c8035ccbc163f180ac2\",\n \"content_id\": \"1d9205fe94b304e211ea0c7365f8629ecd97bc36\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 129,\n \"license_type\": \"no_license\",\n \"max_line_length\": 118,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"niuwan1/PU-gbdt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# PU-gbdt\\nConvert the loss function in gbdt to PU-Learning loss function and use the classifier to predict a bank credit dataset\\n\"\n },\n {\n \"alpha_fraction\": 0.6112600564956665,\n \"alphanum_fraction\": 0.622654139995575,\n \"avg_line_length\": 25.192981719970703,\n \"blob_id\": \"6ecbcd53885f2a31729092638447481dd2972541\",\n \"content_id\": \"a4819e17d5c67bebe34bf7a6e21e4c8842f5ef3f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1492,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 57,\n \"path\": \"/PU-gbdt-code/Machine-Learning-From-Scratch-master/gradient_boosting_decision_tree/gbdt_classifier_example.py\",\n \"repo_name\": \"niuwan1/PU-gbdt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from __future__ import division, print_function\\nimport numpy as np\\nfrom sklearn import datasets\\nimport matplotlib.pyplot as plt\\n\\n# Import helper functions\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.metrics import roc_auc_score, accuracy_score\\nfrom sklearn.ensemble import GradientBoostingRegressor\\nimport pandas as pd\\ndef main():\\n\\n print (\\\"-- Gradient Boosting Classification --\\\")\\n\\n path = '/Users/junowang/Desktop/gbdt-code/data/test_new.csv'\\n data = pd.read_csv(path, header=0, encoding = \\\"utf-8\\\")\\n print(data.head())\\n print(data.head)\\n y = data['label']\\n X = data.drop('label')\\n\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)\\n gbdt = GradientBoostingRegressor(\\n loss='ls'\\n , learning_rate=0.1\\n , n_estimators=100\\n , subsample=1\\n , min_samples_split=2\\n , min_samples_leaf=1\\n , max_depth=3\\n , init=None\\n , random_state=None\\n , max_features=None\\n , alpha=0.9\\n , verbose=0\\n , max_leaf_nodes=None\\n , warm_start=False\\n )\\n gbdt.fit(X_train, y_train)\\n y_pred = gbdt.predict(X_test)\\n\\n accuracy = accuracy_score(y_test, y_pred)\\n auc = roc_auc_score(y_test,y_pred)\\n\\n print (\\\"Accuracy:\\\", accuracy)\\n print('AUC', auc)\\n\\n\\n # Plot().plot_in_2d(X_test, y_pred,\\n #title=\\\"Gradient Boosting\\\",\\n #accuracy=accuracy,\\n #legend_labels=data.target_names)\\n\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":6,"string":"6"}}},{"rowIdx":2983080,"cells":{"repo_name":{"kind":"string","value":"MonsieurShaik/nltk-tutorials"},"repo_url":{"kind":"string","value":"https://github.com/MonsieurShaik/nltk-tutorials"},"snapshot_id":{"kind":"string","value":"95b3ee88311cf0abc3479b67467f71534d8260eb"},"revision_id":{"kind":"string","value":"fa3361dc3f939629ec925c7cb5aa5b6092e16e6f"},"directory_id":{"kind":"string","value":"9197a63774ea1e1ddda73c3cbff953fd7fdda9bc"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2018-02-09T12:35:55.550063","string":"2018-02-09T12:35:55.550063"},"revision_date":{"kind":"timestamp","value":"2017-07-11T07:24:16","string":"2017-07-11T07:24:16"},"committer_date":{"kind":"timestamp","value":"2017-07-11T07:24:16","string":"2017-07-11T07:24:16"},"github_id":{"kind":"number","value":96747196,"string":"96,747,196"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.800000011920929,"alphanum_fraction":0.8166666626930237,"avg_line_length":44,"blob_id":"a1eb3181acb184d738f968d7c131abe9344f1cbe","content_id":"e2a564a6a95de2a6197327fe02db55f3985be6d1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":180,"license_type":"no_license","max_line_length":83,"num_lines":4,"path":"/README.md","repo_name":"MonsieurShaik/nltk-tutorials","src_encoding":"UTF-8","text":"# nltk-tutorials\nAll the code that I write while taking NLTK tutorials.\n# The course I'm taking\nhttps://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL\n"},{"alpha_fraction":0.7065368294715881,"alphanum_fraction":0.7121001482009888,"avg_line_length":27.799999237060547,"blob_id":"2224ddd07117642d8cb6307d3912b2f0053adfa5","content_id":"a9478611d6b3535304f1136dd862742b7d160ef0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":719,"license_type":"no_license","max_line_length":70,"num_lines":25,"path":"/tutorial-code/nltk-text-classification.py","repo_name":"MonsieurShaik/nltk-tutorials","src_encoding":"UTF-8","text":"import nltk\nimport random\nfrom nltk.corpus import movie_reviews\n\ndocument = [(list(movie_reviews.words(fileid)), category)\n for category in movie_reviews.categories()\n for fileid in movie_reviews.fileids(category)]\n\n# document = []\n\n# for category in movie_reviews.categories():\n# for fileid in movie_reviews.fileids(category):\n# document.append(list(movie_reviews.words(fileid)), category)\n\nrandom.shuffle(document)\n\nall_words = []\nfor w in movie_reviews.words():\n all_words.append(w.lower())\n\nall_words = nltk.FreqDist(all_words)\n# get the top 15 most common words\nprint(all_words.most_common(15))\n# Get the number of times \"Stupid\" occurs in the review\nprint(all_words[\"stupid\"])"},{"alpha_fraction":0.7641128897666931,"alphanum_fraction":0.7641128897666931,"avg_line_length":28.235294342041016,"blob_id":"ca1bf9d20ce76b7fc4ba79bea041dbc0fbde143d","content_id":"5e70667430cd5647ff0a00bebe801b7a3ebfbcb3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":496,"license_type":"no_license","max_line_length":85,"num_lines":17,"path":"/tutorial-code/nltk-2.py","repo_name":"MonsieurShaik/nltk-tutorials","src_encoding":"UTF-8","text":"from nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\n\nexample_sentence = \"This is an example to demo stop word filtration.\"\n# The words that are kind of useless for the language processor are called stopwords.\nstop_words = set(stopwords.words(\"english\"))\nprint(stop_words)\n\nwords = word_tokenize(example_sentence)\nfiltered_sentence = []\n\n# Filter the sentence.\nfor word in words:\n if word not in stop_words:\n filtered_sentence.append(word)\n\nprint(filtered_sentence)"},{"alpha_fraction":0.6848214268684387,"alphanum_fraction":0.6848214268684387,"avg_line_length":36.33333206176758,"blob_id":"b5d980c8b05cc9f8f872b7725691efa63f039819","content_id":"d9beb23016451c5e43e119bd4d11a072e777b7f1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1120,"license_type":"no_license","max_line_length":119,"num_lines":30,"path":"/tutorial-code/nltk-1.py","repo_name":"MonsieurShaik/nltk-tutorials","src_encoding":"UTF-8","text":"# Types of tokenizers\n# Word tokenizers - Tokenize by word.\n# Sentence tokenizers - Tokenize by sentences.\n\n# Corpora - Body of text. Eg: Medical journals, tech documentation.\n# Anything in English language.\n\n# Lexicon - Words and their meanings\n\n# Meanings are different in different speaks\n# Eg: Investor-speak vs English-speak\n# 'Bull' has different meanings in Investor-speak and English-speak.\n\n# Code on a using a tokenizer.\n\n# sent_tokenize = Sentence tokenizer, word_tokenize = Word tokenizer.\nfrom nltk.tokenize import sent_tokenize, word_tokenize\n\nexample_text = \"Hello Mr. Sameer! How are you? This is the first line of this code. This is simple, and important too.\"\n\n# Tokenize by sentence\n# Output: ['Hello Mr.', 'Guy!', 'How are you?', 'This is the first line of this code\n# .', 'This is simple, and important too.']\nprint(sent_tokenize(example_text))\n\n# Tokenize by word\n# Output: ['Hello', 'Mr.', 'Sameer', '!', 'How', 'are', 'you', '?', 'This', 'is', 't\n# he', 'first', 'line', 'of', 'this', 'code', '.', 'This', 'is', 'simple', '\n# ', 'and', 'important', 'too', '.']\nprint(word_tokenize(example_text))\n"},{"alpha_fraction":0.6269841194152832,"alphanum_fraction":0.6643990874290466,"avg_line_length":19.045454025268555,"blob_id":"2bdf3f6a18c022fc1f5711877d8eaf0e922bac72","content_id":"a4b66f1c95fb8e42e033c7c38fefffaaddd3f7f6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":882,"license_type":"no_license","max_line_length":53,"num_lines":44,"path":"/tutorial-code/nltk-wordnet.py","repo_name":"MonsieurShaik/nltk-tutorials","src_encoding":"UTF-8","text":"from nltk.corpus import wordnet\n\nsyns = wordnet.synsets(\"program\")\n\n# synset\nprint(syns[0].name())\n\n# Just the word\nprint(syns[0].lemmas()[0].name())\n\n# definition\nprint(syns[0].definition())\n\n# examples\nprint(syns[0].examples())\n\nsynonyms = []\nantonyms = []\n\nfor syn in wordnet.synsets(\"good\"):\n for lem in syn.lemmas():\n synonyms.append(lem.name())\n if lem.antonyms():\n antonyms.append(lem.antonyms()[0].name())\n\nprint(set(synonyms))\nprint(set(antonyms))\n\n# Semantic similarities\n# ship.n.01 -> Ship - Noun - 1st of results\n# ship vs boat\nw1 = wordnet.synset(\"ship.n.01\")\nw2 = wordnet.synset(\"boat.n.01\")\nprint(w1.wup_similarity(w2))\n\n# ship vs car\nw1 = wordnet.synset(\"ship.n.01\")\nw2 = wordnet.synset(\"car.n.01\")\nprint(w1.wup_similarity(w2))\n\n# ship vs dog\nw1 = wordnet.synset(\"ship.n.01\")\nw2 = wordnet.synset(\"dog.n.01\")\nprint(w1.wup_similarity(w2))\n"},{"alpha_fraction":0.7444444298744202,"alphanum_fraction":0.7444444298744202,"avg_line_length":29.11111068725586,"blob_id":"bdc3323605ed4b1eb0bef8c3f7d8880ee045dfbe","content_id":"ad09c9b33f40c92db6ef6c7d796eec61166e1de4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":270,"license_type":"no_license","max_line_length":75,"num_lines":9,"path":"/tutorial-code/nltk-stemming.py","repo_name":"MonsieurShaik/nltk-tutorials","src_encoding":"UTF-8","text":"from nltk.stem import PorterStemmer\nfrom nltk.tokenize import word_tokenize\n\nps = PorterStemmer()\n\n# Declare example words and stem the words.\nexample_words = [\"python\", \"pythoner\", \"pythoning\", \"pythoned\", \"pythonly\"]\nfor word in example_words:\n print(ps.stem(word))"},{"alpha_fraction":0.6680541038513184,"alphanum_fraction":0.6774193644523621,"avg_line_length":35.92307662963867,"blob_id":"017c32bb0018e1f233a7b0aa21f99bb3ae4ce533","content_id":"840afd0d52d57f11ae8b8be2eb43c46455e14286","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":961,"license_type":"no_license","max_line_length":137,"num_lines":26,"path":"/tutorial-code/nltk-ner.py","repo_name":"MonsieurShaik/nltk-tutorials","src_encoding":"UTF-8","text":"import nltk\nfrom nltk.corpus import state_union\n# PunktSentenceTokenizer is an unsupervised machine learning sentence tokenizer. It comes pre-trained but you can retrain it if required.\nfrom nltk.tokenize import PunktSentenceTokenizer\n\ntrain_text = state_union.raw(\"2005-GWBush.txt\")\nsample_text = state_union.raw(\"2006-GWBush.txt\")\ncustom_sent_tokenizer = PunktSentenceTokenizer(sample_text)\ntokenized = custom_sent_tokenizer.tokenize(sample_text)\n\ndef process_content():\n try:\n for i in tokenized[5:]:\n words = nltk.word_tokenize(i)\n # Here pos = Part of speech\n tagged = nltk.pos_tag(words)\n\n # nltk.ne_chunk is used for Named entity recognition.\n # Error rate is kind of high for NLTK NER.\n namedEnt = nltk.ne_chunk(tagged)\n # namedEnt = nltk.ne_chunk(tagged, binary=True)\n namedEnt.draw()\n except Exception as e:\n print(str(e))\n\nprocess_content()\n\n"},{"alpha_fraction":0.7875000238418579,"alphanum_fraction":0.7875000238418579,"avg_line_length":35.3636360168457,"blob_id":"1314908e42dd36031bdde03844825ae559abfcfd","content_id":"564955e31f35d91ca54fade7c5467fcb7d224ef9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":400,"license_type":"no_license","max_line_length":53,"num_lines":11,"path":"/tutorial-code/nltk-lemmatizing.py","repo_name":"MonsieurShaik/nltk-tutorials","src_encoding":"UTF-8","text":"from nltk.stem import WordNetLemmatizer\n\nlemmatizer = WordNetLemmatizer()\n# Default part of speech tag is noun for lemmatizing.\nprint(lemmatizer.lemmatize(\"cats\"))\nprint(lemmatizer.lemmatize(\"cacti\"))\nprint(lemmatizer.lemmatize(\"brooms\"))\nprint(lemmatizer.lemmatize(\"mops\"))\nprint(lemmatizer.lemmatize(\"bikes\"))\nprint(lemmatizer.lemmatize(\"better\", pos=\"a\"))\nprint(lemmatizer.lemmatize(\"implement\"))\n"}],"string":"[\n {\n \"alpha_fraction\": 0.800000011920929,\n \"alphanum_fraction\": 0.8166666626930237,\n \"avg_line_length\": 44,\n \"blob_id\": \"a1eb3181acb184d738f968d7c131abe9344f1cbe\",\n \"content_id\": \"e2a564a6a95de2a6197327fe02db55f3985be6d1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 180,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 4,\n \"path\": \"/README.md\",\n \"repo_name\": \"MonsieurShaik/nltk-tutorials\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# nltk-tutorials\\nAll the code that I write while taking NLTK tutorials.\\n# The course I'm taking\\nhttps://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL\\n\"\n },\n {\n \"alpha_fraction\": 0.7065368294715881,\n \"alphanum_fraction\": 0.7121001482009888,\n \"avg_line_length\": 27.799999237060547,\n \"blob_id\": \"2224ddd07117642d8cb6307d3912b2f0053adfa5\",\n \"content_id\": \"a9478611d6b3535304f1136dd862742b7d160ef0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 719,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 25,\n \"path\": \"/tutorial-code/nltk-text-classification.py\",\n \"repo_name\": \"MonsieurShaik/nltk-tutorials\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import nltk\\nimport random\\nfrom nltk.corpus import movie_reviews\\n\\ndocument = [(list(movie_reviews.words(fileid)), category)\\n for category in movie_reviews.categories()\\n for fileid in movie_reviews.fileids(category)]\\n\\n# document = []\\n\\n# for category in movie_reviews.categories():\\n# for fileid in movie_reviews.fileids(category):\\n# document.append(list(movie_reviews.words(fileid)), category)\\n\\nrandom.shuffle(document)\\n\\nall_words = []\\nfor w in movie_reviews.words():\\n all_words.append(w.lower())\\n\\nall_words = nltk.FreqDist(all_words)\\n# get the top 15 most common words\\nprint(all_words.most_common(15))\\n# Get the number of times \\\"Stupid\\\" occurs in the review\\nprint(all_words[\\\"stupid\\\"])\"\n },\n {\n \"alpha_fraction\": 0.7641128897666931,\n \"alphanum_fraction\": 0.7641128897666931,\n \"avg_line_length\": 28.235294342041016,\n \"blob_id\": \"ca1bf9d20ce76b7fc4ba79bea041dbc0fbde143d\",\n \"content_id\": \"5e70667430cd5647ff0a00bebe801b7a3ebfbcb3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 496,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 17,\n \"path\": \"/tutorial-code/nltk-2.py\",\n \"repo_name\": \"MonsieurShaik/nltk-tutorials\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from nltk.corpus import stopwords\\nfrom nltk.tokenize import word_tokenize\\n\\nexample_sentence = \\\"This is an example to demo stop word filtration.\\\"\\n# The words that are kind of useless for the language processor are called stopwords.\\nstop_words = set(stopwords.words(\\\"english\\\"))\\nprint(stop_words)\\n\\nwords = word_tokenize(example_sentence)\\nfiltered_sentence = []\\n\\n# Filter the sentence.\\nfor word in words:\\n if word not in stop_words:\\n filtered_sentence.append(word)\\n\\nprint(filtered_sentence)\"\n },\n {\n \"alpha_fraction\": 0.6848214268684387,\n \"alphanum_fraction\": 0.6848214268684387,\n \"avg_line_length\": 36.33333206176758,\n \"blob_id\": \"b5d980c8b05cc9f8f872b7725691efa63f039819\",\n \"content_id\": \"d9beb23016451c5e43e119bd4d11a072e777b7f1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1120,\n \"license_type\": \"no_license\",\n \"max_line_length\": 119,\n \"num_lines\": 30,\n \"path\": \"/tutorial-code/nltk-1.py\",\n \"repo_name\": \"MonsieurShaik/nltk-tutorials\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Types of tokenizers\\n# Word tokenizers - Tokenize by word.\\n# Sentence tokenizers - Tokenize by sentences.\\n\\n# Corpora - Body of text. Eg: Medical journals, tech documentation.\\n# Anything in English language.\\n\\n# Lexicon - Words and their meanings\\n\\n# Meanings are different in different speaks\\n# Eg: Investor-speak vs English-speak\\n# 'Bull' has different meanings in Investor-speak and English-speak.\\n\\n# Code on a using a tokenizer.\\n\\n# sent_tokenize = Sentence tokenizer, word_tokenize = Word tokenizer.\\nfrom nltk.tokenize import sent_tokenize, word_tokenize\\n\\nexample_text = \\\"Hello Mr. Sameer! How are you? This is the first line of this code. This is simple, and important too.\\\"\\n\\n# Tokenize by sentence\\n# Output: ['Hello Mr.', 'Guy!', 'How are you?', 'This is the first line of this code\\n# .', 'This is simple, and important too.']\\nprint(sent_tokenize(example_text))\\n\\n# Tokenize by word\\n# Output: ['Hello', 'Mr.', 'Sameer', '!', 'How', 'are', 'you', '?', 'This', 'is', 't\\n# he', 'first', 'line', 'of', 'this', 'code', '.', 'This', 'is', 'simple', '\\n# ', 'and', 'important', 'too', '.']\\nprint(word_tokenize(example_text))\\n\"\n },\n {\n \"alpha_fraction\": 0.6269841194152832,\n \"alphanum_fraction\": 0.6643990874290466,\n \"avg_line_length\": 19.045454025268555,\n \"blob_id\": \"2bdf3f6a18c022fc1f5711877d8eaf0e922bac72\",\n \"content_id\": \"a4b66f1c95fb8e42e033c7c38fefffaaddd3f7f6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 882,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 44,\n \"path\": \"/tutorial-code/nltk-wordnet.py\",\n \"repo_name\": \"MonsieurShaik/nltk-tutorials\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from nltk.corpus import wordnet\\n\\nsyns = wordnet.synsets(\\\"program\\\")\\n\\n# synset\\nprint(syns[0].name())\\n\\n# Just the word\\nprint(syns[0].lemmas()[0].name())\\n\\n# definition\\nprint(syns[0].definition())\\n\\n# examples\\nprint(syns[0].examples())\\n\\nsynonyms = []\\nantonyms = []\\n\\nfor syn in wordnet.synsets(\\\"good\\\"):\\n for lem in syn.lemmas():\\n synonyms.append(lem.name())\\n if lem.antonyms():\\n antonyms.append(lem.antonyms()[0].name())\\n\\nprint(set(synonyms))\\nprint(set(antonyms))\\n\\n# Semantic similarities\\n# ship.n.01 -> Ship - Noun - 1st of results\\n# ship vs boat\\nw1 = wordnet.synset(\\\"ship.n.01\\\")\\nw2 = wordnet.synset(\\\"boat.n.01\\\")\\nprint(w1.wup_similarity(w2))\\n\\n# ship vs car\\nw1 = wordnet.synset(\\\"ship.n.01\\\")\\nw2 = wordnet.synset(\\\"car.n.01\\\")\\nprint(w1.wup_similarity(w2))\\n\\n# ship vs dog\\nw1 = wordnet.synset(\\\"ship.n.01\\\")\\nw2 = wordnet.synset(\\\"dog.n.01\\\")\\nprint(w1.wup_similarity(w2))\\n\"\n },\n {\n \"alpha_fraction\": 0.7444444298744202,\n \"alphanum_fraction\": 0.7444444298744202,\n \"avg_line_length\": 29.11111068725586,\n \"blob_id\": \"bdc3323605ed4b1eb0bef8c3f7d8880ee045dfbe\",\n \"content_id\": \"ad09c9b33f40c92db6ef6c7d796eec61166e1de4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 270,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 9,\n \"path\": \"/tutorial-code/nltk-stemming.py\",\n \"repo_name\": \"MonsieurShaik/nltk-tutorials\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from nltk.stem import PorterStemmer\\nfrom nltk.tokenize import word_tokenize\\n\\nps = PorterStemmer()\\n\\n# Declare example words and stem the words.\\nexample_words = [\\\"python\\\", \\\"pythoner\\\", \\\"pythoning\\\", \\\"pythoned\\\", \\\"pythonly\\\"]\\nfor word in example_words:\\n print(ps.stem(word))\"\n },\n {\n \"alpha_fraction\": 0.6680541038513184,\n \"alphanum_fraction\": 0.6774193644523621,\n \"avg_line_length\": 35.92307662963867,\n \"blob_id\": \"017c32bb0018e1f233a7b0aa21f99bb3ae4ce533\",\n \"content_id\": \"840afd0d52d57f11ae8b8be2eb43c46455e14286\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 961,\n \"license_type\": \"no_license\",\n \"max_line_length\": 137,\n \"num_lines\": 26,\n \"path\": \"/tutorial-code/nltk-ner.py\",\n \"repo_name\": \"MonsieurShaik/nltk-tutorials\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import nltk\\nfrom nltk.corpus import state_union\\n# PunktSentenceTokenizer is an unsupervised machine learning sentence tokenizer. It comes pre-trained but you can retrain it if required.\\nfrom nltk.tokenize import PunktSentenceTokenizer\\n\\ntrain_text = state_union.raw(\\\"2005-GWBush.txt\\\")\\nsample_text = state_union.raw(\\\"2006-GWBush.txt\\\")\\ncustom_sent_tokenizer = PunktSentenceTokenizer(sample_text)\\ntokenized = custom_sent_tokenizer.tokenize(sample_text)\\n\\ndef process_content():\\n try:\\n for i in tokenized[5:]:\\n words = nltk.word_tokenize(i)\\n # Here pos = Part of speech\\n tagged = nltk.pos_tag(words)\\n\\n # nltk.ne_chunk is used for Named entity recognition.\\n # Error rate is kind of high for NLTK NER.\\n namedEnt = nltk.ne_chunk(tagged)\\n # namedEnt = nltk.ne_chunk(tagged, binary=True)\\n namedEnt.draw()\\n except Exception as e:\\n print(str(e))\\n\\nprocess_content()\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7875000238418579,\n \"alphanum_fraction\": 0.7875000238418579,\n \"avg_line_length\": 35.3636360168457,\n \"blob_id\": \"1314908e42dd36031bdde03844825ae559abfcfd\",\n \"content_id\": \"564955e31f35d91ca54fade7c5467fcb7d224ef9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 400,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 11,\n \"path\": \"/tutorial-code/nltk-lemmatizing.py\",\n \"repo_name\": \"MonsieurShaik/nltk-tutorials\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from nltk.stem import WordNetLemmatizer\\n\\nlemmatizer = WordNetLemmatizer()\\n# Default part of speech tag is noun for lemmatizing.\\nprint(lemmatizer.lemmatize(\\\"cats\\\"))\\nprint(lemmatizer.lemmatize(\\\"cacti\\\"))\\nprint(lemmatizer.lemmatize(\\\"brooms\\\"))\\nprint(lemmatizer.lemmatize(\\\"mops\\\"))\\nprint(lemmatizer.lemmatize(\\\"bikes\\\"))\\nprint(lemmatizer.lemmatize(\\\"better\\\", pos=\\\"a\\\"))\\nprint(lemmatizer.lemmatize(\\\"implement\\\"))\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":8,"string":"8"}}},{"rowIdx":2983081,"cells":{"repo_name":{"kind":"string","value":"jstestsuite/ros-test"},"repo_url":{"kind":"string","value":"https://github.com/jstestsuite/ros-test"},"snapshot_id":{"kind":"string","value":"c33e244794287e2665cace847a9faf1b596ddf92"},"revision_id":{"kind":"string","value":"b0bb5b06c1c18b804c0f0a4e2deb4d88be894d1f"},"directory_id":{"kind":"string","value":"bdf3c98e33c6bc9c8e0332043cc5a3a3f5e8ea42"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-04-11T16:54:41.401198","string":"2020-04-11T16:54:41.401198"},"revision_date":{"kind":"timestamp","value":"2019-02-13T00:44:14","string":"2019-02-13T00:44:14"},"committer_date":{"kind":"timestamp","value":"2019-02-13T00:44:14","string":"2019-02-13T00:44:14"},"github_id":{"kind":"number","value":161938209,"string":"161,938,209"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5413343906402588,"alphanum_fraction":0.546482264995575,"avg_line_length":24.399999618530273,"blob_id":"471f415a1a75a40ea734d12c453c00ad569f2c60","content_id":"40815c68682e4dfa352f4ab4e554441cd5b85599","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":19814,"license_type":"no_license","max_line_length":102,"num_lines":780,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/PrioritizedSweeping.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include \"PrioritizedSweeping.hh\"\n#include <algorithm>\n\n//#include <time.h>\n#include <sys/time.h>\n\n\nPrioritizedSweeping::PrioritizedSweeping(int numactions, float gamma,\n float MAX_TIME, bool onlyAddLastSA, int modelType,\n const std::vector<float> &fmax, \n const std::vector<float> &fmin, \n Random r):\n numactions(numactions), gamma(gamma), MAX_TIME(MAX_TIME),\n onlyAddLastSA(onlyAddLastSA), modelType(modelType)\n{\n rng = r;\n nstates = 0;\n nactions = 0;\n\n timingType = false; //true;\n\n model = NULL;\n planTime = getSeconds();\n\n // algorithm options\n MAX_STEPS = 10; //50; //60; //80; //0; //5; //10;\n\n lastModelUpdate = -1;\n\n PLANNERDEBUG = false;\n POLICYDEBUG = false; //true; //false;\n ACTDEBUG = false; //true;\n MODELDEBUG = false; //true;\n LISTDEBUG = false; // true; //false;\n\n featmax = fmax;\n featmin = fmin;\n\n}\n\nPrioritizedSweeping::~PrioritizedSweeping() {}\n\nvoid PrioritizedSweeping::setModel(MDPModel* m){\n\n model = m;\n\n}\n\n\n/////////////////////////////\n// Functional functions :) //\n/////////////////////////////\n\n\nvoid PrioritizedSweeping::initNewState(state_t s){\n if (PLANNERDEBUG) cout << \"initNewState(s = \" << s\n << \") size = \" << s->size() << endl;\n\n if (MODELDEBUG) cout << \"New State: \" << endl;\n\n // create state info and add to hash map\n state_info* info = &(statedata[s]);\n initStateInfo(info);\n\n // init these from model\n for (int i = 0; i < numactions; i++){\n model->getStateActionInfo(*s, i, &(info->modelInfo[i]));\n }\n\n // we have to make sure q-values are initialized properly\n // or we'll get bizarre results (if these aren't swept over)\n for (int j = 0; j < numactions; j++){\n // update q values\n updateQValues(*s, j);\n info->Q[j] += rng.uniform(0,0.01);\n }\n\n if (PLANNERDEBUG) cout << \"done with initNewState()\" << endl;\n\n}\n\n/** Use the latest experience to update state info and the model. */\nbool PrioritizedSweeping::updateModelWithExperience(const std::vector<float> &laststate,\n int lastact,\n const std::vector<float> &currstate,\n float reward, bool term){\n if (PLANNERDEBUG) cout << \"updateModelWithExperience(last = \" << &laststate\n << \", curr = \" << &currstate\n << \", lastact = \" << lastact\n << \", r = \" << reward\n << \")\" << endl;\n\n if (!timingType)\n planTime = getSeconds();\n\n // canonicalize these things\n state_t last = canonicalize(laststate);\n state_t curr = canonicalize(currstate);\n\n prevstate = laststate;\n prevact = lastact;\n\n // if not transition to terminal\n if (curr == NULL)\n return false;\n\n // get state info\n state_info* info = &(statedata[last]);\n\n // update the state visit count\n info->visits[lastact]++;\n\n // init model?\n if (model == NULL){\n cout << \"ERROR IN MODEL OR MODEL SIZE\" << endl;\n exit(-1);\n }\n\n experience e;\n e.s = *last;\n e.next = *curr;\n e.act = lastact;\n e.reward = reward;\n e.terminal = term;\n bool modelChanged = model->updateWithExperience(e);\n\n if (PLANNERDEBUG) cout << \"Added exp: \" << modelChanged << endl;\n if (timingType)\n planTime = getSeconds();\n\n return modelChanged;\n\n}\n\n\n\n/** Update our state info's from the model by calling the model function */\nvoid PrioritizedSweeping::updateStatesFromModel(){\n if (PLANNERDEBUG || LISTDEBUG) cout << \"updateStatesFromModel()\" << endl;\n\n // for each state\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n for (int j = 0; j < numactions; j++){\n updateStateActionFromModel(*i, j);\n }\n\n }\n\n}\n\n\n\n\n/** Choose the next action */\nint PrioritizedSweeping::getBestAction(const std::vector<float> &state){\n if (PLANNERDEBUG) cout << \"getBestAction(s = \" << &state\n << \")\" << endl;\n\n state_t s = canonicalize(state);\n\n // get state info\n state_info* info = &(statedata[s]);\n\n // Get Q values\n std::vector<float> &Q = info->Q;\n\n // Choose an action\n const std::vector<float>::iterator a =\n random_max_element(Q.begin(), Q.end()); // Choose maximum\n\n int act = a - Q.begin();\n float val = *a;\n\n if (ACTDEBUG){\n cout << endl << \"chooseAction State \" << (*s)[0] << \",\" << (*s)[1]\n << \" act: \" << act << \" val: \" << val << endl;\n for (int iAct = 0; iAct < numactions; iAct++){\n cout << \" Action: \" << iAct\n << \" val: \" << Q[iAct]\n << \" visits: \" << info->visits[iAct]\n << \" modelsAgree: \" << info->modelInfo[iAct].known << endl;\n }\n }\n\n nactions++;\n\n // return index of action\n return act;\n}\n\n\n\nvoid PrioritizedSweeping::planOnNewModel(){\n\n // update model info\n\n // print state\n if (PLANNERDEBUG){\n cout << endl << endl << \"Before update\" << endl << endl;\n printStates();\n }\n\n // tabular - can just update last state-action from model.\n if (false && modelType == RMAX){\n updateStateActionFromModel(prevstate, prevact);\n }\n else {\n updateStatesFromModel();\n }\n\n // just add last state action (this is normal prioritized sweeping).\n // if bool was false, will have checked for differences and added them in update above\n if (onlyAddLastSA || modelType == RMAX){\n float diff = updateQValues(prevstate, prevact);\n addSAToList(prevstate, prevact, diff);\n }\n\n if (PLANNERDEBUG){\n cout << endl << endl << \"After update\" << endl << endl;\n printStates();\n }\n\n // run value iteration\n createPolicy();\n\n}\n\n\n////////////////////////////\n// Helper Functions //\n////////////////////////////\n\nPrioritizedSweeping::state_t PrioritizedSweeping::canonicalize(const std::vector<float> &s) {\n if (PLANNERDEBUG) cout << \"canonicalize(s = \" << s[0] << \", \"\n << s[1] << \")\" << endl;\n\n // get state_t for pointer if its in statespace\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\n statespace.insert(s);\n state_t retval = &*result.first; // Dereference iterator then get pointer\n\n if (PLANNERDEBUG) cout << \" returns \" << retval\n << \" New: \" << result.second << endl;\n\n // if not, init this new state\n if (result.second) { // s is new, so initialize Q(s,a) for all a\n initNewState(retval);\n if (PLANNERDEBUG) cout << \" New state initialized\" << endl;\n }\n\n\n return retval;\n}\n\n// init state info\nvoid PrioritizedSweeping::initStateInfo(state_info* info){\n if (PLANNERDEBUG) cout << \"initStateInfo()\";\n\n info->id = nstates++;\n if (PLANNERDEBUG) cout << \" id = \" << info->id << endl;\n\n info->fresh = true;\n\n // model data (transition, reward, known)\n info->modelInfo = new StateActionInfo[numactions];\n\n // model q values, visit counts\n info->visits.resize(numactions, 0);\n info->Q.resize(numactions, 0);\n info->lastUpdate.resize(numactions, nactions);\n\n for (int i = 0; i < numactions; i++){\n info->Q[i] = rng.uniform(0,1);\n }\n\n if (PLANNERDEBUG) cout << \"done with initStateInfo()\" << endl;\n\n}\n\n\n/** Print state info for debugging. */\nvoid PrioritizedSweeping::printStates(){\n\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n state_t s = canonicalize(*i);\n\n state_info* info = &(statedata[s]);\n\n cout << endl << \"State \" << info->id << \": \";\n for (unsigned j = 0; j < s->size(); j++){\n cout << (*s)[j] << \", \";\n }\n cout << endl;\n\n for (int act = 0; act < numactions; act++){\n cout << \" visits[\" << act << \"] = \" << info->visits[act]\n << \" Q: \" << info->Q[act]\n << \" R: \" << info->modelInfo[act].reward << endl;\n\n cout << \" Next states: \" << endl;\n for (std::map<std::vector<float>, float>::iterator outIt\n = info->modelInfo[act].transitionProbs.begin();\n outIt != info->modelInfo[act].transitionProbs.end(); outIt++){\n\n std::vector<float> nextstate = (*outIt).first;\n float prob = (*outIt).second;\n\n cout << \" State \";\n for (unsigned k = 0; k < nextstate.size(); k++){\n cout << nextstate[k] << \", \";\n }\n cout << \" prob: \" << prob << endl;\n\n } // end of next states\n\n } // end of actions\n\n // print predecessors\n for (std::list<saqPair>::iterator x = info->pred.begin();\n x != info->pred.end(); x++){\n\n std::vector<float> s = (*x).s;\n int a = (*x).a;\n\n cout << \"Has predecessor state: \";\n for (unsigned k = 0; k < s.size(); k++){\n cout << s[k] << \", \";\n }\n cout << \" action: \" << a << endl;\n }\n\n }\n}\n\n\n\n\n\nvoid PrioritizedSweeping::deleteInfo(state_info* info){\n\n delete [] info->modelInfo;\n\n}\n\n\ndouble PrioritizedSweeping::getSeconds(){\n struct timezone tz;\n timeval timeT;\n gettimeofday(&timeT, &tz);\n return timeT.tv_sec + (timeT.tv_usec / 1000000.0);\n}\n\n\n/** Create policy through prioritized sweeping. */\nvoid PrioritizedSweeping::createPolicy(){\n if (POLICYDEBUG) cout << endl << \"createPolicy()\" << endl;\n\n /*\n // loop through all states, add them all to queue with some high value.\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n saqPair saq;\n saq.s = *i;\n saq.q = 100.0;\n\n for (int j = 0; j < numactions; j++){\n saq.a = j;\n\n if (LISTDEBUG){\n cout << \"Added state \";\n for (unsigned k = 0; k < saq.s.size(); k++){\n cout << saq.s[k] << \", \";\n }\n cout << \" action: \" << saq.a << endl;\n }\n\n priorityList.push_front(saq);\n }\n }\n */\n\n // add last state-action to priority list\n //addSAToList(prevstate, prevact, 100.0);\n\n int updates = 0;\n\n // go through queue, doing prioritized sweeping. until nothing left on queue.\n while (!priorityList.empty()){\n\n if ((getSeconds() - planTime) > MAX_TIME)\n break;\n\n // print list!\n if (LISTDEBUG){\n cout << endl << \"Current List (\" << updates << \"):\" << endl;\n for (std::list<saqPair>::iterator k = priorityList.begin(); k != priorityList.end(); k++){\n cout << \"State: \";\n for (unsigned l = 0; l < (*k).s.size(); l++){\n cout << (*k).s[l] << \", \";\n }\n cout << \" act: \" << (*k).a << \" Q: \" << (*k).q << endl;\n }\n }\n\n updates++;\n\n // pull off first item\n saqPair currUpdate = priorityList.front();\n priorityList.pop_front();\n\n state_t s = canonicalize(currUpdate.s);\n\n // get state's info\n state_info* info = &(statedata[s]);\n\n updatePriorityList(info, *s);\n\n } // is list empty loop\n\n\n priorityList.clear();\n\n if (LISTDEBUG)\n cout << \"priority list complete after updates to \"\n << updates << \" states.\" <<endl;\n\n}\n\n\nvoid PrioritizedSweeping::updatePriorityList(state_info* info,\n const std::vector<float> &next){\n if (LISTDEBUG) cout << \"update priority list\" << endl;\n\n float MIN_ERROR = 0.01;\n\n // find maxq at this state\n std::vector<float>::iterator maxAct =\n std::max_element(info->Q.begin(),\n info->Q.end());\n float maxval = *maxAct;\n\n if (LISTDEBUG) cout << \" maxQ at this state: \" << maxval << endl;\n\n // loop through all s,a predicted to lead to this state\n for (std::list<saqPair>::iterator i = info->pred.begin();\n i != info->pred.end(); i++){\n\n if ((getSeconds() - planTime) > MAX_TIME)\n break;\n\n std::vector<float> s = (*i).s;\n int a = (*i).a;\n\n if (LISTDEBUG) {\n cout << endl << \" For predecessor state: \";\n for (unsigned j = 0; j < s.size(); j++){\n cout << s[j] << \", \";\n }\n cout << \" action: \" << a << endl;\n }\n\n // figure out amount of update\n float diff = updateQValues(s, a);\n\n if (LISTDEBUG) {\n cout << \" diff: \" << diff << endl;\n }\n // possibly add to queue\n if (diff > MIN_ERROR){\n saqPair saq;\n saq.s = s;\n saq.a = a;\n saq.q = diff;\n\n // find spot for it in queue\n if (priorityList.empty()){\n if (LISTDEBUG) cout << \" empty list\" << endl;\n priorityList.push_front(saq);\n }\n else {\n\n // check that its not already in queue\n for (std::list<saqPair>::iterator k = priorityList.begin(); k != priorityList.end(); k++){\n // matched\n if (saqPairMatch(saq, *k)){\n if (LISTDEBUG)\n cout << \" found matching element already in list\" << endl;\n\n priorityList.erase(k);\n break;\n }\n\n }\n\n int l = 0;\n std::list<saqPair>::iterator k;\n for (k = priorityList.begin(); k != priorityList.end(); k++){\n if (LISTDEBUG)\n cout << \" Element \" << l << \" has q value \" << (*k).q << endl;\n if (diff > (*k).q){\n if (LISTDEBUG)\n cout << \" insert at \" << l << endl;\n priorityList.insert(k, saq);\n break;\n }\n l++;\n }\n // put this at the end\n if (k == priorityList.end()){\n if (LISTDEBUG)\n cout << \" insert at end\" << endl;\n priorityList.push_back(saq);\n }\n\n } // not empty\n\n\n } else {\n if (LISTDEBUG){\n cout << \" Error \" << diff << \" not big enough to put on list.\" << endl;\n }\n }\n }\n}\n\n\nbool PrioritizedSweeping::saqPairMatch(saqPair a, saqPair b){\n if (a.a != b.a)\n return false;\n\n for (unsigned i = 0; i < a.s.size(); i++){\n if (a.s[i] != b.s[i])\n return false;\n }\n\n return true;\n}\n\n\n\nfloat PrioritizedSweeping::updateQValues(const std::vector<float> &state, int act){\n\n state_t s = canonicalize(state);\n\n // get state's info\n state_info* info = &(statedata[s]);\n\n // see if we should update mode for this state,action\n /*\n if (info->lastUpdate[act] < lastModelUpdate){\n if (LISTDEBUG) {\n cout << \"Updating this state action. Last updated at \"\n << info->lastUpdate[act]\n << \" last model update: \" << lastModelUpdate << endl;\n }\n updateStateActionFromModel(state, act);\n }\n */\n\n if (LISTDEBUG || POLICYDEBUG){\n cout << endl << \" State: id: \" << info->id << \": \" ;\n for (unsigned si = 0; si < s->size(); si++){\n cout << (*s)[si] << \",\";\n }\n }\n\n // get state action info for this action\n StateActionInfo *modelInfo = &(info->modelInfo[act]);\n\n if (LISTDEBUG || POLICYDEBUG)\n cout << \" Action: \" << act\n << \" State visits: \" << info->visits[act] << endl;\n\n // Q = R + discounted val of next state\n // this is the R part :)\n float newQ = modelInfo->reward;\n\n float probSum = modelInfo->termProb;\n\n // for all next states, add discounted value appropriately\n // loop through next state's that are in this state-actions list\n for (std::map<std::vector<float>, float>::iterator outIt\n = modelInfo->transitionProbs.begin();\n outIt != modelInfo->transitionProbs.end(); outIt++){\n\n std::vector<float> nextstate = (*outIt).first;\n\n if (POLICYDEBUG){\n cout << \" Next state was: \";\n for (unsigned oi = 0; oi < nextstate.size(); oi++){\n cout << nextstate[oi] << \",\";\n }\n cout << endl;\n }\n\n // get transition probability\n float transitionProb = (1.0-modelInfo->termProb) *\n modelInfo->transitionProbs[nextstate];\n\n probSum += transitionProb;\n\n if (POLICYDEBUG)\n cout << \" prob: \" << transitionProb << endl;\n\n if (transitionProb < 0 || transitionProb > 1.0001){\n cout << \"Error with transitionProb: \" << transitionProb << endl;\n exit(-1);\n }\n\n // if there is some probability of this transition\n if (transitionProb > 0.0){\n\n // assume maxval of qmax if we don't know the state\n float maxval = 0.0;\n\n // make sure its a real state\n bool realState = true;\n\n\n for (unsigned b = 0; b < nextstate.size(); b++){\n if (nextstate[b] < (featmin[b]-EPSILON)\n || nextstate[b] > (featmax[b]+EPSILON)){\n realState = false;\n if (POLICYDEBUG)\n cout << \" Next state is not valid (feature \"\n << b << \" out of range)\" << endl;\n break;\n }\n }\n\n\n\n // update q values for any states within MAX_STEPS of visited states\n if (realState){\n\n state_t next = canonicalize(nextstate);\n\n state_info* nextinfo = &(statedata[next]);\n //nextinfo->fresh = false;\n\n // find the max value of this next state\n std::vector<float>::iterator maxAct =\n std::max_element(nextinfo->Q.begin(),\n nextinfo->Q.end());\n maxval = *maxAct;\n\n } // within max steps\n else {\n maxval = 0.0;\n if (POLICYDEBUG){\n cout << \"This state is too far away, state: \";\n for (unsigned si = 0; si < s->size(); si++){\n cout << (*s)[si] << \",\";\n }\n cout << \" Action: \" << act << endl;\n }\n }\n\n nextstate.clear();\n\n if (POLICYDEBUG) cout << \" Max value: \" << maxval << endl;\n\n // update q value with this value\n newQ += (gamma * transitionProb * maxval);\n\n } // transition probability > 0\n\n } // outcome loop\n\n\n if (probSum < 0.9999 || probSum > 1.0001){\n cout << \"Error: transition probabilities do not add to 1: Sum: \"\n << probSum << endl;\n exit(-1);\n }\n\n\n // set q value\n float tdError = fabs(info->Q[act] - newQ);\n if (LISTDEBUG || POLICYDEBUG) cout << \" NewQ: \" << newQ\n << \" OldQ: \" << info->Q[act] << endl;\n info->Q[act] = newQ;\n\n return tdError;\n}\n\nvoid PrioritizedSweeping::addSAToList(const std::vector<float> &s, int act, float q){\n\n saqPair saq;\n saq.s = s;\n saq.a = act;\n saq.q = q;\n\n if (LISTDEBUG){\n cout << \"Added state \";\n for (unsigned k = 0; k < saq.s.size(); k++){\n cout << saq.s[k] << \", \";\n }\n cout << \" action: \" << saq.a\n << \" value: \" << saq.q << endl;\n }\n\n priorityList.push_front(saq);\n\n}\n\n\n\n/** Update a single state-action from the model */\nvoid PrioritizedSweeping::updateStateActionFromModel(const std::vector<float> &state, int a){\n\n if ((getSeconds() - planTime) > MAX_TIME)\n return;\n\n state_t s = canonicalize(state);\n\n // get state's info\n state_info* info = &(statedata[s]);\n\n int j = a;\n\n // get updated model\n model->getStateActionInfo(*s, j, &(info->modelInfo[j]));\n info->lastUpdate[j] = nactions;\n\n if (info->modelInfo[j].termProb >= 1.0)\n return;\n\n // go through next states, for each one, add self to predecessor list\n for (std::map<std::vector<float>, float>::iterator outIt\n = info->modelInfo[j].transitionProbs.begin();\n outIt != info->modelInfo[j].transitionProbs.end(); outIt++){\n\n std::vector<float> nextstate = (*outIt).first;\n state_t next = canonicalize(nextstate);\n state_info* nextinfo = &(statedata[next]);\n //float prob = (*outIt).second;\n\n if (LISTDEBUG){\n cout << \"State \";\n for (unsigned k = 0; k < nextstate.size(); k++){\n cout << nextstate[k] << \", \";\n }\n cout << \" has predecessor: \";\n for (unsigned k = 0; k < nextstate.size(); k++){\n cout << (*s)[k] << \", \";\n }\n cout << \" action: \" << j << endl;\n }\n\n saqPair saq;\n saq.s = *s;\n saq.a = j;\n saq.q = 0.0;\n\n // add to list\n // check that its not already here\n bool nothere = true;\n for (std::list<saqPair>::iterator k = nextinfo->pred.begin();\n k != nextinfo->pred.end(); k++){\n if (saqPairMatch(saq, *k)){\n nothere = false;\n break;\n }\n }\n if (nothere)\n nextinfo->pred.push_front(saq);\n\n }\n\n info->fresh = false;\n\n //if (PLANNERDEBUG || LISTDEBUG) cout << \" updateStatesFromModel i = \" << &i << \" complete\" << endl;\n\n}\n\n\n"},{"alpha_fraction":0.7811634540557861,"alphanum_fraction":0.7811634540557861,"avg_line_length":35.099998474121094,"blob_id":"87306360657ef24f3ab2f5b64ad9cbb0c2099d16","content_id":"72d1d7ea8a5e17cb7b241e9fb6e72f01b6271b8d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":361,"license_type":"no_license","max_line_length":76,"num_lines":10,"path":"/build/roundbot_control/CMakeFiles/diff_controller.dir/cmake_clean.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"file(REMOVE_RECURSE\n \"CMakeFiles/diff_controller.dir/src/DiffController.cpp.o\"\n \"/home/justin/ros_test/devel/lib/libdiff_controller.pdb\"\n \"/home/justin/ros_test/devel/lib/libdiff_controller.so\"\n)\n\n# Per-language clean rules from dependency scanning.\nforeach(lang CXX)\n include(CMakeFiles/diff_controller.dir/cmake_clean_${lang}.cmake OPTIONAL)\nendforeach()\n"},{"alpha_fraction":0.7570093274116516,"alphanum_fraction":0.7570093274116516,"avg_line_length":31.100000381469727,"blob_id":"f2ce96247d9f3ac15de13b83070a3c92ecc8aaa5","content_id":"d052ce1154538e7d3e43ba3fb5f67d584484a97f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":321,"license_type":"no_license","max_line_length":66,"num_lines":10,"path":"/build/rl-texplore-ros-pkg-master/src/rl_agent/CMakeFiles/agent.dir/cmake_clean.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"file(REMOVE_RECURSE\n \"CMakeFiles/agent.dir/src/agent.cpp.o\"\n \"/home/justin/ros_test/devel/lib/rl_agent/agent.pdb\"\n \"/home/justin/ros_test/devel/lib/rl_agent/agent\"\n)\n\n# Per-language clean rules from dependency scanning.\nforeach(lang CXX)\n include(CMakeFiles/agent.dir/cmake_clean_${lang}.cmake OPTIONAL)\nendforeach()\n"},{"alpha_fraction":0.5434399843215942,"alphanum_fraction":0.5663340091705322,"avg_line_length":21.051780700683594,"blob_id":"b7bf56aea894f634851a620ab1848b299a9c81cc","content_id":"4432f10a2f929c15b3062c31583f76e9666015a4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":6814,"license_type":"no_license","max_line_length":106,"num_lines":309,"path":"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/taxi.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file taxi.cc\n Implements the taxi domain, from:\n Dietterich, \"The MAXQ method for hierarchical reinforcement learning,\" ICML 1998.\n \\author Todd Hester\n \\author Nick Jong\n*/\n\n#include <rl_env/taxi.hh>\n\nconst Taxi::DefaultLandmarks Taxi::defaultlandmarks;\n\nTaxi::DefaultLandmarks::DefaultLandmarks() {\n push_back(value_type(4.,0.));\n push_back(value_type(0.,3.));\n push_back(value_type(4.,4.));\n push_back(value_type(0.,0.));\n}\n\nTaxi::Taxi(Random &rand, const Gridworld *gridworld, bool stochastic):\n grid(gridworld), landmarks(4), noisy(stochastic), rng(rand),\n s(4),\n ns(s[0]),\n ew(s[1]),\n pass(s[2]),\n dest(s[3])\n{\n randomize_landmarks_to_corners();\n reset();\n}\n\nTaxi::Taxi(Random &rand):\n grid(create_default_map()),\n landmarks(defaultlandmarks),\n noisy(false),\n rng(rand),\n s(4),\n ns(s[0]),\n ew(s[1]),\n pass(s[2]),\n dest(s[3])\n{\n reset();\n}\n\nTaxi::Taxi(Random &rand, bool stochastic):\n grid(create_default_map()),\n landmarks(defaultlandmarks),\n noisy(stochastic),\n rng(rand),\n s(4),\n ns(s[0]),\n ew(s[1]),\n pass(s[2]),\n dest(s[3])\n{\n reset();\n}\n\nTaxi::Taxi(Random &rand, unsigned width, unsigned height, bool stochastic):\n grid(new Gridworld(height, width, rand)),\n landmarks(4), noisy(stochastic), rng(rand),\n s(4),\n ns(s[0]),\n ew(s[1]),\n pass(s[2]),\n dest(s[3])\n{\n randomize_landmarks_to_corners();\n reset();\n}\n\nTaxi::~Taxi() { delete grid; }\n\nconst std::vector<float> &Taxi::sensation() const { return s; }\n\nfloat Taxi::apply(int action) {\n const taxi_action_t effect =\n noisy\n ? add_noise(static_cast<taxi_action_t>(action))\n : static_cast<taxi_action_t>(action);\n switch(effect) {\n case NORTH:\n if (!grid->wall(static_cast<unsigned>(ns),\n static_cast<unsigned>(ew),\n effect))\n {\n ++ns;\n apply_fickle_passenger();\n }\n return -1;\n case SOUTH:\n if (!grid->wall(static_cast<unsigned>(ns),\n static_cast<unsigned>(ew),\n effect))\n {\n --ns;\n apply_fickle_passenger();\n }\n return -1;\n case EAST:\n if (!grid->wall(static_cast<unsigned>(ns),\n static_cast<unsigned>(ew),\n effect))\n {\n ++ew;\n apply_fickle_passenger();\n }\n return -1;\n case WEST:\n if (!grid->wall(static_cast<unsigned>(ns),\n static_cast<unsigned>(ew),\n effect))\n {\n --ew;\n apply_fickle_passenger();\n }\n return -1;\n case PICKUP: {\n if (pass < landmarks.size()\n && coord_t(ns,ew) == landmarks[static_cast<unsigned>(pass)])\n {\n pass = landmarks.size();\n fickle = noisy;\n return -1;\n } else\n return -10;\n }\n case PUTDOWN:\n if (pass == landmarks.size()\n && coord_t(ns,ew) == landmarks[static_cast<unsigned>(dest)]) {\n pass = dest;\n return 20;\n } else\n return -10;\n }\n std::cerr << \"Unreachable point reached in Taxi::apply!!!\\n\";\n return 0; // unreachable, I hope\n}\n\nbool Taxi::terminal() const {\n return pass == dest;\n}\n\nvoid Taxi::reset() {\n ns = rng.uniformDiscrete(1, grid->height()) - 1;\n ew = rng.uniformDiscrete(1, grid->width()) - 1;\n pass = rng.uniformDiscrete(1, landmarks.size()) - 1;\n do dest = rng.uniformDiscrete(1, landmarks.size()) - 1;\n while (dest == pass);\n fickle = false;\n}\n\n\n\nint Taxi::getNumActions() {\n return 6;\n}\n\n\nconst Gridworld *Taxi::create_default_map() {\n std::vector<std::vector<bool> > nsv(5, std::vector<bool>(4,false));\n std::vector<std::vector<bool> > ewv(5, std::vector<bool>(4,false));\n ewv[0][0] = true;\n ewv[0][2] = true;\n ewv[1][0] = true;\n ewv[1][2] = true;\n ewv[3][1] = true;\n ewv[4][1] = true;\n return new Gridworld(5,5,nsv,ewv);\n}\n\nTaxi::taxi_action_t Taxi::add_noise(taxi_action_t action) {\n switch(action) {\n case NORTH:\n case SOUTH:\n return rng.bernoulli(0.8) ? action : (rng.bernoulli(0.5) ? EAST : WEST);\n case EAST:\n case WEST:\n return rng.bernoulli(0.8) ? action : (rng.bernoulli(0.5) ? NORTH : SOUTH);\n default:\n return action;\n }\n}\n\nvoid Taxi::apply_fickle_passenger() {\n\n if (fickle) {\n fickle = false;\n if (rng.bernoulli(0.3)) {\n dest += rng.uniformDiscrete(1, landmarks.size() - 1);\n dest = static_cast<int>(dest) % landmarks.size();\n }\n }\n\n}\n\nvoid Taxi::randomize_landmarks() {\n std::vector<unsigned> indices(landmarks.size());\n const unsigned n = grid->height() * grid->width();\n for (unsigned i = 0; i < indices.size(); ++i) {\n unsigned index;\n bool duplicate;\n do {\n index = rng.uniformDiscrete(1,n) - 1;\n duplicate = false;\n for (unsigned j = 0; j < i; ++j)\n if (index == indices[j])\n duplicate = true;\n } while (duplicate);\n indices[i] = index;\n }\n for (unsigned i = 0; i < indices.size(); ++i)\n landmarks[i] = coord_t(indices[i] / grid->width(),\n indices[i] % grid->width());\n}\n\nvoid Taxi::randomize_landmarks_to_corners() {\n for (unsigned i = 0; i < landmarks.size(); ++i) {\n int ns = rng.uniformDiscrete(0,1);\n int ew = rng.uniformDiscrete(0,1);\n if (1 == i/2)\n ns = grid->height() - ns - 1;\n if (1 == i%2)\n ew = grid->width() - ew - 1;\n landmarks[i] = coord_t(ns,ew);\n }\n}\n\n\nvoid Taxi::setSensation(std::vector<float> newS){\n if (s.size() != newS.size()){\n cerr << \"Error in sensation sizes\" << endl;\n }\n\n for (unsigned i = 0; i < newS.size(); i++){\n s[i] = (int)newS[i];\n }\n}\n\nstd::vector<experience> Taxi::getSeedings() {\n\n // return seedings\n std::vector<experience> seeds;\n\n if (true)\n return seeds;\n // REMOVE THIS TO USE SEEDINGS\n\n // single seed for each of 4 drop off and pickup cases\n for (int i = 0; i < 4; i++){\n // drop off\n seeds.push_back(getExp(landmarks[i].first, landmarks[i].second, 4, i, PUTDOWN));\n // pick up\n seeds.push_back(getExp(landmarks[i].first, landmarks[i].second, i, rng.uniformDiscrete(0,3), PICKUP));\n }\n\n reset();\n\n return seeds;\n\n}\n\nexperience Taxi::getExp(float s0, float s1, float s2, float s3, int a){\n\n experience e;\n\n e.s.resize(4, 0.0);\n e.next.resize(4, 0.0);\n\n ns = s0;\n ew = s1;\n pass = s2;\n dest = s3;\n\n e.act = a;\n e.s = sensation();\n e.reward = apply(e.act);\n\n e.terminal = terminal();\n e.next = sensation();\n\n return e;\n}\n\n\nvoid Taxi::getMinMaxFeatures(std::vector<float> *minFeat,\n std::vector<float> *maxFeat){\n\n minFeat->resize(s.size(), 0.0);\n maxFeat->resize(s.size(), 1.0);\n\n (*minFeat)[0] = 0.0;\n (*maxFeat)[0] = 4.0;\n (*minFeat)[1] = 0.0;\n (*maxFeat)[1] = 4.0;\n (*minFeat)[2] = 0.0;\n (*maxFeat)[2] = 4.0;\n (*minFeat)[3] = 0.0;\n (*maxFeat)[3] = 3.0;\n\n}\n\nvoid Taxi::getMinMaxReward(float *minR,\n float *maxR){\n\n *minR = -10.0;\n *maxR = 20.0;\n\n}\n"},{"alpha_fraction":0.7567567825317383,"alphanum_fraction":0.7567567825317383,"avg_line_length":32.29999923706055,"blob_id":"6f94022d1f9f61740962da06206a3817e3a2a8e8","content_id":"993ab6cbb787a97503513274471cc0195c54ac50","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":333,"license_type":"no_license","max_line_length":70,"num_lines":10,"path":"/build/mybot_cpp/CMakeFiles/mybot_cpp.dir/cmake_clean.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"file(REMOVE_RECURSE\n \"CMakeFiles/mybot_cpp.dir/pub.cpp.o\"\n \"/home/justin/ros_test/devel/lib/mybot_cpp/mybot_cpp.pdb\"\n \"/home/justin/ros_test/devel/lib/mybot_cpp/mybot_cpp\"\n)\n\n# Per-language clean rules from dependency scanning.\nforeach(lang CXX)\n include(CMakeFiles/mybot_cpp.dir/cmake_clean_${lang}.cmake OPTIONAL)\nendforeach()\n"},{"alpha_fraction":0.7931309938430786,"alphanum_fraction":0.8011181950569153,"avg_line_length":72.70587921142578,"blob_id":"922a455502648a0ba52cade1639c1ef3385a4a26","content_id":"6cb7209c8c928c43d04ae8a3e7f726379dffd073","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":1252,"license_type":"no_license","max_line_length":188,"num_lines":17,"path":"/src/hector_example/catkin_generated/setup_cached.sh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#!/usr/bin/env sh\n# generated from catkin/python/catkin/environment_cache.py\n\n# based on a snapshot of the environment before and after calling the setup script\n# it emulates the modifications of the setup script without recurring computations\n\n# new environment variables\n\n# modified environment variables\nexport CMAKE_PREFIX_PATH=\"/home/jman/ros/src/ugv_course/hector_example/devel:$CMAKE_PREFIX_PATH\"\nexport CPATH=\"/home/jman/ros/src/ugv_course/hector_example/devel/include:$CPATH\"\nexport LD_LIBRARY_PATH=\"/home/jman/ros/src/ugv_course/hector_example/devel/lib:/home/jman/ros/src/ugv_course/hector_example/devel/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH\"\nexport PATH=\"/home/jman/ros/src/ugv_course/hector_example/devel/bin:$PATH\"\nexport PKG_CONFIG_PATH=\"/home/jman/ros/src/ugv_course/hector_example/devel/lib/pkgconfig:/home/jman/ros/src/ugv_course/hector_example/devel/lib/x86_64-linux-gnu/pkgconfig:$PKG_CONFIG_PATH\"\nexport PYTHONPATH=\"/home/jman/ros/src/ugv_course/hector_example/devel/lib/python2.7/dist-packages:$PYTHONPATH\"\nexport ROSLISP_PACKAGE_DIRECTORIES=\"/home/jman/ros/src/ugv_course/hector_example/devel/share/common-lisp:$ROSLISP_PACKAGE_DIRECTORIES\"\nexport ROS_PACKAGE_PATH=\"/home/jman/ros/src/ugv_course/hector_example:$ROS_PACKAGE_PATH\""},{"alpha_fraction":0.5320783853530884,"alphanum_fraction":0.5386093258857727,"avg_line_length":24.513071060180664,"blob_id":"28b17c3283fb34e1704f4e1890e244e45e9c6686","content_id":"07a400ed2d86ebf50b74d52f1df9936fdaa6f027","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":7809,"license_type":"no_license","max_line_length":144,"num_lines":306,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Agent/Sarsa.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include <rl_agent/Sarsa.hh>\n#include <algorithm>\n\nSarsa::Sarsa(int numactions, float gamma,\n float initialvalue, float alpha, float ep, float lambda,\n Random rng):\n numactions(numactions), gamma(gamma),\n initialvalue(initialvalue), alpha(alpha),\n epsilon(ep), lambda(lambda),\n rng(rng)\n{\n\n currentq = NULL;\n ACTDEBUG = false; //true; //false;\n ELIGDEBUG = false;\n\n}\n\nSarsa::~Sarsa() {}\n\nint Sarsa::first_action(const std::vector<float> &s) {\n\n if (ACTDEBUG){\n cout << \"First - in state: \";\n printState(s);\n cout << endl;\n }\n\n // clear all eligibility traces\n for (std::map<state_t, std::vector<float> >::iterator i = eligibility.begin();\n i != eligibility.end(); i++){\n\n std::vector<float> & elig_s = (*i).second;\n for (int j = 0; j < numactions; j++){\n elig_s[j] = 0.0;\n }\n }\n\n // Get action values\n state_t si = canonicalize(s);\n std::vector<float> &Q_s = Q[si];\n\n // Choose an action\n const std::vector<float>::iterator a =\n rng.uniform() < epsilon\n ? Q_s.begin() + rng.uniformDiscrete(0, numactions - 1) // Choose randomly\n : random_max_element(Q_s.begin(), Q_s.end()); // Choose maximum\n\n // set eligiblity to 1\n std::vector<float> &elig_s = eligibility[si];\n elig_s[a-Q_s.begin()] = 1.0;\n\n if (ACTDEBUG){\n cout << \" act: \" << (a-Q_s.begin()) << \" val: \" << *a << endl;\n for (int iAct = 0; iAct < numactions; iAct++){\n cout << \" Action: \" << iAct \n\t << \" val: \" << Q_s[iAct] << endl;\n }\n cout << \"Took action \" << (a-Q_s.begin()) << \" from state \";\n printState(s);\n cout << endl;\n }\n\n return a - Q_s.begin();\n}\n\nint Sarsa::next_action(float r, const std::vector<float> &s) {\n\n if (ACTDEBUG){\n cout << \"Next: got reward \" << r << \" in state: \";\n printState(s);\n cout << endl;\n }\n\n // Get action values\n state_t st = canonicalize(s);\n std::vector<float> &Q_s = Q[st];\n const std::vector<float>::iterator max =\n random_max_element(Q_s.begin(), Q_s.end());\n\n // Choose an action\n const std::vector<float>::iterator a =\n rng.uniform() < epsilon\n ? Q_s.begin() + rng.uniformDiscrete(0, numactions - 1)\n : max;\n\n // Update value for all with positive eligibility\n for (std::map<state_t, std::vector<float> >::iterator i = eligibility.begin();\n i != eligibility.end(); i++){\n\n state_t si = (*i).first;\n std::vector<float> & elig_s = (*i).second;\n for (int j = 0; j < numactions; j++){\n if (elig_s[j] > 0.0){\n if (ELIGDEBUG) {\n cout << \"updating state \" << (*((*i).first))[0] << \", \" << (*((*i).first))[1] << \" act: \" << j << \" with elig: \" << elig_s[j] << endl;\n }\n // update\n Q[si][j] += alpha * elig_s[j] * (r + gamma * (*a) - Q[si][j]);\n elig_s[j] *= lambda;\n }\n }\n \n }\n\n // Set elig to 1\n eligibility[st][a-Q_s.begin()] = 1.0;\n\n if (ACTDEBUG){\n cout << \" act: \" << (a-Q_s.begin()) << \" val: \" << *a << endl;\n for (int iAct = 0; iAct < numactions; iAct++){\n cout << \" Action: \" << iAct \n\t << \" val: \" << Q_s[iAct] << endl;\n }\n cout << \"Took action \" << (a-Q_s.begin()) << \" from state \";\n printState(s);\n cout << endl;\n }\n\n return a - Q_s.begin();\n}\n\nvoid Sarsa::last_action(float r) {\n\n if (ACTDEBUG){\n cout << \"Last: got reward \" << r << endl;\n }\n\n // Update value for all with positive eligibility\n for (std::map<state_t, std::vector<float> >::iterator i = eligibility.begin();\n i != eligibility.end(); i++){\n \n state_t si = (*i).first;\n std::vector<float> & elig_s = (*i).second;\n for (int j = 0; j < numactions; j++){\n if (elig_s[j] > 0.0){\n if (ELIGDEBUG){\n cout << \"updating state \" << (*((*i).first))[0] << \", \" << (*((*i).first))[1] << \" act: \" << j << \" with elig: \" << elig_s[j] << endl;\n }\n // update\n Q[si][j] += alpha * elig_s[j] * (r - Q[si][j]);\n elig_s[j] = 0.0;\n }\n } \n }\n \n}\n\nSarsa::state_t Sarsa::canonicalize(const std::vector<float> &s) {\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\n statespace.insert(s);\n state_t retval = &*result.first; // Dereference iterator then get pointer \n if (result.second) { // s is new, so initialize Q(s,a) for all a\n std::vector<float> &Q_s = Q[retval];\n Q_s.resize(numactions,initialvalue);\n std::vector<float> &elig = eligibility[retval];\n elig.resize(numactions,0);\n }\n return retval; \n}\n\n\n\n std::vector<float>::iterator\nSarsa::random_max_element(\n\t\t\t std::vector<float>::iterator start,\n\t\t\t std::vector<float>::iterator end) {\n\n std::vector<float>::iterator max =\n std::max_element(start, end);\n int n = std::count(max, end, *max);\n if (n > 1) {\n n = rng.uniformDiscrete(1, n);\n while (n > 1) {\n max = std::find(max + 1, end, *max);\n --n;\n }\n }\n return max;\n}\n\n\n\n\nvoid Sarsa::setDebug(bool d){\n ACTDEBUG = d;\n}\n\n\nvoid Sarsa::printState(const std::vector<float> &s){\n for (unsigned j = 0; j < s.size(); j++){\n cout << s[j] << \", \";\n }\n}\n\n\n\nvoid Sarsa::seedExp(std::vector<experience> seeds){\n\n // for each seeding experience, update our model\n for (unsigned i = 0; i < seeds.size(); i++){\n experience e = seeds[i];\n \n std::vector<float> &Q_s = Q[canonicalize(e.s)];\n \n // Get q value for action taken\n const std::vector<float>::iterator a = Q_s.begin() + e.act;\n\n // Update value of action just executed\n Q_s[e.act] += alpha * (e.reward + gamma * (*a) - Q_s[e.act]);\n \n \n /*\n cout << \"Seeding with experience \" << i << endl;\n cout << \"last: \" << (e.s)[0] << \", \" << (e.s)[1] << \", \" \n\t << (e.s)[2] << endl;\n cout << \"act: \" << e.act << \" r: \" << e.reward << endl;\n cout << \"next: \" << (e.next)[0] << \", \" << (e.next)[1] << \", \" \n\t << (e.next)[2] << \", \" << e.terminal << endl;\n cout << \"Q: \" << *currentq << \" max: \" << *max << endl;\n */\n\n }\n\n\n}\n\nvoid Sarsa::logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax){\n std::vector<float> s;\n s.resize(2, 0.0);\n for (int i = xmin ; i < xmax; i++){\n for (int j = ymin; j < ymax; j++){\n s[0] = j;\n s[1] = i;\n std::vector<float> &Q_s = Q[canonicalize(s)];\n const std::vector<float>::iterator max =\n random_max_element(Q_s.begin(), Q_s.end());\n *of << (*max) << \",\";\n }\n }\n}\n\n\nfloat Sarsa::getValue(std::vector<float> state){\n\n state_t s = canonicalize(state);\n\n // Get Q values\n std::vector<float> &Q_s = Q[s];\n\n // Choose an action\n const std::vector<float>::iterator a =\n random_max_element(Q_s.begin(), Q_s.end()); // Choose maximum\n\n // Get avg value\n float valSum = 0.0;\n float cnt = 0;\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n state_t s = canonicalize(*i);\n\n // get state's info\n std::vector<float> &Q_s = Q[s];\n \n for (int j = 0; j < numactions; j++){\n valSum += Q_s[j];\n cnt++;\n }\n }\n\n cout << \"Avg Value: \" << (valSum / cnt) << endl;\n\n return *a;\n}\n\n\nvoid Sarsa::savePolicy(const char* filename){\n\n ofstream policyFile(filename, ios::out | ios::binary | ios::trunc);\n\n // first part, save the vector size\n std::set< std::vector<float> >::iterator i = statespace.begin();\n int fsize = (*i).size();\n policyFile.write((char*)&fsize, sizeof(int));\n\n // save numactions\n policyFile.write((char*)&numactions, sizeof(int));\n\n // go through all states, and save Q values\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n state_t s = canonicalize(*i);\n std::vector<float> *Q_s = &(Q[s]);\n\n // save state\n policyFile.write((char*)&((*i)[0]), sizeof(float)*fsize);\n\n // save q-values\n policyFile.write((char*)&((*Q_s)[0]), sizeof(float)*numactions);\n\n }\n\n policyFile.close();\n}\n\n\n"},{"alpha_fraction":0.7650602459907532,"alphanum_fraction":0.7710843086242676,"avg_line_length":40.5625,"blob_id":"33551b996deb4a0055d4f4f4d1bb37c33bb8da23","content_id":"ff838ccb4b73a5486cd557e81063004350d34a23","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":664,"license_type":"no_license","max_line_length":49,"num_lines":16,"path":"/build/ugv_course_gazebo/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"ugv_course_gazebo\")\nset(ugv_course_gazebo_VERSION \"0.0.0\")\nset(ugv_course_gazebo_MAINTAINER \"abc <abc@todo.todo>\")\nset(ugv_course_gazebo_PACKAGE_FORMAT \"1\")\nset(ugv_course_gazebo_BUILD_DEPENDS )\nset(ugv_course_gazebo_BUILD_EXPORT_DEPENDS )\nset(ugv_course_gazebo_BUILDTOOL_DEPENDS \"catkin\")\nset(ugv_course_gazebo_BUILDTOOL_EXPORT_DEPENDS )\nset(ugv_course_gazebo_EXEC_DEPENDS )\nset(ugv_course_gazebo_RUN_DEPENDS )\nset(ugv_course_gazebo_TEST_DEPENDS )\nset(ugv_course_gazebo_DOC_DEPENDS )\nset(ugv_course_gazebo_URL_WEBSITE \"\")\nset(ugv_course_gazebo_URL_BUGTRACKER \"\")\nset(ugv_course_gazebo_URL_REPOSITORY \"\")\nset(ugv_course_gazebo_DEPRECATED \"\")"},{"alpha_fraction":0.7676348686218262,"alphanum_fraction":0.7676348686218262,"avg_line_length":31.066667556762695,"blob_id":"e7708c1545a29cb65ed5528b1b09049f2715b18a","content_id":"ccdc9331e4d934900885a391b12188b1e12be3ad","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":482,"license_type":"no_license","max_line_length":62,"num_lines":15,"path":"/devel/share/gennodejs/ros/rl_msgs/msg/_index.js","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"\n\"use strict\";\n\nlet RLStateReward = require('./RLStateReward.js');\nlet RLAction = require('./RLAction.js');\nlet RLEnvDescription = require('./RLEnvDescription.js');\nlet RLExperimentInfo = require('./RLExperimentInfo.js');\nlet RLEnvSeedExperience = require('./RLEnvSeedExperience.js');\n\nmodule.exports = {\n RLStateReward: RLStateReward,\n RLAction: RLAction,\n RLEnvDescription: RLEnvDescription,\n RLExperimentInfo: RLExperimentInfo,\n RLEnvSeedExperience: RLEnvSeedExperience,\n};\n"},{"alpha_fraction":0.7051349878311157,"alphanum_fraction":0.7051349878311157,"avg_line_length":27.621212005615234,"blob_id":"66eac39e7f2043d27c101c9a846d8fb6e3280955","content_id":"299a671d821f68cf22409c0d891128a517a4fcf1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":1889,"license_type":"no_license","max_line_length":70,"num_lines":66,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/include/rl_agent/SavedPolicy.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file\n Interface for an implementation of the saved policy\n algorithm. */\n\n#ifndef _SAVEDPOLICY_HH_\n#define _SAVEDPOLICY_HH_\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n\n#include <map>\n#include <set>\n#include <vector>\n\n/** Agent that uses a saved policy from a file. */\nclass SavedPolicy: public Agent {\npublic:\n /** Standard constructor\n \\param numactions The number of possible actions\n */\n SavedPolicy(int numactions, const char* filename);\n\n virtual ~SavedPolicy();\n\n virtual int first_action(const std::vector<float> &s);\n virtual int next_action(float r, const std::vector<float> &s);\n virtual void last_action(float r);\n virtual void setDebug(bool d) {};\n virtual void seedExp(std::vector<experience>);\n\n void loadPolicy(const char* filename);\n\nprotected:\n /** The implementation maps all sensations to a set of canonical\n pointers, which serve as the internal representation of\n environment state. */\n typedef const std::vector<float> *state_t;\n\n /** Produces a canonical representation of the given sensation.\n \\param s The current sensation from the environment.\n \\return A pointer to an equivalent state in statespace. */\n state_t canonicalize(const std::vector<float> &s);\n void printState(const std::vector<float> &s);\n\n\nprivate:\n /** Set of all distinct sensations seen. Pointers to elements of\n this set serve as the internal representation of the environment\n state. */\n std::set<std::vector<float> > statespace;\n\n /** The primary data structure of the learning algorithm, the value\n function Q. For state_t s and int a, Q[s][a] gives the\n learned maximum expected future discounted reward conditional on\n executing action a in state s. */\n std::map<state_t, std::vector<float> > Q;\n\n const int numactions;\n\n bool ACTDEBUG;\n bool LOADDEBUG;\n bool loaded;\n\n};\n\n#endif\n"},{"alpha_fraction":0.6567164063453674,"alphanum_fraction":0.6641790866851807,"avg_line_length":15.75,"blob_id":"c77a61c084810b6bf527c08a803d2a3904d65f3d","content_id":"e8ecaf547f34d5f8538b16c639340ec8f0832f97","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":134,"license_type":"no_license","max_line_length":30,"num_lines":8,"path":"/src/turtlebot3_ddpg/nodes/test.py","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"import rospy\n \nrospy.init_node('test')\nrate = rospy.Rate(1)\n \nwhile not rospy.is_shutdown():\n print \"Hello there\"\n rate.sleep()\n"},{"alpha_fraction":0.6922787427902222,"alphanum_fraction":0.6952918767929077,"avg_line_length":22.469026565551758,"blob_id":"44d481bd7625faf70828cee5213d2cc701a90370","content_id":"915294e5436094fd7bfae97c82bbdc19d3259520","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":2655,"license_type":"no_license","max_line_length":89,"num_lines":113,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/Stump.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#ifndef _STUMP_HH_\n#define _STUMP_HH_\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n#include <vector>\n#include <set>\n#include <map>\n\n#define N_STUMP_EXP 250000\n\n/** C4.5 decision stump class */\nclass Stump: public Classifier {\n\npublic:\n\n // mode - re-build stump every step? \n // re-build only on misclassifications? or rebuild every 'trainFreq' steps\n Stump(int id, int trainMode, int trainFreq, int m, float featPct, Random rng);\n\n Stump(const Stump&);\n virtual Stump* getCopy();\n\n ~Stump();\n\n // structs to be defined\n struct stump_experience;\n \n struct stump_experience {\n std::vector<float> input;\n float output;\n int id;\n };\n \n enum splitTypes{\n ONLY,\n CUT\n };\n\n bool trainInstances(std::vector<classPair> &instances);\n bool trainInstance(classPair &instance);\n void testInstance(const std::vector<float> &input, std::map<float, float>* retval);\n float getConf(const std::vector<float> &input);\n\n void buildStump();\n\n // helper functions\n void initStump();\n bool passTest(int dim, float val, int type, const std::vector<float> &input);\n float calcGainRatio(int dim, float val, int type,float I);\n float* sortOnDim(int dim);\n float calcIofP(float* P, int size);\n float calcIforSet(const std::vector<stump_experience*> &instances);\n void printStump();\n void testPossibleSplits(float *bestGainRatio, int *bestDim, \n\t\t\t float *bestVal, int *bestType);\n void implementSplit(float bestGainRatio, int bestDim,\n\t\t float bestVal, int bestType);\n void compareSplits(float gainRatio, int dim, float val, int type, \n\t\t int *nties, float *bestGainRatio, int *bestDim, \n\t\t float *bestVal, int *bestType);\n //std::vector<float> calcChiSquare(std::vector<stump_experience*> instances);\n void outputProbabilities(std::multiset<float> outputs, std::map<float, float>* retval);\n int findMatching(const std::vector<stump_experience*> &instances, int dim, \n\t\t int val, int minConf);\n\n void setParams(float margin, float forestPct, float minRatio);\n\n bool ALLOW_ONLY_SPLITS;\n\n bool STDEBUG;\n bool SPLITDEBUG;\n int nExperiences;\n\n float SPLIT_MARGIN;\n float MIN_GAIN_RATIO; \n float REBUILD_RATIO;\n float LOSS_MARGIN;\n\nprivate:\n\n const int id;\n \n const int mode;\n const int freq;\n const int M;\n float featPct;\n\n Random rng;\n\n int nOutput;\n int nnodes;\n\n // INSTANCES\n std::vector<stump_experience*> experiences;\n stump_experience allExp[N_STUMP_EXP];\n\n // split criterion\n int dim;\n float val;\n int type;\n float gainRatio;\n \n // set of all outputs seen at this leaf/node\n std::multiset<float> leftOutputs;\n std::multiset<float> rightOutputs;\n\n\n\n};\n\n\n#endif\n \n"},{"alpha_fraction":0.760869562625885,"alphanum_fraction":0.782608687877655,"avg_line_length":16.125,"blob_id":"71f830936db45e55473cfca0e70d21c8e4b9d999","content_id":"8ff6323440fff85b6a3c953b185aea2bd479981a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":138,"license_type":"no_license","max_line_length":39,"num_lines":8,"path":"/src/roundbot_gazebo/CMakeLists.txt","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"cmake_minimum_required(VERSION 2.8.3)\nproject(roundbot_gazebo)\n\nfind_package(catkin REQUIRED COMPONENTS\n gazebo_ros\n)\n\ncatkin_package()\n\n"},{"alpha_fraction":0.7785016298294067,"alphanum_fraction":0.7882736325263977,"avg_line_length":33.11111068725586,"blob_id":"b77d892c7ef8a2d120f9f09c6c268ffb03583462","content_id":"b3a02c245a926e74a02b9ada3e2e8aef9d658382","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":307,"license_type":"no_license","max_line_length":92,"num_lines":9,"path":"/build/homework2/CMakeFiles/homework2_generate_messages_cpp.dir/cmake_clean.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"file(REMOVE_RECURSE\n \"CMakeFiles/homework2_generate_messages_cpp\"\n \"/home/justin/ros_test/devel/include/homework2/string_cat.h\"\n)\n\n# Per-language clean rules from dependency scanning.\nforeach(lang )\n include(CMakeFiles/homework2_generate_messages_cpp.dir/cmake_clean_${lang}.cmake OPTIONAL)\nendforeach()\n"},{"alpha_fraction":0.5395956039428711,"alphanum_fraction":0.5796124935150146,"avg_line_length":18.068273544311523,"blob_id":"35b163dec61079aab406487cb3fb69c51534dd31","content_id":"38e65a56e2635a82c54acbaa1382368801b19e5a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":4748,"license_type":"no_license","max_line_length":124,"num_lines":249,"path":"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/CartPole.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file CartPole.cc\n Implements the Cart-Pole balancing domain, with possible noise.\n \\author Todd Hester\n*/\n\n#include <rl_env/CartPole.hh>\n\n \nCartPole::CartPole(Random &rand):\n noisy(false),\n rng(rand),\n s(4),\n cartPos(s[0]),\n cartVel(s[1]),\n poleAngle(s[2]),\n poleVel(s[3])\n{\n reset();\n //cout << *this << endl;\n}\n \n\nCartPole::CartPole(Random &rand, bool stochastic):\n noisy(stochastic),\n rng(rand),\n s(4),\n cartPos(s[0]),\n cartVel(s[1]),\n poleAngle(s[2]),\n poleVel(s[3])\n{\n reset();\n}\n\n\nCartPole::~CartPole() { }\n\nconst std::vector<float> &CartPole::sensation() const { \n //cout << \"At state \" << s[0] << \", \" << s[1] << endl;\n\n return s; \n}\n\n\nfloat CartPole::transition(float force){\n\n // transition\n\n float xacc;\n float thetaacc;\n float costheta;\n float sintheta;\n float temp;\n \n //Noise of 1.0 means possibly halfway to opposite action\n if (noisy){\n float thisNoise=1.0*FORCE_MAG*(rng.uniform(-0.5, 0.5)); \n force+=thisNoise;\n }\n\n costheta = cos(poleAngle);\n sintheta = sin(poleAngle);\n\n temp = (force + POLEMASS_LENGTH * poleVel * poleVel * sintheta) / TOTAL_MASS;\n\n thetaacc = (GRAVITY * sintheta - costheta * temp) / (LENGTH * (FOURTHIRDS - MASSPOLE * costheta * costheta / TOTAL_MASS));\n\n xacc = temp - POLEMASS_LENGTH * thetaacc * costheta / TOTAL_MASS;\n\n // Update the four state variables, using Euler's method. \n cartPos += TAU * cartVel;\n cartVel += TAU * xacc;\n poleAngle += TAU * poleVel;\n poleVel += TAU * thetaacc;\n \n // These probably never happen because the pole would crash \n while (poleAngle >= M_PI) {\n poleAngle -= 2.0 * M_PI;\n }\n while (poleAngle < -M_PI) {\n poleAngle += 2.0 * M_PI;\n }\n\n // dont velocities go past ranges\n if (fabs(cartVel) > 3){\n // cout << \"cart velocity out of range: \" << cartVel << endl;\n if (cartVel > 0)\n cartVel = 3;\n else\n cartVel = -3;\n }\n if (fabs(poleVel) > M_PI){\n // cout << \"pole velocity out of range: \" << poleVel << endl;\n if (poleVel > 0)\n poleVel = M_PI;\n else\n poleVel = -M_PI;\n }\n\n return reward();\n\n}\n\n\nfloat CartPole::apply(int action) {\n\n float force = 0;\n if (action == 1) {\n force = FORCE_MAG;\n } else {\n force = -FORCE_MAG;\n }\n\n return transition(force);\n}\n\n \n\nfloat CartPole::reward() {\n\n // normally +1 and 0 on goal\n if (terminal())\n return 0.0;\n else\n return 1.0;\n}\n\n\n\nbool CartPole::terminal() const {\n // current position past termination conditions (off track, pole angle)\n return (fabs(poleAngle) > (DEG_T_RAD*12.0) || fabs(cartPos) > 2.4);\n}\n\n\n\nvoid CartPole::reset() {\n\n GRAVITY = 9.8;\n MASSCART = 1.0;\n MASSPOLE = 0.1;\n TOTAL_MASS = (MASSPOLE + MASSCART);\n LENGTH = 0.5;\t // actually half the pole's length \n \n POLEMASS_LENGTH = (MASSPOLE * LENGTH);\n FORCE_MAG = 10.0;\n TAU = 0.02;\t // seconds between state updates \n \n FOURTHIRDS = 4.0 / 3.0;\n DEG_T_RAD = 0.01745329;\n RAD_T_DEG = 1.0/DEG_T_RAD;\n\n if (noisy){\n cartPos = rng.uniform(-0.5, 0.5);\n cartVel = rng.uniform(-0.5, 0.5);\n poleAngle = rng.uniform(-0.0625, 0.0625);\n poleVel = rng.uniform(-0.0625, 0.0625);\n } else {\n cartPos = 0.0;\n cartVel = 0.0;\n poleAngle = 0.0;\n poleVel = 0.0;\n }\n\n}\n\n\n\nint CartPole::getNumActions(){\n return 2;\n}\n\n\nvoid CartPole::setSensation(std::vector<float> newS){\n if (s.size() != newS.size()){\n cerr << \"Error in sensation sizes\" << endl;\n }\n\n for (unsigned i = 0; i < 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Hester\n*/\n\n#include \"ExplorationModel.hh\"\n\n\n\n\nExplorationModel::ExplorationModel(MDPModel* innermodel, int modelType, int exploreType,\n int predType, int nModels,\n float m, int numactions,\n float rmax, float qmax, float rrange,\n int nfactors, float v, float n,\n const std::vector<float> &fmax,\n const std::vector<float> &fmin, Random rng):\n modelType(modelType), exploreType(exploreType), predType(predType),\n nModels(nModels),\n M(m), numactions(numactions), rmax(rmax), qmax(qmax), rrange(rrange),\n nfactors(nfactors), v(v), n(n), rng(rng)\n{\n\n model = innermodel;\n\n MODEL_DEBUG = false; //true;\n\n cout << \"Exploration Model \" << exploreType << \", v: \" << v << \", n: \" << n << endl;\n\n featmax = fmax;\n featmin = fmin;\n\n}\n\nExplorationModel::ExplorationModel(const ExplorationModel &em):\nmodelType(em.modelType), exploreType(em.exploreType), predType(em.predType),\n nModels(em.nModels),\n M(em.M), numactions(em.numactions), rmax(em.rmax), qmax(em.qmax), rrange(em.rrange),\nnfactors(em.nfactors), v(em.v), n(em.n), rng(em.rng)\n{\n model = em.model->getCopy();\n MODEL_DEBUG = em.MODEL_DEBUG;\n featmax = em.featmax;\n featmin = em.featmin;\n statespace = em.statespace;\n}\n\nExplorationModel* ExplorationModel::getCopy(){\n ExplorationModel* copy = new ExplorationModel(*this);\n return copy;\n}\n\n\nExplorationModel::~ExplorationModel() {\n delete model;\n}\n\n\n\nbool ExplorationModel::updateWithExperiences(std::vector<experience> &instances){\n bool changed = model->updateWithExperiences(instances);\n bool visitChange = false;\n\n // keep track of which states we've been to for this mode\n for (unsigned i = 0; i < instances.size(); i++){\n if (exploreType == UNVISITED_BONUS){\n bool retval = addStateToSet(instances[i].s);\n visitChange = visitChange || retval;\n }\n\n if (exploreType == UNVISITED_ACT_BONUS || exploreType == DIFF_AND_VISIT_BONUS || exploreType == NOVEL_STATE_BONUS || exploreType == DIFF_AND_NOVEL_BONUS){\n std::vector<float> last2 = instances[i].s;\n last2.push_back(instances[i].act);\n bool retval = addStateToSet(last2);\n visitChange = visitChange || retval;\n }\n }\n\n return (changed || visitChange);\n}\n\n\n// update all the counts, check if model has changed\n// stop counting at M\nbool ExplorationModel::updateWithExperience(experience &e){\n //if (MODEL_DEBUG) cout << \"updateWithExperience \" << &last << \", \" << act\n // << \", \" << &curr << \", \" << reward << endl;\n\n bool changed = model->updateWithExperience(e);\n bool visitChange = false;\n\n // keep track of which states we've been to for this mode\n if (exploreType == UNVISITED_BONUS){\n bool retval = addStateToSet(e.s);\n visitChange = visitChange || retval;\n }\n\n if (exploreType == UNVISITED_ACT_BONUS || exploreType == DIFF_AND_VISIT_BONUS || exploreType == NOVEL_STATE_BONUS || exploreType == DIFF_AND_NOVEL_BONUS){\n std::vector<float> last2 = e.s;\n last2.push_back(e.act);\n bool retval = addStateToSet(last2);\n visitChange = visitChange || retval;\n }\n\n return (changed || visitChange);\n}\n\n\n// calculate state info such as transition probs, known/unknown, reward prediction\nfloat ExplorationModel::getStateActionInfo(const std::vector<float> &state, int act, StateActionInfo* retval){\n //if (MODEL_DEBUG) cout << \"getStateActionInfo, \" << &state << \", \" << act << endl;\n\n retval->transitionProbs.clear();\n\n float conf = model->getStateActionInfo(state, act, retval);\n\n\n //cout << \"state: \" << state[0] << \" act: \" << act;\n\n if (MODEL_DEBUG)// || (conf > 0.0 && conf < 1.0))\n cout << \"reward: \" << retval->reward << \" conf: \" << conf << endl;\n\n // check exploration bonuses\n\n // use qmax if state is unknown\n if (exploreType == EXPLORE_UNKNOWN){\n if (!retval->known){\n if (MODEL_DEBUG){\n cout << \"State-Action Unknown in model: conf: \" << conf << \" \";\n for (unsigned si = 0; si < state.size(); si++){\n cout << (state)[si] << \",\";\n }\n cout << \" Action: \" << act << endl;\n }\n retval->reward = qmax;\n retval->termProb = 1.0;\n if (MODEL_DEBUG || MODEL_DEBUG)\n cout << \" State-Action Unknown in model, using qmax \"\n << qmax << endl;\n }\n }\n\n // small bonus for unvisited states\n if (exploreType == UNVISITED_BONUS){\n if (!checkForState(state)){\n // modify reward with a bonus of n\n float newQ =retval->reward + n;\n if (MODEL_DEBUG){\n cout << \" State unvisited bonus, orig R: \"\n << retval->reward\n << \" adding n: \" << n\n << \" new value : \" << newQ\n << endl;\n }\n retval->reward = newQ;\n }\n }\n\n // small bonus for unvisited state-actions\n if (exploreType == UNVISITED_ACT_BONUS || exploreType == DIFF_AND_VISIT_BONUS){\n std::vector<float> state2 = state;\n state2.push_back(act);\n if (!checkForState(state2)){\n // modify reward with a bonus of n\n float newQ =retval->reward + n;\n if (MODEL_DEBUG){\n cout << \" State-Action unvisited bonus, orig R: \"\n << retval->reward\n << \" adding n: \" << n\n << \" new value : \" << newQ\n << endl;\n }\n retval->reward = newQ;\n }\n }\n\n // small bonus for states far from visited states with same action\n if (exploreType == NOVEL_STATE_BONUS || exploreType == DIFF_AND_NOVEL_BONUS){\n std::vector<float> state2 = state;\n state2.push_back(act);\n float featDist = getFeatDistToVisitedSA(state2);\n if (featDist > 0){\n // modify reward with proportional bonus of n\n float bonus = featDist * n;\n if (MODEL_DEBUG){\n cout << \" State-Action novel state bonus, dist: \" << featDist\n << \" n: \" << n << \", bonus, \" << bonus << endl;\n }\n retval->reward += bonus;\n }\n }\n\n // use some % of v if we're doing continuous terminal bonus\n if (exploreType == CONTINUOUS_BONUS){\n if (conf < 1.0){\n // percent of conf\n float bonus = (1.0-conf)*v;\n if (MODEL_DEBUG){\n cout << \" State-Action continuous bonus conf: \"\n << conf\n << \", using v*(1-conf): \"\n << bonus << endl;\n }\n retval->reward = bonus;\n retval->termProb = 1.0;\n }\n }\n\n // use some % of v if we're doing continuous bonus\n if (exploreType == CONTINUOUS_BONUS_R || exploreType == DIFF_AND_VISIT_BONUS || exploreType == DIFF_AND_NOVEL_BONUS){\n if (conf < 1.0){\n // percent of conf\n float bonus = (1.0-conf)*v;\n retval->reward += bonus;\n if (MODEL_DEBUG){\n cout << \" State-Action continuous bonus conf: \"\n << conf\n << \", using v*(1-conf): \"\n << bonus << endl;\n }\n }\n }\n\n // use qmax if we're doing threshold terminal bonus and conf under threshold\n if (exploreType == THRESHOLD_BONUS){\n if (conf < 0.5){\n float bonus = v;\n if (MODEL_DEBUG){\n cout << \" State-Action conf< thresh: \"\n << conf\n << \" M: \" << M\n << \", using v \"\n << v << endl;\n }\n retval->reward = bonus;\n retval->termProb = 1.0;\n }\n }\n\n // use rmax for additional thresh bonus and conf under thresh\n if (exploreType == THRESHOLD_BONUS_R){\n if (conf < 0.9){\n float bonus = v;\n retval->reward += bonus;\n if (MODEL_DEBUG){\n cout << \" State-Action conf< thresh: \"\n << conf\n << \" M: \" << M\n << \", using v \"\n << v << endl;\n }\n }\n }\n\n // visits conf\n if (exploreType == VISITS_CONF){\n if (conf < 0.5){\n float bonus = qmax;\n retval->reward += bonus;\n if (MODEL_DEBUG){\n cout << \" State-Action conf< thresh or 0 visits: \"\n << conf\n << \" M: \" << M\n << \", using qmax \"\n << qmax << endl;\n }\n retval->reward = bonus;\n retval->termProb = 1.0;\n }\n }\n\n\n if (MODEL_DEBUG)\n cout << \" Conf: \" << conf << \" Avg reward: \" << retval->reward << endl;\n if (isnan(retval->reward))\n cout << \"ERROR: Model returned reward of NaN\" << endl;\n\n return true;\n\n}\n\n// add state to set (if its not already in it)\nbool ExplorationModel::addStateToSet(const std::vector<float> &s){\n std::pair<std::set<std::vector<float> >::iterator, bool> retval;\n retval = statespace.insert(s);\n return retval.second;\n}\n\n\n// check if state is in set (so we know if we've visited it)\nbool ExplorationModel::checkForState(const std::vector<float> &s){\n return (statespace.count(s) == 1);\n}\n\n// get distance in feature space from this state to one we've visited\nfloat ExplorationModel::getFeatDistToVisitedSA(const std::vector<float> &s){\n\n // if we've visited this exact s,a then dist is 0\n if (checkForState(s)){\n return 0;\n }\n\n // otherwise go through all states and find minimum distance\n float maxDist = 0;\n unsigned nfeats = s.size()-1;\n std::vector<float> featRange(nfeats, 0);\n for (unsigned i = 0; i < nfeats; i++){\n featRange[i] = featmax[i] - featmin[i];\n maxDist += 1.0;//featmax[i] - featmin[i];\n\n //cout << \"feat \" << i << \" diff: \" << (featmax[i] - featmin[i]) << \" max: \" << maxDist << endl;\n }\n\n float minDist = maxDist;//nfeats;\n unsigned actionIndex = nfeats;\n\n for (std::set<std::vector<float> >::iterator i = statespace.begin(); i != statespace.end(); i++){\n // ignore if not the same action\n if (s[actionIndex] != (*i)[actionIndex]) continue;\n\n // otherwise, sum all features that are different\n float count = 0;\n for (unsigned j = 0; j < nfeats; j++){\n // distance based on magnitude of feature difference\n // normalize by feature range\n count += fabs(s[j] - (*i)[j]) / featRange[j];\n }\n if (count < minDist) minDist = count;\n\n }\n\n return (float)minDist/(float)nfeats;\n\n}\n\n\n"},{"alpha_fraction":0.7662650346755981,"alphanum_fraction":0.7939758896827698,"avg_line_length":50.9375,"blob_id":"67c6afa2982132708d8115dc24f100c85e7dc04a","content_id":"8d1df2765f6af397c976f371b1f494ba65bbc8fa","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":830,"license_type":"no_license","max_line_length":103,"num_lines":16,"path":"/build/turtlebot3_ddpg/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"turtlebot3_ddpg\")\nset(turtlebot3_ddpg_VERSION \"1.0.0\")\nset(turtlebot3_ddpg_MAINTAINER \"Pyo <pyo@robotis.com>\")\nset(turtlebot3_ddpg_PACKAGE_FORMAT \"2\")\nset(turtlebot3_ddpg_BUILD_DEPENDS \"rospy\")\nset(turtlebot3_ddpg_BUILD_EXPORT_DEPENDS \"rospy\")\nset(turtlebot3_ddpg_BUILDTOOL_DEPENDS \"catkin\")\nset(turtlebot3_ddpg_BUILDTOOL_EXPORT_DEPENDS )\nset(turtlebot3_ddpg_EXEC_DEPENDS \"rospy\")\nset(turtlebot3_ddpg_RUN_DEPENDS \"rospy\")\nset(turtlebot3_ddpg_TEST_DEPENDS )\nset(turtlebot3_ddpg_DOC_DEPENDS )\nset(turtlebot3_ddpg_URL_WEBSITE \"http://wiki.ros.org/turtlebot3_machine_learning\")\nset(turtlebot3_ddpg_URL_BUGTRACKER \"https://github.com/ROBOTIS-GIT/turtlebot3_machine_learning/issues\")\nset(turtlebot3_ddpg_URL_REPOSITORY \"https://github.com/ROBOTIS-GIT/turtlebot3_machine_learning\")\nset(turtlebot3_ddpg_DEPRECATED \"\")"},{"alpha_fraction":0.5780776739120483,"alphanum_fraction":0.5962544679641724,"avg_line_length":32.724456787109375,"blob_id":"68e7ec697b6895e8d2f2297c43308ca779fc7465","content_id":"1501fe8090f97fb4f45f2a3ad5568927a52b6c39","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":10893,"license_type":"no_license","max_line_length":110,"num_lines":323,"path":"/build/turtlebot3_ddpg/catkin_generated/installspace/turtlebot3_ddpg_stage_1","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#!/usr/bin/env python2\n#################################################################################\n# Copyright 2018 ROBOTIS CO., LTD.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#################################################################################\n\n# Authors: Gilbert #\n\nimport rospy\nimport os\nimport json\nimport numpy as np\nimport random\nimport time\nimport sys\nsys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))\nfrom collections import deque\nfrom std_msgs.msg import Float32MultiArray\nfrom src.turtlebot3_ddpg.environment_stage_1 import Env\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential, load_model\nfrom tensorflow.keras.optimizers import RMSprop\nfrom tensorflow.keras.layers import Dense, Dropout, Activation, Input\n\nfrom tensorflow import keras\n\nfrom tensorflow.keras.models import Model\n\n\nEPISODES = 3000\nGAMMA = 0.99\nTAU = 0.001\n\nclass ReinforceAgent():\n def __init__(self, state_size, action_size):\n self.pub_result = rospy.Publisher('result', Float32MultiArray, queue_size=5)\n self.dirPath = os.path.dirname(os.path.realpath(__file__))\n self.dirPath = self.dirPath.replace('turtlebot3_ddpg/nodes', 'turtlebot3_ddpg/save_model/stage_1_')\n self.result = Float32MultiArray()\n self.load_model = False\n self.load_episode = 0\n self.state_size = state_size\n self.action_size = action_size\n self.episode_step = 6000\n self.target_update = 2000\n self.learning_rate = 0.00025\n self.epsilon = 1.0\n self.epsilon_decay = 0.99\n self.epsilon_min = 0.05\n self.batch_size = 32\n self.train_start = 32\n self.memory = deque(maxlen=1000000)\n\n\n\tself.TAU = 0.001 #Target Network HyperParameters\n \tself.LRA = 0.0001 #Learning rate for Actor\n\tself.LRC = 0.001 #Lerning rate for Critic\n\n self.model, self.weights, self.state2 = self.buildActor()\n self.target_model, self.target_weights, self.target_state = self.buildActor()\n\n self.critic, self.state, self.action = self.buildCritic()\n self.target_critic, _, _ = self.buildCritic()\n\n self.updateTargetModel()\n\n#added\n\n self.sess = tf.Session()\n \n self.action_gradient = tf.placeholder(tf.float32,[None, 2])\n self.params_grad = tf.gradients(self.model.output, self.model.weights, -self.action_gradient)\n grads = zip(self.params_grad, self.model.weights)\n self.optimize = tf.train.AdamOptimizer(self.learning_rate).apply_gradients(grads)\n self.sess.run(tf.global_variables_initializer())\n\n self.action_grads = tf.gradients(self.critic.output, self.action) #GRADIENTS for policy update\n#end add\n\n\n\n def buildCritic(self):\n s = keras.layers.Input(shape=(14,), name='state')\n\n d1 = Dense(512, activation='relu')(s)\n\n\n a = keras.layers.Input(shape=(2,), name='action')\n x = keras.layers.concatenate([d1, a])\n\n x = Dense(512, activation='relu')(x)\n x = Dense(512, activation='relu')(x)\n\n\n\n q_val= Dense(1, activation='sigmoid', name='main_output')(x)\n\n critic = Model(inputs=[s, a], outputs=[q_val])\n #critic.summary()\n\n critic.compile(optimizer=tf.train.AdamOptimizer(), \n loss='mse',\n metrics=['accuracy'])\n return critic, s, a\n\n def buildActor(self):\n state = keras.layers.Input(shape=(14,), name='main_input')\n\n\n x = Dense(512, activation='relu')(state)\n x = Dense(512, activation='relu')(x)\n x = Dense(512, activation='relu')(x)\n\n\n lin_vel = Dense(1, activation='sigmoid', name='lin_vel')(x)\n ang_vel = Dense(1, activation='tanh', name='ang_vel')(x)\n output = keras.layers.concatenate([lin_vel, ang_vel])\n\n actor = Model(inputs=[state], outputs=[output])\n\t#actor.summary()\n \n actor.compile(optimizer=tf.train.AdamOptimizer(), \n loss='mse',\n metrics=['accuracy'])\n\n return actor, actor.trainable_weights, state\n\n\n#added\n def train(self, states, action_grads):\n self.sess.run(self.optimize, feed_dict={\n self.state2: states,\n self.action_gradient: action_grads\n })\n\n def target_train(self):\n actor_weights = self.model.get_weights()\n actor_target_weights = self.target_model.get_weights()\n for i in xrange(len(actor_weights)):\n actor_target_weights[i] = self.TAU * actor_weights[i] + (1 - self.TAU)* actor_target_weights[i]\n self.target_model.set_weights(actor_target_weights)\n\n\n def target_train_c(self):\n critic_weights = self.target_critic.get_weights()\n critic_target_weights = self.target_critic.get_weights()\n for i in xrange(len(critic_weights)):\n critic_target_weights[i] = self.TAU * critic_weights[i] + (1 - self.TAU)* critic_target_weights[i]\n\tself.target_critic.set_weights(critic_target_weights)\n\n def gradients(self, states, actions):\n grad = self.sess.run(self.action_grads, feed_dict={\n self.state: states,\n self.action: actions\n\t})[0]\n\treturn grad\n\n#end add\n\n#Need to update\n\n#should be OK\n def updateTargetModel(self):\n #self.target_model.set_weights(self.model.get_weights())\n #self.target_critic.set_weights(self.critic.get_weights())\n\tself.target_train()\n\tself.target_train_c()\n\n\n#should be OK\n def getAction(self, state):\n if np.random.rand() <= self.epsilon:\n self.q_value = [random.uniform(0, 1.0), random.uniform(-1.0, 1.0)]\n return self.q_value\n else:\n q_value = self.target_model.predict(state.reshape(1, len(state)))\n\t q_value= q_value.flatten()\n self.q_value = q_value\n return q_value\n\n\n#should be OK\n def appendMemory(self, state, action, reward, next_state, done):\n self.memory.append((state, action, reward, next_state, done))\n\n\n#need to analyze\n####################### Put the new train and stuff in here\n###################### also make sure you actually understand what you are doing...\n\n def trainModel(self, target=False):\n env.pause_proxy()\n mini_batch = random.sample(self.memory, self.batch_size)\n X_batch = np.empty(([0,14]), dtype=np.float64)\n X_batch2 = np.empty(([0,2]), dtype=np.float64)\n Y_batch = np.empty((0), dtype=np.float64)\n\n for i in range(self.batch_size):\n states = mini_batch[i][0]\n actions = mini_batch[i][1]\n rewards = mini_batch[i][2]\n next_states = mini_batch[i][3]\n dones = mini_batch[i][4]\n\t #print len(states), \"\\n\"\n q_value = self.model.predict(states.reshape(1, len(states)))\n\n\n next_target = self.target_model.predict(next_states.reshape(1, len(next_states)))\n\n\t next_target = next_target[0][:]\n next_target = next_target.reshape(1, len())\n\n \n\t next_q = self.target_critic.predict([next_states.reshape(1, len(next_states)), next_target ])\n\n Y_sample = rewards+GAMMA*next_q\n\n\t test = states.reshape(1, len(next_states))\n X_batch = np.append(X_batch, test, axis = 0)\n X_batch2 = np.append(X_batch2, np.asarray(actions).reshape(1,2), axis = 0) \n\n\n Y_batch = np.append(Y_batch, Y_sample)\n\n\n\n self.critic.fit([X_batch, X_batch2], Y_batch, batch_size=self.batch_size, epochs=1, verbose=0)\n\twith tf.Session() as sess:\n\t #grads=self.action_grads, feed_dict={\n \t#self.state: states,\n \t#self.action: actions}\t\n\t\tgrads =self.gradients(X_batch, X_batch2)\n\t #print X_batch.shape\n\t #print grads.shape\n\t\tself.train(X_batch, grads)\n\n env.unpause_proxy()\n\n\n#need to analyze\nif __name__ == '__main__':\n rospy.init_node('turtlebot3_dqn_stage_1')\n pub_result = rospy.Publisher('result', Float32MultiArray, queue_size=5)\n pub_get_action = rospy.Publisher('get_action', Float32MultiArray, queue_size=5)\n result = Float32MultiArray()\n get_action = Float32MultiArray()\n past_action = [0.0,0.0]\n state_size = 12\n action_size = 5\n\n env = Env(action_size)\n\n agent = ReinforceAgent(state_size, action_size)\n scores, episodes = [], []\n global_step = 0\n start_time = time.time()\n\n for e in range(agent.load_episode + 1, EPISODES):\n done = False\n state = env.reset()\n score = 0\n\t\n for t in range(agent.episode_step):\n action = agent.getAction(state)\n\t #print action\n\t #print \"ACTION IS PRINTED\"\n next_state, reward, done = env.step(action, past_action)\n\t #print len(state), \"\\n\"\n agent.appendMemory(state, action, reward, next_state, done)\n\n if len(agent.memory) >= agent.train_start:\n if global_step <= agent.target_update:\n agent.trainModel()\n else:\n agent.trainModel(True)\n\n score += reward\n state = next_state\n get_action.data = [action, score, reward]\n pub_get_action.publish(get_action)\n\t past_action = action\n #if e % 10 == 0:#\n # agent.model.save(agent.dirPath + str(e) + '.h5')#\n # with open(agent.dirPath + str(e) + '.json', 'w') as outfile:\n # json.dump(param_dictionary, outfile)\n\n if t >= 400:\n rospy.loginfo(\"Time out!!\")\n done = True\n\n if done:\n result.data = [score, np.max(agent.q_value)]\n pub_result.publish(result)\n agent.updateTargetModel()\n scores.append(score)\n episodes.append(e)\n m, s = divmod(int(time.time() - start_time), 60)\n h, m = divmod(m, 60)\n\n rospy.loginfo('Ep: %d score: %.2f memory: %d epsilon: %.2f time: %d:%02d:%02d',\n e, score, len(agent.memory), agent.epsilon, h, m, s)\n param_keys = ['epsilon']\n param_values = [agent.epsilon]\n param_dictionary = dict(zip(param_keys, param_values))\n break\n\n global_step += 1\n # if global_step % agent.target_update == 0:\n # rospy.loginfo(\"UPDATE TARGET NETWORK\")\n\n if agent.epsilon > agent.epsilon_min:\n agent.epsilon *= agent.epsilon_decay\n"},{"alpha_fraction":0.7103658318519592,"alphanum_fraction":0.7198932766914368,"avg_line_length":29.160919189453125,"blob_id":"13fa647f1b807f2edb5580f61e8dec29ff5c0cbd","content_id":"9a1f83f36d4a8b7ffc43f4e597924f425008c2b4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":2624,"license_type":"no_license","max_line_length":135,"num_lines":87,"path":"/src/rl-texplore-ros-pkg-master/src/rl_env/include/rl_env/RobotCarVel.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file RobotCarVel.hh\n This domain is a simulation of velocity control for the Austin Robot \n Technology autonomous vehicle. \n This vehicle is described in:\n Beeson et al, \"Multiagent Interactions in Urban Driving,\" Journal of Physical Agents, March 2008.\n The velocity control task is described in:\n Hester, Quinlan, and Stone, \"A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control\", arXiv 1105.1749, 2011.\n \\author Todd Hester\n*/\n\n#ifndef _ROBOTCAR_H_\n#define _ROBOTCAR_H_\n\n#include <set>\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n\n/** This class defines a domain that is a simulation of velocity control for the Austin Robot \n Technology autonomous vehicle. */\nclass RobotCarVel: public Environment {\npublic:\n\n /** Creates a RobotCarVel domain.\n \\param rand Random number generator \n \\param randomVel Use random starting and target velocities\n \\param upVel For specific velocity pair (not random), do target > starting vel\n \\param tenToSix Use 10 and 6 m/s for specific velocity pair, rather than 7 and 2\n \\param lag Implements lag on the brake actuator to fully model the real vehicle.\n */\n RobotCarVel(Random &rand, bool randomVel, bool upVel, bool tenToSix, bool lag);\n\n virtual ~RobotCarVel();\n\n virtual const std::vector<float> &sensation() const;\n virtual float apply(int action);\n\n virtual bool terminal() const;\n virtual void reset();\n\n virtual int getNumActions();\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\n virtual void getMinMaxReward(float* minR, float* maxR);\n\n /** Set the state vector for debug purposes. */\n void setSensation(std::vector<float> newS);\n\n virtual std::vector<experience> getSeedings();\n\n /** Get an experience seed at a random velocity for the given target velocity. */\n experience getRandomVelSeed(float target);\n\n /** Get an example experience for the given state-action. */\n experience getExp(float s0, float s1, float s2, float s3, int a);\n\n //virtual bool isEpisodic() { return false; };\n\n /** Bound a value between the given min and max. */\n float bound(float val, float min, float max);\n\nprotected:\n enum car_action_t {NOTHING, THROTTLE_UP, THROTTLE_DOWN, BRAKE_UP, BRAKE_DOWN};\n\nprivate:\n\n Random &rng;\n\n std::vector<float> s;\n std::vector<float> hidden;\n \n float &targetVel;\n float &currVel;\n float &trueThrottle;\n float &trueBrake;\n float &throttleTarget;\n float &brakeTarget;\n\n const bool randomVel;\n const bool upVel;\n const bool tenToSix;\n const bool lag;\n\n float brakePosVel;\n int actNum;\n\n};\n\n#endif\n"},{"alpha_fraction":0.6919922828674316,"alphanum_fraction":0.694298505783081,"avg_line_length":32.35470199584961,"blob_id":"3a364e4d421c6bb303de9a185230e9f0e7f4acb1","content_id":"b2e4caa1d6820ada4c58cbc238408e22dbd194f1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":7806,"license_type":"no_license","max_line_length":322,"num_lines":234,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/PO_ETUCT.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file PO_ETUCT.hh\n Defines UCT with eligiblity traces, and plans over states augmented with k-action histories.\n A modified version of UCT \n as presented in:\n L. Kocsis and C. Szepesv´ari, \"Bandit based monte-carlo planning,\" in\n ECML-06. Number 4212 in LNCS. Springer, 2006, pp. 282-293.\n \\author Todd Hester\n*/\n\n#ifndef _PO_ETUCT_HH_\n#define _PO_ETUCT_HH_\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n\n#include \"../Models/FactoredModel.hh\"\n\n#include <set>\n#include <vector>\n#include <map>\n#include <deque>\n\n/** This class defines a modified version of UCT, which plans on a model using Monte Carlo rollouts. Unlike the original UCT, it does not separate values by tree depth, and it incorporates eligibility traces. This version plans over states augmented with k-action histories, for delayed or partially observable domains. */\nclass PO_ETUCT: public Planner {\npublic:\n\n /** Standard constructor\n \\param numactions, numactions in the domain\n \\param gamma discount factor\n \\param rrange range of one-step rewards in the domain\n \\param lambda for use with eligibility traces\n \\param MAX_ITER maximum number of MC rollouts to perform\n \\param MAX_TIME maximum amount of time to run Monte Carlo rollouts\n \\param MAX_DEPTH maximum depth to perform rollout to\n \\param modelType specifies model type\n \\param featmax maximum value of each feature\n \\param featmin minimum value of each feature\n \\param statesPerDim # of values to discretize each feature into\n \\param trackActual track actual real-valued states (or just discrete states)\n \\param history # of previous actions to use for delayed domains\n \\param rng random number generator\n */\n PO_ETUCT(int numactions, float gamma, float rrange, float lambda,\n int MAX_ITER, float MAX_TIME, int MAX_DEPTH, int modelType,\n const std::vector<float> &featmax, const std::vector<float> &featmin,\n const std::vector<int> &statesPerDim, bool trackActual, \n int history, Random rng = Random());\n \n /** Unimplemented copy constructor: internal state cannot be simply\n copied. */\n PO_ETUCT(const PO_ETUCT &);\n\n virtual ~PO_ETUCT();\n\n virtual void setModel(MDPModel* model);\n virtual bool updateModelWithExperience(const std::vector<float> &last, \n int act, \n const std::vector<float> &curr, \n float reward, bool term);\n virtual void planOnNewModel();\n virtual int getBestAction(const std::vector<float> &s);\n\n virtual void setSeeding(bool seed);\n virtual void setFirst();\n\n /** Output value function to a file */\n void logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax);\n \n /** Return a discretized version of the input state. */\n std::vector<float> discretizeState(const std::vector<float> &s);\n\n bool PLANNERDEBUG;\n bool MODELDEBUG;\n bool ACTDEBUG;\n bool UCTDEBUG;\n bool REALSTATEDEBUG;\n bool HISTORYDEBUG;\n\n /** MDPModel that we're using with planning */\n MDPModel* model;\n\n /** The implementation maps all sensations to a set of canonical\n pointers, which serve as the internal representation of\n environment state. */\n typedef const std::vector<float> *state_t;\n\n\nprotected:\n\n\n struct state_info;\n struct model_info;\n\n /** A struct that contains a vector of possible next state samples, weighted by their probabilities. */\n struct state_samples {\n std::vector<state_t> samples;\n };\n\n /** State info struct. Maintains visit counts, models, and q-values for state-actions. */\n struct state_info {\n\n // data filled in from models\n StateActionInfo* model;\n\n // q values from policy creation\n std::vector<float> Q;\n\n // uct experience data\n int uctVisits;\n std::vector<int> uctActions;\n short unsigned int visited;\n short unsigned int id;\n\n // needs update\n bool needsUpdate;\n\n };\n\n\n /** Initialize state info struct */\n void initStateInfo(state_t s, state_info* info);\n \n /** Produces a canonical representation of the given sensation.\n \\param s The current sensation from the environment.\n \\return A pointer to an equivalent state in statespace. */\n state_t canonicalize(const std::vector<float> &s);\n\n /** Delete a state_info struct */\n void deleteInfo(state_info* info);\n \n /** Initialize a new state */\n void initNewState(state_t s);\n \n /** Compuate a policy from a model */\n void createPolicy();\n \n /** Print information for each state. */\n void printStates();\n \n /** Calculate which states are reachable from states the agent has actually visited. */\n void calculateReachableStates();\n \n /** Remove states from set that were deemed unreachable. */\n void removeUnreachableStates();\n\n /** Update the state_info copy of the model for the given state-action from the MDPModel */\n void updateStateActionFromModel(state_t s, int a, state_info* info);\n\n /** Update the state_info copy of the model for the given state-action and k-action history from the MDPModel. */\n void updateStateActionHistoryFromModel(const std::vector<float> modState, int a, StateActionInfo *newModel);\n\n /** Get the current time in seconds */\n double getSeconds();\n\n /** Reset UCT visit counts to some baseline level (to decrease our confidence in q-values because model has changed. */\n void resetUCTCounts();\n \n /** Perform UCT/Monte Carlo rollout from the given state.\n If terminal or at depth, return some reward.\n Otherwise, select an action based on UCB.\n Simulate action to get reward and next state.\n Call search on next state at depth+1 to get reward return from there on.\n Update q value towards new value: reward + gamma * searchReturn\n Update visit counts for confidence bounds\n Return q\n \n From \"Bandit Based Monte Carlo Planning\" by Kocsis and Szepesv´ari.\n */\n float uctSearch(const std::vector<float> &actualS, state_t state, int depth);\n \n /** Return a sampled state from the next state distribution of the model. \n Simulate the next state from the given state, action, and possibly history of past actions. */\n std::vector<float> simulateNextState(const std::vector<float> &actualState, state_t discState, state_info* info, int action, float* reward, bool* term);\n \n /** Select UCT action based on UCB1 algorithm. */\n int selectUCTAction(state_info* info);\n \n /** Canonicalize all the next states predicted by this model. */\n void canonNextStates(StateActionInfo* modelInfo);\n \n virtual void savePolicy(const char* filename);\n \n /** Add two vectors together. */\n std::vector<float> addVec(const std::vector<float> &a, const std::vector<float> &b);\n \n /** Subtract two vectors. */\n std::vector<float> subVec(const std::vector<float> &a, const std::vector<float> &b);\n\nprivate:\n\n /** Set of all distinct sensations seen. Pointers to elements of\n this set serve as the internal representation of the environment\n state. */\n std::set<std::vector<float> > statespace;\n\n /** Hashmap mapping state vectors to their state_info structs. */\n std::map<state_t, state_info> statedata;\n\n /** Current history of previous actions. */\n std::deque<float> saHistory;\n\n std::vector<float> featmax;\n std::vector<float> featmin;\n \n state_t prevstate;\n int prevact;\n state_info* previnfo;\n\n double planTime;\n\n bool seedMode;\n\n int nstates;\n int nactions; \n int lastUpdate;\n bool timingType;\n\n const int numactions;\n const float gamma;\n const float rrange;\n const float lambda;\n\n const int MAX_ITER;\n const float MAX_TIME;\n const int MAX_DEPTH;\n const int modelType;\n const std::vector<int> &statesPerDim;\n const bool trackActual;\n const int HISTORY_SIZE;\n const int HISTORY_FL_SIZE;\n\n};\n\n#endif\n"},{"alpha_fraction":0.7769516706466675,"alphanum_fraction":0.7918215394020081,"avg_line_length":35.681819915771484,"blob_id":"326f9a3c1b3c6e789c178af895c869ec61d98c3a","content_id":"b01f7b0298b9ca590cac4c79a58bbac46e590be3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":1614,"license_type":"no_license","max_line_length":71,"num_lines":44,"path":"/build/CTestTestfile.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"# CMake generated Testfile for \n# Source directory: /home/justin/ros_test/src\n# Build directory: /home/justin/ros_test/build\n# \n# This file includes the relevant testing commands required for \n# testing this directory and lists subdirectories to be tested as well.\nsubdirs(\"gtest\")\nsubdirs(\"boost-python-catkin-example-master\")\nsubdirs(\"gmapping_example\")\nsubdirs(\"hector_example\")\nsubdirs(\"mybot_control\")\nsubdirs(\"mybot_description\")\nsubdirs(\"mybot_gazebo\")\nsubdirs(\"mybot_navigation\")\nsubdirs(\"robot_sensors\")\nsubdirs(\"rl-texplore-ros-pkg-master/src/rl_msgs\")\nsubdirs(\"turtlebot3/turtlebot3\")\nsubdirs(\"turtlebot3_machine_learning\")\nsubdirs(\"turtlebot3_msgs\")\nsubdirs(\"turtlebot3/turtlebot3_navigation\")\nsubdirs(\"turtlebot3_simulations/turtlebot3_simulations\")\nsubdirs(\"ugv_course_gazebo\")\nsubdirs(\"ugv_course_launch\")\nsubdirs(\"mybot_cpp\")\nsubdirs(\"rl-texplore-ros-pkg-master/src/rl_common\")\nsubdirs(\"roundbot_control\")\nsubdirs(\"simple_navigation_goals\")\nsubdirs(\"mantis_model\")\nsubdirs(\"rl-texplore-ros-pkg-master/src/rl_agent\")\nsubdirs(\"rl-texplore-ros-pkg-master/src/rl_env\")\nsubdirs(\"rl-texplore-ros-pkg-master/src/rl_experiment\")\nsubdirs(\"roundbot_gazebo\")\nsubdirs(\"state_space_example\")\nsubdirs(\"turtlebot3/turtlebot3_bringup\")\nsubdirs(\"turtlebot3_ddpg\")\nsubdirs(\"turtlebot3_dqn\")\nsubdirs(\"turtlebot3/turtlebot3_example\")\nsubdirs(\"turtlebot3_simulations/turtlebot3_fake\")\nsubdirs(\"turtlebot3_simulations/turtlebot3_gazebo\")\nsubdirs(\"turtlebot3/turtlebot3_slam\")\nsubdirs(\"turtlebot3/turtlebot3_teleop\")\nsubdirs(\"ugv_course_libs\")\nsubdirs(\"roundbot_description\")\nsubdirs(\"turtlebot3/turtlebot3_description\")\n"},{"alpha_fraction":0.7142857313156128,"alphanum_fraction":0.7142857313156128,"avg_line_length":13,"blob_id":"fd5c121a3e6fdfbbae17966692f9c9a921b7580a","content_id":"e878f4337a055f06effaaa30c97642c5a628c5cc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":28,"license_type":"no_license","max_line_length":16,"num_lines":2,"path":"/README.md","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"# ros-test\nthis is ros test\n"},{"alpha_fraction":0.7425464987754822,"alphanum_fraction":0.7579454779624939,"avg_line_length":37.71428680419922,"blob_id":"ca16078cf570611015dcc8d48da30f78b6c07ae0","content_id":"f817eba68d04b4a42d4bba26fdb3292725dd7aff","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Makefile","length_bytes":20326,"license_type":"no_license","max_line_length":214,"num_lines":525,"path":"/build/homework2/Makefile","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"# CMAKE generated file: DO NOT EDIT!\n# Generated by \"Unix Makefiles\" Generator, CMake Version 3.10\n\n# Default target executed when no arguments are given to make.\ndefault_target: all\n\n.PHONY : default_target\n\n# Allow only one \"make -f Makefile2\" at a time, but pass parallelism.\n.NOTPARALLEL:\n\n\n#=============================================================================\n# Special targets provided by cmake.\n\n# Disable implicit rules so canonical targets will work.\n.SUFFIXES:\n\n\n# Remove some rules from gmake that .SUFFIXES does not remove.\nSUFFIXES =\n\n.SUFFIXES: .hpux_make_needs_suffix_list\n\n\n# Suppress display of executed commands.\n$(VERBOSE).SILENT:\n\n\n# A target that is always out of date.\ncmake_force:\n\n.PHONY : cmake_force\n\n#=============================================================================\n# Set environment variables for the build.\n\n# The shell in which to execute make rules.\nSHELL = /bin/sh\n\n# The CMake executable.\nCMAKE_COMMAND = /usr/bin/cmake\n\n# The command to remove a file.\nRM = /usr/bin/cmake -E remove -f\n\n# Escaping for special characters.\nEQUALS = =\n\n# The top-level source directory on which CMake was run.\nCMAKE_SOURCE_DIR = /home/justin/ros_test/src\n\n# The top-level build directory on which CMake was run.\nCMAKE_BINARY_DIR = /home/justin/ros_test/build\n\n#=============================================================================\n# Targets provided globally by CMake.\n\n# Special rule for the target install/strip\ninstall/strip: preinstall\n\t@$(CMAKE_COMMAND) -E cmake_echo_color --switch=$(COLOR) --cyan \"Installing the project stripped...\"\n\t/usr/bin/cmake -DCMAKE_INSTALL_DO_STRIP=1 -P cmake_install.cmake\n.PHONY : install/strip\n\n# Special rule for the target install/strip\ninstall/strip/fast: preinstall/fast\n\t@$(CMAKE_COMMAND) -E cmake_echo_color --switch=$(COLOR) --cyan \"Installing the project stripped...\"\n\t/usr/bin/cmake -DCMAKE_INSTALL_DO_STRIP=1 -P cmake_install.cmake\n.PHONY : install/strip/fast\n\n# Special rule for the target install\ninstall: preinstall\n\t@$(CMAKE_COMMAND) -E cmake_echo_color --switch=$(COLOR) --cyan \"Install the project...\"\n\t/usr/bin/cmake -P cmake_install.cmake\n.PHONY : install\n\n# Special rule for the target install\ninstall/fast: preinstall/fast\n\t@$(CMAKE_COMMAND) -E cmake_echo_color --switch=$(COLOR) --cyan \"Install the project...\"\n\t/usr/bin/cmake -P cmake_install.cmake\n.PHONY : install/fast\n\n# 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event.current_real;\n transform.setOrigin(tf::Vector3(current_estimate.position.x, current_estimate.position.y, 0));\n transform.setRotation(tf::Quaternion(0, 0, current_estimate.orientation.z, current_estimate.orientation.w));\n broadcaster.sendTransform(transform);\n\n // Populate and publish odometry\n nav_msgs::Odometry odom_msg;\n odom_msg.header.stamp = event.current_real;\n odom_msg.header.frame_id = parent_frame;\n odom_msg.child_frame_id = child_frame;\n odom_msg.pose.pose = current_estimate;\n odom_msg.twist.twist = current_twist;\n pub_odom.publish(odom_msg);\n}\n\nint main(int argc, char** argv)\n{\n ros::init(argc, argv, \"state_space_example\");\n ros::NodeHandle node;\n ros::NodeHandle private_node(\"~\");\n\n ros::Subscriber sub_twist = node.subscribe(\"twist\", 1, recvTwist);\n pub_odom = node.advertise<nav_msgs::Odometry>(\"odom\", 1);\n\n private_node.param(\"sample_time\", sample_time, 0.02);\n private_node.param(\"parent_frame\", parent_frame, 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<sys/time.h>\n\n\nValueIteration::ValueIteration(int numactions, float gamma,\n int MAX_LOOPS, float MAX_TIME, int modelType,\n const std::vector<float> &fmax, \n const std::vector<float> &fmin, \n const std::vector<int> &n, Random newRng):\n numactions(numactions), gamma(gamma),\n MAX_LOOPS(MAX_LOOPS), MAX_TIME(MAX_TIME), modelType(modelType),\n statesPerDim(n)\n{\n rng = newRng;\n\n nstates = 0;\n nactions = 0;\n\n timingType = false; //true;\n\n model = NULL;\n planTime = getSeconds();\n\n // algorithm options\n MAX_STEPS = 100; //50; //60; //80; //0; //5; //10;\n\n PLANNERDEBUG = false;\n POLICYDEBUG = false;\n ACTDEBUG = false;\n MODELDEBUG = false;\n\n featmax = fmax;\n featmin = fmin;\n\n if (statesPerDim[0] > 0){\n cout << \"Planner VI using discretization of \" << statesPerDim[0] << endl;\n }\n\n\n}\n\nValueIteration::~ValueIteration() {\n for (std::map<state_t, state_info>::iterator i = statedata.begin();\n i != statedata.end(); i++){\n \n // get state's info\n //cout << \" planner got info\" << endl;\n state_info* info = &((*i).second);\n\n deleteInfo(info);\n }\n\n statedata.clear();\n \n}\n\nvoid ValueIteration::setModel(MDPModel* m){\n\n model = m;\n\n // initStates();\n\n}\n\n\n// canonicalize all the states so we already have them in our statespace\nvoid ValueIteration::initStates(){\n cout << \"init states\" << endl;\n std::vector<float> s(featmin.size());\n\n fillInState(s,0);\n cout << \"init states complete\" << endl;\n}\n\nvoid ValueIteration::fillInState(std::vector<float>s, int depth){\n\n // if depth == size, canonicalize and return\n if (depth == (int)featmin.size()){\n canonicalize(s);\n return;\n }\n\n // go through all features at depth\n for (float i = featmin[depth]; i < featmax[depth]+1; i++){\n s[depth] = i;\n fillInState(s, depth+1);\n }\n}\n\n\n/////////////////////////////\n// Functional functions :) //\n/////////////////////////////\n\n\nvoid ValueIteration::initNewState(state_t s){\n if (PLANNERDEBUG) cout << \"initNewState(s = \" << s\n << \") size = \" << s->size() << endl;\n\n if (MODELDEBUG) cout << \"New State: \" << endl;\n\n // create state info and add to hash map\n state_info* info = &(statedata[s]);\n initStateInfo(info);\n\n // init these from model\n for (int i = 0; i < numactions; i++){\n model->getStateActionInfo(*s, i, &(info->modelInfo[i]));\n }\n\n\n if (PLANNERDEBUG) cout << \"done with initNewState()\" << endl;\n\n}\n\nbool ValueIteration::updateModelWithExperience(const std::vector<float> &laststate,\n int lastact,\n const std::vector<float> &currstate,\n float reward, bool term){\n if (PLANNERDEBUG) cout << \"updateModelWithExperience(last = \" << &laststate\n << \", curr = \" << &currstate\n << \", lastact = \" << lastact\n << \", r = \" << reward\n << \")\" << endl;\n\n if (!timingType)\n planTime = getSeconds();\n\n // canonicalize these things\n state_t last = canonicalize(laststate);\n state_t curr = canonicalize(currstate);\n\n prevstate = laststate;\n prevact = lastact;\n\n // if not transition to terminal\n if (curr == NULL)\n return false;\n\n // get state info\n state_info* info = &(statedata[last]);\n\n // update the state visit count\n info->visits[lastact]++;\n\n // init model?\n if (model == NULL){\n cout << \"ERROR IN MODEL OR MODEL SIZE\" << endl;\n exit(-1);\n }\n\n experience e;\n e.s = laststate;\n e.next = currstate;\n e.act = lastact;\n e.reward = reward;\n e.terminal = term;\n bool modelChanged = model->updateWithExperience(e);\n\n if (PLANNERDEBUG) cout << \"VI Added exp: \" << modelChanged << endl;\n if (timingType)\n planTime = getSeconds();\n\n return modelChanged;\n\n}\n\n\nvoid ValueIteration::updateStateActionFromModel(const std::vector<float> &state, int a){\n if (PLANNERDEBUG) cout << \"updateStateActionFromModel()\" << endl;\n\n state_t s = canonicalize(state);\n\n // get state's info\n state_info* info = &(statedata[s]);\n\n // update state info\n // get state action info for each action\n model->getStateActionInfo(state, a, &(info->modelInfo[a]));\n\n info->fresh = false;\n\n}\n\n\nvoid ValueIteration::updateStatesFromModel(){\n if (PLANNERDEBUG) cout << \"updateStatesFromModel()\" << endl;\n\n // for each state\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n if (PLANNERDEBUG){\n cout << \"updateStatesFromModel i = \" << &(*i) << endl;\n cout << \"State is \";\n for (unsigned j = 0; j < (*i).size(); j++){\n cout << (*i)[j] << \", \";\n }\n cout << endl;\n }\n\n state_t s = canonicalize(*i);\n\n // get state's info\n state_info* info = &(statedata[s]);\n\n // update state info\n // get state action info for each action\n for (int j = 0; j < numactions; j++){\n model->getStateActionInfo(*s, j, &(info->modelInfo[j]));\n }\n\n //s2.clear();\n\n info->fresh = false;\n\n if (PLANNERDEBUG) cout << \"updateStatesFromModel i = \" << &i << \" complete\" << endl;\n\n }\n\n if (PLANNERDEBUG) cout << \"updateStatesFromModel \" << \" totally complete\" << endl;\n\n}\n\n\nint ValueIteration::getBestAction(const std::vector<float> &state){\n if (PLANNERDEBUG) cout << \"getBestAction(s = \" << &state\n << \")\" << endl;\n\n state_t s = canonicalize(state);\n\n // get state info\n state_info* info = &(statedata[s]);\n\n // Get Q values\n std::vector<float> &Q = info->Q;\n\n // Choose an action\n const std::vector<float>::iterator a =\n random_max_element(Q.begin(), Q.end()); // Choose maximum\n\n int act = a - Q.begin();\n float val = *a;\n if (ACTDEBUG){\n cout << endl << \"chooseAction State \" << (*s)[0] << \",\" << (*s)[1]\n << \" act: \" << act << \" val: \" << val << endl;\n for (int iAct = 0; iAct < numactions; iAct++){\n cout << \" Action: \" << iAct\n << \" val: \" << Q[iAct]\n << \" visits: \" << info->visits[iAct]\n << \" modelsAgree: \" << info->modelInfo[iAct].known << endl;\n }\n }\n\n nactions++;\n\n // return index of action\n return act;\n}\n\n\n\n\n// use VI to compute new policy using model\nvoid ValueIteration::createPolicy(){\n if (PLANNERDEBUG || POLICYDEBUG) cout << endl << \"createPolicy()\" << endl;\n\n float maxError = 5000;\n int nloops = 0;\n\n calculateReachableStates();\n\n float MIN_ERROR = 0.0001;\n //float initTime = getSeconds();\n //cout << \"max time \" << MAX_TIME << \" max loops: \" << MAX_LOOPS << endl;\n int statesUpdated = 0;\n\n // until convergence (always at least MIN_LOOPS)\n while (maxError > MIN_ERROR){ // && nloops < MAX_LOOPS){\n\n //if ((getSeconds() - initTime) > MAX_TIME)\n // break;\n\n // if ((getSeconds() - planTime) > MAX_TIME)\n //break;\n\n if (POLICYDEBUG)\n cout << \"max error: \" << maxError << \" nloops: \" << nloops\n << endl;\n\n maxError = 0;\n nloops++;\n\n // for all states\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n // if ((getSeconds() - planTime) > MAX_TIME)\n //break;\n\n statesUpdated++;\n state_t s = canonicalize(*i);\n\n // get state's info\n state_info* info = &(statedata[s]);\n\n if (POLICYDEBUG){\n cout << endl << \" State: id: \" << info->id << \": \" ;\n for (unsigned si = 0; si < s->size(); si++){\n cout << (*s)[si] << \",\";\n }\n cout << \" Steps: \" << info->stepsAway << endl;\n\n }\n\n // skip states we can't reach\n if (info->stepsAway > 99999){\n if (POLICYDEBUG)\n cout << \"State not reachable, ignoring.\" << endl;\n continue;\n }\n\n // for each action\n for (int act = 0; act < numactions; act++){\n\n\n\n // get state action info for this action\n StateActionInfo *modelInfo = &(info->modelInfo[act]);\n\n if (POLICYDEBUG)\n cout << \" Action: \" << act\n << \" State visits: \" << info->visits[act]\n << \" reward: \" << modelInfo->reward \n\t << \" term: \" << modelInfo->termProb << endl;\n\n // Q = R + discounted val of next state\n // this is the R part :)\n float newQ = modelInfo->reward;\n\n float probSum = modelInfo->termProb;\n\n // for all next states, add discounted value appropriately\n // loop through next state's that are in this state-actions list\n for (std::map<std::vector<float>, float>::iterator outIt\n = modelInfo->transitionProbs.begin();\n outIt != modelInfo->transitionProbs.end(); outIt++){\n\n std::vector<float> nextstate = (*outIt).first;\n\n if (POLICYDEBUG){\n cout << \" Next state was: \";\n for (unsigned oi = 0; oi < nextstate.size(); oi++){\n cout << nextstate[oi] << \",\";\n }\n cout << endl;\n }\n\n // get transition probability\n float transitionProb = (1.0-modelInfo->termProb) *\n modelInfo->transitionProbs[nextstate];\n\n probSum += transitionProb;\n\n if (POLICYDEBUG)\n cout << \" prob: \" << transitionProb << endl;\n\n if (transitionProb < 0 || transitionProb > 1.0001){\n cout << \"Error with transitionProb: \" << transitionProb << endl;\n exit(-1);\n }\n\n // if there is some probability of this transition\n if (transitionProb > 0.0){\n\n float maxval = 0.0;\n\n // make sure its a real state\n bool realState = true;\n\n for (unsigned b = 0; b < nextstate.size(); b++){\n if (nextstate[b] < (featmin[b]-EPSILON)\n || nextstate[b] > (featmax[b]+EPSILON)){\n realState = false;\n if (POLICYDEBUG)\n cout << \" Next state is not valid (feature \"\n << b << \" out of range)\" << endl;\n break;\n }\n }\n\n state_t next;\n \n // update q values for any states within MAX_STEPS of visited states\n if (info->stepsAway >= MAX_STEPS || !realState){\n next = s;\n } else {\n next = canonicalize(nextstate);\n }\n \n state_info* nextinfo = &(statedata[next]);\n //nextinfo->fresh = false;\n \n int newSteps = info->stepsAway + 1;\n if (newSteps < nextinfo->stepsAway){\n if (POLICYDEBUG) {\n cout << \" Setting state to \"\n << newSteps << \" steps away.\" << endl;\n }\n nextinfo->stepsAway = newSteps;\n }\n \n // find the max value of this next state\n std::vector<float>::iterator maxAct =\n std::max_element(nextinfo->Q.begin(),\n nextinfo->Q.end());\n maxval = *maxAct;\n \n nextstate.clear();\n \n if (POLICYDEBUG) cout << \" Max value: \" << maxval << endl;\n\n // update q value with this value\n newQ += (gamma * transitionProb * maxval);\n\n } // transition probability > 0\n\n } // outcome loop\n\n\n if (probSum < 0.9999 || probSum > 1.0001){\n cout << \"Error: transition probabilities do not add to 1: Sum: \"\n << probSum << endl;\n exit(-1);\n }\n\n\n // set q value\n float tdError = fabs(info->Q[act] - newQ);\n if (POLICYDEBUG) cout << \" NewQ: \" << newQ\n << \" OldQ: \" << info->Q[act] << endl;\n info->Q[act] = newQ;\n\n // check max error\n if (tdError > maxError)\n maxError = tdError;\n\n if (POLICYDEBUG)\n cout << \" TD error: \" << tdError\n << \" Max error: \" << maxError << endl;\n\n } // action loop\n\n } // state loop\n\n } // while not converged loop\n\n if (false || nloops >= MAX_LOOPS){\n cout << nactions << \" Policy creation ended with maxError: \" << maxError\n << \" nloops: \" << nloops << \" time: \" << (getSeconds()-planTime)\n << \" states: \" << statesUpdated\n << endl;\n }\n\n // remove unreachable states\n removeUnreachableStates();\n\n if (POLICYDEBUG) cout << nactions\n << \" policy creation complete: maxError: \"\n << maxError << \" nloops: \" << nloops\n << endl;\n\n\n}\n\n\nvoid ValueIteration::planOnNewModel(){\n\n // update model info\n // can just update one for tabular model\n if (modelType == RMAX){\n updateStateActionFromModel(prevstate, prevact);\n }\n else {\n updateStatesFromModel();\n }\n\n // run value iteration\n createPolicy();\n\n}\n\n\n////////////////////////////\n// Helper Functions //\n////////////////////////////\n\nValueIteration::state_t ValueIteration::canonicalize(const std::vector<float> &s) {\n if (PLANNERDEBUG) cout << \"canonicalize(s = \" << s[0] << \", \"\n << s[1] << \")\" << endl;\n\n std::vector<float> s2;\n if (statesPerDim[0] > 0){\n s2 = discretizeState(s);\n } else {\n s2 = s;\n }\n\n if (PLANNERDEBUG) cout << \"discretized(\" << s2[0] << \", \" << s2[1] << \")\" << endl;\n\n // get state_t for pointer if its in statespace\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\n statespace.insert(s2);\n state_t retval = &*result.first; // Dereference iterator then get pointer\n\n if (PLANNERDEBUG) cout << \" returns \" << retval\n << \" New: \" << result.second << endl;\n\n // if not, init this new state\n if (result.second) { // s is new, so initialize Q(s,a) for all a\n initNewState(retval);\n if (PLANNERDEBUG) cout << \" New state initialized\" << endl;\n }\n\n return retval;\n}\n\n\n\n// init state info\nvoid ValueIteration::initStateInfo(state_info* info){\n if (PLANNERDEBUG) cout << \"initStateInfo()\";\n\n info->id = nstates++;\n if (PLANNERDEBUG) cout << \" id = \" << info->id << endl;\n\n info->fresh = true;\n info->stepsAway = 100000;\n\n // model data (transition, reward, known)\n info->modelInfo = new StateActionInfo[numactions];\n\n // model q values, visit counts\n info->visits.resize(numactions, 0);\n info->Q.resize(numactions, 0);\n\n for (int i = 0; i < numactions; i++){\n info->Q[i] = rng.uniform(0,0.01);\n }\n\n if (PLANNERDEBUG) cout << \"done with initStateInfo()\" << endl;\n\n}\n\n\nvoid ValueIteration::printStates(){\n\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n state_t s = canonicalize(*i);\n\n state_info* info = &(statedata[s]);\n\n cout << \"State \" << info->id << \": \";\n for (unsigned j = 0; j < s->size(); j++){\n cout << (*s)[j] << \", \";\n }\n cout << endl;\n\n for (int act = 0; act < numactions; act++){\n cout << \" visits[\" << act << \"] = \" << info->visits[act]\n << \" Q: \" << info->Q[act]\n << \" R: \" << info->modelInfo[act].reward << endl;\n }\n\n }\n}\n\n\n\n\nvoid ValueIteration::calculateReachableStates(){\n // for all states\n // set plausible flag to see if we need to calc q value for this state\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n state_t s = canonicalize(*i);\n\n // get state's info\n state_info* info = &(statedata[s]);\n\n info->stepsAway = 100000;\n\n //if (info->fresh){\n // info->stepsAway = 0;\n // continue;\n //}\n\n for (int j = 0; j < numactions; j++){\n if (info->visits[j] > 0){\n info->stepsAway = 0;\n break;\n }\n }\n }\n}\n\nvoid ValueIteration::removeUnreachableStates(){\n\n return;\n\n // for all states\n // set plausible flag to see if we need to calc q value for this state\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n state_t s = canonicalize(*i);\n\n // get state's info\n state_info* info = &(statedata[s]);\n\n // state is unreachable\n if (info->stepsAway > MAX_STEPS){\n\n if (POLICYDEBUG){\n cout << \"Removing unreachable state: \" << (*s)[0];\n for (unsigned i = 1; i < s->size(); i++){\n cout << \", \" << (*s)[i];\n }\n cout << endl;\n }\n\n // delete state\n deleteInfo(info);\n\n // remove from statespace\n statespace.erase(*s);\n\n // remove from statedata hashmap\n statedata.erase(s);\n\n }\n }\n}\n\n\n\n\n\n\nvoid ValueIteration::deleteInfo(state_info* info){\n\n delete [] info->modelInfo;\n\n}\n\n\ndouble ValueIteration::getSeconds(){\n struct timezone tz;\n timeval timeT;\n gettimeofday(&timeT, &tz);\n return timeT.tv_sec + (timeT.tv_usec / 1000000.0);\n}\n\n\nvoid ValueIteration::savePolicy(const char* filename){\n\n ofstream policyFile(filename, ios::out | ios::binary | ios::trunc);\n\n // first part, save the vector size\n int fsize = featmin.size();\n policyFile.write((char*)&fsize, sizeof(int));\n\n // save numactions\n policyFile.write((char*)&numactions, sizeof(int));\n\n // go through all states, and save Q values\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n state_t s = canonicalize(*i);\n state_info* info = &(statedata[s]);\n\n // save state\n policyFile.write((char*)&((*i)[0]), sizeof(float)*fsize);\n\n // save q-values\n policyFile.write((char*)&(info->Q[0]), sizeof(float)*numactions);\n\n }\n\n policyFile.close();\n}\n\n\n// should do it such that an already discretized state stays the same\n// mainly the numerical value of each bin should be the average of that bin\nstd::vector<float> ValueIteration::discretizeState(const std::vector<float> &s){\n std::vector<float> ds(s.size());\n\n for (unsigned i = 0; i < s.size(); i++){\n \n // since i'm sometimes doing this for discrete domains\n // want to center bins on 0, not edge on 0\n //cout << \"feat \" << i << \" range: \" << featmax[i] << \" \" << featmin[i] << \" \" << (featmax[i]-featmin[i]) << \" n: \" << (float)statesPerDim;\n\n float factor = (featmax[i] - featmin[i]) / (float)statesPerDim[i];\n int bin = 0;\n if (s[i] > 0){\n bin = (int)((s[i]+factor/2) / factor);\n } else {\n bin = (int)((s[i]-factor/2) / factor);\n }\n\n ds[i] = factor*bin;\n //cout << \" factor: \" << factor << \" bin: \" << bin;\n //cout << \" Original: \" << s[i] << \" Discrete: \" << ds[i] << endl;\n }\n\n return 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grid(new Gridworld(height, width, rand)),\n goal(coord_t(2.,2.)),\n noisy(stochastic), rng(rand),\n doorway(NULL),\n s(2),\n ns(s[0]),\n ew(s[1])\n {\n randomize_goal();\n reset();\n }\n*/\n\nEnergyRooms::~EnergyRooms() { delete grid; }\n\nconst std::vector<float> &EnergyRooms::sensation() const {\n //cout << \"At state \" << s[0] << \", \" << s[1] << endl;\n\n return s;\n}\n\nfloat EnergyRooms::apply(int action) {\n\n //cout << \"Taking action \" << static_cast<room_action_t>(action) << endl;\n\n // 80% of the time, lose one energy\n if (!noisy || rng.bernoulli(0.8))\n energy--;\n\n // many fuel squares, with varying amounts of fuel and reward\n if (fuel){\n if ((int)ns % 3 == 0 && (int)ew % 3 == 0){\n energy += (int)(5 + ns/3 + (11-ew)/3);\n }\n if (energy > 20.0)\n energy = 20.0;\n }\n else {\n // certain squares reset you to 10\n if (ns == 7 && ew == 3)\n energy = 10;\n if (ns == 7 && ew == 7)\n energy = 10;\n if (ns == 3 && ew == 3)\n energy = 10;\n if (ns == 7 && ew == 7)\n energy = 10;\n }\n\n // never go below 0\n if (energy < 0.0)\n energy = 0;\n\n\n const room_action_t effect =\n noisy\n ? add_noise(static_cast<room_action_t>(action))\n : static_cast<room_action_t>(action);\n switch(effect) {\n case NORTH:\n if (!grid->wall(static_cast<unsigned>(ns),\n static_cast<unsigned>(ew),\n effect))\n {\n ++ns;\n }\n return reward();\n case SOUTH:\n if (!grid->wall(static_cast<unsigned>(ns),\n static_cast<unsigned>(ew),\n effect))\n {\n --ns;\n }\n return reward();\n case EAST:\n if (!grid->wall(static_cast<unsigned>(ns),\n static_cast<unsigned>(ew),\n effect))\n {\n ++ew;\n }\n return reward();\n case WEST:\n if (!grid->wall(static_cast<unsigned>(ns),\n static_cast<unsigned>(ew),\n effect))\n {\n --ew;\n }\n return reward();\n }\n\n std::cerr << \"Unreachable point reached in EnergyRooms::apply!!!\\n\";\n return 0; // unreachable, I hope\n}\n\n\nfloat EnergyRooms::reward() {\n\n if (negReward){\n // normally -1 and 0 on goal\n if (terminal())\n return 0;\n else if (energy <= 0.1){\n if (fuel)\n return -10;\n else\n return -2;\n }\n // normal square and normal energy\n else{\n // many squares of random cost\n if (fuel){\n if ((int)ns % 3 == 0 && (int)ew % 3 == 0)\n return -2 + (int)(-ew/5 -((int)ns%4));\n\n else\n return -1;\n }\n else\n return -1;\n }\n\n } // not negReward\n else{\n\n // or we could do 0 and 1 on goal\n if (terminal())\n return 1;\n else if (energy <= 0.1){\n if (fuel)\n return -10;\n else\n return -2;\n }else\n return 0;\n }\n}\n\n\nbool EnergyRooms::terminal() const {\n // current position equal to goal??\n return coord_t(ns,ew) == goal;\n}\n\n\nvoid EnergyRooms::reset() {\n // start randomly in upper left room (goal is lower right)\n ns = rng.uniformDiscrete(6, grid->height()-1);\n ew = rng.uniformDiscrete(0, 4);\n energy = 10;\n\n // if fuel, start with random amount of fuel\n if (fuel) {\n energy = rng.uniformDiscrete(1, 20);\n }\n\n //ns = 8;\n //ew = 2;\n\n //ns = 4;\n //ew = 9;\n}\n\n\nstd::vector<std::vector<float> > EnergyRooms::getSubgoals(){\n\n //cout << \"Getting room subgoals \" << endl;\n\n // Create vector of state representations, each is a subgoal\n std::vector<std::vector<float> > subgoals;\n\n\n std::vector<float> subgoal(2);\n\n // between two left rooms\n subgoal[0] = 5;\n subgoal[1] = 1;\n subgoals.push_back(subgoal);\n\n // between two right rooms\n subgoal[0] = 4;\n subgoal[1] = 8;\n subgoals.push_back(subgoal);\n\n // between two top rooms\n subgoal[0] = 8;\n subgoal[1] = 5;\n subgoals.push_back(subgoal);\n\n // between two lower rooms\n subgoal[0] = 1;\n subgoal[1] = 5;\n subgoals.push_back(subgoal);\n\n if (goalOption){\n // actual goal\n subgoal[0] = 1;\n subgoal[1] = 10;\n subgoals.push_back(subgoal);\n }\n\n return subgoals;\n\n}\n\n\nint EnergyRooms::getNumActions(){\n return 4;\n}\n\n\nstd::ostream &operator<<(std::ostream &out, const EnergyRooms &rooms) {\n out << \"Map:\\n\" << *rooms.grid;\n\n // print goal\n out << \"Goal: row \" << rooms.goal.first\n << \", column \" << rooms.goal.second << \"\\n\";\n\n // print doorway\n out << \"Doorway: row \" << rooms.doorway.first\n << \", column \" << rooms.doorway.second << \"\\n\";\n\n return out;\n}\n\nconst Gridworld *EnergyRooms::create_default_map() {\n int width = 11;\n int height = 11;\n std::vector<std::vector<bool> > nsv(width, std::vector<bool>(height-1,false));\n std::vector<std::vector<bool> > ewv(height, std::vector<bool>(width-1,false));\n\n // put the vertical wall between the two rooms\n for (int j = 0; j < height; j++){\n // skip doorways at 1 and 8\n if (j == 1 || j == 8)\n continue;\n ewv[j][4] = true;\n ewv[j][5] = true;\n }\n\n nsv[5][0] = true;\n nsv[5][1] = true;\n nsv[5][7] = true;\n nsv[5][8] = true;\n\n // put the horizontal wall for the left room\n for (int i = 0; i < 6; i++){\n // skip doorway at 1\n if (i == 1)\n continue;\n nsv[i][4] = true;\n nsv[i][5] = true;\n }\n\n ewv[5][0] = true;\n ewv[5][1] = true;\n\n // put the horizontal wall for the right room\n for (int i = 5; i < width; i++){\n // skip doorway at 8\n if (i == 8)\n continue;\n nsv[i][3] = true;\n nsv[i][4] = true;\n }\n\n ewv[4][7] = true;\n ewv[4][8] = true;\n\n return new Gridworld(height, width, nsv, ewv);\n}\n\nEnergyRooms::room_action_t EnergyRooms::add_noise(room_action_t action) {\n switch(action) {\n case NORTH:\n case SOUTH:\n return rng.bernoulli(0.8) ? action : (rng.bernoulli(0.5) ? EAST : WEST);\n case EAST:\n case WEST:\n return rng.bernoulli(0.8) ? action : (rng.bernoulli(0.5) ? NORTH : SOUTH);\n default:\n return action;\n }\n}\n\n\nvoid EnergyRooms::randomize_goal() {\n const unsigned n = grid->height() * grid->width();\n unsigned index = rng.uniformDiscrete(1,n) - 1;\n goal = coord_t(index / grid->width(), index % grid->width());\n}\n\n\nvoid EnergyRooms::getMinMaxFeatures(std::vector<float> *minFeat,\n std::vector<float> *maxFeat){\n\n minFeat->resize(s.size(), 0.0);\n maxFeat->resize(s.size(), 10.0);\n\n (*maxFeat)[2] = 20.0;\n\n}\n\nvoid EnergyRooms::getMinMaxReward(float *minR,\n float *maxR){\n\n *minR = -10.0;\n *maxR = 1.0;\n\n}\n"},{"alpha_fraction":0.6167809367179871,"alphanum_fraction":0.6268656849861145,"avg_line_length":22.580951690673828,"blob_id":"cfe324d342edf7457aa92ff2b7aca683e094449b","content_id":"8a05de0980eec228cf625940be46bd2d6711f542","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":2479,"license_type":"no_license","max_line_length":138,"num_lines":105,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/MultipleModels.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include \"MultipleModels.hh\"\n#include \"NeuralNetwork.hh\"\n#include \"DecisionTree.hh\"\n#include \"StochDecisionTree.hh\"\n#include \"KNN.hh\"\n\n\nconst bool MM_DEBUG = false; //true;\n\n\nMultipleModels::MultipleModels(int id, int nIn, int nOut, int modeltype, Random rng):\n id(id), nInput(nIn), nOutput(nOut), type(modeltype), rng(rng)\n{\n\n \n if (MM_DEBUG) \n cout << \"nIn: \" << nInput << \" nOut: \" << nOutput << endl;\n\n // create a model for each output variable\n createModels();\n\n if (MM_DEBUG)\n cout << \"multiple models created.\" << endl;\n\n}\n\nMultipleModels::~MultipleModels() {}\n\n\n\nbool MultipleModels::trainInstance(std::vector<float> input, \n\t\t\t\t std::vector<float> targetOutput){\n if (MM_DEBUG) cout << \"Multimodel trainInstance()\" << endl;\n bool modelChanged = false;\n\n // for each model\n for (int i = 0; i < nOutput; i++){\n\n std::vector<float> output;\n output.push_back(targetOutput[i]);\n\n // call train instance, with just its output variable\n bool singleChange = models[i]->trainInstance(input, output);\n if (singleChange)\n modelChanged = true;\n }\n\n return modelChanged;\n\n}\n\n\n\n// TODO: figure out how this will work. for each output feature, we'll actually want a vector with the probability of each possible value\n// TODO: for now, just assume deterministic and return the only outcome\nstd::vector<float> MultipleModels::testInstance(std::vector<float> input){\n if (MM_DEBUG) cout << \"Multimodel testInstance()\" << endl;\n\n std::vector<float> outputs;\n outputs.resize(nOutput);\n\n // call each model\n for (int i = 0; i < nOutput; i++){\n\n // combine output variables into one vector\n outputs[i] = models[i]->testInstance(input)[0];\n\n }\n\n return outputs;\n\n}\n\nvoid MultipleModels::createModels(){\n if (MM_DEBUG) cout << \"createModels\" << endl;\n\n // create a model for each output variable\n models.resize(nOutput);\n\n for (int i = 0; i < nOutput; i++){\n\n // NN\n if (type == 0){\n //models[i] = new NeuralNetwork(id*nOutput + i, nInput, \n //\t\t\t\t 10, 3, 0.3, 0.7, rng);\n }\n\n // DT\n else if (type == 1){\n if (MM_DEBUG) cout << \"Creating DT for output \" << i << endl;\n models[i] = new DecisionTree(id*nOutput + i, 1, 1000, rng);\n //models[i] = new StochDecisionTree(id*nOutput + i, 1, 1000, rng);\n }\n\n // KNN\n else {\n if (MM_DEBUG) cout << \"Creating KNN for output \" << i \n\t\t\t << \" with k \" << id+1 << endl;\n models[i] = new KNN(id*nOutput + i, id+2, rng);\n\n }\n\n\n }\n}\n\n\n\n"},{"alpha_fraction":0.7873753905296326,"alphanum_fraction":0.7873753905296326,"avg_line_length":49.16666793823242,"blob_id":"4c36993ff777d3ce4e79aece77a18e85d54a1ff9","content_id":"7c15fb3428ea3ad17db45e1d043d5929d34dbe0e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":301,"license_type":"no_license","max_line_length":71,"num_lines":6,"path":"/build/ugv_course_launch/CTestTestfile.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"# CMake generated Testfile for \n# Source directory: /home/justin/ros_test/src/ugv_course_launch\n# Build directory: /home/justin/ros_test/build/ugv_course_launch\n# \n# This file includes the relevant testing commands required for \n# testing this directory and lists subdirectories to be tested as well.\n"},{"alpha_fraction":0.755033552646637,"alphanum_fraction":0.7651006579399109,"avg_line_length":41.71428680419922,"blob_id":"4f8316850ddc50d3874571545cd6a699f4117c6b","content_id":"00343379606cc9d93e89af9a9c706858219a79a8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":298,"license_type":"no_license","max_line_length":49,"num_lines":7,"path":"/src/ugv_course_launch/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"ugv_course_launch\")\nset(ugv_course_launch_MAINTAINER \"abc <abc@todo.todo>\")\nset(ugv_course_launch_DEPRECATED \"\")\nset(ugv_course_launch_VERSION \"0.0.0\")\nset(ugv_course_launch_BUILD_DEPENDS )\nset(ugv_course_launch_RUN_DEPENDS )\nset(ugv_course_launch_BUILDTOOL_DEPENDS \"catkin\")"},{"alpha_fraction":0.6607614755630493,"alphanum_fraction":0.6607614755630493,"avg_line_length":23.71895408630371,"blob_id":"d2bb4862772a99f0b0125da1e113932cf6479e09","content_id":"64c835c0bafd6119e9c1108e5d1e23a8cdde7971","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":3782,"license_type":"no_license","max_line_length":74,"num_lines":153,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/PolicyIteration.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#ifndef _POLICYITERATION_HH_\n#define _POLICYITERATION_HH_\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n\n#include <set>\n#include <vector>\n#include <map>\n\n\nclass PolicyIteration: public Planner {\npublic:\n\n /** The implementation maps all sensations to a set of canonical\n pointers, which serve as the internal representation of\n environment state. */\n typedef const std::vector<float> *state_t;\n\n\n /** Standard constructor\n \\param numactions, numactions in the domain\n \\param gamma discount factor\n \\param maxloops\n \\param max time\n \\param rng random\n */\n PolicyIteration(int numactions, float gamma,\n int MAX_LOOPS, float MAX_TIME, int modelType,\n const std::vector<float> &featmax, \n const std::vector<float> &featmin,\n const std::vector<int> &statesPerDim,\n Random rng = Random());\n\n /** Unimplemented copy constructor: internal state cannot be simply\n copied. */\n PolicyIteration(const PolicyIteration &);\n\n virtual ~PolicyIteration();\n\n virtual void setModel(MDPModel* model);\n virtual bool updateModelWithExperience(const std::vector<float> &last, \n int act, \n const std::vector<float> &curr, \n float reward, bool term);\n virtual void planOnNewModel();\n virtual int getBestAction(const std::vector<float> &s);\n virtual void savePolicy(const char* filename);\n\n bool PLANNERDEBUG;\n bool POLICYDEBUG; //= false; //true;\n bool MODELDEBUG;\n bool ACTDEBUG;\n\n /** Model that we're using */\n MDPModel* model;\n\n\n\nprotected:\n\n\n struct state_info;\n struct model_info;\n\n /** State info struct */\n struct state_info {\n int id;\n\n int stepsAway;\n bool fresh;\n\n // experience data\n std::vector<int> visits;\n\n // data filled in from models\n StateActionInfo* modelInfo;\n\n //std::map<state_t, std::vector<float> > P;\n //std::vector<float> R;\n //std::vector<bool> known;\n\n // q values from policy creation\n\n float value;\n int bestAction;\n\n };\n\n\n\n // various helper functions that we need\n void initStateInfo(state_info* info);\n \n /** Produces a canonical representation of the given sensation.\n \\param s The current sensation from the environment.\n \\return A pointer to an equivalent state in statespace. */\n state_t canonicalize(const std::vector<float> &s);\n\n // Operational functions\n void deleteInfo(state_info* info);\n void initNewState(state_t s);\n void createPolicy();\n void printStates();\n void calculateReachableStates();\n void removeUnreachableStates();\n\n // functions to update our models and get info from them\n void updateStatesFromModel();\n void updateStateActionFromModel(const std::vector<float> &state, int j);\n\n double getSeconds();\n\n // for policy iter\n void policyEvaluation();\n float getActionValue(state_t s, state_info* info, int act);\n bool policyImprovement();\n std::vector<float> discretizeState(const std::vector<float> &s);\n\nprivate:\n\n /** Set of all distinct sensations seen. Pointers to elements of\n this set serve as the internal representation of the environment\n state. */\n std::set<std::vector<float> > statespace;\n\n /** Hashmap mapping state vectors to their state_info structs. */\n std::map<state_t, state_info> statedata;\n\n std::vector<float> featmax;\n std::vector<float> featmin;\n\n std::vector<float> prevstate;\n int prevact;\n\n double planTime;\n int nstates;\n int nactions; \n \n int MAX_STEPS;\n bool timingType;\n\n const int numactions;\n const float gamma;\n\n const int MAX_LOOPS;\n const float MAX_TIME;\n const int modelType;\n const std::vector<int> &statesPerDim;\n\n};\n\n#endif\n"},{"alpha_fraction":0.6837209463119507,"alphanum_fraction":0.6837209463119507,"avg_line_length":85,"blob_id":"6cd22510bdc385ab3cf4c0c1f1280ee68ccff94d","content_id":"7c3624e86d1ff63e685ecc3e032be5729f5a1993","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":430,"license_type":"no_license","max_line_length":201,"num_lines":5,"path":"/build/catkin_generated/order_packages.py","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"# generated from catkin/cmake/template/order_packages.context.py.in\nsource_root_dir = \"/home/justin/ros_test/src\"\nwhitelisted_packages = \"\".split(';') if \"\" != \"\" else []\nblacklisted_packages = \"\".split(';') if \"\" != \"\" else []\nunderlay_workspaces = \"/home/justin/catkin_ws/devel;/home/justin/ros_test/devel;/opt/ros/melodic\".split(';') if \"/home/justin/catkin_ws/devel;/home/justin/ros_test/devel;/opt/ros/melodic\" != \"\" else []\n"},{"alpha_fraction":0.6606461405754089,"alphanum_fraction":0.6606461405754089,"avg_line_length":24.03821563720703,"blob_id":"66b8c1e0fd3a6abc75011934d41e0d539ac541e8","content_id":"0b055ba257424d44b4d66e4bbf49c7a2819e19bd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":3931,"license_type":"no_license","max_line_length":76,"num_lines":157,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/PrioritizedSweeping.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#ifndef _PRIORITIZEDSWEEPING_HH_\n#define _PRIORITIZEDSWEEPING_HH_\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n\n#include <set>\n#include <vector>\n#include <map>\n\n\nclass PrioritizedSweeping: public Planner {\npublic:\n\n /** The implementation maps all sensations to a set of canonical\n pointers, which serve as the internal representation of\n environment state. */\n typedef const std::vector<float> *state_t;\n\n\n /** Standard constructor\n \\param numactions, numactions in the domain\n \\param gamma discount factor\n \\param rng random\n */\n PrioritizedSweeping(int numactions, float gamma, float MAX_TIME,\n bool onlyAddLastSA, int modelType,\n const std::vector<float> &featmax, \n const std::vector<float> &featmin,\n Random rng = Random());\n\n /** Unimplemented copy constructor: internal state cannot be simply\n copied. */\n PrioritizedSweeping(const PrioritizedSweeping &);\n\n virtual ~PrioritizedSweeping();\n\n virtual void setModel(MDPModel* model);\n virtual bool updateModelWithExperience(const std::vector<float> &last, \n int act, \n const std::vector<float> &curr, \n float reward, bool term);\n virtual void planOnNewModel();\n virtual int getBestAction(const std::vector<float> &s);\n\n bool PLANNERDEBUG;\n bool POLICYDEBUG; //= false; //true;\n bool MODELDEBUG;\n bool ACTDEBUG;\n bool LISTDEBUG;\n\n /** Model that we're using */\n MDPModel* model;\n\n\n\nprotected:\n\n\n struct state_info;\n struct model_info;\n\n struct saqPair {\n std::vector<float> s;\n int a;\n float q;\n };\n\n /** State info struct */\n struct state_info {\n int id;\n\n bool fresh;\n\n // experience data\n std::vector<int> visits;\n\n // data filled in from models\n StateActionInfo* modelInfo;\n\n //std::map<state_t, std::vector<float> > P;\n //std::vector<float> R;\n //std::vector<bool> known;\n\n // q values from policy creation\n std::vector<float> Q;\n \n // which states lead to this state?\n std::list<saqPair> pred;\n std::vector<int> lastUpdate;\n\n };\n\n\n\n // various helper functions that we need\n void initStateInfo(state_info* info);\n \n /** Produces a canonical representation of the given sensation.\n \\param s The current sensation from the environment.\n \\return A pointer to an equivalent state in statespace. */\n state_t canonicalize(const std::vector<float> &s);\n\n // Operational functions\n void deleteInfo(state_info* info);\n void initNewState(state_t s);\n void createPolicy();\n void printStates();\n\n // functions to update our models and get info from them\n void updateStatesFromModel();\n\n double getSeconds();\n\n // for prioritized sweeping\n void updatePriorityList(state_info* info, const std::vector<float> &next);\n bool saqPairMatch(saqPair a, saqPair b);\n float updateQValues(const std::vector<float> &state, int act);\n void addSAToList(const std::vector<float> &s, int act, float q);\n void updateStateActionFromModel(const std::vector<float> &state, int a);\n\nprivate:\n\n /** Set of all distinct sensations seen. Pointers to elements of\n this set serve as the internal representation of the environment\n state. */\n std::set<std::vector<float> > statespace;\n\n /** Hashmap mapping state vectors to their state_info structs. */\n std::map<state_t, state_info> statedata;\n\n /** priority list for prioritized sweeping */\n std::list< saqPair> priorityList;\n\n std::vector<float> featmax;\n std::vector<float> featmin;\n\n std::vector<float> prevstate;\n int prevact;\n \n double planTime;\n int nstates;\n int nactions; \n int lastModelUpdate;\n\n int MAX_STEPS;\n bool timingType;\n\n const int numactions;\n const float gamma;\n const float MAX_TIME;\n const bool onlyAddLastSA;\n const int modelType;\n\n};\n\n#endif\n"},{"alpha_fraction":0.7082785964012146,"alphanum_fraction":0.7135348320007324,"avg_line_length":46.625,"blob_id":"145aa3e3baa4e35992769277b2a5e5b62c666696","content_id":"dae4f113dca76867e6da1924c9e391ee06e7cb21","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":761,"license_type":"no_license","max_line_length":99,"num_lines":16,"path":"/build/rl-texplore-ros-pkg-master/src/rl_agent/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"rl_agent\")\nset(rl_agent_VERSION \"0.0.0\")\nset(rl_agent_MAINTAINER \"Todd Hester <todd.hester@gmail.com>\")\nset(rl_agent_PACKAGE_FORMAT \"1\")\nset(rl_agent_BUILD_DEPENDS \"roscpp\" \"rl_msgs\" \"rl_common\" \"tf\" \"std_msgs\")\nset(rl_agent_BUILD_EXPORT_DEPENDS \"message_runtime\" \"roscpp\" \"rl_msgs\" \"tf\" \"rl_common\" \"std_msgs\")\nset(rl_agent_BUILDTOOL_DEPENDS \"catkin\")\nset(rl_agent_BUILDTOOL_EXPORT_DEPENDS )\nset(rl_agent_EXEC_DEPENDS \"message_runtime\" \"roscpp\" \"rl_msgs\" \"tf\" \"rl_common\" \"std_msgs\")\nset(rl_agent_RUN_DEPENDS \"message_runtime\" \"roscpp\" \"rl_msgs\" \"tf\" \"rl_common\" \"std_msgs\")\nset(rl_agent_TEST_DEPENDS )\nset(rl_agent_DOC_DEPENDS )\nset(rl_agent_URL_WEBSITE \"\")\nset(rl_agent_URL_BUGTRACKER \"\")\nset(rl_agent_URL_REPOSITORY \"\")\nset(rl_agent_DEPRECATED \"\")"},{"alpha_fraction":0.6536796689033508,"alphanum_fraction":0.6536796689033508,"avg_line_length":27.875,"blob_id":"cb461d47731eb47a3d77a9a1eba234f37730f728","content_id":"f9e24f517592b5b715ff8a827d8cd00dab112bf4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":1155,"license_type":"no_license","max_line_length":77,"num_lines":40,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/include/rl_agent/DiscretizationAgent.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#ifndef _DISCAGENT_HH_\n#define _DISCAGENT_HH_\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n\n#include <vector>\n\n\nclass DiscretizationAgent: public Agent {\npublic:\n\n DiscretizationAgent(int statesPerDim, Agent* a, \n std::vector<float> featmin, std::vector<float> featmax,\n bool d);\n DiscretizationAgent(std::vector<int> statesPerDim, Agent* a, \n std::vector<float> featmin, std::vector<float> featmax,\n bool d);\n ~DiscretizationAgent();\n \n void initEverything(Agent* a, std::vector<float> fmin,\n std::vector<float> fmax, bool d);\n virtual int first_action(const std::vector<float> &s);\n virtual int next_action(float r, const std::vector<float> &s);\n virtual void last_action(float r);\n virtual void setDebug(bool b);\n virtual void seedExp(std::vector<experience> seeds);\n virtual void savePolicy(const char* filename);\n\n \n std::vector<float> discretizeState(const std::vector<float> &s);\n std::vector<int> statesPerDim;\n Agent* agent;\n std::vector<float> featmin;\n std::vector<float> featmax;\n bool DEBUG;\n\n};\n\n#endif\n"},{"alpha_fraction":0.5563433766365051,"alphanum_fraction":0.5607748627662659,"avg_line_length":23.752351760864258,"blob_id":"519c443e326d9c1e154525d2c44f6788d6098bc8","content_id":"fb72ccdcfb44094b0e42d60e500d911e953ada8a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":7898,"license_type":"no_license","max_line_length":79,"num_lines":319,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Agent/QLearner.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include <rl_agent/QLearner.hh>\n#include <algorithm>\n\nQLearner::QLearner(int numactions, float gamma,\n float initialvalue, float alpha, float ep,\n Random rng):\n numactions(numactions), gamma(gamma),\n initialvalue(initialvalue), alpha(alpha),\n rng(rng), currentq(NULL)\n{\n\n epsilon = ep;\n ACTDEBUG = false; //true; //false;\n\n}\n\nQLearner::~QLearner() {}\n\nint QLearner::first_action(const std::vector<float> &s) {\n\n if (ACTDEBUG){\n cout << \"First - in state: \";\n printState(s);\n cout << endl;\n }\n\n // Get action values\n std::vector<float> &Q_s = Q[canonicalize(s)];\n\n // Choose an action\n const std::vector<float>::iterator a =\n rng.uniform() < epsilon\n ? Q_s.begin() + rng.uniformDiscrete(0, numactions - 1) // Choose randomly\n : random_max_element(Q_s.begin(), Q_s.end()); // Choose maximum\n\n // Store location to update value later\n currentq = &*a;\n\n if (ACTDEBUG){\n cout << \" act: \" << (a-Q_s.begin()) << \" val: \" << *a << endl;\n for (int iAct = 0; iAct < numactions; iAct++){\n cout << \" Action: \" << iAct\n << \" val: \" << Q_s[iAct] << endl;\n }\n cout << \"Took action \" << (a-Q_s.begin()) << \" from state \";\n printState(s);\n cout << endl;\n }\n\n return a - Q_s.begin();\n}\n\nint QLearner::next_action(float r, const std::vector<float> &s) {\n\n if (ACTDEBUG){\n cout << \"Next: got reward \" << r << \" in state: \";\n printState(s);\n cout << endl;\n }\n\n // Get action values\n std::vector<float> &Q_s = Q[canonicalize(s)];\n const std::vector<float>::iterator max =\n random_max_element(Q_s.begin(), Q_s.end());\n\n // Update value of action just executed\n *currentq += alpha * (r + gamma * (*max) - *currentq);\n\n // Choose an action\n const std::vector<float>::iterator a =\n rng.uniform() < epsilon\n ? Q_s.begin() + rng.uniformDiscrete(0, numactions - 1)\n : max;\n\n // Store location to update value later\n currentq = &*a;\n\n if (ACTDEBUG){\n cout << \" act: \" << (a-Q_s.begin()) << \" val: \" << *a << endl;\n for (int iAct = 0; iAct < numactions; iAct++){\n cout << \" Action: \" << iAct\n << \" val: \" << Q_s[iAct] << endl;\n }\n cout << \"Took action \" << (a-Q_s.begin()) << \" from state \";\n printState(s);\n cout << endl;\n }\n\n return a - Q_s.begin();\n}\n\nvoid QLearner::last_action(float r) {\n\n if (ACTDEBUG){\n cout << \"Last: got reward \" << r << endl;\n }\n\n *currentq += alpha * (r - *currentq);\n currentq = NULL;\n}\n\nQLearner::state_t QLearner::canonicalize(const std::vector<float> &s) {\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\n statespace.insert(s);\n state_t retval = &*result.first; // Dereference iterator then get pointer\n if (result.second) { // s is new, so initialize Q(s,a) for all a\n std::vector<float> &Q_s = Q[retval];\n Q_s.resize(numactions,initialvalue);\n }\n return retval;\n}\n\n\n\nstd::vector<float>::iterator\nQLearner::random_max_element(\n std::vector<float>::iterator start,\n std::vector<float>::iterator end) {\n\n std::vector<float>::iterator max =\n std::max_element(start, end);\n int n = std::count(max, end, *max);\n if (n > 1) {\n n = rng.uniformDiscrete(1, n);\n while (n > 1) {\n max = std::find(max + 1, end, *max);\n --n;\n }\n }\n return max;\n}\n\n\n\n\nvoid QLearner::setDebug(bool d){\n ACTDEBUG = d;\n}\n\n\nvoid QLearner::printState(const std::vector<float> &s){\n for (unsigned j = 0; j < s.size(); j++){\n cout << s[j] << \", \";\n }\n}\n\n\n\nvoid QLearner::seedExp(std::vector<experience> seeds){\n\n // for each seeding experience, update our model\n for (unsigned i = 0; i < seeds.size(); i++){\n experience e = seeds[i];\n\n std::vector<float> &Q_s = Q[canonicalize(e.s)];\n std::vector<float> &Q_next = Q[canonicalize(e.next)];\n\n // get max value of next state\n const std::vector<float>::iterator max =\n random_max_element(Q_next.begin(), Q_next.end());\n\n // Get q value for action taken\n const std::vector<float>::iterator a = Q_s.begin() + e.act;\n currentq = &*a;\n\n // Update value of action just executed\n *currentq += alpha * (e.reward + gamma * (*max) - *currentq);\n\n\n /*\n cout << \"Seeding with experience \" << i << endl;\n cout << \"last: \" << (e.s)[0] << \", \" << (e.s)[1] << \", \"\n << (e.s)[2] << endl;\n cout << \"act: \" << e.act << \" r: \" << e.reward << endl;\n cout << \"next: \" << (e.next)[0] << \", \" << (e.next)[1] << \", \"\n << (e.next)[2] << \", \" << e.terminal << endl;\n cout << \"Q: \" << *currentq << \" max: \" << *max << endl;\n */\n\n }\n\n\n}\n\nvoid QLearner::logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax){\n std::vector<float> s;\n s.resize(2, 0.0);\n for (int i = xmin ; i < xmax; i++){\n for (int j = ymin; j < ymax; j++){\n s[0] = j;\n s[1] = i;\n std::vector<float> &Q_s = Q[canonicalize(s)];\n const std::vector<float>::iterator max =\n random_max_element(Q_s.begin(), Q_s.end());\n *of << (*max) << \",\";\n }\n }\n}\n\n\nfloat QLearner::getValue(std::vector<float> state){\n\n state_t s = canonicalize(state);\n\n // Get Q values\n std::vector<float> &Q_s = Q[s];\n\n // Choose an action\n const std::vector<float>::iterator a =\n random_max_element(Q_s.begin(), Q_s.end()); // Choose maximum\n\n // Get avg value\n float valSum = 0.0;\n float cnt = 0;\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n state_t s = canonicalize(*i);\n\n // get state's info\n std::vector<float> &Q_s = Q[s];\n\n for (int j = 0; j < numactions; j++){\n valSum += Q_s[j];\n cnt++;\n }\n }\n\n cout << \"Avg Value: \" << (valSum / cnt) << endl;\n\n return *a;\n}\n\n\nvoid QLearner::savePolicy(const char* filename){\n\n ofstream policyFile(filename, ios::out | ios::binary | ios::trunc);\n\n // first part, save the vector size\n std::set< std::vector<float> >::iterator i = statespace.begin();\n int fsize = (*i).size();\n policyFile.write((char*)&fsize, sizeof(int));\n\n // save numactions\n policyFile.write((char*)&numactions, sizeof(int));\n\n // go through all states, and save Q values\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n state_t s = canonicalize(*i);\n std::vector<float> *Q_s = &(Q[s]);\n\n // save state\n policyFile.write((char*)&((*i)[0]), sizeof(float)*fsize);\n\n // save q-values\n policyFile.write((char*)&((*Q_s)[0]), sizeof(float)*numactions);\n\n }\n\n policyFile.close();\n}\n\n\nvoid QLearner::loadPolicy(const char* filename){\n bool LOADDEBUG = false;\n\n ifstream policyFile(filename, ios::in | ios::binary);\n if (!policyFile.is_open())\n return;\n\n // first part, save the vector size\n int fsize;\n policyFile.read((char*)&fsize, sizeof(int));\n if (LOADDEBUG) cout << \"Numfeats loaded: \" << fsize << endl;\n\n // save numactions\n int nact;\n policyFile.read((char*)&nact, sizeof(int));\n\n if (nact != numactions){\n cout << \"this policy is not valid loaded nact: \" << nact\n << \" was told: \" << numactions << endl;\n exit(-1);\n }\n\n // go through all states, loading q values\n while(!policyFile.eof()){\n std::vector<float> state;\n state.resize(fsize, 0.0);\n\n // load state\n policyFile.read((char*)&(state[0]), sizeof(float)*fsize);\n if (LOADDEBUG){\n cout << \"load policy for state: \";\n printState(state);\n }\n\n state_t s = canonicalize(state);\n std::vector<float> *Q_s = &(Q[s]);\n\n if (policyFile.eof()) break;\n\n // load q values\n policyFile.read((char*)&((*Q_s)[0]), sizeof(float)*numactions);\n\n if (LOADDEBUG){\n cout << \"Q values: \" << endl;\n for (int iAct = 0; iAct < numactions; iAct++){\n cout << \" Action: \" << iAct << \" val: \" << (*Q_s)[iAct] << endl;\n }\n }\n }\n\n policyFile.close();\n cout << \"Policy loaded!!!\" << endl;\n //loaded = true;\n}\n\n\n"},{"alpha_fraction":0.7370242476463318,"alphanum_fraction":0.7474048733711243,"avg_line_length":40.42856979370117,"blob_id":"262e9904ea28ff03f7daf34421e5846ed3dc9a66","content_id":"eb60c4f29be1f16bb28f588e6202e630fe70b686","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":289,"license_type":"no_license","max_line_length":45,"num_lines":7,"path":"/src/mantis_model/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"mantis_model\")\nset(mantis_model_MAINTAINER \"abc <abc@todo.todo>\")\nset(mantis_model_DEPRECATED \"\")\nset(mantis_model_VERSION \"0.0.0\")\nset(mantis_model_BUILD_DEPENDS \"roscpp\" \"tf\")\nset(mantis_model_RUN_DEPENDS \"roscpp\" \"tf\")\nset(mantis_model_BUILDTOOL_DEPENDS \"catkin\")"},{"alpha_fraction":0.7683284282684326,"alphanum_fraction":0.7683284282684326,"avg_line_length":33.099998474121094,"blob_id":"e0d61c2bac0cc91d03ad1be9ee4c5bc87c897c49","content_id":"c9bf1b15776da9976f75411bbea1db89021dee7e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":341,"license_type":"no_license","max_line_length":72,"num_lines":10,"path":"/build/ugv_course_gazebo_plugins/CMakeFiles/pose_plugin.dir/cmake_clean.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"file(REMOVE_RECURSE\n \"CMakeFiles/pose_plugin.dir/src/PosePlugin.cpp.o\"\n \"/home/justin/ros_test/devel/lib/libpose_plugin.pdb\"\n \"/home/justin/ros_test/devel/lib/libpose_plugin.so\"\n)\n\n# Per-language clean rules from dependency scanning.\nforeach(lang CXX)\n include(CMakeFiles/pose_plugin.dir/cmake_clean_${lang}.cmake OPTIONAL)\nendforeach()\n"},{"alpha_fraction":0.7752161622047424,"alphanum_fraction":0.7752161622047424,"avg_line_length":33.70000076293945,"blob_id":"8075295b9f2d9bbaa76d6c8ce72916bdbee26ca2","content_id":"fda1f064127a75250fafb78eddd3c843053a1c7d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":347,"license_type":"no_license","max_line_length":71,"num_lines":10,"path":"/build/rl-texplore-ros-pkg-master/src/rl_experiment/CMakeFiles/experiment.dir/cmake_clean.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"file(REMOVE_RECURSE\n \"CMakeFiles/experiment.dir/src/rl.cc.o\"\n \"/home/justin/ros_test/devel/lib/rl_experiment/experiment.pdb\"\n \"/home/justin/ros_test/devel/lib/rl_experiment/experiment\"\n)\n\n# Per-language clean rules from dependency scanning.\nforeach(lang CXX)\n include(CMakeFiles/experiment.dir/cmake_clean_${lang}.cmake OPTIONAL)\nendforeach()\n"},{"alpha_fraction":0.6979137063026428,"alphanum_fraction":0.700200080871582,"avg_line_length":32.32381057739258,"blob_id":"768bb4f60136538b5edf0a66e5d7b21147838f9b","content_id":"73779c240983738ef0fad691507d66e0d2fb92bc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":3499,"license_type":"no_license","max_line_length":159,"num_lines":105,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/ExplorationModel.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file ExplorationModel.hh\n Defines the ExplorationModel class.\n Reward bonuses based on the variance in model predictions are described in: Hester and Stone, \"Real Time Targeted Exploration in Large Domains\", ICDL 2010.\n And intrinsic reward bonuses based on variance novelty as described in:\n Hester and Stone, \"Intinrisically Motivated Model Learning for a Developing Curious Agent\", AAMAS ALA 2012.\n \\author Todd Hester\n*/\n\n#ifndef _EXPLOREMODEL_HH_\n#define _EXPLOREMODEL_HH_\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n#include <vector>\n#include <map>\n#include <set>\n\n\n/** This model wraps an another model and adds reward\n bonuses based on model confidence, # of visits, or other metrics. */\nclass ExplorationModel: public MDPModel {\n\npublic:\n\n /** Default contstructor\n \\param model The underlying MDP Model being used.\n \\param modelType the type of model being used.\n \\param exploreType type of reward bonuses to be added on top of model\n \\param predType the way in which ensemble models combine their models\n \\param nModels # of models to use for ensemble models (i.e. random forests)\n \\param m # of visits for a given state-action to be considered known\n \\param numactions # of actions in the domain\n \\param rmax maximum one-step reward in the domain\n \\param qmax maximum possible q-value in a domain\n \\param rrange range of one-step rewards in the domain\n \\param nfactors # of state features in the domain\n \\param b/v coefficient to determine magnitude of variance reward \n \\param n coefficient to determine magnitude of novelty reward\n \\param featmax the maximum value of each state feature\n \\param featmin the minimum value of each state feature\n \\param rng Random Number Generator\n */\n ExplorationModel(MDPModel * model, int modelType, \n int exploreType, int predType, int nModels,\n float m, int numactions, float rmax, \n float qmax, float rrange, int nfactors, float v, float n,\n const std::vector<float> &featmax, \n const std::vector<float> &featmin, \n Random rng); \n\n /** Copy constructor */\n ExplorationModel(const ExplorationModel&);\n\n virtual ~ExplorationModel();\n virtual ExplorationModel* getCopy();\n\n virtual bool updateWithExperiences(std::vector<experience> &instances);\n virtual bool updateWithExperience(experience &e);\n virtual float getStateActionInfo(const std::vector<float> &state, int act, StateActionInfo* retval);\n\n /** Add state to a set of visited states */\n bool addStateToSet(const std::vector<float> &s);\n\n /** Check if the given state is in the set of visited states */\n bool checkForState(const std::vector<float> &s);\n\n /** Find distance in feature space to nearest visited state-action */\n float getFeatDistToVisitedSA(const std::vector<float> &s);\n\n\n bool MODEL_DEBUG;\n\nprotected:\n\n\n\nprivate:\n \n /** Set of all distinct sensations seen. \n This way we can know what we've visited. */\n std::set<std::vector<float> > statespace;\n\n /** Underlying MDP model that we've wrapped and that we add bonus rewards onto. */\n MDPModel* model;\n\n std::vector<float> featmax;\n std::vector<float> featmin;\n\n int modelType; \n int exploreType; \n int predType;\n int nModels;\n float M; \n int numactions; float rmax; float qmax; float rrange;\n int nfactors; \n const float v;\n const float n;\n\n Random rng;\n \n};\n\n\n\n#endif\n"},{"alpha_fraction":0.7820512652397156,"alphanum_fraction":0.7897436022758484,"avg_line_length":64.16666412353516,"blob_id":"b0a85ff62a77cf891b5bf1261bc776a3a6571f14","content_id":"1cf3f8adba814379e227e153c737de5cf1db151e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":390,"license_type":"no_license","max_line_length":88,"num_lines":6,"path":"/build/boost-python-catkin-example-master/catkin_generated/setup_py_interrogation.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(boost_python_catkin_example_SETUP_PY_VERSION \"1.0.0\")\nset(boost_python_catkin_example_SETUP_PY_SCRIPTS \"\")\nset(boost_python_catkin_example_SETUP_PY_PACKAGES \"boost_python_catkin_example\")\nset(boost_python_catkin_example_SETUP_PY_PACKAGE_DIRS \"src/boost_python_catkin_example\")\nset(boost_python_catkin_example_SETUP_PY_MODULES \"\")\nset(boost_python_catkin_example_SETUP_PY_MODULE_DIRS \"\")"},{"alpha_fraction":0.702464759349823,"alphanum_fraction":0.702464759349823,"avg_line_length":30.208791732788086,"blob_id":"d0578ffd421d58f693e792d73a41a8e1301f7942","content_id":"bca3a96902f77125d7e56935e42fa5fbb44d2019","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":2840,"license_type":"no_license","max_line_length":71,"num_lines":91,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/include/rl_agent/Sarsa.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\File\n Interface for an implementation of the canonical Sarsa lambda\n algorithm. */\n\n#ifndef _SARSA_HH_\n#define _SARSA_HH_\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n\n#include <map>\n#include <set>\n#include <vector>\n\n/** Agent that uses straight Sarsa Lambda, with no generalization and\n epsilon-greedy exploration. */\nclass Sarsa: public Agent {\npublic:\n /** Standard constructor\n \\param numactions The number of possible actions\n \\param gamma The discount factor\n \\param initialvalue The initial value of each Q(s,a)\n \\param alpha The learning rate\n \\param epsilon The probability of taking a random action\n \\param rng Initial state of the random number generator to use */\n Sarsa(int numactions, float gamma,\n float initialvalue, float alpha, float epsilon, float lambda, \n Random rng = Random());\n\n /** Unimplemented copy constructor: internal state cannot be simply\n copied. */\n Sarsa(const Sarsa &);\n\n virtual ~Sarsa();\n\n virtual int first_action(const std::vector<float> &s);\n virtual int next_action(float r, const std::vector<float> &s);\n virtual void last_action(float r);\n virtual void setDebug(bool d);\n virtual void seedExp(std::vector<experience>);\n virtual void savePolicy(const char* filename);\n\n void printState(const std::vector<float> &s);\n float getValue(std::vector<float> state);\n \n std::vector<float>::iterator random_max_element(\n\t\t\t\t\t\t std::vector<float>::iterator start,\n\t\t\t\t\t\t std::vector<float>::iterator end);\n\n void logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax);\n\nprotected:\n /** The implementation maps all sensations to a set of canonical\n pointers, which serve as the internal representation of\n environment state. */\n typedef const std::vector<float> *state_t;\n\n /** Produces a canonical representation of the given sensation.\n \\param s The current sensation from the environment.\n \\return A pointer to an equivalent state in statespace. */\n state_t canonicalize(const std::vector<float> &s);\n\nprivate:\n /** Set of all distinct sensations seen. Pointers to elements of\n this set serve as the internal representation of the environment\n state. */\n std::set<std::vector<float> > statespace;\n\n /** The primary data structure of the learning algorithm, the value\n function Q. For state_t s and int a, Q[s][a] gives the\n learned maximum expected future discounted reward conditional on\n executing action a in state s. */\n std::map<state_t, std::vector<float> > Q;\n std::map<state_t, std::vector<float> > eligibility;\n\n const int numactions;\n const float gamma;\n\n const float initialvalue;\n const float alpha;\n const float epsilon;\n const float lambda;\n\n Random rng;\n float *currentq;\n\n bool ACTDEBUG;\n bool ELIGDEBUG;\n};\n\n#endif\n"},{"alpha_fraction":0.7648809552192688,"alphanum_fraction":0.7648809552192688,"avg_line_length":32.599998474121094,"blob_id":"6120e4166c1ec91d795c74fb66ee519d410d525c","content_id":"3d3c8cb53d9e4edc0c26107897b04145628bec13","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":336,"license_type":"no_license","max_line_length":71,"num_lines":10,"path":"/build/ugv_course_gazebo_plugins/CMakeFiles/gps_plugin.dir/cmake_clean.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"file(REMOVE_RECURSE\n \"CMakeFiles/gps_plugin.dir/src/GpsPlugin.cpp.o\"\n \"/home/justin/ros_test/devel/lib/libgps_plugin.pdb\"\n \"/home/justin/ros_test/devel/lib/libgps_plugin.so\"\n)\n\n# Per-language clean rules from dependency scanning.\nforeach(lang CXX)\n include(CMakeFiles/gps_plugin.dir/cmake_clean_${lang}.cmake OPTIONAL)\nendforeach()\n"},{"alpha_fraction":0.57459557056427,"alphanum_fraction":0.5775913596153259,"avg_line_length":25.492063522338867,"blob_id":"a9a00a8edb6278bcf8134697d6f1a3ca0f1648c4","content_id":"64a6bdb43f52534d51e955dcc2486b5e3e59fa58","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":3338,"license_type":"no_license","max_line_length":142,"num_lines":126,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/MBS.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include \"MBS.hh\"\n#include <algorithm>\n\n//#include <time.h>\n#include <sys/time.h>\n\n\nMBS::MBS(int numactions, float gamma,\n int MAX_LOOPS, float MAX_TIME, int modelType,\n const std::vector<float> &fmax, \n const std::vector<float> &fmin, \n const std::vector<int> &n, \n const int k, Random newRng):\n k(k)\n{\n \n vi = new ValueIteration(numactions, gamma, MAX_LOOPS, MAX_TIME, modelType,\n fmax, fmin, n, newRng);\n DELAYDEBUG = false; //true;\n seedMode = false;\n\n if (DELAYDEBUG) cout << \"MBS delay planner with k = \" << k << endl;\n}\n\nMBS::~MBS() {\n delete vi;\n}\n\nvoid MBS::setModel(MDPModel* m){\n vi->setModel(m);\n model = m;\n}\n\n\n/** Use the latest experience to update state info and the model. */\nbool MBS::updateModelWithExperience(const std::vector<float> &laststate,\n int lastact,\n const std::vector<float> &currstate,\n float reward, bool term){\n\n if (seedMode) return false;\n \n // add this action to our history list\n if (DELAYDEBUG) cout << \"add new action \" << lastact << \" to list\" << endl;\n actHistory.push_back(lastact);\n \n if (actHistory.size() > k){\n int effectiveAction = actHistory.front();\n actHistory.pop_front();\n \n // if history size is >= k\n // then we can add this experience\n if (DELAYDEBUG){\n cout << \"update with old act: \" << effectiveAction << endl;\n cout << \"from: \" << laststate[0] << \", \" << laststate[1];\n cout << \" to: \" << currstate[0] << \", \" << currstate[1];\n cout << \" reward: \" << reward << \" term: \" << term << endl;\n }\n \n\n return vi->updateModelWithExperience(laststate, effectiveAction,\n currstate, reward, term);\n }\n\n return false;\n\n}\n\n\n/** Choose the next action */\nint MBS::getBestAction(const std::vector<float> &state){\n std::vector<float> statePred = state;\n\n // figure out what state we think we're in\n for (unsigned i = 0; i < actHistory.size(); i++){\n if (DELAYDEBUG) cout << i << \" prediction: \" \n << statePred[0] << \", \" << statePred[1] \n << \" pred for act: \" << actHistory[i] << endl;\n StateActionInfo prediction;\n model->getStateActionInfo(statePred, actHistory[i], &prediction);\n\n // find most likely next state\n std::vector<float> possibleNext;\n float maxProb = -1;\n for (std::map<std::vector<float>, float>::iterator it = prediction.transitionProbs.begin(); it != prediction.transitionProbs.end(); it++){\n \n float prob = (*it).second;\n if (prob > maxProb){\n possibleNext = (*it).first;\n maxProb = prob;\n }\n }\n statePred = possibleNext;\n\n }\n if (DELAYDEBUG) cout << \"predict current state is \" << statePred[0] << \", \" << statePred[1] << endl;\n \n // call get best action for that state\n int act = vi->getBestAction(statePred);\n\n if (DELAYDEBUG) cout << \"best action is \" << act << endl << endl;\n\n return act;\n\n}\n\n\nvoid MBS::planOnNewModel(){\n vi->planOnNewModel();\n}\n\nvoid MBS::savePolicy(const char* filename){\n vi->savePolicy(filename);\n}\n\nvoid MBS::setSeeding(bool seeding){\n\n seedMode = seeding;\n\n}\n\n\nvoid MBS::setFirst(){ \n // first action, reset history vector\n actHistory.clear();\n}\n"},{"alpha_fraction":0.6966953277587891,"alphanum_fraction":0.6996831297874451,"avg_line_length":33.089508056640625,"blob_id":"ed4d1a7c1887cc41114eaadb6866fe67cf07b606","content_id":"2190557f3ffcf6799c6d8315d6b01f461075933f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":11046,"license_type":"no_license","max_line_length":345,"num_lines":324,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/ParallelETUCT.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file ParallelETUCT.hh\n Defines my real-time model-based RL architecture which uses UCT with eligiblity traces for planning. \n The modified version of UCT used is presented in:\n L. Kocsis and C. Szepesv´ari, \"Bandit based monte-carlo planning,\" in\n ECML-06. Number 4212 in LNCS. Springer, 2006, pp. 282-293.\n The real-time architecture is presented in: \n Hester, Quinlan, and Stone, \"A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control\", arXiv 1105.1749, 2011.\n \\author Todd Hester\n*/\n\n#ifndef _ParallelETUCT_HH_\n#define _ParallelETUCT_HH_\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n#include <rl_common/ExperienceFile.hh>\n\n#include \"../Models/FactoredModel.hh\"\n#include \"../Models/C45Tree.hh\"\n\n#include <set>\n#include <vector>\n#include <map>\n#include <sstream>\n#include <deque>\n\n/** Parallel thread that continually does uct search from agent's current state. */\nvoid* parallelSearchStart(void* arg);\n\n/** Thread that loops, continually updating model with new experiences. */\nvoid* parallelModelLearningStart(void* arg);\n\n/** This class defines my real-time model-based RL architecture, which does model learning, planning, and acting on 3 separate threads. It uses a modified version of UCT for planning, which plans on a model using Monte Carlo rollouts. Unlike the original UCT, it does not separate values by tree depth, and it incorporates eligibility traces. */\nclass ParallelETUCT: public Planner {\npublic:\n\n\n /** Standard constructor\n \\param numactions, numactions in the domain\n \\param gamma discount factor\n \\param rrange range of one-step rewards in the domain\n \\param lambda for use with eligibility traces\n \\param MAX_ITER maximum number of MC rollouts to perform\n \\param MAX_TIME maximum amount of time to run Monte Carlo rollouts\n \\param MAX_DEPTH maximum depth to perform rollout to\n \\param modelType specifies model type\n \\param featmax maximum value of each feature\n \\param featmin minimum value of each feature\n \\param statesPerDim # of values to discretize each feature into\n \\param trackActual track actual real-valued states (or just discrete states)\n \\param historySize # of previous actions to use for delayed domains\n \\param rng random number generator\n */\n ParallelETUCT(int numactions, float gamma, float rrange, float lambda,\n int MAX_ITER, float MAX_TIME, int MAX_DEPTH, int modelType,\n const std::vector<float> &featmax, const std::vector<float> &featmin,\n const std::vector<int> &statesPerDim, bool trackActual, int historySize, Random rng = Random());\n \n /** Unimplemented copy constructor: internal state cannot be simply\n copied. */\n ParallelETUCT(const ParallelETUCT &);\n\n virtual ~ParallelETUCT();\n\n virtual void setModel(MDPModel* model);\n virtual bool updateModelWithExperience(const std::vector<float> &last, \n int act, \n const std::vector<float> &curr, \n float reward, bool term);\n virtual void planOnNewModel();\n virtual int getBestAction(const std::vector<float> &s);\n\n virtual void setSeeding(bool seed);\n virtual void setFirst();\n\n bool PLANNERDEBUG;\n bool POLICYDEBUG; //= false; //true;\n bool MODELDEBUG;\n bool ACTDEBUG;\n bool UCTDEBUG;\n bool PTHREADDEBUG;\n bool ATHREADDEBUG;\n bool MTHREADDEBUG;\n bool TIMINGDEBUG;\n bool REALSTATEDEBUG;\n bool HISTORYDEBUG;\n\n /** MDPModel that we're using with planning */\n MDPModel* model;\n\n /** Copy of our model used when updating the model, so the original can still be queried by the other threads. */\n MDPModel* modelcopy;\n\n /** The implementation maps all sensations to a set of canonical\n pointers, which serve as the internal representation of\n environment state. */\n typedef const std::vector<float> *state_t;\n\n ///////////////////////////////\n // parallel stuff\n ///////////////////////////////\n\n // tell if thread started\n bool modelThreadStarted;\n bool planThreadStarted;\n\n /** Thread that performs planning using UCT. */\n pthread_t planThread;\n\n /** Thread that performs model updates. */\n pthread_t modelThread;\n\n // some variables that are locked\n /** List of experiences from action thread to be added into model */\n std::vector<experience> expList;\n\n /** Agent's current discrete state that UCT rollouts should start from. */\n state_t discPlanState;\n \n /** Agent's current actual real-valued state that UCT rollouts should start from. */\n std::vector<float> actualPlanState;\n\n /** Canonical pointer to agent's current state that UCT rollouts should start from. */\n state_t startState;\n\n // lock over simple objects\n /** Mutex around the last frame that the model was updated. */\n pthread_mutex_t update_mutex;\n /** Mutex around the agent's current state to plan from. */\n pthread_mutex_t plan_state_mutex;\n /** Mutex around the model. */\n pthread_mutex_t model_mutex;\n /** Mutex around the list of experiences to be added to the model. */\n pthread_mutex_t list_mutex;\n /** Mutex around the history of previous actions of the agent. */\n pthread_mutex_t history_mutex;\n /** Mutex around the counter of how many actions the agent has taken. */\n pthread_mutex_t nactions_mutex;\n\n /** Mutex around the set of states. */\n pthread_mutex_t statespace_mutex;\n\n // condition for when list is updated\n pthread_cond_t list_cond; \n\n /** Perform UCT/Monte Carlo rollout from the given state.\n If terminal or at depth, return some reward.\n Otherwise, select an action based on UCB.\n Simulate action to get reward and next state.\n Call search on next state at depth+1 to get reward return from there on.\n Update q value towards new value: reward + gamma * searchReturn\n Update visit counts for confidence bounds\n Return q\n \n From \"Bandit Based Monte Carlo Planning\" by Kocsis and Szepesv´ari.\n */\n float uctSearch(const std::vector<float> &actS, state_t state, int depth, std::deque<float> &history);\n\n /** Select a random previously visited state. */\n std::vector<float> selectRandomState();\n\n /** Start the parallel model learning thread. */\n void parallelModelLearning();\n\n /** Start the parallel UCT planning thread. */\n void parallelSearch();\n\n /** Load a policy from a file. */\n void loadPolicy(const char* filename);\n\n /** Output value function to a file */\n void logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax);\n\n /** Add two vectors together. */\n std::vector<float> addVec(const std::vector<float> &a, const std::vector<float> &b);\n\n /** Subtract two vectors. */\n std::vector<float> subVec(const std::vector<float> &a, const std::vector<float> &b);\n\nprotected:\n\n\n struct state_info;\n struct model_info;\n\n /** A struct that contains a vector of possible next state samples, weighted by their probabilities. */\n struct state_samples {\n std::vector<state_t> samples;\n };\n\n /** State info struct. Maintains visit counts, models, and q-values for state-actions. */\n struct state_info {\n\n // data filled in from models\n std::map< std::deque<float>, StateActionInfo>* historyModel;\n\n // q values from policy creation\n std::vector<float> Q;\n\n // uct experience data\n int uctVisits;\n std::vector<int> uctActions;\n short unsigned int visited;\n short unsigned int id;\n\n // needs update\n bool needsUpdate;\n\n // mutex for model info, samples, everything else\n pthread_mutex_t statemodel_mutex;\n pthread_mutex_t stateinfo_mutex;\n\n };\n\n\n\n /** Initialize state info struct */\n void initStateInfo(state_t s,state_info* info, int id);\n \n /** Produces a canonical representation of the given sensation.\n \\param s The current sensation from the environment.\n \\return A pointer to an equivalent state in statespace. */\n state_t canonicalize(const std::vector<float> &s);\n\n /** Delete a state_info struct */\n void deleteInfo(state_info* info);\n \n /** Compuate a policy from a model */\n void createPolicy();\n \n /** Print information for each state. */\n void printStates();\n \n /** Calculate which states are reachable from states the agent has actually visited. */\n void calculateReachableStates();\n \n /** Remove states from set that were deemed unreachable. */\n void removeUnreachableStates();\n\n /** Update the state_info copy of the model for the given state-action from the MDPModel */\n void updateStateActionFromModel(state_t s, int a, state_info* info);\n \n /** Update the state_info copy of the model for the given state-action and k-action history from the MDPModel. */\n void updateStateActionHistoryFromModel(const std::vector<float> modState, int a, StateActionInfo *newModel);\n\n /** Get the current time in seconds */\n double getSeconds();\n\n // uct stuff\n /** Reset UCT visit counts to some baseline level (to decrease our confidence in q-values because model has changed. */\n void resetAndUpdateStateActions();\n \n /** Return a sampled state from the next state distribution of the model. \n Simulate the next state from the given state, action, and possibly history of past actions. */\n std::vector<float> simulateNextState(const std::vector<float> &actS, state_t state, state_info* info, const std::deque<float> &history, int action, float* reward, bool* term);\n \n /** Select UCT action based on UCB1 algorithm. */\n int selectUCTAction(state_info* info);\n \n /** Canonicalize all the next states predicted by this model. */\n void canonNextStates(StateActionInfo* modelInfo);\n\n /** Initialize the states for this domain (based on featmin and featmax) */\n void initStates();\n \n /** Fill in a state based on featmin and featmax */\n void fillInState(std::vector<float>s, int depth);\n\n virtual void savePolicy(const char* filename);\n \n /** Return a discretized version of the input state. */\n std::vector<float> discretizeState(const std::vector<float> &s);\n\nprivate:\n\n /** Set of all distinct sensations seen. Pointers to elements of\n this set serve as the internal representation of the environment\n state. */\n std::set<std::vector<float> > statespace;\n\n /** Hashmap mapping state vectors to their state_info structs. */\n std::map<state_t, state_info> statedata;\n\n std::vector<float> featmax;\n std::vector<float> featmin;\n\n /** Current history of previous actions. */\n std::deque<float> saHistory;\n\n state_t prevstate;\n int prevact;\n state_info* previnfo;\n\n double planTime;\n double initTime;\n double setTime;\n bool seedMode;\n\n int nstates;\n int nsaved;\n int nactions;\n int lastUpdate;\n\n bool timingType;\n\n const int numactions;\n const float gamma;\n const float rrange;\n const float lambda;\n\n const int MAX_ITER;\n const float MAX_TIME;\n const int MAX_DEPTH;\n const int modelType;\n const std::vector<int> &statesPerDim;\n const bool trackActual;\n const int HISTORY_SIZE;\n const int HISTORY_FL_SIZE;\n\n const unsigned CLEAR_SIZE;\n ExperienceFile expfile;\n};\n\n#endif\n"},{"alpha_fraction":0.6664590835571289,"alphanum_fraction":0.6772947907447815,"avg_line_length":28.302919387817383,"blob_id":"dbcd0028e3d7daaf31dfe877586eb43391a59103","content_id":"0e7aada902d45bd77880e42561ee4c2103727513","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":8029,"license_type":"no_license","max_line_length":441,"num_lines":274,"path":"/devel/include/rl_msgs/RLEnvDescription.h","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"// Generated by gencpp from file rl_msgs/RLEnvDescription.msg\n// DO NOT EDIT!\n\n\n#ifndef RL_MSGS_MESSAGE_RLENVDESCRIPTION_H\n#define RL_MSGS_MESSAGE_RLENVDESCRIPTION_H\n\n\n#include <string>\n#include <vector>\n#include <map>\n\n#include <ros/types.h>\n#include <ros/serialization.h>\n#include <ros/builtin_message_traits.h>\n#include <ros/message_operations.h>\n\n\nnamespace rl_msgs\n{\ntemplate <class ContainerAllocator>\nstruct RLEnvDescription_\n{\n typedef RLEnvDescription_<ContainerAllocator> Type;\n\n RLEnvDescription_()\n : num_actions(0.0)\n , num_states(0.0)\n , min_state_range()\n , max_state_range()\n , max_reward(0.0)\n , reward_range(0.0)\n , stochastic(false)\n , episodic(false)\n , title() {\n }\n RLEnvDescription_(const ContainerAllocator& _alloc)\n : num_actions(0.0)\n , num_states(0.0)\n , min_state_range(_alloc)\n , max_state_range(_alloc)\n , max_reward(0.0)\n , reward_range(0.0)\n , stochastic(false)\n , episodic(false)\n , title(_alloc) {\n (void)_alloc;\n }\n\n\n\n typedef float _num_actions_type;\n _num_actions_type num_actions;\n\n typedef float _num_states_type;\n _num_states_type num_states;\n\n typedef std::vector<float, typename ContainerAllocator::template rebind<float>::other > _min_state_range_type;\n _min_state_range_type min_state_range;\n\n typedef std::vector<float, typename ContainerAllocator::template rebind<float>::other > _max_state_range_type;\n _max_state_range_type max_state_range;\n\n typedef float _max_reward_type;\n _max_reward_type max_reward;\n\n typedef float _reward_range_type;\n _reward_range_type reward_range;\n\n typedef uint8_t _stochastic_type;\n _stochastic_type stochastic;\n\n typedef uint8_t _episodic_type;\n _episodic_type episodic;\n\n typedef std::basic_string<char, std::char_traits<char>, typename ContainerAllocator::template rebind<char>::other > _title_type;\n _title_type title;\n\n\n\n\n\n typedef boost::shared_ptr< ::rl_msgs::RLEnvDescription_<ContainerAllocator> > Ptr;\n typedef boost::shared_ptr< ::rl_msgs::RLEnvDescription_<ContainerAllocator> const> ConstPtr;\n\n}; // struct RLEnvDescription_\n\ntypedef ::rl_msgs::RLEnvDescription_<std::allocator<void> > RLEnvDescription;\n\ntypedef boost::shared_ptr< ::rl_msgs::RLEnvDescription > RLEnvDescriptionPtr;\ntypedef boost::shared_ptr< ::rl_msgs::RLEnvDescription const> RLEnvDescriptionConstPtr;\n\n// constants requiring out of line definition\n\n\n\ntemplate<typename ContainerAllocator>\nstd::ostream& operator<<(std::ostream& s, const ::rl_msgs::RLEnvDescription_<ContainerAllocator> & v)\n{\nros::message_operations::Printer< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >::stream(s, \"\", v);\nreturn s;\n}\n\n} // namespace rl_msgs\n\nnamespace ros\n{\nnamespace message_traits\n{\n\n\n\n// BOOLTRAITS {'IsFixedSize': False, 'IsMessage': True, 'HasHeader': False}\n// {'rl_msgs': ['/home/justin/ros_test/src/rl-texplore-ros-pkg-master/src/rl_msgs/msg'], 'std_msgs': ['/opt/ros/melodic/share/std_msgs/cmake/../msg']}\n\n// !!!!!!!!!!! ['__class__', '__delattr__', '__dict__', '__doc__', '__eq__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_parsed_fields', 'constants', 'fields', 'full_name', 'has_header', 'header_present', 'names', 'package', 'parsed_fields', 'short_name', 'text', 'types']\n\n\n\n\ntemplate <class ContainerAllocator>\nstruct IsFixedSize< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\n : FalseType\n { };\n\ntemplate <class ContainerAllocator>\nstruct IsFixedSize< ::rl_msgs::RLEnvDescription_<ContainerAllocator> const>\n : FalseType\n { };\n\ntemplate <class ContainerAllocator>\nstruct IsMessage< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\n : TrueType\n { };\n\ntemplate <class ContainerAllocator>\nstruct IsMessage< ::rl_msgs::RLEnvDescription_<ContainerAllocator> const>\n : TrueType\n { };\n\ntemplate <class ContainerAllocator>\nstruct HasHeader< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\n : FalseType\n { };\n\ntemplate <class ContainerAllocator>\nstruct HasHeader< ::rl_msgs::RLEnvDescription_<ContainerAllocator> const>\n : FalseType\n { };\n\n\ntemplate<class ContainerAllocator>\nstruct MD5Sum< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\n{\n static const char* value()\n {\n return \"41ee0d621b8031a958ff6d1f587a3860\";\n }\n\n static const char* value(const ::rl_msgs::RLEnvDescription_<ContainerAllocator>&) { return value(); }\n static const uint64_t static_value1 = 0x41ee0d621b8031a9ULL;\n static const uint64_t static_value2 = 0x58ff6d1f587a3860ULL;\n};\n\ntemplate<class ContainerAllocator>\nstruct DataType< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\n{\n static const char* value()\n {\n return \"rl_msgs/RLEnvDescription\";\n }\n\n static const char* value(const ::rl_msgs::RLEnvDescription_<ContainerAllocator>&) { return value(); }\n};\n\ntemplate<class ContainerAllocator>\nstruct Definition< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\n{\n static const char* value()\n {\n return \"# Message that contains details about an RL enviroment/task\\n\\\nfloat32 num_actions\\n\\\nfloat32 num_states\\n\\\n\\n\\\n#Optional information to describe the range of a continous state\\n\\\n#Some RL algorithms may need this to discretize the state space\\n\\\nfloat32[] min_state_range\\n\\\nfloat32[] max_state_range\\n\\\n\\n\\\n#Info needed for r-max some other methods\\n\\\nfloat32 max_reward\\n\\\nfloat32 reward_range\\n\\\n\\n\\\nbool stochastic\\n\\\nbool episodic\\n\\\nstring title\\n\\\n\";\n }\n\n static const char* value(const ::rl_msgs::RLEnvDescription_<ContainerAllocator>&) { return value(); }\n};\n\n} // namespace message_traits\n} // namespace ros\n\nnamespace ros\n{\nnamespace serialization\n{\n\n template<class ContainerAllocator> struct Serializer< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\n {\n template<typename Stream, typename T> inline static void allInOne(Stream& stream, T m)\n {\n stream.next(m.num_actions);\n stream.next(m.num_states);\n stream.next(m.min_state_range);\n stream.next(m.max_state_range);\n stream.next(m.max_reward);\n stream.next(m.reward_range);\n stream.next(m.stochastic);\n stream.next(m.episodic);\n stream.next(m.title);\n }\n\n ROS_DECLARE_ALLINONE_SERIALIZER\n }; // struct RLEnvDescription_\n\n} // namespace serialization\n} // namespace ros\n\nnamespace ros\n{\nnamespace message_operations\n{\n\ntemplate<class ContainerAllocator>\nstruct Printer< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\n{\n template<typename Stream> static void stream(Stream& s, const std::string& indent, const ::rl_msgs::RLEnvDescription_<ContainerAllocator>& v)\n {\n s << indent << \"num_actions: \";\n Printer<float>::stream(s, indent + \" \", v.num_actions);\n s << indent << \"num_states: \";\n Printer<float>::stream(s, indent + \" \", v.num_states);\n s << indent << \"min_state_range[]\" << std::endl;\n for (size_t i = 0; i < v.min_state_range.size(); ++i)\n {\n s << indent << \" min_state_range[\" << i << \"]: \";\n Printer<float>::stream(s, indent + \" \", v.min_state_range[i]);\n }\n s << indent << \"max_state_range[]\" << std::endl;\n for (size_t i = 0; i < v.max_state_range.size(); ++i)\n {\n s << indent << \" max_state_range[\" << i << \"]: \";\n Printer<float>::stream(s, indent + \" \", v.max_state_range[i]);\n }\n s << indent << \"max_reward: \";\n Printer<float>::stream(s, indent + \" \", v.max_reward);\n s << indent << \"reward_range: \";\n Printer<float>::stream(s, indent + \" \", v.reward_range);\n s << indent << \"stochastic: \";\n Printer<uint8_t>::stream(s, indent + \" \", v.stochastic);\n s << indent << \"episodic: \";\n Printer<uint8_t>::stream(s, indent + \" \", v.episodic);\n s << indent << \"title: \";\n Printer<std::basic_string<char, std::char_traits<char>, typename ContainerAllocator::template rebind<char>::other > >::stream(s, indent + \" \", v.title);\n }\n};\n\n} // namespace message_operations\n} // namespace ros\n\n#endif // RL_MSGS_MESSAGE_RLENVDESCRIPTION_H\n"},{"alpha_fraction":0.699404776096344,"alphanum_fraction":0.699404776096344,"avg_line_length":24.627119064331055,"blob_id":"fff286fa5a74767a9e0516e8aebc92df60e3addd","content_id":"680b0953284cb08534df0ddf4fa43eb944b51307","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":3024,"license_type":"no_license","max_line_length":91,"num_lines":118,"path":"/src/rl-texplore-ros-pkg-master/src/rl_env/include/rl_env/fourrooms.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#ifndef _FOURROOMS_H_\n#define _FOURROOMS_H_\n\n#include <set>\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n#include \"gridworld.hh\"\n\n/*\ninline ostream &operator<<(ostream &out, const room_action_t &a) {\n switch(a) {\n case NORTH: return out << \"NORTH\";\n case SOUTH: return out << \"SOUTH\";\n case EAST: return out << \"EAST\";\n case WEST: return out << \"WEST\";\n }\n return out;\n}\n*/\n\nclass FourRooms: public Environment {\npublic:\n /** Creates a FourRooms domain using the specified map.\n \\param rand Random number generator to use.\n \\param gridworld The map to use.\n \\param stochastic Whether to use nondeterministic actions. */\n FourRooms(Random &rand, const Gridworld *gridworld, bool stochastic);\n\n /** Creates a deterministic FourRooms domain.\n \\param rand Random number generator used solely for random\n initial states. \n \\param negReward Whether negative rewards are on/off.\n */\n FourRooms(Random &rand);\n\n /** Creates a possibly noisy FourRooms domain. */\n FourRooms(Random &rand, bool stochastic, bool negReward, bool exReward);\n\n /** Creates a possibly noisy FourRooms domain with distances to walls. */\n FourRooms(Random &rand, bool stochastic, bool negReward);\n\n /** Creates a Four Rooms domain with distance and reward distances */\n FourRooms(Random &rand, bool stochastic);\n\n /** Creates a random FourRooms domain of the given size. */\n FourRooms(Random &rand, unsigned width, unsigned height, bool stochastic);\n\n virtual ~FourRooms();\n\n virtual const std::vector<float> &sensation() const;\n virtual float apply(int action);\n\n virtual bool terminal() const;\n virtual void reset();\n\n virtual int getNumActions();\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\n virtual void getMinMaxReward(float* minR, float* maxR);\n\n const Gridworld &gridworld() const { return *grid; }\n\n friend std::ostream &operator<<(std::ostream &out, const FourRooms &rooms);\n\n std::vector<std::vector<float> > getSubgoals();\n void calcWallDistances();\n\n void setSensation(std::vector<float> newS);\n\nprotected:\n typedef std::pair<float,float> coord_t;\n enum room_action_t {NORTH, SOUTH, EAST, WEST};\n\nprivate:\n const Gridworld *const grid;\n coord_t goal;\n\n const bool negReward;\n const bool noisy;\n const bool extraReward;\n const bool rewardSensor;\n\n Random &rng;\n\n coord_t doorway;\n\n std::vector<float> s;\n std::vector<float> unused;\n\n float &ns;\n float &ew;\n\n float &distN;\n float &distS;\n float &distE;\n float &distW;\n\n float &rewardEW;\n float &rewardNS;\n\n const bool goalOption;\n\n const Gridworld *create_default_map();\n\n /** Corrupts a movement action.\n \\param action The intended action\n \\return The action actually executed */\n room_action_t add_noise(room_action_t action);\n\n /** Randomly assigns the goal to any random \n position in the world. */\n void randomize_goal();\n\n /** Return the correct reward based on the current state. */\n float reward(int effect);\n\n};\n\n#endif\n"},{"alpha_fraction":0.7690058350563049,"alphanum_fraction":0.7777777910232544,"avg_line_length":17.052631378173828,"blob_id":"83f94f19fedef334c928a152cb1d32ba6167c8fd","content_id":"0d699c0b65c0d795fa3805f6dbff7b17bee6ee04","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":342,"license_type":"no_license","max_line_length":63,"num_lines":19,"path":"/src/state_space_example/CMakeLists.txt","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"cmake_minimum_required(VERSION 2.8.3)\nproject(state_space_example)\n\nfind_package(catkin REQUIRED COMPONENTS\n roscpp\n tf\n visualization_msgs\n)\n\ncatkin_package()\n\ninclude_directories(\n ${catkin_INCLUDE_DIRS}\n)\n\nadd_executable(state_space_example src/state_space_example.cpp)\ntarget_link_libraries(state_space_example\n ${catkin_LIBRARIES}\n)"},{"alpha_fraction":0.5288340449333191,"alphanum_fraction":0.5650969743728638,"avg_line_length":16.727678298950195,"blob_id":"dc226f28144a528a96707845e72b55a676617826","content_id":"27b0d631bc902b407e1a48bed5140e8e8ba626d8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":3971,"license_type":"no_license","max_line_length":82,"num_lines":224,"path":"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/MountainCar.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file MountainCar.cc\n Implements the Mountain Car domain, with possible action delays or linearized \n transition dynamics.\n \\author Todd Hester\n*/\n\n#include <rl_env/MountainCar.hh>\n\n \nMountainCar::MountainCar(Random &rand):\n noisy(false),\n rng(rand),\n s(2),\n pos(s[0]),\n vel(s[1]),\n linear(false),\n delay(0)\n{\n reset();\n //cout << *this << endl;\n}\n \n\nMountainCar::MountainCar(Random &rand, bool stochastic, bool lin, int delay):\n noisy(stochastic),\n rng(rand),\n s(2),\n pos(s[0]),\n vel(s[1]),\n linear(lin),\n delay(delay)\n{\n reset();\n}\n\n\nMountainCar::~MountainCar() { }\n\nconst std::vector<float> &MountainCar::sensation() const { \n //cout << \"At state \" << s[0] << \", \" << s[1] << endl;\n\n return s; \n}\n\nfloat MountainCar::apply(int action) {\n\n //cout << \"Taking action \" << action << endl;\n\n float actVal = ((float)action-1.0);\n if (noisy){\n actVal += rng.uniform(-0.5, 0.5);\n }\n\n float newVel = vel;\n if (linear){\n // for now, make this linear\n newVel = vel + 0.001 * actVal + -0.0075*pos;\n } else {\n newVel = vel + 0.001 * actVal + -0.0025*cos(3.0*pos);\n }\n\n newVel = bound(newVel, -0.07, 0.07);\n\n float newPos = pos + vel;\n if (newPos < -1.2f && newVel < 0.0f)\n newVel = 0.0;\n newPos = bound(newPos, -1.2, 0.6);\n\n pos = newPos;\n vel = newVel;\n\n if (delay > 0){\n posHistory.push_back(newPos);\n pos = posHistory.front();\n posHistory.pop_front();\n velHistory.push_back(newVel);\n vel = velHistory.front();\n velHistory.pop_front();\n // cout << \"new pos: \" << newPos << \" observed: \" << pos << endl;\n //cout << \"new vel: \" << newVel << \" observed: \" << vel << endl;\n }\n\n return reward();\n\n}\n\nfloat MountainCar::bound(float val, float min, float max){\n if (val < min)\n return min;\n if (val > max)\n return max;\n return val;\n}\n\n\nfloat MountainCar::reward() {\n \n // normally -1 and 0 on goal\n if (terminal())\n return 0;\n else \n return -1;\n \n}\n\n\nbool MountainCar::terminal() const {\n // current position equal to goal??\n return (pos >= 0.6); \n}\n\n\n\nvoid MountainCar::reset() {\n\n if (noisy){\n pos = rng.uniform(-1.2, 0.59);\n vel = rng.uniform(-0.07, 0.07);\n } else {\n pos = 0;\n vel = 0;\n }\n\n pos = rng.uniform(-1.2, 0.59);\n vel = rng.uniform(-0.07, 0.07);\n\n if (delay > 0){\n posHistory.clear();\n velHistory.clear();\n for (int i = 0; i < delay; i++){\n posHistory.push_back(pos);\n velHistory.push_back(vel);\n }\n }\n\n}\n\n\nint MountainCar::getNumActions(){\n return 3;\n}\n\n\nvoid MountainCar::setSensation(std::vector<float> newS){\n if (s.size() != newS.size()){\n cerr << \"Error in sensation sizes\" << endl;\n }\n\n for (unsigned i = 0; i < newS.size(); i++){\n s[i] = newS[i];\n }\n}\n\nstd::vector<experience> MountainCar::getSeedings() {\n\n int origDelay = delay;\n delay = 0;\n\n // return seedings\n std::vector<experience> seeds;\n\n // two seeds of terminal state\n seeds.push_back(getExp(0.58, 0.03, 2));\n //seeds.push_back(getExp(0.57, 0.06, 2));\n\n // random seed of each action\n for (int i = 0; i < getNumActions(); i++){\n float p = rng.uniform(-1.2, 0.6);\n float v = rng.uniform(-0.07, 0.07);\n seeds.push_back(getExp(p, v, i));\n }\n\n delay = origDelay;\n\n reset();\n\n return seeds;\n\n}\n\nexperience MountainCar::getExp(float s0, float s1, int a){\n\n experience e;\n\n e.s.resize(2, 0.0);\n e.next.resize(2, 0.0);\n\n pos = s0;\n vel = s1;\n\n e.act = a;\n e.s = sensation();\n e.reward = apply(e.act);\n\n e.terminal = terminal();\n e.next = sensation();\n\n reset();\n\n return e;\n}\n\n\nvoid MountainCar::getMinMaxFeatures(std::vector<float> *minFeat,\n std::vector<float> *maxFeat){\n \n minFeat->resize(s.size(), 0.0);\n maxFeat->resize(s.size(), 1.0);\n\n (*minFeat)[0] = -1.2;\n (*maxFeat)[0] = 0.6;\n\n (*minFeat)[1] = -0.07;\n (*maxFeat)[1] = 0.07;\n\n}\n\nvoid MountainCar::getMinMaxReward(float *minR,\n float *maxR){\n \n *minR = -1.0;\n *maxR = 0.0; \n 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gazebo_msgs_generate_messages_nodejs\"\n.PHONY : help\n\n\n\n#=============================================================================\n# Special targets to cleanup operation of make.\n\n# Special rule to run CMake to check the build system integrity.\n# No rule that depends on this can have commands that come from listfiles\n# because they might be regenerated.\ncmake_check_build_system:\n\tcd /home/justin/ros_test/build && $(CMAKE_COMMAND) -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR) --check-build-system CMakeFiles/Makefile.cmake 0\n.PHONY : 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solely for random\n initial states. \n */\n CartPole(Random &rand);\n\n /** Creates a Cart-Pole domain, possibly with noise.\n \\param rand Random number generator\n \\param stochastic noisy transitions? \n */\n CartPole(Random &rand, bool stochastic);\n\n virtual ~CartPole();\n\n virtual const std::vector<float> &sensation() const;\n virtual float apply(int action);\n\n /** Calculate the new state and reward for the given force */\n float transition(float force);\n\n virtual bool terminal() const;\n virtual void reset();\n\n virtual int getNumActions();\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\n virtual void getMinMaxReward(float* minR, float* maxR);\n\n /** Set the state vector (for debug purposes) */\n void setSensation(std::vector<float> newS);\n\n virtual std::vector<experience> getSeedings();\n\n /** Get an experience for the given state-action */\n experience getExp(float s0, float s1, float s2, float s3, int a);\n \nprotected:\n enum car_action_t {LEFT, RIGHT};\n\nprivate:\n\n float GRAVITY;\n float MASSCART;\n float MASSPOLE;\n float TOTAL_MASS;\n float LENGTH;\n \n float POLEMASS_LENGTH;\n float FORCE_MAG;\n float TAU;\n \n float FOURTHIRDS;\n float DEG_T_RAD;\n float RAD_T_DEG;\n\n const bool noisy;\n Random &rng;\n\n std::vector<float> s;\n \n float &cartPos;\n float &cartVel;\n float &poleAngle;\n float &poleVel;\n\n float reward();\n\n};\n\n#endif\n"},{"alpha_fraction":0.7238317728042603,"alphanum_fraction":0.7254672646522522,"avg_line_length":34.96638488769531,"blob_id":"60f64263a842d0b0917c8c855d05deb554633e23","content_id":"5e531cb236a5257837126028cbee49b949dd02cd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":4280,"license_type":"no_license","max_line_length":185,"num_lines":119,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/FactoredModel.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file FactoredModel.hh\n Defines the FactoredModel class\n Please cite: Hester and Stone, \"Real Time Targeted Exploration in Large Domains\", ICDL 2010.\n \\author Todd Hester\n*/\n\n#ifndef _FACTOREDMODEL_HH_\n#define _FACTOREDMODEL_HH_\n\n#include \"../Models/C45Tree.hh\"\n#include \"../Models/M5Tree.hh\"\n#include \"../Models/LinearSplitsTree.hh\"\n#include \"../Models/Stump.hh\"\n#include \"../Models/MultipleClassifiers.hh\"\n#include \"../Models/SepPlanExplore.hh\"\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n#include <vector>\n\n\n/** Builds an mdp model consisting of a tree (or ensemble of trees) to predict each feature, reward, and termination probability. Thus forming a complete model of the MDP. */\nclass FactoredModel: public MDPModel {\npublic:\n\n /** Default constructor\n \\param id identify the model\n \\param numactions # of actions in the domain\n \\param M # of visits for a given state-action to be considered known\n \\param modelType identifies which type of model to use\n \\param predType identifies how to combine multiple models\n \\param nModels # of models to use for ensemble models (i.e. random forests)\n \\param treeThreshold determines the amount of error to be tolerated in the tree (prevents over-fitting with larger and larger trees)\n \\param featRange range of each feature in the domain\n \\param rRange range of reward values in domain\n \\param needConf do we need confidence measures?\n \\param dep assume dependent transitions between features (or indep)\n \\param relTrans model relative transitions of features (or absolute)\n \\param featPct pct of features to remove from set used for each tree split\n \\param stoch if the domain is stochastic or deterministic\n \\param episodic if the domain is episodic\n \\param rng Random Number Generator \n */\n FactoredModel(int id, int numactions, int M, int modelType, \n int predType, int nModels, float treeThreshold,\n const std::vector<float> &featRange, float rRange,\n bool needConf, bool dep, bool relTrans, float featPct, \n\t bool stoch, bool episodic, Random rng = Random());\n\n /** Copy Constructor for MDP Tree */\n FactoredModel(const FactoredModel &);\n\n virtual ~FactoredModel();\n\n virtual bool updateWithExperiences(std::vector<experience> &instances);\n virtual bool updateWithExperience(experience &e);\n\n /** Initialize the MDP model with the given # of state features */\n bool initMDPModel(int nfactors);\n virtual float getStateActionInfo(const std::vector<float> &state, int act, StateActionInfo* retval);\n virtual FactoredModel* getCopy();\n\n /** Method to get a single sample of the predicted next state for the given state-action, rather than the full distribution given by getStateActionInfo */\n float getSingleSAInfo(const std::vector<float> &state, int act, StateActionInfo* retval);\n\n /** Combines predictions for each separate state feature into probabilities of the overall state vector */\n void addFactorProb(float* probs, std::vector<float>* next, std::vector<float> x, StateActionInfo* retval, int index, std::vector< std::map<float,float> > predictions, float* confSum);\n\n /** Set some parameters of the subtrees */\n void setTreeParams(float margin, float forestPct, float minRatio);\n\n /** Helper function to add two vectors together */\n std::vector<float> addVec(const std::vector<float> &a, const std::vector<float> &b);\n\n /** Helper function to subtract two vectors */\n std::vector<float> subVec(const std::vector<float> &a, const std::vector<float> &b);\n \nprivate:\n \n /** Classifier to predict each feature */\n std::vector<Classifier*> outputModels;\n\n /** Classifier to predict reward */\n Classifier* rewardModel;\n\n /** Classifier to prediction termination probability */\n Classifier* terminalModel;\n\n int id;\n int nfactors;\n const int nact;\n const int M;\n const int modelType;\n const int predType;\n const int nModels;\n const int treeBuildType;\n const float treeThresh;\n\n const std::vector<float> featRange;\n const float rRange;\n\n const bool needConf;\n const bool dep;\n const bool relTrans;\n const float FEAT_PCT;\n const bool stoch;\n const bool episodic;\n Random rng;\n \n float EXP_PCT;\n\n bool MODEL_DEBUG;\n bool COPYDEBUG;\n\n};\n\n\n\n#endif\n"},{"alpha_fraction":0.6179324984550476,"alphanum_fraction":0.6217840313911438,"avg_line_length":26.388185501098633,"blob_id":"96ae44dc8a3f9ec544a1c988f3782a7fe54b00ec","content_id":"d05c1eba14fb397d604b2586f10d2bb71ca4b36e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":6491,"license_type":"no_license","max_line_length":106,"num_lines":237,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/RMaxModel.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file RMaxModel.cc\n Implements the RMaxModel class.\n \\author Todd Hester\n*/\n\n#include \"RMaxModel.hh\"\n\n\n\n\nRMaxModel::RMaxModel(int m, int nact, Random rng):\n M(m), nact(nact), rng(rng)\n{\n\n nstates = 0;\n RMAX_DEBUG = false; //true;\n //initMDPModel(nfactors); \n}\n\nRMaxModel::RMaxModel(const RMaxModel &rm):\nM(rm.M), nact(rm.nact), rng(rm.rng)\n{\n\n nstates = rm.nstates;\n RMAX_DEBUG = rm.RMAX_DEBUG;\n\n statespace = rm.statespace;\n\n // copy info into map for each of these\n // because map is by address, must get correct address for each\n for (std::map<state_t, state_info>::const_iterator i = rm.statedata.begin();\n i != rm.statedata.end(); i++){\n\n state_t s = canonicalize(*((*i).first));\n statedata[s] = (*i).second;\n\n }\n\n}\n\nRMaxModel* RMaxModel::getCopy(){\n RMaxModel* copy = new RMaxModel(*this);\n return copy;\n}\n\nRMaxModel::~RMaxModel() {}\n\n\nbool RMaxModel::updateWithExperiences(std::vector<experience> &instances){\n\n bool changed = false;\n \n for (unsigned i = 0; i < instances.size(); i++){\n bool singleChange = updateWithExperience(instances[i]);\n changed = changed || singleChange;\n }\n return changed;\n}\n\n\n// update all the counts, check if model has changed\n// stop counting at M\nbool RMaxModel::updateWithExperience(experience &e){\n if (RMAX_DEBUG) cout << \"updateWithExperience \" << &(e.s) << \", \" << e.act \n << \", \" << &(e.next) << \", \" << e.reward << endl;\n\n // get state info for last state\n state_t l = canonicalize(e.s);\n state_info* info = &(statedata[l]);\n\n bool modelChanged = false;\n\n // stop at M\n //if (info->visits[e.act] == M)\n if (info->known[e.act])\n return false;\n\n // update visit count for action just executed\n info->visits[e.act]++;\n\n // update termination count\n if (e.terminal) info->terminations[e.act]++;\n\n // update reward sum for this action\n info->Rsum[e.act] += e.reward;\n\n // update transition count for outcome that occured\n std::vector<int> &transCounts = info->outCounts[e.next];\n\n // first, check that we have resized this counter\n checkTransitionCountSize(&transCounts);\n\n // only update state transition counts for non-terminal transitions\n if (!e.terminal){\n // then update transition count for this action/outcome\n transCounts[e.act]++;\n } \n\n // check if count becomes known \n if (!info->known[e.act] && info->visits[e.act] >= M){\n info->known[e.act] = true;\n modelChanged = true;\n }\n\n if (RMAX_DEBUG) cout << \"s\" << info->id << \" act: \" << e.act \n << \" transCounts[act] = \" << transCounts[e.act] \n << \" visits[act] = \" << info->visits[e.act] << endl;\n \n // anything that got past the 'return false' above is a change in conf or predictions\n return true; //modelChanged;\n\n}\n\n\n// calculate state info such as transition probs, known/unknown, reward prediction\nfloat RMaxModel::getStateActionInfo(const std::vector<float> &state, int act, StateActionInfo* retval){\n if (RMAX_DEBUG) cout << \"getStateActionInfo, \" << &state << \", \" << act << endl;\n\n\n retval->transitionProbs.clear();\n\n // get state-action info for this state\n state_t l = canonicalize(state);\n state_info* info = &(statedata[l]);\n\n \n // see if it has any visits (could still be unknown)\n if (info->visits[act] == 0){\n if (RMAX_DEBUG) cout << \"This outcome is unknown\" << endl;\n retval->reward = -0.001;\n\n // add to transition map\n retval->transitionProbs[state] = 1.0;\n retval->known = false;\n retval->termProb = 0.0;\n return 0;\n }\n \n \n // fill in transition probs\n for (std::map<std::vector<float>, std::vector<int> >::iterator it = info->outCounts.begin(); \n it != info->outCounts.end(); it++){\n\n // get key from iterator\n std::vector<float> next = (*it).first;\n int count = ((*it).second)[act];\n\n // add to transition map\n if (count > 0.0){\n retval->transitionProbs[next] = (float)count / (float)(info->visits[act] - info->terminations[act]);\n if (RMAX_DEBUG) cout << \"Outcome \" << &next << \" has prob \" << retval->transitionProbs[next]\n\t\t\t << \" from count of \" << count << \" on \" \n\t\t\t << info->visits[act] << \" visits.\" << endl;\n }\n }\n \n\n // add in avg rewrad\n retval->reward = (float)info->Rsum[act] / (float)info->visits[act];\n if (RMAX_DEBUG) cout << \"Avg Reward of \" << retval->reward << \" from reward sum of \" \n\t\t << info->Rsum[act] \n\t\t << \" on \" << info->visits[act] << \" visits.\" << endl;\n\n // termination probability\n retval->termProb = (float)info->terminations[act] / (float)info->visits[act];\n if (RMAX_DEBUG) cout << \"termProb: \" << retval->termProb << endl;\n if (retval->termProb < 0 || retval->termProb > 1){\n cout << \"Problem with termination probability: \" << retval->termProb << endl;\n }\n\n\n retval->known = info->known[act];\n // conf as a pct of float m (so 0.5 is exactly M)\n float conf = (float)info->visits[act]/ (2.0 * (float)M);\n\n return conf;\n\n}\n\n\n\n\n\nRMaxModel::state_t RMaxModel::canonicalize(const std::vector<float> &s) {\n if (RMAX_DEBUG) cout << \"canonicalize, s = \" << &s << endl;\n\n // get state_t for pointer if its in statespace\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\n statespace.insert(s);\n state_t retval = &*result.first; // Dereference iterator then get pointer \n\n if (RMAX_DEBUG) cout << \" returns \" << retval << endl;\n\n // if not, init this new state\n if (result.second) { // s is new, so initialize Q(s,a) for all a\n initNewState(retval);\n }\n\n return retval; \n}\n\nvoid RMaxModel::initNewState(state_t s){\n if (RMAX_DEBUG) cout << \"initNewState(s = \" << s \n\t\t << \")\" << endl;\n \n // create state info and add to hash map\n state_info* info = &(statedata[s]);\n initStateInfo(info);\n\n}\n\n\n// init state info\nvoid RMaxModel::initStateInfo(state_info* info){\n if (RMAX_DEBUG) cout << \"initStateInfo()\";\n\n info->id = nstates++;\n if (RMAX_DEBUG) cout << \" id = \" << info->id << endl;\n\n // model data (q values, state-action counts, transition, etc)\n info->visits.resize(nact, 0);\n info->Rsum.resize(nact, 0);\n info->known.resize(nact, false);\n info->terminations.resize(nact, 0);\n\n}\n\n\nvoid RMaxModel::checkTransitionCountSize(std::vector<int>* transCounts){\n if (RMAX_DEBUG) cout << \"checkTransitionCountSize(transCounts) \" \n\t\t << \"size: \" << transCounts->size() << endl;\n\n // resize to numactions if not initialized for this outcome yet\n if (transCounts->size() == 0)\n transCounts->resize(nact, 0);\n\n}\n"},{"alpha_fraction":0.7881137132644653,"alphanum_fraction":0.7932816743850708,"avg_line_length":47.4375,"blob_id":"c4085ed04f7c5fc5456315414b25c63ebbcef847","content_id":"cfef3dfd824b9134640b05cc88e35c389a9ea695","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":774,"license_type":"no_license","max_line_length":74,"num_lines":16,"path":"/build/roundbot_control/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"roundbot_control\")\nset(roundbot_control_VERSION \"0.0.0\")\nset(roundbot_control_MAINTAINER \"micho <micho@todo.todo>\")\nset(roundbot_control_PACKAGE_FORMAT \"1\")\nset(roundbot_control_BUILD_DEPENDS \"controller_interface\")\nset(roundbot_control_BUILD_EXPORT_DEPENDS \"controller_interface\" \"roscpp\")\nset(roundbot_control_BUILDTOOL_DEPENDS \"catkin\" \"roscpp\")\nset(roundbot_control_BUILDTOOL_EXPORT_DEPENDS )\nset(roundbot_control_EXEC_DEPENDS \"controller_interface\" \"roscpp\")\nset(roundbot_control_RUN_DEPENDS \"controller_interface\" \"roscpp\")\nset(roundbot_control_TEST_DEPENDS )\nset(roundbot_control_DOC_DEPENDS )\nset(roundbot_control_URL_WEBSITE \"\")\nset(roundbot_control_URL_BUGTRACKER \"\")\nset(roundbot_control_URL_REPOSITORY \"\")\nset(roundbot_control_DEPRECATED \"\")"},{"alpha_fraction":0.7630208134651184,"alphanum_fraction":0.7708333134651184,"avg_line_length":54,"blob_id":"48d1a3f6b4ed1166e33d4a97209921d2f3a64f21","content_id":"5c8574684bc3ad9441d8300a1627fc86f76f8883","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":384,"license_type":"no_license","max_line_length":73,"num_lines":7,"path":"/src/state_space_example/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"state_space_example\")\nset(state_space_example_MAINTAINER \"student <student@todo.todo>\")\nset(state_space_example_DEPRECATED \"\")\nset(state_space_example_VERSION \"0.0.0\")\nset(state_space_example_BUILD_DEPENDS \"roscpp\" \"tf\" \"visualization_msgs\")\nset(state_space_example_RUN_DEPENDS \"roscpp\" \"tf\" \"visualization_msgs\")\nset(state_space_example_BUILDTOOL_DEPENDS \"catkin\")"},{"alpha_fraction":0.5211640000343323,"alphanum_fraction":0.529629647731781,"avg_line_length":18.6875,"blob_id":"c75cc5357c1696d4a63cac010fda443fbdc422ee","content_id":"f0badfbc2c8094ad659c731cbd21d725d381fe70","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":3780,"license_type":"no_license","max_line_length":79,"num_lines":192,"path":"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/stocks.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include <rl_env/stocks.hh>\n\n\nStocks::Stocks(Random &rand, bool stochastic, int nsectors, int nstocks):\n nsectors(nsectors),\n nstocks(nstocks),\n noisy(stochastic),\n rng(rand),\n s(nsectors*nstocks + nsectors)\n{\n STOCK_DEBUG = false; //true;\n\n initStocks();\n reset();\n}\n\n\nStocks::Stocks(Random &rand, bool stochastic):\n nsectors(3),\n nstocks(2),\n noisy(stochastic),\n rng(rand),\n s(nsectors*nstocks + nsectors)\n{\n STOCK_DEBUG = false; //true;\n\n initStocks();\n reset();\n}\n\n\nStocks::~Stocks() {\n delete [] owners;\n for (int i = 0; i < nsectors; i++){\n delete [] rising[i];\n }\n delete [] rising;\n}\n\nconst std::vector<float> &Stocks::sensation() const { return s; }\n\nfloat Stocks::apply(int action) {\n\n // figure out ownership of stocks with this action\n if (action < nsectors){\n // flip that bit of sensation array\n s[owners[action]] = !s[owners[action]];\n }\n else if (action == nsectors){\n // do nothing\n }\n else {\n cout << \"Invalid action!\" << endl;\n }\n\n if (STOCK_DEBUG){\n cout << \"Action: \" << action << \" Ownership now: \";\n for (int i = 0; i < nsectors; i++){\n cout << s[owners[i]] << \", \";\n }\n cout << endl;\n }\n\n float r = reward();\n\n calcStockRising();\n\n return r;\n\n}\n\nvoid Stocks::calcStockRising() {\n\n // for each sector\n for (int i = 0; i < nsectors; i++){\n // get average of stocks\n float sum = 0;\n for (int j = 0; j < nstocks; j++){\n sum += s[rising[i][j]];\n }\n float riseProb = 0.1 + 0.8 * (sum / (float)nstocks);\n\n // set value for each stock\n for (int j = 0; j < nstocks; j++){\n if (noisy) {\n s[rising[i][j]] = rng.bernoulli(riseProb);\n } else {\n s[rising[i][j]] = (riseProb > 0.5);\n }\n }\n\n if (STOCK_DEBUG){\n cout << \"S\" << i << \" Prob: \" << riseProb << \" stocks now: \";\n for (int j = 0; j < nstocks; j++){\n cout << s[rising[i][j]] << \", \";\n }\n cout << endl;\n }\n\n }\n\n}\n\nfloat Stocks::reward() {\n\n float sum = 0.0;\n\n // for each sector\n for (int i = 0; i < nsectors; i++){\n // skip if we don't own it\n if (!s[owners[i]])\n continue;\n\n if (STOCK_DEBUG) cout << \"Own sector \" << i << \": \";\n\n\n // add 1 for each rising, sub 1 for each not\n for (int j = 0; j < nstocks; j++){\n if (s[rising[i][j]]){\n sum++;\n if (STOCK_DEBUG) cout << \"rising, \";\n } else {\n sum--;\n if (STOCK_DEBUG) cout << \"falling, \";\n }\n }\n\n if (STOCK_DEBUG) cout << endl;\n }\n\n if (STOCK_DEBUG)\n cout << \"Reward is \" << sum << endl;\n\n return sum;\n\n}\n\nbool Stocks::terminal() const {\n return false;\n}\n\nvoid Stocks::initStocks(){\n // set owners and rising variables\n owners = new int[nsectors];\n rising = new int*[nsectors];\n for (int i = 0; i < nsectors; i++){\n rising[i] = new int[nstocks];\n owners[i] = i;\n if (STOCK_DEBUG)\n cout << \"Owners[\" << i << \"] is \" << owners[i] << endl;\n for (int j = 0; j < nstocks; j++){\n rising[i][j] = nsectors + (i*nstocks) + j;\n if (STOCK_DEBUG)\n cout << \"Rising[\" << i << \"][\" << j << \"] is \" << rising[i][j] << endl;\n }\n }\n}\n\nvoid Stocks::reset() {\n\n // random values for each variable\n for (unsigned i = 0; i < s.size(); i++){\n s[i] = rng.uniformDiscrete(0,1);\n }\n}\n\n\nint Stocks::getNumActions() {\n return nsectors+1;\n}\n\nvoid Stocks::setSensation(std::vector<float> sIn){\n for (unsigned i = 0; i < s.size(); i++){\n s[i] = sIn[i];\n }\n}\n\n\nvoid Stocks::getMinMaxFeatures(std::vector<float> *minFeat,\n std::vector<float> *maxFeat){\n\n minFeat->resize(s.size(), 0.0);\n maxFeat->resize(s.size(), 1.0);\n\n}\n\nvoid Stocks::getMinMaxReward(float *minR,\n float *maxR){\n\n *minR = -(nsectors*nstocks);\n *maxR = (nsectors*nstocks);\n}\n"},{"alpha_fraction":0.74842768907547,"alphanum_fraction":0.7547169923782349,"avg_line_length":38.8125,"blob_id":"23fb2095d804446a816fcd243e04c6c2d9b9258b","content_id":"d0991a771cccb8cebd8d331cc653fdee790873c6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":636,"license_type":"no_license","max_line_length":52,"num_lines":16,"path":"/build/mantis_model/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"mantis_model\")\nset(mantis_model_VERSION \"0.0.0\")\nset(mantis_model_MAINTAINER \"abc <abc@todo.todo>\")\nset(mantis_model_PACKAGE_FORMAT \"1\")\nset(mantis_model_BUILD_DEPENDS \"roscpp\" \"tf\")\nset(mantis_model_BUILD_EXPORT_DEPENDS \"roscpp\" \"tf\")\nset(mantis_model_BUILDTOOL_DEPENDS \"catkin\")\nset(mantis_model_BUILDTOOL_EXPORT_DEPENDS )\nset(mantis_model_EXEC_DEPENDS \"roscpp\" \"tf\")\nset(mantis_model_RUN_DEPENDS \"roscpp\" \"tf\")\nset(mantis_model_TEST_DEPENDS )\nset(mantis_model_DOC_DEPENDS )\nset(mantis_model_URL_WEBSITE \"\")\nset(mantis_model_URL_BUGTRACKER \"\")\nset(mantis_model_URL_REPOSITORY \"\")\nset(mantis_model_DEPRECATED \"\")"},{"alpha_fraction":0.7963836193084717,"alphanum_fraction":0.8042452931404114,"avg_line_length":73.88235473632812,"blob_id":"8b8d7133e3269cfff55be05cbb6fa586d53ddd68","content_id":"9861eed83234d81cf7cec85abb9074573955163e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":1272,"license_type":"no_license","max_line_length":192,"num_lines":17,"path":"/src/gmapping_example/catkin_generated/setup_cached.sh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#!/usr/bin/env sh\n# generated from catkin/python/catkin/environment_cache.py\n\n# based on a snapshot of the environment before and after calling the setup script\n# it emulates the modifications of the setup script without recurring computations\n\n# new environment variables\n\n# modified environment variables\nexport CMAKE_PREFIX_PATH=\"/home/jman/ros/src/ugv_course/gmapping_example/devel:$CMAKE_PREFIX_PATH\"\nexport CPATH=\"/home/jman/ros/src/ugv_course/gmapping_example/devel/include:$CPATH\"\nexport LD_LIBRARY_PATH=\"/home/jman/ros/src/ugv_course/gmapping_example/devel/lib:/home/jman/ros/src/ugv_course/gmapping_example/devel/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH\"\nexport PATH=\"/home/jman/ros/src/ugv_course/gmapping_example/devel/bin:$PATH\"\nexport PKG_CONFIG_PATH=\"/home/jman/ros/src/ugv_course/gmapping_example/devel/lib/pkgconfig:/home/jman/ros/src/ugv_course/gmapping_example/devel/lib/x86_64-linux-gnu/pkgconfig:$PKG_CONFIG_PATH\"\nexport PYTHONPATH=\"/home/jman/ros/src/ugv_course/gmapping_example/devel/lib/python2.7/dist-packages:$PYTHONPATH\"\nexport ROSLISP_PACKAGE_DIRECTORIES=\"/home/jman/ros/src/ugv_course/gmapping_example/devel/share/common-lisp:$ROSLISP_PACKAGE_DIRECTORIES\"\nexport ROS_PACKAGE_PATH=\"/home/jman/ros/src/ugv_course/gmapping_example:$ROS_PACKAGE_PATH\""},{"alpha_fraction":0.6051876544952393,"alphanum_fraction":0.6110495924949646,"avg_line_length":26.971851348876953,"blob_id":"4d28e1fe68d102ff7df5a3952c6df8208299b07e","content_id":"64c6a4db0ecb62eda0c53b02daaf261857cc3547","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":39748,"license_type":"no_license","max_line_length":201,"num_lines":1421,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/ParallelETUCT.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file ParallelETUCT.cc\n Implements my real-time model-based RL architecture which uses UCT with eligiblity traces for planning.\n The modified version of UCT used is presented in:\n L. Kocsis and C. Szepesv´ari, \"Bandit based monte-carlo planning,\" in\n ECML-06. Number 4212 in LNCS. Springer, 2006, pp. 282-293.\n The real-time architecture is presented in:\n Hester, Quinlan, and Stone, \"A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control\", arXiv 1105.1749, 2011.\n \\author Todd Hester\n*/\n\n#include \"ParallelETUCT.hh\"\n#include <algorithm>\n\n#include <sys/time.h>\n\n\nParallelETUCT::ParallelETUCT(int numactions, float gamma, float rrange, float lambda,\n int MAX_ITER, float MAX_TIME, int MAX_DEPTH, int modelType,\n const std::vector<float> &fmax, const std::vector<float> &fmin,\n const std::vector<int> &nstatesPerDim, bool trackActual, int historySize, Random r):\n numactions(numactions), gamma(gamma), rrange(rrange), lambda(lambda),\n MAX_ITER(MAX_ITER), MAX_TIME(MAX_TIME),\n MAX_DEPTH(MAX_DEPTH), modelType(modelType), statesPerDim(nstatesPerDim),\n trackActual(trackActual), HISTORY_SIZE(historySize),\n HISTORY_FL_SIZE(historySize*numactions),\n CLEAR_SIZE(25)\n{\n rng = r;\n\n nstates = 0;\n nsaved = 0;\n nactions = 0;\n lastUpdate = -1;\n\n seedMode = false;\n timingType = true;\n\n previnfo = NULL;\n model = NULL;\n planTime = getSeconds();\n initTime = getSeconds();\n setTime = getSeconds();\n\n PLANNERDEBUG = false;\n POLICYDEBUG = false; //true; //false; //true; //false;\n ACTDEBUG = false; //true;\n MODELDEBUG = false;\n UCTDEBUG = false;\n PTHREADDEBUG = false;\n ATHREADDEBUG = false;//true;\n MTHREADDEBUG = false; //true;\n TIMINGDEBUG = false;\n REALSTATEDEBUG = false;\n HISTORYDEBUG = false; //true;\n\n if (statesPerDim[0] > 0){\n cout << \"Planner Parallel ETUCT using discretization of \" << statesPerDim[0] << endl;\n }\n if (trackActual){\n cout << \"Parallel ETUCT tracking real state values\" << endl;\n }\n cout << \"Planner using history size: \" << HISTORY_SIZE << endl;\n\n featmax = fmax;\n featmin = fmin;\n\n pthread_mutex_init(&update_mutex, NULL);\n pthread_mutex_init(&history_mutex, NULL);\n pthread_mutex_init(&nactions_mutex, NULL);\n pthread_mutex_init(&plan_state_mutex, NULL);\n pthread_mutex_init(&statespace_mutex, NULL);\n pthread_mutex_init(&model_mutex, NULL);\n pthread_mutex_init(&list_mutex, NULL);\n pthread_cond_init(&list_cond, NULL);\n\n // start parallel search thread\n actualPlanState = std::vector<float>(featmax.size());\n discPlanState = NULL;\n modelThreadStarted = false;\n planThreadStarted = false;\n expList.clear();\n\n if (HISTORY_SIZE == 0){\n saHistory.push_back(0.0);\n }\n else {\n if (HISTORYDEBUG) {\n cout << \"History size of \" << HISTORY_SIZE\n << \" float size of \" << HISTORY_FL_SIZE\n << \" with state size: \" << fmin.size()\n << \" and numact: \" << numactions << endl;\n }\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\n saHistory.push_back(0.0);\n }\n }\n\n // initStates();\n expfile.initFile(\"experiences.bin\", featmax.size());\n}\n\nParallelETUCT::~ParallelETUCT() {\n // join threads\n\n //pthread_kill(planThread);\n //pthread_kill(modelThread);\n\n pthread_detach(planThread);//, NULL);\n pthread_detach(modelThread);//, NULL);\n\n pthread_cancel(planThread);//, NULL);\n pthread_cancel(modelThread);//, NULL);\n\n //pthread_join(planThread, NULL);\n //pthread_join(modelThread, NULL);\n\n //pthread_detach(planThread);//, NULL);\n //pthread_detach(modelThread);//, NULL);\n\n\n pthread_mutex_lock(&plan_state_mutex);\n pthread_mutex_lock(&statespace_mutex);\n pthread_mutex_lock(&model_mutex);\n pthread_mutex_lock(&list_mutex);\n\n // delete exp list\n expList.clear();\n\n for (std::map<state_t, state_info>::iterator i = statedata.begin();\n i != statedata.end(); i++){\n\n // get state's info\n //cout << \" planner got info\" << endl;\n state_info* info = &((*i).second);\n\n deleteInfo(info);\n }\n\n featmax.clear();\n featmin.clear();\n\n statespace.clear();\n statedata.clear();\n\n pthread_mutex_unlock(&plan_state_mutex);\n pthread_mutex_unlock(&statespace_mutex);\n pthread_mutex_unlock(&model_mutex);\n pthread_mutex_unlock(&list_mutex);\n\n}\n\nvoid ParallelETUCT::setModel(MDPModel* m){\n\n model = m;\n\n}\n\n\n/////////////////////////////\n// Functional functions :) //\n/////////////////////////////\n\n\n\n/** Use the latest experience to update state info and the model. */\nbool ParallelETUCT::updateModelWithExperience(const std::vector<float> &laststate,\n int lastact,\n const std::vector<float> &currstate,\n float reward, bool term){\n // if (PLANNERDEBUG) cout << \"updateModelWithExperience(last = \" << &laststate\n // << \", curr = \" << &currstate\n // << \", lastact = \" << lastact\n // << \", r = \" << reward\n // << \", term = \" << term\n // << \")\" << endl;\n\n //cout << \"updateModel\" << endl << flush;\n\n if (!timingType)\n planTime = getSeconds();\n initTime = getSeconds();\n\n // canonicalize these things\n state_t last = canonicalize(laststate);\n\n prevstate = last;\n prevact = lastact;\n\n // get state info\n pthread_mutex_lock(&statespace_mutex);\n previnfo = &(statedata[last]);\n pthread_mutex_unlock(&statespace_mutex);\n\n if (MODELDEBUG){\n cout << \"Update with exp from state: \";\n for (unsigned i = 0; i < last->size(); i++){\n cout << (laststate)[i] << \", \";\n }\n cout << \" action: \" << lastact;\n cout << \" to state: \";\n for (unsigned i = 0; i < currstate.size(); i++){\n cout << (currstate)[i] << \", \";\n }\n cout << \" and reward: \" << reward << endl;\n }\n\n // add experiences to list to later be updated into model\n if (ATHREADDEBUG)\n cout << \"*** Action thread wants list lock ***\" << endl << flush;\n if (TIMINGDEBUG) cout << \"Want list mutex, time: \" << (getSeconds()-initTime) << endl;\n pthread_mutex_lock(&list_mutex);\n if (TIMINGDEBUG) cout << \"got list mutex, time: \" << (getSeconds()-initTime) << endl;\n experience e;\n e.s = laststate;\n e.next = currstate;\n e.act = lastact;\n e.reward = reward;\n e.terminal = term;\n\n if (HISTORY_SIZE > 0){\n if (HISTORYDEBUG) {\n cout << \"Original state vector (size \" << e.s.size() << \": \" << e.s[0];\n for (unsigned i = 1; i < e.s.size(); i++){\n cout << \",\" << e.s[i];\n }\n cout << endl;\n }\n // add history onto e.s\n pthread_mutex_lock(&history_mutex);\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\n e.s.push_back(saHistory[i]);\n }\n pthread_mutex_unlock(&history_mutex);\n\n if (HISTORYDEBUG) {\n cout << \"New state vector (size \" << e.s.size() << \": \" << e.s[0];\n for (unsigned i = 1; i < e.s.size(); i++){\n cout << \",\" << e.s[i];\n }\n cout << endl;\n }\n\n if (!seedMode){\n pthread_mutex_lock(&history_mutex);\n // push this state and action onto the history vector\n /*\n for (unsigned i = 0; i < last->size(); i++){\n saHistory.push_back((*last)[i]);\n saHistory.pop_front();\n }\n\n saHistory.push_back((*last)[3]);\n saHistory.pop_front();\n */\n for (int i = 0; i < numactions; i++){\n if (i == lastact)\n saHistory.push_back(1.0);\n else\n saHistory.push_back(0.0);\n saHistory.pop_front();\n }\n\n // saHistory.push_back(lastact);\n //saHistory.pop_front();\n if (HISTORYDEBUG) {\n cout << \"New history vector (size \" << saHistory.size() << \": \" << saHistory[0];\n for (unsigned i = 1; i < saHistory.size(); i++){\n cout << \",\" << saHistory[i];\n }\n cout << endl;\n }\n pthread_mutex_unlock(&history_mutex);\n }\n }\n\n expList.push_back(e);\n //expfile.saveExperience(e);\n if (ATHREADDEBUG || MTHREADDEBUG)\n cout << \"added exp to list, size: \" << expList.size() << endl << flush;\n if (TIMINGDEBUG) cout << \"list updated, time: \" << (getSeconds()-initTime) << endl;\n pthread_cond_signal(&list_cond);\n pthread_mutex_unlock(&list_mutex);\n\n /*\n if (e.reward > -0.5 && e.reward < 0){\n expfile.saveExperience(e);\n nsaved++;\n cout << \"Saved Experience \" << e.reward << endl;\n }\n */\n\n if (timingType)\n planTime = getSeconds();\n\n if (TIMINGDEBUG) cout << \"leaving updateModel, time: \" << (getSeconds()-initTime) << endl;\n\n\n return false;\n\n}\n\n/** Update a single state-action from the model */\nvoid ParallelETUCT::updateStateActionFromModel(state_t s, int a, state_info* info){\n\n if (HISTORY_SIZE == 0){\n pthread_mutex_lock(&info->statemodel_mutex);\n\n std::deque<float> history(1,0.0);\n StateActionInfo* newModel = NULL;\n newModel = &(info->historyModel[a][history]);\n\n updateStateActionHistoryFromModel(*s, a, newModel);\n pthread_mutex_unlock(&info->statemodel_mutex);\n\n }\n\n else {\n pthread_mutex_lock(&info->statemodel_mutex);\n\n // clear large ones\n if (info->historyModel[a].size() > CLEAR_SIZE){\n\n // cout << \"clearing model map of size \" << info->historyModel[a].size() << endl;\n\n // instead, clear because these take too much memory to keep around\n info->historyModel[a].clear();\n\n } else {\n\n // fill in for all histories???\n for (std::map< std::deque<float>, StateActionInfo>::iterator it = info->historyModel[a].begin();\n it != info->historyModel[a].end(); it++){\n\n std::deque<float> oneHist = (*it).first;\n StateActionInfo* newModel = &((*it).second);\n\n // add history to vector\n std::vector<float> modState = *s;\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\n modState.push_back(oneHist[i]);\n }\n updateStateActionHistoryFromModel(modState, a, newModel);\n }\n }\n\n pthread_mutex_unlock(&info->statemodel_mutex);\n }\n\n}\n\n/** Update a single state-action from the model */\nvoid ParallelETUCT::updateStateActionHistoryFromModel(const std::vector<float> modState, int a, StateActionInfo *newModel){\n\n // update state info\n // get state action info for each action\n pthread_mutex_lock(&model_mutex);\n\n model->getStateActionInfo(modState, a, newModel);\n\n pthread_mutex_lock(&nactions_mutex);\n newModel->frameUpdated = nactions;\n pthread_mutex_unlock(&nactions_mutex);\n\n pthread_mutex_unlock(&model_mutex);\n\n //canonNextStates(newModel);\n\n}\n\nvoid ParallelETUCT::canonNextStates(StateActionInfo* modelInfo){\n\n\n // loop through all next states\n for (std::map<std::vector<float>, float>::iterator outIt\n = modelInfo->transitionProbs.begin();\n outIt != modelInfo->transitionProbs.end(); outIt++){\n\n std::vector<float> nextstate = (*outIt).first;\n bool badState = false;\n\n // check that it is valid, otherwise replace with current\n for (unsigned j = 0; j < nextstate.size(); j++){\n float factor = EPSILON;\n if (statesPerDim[j] > 0)\n factor = (featmax[j] - featmin[j]) / (float)statesPerDim[j];\n if (nextstate[j] < (featmin[j]-factor)\n || nextstate[j] > (featmax[j]+factor)){\n //cout << \"next state out of range \" << nextstate[j] << endl;\n badState = true;\n break;\n }\n }\n\n if (!badState){\n\n canonicalize(nextstate);\n }\n }\n}\n\n\n\n\n/** Choose the next action */\nint ParallelETUCT::getBestAction(const std::vector<float> &state){\n // if (PLANNERDEBUG) cout << \"getBestAction(s = \" << &state << \")\" << endl;\n\n pthread_mutex_lock(&nactions_mutex);\n nactions++;\n pthread_mutex_unlock(&nactions_mutex);\n\n if (TIMINGDEBUG) cout << \"getBestAction, time: \" << (getSeconds()-initTime) << endl;\n\n\n state_t s = canonicalize(state);\n\n // set plan state so uct will search from here\n if (ATHREADDEBUG)\n cout << \"*** Action thread wants plan state lock ***\" << endl << flush;\n if (TIMINGDEBUG) cout << \"want planStateMut, time: \" << (getSeconds()-initTime) << endl;\n\n pthread_mutex_lock(&(plan_state_mutex));\n if (TIMINGDEBUG) cout << \"got planStateMut, time: \" << (getSeconds()-initTime) << endl;\n\n actualPlanState = state;\n discPlanState = s;\n setTime = getSeconds();\n\n if (ATHREADDEBUG){\n cout << \"Set planning state as: \";\n for (unsigned i = 0; i < state.size(); i++){\n cout << state[i] << \", \";\n }\n cout << endl << flush;\n }\n\n // call uct search on it\n pthread_mutex_unlock(&(plan_state_mutex));\n if (TIMINGDEBUG) cout << \"set planState, time: \" << (getSeconds()-initTime) << endl;\n\n // get state info\n pthread_mutex_lock(&statespace_mutex);\n state_info* info = &(statedata[s]);\n pthread_mutex_unlock(&statespace_mutex);\n\n // wait a bit for some planning from this state\n\n // depending on how you run the code, this has to be setup differently\n // if someone else calls this method at the appropriate rate, do nothing here\n\n // or this can be where we wait to ensure we run at some rate:\n while (((getSeconds()- initTime) < MAX_TIME)){\n if (TIMINGDEBUG)\n cout << \"waiting for time: \" << (getSeconds()-initTime) << endl;\n\n pthread_yield();\n }\n\n if (TIMINGDEBUG) cout << \"time up: \" << (getSeconds()-initTime) << endl;\n\n if (TIMINGDEBUG && (getSeconds()-initTime) > 0.15) cout << \"**********\" << endl;\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n // Get Q values\n std::vector<float> &Q = info->Q;\n\n\n if (ATHREADDEBUG) {\n if (previnfo != NULL)\n cout << \" ... now \" << previnfo->uctVisits << \" times.\" << endl;\n cout << \"Getting best action from state \";\n for (unsigned i = 0; i < s->size(); i++){\n cout << (*s)[i] << \", \";\n }\n cout << \" sampled \" << info->uctVisits << \" times.\";// << endl << flush;\n }\n\n // Choose an action\n const std::vector<float>::iterator a =\n random_max_element(Q.begin(), Q.end()); // Choose maximum\n int act = a - Q.begin();\n\n if (TIMINGDEBUG) cout << \"got action: \" << (getSeconds()-initTime) << endl;\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n // return index of action\n return act;\n}\n\n\n\n\n\n\nvoid ParallelETUCT::planOnNewModel(){\n //return;\n // cout << \"planOnNewModel\" << endl << flush;\n // start model learning thread here\n if (!modelThreadStarted){\n modelThreadStarted = true;\n pthread_create(&modelThread, NULL, parallelModelLearningStart, this);\n }\n\n if (!planThreadStarted){\n planThreadStarted = true;\n pthread_create(&(planThread), NULL, parallelSearchStart, this);\n }\n\n}\n\n\nvoid* parallelModelLearningStart(void* arg){\n cout << \"Start model learning thread\" << endl << flush;\n ParallelETUCT* pe = reinterpret_cast<ParallelETUCT*>(arg);\n while(true){\n pe->parallelModelLearning();\n /*\n if (!pe->planThreadStarted){\n pe->planThreadStarted = true;\n pthread_create(&(pe->planThread), NULL, parallelSearchStart, pe);\n }\n */\n }\n return NULL;\n}\n\nvoid ParallelETUCT::parallelModelLearning(){\n //while(true){\n\n // wait for experience list to be non-empty\n pthread_mutex_lock(&list_mutex);\n while (expList.size() == 0){\n pthread_cond_wait(&list_cond,&list_mutex);\n }\n pthread_mutex_unlock(&list_mutex);\n\n // copy over experience list\n std::vector<experience> updateList;\n if (MTHREADDEBUG) cout << \" *** Model thread wants list lock ***\" << endl << flush;\n pthread_mutex_lock(&list_mutex);\n updateList = expList;\n expList.clear();\n if (MTHREADDEBUG) cout << \" *** Model thread done with list lock ***\" << endl << flush;\n pthread_mutex_unlock(&list_mutex);\n\n /*\n // update model\n //cout << \"*** Model thread wants tree lock ***\" << endl << flush;\n pthread_mutex_lock(&model_mutex);\n if (MTHREADDEBUG) cout << \" Model thread: going to update model with \" << updateList.size() << \" new experiences\" << endl << flush;\n //cout << \"****update tree with \" << updateList.size() << endl << flush;\n bool modelChanged = model->updateWithExperiences(updateList);\n if (MTHREADDEBUG) cout << \" Model updated\" << endl << flush;\n pthread_mutex_unlock(&model_mutex);\n */\n\n modelcopy = model->getCopy();\n //if (COPYDEBUG) cout << \"*** PO: model copied\" << endl;\n\n // update model copy with new experience\n bool modelChanged = modelcopy->updateWithExperiences(updateList);\n\n // set model pointer to point at copy, delete original model cout << \"acquire model_mutex for update\" << endl;\n pthread_mutex_lock(&model_mutex);\n //cout << \"model_mutex acquired for update\" << endl;\n //if (COPYDEBUG) cout << \"*** PO: delete original model and change pointer\" << endl;\n delete model;\n model = modelcopy;\n if (MTHREADDEBUG) cout << \" Model updated\" << endl << flush;\n //if (COPYDEBUG) cout << \"*** PO: pointer set to updated model copy\" << endl;\n pthread_mutex_unlock(&model_mutex);\n\n\n\n // if it changed, reset counts, update state actions\n if (modelChanged) resetAndUpdateStateActions();\n\n pthread_yield();\n\n //}// while loop\n} // method\n\n\n\n\nvoid ParallelETUCT::resetAndUpdateStateActions(){\n //cout << \"*** Model changed, updating state actions ***\" << endl << flush;\n const int MIN_VISITS = 10;\n\n pthread_mutex_lock(&nactions_mutex);\n int updateTime = nactions;\n pthread_mutex_unlock(&nactions_mutex);\n\n // loop through here\n\n pthread_mutex_lock(&statespace_mutex);\n\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n pthread_mutex_unlock(&statespace_mutex);\n\n state_t s = canonicalize(*i);\n\n if (MTHREADDEBUG) cout << \" *** Model thread wants search lock ***\" << endl;\n\n if (MTHREADDEBUG) cout << \" *** Model thread got search lock \" << endl;\n\n pthread_mutex_lock(&statespace_mutex);\n state_info* info = &(statedata[s]);\n pthread_mutex_unlock(&statespace_mutex);\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n if (info->uctVisits > (MIN_VISITS * numactions))\n info->uctVisits = MIN_VISITS * numactions;\n\n for (int j = 0; j < numactions; j++){\n if (info->uctActions[j] > MIN_VISITS)\n info->uctActions[j] = MIN_VISITS;\n if (info->needsUpdate || info->historyModel[j].size() > CLEAR_SIZE){\n updateStateActionFromModel(s, j, info);\n }\n }\n info->needsUpdate = false;\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n pthread_yield();\n\n pthread_mutex_lock(&statespace_mutex);\n\n }\n pthread_mutex_unlock(&statespace_mutex);\n\n pthread_mutex_lock(&update_mutex);\n lastUpdate = updateTime;\n pthread_mutex_unlock(&update_mutex);\n\n}\n\n\n\n\n////////////////////////////\n// Helper Functions //\n////////////////////////////\n\nParallelETUCT::state_t ParallelETUCT::canonicalize(const std::vector<float> &s) {\n if (PLANNERDEBUG) cout << \"canonicalize(s = \" << s[0] << \", \"\n << s[1] << \")\" << endl;\n\n // discretize it\n std::vector<float> s2;\n if (statesPerDim[0] > 0){\n s2 = discretizeState(s);\n } else {\n s2 = s;\n }\n\n pthread_mutex_lock(&statespace_mutex);\n\n // get state_t for pointer if its in statespace\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\n statespace.insert(s2);\n state_t retval = &*result.first; // Dereference iterator then get pointer\n\n\n // if not, init this new state\n if (result.second) { // s is new, so initialize Q(s,a) for all a\n state_info* info = &(statedata[retval]);\n int id = nstates++;\n pthread_mutex_unlock(&statespace_mutex);\n initStateInfo(retval, info, id);\n } else {\n pthread_mutex_unlock(&statespace_mutex);\n }\n\n return retval;\n}\n\n\n// init state info\nvoid ParallelETUCT::initStateInfo(state_t s, state_info* info, int id){\n //if (PLANNERDEBUG) cout << \"initStateInfo()\";\n\n // init mutex's for this state info\n pthread_mutex_init(&info->statemodel_mutex, NULL);\n pthread_mutex_init(&info->stateinfo_mutex, NULL);\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n // model data (transition, reward, known)\n\n pthread_mutex_lock(&info->statemodel_mutex);\n info->historyModel = new std::map< std::deque<float>, StateActionInfo>[numactions];\n pthread_mutex_unlock(&info->statemodel_mutex);\n\n info->id = id;\n if (PLANNERDEBUG) cout << \" id = \" << info->id << endl;\n\n // model q values, visit counts\n info->Q.resize(numactions, 0);\n info->uctActions.resize(numactions, 1);\n info->uctVisits = 1;\n info->visited = 0; //false;\n\n for (int i = 0; i < numactions; i++){\n info->Q[i] = rng.uniform(0,0.01);\n }\n\n info->needsUpdate = true;\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n //if (PLANNERDEBUG) cout << \"done with initStateInfo()\" << endl;\n\n}\n\n\n/** Print state info for debugging. */\nvoid ParallelETUCT::printStates(){\n\n pthread_mutex_lock(&statespace_mutex);\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n pthread_mutex_unlock(&statespace_mutex);\n\n state_t s = canonicalize(*i);\n\n pthread_mutex_lock(&statespace_mutex);\n state_info* info = &(statedata[s]);\n pthread_mutex_unlock(&statespace_mutex);\n\n cout << \"State \" << info->id << \": \";\n for (unsigned j = 0; j < s->size(); j++){\n cout << (*s)[j] << \", \";\n }\n cout << endl;\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n //pthread_mutex_lock(&info->statemodel_mutex);\n for (int act = 0; act < numactions; act++){\n cout << \" Q: \" << info->Q[act] << endl;\n // << \" R: \" << info->modelInfo[act].reward << endl;\n }\n // pthread_mutex_unlock(&info->statemodel_mutex);\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n pthread_mutex_lock(&statespace_mutex);\n\n }\n pthread_mutex_unlock(&statespace_mutex);\n\n}\n\n\nvoid ParallelETUCT::deleteInfo(state_info* info){\n\n delete [] info->historyModel;\n\n}\n\n\n\ndouble ParallelETUCT::getSeconds(){\n struct timezone tz;\n timeval timeT;\n gettimeofday(&timeT, &tz);\n return timeT.tv_sec + (timeT.tv_usec / 1000000.0);\n}\n\n\n/** Execute the uct search from state state at depth depth.\n If terminal or at depth, return some reward.\n Otherwise, select an action based on UCB.\n Simulate action to get reward and next state.\n Call search on next state at depth+1 to get reward return from there on.\n Update q value towards new value: reward + gamma * searchReturn\n Update visit counts for confidence bounds\n Return q\n\n From \"Bandit Based Monte Carlo Planning\" by Kocsis and Csaba.\n*/\nfloat ParallelETUCT::uctSearch(const std::vector<float> &actS, state_t discS, int depth, std::deque<float> &searchHistory){\n if (UCTDEBUG){\n cout << \" uctSearch state \";\n for (unsigned i = 0; i < actS.size(); i++){\n cout << actS[i] << \", \";\n }\n cout << \" at depth \" << depth << endl;\n }\n\n pthread_mutex_lock(&statespace_mutex);\n state_info* info = &(statedata[discS]);\n pthread_mutex_unlock(&statespace_mutex);\n\n // if max depth\n // iterative deepening (probability inversely proportional to visits)\n //float terminateProb = 1.0/(2.0+(float)info->uctVisits);\n\n // already visited, stop here\n if (depth > MAX_DEPTH){\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n // return max q value here\n std::vector<float>::iterator maxAct =\n std::max_element(info->Q.begin(),\n info->Q.end());\n float maxval = *maxAct;\n\n if (UCTDEBUG)\n cout << \"Terminated after depth: \" << depth\n // << \" prob: \" << terminateProb\n << \" Q: \" << maxval\n << \" visited: \" << info->visited << endl;\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n return maxval;\n }\n\n // select action\n int action = selectUCTAction(info);\n\n // simulate action to get next state and reward\n // depending on exploration, may also terminate us\n float reward = 0;\n bool term = false;\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n float learnRate;\n //float learnRate = 0.001;\n //float learnRate = 1.0 / info->uctActions[action];\n // learnRate = 10.0 / (info->uctActions[action] + 100.0);\n learnRate = 10.0 / (info->uctActions[action] + 10.0);\n //if (learnRate < 0.001 && MAX_TIME < 0.5)\n //learnRate = 0.001;\n //learnRate = 0.05;\n //learnRate = 1.0;\n\n // tell model learning thread to update this state since we've visited it\n info->needsUpdate = true;\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n std::vector<float> actualNext = simulateNextState(actS, discS, info, searchHistory, action, &reward, &term);\n\n // simulate reward from this action\n if (term){\n // this one terminated\n if (UCTDEBUG) cout << \" Terminated on exploration condition\" << endl;\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n info->Q[action] += learnRate * (reward - info->Q[action]);\n info->uctVisits++;\n info->uctActions[action]++;\n\n if (UCTDEBUG)\n cout << \" Depth: \" << depth << \" Selected action \" << action\n << \" r: \" << reward\n << \" StateVisits: \" << info->uctVisits\n << \" ActionVisits: \" << info->uctActions[action] << endl;\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n return reward;\n }\n\n // simulate next state from this action\n state_t discNext = canonicalize(actualNext);\n\n if (UCTDEBUG)\n cout << \" Depth: \" << depth << \" Selected action \" << action\n << \" r: \" << reward << endl;\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n info->visited++; // = true;\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n if (HISTORY_SIZE > 0){\n // update history vector for this state\n /*\n for (unsigned i = 0; i < (*discS).size(); i++){\n searchHistory.push_back((*discS)[i]);\n searchHistory.pop_front();\n }\n\n searchHistory.push_back((*discS)[3]);\n searchHistory.pop_front();\n */\n for (int i = 0; i < numactions; i++){\n if (i == action)\n searchHistory.push_back(1.0);\n else\n searchHistory.push_back(0.0);\n searchHistory.pop_front();\n }\n\n // searchHistory.push_back(action);\n //searchHistory.pop_front();\n if (HISTORYDEBUG) {\n cout << \"New planning history vector (size \" << searchHistory.size() << \": \" << searchHistory[0];\n for (unsigned i = 1; i < searchHistory.size(); i++){\n cout << \",\" << searchHistory[i];\n }\n cout << endl;\n }\n }\n\n // new q value\n float newQ = reward + gamma * uctSearch(actualNext, discNext, depth+1, searchHistory);\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n if (info->visited == 1){\n\n // update q and visit counts\n info->Q[action] += learnRate * (newQ - info->Q[action]);\n info->uctVisits++;\n info->uctActions[action]++;\n\n if (UCTDEBUG)\n cout << \" Depth: \" << depth << \" newQ: \" << newQ\n << \" StateVisits: \" << info->uctVisits\n << \" ActionVisits: \" << info->uctActions[action] << endl;\n\n if (lambda < 1.0){\n\n // new idea, return max of Q or new q\n std::vector<float>::iterator maxAct =\n std::max_element(info->Q.begin(),\n info->Q.end());\n float maxval = *maxAct;\n\n if (UCTDEBUG)\n cout << \" Replacing newQ: \" << newQ;\n\n // replace with w avg of maxq and new val\n newQ = (lambda * newQ) + ((1.0-lambda) * maxval);\n\n if (UCTDEBUG)\n cout << \" with wAvg: \" << newQ << endl;\n }\n\n }\n\n info->visited--;\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n // return q\n return newQ;\n\n}\n\n\nint ParallelETUCT::selectUCTAction(state_info* info){\n // if (UCTDEBUG) cout << \" selectUCTAction\" << endl;\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n std::vector<float> &Q = info->Q;\n\n if (info->uctActions.size() < (unsigned)numactions){\n cout << \"ERROR: uctActions has size \" << info->uctActions.size() << endl << flush;\n info->uctActions.resize(numactions);\n }\n\n // loop through\n float rewardBound = rrange;\n if (rewardBound < 1.0)\n rewardBound = 1.0;\n rewardBound /= (1.0 - gamma);\n if (UCTDEBUG) cout << \"Reward bound: \" << rewardBound << endl;\n\n std::vector<float> uctQ(numactions, 0.0);\n\n for (int i = 0; i < numactions; i++){\n\n // this actions value is Q + rMax * 2 sqrt (log N(s) / N(s,a))\n uctQ[i] = Q[i] +\n rewardBound * 2.0 * sqrt(log((float)info->uctVisits) /\n (float)info->uctActions[i]);\n\n if (UCTDEBUG)\n cout << \" Action: \" << i << \" Q: \" << Q[i]\n << \" visits: \" << info->uctActions[i]\n << \" value: \" << uctQ[i] << endl;\n }\n\n // max element of uctQ\n std::vector<float>::iterator maxAct =\n max_element(uctQ.begin(), uctQ.end());\n float maxval = *maxAct;\n int act = maxAct - uctQ.begin();\n\n if (UCTDEBUG)\n cout << \" Selected \" << act << \" val: \" << maxval << endl;\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n return act;\n\n}\n\n/** sample from next state distribution */\nstd::vector<float> ParallelETUCT::simulateNextState(const std::vector<float> &actualState, state_t discState, state_info* info, const std::deque<float> &history, int action, float* reward, bool* term){\n //if (UCTDEBUG) cout << \" simulateNextState\" << endl;\n\n\n // check if its up to date\n pthread_mutex_lock(&info->statemodel_mutex);\n StateActionInfo* modelInfo = NULL;\n modelInfo = &(info->historyModel[action][history]);\n\n pthread_mutex_lock(&update_mutex);\n bool upToDate = modelInfo->frameUpdated >= lastUpdate;\n pthread_mutex_unlock(&update_mutex);\n\n if (!upToDate){\n // must put in appropriate history\n if (HISTORY_SIZE > 0){\n std::vector<float> modState = *discState;\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\n modState.push_back(history[i]);\n }\n updateStateActionHistoryFromModel(modState, action, modelInfo);\n } else {\n updateStateActionHistoryFromModel(*discState, action, modelInfo);\n }\n }\n\n *reward = modelInfo->reward;\n *term = (rng.uniform() < modelInfo->termProb);\n\n if (*term){\n pthread_mutex_unlock(&info->statemodel_mutex);\n return actualState;\n }\n\n float randProb = rng.uniform();\n\n float probSum = 0.0;\n std::vector<float> nextstate;\n\n if (REALSTATEDEBUG) cout << \"randProb: \" << randProb << \" numNext: \" << modelInfo->transitionProbs.size() << endl;\n\n //if (modelInfo->transitionProbs.size() == 0)\n nextstate = actualState;\n\n for (std::map<std::vector<float>, float>::iterator outIt\n = modelInfo->transitionProbs.begin();\n outIt != modelInfo->transitionProbs.end(); outIt++){\n\n float prob = (*outIt).second;\n probSum += prob;\n if (REALSTATEDEBUG) cout << randProb << \", \" << probSum << \", \" << prob << endl;\n\n if (randProb <= probSum){\n nextstate = (*outIt).first;\n if (REALSTATEDEBUG) cout << \"selected state \" << randProb << \", \" << probSum << \", \" << prob << endl;\n break;\n }\n }\n\n pthread_mutex_unlock(&info->statemodel_mutex);\n\n if (trackActual){\n\n // find the relative change from discrete center\n std::vector<float> relChange = subVec(nextstate, *discState);\n\n // add that on to actual current state value\n nextstate = addVec(actualState, relChange);\n\n }\n\n // check that next state is valid\n for (unsigned j = 0; j < nextstate.size(); j++){\n float factor = EPSILON;\n if (statesPerDim[j] > 0)\n factor = (featmax[j] - featmin[j]) / (float)statesPerDim[j];\n if (nextstate[j] < (featmin[j]-factor)\n || nextstate[j] > (featmax[j]+factor)){\n return actualState;\n }\n }\n\n // return new actual state\n return nextstate;\n\n}\n\nstd::vector<float> ParallelETUCT::selectRandomState(){\n\n pthread_mutex_lock(&statespace_mutex);\n if (statespace.size() == 0){\n pthread_mutex_unlock(&statespace_mutex);\n return std::vector<float>(featmax.size());\n }\n pthread_mutex_unlock(&statespace_mutex);\n\n // take a random state from the space of ones we've visited\n int index = 0;\n std::vector<float> state;\n\n pthread_mutex_lock(&statespace_mutex);\n if (statespace.size() > 1){\n index = rng.uniformDiscrete(0, statespace.size()-1);\n }\n pthread_mutex_unlock(&statespace_mutex);\n\n int cnt = 0;\n\n if (PTHREADDEBUG) cout << \"*** Planning thread wants search lock (randomstate) ***\" << endl << flush;\n\n pthread_mutex_lock(&statespace_mutex);\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++, cnt++){\n if (cnt == index){\n state = *i;\n break;\n }\n }\n pthread_mutex_unlock(&statespace_mutex);\n\n return state;\n}\n\n\nvoid* parallelSearchStart(void* arg){\n ParallelETUCT* pe = reinterpret_cast<ParallelETUCT*>(arg);\n\n cout << \"start parallel uct planning search thread\" << endl << flush;\n\n while(true){\n pe->parallelSearch();\n }\n\n return NULL;\n}\n\nvoid ParallelETUCT::parallelSearch(){\n\n std::vector<float> actS;\n state_t discS;\n std::deque<float> searchHistory;\n\n // get new planning state\n if (PTHREADDEBUG) {\n cout << \"*** Planning thread wants planning state lock ***\" << endl << flush;\n }\n pthread_mutex_lock(&(plan_state_mutex));\n if (HISTORY_SIZE > 0) pthread_mutex_lock(&history_mutex);\n\n // take the state we're in \n actS = actualPlanState;\n discS = discPlanState;\n searchHistory = saHistory;\n\n\n // wait for non-null\n if (discS == NULL){\n pthread_mutex_unlock(&(plan_state_mutex));\n if (HISTORY_SIZE > 0) pthread_mutex_unlock(&history_mutex);\n return;\n }\n\n if (PTHREADDEBUG){\n pthread_mutex_lock(&statespace_mutex);\n cout << \" uct search from state s (\"\n << statedata[discS].uctVisits <<\"): \";\n pthread_mutex_unlock(&statespace_mutex);\n\n for (unsigned i = 0; i < discS->size(); i++){\n cout << (*discS)[i] << \", \";\n }\n cout << endl << flush;\n }\n\n // call uct search on it\n pthread_mutex_unlock(&(plan_state_mutex));\n if (HISTORY_SIZE > 0) pthread_mutex_unlock(&history_mutex);\n\n if (PTHREADDEBUG) cout << \"*** Planning thread wants search lock ***\" << endl;\n uctSearch(actS, discS, 0, searchHistory);\n\n pthread_yield();\n\n}\n\n\n// canonicalize all the states so we already have them in our statespace\nvoid ParallelETUCT::initStates(){\n cout << \"init states\" << endl;\n std::vector<float> s(featmin.size());\n\n fillInState(s,0);\n}\n\nvoid ParallelETUCT::fillInState(std::vector<float>s, int depth){\n\n // if depth == size, canonicalize and return\n if (depth == (int)featmin.size()){\n canonicalize(s);\n return;\n }\n\n // go through all features at depth\n for (float i = featmin[depth]; i < featmax[depth]+1; i++){\n s[depth] = i;\n fillInState(s, depth+1);\n }\n}\n\n\n\nvoid ParallelETUCT::savePolicy(const char* filename){\n\n ofstream policyFile(filename, ios::out | ios::binary | ios::trunc);\n\n // first part, save the vector size\n int fsize = featmin.size();\n policyFile.write((char*)&fsize, sizeof(int));\n\n // save numactions\n policyFile.write((char*)&numactions, sizeof(int));\n\n // go through all states, and save Q values\n pthread_mutex_lock(&statespace_mutex);\n\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n pthread_mutex_unlock(&statespace_mutex);\n\n state_t s = canonicalize(*i);\n\n pthread_mutex_lock(&statespace_mutex);\n state_info* info = &(statedata[s]);\n pthread_mutex_unlock(&statespace_mutex);\n\n // save state\n policyFile.write((char*)&((*i)[0]), sizeof(float)*fsize);\n\n // save q-values\n pthread_mutex_lock(&info->stateinfo_mutex);\n policyFile.write((char*)&(info->Q[0]), sizeof(float)*numactions);\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n pthread_mutex_lock(&statespace_mutex);\n }\n pthread_mutex_unlock(&statespace_mutex);\n\n policyFile.close();\n}\n\n\n\nvoid ParallelETUCT::loadPolicy(const char* filename){\n\n ifstream policyFile(filename, ios::in | ios::binary);\n\n // first part, save the vector size\n int fsize;\n policyFile.read((char*)&fsize, sizeof(int));\n cout << \"Numfeats loaded: \" << fsize << endl << flush;\n\n // save numactions\n int nact;\n policyFile.read((char*)&nact, sizeof(int));\n cout << \"nact loaded: \" << nact << endl << flush;\n cout << \" numactions: \" << numactions << endl << flush;\n\n if (nact != numactions){\n cout << \"this policy is not valid loaded nact: \" << nact\n << \" was told: \" << numactions << endl << flush;\n exit(-1);\n }\n\n // go through all states, loading q values\n while(!policyFile.eof()){\n std::vector<float> state(fsize, 0.0);\n\n // load state\n policyFile.read((char*)&(state[0]), sizeof(float)*fsize);\n //if (LOADDEBUG){\n //cout << \"load policy for state: \";\n // printState(state);\n //}\n\n state_t s = canonicalize(state);\n\n pthread_mutex_lock(&statespace_mutex);\n state_info* info = &(statedata[s]);\n pthread_mutex_unlock(&statespace_mutex);\n\n if (policyFile.eof()) break;\n\n // load q values\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n policyFile.read((char*)&(info->Q[0]), sizeof(float)*numactions);\n\n info->uctVisits = numactions * 100;\n\n for (int j = 0; j < numactions; j++){\n info->uctActions[j] = 100;\n }\n\n info->needsUpdate = true;\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n //if (LOADDEBUG){\n //cout << \"Q values: \" << endl;\n //for (int iAct = 0; iAct < numactions; iAct++){\n // cout << \" Action: \" << iAct << \" val: \" << info->Q[iAct] << endl;\n //}\n //}\n }\n\n policyFile.close();\n cout << \"Policy loaded!!!\" << endl << flush;\n}\n\nvoid ParallelETUCT::logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax){\n std::vector<float> state(2, 0.0);\n for (int i = xmin ; i < xmax; i++){\n for (int j = ymin; j < ymax; j++){\n state[0] = j;\n state[1] = i;\n state_t s = canonicalize(state);\n\n pthread_mutex_lock(&statespace_mutex);\n state_info* info = &(statedata[s]);\n pthread_mutex_unlock(&statespace_mutex);\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n std::vector<float> &Q_s = info->Q;\n const std::vector<float>::iterator max =\n random_max_element(Q_s.begin(), Q_s.end());\n *of << (*max) << \",\";\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n }\n }\n}\n\n\n// should do it such that an already discretized state stays the same\n// mainly the numerical value of each bin should be the average of that bin\nstd::vector<float> ParallelETUCT::discretizeState(const std::vector<float> &s){\n std::vector<float> ds(s.size());\n\n for (unsigned i = 0; i < s.size(); i++){\n\n // since i'm sometimes doing this for discrete domains\n // want to center bins on 0, not edge on 0\n //cout << \"feat \" << i << \" range: \" << featmax[i] << \" \" << featmin[i] << \" \" << (featmax[i]-featmin[i]) << \" n: \" << (float)statesPerDim;\n\n float factor = (featmax[i] - featmin[i]) / (float)statesPerDim[i];\n int bin = 0;\n if (s[i] > 0){\n bin = (int)((s[i]+factor/2) / factor);\n } else {\n bin = (int)((s[i]-factor/2) / factor);\n }\n\n ds[i] = factor*bin;\n //cout << \" factor: \" << factor << \" bin: \" << bin;\n //cout << \" Original: \" << s[i] << \" Discrete: \" << ds[i] << endl;\n }\n\n return ds;\n}\n\n\nstd::vector<float> ParallelETUCT::addVec(const std::vector<float> &a, const std::vector<float> &b){\n if (a.size() != b.size())\n cout << \"ERROR: add vector sizes wrong \" << a.size() << \", \" << b.size() << endl;\n\n std::vector<float> c(a.size(), 0.0);\n for (unsigned i = 0; i < a.size(); i++){\n c[i] = a[i] + b[i];\n }\n\n return c;\n}\n\nstd::vector<float> ParallelETUCT::subVec(const std::vector<float> &a, const std::vector<float> &b){\n if (a.size() != b.size())\n cout << \"ERROR: sub vector sizes wrong \" << a.size() << \", \" << b.size() << endl;\n\n std::vector<float> c(a.size(), 0.0);\n for (unsigned i = 0; i < a.size(); i++){\n c[i] = a[i] - b[i];\n }\n\n return c;\n}\n\nvoid ParallelETUCT::setFirst(){\n if (HISTORY_SIZE == 0) return;\n\n if (HISTORYDEBUG) cout << \"first action, set sahistory to 0s\" << endl;\n\n pthread_mutex_lock(&(history_mutex));\n // first action, reset history vector\n saHistory.resize(saHistory.size(), 0.0);\n pthread_mutex_unlock(&(history_mutex));\n}\n\nvoid ParallelETUCT::setSeeding(bool seeding){\n\n if (HISTORYDEBUG) cout << \"set seed mode to \" << seeding << endl;\n seedMode = seeding;\n\n}\n"},{"alpha_fraction":0.7761467695236206,"alphanum_fraction":0.7798165082931519,"avg_line_length":67.1875,"blob_id":"6bf312cbc00773f2ed4d16be2aabe4f1b121cfbe","content_id":"966a0b05bb13ca4ab6622ac6810a50ea5ce1b1db","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":1090,"license_type":"no_license","max_line_length":126,"num_lines":16,"path":"/build/ugv_course_gazebo_plugins/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"ugv_course_gazebo_plugins\")\nset(ugv_course_gazebo_plugins_VERSION \"0.0.0\")\nset(ugv_course_gazebo_plugins_MAINTAINER \"micho <micho@todo.todo>\")\nset(ugv_course_gazebo_plugins_PACKAGE_FORMAT \"1\")\nset(ugv_course_gazebo_plugins_BUILD_DEPENDS \"dynamic_reconfigure\" \"roscpp\" \"tf\" \"ugv_course_libs\" \"visualization_msgs\")\nset(ugv_course_gazebo_plugins_BUILD_EXPORT_DEPENDS \"dynamic_reconfigure\" \"roscpp\" \"tf\" 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<rl_common/Random.h>\n#include <rl_common/core.hh>\n\n\nclass LightWorld: public Environment {\npublic:\n /** Creates a PlayRoom domain using the specified map.\n Puts objects in random locations.\n \\param rand Random number generator to use.\n \\param stochastic Whether to use nondeterministic actions \n \\param nrooms Number of rooms to have */\n LightWorld(Random &rand, bool stochastic, int nrooms);\n\n virtual ~LightWorld();\n\n virtual const std::vector<float> &sensation() const;\n virtual float apply(int action);\n\n virtual bool terminal() const;\n virtual void reset();\n virtual int getNumActions();\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\n virtual bool isEpisodic() { return false; };\n virtual void getMinMaxReward(float* minR, float* maxR);\n\n\n friend std::ostream &operator<<(std::ostream &out, const LightWorld &playroom);\n\n\n void resetKey();\n void setKey(std::vector<float> testS);\n\n struct room_info {\n int height;\n int width;\n int key_ns;\n int key_ew;\n int lock_ns;\n int lock_ew;\n int door_ns;\n int door_ew;\n };\n\n typedef std::pair<float,float> coord_t;\n enum lightworld_action_t {NORTH, EAST, WEST, SOUTH, PICKUP, PRESS, NUM_ACTIONS};\n\n bool LWDEBUG;\n\n const bool noisy;\n int nrooms;\n Random &rng;\n\n std::vector<float> s;\n float& ns;\n float& ew;\n float& have_key;\n float& door_open;\n float& room_id;\n float& key_n;\n float& key_e;\n float& key_w;\n float& key_s;\n float& lock_n;\n float& lock_e;\n float& lock_w;\n float& lock_s;\n float& door_n;\n float& door_e;\n float& door_w; \n float& door_s;\n\n std::vector<room_info> rooms;\n\n void updateSensors();\n int applyNoise(int action);\n\n /** Prints the current state */\n void print_state() const;\n\n /** Prints the current map. */\n void print_map() const;\n\n void updateVisits();\n\n int totalVisited;\n int keyVisited;\n int lockVisited;\n int doorVisited;\n int haveKey;\n int doorOpen;\n int leaveRoom;\n int 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rectangular grid with interior walls that\n allows users easily to determine available directions of travel in\n each cell. */\nclass Gridworld {\npublic:\n /** Creates a gridworld using the given wall occupancy matrices.\n \\param height Height of the gridworld.\n \\param width Width of the gridworld.\n \\param northsouth Whether each interior wall blocking NS\n movement exists, organized first by [w]\n columns and then by [h-1] rows.\n \\param eastwest Whether each interior wall blocking EW movement\n exists, organized first by [h] rows and then by\n [w-1] columns. */\n Gridworld(unsigned height, unsigned width,\n\t const std::vector<std::vector<bool> > &northsouth,\n\t const std::vector<std::vector<bool> > &eastwest);\n\n /** Creates a random gridworld with the desired dimensions. */\n Gridworld(unsigned width, unsigned height, Random &rng);\n\n unsigned height() const { return h; }\n unsigned width() const { return w; }\n\n /** Checks if a wall blocks movement in a given direction from a\n given coordinate.\n \\param nsCoord The coordinate along the NS direction.\n \\param ewCoord The coordinate along the EW direction.\n \\param dir The direction in which to check movement. 0 is\n north, 1 is south, 2 is east, 3 is west. */\n bool wall(unsigned nsCoord, unsigned ewCoord, unsigned dir) const;\n\n friend std::ostream &operator<<(std::ostream &out, const Gridworld &g);\n\nprotected:\n /** Attempts to add a random wall that must not touch any other\n interior wall. */\n void add_obstacle(Random &rng);\n\n /** Given a segment of wall that could be built, builds a subset of\n it of random length. Always builds from one of the two ends. */\n void chooseSegment(unsigned first,\n\t\t unsigned last,\n\t\t unsigned j,\n\t\t std::vector<std::vector<bool> > ¶llel,\n\t\t Random &rng);\n\nprivate:\n /** Determines if the \"smaller\" endpoint of the given line segment\n is clear: none of the four possible walls using that endpoint\n exist.\n \\param i An index into parallel.\n \\param j An index into parallel[i].\n \\param parallel Occupancy matrix for walls parallel to the wall\n under consiration.\n \\param perpendicular Occupancy matrix for walls perpendicular to\n the wall under consiration. */\n bool isClear(unsigned i, unsigned j,\n\t std::vector<std::vector<bool> > ¶llel,\n\t std::vector<std::vector<bool> > &perpendicular) const\n {\n if (i > parallel.size())\n return false;\n if (i < parallel.size() && parallel[i][j])\n return false;\n if (i > 0 && parallel[i - 1][j])\n return false;\n if (i > 0\n\t&& i <= perpendicular[j].size()\n\t&& perpendicular[j][i - 1])\n return false;\n if (i > 0\n\t&& i <= perpendicular[j + 1].size()\n\t&& perpendicular[j + 1][i - 1])\n return false;\n return true;\n }\n\n const unsigned h;\n const unsigned w;\n\n /** The occupancy matrix for the walls that obstruct NS\n movement. 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this);\n loadParams(n);\n left_target_speed_ = 0.0;\n right_target_speed_ = 0.0;\n\n left_joint_ = hw->getHandle(left_joint_name_);\n right_joint_ = hw->getHandle(right_joint_name_);\n return true;\n}\n\nvoid DiffController::loadParams(ros::NodeHandle& n)\n{\n n.param(\"left_joint\", left_joint_name_, std::string(\"left_wheel\"));\n n.param(\"right_joint\", right_joint_name_, std::string(\"right_wheel\"));\n n.param(\"control_timeout\", timeout_, 0.25);\n n.param(\"wheel_radius\", wheel_radius_, 0.2);\n n.param(\"wheel_separation\", wheel_separation_, 0.9);\n\n double max_speed, max_accel, max_decel;\n n.param(\"max_speed\", max_speed, 10.0);\n n.param(\"max_accel\", max_accel, 1.0);\n n.param(\"max_decel\", max_decel, 1.0);\n\n left_motor_ = new MotorSim(max_speed, max_accel, max_decel);\n right_motor_ = new MotorSim(max_speed, max_accel, max_decel);\n}\n\nvoid DiffController::recvTwist(const geometry_msgs::Twist::ConstPtr& msg)\n{\n command_stamp_ = ros::Time::now();\n cmd_ = 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bool init(hardware_interface::VelocityJointInterface* hw, ros::NodeHandle &n);\n void update(const ros::Time& time, const ros::Duration& period);\n void starting(const ros::Time& time);\n void stopping(const ros::Time& time);\nprivate:\n void recvLeftCommand(const std_msgs::Float64::ConstPtr& msg);\n void recvRightCommand(const std_msgs::Float64::ConstPtr& msg);\n void loadParams(ros::NodeHandle& n);\n\n ros::Subscriber sub_left_command_;\n ros::Subscriber sub_right_command_;\n\n hardware_interface::JointHandle left_joint_;\n hardware_interface::JointHandle right_joint_;\n\n MotorSim* left_motor_;\n MotorSim* right_motor_;\n double left_target_speed_;\n double right_target_speed_;\n ros::Time left_command_stamp_;\n ros::Time right_command_stamp_;\n\n // Parameters\n std::string left_joint_name_;\n std::string right_joint_name_;\n double timeout_;\n};\n\nPLUGINLIB_EXPORT_CLASS(roundbot_control::WheelSpeedController, controller_interface::ControllerBase);\n\n}\n\n#endif /* 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regenerated.\ncmake_check_build_system:\n\tcd /home/justin/ros_test/build && $(CMAKE_COMMAND) -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR) --check-build-system CMakeFiles/Makefile.cmake 0\n.PHONY : cmake_check_build_system\n\n"},{"alpha_fraction":0.5431063771247864,"alphanum_fraction":0.5507646799087524,"avg_line_length":27.5848445892334,"blob_id":"1009eca8749bb61d9b6b09eb741611195a8246b6","content_id":"7654504738dadf367eca00237533819604770fcf","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":44135,"license_type":"no_license","max_line_length":132,"num_lines":1544,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/LinearSplitsTree.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include \"LinearSplitsTree.hh\"\n\n// LinearSplitsTree, from the following sources:\n\n// Include stuff for newmat matrix libraries\n\n#define WANT_MATH // include.h will get math fns\n // newmatap.h will get include.h\n#include \"../newmat/newmatap.h\" // need matrix applications\n#ifdef use_namespace\nusing namespace NEWMAT; // access NEWMAT namespace\n#endif\n\n// TODO:\n// - save regression from split testing rather than re-building it later\n\n\nLinearSplitsTree::LinearSplitsTree(int id, int trainMode, int trainFreq, int m,\n float featPct, bool simple, float min_er,\n Random rng):\n id(id), mode(trainMode), \n freq(trainFreq), M(m),\n featPct(featPct), SIMPLE(simple), \n MIN_ER(min_er), rng(rng)\n{\n\n nnodes = 0;\n nOutput = 0;\n nExperiences = 0;\n hadError = false;\n maxnodes = N_LS_NODES;\n totalnodes = 0;\n\n // how close a split has to be to be randomly selected\n SPLIT_MARGIN = 0.0; //0.02; //5; //01; //0.05; //0.2; //0.05;\n\n LMDEBUG = false;// true;\n DTDEBUG = false;//true;\n SPLITDEBUG = false; //true;\n STOCH_DEBUG = false; //true; //false; //true;\n INCDEBUG = false; //true; //false; //true;\n NODEDEBUG = false;\n COPYDEBUG = false; //true;\n\n cout << \"Created linear splits decision tree \" << id;\n if (SIMPLE) cout << \" simple regression\";\n else cout << \" multivariate regrssion\";\n if (DTDEBUG){\n cout << \" mode: \" << mode << \" freq: \" << freq << endl;\n }\n cout << \" MIN_ER: \" << MIN_ER << endl;\n\n\n initNodes();\n initTree();\n\n}\n\nLinearSplitsTree::LinearSplitsTree(const LinearSplitsTree& ls):\n id(ls.id), mode(ls.mode), \n freq(ls.freq), M(ls.M),\n featPct(ls.featPct), SIMPLE(ls.SIMPLE), \n MIN_ER(ls.MIN_ER), rng(ls.rng)\n{\n COPYDEBUG = ls.COPYDEBUG;\n if (COPYDEBUG) cout << \"LS copy \" << id << endl;\n nnodes = 0;\n nOutput = ls.nOutput;\n nExperiences = ls.nExperiences;\n hadError = ls.hadError;\n totalnodes = 0;\n maxnodes = ls.maxnodes;\n SPLIT_MARGIN = ls.SPLIT_MARGIN; \n LMDEBUG = ls.LMDEBUG;\n DTDEBUG = ls.DTDEBUG;\n SPLITDEBUG = ls.SPLITDEBUG;\n STOCH_DEBUG = ls.STOCH_DEBUG; \n INCDEBUG = ls.INCDEBUG; \n NODEDEBUG = ls.NODEDEBUG;\n\n if (COPYDEBUG) cout << \" LS copy nodes, experiences, root, etc\" << endl;\n // copy all experiences\n for (int i = 0; i < N_LST_EXP; i++){\n allExp[i] = ls.allExp[i];\n }\n if (COPYDEBUG) cout << \" LS copied exp array\" << endl;\n\n // set experience pointers\n experiences.resize(ls.experiences.size());\n for (unsigned i = 0; i < ls.experiences.size(); i++){\n experiences[i] = &(allExp[i]);\n }\n if (COPYDEBUG) cout << \" LS set experience pointers\" << endl;\n\n // now the tricky part, set the pointers inside the tree nodes correctly\n initNodes();\n\n if (COPYDEBUG) cout << \" LS copy tree \" << endl;\n root = allocateNode();\n lastNode = root;\n copyTree(root, ls.root);\n if (COPYDEBUG) cout << \" LS tree copy done\" << endl;\n \n if (COPYDEBUG) {\n cout << endl << \"New tree: \" << endl;\n printTree(root, 0);\n cout << endl;\n cout << \" LS copy done\" << endl;\n }\n\n}\n\nvoid LinearSplitsTree::copyTree(tree_node* newNode, tree_node* origNode){\n\n int nodeId = newNode->id;\n\n if (COPYDEBUG) {\n cout << \" Copy node \" << newNode->id << \" from node \" << origNode->id << endl;\n cout << \" NewAddy \" << newNode << \", old: \" << origNode << endl;\n }\n\n // copy node from t\n *newNode = *origNode;\n newNode->id = nodeId;\n\n // if it has children, allocate and copy them too\n if (origNode->l != NULL && !newNode->leaf){\n newNode->l = allocateNode();\n if (COPYDEBUG) cout << \" Copy left node \" << newNode->l->id << \" from \" << origNode->l->id << endl;\n copyTree(newNode->l, origNode->l);\n } else {\n newNode->l = NULL;\n }\n\n if (origNode->r != NULL && !newNode->leaf){\n newNode->r = allocateNode();\n if (COPYDEBUG) cout << \" Copy right node \" << newNode->r->id << \" from \" << origNode->r->id << endl;\n copyTree(newNode->r, origNode->r);\n } else {\n newNode->r = NULL;\n }\n}\n\nLinearSplitsTree* LinearSplitsTree::getCopy(){\n LinearSplitsTree* copy = new LinearSplitsTree(*this);\n return copy;\n}\n\nLinearSplitsTree::~LinearSplitsTree() {\n deleteTree(root);\n for (unsigned i = N_LST_EXP; i < experiences.size(); i++){\n delete experiences[i];\n }\n experiences.clear();\n}\n\n// here the target output will be a single value\nbool LinearSplitsTree::trainInstance(classPair &instance){\n\n if (DTDEBUG) cout << id << \" trainInstance\" << endl;\n\n bool modelChanged = false;\n\n // simply add this instance to the set of experiences\n\n // take from static array until we run out\n tree_experience *e;\n if (nExperiences < N_LST_EXP){\n // from statically created set of experiences\n e = &(allExp[nExperiences]);\n\n } else {\n // dynamically create experience\n e = new tree_experience;\n }\n\n\n e->input = instance.in;\n e->output = instance.out;\n experiences.push_back(e);\n nExperiences++;\n\n if (nExperiences == 1000000){\n cout << \"Reached limit of # experiences allowed.\" << endl;\n return false;\n }\n\n if (nExperiences != (int)experiences.size())\n cout << \"ERROR: experience size mismatch: \" << nExperiences << \", \" << experiences.size() << endl;\n\n //cout << nExperiences << endl << flush;\n //if (nExperiences == 503 && id == 10){\n // DTDEBUG = true;\n // SPLITDEBUG = true;\n // INCDEBUG = true;\n //}\n\n if ( DTDEBUG) {\n cout << \"Original input: \";\n for (unsigned i = 0; i < instance.in.size(); i++){\n cout << instance.in[i] << \", \";\n }\n cout << endl << \" Original output: \" << instance.out << endl;\n cout << \"Added exp id: \" << nExperiences << \" output: \" << e->output << endl;\n cout << \"Address: \" << e << \" Input : \";\n for (unsigned i = 0; i < e->input.size(); i++){\n cout << e->input[i] << \", \";\n }\n cout << endl << \" Now have \" << nExperiences << \" experiences.\" << endl;\n }\n\n // mode 0: re-build every step\n if (mode == BUILD_EVERY || nExperiences <= 1){\n rebuildTree();\n modelChanged = true;\n }\n\n // mode 1: re-build on error only\n else if (mode == BUILD_ON_ERROR){\n\n // build on misclassification\n // check for misclassify\n std::map<float, float> answer;\n testInstance(e->input, &answer);\n float val = answer.begin()->first;\n float error = fabs(val - e->output);\n\n if (error > 0.0){\n rebuildTree();\n modelChanged = true;\n } \n }\n\n // mode 2: re-build every FREQ steps\n else if (mode == BUILD_EVERY_N){\n // build every freq steps\n if (!modelChanged && (nExperiences % freq) == 0){\n rebuildTree();\n modelChanged = true;\n }\n }\n\n if (modelChanged){\n if (DTDEBUG || SPLITDEBUG) cout << \"DT \" << id << \" tree re-built.\" << endl;\n\n if (DTDEBUG || SPLITDEBUG){\n cout << endl << \"DT: \" << id << endl;\n printTree(root, 0);\n cout << \"Done printing tree\" << endl;\n }\n }\n\n /*\n if (nExperiences % 100 == 0){\n cout << endl << \"DT: \" << id << endl;\n printTree(root, 0);\n cout << \"Done printing tree\" << endl;\n \n // look at error\n float errorSum = 0.0;\n for (int i = 0; i < nExperiences; i++){\n e = experiences[i];\n std::map<float, float> answer;\n testInstance(e->input, &answer);\n float val = answer.begin()->first;\n float error = fabs(val - e->output);\n errorSum += error;\n }\n float avgError = errorSum / (float)nExperiences;\n cout << \"avgError: \" << avgError << endl << endl;\n }\n */\n\n return modelChanged;\n\n}\n\n\n// here the target output will be a single value\nbool LinearSplitsTree::trainInstances(std::vector<classPair> &instances){\n if (DTDEBUG) cout << \"DT trainInstances: \" << instances.size() << endl;\n\n bool modelChanged = false;\n\n bool doBuild = false;\n\n // loop through instances, possibly checking for errors\n for (unsigned a = 0; a < instances.size(); a++){\n classPair instance = instances[a];\n\n // simply add this instance to the set of experiences\n\n // take from static array until we run out\n tree_experience *e;\n if (nExperiences < N_LST_EXP){\n // from statically created set of experiences\n e = &(allExp[nExperiences]);\n\n } else {\n // dynamically create experience\n e = new tree_experience;\n }\n\n\n e->input = instance.in;\n e->output = instance.out;\n experiences.push_back(e);\n nExperiences++;\n\n if (nExperiences == 1000000){\n cout << \"Reached limit of # experiences allowed.\" << endl;\n return false;\n }\n\n if (nExperiences != (int)experiences.size())\n cout << \"ERROR: experience size mismatch: \" << nExperiences << \", \" << experiences.size() << endl;\n\n if (DTDEBUG) {\n cout << \"Original input: \";\n for (unsigned i = 0; i < instance.in.size(); i++){\n cout << instance.in[i] << \", \";\n }\n cout << endl << \" Original output: \" << instance.out << endl;\n cout << \"Added exp id: \" << nExperiences << \" output: \" << e->output << endl;\n cout << \"Address: \" << e << \" Input : \";\n for (unsigned i = 0; i < e->input.size(); i++){\n cout << e->input[i] << \", \";\n }\n cout << endl << \" Now have \" << nExperiences << \" experiences.\" << endl;\n }\n\n /*\n if (nExperiences % 100 == 0){\n cout << endl << \"DT: \" << id << endl;\n printTree(root, 0);\n cout << \"Done printing tree\" << endl;\n \n // look at error\n float errorSum = 0.0;\n for (int i = 0; i < nExperiences; i++){\n e = experiences[i];\n std::map<float, float> answer;\n testInstance(e->input, &answer);\n float val = answer.begin()->first;\n float error = fabs(val - e->output);\n errorSum += error;\n }\n float avgError = errorSum / (float)nExperiences;\n cout << \"avgError: \" << avgError << endl << endl;\n }\n */\n\n // depending on mode/etc, maybe re-build tree\n\n // don't need to check if we've already decided\n if (doBuild) continue;\n\n // mode 0: re-build every step\n if (mode == BUILD_EVERY || nExperiences <= 1){\n doBuild = true;\n }\n\n // mode 1: re-build on error only\n else if (mode == BUILD_ON_ERROR){\n\n // build on misclassification\n // check for misclassify\n std::map<float, float> answer;\n testInstance(e->input, &answer);\n float val = answer.begin()->first;\n float error = fabs(val - e->output);\n\n if (error > 0.0){\n doBuild = true;\n }\n\n }\n\n // mode 2: re-build every FREQ steps\n else if (mode == BUILD_EVERY_N){\n // build every freq steps\n if (!modelChanged && (nExperiences % freq) == 0){\n doBuild = true;\n }\n }\n\n } // loop of new instances\n\n if (DTDEBUG) cout << \"Added \" << instances.size() << \" new instances. doBuild = \" << doBuild << endl;\n\n if (doBuild){\n rebuildTree();\n modelChanged = true;\n }\n\n if (modelChanged){\n if (DTDEBUG || SPLITDEBUG) cout << \"DT \" << id << \" tree re-built.\" << endl;\n\n if (DTDEBUG || SPLITDEBUG){\n cout << endl << \"DT: \" << id << endl;\n printTree(root, 0);\n cout << \"Done printing tree\" << endl;\n }\n }\n\n return modelChanged;\n\n}\n\n\nvoid LinearSplitsTree::rebuildTree(){\n //cout << \"rebuild tree \" << id << \" on exp: \" << nExperiences << endl;\n //deleteTree(root);\n\n // re-calculate avg error for root\n root->avgError = calcAvgErrorforSet(experiences);\n\n buildTree(root, experiences, false);\n //cout << \"tree \" << id << \" rebuilt. \" << endl;\n}\n\n\n// TODO: here we want to return the probability of the output value being each of the possible values, in the stochastic case\nvoid LinearSplitsTree::testInstance(const std::vector<float> &input, std::map<float, float>* retval){\n if (DTDEBUG) cout << \"testInstance on tree \" << id << endl;\n\n retval->clear();\n\n // in case the tree is empty\n if (experiences.size() == 0){\n (*retval)[0.0] = 1.0;\n return;\n }\n\n // follow through tree to leaf\n tree_node* leaf = traverseTree(root, input);\n lastNode = leaf;\n\n // and return mapping of outputs and their probabilities\n leafPrediction(leaf, input, retval);\n\n}\n\nfloat LinearSplitsTree::getConf(const std::vector<float> &input){\n if (DTDEBUG) cout << \"numVisits\" << endl;\n\n // in case the tree is empty\n if (experiences.size() == 0){\n return 0;\n }\n\n if (lastNode == NULL)\n return 0;\n\n // follow through tree to leaf\n //tree_node* leaf = traverseTree(root, input);\n\n // and return # in this leaf\n float conf = (float)lastNode->nInstances / (float)(2.0*M);\n if (conf > 1.0)\n return 1.0;\n else\n return conf;\n\n}\n\n// check to see if this state is one we should explore\n// to get more info on potential splits\n\n\n\n// init the tree\nvoid LinearSplitsTree::initTree(){\n if (DTDEBUG) cout << \"initTree()\" << endl;\n root = allocateNode();\n\n if (DTDEBUG) cout << \" root id = \" << root->id << endl;\n\n // just to ensure the diff models are on different random values\n for (int i = 0; i < id; i++){\n rng.uniform(0, 1);\n }\n\n}\n\n\n\n// init a tree node\nvoid LinearSplitsTree::initTreeNode(tree_node* node){\n if (DTDEBUG) cout << \"initTreeNode()\";\n\n node->id = nnodes++;\n if (DTDEBUG) cout << \" id = \" << node->id << endl;\n\n totalnodes++;\n if (totalnodes > maxnodes){\n maxnodes = totalnodes;\n if (DTDEBUG) cout << id << \" LS MAX nodes: \" << maxnodes << endl;\n }\n\n // split criterion\n node->dim = -1;\n node->val = -1;\n\n // current data\n node->nInstances = 0;\n\n // coefficients\n node->constant = 0.0;\n\n // coefficients will get resized later\n // node->coefficients.resize(2,0);\n\n // next nodes in tree\n node->l = NULL;\n node->r = NULL;\n\n node->leaf = true;\n node->avgError = 10000;\n\n}\n\n/** delete current tree */\nvoid LinearSplitsTree::deleteTree(tree_node* node){\n if (DTDEBUG) cout << \"deleteTree, node=\" << node->id << endl;\n\n if (node==NULL)\n return;\n\n totalnodes--;\n\n node->nInstances = 0;\n node->coefficients.clear();\n \n //recursively call deleteTree on children\n // then delete them\n if (!node->leaf){\n // left child\n if (node->l != NULL){\n deleteTree(node->l);\n deallocateNode(node->l);\n node->l = NULL;\n }\n\n // right child\n if (node->r != NULL){\n deleteTree(node->r);\n deallocateNode(node->r);\n node->r = NULL;\n }\n }\n\n node->dim = -1;\n node->leaf = true;\n node->avgError = 10000;\n node->val = -1;\n node->constant = 0;\n}\n\n\n/** Get the correct child of this node based on the input */\nLinearSplitsTree::tree_node* LinearSplitsTree::getCorrectChild(tree_node* node,\n const std::vector<float> &input){\n\n if (DTDEBUG) cout << \"getCorrectChild, node=\" << node->id << endl;\n\n if (passTest(node->dim, node->val, input))\n return node->l;\n else\n return node->r;\n\n}\n\n/** Traverse the tree to the leaf for this input. */\nLinearSplitsTree::tree_node* LinearSplitsTree::traverseTree(tree_node* node,\n const std::vector<float> &input){\n\n if (DTDEBUG) cout << \"traverseTree, node=\" << node->id << endl;\n\n while (!node->leaf){\n node = getCorrectChild(node, input);\n }\n\n return node;\n}\n\n\n/** Decide if this passes the test */\nbool LinearSplitsTree::passTest(int dim, float val, const std::vector<float> &input){\n if (DTDEBUG) cout << \"passTest, dim=\" << dim << \",val=\" << val \n << \",input[\"<<dim<<\"]=\" << input[dim] <<endl;\n\n \n if (input[dim] > val)\n return false;\n else\n return true;\n\n}\n\n\n/** Build the tree from this node down using this set of experiences. */\nvoid LinearSplitsTree::buildTree(tree_node *node,\n const std::vector<tree_experience*> &instances,\n bool changed){\n if(DTDEBUG) cout << \"buildTree, node=\" << node->id\n << \",nInstances:\" << instances.size()\n << \",chg:\" << changed << endl;\n\n if (instances.size() == 0){\n cout << \"Error: buildTree called on tree \" << id << \" node \" << node->id << \" with no instances.\" << endl;\n exit(-1);\n }\n\n\n // TODO: what about stochastic data?\n //std::vector<float> chiSquare = calcChiSquare(instances);\n\n // first, add instances to tree\n node->nInstances = instances.size();\n\n // add each output to this node\n bool allSame = true;\n float val0 = instances[0]->output;\n for (unsigned i = 1; i < instances.size(); i++){\n if (instances[i]->output != val0){\n allSame = false;\n break;\n }\n }\n\n // see if they're all the same\n if (allSame){\n makeLeaf(node, instances);\n if (DTDEBUG){\n cout << \"Tree \" << id << \" node \" << node->id \n << \" All \" << node->nInstances\n << \" classified with output \"\n << instances[0]->output << \", \" << node->constant << endl;\n }\n return;\n }\n\n // check if linear model has no error\n if (node != root && node->avgError < MIN_ER){\n makeLeaf(node, instances);\n if (node->avgError < MIN_ER){\n if (DTDEBUG) {\n cout << \"Tree \" << id << \" node \" << node->id\n << \" has low error \" << node->avgError << \" keeping as leaf\" << endl;\n }\n return;\n }\n }\n \n // if not, calculate ER to determine best split\n if (SPLITDEBUG) cout << endl << \"Creating new decision node \" << id << \"-\" << node->id << endl;\n\n node->leaf = false;\n //node->nInstances++;\n\n float bestER = -1.0;\n int bestDim = -1;\n float bestVal = -1;\n std::vector<tree_experience*> bestLeft;\n std::vector<tree_experience*> bestRight;\n float leftError = 10000;\n float rightError = 10000;\n\n testPossibleSplits(node->avgError, instances, &bestER, &bestDim, &bestVal, &bestLeft, &bestRight, &leftError, &rightError);\n\n implementSplit(node, instances, bestER, bestDim, bestVal, bestLeft, bestRight, changed, leftError, rightError);\n\n}\n\n\nvoid LinearSplitsTree::makeLeaf(tree_node* node, const std::vector<tree_experience*> &instances){\n\n // check on children\n if (node->l != NULL){\n deleteTree(node->l);\n deallocateNode(node->l);\n node->l = NULL;\n }\n\n if (node->r != NULL){\n deleteTree(node->r);\n deallocateNode(node->r);\n node->r = NULL;\n }\n\n node->leaf = true;\n\n // fit linear model\n if (SIMPLE)\n node->avgError = fitSimpleLinearModel(instances, &(node->constant), &(node->coefficients));\n else \n node->avgError = fitMultiLinearModel(instances, &(node->constant), &(node->coefficients));\n\n if (DTDEBUG || LMDEBUG){\n cout << \"make leaf, fit linear model with constant \" << node->constant \n << \" error: \" << node->avgError << endl;\n }\n\n}\n\n\nfloat LinearSplitsTree::fitMultiLinearModel(const std::vector<tree_experience*> &instances,\n float *bestConstant, \n std::vector<float> *bestCoefficients){\n if(DTDEBUG || LMDEBUG) cout << \"fitMultiLinearModel\"\n << \",nInstances:\" << instances.size() << endl;\n\n // make sure there are enough coefficients for all the features\n if (bestCoefficients->size() != instances[0]->input.size()){\n bestCoefficients->resize(instances[0]->input.size(), 0); \n }\n\n // in case of size 1\n if (instances.size() == 1){\n *bestConstant = instances[0]->output;\n for (unsigned i = 0; i < bestCoefficients->size(); i++){\n (*bestCoefficients)[i] = 0.0;\n }\n if (LMDEBUG || SPLITDEBUG){\n cout << \" Singleton constant: \" \n << *bestConstant << \" avgE: \" << 0 << endl;\n }\n return 0;\n }\n\n // loop through all features, try simple single variable regression\n // keep track of error/coefficient of each\n float bestError = 100000;\n float avgError = 100000;\n bool doRegression = true;\n int ntimes = 0;\n\n std::vector<bool> featureMask(bestCoefficients->size(), true);\n\n while (doRegression){\n //cout << id << \" Attempt linear model \" << ntimes << endl;\n ntimes++;\n\n int nObs = (int)instances.size();\n\n int nFeats = 0;\n for (unsigned i = 0; i < featureMask.size(); i++){\n if (featureMask[i]) nFeats++;\n }\n if (nFeats < 1)\n break;\n\n // no feats or obs, no model to build\n if (nObs == 0 || nFeats == 0)\n break;\n\n Matrix X(nObs, nFeats);\n ColumnVector Y(nObs);\n\n std::vector<int> featIndices(nFeats);\n\n std::vector<bool> constants(nFeats,true);\n bool foundProblem = false;\n \n // load up matrices\n for (int i = 0; i < nObs; i++){\n tree_experience *e = instances[i];\n if (LMDEBUG) cout << \"Obs: \" << i;\n\n // go through all features\n int featIndex = 1;\n for (unsigned j = 0; j < featureMask.size(); j++){\n (*bestCoefficients)[j] = 0;\n if (!featureMask[j])\n continue;\n\n if (constants[j] && e->input[j] != instances[0]->input[j]){\n constants[j] = false;\n }\n\n if (i == nObs-1 && constants[j]){\n //cout << \"PROBLEM: feat \" << j << \" is constant!\" << endl;\n foundProblem = true;\n featureMask[j] = false;\n nFeats--;\n if (nFeats > 0)\n continue;\n else\n break;\n }\n\n featIndices[featIndex-1] = j;\n // HACK: I'm adding random noise here to prevent colinear features\n X(i+1,featIndex) = e->input[j]; // + rng.uniform(-0.00001, 0.00001);\n if (LMDEBUG){\n cout << \" Feat \" << featIndex << \" index \" << j\n << \" val \" << X(i+1,featIndex) << \",\";\n }\n featIndex++;\n }\n\n Y(i+1) = e->output;\n if (LMDEBUG) cout << \" out: \" << e->output << endl;\n }\n\n if (foundProblem)\n continue;\n\n // make vector of 1s\n ColumnVector Ones(nObs); Ones = 1.0;\n\n // calculate means (averages) of x1 and x2 [ .t() takes transpose]\n RowVector Mrow = Ones.t() * X / nObs;\n\n // and subtract means from x1 and x1\n Matrix XC(nObs,nFeats);\n XC = X - Ones * Mrow;\n\n // do the same to Y [use Sum to get sum of elements]\n ColumnVector YC(nObs);\n Real mval = Sum(Y) / nObs;\n YC = Y - Ones * mval;\n\n // form sum of squares and product matrix\n // [use << rather than = for copying Matrix into SymmetricMatrix]\n SymmetricMatrix SSQ;\n SSQ << XC.t() * XC;\n\n Try {\n\n ///////////////////////////\n // Cholesky Method\n LowerTriangularMatrix L = Cholesky(SSQ);\n\n // calculate estimate\n ColumnVector A = L.t().i() * (L.i() * (XC.t() * YC));\n ///////////////////////////\n \n //////////////////////////\n // Least Squares Method\n /*\n // calculate estimate\n // [bracket last two terms to force this multiplication first]\n // [ .i() means inverse, but inverse is not explicity calculated]\n ColumnVector A = SSQ.i() * (XC.t() * YC);\n //////////////////////////\n */\n \n // calculate estimate of constant term\n // [AsScalar converts 1x1 matrix to Real]\n Real a = mval - (Mrow * A).AsScalar();\n\n // Calculate fitted values and residuals\n //int npred1 = nFeats+1;\n ColumnVector Fitted = X * A + a;\n ColumnVector Residual = Y - Fitted;\n //Real ResVar = Residual.SumSquare() / (nObs-npred1);\n\n // print out answers\n // for each instance\n bestError = 0;\n for (int i = 0; i < nObs; i++){\n if (DTDEBUG || LMDEBUG){\n cout << \"instance \" << i << \" linear model predicted: \" << Fitted(i+1)\n << \" actual: \" << instances[i]->output\n << \" error: \" << Residual(i+1) << endl;\n }\n bestError += fabs(Residual(i+1));\n }\n avgError = bestError / (float)nObs;\n\n // coeff\n for (int i = 0; i < nFeats; i++){\n if (DTDEBUG || LMDEBUG) cout << \"Coeff \" << i << \" on feat: \" << featIndices[i] << \" is \" << A(i+1) << endl;\n (*bestCoefficients)[featIndices[i]] = A(i+1);\n }\n if (DTDEBUG || LMDEBUG) cout << \"constant is \" << a << endl;\n *bestConstant = a;\n\n }\n\n CatchAll {\n // had an error trying the linear regression.\n // HACK TODO: for now, turn off one variable\n //cout << ntimes << \" linear regression had exception\" << endl;\n //<< BaseException::what() <<endl;\n\n /*\n for (int i = 0; i < nObs; i++){\n tree_experience *e = instances[i];\n cout << \"Obs: \" << i;\n \n // go through all features\n int featIndex = 1;\n for (unsigned j = 0; j < featureMask.size(); j++){\n node->coefficients[j] = 0;\n if (!featureMask[j])\n continue;\n \n \n cout << \" Feat \" << featIndex << \" index \" << j\n << \" val \" << X(i+1,featIndex) << \",\";\n \n featIndex++;\n }\n \n cout << \" out: \" << e->output << endl;\n }\n */\n\n // tried this already, stop now\n if (ntimes > 2 || nFeats < 2){\n //cout << \"max regression\" << endl;\n doRegression = false;\n break;\n }\n for (unsigned j = 0; j < featureMask.size(); j++){\n if (featureMask[j]){\n //cout << \"remove feature \" << j << endl;\n featureMask[j] = false;\n nFeats--;\n break;\n }\n }\n continue;\n } // catch\n\n // it worked, dont need to do it again\n doRegression = false;\n\n }\n\n // return error\n return avgError;\n\n}\n\n\nfloat LinearSplitsTree::fitSimpleLinearModel(const std::vector<tree_experience*> &instances,\n float *bestConstant, std::vector<float> *bestCoefficients){\n if(DTDEBUG || LMDEBUG || SPLITDEBUG) cout << \" fitSimpleLinearModel, \"\n << \",nInstances:\" << instances.size() << endl;\n \n // make sure there are enough coefficients for all the features\n if (bestCoefficients->size() != instances[0]->input.size()){\n bestCoefficients->resize(instances[0]->input.size(), 0); \n }\n\n // loop through all features, try simple single variable regression\n // keep track of error/coefficient of each\n int bestFeat = -1;\n float bestCoeff = -1;\n float bestError = 100000;\n *bestConstant = -1;\n\n // in case of size 1\n if (instances.size() == 1){\n bestFeat = 0;\n *bestConstant = instances[0]->output;\n for (unsigned i = 0; i < bestCoefficients->size(); i++){\n (*bestCoefficients)[i] = 0.0;\n }\n bestError = 0;\n if (LMDEBUG || SPLITDEBUG){\n cout << \" Singleton constant: \" \n << *bestConstant << \" avgE: \" << bestError << endl;\n }\n return 0;\n }\n\n std::vector<float> xsum(instances[0]->input.size(), 0);\n std::vector<float> xysum(instances[0]->input.size(), 0);\n std::vector<float> x2sum(instances[0]->input.size(), 0);\n float ysum = 0;\n\n int nObs = (int)instances.size();\n for (int i = 0; i < nObs; i++){\n tree_experience *e = instances[i];\n if (LMDEBUG) cout << \"Obs: \" << i;\n \n // go through all features\n for (unsigned j = 0; j < instances[0]->input.size(); j++){\n if (LMDEBUG) cout << \", F\" << j << \": \" << e->input[j];\n xsum[j] += e->input[j];\n xysum[j] += (e->input[j] * e->output);\n x2sum[j] += (e->input[j] * e->input[j]);\n }\n ysum += e->output;\n if (LMDEBUG) cout << \", out: \" << e->output << endl;\n }\n\n // now go through all features and calc coeff and constant\n for (unsigned j = 0; j < instances[0]->input.size(); j++){\n float coeff = (xysum[j] - xsum[j]*ysum/nObs)/(x2sum[j]-(xsum[j]*xsum[j])/nObs);\n float constant = (ysum/nObs) - coeff*(xsum[j]/nObs);\n\n // so we don't get absurd coefficients that get rounded off earlier\n if (fabs(coeff) < 1e-5){\n coeff = 0.0;\n }\n if (fabs(constant) < 1e-5){\n constant = 0.0;\n }\n\n if (LMDEBUG) {\n cout << \"Feat \" << j << \" coeff: \" << coeff << \", constant: \" \n << constant << endl;\n }\n\n // now try to make predictions and see what error is\n float errorSum = 0;\n for (int i = 0; i < nObs; i++){\n tree_experience *e = instances[i];\n float pred = constant + coeff * e->input[j];\n float error = fabs(pred - e->output);\n if (LMDEBUG) cout << \"Instance \" << i << \" error: \" << error << endl;\n errorSum += error;\n }\n float avgError = errorSum / (float)nObs;\n if (LMDEBUG) cout << \"avgError: \" << avgError << endl;\n\n // check if this is the best\n if (avgError < bestError){\n bestError = avgError;\n bestFeat = j;\n *bestConstant = constant;\n bestCoeff = coeff;\n }\n }\n \n if (LMDEBUG || SPLITDEBUG){\n cout << \" LST feat: \" << bestFeat << \" coeff: \" \n << bestCoeff << \" constant: \" \n << *bestConstant << \" avgE: \" << bestError << endl;\n }\n\n if (bestFeat < 0 || bestFeat > (int)instances[0]->input.size()){\n for (unsigned i = 0; i < bestCoefficients->size(); i++){\n (*bestCoefficients)[i] = 0.0;\n }\n *bestConstant = 0.0;\n return 100000;\n }\n\n // fill in coeff vector\n for (unsigned i = 0; i < bestCoefficients->size(); i++){\n if (i == (unsigned)bestFeat)\n (*bestCoefficients)[i] = bestCoeff;\n else\n (*bestCoefficients)[i] = 0.0; \n }\n\n return bestError;\n \n}\n\n\n\n\nvoid LinearSplitsTree::implementSplit(tree_node* node, \n const std::vector<tree_experience*> &instances,\n float bestER, int bestDim,\n float bestVal, \n const std::vector<tree_experience*> &bestLeft,\n const std::vector<tree_experience*> &bestRight,\n bool changed, float leftError, float rightError){\n if (DTDEBUG) cout << \"implementSplit node=\" << node->id\n << \",er=\" << bestER\n << \",dim=\" << bestDim\n << \",val=\" << bestVal \n << \",chg=\" << changed << endl;\n\n\n // see if this should still be a leaf node\n if (bestER < MIN_ER){\n makeLeaf(node, instances);\n\n if (SPLITDEBUG || STOCH_DEBUG){\n cout << \" DT \" << id << \" Node \" << node->id << \" Poor er \"\n << node->nInstances\n << \" instances classified at leaf \" << node->id\n << \" with er \" << bestER << \" constant: \" << node->constant << endl;\n }\n return;\n }\n\n //cout << id << \" implement split with er \" << bestER << endl;\n\n // see if this split changed or not\n // assuming no changes above\n if (!changed && node->dim == bestDim && node->val == bestVal\n && !node->leaf && node->l != NULL && node->r != NULL){\n // same split as before.\n if (DTDEBUG || SPLITDEBUG) cout << \"Same split as before \" << node->id << endl;\n node->l->avgError = leftError;\n node->r->avgError = rightError;\n\n\n // see which leaf changed\n if (bestLeft.size() > (unsigned)node->l->nInstances){\n // redo left side\n if (DTDEBUG) cout << \"Rebuild left side of tree\" << endl;\n buildTree(node->l, bestLeft, changed);\n }\n\n if (bestRight.size() > (unsigned)node->r->nInstances){\n // redo right side\n if (DTDEBUG) cout << \"Rebuild right side of tree\" << endl;\n buildTree(node->r, bestRight, changed);\n }\n return;\n }\n\n\n // totally new\n // set the best split here\n node->leaf = false;\n node->dim = bestDim;\n node->val = bestVal;\n\n if (SPLITDEBUG) cout << \"Best split was cut with val \" << node->val\n << \" on dim \" << node->dim\n << \" with er: \" << bestER << endl;\n\n if (DTDEBUG) cout << \"Left has \" << bestLeft.size()\n << \", right has \" << bestRight.size() << endl;\n\n // make sure both instances\n if (bestLeft.size() == 0 || bestRight.size() == 0){\n cout << \"ERROR: DT \" << id << \" node \" << node->id << \" has 0 instances: left: \" << bestLeft.size()\n << \" right: \" << bestRight.size() << endl;\n cout << \"Split was cut with val \" << node->val\n << \" on dim \" << node->dim\n << \" with er: \" << bestER << endl;\n exit(-1);\n }\n\n\n // check if these already exist\n if (node->l == NULL){\n if (DTDEBUG) cout << \"Init new left tree nodes \" << endl;\n node->l = allocateNode();\n }\n if (node->r == NULL){\n if (DTDEBUG) cout << \"Init new right tree nodes \" << endl;\n node->r = allocateNode();\n }\n\n // recursively build the sub-trees to this one\n if (DTDEBUG) cout << \"Building left tree for node \" << node->id << endl;\n node->l->avgError = leftError;\n buildTree(node->l, bestLeft, true);\n if (DTDEBUG) cout << \"Building right tree for node \" << node->id << endl;\n node->r->avgError = rightError;\n buildTree(node->r, bestRight, true);\n\n}\n\n\nvoid LinearSplitsTree::testPossibleSplits(float avgError, const std::vector<tree_experience*> &instances,\n float *bestER, int *bestDim,\n float *bestVal, \n std::vector<tree_experience*> *bestLeft,\n std::vector<tree_experience*> *bestRight,\n float *bestLeftError, float *bestRightError) {\n if (DTDEBUG || SPLITDEBUG) cout << \"testPossibleSplits, error=\" << avgError << endl;\n\n int nties = 0;\n\n // for each possible split, calc standard deviation reduction\n for (unsigned idim = 0; idim < instances[0]->input.size(); idim++){\n\n //float* sorted = sortOnDim(idim, instances);\n float minVal, maxVal;\n std::set<float> uniques = getUniques(idim, instances, minVal, maxVal);\n\n for (std::set<float>::iterator j = uniques.begin(); j != uniques.end(); j++){\n\n // skip max val, not a valid cut for either\n if ((*j) == maxVal)\n continue;\n\n // if this is a random forest, we eliminate some random number of splits\n // here (decision is taken from the random set that are left)\n if (rng.uniform() < featPct)\n continue;\n \n std::vector<tree_experience*> left;\n std::vector<tree_experience*> right;\n float leftError = 10000;\n float rightError = 10000;\n\n // splits that are cuts\n float splitval = (*j);\n float er = calcER(idim, splitval, instances, avgError, left, right, &leftError, &rightError);\n\n if (SPLITDEBUG){\n cout << id << \" CUT split val \" << splitval\n << \" on dim: \" << idim << \" had er \"\n << er << endl;\n }\n\n // see if this is the new best er\n compareSplits(er, idim, splitval, left, right, &nties, leftError, rightError,\n bestER, bestDim, bestVal,bestLeft, bestRight, bestLeftError, bestRightError);\n\n\n } // j loop\n }\n}\n\n\n\n/** Decide if this split is better. */\nvoid LinearSplitsTree::compareSplits(float er, int dim, float val, \n const std::vector<tree_experience*> &left, \n const std::vector<tree_experience*> &right,\n int *nties, float leftError, float rightError,\n float *bestER, int *bestDim,\n float *bestVal, \n std::vector<tree_experience*> *bestLeft,\n std::vector<tree_experience*> *bestRight,\n float *bestLeftError, float *bestRightError){\n if (DTDEBUG) cout << \"compareSplits er=\" << er << \",dim=\" << dim\n << \",val=\" << val <<endl;\n\n\n bool newBest = false;\n\n // if its a virtual tie, break it randomly\n if (fabs(*bestER - er) < SPLIT_MARGIN){\n //cout << \"Split tie, making random decision\" << endl;\n\n (*nties)++;\n float randomval = rng.uniform(0,1);\n float newsplitprob = (1.0 / (float)*nties);\n\n if (randomval < newsplitprob){\n newBest = true;\n if (SPLITDEBUG) cout << \" Tie on split. DT: \" << id << \" rand: \" << randomval\n << \" splitProb: \" << newsplitprob << \", selecting new split \" << endl;\n }\n else\n if (SPLITDEBUG) cout << \" Tie on split. DT: \" << id << \" rand: \" << randomval\n << \" splitProb: \" << newsplitprob << \", staying with old split \" << endl;\n }\n\n // if its clearly better, set this as the best split\n else if (er > *bestER){\n newBest = true;\n *nties = 1;\n }\n\n\n // set the split features\n if (newBest){\n *bestER = er;\n *bestDim = dim;\n *bestVal = val;\n *bestLeft = left;\n *bestRight = right;\n *bestLeftError = leftError;\n *bestRightError = rightError;\n if (SPLITDEBUG){\n cout << \" New best er: \" << *bestER\n << \" with val \" << *bestVal\n << \" on dim \" << *bestDim << endl;\n }\n } // newbest\n}\n\n/** Calculate error reduction for this possible split. */\nfloat LinearSplitsTree::calcER(int dim, float val,\n const std::vector<tree_experience*> &instances,\n float avgError,\n std::vector<tree_experience*> &left,\n std::vector<tree_experience*> &right,\n float *leftError, float *rightError){\n if (DTDEBUG || SPLITDEBUG) cout << \"calcER, dim=\" << dim\n << \" val=\" << val\n << \" err=\" << avgError\n << \" nInstances= \" << instances.size() << endl;\n\n left.clear();\n right.clear();\n\n // split into two sides\n for (unsigned i = 0; i < instances.size(); i++){\n if (DTDEBUG) cout << \" calcER - Classify instance \" << i << \" on new split \" << endl;\n\n if (passTest(dim, val, instances[i]->input)){\n left.push_back(instances[i]);\n }\n else{\n right.push_back(instances[i]);\n }\n }\n\n if (DTDEBUG) cout << \"Left has \" << left.size()\n << \", right has \" << right.size() << endl;\n\n // get sd for both sides\n *leftError = calcAvgErrorforSet(left);\n *rightError = calcAvgErrorforSet(right);\n\n float leftRatio = (float)left.size() / (float)instances.size();\n float rightRatio = (float)right.size() / (float)instances.size();\n float newError = (leftRatio * (*leftError) + rightRatio * (*rightError));\n\n float er = avgError - newError;\n\n if (DTDEBUG || SPLITDEBUG){\n cout << \"LeftError: \" << *leftError\n << \" RightError: \" << *rightError\n << \" NewError: \" << newError\n << \" NodeError: \" << avgError\n << \" ER: \" << er\n << endl;\n }\n\n return er;\n\n}\n\n/** Calculate std deviation for set. */\nfloat LinearSplitsTree::calcAvgErrorforSet(const std::vector<tree_experience*> &instances){\n if (DTDEBUG) cout << \"calcAvgErrorforSet\" << endl;\n\n int n = instances.size();\n\n if (n == 0)\n return 0;\n\n // fit a linear model to instances\n // and figure out avg error of it\n float constant;\n float avgError = 0.0;\n std::vector<float> coeff;\n if (SIMPLE) \n avgError = fitSimpleLinearModel(instances, &constant, &coeff);\n else \n avgError = fitMultiLinearModel(instances, &constant, &coeff);\n\n return avgError;\n}\n\n\n/** Returns the unique elements at this index */\nstd::set<float> LinearSplitsTree::getUniques(int dim, const std::vector<tree_experience*> &instances, float& minVal, float& maxVal){\n if (DTDEBUG) cout << \"getUniques,dim = \" << dim;\n\n std::set<float> uniques;\n\n for (int i = 0; i < (int)instances.size(); i++){\n if (i == 0 || instances[i]->input[dim] < minVal)\n minVal = instances[i]->input[dim];\n if (i == 0 || instances[i]->input[dim] > maxVal)\n maxVal = instances[i]->input[dim];\n\n uniques.insert(instances[i]->input[dim]);\n }\n\n \n // lets not try more than 100 possible splits per dimension\n if (uniques.size() > 100){\n float rangeInc = (maxVal - minVal) / 100.0;\n uniques.clear();\n for (float i = minVal; i < maxVal; i+= rangeInc){\n uniques.insert(i);\n }\n }\n uniques.insert(maxVal);\n \n\n if (DTDEBUG) cout << \" #: \" << uniques.size() << endl;\n return uniques;\n}\n\n\n/** Returns a list of the attributes in this dimension sorted\n from lowest to highest. */\nfloat* LinearSplitsTree::sortOnDim(int dim, const std::vector<tree_experience*> &instances){\n if (DTDEBUG) cout << \"sortOnDim,dim = \" << dim << endl;\n\n float* values = new float[instances.size()];\n\n for (int i = 0; i < (int)instances.size(); i++){\n //cout << \"Instance \" << i << endl;\n\n float val = instances[i]->input[dim];\n //cout << \" val: \" << val << endl;\n\n // find where this should go\n for (int j = 0; j <= i; j++){\n //cout << \" j: \" << j << endl;\n\n // get to i, this is the spot then\n if (j==i){\n values[j] = val;\n //cout << \" At i, putting value in slot j: \" << j << endl;\n }\n\n // if this is the spot\n else if (val < values[j]){\n //cout << \" Found slot at j: \" << j << endl;\n\n // slide everything forward to make room\n for (int k = i; k > j; k--){\n //cout << \" k = \" << k << \" Sliding value from k-1 to k\" << endl;\n values[k] = values[k-1];\n }\n\n // put value in its spot at j\n //cout << \" Putting value at slot j: \" << j << endl;\n values[j] = val;\n\n // break\n break;\n }\n\n }\n }\n\n if (DTDEBUG){\n cout << \"Sorted array: \" << values[0];\n for (int i = 1; i < (int)instances.size(); i++){\n cout << \", \" << values[i];\n }\n cout << endl;\n }\n\n return values;\n\n}\n\n\n/** Print the tree for debug purposes. */\nvoid LinearSplitsTree::printTree(tree_node *t, int level){\n\n for (int i = 0; i < level; i++){\n cout << \".\";\n }\n\n cout << \"Node \" << t->id << \" nInstances: \" << t->nInstances ;\n\n // Leaf, print regression stuff\n if (t->leaf){\n cout << \" Constant: \" << t->constant << \" coeff: \";\n for (unsigned j = 0; j < t->coefficients.size(); j++){\n if (!SIMPLE)\n cout << t->coefficients[j] << \", \";\n if (SIMPLE && t->coefficients[j] != 0)\n cout << \" on feat \" << j << \": \" << t->coefficients[j];\n }\n cout << endl;\n return;\n }\n\n // otherwise, print split info, next nodes\n\n cout << \" Type: CUT\";\n cout << \" Dim: \" << t->dim << \" Val: \" << t->val;\n cout << \" Left: \" << t->l->id << \" Right: \" << t->r->id << endl;\n\n // print children\n if (t->dim != -1 && !t->leaf){\n printTree(t->l, level+1);\n printTree(t->r, level+1);\n }\n\n}\n\n\n// output a map of outcomes and their probabilities for this leaf node\nvoid LinearSplitsTree::leafPrediction(tree_node* leaf, const std::vector<float> &input,\n std::map<float, float>* retval){\n if (DTDEBUG)\n cout << \"Calculating output for leaf \" << leaf->id << endl;\n\n float prediction = leaf->constant;\n if (DTDEBUG) cout << \"leaf constant: \" << leaf->constant << endl;\n\n // plus each coefficient\n for (unsigned i = 0; i < input.size(); 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It can optionally be stochastic, have action\n delays, or multiple goals (with partial observability). */\nclass TwoRooms: public Environment {\npublic:\n\n /** Standard Constructor\n \\param rand Random Number generator\n \\param stochastic Make the domain stochastic \n \\param rewardType Create -1 per step and 0 on termination (vs 0 and 1)\n \\param actDelay # of steps to delay actions\n \\param multiGoal create mulitple goals that are randomly selected from each episode\n */\n TwoRooms(Random &rand, bool stochastic, bool rewardType, int actDelay, bool multiGoal);\n\n virtual ~TwoRooms();\n\n virtual const std::vector<float> &sensation() const;\n virtual float apply(int action);\n\n virtual bool terminal() const;\n virtual void reset();\n\n virtual int getNumActions();\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\n virtual void getMinMaxReward(float* minR, float* maxR);\n\n /** Create an experience tuple for the given state-action. */\n experience getExp(float s0, float s1, int a);\n\n virtual std::vector<experience> getSeedings();\n\nprotected:\n typedef std::pair<float,float> coord_t;\n enum room_action_t {NORTH, SOUTH, EAST, WEST};\n\nprivate:\n const Gridworld *const grid;\n coord_t goal;\n coord_t goal2;\n std::deque<int> actHistory;\n bool useGoal2;\n\n const bool negReward;\n const bool noisy;\n const int actDelay;\n const bool multiGoal;\n\n Random &rng;\n\n coord_t doorway;\n\n std::vector<float> s;\n\n float &ns;\n float &ew;\n\n /** Create default two room gridworld */\n const Gridworld *create_default_map();\n\n /** Corrupts a movement action.\n \\param action The intended action\n \\return The action actually executed */\n room_action_t add_noise(room_action_t action);\n\n /** Randomly assigns the goal to any random \n position in the world. */\n void randomize_goal();\n\n /** Return the correct reward based on the current state. */\n float reward();\n\n};\n\n#endif\n"},{"alpha_fraction":0.5695687532424927,"alphanum_fraction":0.5732302665710449,"avg_line_length":20.373912811279297,"blob_id":"be23ec9b3ab12f8a319d206fc716b16c9d0e14c7","content_id":"6f31c73dc46e1164426dec166846cba60a8f6e72","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":2458,"license_type":"no_license","max_line_length":71,"num_lines":115,"path":"/src/rl-texplore-ros-pkg-master/src/rl_common/include/rl_common/ExperienceFile.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#ifndef _EXPFILE_HH_\n#define _EXPFILE_HH_\n\n#include \"Random.h\"\n#include \"core.hh\"\n\n#include <vector>\n#include <algorithm>\n\n#include <sys/time.h>\n\nclass ExperienceFile {\npublic:\n /** Standard constructor\n */\n\n ofstream vectorFile;\n int expNum;\n\n ExperienceFile(){\n expNum = 0;\n }\n\n ~ExperienceFile(){\n if (vectorFile.is_open())\n vectorFile.close();\n }\n\n void initFile(const char* filename, int nfeats){\n vectorFile.open(filename, ios::out | ios::binary);\n\n // first part, save the vector size\n vectorFile.write((char*)&nfeats, sizeof(int));\n }\n\n void saveExperience(experience e){\n if (!vectorFile.is_open())\n return;\n\n /*\n if (expNum == 50){\n vectorFile.close();\n return;\n }\n */\n\n vectorFile.write((char*)&(e.s[0]), e.s.size()*sizeof(float));\n vectorFile.write((char*)&(e.next[0]), e.next.size()*sizeof(float));\n vectorFile.write((char*)&e.act, sizeof(int));\n vectorFile.write((char*)&e.reward, sizeof(float));\n vectorFile.write((char*)&e.terminal, sizeof(bool));\n\n //cout << \"Experience \" << expNum << endl;\n expNum++;\n //printExperience(e);\n }\n\n void printExperience(experience e){\n\n cout << \"State s: \";\n for(unsigned i = 0; i < e.s.size(); i++){\n cout << e.s[i] << \", \";\n }\n cout << endl << \" Next: \";\n for(unsigned i = 0; i < e.next.size(); i++){\n cout << e.next[i] << \", \";\n }\n cout << endl;\n cout << \"action: \" << e.act << \" reward: \" << e.reward << endl;\n\n }\n\n\n\n std::vector<experience> loadExperiences(const char* filename){\n ifstream inFile (filename, ios::in | ios::binary);\n\n int numFeats;\n inFile.read((char*)&numFeats, sizeof(int));\n\n std::vector<experience> seeds;\n\n // while file is not empty\n while(!inFile.eof()){\n experience e;\n e.s.resize(numFeats);\n e.next.resize(numFeats);\n\n inFile.read((char*)&(e.s[0]), e.s.size()*sizeof(float));\n if (inFile.eof()) break;\n inFile.read((char*)&(e.next[0]), e.next.size()*sizeof(float));\n if (inFile.eof()) break;\n inFile.read((char*)&e.act, sizeof(int));\n inFile.read((char*)&e.reward, sizeof(float));\n inFile.read((char*)&e.terminal, sizeof(bool));\n\n //cout << \"Experience \" << seeds.size() << endl;\n //printExperience(e);\n\n seeds.push_back(e);\n }\n\n inFile.close();\n\n return seeds;\n }\n\n void closeFile(){\n if (vectorFile.is_open())\n vectorFile.close();\n 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constructor\n \\param numactions The number of possible actions\n \\param gamma The discount factor\n \\param rmax max reward value, given out for unknown states\n \\param rrange range between max and min reward in domain\n \\param modelType specifies model type\n \\param exploreType specifies exploration type\n \\param predType specifies how to combine multiple models\n \\param nModels number of models in ensemble\n \\param plannerType specifies planner type\n \\param epsilon used for epsilon-greedy action selection\n \\param lambda used for eligibility traces in uct planning\n \\param MAX_TIME amount of time the uct planners are given for planning\n \\param m # visits required for a state to become known when doing rmax exploration\n \\param featmin min values of each feature\n \\param featmax max values of each feature\n \\param statesPerDim # of values to discretize each feature into\n \\param history # of previous actions to use for delayed domains\n \\param b\\v bonus reward used when models disagree\n \\param n bonus reward used for novel states\n \\param depTrans assume dependent or indep. feature transitions\n \\param relTrans model transitions relatively vs absolutely\n \\param featPct pct of feature to remove from set used for each split in tree\n \\param stoch is the domain stochastic?\n \\param episodic is the domain episodic?\n \\param rng Initial state of the random number generator to use */\n ModelBasedAgent(int numactions, float gamma, float rmax, float rrange, \n int modelType, int exploreType, \n int predType, int nModels, int plannerType,\n float epsilon, float lambda, float MAX_TIME,\n float m, const std::vector<float> &featmin, \n const std::vector<float> &featmax,\n int statesPerDim, int history, float v, float n,\n bool depTrans, bool relTrans, float featPct,\n bool stoch, bool episodic, Random rng = Random());\n\n /** Standard constructor \n \\param numactions The number of possible actions\n \\param gamma The discount factor\n \\param rmax max reward value, given out for unknown states\n \\param rrange range between max and min reward in domain\n \\param modelType specifies model type\n \\param exploreType specifies exploration type\n \\param predType specifies how to combine multiple models\n \\param nModels number of models in ensemble\n \\param plannerType specifies planner type\n \\param epsilon used for epsilon-greedy action selection\n \\param lambda used for eligibility traces in uct planning\n \\param MAX_TIME amount of time the uct planners are given for planning\n \\param m # visits required for a state to become known when doing rmax exploration\n \\param featmin min values of each feature\n \\param featmax max values of each feature\n \\param statesPerDim # of values to discretize each feature into\n \\param history # of previous actions to use for delayed domains\n \\param b bonus reward used when models disagree\n \\param depTrans assume dependent or indep. feature transitions\n \\param relTrans model transitions relatively vs absolutely\n \\param featPct pct of feature to remove from set used for each split in tree\n \\param stoch is the domain stochastic?\n \\param episodic is the domain episodic?\n \\param rng Initial state of the random number generator to use*/\n ModelBasedAgent(int numactions, float gamma, float rmax, float rrange, \n int modelType, int exploreType, \n int predType, int nModels, int plannerType,\n float epsilon, float lambda, float MAX_TIME,\n float m, const std::vector<float> &featmin, \n const std::vector<float> &featmax,\n std::vector<int> statesPerDim, int history, float v, float n,\n bool depTrans, bool relTrans, float featPct,\n bool stoch, bool episodic, Random rng = Random());\n \n /** Init params for both constructors */\n void initParams();\n\n /** Unimplemented copy constructor: internal state cannot be simply\n copied. */\n ModelBasedAgent(const ModelBasedAgent &);\n\n virtual ~ModelBasedAgent();\n\n virtual int first_action(const std::vector<float> &s);\n virtual int next_action(float r, const std::vector<float> &s);\n virtual void last_action(float r);\n virtual void seedExp(std::vector<experience> seeds);\n virtual void setDebug(bool d);\n virtual void savePolicy(const char* filename);\n\n /** Output value function to a file */\n void logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax);\n\n bool AGENTDEBUG;\n bool POLICYDEBUG; //= false; //true;\n bool ACTDEBUG;\n bool SIMPLEDEBUG;\n bool TIMEDEBUG;\n\n bool seeding;\n\n /** Model that we're using */\n MDPModel* model;\n\n /** Planner that we're using */\n Planner* planner;\n\n float planningTime;\n float modelUpdateTime;\n float actionTime;\n\n std::vector<float> featmin;\n std::vector<float> featmax;\n\nprotected:\n\n /** The implementation maps all sensations to a set of canonical\n pointers, which serve as the internal representation of\n environment state. */\n typedef const std::vector<float> *state_t;\n\n /** Saves state and action to use 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\"catkin\")\nset(boost_python_catkin_example_BUILDTOOL_EXPORT_DEPENDS )\nset(boost_python_catkin_example_EXEC_DEPENDS )\nset(boost_python_catkin_example_RUN_DEPENDS )\nset(boost_python_catkin_example_TEST_DEPENDS )\nset(boost_python_catkin_example_DOC_DEPENDS )\nset(boost_python_catkin_example_URL_WEBSITE \"https://github.com/luator/ros-boost-python\")\nset(boost_python_catkin_example_URL_BUGTRACKER \"\")\nset(boost_python_catkin_example_URL_REPOSITORY \"\")\nset(boost_python_catkin_example_DEPRECATED \"\")"},{"alpha_fraction":0.5498554706573486,"alphanum_fraction":0.5698802471160889,"avg_line_length":23.14214515686035,"blob_id":"4110c4d347e596a05cb20334e6b5d7e436da25d5","content_id":"22ce8f04dd66435de0cb2371ab4a4c22914444d5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":9688,"license_type":"no_license","max_line_length":138,"num_lines":401,"path":"/src/boost-python-catkin-example-master/src/mycpplib.cpp","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"\n\n\n\n\n\n\n#include <iostream>\n#include <boost/python.hpp>\n#include <boost/python/list.hpp>\n#include <boost/python/extract.hpp>\n#include <string>\n#include <sstream>\n#include <vector>\n#include <cmath>\n#include <ros/ros.h>\n#include <std_msgs/Float64.h>\n#include <geometry_msgs/Twist.h>\n#include <geometry_msgs/Pose.h>\n#include <std_srvs/Empty.h>\n#include <gazebo_msgs/SpawnModel.h>\n#include <gazebo_msgs/DeleteModel.h>\n#include <tf/tf.h>\n#include <geometry_msgs/Pose.h>\n#include <nav_msgs/Odometry.h>\n#include <vector>\n#include <boost/python/suite/indexing/vector_indexing_suite.hpp>\n#include <boost/python/numpy.hpp>\n#include <sensor_msgs/LaserScan.h>\n#include <std_msgs/String.h>\n#include <algorithm> \n#include <cstdlib>\n#include <fstream>\n#include <tf2/LinearMath/Quaternion.h>\n\n//#include <condition_variable> // std::condition_variable\n\n\n\n//using namespace std;\n //using namespace std::placeholders; \n//namespace p = boost::python;\n//namespace np = boost::python::numpy;\ntypedef std::vector<float> MyList;\nclass World\n{\nprivate:\n\t\n float goal_angle =0.0;\n float heading = 0.0;\n float min_range = 0.15;\n float goal_x = 0.0;\n float goal_y = 0.0;\n std::string mMsg;\n int count = 0;\n float goal_distance = 0.0;\n int old_count = 0;\n float past_distance = 0.0;\n float pos_x = 0.0;\n float pos_y = 0.0;\n geometry_msgs::Twist msg;\n geometry_msgs::Twist msg_empty;\n ros::NodeHandle n;\n MyList action = MyList(2);\n MyList past_action = MyList(2);\n MyList state = MyList(15);\n bool done = false;\n bool get_goalbox = false;\n float distance = 0.0;\n float reward = 0;\n //float past_action = 0;\n geometry_msgs::Pose post;\n MyList goal_x_list = {0.6, 1.9, 0.5, 0.2, -0.8, -1, -1.9, 0.5, 2, 0.5, 0, -0.1, -2};\n MyList goal_y_list = {0, -0.5, -1.9, 1.5, -0.9, 1, 1.1, -1.5, 1.5, 1.8, -1, 1.6, -0.8};\n // int rand();\n double yaw = 0;\n bool init_goal = true;\n \n\n\n\n\npublic: \n\n //nav_msgs::Odometry::ConstPtr msg;\n\n ros::Publisher chatter_pub = n.advertise<geometry_msgs::Twist>(\"cmd_vel\", 5); \n //ros::Subscriber odom_sub = n.subscribe(\"odom\", 1, std::bind(getOdometry, &msg));\n ros::Subscriber sub_t = n.subscribe(\"odom\", 1, &World::getOdometry, this);\n //ros::Subscriber scan = n.subscribe(\"scan\", 1, &World::getScan, this);\nsensor_msgs::LaserScanConstPtr test3 = ros::topic::waitForMessage<sensor_msgs::LaserScan>(\"scan\");\n\n\n\n//SOMETHING HERE \n //self.respawn_goal = Respawn()\n\n\n ros::ServiceClient reset_proxy = n.serviceClient<std_srvs::Empty>(\"gazebo/reset_simulation\"); \n ros::ServiceClient pauseGazebo = n.serviceClient<std_srvs::Empty>(\"/gazebo/pause_physics\");\n ros::ServiceClient unpauseGazebo = n.serviceClient<std_srvs::Empty>(\"/gazebo/unpause_physics\");\n std_srvs::Empty emptySrv;\n //pauseGazebo.call(emptySrv);\n //msg_empty.linear.x = 0.0;\n // msg_empty.angular.z = 0.0;\n\n\n\n\n//unpause_proxy\n void unpause_proxy() {\n unpauseGazebo.call(emptySrv);\n }\n\n//pause_proxy\n void pause_proxy() {\n pauseGazebo.call(emptySrv);\n }\n\n\n//getGoaldistance\n float getGoalDistance() {\n\tgoal_distance = hypot(post.position.x - pos_x, post.position.y-pos_y);\n\tstate[13] = goal_distance;\n\n }\n\n//getOdometry\n\n void getOdometry(const nav_msgs::Odometry::ConstPtr& msg) {\n\tpos_x = msg->pose.pose.position.x;\n\tpos_y = msg->pose.pose.position.y;\n\n\tgeometry_msgs::Quaternion q = msg->pose.pose.orientation;\n\tdouble siny_cosp = +2.0 * (q.w * q.z + q.x * q.y);\n\tdouble cosy_cosp = +1.0 - 2.0 * (q.y * q.y + q.z * q.z); \n\tyaw = atan2(siny_cosp, cosy_cosp);\n\tgoal_angle = atan2(post.position.x - pos_x, post.position.y-pos_y);\n\theading = goal_angle - yaw;\n\n\tif (heading > 3.14)\n heading -= 2 * 3.14;\n\n else if (heading < -3.14)\n heading += 2 * 3.14;\n\tstate[12] = heading;\n }\n\n//getScan\n void getScan(const sensor_msgs::LaserScan::ConstPtr& msg) {\n\tfor (int i =0; i <10; ++i) {\n\t state[i] = msg->ranges[i];\n\n\t if (state[i] > 3.5)\n\t\tstate[i] = 3.5;\t \t \t\n\t}\n\t count = msg->header.seq;\n }\n\n//getstate\n void getState() {\n\n\tfor (int i =0; i<10; ++i) {\n\t\t//std::min_element(state.begin(), state.end());\n\t if (state[i] < min_range) \n\t\t{done = true;\n\t\t//state[12] = state[i];\n\t\t//state[13] = 5;\n\t\tbreak;\n\t\t}\n\t}\n\tgetGoalDistance();\n\t//distance = hypot(goal_x - pos_x, goal_y-pos_y);\t \n\tif (goal_distance < 0.2) \n\t get_goalbox = true;\n }\n\n//setreward\n void setReward() {\n\t\n float distance_rate = past_distance - goal_distance;\n\treward = (500 * distance_rate);\n\tpast_distance = goal_distance;\n\t\n\tif (done) {\n\t ROS_INFO(\"Collision!\");\n\t reward = -150;\n\t chatter_pub.publish(msg_empty);\n\t reset();\n\t done = false;\n\t}\n\tif (get_goalbox) {\n\t ROS_INFO(\"Goal!\");\n\t reward = 200;\n\t deleteGoal();\n\t spawnGoal();\n\n\t ///// new goal and get position?\n\t /////////////////////////////////\n\t /////////////////////////////////\n\t getGoalDistance();\n\t get_goalbox = false;\n\t}\n\n\t//current_distance = \n\n }\n\n//step\n MyList step(boost::python::list msgs, boost::python::list msgs2) {\n long l = len(msgs);\n long l2 = len(msgs2);\n\n for (long i = 0; i<l; ++i) {\n\n action[i] = boost::python::extract<float>(msgs[i]);\n\t}\t\n for (long i = 0; i<l2; ++i) {\n\n past_action[i] = boost::python::extract<float>(msgs2[i]);\n\t}\t\n if (action[1] > 1.0) {\n action[1] = 1.0;\n\t }\n\t else if (action[1] < -1.0) {\n\t\taction[1] = -1.0;\n\t }\n\tstate[11] = past_action[0];\n\tstate[12] = past_action[1];\n\tmsg.linear.x = action[0]*0.3;\n\tmsg.angular.z = action[1];\n\twhile (test3 == NULL)\n\t{\n\t} \n\t\n\tfor (int i =0; i <10; ++i) {\n\t state[i] = test3->ranges[i];\n\n\t if (state[i] > 3.5)\n\t\tstate[i] = 3.5;\t \t \t\n\t}\n\t//state[13] = test3->header.seq;\n\n\n\ttest3 = ros::topic::waitForMessage<sensor_msgs::LaserScan>(\"scan\");\n\n\tchatter_pub.publish(msg);\n\t//////////////////////////////////////////\n //need to make so this function can only run once per laser scan...\n\t//currently it will not publis until there is a new laser scan, I need it to run only once per scan \n\t///////////////////////////////////////////\n\t//////////////////////////////////////////\n\t/////////////////////////////////////////\n\n\told_count = count;\n\tgetState();\n\tsetReward();\n\tstate[14] = reward;\n\n\treturn state;\n\n }\n\n//reset\n void reset() {\n\tros::service::waitForService(\"gazebo/reset_simulation\");\n\ttry {\n\t reset_proxy.call(emptySrv);\n\t}\n\tcatch (int e) {\n\t ROS_INFO(\"Unable to reset Gazebo. Service call failed\");\n\t}\n\t\n\tif (init_goal == true) {\n\t spawnGoal();\n\t init_goal = false;\n\t}\n\t\n\tgetGoalDistance();\n\t\n }\n\n void spawnGoal() {\n\n \tros::service::waitForService(\"gazebo/spawn_sdf_model\");\n \tgazebo_msgs::SpawnModel model;\n \n \tros::ServiceClient spawn_model = n.serviceClient<gazebo_msgs::SpawnModel>(\"gazebo/spawn_sdf_model\");\n \tstd::ifstream file(\"/home/justin/ros_test/src/turtlebot3_simulations/turtlebot3_gazebo/models/turtlebot3_square/goal_box/model.sdf\");\n\n \tstd::string line;\n \n \twhile(!file.eof()) // Parse the contents of the given urdf in a string\n \t{\n \t std::getline(file,line);\n model.request.model_xml+=line;\n \t}\n \tfile.close();\n\t//int temp = rand();\n \tpost.position.x = goal_x_list[rand()%13];\n \tpost.position.y = goal_y_list[rand()%13];\n \n \tmodel.request.model_name=\"goal\";\n \tmodel.request.reference_frame=\"world\";\n \tmodel.request.initial_pose = post;\n \tspawn_model.call(model); //Call the service\n\n\tgetGoalDistance();\n\n }\n\n\n void deleteGoal() {\n\t\n\tros::service::waitForService(\"gazebo/delete_model\");\n \tgazebo_msgs::DeleteModel model;\n\tros::ServiceClient delete_model = n.serviceClient<gazebo_msgs::DeleteModel>(\"gazebo/delete_model\");\n\tmodel.request.model_name = \"goal\";\n\tdelete_model.call(model);\n\n\t//send an 0 cmd_vel command\n\t//chatter_pub.publish(msg_empty);\n\n }\n\n\n\n/*\n\n\n\n get position\ncheck model\n def deleteModel(self):\n while True:\n if self.check_model:\n rospy.wait_for_service('gazebo/delete_model')\n del_model_prox = rospy.ServiceProxy('gazebo/delete_model', DeleteModel)\n del_model_prox(self.modelName)\n break\n else:\n pass\n\n\n*/\n\n\n//used for learning\n void many(boost::python::list msgs) {\n long l = len(msgs);\n std::stringstream ss;\n for (long i = 0; i<l; ++i) {\n if (i>0) ss << \", \";\n std::string s = boost::python::extract<std::string>(msgs[i]);\n ss << s;\n }\n mMsg = ss.str();\n }\n\n//used for learing\n void chat() {\n \tmsg.linear.x = pos_x;\n \tmsg.angular.z = 5; \n chatter_pub.publish(msg);\n }\n\n\n//returns value\n MyList greet() { return state; }\n\n\n\n\n};\n\n\n///////////////////////////////////////////////////////////////////////////////////\n\n//namespace bn = boost::python::numpy;\n\n\n///////////////////////////////////////////////////////////////////////////////////////////\nusing namespace boost::python;\n\nBOOST_PYTHON_MODULE(mycpplib)\n{\n\n //boost::python::numpy::initialize();\n\n //def(\"testf\", testf);\n\n class_<MyList>(\"MyList\")\n .def(vector_indexing_suite<MyList>() );\n\n class_<World>(\"World\")\n\n\n .def(\"greet\", &World::greet)\n .def(\"pause_proxy\", &World::pause_proxy)\n .def(\"unpause_proxy\", &World::unpause_proxy)\n .def(\"reset\", &World::reset) \n .def(\"step\", &World::step)\n .def(\"many\", &World::many)\n .def(\"chat\", &World::chat)\n .def(\"spawnGoal\", &World::spawnGoal)\n .def(\"deleteGoal\", &World::deleteGoal)\n ;\n};\n"},{"alpha_fraction":0.7618767619132996,"alphanum_fraction":0.7736359238624573,"avg_line_length":33.01599884033203,"blob_id":"f4db42256adbd2023e74e267103cf49c276c2a98","content_id":"5aa201566dc316c7b034479e99bb80c4408c297d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":8504,"license_type":"no_license","max_line_length":144,"num_lines":250,"path":"/build/homework2/cmake/homework2-genmsg.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"# generated from genmsg/cmake/pkg-genmsg.cmake.em\n\nmessage(STATUS \"homework2: 0 messages, 1 services\")\n\nset(MSG_I_FLAGS \"-Istd_msgs:/opt/ros/melodic/share/std_msgs/cmake/../msg\")\n\n# Find all generators\nfind_package(gencpp REQUIRED)\nfind_package(geneus REQUIRED)\nfind_package(genlisp REQUIRED)\nfind_package(gennodejs REQUIRED)\nfind_package(genpy REQUIRED)\n\nadd_custom_target(homework2_generate_messages ALL)\n\n# verify that message/service dependencies have not changed since configure\n\n\n\nget_filename_component(_filename \"/home/justin/ros_test/src/homework2/srv/string_cat.srv\" NAME_WE)\nadd_custom_target(_homework2_generate_messages_check_deps_${_filename}\n COMMAND ${CATKIN_ENV} ${PYTHON_EXECUTABLE} ${GENMSG_CHECK_DEPS_SCRIPT} \"homework2\" \"/home/justin/ros_test/src/homework2/srv/string_cat.srv\" \"\"\n)\n\n#\n# langs = gencpp;geneus;genlisp;gennodejs;genpy\n#\n\n### Section generating for lang: gencpp\n### Generating Messages\n\n### Generating Services\n_generate_srv_cpp(homework2\n \"/home/justin/ros_test/src/homework2/srv/string_cat.srv\"\n \"${MSG_I_FLAGS}\"\n \"\"\n ${CATKIN_DEVEL_PREFIX}/${gencpp_INSTALL_DIR}/homework2\n)\n\n### Generating Module File\n_generate_module_cpp(homework2\n ${CATKIN_DEVEL_PREFIX}/${gencpp_INSTALL_DIR}/homework2\n \"${ALL_GEN_OUTPUT_FILES_cpp}\"\n)\n\nadd_custom_target(homework2_generate_messages_cpp\n DEPENDS ${ALL_GEN_OUTPUT_FILES_cpp}\n)\nadd_dependencies(homework2_generate_messages homework2_generate_messages_cpp)\n\n# add dependencies to all check dependencies targets\nget_filename_component(_filename \"/home/justin/ros_test/src/homework2/srv/string_cat.srv\" NAME_WE)\nadd_dependencies(homework2_generate_messages_cpp _homework2_generate_messages_check_deps_${_filename})\n\n# target for backward compatibility\nadd_custom_target(homework2_gencpp)\nadd_dependencies(homework2_gencpp homework2_generate_messages_cpp)\n\n# register target for catkin_package(EXPORTED_TARGETS)\nlist(APPEND ${PROJECT_NAME}_EXPORTED_TARGETS homework2_generate_messages_cpp)\n\n### Section generating for lang: geneus\n### Generating Messages\n\n### Generating Services\n_generate_srv_eus(homework2\n \"/home/justin/ros_test/src/homework2/srv/string_cat.srv\"\n \"${MSG_I_FLAGS}\"\n \"\"\n ${CATKIN_DEVEL_PREFIX}/${geneus_INSTALL_DIR}/homework2\n)\n\n### Generating Module File\n_generate_module_eus(homework2\n ${CATKIN_DEVEL_PREFIX}/${geneus_INSTALL_DIR}/homework2\n \"${ALL_GEN_OUTPUT_FILES_eus}\"\n)\n\nadd_custom_target(homework2_generate_messages_eus\n DEPENDS ${ALL_GEN_OUTPUT_FILES_eus}\n)\nadd_dependencies(homework2_generate_messages homework2_generate_messages_eus)\n\n# add dependencies to all check dependencies targets\nget_filename_component(_filename \"/home/justin/ros_test/src/homework2/srv/string_cat.srv\" NAME_WE)\nadd_dependencies(homework2_generate_messages_eus _homework2_generate_messages_check_deps_${_filename})\n\n# target for backward compatibility\nadd_custom_target(homework2_geneus)\nadd_dependencies(homework2_geneus homework2_generate_messages_eus)\n\n# register target for catkin_package(EXPORTED_TARGETS)\nlist(APPEND ${PROJECT_NAME}_EXPORTED_TARGETS homework2_generate_messages_eus)\n\n### Section generating for lang: genlisp\n### Generating Messages\n\n### Generating Services\n_generate_srv_lisp(homework2\n \"/home/justin/ros_test/src/homework2/srv/string_cat.srv\"\n \"${MSG_I_FLAGS}\"\n \"\"\n ${CATKIN_DEVEL_PREFIX}/${genlisp_INSTALL_DIR}/homework2\n)\n\n### Generating Module File\n_generate_module_lisp(homework2\n ${CATKIN_DEVEL_PREFIX}/${genlisp_INSTALL_DIR}/homework2\n \"${ALL_GEN_OUTPUT_FILES_lisp}\"\n)\n\nadd_custom_target(homework2_generate_messages_lisp\n DEPENDS ${ALL_GEN_OUTPUT_FILES_lisp}\n)\nadd_dependencies(homework2_generate_messages homework2_generate_messages_lisp)\n\n# add dependencies to all check dependencies targets\nget_filename_component(_filename \"/home/justin/ros_test/src/homework2/srv/string_cat.srv\" NAME_WE)\nadd_dependencies(homework2_generate_messages_lisp _homework2_generate_messages_check_deps_${_filename})\n\n# target for backward compatibility\nadd_custom_target(homework2_genlisp)\nadd_dependencies(homework2_genlisp homework2_generate_messages_lisp)\n\n# register target for catkin_package(EXPORTED_TARGETS)\nlist(APPEND ${PROJECT_NAME}_EXPORTED_TARGETS homework2_generate_messages_lisp)\n\n### Section generating for lang: gennodejs\n### Generating Messages\n\n### Generating Services\n_generate_srv_nodejs(homework2\n \"/home/justin/ros_test/src/homework2/srv/string_cat.srv\"\n \"${MSG_I_FLAGS}\"\n \"\"\n ${CATKIN_DEVEL_PREFIX}/${gennodejs_INSTALL_DIR}/homework2\n)\n\n### Generating Module File\n_generate_module_nodejs(homework2\n ${CATKIN_DEVEL_PREFIX}/${gennodejs_INSTALL_DIR}/homework2\n \"${ALL_GEN_OUTPUT_FILES_nodejs}\"\n)\n\nadd_custom_target(homework2_generate_messages_nodejs\n DEPENDS ${ALL_GEN_OUTPUT_FILES_nodejs}\n)\nadd_dependencies(homework2_generate_messages homework2_generate_messages_nodejs)\n\n# add dependencies to all check dependencies targets\nget_filename_component(_filename \"/home/justin/ros_test/src/homework2/srv/string_cat.srv\" NAME_WE)\nadd_dependencies(homework2_generate_messages_nodejs _homework2_generate_messages_check_deps_${_filename})\n\n# target for backward compatibility\nadd_custom_target(homework2_gennodejs)\nadd_dependencies(homework2_gennodejs homework2_generate_messages_nodejs)\n\n# register target for catkin_package(EXPORTED_TARGETS)\nlist(APPEND ${PROJECT_NAME}_EXPORTED_TARGETS homework2_generate_messages_nodejs)\n\n### Section generating for lang: genpy\n### Generating Messages\n\n### Generating Services\n_generate_srv_py(homework2\n \"/home/justin/ros_test/src/homework2/srv/string_cat.srv\"\n \"${MSG_I_FLAGS}\"\n \"\"\n ${CATKIN_DEVEL_PREFIX}/${genpy_INSTALL_DIR}/homework2\n)\n\n### Generating Module File\n_generate_module_py(homework2\n ${CATKIN_DEVEL_PREFIX}/${genpy_INSTALL_DIR}/homework2\n \"${ALL_GEN_OUTPUT_FILES_py}\"\n)\n\nadd_custom_target(homework2_generate_messages_py\n DEPENDS ${ALL_GEN_OUTPUT_FILES_py}\n)\nadd_dependencies(homework2_generate_messages homework2_generate_messages_py)\n\n# add dependencies to all check dependencies targets\nget_filename_component(_filename \"/home/justin/ros_test/src/homework2/srv/string_cat.srv\" NAME_WE)\nadd_dependencies(homework2_generate_messages_py _homework2_generate_messages_check_deps_${_filename})\n\n# target for backward compatibility\nadd_custom_target(homework2_genpy)\nadd_dependencies(homework2_genpy homework2_generate_messages_py)\n\n# register target for catkin_package(EXPORTED_TARGETS)\nlist(APPEND ${PROJECT_NAME}_EXPORTED_TARGETS homework2_generate_messages_py)\n\n\n\nif(gencpp_INSTALL_DIR AND EXISTS ${CATKIN_DEVEL_PREFIX}/${gencpp_INSTALL_DIR}/homework2)\n # install generated code\n install(\n DIRECTORY ${CATKIN_DEVEL_PREFIX}/${gencpp_INSTALL_DIR}/homework2\n DESTINATION ${gencpp_INSTALL_DIR}\n )\nendif()\nif(TARGET std_msgs_generate_messages_cpp)\n add_dependencies(homework2_generate_messages_cpp std_msgs_generate_messages_cpp)\nendif()\n\nif(geneus_INSTALL_DIR AND EXISTS ${CATKIN_DEVEL_PREFIX}/${geneus_INSTALL_DIR}/homework2)\n # install generated code\n install(\n DIRECTORY ${CATKIN_DEVEL_PREFIX}/${geneus_INSTALL_DIR}/homework2\n DESTINATION ${geneus_INSTALL_DIR}\n )\nendif()\nif(TARGET std_msgs_generate_messages_eus)\n add_dependencies(homework2_generate_messages_eus std_msgs_generate_messages_eus)\nendif()\n\nif(genlisp_INSTALL_DIR AND EXISTS ${CATKIN_DEVEL_PREFIX}/${genlisp_INSTALL_DIR}/homework2)\n # install generated code\n install(\n DIRECTORY ${CATKIN_DEVEL_PREFIX}/${genlisp_INSTALL_DIR}/homework2\n DESTINATION ${genlisp_INSTALL_DIR}\n )\nendif()\nif(TARGET std_msgs_generate_messages_lisp)\n add_dependencies(homework2_generate_messages_lisp std_msgs_generate_messages_lisp)\nendif()\n\nif(gennodejs_INSTALL_DIR AND EXISTS ${CATKIN_DEVEL_PREFIX}/${gennodejs_INSTALL_DIR}/homework2)\n # install generated code\n install(\n DIRECTORY ${CATKIN_DEVEL_PREFIX}/${gennodejs_INSTALL_DIR}/homework2\n DESTINATION ${gennodejs_INSTALL_DIR}\n )\nendif()\nif(TARGET std_msgs_generate_messages_nodejs)\n add_dependencies(homework2_generate_messages_nodejs std_msgs_generate_messages_nodejs)\nendif()\n\nif(genpy_INSTALL_DIR AND EXISTS ${CATKIN_DEVEL_PREFIX}/${genpy_INSTALL_DIR}/homework2)\n install(CODE \"execute_process(COMMAND \\\"/usr/bin/python2\\\" -m compileall 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R. Quinlan, \"Induction of decision trees,\" Machine Learning, vol 1. pp 81-106, 1986.\n \\author Todd Hester\n*/\n\n\n#ifndef _C45TREE_HH_\n#define _C45TREE_HH_\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n#include <vector>\n#include <set>\n#include <map>\n\n#define N_C45_EXP 200000\n#define N_C45_NODES 2500\n\n#define BUILD_EVERY 0\n#define BUILD_ON_ERROR 1\n#define BUILD_EVERY_N 2\n#define BUILD_ON_TERMINAL 3\n#define BUILD_ON_TERMINAL_AND_ERROR 4\n\n\n/** C4.5 decision tree class. */\nclass C45Tree: public Classifier {\n\npublic:\n\n /** Default constructor \n \\param id id of the tree for debug\n \\param trainMode build every step? only on errors? every freq steps?\n \\param trainFreq frequency of model building if using latter mode\n \\param m # of visits for a given state-action to be considered known\n \\param featPct pct of features to remove from set used for each tree split\n \\param rng Random Number Generator \n */\n C45Tree(int id, int trainMode, int trainFreq, int m, \n\t float featPct, Random rng);\n\n /** Copy constructor */\n C45Tree(const C45Tree&);\n\n ~C45Tree();\n\n // structs to be defined\n struct tree_node;\n struct tree_experience;\n \n /** Make a copy of the subtree from origNode to newNode */\n void copyTree(tree_node* newNode, tree_node* origNode);\n\n virtual C45Tree* getCopy();\n\n /** Tree node struct. For decision nodes, it contains split information and pointers to child nodes. For leaf nodes, it contains all outputs that went into this leaf during trainiing. */\n struct tree_node {\n int id;\n\n // split criterion\n int dim;\n float val;\n bool type;\n\n // set of all outputs seen at this leaf/node\n std::map<float,int> outputs;\n int nInstances;\n\n // next nodes in tree\n tree_node *l;\n tree_node *r;\n\n bool leaf;\n };\n\n /** Experiences the tree is trained on. A vector of inputs and one float output to predict */\n struct tree_experience {\n std::vector<float> input;\n float output;\n };\n \n /** The types of splits. Split on ONLY meaning is input == x, or CUT meaning is input > x */\n enum splitTypes{\n ONLY,\n CUT\n };\n\n virtual bool trainInstance(classPair &instance);\n virtual bool trainInstances(std::vector<classPair> &instances);\n virtual void testInstance(const std::vector<float> &input, std::map<float, float>* retval);\n virtual float getConf(const std::vector<float> &input);\n\n /** Build the tree with the given instances from the given tree node */\n bool buildTree(tree_node* node, const std::vector<tree_experience*> &instances, bool changed);\n\n // helper functions\n /** Initialize the tree */\n void initTree();\n\n /** Rebuild the tree */\n bool rebuildTree();\n\n /** Initialize the tree_node struct */\n void initTreeNode(tree_node* node);\n\n /** Traverse the tree to a leaf for the given input */\n tree_node* traverseTree(tree_node* node, const std::vector<float> &input);\n\n /** Get the correct child of this node for a given input */\n tree_node* getCorrectChild(tree_node* node, const std::vector<float> &input);\n\n /** Determine if the input passes the test defined by dim, val, type */\n bool passTest(int dim, float val, bool type, const std::vector<float> &input);\n\n /** Calculate the gain ratio for the given split of instances */\n float calcGainRatio(int dim, float val, bool type,\n\t\t const std::vector<tree_experience*> &instances, float I,\n\t\t std::vector<tree_experience*> &left,\n\t\t std::vector<tree_experience*> &right);\n\n /** Returns an array of the values of features at the index dim, sorted from lowest to highest */\n float* sortOnDim(int dim, const std::vector<tree_experience*> &instances);\n\n /** Get all the unique values of the features on dimension dim */\n std::set<float> getUniques(int dim, const std::vector<tree_experience*> &instances, float & minVal, float& maxVal);\n\n /** Delete this tree node and all nodes below it in the tree. */\n void deleteTree(tree_node* node);\n\n /** Calculate I(P) */\n float calcIofP(float* P, int size);\n\n /** Calculate I(P) for set. */\n float calcIforSet(const std::vector<tree_experience*> &instances);\n\n /** Print the tree for debug purposes. */\n void printTree(tree_node *t, int level);\n\n /** Test the possible splits for the given set of instances */\n void testPossibleSplits(const std::vector<tree_experience*> &instances, float *bestGainRatio, int *bestDim, \n float *bestVal, bool *bestType,\n std::vector<tree_experience*> *bestLeft, std::vector<tree_experience*> *bestRight);\n\n /** Implement the given split at the given node */\n bool implementSplit(tree_node* node, float bestGainRatio, int bestDim,\n float bestVal, bool bestType, \n const std::vector<tree_experience*> &left, \n const std::vector<tree_experience*> &right, bool changed);\n\n /** Compare the current split to determine if it is the best split. */\n void compareSplits(float gainRatio, int dim, float val, bool type, \n const std::vector<tree_experience*> &left, \n const std::vector<tree_experience*> &right,\n int *nties, float *bestGainRatio, int *bestDim, \n float *bestVal, bool *bestType,\n std::vector<tree_experience*> *bestLeft, \n std::vector<tree_experience*> *bestRight);\n\n /** Get the probability distribution for the given leaf node. */\n void outputProbabilities(tree_node *t, std::map<float, float>* retval);\n\n /** Make the given node into a leaf node. */\n bool makeLeaf(tree_node* node);\n\n /** Allocate a new node from our pre-allocated store of tree nodes */\n tree_node* allocateNode();\n\n /** Return tree node back to store of nodes */\n void deallocateNode(tree_node* node);\n\n /** Initialize our store of tree nodes */\n void initNodes();\n\n\n bool INCDEBUG;\n bool DTDEBUG;\n bool SPLITDEBUG;\n bool STOCH_DEBUG;\n int nExperiences;\n bool NODEDEBUG;\n bool COPYDEBUG;\n\n float SPLIT_MARGIN;\n float MIN_GAIN_RATIO; \n\nprivate:\n\n const int id;\n \n const int mode;\n const int freq;\n const int M;\n const float featPct; \n const bool ALLOW_ONLY_SPLITS;\n\n Random rng;\n\n int nOutput;\n int nnodes;\n bool hadError;\n int maxnodes;\n int totalnodes;\n\n /** Vector of all experiences used to train the tree */\n std::vector<tree_experience*> experiences;\n\n /** Pre-allocated array of experiences to be filled during training. */\n tree_experience allExp[N_C45_EXP];\n\n /** Pre-allocated array of tree nodes to be used for tree */\n tree_node allNodes[N_C45_NODES];\n std::vector<int> freeNodes;\n\n // TREE\n /** Pointer to root node of tree. */\n tree_node* root;\n /** Pointer to last node of tree used (leaf used in last prediction made). */\n tree_node* lastNode;\n\n};\n\n\n#endif\n 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\"../Planners/PO_ETUCT.hh\"\n#include \"../Planners/PO_ParallelETUCT.hh\"\n#include \"../Planners/MBS.hh\"\n\n// models\n#include \"../Models/RMaxModel.hh\"\n#include \"../Models/FactoredModel.hh\"\n#include \"../Models/ExplorationModel.hh\"\n\nModelBasedAgent::ModelBasedAgent(int numactions, float gamma, \n float rmax, float rrange,\n int modelType,\tint exploreType, \n int predType, int nModels, int plannerType, \n float epsilon, float lambda, float MAX_TIME,\n float m, const std::vector<float> &featmin,\n const std::vector<float> &featmax, \n std::vector<int> nstatesPerDim, int history, float v, float n,\n bool depTrans, bool relTrans, float featPct, bool stoch, bool episodic,\n Random rng):\n featmin(featmin), featmax(featmax),\n numactions(numactions), gamma(gamma), rmax(rmax), rrange(rrange),\n qmax(rmax/(1.0-gamma)), \n modelType(modelType), exploreType(exploreType), \n predType(predType), nModels(nModels), plannerType(plannerType),\n epsilon(epsilon), lambda(lambda), MAX_TIME(MAX_TIME),\n M(m), statesPerDim(nstatesPerDim), history(history), v(v), n(n),\n depTrans(depTrans), relTrans(relTrans), featPct(featPct),\n stoch(stoch), episodic(episodic), rng(rng)\n{\n\n if (statesPerDim[0] > 0){\n cout << \"MBA: Planner will use states discretized by various amounts per dim with continuous model\" << endl;\n }\n\n initParams();\n\n}\n\n\n\nModelBasedAgent::ModelBasedAgent(int numactions, float gamma, \n float rmax, float rrange,\n int modelType,\tint exploreType, \n int predType, int nModels, int plannerType, \n float epsilon, float lambda, float MAX_TIME,\n float m, const std::vector<float> &featmin,\n const std::vector<float> &featmax, \n int nstatesPerDim, int history, float v, float n,\n bool depTrans, bool relTrans, float featPct,\n\t\t\t\t bool stoch, bool episodic, Random rng):\n featmin(featmin), featmax(featmax),\n numactions(numactions), gamma(gamma), rmax(rmax), rrange(rrange),\n qmax(rmax/(1.0-gamma)), \n modelType(modelType), exploreType(exploreType), \n predType(predType), nModels(nModels), plannerType(plannerType),\n epsilon(epsilon), lambda(lambda), MAX_TIME(MAX_TIME),\n M(m), statesPerDim(featmin.size(),nstatesPerDim), history(history), v(v), n(n),\n depTrans(depTrans), relTrans(relTrans), featPct(featPct),\n stoch(stoch), episodic(episodic), rng(rng)\n{\n\n if (statesPerDim[0] > 0){\n cout << \"MBA: Planner will use states discretized by \" << statesPerDim[0] << \" with continuous model\" << endl;\n }\n\n initParams();\n\n}\n\n\nvoid ModelBasedAgent::initParams(){\n\n nstates = 0;\n nactions = 0;\n\n model = NULL;\n planner = NULL;\n\n modelUpdateTime = 0.0;\n planningTime = 0.0;\n actionTime = 0.0;\n \n modelChanged = false;\n\n \n BATCH_FREQ = 1; //50;\n\n TIMEDEBUG = false; //true;\n AGENTDEBUG = false;\n ACTDEBUG = false;//true;\n SIMPLEDEBUG = false; //true; //false; //true;\n\n // check\n if (qmax <= 0.1 && (exploreType == TWO_MODE_PLUS_R || \n\t\t exploreType == CONTINUOUS_BONUS_R || \n\t\t exploreType == CONTINUOUS_BONUS ||\n\t\t exploreType == THRESHOLD_BONUS_R)) {\n std::cerr << \"For this exploration type, rmax needs to be an additional positive bonus value, not a replacement for the q-value\" << endl;\n exit(-1);\n }\n\n if (exploreType == TWO_MODE || exploreType == TWO_MODE_PLUS_R){\n std::cerr << \"This exploration type does not work in this agent.\" << endl;\n exit(-1);\n }\n\n seeding = false;\n \n if (SIMPLEDEBUG)\n cout << \"qmax: \" << qmax << endl;\n\n}\n\nModelBasedAgent::~ModelBasedAgent() {\n delete planner;\n delete model;\n featmin.clear();\n featmax.clear();\n prevstate.clear();\n}\n\nint ModelBasedAgent::first_action(const std::vector<float> &s) {\n if (AGENTDEBUG) cout << \"first_action(s)\" << endl;\n\n if (model == NULL)\n initModel(s.size());\n\n planner->setFirst();\n\n // in case we didn't do it after seeding\n if (plannerType == PARALLEL_ET_UCT || plannerType == PAR_ETUCT_ACTUAL)\n planner->planOnNewModel();\n\n // choose an action\n int act = chooseAction(s);\n\n // save curr state/action for next time\n saveStateAndAction(s, act);\n\n if (ACTDEBUG)\n cout << \"Took action \" << act << \" from state \" \n\t << s[0] << \",\" << s[1] \n\t << endl;\n\n // return that action\n return act;\n\n}\n\nint ModelBasedAgent::next_action(float r, const std::vector<float> &s) {\n if (AGENTDEBUG) {\n cout << \"next_action(r = \" << r \n\t << \", s = \" << &s << \")\" << endl;\n }\n \n if (SIMPLEDEBUG) cout << \"Got Reward \" << r;\n \n // update our models\n // this is where we possibly plan again if model changes\n updateWithNewExperience(prevstate, s, prevact, r, false);\n\n // choose an action\n int act = chooseAction(s);\n \n // save curr state/action for next time\n saveStateAndAction(s, act);\n\n if (ACTDEBUG){\n cout << \"Took action \" << act << \" from state \" \n\t << (s)[0];\n for (unsigned i = 1; i < s.size(); i++){\n cout << \",\" << (s)[i];\n }\n cout << endl;\n }\n\n // return that action\n return act;\n\n}\n\nvoid ModelBasedAgent::last_action(float r) {\n if (AGENTDEBUG) cout << \"last_action(r = \" << r\n\t\t << \")\" << endl;\n\n if (AGENTDEBUG) cout << \"Got Reward \" << r;\n\n // update our models\n // this is where we possibly plan again if model changes\n updateWithNewExperience(prevstate, prevstate, prevact, r, true);\n\n}\n\n\n\n/////////////////////////////\n// Functional functions :) //\n/////////////////////////////\n\n\nvoid ModelBasedAgent::initModel(int nfactors){\n if ( AGENTDEBUG) cout << \"initModel nfactors: \" << nfactors << endl;\n \n bool needConf = \n (exploreType != NO_EXPLORE && exploreType != EXPLORE_UNKNOWN && \n exploreType != EPSILONGREEDY && exploreType != UNVISITED_BONUS &&\n exploreType != UNVISITED_ACT_BONUS);\n\n std::vector<float> featRange(featmax.size(), 0);\n for (unsigned i = 0; i < featmax.size(); i++){\n featRange[i] = featmax[i] - featmin[i];\n cout << \"feature \" << i << \" has range \" << featRange[i] << endl;\n }\n cout << \"reward range: \" << rrange << endl;\n\n float treeRangePct = 0.0001;\n \n // m5 tree\n if (modelType == M5MULTI || modelType == M5SINGLE ||\n modelType == M5ALLMULTI || modelType == M5ALLSINGLE ||\n modelType == ALLM5TYPES){\n treeRangePct = 0.0001;\n }\n \n // just to ensure the diff models are on different random values\n for (int i = 0; i < modelType; i++){\n rng.uniform(0, 1);\n }\n \n // 0 - traditional rmax model (unknown until m visits, then ML)\n if (modelType == RMAX) {\n model = new RMaxModel(M, numactions, rng);\n }\n \n // any tree or stump will be mdptree\n else if (modelType == C45TREE || modelType == STUMP ||\n modelType == M5MULTI || modelType == M5SINGLE ||\n modelType == M5ALLMULTI || modelType == M5ALLSINGLE ||\n modelType == ALLM5TYPES ||\n modelType == LSTMULTI || modelType == LSTSINGLE ||\n modelType == GPREGRESS || modelType == GPTREE){\n\n model = new FactoredModel(0,numactions, M, modelType, predType, nModels, treeRangePct, featRange, rrange, needConf, depTrans, relTrans, featPct, stoch, episodic, rng);\n }\n \n /*\n else if (modelType == GPREGRESS){\n model = new GPmdp(0, numactions, relTrans, rng);\n }\n */\n\n // pass model into exploration model wrapper model\n if (exploreType != NO_EXPLORE && exploreType != EPSILONGREEDY){\n MDPModel* m2 = model;\n\n model = new ExplorationModel(m2, modelType, exploreType,\n predType, nModels, M, numactions,\n rmax, qmax, rrange, nfactors, v, n,\n featmax, featmin, rng);\n \n }\n \n initPlanner();\n planner->setModel(model);\n\n}\n\nvoid ModelBasedAgent::initPlanner(){\n if (AGENTDEBUG) cout << \"InitPlanner type: \" << plannerType << endl;\n\n int max_path = 200; //500;\n\n // init planner based on type\n if (plannerType == VALUE_ITERATION){\n planner = new ValueIteration(numactions, gamma, 500000, 10.0, modelType, featmax, featmin, statesPerDim, rng);\n }\n else if (plannerType == MBS_VI){\n planner = new MBS(numactions, gamma, 500000, 10.0, modelType, featmax, featmin, statesPerDim, history, rng);\n }\n else if (plannerType == POLICY_ITERATION){\n planner = new PolicyIteration(numactions, gamma, 500000, 10.0, modelType, featmax, featmin, statesPerDim, rng);\n }\n else if (plannerType == PRI_SWEEPING){\n planner = new PrioritizedSweeping(numactions, gamma, 10.0, true, modelType, featmax, featmin, rng);\n }\n else if (plannerType == MOD_PRI_SWEEPING){\n planner = new PrioritizedSweeping(numactions, gamma, 10.0, false, modelType, featmax, featmin, rng);\n }\n else if (plannerType == ET_UCT){\n planner = new ETUCT(numactions, gamma, rrange, lambda, 500000, MAX_TIME, max_path, modelType, featmax, featmin, statesPerDim, false, history, rng);\n }\n else if (plannerType == POMDP_ETUCT){\n planner = new PO_ETUCT(numactions, gamma, rrange, lambda, 500000, MAX_TIME, max_path, modelType, featmax, featmin, statesPerDim, true, history, rng);\n }\n else if (plannerType == POMDP_PAR_ETUCT){\n planner = new PO_ParallelETUCT(numactions, gamma, rrange, lambda, 500000, MAX_TIME, max_path, modelType, featmax, featmin, statesPerDim, true, history, rng);\n }\n else if (plannerType == ET_UCT_ACTUAL){\n planner = new ETUCT(numactions, gamma, rrange, lambda, 500000, MAX_TIME, max_path, modelType, featmax, featmin, statesPerDim, true, history, rng);\n }\n else if (plannerType == PARALLEL_ET_UCT){\n planner = new ParallelETUCT(numactions, gamma, rrange, lambda, 500000, MAX_TIME, max_path, modelType, featmax, featmin, statesPerDim, false, history, rng);\n }\n else if (plannerType == PAR_ETUCT_ACTUAL){\n planner = new ParallelETUCT(numactions, gamma, rrange, lambda, 500000, MAX_TIME, max_path, modelType, featmax, featmin, statesPerDim, true, history, rng);\n }\n else if (plannerType == ET_UCT_L1){\n planner = new ETUCT(numactions, gamma, rrange, 1.0, 500000, MAX_TIME, max_path, modelType, featmax, featmin, statesPerDim, false, history, rng);\n }\n else {\n std::cerr << \"ERROR: invalid planner type: \" << plannerType << endl;\n exit(-1);\n }\n\n}\n\nvoid ModelBasedAgent::updateWithNewExperience(const std::vector<float> &last, \n const std::vector<float> &curr, \n int lastact, float reward, \n bool terminal){\n if (AGENTDEBUG) cout << \"updateWithNewExperience(last = \" << &last \n << \", curr = \" << &curr\n << \", lastact = \" << lastact \n << \", r = \" << reward\n << \", t = \" << terminal\n << \")\" << endl;\n \n double initTime = 0;\n double timeTwo = 0;\n double timeThree = 0;\n\n if (model == NULL)\n initModel(last.size());\n\n // update our models and see if they change\n if (false || TIMEDEBUG) initTime = getSeconds();\n\n modelChanged = planner->updateModelWithExperience(last, lastact, curr, reward, terminal) || modelChanged;\n\n if (false || TIMEDEBUG) timeTwo = getSeconds();\n\n if (AGENTDEBUG) cout << \"Agent Added exp: \" << modelChanged << endl;\n\n // tell the planner to update with the updated model\n if ((modelChanged && (!seeding || modelType == RMAX) \n && (nactions % BATCH_FREQ == 0))){\n planner->planOnNewModel();\n modelChanged = false;\n }\n\n if (TIMEDEBUG){\n\n timeThree = getSeconds();\n \n planningTime += (timeThree-timeTwo);\n modelUpdateTime += (timeTwo - initTime);\n \n if (nactions % 10 == 0){\n cout << nactions \n\t << \" UpdateModel \" << modelUpdateTime/ (float)nactions\n\t << \" createPolicy \" << planningTime/(float)nactions << endl;\n \n }\n }\n\n\n}\n\n\nint ModelBasedAgent::chooseAction(const std::vector<float> &s){\n if (AGENTDEBUG) cout << \"chooseAction(s = \" << &s \n\t\t << \")\" << endl;\n\n double initTime = 0;\n double timeTwo = 0;\n\n // get action to take from planner\n if (TIMEDEBUG) initTime = getSeconds();\n int act = planner->getBestAction(s);\n if (TIMEDEBUG) {\n timeTwo = getSeconds();\n planningTime += (timeTwo - initTime);\n }\n\n if (exploreType == EPSILONGREEDY && rng.bernoulli(epsilon)){\n //if (true) cout << \"Random action\" << endl;\n act = rng.uniformDiscrete(0, numactions-1);\n }\n\n if (SIMPLEDEBUG){\n cout << endl << \"Action \" << nactions\n\t << \": State \" << (s)[0];\n for (unsigned i = 1; i < s.size(); i++){\n cout << \",\" << (s)[i];\n }\n cout << \", Took action \" << act << \", \";\n }\n\n nactions++;\n\n // return index of action\n return act;\n}\n\nvoid ModelBasedAgent::saveStateAndAction(const std::vector<float> &s, int act){\n if (AGENTDEBUG) cout << \"saveStateAndAction(s = \" << &s \n\t\t << \", act = \" << act\n\t\t << \")\" << endl;\n prevstate = s;\n prevact = act;\n\n}\n\n\n\n\n\ndouble ModelBasedAgent::getSeconds(){\n struct timezone tz;\n timeval timeT;\n gettimeofday(&timeT, &tz);\n return timeT.tv_sec + (timeT.tv_usec / 1000000.0);\n}\n\n\nvoid ModelBasedAgent::seedExp(std::vector<experience> seeds){\n if (AGENTDEBUG) cout << \"seed experiences\" << endl;\n\n if (seeds.size() == 0) return;\n\n if (model == NULL)\n initModel(seeds[0].s.size());\n\n seeding = true;\n planner->setSeeding(true);\n\n // for each seeding experience, update our model\n for (unsigned i = 0; i < seeds.size(); i++){\n experience e = seeds[i];\n\n // update our models\n // this is where we possibly run qmax again if model(s) change\n updateWithNewExperience(e.s, e.next, e.act, e.reward, e.terminal);\n\n /*\n cout << \"Seeding with experience \" << i << endl;\n cout << \"last: \" << (*curr)[0] << \", \" << (*curr)[1] << \", \" \n\t << (*curr)[2] << endl;\n cout << \"act: \" << e.act << \" r: \" << e.reward << endl;\n cout << \"next: \" << (*next)[0] << \", \" << (*next)[1] << \", \" \n\t << (*next)[2] << \", \" << e.terminal << endl;\n */\n\n }\n\n seeding = false;\n planner->setSeeding(false);\n\n if (seeds.size() > 0)\n planner->planOnNewModel();\n\n}\n\n\n void ModelBasedAgent::setDebug(bool d){\n AGENTDEBUG = d;\n }\n\nvoid ModelBasedAgent::savePolicy(const char* filename){\n planner->savePolicy(filename);\n}\n\n\n\nvoid ModelBasedAgent::logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax){\n\n // call planner\n if (plannerType == PARALLEL_ET_UCT){\n ((ParallelETUCT*)planner)->logValues(of, xmin, xmax, ymin, ymax);\n }\n if (plannerType == ET_UCT){\n ((ETUCT*)planner)->logValues(of, xmin, xmax, ymin, ymax);\n }\n\n}\n"},{"alpha_fraction":0.7727272510528564,"alphanum_fraction":0.7792207598686218,"avg_line_length":37.5625,"blob_id":"79c1da0170b91951169d82c1095eb04c36e5e444","content_id":"68e128ef79310769af70a796ca9966df8af3654f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":616,"license_type":"no_license","max_line_length":46,"num_lines":16,"path":"/build/hector_example/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"hector_example\")\nset(hector_example_VERSION \"0.0.0\")\nset(hector_example_MAINTAINER \"abc <abc@todo.todo>\")\nset(hector_example_PACKAGE_FORMAT \"1\")\nset(hector_example_BUILD_DEPENDS )\nset(hector_example_BUILD_EXPORT_DEPENDS )\nset(hector_example_BUILDTOOL_DEPENDS \"catkin\")\nset(hector_example_BUILDTOOL_EXPORT_DEPENDS )\nset(hector_example_EXEC_DEPENDS )\nset(hector_example_RUN_DEPENDS )\nset(hector_example_TEST_DEPENDS )\nset(hector_example_DOC_DEPENDS )\nset(hector_example_URL_WEBSITE \"\")\nset(hector_example_URL_BUGTRACKER \"\")\nset(hector_example_URL_REPOSITORY \"\")\nset(hector_example_DEPRECATED \"\")"},{"alpha_fraction":0.6762574315071106,"alphanum_fraction":0.6888320446014404,"avg_line_length":23.825397491455078,"blob_id":"11278b6e630af56415bb4bd2be2cc0efb98a51af","content_id":"d86f037000d0514cdd240a3f73ab317ff4b3f50c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":4692,"license_type":"no_license","max_line_length":441,"num_lines":189,"path":"/devel/include/rl_msgs/RLAction.h","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"// Generated by gencpp from file rl_msgs/RLAction.msg\n// DO NOT EDIT!\n\n\n#ifndef RL_MSGS_MESSAGE_RLACTION_H\n#define RL_MSGS_MESSAGE_RLACTION_H\n\n\n#include <string>\n#include <vector>\n#include <map>\n\n#include <ros/types.h>\n#include <ros/serialization.h>\n#include <ros/builtin_message_traits.h>\n#include <ros/message_operations.h>\n\n\nnamespace rl_msgs\n{\ntemplate <class ContainerAllocator>\nstruct RLAction_\n{\n typedef RLAction_<ContainerAllocator> Type;\n\n RLAction_()\n : action(0) {\n }\n RLAction_(const ContainerAllocator& _alloc)\n : action(0) {\n (void)_alloc;\n }\n\n\n\n typedef int32_t _action_type;\n _action_type action;\n\n\n\n\n\n typedef boost::shared_ptr< ::rl_msgs::RLAction_<ContainerAllocator> > Ptr;\n typedef boost::shared_ptr< ::rl_msgs::RLAction_<ContainerAllocator> const> ConstPtr;\n\n}; // struct RLAction_\n\ntypedef ::rl_msgs::RLAction_<std::allocator<void> > RLAction;\n\ntypedef boost::shared_ptr< ::rl_msgs::RLAction > RLActionPtr;\ntypedef boost::shared_ptr< ::rl_msgs::RLAction const> RLActionConstPtr;\n\n// constants requiring out of line definition\n\n\n\ntemplate<typename ContainerAllocator>\nstd::ostream& operator<<(std::ostream& s, const ::rl_msgs::RLAction_<ContainerAllocator> & v)\n{\nros::message_operations::Printer< ::rl_msgs::RLAction_<ContainerAllocator> >::stream(s, \"\", v);\nreturn s;\n}\n\n} // namespace rl_msgs\n\nnamespace ros\n{\nnamespace message_traits\n{\n\n\n\n// BOOLTRAITS {'IsFixedSize': True, 'IsMessage': True, 'HasHeader': False}\n// {'rl_msgs': ['/home/justin/ros_test/src/rl-texplore-ros-pkg-master/src/rl_msgs/msg'], 'std_msgs': ['/opt/ros/melodic/share/std_msgs/cmake/../msg']}\n\n// !!!!!!!!!!! ['__class__', '__delattr__', '__dict__', '__doc__', '__eq__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_parsed_fields', 'constants', 'fields', 'full_name', 'has_header', 'header_present', 'names', 'package', 'parsed_fields', 'short_name', 'text', 'types']\n\n\n\n\ntemplate <class ContainerAllocator>\nstruct IsFixedSize< ::rl_msgs::RLAction_<ContainerAllocator> >\n : TrueType\n { };\n\ntemplate <class ContainerAllocator>\nstruct IsFixedSize< ::rl_msgs::RLAction_<ContainerAllocator> const>\n : TrueType\n { };\n\ntemplate <class ContainerAllocator>\nstruct IsMessage< ::rl_msgs::RLAction_<ContainerAllocator> >\n : TrueType\n { };\n\ntemplate <class ContainerAllocator>\nstruct IsMessage< ::rl_msgs::RLAction_<ContainerAllocator> const>\n : TrueType\n { };\n\ntemplate <class ContainerAllocator>\nstruct HasHeader< ::rl_msgs::RLAction_<ContainerAllocator> >\n : FalseType\n { };\n\ntemplate <class ContainerAllocator>\nstruct HasHeader< ::rl_msgs::RLAction_<ContainerAllocator> const>\n : FalseType\n { };\n\n\ntemplate<class ContainerAllocator>\nstruct MD5Sum< ::rl_msgs::RLAction_<ContainerAllocator> >\n{\n static const char* value()\n {\n return \"b028501ac85c840a01d50342b4cc9b6e\";\n }\n\n static const char* value(const ::rl_msgs::RLAction_<ContainerAllocator>&) { return value(); }\n static const uint64_t static_value1 = 0xb028501ac85c840aULL;\n static const uint64_t static_value2 = 0x01d50342b4cc9b6eULL;\n};\n\ntemplate<class ContainerAllocator>\nstruct DataType< ::rl_msgs::RLAction_<ContainerAllocator> >\n{\n static const char* value()\n {\n return \"rl_msgs/RLAction\";\n }\n\n static const char* value(const ::rl_msgs::RLAction_<ContainerAllocator>&) { return value(); }\n};\n\ntemplate<class ContainerAllocator>\nstruct Definition< ::rl_msgs::RLAction_<ContainerAllocator> >\n{\n static const char* value()\n {\n return \"# Message for describing an action in RL\\n\\\n\\n\\\nint32 action\\n\\\n\";\n }\n\n static const char* value(const ::rl_msgs::RLAction_<ContainerAllocator>&) { return value(); }\n};\n\n} // namespace message_traits\n} // namespace ros\n\nnamespace ros\n{\nnamespace serialization\n{\n\n template<class ContainerAllocator> struct Serializer< ::rl_msgs::RLAction_<ContainerAllocator> >\n {\n template<typename Stream, typename T> inline static void allInOne(Stream& stream, T m)\n {\n stream.next(m.action);\n }\n\n ROS_DECLARE_ALLINONE_SERIALIZER\n }; // struct RLAction_\n\n} // namespace serialization\n} // namespace ros\n\nnamespace ros\n{\nnamespace message_operations\n{\n\ntemplate<class ContainerAllocator>\nstruct Printer< ::rl_msgs::RLAction_<ContainerAllocator> >\n{\n template<typename Stream> static void stream(Stream& s, const std::string& indent, const ::rl_msgs::RLAction_<ContainerAllocator>& v)\n {\n s << indent << \"action: \";\n Printer<int32_t>::stream(s, indent + \" \", v.action);\n }\n};\n\n} // namespace message_operations\n} // namespace ros\n\n#endif // RL_MSGS_MESSAGE_RLACTION_H\n"},{"alpha_fraction":0.7804877758026123,"alphanum_fraction":0.7804877758026123,"avg_line_length":50.25,"blob_id":"7b811105ddeb5d6231bb8b5acde4376c4b23d510","content_id":"bdcbb9fd3b2e2a954ad8d18580368520701e5c90","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":205,"license_type":"no_license","max_line_length":100,"num_lines":4,"path":"/devel/share/rl_msgs/cmake/rl_msgs-msg-paths.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"# generated from genmsg/cmake/pkg-msg-paths.cmake.develspace.in\n\nset(rl_msgs_MSG_INCLUDE_DIRS \"/home/justin/ros_test/src/rl-texplore-ros-pkg-master/src/rl_msgs/msg\")\nset(rl_msgs_MSG_DEPENDENCIES std_msgs)\n"},{"alpha_fraction":0.5638216733932495,"alphanum_fraction":0.5668789744377136,"avg_line_length":22.363094329833984,"blob_id":"c5dbc6fad2536e5b928959066f3e9a453e1c0561","content_id":"fff02928d22460c17087cf1bd56c3f38c89a8736","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":3925,"license_type":"no_license","max_line_length":77,"num_lines":168,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Agent/SavedPolicy.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include <rl_agent/SavedPolicy.hh>\n#include <algorithm>\n\nSavedPolicy::SavedPolicy(int numactions, const char* filename):\n numactions(numactions)\n{\n\n ACTDEBUG = false;\n LOADDEBUG = false;\n loaded = false;\n\n loadPolicy(filename);\n\n \n}\n\nSavedPolicy::~SavedPolicy() {}\n\nint SavedPolicy::first_action(const std::vector<float> &s) {\n\n if (ACTDEBUG){\n cout << \"First - in state: \";\n printState(s);\n cout << endl;\n }\n\n // Get action values\n std::vector<float> &Q_s = Q[canonicalize(s)];\n\n // Choose an action\n const std::vector<float>::iterator a =\n std::max_element(Q_s.begin(), Q_s.end()); // Choose maximum\n\n if (ACTDEBUG){\n cout << \" act: \" << (a-Q_s.begin()) << \" val: \" << *a << endl;\n for (int iAct = 0; iAct < numactions; iAct++){\n cout << \" Action: \" << iAct \n\t << \" val: \" << Q_s[iAct] << endl;\n }\n cout << \"Took action \" << (a-Q_s.begin()) << \" from state \";\n printState(s);\n cout << endl;\n }\n\n return a - Q_s.begin();\n}\n\nint SavedPolicy::next_action(float r, const std::vector<float> &s) {\n\n if (ACTDEBUG){\n cout << \"Next: got reward \" << r << \" in state: \";\n printState(s);\n cout << endl;\n }\n\n // Get action values\n std::vector<float> &Q_s = Q[canonicalize(s)];\n const std::vector<float>::iterator max =\n std::max_element(Q_s.begin(), Q_s.end());\n\n // Choose an action\n const std::vector<float>::iterator a = max;\n\n if (ACTDEBUG){\n cout << \" act: \" << (a-Q_s.begin()) << \" val: \" << *a << endl;\n for (int iAct = 0; iAct < numactions; iAct++){\n cout << \" Action: \" << iAct \n\t << \" val: \" << Q_s[iAct] << endl;\n }\n cout << \"Took action \" << (a-Q_s.begin()) << \" from state \";\n printState(s);\n cout << endl;\n }\n\n return a - Q_s.begin();\n}\n\nvoid SavedPolicy::last_action(float r) {\n\n if (ACTDEBUG){\n cout << \"Last: got reward \" << r << endl;\n }\n\n}\n\nSavedPolicy::state_t SavedPolicy::canonicalize(const std::vector<float> &s) {\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\n statespace.insert(s);\n state_t retval = &*result.first; // Dereference iterator then get pointer \n if (result.second) { // s is new, so initialize Q(s,a) for all a\n if (loaded){\n cout << \"State unknown in policy!!!\" << endl;\n for (unsigned i = 0; i < s.size(); i++){\n cout << s[i] << \", \";\n }\n cout << endl;\n }\n std::vector<float> &Q_s = Q[retval];\n Q_s.resize(numactions,0.0);\n }\n return retval; \n}\n\n\n\nvoid SavedPolicy::printState(const std::vector<float> &s){\n for (unsigned j = 0; j < s.size(); j++){\n cout << s[j] << \", \";\n }\n}\n\n\n\nvoid SavedPolicy::seedExp(std::vector<experience> seeds){\n return;\n}\n\n\nvoid SavedPolicy::loadPolicy(const char* filename){\n\n ifstream policyFile(filename, ios::in | ios::binary);\n\n // first part, save the vector size\n int fsize;\n policyFile.read((char*)&fsize, sizeof(int));\n if (LOADDEBUG) cout << \"Numfeats loaded: \" << fsize << endl;\n\n // save numactions\n int nact;\n policyFile.read((char*)&nact, sizeof(int));\n\n if (nact != numactions){\n cout << \"this policy is not valid loaded nact: \" << nact \n << \" was told: \" << numactions << endl;\n exit(-1);\n }\n\n // go through all states, loading q values\n while(!policyFile.eof()){\n std::vector<float> state;\n state.resize(fsize, 0.0);\n\n // load state\n policyFile.read((char*)&(state[0]), sizeof(float)*fsize);\n if (LOADDEBUG){\n cout << \"load policy for state: \";\n printState(state);\n }\n\n state_t s = canonicalize(state);\n\n if (policyFile.eof()) break;\n\n // load q values\n policyFile.read((char*)&(Q[s][0]), sizeof(float)*numactions);\n \n if (LOADDEBUG){\n cout << \"Q values: \" << endl;\n for (int iAct = 0; iAct < numactions; iAct++){\n cout << \" Action: \" << iAct << \" val: \" << Q[s][iAct] << endl;\n }\n }\n }\n \n policyFile.close();\n cout << \"Policy loaded!!!\" << endl;\n loaded = true;\n}\n"},{"alpha_fraction":0.6638965606689453,"alphanum_fraction":0.6906740665435791,"avg_line_length":24.785715103149414,"blob_id":"e702eaa916d6f6a4d7a106cbe85cdbcdb4554489","content_id":"d8a8e98c902b3a5aef1fc70d6a3de30d6ab26368","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":1083,"license_type":"no_license","max_line_length":71,"num_lines":42,"path":"/src/mantis_model/src/joint_state_pub.cpp","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include <ros/ros.h>\n#include <sensor_msgs/JointState.h>\n\nsensor_msgs::JointState joint_state;\nros::Publisher pub_joints;\n\nvoid timerCallback(const ros::TimerEvent& event)\n{\n double constant_rate = 2.0;\n\n joint_state.header.stamp = event.current_real;\n joint_state.position[0] += 0.02 * constant_rate;\n joint_state.position[1] += 0.02 * constant_rate;\n\n joint_state.velocity[0] = constant_rate;\n joint_state.velocity[1] = constant_rate;\n\n joint_state.effort[0] = 5;\n joint_state.effort[1] = 5;\n pub_joints.publish(joint_state);\n}\n\nint main(int argc, char** argv)\n{\n ros::init(argc, argv, \"joint_state_pub\");\n ros::NodeHandle n;\n\n joint_state.header.frame_id = \"base_footprint\";\n joint_state.name.resize(2);\n joint_state.position.resize(2, 0);\n joint_state.velocity.resize(2, 0);\n joint_state.effort.resize(2, 0);\n\n joint_state.name[0] = \"left_wheel_joint\";\n joint_state.name[1] = \"right_wheel_joint\";\n\n pub_joints = n.advertise<sensor_msgs::JointState>(\"joint_states\", 1);\n\n ros::Timer timer = n.createTimer(ros::Duration(0.02), timerCallback);\n\n ros::spin();\n}\n"},{"alpha_fraction":0.7407407164573669,"alphanum_fraction":0.7460317611694336,"avg_line_length":46.3125,"blob_id":"f1a756eb0d25827008d9222cb8b30d57cfdc4781","content_id":"d9c7ae1e805ed0d7a4183d9695ca56abdbd386c3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":756,"license_type":"no_license","max_line_length":73,"num_lines":16,"path":"/build/gps_sim_project/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"gps_sim_project\")\nset(gps_sim_project_VERSION \"0.0.0\")\nset(gps_sim_project_MAINTAINER \"abc <abc@todo.todo>\")\nset(gps_sim_project_PACKAGE_FORMAT \"1\")\nset(gps_sim_project_BUILD_DEPENDS \"roscpp\" \"ugv_course_libs\" \"tf\")\nset(gps_sim_project_BUILD_EXPORT_DEPENDS \"roscpp\" \"ugv_course_libs\" \"tf\")\nset(gps_sim_project_BUILDTOOL_DEPENDS 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MOTORSIM_H_\n\nnamespace roundbot_control{\n\nclass MotorSim{\npublic:\n MotorSim(double max_speed, double max_accel, double max_decel);\n\n double iterate(double target_speed, double dt);\nprivate:\n\n double current_speed_;\n\n double max_speed_;\n double max_accel_;\n double max_decel_;\n};\n\n}\n\n\n\n#endif /* MOTORSIM_H_ */\n"},{"alpha_fraction":0.7580246925354004,"alphanum_fraction":0.7580246925354004,"avg_line_length":39.5,"blob_id":"df097f6c7921ca55195230b4ba15cfba619d3635","content_id":"92ad028630719f8f91fb3fed010e323fc3d2a75a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":810,"license_type":"no_license","max_line_length":67,"num_lines":20,"path":"/build/rl-texplore-ros-pkg-master/src/rl_env/CMakeFiles/envlib.dir/cmake_clean.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"file(REMOVE_RECURSE\n \"CMakeFiles/envlib.dir/src/Env/tworooms.cc.o\"\n \"CMakeFiles/envlib.dir/src/Env/taxi.cc.o\"\n 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RLEnvSeedExperience_<ContainerAllocator> Type;\n\n RLEnvSeedExperience_()\n : from_state()\n , action(0)\n , to_state()\n , reward(0.0)\n , terminal(false) {\n }\n RLEnvSeedExperience_(const ContainerAllocator& _alloc)\n : from_state(_alloc)\n , action(0)\n , to_state(_alloc)\n , reward(0.0)\n , terminal(false) {\n (void)_alloc;\n }\n\n\n\n typedef std::vector<float, typename ContainerAllocator::template rebind<float>::other > _from_state_type;\n _from_state_type from_state;\n\n typedef int32_t _action_type;\n _action_type action;\n\n typedef std::vector<float, typename ContainerAllocator::template rebind<float>::other > _to_state_type;\n _to_state_type to_state;\n\n typedef float _reward_type;\n _reward_type reward;\n\n typedef uint8_t _terminal_type;\n _terminal_type terminal;\n\n\n\n\n\n typedef boost::shared_ptr< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> > Ptr;\n typedef boost::shared_ptr< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> const> ConstPtr;\n\n}; // struct RLEnvSeedExperience_\n\ntypedef ::rl_msgs::RLEnvSeedExperience_<std::allocator<void> > RLEnvSeedExperience;\n\ntypedef boost::shared_ptr< ::rl_msgs::RLEnvSeedExperience > RLEnvSeedExperiencePtr;\ntypedef boost::shared_ptr< ::rl_msgs::RLEnvSeedExperience const> RLEnvSeedExperienceConstPtr;\n\n// constants requiring out of line definition\n\n\n\ntemplate<typename ContainerAllocator>\nstd::ostream& operator<<(std::ostream& s, const ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> & v)\n{\nros::message_operations::Printer< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >::stream(s, \"\", v);\nreturn s;\n}\n\n} // namespace rl_msgs\n\nnamespace ros\n{\nnamespace message_traits\n{\n\n\n\n// BOOLTRAITS {'IsFixedSize': False, 'IsMessage': True, 'HasHeader': False}\n// {'rl_msgs': ['/home/justin/ros_test/src/rl-texplore-ros-pkg-master/src/rl_msgs/msg'], 'std_msgs': ['/opt/ros/melodic/share/std_msgs/cmake/../msg']}\n\n// !!!!!!!!!!! ['__class__', '__delattr__', '__dict__', '__doc__', '__eq__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_parsed_fields', 'constants', 'fields', 'full_name', 'has_header', 'header_present', 'names', 'package', 'parsed_fields', 'short_name', 'text', 'types']\n\n\n\n\ntemplate <class ContainerAllocator>\nstruct IsFixedSize< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\n : FalseType\n { };\n\ntemplate <class ContainerAllocator>\nstruct IsFixedSize< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> const>\n : FalseType\n { };\n\ntemplate <class ContainerAllocator>\nstruct IsMessage< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\n : TrueType\n { };\n\ntemplate <class ContainerAllocator>\nstruct IsMessage< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> const>\n : TrueType\n { };\n\ntemplate <class ContainerAllocator>\nstruct HasHeader< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\n : FalseType\n { };\n\ntemplate <class ContainerAllocator>\nstruct HasHeader< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> const>\n : FalseType\n { };\n\n\ntemplate<class ContainerAllocator>\nstruct MD5Sum< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\n{\n static const char* value()\n {\n return \"e195f7b7c1a41138d96a4af2de05b1b1\";\n }\n\n static const char* value(const ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator>&) { return value(); }\n static const uint64_t static_value1 = 0xe195f7b7c1a41138ULL;\n static const uint64_t static_value2 = 0xd96a4af2de05b1b1ULL;\n};\n\ntemplate<class ContainerAllocator>\nstruct DataType< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\n{\n static const char* value()\n {\n return \"rl_msgs/RLEnvSeedExperience\";\n }\n\n static const char* value(const ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator>&) { return value(); }\n};\n\ntemplate<class ContainerAllocator>\nstruct Definition< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\n{\n static const char* value()\n {\n return \"# Message that contains a seed experience to initialize the model\\n\\\nfloat32[] from_state\\n\\\nint32 action\\n\\\nfloat32[] to_state\\n\\\nfloat32 reward\\n\\\nbool terminal\\n\\\n\\n\\\n\";\n }\n\n static const char* value(const ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator>&) { return value(); }\n};\n\n} // namespace message_traits\n} // namespace ros\n\nnamespace ros\n{\nnamespace serialization\n{\n\n template<class ContainerAllocator> struct Serializer< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\n {\n template<typename Stream, typename T> inline static void allInOne(Stream& stream, T m)\n {\n stream.next(m.from_state);\n stream.next(m.action);\n stream.next(m.to_state);\n stream.next(m.reward);\n stream.next(m.terminal);\n }\n\n ROS_DECLARE_ALLINONE_SERIALIZER\n }; // struct RLEnvSeedExperience_\n\n} // namespace serialization\n} // namespace ros\n\nnamespace ros\n{\nnamespace message_operations\n{\n\ntemplate<class ContainerAllocator>\nstruct Printer< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\n{\n template<typename Stream> static void stream(Stream& s, const std::string& indent, const ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator>& v)\n {\n s << indent << \"from_state[]\" << std::endl;\n for (size_t i = 0; i < v.from_state.size(); ++i)\n {\n s << indent << \" from_state[\" << i << \"]: \";\n Printer<float>::stream(s, indent + \" \", v.from_state[i]);\n }\n s << indent << \"action: \";\n Printer<int32_t>::stream(s, indent + \" \", v.action);\n s << indent << \"to_state[]\" << std::endl;\n for (size_t i = 0; i < v.to_state.size(); ++i)\n {\n s << indent << \" to_state[\" << i << \"]: \";\n Printer<float>::stream(s, indent + \" \", v.to_state[i]);\n }\n s << indent << \"reward: \";\n Printer<float>::stream(s, indent + \" \", v.reward);\n s << indent << \"terminal: \";\n Printer<uint8_t>::stream(s, indent + \" \", v.terminal);\n }\n};\n\n} // namespace message_operations\n} // namespace ros\n\n#endif // RL_MSGS_MESSAGE_RLENVSEEDEXPERIENCE_H\n"},{"alpha_fraction":0.5189234018325806,"alphanum_fraction":0.5412456393241882,"avg_line_length":25.163461685180664,"blob_id":"0947109f50aebad0cf0de8b3eb2eaf8f008b009e","content_id":"d7a775a5ff5ca15d149bdf76aa035d2ad65f2311","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":10886,"license_type":"no_license","max_line_length":290,"num_lines":416,"path":"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/LightWorld.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/**\n The LightWorld domain from\n \"Building Portable Options: Skill Transfer in Reinforcement Learning\"\n by Konidaris and Barto\n*/\n\n#include <rl_env/LightWorld.hh>\n\n\nLightWorld::LightWorld(Random &rand, bool stochastic, int nrooms):\n noisy(stochastic),\n nrooms(nrooms),\n rng(rand),\n s(17),\n ns(s[0]),\n ew(s[1]),\n have_key(s[2]),\n door_open(s[3]),\n room_id(s[4]),\n key_n(s[5]),\n key_e(s[6]),\n key_w(s[7]),\n key_s(s[8]),\n lock_n(s[9]),\n lock_e(s[10]),\n lock_w(s[11]),\n lock_s(s[12]),\n door_n(s[13]),\n door_e(s[14]),\n door_w(s[15]),\n door_s(s[16])\n{\n\n LWDEBUG = false;\n MAX_SENSE = 10;\n\n\n totalVisited = 0;\n keyVisited = 0;\n lockVisited = 0;\n doorVisited = 0;\n haveKey = 0;\n doorOpen = 0;\n leaveRoom = 0;\n pressKey = 0;\n pressLockCorrect = 0;\n pressLockIncorrect = 0;\n pressDoor = 0;\n pressOther = 0;\n pickupKeyCorrect = 0;\n pickupKeyIncorrect = 0;\n pickupLock = 0;\n pickupDoor = 0;\n pickupOther = 0;\n\n reset();\n}\n\n\n\nLightWorld::~LightWorld() { }\n\n\nconst std::vector<float> &LightWorld::sensation() const {\n if (LWDEBUG) print_map();\n return s;\n}\n\n\nint LightWorld::applyNoise(int action){\n switch(action) {\n case NORTH:\n case SOUTH:\n return rng.bernoulli(0.9) ? action : (rng.bernoulli(0.5) ? EAST : WEST);\n case EAST:\n case WEST:\n return rng.bernoulli(0.9) ? action : (rng.bernoulli(0.5) ? NORTH : SOUTH);\n case PRESS:\n case PICKUP:\n return rng.bernoulli(0.9) ? action : -1;\n default:\n return action;\n }\n}\n\nfloat LightWorld::apply(int origAction) {\n\n int reward = 0;\n\n int action = origAction;\n if (noisy)\n action = applyNoise(origAction);\n\n if (action == NORTH){\n if (((ns < rooms[room_id].height-2) || (rooms[room_id].lock_ns == rooms[room_id].height-1 && ew == rooms[room_id].lock_ew) || (rooms[room_id].door_ns == rooms[room_id].height-1 && ew == rooms[room_id].door_ew)) && ew > 0 && ew < rooms[room_id].width-1 && ns < rooms[room_id].height-1) {\n ns++;\n } else if (door_open && ns == rooms[room_id].door_ns && ew == rooms[room_id].door_ew && rooms[room_id].door_ns == rooms[room_id].height-1){\n leaveRoom++;\n room_id++;\n if (room_id >= nrooms) room_id = 0;\n have_key = false;\n door_open = false;\n if (rooms[room_id].key_ns < 0) have_key = true;\n ns = 0;\n resetKey();\n reward+=10;\n }\n }\n if (action == EAST){\n if (((ew < rooms[room_id].width-2) || (rooms[room_id].lock_ew == rooms[room_id].width-1 && ns == rooms[room_id].lock_ns) || (rooms[room_id].door_ew == rooms[room_id].width-1 && ns == rooms[room_id].door_ns)) && ns > 0 && ns < rooms[room_id].height-1 && ew < rooms[room_id].width-1) {\n ew++;\n } else if (door_open && ew == rooms[room_id].door_ew && ns == rooms[room_id].door_ns && rooms[room_id].door_ew == rooms[room_id].width-1){\n leaveRoom++;\n room_id++;\n if (room_id >= nrooms) room_id = 0;\n have_key = false;\n door_open = false;\n if (rooms[room_id].key_ns < 0) have_key = true;\n ew = 0;\n if (room_id == 1) ns = 3;\n resetKey();\n reward+=10;\n }\n }\n if (action == SOUTH){\n if (((ns > 1) || (rooms[room_id].lock_ns == 0 && ew == rooms[room_id].lock_ew) || (rooms[room_id].door_ns == 0 && ew == rooms[room_id].door_ew)) && ew > 0 && ew < rooms[room_id].width-1 && ns > 0) {\n ns--;\n } else if (door_open && ns == rooms[room_id].door_ns && ew == rooms[room_id].door_ew && rooms[room_id].door_ns == 0){\n leaveRoom++;\n room_id++;\n if (room_id >= nrooms) room_id = 0;\n have_key = false;\n door_open = false;\n if (rooms[room_id].key_ns < 0) have_key = true;\n ns = rooms[room_id].height-1;\n resetKey();\n reward+=10;\n }\n }\n if (action == WEST){\n if (((ew > 1) || (rooms[room_id].lock_ew == 0 && ns == rooms[room_id].lock_ns) || (rooms[room_id].door_ew == 0 && ns == rooms[room_id].door_ns)) && ns > 0 && ns < rooms[room_id].height-1 && ew > 0) {\n ew--;\n } else if (door_open && ew == rooms[room_id].door_ew && ns == rooms[room_id].door_ns && rooms[room_id].door_ew == 0){\n leaveRoom++;\n room_id++;\n if (room_id >= nrooms) room_id = 0;\n have_key = false;\n door_open = false;\n if (rooms[room_id].key_ns < 0) have_key = true;\n ew = rooms[room_id].width-1;\n resetKey();\n reward+=10;\n }\n }\n\n\n if (action == PICKUP){\n if (ns == rooms[room_id].key_ns && ew == rooms[room_id].key_ew){\n if (!have_key) pickupKeyCorrect++;\n else pickupKeyIncorrect++;\n have_key = true;\n }\n else if (ns == rooms[room_id].lock_ns && ew == rooms[room_id].lock_ew)\n pickupLock++;\n else if (ns == rooms[room_id].door_ns && ew == rooms[room_id].door_ew)\n pickupDoor++;\n else\n pickupOther++;\n }\n\n if (action == PRESS){\n if (ns == rooms[room_id].lock_ns && ew == rooms[room_id].lock_ew){\n if (have_key) {\n if (!door_open) pressLockCorrect++;\n else pressLockIncorrect++;\n door_open = true;\n } else {\n pressLockIncorrect++;\n }\n }\n else if (ns == rooms[room_id].key_ns && ew == rooms[room_id].key_ew)\n pressKey++;\n else if (ns == rooms[room_id].door_ns && ew == rooms[room_id].door_ew)\n pressDoor++;\n else\n pressOther++;\n }\n\n updateSensors();\n\n return reward;\n\n}\n\nvoid LightWorld::updateSensors() {\n\n // set all to 0\n key_n = 0;\n key_e = 0;\n key_w = 0;\n key_s = 0;\n lock_n = 0;\n lock_e = 0;\n lock_w = 0;\n lock_s = 0;\n door_n = 0;\n door_e = 0;\n door_w = 0;\n door_s = 0;\n\n if (!have_key){\n if (rooms[room_id].key_ns <= ns){\n key_s = MAX_SENSE - (ns - rooms[room_id].key_ns);\n }\n if (rooms[room_id].key_ns >= ns){\n key_n = MAX_SENSE - (rooms[room_id].key_ns - ns);\n }\n if (rooms[room_id].key_ew <= ew){\n key_w = MAX_SENSE - (ew - rooms[room_id].key_ew);\n }\n if (rooms[room_id].key_ew >= ew){\n key_e = MAX_SENSE - (rooms[room_id].key_ew - ew);\n }\n }\n\n if (door_open){\n if (rooms[room_id].door_ns <= ns){\n door_s = MAX_SENSE - (ns - rooms[room_id].door_ns);\n }\n if (rooms[room_id].door_ns >= ns){\n door_n = MAX_SENSE - (rooms[room_id].door_ns - ns);\n }\n if (rooms[room_id].door_ew <= ew){\n door_w = MAX_SENSE - (ew - rooms[room_id].door_ew);\n }\n if (rooms[room_id].door_ew >= ew){\n door_e = MAX_SENSE - (rooms[room_id].door_ew - ew);\n }\n }\n\n if (rooms[room_id].lock_ns <= ns){\n lock_s = MAX_SENSE - (ns - rooms[room_id].lock_ns);\n }\n if (rooms[room_id].lock_ns >= ns){\n lock_n = MAX_SENSE - (rooms[room_id].lock_ns - ns);\n }\n if (rooms[room_id].lock_ew <= ew){\n lock_w = MAX_SENSE - (ew - rooms[room_id].lock_ew);\n }\n if (rooms[room_id].lock_ew >= ew){\n lock_e = MAX_SENSE - (rooms[room_id].lock_ew - ew);\n }\n}\n\n\nvoid LightWorld::resetKey() {\n if (!have_key && rooms[room_id].key_ns > -1){\n rooms[room_id].key_ns = rng.uniformDiscrete(1, rooms[room_id].height-2);\n rooms[room_id].key_ew = rng.uniformDiscrete(1, rooms[room_id].width-2);\n }\n}\n\nvoid LightWorld::setKey(std::vector<float> testS){\n if (!have_key){\n float nsDist = 0;\n if (testS[5] > 0)\n nsDist = MAX_SENSE - testS[5];\n else\n nsDist = -MAX_SENSE + testS[8];\n rooms[room_id].key_ns = testS[0] + nsDist;\n float ewDist = 0;\n if (testS[6] > 0)\n ewDist = MAX_SENSE - testS[6];\n else\n ewDist = -MAX_SENSE + testS[7];\n rooms[room_id].key_ew = testS[1] + ewDist;\n }\n}\n\n\n\nbool LightWorld::terminal() const {\n // TODO: different terminal condition\n //return scream || (dangerous && (under_eye == 6 || under_hand == 6));\n return false;\n}\n\nvoid LightWorld::reset() {\n\n // init rooms\n rooms.resize(nrooms);\n\n rooms[0].height = 8;\n rooms[0].width = 7;\n rooms[0].key_ns = 3;\n rooms[0].key_ew = 1;\n rooms[0].lock_ns = 2;\n rooms[0].lock_ew = 6;\n rooms[0].door_ns = 5;\n rooms[0].door_ew = 6;\n\n rooms[1].height = 6;\n rooms[1].width = 5;\n rooms[1].key_ns = 3;\n rooms[1].key_ew = 2;\n rooms[1].lock_ns = 0;\n rooms[1].lock_ew = 3;\n rooms[1].door_ns = 0;\n rooms[1].door_ew = 1;\n\n rooms[2].height = 8;\n rooms[2].width = 5;\n rooms[2].key_ns = -1;\n rooms[2].key_ew = -1;\n rooms[2].lock_ns = 3;\n rooms[2].lock_ew = 4;\n rooms[2].door_ns = 3;\n rooms[2].door_ew = 0;\n\n rooms[3].height = 6;\n rooms[3].width = 7;\n rooms[3].key_ns = 1;\n rooms[3].key_ew = 1;\n rooms[3].lock_ns = 0;\n rooms[3].lock_ew = 4;\n rooms[3].door_ns = 5;\n rooms[3].door_ew = 2;\n\n for (int i = 4; i < nrooms; i++){\n rooms[i].height = i+3;\n rooms[i].width = i+2;\n rooms[i].key_ns = i;\n rooms[i].key_ew = i-1;\n rooms[i].lock_ns = 0;\n rooms[i].lock_ew = i-2;\n rooms[i].door_ns = 0;\n rooms[i].door_ew = i;\n }\n\n // random spot in first room\n room_id = 0;\n ns = rng.uniformDiscrete(1, rooms[0].height-2);\n ew = rng.uniformDiscrete(1, rooms[0].width-2);\n have_key = false;\n door_open = false;\n resetKey();\n updateSensors();\n\n if (LWDEBUG) print_map();\n\n}\n\n\nint LightWorld::getNumActions() {\n if (LWDEBUG) cout << \"Return number of actions: \" << NUM_ACTIONS << endl;\n return NUM_ACTIONS; //num_actions;\n}\n\n\nvoid LightWorld::print_map() const{\n // TODO: print map in rows, including symbols for all objects\n\n cout << \"\\nLightWorld, Room \" << room_id << endl;\n\n // for each row\n for (int j = rooms[room_id].height-1; j >= 0; --j){\n // for each column\n for (int i = 0; i < rooms[room_id].width; i++){\n if (ns == j && ew == i) cout << \"A\";\n else if (j == rooms[room_id].key_ns && i == rooms[room_id].key_ew && !have_key) cout << \"K\";\n else if (j == rooms[room_id].lock_ns && i == rooms[room_id].lock_ew) cout << \"L\";\n else if (j == rooms[room_id].door_ns && i == rooms[room_id].door_ew) cout << \"D\";\n else if (j == 0 || i == 0 || j == rooms[room_id].height-1 || i == rooms[room_id].width-1) cout << \"X\";\n else cout << \".\";\n } // last col of row\n cout << endl;\n } // last row\n\n cout << \"at \" << ns << \", \" << ew << endl;\n cout << \"Key: \" << have_key << \" door: \"<< door_open << endl;\n cout << \"NORTH: key: \" << key_n << \", door: \" << door_n << \", lock: \" << lock_n << endl;\n cout << \"EAST: key: \" << key_e << \", door: \" << door_e << \", lock: \" << lock_e << endl;\n cout << \"SOUTH: key: \" << key_s << \", door: \" << door_s << \", lock: \" << lock_s << endl;\n cout << \"WEST: key: \" << key_w << \", door: \" << door_w << \", lock: \" << lock_w << endl;\n\n\n}\n\n\n\nvoid LightWorld::getMinMaxFeatures(std::vector<float> *minFeat,\n std::vector<float> *maxFeat){\n\n minFeat->resize(s.size(), 0.0);\n maxFeat->resize(s.size(), MAX_SENSE);\n\n // room id only goes to 2\n (*maxFeat)[4] = 2;\n\n // have_key and door_open are boolean\n (*maxFeat)[2] = 1;\n (*maxFeat)[3] = 1;\n\n // room sizes only go to 8\n (*maxFeat)[0] = 8;\n (*maxFeat)[1] = 8;\n\n}\n\nvoid LightWorld::getMinMaxReward(float *minR,\n float *maxR){\n\n *minR = 0.0;\n *maxR = 10.0;\n\n}\n\n\n"},{"alpha_fraction":0.6338916420936584,"alphanum_fraction":0.6452034115791321,"avg_line_length":29.633333206176758,"blob_id":"4303907cf941162532c3e7695a8b06a2b35f1bf7","content_id":"bb2fcd891c1a3ed0cbee1d9ecbdc268e3de76179","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":4597,"license_type":"no_license","max_line_length":113,"num_lines":150,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/SepPlanExplore.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include \"SepPlanExplore.hh\"\n\n\n\nSepPlanExplore::SepPlanExplore(int id, int modelType, int predType,\n int nModels, int trainMode,\n int trainFreq,\n float featPct, float expPct,\n float treeThreshold, bool stoch,\n float featRange, Random rng):\n id(id), modelType(modelType), predType(predType), nModels(nModels),\n mode(trainMode), freq(trainFreq),\n featPct(featPct), expPct(expPct),\n treeThresh(treeThreshold), stoch(stoch),\n featRange(featRange), rng(rng)\n{\n SPEDEBUG = false;//true;\n\n cout << \"Created Sep Plan & Explore models \" << id << \" with nModels: \" << nModels << endl;\n\n for (int i = 0; i < id; i++)\n rng.uniform(0,1);\n\n initModels();\n\n}\n\nSepPlanExplore::SepPlanExplore(const SepPlanExplore& spe):\n id(spe.id), modelType(spe.modelType), \n predType(spe.predType), nModels(spe.nModels),\n mode(spe.mode), freq(spe.freq),\n featPct(spe.featPct), expPct(spe.expPct),\n treeThresh(spe.treeThresh), stoch(spe.stoch),\n featRange(spe.featRange), rng(spe.rng)\n{\n cout << \"spe get copy\" << endl;\n SPEDEBUG = spe.SPEDEBUG;\n expModel = spe.expModel->getCopy();\n planModel = spe.planModel->getCopy();\n}\n\nSepPlanExplore* SepPlanExplore::getCopy(){\n SepPlanExplore* copy = new SepPlanExplore(*this);\n return copy;\n}\n\nSepPlanExplore::~SepPlanExplore() {\n delete expModel;\n delete planModel;\n}\n\n\nbool SepPlanExplore::trainInstances(std::vector<classPair> &instances){\n if (SPEDEBUG) cout << id << \"SPE trainInstances: \" << instances.size() << endl;\n\n // train both\n bool expChanged = expModel->trainInstances(instances);\n bool planChanged = planModel->trainInstances(instances);\n\n return (expChanged || planChanged);\n\n}\n\n\n\n// here the target output will be a single value\nbool SepPlanExplore::trainInstance(classPair &instance){\n if (SPEDEBUG) cout << id << \"SPE trainInstance: \" << endl;\n\n // train both\n bool expChanged = expModel->trainInstance(instance);\n bool planChanged = planModel->trainInstance(instance);\n\n return (expChanged || planChanged);\n\n}\n\n// get all the models outputs and combine them somehow\nvoid SepPlanExplore::testInstance(const std::vector<float> &input, std::map<float, float>* retval){\n if (SPEDEBUG) cout << id << \" testInstance\" << endl;\n\n retval->clear();\n\n // use planning model to get predictions\n planModel->testInstance(input, retval);\n\n}\n\n\n\nfloat SepPlanExplore::getConf(const std::vector<float> &input){\n if (SPEDEBUG) cout << \"getConf\" << endl;\n\n // use exploreation model for confidence measure\n return expModel->getConf(input);\n\n}\n\n\n// init models\nvoid SepPlanExplore::initModels(){\n if (SPEDEBUG) cout << \"initModels()\" << endl;\n\n if (nModels < 2){\n cout << \"Should really use Sep plan & explore models with multiple models\" << endl;\n exit(-1);\n }\n\n // explore model should be of type MultipleClassifiers\n expModel = new MultipleClassifiers(id, modelType, predType,\n nModels, mode, freq,\n featPct, expPct, treeThresh, stoch, featRange, rng);\n\n // init the trees or stumps\n if (modelType == C45TREE){\n planModel = new C45Tree(id, mode, freq, 0, 0.0, rng);\n }\n else if (modelType == M5MULTI){\n planModel = new M5Tree(id, mode, freq, 0, 0.0, false, false, treeThresh, rng);\n }\n else if (modelType == M5ALLMULTI){\n planModel = new M5Tree(id, mode, freq, 0, 0.0, false, true, treeThresh, rng);\n }\n else if (modelType == M5ALLSINGLE){\n planModel = new M5Tree(id, mode, freq, 0, 0.0, true, true, treeThresh, rng);\n }\n else if (modelType == M5SINGLE){\n planModel = new M5Tree(id, mode, freq, 0, 0.0, true, false, treeThresh, rng);\n }\n else if (modelType == LSTSINGLE){\n planModel = new LinearSplitsTree(id, mode, freq, 0, 0.0, true, treeThresh, rng);\n }\n else if (modelType == LSTMULTI){\n planModel = new LinearSplitsTree(id, mode, freq, 0, 0.0, false, treeThresh, rng);\n }\n else if (modelType == STUMP){\n planModel = new Stump(id, mode, freq, 0, 0.0, rng);\n }\n else if (modelType == ALLM5TYPES){\n // select an m5 type randomly. so multivariate v single and allfeats v subtree feats\n bool simple = rng.bernoulli(0.5);\n bool allFeats = rng.bernoulli(0.5);\n //cout << \"ALL types init tree \" << i << \" with simple: \" << simple << \" and allFeats: \" << allFeats << endl;\n planModel = new M5Tree(id, mode, freq, 0, 0.0, simple, allFeats, treeThresh, rng);\n }\n else {\n cout << \"Invalid model type for this committee\" << endl;\n exit(-1);\n }\n}\n\n\n"},{"alpha_fraction":0.7492711544036865,"alphanum_fraction":0.7492711544036865,"avg_line_length":25.365385055541992,"blob_id":"20da0718e5928f3fa6086f758e4ef33ff5f96017","content_id":"24a9c8b1c1eb126b75ce408f5897a51a49815607","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":1372,"license_type":"no_license","max_line_length":106,"num_lines":52,"path":"/src/roundbot_control/include/roundbot_control/DiffController.h","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"\n#ifndef DIFFCONTROLLER_H_\n#define DIFFCONTROLLER_H_\n\n#include <controller_interface/controller.h>\n#include <hardware_interface/joint_command_interface.h>\n#include <pluginlib/class_list_macros.h>\n\n#include <geometry_msgs/Twist.h>\n#include <roundbot_control/MotorSim.h>\n\nnamespace roundbot_control\n{\n\nclass DiffController : public controller_interface::Controller<hardware_interface::VelocityJointInterface>\n{\npublic:\n bool init(hardware_interface::VelocityJointInterface* hw, ros::NodeHandle &n);\n void update(const ros::Time& time, const ros::Duration& period);\n void starting(const ros::Time& time);\n void stopping(const ros::Time& time);\nprivate:\n void recvTwist(const geometry_msgs::Twist::ConstPtr& msg);\n void loadParams(ros::NodeHandle& n);\n\n ros::Subscriber sub_twist_;\n\n hardware_interface::JointHandle left_joint_;\n hardware_interface::JointHandle right_joint_;\n\n MotorSim* left_motor_;\n MotorSim* right_motor_;\n geometry_msgs::Twist cmd_;\n double left_target_speed_;\n double right_target_speed_;\n ros::Time left_command_stamp_;\n ros::Time command_stamp_;\n\n // Parameters\n std::string left_joint_name_;\n std::string right_joint_name_;\n double timeout_;\n double wheel_radius_;\n double wheel_separation_;\n};\n\nPLUGINLIB_EXPORT_CLASS(roundbot_control::DiffController, controller_interface::ControllerBase);\n\n}\n\n\n\n#endif /* DIFFCONTROLLER_H_ */\n"},{"alpha_fraction":0.5477648377418518,"alphanum_fraction":0.5517452359199524,"avg_line_length":27.399999618530273,"blob_id":"fde370e2a8e232497e33b03a8838aedd6ba75238","content_id":"9481a8e158ab6eca3c544cd069bfbef2f0a980fe","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":3266,"license_type":"no_license","max_line_length":141,"num_lines":115,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Agent/DiscretizationAgent.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include <rl_agent/DiscretizationAgent.hh>\n#include <algorithm>\n\nDiscretizationAgent::DiscretizationAgent(int n, Agent* a, \n std::vector<float> fmin,\n std::vector<float> fmax,\n bool d){\n\n statesPerDim.resize(fmin.size(), n);\n initEverything(a, fmin, fmax, d);\n\n}\n\nDiscretizationAgent::DiscretizationAgent(std::vector<int> n, Agent* a, \n std::vector<float> fmin,\n std::vector<float> fmax,\n bool d){\n\n if (n.size() != fmin.size()){\n cout << \"ERROR: discretition vector size is different than # features\" << endl;\n exit(-1);\n }\n\n statesPerDim = n;\n initEverything(a, fmin, fmax, d);\n\n}\n\nvoid DiscretizationAgent::initEverything(Agent* a, \n std::vector<float> fmin,\n std::vector<float> fmax,\n bool d){\n agent = a;\n featmin = fmin;\n featmax = fmax;\n DEBUG = d;\n\n // print number of features\n int totalFeatures = 1;\n for (unsigned i = 0; i < featmin.size(); i++){\n if (DEBUG) cout << \"Dim \" << i << \" has \" \n << (1+statesPerDim[i]) << \" values.\" << endl;\n totalFeatures *= (1+statesPerDim[i]);\n }\n if (DEBUG) cout << \"Total # States: \" << totalFeatures << endl;\n\n}\n\nDiscretizationAgent::~DiscretizationAgent(){\n delete agent;\n}\n\nint DiscretizationAgent::first_action(const std::vector<float> &s) {\n std::vector<float> ds = discretizeState(s);\n\n return agent->first_action(ds);\n}\n\nint DiscretizationAgent::next_action(float r, const std::vector<float> &s) {\n std::vector<float> ds = discretizeState(s);\n\n return agent->next_action((int)r, ds);\n}\n\nvoid DiscretizationAgent::last_action(float r) {\n return agent->last_action((int)r);\n}\n\n\nvoid DiscretizationAgent::setDebug(bool b){}\n\nstd::vector<float> DiscretizationAgent::discretizeState(const std::vector<float> &s){\n std::vector<float> ds;\n ds.resize(s.size());\n \n // since i'm sometimes doing this for discrete domains\n // want to center bins on 0, not edge on 0\n //cout << \"feat \" << i << \" range: \" << featmax[i] << \" \" << featmin[i] << \" \" << (featmax[i]-featmin[i]) << \" n: \" << (float)statesPerDim;\n for (unsigned i = 0; i < s.size(); i++){\n float factor = (featmax[i] - featmin[i]) / (float)statesPerDim[i];\n int bin = 0;\n if (s[i] > 0){\n bin = (int)((s[i]+factor/2) / factor);\n } else {\n bin = (int)((s[i]-factor/2) / factor);\n }\n \n ds[i] = factor*bin;\n //cout << \"DA factor: \" << factor << \" bin: \" << bin;\n //cout << \" Original: \" << s[i] << \" Discrete: \" << ds[i] << endl;\n }\n\n return ds;\n\n}\n\n\nvoid DiscretizationAgent::seedExp(std::vector<experience> seedings){\n // discretize each experience\n for (unsigned i = 0; i < seedings.size(); i++){\n experience* e = &(seedings[i]);\n\n e->next = discretizeState(e->next);\n e->s = discretizeState(e->s);\n e->reward = ((int)e->reward);\n\n }\n\n // and pass to internal agent\n agent->seedExp(seedings);\n}\n\nvoid DiscretizationAgent::savePolicy(const char* filename){\n agent->savePolicy(filename);\n}\n"},{"alpha_fraction":0.5541346073150635,"alphanum_fraction":0.5619643926620483,"avg_line_length":25.303203582763672,"blob_id":"42357a8bb1fb9fdacef95df45ab8ec3823e44a3d","content_id":"ae2bde877e7cb95398a2e904102b78b251f3a64e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":22989,"license_type":"no_license","max_line_length":125,"num_lines":874,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/Stump.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include \"Stump.hh\"\n\n\n\nStump::Stump(int id, int trainMode, int trainFreq, int m, float featPct, Random rng):\n id(id), mode(trainMode), freq(trainFreq), M(m), featPct(featPct), rng(rng)\n{\n\n nOutput = 0;\n nExperiences = 0;\n\n // how close a split has to be to be randomly selected\n SPLIT_MARGIN = 0.02; //5; //01; //0.05; //0.2; //0.05;\n\n LOSS_MARGIN = 0.0; //0.02;\n REBUILD_RATIO = 0.05;\n\n MIN_GAIN_RATIO = 0.0001; //0.0004; //0.001; //0.0002; //0.001;\n\n ALLOW_ONLY_SPLITS = true; //false;\n\n STDEBUG = false;\n SPLITDEBUG = false; //true;\n\n if (STDEBUG) {\n cout << \"Created decision stump \" << id << endl;\n cout << \" mode: \" << mode\n << \" freq: \" << freq << endl;\n }\n\n initStump();\n\n}\n\nStump::Stump(const Stump& s):\nid(s.id), mode(s.mode), freq(s.freq), M(s.M), featPct(s.featPct), rng(s.rng)\n{\n nOutput = s.nOutput;\n nExperiences = s.nExperiences;\n SPLIT_MARGIN = s.SPLIT_MARGIN;\n LOSS_MARGIN = s.LOSS_MARGIN;\n REBUILD_RATIO = s.REBUILD_RATIO;\n MIN_GAIN_RATIO = s.MIN_GAIN_RATIO;\n ALLOW_ONLY_SPLITS = s.ALLOW_ONLY_SPLITS;\n STDEBUG = s.STDEBUG;\n SPLITDEBUG = s.SPLITDEBUG;\n\n for (int i = 0; i < N_STUMP_EXP; i++){\n allExp[i] = s.allExp[i];\n }\n\n // set experience pointers\n experiences.resize(s.experiences.size());\n for (unsigned i = 0; i < s.experiences.size(); i++){\n experiences[i] = &(allExp[i]);\n }\n\n // actual split\n dim = s.dim;\n val = s.val;\n type = s.type;\n gainRatio = s.gainRatio;\n leftOutputs = s.leftOutputs;\n rightOutputs = s.rightOutputs;\n}\n\nStump* Stump::getCopy(){\n Stump* copy = new Stump(*this);\n return copy;\n}\n\nStump::~Stump() {\n for (unsigned i = N_STUMP_EXP; i < experiences.size(); i++){\n delete experiences[i];\n }\n experiences.clear();\n}\n\n\n// multiple instances at once\nbool Stump::trainInstances(std::vector<classPair> &instances){\n if (STDEBUG) cout << \"trainInstances: \" << instances.size() << endl;\n\n bool modelChanged = false;\n bool doBuild = false;\n\n // loop through instances, possibly checking for errors\n for (unsigned a = 0; a < instances.size(); a++){\n classPair instance = instances[a];\n\n // take from static array until we run out\n stump_experience *e;\n if (nExperiences < N_STUMP_EXP){\n // from statically created set of experiences\n e = &(allExp[nExperiences]);\n\n } else {\n // dynamically create experience\n e = new stump_experience;\n }\n\n e->input = instance.in;\n e->output = instance.out;\n e->id = nExperiences;\n experiences.push_back(e);\n nExperiences++;\n\n if (nExperiences == 1000000){\n cout << \"Reached limit of # experiences allowed.\" << endl;\n return false;\n }\n\n if (nExperiences != (int)experiences.size())\n cout << \"ERROR: experience size mismatch: \" << nExperiences << \", \" << experiences.size() << endl;\n\n //cout << nExperiences << endl << flush;\n //if (nExperiences == 503 && id == 10){\n // STDEBUG = true;\n // SPLITDEBUG = true;\n // INCDEBUG = true;\n //}\n\n if (STDEBUG) {\n cout << \"Original input: \";\n for (unsigned i = 0; i < instance.in.size(); i++){\n cout << instance.in[i] << \", \";\n }\n cout << endl << \" Original output: \" << instance.out << endl;\n cout << \"Added exp id: \" << e->id << \" output: \" << e->output << endl;\n cout << \"Address: \" << e << \" Input : \";\n for (unsigned i = 0; i < e->input.size(); i++){\n cout << e->input[i] << \", \";\n }\n cout << endl << \" Now have \" << nExperiences << \" experiences.\" << endl;\n }\n\n // mode 0: re-build every step\n if (mode == 0 || mode == 1 || nExperiences <= 1){\n // build every step\n doBuild = true;\n }\n\n // mode 2: re-build every FREQ steps\n else if (mode == 2){\n // build every freq steps\n if (!modelChanged && (nExperiences % freq) == 0){\n doBuild = true;\n }\n }\n\n } // end instance loop\n\n if (doBuild){\n buildStump();\n modelChanged = true;\n }\n\n if (modelChanged){\n if (STDEBUG) cout << \"ST \" << id << \" stump re-built.\" << endl;\n\n if (STDEBUG){\n cout << endl << \"ST: \" << id << endl;\n printStump();\n cout << \"Done printing stump\" << endl;\n }\n }\n\n return modelChanged;\n}\n\n\n// here the target output will be a single value\nbool Stump::trainInstance(classPair &instance){\n if (STDEBUG) cout << \"trainInstance\" << endl;\n\n bool modelChanged = false;\n\n // simply add this instance to the set of experiences\n\n // take from static array until we run out\n stump_experience *e;\n if (nExperiences < N_STUMP_EXP){\n // from statically created set of experiences\n e = &(allExp[nExperiences]);\n\n } else {\n // dynamically create experience\n e = new stump_experience;\n }\n\n\n e->input = instance.in;\n e->output = instance.out;\n e->id = nExperiences;\n experiences.push_back(e);\n nExperiences++;\n\n if (nExperiences == 1000000){\n cout << \"Reached limit of # experiences allowed.\" << endl;\n return false;\n }\n\n if (nExperiences != (int)experiences.size())\n cout << \"ERROR: experience size mismatch: \" << nExperiences << \", \" << experiences.size() << endl;\n\n //cout << nExperiences << endl << flush;\n //if (nExperiences == 503 && id == 10){\n // STDEBUG = true;\n // SPLITDEBUG = true;\n // INCDEBUG = true;\n //}\n\n if (STDEBUG) {\n cout << \"Original input: \";\n for (unsigned i = 0; i < instance.in.size(); i++){\n cout << instance.in[i] << \", \";\n }\n cout << endl << \" Original output: \" << instance.out << endl;\n cout << \"Added exp id: \" << e->id << \" output: \" << e->output << endl;\n cout << \"Address: \" << e << \" Input : \";\n for (unsigned i = 0; i < e->input.size(); i++){\n cout << e->input[i] << \", \";\n }\n cout << endl << \" Now have \" << nExperiences << \" experiences.\" << endl;\n }\n\n // depending on mode/etc, maybe re-build stump\n\n // mode 0: re-build every step\n if (mode == 0 || mode == 1 || nExperiences <= 1){\n\n // build every step\n buildStump();\n modelChanged = true;\n\n }\n\n // mode 2: re-build every FREQ steps\n else if (mode == 2){\n // build every freq steps\n if (!modelChanged && (nExperiences % freq) == 0){\n buildStump();\n modelChanged = true;\n\n }\n }\n\n if (modelChanged){\n if (STDEBUG) cout << \"ST \" << id << \" stump re-built.\" << endl;\n\n if (STDEBUG){\n cout << endl << \"ST: \" << id << endl;\n printStump();\n cout << \"Done printing stump\" << endl;\n }\n }\n\n return modelChanged;\n\n}\n\n\n\n// TODO: here we want to return the probability of the output value being each of the possible values, in the stochastic case\nvoid Stump::testInstance(const std::vector<float> &input, std::map<float, float>* retval){\n if (STDEBUG) cout << \"testInstance ST: \" << id << endl;\n\n retval->clear();\n\n // in case the stump is empty\n if (experiences.size() == 0){\n (*retval)[0.0] = 1.0;\n if (STDEBUG) cout << \"no experiences, return 1.0 prob of 0.0\" << endl;\n return; \n }\n\n // and return mapping of outputs and their probabilities\n if (passTest(dim, val, type, input))\n outputProbabilities(leftOutputs, retval);\n else\n outputProbabilities(rightOutputs, retval);\n}\n\nfloat Stump::getConf(const std::vector<float> &input){\n if (STDEBUG) cout << \"numVisits\" << endl;\n\n // in case the stump is empty\n if (experiences.size() == 0){\n return 0;\n }\n\n // and return #\n float conf = (float)nExperiences / (float)(2.0 *M);\n if (conf > 1.0)\n return 1.0;\n else\n return conf;\n\n}\n\n\nint Stump::findMatching(const std::vector<stump_experience*> &instances, int dim,\n int val, int minConf){\n\n int count = 0;\n\n for (unsigned i = 0; i < instances.size(); i++){\n if (!passTest(dim, val, ONLY, instances[i]->input)){\n count++;\n // no need to continue if this won't be the new min\n if (count >= minConf)\n return count;\n }\n }\n return count;\n}\n\n\n\n\n// init the stump\nvoid Stump::initStump(){\n if (STDEBUG) cout << \"initStump()\" << endl;\n\n dim = -1;\n val = -1;\n type = -1;\n\n // current data\n leftOutputs.clear();\n rightOutputs.clear();\n\n // just to ensure the diff models are on different random values\n for (int i = 0; i < id; i++){\n rng.uniform(0, 1);\n }\n\n}\n\n\n/** Decide if this passes the test */\nbool Stump::passTest(int dim, float val, int type, const std::vector<float> &input){\n if (STDEBUG) cout << \"passTest, dim=\" << dim << \",val=\" << val << \",type=\" << type\n << \",input[\"<<dim<<\"]=\" << input[dim] <<endl;\n\n // TODO: deal with categorical attributes in addition to continuous ones\n\n if (type == CUT){\n if (input[dim] > val)\n return false;\n else\n return true;\n } else if (type == ONLY){\n if (input[dim] == val)\n return false;\n else\n return true;\n } else {\n return false;\n }\n\n}\n\n\n/** Build the stump from this node down using this set of experiences. */\nvoid Stump::buildStump(){\n if(STDEBUG) cout << \"buildStump\" << endl;\n\n if (experiences.size() == 0){\n cout << \"Error: buildStump called on stump \" << id << \" with no instances.\" << endl;\n exit(-1);\n }\n\n if (SPLITDEBUG) cout << endl << \"Creating new decision node\" << endl;\n\n //node->nInstances++;\n\n float bestGainRatio = -1.0;\n int bestDim = -1;\n float bestVal = -1;\n int bestType = -1;\n\n testPossibleSplits(&bestGainRatio, &bestDim, &bestVal, &bestType);\n\n implementSplit(bestGainRatio, bestDim, bestVal, bestType);\n\n}\n\n\nvoid Stump::implementSplit(float bestGainRatio, int bestDim,\n float bestVal, int bestType){\n if (STDEBUG) cout << \"implementSplit gainRatio=\" << bestGainRatio\n << \",dim=\" << bestDim\n << \",val=\" << bestVal << \",type=\" << bestType << endl;\n\n\n // set the best split here\n dim = bestDim;\n val = bestVal;\n type = bestType;\n gainRatio = bestGainRatio;\n if (SPLITDEBUG) cout << \"Best split was type \" << type\n << \" with val \" << val\n << \" on dim \" << dim\n << \" with gainratio: \" << bestGainRatio << endl;\n\n\n // split up the instances based on this split\n std::vector<stump_experience*> left;\n std::vector<stump_experience*> right;\n leftOutputs.clear();\n rightOutputs.clear();\n for (unsigned i = 0; i < experiences.size(); i++){\n if (STDEBUG) cout << \"implmentSplit - Classify instance \" << i << \" id: \"\n << experiences[i]->id << \" on new split \" << endl;\n if (passTest(dim, val, type, experiences[i]->input)){\n left.push_back(experiences[i]);\n leftOutputs.insert(experiences[i]->output);\n }\n else{\n right.push_back(experiences[i]);\n rightOutputs.insert(experiences[i]->output);\n }\n }\n\n if (STDEBUG) cout << \"Left has \" << left.size()\n << \", right has \" << right.size() << endl;\n\n}\n\n\n\nvoid Stump::testPossibleSplits(float *bestGainRatio, int *bestDim,\n float *bestVal, int *bestType) {\n if (STDEBUG) cout << \"testPossibleSplits\" << endl;\n\n\n // pre-calculate some stuff for these splits (namely I, P, C)\n float I = calcIforSet(experiences);\n //if (STDEBUG) cout << \"I: \" << I << endl;\n\n int nties = 0;\n\n // for each possible split, calc gain ratio\n for (unsigned idim = 0; idim < experiences[0]->input.size(); idim++){\n\n // we eliminate some random number of splits\n // here (decision is taken from the random set that are left)\n if (rng.uniform() < featPct)\n continue;\n\n\n float* sorted = sortOnDim(idim);\n\n for (int j = 0; j < (int)experiences.size(); j++){\n\n // splits that are cuts\n // if different from last value, try split in between\n if (j > 0 && sorted[j] != sorted[j-1]){\n float splitval = (sorted[j] + sorted[j-1]) / 2.0;\n float gainRatio = calcGainRatio(idim, splitval, CUT, I);\n\n if (SPLITDEBUG) cout << \" CUT split val \" << splitval\n << \" on dim: \" << idim << \" had gain ratio \"\n << gainRatio << endl;\n\n // see if this is the new best gain ratio\n compareSplits(gainRatio, idim, splitval, CUT, &nties,\n bestGainRatio, bestDim, bestVal, bestType);\n\n\n } // if its a possible cut\n\n if (ALLOW_ONLY_SPLITS){\n // splits that are true only if this value is equal\n if (j == 0 || sorted[j] != sorted[j-1]){\n float splitval = sorted[j];\n\n float gainRatio = calcGainRatio(idim, splitval, ONLY, I);\n\n if (SPLITDEBUG) cout << \" ONLY split val \" << splitval\n << \" on dim: \" << idim << \" had gain ratio \"\n << gainRatio << endl;\n\n // see if this is the new best gain ratio\n compareSplits(gainRatio, idim, splitval, ONLY, &nties,\n bestGainRatio, bestDim, bestVal, bestType);\n\n } // splits with only\n }\n\n } // j loop\n delete[] sorted;\n\n }\n}\n\n\n/** Decide if this split is better. */\nvoid Stump::compareSplits(float gainRatio, int dim, float val, int type,\n int *nties, float *bestGainRatio, int *bestDim,\n float *bestVal, int *bestType){\n if (STDEBUG) cout << \"compareSplits gainRatio=\" << gainRatio << \",dim=\" << dim\n << \",val=\" << val << \",type= \" << type <<endl;\n\n\n bool newBest = false;\n\n // if its a virtual tie, break it randomly\n if (fabs(*bestGainRatio - gainRatio) < SPLIT_MARGIN){\n //cout << \"Split tie, making random decision\" << endl;\n\n (*nties)++;\n float randomval = rng.uniform(0,1);\n float newsplitprob = (1.0 / (float)*nties);\n\n if (randomval < newsplitprob){\n newBest = true;\n if (SPLITDEBUG) cout << \" Tie on split. ST: \" << id << \" rand: \" << randomval\n << \" splitProb: \" << newsplitprob << \", selecting new split \" << endl;\n }\n else\n if (SPLITDEBUG) cout << \" Tie on split. ST: \" << id << \" rand: \" << randomval\n << \" splitProb: \" << newsplitprob << \", staying with old split \" << endl;\n }\n\n // if its clearly better, set this as the best split\n else if (gainRatio > *bestGainRatio){\n newBest = true;\n *nties = 1;\n }\n\n\n // set the split features\n if (newBest){\n *bestGainRatio = gainRatio;\n *bestDim = dim;\n *bestVal = val;\n *bestType = type;\n if (SPLITDEBUG){\n cout << \" New best gain ratio: \" << *bestGainRatio\n << \": type \" << *bestType\n << \" with val \" << *bestVal\n << \" on dim \" << *bestDim << endl;\n }\n } // newbest\n}\n\n/** Calculate gain ratio for this possible split. */\nfloat Stump::calcGainRatio(int dim, float val, int type,\n float I){\n if (STDEBUG) cout << \"calcGainRatio, dim=\" << dim\n << \" val=\" << val\n << \" I=\" << I\n << \" nInstances= \" << experiences.size() << endl;\n\n std::vector<stump_experience*> left;\n std::vector<stump_experience*> right;\n\n\n // array with percentage positive and negative for this test\n float D[2];\n\n // info(T) = I(P): float I;\n\n // Info for this split = Info(X,T)\n float Info;\n\n // Gain for this split = Gain(X,T)\n float Gain;\n\n // SplitInfo for this split = I(|pos|/|T|, |neg|/|T|)\n float SplitInfo;\n\n // GainRatio for this split = GainRatio(X,T) = Gain(X,T) / SplitInfo(X,T)\n float GainRatio;\n\n // see where the instances would go with this split\n for (unsigned i = 0; i < experiences.size(); i++){\n if (STDEBUG) cout << \"calcGainRatio - Classify instance \" << i << \" id: \"\n << experiences[i]->id << \" on new split \" << endl;\n\n if (passTest(dim, val, type, experiences[i]->input)){\n left.push_back(experiences[i]);\n }\n else{\n right.push_back(experiences[i]);\n }\n }\n\n if (STDEBUG) cout << \"Left has \" << left.size()\n << \", right has \" << right.size() << endl;\n\n D[0] = (float)left.size() / (float)experiences.size();\n D[1] = (float)right.size() / (float)experiences.size();\n float leftInfo = calcIforSet(left);\n float rightInfo = calcIforSet(right);\n Info = D[0] * leftInfo + D[1] * rightInfo;\n Gain = I - Info;\n SplitInfo = calcIofP((float*)&D, 2);\n GainRatio = Gain / SplitInfo;\n\n if (STDEBUG){\n cout << \"LeftInfo: \" << leftInfo\n << \" RightInfo: \" << rightInfo\n << \" Info: \" << Info\n << \" Gain: \" << Gain\n << \" SplitInfo: \" << SplitInfo\n << \" GainRatio: \" << GainRatio\n << endl;\n }\n\n return GainRatio;\n\n}\n\nfloat Stump::calcIofP(float* P, int size){\n if (STDEBUG) cout << \"calcIofP, size=\" << size << endl;\n float I = 0;\n for (int i = 0; i < size; i++){\n I -= P[i] * log(P[i]);\n }\n return I;\n}\n\n/** Calculate I(P) for set. */\nfloat Stump::calcIforSet(const std::vector<stump_experience*> &instances){\n if (STDEBUG) cout << \"calcIforSet\" << endl;\n\n std::vector<float> classes;\n std::vector<int> classCounts;\n\n // go through instances and figure count of each type\n for (unsigned i = 0; i < instances.size(); i++){\n\n float val = instances[i]->output;\n bool newValue = true;\n // see if this is a new val\n for (unsigned j = 0; j < classes.size(); j++){\n // not new, increment count for this class\n if (val == classes[j]){\n newValue = false;\n classCounts[j]++;\n break;\n }\n }\n\n // it is a new value\n if (newValue){\n classes.push_back(val);\n classCounts.push_back(1);\n if (STDEBUG) cout << \"found new class with val \" << val << endl;\n }\n }\n\n // now calculate P\n float *P = new float[classes.size()];\n for (int i = 0; i < (int)classCounts.size(); i++){\n P[i] = (float)classCounts[i] / (float)instances.size();\n }\n\n // calculate I(P)\n float I = calcIofP(P, classes.size());\n delete [] P;\n\n return I;\n\n}\n\n/** Returns a list of the attributes in this dimension sorted\n from lowest to highest. */\nfloat* Stump::sortOnDim(int dim){\n if (STDEBUG) cout << \"sortOnDim,dim = \" << dim << endl;\n\n float* values = new float[experiences.size()];\n\n for (int i = 0; i < (int)experiences.size(); i++){\n //cout << \"Instance \" << i << endl;\n\n float val = experiences[i]->input[dim];\n //cout << \" val: \" << val << endl;\n\n // find where this should go\n for (int j = 0; j <= i; j++){\n //cout << \" j: \" << j << endl;\n\n // get to i, this is the spot then\n if (j==i){\n values[j] = val;\n //cout << \" At i, putting value in slot j: \" << j << endl;\n }\n\n // if this is the spot\n else if (val < values[j]){\n //cout << \" Found slot at j: \" << j << endl;\n\n // slide everything forward to make room\n for (int k = i; k > j; k--){\n //cout << \" k = \" << k << \" Sliding value from k-1 to k\" << endl;\n values[k] = values[k-1];\n }\n\n // put value in its spot at j\n //cout << \" Putting value at slot j: \" << j << endl;\n values[j] = val;\n\n // break\n break;\n }\n\n }\n }\n\n if (STDEBUG){\n cout << \"Sorted array: \" << values[0];\n for (int i = 1; i < (int)experiences.size(); i++){\n cout << \", \" << values[i];\n }\n cout << endl;\n }\n\n return values;\n\n}\n\n\n/** Print the stump for debug purposes. */\nvoid Stump::printStump(){\n\n cout << \"Type: \" << type\n << \" Dim: \" << dim << \" Val: \" << val\n << \" nExperiences: \" << nExperiences ;\n\n cout << \" Left Outputs: \";\n for (std::multiset<float>::iterator j = leftOutputs.begin();\n j != leftOutputs.end(); j++){\n cout << *j << \", \";\n }\n cout << endl;\n cout << \" Right Outputs: \";\n for (std::multiset<float>::iterator j = rightOutputs.begin();\n j != rightOutputs.end(); j++){\n cout << *j << \", \";\n }\n cout << endl;\n\n}\n\n/** See if the attributes are independent of the output class\n If they are all independent, we have no more inputs to split on */\n/*\n std::vector<float> Stump::calcChiSquare(std::vector<experience> experiences){\n\n std::vector<float> chiSquare;\n chiSquare.resize(experiences[0].input.size());\n\n // for each input attribute\n for (int i = 0; i < (int)experiences[0].input.size(); i++){\n\n // attribute counts\n std::vector<float> attribValue;\n std::vector<int> attribCount;\n\n // outcome counts\n std::vector<float> outcomeValue;\n std::vector<int> outcomeCount;\n\n for (int k = 0; k < (int)experiences.size(); k++){\n\n experience* exp = &(experiences[k]);\n\n // match attribute value\n bool attribMatch = false;\n for (int l = 0; l < (int)attribValue.size(); l++){\n if (attribValue[l] == exp->input[i]){\n attribMatch = true;\n attribCount[l]++;\n break;\n }\n }\n // no match\n attribValue.push_back(exp->input[i]);\n attribCount.push_back(1);\n\n // match outcome value\n bool outcomeMatch = false;\n for (int l = 0; l < (int)outcomeValue.size(); l++){\n if (outcomeValue[l] == exp->output){\n outcomeMatch = true;\n outcomeCount[l]++;\n break;\n }\n }\n // no match\n outcomeValue.push_back(exp->output);\n outcomeCount.push_back(1);\n\n }\n\n // table\n // TODO\n\n float** actual = new float*[attribValue.size()];//[outcomeValue.size()];\n float** expected = new float*[attribValue.size()];//[outcomeValue.size()];\n for (int k = 0; k < (int)attribValue.size(); k++){\n actual[k] = new float[outcomeValue.size()];\n expected[k] = new float[outcomeValue.size()];\n }\n\n // calculate actual table\n for (int k = 0; k < (int)experiences.size(); k++){\n experience* exp = &(experiences[k]);\n\n // match\n for (int l = 0; l < (int)attribValue.size(); l++){\n if (attribValue[l] != exp->input[i])\n continue;\n for (int j = 0; j < (int)outcomeValue.size(); j++){\n if (outcomeValue[j] != exp->output)\n continue;\n actual[l][j]++;\n }\n }\n }\n\n // calculate expected table and compare\n float x2 = 0.0;\n for (int l = 0; l < (int)attribValue.size(); l++){\n for (int j = 0; j < (int)outcomeValue.size(); j++){\n\n // calculate expected\n expected[l][j] = attribCount[l] * outcomeCount[j] /\n (float)experiences.size();\n\n float cell = (actual[l][j] - expected[l][j]) *\n (actual[l][j] - expected[l][j]) / expected[i][j];\n\n x2 += cell;\n }\n }\n\n delete actual;\n delete expected;\n\n chiSquare[i] = x2;\n cout << \" Feature \" << i << \" Independence: \" << chiSquare[i] << endl;\n\n }\n return chiSquare;\n\n }\n*/\n\n// output a map of outcomes and their probabilities for this leaf node\nvoid Stump::outputProbabilities(std::multiset<float> outputs, std::map<float, float>* retval){\n if (STDEBUG) cout << \" Calculating output probs\" << endl;\n\n // go through all possible output values\n for (std::multiset<float>::iterator it = outputs.begin();\n it != outputs.end(); it = outputs.upper_bound(*it)){\n\n // get count for this value\n float val = *it;\n int count = outputs.count(val);\n if (count > 0){\n (*retval)[val] = (float) count / (float)outputs.size();\n if (STDEBUG) cout << \" Output value \" << val << \" had count of \" << count\n << \" on \"\n << outputs.size() << \" experiences and prob of \"\n << (*retval)[val] << endl;\n }\n }\n\n}\n"},{"alpha_fraction":0.701309323310852,"alphanum_fraction":0.7045826315879822,"avg_line_length":25,"blob_id":"389f8e8a1342dbabb3e19223487deb68250a7358","content_id":"ab07c880416b45c476f1a821dfd62b10871feedf","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":2444,"license_type":"no_license","max_line_length":102,"num_lines":94,"path":"/src/rl-texplore-ros-pkg-master/src/rl_env/include/rl_env/FuelRooms.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file FuelRooms.hh\n Defines the Fuel World domain, with possible noise.\n From the paper:\n Hester and Stone, \"Real Time Targeted Exploration in Large Domains\", ICDL 2010.\n \\author Todd Hester\n*/\n\n#ifndef _FUELROOMS_H_\n#define _FUELROOMS_H_\n\n#include <set>\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n#include \"gridworld.hh\"\n\n\n/** This class defines the Fuel World gridworld domain */\nclass FuelRooms: public Environment {\npublic:\n\n /** Creates a deterministic FuelRooms domain.\n \\param rand Random number generator.\n \\param extraVariation the costs of fuel stations vary even more\n \\param stoch Stochastic or deterministic\n */\n FuelRooms(Random &rand, bool extraVariation, bool stoch);\n\n virtual ~FuelRooms();\n\n virtual const std::vector<float> &sensation() const;\n virtual float apply(int action);\n\n virtual bool terminal() const;\n virtual void reset();\n\n virtual int getNumActions();\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\n virtual void getMinMaxReward(float* minR, float* maxR);\n\n virtual std::vector<experience> getSeedings();\n\n /** Get an example experience for this state-action. */\n experience getExp(int s0, int s1, int s2, int a);\n\n // stuff to check on exploration\n /** For heatmap plots: Keep track of visits to each state, total states, and fuel station states. */\n void checkVisits();\n\n /** For heatmap plots: Reset visit counts for states to 0. */\n void resetVisits();\n\n /** For heatmap plots: Print % of visits to different state types */\n void printVisits();\n\n /** For heatmap plots: Print map of visits to each state cell */\n void printVisitMap(string filename);\n\nprotected:\n typedef std::pair<float,float> coord_t;\n enum room_action_t {NORTH, NORTHEAST, EAST, SOUTHEAST, SOUTH, SOUTHWEST, WEST, NORTHWEST};\n\nprivate:\n const int height;\n const int width;\n coord_t goal;\n\n const bool extraVar;\n const bool noisy;\n Random &rng;\n\n\n std::vector<float> s;\n\n float &ns;\n float &ew;\n float &energy;\n\n // keep track of the agent's exploration so we can look at it later\n int fuelVisited;\n int totalVisited;\n \n int** stateVisits;\n\n /** Corrupts a movement action.\n \\param action The intended action\n \\return The action actually executed */\n room_action_t add_noise(room_action_t action);\n\n /** Return the correct reward based on the current state. */\n float reward(int effect);\n\n};\n\n#endif\n"},{"alpha_fraction":0.7230769395828247,"alphanum_fraction":0.7576923370361328,"avg_line_length":42.5,"blob_id":"884ac901e5dc07d5ceedb806c6f1723f53fc140e","content_id":"b32447b7fe6a87301ff882295918b84306d5104d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":260,"license_type":"no_license","max_line_length":45,"num_lines":6,"path":"/build/turtlebot3_ddpg/catkin_generated/setup_py_interrogation.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(turtlebot3_ddpg_SETUP_PY_VERSION \"1.0.0\")\nset(turtlebot3_ddpg_SETUP_PY_SCRIPTS \"\")\nset(turtlebot3_ddpg_SETUP_PY_PACKAGES \"\")\nset(turtlebot3_ddpg_SETUP_PY_PACKAGE_DIRS \"\")\nset(turtlebot3_ddpg_SETUP_PY_MODULES \"\")\nset(turtlebot3_ddpg_SETUP_PY_MODULE_DIRS \"\")"},{"alpha_fraction":0.5495814085006714,"alphanum_fraction":0.5591315627098083,"avg_line_length":26.578577041625977,"blob_id":"695ee97efa81e1917e386cedca5054a126317c06","content_id":"729e49a1470b5c7a5b5d94d58d82aee21a5f9dbb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":31413,"license_type":"no_license","max_line_length":125,"num_lines":1139,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/C45Tree.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file C45Tree.cc\n Implements the C4.5 decision tree class.\n This is an implementation of C4.5 decision trees described in:\n J. R. Quinlan, \"Induction of decision trees,\" Machine Learning, vol 1. pp 81-106, 1986.\n \\author Todd Hester\n*/\n\n#include \"C45Tree.hh\"\n\n\n\nC45Tree::C45Tree(int id, int trainMode, int trainFreq, int m,\n float featPct, Random rng):\n id(id), mode(trainMode), freq(trainFreq), M(m),\n featPct(featPct), ALLOW_ONLY_SPLITS(true), rng(rng)\n{\n\n nnodes = 0;\n nOutput = 0;\n nExperiences = 0;\n hadError = false;\n maxnodes = N_C45_NODES;\n totalnodes = 0;\n\n // how close a split has to be to be randomly selected\n SPLIT_MARGIN = 0.0; //0.02; //5; //01; //0.05; //0.2; //0.05;\n\n MIN_GAIN_RATIO = 0.0001; //0.0004; //0.001; //0.0002; //0.001;\n\n DTDEBUG = false; //true;\n SPLITDEBUG = false; //true;\n STOCH_DEBUG = false; //true; //false; //true;\n INCDEBUG = false; //true; //false; //true;\n NODEDEBUG = false;\n COPYDEBUG = false;\n\n cout << \"Created C4.5 decision tree \" << id;\n if (ALLOW_ONLY_SPLITS) cout << \" with == and > splits\" << endl;\n else cout << \" with > splits only\" << endl;\n if (DTDEBUG) {\n cout << \" mode: \" << mode << \" freq: \" << freq << endl;\n }\n\n initNodes();\n initTree();\n\n}\n\nC45Tree::C45Tree(const C45Tree &t):\n id(t.id), mode(t.mode), freq(t.freq), M(t.M),\n featPct(t.featPct), ALLOW_ONLY_SPLITS(t.ALLOW_ONLY_SPLITS), rng(t.rng)\n{\n COPYDEBUG = t.COPYDEBUG;\n if (COPYDEBUG) cout << \" C4.5 tree copy constructor id \" << id << endl;\n nnodes = 0;\n nOutput = t.nOutput;\n nExperiences = t.nExperiences;\n hadError = t.hadError;\n maxnodes = t.maxnodes;\n totalnodes = 0;\n\n SPLIT_MARGIN = t.SPLIT_MARGIN;\n MIN_GAIN_RATIO = t.MIN_GAIN_RATIO;\n DTDEBUG = t.DTDEBUG;\n SPLITDEBUG = t.SPLITDEBUG;\n STOCH_DEBUG = t.STOCH_DEBUG;\n INCDEBUG = t.INCDEBUG;\n NODEDEBUG = t.NODEDEBUG;\n \n \n if (COPYDEBUG) cout << \" C4.5 copy nodes, experiences, root, etc\" << endl;\n // copy all experiences\n for (int i = 0; i < N_C45_EXP; i++){\n allExp[i] = t.allExp[i];\n }\n if (COPYDEBUG) cout << \" C4.5 copied exp array\" << endl;\n\n // set experience pointers\n experiences.resize(t.experiences.size());\n for (unsigned i = 0; i < t.experiences.size(); i++){\n experiences[i] = &(allExp[i]);\n }\n if (COPYDEBUG) cout << \" C4.5 set experience pointers\" << endl;\n\n // now the tricky part, set the pointers inside the tree nodes correctly\n initNodes();\n\n if (COPYDEBUG) cout << \" C4.5 copy tree \" << endl;\n root = allocateNode();\n lastNode = root;\n copyTree(root, t.root);\n if (COPYDEBUG) cout << \" C4.5 tree copy done\" << endl;\n \n if (COPYDEBUG) {\n cout << endl << \"New tree: \" << endl;\n printTree(root, 0);\n cout << endl;\n cout << \" c4.5 copy done\" << endl;\n }\n\n}\n\nC45Tree* C45Tree::getCopy(){\n \n C45Tree* copy = new C45Tree(*this);\n return copy;\n\n}\n\nvoid C45Tree::copyTree(tree_node* newNode, tree_node* origNode){\n\n int nodeId = newNode->id;\n\n if (COPYDEBUG) {\n cout << \" Copy node \" << newNode->id << \" from node \" << origNode->id << endl;\n cout << \" NewAddy \" << newNode << \", old: \" << origNode << endl;\n }\n\n // copy node from t\n *newNode = *origNode;\n newNode->id = nodeId;\n\n // if it has children, allocate and copy them too\n if (origNode->l != NULL && !newNode->leaf){\n newNode->l = allocateNode();\n if (COPYDEBUG) cout << \" Copy left node \" << newNode->l->id << \" from \" << origNode->l->id << endl;\n copyTree(newNode->l, origNode->l);\n } else {\n newNode->l = NULL;\n }\n\n if (origNode->r != NULL && !newNode->leaf){\n newNode->r = allocateNode();\n if (COPYDEBUG) cout << \" Copy right node \" << newNode->r->id << \" from \" << origNode->r->id << endl;\n copyTree(newNode->r, origNode->r);\n } else {\n newNode->r = NULL;\n }\n}\n\n\nC45Tree::~C45Tree() {\n deleteTree(root);\n for (unsigned i = N_C45_EXP; i < experiences.size(); i++){\n delete experiences[i];\n }\n experiences.clear();\n}\n\n// here the target output will be a single value\nbool C45Tree::trainInstance(classPair &instance){\n\n if (DTDEBUG) cout << \"trainInstance\" << endl;\n\n bool modelChanged = false;\n\n // simply add this instance to the set of experiences\n\n // take from static array until we run out\n tree_experience *e;\n if (nExperiences < N_C45_EXP){\n // from statically created set of experiences\n e = &(allExp[nExperiences]);\n\n } else {\n // dynamically create experience\n e = new tree_experience;\n }\n\n\n e->input = instance.in;\n e->output = instance.out;\n experiences.push_back(e);\n nExperiences++;\n\n if (nExperiences == 1000000){\n cout << \"Reached limit of # experiences allowed.\" << endl;\n return false;\n }\n\n if (nExperiences != (int)experiences.size())\n cout << \"ERROR: experience size mismatch: \" << nExperiences << \", \" << experiences.size() << endl;\n\n //cout << nExperiences << endl << flush;\n //if (nExperiences == 503 && id == 10){\n // DTDEBUG = true;\n // SPLITDEBUG = true;\n // INCDEBUG = true;\n //}\n\n if (DTDEBUG) {\n cout << \"Original input: \";\n for (unsigned i = 0; i < instance.in.size(); i++){\n cout << instance.in[i] << \", \";\n }\n cout << endl << \" Original output: \" << instance.out << endl;\n cout << \"Added exp id: \" << nExperiences << \" output: \" << e->output << endl;\n cout << \"Address: \" << e << \" Input : \";\n for (unsigned i = 0; i < e->input.size(); i++){\n cout << e->input[i] << \", \";\n }\n cout << endl << \" Now have \" << nExperiences << \" experiences.\" << endl;\n }\n\n // depending on mode/etc, maybe re-build tree\n\n // mode 0: re-build every step\n if (mode == BUILD_EVERY || nExperiences <= 1){\n modelChanged = rebuildTree();\n }\n\n // mode 1: re-build on error only\n else if (mode == BUILD_ON_ERROR){\n\n // build on misclassification\n // check for misclassify\n // get leaf\n tree_node* leaf = traverseTree(root, e->input);\n // find probability for this output\n float count = (float)leaf->outputs[e->output];\n float outputProb = count / (float)leaf->nInstances;\n\n if (outputProb < 0.75){\n modelChanged = rebuildTree();\n modelChanged = true;\n }\n }\n\n // mode 2: re-build every FREQ steps\n else if (mode == BUILD_EVERY_N){\n // build every freq steps\n if (!modelChanged && (nExperiences % freq) == 0){\n modelChanged = rebuildTree();\n }\n }\n\n if (modelChanged){\n if (DTDEBUG) cout << \"DT \" << id << \" tree re-built.\" << endl;\n\n if (DTDEBUG){\n cout << endl << \"DT: \" << id << endl;\n printTree(root, 0);\n cout << \"Done printing tree\" << endl;\n }\n }\n\n /*\n if (nExperiences % 50 == 0){\n cout << endl << \"DT: \" << id << endl;\n printTree(root, 0);\n cout << \"Done printing tree\" << endl;\n }\n */\n\n return modelChanged;\n\n}\n\n\n// here the target output will be a single value\nbool C45Tree::trainInstances(std::vector<classPair> &instances){\n if (DTDEBUG) cout << \"DT \" << id << \" trainInstances: \" << instances.size() << endl;\n\n bool modelChanged = false;\n\n bool doBuild = false;\n\n bool buildOnError = false;\n\n // loop through instances, possibly checking for errors\n for (unsigned a = 0; a < instances.size(); a++){\n classPair instance = instances[a];\n\n // simply add this instance to the set of experiences\n\n // take from static array until we run out\n tree_experience *e;\n if (nExperiences < N_C45_EXP){\n // from statically created set of experiences\n e = &(allExp[nExperiences]);\n\n } else {\n // dynamically create experience\n e = new tree_experience;\n }\n\n\n e->input = instance.in;\n e->output = instance.out;\n experiences.push_back(e);\n nExperiences++;\n\n if (nExperiences == 1000000){\n cout << \"Reached limit of # experiences allowed.\" << endl;\n return false;\n }\n\n if (nExperiences != (int)experiences.size())\n cout << \"ERROR: experience size mismatch: \" << nExperiences << \", \" << experiences.size() << endl;\n\n if (DTDEBUG) {\n cout << \"Original input: \";\n for (unsigned i = 0; i < instance.in.size(); i++){\n cout << instance.in[i] << \", \";\n }\n cout << endl << \" Original output: \" << instance.out << endl;\n cout << \"Added exp id: \" << nExperiences << \" output: \" << e->output << endl;\n cout << \"Address: \" << e << \" Input : \";\n for (unsigned i = 0; i < e->input.size(); i++){\n cout << e->input[i] << \", \";\n }\n cout << endl << \" Now have \" << nExperiences << \" experiences.\" << endl;\n }\n\n // depending on mode/etc, maybe re-build tree\n\n // don't need to check if we've already decided\n if (doBuild) continue;\n\n // mode 0: re-build every step\n if (mode == BUILD_EVERY || nExperiences <= 1){\n doBuild = true;\n }\n\n // mode 1: re-build on error only\n else if (mode == BUILD_ON_ERROR){\n\n // build on misclassification\n // check for misclassify\n // get leaf\n tree_node* leaf = traverseTree(root, e->input);\n // find probability for this output\n float count = (float)leaf->outputs[e->output];\n float outputProb = count / (float)leaf->nInstances;\n\n if (outputProb < 0.75){\n doBuild = true;\n buildOnError = true;\n }\n }\n\n // mode 2: re-build every FREQ steps\n else if (mode == BUILD_EVERY_N){\n // build every freq steps\n if (!modelChanged && (nExperiences % freq) == 0){\n doBuild = true;\n }\n }\n\n } // loop of new instances\n\n if (DTDEBUG) cout << \"Added \" << instances.size() << \" new instances. doBuild = \" << doBuild << endl;\n\n if (doBuild){\n modelChanged = rebuildTree();\n }\n\n if (modelChanged){\n if (DTDEBUG) cout << \"DT \" << id << \" tree re-built.\" << endl;\n\n if (DTDEBUG){\n cout << endl << \"DT: \" << id << endl;\n printTree(root, 0);\n cout << \"Done printing tree\" << endl;\n }\n }\n\n return (modelChanged || buildOnError);\n\n}\n\n\nbool C45Tree::rebuildTree(){\n return buildTree(root, experiences, false);\n}\n\n\n// TODO: here we want to return the probability of the output value being each of the possible values, in the stochastic case\nvoid C45Tree::testInstance(const std::vector<float> &input, std::map<float, float>* retval){\n if (DTDEBUG) cout << \"testInstance\" << endl;\n\n retval->clear();\n\n // in case the tree is empty\n if (experiences.size() == 0){\n (*retval)[0.0] = 1.0;\n return;\n }\n\n // follow through tree to leaf\n tree_node* leaf = traverseTree(root, input);\n lastNode = leaf;\n\n // and return mapping of outputs and their probabilities\n outputProbabilities(leaf, retval);\n\n}\n\nfloat C45Tree::getConf(const std::vector<float> &input){\n if (DTDEBUG) cout << \"numVisits\" << endl;\n\n // in case the tree is empty\n if (experiences.size() == 0){\n return 0;\n }\n\n // follow through tree to leaf\n //tree_node* leaf = traverseTree(root, input);\n\n // and return # in this leaf\n float conf = (float)lastNode->nInstances / (float)(2.0*M);\n if (conf > 1.0)\n return 1.0;\n else\n return conf;\n\n}\n\n// check to see if this state is one we should explore\n// to get more info on potential splits\n\n\n// init the tree\nvoid C45Tree::initTree(){\n if (DTDEBUG) cout << \"initTree()\" << endl;\n root = allocateNode();\n\n if (DTDEBUG) cout << \" root id = \" << root->id << endl;\n\n // just to ensure the diff models are on different random values\n for (int i = 0; i < id; i++){\n rng.uniform(0, 1);\n }\n\n}\n\n\n\n// init a tree node\nvoid C45Tree::initTreeNode(tree_node* node){\n if (DTDEBUG) cout << \"initTreeNode()\";\n\n node->id = nnodes++;\n if (DTDEBUG) cout << \" id = \" << node->id << endl;\n\n totalnodes++;\n if (totalnodes > maxnodes){\n maxnodes = totalnodes;\n if (DTDEBUG) cout << id << \" C4.5 MAX nodes: \" << maxnodes << endl;\n }\n\n // split criterion\n node->dim = -1;\n node->val = -1;\n node->type = -1;\n\n // current data\n node->nInstances = 0;\n node->outputs.clear();\n\n // next nodes in tree\n node->l = NULL;\n node->r = NULL;\n\n node->leaf = true;\n\n}\n\nvoid C45Tree::deleteTree(tree_node* node){\n if (DTDEBUG) cout << \"deleteTree, node=\" << node->id << endl;\n\n if (node==NULL)\n return;\n\n totalnodes--;\n\n node->nInstances = 0;\n node->outputs.clear();\n\n //recursively call deleteTree on children\n // then delete them\n if (!node->leaf){\n // left child\n if (node->l != NULL){\n deleteTree(node->l);\n deallocateNode(node->l);\n node->l = NULL;\n }\n\n // right child\n if (node->r != NULL){\n deleteTree(node->r);\n deallocateNode(node->r);\n node->r = NULL;\n }\n }\n\n node->leaf = true;\n node->dim = -1;\n\n}\n\n\nC45Tree::tree_node* C45Tree::getCorrectChild(tree_node* node,\n const std::vector<float> &input){\n\n if (DTDEBUG) cout << \"getCorrectChild, node=\" << node->id << endl;\n\n if (passTest(node->dim, node->val, node->type, input))\n return node->l;\n else\n return node->r;\n\n}\n\nC45Tree::tree_node* C45Tree::traverseTree(tree_node* node,\n const std::vector<float> &input){\n\n if (DTDEBUG) cout << \"traverseTree, node=\" << node->id << endl;\n\n while (!node->leaf){\n node = getCorrectChild(node, input);\n }\n\n return node;\n}\n\n\nbool C45Tree::passTest(int dim, float val, bool type, const std::vector<float> &input){\n if (DTDEBUG) cout << \"passTest, dim=\" << dim << \",val=\" << val << \",type=\" << type\n << \",input[\"<<dim<<\"]=\" << input[dim] <<endl;\n\n if (type == CUT){\n if (input[dim] > val)\n return false;\n else\n return true;\n } else if (type == ONLY){\n if (input[dim] == val)\n return false;\n else\n return true;\n } else {\n return false;\n }\n\n}\n\n\nbool C45Tree::buildTree(tree_node *node,\n const std::vector<tree_experience*> &instances,\n bool changed){\n if(DTDEBUG) cout << \"buildTree, node=\" << node->id\n << \",nInstances:\" << instances.size() \n << \",chg:\" << changed << endl;\n\n if (instances.size() == 0){\n cout << \"Error: buildTree called on tree \" << id << \" node \" << node->id << \" with no instances.\" << endl;\n exit(-1);\n }\n\n\n // TODO: what about stochastic data?\n //std::vector<float> chiSquare = calcChiSquare(instances);\n\n // first, add instances to tree\n node->nInstances = instances.size();\n\n // add each output to this node\n node->outputs.clear();\n for (unsigned i = 0; i < instances.size(); i++){\n node->outputs[instances[i]->output]++;\n }\n\n // see if they're all the same\n if (node->outputs.size() == 1){\n bool change = makeLeaf(node);\n if (DTDEBUG){\n cout << \"All \" << node->nInstances\n << \" classified with output \"\n << instances[0]->output << endl;\n }\n return change;\n }\n\n // if not, calculate gain ratio to determine best split\n else {\n\n if (SPLITDEBUG) cout << endl << \"Creating new decision node\" << endl;\n\n //node->leaf = false;\n //node->nInstances++;\n\n float bestGainRatio = -1.0;\n int bestDim = -1;\n float bestVal = -1;\n bool bestType = 0;\n std::vector<tree_experience*> bestLeft;\n std::vector<tree_experience*> bestRight;\n\n testPossibleSplits(instances, &bestGainRatio, &bestDim, &bestVal, &bestType, &bestLeft, &bestRight);\n\n return implementSplit(node, bestGainRatio, bestDim, bestVal, bestType, bestLeft, bestRight, changed);\n\n }\n\n}\n\n\nbool C45Tree::makeLeaf(tree_node* node){\n\n // check on children\n if (node->l != NULL){\n deleteTree(node->l);\n deallocateNode(node->l);\n node->l = NULL;\n }\n\n if (node->r != NULL){\n deleteTree(node->r);\n deallocateNode(node->r);\n node->r = NULL;\n }\n\n // changed from not leaf to leaf, or just init'd\n bool change = (!node->leaf || node->type < 0);\n\n node->leaf = true;\n node->type = 0;\n\n return change;\n}\n\nbool C45Tree::implementSplit(tree_node* node, float bestGainRatio, int bestDim,\n float bestVal, bool bestType,\n const std::vector<tree_experience*> &bestLeft, \n const std::vector<tree_experience*> &bestRight,\n bool changed){\n if (DTDEBUG) cout << \"implementSplit node=\" << node->id << \",gainRatio=\" << bestGainRatio\n << \",dim=\" << bestDim\n << \",val=\" << bestVal << \",type=\" << bestType \n << \",chg=\" << changed << endl;\n\n\n // see if this should still be a leaf node\n if (bestGainRatio < MIN_GAIN_RATIO){\n bool change = makeLeaf(node);\n if (SPLITDEBUG || STOCH_DEBUG){\n cout << \"DT \" << id << \" Node \" << node->id << \" Poor gain ratio: \"\n\t << bestGainRatio << \", \" << node->nInstances\n << \" instances classified at leaf \" << node->id\n << \" with multiple outputs \" << endl;\n }\n return change;\n }\n\n // see if this split changed or not\n // assuming no changes above\n if (!changed && node->dim == bestDim && node->val == bestVal\n && node->type == bestType && !node->leaf\n && node->l != NULL && node->r != NULL){\n // same split as before.\n if (DTDEBUG || SPLITDEBUG) cout << \"Same split as before\" << endl;\n bool changeL = false;\n bool changeR = false;\n\n // see which leaf changed\n if (bestLeft.size() > (unsigned)node->l->nInstances){\n // redo left side\n if (DTDEBUG) cout << \"Rebuild left side of tree\" << endl;\n changeL = buildTree(node->l, bestLeft, changed);\n }\n\n if (bestRight.size() > (unsigned)node->r->nInstances){\n // redo right side\n if (DTDEBUG) cout << \"Rebuild right side of tree\" << endl;\n changeR = buildTree(node->r, bestRight, changed);\n }\n\n // everything up to here is the same, check if there were changes below\n return (changeL || changeR);\n }\n\n // totally new\n // set the best split here\n node->leaf = false;\n node->dim = bestDim;\n node->val = bestVal;\n node->type = bestType;\n\n if (SPLITDEBUG) cout << \"Best split was type \" << node->type\n << \" with val \" << node->val\n << \" on dim \" << node->dim\n << \" with gainratio: \" << bestGainRatio << endl;\n\n if (DTDEBUG) cout << \"Left has \" << bestLeft.size()\n << \", right has \" << bestRight.size() << endl;\n\n // make sure both instances\n if (bestLeft.size() == 0 || bestRight.size() == 0){\n cout << \"ERROR: DT \" << id << \" node \" << node->id << \" has 0 instances: left: \" << bestLeft.size()\n << \" right: \" << bestRight.size() << endl;\n cout << \"Split was type \" << node->type\n << \" with val \" << node->val\n << \" on dim \" << node->dim\n << \" with gainratio: \" << bestGainRatio << endl;\n exit(-1);\n }\n\n\n // check if these already exist\n if (node->l == NULL){\n if (DTDEBUG) cout << \"Init new left tree nodes \" << endl;\n node->l = allocateNode();\n }\n if (node->r == NULL){\n if (DTDEBUG) cout << \"Init new right tree nodes \" << endl;\n node->r = allocateNode();\n }\n\n // recursively build the sub-trees to this one\n if (DTDEBUG) cout << \"Building left tree for node \" << node->id << endl;\n buildTree(node->l, bestLeft, true);\n if (DTDEBUG) cout << \"Building right tree for node \" << node->id << endl;\n buildTree(node->r, bestRight, true);\n\n // this one changed, or above changed, no reason to check change of lower parts\n return true;\n \n}\n\n\n\nvoid C45Tree::testPossibleSplits(const std::vector<tree_experience*> &instances, \n float *bestGainRatio, int *bestDim,\n float *bestVal, bool *bestType, \n std::vector<tree_experience*> *bestLeft,\n std::vector<tree_experience*> *bestRight) {\n if (DTDEBUG) cout << \"testPossibleSplits\" << endl;\n\n\n // pre-calculate some stuff for these splits (namely I, P, C)\n float I = calcIforSet(instances);\n //if (DTDEBUG) cout << \"I: \" << I << endl;\n\n int nties = 0;\n\n // for each possible split, calc gain ratio\n for (unsigned idim = 0; idim < instances[0]->input.size(); idim++){\n\n //float* sorted = sortOnDim(idim, instances);\n float minVal, maxVal;\n std::set<float> uniques = getUniques(idim, instances, minVal, maxVal);\n\n for (std::set<float>::iterator j = uniques.begin(); j != uniques.end(); j++){\n\n // skip max val, not a valid cut for either\n if ((*j) == maxVal)\n continue;\n \n // if this is a random forest, we eliminate some random number of splits\n // here (decision is taken from the random set that are left)\n if (rng.uniform() < featPct)\n continue;\n\n std::vector<tree_experience*> left;\n std::vector<tree_experience*> right;\n\n // splits that are cuts\n float splitval = (*j);\n float gainRatio = calcGainRatio(idim, splitval, CUT, instances, I, left, right);\n\n if (SPLITDEBUG) cout << \" CUT split val \" << splitval\n << \" on dim: \" << idim << \" had gain ratio \"\n << gainRatio << endl;\n\n // see if this is the new best gain ratio\n compareSplits(gainRatio, idim, splitval, CUT, left, right, &nties,\n bestGainRatio, bestDim, bestVal, bestType, bestLeft, bestRight);\n\n\n // no minval here, it would be the same as the cut split on minval\n if (ALLOW_ONLY_SPLITS && (*j) != minVal){\n // splits that are true only if this value is equal\n float splitval = (*j);\n\n float gainRatio = calcGainRatio(idim, splitval, ONLY, instances, I, left, right);\n\n if (SPLITDEBUG) cout << \" ONLY split val \" << splitval\n << \" on dim: \" << idim << \" had gain ratio \"\n << gainRatio << endl;\n\n // see if this is the new best gain ratio\n compareSplits(gainRatio, idim, splitval, ONLY, left, right, &nties,\n bestGainRatio, bestDim, bestVal, bestType, bestLeft, bestRight);\n\n } // splits with only\n\n } // j loop\n }\n}\n\n\n\nvoid C45Tree::compareSplits(float gainRatio, int dim, float val, bool type,\n const std::vector<tree_experience*> &left, \n const std::vector<tree_experience*> &right,\n int *nties, float *bestGainRatio, int *bestDim,\n float *bestVal, bool *bestType,\n std::vector<tree_experience*> *bestLeft, std::vector<tree_experience*> *bestRight){\n if (DTDEBUG) cout << \"compareSplits gainRatio=\" << gainRatio << \",dim=\" << dim\n << \",val=\" << val << \",type= \" << type <<endl;\n\n\n bool newBest = false;\n\n // if its a virtual tie, break it randomly\n if (fabs(*bestGainRatio - gainRatio) < SPLIT_MARGIN){\n //cout << \"Split tie, making random decision\" << endl;\n\n (*nties)++;\n float randomval = rng.uniform(0,1);\n float newsplitprob = (1.0 / (float)*nties);\n\n if (randomval < newsplitprob){\n newBest = true;\n if (SPLITDEBUG) cout << \" Tie on split. DT: \" << id << \" rand: \" << randomval\n << \" splitProb: \" << newsplitprob << \", selecting new split \" << endl;\n }\n else\n if (SPLITDEBUG) cout << \" Tie on split. DT: \" << id << \" rand: \" << randomval\n << \" splitProb: \" << newsplitprob << \", staying with old split \" << endl;\n }\n\n // if its clearly better, set this as the best split\n else if (gainRatio > *bestGainRatio){\n newBest = true;\n *nties = 1;\n }\n\n\n // set the split features\n if (newBest){\n *bestGainRatio = gainRatio;\n *bestDim = dim;\n *bestVal = val;\n *bestType = type;\n *bestLeft = left;\n *bestRight = right;\n if (SPLITDEBUG){\n cout << \" New best gain ratio: \" << *bestGainRatio\n << \": type \" << *bestType\n << \" with val \" << *bestVal\n << \" on dim \" << *bestDim << endl;\n }\n } // newbest\n}\n\nfloat C45Tree::calcGainRatio(int dim, float val, bool type,\n const std::vector<tree_experience*> &instances,\n float I,\n std::vector<tree_experience*> &left,\n std::vector<tree_experience*> &right){\n if (DTDEBUG) cout << \"calcGainRatio, dim=\" << dim\n << \" val=\" << val\n << \" I=\" << I\n << \" nInstances= \" << instances.size() << endl;\n\n left.clear();\n right.clear();\n\n // array with percentage positive and negative for this test\n float D[2];\n\n // info(T) = I(P): float I;\n\n // Info for this split = Info(X,T)\n float Info;\n\n // Gain for this split = Gain(X,T)\n float Gain;\n\n // SplitInfo for this split = I(|pos|/|T|, |neg|/|T|)\n float SplitInfo;\n\n // GainRatio for this split = GainRatio(X,T) = Gain(X,T) / SplitInfo(X,T)\n float GainRatio;\n\n // see where the instances would go with this split\n for (unsigned i = 0; i < instances.size(); i++){\n if (DTDEBUG) cout << \"calcGainRatio - Classify instance \" << i \n << \" on new split \" << endl;\n\n if (passTest(dim, val, type, instances[i]->input)){\n left.push_back(instances[i]);\n }\n else{\n right.push_back(instances[i]);\n }\n }\n\n if (DTDEBUG) cout << \"Left has \" << left.size()\n << \", right has \" << right.size() << endl;\n\n D[0] = (float)left.size() / (float)instances.size();\n D[1] = (float)right.size() / (float)instances.size();\n float leftInfo = calcIforSet(left);\n float rightInfo = calcIforSet(right);\n Info = D[0] * leftInfo + D[1] * rightInfo;\n Gain = I - Info;\n SplitInfo = calcIofP((float*)&D, 2);\n GainRatio = Gain / SplitInfo;\n\n if (DTDEBUG){\n cout << \"LeftInfo: \" << leftInfo\n << \" RightInfo: \" << rightInfo\n << \" Info: \" << Info\n << \" Gain: \" << Gain\n << \" SplitInfo: \" << SplitInfo\n << \" GainRatio: \" << GainRatio\n << endl;\n }\n\n return GainRatio;\n\n}\n\nfloat C45Tree::calcIofP(float* P, int size){\n if (DTDEBUG) cout << \"calcIofP, size=\" << size << endl;\n float I = 0;\n for (int i = 0; i < size; i++){\n I -= P[i] * log(P[i]);\n }\n return I;\n}\n\nfloat C45Tree::calcIforSet(const std::vector<tree_experience*> &instances){\n if (DTDEBUG) cout << \"calcIforSet\" << endl;\n\n std::map<float, int> classes;\n\n // go through instances and figure count of each type\n for (unsigned i = 0; i < instances.size(); i++){\n // increment count for this value\n float val = instances[i]->output;\n classes[val]++;\n }\n\n // now calculate P\n float Pval;\n float I = 0;\n for (std::map<float, int>::iterator i = classes.begin(); i != classes.end(); i++){\n Pval = (float)(*i).second / (float)instances.size();\n // calc I of P\n I -= Pval * log(Pval);\n }\n\n return I;\n\n}\n\nstd::set<float> C45Tree::getUniques(int dim, const std::vector<tree_experience*> &instances, float& minVal, float& maxVal){\n if (DTDEBUG) cout << \"getUniques,dim = \" << dim;\n\n std::set<float> uniques;\n\n for (int i = 0; i < (int)instances.size(); i++){\n if (i == 0 || instances[i]->input[dim] < minVal)\n minVal = instances[i]->input[dim];\n if (i == 0 || instances[i]->input[dim] > maxVal)\n maxVal = instances[i]->input[dim];\n\n uniques.insert(instances[i]->input[dim]);\n }\n\n if (DTDEBUG) cout << \" #: \" << uniques.size() << endl;\n return uniques;\n}\n\n\nfloat* C45Tree::sortOnDim(int dim, const std::vector<tree_experience*> &instances){\n if (DTDEBUG) cout << \"sortOnDim,dim = \" << dim << endl;\n\n float* values = new float[instances.size()];\n\n for (int i = 0; i < (int)instances.size(); i++){\n //cout << \"Instance \" << i << endl;\n\n float val = instances[i]->input[dim];\n //cout << \" val: \" << val << endl;\n\n // find where this should go\n for (int j = 0; j <= i; j++){\n //cout << \" j: \" << j << endl;\n\n // get to i, this is the spot then\n if (j==i){\n values[j] = val;\n //cout << \" At i, putting value in slot j: \" << j << endl;\n }\n\n // if this is the spot\n else if (val < values[j]){\n //cout << \" Found slot at j: \" << j << endl;\n\n // slide everything forward to make room\n for (int k = i; k > j; k--){\n //cout << \" k = \" << k << \" Sliding value from k-1 to k\" << endl;\n values[k] = values[k-1];\n }\n\n // put value in its spot at j\n //cout << \" Putting value at slot j: \" << j << endl;\n values[j] = val;\n\n // break\n break;\n }\n\n }\n }\n\n if (DTDEBUG){\n cout << \"Sorted array: \" << values[0];\n for (int i = 1; i < (int)instances.size(); i++){\n cout << \", \" << values[i];\n }\n cout << endl;\n }\n\n return values;\n\n}\n\n\nvoid C45Tree::printTree(tree_node *t, int level){\n\n for (int i = 0; i < level; i++){\n cout << \".\";\n }\n\n cout << \"Node \" << t->id;\n if (t->type == C45Tree::CUT) cout << \" Type: CUT\"; \n else cout << \" Type: ONLY\";\n cout << \" Dim: \" << t->dim << \" Val: \" << t->val\n << \" nInstances: \" << t->nInstances ;\n \n if (t->leaf){\n cout << \" Outputs: \";\n for (std::map<float, int>::iterator j = t->outputs.begin();\n j != t->outputs.end(); j++){\n cout << (*j).first << \": \" << (*j).second << \", \";\n }\n cout << endl;\n }\n else\n cout << \" Left: \" << t->l->id << \" Right: \" << t->r->id << endl;\n\n\n // print children\n if (t->dim != -1 && !t->leaf){\n printTree(t->l, level+1);\n printTree(t->r, level+1);\n }\n\n}\n\n\n\n\n\n// output a map of outcomes and their probabilities for this leaf node\nvoid C45Tree::outputProbabilities(tree_node* leaf, std::map<float, float>* retval){\n if (STOCH_DEBUG) cout << \"Calculating output probs for leaf \" << leaf->id << endl;\n\n // go through all output values at this leaf, turn into probabilities\n for (std::map<float, int>::iterator it = leaf->outputs.begin();\n it != leaf->outputs.end(); it++){\n\n float val = (*it).first;\n float count = (float)(*it).second;\n if (count > 0)\n (*retval)[val] = count / (float)leaf->nInstances;\n\n if (STOCH_DEBUG) \n cout << \"Output value \" << val << \" had count of \" << count << \" on \"\n << leaf->nInstances <<\" instances and prob of \" \n << (*retval)[val] << endl;\n }\n\n}\n\n\nvoid C45Tree::initNodes(){\n\n for (int i = 0; i < N_C45_NODES; i++){\n initTreeNode(&(allNodes[i]));\n freeNodes.push_back(i);\n if (NODEDEBUG) \n cout << \"init node \" << i << \" with id \" << allNodes[i].id \n << \", now \" << freeNodes.size() << \" free nodes.\" << endl;\n }\n\n}\n\nC45Tree::tree_node* C45Tree::allocateNode(){\n if (freeNodes.empty()){\n tree_node* newNode = new tree_node;\n initTreeNode(newNode);\n if (NODEDEBUG) \n cout << id << \" PROBLEM: No more pre-allocated nodes!!!\" << endl\n << \"return new node \" << newNode->id \n << \", now \" << freeNodes.size() << \" free nodes.\" << endl;\n return newNode;\n }\n\n int i = freeNodes.back();\n freeNodes.pop_back();\n if (NODEDEBUG) \n cout << id << \" allocate node \" << i << \" with id \" << allNodes[i].id \n << \", now \" << freeNodes.size() << \" free nodes.\" << endl;\n return &(allNodes[i]);\n}\n\nvoid C45Tree::deallocateNode(tree_node* node){\n\n if (node->id >= N_C45_NODES){\n if (NODEDEBUG) \n cout << id << \" dealloc extra node id \" << node->id \n << \", now \" << freeNodes.size() << \" free nodes.\" << endl;\n delete node;\n return;\n }\n\n freeNodes.push_back(node->id);\n if (NODEDEBUG) \n cout << id << \" dealloc node \" << node->id \n << \", now \" << freeNodes.size() << \" free nodes.\" << endl;\n}\n\n"},{"alpha_fraction":0.7576243877410889,"alphanum_fraction":0.7654207944869995,"avg_line_length":47.988765716552734,"blob_id":"e8b9b102c53e3ba0e977eae0dd1fd3fb9bde4796","content_id":"62f84013087c68290cd986b3335e3f793e635e38","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":4361,"license_type":"no_license","max_line_length":188,"num_lines":89,"path":"/build/homework2/cmake_install.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"# Install script for directory: /home/justin/ros_test/src/homework2\n\n# Set the install prefix\nif(NOT DEFINED CMAKE_INSTALL_PREFIX)\n set(CMAKE_INSTALL_PREFIX \"/home/justin/ros_test/install\")\nendif()\nstring(REGEX REPLACE \"/$\" \"\" CMAKE_INSTALL_PREFIX \"${CMAKE_INSTALL_PREFIX}\")\n\n# Set the install configuration name.\nif(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME)\n if(BUILD_TYPE)\n string(REGEX REPLACE \"^[^A-Za-z0-9_]+\" \"\"\n CMAKE_INSTALL_CONFIG_NAME \"${BUILD_TYPE}\")\n else()\n set(CMAKE_INSTALL_CONFIG_NAME \"\")\n endif()\n message(STATUS \"Install configuration: \\\"${CMAKE_INSTALL_CONFIG_NAME}\\\"\")\nendif()\n\n# Set the component getting installed.\nif(NOT CMAKE_INSTALL_COMPONENT)\n if(COMPONENT)\n message(STATUS \"Install component: \\\"${COMPONENT}\\\"\")\n set(CMAKE_INSTALL_COMPONENT \"${COMPONENT}\")\n else()\n set(CMAKE_INSTALL_COMPONENT)\n endif()\nendif()\n\n# Install shared libraries without execute permission?\nif(NOT DEFINED CMAKE_INSTALL_SO_NO_EXE)\n set(CMAKE_INSTALL_SO_NO_EXE \"1\")\nendif()\n\n# Is this installation the result of a crosscompile?\nif(NOT DEFINED CMAKE_CROSSCOMPILING)\n set(CMAKE_CROSSCOMPILING \"FALSE\")\nendif()\n\nif(\"x${CMAKE_INSTALL_COMPONENT}x\" STREQUAL \"xUnspecifiedx\" OR NOT CMAKE_INSTALL_COMPONENT)\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/share/homework2/srv\" TYPE FILE FILES \"/home/justin/ros_test/src/homework2/srv/string_cat.srv\")\nendif()\n\nif(\"x${CMAKE_INSTALL_COMPONENT}x\" STREQUAL \"xUnspecifiedx\" OR NOT CMAKE_INSTALL_COMPONENT)\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/share/homework2/cmake\" TYPE FILE FILES \"/home/justin/ros_test/build/homework2/catkin_generated/installspace/homework2-msg-paths.cmake\")\nendif()\n\nif(\"x${CMAKE_INSTALL_COMPONENT}x\" STREQUAL \"xUnspecifiedx\" OR NOT CMAKE_INSTALL_COMPONENT)\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/include\" TYPE DIRECTORY FILES \"/home/justin/ros_test/devel/include/homework2\")\nendif()\n\nif(\"x${CMAKE_INSTALL_COMPONENT}x\" STREQUAL \"xUnspecifiedx\" OR NOT CMAKE_INSTALL_COMPONENT)\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/share/roseus/ros\" TYPE DIRECTORY FILES \"/home/justin/ros_test/devel/share/roseus/ros/homework2\")\nendif()\n\nif(\"x${CMAKE_INSTALL_COMPONENT}x\" STREQUAL \"xUnspecifiedx\" OR NOT CMAKE_INSTALL_COMPONENT)\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/share/common-lisp/ros\" TYPE DIRECTORY FILES \"/home/justin/ros_test/devel/share/common-lisp/ros/homework2\")\nendif()\n\nif(\"x${CMAKE_INSTALL_COMPONENT}x\" STREQUAL \"xUnspecifiedx\" OR NOT CMAKE_INSTALL_COMPONENT)\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/share/gennodejs/ros\" TYPE DIRECTORY FILES \"/home/justin/ros_test/devel/share/gennodejs/ros/homework2\")\nendif()\n\nif(\"x${CMAKE_INSTALL_COMPONENT}x\" STREQUAL \"xUnspecifiedx\" OR NOT CMAKE_INSTALL_COMPONENT)\n execute_process(COMMAND \"/usr/bin/python2\" -m compileall \"/home/justin/ros_test/devel/lib/python2.7/dist-packages/homework2\")\nendif()\n\nif(\"x${CMAKE_INSTALL_COMPONENT}x\" STREQUAL \"xUnspecifiedx\" OR NOT CMAKE_INSTALL_COMPONENT)\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/lib/python2.7/dist-packages\" TYPE DIRECTORY FILES \"/home/justin/ros_test/devel/lib/python2.7/dist-packages/homework2\")\nendif()\n\nif(\"x${CMAKE_INSTALL_COMPONENT}x\" STREQUAL \"xUnspecifiedx\" OR NOT CMAKE_INSTALL_COMPONENT)\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/lib/pkgconfig\" TYPE FILE FILES \"/home/justin/ros_test/build/homework2/catkin_generated/installspace/homework2.pc\")\nendif()\n\nif(\"x${CMAKE_INSTALL_COMPONENT}x\" STREQUAL \"xUnspecifiedx\" OR NOT CMAKE_INSTALL_COMPONENT)\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/share/homework2/cmake\" TYPE FILE FILES \"/home/justin/ros_test/build/homework2/catkin_generated/installspace/homework2-msg-extras.cmake\")\nendif()\n\nif(\"x${CMAKE_INSTALL_COMPONENT}x\" STREQUAL \"xUnspecifiedx\" OR NOT CMAKE_INSTALL_COMPONENT)\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/share/homework2/cmake\" TYPE FILE FILES\n \"/home/justin/ros_test/build/homework2/catkin_generated/installspace/homework2Config.cmake\"\n \"/home/justin/ros_test/build/homework2/catkin_generated/installspace/homework2Config-version.cmake\"\n )\nendif()\n\nif(\"x${CMAKE_INSTALL_COMPONENT}x\" STREQUAL \"xUnspecifiedx\" OR NOT CMAKE_INSTALL_COMPONENT)\n file(INSTALL DESTINATION \"${CMAKE_INSTALL_PREFIX}/share/homework2\" TYPE FILE FILES \"/home/justin/ros_test/src/homework2/package.xml\")\nendif()\n\n"},{"alpha_fraction":0.6924026012420654,"alphanum_fraction":0.7031552791595459,"avg_line_length":26.274038314819336,"blob_id":"e00742801859c8e8da579e83890e3a01f1506986","content_id":"96a1a398b32b137fee19cc756b97eadd38236c24","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":5673,"license_type":"no_license","max_line_length":441,"num_lines":208,"path":"/devel/include/rl_msgs/RLExperimentInfo.h","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"// Generated by gencpp from file rl_msgs/RLExperimentInfo.msg\n// DO NOT EDIT!\n\n\n#ifndef RL_MSGS_MESSAGE_RLEXPERIMENTINFO_H\n#define RL_MSGS_MESSAGE_RLEXPERIMENTINFO_H\n\n\n#include <string>\n#include <vector>\n#include <map>\n\n#include <ros/types.h>\n#include <ros/serialization.h>\n#include <ros/builtin_message_traits.h>\n#include <ros/message_operations.h>\n\n\nnamespace rl_msgs\n{\ntemplate <class ContainerAllocator>\nstruct RLExperimentInfo_\n{\n typedef RLExperimentInfo_<ContainerAllocator> Type;\n\n RLExperimentInfo_()\n : episode_number(0)\n , episode_reward(0.0)\n , number_actions(0) {\n }\n RLExperimentInfo_(const ContainerAllocator& _alloc)\n : episode_number(0)\n , episode_reward(0.0)\n , number_actions(0) {\n (void)_alloc;\n }\n\n\n\n typedef int32_t _episode_number_type;\n _episode_number_type episode_number;\n\n typedef float _episode_reward_type;\n _episode_reward_type episode_reward;\n\n typedef int32_t _number_actions_type;\n _number_actions_type number_actions;\n\n\n\n\n\n typedef boost::shared_ptr< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> > Ptr;\n typedef boost::shared_ptr< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> const> ConstPtr;\n\n}; // struct RLExperimentInfo_\n\ntypedef ::rl_msgs::RLExperimentInfo_<std::allocator<void> > RLExperimentInfo;\n\ntypedef boost::shared_ptr< ::rl_msgs::RLExperimentInfo > RLExperimentInfoPtr;\ntypedef boost::shared_ptr< ::rl_msgs::RLExperimentInfo const> RLExperimentInfoConstPtr;\n\n// constants requiring out of line definition\n\n\n\ntemplate<typename ContainerAllocator>\nstd::ostream& operator<<(std::ostream& s, const ::rl_msgs::RLExperimentInfo_<ContainerAllocator> & v)\n{\nros::message_operations::Printer< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >::stream(s, \"\", v);\nreturn s;\n}\n\n} // namespace rl_msgs\n\nnamespace ros\n{\nnamespace message_traits\n{\n\n\n\n// BOOLTRAITS {'IsFixedSize': True, 'IsMessage': True, 'HasHeader': False}\n// {'rl_msgs': ['/home/justin/ros_test/src/rl-texplore-ros-pkg-master/src/rl_msgs/msg'], 'std_msgs': ['/opt/ros/melodic/share/std_msgs/cmake/../msg']}\n\n// !!!!!!!!!!! ['__class__', '__delattr__', '__dict__', '__doc__', '__eq__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_parsed_fields', 'constants', 'fields', 'full_name', 'has_header', 'header_present', 'names', 'package', 'parsed_fields', 'short_name', 'text', 'types']\n\n\n\n\ntemplate <class ContainerAllocator>\nstruct IsFixedSize< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\n : TrueType\n { };\n\ntemplate <class ContainerAllocator>\nstruct IsFixedSize< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> const>\n : TrueType\n { };\n\ntemplate <class ContainerAllocator>\nstruct IsMessage< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\n : TrueType\n { };\n\ntemplate <class ContainerAllocator>\nstruct IsMessage< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> const>\n : TrueType\n { };\n\ntemplate <class ContainerAllocator>\nstruct HasHeader< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\n : FalseType\n { };\n\ntemplate <class ContainerAllocator>\nstruct HasHeader< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> const>\n : FalseType\n { };\n\n\ntemplate<class ContainerAllocator>\nstruct MD5Sum< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\n{\n static const char* value()\n {\n return \"df389dab0f017dc2a66e6cedd44b7a1e\";\n }\n\n static const char* value(const ::rl_msgs::RLExperimentInfo_<ContainerAllocator>&) { return value(); }\n static const uint64_t static_value1 = 0xdf389dab0f017dc2ULL;\n static const uint64_t static_value2 = 0xa66e6cedd44b7a1eULL;\n};\n\ntemplate<class ContainerAllocator>\nstruct DataType< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\n{\n static const char* value()\n {\n return \"rl_msgs/RLExperimentInfo\";\n }\n\n static const char* value(const ::rl_msgs::RLExperimentInfo_<ContainerAllocator>&) { return value(); }\n};\n\ntemplate<class ContainerAllocator>\nstruct Definition< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\n{\n static const char* value()\n {\n return \"# Message displaying / plotting / printing info about experiments\\n\\\n\\n\\\nint32 episode_number\\n\\\nfloat32 episode_reward\\n\\\n\\n\\\nint32 number_actions\\n\\\n\";\n }\n\n static const char* value(const ::rl_msgs::RLExperimentInfo_<ContainerAllocator>&) { return value(); }\n};\n\n} // namespace message_traits\n} // namespace ros\n\nnamespace ros\n{\nnamespace serialization\n{\n\n template<class ContainerAllocator> struct Serializer< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\n {\n template<typename Stream, typename T> inline static void allInOne(Stream& stream, T m)\n {\n stream.next(m.episode_number);\n stream.next(m.episode_reward);\n stream.next(m.number_actions);\n }\n\n ROS_DECLARE_ALLINONE_SERIALIZER\n }; // struct RLExperimentInfo_\n\n} // namespace serialization\n} // namespace ros\n\nnamespace ros\n{\nnamespace message_operations\n{\n\ntemplate<class ContainerAllocator>\nstruct Printer< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\n{\n template<typename Stream> static void stream(Stream& s, const std::string& indent, const ::rl_msgs::RLExperimentInfo_<ContainerAllocator>& v)\n {\n s << indent << \"episode_number: \";\n Printer<int32_t>::stream(s, indent + \" \", v.episode_number);\n s << indent << \"episode_reward: \";\n Printer<float>::stream(s, indent + \" \", v.episode_reward);\n s << indent << \"number_actions: \";\n Printer<int32_t>::stream(s, indent + \" \", v.number_actions);\n }\n};\n\n} // namespace message_operations\n} // namespace ros\n\n#endif // RL_MSGS_MESSAGE_RLEXPERIMENTINFO_H\n"},{"alpha_fraction":0.7677419185638428,"alphanum_fraction":0.7774193286895752,"avg_line_length":43.42856979370117,"blob_id":"bb00b6561cc570104ee7c4f854db30eccb397b4f","content_id":"9a14e2e4a3d23602a415cb069a3c091c1cb62748","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":310,"license_type":"no_license","max_line_length":49,"num_lines":7,"path":"/src/roundbot_gazebo/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"roundbot_gazebo\")\nset(roundbot_gazebo_MAINTAINER \"micho <micho@todo.todo>\")\nset(roundbot_gazebo_DEPRECATED \"\")\nset(roundbot_gazebo_VERSION \"0.0.0\")\nset(roundbot_gazebo_BUILD_DEPENDS \"gazebo_ros\")\nset(roundbot_gazebo_RUN_DEPENDS \"gazebo_ros\")\nset(roundbot_gazebo_BUILDTOOL_DEPENDS \"catkin\")"},{"alpha_fraction":0.7473683953285217,"alphanum_fraction":0.7736842036247253,"avg_line_length":46.5625,"blob_id":"a6d1cf1b6c1889d7076520e31f0641dca42f4bff","content_id":"ffff3049f2fd86605dfb9afde94d4d84550ddd35","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":760,"license_type":"no_license","max_line_length":92,"num_lines":16,"path":"/build/homework4/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"homework4\")\nset(homework4_VERSION \"0.0.0\")\nset(homework4_MAINTAINER \"abc <abc@todo.todo>\")\nset(homework4_PACKAGE_FORMAT \"1\")\nset(homework4_BUILD_DEPENDS \"dynamic_reconfigure\" \"roscpp\" \"tf\" \"visualization_msgs\")\nset(homework4_BUILD_EXPORT_DEPENDS \"dynamic_reconfigure\" \"roscpp\" \"tf\" \"visualization_msgs\")\nset(homework4_BUILDTOOL_DEPENDS \"catkin\")\nset(homework4_BUILDTOOL_EXPORT_DEPENDS )\nset(homework4_EXEC_DEPENDS \"dynamic_reconfigure\" \"roscpp\" \"tf\" \"visualization_msgs\")\nset(homework4_RUN_DEPENDS \"dynamic_reconfigure\" \"roscpp\" \"tf\" \"visualization_msgs\")\nset(homework4_TEST_DEPENDS )\nset(homework4_DOC_DEPENDS )\nset(homework4_URL_WEBSITE \"\")\nset(homework4_URL_BUGTRACKER \"\")\nset(homework4_URL_REPOSITORY \"\")\nset(homework4_DEPRECATED \"\")"},{"alpha_fraction":0.5887850522994995,"alphanum_fraction":0.6093457937240601,"avg_line_length":19.5,"blob_id":"5f136048427f69d1b9b404b75b9ae34ff5427167","content_id":"44b60a9e12b5fca87559fce5df06266322a3bd20","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":535,"license_type":"no_license","max_line_length":85,"num_lines":26,"path":"/src/pub.cpp","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include \"ros/ros.h\"\n#include \"std_msgs/String.h\"\n#include <geometry_msgs/Twist.h>\n\n#include <sstream>\n\nint main(int argc, char **argv)\n{\n ros::init(argc, argv, \"talker\");\n ros::NodeHandle n;\n ros::Publisher chatter_pub = n.advertise<geometry_msgs::Twist>(\"cmd_vel\", 1000); \n ros::Rate loop_rate(10);\n\n int count = 0;\n while (ros::ok())\n {\n geometry_msgs::Twist msg;\n msg.linear.x = 10;\n msg.angular.z = 5; \n chatter_pub.publish(msg);\n ros::spinOnce();\n loop_rate.sleep();\n ++count;\n }\n return 0;\n}\n\n\n"},{"alpha_fraction":0.7298524379730225,"alphanum_fraction":0.7343927621841431,"avg_line_length":54.125,"blob_id":"a1e23ebd6cec047a459c66273021c577c3db4ab9","content_id":"1f97f6cdbf322bd3de86e8640167bd676e4b5c14","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":881,"license_type":"no_license","max_line_length":114,"num_lines":16,"path":"/build/rl-texplore-ros-pkg-master/src/rl_experiment/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"rl_experiment\")\nset(rl_experiment_VERSION \"0.0.0\")\nset(rl_experiment_MAINTAINER \"Todd Hester <todd.hester@gmail.com>\")\nset(rl_experiment_PACKAGE_FORMAT \"1\")\nset(rl_experiment_BUILD_DEPENDS \"roscpp\" \"std_msgs\" \"tf\" \"rl_common\" \"rl_agent\" \"rl_env\")\nset(rl_experiment_BUILD_EXPORT_DEPENDS \"message_runtime\" \"roscpp\" \"std_msgs\" \"tf\" \"rl_common\" \"rl_agent\" \"rl_env\")\nset(rl_experiment_BUILDTOOL_DEPENDS \"catkin\")\nset(rl_experiment_BUILDTOOL_EXPORT_DEPENDS )\nset(rl_experiment_EXEC_DEPENDS \"message_runtime\" \"roscpp\" \"std_msgs\" \"tf\" \"rl_common\" \"rl_agent\" \"rl_env\")\nset(rl_experiment_RUN_DEPENDS \"message_runtime\" \"roscpp\" \"std_msgs\" \"tf\" \"rl_common\" \"rl_agent\" \"rl_env\")\nset(rl_experiment_TEST_DEPENDS )\nset(rl_experiment_DOC_DEPENDS )\nset(rl_experiment_URL_WEBSITE \"\")\nset(rl_experiment_URL_BUGTRACKER \"\")\nset(rl_experiment_URL_REPOSITORY \"\")\nset(rl_experiment_DEPRECATED \"\")"},{"alpha_fraction":0.7841945290565491,"alphanum_fraction":0.7841945290565491,"avg_line_length":49.61538314819336,"blob_id":"859afddd73621f7cb1f0222b1d6037e07d6d99fc","content_id":"63ba2a1802a0ed5fdbda9f20502775b74f5e2735","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":658,"license_type":"no_license","max_line_length":93,"num_lines":13,"path":"/build/rl-texplore-ros-pkg-master/src/rl_msgs/CMakeFiles/rl_msgs_generate_messages_nodejs.dir/cmake_clean.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"file(REMOVE_RECURSE\n \"CMakeFiles/rl_msgs_generate_messages_nodejs\"\n \"/home/justin/ros_test/devel/share/gennodejs/ros/rl_msgs/msg/RLEnvSeedExperience.js\"\n \"/home/justin/ros_test/devel/share/gennodejs/ros/rl_msgs/msg/RLExperimentInfo.js\"\n \"/home/justin/ros_test/devel/share/gennodejs/ros/rl_msgs/msg/RLEnvDescription.js\"\n \"/home/justin/ros_test/devel/share/gennodejs/ros/rl_msgs/msg/RLStateReward.js\"\n \"/home/justin/ros_test/devel/share/gennodejs/ros/rl_msgs/msg/RLAction.js\"\n)\n\n# Per-language clean rules from dependency scanning.\nforeach(lang )\n include(CMakeFiles/rl_msgs_generate_messages_nodejs.dir/cmake_clean_${lang}.cmake OPTIONAL)\nendforeach()\n"},{"alpha_fraction":0.7617328763008118,"alphanum_fraction":0.7725631594657898,"avg_line_length":38.71428680419922,"blob_id":"3664c9f601fd836b301369f0d6dded372873c037","content_id":"9c3158074df90550e937e13077cd070fced7adb1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":277,"license_type":"no_license","max_line_length":46,"num_lines":7,"path":"/src/hector_example/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"hector_example\")\nset(hector_example_MAINTAINER \"abc <abc@todo.todo>\")\nset(hector_example_DEPRECATED \"\")\nset(hector_example_VERSION \"0.0.0\")\nset(hector_example_BUILD_DEPENDS )\nset(hector_example_RUN_DEPENDS )\nset(hector_example_BUILDTOOL_DEPENDS \"catkin\")"},{"alpha_fraction":0.5508750081062317,"alphanum_fraction":0.5613197684288025,"avg_line_length":29.054054260253906,"blob_id":"6d4e977c5e814896bd57563b552f79746eb95b92","content_id":"edd96b20a4b95c162b83d7aacd56de079a516317","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":14457,"license_type":"no_license","max_line_length":176,"num_lines":481,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/MultipleClassifiers.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file MultipleClassifiers.cc\n Implements the Multiple Classifiers class.\n \\author Todd Hester\n*/\n\n#include \"MultipleClassifiers.hh\"\n\n\n\nMultipleClassifiers::MultipleClassifiers(int id, int modelType, int predType,\n int nModels, int trainMode,\n int trainFreq,\n float featPct, float expPct,\n float treeThreshold, bool stoch,\n float featRange, Random rng):\n id(id), modelType(modelType), predType(predType), nModels(nModels),\n mode(trainMode), freq(trainFreq),\n featPct(featPct), expPct(expPct), \n treeThresh(treeThreshold), stoch(stoch), \n addNoise(!stoch && (modelType == M5MULTI || modelType == M5SINGLE || modelType == M5ALLMULTI || modelType == M5ALLSINGLE || modelType == LSTMULTI || modelType == LSTSINGLE)),\n featRange(featRange),\n rng(rng)\n{\n STDEBUG = false;//true;\n ACC_DEBUG = false;//true;\n PRED_DEBUG = false; //true;\n CONF_DEBUG = false;\n COPYDEBUG = false;\n nsteps = 0;\n \n cout << \"Created MultClass \" << id << \" with nModels: \" << nModels << \", addNoise: \" << addNoise << endl;\n\n for (int i = -1; i < id; i++)\n rng.uniform(0,1);\n\n\n initModels();\n\n}\n\nMultipleClassifiers::MultipleClassifiers(const MultipleClassifiers &t):\n id(t.id), modelType(t.modelType), predType(t.predType), nModels(t.nModels),\n mode(t.mode), freq(t.freq),\n featPct(t.featPct), expPct(t.expPct), \n treeThresh(t.treeThresh), stoch(t.stoch), addNoise(t.addNoise),\n featRange(t.featRange), rng(t.rng)\n{\n COPYDEBUG = t.COPYDEBUG;\n if (COPYDEBUG) cout << \" MC copy constructor id \" << id << endl;\n STDEBUG = t.STDEBUG;\n ACC_DEBUG = t.ACC_DEBUG;\n PRED_DEBUG = t.PRED_DEBUG;\n CONF_DEBUG = t.CONF_DEBUG;\n nsteps = t.nsteps;\n\n accuracy = t.accuracy;\n\n infos.resize(nModels);\n models.resize(nModels);\n if (COPYDEBUG) cout << \"models size now \" << models.size() << \" nModels: \" << nModels << endl;\n // copy each model\n for (unsigned i = 0; i < models.size(); i++){\n if (COPYDEBUG)cout << \"MC copy model \" << i << endl;\n models[i] = t.models[i]->getCopy();\n }\n\n if (COPYDEBUG) cout << \"MC copy complete models size: \" << models.size() << endl;\n}\n\nMultipleClassifiers* MultipleClassifiers::getCopy(){\n \n MultipleClassifiers* copy = new MultipleClassifiers(*this);\n return copy;\n\n}\n\nMultipleClassifiers::~MultipleClassifiers() {\n for (unsigned i = 0; i < models.size(); i++){\n delete models[i];\n }\n models.clear();\n accuracy.clear();\n infos.clear();\n}\n\n\nbool MultipleClassifiers::trainInstances(std::vector<classPair> &instances){\n if (STDEBUG) cout << id << \" MultClass trainInstances: \" << instances.size() << endl;\n\n bool changed = false;\n \n std::vector< std::vector<classPair> >subsets(nModels);\n for (int i = 0; i < nModels; i++){\n subsets[i].reserve(instances.size());\n }\n\n for (unsigned j = 0; j < instances.size(); j++){\n bool didUpdate = false;\n \n // train each model\n for (int i = 0; i < nModels; i++){\n \n // check accuracy of this model\n if (predType == BEST || predType == WEIGHTAVG){\n\tfor (unsigned j = 0; j < instances.size(); j++){\n\t updateModelAccuracy(i, instances[j].in, instances[j].out);\n\t}\n }\n\n // create new vector with some random subset of experiences\n if (rng.uniform() < expPct){\n\tfloat origOutput = instances[j].out;\n\tif (addNoise) instances[j].out += rng.uniform(-0.2,0.2)*treeThresh;\n subsets[i].push_back(instances[j]);\n\tif (addNoise) instances[j].out = origOutput;\n\tdidUpdate = true;\n }\n } // model loop\n \n // make sure someone got this instance\n if (!didUpdate){\n int model = rng.uniformDiscrete(0,nModels-1);\n //cout << \"none got instance, update model \" << model << endl;\n if (addNoise) instances[j].out += rng.uniform(-0.2,0.2)*treeThresh;\n subsets[model].push_back(instances[j]);\n }\n } // instances loop\n \n // now update models\n for (int i = 0; i < nModels; i++){\n if (subsets[i].size() > 0){\n if (STDEBUG) cout << id << \" train model \" << i << \" on subset of size \"\n << subsets[i].size() << endl;\n bool singleChange = models[i]->trainInstances(subsets[i]);\n changed = changed || singleChange;\n }\n }\n \n nsteps += instances.size();\n\n return changed;\n}\n\n// here the target output will be a single value\nbool MultipleClassifiers::trainInstance(classPair &instance){\n if (STDEBUG) cout << id << \" trainInstance\" << endl;\n\n bool changed = false;\n\n // train each model\n bool didUpdate = false;\n for (int i = 0; i < nModels; i++){\n\n // check accuracy of this model\n if (predType == BEST || predType == WEIGHTAVG){\n updateModelAccuracy(i, instance.in, instance.out);\n }\n\n // update with new experience\n if (rng.uniform() < expPct){\n didUpdate = true;\n float origOutput = instance.out;\n if (addNoise) instance.out += rng.uniform(-0.2,0.2)*treeThresh;\n bool singleChange = models[i]->trainInstance(instance);\n if (addNoise) instance.out = origOutput;\n changed = changed || singleChange;\n }\n }\n\n // make sure some model got the transition\n if (!didUpdate){\n int model = rng.uniformDiscrete(0,nModels-1);\n if (addNoise) instance.out += rng.uniform(-0.2,0.2)*treeThresh;\n bool singleChange = models[model]->trainInstance(instance);\n changed = singleChange || changed;\n }\n\n nsteps++;\n\n return changed;\n\n}\n\n\n// get all the models outputs and combine them somehow\nvoid MultipleClassifiers::testInstance(const std::vector<float> &input, std::map<float, float>* retval){\n if (STDEBUG) cout << id << \" testInstance\" << endl;\n\n if ((int)infos.size() != nModels){\n infos.resize(nModels);\n }\n\n for (unsigned i = 0; i < infos.size(); i++){\n infos[i].clear();\n }\n\n retval->clear();\n\n // possibly have to init our model\n if ((int)models.size() != nModels)\n initModels();\n\n ///////////////////////////////////\n // return best\n if (predType == BEST){\n float acc = -1.0;\n int best = -1;\n for (int i = 0; i < nModels; i++){\n if (PRED_DEBUG) cout << \"Model \" << i << \" has acc \"\n << accuracy[i] << endl;\n if (accuracy[i] > acc){\n acc = accuracy[i];\n best = i;\n }\n }\n if (PRED_DEBUG) cout << id << \" Returning model \" << best\n << \" with accuracy \" << acc << endl;\n models[best]->testInstance(input, retval);\n return;\n }\n //////////////////////////////////////\n\n\n /////////////////////////////////////////\n // calculate weights for weighted avg\n std::vector<float> weights(nModels, 1.0);\n if (predType == WEIGHTAVG){\n float accSum = 0.0;\n \n for (int j = 0; j < nModels; j++){\n accSum += accuracy[j];\n }\n\n if (accSum > 0.0){\n for (int j = 0; j < nModels; j++){\n weights[j] = (float)nModels * accuracy[j] / accSum;\n if (PRED_DEBUG || ACC_DEBUG) cout << \"Model \" << j << \" acc: \"\n << accuracy[j] << \" weight: \"\n << weights[j] << endl;\n }\n }\n }\n /////////////////////////////////////////\n\n // get state action info from each tree in our set\n for (int j = 0; j < nModels; j++){\n models[j]->testInstance(input, &(infos[j]));\n }\n\n // make a list of all outcomes any model predicted\n for (int i = 0; i < nModels; i++){\n for (std::map<float, float>::iterator it = infos[i].begin();\n it != infos[i].end(); it++){\n\n float outcome = (*it).first;\n float prob = (*it).second;\n\n // only if prob > 0\n if (prob > 0){\n\n // see if its not already in there\n if (!retval->count(outcome)){\n if (PRED_DEBUG) cout << \" new outcome \" << outcome << endl;\n // check if it really is a new one\n for (std::map<float, float>::iterator j = retval->begin();\n j != retval->end(); j++){\n if (fabs(outcome - j->first) < treeThresh){\n if (PRED_DEBUG) cout << \"within treeThresh of \" << j->first << endl;\n outcome = j->first;\n break;\n }\n }\n }\n\n (*retval)[outcome] += (prob * weights[i] / (float)nModels);\n \n if (PRED_DEBUG) cout << \"inserting outcome \" << outcome\n << \" from model \" << i\n << \" with prob: \" << prob\n << \" and weight: \" << weights[i]\n << \" now total prob: \" << (*retval)[outcome] << endl;\n \n }\n }\n }\n}\n\n\n\nfloat MultipleClassifiers::getConf(const std::vector<float> &input){\n if (STDEBUG || CONF_DEBUG) cout << id << \" getConf\" << endl;\n\n if ((int)infos.size() != nModels){\n infos.resize(nModels);\n }\n\n // get predictions if we haven't\n if (predType == BEST || predType == SEPARATE){\n if (CONF_DEBUG) cout << \"conf: getting predictions again\" << endl;\n for (int j = 0; j < nModels; j++){\n infos[j].clear();\n models[j]->testInstance(input, &(infos[j]));\n }\n }\n\n float conf = 0;\n\n // for deterministic trees, calculating a distribution of outcomes\n // calculate kl divergence\n if (modelType == RMAX || modelType == SLF || modelType == C45TREE ||\n modelType == STUMP ){ \n conf = 1.0 - klDivergence(input);\n }\n\n // for continuous trees, providing a single continuous prediction\n // calcluate variance\n else {\n // use scaled sd\n conf = 1.0 - sqrt(variance(input)) / featRange;\n }\n\n if (CONF_DEBUG) cout << \"return conf: \" << conf << endl;\n\n return conf;\n\n}\n\n// calculate kl divergence of these predictions\nfloat MultipleClassifiers::klDivergence(const std::vector<float> &input){\n\n // KL divergence equation\n // D_KL(P||Q) = SUM_i P(i) log (P(i)/Q(i))\n float totalKL = 0.0;\n\n // I guess let's take the avg KL divergence of comparisons \n // between all pairs of models\n for (int i = 0; i < nModels; i++){\n for (int j = 0; j < nModels; j++){\n if (i == j) continue;\n float singleKL = 0.0;\n\n for (std::map<float, float>::iterator it = infos[i].begin();\n it != infos[i].end(); it++){\n \n float outcome = (*it).first;\n float prob = (*it).second;\n \n if (prob == 0){\n continue;\n }\n\n if (CONF_DEBUG) \n cout << \"model \" << i << \" predicts \" << outcome << \" with prob \" \n << prob << \", model \" << j << \" has prob \" \n << infos[j][outcome] << endl;\n\n float jProb = infos[j][outcome];\n if (jProb == 0) jProb = 0.01;\n\n singleKL += prob * log(prob / jProb);\n \n }\n totalKL += singleKL;\n\n if (CONF_DEBUG) \n cout << \"D_KL(\" << i << \"||\" << j << \") = \" << singleKL\n << \" klsum: \" << totalKL << endl;\n\n } // outcomes of model i\n } // models\n\n int npairs = nModels * (nModels - 1);\n float avgKL = totalKL / (float)npairs;\n\n // normalize. since my 0 prob is really 0.01, and log 1/0.01 = 4.60517, we'll divide by that\n avgKL /= 4.60517;\n\n if (CONF_DEBUG) cout << \"AvgKL: \" << avgKL << endl << endl;\n\n return avgKL;\n\n}\n\n// calculate the variance of these predictions\nfloat MultipleClassifiers::variance(const std::vector<float> &input){\n\n float sum = 0.0;\n float sumSqr = 0.0;\n\n for (int i = 0; i < nModels; i++){\n float val = infos[i].begin()->first;\n sum += val;\n sumSqr += (val*val);\n }\n\n float mean = sum / (float)nModels;\n float variance = (sumSqr - sum*mean) / (float)(nModels-1.0);\n\n if (CONF_DEBUG) cout << \"variance of predictions is \" << variance << endl;\n\n return variance;\n\n}\n\n// init models\nvoid MultipleClassifiers::initModels(){\n if (STDEBUG) cout << \"initModels()\" << endl;\n\n models.resize(nModels);\n accuracy.resize(nModels, 0.0);\n\n // init the trees or stumps\n for (int i = 0; i < nModels; i++){\n\n if (modelType == C45TREE){\n models[i] = new C45Tree(id + i*(1+nModels), mode, freq, 0, featPct, rng);\n }\n else if (modelType == M5MULTI){\n models[i] = new M5Tree(id + i*(1+nModels), mode, freq, 0, featPct, false, false, treeThresh, rng);\n }\n else if (modelType == M5ALLMULTI){\n models[i] = new M5Tree(id + i*(1+nModels), mode, freq, 0, featPct, false, true, treeThresh, rng);\n }\n else if (modelType == M5ALLSINGLE){\n models[i] = new M5Tree(id + i*(1+nModels), mode, freq, 0, featPct, true, true, treeThresh, rng);\n }\n else if (modelType == M5SINGLE){\n models[i] = new M5Tree(id + i*(1+nModels), mode, freq, 0, featPct, true, false, treeThresh, rng);\n }\n else if (modelType == LSTSINGLE){\n models[i] = new LinearSplitsTree(id + i*(1+nModels), mode, freq, 0, featPct, true, treeThresh, rng);\n }\n else if (modelType == LSTMULTI){\n models[i] = new LinearSplitsTree(id + i*(1+nModels), mode, freq, 0, featPct, false, treeThresh, rng);\n }\n else if (modelType == STUMP){\n models[i] = new Stump(id + 1*(1+nModels), mode, freq, 0, featPct, rng);\n }\n else if (modelType == ALLM5TYPES){\n // select an m5 type randomly. so multivariate v single and allfeats v subtree feats\n bool simple = rng.bernoulli(0.5);\n bool allFeats = rng.bernoulli(0.5);\n //cout << \"ALL types init tree \" << i << \" with simple: \" << simple << \" and allFeats: \" << allFeats << endl;\n models[i] = new M5Tree(id + i*(1+nModels), mode, freq, 0, featPct, simple, allFeats, treeThresh, rng);\n }\n else {\n cout << \"Invalid model type for this committee\" << endl;\n exit(-1);\n }\n }\n\n}\n\n\nvoid MultipleClassifiers::updateModelAccuracy(int i, const std::vector<float> &in,\n float output){\n\n if (ACC_DEBUG) cout << \"Check model accuracy \" << i << \" curr: \" << accuracy[i] << endl;\n\n // get prediction\n std::map<float, float> pred;\n models[i]->testInstance(in, &pred);\n\n // see if it was correct\n bool correct = true;\n // check trans prob\n // for now, just see if prob of next was ML from this model\n float prob = pred[output];\n if (ACC_DEBUG) cout << \"Model \" << i << \" predicted outcome \"\n << output << \" with prob: \" << prob << endl;\n if (prob <= 0.5)\n correct = false;\n\n // update accuracy\n accuracy[i] = accuracy[i] * (float)nsteps/(float)(nsteps+1.0);\n if (correct)\n accuracy[i] += 1.0/(float)(nsteps+1.0);\n\n if (ACC_DEBUG) cout << \"Model \" << i << \" new accuracy: \" << accuracy[i] << endl;\n\n}\n\n"},{"alpha_fraction":0.7858880758285522,"alphanum_fraction":0.7858880758285522,"avg_line_length":33.25,"blob_id":"4deac065ec057d27f29837a46c4c40fe61ae0f1d","content_id":"b383b9cd383342c634227cde67936c23ee8c4dbc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":411,"license_type":"no_license","max_line_length":76,"num_lines":12,"path":"/src/roundbot_control/CMakeFiles/diff_controller.dir/cmake_clean.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"FILE(REMOVE_RECURSE\n \"CMakeFiles/diff_controller.dir/src/DiffController.cpp.o\"\n \"devel/lib/libdiff_controller.pdb\"\n \"devel/lib/libdiff_controller.so\"\n \"devel/lib/libdiff_controller.pdb\"\n \"CMakeFiles/CMakeRelink.dir/libdiff_controller.so\"\n)\n\n# Per-language clean rules from dependency scanning.\nFOREACH(lang CXX)\n INCLUDE(CMakeFiles/diff_controller.dir/cmake_clean_${lang}.cmake OPTIONAL)\nENDFOREACH(lang)\n"},{"alpha_fraction":0.5141873359680176,"alphanum_fraction":0.5293720364570618,"avg_line_length":30.08300018310547,"blob_id":"fe4ef2e1979648dcaebf1e1991dcdbb6abe74df9","content_id":"1a71eb674bfb3afe0dc392e25a91d7426e0d9204","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":31084,"license_type":"no_license","max_line_length":237,"num_lines":1000,"path":"/src/rl-texplore-ros-pkg-master/src/rl_experiment/src/rl.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file Main file that starts agents and environments\n \\author Todd Hester\n*/\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n\n#include <stdio.h>\n#include <string.h>\n#include <sys/time.h>\n\n//////////////////\n// Environments //\n//////////////////\n#include <rl_env/RobotCarVel.hh>\n#include <rl_env/fourrooms.hh>\n#include <rl_env/tworooms.hh>\n#include <rl_env/taxi.hh>\n#include <rl_env/FuelRooms.hh>\n#include <rl_env/stocks.hh>\n#include <rl_env/energyrooms.hh>\n#include <rl_env/MountainCar.hh>\n#include <rl_env/CartPole.hh>\n#include <rl_env/LightWorld.hh>\n\n\n////////////\n// Agents //\n////////////\n#include <rl_agent/QLearner.hh>\n#include <rl_agent/ModelBasedAgent.hh>\n#include <rl_agent/DiscretizationAgent.hh>\n#include <rl_agent/SavedPolicy.hh>\n#include <rl_agent/Dyna.hh>\n#include <rl_agent/Sarsa.hh>\n\n\n\n#include <vector>\n#include <sstream>\n#include <iostream>\n\n#include <getopt.h>\n#include <stdlib.h>\n\nunsigned NUMEPISODES = 1000; //10; //200; //500; //200;\nconst unsigned NUMTRIALS = 1; //30; //30; //5; //30; //30; //50\nunsigned MAXSTEPS = 1000; // per episode\nbool PRINTS = false;\n\n\nvoid displayHelp(){\n cout << \"\\n Call experiment --agent type --env type [options]\\n\";\n cout << \"Agent types: qlearner sarsa modelbased rmax texplore dyna savedpolicy\\n\";\n cout << \"Env types: taxi tworooms fourrooms energy fuelworld mcar cartpole car2to7 car7to2 carrandom stocks lightworld\\n\";\n\n cout << \"\\n Agent Options:\\n\";\n cout << \"--gamma value (discount factor between 0 and 1)\\n\";\n cout << \"--epsilon value (epsilon for epsilon-greedy exploration)\\n\";\n cout << \"--alpha value (learning rate alpha)\\n\";\n cout << \"--initialvalue value (initial q values)\\n\";\n cout << \"--actrate value (action selection rate (Hz))\\n\";\n cout << \"--lamba value (lamba for eligibility traces)\\n\";\n cout << \"--m value (parameter for R-Max)\\n\";\n cout << \"--k value (For Dyna: # of model based updates to do between each real world update)\\n\";\n cout << \"--history value (# steps of history to use for planning with delay)\\n\";\n cout << \"--filename file (file to load saved policy from for savedpolicy agent)\\n\";\n cout << \"--model type (tabular,tree,m5tree)\\n\";\n cout << \"--planner type (vi,pi,sweeping,uct,parallel-uct,delayed-uct,delayed-parallel-uct)\\n\";\n cout << \"--explore type (unknown,greedy,epsilongreedy,variancenovelty)\\n\";\n cout << \"--combo type (average,best,separate)\\n\";\n cout << \"--nmodels value (# of models)\\n\";\n cout << \"--nstates value (optionally discretize domain into value # of states on each feature)\\n\";\n cout << \"--reltrans (learn relative transitions)\\n\";\n cout << \"--abstrans (learn absolute transitions)\\n\";\n cout << \"--v value (For TEXPLORE: b/v coefficient for rewarding state-actions where models disagree)\\n\";\n cout << \"--n value (For TEXPLORE: n coefficient for rewarding state-actions which are novel)\\n\";\n\n cout << \"\\n Env Options:\\n\";\n cout << \"--deterministic (deterministic version of domain)\\n\";\n cout << \"--stochastic (stochastic version of domain)\\n\";\n cout << \"--delay value (# steps of action delay (for mcar and tworooms)\\n\";\n cout << \"--lag (turn on brake lag for car driving domain)\\n\";\n cout << \"--highvar (have variation fuel costs in Fuel World)\\n\";\n cout << \"--nsectors value (# sectors for stocks domain)\\n\";\n cout << \"--nstocks value (# stocks for stocks domain)\\n\";\n\n cout << \"\\n--prints (turn on debug printing of actions/rewards)\\n\";\n cout << \"--nepisodes value (# of episodes to run (1000 default)\\n\";\n cout << \"--seed value (integer seed for random number generator)\\n\";\n\n cout << \"\\n For more info, see: http://www.ros.org/wiki/rl_experiment\\n\";\n\n exit(-1);\n\n}\n\n\nint main(int argc, char **argv) {\n\n // default params for env and agent\n char* agentType = NULL;\n char* envType = NULL;\n float discountfactor = 0.99;\n float epsilon = 0.1;\n float alpha = 0.3;\n float initialvalue = 0.0;\n float actrate = 10.0;\n float lambda = 0.1;\n int M = 5;\n int modelType = C45TREE;\n int exploreType = GREEDY;\n int predType = BEST;\n int plannerType = PAR_ETUCT_ACTUAL;\n int nmodels = 1;\n bool reltrans = true;\n bool deptrans = false;\n float v = 0;\n float n = 0;\n float featPct = 0.2;\n int nstates = 0;\n int k = 1000;\n char *filename = NULL;\n bool stochastic = true;\n int nstocks = 3;\n int nsectors = 3;\n int delay = 0;\n bool lag = false;\n bool highvar = false;\n int history = 0;\n int seed = 1;\n // change some of these parameters based on command line args\n\n // parse agent type\n bool gotAgent = false;\n for (int i = 1; i < argc-1; i++){\n if (strcmp(argv[i], \"--agent\") == 0){\n gotAgent = true;\n agentType = argv[i+1];\n }\n }\n if (!gotAgent) {\n cout << \"--agent type option is required\" << endl;\n displayHelp();\n }\n\n // set some default options for rmax or texplore\n if (strcmp(agentType, \"rmax\") == 0){\n modelType = RMAX;\n exploreType = EXPLORE_UNKNOWN;\n predType = BEST;\n plannerType = VALUE_ITERATION;\n nmodels = 1;\n reltrans = false;\n M = 5;\n history = 0;\n } else if (strcmp(agentType, \"texplore\") == 0){\n modelType = C45TREE;\n exploreType = DIFF_AND_NOVEL_BONUS;\n v = 0;\n n = 0;\n predType = AVERAGE;\n plannerType = PAR_ETUCT_ACTUAL;\n nmodels = 5;\n reltrans = true;\n M = 0;\n history = 0;\n }\n\n // parse env type\n bool gotEnv = false;\n for (int i = 1; i < argc-1; i++){\n if (strcmp(argv[i], \"--env\") == 0){\n gotEnv = true;\n envType = argv[i+1];\n }\n }\n if (!gotEnv) {\n cout << \"--env type option is required\" << endl;\n displayHelp();\n }\n\n // parse other arguments\n char ch;\n const char* optflags = \"geairlmoxpcn:\";\n int option_index = 0;\n static struct option long_options[] = {\n {\"gamma\", 1, 0, 'g'},\n {\"discountfactor\", 1, 0, 'g'},\n {\"epsilon\", 1, 0, 'e'},\n {\"alpha\", 1, 0, 'a'},\n {\"initialvalue\", 1, 0, 'i'},\n {\"actrate\", 1, 0, 'r'},\n {\"lambda\", 1, 0, 'l'},\n {\"m\", 1, 0, 'm'},\n {\"model\", 1, 0, 'o'},\n {\"explore\", 1, 0, 'x'},\n {\"planner\", 1, 0, 'p'},\n {\"combo\", 1, 0, 'c'},\n {\"nmodels\", 1, 0, '#'},\n {\"reltrans\", 0, 0, 't'},\n {\"abstrans\", 0, 0, '0'},\n {\"seed\", 1, 0, 's'},\n {\"agent\", 1, 0, 'q'},\n {\"prints\", 0, 0, 'd'},\n {\"nstates\", 1, 0, 'w'},\n {\"k\", 1, 0, 'k'},\n {\"filename\", 1, 0, 'f'},\n {\"history\", 1, 0, 'y'},\n {\"b\", 1, 0, 'b'},\n {\"v\", 1, 0, 'v'},\n {\"n\", 1, 0, 'n'},\n\n {\"env\", 1, 0, 1},\n {\"deterministic\", 0, 0, 2},\n {\"stochastic\", 0, 0, 3},\n {\"delay\", 1, 0, 4},\n {\"nsectors\", 1, 0, 5},\n {\"nstocks\", 1, 0, 6},\n {\"lag\", 0, 0, 7},\n {\"nolag\", 0, 0, 8},\n {\"highvar\", 0, 0, 11},\n {\"nepisodes\", 1, 0, 12}\n\n };\n\n bool epsilonChanged = false;\n bool actrateChanged = false;\n bool mChanged = false;\n bool bvnChanged = false;\n bool lambdaChanged = false;\n\n while(-1 != (ch = getopt_long_only(argc, argv, optflags, long_options, &option_index))) {\n switch(ch) {\n\n case 'g':\n discountfactor = std::atof(optarg);\n cout << \"discountfactor: \" << discountfactor << endl;\n break;\n\n case 'e':\n epsilonChanged = true;\n epsilon = std::atof(optarg);\n cout << \"epsilon: \" << epsilon << endl;\n break;\n\n case 'y':\n {\n if (strcmp(agentType, \"texplore\") == 0 || strcmp(agentType, \"modelbased\") == 0){\n history = std::atoi(optarg);\n cout << \"history: \" << history << endl;\n } else {\n cout << \"--history is not a valid option for agent: \" << agentType << endl;\n exit(-1);\n }\n break;\n }\n\n case 'k':\n {\n if (strcmp(agentType, \"dyna\") == 0){\n k = std::atoi(optarg);\n cout << \"k: \" << k << endl;\n } else {\n cout << \"--k is only a valid option for the Dyna agent\" << endl;\n exit(-1);\n }\n break;\n }\n\n case 'f':\n filename = optarg;\n cout << \"policy filename: \" << filename << endl;\n break;\n\n case 'a':\n {\n if (strcmp(agentType, \"qlearner\") == 0 || strcmp(agentType, \"dyna\") == 0 || strcmp(agentType, \"sarsa\") == 0){\n alpha = std::atof(optarg);\n cout << \"alpha: \" << alpha << endl;\n } else {\n cout << \"--alpha option is only valid for Q-Learning, Dyna, and Sarsa\" << endl;\n exit(-1);\n }\n break;\n }\n\n case 'i':\n {\n if (strcmp(agentType, \"qlearner\") == 0 || strcmp(agentType, \"dyna\") == 0 || strcmp(agentType, \"sarsa\") == 0){\n initialvalue = std::atof(optarg);\n cout << \"initialvalue: \" << initialvalue << endl;\n } else {\n cout << \"--initialvalue option is only valid for Q-Learning, Dyna, and Sarsa\" << endl;\n exit(-1);\n }\n break;\n }\n\n case 'r':\n {\n actrateChanged = true;\n if (strcmp(agentType, \"texplore\") == 0 || strcmp(agentType, \"modelbased\") == 0 || strcmp(agentType, \"rmax\") == 0){\n actrate = std::atof(optarg);\n cout << \"actrate: \" << actrate << endl;\n } else {\n cout << \"Model-free methods do not require an action rate\" << endl;\n exit(-1);\n }\n break;\n }\n\n case 'l':\n {\n lambdaChanged = true;\n if (strcmp(agentType, \"texplore\") == 0 || strcmp(agentType, \"modelbased\") == 0 || strcmp(agentType, \"rmax\") == 0 || strcmp(agentType, \"sarsa\") == 0){\n lambda = std::atof(optarg);\n cout << \"lambda: \" << lambda << endl;\n } else {\n cout << \"--lambda option is invalid for this agent: \" << agentType << endl;\n exit(-1);\n }\n break;\n }\n\n case 'm':\n {\n mChanged = true;\n if (strcmp(agentType, \"texplore\") == 0 || strcmp(agentType, \"modelbased\") == 0 || strcmp(agentType, \"rmax\") == 0){\n M = std::atoi(optarg);\n cout << \"M: \" << M << endl;\n } else {\n cout << \"--M option only useful for model-based agents, not \" << agentType << endl;\n exit(-1);\n }\n break;\n }\n\n case 'o':\n {\n if (strcmp(agentType, \"texplore\") == 0 || strcmp(agentType, \"modelbased\") == 0 || strcmp(agentType, \"rmax\") == 0){\n if (strcmp(optarg, \"tabular\") == 0) modelType = RMAX;\n else if (strcmp(optarg, \"tree\") == 0) modelType = C45TREE;\n else if (strcmp(optarg, \"texplore\") == 0) modelType = C45TREE;\n else if (strcmp(optarg, \"c45tree\") == 0) modelType = C45TREE;\n else if (strcmp(optarg, \"m5tree\") == 0) modelType = M5ALLMULTI;\n if (strcmp(agentType, \"rmax\") == 0 && modelType != RMAX){\n cout << \"R-Max should use tabular model\" << endl;\n exit(-1);\n }\n } else {\n cout << \"Model-free methods do not need a model, --model option does nothing for this agent type\" << endl;\n exit(-1);\n }\n cout << \"model: \" << modelNames[modelType] << endl;\n break;\n }\n\n case 'x':\n {\n if (strcmp(optarg, \"unknown\") == 0) exploreType = EXPLORE_UNKNOWN;\n else if (strcmp(optarg, \"greedy\") == 0) exploreType = GREEDY;\n else if (strcmp(optarg, \"epsilongreedy\") == 0) exploreType = EPSILONGREEDY;\n else if (strcmp(optarg, \"unvisitedstates\") == 0) exploreType = UNVISITED_BONUS;\n else if (strcmp(optarg, \"unvisitedactions\") == 0) exploreType = UNVISITED_ACT_BONUS;\n else if (strcmp(optarg, \"variancenovelty\") == 0) exploreType = DIFF_AND_NOVEL_BONUS;\n if (strcmp(agentType, \"rmax\") == 0 && exploreType != EXPLORE_UNKNOWN){\n cout << \"R-Max should use \\\"--explore unknown\\\" exploration\" << endl;\n exit(-1);\n }\n else if (strcmp(agentType, \"texplore\") != 0 && strcmp(agentType, \"modelbased\") != 0 && strcmp(agentType, \"rmax\") != 0 && (exploreType != GREEDY && exploreType != EPSILONGREEDY)) {\n cout << \"Model free methods must use either greedy or epsilon-greedy exploration!\" << endl;\n exploreType = EPSILONGREEDY;\n exit(-1);\n }\n cout << \"explore: \" << exploreNames[exploreType] << endl;\n break;\n }\n\n case 'p':\n {\n if (strcmp(optarg, \"vi\") == 0) plannerType = VALUE_ITERATION;\n else if (strcmp(optarg, \"valueiteration\") == 0) plannerType = VALUE_ITERATION;\n else if (strcmp(optarg, \"policyiteration\") == 0) plannerType = POLICY_ITERATION;\n else if (strcmp(optarg, \"pi\") == 0) plannerType = POLICY_ITERATION;\n else if (strcmp(optarg, \"sweeping\") == 0) plannerType = PRI_SWEEPING;\n else if (strcmp(optarg, \"prioritizedsweeping\") == 0) plannerType = PRI_SWEEPING;\n else if (strcmp(optarg, \"uct\") == 0) plannerType = ET_UCT_ACTUAL;\n else if (strcmp(optarg, \"paralleluct\") == 0) plannerType = PAR_ETUCT_ACTUAL;\n else if (strcmp(optarg, \"realtimeuct\") == 0) plannerType = PAR_ETUCT_ACTUAL;\n else if (strcmp(optarg, \"realtime-uct\") == 0) plannerType = PAR_ETUCT_ACTUAL;\n else if (strcmp(optarg, \"parallel-uct\") == 0) plannerType = PAR_ETUCT_ACTUAL;\n else if (strcmp(optarg, \"delayeduct\") == 0) plannerType = POMDP_ETUCT;\n else if (strcmp(optarg, \"delayed-uct\") == 0) plannerType = POMDP_ETUCT;\n else if (strcmp(optarg, \"delayedparalleluct\") == 0) plannerType = POMDP_PAR_ETUCT;\n else if (strcmp(optarg, \"delayed-parallel-uct\") == 0) plannerType = POMDP_PAR_ETUCT;\n if (strcmp(agentType, \"texplore\") != 0 && strcmp(agentType, \"modelbased\") != 0 && strcmp(agentType, \"rmax\") != 0){\n cout << \"Model-free methods do not require planners, --planner option does nothing with this agent\" << endl;\n exit(-1);\n }\n if (strcmp(agentType, \"rmax\") == 0 && plannerType != VALUE_ITERATION){\n cout << \"Typical implementation of R-Max would use value iteration, but another planner type is ok\" << endl;\n }\n cout << \"planner: \" << plannerNames[plannerType] << endl;\n break;\n }\n\n case 'c':\n {\n if (strcmp(agentType, \"texplore\") == 0 || strcmp(agentType, \"modelbased\") == 0){\n if (strcmp(optarg, \"average\") == 0) predType = AVERAGE;\n else if (strcmp(optarg, \"weighted\") == 0) predType = WEIGHTAVG;\n else if (strcmp(optarg, \"best\") == 0) predType = BEST;\n else if (strcmp(optarg, \"separate\") == 0) predType = SEPARATE;\n cout << \"predType: \" << comboNames[predType] << endl;\n } else {\n cout << \"--combo is an invalid option for agent: \" << agentType << endl;\n exit(-1);\n }\n break;\n }\n\n case '#':\n {\n if (strcmp(agentType, \"texplore\") == 0 || strcmp(agentType, \"modelbased\") == 0){\n nmodels = std::atoi(optarg);\n cout << \"nmodels: \" << nmodels << endl;\n } else {\n cout << \"--nmodels is an invalid option for agent: \" << agentType << endl;\n exit(-1);\n }\n if (nmodels < 1){\n cout << \"nmodels must be > 0\" << endl;\n exit(-1);\n }\n break;\n }\n\n case 't':\n {\n if (strcmp(agentType, \"texplore\") == 0 || strcmp(agentType, \"modelbased\") == 0){\n reltrans = true;\n cout << \"reltrans: \" << reltrans << endl;\n } else {\n cout << \"--reltrans is an invalid option for agent: \" << agentType << endl;\n exit(-1);\n }\n break;\n }\n\n case '0':\n {\n if (strcmp(agentType, \"texplore\") == 0 || strcmp(agentType, \"modelbased\") == 0){\n reltrans = false;\n cout << \"reltrans: \" << reltrans << endl;\n } else {\n cout << \"--abstrans is an invalid option for agent: \" << agentType << endl;\n exit(-1);\n }\n break;\n }\n\n case 's':\n seed = std::atoi(optarg);\n cout << \"seed: \" << seed << endl;\n break;\n\n case 'q':\n // already processed this one\n cout << \"agent: \" << agentType << endl;\n break;\n\n case 'd':\n PRINTS = true;\n break;\n\n case 'w':\n nstates = std::atoi(optarg);\n cout << \"nstates for discretization: \" << nstates << endl;\n break;\n\n case 'v':\n case 'b':\n {\n bvnChanged = true;\n if (strcmp(agentType, \"texplore\") == 0){\n v = std::atof(optarg);\n cout << \"v coefficient (variance bonus): \" << v << endl;\n }\n else {\n cout << \"--v and --b are invalid options for agent: \" << agentType << endl;\n exit(-1);\n }\n break;\n }\n\n case 'n':\n {\n bvnChanged = true;\n if (strcmp(agentType, \"texplore\") == 0){\n n = std::atof(optarg);\n cout << \"n coefficient (novelty bonus): \" << n << endl;\n }\n else {\n cout << \"--n is an invalid option for agent: \" << agentType << endl;\n exit(-1);\n }\n break;\n }\n\n case 2:\n stochastic = false;\n cout << \"stochastic: \" << stochastic << endl;\n break;\n\n case 11:\n {\n if (strcmp(envType, \"fuelworld\") == 0){\n highvar = true;\n cout << \"fuel world fuel cost variation: \" << highvar << endl;\n } else {\n cout << \"--highvar is only a valid option for the fuelworld domain.\" << endl;\n exit(-1);\n }\n break;\n }\n\n case 3:\n stochastic = true;\n cout << \"stochastic: \" << stochastic << endl;\n break;\n\n case 4:\n {\n if (strcmp(envType, \"mcar\") == 0 || strcmp(envType, \"tworooms\") == 0){\n delay = std::atoi(optarg);\n cout << \"delay steps: \" << delay << endl;\n } else {\n cout << \"--delay option is only valid for the mcar and tworooms domains\" << endl;\n exit(-1);\n }\n break;\n }\n\n case 5:\n {\n if (strcmp(envType, \"stocks\") == 0){\n nsectors = std::atoi(optarg);\n cout << \"nsectors: \" << nsectors << endl;\n } else {\n cout << \"--nsectors option is only valid for the stocks domain\" << endl;\n exit(-1);\n }\n break;\n }\n\n case 6:\n {\n if (strcmp(envType, \"stocks\") == 0){\n nstocks = std::atoi(optarg);\n cout << \"nstocks: \" << nstocks << endl;\n } else {\n cout << \"--nstocks option is only valid for the stocks domain\" << endl;\n exit(-1);\n }\n break;\n }\n\n case 7:\n {\n if (strcmp(envType, \"car2to7\") == 0 || strcmp(envType, \"car7to2\") == 0 || strcmp(envType, \"carrandom\") == 0){\n lag = true;\n cout << \"lag: \" << lag << endl;\n } else {\n cout << \"--lag option is only valid for car velocity tasks\" << endl;\n exit(-1);\n }\n break;\n }\n\n case 8:\n {\n if (strcmp(envType, \"car2to7\") == 0 || strcmp(envType, \"car7to2\") == 0 || strcmp(envType, \"carrandom\") == 0){\n lag = false;\n cout << \"lag: \" << lag << endl;\n } else {\n cout << \"--nolag option is only valid for car velocity tasks\" << endl;\n exit(-1);\n }\n break;\n }\n\n case 1:\n // already processed this one\n cout << \"env: \" << envType << endl;\n break;\n\n case 12:\n NUMEPISODES = std::atoi(optarg);\n cout << \"Num Episodes: \" << NUMEPISODES << endl;\n break;\n\n case 'h':\n case '?':\n case 0:\n default:\n displayHelp();\n break;\n }\n }\n\n // default back to greedy if no coefficients\n if (exploreType == DIFF_AND_NOVEL_BONUS && v == 0 && n == 0)\n exploreType = GREEDY;\n\n // check for conflicting options\n // changed epsilon but not doing epsilon greedy exploration\n if (epsilonChanged && exploreType != EPSILONGREEDY){\n cout << \"No reason to change epsilon when not using epsilon-greedy exploration\" << endl;\n exit(-1);\n }\n\n // set history value but not doing uct w/history planner\n if (history > 0 && (plannerType == VALUE_ITERATION || plannerType == POLICY_ITERATION || plannerType == PRI_SWEEPING)){\n cout << \"No reason to set history higher than 0 if not using a UCT planner\" << endl;\n exit(-1);\n }\n\n // set action rate but not doing real-time planner\n if (actrateChanged && (plannerType == VALUE_ITERATION || plannerType == POLICY_ITERATION || plannerType == PRI_SWEEPING)){\n cout << \"No reason to set actrate if not using a UCT planner\" << endl;\n exit(-1);\n }\n\n // set lambda but not doing uct (lambda)\n if (lambdaChanged && (strcmp(agentType, \"texplore\") == 0 || strcmp(agentType, \"modelbased\") == 0 || strcmp(agentType, \"rmax\") == 0) && (plannerType == VALUE_ITERATION || plannerType == POLICY_ITERATION || plannerType == PRI_SWEEPING)){\n cout << \"No reason to set actrate if not using a UCT planner\" << endl;\n exit(-1);\n }\n\n // set n/v/b but not doing that diff_novel exploration\n if (bvnChanged && exploreType != DIFF_AND_NOVEL_BONUS){\n cout << \"No reason to set n or v if not doing variance & novelty exploration\" << endl;\n exit(-1);\n }\n\n // set combo other than best but only doing 1 model\n if (predType != BEST && nmodels == 1){\n cout << \"No reason to have model combo other than best with nmodels = 1\" << endl;\n exit(-1);\n }\n\n // set M but not doing explore unknown\n if (mChanged && exploreType != EXPLORE_UNKNOWN){\n cout << \"No reason to set M if not doing R-max style Explore Unknown exploration\" << endl;\n exit(-1);\n }\n\n if (PRINTS){\n if (stochastic)\n cout << \"Stohastic\\n\";\n else\n cout << \"Deterministic\\n\";\n }\n\n Random rng(1 + seed);\n\n std::vector<int> statesPerDim;\n\n // Construct environment here.\n Environment* e;\n\n if (strcmp(envType, \"cartpole\") == 0){\n if (PRINTS) cout << \"Environment: Cart Pole\\n\";\n e = new CartPole(rng, stochastic);\n }\n\n else if (strcmp(envType, \"mcar\") == 0){\n if (PRINTS) cout << \"Environment: Mountain Car\\n\";\n e = new MountainCar(rng, stochastic, false, delay);\n }\n\n // taxi\n else if (strcmp(envType, \"taxi\") == 0){\n if (PRINTS) cout << \"Environment: Taxi\\n\";\n e = new Taxi(rng, stochastic);\n }\n\n // Light World\n else if (strcmp(envType, \"lightworld\") == 0){\n if (PRINTS) cout << \"Environment: Light World\\n\";\n e = new LightWorld(rng, stochastic, 4);\n }\n\n // two rooms\n else if (strcmp(envType, \"tworooms\") == 0){\n if (PRINTS) cout << \"Environment: TwoRooms\\n\";\n e = new TwoRooms(rng, stochastic, true, delay, false);\n }\n\n // car vel, 2 to 7\n else if (strcmp(envType, \"car2to7\") == 0){\n if (PRINTS) cout << \"Environment: Car Velocity 2 to 7 m/s\\n\";\n e = new RobotCarVel(rng, false, true, false, lag);\n statesPerDim.resize(4,0);\n statesPerDim[0] = 12;\n statesPerDim[1] = 120;\n statesPerDim[2] = 4;\n statesPerDim[3] = 10;\n MAXSTEPS = 100;\n }\n // car vel, 7 to 2\n else if (strcmp(envType, \"car7to2\") == 0){\n if (PRINTS) cout << \"Environment: Car Velocity 7 to 2 m/s\\n\";\n e = new RobotCarVel(rng, false, false, false, lag);\n statesPerDim.resize(4,0);\n statesPerDim[0] = 12;\n statesPerDim[1] = 120;\n statesPerDim[2] = 4;\n statesPerDim[3] = 10;\n MAXSTEPS = 100;\n }\n // car vel, random vels\n else if (strcmp(envType, \"carrandom\") == 0){\n if (PRINTS) cout << \"Environment: Car Velocity Random Velocities\\n\";\n e = new RobotCarVel(rng, true, false, false, lag);\n statesPerDim.resize(4,0);\n statesPerDim[0] = 12;\n statesPerDim[1] = 48;\n statesPerDim[2] = 4;\n statesPerDim[3] = 10;\n MAXSTEPS = 100;\n }\n\n // four rooms\n else if (strcmp(envType, \"fourrooms\") == 0){\n if (PRINTS) cout << \"Environment: FourRooms\\n\";\n e = new FourRooms(rng, stochastic, true, false);\n }\n\n // four rooms with energy level\n else if (strcmp(envType, \"energy\") == 0){\n if (PRINTS) cout << \"Environment: EnergyRooms\\n\";\n e = new EnergyRooms(rng, stochastic, true, false);\n }\n\n // gridworld with fuel (fuel stations on top and bottom with random costs)\n else if (strcmp(envType, \"fuelworld\") == 0){\n if (PRINTS) cout << \"Environment: FuelWorld\\n\";\n e = new FuelRooms(rng, highvar, stochastic);\n }\n\n // stocks\n else if (strcmp(envType, \"stocks\") == 0){\n if (PRINTS) cout << \"Enironment: Stocks with \" << nsectors\n << \" sectors and \" << nstocks << \" stocks\\n\";\n e = new Stocks(rng, stochastic, nsectors, nstocks);\n }\n\n else {\n std::cerr << \"Invalid env type\" << endl;\n exit(-1);\n }\n\n const int numactions = e->getNumActions(); // Most agents will need this?\n\n std::vector<float> minValues;\n std::vector<float> maxValues;\n e->getMinMaxFeatures(&minValues, &maxValues);\n bool episodic = e->isEpisodic();\n\n cout << \"Environment is \";\n if (!episodic) cout << \"NOT \";\n cout << \"episodic.\" << endl;\n\n // lets just check this for now\n for (unsigned i = 0; i < minValues.size(); i++){\n if (PRINTS) cout << \"Feat \" << i << \" min: \" << minValues[i]\n << \" max: \" << maxValues[i] << endl;\n }\n\n // get max/min reward for the domain\n float rMax = 0.0;\n float rMin = -1.0;\n\n e->getMinMaxReward(&rMin, &rMax);\n float rRange = rMax - rMin;\n if (PRINTS) cout << \"Min Reward: \" << rMin\n << \", Max Reward: \" << rMax << endl;\n\n // set rmax as a bonus for certain exploration types\n if (rMax <= 0.0 && (exploreType == TWO_MODE_PLUS_R ||\n exploreType == CONTINUOUS_BONUS_R ||\n exploreType == CONTINUOUS_BONUS ||\n exploreType == THRESHOLD_BONUS_R)){\n rMax = 1.0;\n }\n\n\n float rsum = 0;\n\n if (statesPerDim.size() == 0){\n cout << \"set statesPerDim to \" << nstates << \" for all dim\" << endl;\n statesPerDim.resize(minValues.size(), nstates);\n }\n\n for (unsigned j = 0; j < NUMTRIALS; ++j) {\n\n // Construct agent here.\n Agent* agent;\n\n if (strcmp(agentType, \"qlearner\") == 0){\n if (PRINTS) cout << \"Agent: QLearner\" << endl;\n agent = new QLearner(numactions,\n discountfactor,\n initialvalue, //0.0, // initialvalue\n alpha, // alpha\n epsilon, // epsilon\n rng);\n }\n\n else if (strcmp(agentType, \"dyna\") == 0){\n if (PRINTS) cout << \"Agent: Dyna\" << endl;\n agent = new Dyna(numactions,\n discountfactor,\n initialvalue, //0.0, // initialvalue\n alpha, // alpha\n k, // k\n epsilon, // epsilon\n rng);\n }\n\n else if (strcmp(agentType, \"sarsa\") == 0){\n if (PRINTS) cout << \"Agent: SARSA\" << endl;\n agent = new Sarsa(numactions,\n discountfactor,\n initialvalue, //0.0, // initialvalue\n alpha, // alpha\n epsilon, // epsilon\n lambda,\n rng);\n }\n\n else if (strcmp(agentType, \"modelbased\") == 0 || strcmp(agentType, \"rmax\") || strcmp(agentType, \"texplore\")){\n if (PRINTS) cout << \"Agent: Model Based\" << endl;\n agent = new ModelBasedAgent(numactions,\n discountfactor,\n rMax, rRange,\n modelType,\n exploreType,\n predType,\n nmodels,\n plannerType,\n epsilon, // epsilon\n lambda,\n (1.0/actrate), //0.1, //0.1, //0.01, // max time\n M,\n minValues, maxValues,\n statesPerDim,//0,\n history, v, n,\n deptrans, reltrans, featPct, stochastic, episodic,\n rng);\n }\n\n else if (strcmp(agentType, \"savedpolicy\") == 0){\n if (PRINTS) cout << \"Agent: Saved Policy\" << endl;\n agent = new SavedPolicy(numactions,filename);\n }\n\n else {\n std::cerr << \"ERROR: Invalid agent type\" << endl;\n exit(-1);\n }\n\n // start discrete agent if we're discretizing (if nstates > 0 and not agent type 'c')\n int totalStates = 1;\n Agent* a2 = agent;\n // not for model based when doing continuous model\n if (nstates > 0 && (modelType != M5ALLMULTI || strcmp(agentType, \"qlearner\") == 0)){\n int totalStates = powf(nstates,minValues.size());\n if (PRINTS) cout << \"Discretize with \" << nstates << \", total: \" << totalStates << endl;\n agent = new DiscretizationAgent(nstates, a2,\n minValues, maxValues, PRINTS);\n }\n else {\n totalStates = 1;\n for (unsigned i = 0; i < minValues.size(); i++){\n int range = 1+maxValues[i] - minValues[i];\n totalStates *= range;\n }\n if (PRINTS) cout << \"No discretization, total: \" << totalStates << endl;\n }\n\n // before we start, seed the agent with some experiences\n agent->seedExp(e->getSeedings());\n\n // STEP BY STEP DOMAIN\n if (!episodic){\n\n // performance tracking\n float sum = 0;\n int steps = 0;\n float trialSum = 0.0;\n\n int a = 0;\n float r = 0;\n\n //////////////////////////////////\n // non-episodic\n //////////////////////////////////\n for (unsigned i = 0; i < NUMEPISODES; ++i){\n\n std::vector<float> es = e->sensation();\n\n // first step\n if (i == 0){\n\n // first action\n a = agent->first_action(es);\n r = e->apply(a);\n\n } else {\n // next action\n a = agent->next_action(r, es);\n r = e->apply(a);\n }\n\n // update performance\n sum += r;\n ++steps;\n\n std::cerr << r << endl;\n\n }\n ///////////////////////////////////\n\n rsum += sum;\n trialSum += sum;\n if (PRINTS) cout << \"Rsum(trial \" << j << \"): \" << trialSum << \" Avg: \"\n << (rsum / (float)(j+1))<< endl;\n\n }\n\n // EPISODIC DOMAINS\n else {\n\n //////////////////////////////////\n // episodic\n //////////////////////////////////\n for (unsigned i = 0; i < NUMEPISODES; ++i) {\n\n // performance tracking\n float sum = 0;\n int steps = 0;\n\n // first action\n std::vector<float> es = e->sensation();\n int a = agent->first_action(es);\n float r = e->apply(a);\n\n // update performance\n sum += r;\n ++steps;\n\n while (!e->terminal() && steps < MAXSTEPS) {\n\n // perform an action\n es = e->sensation();\n a = agent->next_action(r, es);\n r = e->apply(a);\n\n // update performance info\n sum += r;\n ++steps;\n\n }\n\n // terminal/last state\n if (e->terminal()){\n agent->last_action(r);\n }else{\n agent->next_action(r, e->sensation());\n }\n\n e->reset();\n std::cerr << sum << endl;\n\n rsum += sum;\n\n }\n\n }\n\n if (NUMTRIALS > 1) delete agent;\n\n }\n\n if (PRINTS) cout << \"Avg Rsum: \" << (rsum / (float)NUMTRIALS) << endl;\n\n} // end main\n\n"},{"alpha_fraction":0.7760140895843506,"alphanum_fraction":0.7760140895843506,"avg_line_length":42.61538314819336,"blob_id":"20bb18408ba86202ee274bd4811ec3400c045948","content_id":"6e731157013cfbae7994d4936bc0117d2417673a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":567,"license_type":"no_license","max_line_length":90,"num_lines":13,"path":"/build/rl-texplore-ros-pkg-master/src/rl_msgs/CMakeFiles/rl_msgs_generate_messages_cpp.dir/cmake_clean.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"file(REMOVE_RECURSE\n \"CMakeFiles/rl_msgs_generate_messages_cpp\"\n \"/home/justin/ros_test/devel/include/rl_msgs/RLEnvSeedExperience.h\"\n \"/home/justin/ros_test/devel/include/rl_msgs/RLExperimentInfo.h\"\n \"/home/justin/ros_test/devel/include/rl_msgs/RLEnvDescription.h\"\n \"/home/justin/ros_test/devel/include/rl_msgs/RLStateReward.h\"\n \"/home/justin/ros_test/devel/include/rl_msgs/RLAction.h\"\n)\n\n# Per-language clean rules from dependency scanning.\nforeach(lang )\n include(CMakeFiles/rl_msgs_generate_messages_cpp.dir/cmake_clean_${lang}.cmake OPTIONAL)\nendforeach()\n"},{"alpha_fraction":0.5642428398132324,"alphanum_fraction":0.5697131156921387,"avg_line_length":22.276397705078125,"blob_id":"a67412fc4e1ee8aa822993c1fc396599337c022e","content_id":"4ae503c12eca89664f4a8e78a75e6ca94c30ec4b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":7495,"license_type":"no_license","max_line_length":77,"num_lines":322,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Agent/Dyna.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include <rl_agent/Dyna.hh>\n#include <algorithm>\n\n#include <sys/time.h>\n\n\nDyna::Dyna(int numactions, float gamma,\n float initialvalue, float alpha, int k, float ep,\n\t\t Random rng):\n numactions(numactions), gamma(gamma),\n initialvalue(initialvalue), alpha(alpha), k(k),\n rng(rng), currentq(NULL), laststate(NULL), lastact(0)\n{\n\n epsilon = ep;\n ACTDEBUG = false; //true; //false;\n cout << \"Dyna agent with k:\" << k << endl;\n\n}\n\nDyna::~Dyna() {}\n\nint Dyna::first_action(const std::vector<float> &s) {\n\n if (ACTDEBUG){\n cout << \"First - in state: \";\n printState(s);\n cout << endl;\n }\n\n return getBestAction(s);\n\n}\n\nint Dyna::getBestAction(const std::vector<float> &s){\n //cout << \"get best action\" << endl;\n\n // for some amount of time, update based on randomly sampled experiences\n int numExp = (int)experiences.size();\n for (int i = 0; i < k && numExp > 0; i++){\n \n // update from randoml sampled action\n int exp = 0;\n if (numExp > 1)\n exp = rng.uniformDiscrete(0, numExp-1);\n //cout << count << \" Update exp \" << exp << endl;\n\n dynaExperience e = experiences[exp];\n\n std::vector<float> &Q_s = Q[e.s];\n if (e.term){\n Q_s[e.a] += alpha * (e.r - Q_s[e.a]);\n } else {\n std::vector<float> &Q_next = Q[e.next];\n const std::vector<float>::iterator max =\n random_max_element(Q_next.begin(), Q_next.end());\n Q_s[e.a] += alpha * (e.r + (gamma * *max) - Q_s[e.a]);\n }\n\n }\n\n\n // then do normal action selection\n // Get action values\n state_t st = canonicalize(s);\n std::vector<float> &Q_s = Q[st];\n\n // Choose an action\n const std::vector<float>::iterator a =\n rng.uniform() < epsilon\n ? Q_s.begin() + rng.uniformDiscrete(0, numactions - 1) // Choose randomly\n : random_max_element(Q_s.begin(), Q_s.end()); // Choose maximum\n\n // Store location to update value later\n currentq = &*a;\n laststate = st;\n lastact = a - Q_s.begin();\n\n if (ACTDEBUG){\n cout << \" act: \" << (a-Q_s.begin()) << \" val: \" << *a << endl;\n for (int iAct = 0; iAct < numactions; iAct++){\n cout << \" Action: \" << iAct \n\t << \" val: \" << Q_s[iAct] << endl;\n }\n cout << \"Took action \" << (a-Q_s.begin()) << \" from state \";\n printState(s);\n cout << endl;\n }\n\n return a - Q_s.begin();\n}\n\nvoid Dyna::addExperience(float r, state_t s, bool term){\n\n dynaExperience e;\n e.s = laststate;\n e.a = lastact;\n e.next = s;\n e.r = r;\n e.term = term;\n\n experiences.push_back(e);\n\n}\n\nint Dyna::next_action(float r, const std::vector<float> &s) {\n\n if (ACTDEBUG){\n cout << \"Next: got reward \" << r << \" in state: \";\n printState(s);\n cout << endl;\n }\n\n state_t st = canonicalize(s);\n\n addExperience(r,st,false);\n\n // Get action values\n std::vector<float> &Q_s = Q[st];\n const std::vector<float>::iterator max =\n random_max_element(Q_s.begin(), Q_s.end());\n\n // Update value of action just executed\n *currentq += alpha * (r + gamma * (*max) - *currentq);\n\n return getBestAction(s);\n\n}\n\n\n\n\nvoid Dyna::last_action(float r) {\n\n if (ACTDEBUG){\n cout << \"Last: got reward \" << r << endl;\n }\n\n addExperience(r,NULL,true);\n\n *currentq += alpha * (r - *currentq);\n currentq = NULL;\n laststate = NULL;\n}\n\nDyna::state_t Dyna::canonicalize(const std::vector<float> &s) {\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\n statespace.insert(s);\n state_t retval = &*result.first; // Dereference iterator then get pointer \n if (result.second) { // s is new, so initialize Q(s,a) for all a\n std::vector<float> &Q_s = Q[retval];\n Q_s.resize(numactions,initialvalue);\n }\n return retval; \n}\n\n\n\n std::vector<float>::iterator\nDyna::random_max_element(\n\t\t\t std::vector<float>::iterator start,\n\t\t\t std::vector<float>::iterator end) {\n\n std::vector<float>::iterator max =\n std::max_element(start, end);\n int n = std::count(max, end, *max);\n if (n > 1) {\n n = rng.uniformDiscrete(1, n);\n while (n > 1) {\n max = std::find(max + 1, end, *max);\n --n;\n }\n }\n return max;\n}\n\n\n\n\nvoid Dyna::setDebug(bool d){\n ACTDEBUG = d;\n}\n\n\nvoid Dyna::printState(const std::vector<float> &s){\n for (unsigned j = 0; j < s.size(); j++){\n cout << s[j] << \", \";\n }\n}\n\n\n\nvoid Dyna::seedExp(std::vector<experience> seeds){\n\n // for each seeding experience, update our model\n for (unsigned i = 0; i < seeds.size(); i++){\n experience e = seeds[i];\n \n laststate = canonicalize(e.s);\n lastact = e.act;\n state_t st = canonicalize(e.next);\n std::vector<float> &Q_s = Q[laststate];\n std::vector<float> &Q_next = Q[st];\n \n // add experience\n addExperience(e.reward,st,e.terminal);\n\n // get max value of next state\n const std::vector<float>::iterator max =\n random_max_element(Q_next.begin(), Q_next.end());\n\n // Get q value for action taken\n const std::vector<float>::iterator a = Q_s.begin() + e.act;\n currentq = &*a;\n\n // Update value of action just executed\n *currentq += alpha * (e.reward + gamma * (*max) - *currentq);\n\n \n /*\n cout << \"Seeding with experience \" << i << endl;\n cout << \"last: \" << (e.s)[0] << \", \" << (e.s)[1] << \", \" \n\t << (e.s)[2] << endl;\n cout << \"act: \" << e.act << \" r: \" << e.reward << endl;\n cout << \"next: \" << (e.next)[0] << \", \" << (e.next)[1] << \", \" \n\t << (e.next)[2] << \", \" << e.terminal << endl;\n cout << \"Q: \" << *currentq << \" max: \" << *max << endl;\n */\n\n }\n\n\n}\n\nvoid Dyna::logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax){\n std::vector<float> s;\n s.resize(2, 0.0);\n for (int i = xmin ; i < xmax; i++){\n for (int j = ymin; j < ymax; j++){\n s[0] = j;\n s[1] = i;\n std::vector<float> &Q_s = Q[canonicalize(s)];\n const std::vector<float>::iterator max =\n random_max_element(Q_s.begin(), Q_s.end());\n *of << (*max) << \",\";\n }\n }\n}\n\n\nfloat Dyna::getValue(std::vector<float> state){\n\n state_t s = canonicalize(state);\n\n // Get Q values\n std::vector<float> &Q_s = Q[s];\n\n // Choose an action\n const std::vector<float>::iterator a =\n random_max_element(Q_s.begin(), Q_s.end()); // Choose maximum\n\n // Get avg value\n float valSum = 0.0;\n float cnt = 0;\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n state_t s = canonicalize(*i);\n\n // get state's info\n std::vector<float> &Q_s = Q[s];\n \n for (int j = 0; j < numactions; j++){\n valSum += Q_s[j];\n cnt++;\n }\n }\n\n cout << \"Avg Value: \" << (valSum / cnt) << endl;\n\n return *a;\n}\n\n\nvoid Dyna::savePolicy(const char* filename){\n\n ofstream policyFile(filename, ios::out | ios::binary | ios::trunc);\n\n // first part, save the vector size\n std::set< std::vector<float> >::iterator i = statespace.begin();\n int fsize = (*i).size();\n policyFile.write((char*)&fsize, sizeof(int));\n\n // save numactions\n policyFile.write((char*)&numactions, sizeof(int));\n\n // go through all states, and save Q values\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n state_t s = canonicalize(*i);\n std::vector<float> *Q_s = &(Q[s]);\n\n // save state\n policyFile.write((char*)&((*i)[0]), sizeof(float)*fsize);\n\n // save q-values\n policyFile.write((char*)&((*Q_s)[0]), sizeof(float)*numactions);\n\n }\n\n policyFile.close();\n}\n\n\n\ndouble Dyna::getSeconds(){\n struct timezone tz;\n timeval timeT;\n gettimeofday(&timeT, &tz);\n return timeT.tv_sec + (timeT.tv_usec / 1000000.0);\n}\n"},{"alpha_fraction":0.7935222387313843,"alphanum_fraction":0.7935222387313843,"avg_line_length":29.875,"blob_id":"7afb77fcfc7ecd248f0b1ace3a00fd25bb65757b","content_id":"44e47469bf420c3692ddd7adbecf4ccea2a23e05","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":247,"license_type":"no_license","max_line_length":92,"num_lines":8,"path":"/src/mantis_model/CMakeFiles/clean_test_results_mantis_model.dir/cmake_clean.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"FILE(REMOVE_RECURSE\n \"CMakeFiles/clean_test_results_mantis_model\"\n)\n\n# Per-language clean rules from dependency scanning.\nFOREACH(lang)\n INCLUDE(CMakeFiles/clean_test_results_mantis_model.dir/cmake_clean_${lang}.cmake OPTIONAL)\nENDFOREACH(lang)\n"},{"alpha_fraction":0.7027310729026794,"alphanum_fraction":0.7037814855575562,"avg_line_length":23.10126495361328,"blob_id":"060cfbd92583345e52d7504116e2260159243c8f","content_id":"90f04235c922975597781d613af1a9a55ec30dc4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":1904,"license_type":"no_license","max_line_length":91,"num_lines":79,"path":"/src/rl-texplore-ros-pkg-master/src/rl_env/include/rl_env/MountainCar.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file MountainCar.hh\n Defines the Mountain Car domain, with possible action delays or linearized \n transition dynamics.\n \\author Todd Hester\n*/\n\n#ifndef _MOUNTAINCAR_H_\n#define _MOUNTAINCAR_H_\n\n#include <set>\n#include <deque>\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n\n/** This class defines the Mountain Car domain, with possible action delays or linearized \n transition dynamics.\n*/\nclass MountainCar: public Environment {\npublic:\n\n /** Creates a deterministic MountainCar domain.\n \\param rand Random number generator used solely for random\n initial states. \n */\n MountainCar(Random &rand);\n\n /** Creates a Mountain Car domain.\n \\param rand Random number generator\n \\param stochastic if transitions are noisy\n \\param lin create linearized transition dynamics\n \\param delay # of steps to delay state observations\n */\n MountainCar(Random &rand, bool stochastic, bool lin, int delay);\n\n virtual ~MountainCar();\n\n virtual const std::vector<float> &sensation() const;\n virtual float apply(int action);\n\n virtual bool terminal() const;\n virtual void reset();\n\n virtual int getNumActions();\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\n virtual void getMinMaxReward(float* minR, float* maxR);\n\n /** Set the state vector (for debug purposes) */\n void setSensation(std::vector<float> newS);\n\n virtual std::vector<experience> getSeedings();\n\n /** Get an experience for the given state-action */\n experience getExp(float s0, float s1, int a);\n \nprotected:\n enum car_action_t {REVERSE, ZERO, FORWARD};\n\nprivate:\n\n std::deque<float> posHistory;\n std::deque<float> velHistory;\n\n const bool noisy;\n Random &rng;\n\n std::vector<float> s;\n \n float &pos;\n float &vel;\n const bool linear;\n int delay;\n\n float reward();\n\n float bound(float val, float min, float max);\n \n};\n\n#endif\n"},{"alpha_fraction":0.6909680962562561,"alphanum_fraction":0.693329393863678,"avg_line_length":28.982301712036133,"blob_id":"8210ce515ff45afaf7800739bfd4c73463dce4e2","content_id":"13644f1556a7d2388bb79d14ec0fdfdf00e2e1fa","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":3388,"license_type":"no_license","max_line_length":91,"num_lines":113,"path":"/src/rl-texplore-ros-pkg-master/src/rl_env/include/rl_env/taxi.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file taxi.hh\n Defines the taxi domain, from:\n Dietterich, \"The MAXQ method for hierarchical reinforcement learning,\" ICML 1998. \n \\author Todd Hester\n \\author Nick Jong\n*/\n\n#ifndef _TAXI_H_\n#define _TAXI_H_\n\n#include <set>\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n#include \"gridworld.hh\"\n\n/** This class defines the Taxi domain. */\nclass Taxi: public Environment {\npublic:\n /** Creates a Taxi domain using the specified map.\n \\param rand Random number generator to use.\n \\param gridworld The map to use.\n \\param stochastic Whether to use nondeterministic actions and\n fickle passenger. */\n Taxi(Random &rand, const Gridworld *gridworld, bool stochastic);\n\n /** Creates a deterministic Taxi domain.\n \\param rand Random number generator used solely for random\n initial states. */\n Taxi(Random &rand);\n\n /** Creates a possibly noisy Taxi domain. \n \\param rand Random number generator to use.\n \\param stochastic Whether to use nondeterministic actions and\n fickle passenger. \n */\n Taxi(Random &rand, bool stochastic);\n\n /** Creates a random Taxi domain of the given size. \n \\param rand Random number generator to use.\n \\param width width of grid\n \\param height height of grid\n \\param stochastic Whether to use nondeterministic actions and\n fickle passenger. \n */ \n Taxi(Random &rand, unsigned width, unsigned height, bool stochastic);\n\n virtual ~Taxi();\n\n virtual const std::vector<float> &sensation() const;\n virtual float apply(int action);\n\n virtual bool terminal() const;\n virtual void reset();\n virtual int getNumActions();\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\n virtual void getMinMaxReward(float* minR, float* maxR);\n virtual std::vector<experience> getSeedings();\n\n /** Get an example experience for the given state-action. */\n experience getExp(float s0, float s1, float s2, float s3, int a);\n\n /** Set the current state (for debug purposes) */\n void setSensation(std::vector<float> newS);\n\nprotected:\n typedef std::pair<float,float> coord_t;\n enum taxi_action_t {NORTH, SOUTH, EAST, WEST, PICKUP, PUTDOWN};\n\n /** The default landmarks for the Taxi domain */\n class DefaultLandmarks: public std::vector<coord_t> {\n public:\n DefaultLandmarks();\n };\n\nprivate:\n const Gridworld *const grid;\n std::vector<coord_t> landmarks; // not const because of randomize_landmarks\n const bool noisy;\n Random &rng;\n\n std::vector<float> s;\n bool fickle;\n\n float &ns;\n float &ew;\n float &pass;\n float &dest;\n\n /** Create the default gridworld */\n static const Gridworld *create_default_map();\n\n static const DefaultLandmarks defaultlandmarks;\n\n /** Corrupts a movement action.\n \\param action The intended action\n \\return The action actually executed */\n taxi_action_t add_noise(taxi_action_t action);\n\n /** If the domain is noisy and the taxi has just taken its first\n step since picking up the passenger, then potentially change the\n destination. */\n void apply_fickle_passenger();\n\n /** Randomly assigns the four landmarks to any four distinct\n positions in the world. */\n void randomize_landmarks();\n\n /** Randomly assigns the landmarks to positions near the corners of\n the map. */\n void randomize_landmarks_to_corners();\n};\n\n#endif\n"},{"alpha_fraction":0.780281662940979,"alphanum_fraction":0.7887324094772339,"avg_line_length":49.85714340209961,"blob_id":"73ef949951bbf549a8827dd540d945076e642dbc","content_id":"0fcefe86ede41a869ec5bf01e39a61aaac40acff","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":355,"license_type":"no_license","max_line_length":65,"num_lines":7,"path":"/src/roundbot_control/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"roundbot_control\")\nset(roundbot_control_MAINTAINER \"micho <micho@todo.todo>\")\nset(roundbot_control_DEPRECATED \"\")\nset(roundbot_control_VERSION \"0.0.0\")\nset(roundbot_control_BUILD_DEPENDS \"controller_interface\")\nset(roundbot_control_RUN_DEPENDS \"controller_interface\" \"roscpp\")\nset(roundbot_control_BUILDTOOL_DEPENDS \"catkin\" \"roscpp\")"},{"alpha_fraction":0.7864372730255127,"alphanum_fraction":0.7894737124443054,"avg_line_length":21.953489303588867,"blob_id":"f40102b51609c4bb77de1cf551756045bd9477cc","content_id":"969c03997aa2582936a87122295036b5140bb712","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":988,"license_type":"no_license","max_line_length":64,"num_lines":43,"path":"/src/roundbot_control/CMakeLists.txt","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"cmake_minimum_required(VERSION 2.8.3)\nproject(roundbot_control)\n\nfind_package(catkin REQUIRED COMPONENTS\n roscpp\n controller_interface\n)\n\ncatkin_package(\n INCLUDE_DIRS include\n LIBRARIES wheel_speed_controller diff_controller\n)\n\ninclude_directories(\n include\n ${catkin_INCLUDE_DIRS}\n)\n\nadd_library(motor_sim src/MotorSim.cpp)\ntarget_link_libraries(motor_sim\n ${catkin_LIBRARIES}\n)\n\nadd_library(wheel_speed_controller src/WheelSpeedController.cpp)\ntarget_link_libraries(wheel_speed_controller\n ${catkin_LIBRARIES}\n motor_sim\n)\n\nadd_library(diff_controller src/DiffController.cpp)\ntarget_link_libraries(diff_controller\n ${catkin_LIBRARIES}\n motor_sim\n)\n\ninstall(TARGETS wheel_speed_controller diff_controller\n ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}\n LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}\n RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}\n )\n\ninstall(FILES roundbot_control_plugins.xml\n DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION})\n\n"},{"alpha_fraction":0.5598581433296204,"alphanum_fraction":0.5926241278648376,"avg_line_length":21.889610290527344,"blob_id":"c5647db6f8ec35b5daa66efaf7e9a341c0ba2f56","content_id":"c5b1032fd53f03afcc38767375a340a911efde58","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":7050,"license_type":"no_license","max_line_length":135,"num_lines":308,"path":"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/RobotCarVel.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file RobotCarVel.cc\n This domain is a simulation of velocity control for the Austin Robot \n Technology autonomous vehicle. \n This vehicle is described in:\n Beeson et al, \"Multiagent Interactions in Urban Driving,\" Journal of Physical Agents, March 2008.\n The velocity control task is described in:\n Hester, Quinlan, and Stone, \"A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control\", arXiv 1105.1749, 2011.\n \\author Todd Hester\n*/\n\n#include <rl_env/RobotCarVel.hh>\n\n// normal: true values of each\nRobotCarVel::RobotCarVel(Random &rand, bool randomVel, bool upVel, bool tenToSix, bool lag):\n rng(rand),\n s(4),\n hidden(2),\n targetVel(s[0]),\n currVel(s[1]),\n trueThrottle(hidden[0]),\n trueBrake(hidden[1]),\n throttleTarget(s[2]),\n brakeTarget(s[3]),\n randomVel(randomVel),\n upVel(upVel),\n tenToSix(tenToSix),\n lag(lag)\n{\n reset();\n}\n \n\n\nRobotCarVel::~RobotCarVel() { }\n\nconst std::vector<float> &RobotCarVel::sensation() const { \n return s; \n}\n\nfloat RobotCarVel::apply(int action) {\n\n float HZ = 10.0;\n\n actNum++;\n\n // figure out reward based on target/curr vel\n float reward = -10.0 * fabs(currVel - targetVel);\n\n float throttleChangePct = 1.0;//0.9; //1.0;\n float brakeChangePct = 1.0;//0.9; //1.0;\n if (lag){\n brakeChangePct = brakePosVel / HZ;\n float brakeVelTarget = 3.0*(brakeTarget - trueBrake);\n brakePosVel += (brakeVelTarget - brakePosVel) * 3.0 / HZ;\n trueBrake += brakeChangePct;\n } else {\n trueBrake += (brakeTarget-trueBrake) * brakeChangePct;\n }\n trueBrake = bound(trueBrake, 0.0, 1.0);\n\n // figure out the change of true brake/throttle position based on last targets\n trueThrottle += (throttleTarget-trueThrottle) * throttleChangePct;\n trueThrottle = bound(trueThrottle, 0.0, 0.4);\n\n // figure out new velocity based on those positions \n // from the stage simulation\n float g = 9.81; // acceleration due to gravity\n float throttle_accel = g;\n float brake_decel = g;\n float rolling_resistance = 0.01 * g;\n float drag_coeff = 0.01;\n float idle_accel = (rolling_resistance\n + drag_coeff * 3.1 * 3.1);\n float wind_resistance = drag_coeff * currVel * currVel;\n float accel = (idle_accel\n + trueThrottle * throttle_accel\n - trueBrake * brake_decel\n - rolling_resistance\n - wind_resistance);\n currVel += (accel / HZ);\n currVel = bound(currVel, 0.0, 12.0);\n \n\n // figure out action's adjustment to throttle/brake targets\n if (action == THROTTLE_UP){\n brakeTarget = 0.0;\n if (throttleTarget < 0.4)\n throttleTarget += 0.1;\n }\n else if (action == THROTTLE_DOWN){\n brakeTarget = 0.0;\n if (throttleTarget > 0.0)\n throttleTarget -= 0.1;\n }\n else if (action == BRAKE_UP){\n throttleTarget = 0.0;\n if (brakeTarget < 1.0)\n brakeTarget += 0.1;\n }\n else if (action == BRAKE_DOWN){\n throttleTarget = 0.0;\n if (brakeTarget > 0.0)\n brakeTarget -= 0.1;\n }\n else if (action != NOTHING){\n cout << \"invalid action \" << action << endl;\n }\n throttleTarget = bound(throttleTarget, 0.0, 0.4);\n brakeTarget = bound(brakeTarget, 0.0, 1.0);\n throttleTarget = 0.1 * (float)((int)(throttleTarget*10.0));\n brakeTarget = 0.1 * (float)((int)(brakeTarget*10.0));\n \n /*\n cout << action << \", throt: \" << throttleTarget << \", brake: \" << brakeTarget\n << \", trueBrake: \" << trueBrake \n << \", currVel: \" << currVel << \", reward: \" << reward << endl;\n */\n\n return reward;\n\n}\n\n\nbool RobotCarVel::terminal() const {\n return false;\n}\n\n\n\nvoid RobotCarVel::reset() {\n\n // for now\n if (randomVel){\n targetVel = rng.uniformDiscrete(0, 11);\n currVel = rng.uniformDiscrete(0, 11);\n } else {\n if (tenToSix){ // 10 to 6\n if (upVel){\n targetVel = 10.0;\n currVel = 6.0;\n } else {\n targetVel = 6.0;\n currVel = 10.0;\n } \n } else { // 7 to 2\n if (upVel){\n targetVel = 7.0;\n currVel = 2.0;\n } else {\n targetVel = 2.0;\n currVel = 7.0;\n } \n }\n }\n\n actNum = 0;\n throttleTarget = rng.uniformDiscrete(0,4) * 0.1;\n brakeTarget = 0.0;\n trueThrottle = throttleTarget;\n trueBrake = brakeTarget;\n brakePosVel = 0.0;\n\n}\n\n\n\nint RobotCarVel::getNumActions(){\n return 5;\n}\n\n\nvoid RobotCarVel::setSensation(std::vector<float> newS){\n if (s.size() != newS.size()){\n cerr << \"Error in sensation sizes\" << endl;\n }\n\n for (unsigned i = 0; i < newS.size(); i++){\n s[i] = newS[i];\n }\n}\n\nstd::vector<experience> RobotCarVel::getSeedings() {\n\n // return seedings\n std::vector<experience> seeds;\n //return seeds;\n\n \n reset();\n\n /*\n // seeds of perfect velocity\n if (randomVel){\n for (int i = 0; i < 12; i++){\n // 3 random of each\n for (int j = 0; j < 3; j++)\n seeds.push_back(getRandomVelSeed(i));\n } \n } else {\n // just for target velocity\n // 5 random seeds\n for (int j = 0; j < 5; j++){\n seeds.push_back(getRandomVelSeed(targetVel));\n }\n }\n */\n\n \n // some completely random (non target)\n for (int i = 0; i < 25; i++){\n float vel = rng.uniform(0,11);\n\n float throt = 0;\n float brake = 0;\n if (rng.bernoulli(0.5)){\n throt = rng.uniformDiscrete(0,4)*0.1;\n } else {\n brake = rng.uniformDiscrete(0,9)*0.1;\n } \n int act = i%getNumActions();\n seeds.push_back(getExp(targetVel,vel,throt,brake,act));\n }\n\n reset();\n\n return seeds;\n}\n\nexperience RobotCarVel::getRandomVelSeed(float target){\n float throt = 0;\n float brake = 0;\n if (rng.bernoulli(0.5)){\n throt = rng.uniformDiscrete(0,4)*0.1;\n } else {\n brake = rng.uniformDiscrete(0,4)*0.1;\n } \n\n return getExp(target, target, throt, brake, rng.uniformDiscrete(0,4));\n}\n\nexperience RobotCarVel::getExp(float s0, float s1, float s2, float s3, int a){\n\n if (!randomVel) s0 = targetVel;\n\n experience e;\n\n e.s.resize(4, 0.0);\n e.next.resize(4, 0.0);\n\n targetVel = s0;\n currVel = s1;\n throttleTarget = s2;\n brakeTarget = s3;\n trueThrottle = throttleTarget;\n trueBrake = brakeTarget;\n brakePosVel = 0.0;\n\n e.act = a;\n e.s = sensation();\n e.reward = apply(e.act);\n\n e.terminal = terminal();\n e.next = sensation();\n\n /*\n cout << \"seed from state: \";\n for (unsigned i = 0; i < e.s.size(); i++){\n cout << e.s[i] << \", \";\n } \n cout << \"act: \" << e.act << \" reward: \" << e.reward << \" next: \";\n for (unsigned i = 0; i < e.next.size(); i++){\n cout << e.next[i] << \", \";\n } \n cout << endl;\n */\n\n reset();\n\n return e;\n}\n\n\nvoid RobotCarVel::getMinMaxFeatures(std::vector<float> *minFeat,\n std::vector<float> *maxFeat){\n \n minFeat->resize(s.size(), 0.0);\n maxFeat->resize(s.size(), 12.0);\n\n (*maxFeat)[2] = 0.4;\n (*maxFeat)[3] = 1.0;\n\n}\n\nvoid RobotCarVel::getMinMaxReward(float *minR,\n float *maxR){\n \n *minR = -120.0;\n *maxR = 0.0; \n \n}\n\nfloat RobotCarVel::bound(float val, float min, float max){\n if (val < min)\n return min;\n if (val > max)\n return max;\n return val;\n}\n"},{"alpha_fraction":0.7822349667549133,"alphanum_fraction":0.7908309698104858,"avg_line_length":49,"blob_id":"c58c6de0972a2ef6b80a3a649aa85176391da3f0","content_id":"4e553c30fd0f94deae2c74a30f94bbb2da801cd9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":349,"license_type":"no_license","max_line_length":54,"num_lines":7,"path":"/src/roundbot_description/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"roundbot_description\")\nset(roundbot_description_MAINTAINER \"micho <micho@todo.todo>\")\nset(roundbot_description_DEPRECATED \"\")\nset(roundbot_description_VERSION \"0.0.0\")\nset(roundbot_description_BUILD_DEPENDS \"urdf\" \"xacro\")\nset(roundbot_description_RUN_DEPENDS \"urdf\" \"xacro\")\nset(roundbot_description_BUILDTOOL_DEPENDS \"catkin\")"},{"alpha_fraction":0.6486238241195679,"alphanum_fraction":0.660717248916626,"avg_line_length":27.34515380859375,"blob_id":"6387bbe93445b4b98e1a394acaaea628b37cc46d","content_id":"458e3021bedd9604acc9a177719abb7856f01ef1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":11990,"license_type":"no_license","max_line_length":118,"num_lines":423,"path":"/src/rl-texplore-ros-pkg-master/src/rl_common/include/rl_common/core.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#ifndef _RLCORE_H_\n#define _RLCORE_H_\n\n#include \"Random.h\"\n#include <vector>\n#include <map>\n\n\n/** \\file\n Fundamental declarations for the universal concepts in the\n reinforcement learning framework.\n \\author Nick Jong\n \\author Todd Hester\n*/\n\n\n// types of models\n#define RMAX 0\n#define TABULAR 0\n#define SLF 1\n#define C45TREE 2\n#define SINGLETREE 3\n#define SVM 4\n#define STUMP 5\n#define M5MULTI 6\n#define M5SINGLE 7\n#define M5ALLMULTI 8\n#define M5ALLSINGLE 9 \n#define LSTMULTI 10\n#define LSTSINGLE 11\n#define ALLM5TYPES 12\n#define GPREGRESS 13\n#define GPTREE 14\n\nconst std::string modelNames[] = {\n \"Tabular\",\n \"SLF\",\n \"C4.5 Tree\",\n \"Single Tree\",\n \"SVM\",\n \"Stump\",\n \"M5 Tree\",\n \"M5 Tree\",\n \"M5 Tree\",\n \"M5 Tree\",\n \"LS Tree\",\n \"LS Tree\",\n \"M5 Combo\",\n \"GP Regression\",\n \"GP Tree\"\n};\n\n// types of model combos\n#define AVERAGE 1\n#define WEIGHTAVG 2\n#define BEST 3\n#define SEPARATE 4 // sep model for planning, and forest for uncertainty\n\nconst std::string comboNames[] = {\n \"Average\",\n \"Weighted Average\",\n \"Best\",\n \"Separate\"\n};\n\n// types of exploration\n#define EXPLORE_UNKNOWN 0\n#define TWO_MODE 1\n#define TWO_MODE_PLUS_R 2\n#define CONTINUOUS_BONUS 3\n#define THRESHOLD_BONUS 4\n#define CONTINUOUS_BONUS_R 5\n#define THRESHOLD_BONUS_R 6\n#define NO_EXPLORE 7\n#define GREEDY 7\n#define EPSILONGREEDY 8\n#define VISITS_CONF 9\n#define UNVISITED_BONUS 11\n#define UNVISITED_ACT_BONUS 13\n#define DIFF_AND_VISIT_BONUS 16\n#define NOVEL_STATE_BONUS 18\n#define DIFF_AND_NOVEL_BONUS 19\n\nconst std::string exploreNames[] = {\n \"Explore Unknowns\",\n \"Two Modes\",\n \"Two Models +R\",\n \"Continuous Bonus\",\n \"Threshold Bonus\",\n \"Continuous Bonus +R\",\n \"Threshold Bonus +R\",\n \"Greedy\",\n \"Epsilon-Greedy\",\n \"Visits Confidence\",\n \"Type 10\",\n \"Unvisited State Bonus\",\n \"Type 12\", \n \"Unvisited Action Bonus\",\n \"Type 14\",\n \"Type 15\",\n \"Model Diff & Visit Bonus\",\n \"Type 17\",\n \"FeatDist Bonus\",\n \"Model Diff & FeatDist Bonus\"\n};\n\n// types of planners\n#define VALUE_ITERATION 0\n#define POLICY_ITERATION 1\n#define PRI_SWEEPING 2\n#define UCT 3\n#define ET_UCT 4\n#define ET_UCT_WITH_ENV 5\n#define SWEEPING_UCT_HYBRID 6\n#define CMAC_PLANNER 7\n#define NN_PLANNER 8\n#define MOD_PRI_SWEEPING 9\n#define ET_UCT_L1 10\n#define UCT_WITH_L 11\n#define UCT_WITH_ENV 12\n#define PARALLEL_ET_UCT 13\n#define ET_UCT_ACTUAL 14\n#define ET_UCT_CORNERS 15\n#define PAR_ETUCT_ACTUAL 16\n#define PAR_ETUCT_CORNERS 17\n#define POMDP_ETUCT 18\n#define POMDP_PAR_ETUCT 19\n#define MBS_VI 20\n\nconst std::string plannerNames[] = {\n \"Value Iteration\",\n \"Policy Iteration\",\n \"Prioritized Sweeping\",\n \"UCT\",\n \"UCT\",\n \"UCT\",\n \"Sweeping UCT Hybrid\",\n \"CMACs\",\n \"NN\",\n \"Mod. Pri Sweeping\",\n \"UCT L=1\",\n \"UCT L\",\n \"UCT Env\",\n \"Parallel UCT\",\n \"Real-Valued UCT\",\n \"Corner UCT\",\n \"Parallel Real-Valued UCT\",\n \"Parallel Corner UCT\",\n \"Delayed UCT\",\n \"Parallel Delayed UCT\",\n \"Model Based Simulation - VI\"\n};\n \n\n\n#define EPSILON 1e-5\n\n/** Experience <s,a,s',r> struct */\nstruct experience {\n std::vector<float> s;\n int act;\n float reward;\n std::vector<float> next;\n bool terminal;\n};\n\n/** Training instances for prediction models */\nstruct modelPair {\n std::vector<float> in;\n std::vector<float> out;\n};\n\n/** Training instances for classification models */\nstruct classPair {\n std::vector<float> in;\n float out;\n};\n\n/** Interface for an environment, whose states can be represented as\n vectors of floats and whose actions can be represented as ints.\n Implementations of the Environment interface determine how actions\n influence sensations. Note that this design assumes only one\n agent: it would be more accurate to name this interface\n EnvironmentAsPerceivedByOneParticularAgent. */\nclass Environment {\npublic:\n /** Provides access to the current sensation that the environment\n gives to the agent.\n \\return The current sensation. */\n virtual const std::vector<float> &sensation() const = 0;\n\n /** Allows an agent to affect its environment.\n \\param action The action the agent wishes to apply.\n \\return The immediate one-step reward caused by the action. */\n virtual float apply(int action) = 0;\n\n /** Determines whether the environment has reached a terminal state.\n \\return true iff the task is episodic and the present episode\n has ended. Nonepisodic tasks should simply always\n return false. */\n virtual bool terminal() const = 0;\n\n /** Resets the internal state of the environment according to some\n initial state distribution. Typically the user calls this only\n for episodic tasks that have reached terminal states, but this\n usage is not required. */\n virtual void reset() = 0;\n\n /** Returns the number of actions available in this environment.\n \\return The number of actions available */\n virtual int getNumActions() = 0;\n\n /** Gets the minimum and maximum of the features in the environment.\n */\n virtual void getMinMaxFeatures(std::vector<float> *minFeat,\n std::vector<float> *maxFeat) = 0;\n\n /** Gets the minimum and maximum one-step reward in the domain. */\n virtual void getMinMaxReward(float *minR, float *maxR) = 0;\n\n /** Returns if the domain is episodic (true by default). */\n virtual bool isEpisodic(){ return true; };\n\n /** Get seeding experiences for agent. */\n virtual std::vector<experience> getSeedings()\n {\n std::vector<experience> e;\n return e;\n } ;\n\n /** Set the current state for testing purposes. */\n virtual void setSensation(std::vector<float> s){};\n\n virtual ~Environment() {};\n\n};\n\n/** Interface for an agent. Implementations of the Agent interface\n determine the choice of actions given previous sensations and\n rewards. */\nclass Agent {\npublic:\n /** Determines the first action that an agent takes in an\n environment. This method implies that the environment is\n currently in an initial state.\n \\param s The initial sensation from the environment.\n \\return The action the agent wishes to take first. */\n virtual int first_action(const std::vector<float> &s) = 0;\n\n /** Determines the next action that an agent takes in an environment\n and gives feedback for the previous action. This method may\n only be called if the last method called was first_action or\n next_action.\n \\param r The one-step reward resulting from the previous action.\n \\param s The current sensation from the environment.\n \\return The action the agent wishes to take next. */\n virtual int next_action(float r, const std::vector<float> &s) = 0;\n\n /** Gives feedback for the last action taken. This method may only\n be called if the last method called was first_action or\n next_action. It implies that the task is episodic and has just\n terminated. Note that terminal sensations (states) are not\n represented.\n \\param r The one-step reward resulting from the previous action. */\n virtual void last_action(float r) = 0;\n\n /** Set some debug flags on/off */\n virtual void setDebug(bool d) = 0;\n\n /** Use the model seeds from the environment to initialize the agent or its model */\n virtual void seedExp(std::vector<experience> seeds) {};\n\n /** Save the current policy to a file */\n virtual void savePolicy(const char* filename) {};\n\n virtual ~Agent() {};\n};\n\n/** Interface for a model that predicts a vector of floats given a vector of floats as input. */\nclass Model {\npublic:\n /** Train the model on a vector of training instances */\n virtual bool trainInstances(std::vector<modelPair> &instances) = 0;\n\n /** Train the model on a single training instance */\n virtual bool trainInstance(modelPair &instance) = 0;\n\n /** Get the model's prediction for a given input */\n virtual std::vector<float> testInstance(const std::vector<float> &in) = 0;\n\n virtual ~Model() {};\n};\n\n/** Interface for a classification model that predicts a class given a vector of floats as input. */\nclass Classifier {\npublic:\n /** Train the model on a vector of training instances */\n virtual bool trainInstances(std::vector<classPair> &instances) = 0;\n\n /** Train the model on a single training instance */\n virtual bool trainInstance(classPair &instance) = 0;\n\n /** Get the model's prediction for a given input */\n virtual void testInstance(const std::vector<float> &in, std::map<float, float>* retval) = 0;\n\n /** Get the model's confidence in its predictions for a given input. */\n virtual float getConf(const std::vector<float> &in) = 0;\n\n /** Get a copy of the model */\n virtual Classifier* getCopy() = 0;\n\n virtual ~Classifier() {};\n};\n\n/** All the relevant information predicted by a model for a given state-action.\n This includes predicted reward, next state probabilities, probability of episod termination, and model confidence.\n*/\nstruct StateActionInfo {\n bool known;\n float reward;\n float termProb;\n int frameUpdated;\n\n // map from outcome state to probability\n std::map< std::vector<float> , float> transitionProbs;\n\n StateActionInfo(){\n known = false;\n reward = 0.0;\n termProb = 0.0;\n frameUpdated = -1;\n };\n};\n\n\n/** Interface for a model of an MDP. */\nclass MDPModel {\npublic:\n /** Update the MDP model with a vector of experiences. */\n virtual bool updateWithExperiences(std::vector<experience> &instances) = 0;\n\n /** Update the MDP model with a single experience. */\n virtual bool updateWithExperience(experience &instance) = 0;\n\n /** Get the predictions of the MDP model for a given state action */\n virtual float getStateActionInfo(const std::vector<float> &state, int action, StateActionInfo* retval) = 0;\n\n /** Get a copy of the MDP Model */\n virtual MDPModel* getCopy() = 0;\n virtual ~MDPModel() {};\n};\n\n/** Interface for planners */\nclass Planner {\npublic:\n /** Give the planner the model being used with the agent */\n virtual void setModel(MDPModel* model) = 0;\n\n /** Update the given model with an experience <s,a,s',r>. */\n virtual bool updateModelWithExperience(const std::vector<float>& last,\n int act,\n const std::vector<float>& curr,\n float reward, bool terminal) = 0;\n\n /** Plan a new policy suing the current model. */\n virtual void planOnNewModel() = 0;\n\n /** Return the best action for a given state. */\n virtual int getBestAction(const std::vector<float> &s) = 0;\n\n /** Save the policy to a file. */\n virtual void savePolicy(const char* filename) {};\n\n /** Set whether the next experiences are seeds or actual experiences from the agent. */\n virtual void setSeeding(bool seeding) {};\n\n /** Set if this is the first experience of the agent. */\n virtual void setFirst() {};\n\n /** A method to return at random one of the maximum values in the vector. \n Such that when more than one actions are optimal, we select one of them at random.\n */\n std::vector<float>::iterator\n random_max_element(std::vector<float>::iterator start,\n\t\t std::vector<float>::iterator end) {\n const float Q_EPSILON = 1e-4;\n \n std::vector<float>::iterator max =\n std::max_element(start, end);\n\n // find # within epsilon of max\n int nfound = 0;\n for (std::vector<float>::iterator it = start; it != end; it++){\n if (fabs(*it - *max) < Q_EPSILON){\n nfound++;\n }\n }\n \n // only 1: take it\n if (nfound == 1)\n return max;\n\n // take one of close to max at random\n for (std::vector<float>::iterator it = start; it != end; it++){\n if (fabs(*it - *max) < Q_EPSILON){\n if (rng.uniform() < (1.0 / (float)nfound)){\n return it;\n }\n nfound--;\n }\n }\n \n return max;\n };\n\n virtual ~Planner() {};\n \n Random rng;\n\n};\n\n\n#endif\n"},{"alpha_fraction":0.6849710941314697,"alphanum_fraction":0.6849710941314697,"avg_line_length":14.311111450195312,"blob_id":"acc207bf7ddeebfd354dc4b2f5d93d902a72130a","content_id":"7b8aba35ac041d179a1b7ffb592c3dc30454694a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":692,"license_type":"no_license","max_line_length":77,"num_lines":45,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/MultipleModels.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#ifndef _MULTIPLEMODELS_HH_\n#define _MULTIPLEMODELS_HH_\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n#include <vector>\n\n\n\n/** Multiple Models */\nclass MultipleModels: public Model {\n\npublic:\n\n MultipleModels(int id, int nInput, int nOutput, int modelType, Random rng);\n\n virtual ~MultipleModels();\n\n\n\n\n\n virtual bool trainInstance(std::vector<float> input, \n\t\t\t std::vector<float> output);\n virtual std::vector<float> testInstance(std::vector<float> input);\n \n // helper functions\n void createModels();\n\nprivate:\n\n const int id;\n const int nInput; \n const int nOutput;\n const int type;\n \n Random rng;\n\n // MODELS\n std::vector<Model*> models;\n\n};\n\n\n#endif\n \n"},{"alpha_fraction":0.781899094581604,"alphanum_fraction":0.781899094581604,"avg_line_length":50.846153259277344,"blob_id":"db771a1f56a940624ccd0256fe9865d768b65f50","content_id":"91292f22f1dba7f06d60b08f0a1ec22f8f2e7ed9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":674,"license_type":"no_license","max_line_length":91,"num_lines":13,"path":"/build/rl-texplore-ros-pkg-master/src/rl_msgs/CMakeFiles/rl_msgs_generate_messages_lisp.dir/cmake_clean.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"file(REMOVE_RECURSE\n \"CMakeFiles/rl_msgs_generate_messages_lisp\"\n \"/home/justin/ros_test/devel/share/common-lisp/ros/rl_msgs/msg/RLEnvSeedExperience.lisp\"\n \"/home/justin/ros_test/devel/share/common-lisp/ros/rl_msgs/msg/RLExperimentInfo.lisp\"\n \"/home/justin/ros_test/devel/share/common-lisp/ros/rl_msgs/msg/RLEnvDescription.lisp\"\n \"/home/justin/ros_test/devel/share/common-lisp/ros/rl_msgs/msg/RLStateReward.lisp\"\n \"/home/justin/ros_test/devel/share/common-lisp/ros/rl_msgs/msg/RLAction.lisp\"\n)\n\n# Per-language clean rules from dependency scanning.\nforeach(lang )\n include(CMakeFiles/rl_msgs_generate_messages_lisp.dir/cmake_clean_${lang}.cmake OPTIONAL)\nendforeach()\n"},{"alpha_fraction":0.7771260738372803,"alphanum_fraction":0.7829912304878235,"avg_line_length":41.6875,"blob_id":"33a8cd993beb49e092b9720fd3ad755e3357cff5","content_id":"16425e57ecbd64f042cd8088102d8e40e86ff2e7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":682,"license_type":"no_license","max_line_length":54,"num_lines":16,"path":"/build/roundbot_gazebo/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"roundbot_gazebo\")\nset(roundbot_gazebo_VERSION \"0.0.0\")\nset(roundbot_gazebo_MAINTAINER \"micho <micho@todo.todo>\")\nset(roundbot_gazebo_PACKAGE_FORMAT \"1\")\nset(roundbot_gazebo_BUILD_DEPENDS \"gazebo_ros\")\nset(roundbot_gazebo_BUILD_EXPORT_DEPENDS \"gazebo_ros\")\nset(roundbot_gazebo_BUILDTOOL_DEPENDS \"catkin\")\nset(roundbot_gazebo_BUILDTOOL_EXPORT_DEPENDS )\nset(roundbot_gazebo_EXEC_DEPENDS \"gazebo_ros\")\nset(roundbot_gazebo_RUN_DEPENDS \"gazebo_ros\")\nset(roundbot_gazebo_TEST_DEPENDS )\nset(roundbot_gazebo_DOC_DEPENDS )\nset(roundbot_gazebo_URL_WEBSITE \"\")\nset(roundbot_gazebo_URL_BUGTRACKER \"\")\nset(roundbot_gazebo_URL_REPOSITORY \"\")\nset(roundbot_gazebo_DEPRECATED \"\")"},{"alpha_fraction":0.7801418304443359,"alphanum_fraction":0.7943262457847595,"avg_line_length":34.25,"blob_id":"3c7d5f23f783d4ab2c58abbbaaf086772de0c155","content_id":"4bd5cd89a281da4e00508780581d1b50c82a08d6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":141,"license_type":"no_license","max_line_length":63,"num_lines":4,"path":"/devel/share/homework2/cmake/homework2-msg-paths.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"# generated from genmsg/cmake/pkg-msg-paths.cmake.develspace.in\n\nset(homework2_MSG_INCLUDE_DIRS \"\")\nset(homework2_MSG_DEPENDENCIES std_msgs)\n"},{"alpha_fraction":0.7114960551261902,"alphanum_fraction":0.7124409675598145,"avg_line_length":30.719999313354492,"blob_id":"fbe45b152ad1b750e1f48a46d4590d914a0b303f","content_id":"abb88e6f44fcb68ada66a54b7177ee266bb2ae4e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":3175,"license_type":"no_license","max_line_length":138,"num_lines":100,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/MultipleClassifiers.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file MultipleClassifiers.hh\n Defines the Multiple Classifiers class, which uses an ensemble of classifiers, such as a set of decision trees in a random forest.\n \\author Todd Hester\n*/\n\n#ifndef _MULTCLASS_HH_\n#define _MULTCLASS_HH_\n\n#include \"../Models/C45Tree.hh\"\n#include \"../Models/M5Tree.hh\"\n#include \"../Models/LinearSplitsTree.hh\"\n\n#include \"../Models/Stump.hh\"\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n#include <vector>\n#include <set>\n#include <map>\n\n/** Multiple Classifiers class: uses an ensemble of classifiers, such as a set of decision trees in a random forest */\nclass MultipleClassifiers: public Classifier {\n\npublic:\n\n /** Default Constructor\n \\param id identify the model\n \\param modelType identifies which type of model to use\n \\param predType identifies how to combine multiple models\n \\param nModels # of models to use for ensemble models (i.e. random forests)\n \\param trainMode build every step? only on errors? every freq steps?\n \\param trainFreq frequency of model building if using latter mode\n \\param featPct pct of features to remove from set used for each tree split\n \\param expPct pct of experiences to give to each model\n \\param treeThreshold determines the amount of error to be tolerated in the tree (prevents over-fitting with larger and larger trees)\n \\param stoch if the domain is stochastic or deterministic\n \\param rng Random Number Generator \n */\n MultipleClassifiers(int id, int modelType, int predType, int nModels, \n int trainMode, int trainFreq,\n float featPct, float expPct, float treeThreshold,\n bool stoch, float featRange, Random rng);\n\n /** Copy constructor */\n MultipleClassifiers(const MultipleClassifiers&);\n virtual MultipleClassifiers* getCopy();\n\n ~MultipleClassifiers();\n\n virtual bool trainInstances(std::vector<classPair> &instances);\n virtual bool trainInstance(classPair &instance);\n virtual void testInstance(const std::vector<float> &input, std::map<float, float>* retval);\n virtual float getConf(const std::vector<float> &s);\n \n /** Update measure of accuracy for model if we're using best model only */\n void updateModelAccuracy(int i, const std::vector<float> &input, float out);\n\n /** Initialize models */\n void initModels();\n\n /** Calculate kl divergence of the model's predicted probability distributions */\n float klDivergence(const std::vector<float> &input);\n\n /** Calculate the variance of the model's predictions of continuous values */\n float variance(const std::vector<float> &input);\n\n bool STDEBUG;\n bool PRED_DEBUG;\n bool ACC_DEBUG;\n bool CONF_DEBUG;\n bool COPYDEBUG;\n\n /** The ensemble of Classifier Models used. */\n std::vector<Classifier*> models;\n\nprivate:\n\n const int id;\n const int modelType;\n const int predType;\n const int nModels;\n const int mode;\n const int freq;\n float featPct;\n float expPct;\n const float treeThresh;\n const bool stoch;\n const bool addNoise;\n const float featRange;\n \n Random rng;\n\n std::vector<float> accuracy;\n int nsteps;\n std::vector<std::map<float, float> >infos;\n\n};\n\n\n#endif\n \n"},{"alpha_fraction":0.5587970614433289,"alphanum_fraction":0.5700989365577698,"avg_line_length":31.204030990600586,"blob_id":"e23d61000708c8fbd7a561360d17875d8b5be507","content_id":"5fbe8d8af54e1328c6515df6a77750ab9def5c78","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":25571,"license_type":"no_license","max_line_length":198,"num_lines":794,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/FactoredModel.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** FactoredModel.cc\n Implements the FactoredModel class, which uses separate supervised learners to model each feature of an MDP.\n Please cite: Hester and Stone, \"Real Time Targeted Exploration in Large Domains\", ICDL 2010.\n \\author Todd Hester\n*/\n\n#include \"FactoredModel.hh\"\n\n\nFactoredModel::FactoredModel(int id, int numactions, int M, int modelType,\n int predType, int nModels, float treeThreshold,\n const std::vector<float> &featRange, float rRange,\n bool needConf, bool dep, bool relTrans, float featPct, \n\t\t bool stoch, bool episodic, Random rng):\n rewardModel(NULL), terminalModel(NULL), \n id(id), nact(numactions), M(M), modelType(modelType),\n predType(predType), nModels(nModels),\n treeBuildType(BUILD_ON_ERROR), // build tree after prediction error\n treeThresh(treeThreshold), featRange(featRange), rRange(rRange),\n needConf(needConf), dep(dep), relTrans(relTrans), FEAT_PCT(featPct), \n stoch(stoch), episodic(episodic), rng(rng)\n{\n\n //cout << \"MDP Tree explore type: \" << predType << endl;\n MODEL_DEBUG = false; //true;\n COPYDEBUG = false;\n\n // percent of experiences to use for each model\n EXP_PCT = 0.55;//6; //0.4;\n\n // just to ensure the diff models are on different random values\n for (int i = 0; i < id; i++){\n rng.uniform(0, 1);\n }\n\n}\n\n\nFactoredModel::FactoredModel(const FactoredModel & m):\n rewardModel(NULL), terminalModel(NULL), \n id(m.id), nact(m.nact), M(m.M), modelType(m.modelType),\n predType(m.predType), nModels(m.nModels),\n treeBuildType(m.treeBuildType),\n treeThresh(m.treeThresh), featRange(m.featRange), rRange(m.rRange),\n needConf(m.needConf), dep(m.dep), relTrans(m.relTrans), FEAT_PCT(m.FEAT_PCT),\n stoch(m.stoch), episodic(m.episodic), rng(m.rng)\n{\n COPYDEBUG = m.COPYDEBUG;\n\n if (COPYDEBUG) cout << \"MDP Tree copy constructor\" << endl;\n MODEL_DEBUG = m.MODEL_DEBUG;\n EXP_PCT = m.EXP_PCT;\n nfactors = m.nfactors;\n\n\n if (m.outputModels.size() > 0){\n if (COPYDEBUG) cout << \" FactoredModel copy trees\" << endl;\n rewardModel = m.rewardModel->getCopy();\n if (m.terminalModel != NULL) terminalModel = m.terminalModel->getCopy();\n if (COPYDEBUG) cout << \" copy output trees\" << endl;\n outputModels.resize(m.outputModels.size());\n for (unsigned i = 0; i < m.outputModels.size(); i++){\n outputModels[i] = m.outputModels[i]->getCopy();\n }\n if (COPYDEBUG) cout << \" FactoredModel trees copied\" << endl;\n }\n if (COPYDEBUG) cout << \"FactoredModel copy complete \" << endl;\n}\n\nFactoredModel* FactoredModel::getCopy(){\n\n FactoredModel* copy = new FactoredModel(*this);\n return copy;\n\n}\n\n\nFactoredModel::~FactoredModel() {\n if (rewardModel != NULL) delete rewardModel;\n if (terminalModel != NULL) delete terminalModel;\n for (unsigned i = 0; i < outputModels.size(); i++){\n delete outputModels[i];\n }\n outputModels.clear();\n}\n\n\n\n// init the trees\nbool FactoredModel::initMDPModel(int nfactors){\n if (MODEL_DEBUG) cout << \"Init trees for each state factor and reward\" << endl;\n\n outputModels.resize(nfactors);\n\n bool simpleRegress = false;\n if (modelType == M5SINGLE || modelType == M5ALLSINGLE || modelType == LSTSINGLE)\n simpleRegress = true;\n\n // institute a model for each state factor, depending on model type\n for (int i = 0; i < nfactors; i++){\n if (modelType == C45TREE && nModels == 1){\n outputModels[i] = new C45Tree((id * (nfactors+1)) + i, treeBuildType, 5, M, 0, rng);\n if (i == 0){\n rewardModel = new C45Tree((id*(nfactors+1))+nfactors, treeBuildType,5, M, 0, rng);\n if (episodic) terminalModel = new C45Tree((id*(nfactors+1))+nfactors+1, treeBuildType,5, M, 0, rng);\n }\n }\n else if ((modelType == M5MULTI || modelType == M5SINGLE) && nModels == 1){\n outputModels[i] = new M5Tree((id * (nfactors+1)) + i, treeBuildType, 5, M, 0, simpleRegress, false, treeThresh *featRange[i], rng);\n if (i == 0){\n rewardModel = new M5Tree((id * (nfactors+1)) + nfactors, treeBuildType, 5, M, 0, simpleRegress, false, treeThresh *rRange, rng);\n if (episodic) terminalModel = new M5Tree((id * (nfactors+1)) + 1+nfactors, treeBuildType, 5, M, 0, simpleRegress, false, treeThresh, rng);\n }\n }\n else if ((modelType == M5ALLMULTI || modelType == M5ALLSINGLE) && nModels == 1){\n outputModels[i] = new M5Tree((id * (nfactors+1)) + i, treeBuildType, 5, M, 0, simpleRegress, true, treeThresh *featRange[i], rng);\n if (i == 0){\n rewardModel = new M5Tree((id * (nfactors+1)) + nfactors, treeBuildType, 5, M, 0, simpleRegress, true, treeThresh *rRange, rng);\n if (episodic) terminalModel = new M5Tree((id * (nfactors+1)) + 1+nfactors, treeBuildType, 5, M, 0, simpleRegress, true, treeThresh, rng);\n }\n }\n else if ((modelType == LSTMULTI || modelType == LSTSINGLE) && nModels == 1){\n outputModels[i] = new LinearSplitsTree((id * (nfactors+1)) + i, treeBuildType, 5, M, 0, simpleRegress, treeThresh *featRange[i], rng);\n if (i == 0){\n rewardModel = new LinearSplitsTree((id * (nfactors+1)) + nfactors, treeBuildType, 5, M, 0, simpleRegress, treeThresh *rRange, rng);\n if (episodic) terminalModel = new LinearSplitsTree((id * (nfactors+1)) + 1+nfactors, treeBuildType, 5, M, 0, simpleRegress, treeThresh, rng);\n }\n }\n else if (modelType == STUMP && nModels == 1){\n outputModels[i] = new Stump((id * (nfactors+1)) + i, 1, 5, M, 0, rng);\n if (i == 0){\n rewardModel = new Stump((id * (nfactors+1)) + nfactors, 1, 5, M, 0, rng);\n if (episodic) terminalModel = new Stump((id * (nfactors+1)) +1+ nfactors, 1, 5, M, 0, rng);\n }\n }\n else if (predType == SEPARATE && nModels > 1){\n outputModels[i] = new SepPlanExplore((id * (nfactors+1)) + i,\n modelType, predType,\n nModels, treeBuildType, 5,\n FEAT_PCT,\n EXP_PCT,\n treeThresh *featRange[i], stoch, featRange[i], rng);\n if (i == 0){\n rewardModel = new SepPlanExplore((id * (nfactors+1)) + nfactors,\n modelType, predType,\n nModels, treeBuildType, 5,\n FEAT_PCT, // remove this pct of feats\n EXP_PCT, treeThresh *rRange, stoch, rRange, rng);\n\tif (episodic){\n\t terminalModel = new SepPlanExplore((id * (nfactors+1)) +1+ nfactors,\n modelType, predType,\n nModels, treeBuildType, 5,\n FEAT_PCT, // remove this pct of feats\n EXP_PCT, treeThresh, stoch, 1.0, rng);\n\t}\n }\n }\n else if (nModels > 1 || modelType == ALLM5TYPES){\n outputModels[i] = new MultipleClassifiers((id * (nfactors+1)) + i,\n modelType, predType,\n nModels, treeBuildType, 5,\n FEAT_PCT,\n EXP_PCT,\n treeThresh *featRange[i], stoch, featRange[i], rng);\n if (i == 0){\n rewardModel = new MultipleClassifiers((id * (nfactors+1)) + nfactors,\n modelType, predType,\n nModels, treeBuildType, 5,\n FEAT_PCT, // remove this pct of feats\n EXP_PCT, treeThresh *rRange, stoch, rRange, rng);\n\tif (episodic){\n\t terminalModel = new MultipleClassifiers((id * (nfactors+1)) +1+ nfactors,\n modelType, predType,\n nModels, treeBuildType, 5,\n FEAT_PCT, // remove this pct of feats\n EXP_PCT, treeThresh, stoch, 1.0, rng);\n\t}\n }\n } else {\n cout << \"Invalid model type for MDP TREE\" << endl;\n exit(-1);\n }\n\n }\n\n return true;\n\n}\n\n\n// update all trees with multiple experiences\nbool FactoredModel::updateWithExperiences(std::vector<experience> &instances){\n if (MODEL_DEBUG) cout << \"FactoredModel updateWithExperiences : \" << instances.size() << endl;\n\n bool changed = false;\n if (outputModels.size() == 0){\n nfactors = instances[0].next.size();\n initMDPModel(instances[0].next.size());\n }\n\n // make sure size is right\n if (outputModels.size() != instances[0].next.size()){\n if (MODEL_DEBUG)\n cout << \"ERROR: size mismatch between input vector and # trees \"\n << outputModels.size() << \", \" << instances[0].next.size() << endl;\n return false;\n exit(-1);\n }\n\n // separate these experience instances into classPairs\n std::vector<std::vector<classPair> > stateData(outputModels.size());\n std::vector<classPair> rewardData(instances.size());\n std::vector<classPair> termData(instances.size());\n\n // count non-terminal experiences\n int nonTerm = 0;\n for (unsigned i = 0; i < instances.size(); i++){\n if (!instances[i].terminal)\n nonTerm++;\n }\n for (unsigned i = 0; i < outputModels.size(); i++){\n stateData[i].resize(nonTerm);\n }\n int nonTermIndex = 0;\n\n for (unsigned i = 0; i < instances.size(); i++){\n experience e = instances[i];\n\n std::vector<float> inputs(e.s.size() + nact);\n\n for (unsigned k = 0; k < e.s.size(); k++){\n inputs[k] = e.s[k];\n }\n // convert to binary vector of length nact\n for (int k = 0; k < nact; k++){\n if (e.act == k)\n inputs[e.s.size()+k] = 1;\n else\n inputs[e.s.size()+k] = 0;\n }\n\n // convert to rel\n if (relTrans)\n e.next = subVec(e.next, e.s);\n\n // reward and terminal models\n classPair cp;\n cp.in = inputs;\n cp.out = e.reward;\n rewardData[i] = cp;\n\n cp.out = e.terminal;\n termData[i] = cp;\n\n // add to each vector\n if (!e.terminal){\n for (unsigned j = 0; j < outputModels.size(); j++){\n classPair cp;\n cp.in = inputs;\n\n // split the outcome and rewards up\n // into each vector\n cp.out = e.next[j];\n stateData[j][nonTermIndex] = cp;\n\n // for dep trees, add this models target to next model's input\n if (dep){\n inputs.push_back(e.next[j]);\n }\n }\n nonTermIndex++;\n }\n\n }\n\n // build trees on all data\n for (unsigned k = 0; k < stateData.size(); k++){\n if (stateData[k].size() > 0){\n bool singleChange = outputModels[k]->trainInstances(stateData[k]);\n changed = changed || singleChange;\n }\n }\n\n bool singleChange = rewardModel->trainInstances(rewardData);\n changed = changed || singleChange;\n\n if (episodic){\n singleChange = terminalModel->trainInstances(termData);\n changed = changed || singleChange;\n }\n\n return changed;\n}\n\n\n// update all the trees, check if model has changed\nbool FactoredModel::updateWithExperience(experience &e){\n if (MODEL_DEBUG) cout << \"updateWithExperience \" << &(e.s) << \", \" << e.act\n << \", \" << &(e.next) << \", \" << e.reward << endl;\n\n if (MODEL_DEBUG){\n cout << \"From: \";\n for (unsigned i = 0; i < e.s.size(); i++){\n cout << e.s[i] << \", \";\n }\n cout << \"Action: \" << e.act << endl;;\n cout << \"To: \";\n for (unsigned i = 0; i < e.next.size(); i++){\n cout << e.next[i] << \", \";\n }\n cout << \"Reward: \" << e.reward \n\t << \" term: \" << e.terminal << endl;\n }\n\n bool changed = false;\n\n if (outputModels.size() == 0){\n nfactors = e.next.size();\n initMDPModel(e.next.size());\n }\n\n // make sure size is right\n if (outputModels.size() != e.next.size()){\n if (MODEL_DEBUG) cout << \"ERROR: size mismatch between input vector and # trees \"\n << outputModels.size() << \", \" << e.next.size() << endl;\n return false;\n exit(-1);\n }\n\n std::vector<float> inputs(e.s.size() + nact);\n for (unsigned i = 0; i < e.s.size(); i++){\n inputs[i] = e.s[i];\n }\n // convert to binary vector of length nact\n for (int k = 0; k < nact; k++){\n if (e.act == k)\n inputs[e.s.size()+k] = 1;\n else\n inputs[e.s.size()+k] = 0;\n }\n\n // convert to rel\n if (relTrans)\n e.next = subVec(e.next, e.s);\n\n // split the outcome and rewards up\n // and train the trees\n classPair cp;\n cp.in = inputs;\n\n // reward model\n cp.out = e.reward;\n bool singleChange = rewardModel->trainInstance(cp);\n changed = changed || singleChange;\n\n // termination model\n if (episodic){\n cp.out = e.terminal;\n singleChange = terminalModel->trainInstance(cp);\n changed = changed || singleChange;\n }\n\n // if not a terminal transition\n if (!e.terminal){\n for (unsigned i = 0; i < e.next.size(); i++){\n cp.in = inputs;\n cp.out = e.next[i];\n\n bool singleChange = outputModels[i]->trainInstance(cp);\n changed = changed || singleChange;\n\n // add this model's target to input for next model\n if (dep){\n inputs.push_back(e.next[i]);\n }\n }\n }\n\n if (MODEL_DEBUG) cout << \"Model updated, changed: \" << changed << endl;\n return changed;\n\n}\n\n\nfloat FactoredModel::getSingleSAInfo(const std::vector<float> &state, int act, StateActionInfo* retval){\n\n retval->transitionProbs.clear();\n\n if (outputModels.size() == 0){\n retval->reward = -0.001;\n\n // add to transition map\n retval->transitionProbs[state] = 1.0;\n retval->known = false;\n retval->termProb = 0.0;\n return 0;\n }\n\n // input we want predictions for\n std::vector<float> inputs(state.size() + nact);\n for (unsigned i = 0; i < state.size(); i++){\n inputs[i] = state[i];\n }\n // convert to binary vector of length nact\n for (int k = 0; k < nact; k++){\n if (act == k)\n inputs[state.size()+k] = 1;\n else\n inputs[state.size()+k] = 0;\n }\n\n // just pick one sample from each feature prediction\n std::vector<float>output(nfactors);\n for (int i = 0; i < nfactors; i++){\n\n // get prediction\n std::map<float, float> outputPreds;\n outputModels[i]->testInstance(inputs, &outputPreds);\n\n // sample a value\n float randProb = rng.uniform();\n float probSum = 0;\n for (std::map<float, float>::iterator it1 = outputPreds.begin(); it1 != outputPreds.end(); it1++){\n\n // get prob\n probSum += (*it1).second;\n\n if (randProb <= probSum){\n\toutput[i] = (*it1).first;\n\tbreak;\n }\n }\n }\n\n retval->transitionProbs[output] = 1.0;\n\n // calculate reward and terminal probabilities\n // calculate expected reward\n float rewardSum = 0.0;\n // each value\n std::map<float, float> rewardPreds;\n rewardModel->testInstance(inputs, &rewardPreds);\n\n float totalVisits = 0.0;\n for (std::map<float, float>::iterator it = rewardPreds.begin(); it != rewardPreds.end(); it++){\n // get key from iterator\n float val = (*it).first;\n float prob = (*it).second;\n totalVisits += prob;\n if (MODEL_DEBUG) cout << \"Reward value \" << val << \" had prob of \" << prob << endl;\n rewardSum += (prob * val);\n }\n\n retval->reward = rewardSum / totalVisits;\n if (MODEL_DEBUG) cout << \"Average reward was \" << retval->reward << endl;\n\n if (isnan(retval->reward))\n cout << \"FactoredModel setting model reward to NaN\" << endl;\n\n\n // get termination prob\n std::map<float, float> termProbs;\n if (!episodic){\n termProbs[0.0] = 1.0;\n } else {\n terminalModel->testInstance(inputs, &termProbs);\n }\n // this needs to be a weighted sum.\n // discrete trees will give some probabilty of termination (outcome 1)\n // where continuous ones will give some value between 0 and 1\n float termSum = 0;\n float probSum = 0;\n for (std::map<float, float>::iterator it = termProbs.begin(); it != termProbs.end(); it++){\n // get key from iterator\n float val = (*it).first;\n if (val > 1.0) val = 1.0;\n if (val < 0.0) val = 0.0;\n float prob = (*it).second;\n if (MODEL_DEBUG) cout << \"Term value \" << val << \" had prob of \" << prob << endl;\n termSum += (prob * val);\n probSum += prob;\n }\n\n retval->termProb = termSum / probSum;\n if (retval->termProb < 0 || retval->termProb > 1){\n cout << \"Invalid termination probability!!! \" << retval->termProb << endl;\n }\n if (MODEL_DEBUG) cout << \"Termination prob is \" << retval->termProb << endl;\n\n return 1.0;\n\n}\n\n\n// fill in StateActionInfo struct and return it\nfloat FactoredModel::getStateActionInfo(const std::vector<float> &state, int act, StateActionInfo* retval){\n if (MODEL_DEBUG) cout << \"getStateActionInfo, \" << &state << \", \" << act << endl;\n\n\n\n if (MODEL_DEBUG){\n for (unsigned i = 0; i < state.size(); i++){\n cout << state[i] << \", \";\n }\n // cout << endl;\n cout << \"a: \" << act << \" has \" << retval->transitionProbs.size() << \" outcomes already\" << endl;\n }\n\n retval->transitionProbs.clear();\n\n if (outputModels.size() == 0){\n retval->reward = -0.001;\n\n // add to transition map\n retval->transitionProbs[state] = 1.0;\n retval->known = false;\n retval->termProb = 0.0;\n return 0;\n }\n\n // input we want predictions for\n std::vector<float> inputs(state.size() + nact);\n for (unsigned i = 0; i < state.size(); i++){\n inputs[i] = state[i];\n }\n // convert to binary vector of length nact\n for (int k = 0; k < nact; k++){\n if (act == k)\n inputs[state.size()+k] = 1;\n else\n inputs[state.size()+k] = 0;\n }\n\n\n // get the separate predictions for each outcome variable from the respective trees\n // combine together for outcome predictions\n\n // combine together and put into StateActionInfo struct\n retval->known = true;\n float confSum = 0.0;\n\n if (nModels == 1 &&\n (modelType == M5MULTI || modelType == M5SINGLE ||\n modelType == M5ALLMULTI || modelType == M5ALLSINGLE ||\n modelType == LSTMULTI || modelType == LSTSINGLE ||\n modelType == ALLM5TYPES)){\n //cout << \"mdptree, combine deterministic outputs for each feature\" << endl;\n ///////////////////////////////////////////\n // alternate version -> assuming one model that gives one prediction\n ///////////////////////////////////////////\n std::vector<float> MLnext(nfactors);\n std::vector<float> inputCopy = inputs;\n for (int i = 0; i < nfactors; i++){\n // get single outcome for this factor\n std::map<float, float> outputPreds;\n outputModels[i]->testInstance(inputCopy, &outputPreds);\n if (needConf && dep) confSum += outputModels[i]->getConf(inputCopy);\n float val = outputPreds.begin()->first;\n if (relTrans) val = val + inputs[i];\n MLnext[i] = val;\n if (dep){\n inputCopy.push_back(val);\n }\n }\n //add this one\n retval->transitionProbs[MLnext] = 1.0;\n if (MODEL_DEBUG){\n cout << \"Final prob of outcome: \";\n for (int i = 0; i < nfactors; i++){\n cout << MLnext[i] << \", \";\n }\n cout << \" is \" << 1.0 << endl;\n }\n ////////////////////////////////////////////\n }\n\n else {\n //cout << \"mdp tree, combine stochastic predictions for each feature\" << endl;\n //////////////////////////////////////////////////////////////////////\n // Full version: assume possibly stochastic prediction for each model\n //////////////////////////////////////////////////////////////////////\n // grab predicted transition probs just once\n std::vector< std::map<float,float> > predictions(nfactors);\n if (!dep){\n for (int i = 0; i < nfactors; i++){\n outputModels[i]->testInstance(inputs, &(predictions[i]));\n }\n }\n\n ///////////////////////////////////////\n // get probability of each transition\n float* probs = new float[nfactors];\n std::vector<float> next(nfactors, 0);\n addFactorProb(probs, &next, inputs, retval, 0, predictions, &confSum);\n delete[] probs;\n\n /////////////////////////////////////////////////\n }\n\n // calculate expected reward\n float rewardSum = 0.0;\n // each value\n std::map<float, float> rewardPreds;\n rewardModel->testInstance(inputs, &rewardPreds);\n\n if (rewardPreds.size() == 0){\n //cout << \"FactoredModel setting state known false\" << endl;\n retval->known = false;\n return 0;\n }\n\n float totalVisits = 0.0;\n for (std::map<float, float>::iterator it = rewardPreds.begin(); it != rewardPreds.end(); it++){\n // get key from iterator\n float val = (*it).first;\n float prob = (*it).second;\n totalVisits += prob;\n if (MODEL_DEBUG) cout << \"Reward value \" << val << \" had prob of \" << prob << endl;\n rewardSum += (prob * val);\n\n }\n\n retval->reward = rewardSum / totalVisits;\n if (MODEL_DEBUG) cout << \"Average reward was \" << retval->reward << endl;\n\n if (isnan(retval->reward))\n cout << \"FactoredModel setting model reward to NaN\" << endl;\n\n\n // get termination prob\n std::map<float, float> termProbs;\n if (!episodic){\n termProbs[0.0] = 1.0;\n } else {\n terminalModel->testInstance(inputs, &termProbs);\n }\n // this needs to be a weighted sum.\n // discrete trees will give some probabilty of termination (outcome 1)\n // where continuous ones will give some value between 0 and 1\n float termSum = 0;\n float probSum = 0;\n for (std::map<float, float>::iterator it = termProbs.begin(); it != termProbs.end(); it++){\n // get key from iterator\n float val = (*it).first;\n if (val > 1.0) val = 1.0;\n if (val < 0.0) val = 0.0;\n float prob = (*it).second;\n if (MODEL_DEBUG) cout << \"Term value \" << val << \" had prob of \" << prob << endl;\n termSum += (prob * val);\n probSum += prob;\n }\n\n retval->termProb = termSum / probSum;\n if (retval->termProb < 0 || retval->termProb > 1){\n cout << \"Invalid termination probability!!! \" << retval->termProb << endl;\n }\n if (MODEL_DEBUG) cout << \"Termination prob is \" << retval->termProb << endl;\n\n // if we need confidence measure\n if (needConf){\n // conf is avg of each variable's model's confidence\n float rConf = rewardModel->getConf(inputs);\n float tConf = 1.0;\n if (episodic)\n tConf = terminalModel->getConf(inputs);\n\n //cout << \"conf is \" << confSum << \", r: \" << rConf << \", \" << tConf << endl;\n\n confSum += rConf + tConf;\n\n if (!dep){\n for (int i = 0; i < nfactors; i++){\n float featConf = outputModels[i]->getConf(inputs);\n confSum += featConf;\n //cout << \"indep, conf for \" << i << \": \" << featConf << endl;\n }\n }\n confSum /= (float)(state.size() + 2.0);\n } else {\n confSum = 1.0;\n }\n\n if (MODEL_DEBUG) cout << \"avg conf returned \" << confSum << endl;\n\n //cout << \"now has \" << retval->transitionProbs.size() << \" outcomes\" << endl;\n\n // return filled-in struct\n retval->known = true;\n return confSum;\n\n}\n\n\n\n// gets the values/probs for index and adds them to the appropriate spot in the array\nvoid FactoredModel::addFactorProb(float* probs, std::vector<float>* next, std::vector<float> x, StateActionInfo* retval, int index, std::vector< std::map<float,float> > predictions, float* confSum){\n\n // get values, probs etc for this index\n std::map<float, float> outputPreds = predictions[index];\n\n // get prediction each time for dep\n if (dep){\n outputModels[index]->testInstance(x, &outputPreds);\n }\n\n // sum up confidences\n if (dep && needConf){\n float conf = outputModels[index]->getConf(x);\n if (index > 0)\n (*confSum) += conf * probs[index-1];\n else\n (*confSum) += conf;\n }\n\n for (std::map<float, float>::iterator it1 = outputPreds.begin(); it1 != outputPreds.end(); it1++){\n // get key from iterator\n float val = (*it1).first;\n\n if (MODEL_DEBUG) cout << \"Prob of outcome \" << val << \" on factor \" << index << \" is \" << (*it1).second << endl;\n\n // ignore it if it has prob 0\n if ((*it1).second == 0){\n if (MODEL_DEBUG) cout << \"Prob 0, ignore\" << endl;\n continue;\n }\n\n if (dep){\n x.push_back(val);\n }\n\n if (relTrans)\n val = val + x[index];\n\n (*next)[index] = val;\n if (index == 0)\n probs[index] = (*it1).second;\n else\n probs[index] = probs[index-1] * (*it1).second;\n\n // if last one, lets set it in our transition prob map\n if (index == nfactors - 1 && probs[index] > 0.0){\n\n if (MODEL_DEBUG){\n cout << \"Final prob of outcome: \";\n for (int i = 0; i < nfactors; i++){\n cout << (*next)[i] << \", \";\n }\n cout << \" is \" << probs[index] << endl;\n cout << \" was \" << retval->transitionProbs[*next] << endl;\n cout << \" now \" << (retval->transitionProbs[*next]+probs[index]) << endl;\n }\n\n retval->transitionProbs[*next] += probs[index];\n continue;\n }\n\n // next factors\n addFactorProb(probs, next, x, retval, index+1, predictions, confSum);\n\n }\n}\n\nstd::vector<float> FactoredModel::addVec(const std::vector<float> &a, const std::vector<float> &b){\n //if (a.size() != b.size())\n // cout << \"ERROR: vector sizes wrong\" << endl;\n\n\n int smaller = a.size();\n if (b.size() < a.size())\n smaller = b.size();\n\n std::vector<float> c(smaller, 0.0);\n for (int i = 0; i < smaller; i++){\n c[i] = a[i] + b[i];\n }\n\n return c;\n}\n\nstd::vector<float> FactoredModel::subVec(const std::vector<float> &a, const std::vector<float> &b){\n //if (a.size() != b.size())\n // cout << \"ERROR: vector sizes wrong\" << endl;\n\n int smaller = a.size();\n if (b.size() < a.size())\n smaller = b.size();\n\n std::vector<float> c(smaller, 0.0);\n for (int i = 0; i < smaller; i++){\n c[i] = a[i] - b[i];\n }\n\n return c;\n}\n\n"},{"alpha_fraction":0.7770700454711914,"alphanum_fraction":0.7770700454711914,"avg_line_length":30.399999618530273,"blob_id":"291c25972a004c919db900f051e489445028423a","content_id":"1bbe3b10a8739f39da0fb088278be984b60e4414","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":157,"license_type":"no_license","max_line_length":35,"num_lines":5,"path":"/devel/lib/python2.7/dist-packages/rl_msgs/msg/__init__.py","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"from ._RLAction import *\nfrom ._RLEnvDescription import *\nfrom ._RLEnvSeedExperience import *\nfrom ._RLExperimentInfo import *\nfrom ._RLStateReward import *\n"},{"alpha_fraction":0.5590455532073975,"alphanum_fraction":0.567432701587677,"avg_line_length":24.696121215820312,"blob_id":"259de638584a6b0c8e7d359c63278e810268546d","content_id":"df9e7d56aa3f765053cb76593087ced7517281b1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":23846,"license_type":"no_license","max_line_length":193,"num_lines":928,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/ETUCT.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file ETUCT.cc\n Implements UCT with eligiblity traces. A modified version of UCT \n as presented in:\n L. Kocsis and C. Szepesv´ari, \"Bandit based monte-carlo planning,\" in\n ECML-06. Number 4212 in LNCS. Springer, 2006, pp. 282-293.\n \\author Todd Hester\n*/\n\n#include \"ETUCT.hh\"\n#include <algorithm>\n\n#include <sys/time.h>\n\n\nETUCT::ETUCT(int numactions, float gamma, float rrange, float lambda,\n int MAX_ITER, float MAX_TIME, int MAX_DEPTH, int modelType,\n const std::vector<float> &fmax, const std::vector<float> &fmin,\n const std::vector<int> &nstatesPerDim, bool trackActual,\n int historySize, Random r):\n numactions(numactions), gamma(gamma), rrange(rrange), lambda(lambda),\n MAX_ITER(MAX_ITER), MAX_TIME(MAX_TIME),\n MAX_DEPTH(MAX_DEPTH), modelType(modelType), statesPerDim(nstatesPerDim),\n trackActual(trackActual), HISTORY_SIZE(historySize),\n HISTORY_FL_SIZE(historySize*numactions)//fmax.size())\n{\n rng = r;\n\n nstates = 0;\n nactions = 0;\n lastUpdate = -1;\n seedMode = false;\n\n timingType = true;\n\n model = NULL;\n planTime = getSeconds();\n\n PLANNERDEBUG = false;//true;\n ACTDEBUG = false; //true;\n MODELDEBUG = false;//true;//false;\n UCTDEBUG = false; //true;//false;\n REALSTATEDEBUG = false;\n HISTORYDEBUG = false; //true; //false;\n\n featmax = fmax;\n featmin = fmin;\n\n if (statesPerDim[0] > 0){\n cout << \"Planner ETUCT using discretization of \" << statesPerDim[0] << endl;\n }\n if (trackActual){\n cout << \"ETUCT tracking real state values\" << endl;\n }\n cout << \"Planner using history size: \" << HISTORY_SIZE << endl;\n\n if (HISTORY_SIZE == 0){\n saHistory.push_back(0.0);\n }\n else {\n if (HISTORYDEBUG) {\n cout << \"History size of \" << HISTORY_SIZE\n << \" float size of \" << HISTORY_FL_SIZE\n << \" with state size: \" << fmin.size()\n << \" and numact: \" << numactions << endl;\n }\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\n saHistory.push_back(0.0);\n }\n }\n\n // initStates();\n\n}\n\nETUCT::~ETUCT() {\n //cout << \"planner delete\" << endl;\n // clear all state info\n\n for (std::map<state_t, state_info>::iterator i = statedata.begin();\n i != statedata.end(); i++){\n\n // get state's info\n //cout << \" planner got info\" << endl;\n state_info* info = &((*i).second);\n\n deleteInfo(info);\n }\n\n featmax.clear();\n featmin.clear();\n\n statespace.clear();\n statedata.clear();\n //cout << \"planner done\" << endl;\n}\n\nvoid ETUCT::setModel(MDPModel* m){\n\n model = m;\n\n}\n\n// canonicalize all the states so we already have them in our statespace\nvoid ETUCT::initStates(){\n cout << \"init states\" << endl;\n std::vector<float> s(featmin.size());\n\n fillInState(s,0);\n}\n\nvoid ETUCT::fillInState(std::vector<float>s, int depth){\n\n // if depth == size, canonicalize and return\n if (depth == (int)featmin.size()){\n canonicalize(s);\n return;\n }\n\n // go through all features at depth\n for (float i = featmin[depth]; i < featmax[depth]+1; i++){\n s[depth] = i;\n fillInState(s, depth+1);\n }\n}\n\n\n/////////////////////////////\n// Functional functions :) //\n/////////////////////////////\n\n\nvoid ETUCT::initNewState(state_t s){\n //if (PLANNERDEBUG) cout << \"initNewState(s = \" << s\n // << \") size = \" << s->size() << endl;\n\n // create state info and add to hash map\n state_info* info = &(statedata[s]);\n initStateInfo(s, info);\n\n // dont init any model info\n // we'll get it during search if we have to\n\n}\n\nbool ETUCT::updateModelWithExperience(const std::vector<float> &laststate,\n int lastact,\n const std::vector<float> &currstate,\n float reward, bool term){\n // if (PLANNERDEBUG) cout << \"updateModelWithExperience(last = \" << &laststate\n // << \", curr = \" << &currstate\n // << \", lastact = \" << lastact\n // << \", r = \" << reward\n // << \", term = \" << term\n // << \")\" << endl;\n\n if (!timingType)\n planTime = getSeconds();\n\n // canonicalize these things\n state_t last = canonicalize(laststate);\n\n prevstate = last;\n prevact = lastact;\n\n // get state info\n previnfo = &(statedata[last]);\n\n // init model?\n if (model == NULL){\n cout << \"ERROR IN MODEL OR MODEL SIZE\" << endl;\n exit(-1);\n }\n\n if (MODELDEBUG){\n cout << \"Update with exp from state: \";\n for (unsigned i = 0; i < last->size(); i++){\n cout << (laststate)[i] << \", \";\n }\n cout << \" action: \" << lastact;\n cout << \" to state: \";\n for (unsigned i = 0; i < currstate.size(); i++){\n cout << currstate[i] << \", \";\n }\n cout << \" and reward: \" << reward << endl;\n }\n\n experience e;\n e.s = laststate;\n e.next = currstate;\n e.act = lastact;\n e.reward = reward;\n e.terminal = term;\n\n if (HISTORY_SIZE > 0){\n if (HISTORYDEBUG) {\n cout << \"Original state vector (size \" << e.s.size() << \": \" << e.s[0];\n for (unsigned i = 1; i < e.s.size(); i++){\n cout << \",\" << e.s[i];\n }\n cout << endl;\n }\n // add history onto e.s\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\n e.s.push_back(saHistory[i]);\n }\n\n if (HISTORYDEBUG) {\n cout << \"New state vector (size \" << e.s.size() << \": \" << e.s[0];\n for (unsigned i = 1; i < e.s.size(); i++){\n cout << \",\" << e.s[i];\n }\n cout << endl;\n }\n\n if (!seedMode){\n // push this state and action onto the history vector\n /*\n for (unsigned i = 0; i < last->size(); i++){\n saHistory.push_back((*last)[i]);\n saHistory.pop_front();\n }\n */\n \n for (int i = 0; i < numactions; i++){\n if (i == lastact)\n saHistory.push_back(1.0);\n else\n saHistory.push_back(0.0);\n saHistory.pop_front();\n }\n \n if (HISTORYDEBUG) {\n cout << \"New history vector (size \" << saHistory.size() << \": \" << saHistory[0];\n for (unsigned i = 1; i < saHistory.size(); i++){\n cout << \",\" << saHistory[i];\n }\n cout << endl;\n }\n }\n }\n\n bool modelChanged = model->updateWithExperience(e);\n\n if (timingType)\n planTime = getSeconds();\n\n return modelChanged;\n\n}\n\nvoid ETUCT::updateStateActionFromModel(state_t s, int a, state_info* info){\n\n if (HISTORY_SIZE == 0){\n\n std::deque<float> history(1,0.0);\n StateActionInfo* newModel = NULL;\n newModel = &(info->historyModel[a][history]);\n\n updateStateActionHistoryFromModel(*s, a, newModel);\n\n }\n\n else {\n\n // fill in for all histories???\n for (std::map< std::deque<float>, StateActionInfo>::iterator it = info->historyModel[a].begin(); it != info->historyModel[a].end(); it++){\n\n std::deque<float> oneHist = (*it).first;\n StateActionInfo* newModel = &((*it).second);\n\n // add history to vector\n std::vector<float> modState = *s;\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\n modState.push_back(oneHist[i]);\n }\n updateStateActionHistoryFromModel(modState, a, newModel);\n }\n\n }\n\n}\n\nvoid ETUCT::updateStateActionHistoryFromModel(const std::vector<float> modState, int a, StateActionInfo *newModel){\n\n // update state info\n // get state action info for each action\n model->getStateActionInfo(modState, a, newModel);\n newModel->frameUpdated = nactions;\n\n //canonNextStates(newModel);\n\n}\n\n\n\nvoid ETUCT::canonNextStates(StateActionInfo* modelInfo){\n\n // loop through all next states\n for (std::map<std::vector<float>, float>::iterator outIt\n = modelInfo->transitionProbs.begin();\n outIt != modelInfo->transitionProbs.end(); outIt++){\n\n std::vector<float> nextstate = (*outIt).first;\n\n // check that it is valid, otherwise replace with current\n bool badState = false;\n for (unsigned j = 0; j < nextstate.size(); j++){\n if (nextstate[j] < (featmin[j]-EPSILON)\n || nextstate[j] > (featmax[j]+EPSILON)){\n //cout << \"next state out of range \" << nextstate[j] << endl;\n badState = true;\n break;\n }\n }\n\n if (!badState){\n canonicalize(nextstate);\n }\n }\n}\n\n\n\n\nint ETUCT::getBestAction(const std::vector<float> &state){\n // if (PLANNERDEBUG) cout << \"getBestAction(s = \" << &state << \")\" << endl;\n\n // resetUCTCounts();\n\n state_t s = canonicalize(state);\n\n int i = 0;\n for (i = 0; i < MAX_ITER; i++){\n\n std::deque<float> searchHistory = saHistory;\n uctSearch(state, s, 0, searchHistory);\n\n // break after some max time\n if ((getSeconds() - planTime) > MAX_TIME){ // && i > 500){\n break;\n }\n\n }\n double currTime = getSeconds();\n if (false || UCTDEBUG){\n cout << \"Search complete after \" << (currTime-planTime) << \" seconds and \"\n << i << \" iterations.\" << endl;\n }\n\n // get state info\n state_info* info = &(statedata[s]);\n\n // Get Q values\n std::vector<float> &Q = info->Q;\n\n // Choose an action\n const std::vector<float>::iterator a =\n random_max_element(Q.begin(), Q.end()); // Choose maximum\n\n int act = a - Q.begin();\n nactions++;\n\n if (false){\n cout << \"State \" << (*s)[0];\n for (unsigned i = 1; i < s->size(); i++){\n cout << \",\" << (*s)[i];\n }\n cout << \", Took action \" << act << \", \"\n << \"value: \" << *a << endl;\n }\n\n // return index of action\n return act;\n}\n\n\n\n\n\n\nvoid ETUCT::planOnNewModel(){\n\n // reset visit counts/q values\n resetUCTCounts();\n\n // for rmax, only s-a's prediction has changed\n if (modelType == RMAX){\n updateStateActionFromModel(prevstate, prevact, previnfo);\n }\n\n // for other model types, it all could change, clear all cached model predictions\n else {\n\n // still update flagged s-a's\n // then less stuff to query while planning\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n state_t s = canonicalize(*i);\n state_info* info = &(statedata[s]);\n if (info->needsUpdate){\n for (int j = 0; j < numactions; j++){\n updateStateActionFromModel(s, j, info);\n }\n info->needsUpdate = false;\n }\n }\n lastUpdate = nactions;\n }\n\n}\n\n\nvoid ETUCT::resetUCTCounts(){\n // if (PLANNERDEBUG) cout << \"Reset UCT Counts\" << endl;\n const int MIN_VISITS = 10;\n\n // loop through here\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n state_t s = canonicalize(*i);\n\n state_info* info = &(statedata[s]);\n\n if (info->uctVisits > (MIN_VISITS * numactions))\n info->uctVisits = MIN_VISITS * numactions;\n\n for (int j = 0; j < numactions; j++){\n if (info->uctActions[j] > MIN_VISITS)\n info->uctActions[j] = MIN_VISITS;\n }\n\n }\n\n}\n\n\n\n\n////////////////////////////\n// Helper Functions //\n////////////////////////////\n\nETUCT::state_t ETUCT::canonicalize(const std::vector<float> &s) {\n //if (PLANNERDEBUG) cout << \"canonicalize(s = \" << s[0] << \", \"\n // << s[1] << \")\" << endl;\n\n // discretize it\n std::vector<float> s2;\n if (statesPerDim[0] > 0){\n s2 = discretizeState(s);\n } else {\n s2 = s;\n }\n\n // get state_t for pointer if its in statespace\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\n statespace.insert(s2);\n state_t retval = &*result.first; // Dereference iterator then get pointer\n\n //if (PLANNERDEBUG) cout << \" returns \" << retval\n // << \" New: \" << result.second << endl;\n\n // if not, init this new state\n if (result.second) { // s is new, so initialize Q(s,a) for all a\n initNewState(retval);\n if (PLANNERDEBUG) {\n cout << \" New state initialized \"\n << \" orig:(\" << s[0] << \",\" << s[1] << \")\"\n << \" disc:(\" << s2[0] << \",\" << s2[1] << \")\" << endl;\n }\n }\n\n\n return retval;\n}\n\n\n// init state info\nvoid ETUCT::initStateInfo(state_t s, state_info* info){\n //if (PLANNERDEBUG) cout << \"initStateInfo()\";\n\n info->id = nstates++;\n if (PLANNERDEBUG){\n cout << \" id = \" << info->id;\n cout << \", (\" << (*s)[0] << \",\" << (*s)[1] << \")\" << endl;\n }\n\n info->historyModel = new std::map< std::deque<float>, StateActionInfo>[numactions];\n\n // model q values, visit counts\n info->Q.resize(numactions, 0);\n info->uctActions.resize(numactions, 1);\n info->uctVisits = 1;\n info->visited = 0; //false;\n info->needsUpdate = true;\n\n for (int i = 0; i < numactions; i++){\n info->Q[i] = rng.uniform(0,0.01);\n }\n\n //if (PLANNERDEBUG) cout << \"done with initStateInfo()\" << endl;\n\n}\n\n\nvoid ETUCT::printStates(){\n\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n state_t s = canonicalize(*i);\n\n state_info* info = &(statedata[s]);\n\n cout << \"State \" << info->id << \": \";\n for (unsigned j = 0; j < s->size(); j++){\n cout << (*s)[j] << \", \";\n }\n cout << endl;\n\n for (int act = 0; act < numactions; act++){\n cout << \" Q: \" << info->Q[act] << endl;\n }\n\n }\n}\n\n\nvoid ETUCT::deleteInfo(state_info* info){\n\n delete [] info->historyModel;\n\n}\n\n\n\ndouble ETUCT::getSeconds(){\n struct timezone tz;\n timeval timeT;\n gettimeofday(&timeT, &tz);\n return timeT.tv_sec + (timeT.tv_usec / 1000000.0);\n}\n\n\nfloat ETUCT::uctSearch(const std::vector<float> &actS, state_t discS, int depth,std::deque<float> searchHistory){\n if (UCTDEBUG){\n cout << \" uctSearch state \";\n for (unsigned i = 0; i < actS.size(); i++){\n cout << actS[i] << \", \";\n }\n cout << \" at depth \" << depth << endl;\n }\n\n state_info* info = &(statedata[discS]);\n\n // if max depth\n // iterative deepening (probability inversely proportional to visits)\n //float terminateProb = 1.0/(2.0+(float)info->uctVisits);\n\n // already visited, stop here\n if (depth > MAX_DEPTH){\n // return max q value here\n std::vector<float>::iterator maxAct =\n std::max_element(info->Q.begin(),\n info->Q.end());\n float maxval = *maxAct;\n\n if (UCTDEBUG)\n cout << \"Terminated after depth: \" << depth\n // << \" prob: \" << terminateProb\n << \" Q: \" << maxval\n << \" visited: \" << info->visited << endl;\n\n return maxval;\n }\n\n // select action\n int action = selectUCTAction(info);\n\n // simulate action to get next state and reward\n // depending on exploration, may also terminate us\n float reward = 0;\n bool term = false;\n\n float learnRate;\n //float learnRate = 0.001;\n //float learnRate = 1.0 / info->uctActions[action];\n // learnRate = 10.0 / (info->uctActions[action] + 100.0);\n learnRate = 10.0 / (info->uctActions[action] + 10.0);\n //if (learnRate < 0.001 && MAX_TIME < 0.5)\n //learnRate = 0.001;\n //learnRate = 0.05;\n //learnRate = 1.0;\n\n // tell model learning thread to update this state since we've visited it\n info->needsUpdate = true;\n\n // simulate next state, reward, terminal\n std::vector<float> actualNext = simulateNextState(actS, discS, info, searchHistory, action, &reward, &term);\n\n // simulate reward from this action\n if (term){\n // this one terminated\n if (UCTDEBUG) cout << \" Terminated on exploration condition\" << endl;\n info->Q[action] += learnRate * (reward - info->Q[action]);\n info->uctVisits++;\n info->uctActions[action]++;\n if (UCTDEBUG)\n cout << \" Depth: \" << depth << \" Selected action \" << action\n << \" r: \" << reward\n << \" StateVisits: \" << info->uctVisits\n << \" ActionVisits: \" << info->uctActions[action] << endl;\n\n return reward;\n }\n\n // get discretized version of next\n state_t discNext = canonicalize(actualNext);\n\n if (UCTDEBUG)\n cout << \" Depth: \" << depth << \" Selected action \" << action\n << \" r: \" << reward << endl;\n\n info->visited++; // = true;\n\n if (HISTORY_SIZE > 0){\n // update history vector for this state\n /*\n for (unsigned i = 0; i < (*discS).size(); i++){\n searchHistory.push_back((*discS)[i]);\n searchHistory.pop_front();\n }\n */\n for (int i = 0; i < numactions; i++){\n if (i == action)\n searchHistory.push_back(1.0);\n else\n searchHistory.push_back(0.0);\n searchHistory.pop_front();\n }\n \n if (HISTORYDEBUG) {\n cout << \"New planning history vector (size \" << searchHistory.size() << \": \" << searchHistory[0];\n for (unsigned i = 1; i < searchHistory.size(); i++){\n cout << \",\" << searchHistory[i];\n }\n cout << endl;\n }\n }\n\n\n // new q value\n float newQ = reward + gamma * uctSearch(actualNext, discNext, depth+1, searchHistory);\n\n if (info->visited == 1){\n\n // update q and visit counts\n info->Q[action] += learnRate * (newQ - info->Q[action]);\n info->uctVisits++;\n info->uctActions[action]++;\n\n if (UCTDEBUG)\n cout << \" Depth: \" << depth << \" newQ: \" << newQ\n << \" StateVisits: \" << info->uctVisits\n << \" ActionVisits: \" << info->uctActions[action] << endl;\n\n if (lambda < 1.0){\n\n // new idea, return max of Q or new q\n std::vector<float>::iterator maxAct =\n std::max_element(info->Q.begin(),\n info->Q.end());\n float maxval = *maxAct;\n\n if (UCTDEBUG)\n cout << \" Replacing newQ: \" << newQ;\n\n // replace with w avg of maxq and new val\n newQ = (lambda * newQ) + ((1.0-lambda) * maxval);\n\n if (UCTDEBUG)\n cout << \" with wAvg: \" << newQ << endl;\n }\n\n }\n\n info->visited--;\n\n // return q\n return newQ;\n\n}\n\n\nint ETUCT::selectUCTAction(state_info* info){\n // if (UCTDEBUG) cout << \" selectUCTAction\" << endl;\n\n std::vector<float> &Q = info->Q;\n\n // loop through\n float rewardBound = rrange;\n if (rewardBound < 1.0)\n rewardBound = 1.0;\n rewardBound /= (1.0 - gamma);\n if (UCTDEBUG) cout << \"Reward bound: \" << rewardBound << endl;\n\n std::vector<float> uctQ(numactions, 0.0);\n\n for (int i = 0; i < numactions; i++){\n\n // this actions value is Q + rMax * 2 sqrt (log N(s) / N(s,a))\n uctQ[i] = Q[i] +\n rewardBound * 2.0 * sqrt(log((float)info->uctVisits) /\n (float)info->uctActions[i]);\n\n if (UCTDEBUG)\n cout << \" Action: \" << i << \" Q: \" << Q[i]\n << \" visits: \" << info->uctActions[i]\n << \" value: \" << uctQ[i] << endl;\n }\n\n // max element of uctQ\n std::vector<float>::iterator maxAct =\n max_element(uctQ.begin(), uctQ.end());\n float maxval = *maxAct;\n int act = maxAct - uctQ.begin();\n\n if (UCTDEBUG)\n cout << \" Selected \" << act << \" val: \" << maxval << endl;\n\n return act;\n\n}\n\n\n\nstd::vector<float> ETUCT::simulateNextState(const std::vector<float> &actualState, state_t discState, state_info* info, const std::deque<float> &history, int action, float* reward, bool* term){\n\n StateActionInfo* modelInfo = &(info->historyModel[action][history]);\n bool upToDate = modelInfo->frameUpdated >= lastUpdate;\n\n if (!upToDate){\n // must put in appropriate history\n if (HISTORY_SIZE > 0){\n std::vector<float> modState = *discState;\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\n modState.push_back(history[i]);\n }\n updateStateActionHistoryFromModel(modState, action, modelInfo);\n } else {\n updateStateActionHistoryFromModel(*discState, action, modelInfo);\n }\n }\n\n\n *reward = modelInfo->reward;\n *term = (rng.uniform() < modelInfo->termProb);\n\n if (*term){\n return actualState;\n }\n\n float randProb = rng.uniform();\n\n float probSum = 0.0;\n std::vector<float> nextstate;\n\n if (REALSTATEDEBUG) cout << \"randProb: \" << randProb << \" numNext: \" << modelInfo->transitionProbs.size() << endl;\n\n if (modelInfo->transitionProbs.size() == 0)\n nextstate = actualState;\n\n for (std::map<std::vector<float>, float>::iterator outIt\n = modelInfo->transitionProbs.begin();\n outIt != modelInfo->transitionProbs.end(); outIt++){\n\n float prob = (*outIt).second;\n probSum += prob;\n if (REALSTATEDEBUG) cout << randProb << \", \" << probSum << \", \" << prob << endl;\n\n if (randProb <= probSum){\n nextstate = (*outIt).first;\n if (REALSTATEDEBUG) cout << \"selected state \" << randProb << \", \" << probSum << \", \" << prob << endl;\n break;\n }\n }\n\n if (trackActual){\n\n\n // find the relative change from discrete center\n std::vector<float> relChange = subVec(nextstate, *discState);\n\n // add that on to actual current state value\n nextstate = addVec(actualState, relChange);\n\n\n }\n\n // check that next state is valid\n for (unsigned j = 0; j < nextstate.size(); j++){\n if (nextstate[j] < (featmin[j]-EPSILON)\n || nextstate[j] > (featmax[j]+EPSILON)){\n return actualState;\n }\n }\n\n if (UCTDEBUG) cout << \"predicted next state: \" << nextstate[0] << \", \" << nextstate[1] << endl;\n\n // return new actual state\n return nextstate;\n\n}\n\n\nvoid ETUCT::savePolicy(const char* filename){\n\n ofstream policyFile(filename, ios::out | ios::binary | ios::trunc);\n\n // first part, save the vector size\n int fsize = featmin.size();\n policyFile.write((char*)&fsize, sizeof(int));\n\n // save numactions\n policyFile.write((char*)&numactions, sizeof(int));\n\n // go through all states, and save Q values\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n\n state_t s = canonicalize(*i);\n state_info* info = &(statedata[s]);\n\n // save state\n policyFile.write((char*)&((*i)[0]), sizeof(float)*fsize);\n\n // save q-values\n policyFile.write((char*)&(info->Q[0]), sizeof(float)*numactions);\n\n }\n\n policyFile.close();\n}\n\nvoid ETUCT::logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax){\n std::vector<float> state(2, 0.0);\n for (int i = xmin ; i < xmax; i++){\n for (int j = ymin; j < ymax; j++){\n state[0] = j;\n state[1] = i;\n state_t s = canonicalize(state);\n state_info* info = &(statedata[s]);\n std::vector<float> &Q_s = info->Q;\n const std::vector<float>::iterator max =\n random_max_element(Q_s.begin(), Q_s.end());\n *of << (*max) << \",\";\n }\n }\n}\n\n\n// should do it such that an already discretized state stays the same\n// mainly the numerical value of each bin should be the average of that bin\nstd::vector<float> ETUCT::discretizeState(const std::vector<float> &s){\n std::vector<float> ds(s.size());\n\n for (unsigned i = 0; i < s.size(); i++){\n\n // since i'm sometimes doing this for discrete domains\n // want to center bins on 0, not edge on 0\n //cout << \"feat \" << i << \" range: \" << featmax[i] << \" \" << featmin[i] << \" \" << (featmax[i]-featmin[i]) << \" n: \" << (float)statesPerDim;\n\n float factor = (featmax[i] - featmin[i]) / (float)statesPerDim[i];\n int bin = 0;\n if (s[i] > 0){\n bin = (int)((s[i]+factor/2) / factor);\n } else {\n bin = (int)((s[i]-factor/2) / factor);\n }\n\n ds[i] = factor*bin;\n //cout << \"P factor: \" << factor << \" bin: \" << bin;\n //cout << \" Original: \" << s[i] << \" Discrete: \" << ds[i] << endl;\n }\n\n return ds;\n}\n\nstd::vector<float> ETUCT::addVec(const std::vector<float> &a, const std::vector<float> &b){\n if (a.size() != b.size())\n cout << \"ERROR: add vector sizes wrong \" << a.size() << \", \" << b.size() << endl;\n\n std::vector<float> c(a.size(), 0.0);\n for (unsigned i = 0; i < a.size(); i++){\n c[i] = a[i] + b[i];\n }\n\n return c;\n}\n\nstd::vector<float> ETUCT::subVec(const std::vector<float> &a, const std::vector<float> &b){\n if (a.size() != b.size())\n cout << \"ERROR: sub vector sizes wrong \" << a.size() << \", \" << b.size() << endl;\n\n std::vector<float> c(a.size(), 0.0);\n for (unsigned i = 0; i < a.size(); i++){\n c[i] = a[i] - b[i];\n }\n\n return c;\n}\n\n\nvoid ETUCT::setFirst(){\n if (HISTORY_SIZE == 0) return;\n\n if (HISTORYDEBUG) cout << \"first action, set sahistory to 0s\" << endl;\n\n // first action, reset history vector\n saHistory.resize(saHistory.size(), 0.0);\n}\n\nvoid ETUCT::setSeeding(bool seeding){\n\n if (HISTORYDEBUG) cout << \"set seed mode to \" << seeding << endl;\n seedMode = 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src/Agent/QLearner.o\"\n\t@echo \"... src/Agent/QLearner.i\"\n\t@echo \"... src/Agent/QLearner.s\"\n\t@echo \"... src/Agent/Sarsa.o\"\n\t@echo \"... src/Agent/Sarsa.i\"\n\t@echo \"... src/Agent/Sarsa.s\"\n\t@echo \"... src/Agent/SavedPolicy.o\"\n\t@echo \"... src/Agent/SavedPolicy.i\"\n\t@echo \"... src/Agent/SavedPolicy.s\"\n\t@echo \"... src/Models/C45Tree.o\"\n\t@echo \"... src/Models/C45Tree.i\"\n\t@echo \"... src/Models/C45Tree.s\"\n\t@echo \"... src/Models/ExplorationModel.o\"\n\t@echo \"... src/Models/ExplorationModel.i\"\n\t@echo \"... src/Models/ExplorationModel.s\"\n\t@echo \"... src/Models/FactoredModel.o\"\n\t@echo \"... src/Models/FactoredModel.i\"\n\t@echo \"... src/Models/FactoredModel.s\"\n\t@echo \"... src/Models/LinearSplitsTree.o\"\n\t@echo \"... src/Models/LinearSplitsTree.i\"\n\t@echo \"... src/Models/LinearSplitsTree.s\"\n\t@echo \"... src/Models/M5Tree.o\"\n\t@echo \"... src/Models/M5Tree.i\"\n\t@echo \"... src/Models/M5Tree.s\"\n\t@echo \"... src/Models/MultipleClassifiers.o\"\n\t@echo \"... src/Models/MultipleClassifiers.i\"\n\t@echo \"... src/Models/MultipleClassifiers.s\"\n\t@echo \"... src/Models/RMaxModel.o\"\n\t@echo \"... src/Models/RMaxModel.i\"\n\t@echo \"... src/Models/RMaxModel.s\"\n\t@echo \"... src/Models/SepPlanExplore.o\"\n\t@echo \"... src/Models/SepPlanExplore.i\"\n\t@echo \"... src/Models/SepPlanExplore.s\"\n\t@echo \"... src/Models/Stump.o\"\n\t@echo \"... src/Models/Stump.i\"\n\t@echo \"... src/Models/Stump.s\"\n\t@echo \"... src/Planners/ETUCT.o\"\n\t@echo \"... src/Planners/ETUCT.i\"\n\t@echo \"... src/Planners/ETUCT.s\"\n\t@echo \"... src/Planners/MBS.o\"\n\t@echo \"... src/Planners/MBS.i\"\n\t@echo \"... src/Planners/MBS.s\"\n\t@echo \"... src/Planners/PO_ETUCT.o\"\n\t@echo \"... src/Planners/PO_ETUCT.i\"\n\t@echo \"... src/Planners/PO_ETUCT.s\"\n\t@echo \"... src/Planners/PO_ParallelETUCT.o\"\n\t@echo \"... src/Planners/PO_ParallelETUCT.i\"\n\t@echo \"... src/Planners/PO_ParallelETUCT.s\"\n\t@echo \"... src/Planners/ParallelETUCT.o\"\n\t@echo \"... src/Planners/ParallelETUCT.i\"\n\t@echo \"... src/Planners/ParallelETUCT.s\"\n\t@echo \"... src/Planners/PolicyIteration.o\"\n\t@echo \"... src/Planners/PolicyIteration.i\"\n\t@echo \"... src/Planners/PolicyIteration.s\"\n\t@echo \"... src/Planners/PrioritizedSweeping.o\"\n\t@echo \"... src/Planners/PrioritizedSweeping.i\"\n\t@echo \"... src/Planners/PrioritizedSweeping.s\"\n\t@echo \"... src/Planners/ValueIteration.o\"\n\t@echo \"... src/Planners/ValueIteration.i\"\n\t@echo \"... src/Planners/ValueIteration.s\"\n\t@echo \"... src/agent.o\"\n\t@echo \"... src/agent.i\"\n\t@echo \"... src/agent.s\"\n\t@echo \"... src/newmat/bandmat.o\"\n\t@echo \"... src/newmat/bandmat.i\"\n\t@echo \"... src/newmat/bandmat.s\"\n\t@echo \"... src/newmat/cholesky.o\"\n\t@echo \"... src/newmat/cholesky.i\"\n\t@echo \"... src/newmat/cholesky.s\"\n\t@echo \"... src/newmat/evalue.o\"\n\t@echo \"... src/newmat/evalue.i\"\n\t@echo \"... src/newmat/evalue.s\"\n\t@echo \"... src/newmat/fft.o\"\n\t@echo \"... src/newmat/fft.i\"\n\t@echo \"... src/newmat/fft.s\"\n\t@echo \"... src/newmat/hholder.o\"\n\t@echo \"... src/newmat/hholder.i\"\n\t@echo \"... src/newmat/hholder.s\"\n\t@echo \"... src/newmat/jacobi.o\"\n\t@echo \"... src/newmat/jacobi.i\"\n\t@echo \"... src/newmat/jacobi.s\"\n\t@echo \"... src/newmat/myexcept.o\"\n\t@echo \"... src/newmat/myexcept.i\"\n\t@echo \"... src/newmat/myexcept.s\"\n\t@echo \"... src/newmat/newfft.o\"\n\t@echo \"... src/newmat/newfft.i\"\n\t@echo \"... src/newmat/newfft.s\"\n\t@echo \"... src/newmat/newmat1.o\"\n\t@echo \"... src/newmat/newmat1.i\"\n\t@echo \"... src/newmat/newmat1.s\"\n\t@echo \"... src/newmat/newmat2.o\"\n\t@echo \"... src/newmat/newmat2.i\"\n\t@echo \"... src/newmat/newmat2.s\"\n\t@echo \"... src/newmat/newmat3.o\"\n\t@echo \"... src/newmat/newmat3.i\"\n\t@echo \"... src/newmat/newmat3.s\"\n\t@echo \"... src/newmat/newmat4.o\"\n\t@echo \"... src/newmat/newmat4.i\"\n\t@echo \"... src/newmat/newmat4.s\"\n\t@echo \"... src/newmat/newmat5.o\"\n\t@echo \"... src/newmat/newmat5.i\"\n\t@echo \"... src/newmat/newmat5.s\"\n\t@echo \"... src/newmat/newmat6.o\"\n\t@echo \"... src/newmat/newmat6.i\"\n\t@echo \"... src/newmat/newmat6.s\"\n\t@echo \"... src/newmat/newmat7.o\"\n\t@echo \"... src/newmat/newmat7.i\"\n\t@echo \"... src/newmat/newmat7.s\"\n\t@echo \"... src/newmat/newmat8.o\"\n\t@echo \"... src/newmat/newmat8.i\"\n\t@echo \"... src/newmat/newmat8.s\"\n\t@echo \"... src/newmat/newmat9.o\"\n\t@echo \"... src/newmat/newmat9.i\"\n\t@echo \"... src/newmat/newmat9.s\"\n\t@echo \"... src/newmat/newmatex.o\"\n\t@echo \"... src/newmat/newmatex.i\"\n\t@echo \"... src/newmat/newmatex.s\"\n\t@echo \"... src/newmat/newmatrm.o\"\n\t@echo \"... src/newmat/newmatrm.i\"\n\t@echo \"... src/newmat/newmatrm.s\"\n\t@echo \"... src/newmat/sort.o\"\n\t@echo \"... src/newmat/sort.i\"\n\t@echo \"... src/newmat/sort.s\"\n\t@echo \"... src/newmat/submat.o\"\n\t@echo \"... src/newmat/submat.i\"\n\t@echo \"... src/newmat/submat.s\"\n\t@echo \"... src/newmat/svd.o\"\n\t@echo \"... src/newmat/svd.i\"\n\t@echo \"... src/newmat/svd.s\"\n.PHONY : help\n\n\n\n#=============================================================================\n# Special targets to cleanup operation of make.\n\n# Special rule to run CMake to check the build system integrity.\n# No rule that depends on this can have commands that come from listfiles\n# because they might be regenerated.\ncmake_check_build_system:\n\tcd /home/justin/ros_test/build && $(CMAKE_COMMAND) -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR) --check-build-system CMakeFiles/Makefile.cmake 0\n.PHONY : cmake_check_build_system\n\n"},{"alpha_fraction":0.540432333946228,"alphanum_fraction":0.5986789464950562,"avg_line_length":23.73267364501953,"blob_id":"45a15d13f598ebfadcf228ed97be5c5874cb1830","content_id":"cc149f5d20573ff8a493affc48a3b64e1c331172","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":9992,"license_type":"no_license","max_line_length":133,"num_lines":404,"path":"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/FuelRooms.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file FuelRooms.cc\n Implements the Fuel World domain, with possible noise.\n From the paper:\n Hester and Stone, \"Real Time Targeted Exploration in Large Domains\", ICDL 2010.\n \\author Todd Hester\n*/\n\n#include <rl_env/FuelRooms.hh>\n\n\nFuelRooms::FuelRooms(Random &rand, bool extraVariation, bool stoch):\n height(20), width(30),\n goal(coord_t(11.0,24.0)), \n extraVar(extraVariation),\n noisy(stoch),\n rng(rand),\n s(3),\n ns(s[0]),\n ew(s[1]),\n energy(s[2])\n{\n\n fuelVisited = 0;\n totalVisited = 0;\n\n stateVisits = new int*[21];\n for (int i = 0; i < 21; i++){\n stateVisits[i] = new int[31];\n }\n\n reset();\n resetVisits();\n}\n\n\n\nFuelRooms::~FuelRooms() { \n for (int i = 0; i < 21; i++){\n delete [] stateVisits[i];\n }\n delete [] stateVisits;\n}\n\nconst std::vector<float> &FuelRooms::sensation() const { \n return s; \n}\n\nfloat FuelRooms::apply(int action) {\n\n checkVisits();\n\n if (terminal())\n return 0.0;\n\n //cout << \"Taking action \" << static_cast<room_action_t>(action) << endl;\n\n //cout << \"state: \" << s[0] << \", \" << s[1] << \", \" << s[2] \n // << \" act: \" << action << endl;\n\n // 20% lose none\n // 20% lose two\n // 80% of the time, lose one energy\n // if (!noisy || rng.bernoulli(0.8))\n energy--;\n\n // many fuel squares, with varying amounts reward\n if ((int)ns == 0 || (int)ns == height){\n energy += 20.0;\n }\n \n\n if (energy > 60.0) \n energy = 60.0; \n\n const room_action_t effect =\n noisy\n ? add_noise(static_cast<room_action_t>(action)) \n : static_cast<room_action_t>(action);\n\n float r = reward(effect);\n\n if (effect == NORTH || effect == NORTHWEST || effect == NORTHEAST)\n if (ns < height)\n ns++;\n\n if (effect == SOUTH || effect == SOUTHWEST || effect == SOUTHEAST)\n if (ns > 0)\n ns--;\n\n if (effect == EAST || effect == SOUTHEAST || effect == NORTHEAST)\n if (ew < width)\n ew++;\n\n if (effect == WEST || effect == SOUTHWEST || effect == NORTHWEST)\n if (ew > 0)\n ew--;\n \n return r;\n \n std::cerr << \"Unreachable point reached in FuelRooms::apply!!!\\n\";\n return 0; // unreachable, I hope\n}\n\n\n\nfloat FuelRooms::reward(int effect) {\n \n if (energy < 0.0){\n return -400.0;\n }\n\n if (terminal()){\n //cout << \"Found goal!!!!\" << endl;\n return 0.0;\n }\n\n // extra cost at fuel stations\n /*\n if (ns == 0 || ns == height){\n return -5.0;\n }\n */\n\n if (ns == 0 || ns == height){\n float base = -10.0;\n if (ns == 0)\n base = -13.0;\n \n // extra variation\n float var = 1.0;\n if (extraVar)\n var = 5.0;\n else\n base -= 8.0;\n\n return base - (((int)ew % 5) * var);\n \n }\n \n if (effect == NORTH || effect == SOUTH || effect == EAST || effect == WEST)\n return -1.0;\n else\n return -1.4;\n \n}\n\n\nbool FuelRooms::terminal() const {\n // current position equal to goal??\n // or out of fuel\n return (coord_t(ns,ew) == goal) || (energy < 0.0);\n}\n\n\nvoid FuelRooms::reset() {\n // start randomly in left region\n ns = rng.uniformDiscrete(7, 12);\n ew = rng.uniformDiscrete(0, 4);\n\n // start with random amount of fuel\n // enough to get to gas stations, not enough to get to goal\n // gas stations up to 9 steps away, goal at least 20 steps away\n energy = rng.uniformDiscrete(14, 18);\n\n}\n\n\nvoid FuelRooms::resetVisits(){\n fuelVisited = 0;\n totalVisited = 0;\n\n for (int i = 0; i < 21; i++)\n for (int j = 0; j < 31; j++)\n stateVisits[i][j] = 0;\n}\n\nvoid FuelRooms::checkVisits(){\n stateVisits[(int)ns][(int)ew]++;\n // first visit to a state\n if (stateVisits[(int)ns][(int)ew] == 1){\n totalVisited++;\n if (ns == 0 || ns == 20)\n fuelVisited++;\n }\n}\n\nvoid FuelRooms::printVisits(){\n float totalStates = 31.0 * 21.0;\n float fuelStates = 31.0 * 2.0;\n float otherStates = totalStates - fuelStates;\n cout << (fuelVisited/fuelStates) << endl << ((totalVisited-fuelVisited)/otherStates) << endl << (totalVisited/totalStates) << endl;\n}\n\nvoid FuelRooms::printVisitMap(string filename){\n ofstream fout(filename.c_str());\n for (int i = 0; i < 21; i++){\n for (int j = 0; j < 31; j++){\n fout << stateVisits[i][j] << \"\\t\";\n }\n fout << endl;\n }\n fout.close();\n}\n\n\nint FuelRooms::getNumActions(){\n return 8;\n}\n\n\nFuelRooms::room_action_t FuelRooms::add_noise(room_action_t action) {\n\n int newAct = rng.bernoulli(0.8) ? action : (rng.bernoulli(0.5) ? action+1 : action-1);\n\n if (newAct < 0)\n newAct = getNumActions()-1;\n if (newAct >= getNumActions())\n newAct = 0;\n\n return (room_action_t)newAct;\n}\n\n\n\nstd::vector<experience> FuelRooms::getSeedings() {\n\n // return seedings\n std::vector<experience> seeds;\n\n // how many copies of each?\n for (int i = 0; i < 1; i++){\n\n // single seed of terminal state\n seeds.push_back(getExp(11,24,rng.uniformDiscrete(2,40),rng.uniformDiscrete(0,7)));\n \n // one seed from each fuel row\n seeds.push_back(getExp(0, rng.uniformDiscrete(1,29),rng.uniformDiscrete(2,40),rng.uniformDiscrete(0,7))); \n seeds.push_back(getExp(20,rng.uniformDiscrete(1,29),rng.uniformDiscrete(2,40),rng.uniformDiscrete(0,7)));\n\n // seed of terminal\n seeds.push_back(getExp(rng.uniformDiscrete(1,19),rng.uniformDiscrete(1,22),0,rng.uniformDiscrete(0,7)));\n }\n\n /*\n // two seeds around the goal state\n seeds.push_back(getExp(10,24,4,NORTH));\n seeds.push_back(getExp(11,25,42,EAST));\n\n // one of death\n seeds.push_back(getExp(9,15,0,SOUTHEAST));\n */\n\n // lots of seeds of various shit\n /*\n for (int i = 0; i < 3; i++){\n\n // each wall\n //seeds.push_back(getExp(0,11,10,SOUTH));\n //seeds.push_back(getExp(0,27,14,SOUTH));\n //seeds.push_back(getExp(0,17,10,NORTH));\n \n //seeds.push_back(getExp(20,12,20,SOUTH));\n //seeds.push_back(getExp(20,28,24,NORTH));\n //seeds.push_back(getExp(20,18,20,NORTH));\n \n //seeds.push_back(getExp(10,30,30,EAST));\n //seeds.push_back(getExp(12,30,34,EAST));\n //seeds.push_back(getExp(10,30,30,WEST));\n \n //seeds.push_back(getExp(13,0,2,WEST));\n //seeds.push_back(getExp(17,0,4,WEST));\n //seeds.push_back(getExp(13,0,2,EAST));\n \n // experiences showing where the goal state is (11,24)\n seeds.push_back(getExp(10,24,20,NORTH));\n seeds.push_back(getExp(12,24,44,SOUTH));\n seeds.push_back(getExp(11,23,52,EAST));\n seeds.push_back(getExp(11,25,7,WEST));\n seeds.push_back(getExp(10,23,11,NORTHEAST));\n seeds.push_back(getExp(10,25,16,NORTHWEST));\n seeds.push_back(getExp(12,23,21,SOUTHEAST));\n seeds.push_back(getExp(12,25,36,SOUTHWEST));\n\n // near the goal state\n seeds.push_back(getExp(11,23,45,NORTH));\n seeds.push_back(getExp(10,24,31,NORTHEAST));\n seeds.push_back(getExp(11,25,11,SOUTH));\n seeds.push_back(getExp(12,24,18,WEST));\n seeds.push_back(getExp(10,23,18,SOUTHWEST));\n\n // a few normal\n seeds.push_back(getExp(17,14,52,SOUTH));\n seeds.push_back(getExp(1,6,43,EAST));\n seeds.push_back(getExp(9,18,24,NORTH));\n seeds.push_back(getExp(12,8,3,WEST));\n seeds.push_back(getExp(7,1,42,SOUTHEAST));\n seeds.push_back(getExp(6,9,7,NORTHEAST));\n seeds.push_back(getExp(19,28,28,NORTHWEST));\n seeds.push_back(getExp(2,18,33,SOUTHWEST));\n\n // actions do different things from one state!\n seeds.push_back(getExp(9,3,19,SOUTHEAST));\n seeds.push_back(getExp(9,3,19,EAST));\n seeds.push_back(getExp(9,3,19,NORTHWEST));\n seeds.push_back(getExp(9,3,19,WEST));\n seeds.push_back(getExp(9,3,19,NORTH));\n seeds.push_back(getExp(9,3,19,SOUTHWEST));\n\n // and fuel running out\n seeds.push_back(getExp(7,4,0,NORTH));\n seeds.push_back(getExp(19,21,0,SOUTHWEST));\n seeds.push_back(getExp(3,16,0,NORTHEAST));\n seeds.push_back(getExp(13,1,0,SOUTH));\n \n // general gas stations \n seeds.push_back(getExp(0,5,12,EAST));\n seeds.push_back(getExp(0,23,9,WEST));\n seeds.push_back(getExp(0,26,3,NORTHWEST));\n seeds.push_back(getExp(20,7,7,SOUTHEAST));\n seeds.push_back(getExp(20,18,4,SOUTH));\n seeds.push_back(getExp(20,25,4,SOUTHWEST));\n\n // terminal states\n seeds.push_back(getExp(3,24,-1,WEST));\n seeds.push_back(getExp(9,14,-1,SOUTH));\n seeds.push_back(getExp(7,4,-1,EAST));\n seeds.push_back(getExp(14,18,-1,NORTH));\n seeds.push_back(getExp(7,23,-1,NORTHWEST));\n seeds.push_back(getExp(16,5,-1,SOUTHWEST));\n seeds.push_back(getExp(17,14,-1,NORTHEAST));\n seeds.push_back(getExp(4,28,-1,SOUTHEAST));\n\n seeds.push_back(getExp(11,24,12,NORTH));\n seeds.push_back(getExp(11,24,22,WEST));\n seeds.push_back(getExp(11,24,32,EAST));\n seeds.push_back(getExp(11,24,2,SOUTH));\n seeds.push_back(getExp(11,24,17,NORTHWEST));\n seeds.push_back(getExp(11,24,27,SOUTHWEST));\n seeds.push_back(getExp(11,24,37,NORTHEAST));\n seeds.push_back(getExp(11,24,7,SOUTHEAST));\n }\n */\n\n /*\n // a bunch of random seeds\n for (int j = 0; j < 1000; j++){\n seeds.push_back(getExp(rng.uniformDiscrete(0,20),rng.uniformDiscrete(0,30),rng.uniformDiscrete(-1,60),rng.uniformDiscrete(0,3)));\n }\n */\n\n reset();\n resetVisits();\n\n return seeds;\n\n}\n\n\nexperience FuelRooms::getExp(int s0, int s1, int s2, int a){\n\n experience e;\n\n e.s.resize(3, 0.0);\n e.next.resize(3, 0.0);\n\n ns = s0;\n ew = s1;\n energy = s2;\n e.act = a;\n e.s = sensation();\n e.reward = apply(e.act);\n\n e.terminal = terminal();\n e.next = sensation();\n\n /*\n cout << \"Seed experience from state (\" << e.s[0] << \", \"\n << e.s[1] << \", \" << e.s[2] << \") action: \" << e.act\n << \" to (\" << e.next[0] << \", \" << e.next[1] << \", \" << e.next[2] \n << \") with reward \" << e.reward << \" and term: \" << e.terminal << endl;\n */\n\n return e;\n}\n\nvoid FuelRooms::getMinMaxFeatures(std::vector<float> *minFeat,\n std::vector<float> *maxFeat){\n \n minFeat->resize(s.size(), 0.0);\n maxFeat->resize(s.size(), 10.0);\n\n (*maxFeat)[0] = 20.0;\n (*maxFeat)[1] = 30.0;\n (*maxFeat)[2] = 60.0;\n (*minFeat)[2] = -1.0;\n}\n\nvoid FuelRooms::getMinMaxReward(float *minR,\n float *maxR){\n \n *minR = -400.0;\n *maxR = 0.0; \n \n}\n"},{"alpha_fraction":0.7339312434196472,"alphanum_fraction":0.7399103045463562,"avg_line_length":40.875,"blob_id":"2914dd387aa701808de524ffccfd5a98b4fd0b9f","content_id":"5d0f3f5a0ffa50d405a9e60abe77b0f245ecc4ae","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":669,"license_type":"no_license","max_line_length":73,"num_lines":16,"path":"/build/rl-texplore-ros-pkg-master/src/rl_common/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"rl_common\")\nset(rl_common_VERSION \"0.0.0\")\nset(rl_common_MAINTAINER \"Todd Hester <todd.hester@gmail.com>\")\nset(rl_common_PACKAGE_FORMAT \"1\")\nset(rl_common_BUILD_DEPENDS \"roscpp\" \"std_msgs\")\nset(rl_common_BUILD_EXPORT_DEPENDS \"message_runtime\" \"roscpp\" \"std_msgs\")\nset(rl_common_BUILDTOOL_DEPENDS \"catkin\")\nset(rl_common_BUILDTOOL_EXPORT_DEPENDS )\nset(rl_common_EXEC_DEPENDS \"message_runtime\" \"roscpp\" \"std_msgs\")\nset(rl_common_RUN_DEPENDS \"message_runtime\" \"roscpp\" \"std_msgs\")\nset(rl_common_TEST_DEPENDS )\nset(rl_common_DOC_DEPENDS )\nset(rl_common_URL_WEBSITE \"\")\nset(rl_common_URL_BUGTRACKER \"\")\nset(rl_common_URL_REPOSITORY \"\")\nset(rl_common_DEPRECATED \"\")"},{"alpha_fraction":0.5432450175285339,"alphanum_fraction":0.5525165796279907,"avg_line_length":26.980234146118164,"blob_id":"b2b09959625543b5798ad5402e286b2e44c75b06","content_id":"9fca810f21c104eac6907dcacf49e66744b490df","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":45300,"license_type":"no_license","max_line_length":125,"num_lines":1619,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/M5Tree.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file M5Tree.cc\n Implements the M5 Decision tree, as described in:\n \"Learning with Continuous Classes\" by J.R. Quinlan\n \"Inducing Model Trees for Continuous Classes\" by Y. Wang and I.H. Witten\n \\author Todd Hester\n*/\n\n#include \"M5Tree.hh\"\n\n\n// Include stuff for newmat matrix libraries\n\n#define WANT_MATH // include.h will get math fns\n // newmatap.h will get include.h\n#include \"../newmat/newmatap.h\" // need matrix applications\n#ifdef use_namespace\nusing namespace NEWMAT; // access NEWMAT namespace\n#endif\n\n\n\nM5Tree::M5Tree(int id, int trainMode, int trainFreq, int m,\n float featPct, bool simple, bool allowAllFeats, \n\t float min_sdr, Random rng):\n id(id), mode(trainMode), freq(trainFreq), M(m),\n featPct(featPct), SIMPLE(simple), ALLOW_ALL_FEATS(allowAllFeats),\n MIN_SDR(min_sdr), rng(rng)\n{\n\n nnodes = 0;\n nOutput = 0;\n nExperiences = 0;\n hadError = false;\n totalnodes = 0;\n maxnodes = N_M5_NODES;\n\n\n // how close a split has to be to be randomly selected\n SPLIT_MARGIN = 0.0; //0.02; //5; //01; //0.05; //0.2; //0.05;\n\n LMDEBUG = false;\n DTDEBUG = false;///true;\n SPLITDEBUG = false;//true;\n STOCH_DEBUG = false; //true; //false; //true;\n INCDEBUG = false; //true; //false; //true;\n NODEDEBUG = false;\n COPYDEBUG = false; //true;\n nfeat = 4;\n\n cout << \"Created m5 decision tree \" << id;\n if (SIMPLE) cout << \" simple regression\";\n else cout << \" multivariate regression\";\n if (ALLOW_ALL_FEATS) cout << \" (all feats)\";\n else cout << \" (subtree feats)\";\n if (DTDEBUG){\n cout << \" mode: \" << mode << \" freq: \" << freq << endl;\n }\n cout << \" MIN_SDR: \" << MIN_SDR << endl;\n\n initNodes();\n initTree();\n\n\n}\n\nM5Tree::M5Tree(const M5Tree& m5):\n id(m5.id), mode(m5.mode), freq(m5.freq), M(m5.M),\n featPct(m5.featPct), SIMPLE(m5.SIMPLE), ALLOW_ALL_FEATS(m5.ALLOW_ALL_FEATS),\n MIN_SDR(m5.MIN_SDR), rng(m5.rng)\n{\n COPYDEBUG = m5.COPYDEBUG;\n if (COPYDEBUG) cout << \"m5 copy \" << id << endl;\n nnodes = 0;\n nOutput = m5.nOutput;\n nExperiences = m5.nExperiences;\n hadError = m5.hadError;\n totalnodes = 0;\n maxnodes = m5.maxnodes;\n SPLIT_MARGIN = m5.SPLIT_MARGIN; \n LMDEBUG = m5.LMDEBUG;\n DTDEBUG = m5.DTDEBUG;\n SPLITDEBUG = m5.SPLITDEBUG;\n STOCH_DEBUG = m5.STOCH_DEBUG; \n INCDEBUG = m5.INCDEBUG; \n NODEDEBUG = m5.NODEDEBUG;\n nfeat = m5.nfeat;\n\n if (COPYDEBUG) cout << \" M5 copy nodes, experiences, root, etc\" << endl;\n // copy all experiences\n for (int i = 0; i < N_M5_EXP; i++){\n allExp[i] = m5.allExp[i];\n }\n if (COPYDEBUG) cout << \" M5 copied exp array\" << endl;\n\n // set experience pointers\n experiences.resize(m5.experiences.size());\n for (unsigned i = 0; i < m5.experiences.size(); i++){\n experiences[i] = &(allExp[i]);\n }\n if (COPYDEBUG) cout << \" M5 set experience pointers\" << endl;\n\n // now the tricky part, set the pointers inside the tree nodes correctly\n initNodes();\n\n if (COPYDEBUG) cout << \" M5 copy tree \" << endl;\n root = allocateNode();\n lastNode = root;\n copyTree(root, m5.root);\n if (COPYDEBUG) cout << \" M5 tree copy done\" << endl;\n \n if (COPYDEBUG) {\n cout << endl << \"New tree: \" << endl;\n printTree(root, 0);\n cout << endl;\n cout << \" m5 copy done\" << endl;\n }\n\n}\n\nvoid M5Tree::copyTree(tree_node* newNode, tree_node* origNode){\n\n int nodeId = newNode->id;\n\n if (COPYDEBUG) {\n cout << \" Copy node \" << newNode->id << \" from node \" << origNode->id << endl;\n cout << \" NewAddy \" << newNode << \", old: \" << origNode << endl;\n }\n\n // copy node from t\n *newNode = *origNode;\n newNode->id = nodeId;\n\n // if it has children, allocate and copy them too\n if (origNode->l != NULL && !newNode->leaf){\n newNode->l = allocateNode();\n if (COPYDEBUG) cout << \" Copy left node \" << newNode->l->id << \" from \" << origNode->l->id << endl;\n copyTree(newNode->l, origNode->l);\n } else {\n newNode->l = NULL;\n }\n\n if (origNode->r != NULL && !newNode->leaf){\n newNode->r = allocateNode();\n if (COPYDEBUG) cout << \" Copy right node \" << newNode->r->id << \" from \" << origNode->r->id << endl;\n copyTree(newNode->r, origNode->r);\n } else {\n newNode->r = NULL;\n }\n}\n\nM5Tree* M5Tree::getCopy(){\n M5Tree* copy = new M5Tree(*this);\n return copy;\n}\n\nM5Tree::~M5Tree() {\n deleteTree(root);\n for (unsigned i = N_M5_EXP; i < experiences.size(); i++){\n delete experiences[i];\n }\n experiences.clear();\n}\n\n// here the target output will be a single value\nbool M5Tree::trainInstance(classPair &instance){\n\n if (DTDEBUG) cout << id << \" trainInstance\" << endl;\n\n // featPct *= 0.99;\n\n nfeat = instance.in.size();\n\n bool modelChanged = false;\n\n // simply add this instance to the set of experiences\n\n // take from static array until we run out\n tree_experience *e;\n if (nExperiences < N_M5_EXP){\n // from statically created set of experiences\n e = &(allExp[nExperiences]);\n\n } else {\n // dynamically create experience\n e = new tree_experience;\n }\n\n\n e->input = instance.in;\n e->output = instance.out;\n experiences.push_back(e);\n nExperiences++;\n\n if (nExperiences == 1000000){\n cout << \"Reached limit of # experiences allowed.\" << endl;\n return false;\n }\n\n if (nExperiences != (int)experiences.size())\n cout << \"ERROR: experience size mismatch: \" << nExperiences << \", \" << experiences.size() << endl;\n\n //cout << nExperiences << endl << flush;\n //if (nExperiences == 503 && id == 10){\n // DTDEBUG = true;\n // SPLITDEBUG = true;\n // INCDEBUG = true;\n //}\n\n if ( DTDEBUG) {\n cout << \"Original input: \";\n for (unsigned i = 0; i < instance.in.size(); i++){\n cout << instance.in[i] << \", \";\n }\n cout << endl << \" Original output: \" << instance.out << endl;\n cout << \"Added exp id: \" << nExperiences << \" output: \" << e->output << endl;\n cout << \"Address: \" << e << \" Input : \";\n for (unsigned i = 0; i < e->input.size(); i++){\n cout << e->input[i] << \", \";\n }\n cout << endl << \" Now have \" << nExperiences << \" experiences.\" << endl;\n }\n\n // depending on mode/etc, maybe re-build tree\n\n // mode 0: re-build every step\n if (mode == BUILD_EVERY || nExperiences <= 1){\n rebuildTree();\n modelChanged = true;\n }\n\n // mode 1: re-build on error only\n else if (mode == BUILD_ON_ERROR){\n\n // build on misclassification\n // check for misclassify\n std::map<float, float> answer;\n testInstance(e->input, &answer);\n float val = answer.begin()->first;\n float error = fabs(val - e->output);\n\n if (error > 0.0){\n rebuildTree();\n modelChanged = true;\n }\n }\n\n // mode 2: re-build every FREQ steps\n else if (mode == BUILD_EVERY_N){\n // build every freq steps\n if (!modelChanged && (nExperiences % freq) == 0){\n rebuildTree();\n modelChanged = true;\n }\n }\n\n if (modelChanged){\n if (DTDEBUG) cout << \"DT \" << id << \" tree re-built.\" << endl;\n\n if (DTDEBUG){\n cout << endl << \"DT: \" << id << endl;\n printTree(root, 0);\n cout << \"Done printing tree\" << endl;\n }\n }\n\n return modelChanged;\n\n}\n\n\n// here the target output will be a single value\nbool M5Tree::trainInstances(std::vector<classPair> &instances){\n if (DTDEBUG) cout << id << \" DT trainInstances: \" \n << instances.size() \n << \" nExp: \" << nExperiences << endl;\n nfeat = instances[0].in.size();\n \n // featPct *= 0.99;\n \n bool modelChanged = false;\n\n bool doBuild = false;\n\n // loop through instances, possibly checking for errors\n for (unsigned a = 0; a < instances.size(); a++){\n classPair instance = instances[a];\n\n // simply add this instance to the set of experiences\n\n // take from static array until we run out\n tree_experience *e;\n if (nExperiences < N_M5_EXP){\n // from statically created set of experiences\n e = &(allExp[nExperiences]);\n\n } else {\n // dynamically create experience\n e = new tree_experience;\n }\n\n\n e->input = instance.in;\n e->output = instance.out;\n experiences.push_back(e);\n nExperiences++;\n\n if (nExperiences == 1000000){\n cout << \"Reached limit of # experiences allowed.\" << endl;\n return false;\n }\n\n if (nExperiences != (int)experiences.size())\n cout << \"ERROR: experience size mismatch: \" << nExperiences << \", \" << experiences.size() << endl;\n\n if (DTDEBUG) {\n cout << \"Original input: \";\n for (unsigned i = 0; i < instance.in.size(); i++){\n cout << instance.in[i] << \", \";\n }\n cout << endl << \" Original output: \" << instance.out << endl;\n cout << \"Added exp id: \" << nExperiences << \" output: \" << e->output << endl;\n cout << \"Address: \" << e << \" Input : \";\n for (unsigned i = 0; i < e->input.size(); i++){\n cout << e->input[i] << \", \";\n }\n cout << endl << \" Now have \" << nExperiences << \" experiences.\" << endl;\n }\n\n // depending on mode/etc, maybe re-build tree\n\n // don't need to check if we've already decided\n if (doBuild) continue;\n\n // mode 0: re-build every step\n if (mode == BUILD_EVERY || nExperiences <= 1){\n doBuild = true;\n }\n\n // mode 1: re-build on error only\n else if (mode == BUILD_ON_ERROR){\n\n // build on misclassification\n // check for misclassify\n std::map<float, float> answer;\n testInstance(e->input, &answer);\n float val = answer.begin()->first;\n float error = fabs(val - e->output);\n\n if (error > 0.0){\n doBuild = true;\n }\n\n }\n\n // mode 2: re-build every FREQ steps\n else if (mode == BUILD_EVERY_N){\n // build every freq steps\n if (!modelChanged && (nExperiences % freq) == 0){\n doBuild = true;\n }\n }\n\n } // loop of new instances\n\n if (DTDEBUG) cout << \"Added \" << instances.size() << \" new instances. doBuild = \" << doBuild << endl;\n\n if (doBuild){\n rebuildTree();\n modelChanged = true;\n }\n\n if (modelChanged){\n if (DTDEBUG) cout << \"DT \" << id << \" tree re-built.\" << endl;\n\n if (DTDEBUG){\n cout << endl << \"DT: \" << id << endl;\n printTree(root, 0);\n cout << \"Done printing tree\" << endl;\n }\n }\n\n return modelChanged;\n\n}\n\n\nvoid M5Tree::rebuildTree(){\n //cout << \"rebuild tree \" << id << \" on exp: \" << nExperiences << endl;\n // deleteTree(root);\n buildTree(root, experiences, false);\n //cout << \"tree \" << id << \" rebuilt. \" << endl;\n}\n\n\n// TODO: here we want to return the probability of the output value being each of the possible values, in the stochastic case\nvoid M5Tree::testInstance(const std::vector<float> &input, std::map<float, float>* retval){\n if (DTDEBUG) cout << \"testInstance\" << endl;\n\n retval->clear();\n\n // in case the tree is empty\n if (experiences.size() == 0){\n (*retval)[0.0] = 1.0;\n return;\n }\n\n // follow through tree to leaf\n tree_node* leaf = traverseTree(root, input);\n lastNode = leaf;\n\n // and return mapping of outputs and their probabilities\n leafPrediction(leaf, input, retval);\n\n}\n\nfloat M5Tree::getConf(const std::vector<float> &input){\n if (DTDEBUG) cout << \"numVisits\" << endl;\n\n // in case the tree is empty\n if (experiences.size() == 0){\n return 0;\n }\n\n if (lastNode == NULL){\n return 0;\n }\n\n // follow through tree to leaf\n //tree_node* leaf = traverseTree(root, input);\n\n // and return # in this leaf\n float conf = (float)lastNode->nInstances / (float)(2.0*M);\n if (conf > 1.0)\n return 1.0;\n else\n return conf;\n\n}\n\n// check to see if this state is one we should explore\n// to get more info on potential splits\n\n\n// init the tree\nvoid M5Tree::initTree(){\n if (DTDEBUG) cout << \"initTree()\" << endl;\n root = allocateNode();\n\n if (DTDEBUG) cout << \" root id = \" << root->id << endl;\n\n // just to ensure the diff models are on different random values\n for (int i = 0; i < id; i++){\n rng.uniform(0, 1);\n }\n\n}\n\n\n\n// init a tree node\nvoid M5Tree::initTreeNode(tree_node* node){\n if (DTDEBUG) cout << \"initTreeNode()\";\n\n node->id = nnodes++;\n if (DTDEBUG) cout << \" id = \" << node->id << endl;\n\n totalnodes++;\n if (totalnodes > maxnodes){\n maxnodes = totalnodes;\n if (DTDEBUG) cout << id << \" M5 MAX nodes: \" << maxnodes << endl;\n }\n\n // split criterion\n node->dim = -1;\n node->val = -1;\n\n // current data\n node->nInstances = 0;\n node->constant = 0;\n\n // coefficients will get resized later\n // node->coefficients.resize(2,0);\n\n // next nodes in tree\n node->l = NULL;\n node->r = NULL;\n\n node->leaf = true;\n\n}\n\nvoid M5Tree::deleteTree(tree_node* node){\n if (DTDEBUG) cout << \"deleteTree, node=\" << node->id << endl;\n\n if (node==NULL)\n return;\n\n totalnodes--;\n\n node->nInstances = 0;\n node->coefficients.clear();\n\n //recursively call deleteTree on children\n // then delete them\n if (!node->leaf){\n // left child\n if (node->l != NULL){\n deleteTree(node->l);\n deallocateNode(node->l);\n node->l = NULL;\n }\n\n // right child\n if (node->r != NULL){\n deleteTree(node->r);\n deallocateNode(node->r);\n node->r = NULL;\n }\n }\n\n node->leaf = true;\n node->dim = -1;\n node->val = -1;\n node->constant = 0;\n}\n\n\nM5Tree::tree_node* M5Tree::getCorrectChild(tree_node* node,\n const std::vector<float> &input){\n\n if (DTDEBUG) cout << \"getCorrectChild, node=\" << node->id << endl;\n\n if (passTest(node->dim, node->val, input))\n return node->l;\n else\n return node->r;\n\n}\n\nM5Tree::tree_node* M5Tree::traverseTree(tree_node* node,\n const std::vector<float> &input){\n\n if (DTDEBUG) cout << \"traverseTree, node=\" << node->id << endl;\n\n while (!node->leaf){\n node = getCorrectChild(node, input);\n }\n\n return node;\n}\n\n\nbool M5Tree::passTest(int dim, float val, const std::vector<float> &input){\n if (DTDEBUG) cout << \"passTest, dim=\" << dim << \",val=\" << val \n << \",input[\"<<dim<<\"]=\" << input[dim] <<endl;\n\n if (input[dim] > val)\n return false;\n else\n return true;\n\n}\n\n\nvoid M5Tree::buildTree(tree_node *node,\n const std::vector<tree_experience*> &instances,\n bool changed){\n if(DTDEBUG) cout << \"buildTree, node=\" << node->id\n << \",nInstances:\" << instances.size()\n << \",chg:\" << changed << endl;\n\n if (instances.size() == 0){\n cout << \"Error: buildTree called on tree \" << id << \" node \" << node->id << \" with no instances.\" << endl;\n exit(-1);\n }\n\n\n // TODO: what about stochastic data?\n //std::vector<float> chiSquare = calcChiSquare(instances);\n\n // first, add instances to tree\n node->nInstances = instances.size();\n\n bool allSame = true;\n float val0 = instances[0]->output;\n for (unsigned i = 1; i < instances.size(); i++){\n if (instances[i]->output != val0){\n allSame = false;\n break;\n }\n }\n\n // see if they're all the same\n if (allSame){\n makeLeaf(node);\n node->constant = instances[0]->output;\n if (DTDEBUG){\n cout << \"Tree \" << id << \" node \" << node->id \n << \" All \" << node->nInstances\n << \" classified with output \"\n << instances[0]->output << endl;\n }\n return;\n }\n\n // check if this is a leaf and linear model has no error\n if (node->leaf && node->coefficients.size() > 0) {\n //cout << \"Node \" << node->id << \" is leaf, checking lm\" << endl;\n float errorSum = 0;\n for (unsigned i = 0; i < instances.size(); i++){\n // get prediction for instance and compare with actual output\n tree_node* leaf = traverseTree(node, instances[i]->input);\n std::map<float, float> retval;\n leafPrediction(leaf, instances[i]->input, &retval);\n float prediction = retval.begin()->first;\n float absError = fabs(prediction - instances[i]->output);\n errorSum += absError;\n //cout << \"instance \" << i << \" leaf predicted: \" << prediction\n // << \" actual: \" << instances[i]->output\n // << \" error: \" << absError << endl;\n }\n float avgError = errorSum / (float)instances.size();\n if (avgError < 0.001){\n // cout << \"stick with linear model\" << endl;\n return;\n }\n }\n\n // if not, calculate SDR to determine best split\n if (SPLITDEBUG) cout << endl << \"Creating new decision node\" << endl;\n\n node->leaf = false;\n //node->nInstances++;\n\n float bestSDR = -1.0;\n int bestDim = -1;\n float bestVal = -1;\n std::vector<tree_experience*> bestLeft;\n std::vector<tree_experience*> bestRight;\n\n testPossibleSplits(instances, &bestSDR, &bestDim, &bestVal, &bestLeft, &bestRight);\n\n implementSplit(node, instances, bestSDR, bestDim, bestVal, bestLeft, bestRight, changed);\n\n // possibly replace split node with linear regression model\n pruneTree(node, instances);\n\n}\n\n\nvoid M5Tree::makeLeaf(tree_node* node){\n\n removeChildren(node);\n\n // make sure there are enough coefficients for all the features\n // and that they are 0\n if (node->coefficients.size() != (unsigned)nfeat){\n node->coefficients.resize(nfeat, 0);\n }\n for (unsigned i = 0; i < node->coefficients.size(); i++){\n node->coefficients[i] = 0;\n }\n\n}\n\n\nvoid M5Tree::removeChildren(tree_node* node){\n // check on children\n if (node->l != NULL){\n deleteTree(node->l);\n deallocateNode(node->l);\n node->l = NULL;\n }\n\n if (node->r != NULL){\n deleteTree(node->r);\n deallocateNode(node->r);\n node->r = NULL;\n }\n\n node->leaf = true;\n}\n\n\nvoid M5Tree::pruneTree(tree_node *node,\n const std::vector<tree_experience*> &instances){\n if (LMDEBUG || DTDEBUG){\n printTree(root, 0);\n cout << \"pruneTree, node=\" << node->id\n << \",nInstances:\" << instances.size() << endl;\n }\n\n // TODO: no pruning right now\n // return;\n\n // calculate error of current subtree\n float subtreeErrorSum = 0;\n for (unsigned i = 0; i < instances.size(); i++){\n \n // get prediction for instance and compare with actual output\n tree_node* leaf = traverseTree(node, instances[i]->input);\n std::map<float, float> retval;\n leafPrediction(leaf, instances[i]->input, &retval);\n float prediction = retval.begin()->first;\n float absError = fabs(prediction - instances[i]->output);\n subtreeErrorSum += absError;\n if (LMDEBUG || DTDEBUG){\n cout << \"instance \" << i << \" subtree predicted: \" << prediction\n << \" actual: \" << instances[i]->output\n << \" error: \" << absError << endl;\n }\n }\n if (instances.size() < 3){\n if (LMDEBUG || DTDEBUG) cout << \"instances size <= 2!!!\" << endl;\n return;\n }\n\n float avgTreeError = subtreeErrorSum / (float)instances.size();\n\n // if this is zero error, we're not going to replace it\n if (false && avgTreeError <= 0.0001){\n if (LMDEBUG || DTDEBUG) \n cout << \"Sub-tree is perfect (\" << avgTreeError\n << \"), do not attempt LM\" << endl;\n return;\n }\n \n // figure out tree feats used\n std::vector<bool> treeFeatsUsed(instances[0]->input.size(), false);\n getFeatsUsed(node, &treeFeatsUsed);\n int nTreeFeatsUsed = 0;\n for (unsigned i = 0; i < treeFeatsUsed.size(); i++){\n if (treeFeatsUsed[i])\n nTreeFeatsUsed++;\n }\n\n // Just use them all... otherwise we ignore some that weren't good enough\n // for splitting, but are good here\n std::vector<bool> featsUsed(instances[0]->input.size(), true);\n int nFeatsUsed = featsUsed.size();\n\n // or just use ones allowed by subtree\n if (!ALLOW_ALL_FEATS){\n featsUsed = treeFeatsUsed;\n nFeatsUsed = nTreeFeatsUsed;\n }\n\n // no features to build linear model on\n if (nFeatsUsed == 0){\n if (LMDEBUG || DTDEBUG) cout << \"No features for LM\" << endl;\n return;\n }\n\n // add on error bonus based on nInstances and nParams\n float treeErrorEst = avgTreeError;\n float denom = (instances.size() - nTreeFeatsUsed);\n if (denom < 1){\n denom = 0.5;\n if (LMDEBUG) {\n cout << \"denom of tree error factor is \" << denom \n << \" with nInst \" << instances.size() \n << \" nfeats: \" << nTreeFeatsUsed << endl;\n }\n }\n treeErrorEst *= (instances.size() + nTreeFeatsUsed) / denom;\n\n // fit linear model to this set of instances\n float lmErrorSum = 0;\n int nlmFeats = 0;\n if (SIMPLE)\n nlmFeats = fitSimpleLinearModel(node, instances, featsUsed, nFeatsUsed, &lmErrorSum);\n else \n nlmFeats = fitLinearModel(node, instances, featsUsed, nFeatsUsed, &lmErrorSum);\n\n float avgLMError = lmErrorSum / (float)instances.size();\n\n float lmErrorEst = avgLMError;\n float denom2 = (instances.size() - nlmFeats);\n if (denom2 < 1){\n denom2 = 0.5;\n if (LMDEBUG) {\n cout << \"denom2 of lm error factor is \" << denom2\n << \" with nInst \" << instances.size() \n << \" nfeats: \" << nlmFeats << endl;\n }\n }\n lmErrorEst *= (instances.size() + nlmFeats) / denom2; \n\n // replace subtree with linear model?\n if (LMDEBUG || DTDEBUG) {\n cout << \"Sub-Tree Error: \" << treeErrorEst << \", lm Error: \"\n << lmErrorEst << endl;\n }\n // if (lmErrorEst < (treeErrorEst + 0.0001)){\n if (lmErrorEst < (treeErrorEst + 0.1*MIN_SDR)){\n //if (lmErrorEst < treeErrorEst){\n if (LMDEBUG || DTDEBUG)\n cout << node->id << \" replace tree with linear model\" << endl;\n removeChildren(node);\n } else {\n // remove coefficients again, for memory\n // node->coefficients.clear();\n }\n\n}\n\n\nint M5Tree::fitLinearModel(tree_node *node,\n const std::vector<tree_experience*> &instances,\n std::vector<bool> featureMask,\n int nFeats, float* resSum){\n \n if(DTDEBUG || LMDEBUG) cout << \"fitLinearModel, node=\" << node->id\n << \",nInstances:\" << instances.size() << endl;\n\n // make sure there are enough coefficients for all the features\n if (node->coefficients.size() != instances[0]->input.size()){\n node->coefficients.resize(instances[0]->input.size(), 0);\n }\n\n node->constant = 0.0;\n bool doRegression = true;\n int ntimes = 0;\n int nlmFeats = 10;\n (*resSum) = 1000000;\n\n while (doRegression){\n //cout << id << \" Attempt linear model \" << ntimes << endl;\n ntimes++;\n\n int nObs = (int)instances.size();\n\n //cout << \"with nObs: \" << nObs << \" and nFeats: \" << nFeats << endl;\n\n // no feats or obs, no model to build\n if (nObs == 0 || nFeats == 0)\n break;\n\n Matrix X(nObs, nFeats);\n ColumnVector Y(nObs);\n\n std::vector<int> featIndices(nFeats);\n\n std::vector<bool> constants(nFeats,true);\n bool foundProblem = false;\n \n // load up matrices\n for (int i = 0; i < nObs; i++){\n tree_experience *e = instances[i];\n if (LMDEBUG) cout << \"Obs: \" << i;\n\n // go through all features\n int featIndex = 1;\n for (unsigned j = 0; j < featureMask.size(); j++){\n node->coefficients[j] = 0;\n if (!featureMask[j])\n continue;\n \n if (constants[j] && e->input[j] != instances[0]->input[j]){\n constants[j] = false;\n }\n\n\t/*\n if (rng.uniform() < featPct){\n featureMask[j] = false;\n nFeats--;\n if (nFeats > 0)\n continue;\n else\n break;\n }\n\t*/\n\n if (i == nObs-1 && constants[j]){\n //cout << \"PROBLEM: feat \" << j << \" is constant!\" << endl;\n foundProblem = true;\n featureMask[j] = false;\n nFeats--;\n if (nFeats > 0)\n continue;\n else\n break;\n }\n\n featIndices[featIndex-1] = j;\n // HACK: I'm adding random noise here to prevent colinear features\n X(i+1,featIndex) = e->input[j]; // + rng.uniform(-0.00001, 0.00001);\n if (LMDEBUG){\n cout << \" Feat \" << featIndex << \" index \" << j\n << \" val \" << X(i+1,featIndex) << \",\";\n }\n featIndex++;\n }\n\n Y(i+1) = e->output;\n if (LMDEBUG) cout << \" out: \" << e->output << endl;\n }\n\n if (foundProblem || nFeats == 0)\n continue;\n\n // make vector of 1s\n ColumnVector Ones(nObs); Ones = 1.0;\n\n // calculate means (averages) of x1 and x2 [ .t() takes transpose]\n RowVector Mrow = Ones.t() * X / nObs;\n\n // and subtract means from x1 and x1\n Matrix XC(nObs,nFeats);\n XC = X - Ones * Mrow;\n\n // do the same to Y [use Sum to get sum of elements]\n ColumnVector YC(nObs);\n Real mval = Sum(Y) / nObs;\n YC = Y - Ones * mval;\n\n Try {\n\n // form sum of squares and product matrix\n // [use << rather than = for copying Matrix into SymmetricMatrix]\n SymmetricMatrix SSQ;\n SSQ << XC.t() * XC;\n \n ///////////////////////////\n // Cholesky Method\n LowerTriangularMatrix L = Cholesky(SSQ);\n\n // calculate estimate\n ColumnVector A = L.t().i() * (L.i() * (XC.t() * YC));\n ///////////////////////////\n \n //////////////////////////\n // Least Squares Method\n // calculate estimate\n // [bracket last two terms to force this multiplication first]\n // [ .i() means inverse, but inverse is not explicity calculated]\n //ColumnVector A = SSQ.i() * (XC.t() * YC);\n //////////////////////////\n \n \n // calculate estimate of constant term\n // [AsScalar converts 1x1 matrix to Real]\n Real a = mval - (Mrow * A).AsScalar();\n\n // Calculate fitted values and residuals\n //int npred1 = nFeats+1;\n ColumnVector Fitted = X * A + a;\n ColumnVector Residual = Y - Fitted;\n //Real ResVar = Residual.SumSquare() / (nObs-npred1);\n\n // print out answers\n // for each instance\n (*resSum) = 0;\n for (int i = 0; i < nObs; i++){\n if (DTDEBUG || LMDEBUG){\n cout << \"instance \" << i << \" linear model predicted: \" << Fitted(i+1)\n << \" actual: \" << instances[i]->output\n << \" error: \" << Residual(i+1) << endl;\n }\n (*resSum) += fabs(Residual(i+1));\n }\n\n\n // coeff\n nlmFeats = 0;\n for (int i = 0; i < nFeats; i++){\n if (DTDEBUG || LMDEBUG) cout << \"Coeff \" << i << \" on feat: \" << featIndices[i] << \" is \" << A(i+1) << endl;\n node->coefficients[featIndices[i]] = A(i+1);\n if (A(i+1) != 0)\n nlmFeats++;\n }\n if (DTDEBUG || LMDEBUG) cout << \"constant is \" << a << endl;\n node->constant = a;\n\n\n }\n\n CatchAll {\n // had an error trying the linear regression.\n // HACK TODO: for now, turn off one variable\n //cout << ntimes << \" linear regression had exception\" << endl;\n //<< BaseException::what() <<endl;\n\n // tried this already, stop now\n if (ntimes > 1 || nFeats < 2){\n //cout << \"max regression\" << endl;\n doRegression = false;\n break;\n }\n for (unsigned j = 0; j < featureMask.size(); j++){\n if (featureMask[j]){\n //cout << \"remove feature \" << j << endl;\n featureMask[j] = false;\n nFeats--;\n break;\n }\n }\n continue;\n }\n\n // it worked, dont need to do it again\n doRegression = false;\n\n }\n\n // return # features used\n return nlmFeats;\n\n}\n\nint M5Tree::fitSimpleLinearModel(tree_node *node,\n const std::vector<tree_experience*> &instances,\n std::vector<bool> featureMask,\n int nFeats, float* resSum){\n if(DTDEBUG || LMDEBUG) cout << \"fitSimpleLinearModel, node=\" << node->id\n << \",nInstances:\" << instances.size() << endl;\n \n // make sure there are enough coefficients for all the features\n if (node->coefficients.size() != instances[0]->input.size()){\n node->coefficients.resize(instances[0]->input.size(), 0);\n }\n\n // loop through all features, try simple single variable regression\n // keep track of error/coefficient of each\n int bestFeat = -1;\n float bestCoeff = -1;\n float bestError = 1000000;\n float bestConstant = -1;\n\n std::vector<float> xsum(instances[0]->input.size(), 0);\n std::vector<float> xysum(instances[0]->input.size(), 0);\n std::vector<float> x2sum(instances[0]->input.size(), 0);\n float ysum = 0;\n\n int nObs = (int)instances.size();\n for (int i = 0; i < nObs; i++){\n tree_experience *e = instances[i];\n if (LMDEBUG) cout << \"Obs: \" << i;\n \n // go through all features\n for (unsigned j = 0; j < instances[0]->input.size(); j++){\n if (!featureMask[j]) continue;\n if (LMDEBUG) cout << \", F\" << j << \": \" << e->input[j];\n xsum[j] += e->input[j];\n xysum[j] += (e->input[j] * e->output);\n x2sum[j] += (e->input[j] * e->input[j]);\n }\n ysum += e->output;\n if (LMDEBUG) cout << \", out: \" << e->output << endl;\n }\n \n // now go through all features and calc coeff and constant\n for (unsigned j = 0; j < instances[0]->input.size(); j++){\n if (!featureMask[j]) continue;\n /*\n if (rng.uniform() < featPct){\n continue;\n }\n */\n float coeff = (xysum[j] - xsum[j]*ysum/nObs)/(x2sum[j]-(xsum[j]*xsum[j])/nObs);\n float constant = (ysum/nObs) - coeff*(xsum[j]/nObs);\n \n if (LMDEBUG) {\n cout << \"Feat \" << j << \" coeff: \" << coeff << \", constant: \" \n << constant << \" mask: \" << featureMask[j] << endl;\n }\n\n // now try to make predictions and see what error is\n float errorSum = 0;\n for (int i = 0; i < nObs; i++){\n tree_experience *e = instances[i];\n float pred = constant + coeff * e->input[j];\n float error = fabs(pred - e->output);\n if (LMDEBUG) cout << \"Instance \" << i << \" error: \" << error << endl;\n errorSum += error;\n }\n if (LMDEBUG) cout << \"eSum: \" << errorSum << endl;\n\n // check if this is the best\n if (errorSum < bestError){\n bestError = errorSum;\n bestFeat = j;\n bestConstant = constant;\n bestCoeff = coeff;\n }\n }\n \n if (LMDEBUG){\n cout << \"SLM feat: \" << bestFeat << \" coeff: \" \n << bestCoeff << \" constant: \" \n << bestConstant << \" avgE: \" << (bestError/nObs) << endl;\n }\n\n if (bestFeat < 0 || bestFeat > (int)instances[0]->input.size()){\n node->constant = 0.0;\n for (unsigned i = 0; i < node->coefficients.size(); i++){\n node->coefficients[i] = 0.0;\n }\n (*resSum) = 1000000;\n return 10;\n }\n\n // pick best feature somehow\n node->constant = bestConstant;\n for (unsigned i = 0; i < node->coefficients.size(); i++){\n if (i == (unsigned)bestFeat){\n node->coefficients[i] = bestCoeff;\n if (LMDEBUG) cout << \"Set coefficient on feat \" << i << \" to \" << bestCoeff << endl;\n }\n else {\n node->coefficients[i] = 0.0;\n }\n }\n\n (*resSum) = bestError;\n\n // only use 1 input feature this way\n return 1;\n \n}\n\n\n\n\nvoid M5Tree::implementSplit(tree_node* node, \n const std::vector<tree_experience*> &instances,\n float bestSDR, int bestDim,\n float bestVal, \n const std::vector<tree_experience*> &bestLeft,\n const std::vector<tree_experience*> &bestRight,\n bool changed){\n if (DTDEBUG) cout << \"implementSplit node=\" << node->id\n << \",sdr=\" << bestSDR\n << \",dim=\" << bestDim\n << \",val=\" << bestVal \n << \",chg=\" << changed << endl;\n\n\n // see if this should still be a leaf node\n if (bestSDR < MIN_SDR){\n makeLeaf(node);\n float valSum = 0;\n for (unsigned i = 0; i < instances.size(); i++){\n valSum += instances[i]->output;\n }\n float avg = valSum / (float)(instances.size());\n node->constant = avg;\n if (SPLITDEBUG || STOCH_DEBUG){\n cout << \"DT \" << id << \" Node \" << node->id << \" Poor sdr \"\n << node->nInstances\n << \" instances classified at leaf \" << node->id\n << \" with multiple outputs \" << endl;\n }\n return;\n }\n\n // see if this split changed or not\n // assuming no changes above\n if (!changed && node->dim == bestDim && node->val == bestVal\n && !node->leaf && node->l != NULL && node->r != NULL){\n // same split as before.\n if (DTDEBUG || SPLITDEBUG) cout << \"Same split as before\" << endl;\n\n // see which leaf changed\n if (bestLeft.size() > (unsigned)node->l->nInstances){\n // redo left side\n if (DTDEBUG) cout << \"Rebuild left side of tree\" << endl;\n buildTree(node->l, bestLeft, changed);\n }\n\n if (bestRight.size() > (unsigned)node->r->nInstances){\n // redo right side\n if (DTDEBUG) cout << \"Rebuild right side of tree\" << endl;\n buildTree(node->r, bestRight, changed);\n }\n return;\n }\n\n\n // totally new\n // set the best split here\n node->leaf = false;\n node->dim = bestDim;\n node->val = bestVal;\n\n if (SPLITDEBUG) cout << \"Best split was cut with val \" << node->val\n << \" on dim \" << node->dim\n << \" with sdr: \" << bestSDR << endl;\n\n if (DTDEBUG) cout << \"Left has \" << bestLeft.size()\n << \", right has \" << bestRight.size() << endl;\n\n // make sure both instances\n if (bestLeft.size() == 0 || bestRight.size() == 0){\n cout << \"ERROR: DT \" << id << \" node \" << node->id << \" has 0 instances: left: \" << bestLeft.size()\n << \" right: \" << bestRight.size() << endl;\n cout << \"Split was cut with val \" << node->val\n << \" on dim \" << node->dim\n << \" with sdr: \" << bestSDR << endl;\n exit(-1);\n }\n\n\n // check if these already exist\n if (node->l == NULL){\n if (DTDEBUG) cout << \"Init new left tree nodes \" << endl;\n node->l = allocateNode();\n }\n if (node->r == NULL){\n if (DTDEBUG) cout << \"Init new right tree nodes \" << endl;\n node->r = allocateNode();\n }\n\n // recursively build the sub-trees to this one\n if (DTDEBUG) cout << \"Building left tree for node \" << node->id << endl;\n buildTree(node->l, bestLeft, true);\n if (DTDEBUG) cout << \"Building right tree for node \" << node->id << endl;\n buildTree(node->r, bestRight, true);\n\n}\n\n\nvoid M5Tree::getFeatsUsed(tree_node* node, std::vector<bool> *featsUsed){\n\n // if leaf, ones from linear model\n if (node->leaf){\n //cout << \"coeff size: \" << node->coefficients.size() << endl;\n for (unsigned i = 0; i < node->coefficients.size(); i++){\n if (node->coefficients[i] != 0){\n if (LMDEBUG || DTDEBUG) cout << \"Leaf node, used coeff \" << i << endl;\n (*featsUsed)[i] = true;\n }\n }\n return;\n }\n\n // otherwise see what split was used\n // and call for left and right sub-trees\n (*featsUsed)[node->dim] = true;\n if (LMDEBUG || DTDEBUG) cout << \"Split node, used feat \" << node->dim << endl;\n\n getFeatsUsed(node->l, featsUsed);\n getFeatsUsed(node->r, featsUsed);\n\n return;\n}\n\n\nvoid M5Tree::testPossibleSplits(const std::vector<tree_experience*> &instances,\n float *bestSDR, int *bestDim,\n float *bestVal, \n std::vector<tree_experience*> *bestLeft,\n std::vector<tree_experience*> *bestRight) {\n if (DTDEBUG) cout << \"testPossibleSplits\" << endl;\n\n\n // calculate sd for the set\n float sd = calcSDforSet(instances);\n //if (DTDEBUG) cout << \"I: \" << I << endl;\n\n int nties = 0;\n\n // for each possible split, calc standard deviation reduction\n for (unsigned idim = 0; idim < instances[0]->input.size(); idim++){\n\n //float* sorted = sortOnDim(idim, instances);\n float minVal, maxVal;\n std::set<float> uniques = getUniques(idim, instances, minVal, maxVal);\n\n for (std::set<float>::iterator j = uniques.begin(); j != uniques.end(); j++){\n\n // skip max val, not a valid cut for either\n if ((*j) == maxVal)\n continue;\n\n // if this is a random forest, we eliminate some random number of splits\n // here (decision is taken from the random set that are left)\n if (rng.uniform() < featPct)\n continue;\n\n std::vector<tree_experience*> left;\n std::vector<tree_experience*> right;\n\n // splits that are cuts\n float splitval = (*j);\n float sdr = calcSDR(idim, splitval, instances, sd, left, right);\n\n if (SPLITDEBUG) cout << \" CUT split val \" << splitval\n << \" on dim: \" << idim << \" had sdr \"\n << sdr << endl;\n\n // see if this is the new best sdr\n compareSplits(sdr, idim, splitval, left, right, &nties,\n bestSDR, bestDim, bestVal, bestLeft, bestRight);\n\n\n } // j loop\n }\n}\n\n\n\nvoid M5Tree::compareSplits(float sdr, int dim, float val, \n const std::vector<tree_experience*> &left, \n const std::vector<tree_experience*> &right,\n int *nties, float *bestSDR, int *bestDim,\n float *bestVal, \n std::vector<tree_experience*> *bestLeft,\n std::vector<tree_experience*> *bestRight){\n if (DTDEBUG) cout << \"compareSplits sdr=\" << sdr << \",dim=\" << dim\n << \",val=\" << val <<endl;\n\n\n bool newBest = false;\n\n // if its a virtual tie, break it randomly\n if (fabs(*bestSDR - sdr) < SPLIT_MARGIN){\n //cout << \"Split tie, making random decision\" << endl;\n\n (*nties)++;\n float randomval = rng.uniform(0,1);\n float newsplitprob = (1.0 / (float)*nties);\n\n if (randomval < newsplitprob){\n newBest = true;\n if (SPLITDEBUG) cout << \" Tie on split. DT: \" << id << \" rand: \" << randomval\n << \" splitProb: \" << newsplitprob << \", selecting new split \" << endl;\n }\n else\n if (SPLITDEBUG) cout << \" Tie on split. DT: \" << id << \" rand: \" << randomval\n << \" splitProb: \" << newsplitprob << \", staying with old split \" << endl;\n }\n\n // if its clearly better, set this as the best split\n else if (sdr > *bestSDR){\n newBest = true;\n *nties = 1;\n }\n\n\n // set the split features\n if (newBest){\n *bestSDR = sdr;\n *bestDim = dim;\n *bestVal = val;\n *bestLeft = left;\n *bestRight = right;\n if (SPLITDEBUG){\n cout << \" New best sdr: \" << *bestSDR\n << \" with val \" << *bestVal\n << \" on dim \" << *bestDim << endl;\n }\n } // newbest\n}\n\nfloat M5Tree::calcSDR(int dim, float val, \n const std::vector<tree_experience*> &instances,\n float sd,\n std::vector<tree_experience*> &left,\n std::vector<tree_experience*> &right){\n if (DTDEBUG) cout << \"calcSDR, dim=\" << dim\n << \" val=\" << val\n << \" sd=\" << sd\n << \" nInstances= \" << instances.size() << endl;\n\n left.clear();\n right.clear();\n\n // split into two sides\n for (unsigned i = 0; i < instances.size(); i++){\n if (DTDEBUG) cout << \"calcSDR - Classify instance \" << i \n << \" on new split \" << endl;\n\n if (passTest(dim, val, instances[i]->input)){\n left.push_back(instances[i]);\n }\n else{\n right.push_back(instances[i]);\n }\n }\n\n if (DTDEBUG) cout << \"Left has \" << left.size()\n << \", right has \" << right.size() << endl;\n\n // get sd for both sides\n float sdLeft = calcSDforSet(left);\n float sdRight = calcSDforSet(right);\n\n float leftRatio = (float)left.size() / (float)instances.size();\n float rightRatio = (float)right.size() / (float)instances.size();\n\n float sdr = sd - (leftRatio * sdLeft + rightRatio * sdRight);\n\n if (DTDEBUG){\n cout << \"LeftSD: \" << sdLeft\n << \" RightSD: \" << sdRight\n << \" SD: \" << sd\n << \" SDR: \" << sdr\n << endl;\n }\n\n return sdr;\n\n}\n\nfloat M5Tree::calcSDforSet(const std::vector<tree_experience*> &instances){\n if (DTDEBUG) cout << \"calcSDforSet\" << endl;\n\n int n = instances.size();\n\n if (n == 0)\n return 0;\n\n double sum = 0;\n double sumSqr = 0;\n\n // go through instances and calculate sums, sum of squares\n for (unsigned i = 0; i < instances.size(); i++){\n float val = instances[i]->output;\n sum += val;\n sumSqr += (val * val);\n }\n\n double mean = sum / (double)n;\n double variance = (sumSqr - sum*mean)/(double)n;\n float sd = sqrt(variance);\n\n return sd;\n\n}\n\nstd::set<float> M5Tree::getUniques(int dim, const std::vector<tree_experience*> &instances, float& minVal, float& maxVal){\n if (DTDEBUG) cout << \"getUniques,dim = \" << dim;\n\n std::set<float> uniques;\n\n for (int i = 0; i < (int)instances.size(); i++){\n if (i == 0 || instances[i]->input[dim] < minVal)\n minVal = instances[i]->input[dim];\n if (i == 0 || instances[i]->input[dim] > maxVal)\n maxVal = instances[i]->input[dim];\n\n uniques.insert(instances[i]->input[dim]);\n }\n\n // lets not try more than 100 possible splits per dimension\n if (uniques.size() > 100){\n float rangeInc = (maxVal - minVal) / 100.0;\n uniques.clear();\n for (float i = minVal; i < maxVal; i+= rangeInc){\n uniques.insert(i);\n }\n }\n uniques.insert(maxVal);\n\n if (DTDEBUG) cout << \" #: \" << uniques.size() << endl;\n return uniques;\n}\n\n\nfloat* M5Tree::sortOnDim(int dim, const std::vector<tree_experience*> &instances){\n if (DTDEBUG) cout << \"sortOnDim,dim = \" << dim << endl;\n\n float* values = new float[instances.size()];\n\n for (int i = 0; i < (int)instances.size(); i++){\n //cout << \"Instance \" << i << endl;\n\n float val = instances[i]->input[dim];\n //cout << \" val: \" << val << endl;\n\n // find where this should go\n for (int j = 0; j <= i; j++){\n //cout << \" j: \" << j << endl;\n\n // get to i, this is the spot then\n if (j==i){\n values[j] = val;\n //cout << \" At i, putting value in slot j: \" << j << endl;\n }\n\n // if this is the spot\n else if (val < values[j]){\n //cout << \" Found slot at j: \" << j << endl;\n\n // slide everything forward to make room\n for (int k = i; k > j; k--){\n //cout << \" k = \" << k << \" Sliding value from k-1 to k\" << endl;\n values[k] = values[k-1];\n }\n\n // put value in its spot at j\n //cout << \" Putting value at slot j: \" << j << endl;\n values[j] = val;\n\n // break\n break;\n }\n\n }\n }\n\n if (DTDEBUG){\n cout << \"Sorted array: \" << values[0];\n for (int i = 1; i < (int)instances.size(); i++){\n cout << \", \" << values[i];\n }\n cout << endl;\n }\n\n return values;\n\n}\n\n\nvoid M5Tree::printTree(tree_node *t, int level){\n\n for (int i = 0; i < level; i++){\n cout << \".\";\n }\n\n cout << \"Node \" << t->id << \" nInstances: \" << t->nInstances ;\n\n // Leaf, print regression stuff\n if (t->leaf){\n cout << \" Constant: \" << t->constant << \" coeff: \";\n for (unsigned j = 0; j < t->coefficients.size(); j++){\n if (!SIMPLE)\n cout << t->coefficients[j] << \", \";\n if (SIMPLE && t->coefficients[j] != 0)\n cout << \" on feat \" << j << \": \" << t->coefficients[j];\n }\n cout << endl;\n return;\n }\n\n // otherwise, print split info, next nodes\n\n cout << \" Type: CUT\";\n cout << \" Dim: \" << t->dim << \" Val: \" << t->val;\n cout << \" Left: \" << t->l->id << \" Right: \" << t->r->id << endl;\n\n // print children\n if (t->dim != -1 && !t->leaf){\n printTree(t->l, level+1);\n printTree(t->r, level+1);\n }\n\n}\n\n\n// output a map of outcomes and their probabilities for this leaf node\nvoid M5Tree::leafPrediction(tree_node* leaf, const std::vector<float> &input, std::map<float, float>* retval){\n if (DTDEBUG)\n cout << \"Calculating output for leaf \" << leaf->id << endl;\n\n float prediction = leaf->constant;\n if (DTDEBUG) cout << \"leaf constant: \" << leaf->constant << endl;\n\n // plus each coefficient\n for (unsigned i = 0; i < input.size(); i++){\n prediction += leaf->coefficients[i] * input[i];\n if (DTDEBUG) {\n cout << \"feat \" << i << \" coeff: \" << leaf->coefficients[i]\n << \" on input \" << input[i] << endl;\n }\n }\n\n if (DTDEBUG) cout << \" prediction: \" << prediction << endl;\n\n (*retval)[prediction] = 1.0;\n}\n\n\nvoid M5Tree::initNodes(){\n\n for (int i = 0; i < N_M5_NODES; i++){\n initTreeNode(&(allNodes[i]));\n freeNodes.push_back(i);\n if (NODEDEBUG) \n cout << \"init node \" << i << \" with id \" << allNodes[i].id \n << \", now \" << freeNodes.size() << \" free nodes.\" << endl;\n }\n\n}\n\nM5Tree::tree_node* M5Tree::allocateNode(){\n if (freeNodes.empty()){\n tree_node* newNode = new tree_node;\n initTreeNode(newNode);\n if (NODEDEBUG) \n cout << \"PROBLEM: No more pre-allocated nodes!!!\" << endl\n << \"return new node \" << newNode->id \n << \", now \" << freeNodes.size() << \" free nodes.\" << endl;\n return newNode;\n }\n\n int i = freeNodes.back();\n freeNodes.pop_back();\n if (NODEDEBUG) \n cout << \"allocate node \" << i << \" with id \" << allNodes[i].id \n << \", now \" << freeNodes.size() << \" free nodes.\" << endl;\n return &(allNodes[i]);\n}\n\nvoid M5Tree::deallocateNode(tree_node* node){\n if (node->id >= N_M5_NODES){\n if (NODEDEBUG) \n cout << \"dealloc extra node id \" << node->id \n << \", now \" << freeNodes.size() << \" free nodes.\" << endl;\n delete node;\n return;\n }\n\n freeNodes.push_back(node->id);\n if (NODEDEBUG) \n cout << \"dealloc node \" << node->id \n << \", now \" << freeNodes.size() << \" free nodes.\" << endl;\n}\n"},{"alpha_fraction":0.6485013365745544,"alphanum_fraction":0.6498637795448303,"avg_line_length":21.24242401123047,"blob_id":"78bd435b874e44fd0a7f84414ec989ee0bbf845c","content_id":"69e34b24d7b1882c6e1c684c23df466d5f7e7164","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":734,"license_type":"no_license","max_line_length":72,"num_lines":33,"path":"/src/roundbot_control/src/MotorSim.cpp","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include <roundbot_control/MotorSim.h>\n\nnamespace roundbot_control{\n\nMotorSim::MotorSim(double max_speed, double max_accel, double max_decel)\n{\n current_speed_ = 0;\n max_speed_ = max_speed;\n max_accel_ = max_accel;\n max_decel_ = max_decel;\n}\n\ndouble MotorSim::iterate(double target_speed, double dt)\n{\n double target_accel = (target_speed - current_speed_) / dt;\n\n if (target_accel > max_accel_){\n target_accel = max_accel_;\n }else if (target_accel < -max_decel_){\n target_accel = -max_decel_;\n }\n\n current_speed_ += (dt * target_accel);\n if (current_speed_ > max_speed_){\n current_speed_ = max_speed_;\n }else if (current_speed_ < -max_speed_){\n current_speed_ = -max_speed_;\n }\n\n return current_speed_;\n}\n\n}\n"},{"alpha_fraction":0.7360248565673828,"alphanum_fraction":0.7453415989875793,"avg_line_length":45.14285659790039,"blob_id":"7db568eb77b8805a09cc7d163c40a14f8703acd1","content_id":"70d4af0e785a671025579586d5080238ae9c701b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":322,"license_type":"no_license","max_line_length":53,"num_lines":7,"path":"/src/ugv_course_libs/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"ugv_course_libs\")\nset(ugv_course_libs_MAINTAINER \"micho <micho@todo.todo>\")\nset(ugv_course_libs_DEPRECATED \"\")\nset(ugv_course_libs_VERSION \"0.0.0\")\nset(ugv_course_libs_BUILD_DEPENDS \"sensor_msgs\" \"tf\")\nset(ugv_course_libs_RUN_DEPENDS \"sensor_msgs\" \"tf\")\nset(ugv_course_libs_BUILDTOOL_DEPENDS \"catkin\")"},{"alpha_fraction":0.602712869644165,"alphanum_fraction":0.6084104180335999,"avg_line_length":26.7864933013916,"blob_id":"8aea4675025d25e599395537e1fa27da4e1f7486","content_id":"7151fd6171cc77a782e97d03293f8b1106aa2b38","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":38262,"license_type":"no_license","max_line_length":180,"num_lines":1377,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/PO_ParallelETUCT.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file PO_ParallelETUCT.cc\n Implements my real-time model-based RL architecture which uses UCT with eligiblity traces for planning. This version of UCT plans over states augmented with k-action histories.\n The modified version of UCT used is presented in:\n L. Kocsis and C. Szepesv´ari, \"Bandit based monte-carlo planning,\" in\n ECML-06. Number 4212 in LNCS. Springer, 2006, pp. 282-293.\n The real-time architecture is presented in:\n Hester, Quinlan, and Stone, \"A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control\", arXiv 1105.1749, 2011.\n \\author Todd Hester\n*/\n\n#include \"PO_ParallelETUCT.hh\"\n#include <algorithm>\n\n#include <sys/time.h>\n\n\nPO_ParallelETUCT::PO_ParallelETUCT(int numactions, float gamma, float rrange, float lambda,\n int MAX_ITER, float MAX_TIME, int MAX_DEPTH, int modelType,\n const std::vector<float> &fmax, const std::vector<float> &fmin,\n const std::vector<int> &nstatesPerDim, bool trackActual, int historySize, Random r):\n numactions(numactions), gamma(gamma), rrange(rrange), lambda(lambda),\n MAX_ITER(MAX_ITER), MAX_TIME(MAX_TIME),\n MAX_DEPTH(MAX_DEPTH), modelType(modelType), statesPerDim(nstatesPerDim),\n trackActual(trackActual), HISTORY_SIZE(historySize),\n HISTORY_FL_SIZE(historySize*numactions)\n{\n rng = r;\n\n nstates = 0;\n nsaved = 0;\n nactions = 0;\n lastUpdate = -1;\n\n seedMode = false;\n timingType = true;\n\n previnfo = NULL;\n model = NULL;\n planTime = getSeconds();\n initTime = getSeconds();\n setTime = getSeconds();\n\n PLANNERDEBUG = false;\n POLICYDEBUG = false; //true; //false; //true; //false;\n ACTDEBUG = false; //true;\n MODELDEBUG = false;\n UCTDEBUG = false;//true; //false;\n PTHREADDEBUG = false;\n ATHREADDEBUG = false;// true;\n MTHREADDEBUG = false;//true;\n TIMINGDEBUG = false;\n REALSTATEDEBUG = false;\n HISTORYDEBUG = false;//true;\n\n if (statesPerDim[0] > 0){\n cout << \"Planner Parallel ETUCT using discretization of \" << statesPerDim[0] << endl;\n }\n if (trackActual){\n cout << \"Parallel ETUCT tracking real state values\" << endl;\n }\n\n featmax = fmax;\n featmin = fmin;\n\n pthread_mutex_init(&update_mutex, NULL);\n pthread_mutex_init(&nactions_mutex, NULL);\n pthread_mutex_init(&history_mutex, NULL);\n pthread_mutex_init(&plan_state_mutex, NULL);\n pthread_mutex_init(&statespace_mutex, NULL);\n pthread_mutex_init(&model_mutex, NULL);\n pthread_mutex_init(&list_mutex, NULL);\n pthread_cond_init(&list_cond, NULL);\n\n // start parallel search thread\n actualPlanState = std::vector<float>(featmax.size());\n discPlanState = NULL;\n modelThreadStarted = false;\n planThreadStarted = false;\n expList.clear();\n\n if (HISTORY_SIZE == 0){\n saHistory.push_back(0.0);\n }\n else {\n if (HISTORYDEBUG) {\n cout << \"History size of \" << HISTORY_SIZE\n << \" float size of \" << HISTORY_FL_SIZE\n << \" with state size: \" << fmin.size()\n << \" and numact: \" << numactions << endl;\n }\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\n saHistory.push_back(0.0);\n }\n }\n\n // initStates();\n expfile.initFile(\"experiences.bin\", featmax.size());\n}\n\nPO_ParallelETUCT::~PO_ParallelETUCT() {\n // join threads\n\n //pthread_kill(planThread);\n //pthread_kill(modelThread);\n\n pthread_detach(planThread);//, NULL);\n pthread_detach(modelThread);//, NULL);\n\n pthread_cancel(planThread);//, NULL);\n pthread_cancel(modelThread);//, NULL);\n\n //pthread_join(planThread, NULL);\n //pthread_join(modelThread, NULL);\n\n //pthread_detach(planThread);//, NULL);\n //pthread_detach(modelThread);//, NULL);\n\n\n pthread_mutex_lock(&plan_state_mutex);\n pthread_mutex_lock(&statespace_mutex);\n pthread_mutex_lock(&model_mutex);\n pthread_mutex_lock(&list_mutex);\n pthread_mutex_lock(&history_mutex);\n\n // delete exp list\n expList.clear();\n\n for (std::map<state_t, state_info>::iterator i = statedata.begin();\n i != statedata.end(); i++){\n\n // get state's info\n //cout << \" planner got info\" << endl;\n state_info* info = &((*i).second);\n\n deleteInfo(info);\n }\n\n featmax.clear();\n featmin.clear();\n\n statespace.clear();\n statedata.clear();\n\n pthread_mutex_unlock(&plan_state_mutex);\n pthread_mutex_unlock(&statespace_mutex);\n pthread_mutex_unlock(&model_mutex);\n pthread_mutex_unlock(&list_mutex);\n pthread_mutex_unlock(&history_mutex);\n\n}\n\nvoid PO_ParallelETUCT::setModel(MDPModel* m){\n\n model = m;\n\n}\n\n\n/////////////////////////////\n// Functional functions :) //\n/////////////////////////////\n\n\n\nbool PO_ParallelETUCT::updateModelWithExperience(const std::vector<float> &laststate,\n int lastact,\n const std::vector<float> &currstate,\n float reward, bool term){\n // if (PLANNERDEBUG) cout << \"updateModelWithExperience(last = \" << &laststate\n // << \", curr = \" << &currstate\n // << \", lastact = \" << lastact\n // << \", r = \" << reward\n // << \", term = \" << term\n // << \")\" << endl;\n\n //cout << \"updateModel\" << endl << flush;\n\n if (!timingType)\n planTime = getSeconds();\n initTime = getSeconds();\n\n // canonicalize these things\n state_t last = NULL;\n\n // add one history to last state\n if (HISTORY_SIZE > 0){\n std::vector<float> modState = laststate;\n if (HISTORYDEBUG) {\n cout << \"Original state vector (size \" << modState.size() << \": \" << modState[0];\n for (unsigned i = 1; i < modState.size(); i++){\n cout << \",\" << modState[i];\n }\n cout << endl;\n }\n // add history onto modState\n pthread_mutex_lock(&history_mutex);\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\n modState.push_back(saHistory[i]);\n }\n pthread_mutex_unlock(&history_mutex);\n\n if (HISTORYDEBUG) {\n cout << \"New state vector (size \" << modState.size() << \": \" << modState[0];\n for (unsigned i = 1; i < modState.size(); i++){\n cout << \",\" << modState[i];\n }\n cout << endl;\n }\n\n last = canonicalize(modState);\n\n if (!seedMode){\n // push this state and action onto the history vector\n /*\n for (unsigned i = 0; i < last->size(); i++){\n saHistory.push_back((*last)[i]);\n saHistory.pop_front();\n }\n */\n pthread_mutex_lock(&history_mutex);\n for (int i = 0; i < numactions; i++){\n if (i == lastact)\n saHistory.push_back(1.0);\n else\n saHistory.push_back(0.0);\n saHistory.pop_front();\n }\n if (HISTORYDEBUG) {\n cout << \"New history vector (size \" << saHistory.size() << \": \" << saHistory[0];\n for (unsigned i = 1; i < saHistory.size(); i++){\n cout << \",\" << saHistory[i];\n }\n cout << endl;\n }\n pthread_mutex_unlock(&history_mutex);\n }\n }\n\n // no history\n else {\n\n // canonicalize these things\n last = canonicalize(laststate);\n }\n\n prevstate = last;\n prevact = lastact;\n\n // get state info\n pthread_mutex_lock(&statespace_mutex);\n previnfo = &(statedata[last]);\n pthread_mutex_unlock(&statespace_mutex);\n\n if (MODELDEBUG){\n cout << \"Update with exp from state: \";\n for (unsigned i = 0; i < last->size(); i++){\n cout << (laststate)[i] << \", \";\n }\n cout << \" action: \" << lastact;\n cout << \" to state: \";\n for (unsigned i = 0; i < currstate.size(); i++){\n cout << (currstate)[i] << \", \";\n }\n cout << \" and reward: \" << reward << endl;\n }\n\n // add experiences to list to later be updated into model\n if (ATHREADDEBUG)\n cout << \"*** Action thread wants list lock ***\" << endl << flush;\n if (TIMINGDEBUG) cout << \"Want list mutex, time: \" << (getSeconds()-initTime) << endl;\n pthread_mutex_lock(&list_mutex);\n if (TIMINGDEBUG) cout << \"got list mutex, time: \" << (getSeconds()-initTime) << endl;\n experience e;\n e.s = *last;\n e.next = currstate;\n e.act = lastact;\n e.reward = reward;\n e.terminal = term;\n\n expList.push_back(e);\n //expfile.saveExperience(e);\n if (ATHREADDEBUG || MTHREADDEBUG)\n cout << \"added exp to list, size: \" << expList.size() << endl << flush;\n if (TIMINGDEBUG) cout << \"list updated, time: \" << (getSeconds()-initTime) << endl;\n pthread_cond_signal(&list_cond);\n pthread_mutex_unlock(&list_mutex);\n\n /*\n if (e.reward > -0.5 && e.reward < 0){\n expfile.saveExperience(e);\n nsaved++;\n cout << \"Saved Experience \" << e.reward << endl;\n }\n */\n\n if (timingType)\n planTime = getSeconds();\n\n if (TIMINGDEBUG) cout << \"leaving updateModel, time: \" << (getSeconds()-initTime) << endl;\n\n\n return false;\n\n}\n\nvoid PO_ParallelETUCT::updateStateActionFromModel(state_t s, int a, state_info* info){\n\n pthread_mutex_lock(&info->statemodel_mutex);\n StateActionInfo* newModel = &(info->model[a]);\n updateStateActionHistoryFromModel(*s, a, newModel);\n pthread_mutex_unlock(&info->statemodel_mutex);\n\n}\n\nvoid PO_ParallelETUCT::updateStateActionHistoryFromModel(const std::vector<float> modState, int a, StateActionInfo *newModel){\n\n // update state info\n // get state action info for each action\n pthread_mutex_lock(&model_mutex);\n\n model->getStateActionInfo(modState, a, newModel);\n\n pthread_mutex_lock(&nactions_mutex);\n newModel->frameUpdated = nactions;\n pthread_mutex_unlock(&nactions_mutex);\n\n pthread_mutex_unlock(&model_mutex);\n\n if (HISTORY_SIZE > 0){\n\n // figure out new history\n std::deque<float> newHistory;\n int stateSize = modState.size() - HISTORY_FL_SIZE;\n\n if (HISTORYDEBUG) cout << \"input history was: \";\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\n newHistory.push_back(modState[i+stateSize]);\n if (HISTORYDEBUG) cout << modState[i+stateSize] << \", \";\n }\n if (HISTORYDEBUG) cout << endl;\n\n // now add on for action\n for (int i = 0; i < numactions; i++){\n if (i == a)\n newHistory.push_back(1.0);\n else\n newHistory.push_back(0.0);\n newHistory.pop_front();\n }\n\n if (HISTORYDEBUG){\n cout << \"act: \" << a << \", new history:\";\n for (unsigned i = 0; i < newHistory.size(); i++){\n cout << newHistory[i] << \", \";\n }\n cout << endl;\n }\n\n // add outcome histories onto newModel predictions\n std::map< std::vector<float>, float> oldProbs = newModel->transitionProbs;\n newModel->transitionProbs.clear();\n\n for (std::map<std::vector<float>, float>::iterator outIt\n = oldProbs.begin();\n outIt != oldProbs.end(); outIt++){\n\n float prob = (*outIt).second;\n std::vector<float> next = (*outIt).first;\n\n for (unsigned i = 0; i < newHistory.size(); i++){\n next.push_back(newHistory[i]);\n }\n\n if (HISTORYDEBUG){\n cout << \"add history onto prediction of state: \";\n for (unsigned i = 0; i < next.size(); i++){\n cout << next[i] << \", \";\n }\n cout << \" with prob \" << prob << endl;\n }\n\n newModel->transitionProbs[next] = prob;\n }\n }\n\n\n //canonNextStates(newModel);\n\n}\n\nvoid PO_ParallelETUCT::canonNextStates(StateActionInfo* modelInfo){\n\n\n // loop through all next states\n for (std::map<std::vector<float>, float>::iterator outIt\n = modelInfo->transitionProbs.begin();\n outIt != modelInfo->transitionProbs.end(); outIt++){\n\n std::vector<float> nextstate = (*outIt).first;\n bool badState = false;\n\n // check that it is valid, otherwise replace with current\n for (unsigned j = 0; j < featmax.size(); j++){\n if (nextstate[j] < (featmin[j]-EPSILON)\n || nextstate[j] > (featmax[j]+EPSILON)){\n //cout << \"next state out of range \" << nextstate[j] << endl;\n badState = true;\n break;\n }\n }\n\n if (!badState){\n canonicalize(nextstate);\n }\n }\n}\n\nint PO_ParallelETUCT::getBestAction(const std::vector<float> &state){\n // if (PLANNERDEBUG) cout << \"getBestAction(s = \" << &state << \")\" << endl;\n\n pthread_mutex_lock(&nactions_mutex);\n nactions++;\n pthread_mutex_unlock(&nactions_mutex);\n\n\n if (TIMINGDEBUG) cout << \"getBestAction, time: \" << (getSeconds()-initTime) << endl;\n\n // add current history on top\n pthread_mutex_lock(&history_mutex);\n std::vector<float> modState = state;\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\n modState.push_back(saHistory[i]);\n }\n pthread_mutex_unlock(&history_mutex);\n\n state_t s = canonicalize(modState);\n\n // set plan state so uct will search from here\n if (ATHREADDEBUG)\n cout << \"*** Action thread wants plan state lock ***\" << endl << flush;\n if (TIMINGDEBUG) cout << \"want planStateMut, time: \" << (getSeconds()-initTime) << endl;\n\n pthread_mutex_lock(&(plan_state_mutex));\n if (TIMINGDEBUG) cout << \"got planStateMut, time: \" << (getSeconds()-initTime) << endl;\n\n actualPlanState = modState;\n discPlanState = s;\n setTime = getSeconds();\n\n if (ATHREADDEBUG){\n cout << \"Set planning state as: \";\n for (unsigned i = 0; i < modState.size(); i++){\n cout << modState[i] << \", \";\n }\n cout << endl << flush;\n }\n\n // call uct search on it\n pthread_mutex_unlock(&(plan_state_mutex));\n if (TIMINGDEBUG) cout << \"set planState, time: \" << (getSeconds()-initTime) << endl;\n\n // get state info\n pthread_mutex_lock(&statespace_mutex);\n state_info* info = &(statedata[s]);\n pthread_mutex_unlock(&statespace_mutex);\n\n // wait a bit for some planning from this state\n\n // depending on how you run the code, this has to be setup differently\n // if someone else calls this method at the appropriate rate, do nothing here\n\n // or this can be where we wait to ensure we run at some rate:\n while (((getSeconds()- initTime) < MAX_TIME)){\n if (TIMINGDEBUG)\n cout << \"waiting for time: \" << (getSeconds()-initTime) << endl;\n\n pthread_yield();\n }\n\n if (TIMINGDEBUG) cout << \"time up: \" << (getSeconds()-initTime) << endl;\n\n if (TIMINGDEBUG && (getSeconds()-initTime) > 0.15) cout << \"**********\" << endl;\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n // Get Q values\n std::vector<float> &Q = info->Q;\n\n\n if (ATHREADDEBUG) {\n if (previnfo != NULL)\n cout << \" ... now \" << previnfo->uctVisits << \" times.\" << endl;\n cout << \"Getting best action from state \";\n for (unsigned i = 0; i < s->size(); i++){\n cout << (*s)[i] << \", \";\n }\n cout << \" sampled \" << info->uctVisits << \" times.\";// << endl << flush;\n }\n\n // Choose an action\n const std::vector<float>::iterator a =\n random_max_element(Q.begin(), Q.end()); // Choose maximum\n int act = a - Q.begin();\n\n if (TIMINGDEBUG) cout << \"got action: \" << (getSeconds()-initTime) << endl;\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n // return index of action\n return act;\n}\n\n\n\n\n\n\nvoid PO_ParallelETUCT::planOnNewModel(){\n //return;\n // cout << \"planOnNewModel\" << endl << flush;\n // start model learning thread here\n if (!modelThreadStarted){\n modelThreadStarted = true;\n pthread_create(&modelThread, NULL, poParallelModelLearningStart, this);\n }\n\n if (!planThreadStarted){\n planThreadStarted = true;\n pthread_create(&(planThread), NULL, poParallelSearchStart, this);\n }\n\n}\n\nvoid* poParallelModelLearningStart(void* arg){\n cout << \"Start model learning thread\" << endl << flush;\n PO_ParallelETUCT* pe = reinterpret_cast<PO_ParallelETUCT*>(arg);\n while(true){\n pe->parallelModelLearning();\n /*\n if (!pe->planThreadStarted){\n pe->planThreadStarted = true;\n pthread_create(&(pe->planThread), NULL, poParallelSearchStart, pe);\n }\n */\n }\n return NULL;\n}\n\nvoid PO_ParallelETUCT::parallelModelLearning(){\n //while(true){\n\n // wait for experience list to be non-empty\n pthread_mutex_lock(&list_mutex);\n while (expList.size() == 0){\n pthread_cond_wait(&list_cond,&list_mutex);\n }\n pthread_mutex_unlock(&list_mutex);\n\n // copy over experience list\n std::vector<experience> updateList;\n if (MTHREADDEBUG) cout << \" *** Model thread wants list lock ***\" << endl << flush;\n pthread_mutex_lock(&list_mutex);\n updateList = expList;\n expList.clear();\n if (MTHREADDEBUG) cout << \" *** Model thread done with list lock ***\" << endl << flush;\n pthread_mutex_unlock(&list_mutex);\n\n /*\n // update model\n //cout << \"*** Model thread wants tree lock ***\" << endl << flush;\n pthread_mutex_lock(&model_mutex);\n if (MTHREADDEBUG) cout << \" Model thread: going to update model with \" << updateList.size() << \" new experiences\" << endl << flush;\n //cout << \"****update tree with \" << updateList.size() << endl << flush;\n bool modelChanged = model->updateWithExperiences(updateList);\n if (MTHREADDEBUG) cout << \" Model updated\" << endl << flush;\n pthread_mutex_unlock(&model_mutex);\n */\n\n modelcopy = model->getCopy();\n //if (COPYDEBUG) cout << \"*** PO: model copied\" << endl;\n\n // update model copy with new experience\n bool modelChanged = modelcopy->updateWithExperiences(updateList);\n\n // set model pointer to point at copy, delete original model cout << \"acquire model_mutex for update\" << endl;\n pthread_mutex_lock(&model_mutex);\n //cout << \"model_mutex acquired for update\" << endl;\n //if (COPYDEBUG) cout << \"*** PO: delete original model and change pointer\" << endl;\n delete model;\n model = modelcopy;\n if (MTHREADDEBUG) cout << \" Model updated\" << endl << flush;\n //if (COPYDEBUG) cout << \"*** PO: pointer set to updated model copy\" << endl;\n pthread_mutex_unlock(&model_mutex);\n\n\n\n // if it changed, reset counts, update state actions\n if (modelChanged) resetAndUpdateStateActions();\n\n pthread_yield();\n\n //}// while loop\n} // method\n\n\n\n\nvoid PO_ParallelETUCT::resetAndUpdateStateActions(){\n //cout << \"*** Model changed, updating state actions ***\" << endl << flush;\n const int MIN_VISITS = 10;\n\n pthread_mutex_lock(&nactions_mutex);\n int updateTime = nactions;\n pthread_mutex_unlock(&nactions_mutex);\n\n // loop through here\n\n pthread_mutex_lock(&statespace_mutex);\n\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n pthread_mutex_unlock(&statespace_mutex);\n\n state_t s = canonicalize(*i);\n\n if (MTHREADDEBUG) cout << \" *** Model thread wants search lock ***\" << endl;\n\n if (MTHREADDEBUG) cout << \" *** Model thread got search lock \" << endl;\n\n pthread_mutex_lock(&statespace_mutex);\n state_info* info = &(statedata[s]);\n pthread_mutex_unlock(&statespace_mutex);\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n if (info->uctVisits > (MIN_VISITS * numactions))\n info->uctVisits = MIN_VISITS * numactions;\n\n for (int j = 0; j < numactions; j++){\n if (info->needsUpdate){\n updateStateActionFromModel(s, j, info);\n }\n if (info->uctActions[j] > MIN_VISITS)\n info->uctActions[j] = MIN_VISITS;\n }\n info->needsUpdate = false;\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n pthread_yield();\n\n pthread_mutex_lock(&statespace_mutex);\n\n }\n pthread_mutex_unlock(&statespace_mutex);\n\n pthread_mutex_lock(&update_mutex);\n lastUpdate = updateTime;\n pthread_mutex_unlock(&update_mutex);\n\n}\n\n\n\n\n////////////////////////////\n// Helper Functions //\n////////////////////////////\n\nPO_ParallelETUCT::state_t PO_ParallelETUCT::canonicalize(const std::vector<float> &s) {\n if (PLANNERDEBUG) cout << \"canonicalize(s = \" << s[0] << \", \"\n << s[1] << \")\" << endl;\n\n // discretize it\n std::vector<float> s2;\n if (statesPerDim[0] > 0){\n s2 = discretizeState(s);\n } else {\n s2 = s;\n }\n\n pthread_mutex_lock(&statespace_mutex);\n\n // get state_t for pointer if its in statespace\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\n statespace.insert(s2);\n state_t retval = &*result.first; // Dereference iterator then get pointer\n\n // if not, init this new state\n if (result.second) { // s is new, so initialize Q(s,a) for all a\n state_info* info = &(statedata[retval]);\n int id = nstates++;\n pthread_mutex_unlock(&statespace_mutex);\n initStateInfo(retval, info, id);\n } else {\n pthread_mutex_unlock(&statespace_mutex);\n }\n\n return retval;\n}\n\n\n// init state info\nvoid PO_ParallelETUCT::initStateInfo(state_t s, state_info* info, int id){\n //if (PLANNERDEBUG) cout << \"initStateInfo()\";\n\n // init mutex's for this state info\n pthread_mutex_init(&info->statemodel_mutex, NULL);\n pthread_mutex_init(&info->stateinfo_mutex, NULL);\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n // model data (transition, reward, known)\n\n pthread_mutex_lock(&info->statemodel_mutex);\n info->model = new StateActionInfo[numactions];\n pthread_mutex_unlock(&info->statemodel_mutex);\n\n\n\n info->id = id;\n if (PLANNERDEBUG) cout << \" id = \" << info->id << endl;\n\n // model q values, visit counts\n info->Q.resize(numactions, 0);\n info->uctActions.resize(numactions, 1);\n info->uctVisits = 1;\n info->visited = 0; //false;\n\n for (int i = 0; i < numactions; i++){\n info->Q[i] = rng.uniform(0, 0.01);\n }\n\n info->needsUpdate = true;\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n //if (PLANNERDEBUG) cout << \"done with initStateInfo()\" << endl;\n\n}\n\n\nvoid PO_ParallelETUCT::printStates(){\n\n pthread_mutex_lock(&statespace_mutex);\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n pthread_mutex_unlock(&statespace_mutex);\n\n state_t s = canonicalize(*i);\n\n pthread_mutex_lock(&statespace_mutex);\n state_info* info = &(statedata[s]);\n pthread_mutex_unlock(&statespace_mutex);\n\n cout << \"State \" << info->id << \": \";\n for (unsigned j = 0; j < s->size(); j++){\n cout << (*s)[j] << \", \";\n }\n cout << endl;\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n //pthread_mutex_lock(&info->statemodel_mutex);\n for (int act = 0; act < numactions; act++){\n cout << \" Q: \" << info->Q[act] << endl;\n // << \" R: \" << info->modelInfo[act].reward << endl;\n }\n // pthread_mutex_unlock(&info->statemodel_mutex);\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n pthread_mutex_lock(&statespace_mutex);\n\n }\n pthread_mutex_unlock(&statespace_mutex);\n\n}\n\n\nvoid PO_ParallelETUCT::deleteInfo(state_info* info){\n\n pthread_mutex_lock(&info->statemodel_mutex);\n delete [] info->model;\n pthread_mutex_unlock(&info->statemodel_mutex);\n\n}\n\n\n\ndouble PO_ParallelETUCT::getSeconds(){\n struct timezone tz;\n timeval timeT;\n gettimeofday(&timeT, &tz);\n return timeT.tv_sec + (timeT.tv_usec / 1000000.0);\n}\n\n\nfloat PO_ParallelETUCT::uctSearch(const std::vector<float> &actS, state_t discS, int depth){\n if (UCTDEBUG){\n cout << \" uctSearch state \";\n for (unsigned i = 0; i < actS.size(); i++){\n cout << actS[i] << \", \";\n }\n cout << \" at depth \" << depth << endl;\n }\n\n pthread_mutex_lock(&statespace_mutex);\n state_info* info = &(statedata[discS]);\n pthread_mutex_unlock(&statespace_mutex);\n\n // if max depth\n // iterative deepening (probability inversely proportional to visits)\n //float terminateProb = 1.0/(2.0+(float)info->uctVisits);\n\n // already visited, stop here\n if (depth > MAX_DEPTH){\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n // return max q value here\n std::vector<float>::iterator maxAct =\n std::max_element(info->Q.begin(),\n info->Q.end());\n float maxval = *maxAct;\n\n if (UCTDEBUG)\n cout << \"Terminated after depth: \" << depth\n // << \" prob: \" << terminateProb\n << \" Q: \" << maxval\n << \" visited: \" << info->visited << endl;\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n return maxval;\n }\n\n // select action\n int action = selectUCTAction(info);\n\n // simulate action to get next state and reward\n // depending on exploration, may also terminate us\n float reward = 0;\n bool term = false;\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n float learnRate;\n //float learnRate = 0.001;\n //float learnRate = 1.0 / info->uctActions[action];\n // learnRate = 10.0 / (info->uctActions[action] + 100.0);\n learnRate = 10.0 / (info->uctActions[action] + 10.0);\n //if (learnRate < 0.001 && MAX_TIME < 0.5)\n //learnRate = 0.001;\n //learnRate = 0.05;\n //learnRate = 1.0;\n\n // tell model learning thread to update this state since we've visited it\n info->needsUpdate = true;\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n std::vector<float> actualNext = simulateNextState(actS, discS, info, action, &reward, &term);\n\n // simulate reward from this action\n if (term){\n // this one terminated\n if (UCTDEBUG) cout << \" Terminated on exploration condition\" << endl;\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n info->Q[action] += learnRate * (reward - info->Q[action]);\n info->uctVisits++;\n info->uctActions[action]++;\n\n if (UCTDEBUG)\n cout << \" Depth: \" << depth << \" Selected action \" << action\n << \" r: \" << reward\n << \" StateVisits: \" << info->uctVisits\n << \" ActionVisits: \" << info->uctActions[action] << endl;\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n return reward;\n }\n\n // simulate next state from this action\n state_t discNext = canonicalize(actualNext);\n\n if (UCTDEBUG)\n cout << \" Depth: \" << depth << \" Selected action \" << action\n << \" r: \" << reward << endl;\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n info->visited++; // = true;\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n // new q value\n float newQ = reward + gamma * uctSearch(actualNext, discNext, depth+1);\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n if (info->visited == 1){\n\n // update q and visit counts\n info->Q[action] += learnRate * (newQ - info->Q[action]);\n info->uctVisits++;\n info->uctActions[action]++;\n\n if (UCTDEBUG)\n cout << \" Depth: \" << depth << \" newQ: \" << newQ\n << \" StateVisits: \" << info->uctVisits\n << \" ActionVisits: \" << info->uctActions[action] << endl;\n\n if (lambda < 1.0){\n\n // new idea, return max of Q or new q\n std::vector<float>::iterator maxAct =\n std::max_element(info->Q.begin(),\n info->Q.end());\n float maxval = *maxAct;\n\n if (UCTDEBUG)\n cout << \" Replacing newQ: \" << newQ;\n\n // replace with w avg of maxq and new val\n newQ = (lambda * newQ) + ((1.0-lambda) * maxval);\n\n if (UCTDEBUG)\n cout << \" with wAvg: \" << newQ << endl;\n }\n\n }\n\n info->visited--;\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n // return q\n return newQ;\n\n}\n\n\nint PO_ParallelETUCT::selectUCTAction(state_info* info){\n // if (UCTDEBUG) cout << \" selectUCTAction\" << endl;\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n std::vector<float> &Q = info->Q;\n\n if (info->uctActions.size() < (unsigned)numactions){\n cout << \"ERROR: uctActions has size \" << info->uctActions.size() << endl << flush;\n info->uctActions.resize(numactions);\n }\n\n // loop through\n float rewardBound = rrange;\n if (rewardBound < 1.0)\n rewardBound = 1.0;\n rewardBound /= (1.0 - gamma);\n if (UCTDEBUG) cout << \"Reward bound: \" << rewardBound << endl;\n\n std::vector<float> uctQ(numactions, 0.0);\n\n for (int i = 0; i < numactions; i++){\n\n // this actions value is Q + rMax * 2 sqrt (log N(s) / N(s,a))\n uctQ[i] = Q[i] +\n rewardBound * 2.0 * sqrt(log((float)info->uctVisits) /\n (float)info->uctActions[i]);\n\n if (UCTDEBUG)\n cout << \" Action: \" << i << \" Q: \" << Q[i]\n << \" visits: \" << info->uctActions[i]\n << \" value: \" << uctQ[i] << endl;\n }\n\n // max element of uctQ\n std::vector<float>::iterator maxAct =\n max_element(uctQ.begin(), uctQ.end());\n float maxval = *maxAct;\n int act = maxAct - uctQ.begin();\n\n if (UCTDEBUG)\n cout << \" Selected \" << act << \" val: \" << maxval << endl;\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n return act;\n\n}\n\nstd::vector<float> PO_ParallelETUCT::simulateNextState(const std::vector<float> &actualState, state_t discState, state_info* info, int action, float* reward, bool* term){\n //if (UCTDEBUG) cout << \" simulateNextState\" << endl;\n\n\n // check if its up to date\n pthread_mutex_lock(&info->statemodel_mutex);\n StateActionInfo* modelInfo = NULL;\n modelInfo = &(info->model[action]);\n pthread_mutex_lock(&update_mutex);\n bool upToDate = modelInfo->frameUpdated >= lastUpdate;\n pthread_mutex_unlock(&update_mutex);\n\n if (!upToDate){\n updateStateActionHistoryFromModel(*discState, action, modelInfo);\n }\n\n *reward = modelInfo->reward;\n *term = (rng.uniform() < modelInfo->termProb);\n\n if (*term){\n pthread_mutex_unlock(&info->statemodel_mutex);\n return actualState;\n }\n\n float randProb = rng.uniform();\n\n float probSum = 0.0;\n std::vector<float> nextstate;\n\n if (REALSTATEDEBUG) cout << \"randProb: \" << randProb << \" numNext: \" << modelInfo->transitionProbs.size() << endl;\n\n if (modelInfo->transitionProbs.size() == 0)\n nextstate = actualState;\n\n for (std::map<std::vector<float>, float>::iterator outIt\n = modelInfo->transitionProbs.begin();\n outIt != modelInfo->transitionProbs.end(); outIt++){\n\n float prob = (*outIt).second;\n probSum += prob;\n if (REALSTATEDEBUG) cout << randProb << \", \" << probSum << \", \" << prob << endl;\n\n if (randProb <= probSum){\n nextstate = (*outIt).first;\n if (REALSTATEDEBUG) cout << \"selected state \" << randProb << \", \" << probSum << \", \" << prob << endl;\n break;\n }\n }\n\n pthread_mutex_unlock(&info->statemodel_mutex);\n\n if (trackActual){\n\n\n // find the relative change from discrete center\n std::vector<float> relChange = subVec(nextstate, *discState);\n\n // add that on to actual current state value\n nextstate = addVec(actualState, relChange);\n\n\n }\n\n // check that next state is valid\n for (unsigned j = 0; j < featmin.size(); j++){\n if (nextstate[j] < (featmin[j]-EPSILON)\n || nextstate[j] > (featmax[j]+EPSILON)){\n\n if (HISTORY_SIZE == 0) return actualState;\n\n // still tack on correct history\n std::vector<float> modState = actualState;\n int stateOnlySize = modState.size()-HISTORY_FL_SIZE;\n for (int i = stateOnlySize; i < (int)modState.size(); i++){\n if (action == (i - stateOnlySize))\n modState[i] = 1;\n else\n modState[i] = 0;\n }\n return modState;\n\n }\n }\n\n // return new actual state\n return nextstate;\n\n}\n\nstd::vector<float> PO_ParallelETUCT::selectRandomState(){\n\n pthread_mutex_lock(&statespace_mutex);\n if (statespace.size() == 0){\n pthread_mutex_unlock(&statespace_mutex);\n return std::vector<float>(featmax.size());\n }\n pthread_mutex_unlock(&statespace_mutex);\n\n // take a random state from the space of ones we've visited\n int index = 0;\n std::vector<float> state;\n\n pthread_mutex_lock(&statespace_mutex);\n if (statespace.size() > 1){\n index = rng.uniformDiscrete(0, statespace.size()-1);\n }\n pthread_mutex_unlock(&statespace_mutex);\n\n int cnt = 0;\n\n if (PTHREADDEBUG) cout << \"*** Planning thread wants search lock (randomstate) ***\" << endl << flush;\n\n pthread_mutex_lock(&statespace_mutex);\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++, cnt++){\n if (cnt == index){\n state = *i;\n break;\n }\n }\n pthread_mutex_unlock(&statespace_mutex);\n\n return state;\n}\n\n\nvoid* poParallelSearchStart(void* arg){\n PO_ParallelETUCT* pe = reinterpret_cast<PO_ParallelETUCT*>(arg);\n\n cout << \"start parallel uct planning search thread\" << endl << flush;\n\n while(true){\n pe->parallelSearch();\n }\n\n return NULL;\n}\n\nvoid PO_ParallelETUCT::parallelSearch(){\n\n std::vector<float> actS;\n state_t discS;\n\n // get new planning state\n if (PTHREADDEBUG) {\n cout << \"*** Planning thread wants planning state lock ***\" << endl << flush;\n }\n pthread_mutex_lock(&(plan_state_mutex));\n\n // take the state we're in (during episodes)\n actS = actualPlanState;\n discS = discPlanState;\n\n // wait for non-null\n if (discS == NULL){\n pthread_mutex_unlock(&(plan_state_mutex));\n return;\n }\n\n if (PTHREADDEBUG){\n pthread_mutex_lock(&statespace_mutex);\n cout << \" uct search from state s (\"\n << statedata[discS].uctVisits <<\"): \";\n pthread_mutex_unlock(&statespace_mutex);\n\n for (unsigned i = 0; i < discS->size(); i++){\n cout << (*discS)[i] << \", \";\n }\n cout << endl << flush;\n }\n\n // call uct search on it\n pthread_mutex_unlock(&(plan_state_mutex));\n\n if (PTHREADDEBUG) cout << \"*** Planning thread wants search lock ***\" << endl;\n uctSearch(actS, discS, 0);\n\n pthread_yield();\n\n}\n\n\n\nvoid PO_ParallelETUCT::savePolicy(const char* filename){\n\n ofstream policyFile(filename, ios::out | ios::binary | ios::trunc);\n\n // first part, save the vector size\n int fsize = featmin.size();\n policyFile.write((char*)&fsize, sizeof(int));\n\n // save numactions\n policyFile.write((char*)&numactions, sizeof(int));\n\n // go through all states, and save Q values\n pthread_mutex_lock(&statespace_mutex);\n\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\n i != statespace.end(); i++){\n pthread_mutex_unlock(&statespace_mutex);\n\n state_t s = canonicalize(*i);\n\n pthread_mutex_lock(&statespace_mutex);\n state_info* info = &(statedata[s]);\n pthread_mutex_unlock(&statespace_mutex);\n\n // save state\n policyFile.write((char*)&((*i)[0]), sizeof(float)*fsize);\n\n // save q-values\n pthread_mutex_lock(&info->stateinfo_mutex);\n policyFile.write((char*)&(info->Q[0]), sizeof(float)*numactions);\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n pthread_mutex_lock(&statespace_mutex);\n }\n pthread_mutex_unlock(&statespace_mutex);\n\n policyFile.close();\n}\n\n\n\nvoid PO_ParallelETUCT::loadPolicy(const char* filename){\n\n ifstream policyFile(filename, ios::in | ios::binary);\n\n // first part, save the vector size\n int fsize;\n policyFile.read((char*)&fsize, sizeof(int));\n cout << \"Numfeats loaded: \" << fsize << endl << flush;\n\n // save numactions\n int nact;\n policyFile.read((char*)&nact, sizeof(int));\n cout << \"nact loaded: \" << nact << endl << flush;\n cout << \" numactions: \" << numactions << endl << flush;\n\n if (nact != numactions){\n cout << \"this policy is not valid loaded nact: \" << nact\n << \" was told: \" << numactions << endl << flush;\n exit(-1);\n }\n\n // go through all states, loading q values\n while(!policyFile.eof()){\n std::vector<float> state(fsize, 0.0);\n\n // load state\n policyFile.read((char*)&(state[0]), sizeof(float)*fsize);\n //if (LOADDEBUG){\n //cout << \"load policy for state: \";\n // printState(state);\n //}\n\n state_t s = canonicalize(state);\n\n pthread_mutex_lock(&statespace_mutex);\n state_info* info = &(statedata[s]);\n pthread_mutex_unlock(&statespace_mutex);\n\n if (policyFile.eof()) break;\n\n // load q values\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n policyFile.read((char*)&(info->Q[0]), sizeof(float)*numactions);\n\n info->uctVisits = numactions * 100;\n\n for (int j = 0; j < numactions; j++){\n info->uctActions[j] = 100;\n }\n\n info->needsUpdate = true;\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n //if (LOADDEBUG){\n //cout << \"Q values: \" << endl;\n //for (int iAct = 0; iAct < numactions; iAct++){\n // cout << \" Action: \" << iAct << \" val: \" << info->Q[iAct] << endl;\n //}\n //}\n }\n\n policyFile.close();\n cout << \"Policy loaded!!!\" << endl << flush;\n}\n\nvoid PO_ParallelETUCT::logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax){\n std::vector<float> state(2, 0.0);\n for (int i = xmin ; i < xmax; i++){\n for (int j = ymin; j < ymax; j++){\n state[0] = j;\n state[1] = i;\n state_t s = canonicalize(state);\n\n pthread_mutex_lock(&statespace_mutex);\n state_info* info = &(statedata[s]);\n pthread_mutex_unlock(&statespace_mutex);\n\n pthread_mutex_lock(&info->stateinfo_mutex);\n\n std::vector<float> &Q_s = info->Q;\n const std::vector<float>::iterator max =\n random_max_element(Q_s.begin(), Q_s.end());\n *of << (*max) << \",\";\n\n pthread_mutex_unlock(&info->stateinfo_mutex);\n\n }\n }\n}\n\n\n// should do it such that an already discretized state stays the same\n// mainly the numerical value of each bin should be the average of that bin\nstd::vector<float> PO_ParallelETUCT::discretizeState(const std::vector<float> &s){\n std::vector<float> ds(s.size());\n\n for (unsigned i = 0; i < statesPerDim.size(); i++){\n\n // since i'm sometimes doing this for discrete domains\n // want to center bins on 0, not edge on 0\n //cout << \"feat \" << i << \" range: \" << featmax[i] << \" \" << featmin[i] << \" \" << (featmax[i]-featmin[i]) << \" n: \" << (float)statesPerDim;\n\n float factor = (featmax[i] - featmin[i]) / (float)statesPerDim[i];\n int bin = 0;\n if (s[i] > 0){\n bin = (int)((s[i]+factor/2) / factor);\n } else {\n bin = (int)((s[i]-factor/2) / factor);\n }\n\n ds[i] = factor*bin;\n //cout << \" factor: \" << factor << \" bin: \" << bin;\n //cout << \" Original: \" << s[i] << \" Discrete: \" << ds[i] << endl;\n }\n\n for (unsigned i = statesPerDim.size(); i < s.size(); i++){\n ds[i] = s[i];\n }\n\n return ds;\n}\n\n\nstd::vector<float> PO_ParallelETUCT::addVec(const std::vector<float> &a, const std::vector<float> &b){\n if (a.size() != b.size())\n cout << \"ERROR: add vector sizes wrong \" << a.size() << \", \" << b.size() << endl;\n\n std::vector<float> c(a.size(), 0.0);\n for (unsigned i = 0; i < a.size(); i++){\n c[i] = a[i] + b[i];\n }\n\n return c;\n}\n\nstd::vector<float> PO_ParallelETUCT::subVec(const std::vector<float> &a, const std::vector<float> &b){\n if (a.size() != b.size())\n cout << \"ERROR: sub vector sizes wrong \" << a.size() << \", \" << b.size() << endl;\n\n std::vector<float> c(a.size(), 0.0);\n for (unsigned i = 0; i < a.size(); i++){\n c[i] = a[i] - b[i];\n }\n\n return c;\n}\n\nvoid PO_ParallelETUCT::setFirst(){\n if (HISTORY_SIZE == 0) return;\n\n if (HISTORYDEBUG) cout << \"first action, set sahistory to 0s\" << endl;\n\n pthread_mutex_lock(&(history_mutex));\n // first action, reset history vector\n saHistory.resize(saHistory.size(), 0.0);\n pthread_mutex_unlock(&(history_mutex));\n}\n\nvoid PO_ParallelETUCT::setSeeding(bool seeding){\n\n if (HISTORYDEBUG) cout << \"set seed mode to \" << seeding << endl;\n seedMode = seeding;\n\n}\n"},{"alpha_fraction":0.7757256031036377,"alphanum_fraction":0.7757256031036377,"avg_line_length":36.900001525878906,"blob_id":"2540ff83bdc7e30c83bdd9097e27e75cdcb16d9a","content_id":"754bc976d1ad50a87af8c41b198721bab185c2b0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":379,"license_type":"no_license","max_line_length":74,"num_lines":10,"path":"/build/nav_stack_example/CMakeFiles/local_planner.dir/cmake_clean.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"file(REMOVE_RECURSE\n \"CMakeFiles/local_planner.dir/src/local_planner.cpp.o\"\n \"/home/justin/ros_test/devel/lib/nav_stack_example/local_planner.pdb\"\n \"/home/justin/ros_test/devel/lib/nav_stack_example/local_planner\"\n)\n\n# Per-language clean rules from dependency scanning.\nforeach(lang CXX)\n include(CMakeFiles/local_planner.dir/cmake_clean_${lang}.cmake OPTIONAL)\nendforeach()\n"},{"alpha_fraction":0.5256774425506592,"alphanum_fraction":0.5504074096679688,"avg_line_length":17.982013702392578,"blob_id":"6aeb26ff4bc01f5398987712d3d7c237d25bdfa8","content_id":"65528181b98eed38a2c1be1b0c28c38f263275ff","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":10554,"license_type":"no_license","max_line_length":85,"num_lines":556,"path":"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/fourrooms.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include <rl_env/fourrooms.hh>\n\n/*\nFourRooms::FourRooms(Random &rand, const Gridworld *gridworld, bool stochastic):\n grid(gridworld), goal(coord_t(2.,2.)), noisy(stochastic), rng(rand),\n s(2),\n ns(s[0]),\n ew(s[1])\n{\n randomize_goal();\n reset();\n}\n*/\n\n \nFourRooms::FourRooms(Random &rand):\n grid(create_default_map()),\n goal(coord_t(1.,10.)), \n negReward(true),\n noisy(false),\n extraReward(false),\n rewardSensor(false),\n rng(rand),\n doorway(coord_t(2.,4.)),\n s(2),\n unused(6),\n ns(s[0]),\n ew(s[1]),\n distN(unused[0]),\n distS(unused[1]),\n distE(unused[2]),\n distW(unused[3]),\n rewardEW(unused[4]),\n rewardNS(unused[5]),\n goalOption(false)\n{\n reset();\n //cout << *this << endl;\n}\n \n\nFourRooms::FourRooms(Random &rand, bool stochastic, bool negReward, \n\t\t bool exReward):\n grid(create_default_map()),\n goal(coord_t(1.,10.)), \n negReward(negReward),\n noisy(stochastic),\n extraReward(exReward),\n rewardSensor(false),\n rng(rand),\n doorway(coord_t(2.,4.)),\n s(2),\n unused(6),\n ns(s[0]),\n ew(s[1]),\n distN(unused[0]),\n distS(unused[1]),\n distE(unused[2]),\n distW(unused[3]),\n rewardEW(unused[4]),\n rewardNS(unused[5]),\n goalOption(goalOption)\n{\n reset();\n}\n\n// Create the version with extra state features for wall distances\nFourRooms::FourRooms(Random &rand, bool stochastic, bool negReward):\n grid(create_default_map()),\n goal(coord_t(1.,10.)), \n negReward(negReward),\n noisy(stochastic),\n extraReward(true), //false),\n rewardSensor(false),\n rng(rand),\n doorway(coord_t(2.,4.)),\n s(6),\n unused(2),\n ns(s[0]),\n ew(s[1]),\n distN(s[2]),\n distS(s[3]),\n distE(s[4]),\n distW(s[5]),\n rewardEW(unused[0]),\n rewardNS(unused[1]),\n goalOption(false)\n{\n reset();\n}\n\n\n// Create the version with extra state features for wall distances and \n// reward distance\nFourRooms::FourRooms(Random &rand, bool stochastic):\n grid(create_default_map()),\n goal(coord_t(1.,10.)),\n negReward(true),\n noisy(stochastic),\n extraReward(false),\n rewardSensor(false),\n rng(rand),\n doorway(coord_t(2.,4.)),\n s(8),\n unused(0),\n ns(s[0]),\n ew(s[1]),\n distN(s[2]),\n distS(s[3]),\n distE(s[4]),\n distW(s[5]),\n rewardEW(s[6]),\n rewardNS(s[7]),\n goalOption(false)\n{\n // cout << \"Four room with wall dist and reward sensor\" << endl;\n reset();\n}\n\n\n\n/*\nFourRooms::FourRooms(Random &rand, unsigned width, unsigned height, bool stochastic):\n grid(new Gridworld(height, width, rand)),\n goal(coord_t(2.,2.)), \n noisy(stochastic), rng(rand),\n doorway(NULL), \n s(2),\n ns(s[0]),\n ew(s[1])\n{\n randomize_goal();\n reset();\n}\n*/\n\nFourRooms::~FourRooms() { delete grid; }\n\nconst std::vector<float> &FourRooms::sensation() const { \n //cout << \"At state \" << s[0] << \", \" << s[1] << endl;\n\n return s; \n}\n\nfloat FourRooms::apply(int action) {\n\n //cout << \"Taking action \" << static_cast<room_action_t>(action) << endl;\n\n const room_action_t effect =\n noisy\n ? add_noise(static_cast<room_action_t>(action)) \n : static_cast<room_action_t>(action);\n switch(effect) {\n case NORTH:\n if (!grid->wall(static_cast<unsigned>(ns),\n\t\t static_cast<unsigned>(ew),\n\t\t effect))\n {\n\t++ns;\n\tcalcWallDistances();\n }\n return reward(effect);\n case SOUTH:\n if (!grid->wall(static_cast<unsigned>(ns),\n\t\t static_cast<unsigned>(ew),\n\t\t effect))\n {\n\t--ns;\n\tcalcWallDistances();\n }\n return reward(effect);\n case EAST:\n if (!grid->wall(static_cast<unsigned>(ns),\n\t\t static_cast<unsigned>(ew),\n\t\t effect))\n {\n\t++ew;\n\tcalcWallDistances();\n }\n return reward(effect);\n case WEST:\n if (!grid->wall(static_cast<unsigned>(ns),\n\t\t static_cast<unsigned>(ew),\n\t\t effect))\n {\n\t--ew;\n\tcalcWallDistances();\n }\n return reward(effect);\n }\n std::cerr << \"Unreachable point reached in FourRooms::apply!!!\\n\";\n return 0; // unreachable, I hope\n}\n\n\nfloat FourRooms::reward(int effect) {\n \n if (extraReward){\n // 0 on goal\n if (terminal())\n return 0;\n\n // 0 when heading right dir towards door\n // towards top middle door\n if (ew < 6 && ns == 8 && effect == EAST){\n return -1;\n }\n\n // towards left door\n if (ew == 1 && ns > 4 && effect == SOUTH){\n return -1;\n }\n\n // towards right door\n if (ew == 8 && ns > 3 && effect == SOUTH){\n return -1;\n }\n\n // towrads bottom door\n if (ew < 6 && ns == 1 && effect == EAST){\n return -1;\n }\n\n // 0 when heading towards goal\n if (ns == 1 && effect == EAST){\n return -1;\n }\n if (ew == 10 && ns < 4 && effect == SOUTH){\n return -1;\n }\n\n // normally -2\n return -2;\n\n }\n\n\n if (negReward){\n // normally -1 and 0 on goal\n if (terminal())\n return 0;\n else \n return -1;\n \n }else{\n\n // or we could do 0 and 1 on goal\n if (terminal())\n return 1;\n else \n return 0;\n }\n}\n\n\nbool FourRooms::terminal() const {\n // current position equal to goal??\n return coord_t(ns,ew) == goal;\n}\n\n\nvoid FourRooms::calcWallDistances(){\n\n // calculate distances East and West\n // if we're not in the same row as a doorway\n if (ns != 1 && ns != 8){\n // left side of wall\n if (ew < 5){\n distW = ew;\n distE = 4 - ew;\n }\n // right side of wall\n else {\n distW = ew - 6;\n distE = 10 - ew;\n }\n } \n // doorway\n else {\n distW = ew;\n distE = 10 - ew;\n }\n\n // in a vertical doorway\n if (ns == 5 && ew == 1){\n distW = 0;\n distE = 0;\n }\n if (ns == 4 && ew == 8){\n distW = 0;\n distE = 0;\n }\n\n // calculate NS\n // left side\n if (ew < 5){\n // not in doorway column\n if (ew != 1){\n // top room\n if (ns > 5){\n\tdistN = 10 - ns;\n\tdistS = ns - 6;\n }\n // bottom room\n else {\n\tdistN = 4 - ns;\n\tdistS = ns;\n }\n }\n // doorway column\n else {\n distN = 10 - ns;\n distS = ns;\n }\n }\n // right side\n else {\n // not in doorway column\n if (ew != 8){\n // top room\n if (ns > 4){\n\tdistN = 10-ns;\n\tdistS = ns - 5;\n }\n // bottom room\n else {\n\tdistN = 3 - ns;\n\tdistS = ns;\n }\n }\n // doorway column\n else {\n distN = 10-ns;\n distS = ns;\n }\n }\n\n // in horiz doorway\n if (ew == 5 && (ns == 1 || ns == 8)){\n distN = 0;\n distS = 0;\n }\n\n\n // calculate reward distances\n // can see it e/w\n if (ns == 1){\n rewardEW = 10 - ew;\n }\n else {\n rewardEW = 100;\n }\n\n // can see ns\n if (ew == 10 && ns < 4){\n rewardNS = 1 - ns;\n }\n else {\n rewardNS = 100;\n }\n \n /*\n cout << \"x,y: \" << ew << \", \" << ns << \" N,S,E,W: \" \n << distN << \", \" << distS << \", \" \n << distE << \", \" << distW << \" reward EW, NS: \" \n << rewardEW << \", \" << rewardNS << endl;\n */\n}\n\n\nvoid FourRooms::reset() {\n // start randomly in upper left room (goal is lower right)\n ns = rng.uniformDiscrete(6, grid->height()-1);\n ew = rng.uniformDiscrete(0, 4);\n\n //ns = 8;\n //ew = 2;\n\n //ns = 4;\n //ew = 9;\n\n calcWallDistances();\n}\n\n\nstd::vector<std::vector<float> > FourRooms::getSubgoals(){\n\n //cout << \"Getting room subgoals \" << endl;\n\n // Create vector of state representations, each is a subgoal\n std::vector<std::vector<float> > subgoals;\n\n \n std::vector<float> subgoal(2);\n\n // between two left rooms\n subgoal[0] = 5;\n subgoal[1] = 1;\n subgoals.push_back(subgoal);\n \n // between two right rooms\n subgoal[0] = 4;\n subgoal[1] = 8;\n subgoals.push_back(subgoal);\n \n // between two top rooms\n subgoal[0] = 8;\n subgoal[1] = 5;\n subgoals.push_back(subgoal);\n \n // between two lower rooms\n subgoal[0] = 1;\n subgoal[1] = 5;\n subgoals.push_back(subgoal);\n\n if (goalOption){\n // actual goal\n subgoal[0] = 1;\n subgoal[1] = 10;\n subgoals.push_back(subgoal);\n }\n\n return subgoals;\n\n}\n\n\nint FourRooms::getNumActions(){\n return 4;\n}\n\n\nstd::ostream &operator<<(std::ostream &out, const FourRooms &rooms) {\n out << \"Map:\\n\" << *rooms.grid;\n\n // print goal\n out << \"Goal: row \" << rooms.goal.first\n << \", column \" << rooms.goal.second << \"\\n\";\n\n // print doorway\n out << \"Doorway: row \" << rooms.doorway.first\n << \", column \" << rooms.doorway.second << \"\\n\";\n\n return out;\n}\n\nconst Gridworld *FourRooms::create_default_map() {\n int width = 11;\n int height = 11;\n std::vector<std::vector<bool> > nsv(width, std::vector<bool>(height-1,false));\n std::vector<std::vector<bool> > ewv(height, std::vector<bool>(width-1,false));\n\n // put the vertical wall between the two rooms\n for (int j = 0; j < height; j++){\n \t// skip doorways at 1 and 8\n \tif (j == 1 || j == 8)\n \t\tcontinue;\n ewv[j][4] = true;\n ewv[j][5] = true;\n }\n \n nsv[5][0] = true;\n nsv[5][1] = true;\n nsv[5][7] = true;\n nsv[5][8] = true;\n\n // put the horizontal wall for the left room\n for (int i = 0; i < 6; i++){\n \t// skip doorway at 1\n \tif (i == 1)\n \t\tcontinue;\n \tnsv[i][4] = true;\n \tnsv[i][5] = true;\n }\n\n ewv[5][0] = true;\n ewv[5][1] = true;\n\n // put the horizontal wall for the right room\n for (int i = 5; i < width; i++){\n \t// skip doorway at 8\n \tif (i == 8)\n \t\tcontinue;\n \tnsv[i][3] = true;\n \tnsv[i][4] = true;\n } \t\n \n ewv[4][7] = true;\n ewv[4][8] = true;\n\n return new Gridworld(height, width, nsv, ewv);\n}\n\nFourRooms::room_action_t FourRooms::add_noise(room_action_t action) {\n switch(action) {\n case NORTH:\n case SOUTH:\n return rng.bernoulli(0.8) ? action : (rng.bernoulli(0.5) ? EAST : WEST);\n case EAST:\n case WEST:\n return rng.bernoulli(0.8) ? action : (rng.bernoulli(0.5) ? NORTH : SOUTH);\n default:\n return action;\n }\n}\n\n\nvoid FourRooms::randomize_goal() {\n const unsigned n = grid->height() * grid->width();\n unsigned index = rng.uniformDiscrete(1,n) - 1;\n goal = coord_t(index / grid->width(), index % grid->width());\n}\n\n\n/** For special use to test true transitions */\nvoid FourRooms::setSensation(std::vector<float> newS){\n if (s.size() != newS.size()){\n cerr << \"Error in sensation sizes\" << endl;\n }\n\n for (unsigned i = 0; i < newS.size(); i++){\n s[i] = (int)newS[i];\n }\n}\n\n\nvoid FourRooms::getMinMaxFeatures(std::vector<float> *minFeat,\n std::vector<float> *maxFeat){\n \n minFeat->resize(s.size(), 0.0);\n maxFeat->resize(s.size(), 10.0);\n \n if (s.size() > 2) {\n for (unsigned i = 2; i < s.size(); i++){\n (*minFeat)[i] = -10.0;\n }\n }\n\n}\n\nvoid FourRooms::getMinMaxReward(float *minR,\n float *maxR){\n \n if (extraReward){\n *minR = -2.0;\n *maxR = 0.0;\n }\n else if (negReward){\n *minR = -1.0;\n *maxR = 0.0; \n }else{\n *minR = 0.0;\n *maxR = 1.0;\n }\n\n}\n"},{"alpha_fraction":0.6913043260574341,"alphanum_fraction":0.6913043260574341,"avg_line_length":23.80392074584961,"blob_id":"22d9dcfca76df4b857d7553b0202f5b335a3e5d8","content_id":"1a9e3732b99b18d9dfcba465256dd6149be12956","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":2530,"license_type":"no_license","max_line_length":168,"num_lines":102,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/RMaxModel.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"/** \\file RMaxModel.hh\n Defines the RMaxModel class.\n \\author Todd Hester\n*/\n\n#ifndef _RMAXMODEL_HH_\n#define _RMAXMODEL_HH_\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n#include <vector>\n#include <map>\n#include <set>\n\n\n/** MDPModel used for RMax. Tabular model with Maximum Likelihood model for each state-action. */\nclass RMaxModel: public MDPModel {\n\npublic:\n\n /** Default constructor\n \\param m # of visits before a state-actions becomes known.\n \\param nact # of actions in the domain\n \\param rng Random Number Generator \n */\n RMaxModel(int m, int nact, Random rng);\n \n /** Copy constructor */\n RMaxModel(const RMaxModel&);\n\n virtual ~RMaxModel();\n virtual RMaxModel* getCopy();\n\n virtual bool updateWithExperiences(std::vector<experience> &instances);\n virtual bool updateWithExperience(experience &e);\n virtual float getStateActionInfo(const std::vector<float> &state, int act, StateActionInfo* retval);\n\n\n // structs to be defined\n struct state_info;\n\n\n /** State info struct. Maintaints visit counts, outcome counts,\n reward sums, terminal transitions, and whether the state-action is \n considered 'known' (>= m visits) \n */\n struct state_info {\n int id;\n\n // model data (visit counts, outcome counts, reward sums, known)\n std::vector<int> visits;\n\n std::map< std::vector<float> , std::vector<int> > outCounts;\n std::vector<float> Rsum;\n std::vector<int> terminations;\n\n std::vector<bool> known;\n\n };\n\nprotected:\n typedef const std::vector<float> *state_t;\n\n // various helper functions that we need\n\n /** Initialize a state_info struct */\n void initStateInfo(state_info* info);\n \n /** Add the given state to the state set. initializes state info for new states not yet in set. The pointer to the state in the set is used for map of state_info's */\n state_t canonicalize(const std::vector<float> &s);\n\n /** Make sure the transition count vector is sized properly before indexing into it. */\n void checkTransitionCountSize(std::vector<int> *transCounts);\n\n /** Initialize a new state */\n void initNewState(state_t s);\n\n\n\nprivate:\n \n /** Set of all distinct sensations seen. Pointers to elements of\n this set serve as the internal representation of the environment\n state. */\n std::set<std::vector<float> > statespace;\n\n /** Hashmap mapping state vectors to their state_info structs. */\n std::map<state_t, state_info> statedata;\n\n int nstates;\n\n int M;\n int nact;\n Random rng;\n\n bool RMAX_DEBUG;\n \n};\n\n\n\n#endif\n"},{"alpha_fraction":0.7666666507720947,"alphanum_fraction":0.7733333110809326,"avg_line_length":36.5625,"blob_id":"5567893295f62d937d92095678d347ae8798a147","content_id":"2f814463d190520e414be125ca71e6a61f77ecca","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":600,"license_type":"no_license","max_line_length":45,"num_lines":16,"path":"/build/robot_sensors/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"robot_sensors\")\nset(robot_sensors_VERSION \"0.0.0\")\nset(robot_sensors_MAINTAINER \"abc <abc@todo.todo>\")\nset(robot_sensors_PACKAGE_FORMAT \"1\")\nset(robot_sensors_BUILD_DEPENDS )\nset(robot_sensors_BUILD_EXPORT_DEPENDS )\nset(robot_sensors_BUILDTOOL_DEPENDS \"catkin\")\nset(robot_sensors_BUILDTOOL_EXPORT_DEPENDS )\nset(robot_sensors_EXEC_DEPENDS )\nset(robot_sensors_RUN_DEPENDS )\nset(robot_sensors_TEST_DEPENDS )\nset(robot_sensors_DOC_DEPENDS )\nset(robot_sensors_URL_WEBSITE \"\")\nset(robot_sensors_URL_BUGTRACKER \"\")\nset(robot_sensors_URL_REPOSITORY \"\")\nset(robot_sensors_DEPRECATED \"\")"},{"alpha_fraction":0.7524752616882324,"alphanum_fraction":0.7623762488365173,"avg_line_length":15.833333015441895,"blob_id":"191d96f5b4416e0571a154349d706a544d021b0f","content_id":"a1507ebaa6dce73425530098c0014763adfd194d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":303,"license_type":"no_license","max_line_length":55,"num_lines":18,"path":"/src/mantis_model/CMakeLists.txt","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"cmake_minimum_required(VERSION 2.8.3)\nproject(mantis_model)\n\nfind_package(catkin REQUIRED COMPONENTS\n roscpp\n tf\n)\n\ncatkin_package()\n\ninclude_directories(\n ${catkin_INCLUDE_DIRS}\n)\n\nadd_executable(joint_state_pub src/joint_state_pub.cpp)\ntarget_link_libraries(joint_state_pub\n ${catkin_LIBRARIES}\n)\n"},{"alpha_fraction":0.5678496956825256,"alphanum_fraction":0.581071674823761,"avg_line_length":28.030303955078125,"blob_id":"86a85e34ed2b215469db7e7a9619667bdf1cd555","content_id":"f618c5253ee28912ae7e8056a92456ae898f5c4f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":2874,"license_type":"no_license","max_line_length":78,"num_lines":99,"path":"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/gridworld.cc","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#include <rl_env/gridworld.hh>\n#include <cmath>\n\nstd::ostream &operator<<(std::ostream &out, const Gridworld &g) {\n for (unsigned i = 0; i < g.ns.size(); ++i)\n out << \" -\";\n out << \" \\n\";\n\n for (unsigned h = g.ew.size() - 1; h > 0; --h) {\n out << \"| \";\n for (unsigned w = 0; w < g.ew[h].size(); ++w)\n out << (g.ew[h][w] ? \"| \" : \" \");\n out << \"|\\n\";\n\n for (unsigned w = 0; w < g.ns.size(); ++w)\n out << (g.ns[w][h-1] ? \" -\" : \" \");\n out << \" \\n\";\n }\n\n out << \"| \";\n for (unsigned w = 0; w < g.ew[0].size(); ++w)\n out << (g.ew[0][w] ? \"| \" : \" \");\n out << \"|\\n\";\n\n for (unsigned i = 0; i < g.ns.size(); ++i)\n out << \" -\";\n out << \" \\n\";\n\n return out;\n}\n\nGridworld::Gridworld(unsigned height, unsigned width, \n\t\t const std::vector<std::vector<bool> > &northsouth,\n\t\t const std::vector<std::vector<bool> > &eastwest):\n h(height), w(width), ns(northsouth), ew(eastwest)\n{}\n\nGridworld::Gridworld(unsigned height, unsigned width, Random &rng):\n h(height), w(width),\n ns(w, std::vector<bool>(h - 1, false)),\n ew(h, std::vector<bool>(w - 1, false))\n{\n const unsigned n = static_cast<unsigned>(sqrt(static_cast<float>(w*h)));\n for (unsigned i = 0; i < n; ++i)\n add_obstacle(rng);\n}\n\nbool Gridworld::wall(unsigned nsCoord, unsigned ewCoord, unsigned dir) const {\n const bool isNS = 0 == dir/2;\n const bool isIncr = 0 == dir%2;\n const std::vector<std::vector<bool> > &walls = isNS ? ns : ew;\n unsigned major = isNS ? ewCoord : nsCoord;\n unsigned minor = isNS ? nsCoord : ewCoord;\n if (!isIncr) {\n if (minor == 0)\n return true;\n --minor;\n }\n if (minor >= walls[major].size())\n return true;\n return walls[major][minor];\n}\n\nvoid Gridworld::add_obstacle(Random &rng) {\n bool direction = rng.bernoulli(0.5);\n std::vector<std::vector<bool> > ¶llel = direction ? ns : ew;\n std::vector<std::vector<bool> > &perpendicular = direction ? ew : ns;\n\n unsigned seedi = rng.uniformDiscrete(1, parallel.size()) - 1;\n unsigned seedj = rng.uniformDiscrete(1, parallel[seedi].size()) - 1;\n\n unsigned first = seedi + 1;\n while (isClear(first - 1, seedj, parallel, perpendicular))\n --first;\n unsigned last = seedi;\n while (isClear(last + 1, seedj, parallel, perpendicular))\n ++last;\n\n chooseSegment(first, last, seedj, parallel, rng);\n}\n\nvoid Gridworld::chooseSegment(unsigned first,\n\t\t\t unsigned last,\n\t\t\t unsigned j,\n\t\t\t std::vector<std::vector<bool> > ¶llel,\n\t\t\t Random &rng)\n{\n if (last <= first)\n return;\n unsigned maxLength = last - first;\n if (maxLength >= parallel.size())\n maxLength = parallel.size() - 1;\n unsigned length = maxLength > 1 ? rng.uniformDiscrete(1,maxLength) : 1;\n int dir = 1 - 2*rng.uniformDiscrete(0,1);\n unsigned start = (dir > 0) ? first : (last - 1);\n\n for (unsigned i = 0; i < length; ++i)\n parallel[start + i*dir][j] = true;\n}\n"},{"alpha_fraction":0.5573731064796448,"alphanum_fraction":0.5785422325134277,"avg_line_length":33.599998474121094,"blob_id":"78a8f915381ef3907b755ee29d061df3915b5c6d","content_id":"9e08ece872e495d00477d4435c94643477a62e71","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4157,"license_type":"no_license","max_line_length":110,"num_lines":120,"path":"/src/run_model.py","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"\n#!/usr/bin/env python\nimport rospy\nimport tensorflow as tf\nfrom nav_msgs.msg import Odometry\nfrom move_base_msgs.msg import *\nfrom geometry_msgs.msg import Twist\nimport math\nfrom tensorflow import keras\nimport numpy as np\n\ngoal = np.empty([1,1,2])\nML_in = np.empty([1,1,5])\npredictions = np.empty([2])\n\nvel_msg = Twist()\n\ndef create_model():\n model = keras.Sequential([\n keras.layers.Flatten(input_shape=(1, 5)),\n keras.layers.Dense(64, activation=tf.nn.tanh),\n keras.layers.Dense(32, activation=tf.nn.tanh),\n keras.layers.Dense(16, activation=tf.nn.tanh),\n keras.layers.Dense(2, activation=tf.nn.tanh)\n ])\n\n\n \n model.compile(optimizer='adam', loss=tf.keras.losses.sparse_categorical_crossentropy, metrics=['accuracy'])\n \n return model\n\n\ndef goal_got(msg):\n rospy.loginfo(rospy.get_caller_id() + \"I heard %s\", msg.goal.target_pose.pose)\n goal[0][0][0] = msg.goal.target_pose.pose.position.x\n goal[0][0][1] = msg.goal.target_pose.pose.position.y\n #rospy.loginfo(rospy.get_caller_id() + \"I heard %s\", goal[0][0][0])\n\n\ndef odom_got(msg):\n #rospy.loginfo(rospy.get_caller_id() + \"I heard %s\", msg.pose.pose)\n #should do this as an NP array.\n x_loc = msg.pose.pose.position.x\n y_loc = msg.pose.pose.position.y\n #ML_in[0][0][0]=math.sqrt((goal[0][0][0]-x_loc)**2+(goal[0][0][1]-y_loc)**2)\n #ML_in[0][0][1] = math.atan2((goal[0][0][1]-y_loc),(goal[0][0][0]-x_loc))\n ML_in[0][0][2]=msg.pose.pose.orientation.z\n qz = msg.pose.pose.orientation.z\n qw = msg.pose.pose.orientation.w\n qx = msg.pose.pose.orientation.x\n qy = msg.pose.pose.orientation.y\n\n #rospy.loginfo(rospy.get_caller_id() + \"I heard %s\", ML_in)\n siny_cosp = +2.0 * (qw * qz + qx * qy);\n cosy_cosp = +1.0 - 2.0 * (qy * qy + qz * qz); \n yaw = math.atan2(siny_cosp, cosy_cosp);\n rospy.loginfo(\"Distance %s\", yaw)\n ML_in[0][0][0]=math.sqrt((5-x_loc)**2+(2-y_loc)**2)\n ML_in[0][0][1] = math.atan2((2-y_loc),(5-x_loc))\n #rospy.loginfo(\"Distance %s\", (5-x_loc)**2+(2-y_loc)**2)\n #rospy.loginfo(rospy.get_caller_id() + \"I heard %s\", ML_in)\n\n\n\n#####################################################\n#####################################################\n############################################\ndef prep_ml():\n predictions = model.predict(ML_in)\n rospy.loginfo(\"I heard %s\", predictions)\n \n\n###########need to do basic math then call model with it, then send cmd##########################\n################ ########################\n########################################\n########################################\ndef listener():\n\n # In ROS, nodes are uniquely named. If two nodes with the same\n # name are launched, the previous one is kicked off. The\n # anonymous=True flag means that rospy will choose a unique\n # name for our 'listener' node so that multiple listeners can\n # run simultaneously.\n\n # subscribe to odom\n #rospy.init_node('odometry', anonymous=True) #make node \n rospy.Subscriber('odom',Odometry,odom_got)\n\n # subscribe to goal\n #rospy.init_node('goal', anonymous=True) #make node \n rospy.Subscriber('move_base/goal',MoveBaseActionGoal,goal_got)\n # spin() simply keeps python from exiting until this node is stopped\n #rospy.spin()\n\ndef talker():\n pub = rospy.Publisher('cmd_vel', Twist, queue_size=10)\n #vel_msg = Twist()\n #prep_ml()\n \n ML_in[0][0][3]=vel_msg.linear.x\n ML_in[0][0][4]= vel_msg.angular.z\n predictions = model.predict(ML_in)\n vel_msg.linear.x = predictions[0][0]\n vel_msg.angular.z =predictions[0][1]\n #rospy.loginfo(\"Sent %s\", predictions)\n #rospy.loginfo(\"Distance %s\", ML_in)\n #hello_str = \"hello world %s\" % rospy.get_time()\n #rospy.loginfo(rospy.get_caller_id() + \"I heard %s\")\n #rospy.loginfo(hello_str)\n pub.publish(vel_msg)\n\nif __name__ == '__main__':\n rospy.init_node('talker', anonymous=True) \n rate = rospy.Rate(1) # 10hz\n model = create_model()\n model.load_weights('./checkpoints/my_checkpoint') \n while not rospy.is_shutdown(): \n\ttalker() \n \tlistener()\n rate.sleep()\n\n\n\n\n"},{"alpha_fraction":0.767123281955719,"alphanum_fraction":0.767123281955719,"avg_line_length":35.5,"blob_id":"6c9c363aa9be41f49f462099010d1aabba5a61f2","content_id":"dbcae5e6f6175ac357447dfbd6c76d088796dca0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":365,"license_type":"no_license","max_line_length":72,"num_lines":10,"path":"/build/gps_sim_project/CMakeFiles/gps_project.dir/cmake_clean.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"file(REMOVE_RECURSE\n \"CMakeFiles/gps_project.dir/src/gps_project.cpp.o\"\n \"/home/justin/ros_test/devel/lib/gps_sim_project/gps_project.pdb\"\n \"/home/justin/ros_test/devel/lib/gps_sim_project/gps_project\"\n)\n\n# Per-language clean rules from dependency scanning.\nforeach(lang CXX)\n include(CMakeFiles/gps_project.dir/cmake_clean_${lang}.cmake OPTIONAL)\nendforeach()\n"},{"alpha_fraction":0.704402506351471,"alphanum_fraction":0.704402506351471,"avg_line_length":22.459016799926758,"blob_id":"631e12d31da4d150bfff896b08b1b922e912bdc8","content_id":"b2db5ea353d52a3460cacfe9c8d6fc2f54948f15","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":1431,"license_type":"no_license","max_line_length":91,"num_lines":61,"path":"/src/rl-texplore-ros-pkg-master/src/rl_env/include/rl_env/stocks.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#ifndef _STOCKS_H_\n#define _STOCKS_H_\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n#include <set>\n\n\nclass Stocks: public Environment {\npublic:\n /** Creates a Stocks domain using the specified map.\n \\param rand Random number generator to use.\n \\param stochastic Whether to use nondeterministism. \n \\param nsectors number of stock sectors\n \\param nstocks number of stocks per sector */\n Stocks(Random &rand, bool stochastic, int nsectors, int nstocks);\n\n /** Creates a deterministic Stocks domain.\n \\param rand Random number generator used.\n \\param stochastic Whether to use nondeterministism. */\n Stocks(Random &rand, bool stochastic);\n\n virtual ~Stocks();\n\n virtual const std::vector<float> &sensation() const;\n virtual float apply(int action);\n\n virtual bool terminal() const;\n virtual void reset();\n virtual int getNumActions();\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\n virtual bool isEpisodic() { return false; };\n virtual void getMinMaxReward(float* minR, float* maxR);\n\n void calcStockRising();\n float reward();\n void setSensation(std::vector<float> s);\n void initStocks();\n \nprotected:\n\n\n\nprivate:\n\n const int nsectors;\n const int nstocks;\n const bool noisy;\n Random &rng;\n\n std::vector<float> s;\n\n // lets just have an index into the array\n int** rising;\n int* owners;\n\n bool STOCK_DEBUG;\n\n};\n\n#endif\n"},{"alpha_fraction":0.7703348994255066,"alphanum_fraction":0.7751196026802063,"avg_line_length":51.3125,"blob_id":"ac295681c01b340ed18c723505778797843f68b4","content_id":"d7e9bed00d53e104688836f1f6ea2df27e735140","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":836,"license_type":"no_license","max_line_length":80,"num_lines":16,"path":"/build/state_space_example/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"state_space_example\")\nset(state_space_example_VERSION \"0.0.0\")\nset(state_space_example_MAINTAINER \"student <student@todo.todo>\")\nset(state_space_example_PACKAGE_FORMAT \"1\")\nset(state_space_example_BUILD_DEPENDS \"roscpp\" \"tf\" \"visualization_msgs\")\nset(state_space_example_BUILD_EXPORT_DEPENDS \"roscpp\" \"tf\" \"visualization_msgs\")\nset(state_space_example_BUILDTOOL_DEPENDS \"catkin\")\nset(state_space_example_BUILDTOOL_EXPORT_DEPENDS )\nset(state_space_example_EXEC_DEPENDS \"roscpp\" \"tf\" \"visualization_msgs\")\nset(state_space_example_RUN_DEPENDS \"roscpp\" \"tf\" \"visualization_msgs\")\nset(state_space_example_TEST_DEPENDS )\nset(state_space_example_DOC_DEPENDS )\nset(state_space_example_URL_WEBSITE \"\")\nset(state_space_example_URL_BUGTRACKER \"\")\nset(state_space_example_URL_REPOSITORY \"\")\nset(state_space_example_DEPRECATED \"\")"},{"alpha_fraction":0.7922077775001526,"alphanum_fraction":0.7974026203155518,"avg_line_length":47.1875,"blob_id":"4f6a57efb914d57c18dc2cb72ed412816a351bb0","content_id":"96a77923f05c0e9fc4efe20b53dee98143b045d1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":770,"license_type":"no_license","max_line_length":61,"num_lines":16,"path":"/build/roundbot_description/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"roundbot_description\")\nset(roundbot_description_VERSION \"0.0.0\")\nset(roundbot_description_MAINTAINER \"micho <micho@todo.todo>\")\nset(roundbot_description_PACKAGE_FORMAT \"1\")\nset(roundbot_description_BUILD_DEPENDS \"urdf\" \"xacro\")\nset(roundbot_description_BUILD_EXPORT_DEPENDS \"urdf\" \"xacro\")\nset(roundbot_description_BUILDTOOL_DEPENDS \"catkin\")\nset(roundbot_description_BUILDTOOL_EXPORT_DEPENDS 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of the environment before and after calling the setup script\n# it emulates the modifications of the setup script without recurring computations\n\n# new environment variables\n\n# modified environment variables\nexport CMAKE_PREFIX_PATH=\"/home/jman/ros/src/ugv_course/robot_sensors/devel:$CMAKE_PREFIX_PATH\"\nexport CPATH=\"/home/jman/ros/src/ugv_course/robot_sensors/devel/include:$CPATH\"\nexport LD_LIBRARY_PATH=\"/home/jman/ros/src/ugv_course/robot_sensors/devel/lib:/home/jman/ros/src/ugv_course/robot_sensors/devel/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH\"\nexport PATH=\"/home/jman/ros/src/ugv_course/robot_sensors/devel/bin:$PATH\"\nexport PKG_CONFIG_PATH=\"/home/jman/ros/src/ugv_course/robot_sensors/devel/lib/pkgconfig:/home/jman/ros/src/ugv_course/robot_sensors/devel/lib/x86_64-linux-gnu/pkgconfig:$PKG_CONFIG_PATH\"\nexport PYTHONPATH=\"/home/jman/ros/src/ugv_course/robot_sensors/devel/lib/python2.7/dist-packages:$PYTHONPATH\"\nexport 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<ros/serialization.h>\n#include <ros/builtin_message_traits.h>\n#include <ros/message_operations.h>\n\n\nnamespace rl_msgs\n{\ntemplate <class ContainerAllocator>\nstruct RLStateReward_\n{\n typedef RLStateReward_<ContainerAllocator> Type;\n\n RLStateReward_()\n : state()\n , reward(0.0)\n , terminal(false) {\n }\n RLStateReward_(const ContainerAllocator& _alloc)\n : state(_alloc)\n , reward(0.0)\n , terminal(false) {\n (void)_alloc;\n }\n\n\n\n typedef std::vector<float, typename ContainerAllocator::template rebind<float>::other > _state_type;\n _state_type state;\n\n typedef float _reward_type;\n _reward_type reward;\n\n typedef uint8_t _terminal_type;\n _terminal_type terminal;\n\n\n\n\n\n typedef boost::shared_ptr< ::rl_msgs::RLStateReward_<ContainerAllocator> > Ptr;\n typedef boost::shared_ptr< ::rl_msgs::RLStateReward_<ContainerAllocator> const> ConstPtr;\n\n}; // struct RLStateReward_\n\ntypedef ::rl_msgs::RLStateReward_<std::allocator<void> > RLStateReward;\n\ntypedef boost::shared_ptr< ::rl_msgs::RLStateReward > RLStateRewardPtr;\ntypedef boost::shared_ptr< ::rl_msgs::RLStateReward const> RLStateRewardConstPtr;\n\n// constants requiring out of line definition\n\n\n\ntemplate<typename ContainerAllocator>\nstd::ostream& operator<<(std::ostream& s, const ::rl_msgs::RLStateReward_<ContainerAllocator> & v)\n{\nros::message_operations::Printer< ::rl_msgs::RLStateReward_<ContainerAllocator> >::stream(s, \"\", v);\nreturn s;\n}\n\n} // namespace rl_msgs\n\nnamespace ros\n{\nnamespace message_traits\n{\n\n\n\n// BOOLTRAITS {'IsFixedSize': False, 'IsMessage': True, 'HasHeader': False}\n// {'rl_msgs': ['/home/justin/ros_test/src/rl-texplore-ros-pkg-master/src/rl_msgs/msg'], 'std_msgs': ['/opt/ros/melodic/share/std_msgs/cmake/../msg']}\n\n// !!!!!!!!!!! ['__class__', '__delattr__', '__dict__', '__doc__', '__eq__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_parsed_fields', 'constants', 'fields', 'full_name', 'has_header', 'header_present', 'names', 'package', 'parsed_fields', 'short_name', 'text', 'types']\n\n\n\n\ntemplate <class ContainerAllocator>\nstruct IsFixedSize< ::rl_msgs::RLStateReward_<ContainerAllocator> >\n : FalseType\n { };\n\ntemplate <class ContainerAllocator>\nstruct IsFixedSize< ::rl_msgs::RLStateReward_<ContainerAllocator> const>\n : FalseType\n { };\n\ntemplate <class ContainerAllocator>\nstruct IsMessage< ::rl_msgs::RLStateReward_<ContainerAllocator> >\n : TrueType\n { };\n\ntemplate <class ContainerAllocator>\nstruct IsMessage< ::rl_msgs::RLStateReward_<ContainerAllocator> const>\n : TrueType\n { };\n\ntemplate <class ContainerAllocator>\nstruct HasHeader< ::rl_msgs::RLStateReward_<ContainerAllocator> >\n : FalseType\n { };\n\ntemplate <class ContainerAllocator>\nstruct HasHeader< ::rl_msgs::RLStateReward_<ContainerAllocator> const>\n : FalseType\n { };\n\n\ntemplate<class ContainerAllocator>\nstruct MD5Sum< ::rl_msgs::RLStateReward_<ContainerAllocator> >\n{\n static const char* value()\n {\n return \"d7e0cc8b9cf2889f09d7f096a11a2873\";\n }\n\n static const char* value(const ::rl_msgs::RLStateReward_<ContainerAllocator>&) { return value(); }\n static const uint64_t static_value1 = 0xd7e0cc8b9cf2889fULL;\n static const uint64_t static_value2 = 0x09d7f096a11a2873ULL;\n};\n\ntemplate<class ContainerAllocator>\nstruct DataType< ::rl_msgs::RLStateReward_<ContainerAllocator> >\n{\n static const char* value()\n {\n return \"rl_msgs/RLStateReward\";\n }\n\n static const char* value(const ::rl_msgs::RLStateReward_<ContainerAllocator>&) { return value(); }\n};\n\ntemplate<class ContainerAllocator>\nstruct Definition< ::rl_msgs::RLStateReward_<ContainerAllocator> >\n{\n static const char* value()\n {\n return \"# Message for returning the current sensation vector \\n\\\n# (i.e. state or observation or sensor readings) and a\\n\\\n# reward from an environment\\n\\\n\\n\\\nfloat32[] state\\n\\\nfloat32 reward\\n\\\nbool terminal\\n\\\n\";\n }\n\n static const char* value(const ::rl_msgs::RLStateReward_<ContainerAllocator>&) { return value(); }\n};\n\n} // namespace message_traits\n} // namespace ros\n\nnamespace ros\n{\nnamespace serialization\n{\n\n template<class ContainerAllocator> struct Serializer< ::rl_msgs::RLStateReward_<ContainerAllocator> >\n {\n template<typename Stream, typename T> inline static void allInOne(Stream& stream, T m)\n {\n stream.next(m.state);\n stream.next(m.reward);\n stream.next(m.terminal);\n }\n\n ROS_DECLARE_ALLINONE_SERIALIZER\n }; // struct RLStateReward_\n\n} // namespace serialization\n} // namespace ros\n\nnamespace ros\n{\nnamespace message_operations\n{\n\ntemplate<class ContainerAllocator>\nstruct Printer< ::rl_msgs::RLStateReward_<ContainerAllocator> >\n{\n template<typename Stream> static void stream(Stream& s, const std::string& indent, const ::rl_msgs::RLStateReward_<ContainerAllocator>& v)\n {\n s << indent << \"state[]\" << std::endl;\n for (size_t i = 0; i < v.state.size(); ++i)\n {\n s << indent << \" state[\" << i << \"]: \";\n Printer<float>::stream(s, indent + \" \", v.state[i]);\n }\n s << indent << \"reward: \";\n Printer<float>::stream(s, indent + \" \", v.reward);\n s << indent << \"terminal: \";\n Printer<uint8_t>::stream(s, indent + \" \", v.terminal);\n }\n};\n\n} // namespace message_operations\n} // namespace ros\n\n#endif // RL_MSGS_MESSAGE_RLSTATEREWARD_H\n"},{"alpha_fraction":0.6990163922309875,"alphanum_fraction":0.7022950649261475,"avg_line_length":22.41538429260254,"blob_id":"51e1f9b78a4287697c9fed4f1ea2efbacab5e222","content_id":"8ed670f8590e2f8769d782eea1f544c38cb5cbc8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":1525,"license_type":"no_license","max_line_length":93,"num_lines":65,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/SepPlanExplore.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#ifndef _SEPPLANEXPLORE_HH_\n#define _SEPPLANEXPLORE_HH_\n\n#include \"../Models/C45Tree.hh\"\n#include \"../Models/M5Tree.hh\"\n#include \"../Models/LinearSplitsTree.hh\"\n#include \"../Models/MultipleClassifiers.hh\"\n\n#include \"../Models/Stump.hh\"\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n#include <vector>\n#include <set>\n#include <map>\n\n/** C4.5 decision stump class */\nclass SepPlanExplore: public Classifier {\n\npublic:\n\n // mode - re-build stump every step? \n // re-build only on misclassifications? or rebuild every 'trainFreq' steps\n SepPlanExplore(int id, int modelType, int predType, int nModels, \n int trainMode, int trainFreq,\n float featPct, float expPct, float treeThreshold, bool stoch,\n float featRange, Random rng);\n\n SepPlanExplore(const SepPlanExplore&);\n virtual SepPlanExplore* getCopy();\n\n ~SepPlanExplore();\n\n virtual bool trainInstances(std::vector<classPair> &instances);\n virtual bool trainInstance(classPair &instance);\n virtual void testInstance(const std::vector<float> &input, std::map<float, float>* retval);\n virtual float getConf(const std::vector<float> &s);\n \n void initModels();\n\n bool SPEDEBUG;\n\nprivate:\n\n const int id;\n const int modelType;\n const int predType;\n const int nModels;\n const int mode;\n const int freq;\n const float featPct;\n const float expPct;\n const float treeThresh;\n const bool stoch;\n const float featRange; \n\n Random rng;\n\n Classifier* expModel;\n Classifier* planModel;\n\n};\n\n\n#endif\n \n"},{"alpha_fraction":0.7555555701255798,"alphanum_fraction":0.7666666507720947,"avg_line_length":37.71428680419922,"blob_id":"57ae7c12fef2dcd2c309e6bde8bdb3880a62be33","content_id":"417afc43b48b0de0098a7f8009791fe289170f7b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"CMake","length_bytes":270,"license_type":"no_license","max_line_length":45,"num_lines":7,"path":"/src/robot_sensors/catkin_generated/package.cmake","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"set(_CATKIN_CURRENT_PACKAGE \"robot_sensors\")\nset(robot_sensors_MAINTAINER \"abc <abc@todo.todo>\")\nset(robot_sensors_DEPRECATED \"\")\nset(robot_sensors_VERSION \"0.0.0\")\nset(robot_sensors_BUILD_DEPENDS )\nset(robot_sensors_RUN_DEPENDS )\nset(robot_sensors_BUILDTOOL_DEPENDS \"catkin\")"},{"alpha_fraction":0.6356500387191772,"alphanum_fraction":0.6387133598327637,"avg_line_length":29.526315689086914,"blob_id":"148d882c82c04048493cd33bf6e277f8be6cf12f","content_id":"8d79a798f312842ee89ad72e88b04f7c99953e7b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":5223,"license_type":"no_license","max_line_length":117,"num_lines":171,"path":"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/LinearSplitsTree.hh","repo_name":"jstestsuite/ros-test","src_encoding":"UTF-8","text":"#ifndef _LINEARSPLITS_HH_\n#define _LINEARSPLITS_HH_\n\n#include <rl_common/Random.h>\n#include <rl_common/core.hh>\n#include <vector>\n#include <set>\n#include <map>\n\n#define N_LST_EXP 200000\n#define N_LS_NODES 2500\n\n#define BUILD_EVERY 0\n#define BUILD_ON_ERROR 1\n#define BUILD_EVERY_N 2\n#define BUILD_ON_TERMINAL 3\n#define BUILD_ON_TERMINAL_AND_ERROR 4\n\n/** M5 decision tree class */\nclass LinearSplitsTree: public Classifier {\n\npublic:\n\n // mode - re-build tree every step? \n // re-build only on misclassifications? or rebuild every 'trainFreq' steps\n LinearSplitsTree(int id, int trainMode, int trainFreq, int m, \n float featPct, bool simple, float min_er, Random rng);\n\n LinearSplitsTree(const LinearSplitsTree&);\n virtual LinearSplitsTree* getCopy();\n\n ~LinearSplitsTree();\n\n // structs to be defined\n struct tree_node;\n struct tree_experience;\n \n \n /** Tree node struct */\n struct tree_node {\n int id;\n\n // split criterion\n int dim;\n float val;\n float avgError;\n\n bool leaf;\n \n // for regression model\n float constant;\n std::vector<float> coefficients;\n\n // next nodes in tree\n tree_node *l;\n tree_node *r;\n\n // set of all outputs seen at this leaf/node\n int nInstances;\n\n };\n\n struct tree_experience {\n std::vector<float> input;\n float output;\n };\n\n bool trainInstance(classPair &instance);\n bool trainInstances(std::vector<classPair> &instances);\n void testInstance(const std::vector<float> &input, std::map<float, float>* retval);\n float getConf(const std::vector<float> &input);\n\n void buildTree(tree_node* node, const std::vector<tree_experience*> &instances,\n bool changed);\n void copyTree(tree_node* newNode, tree_node* origNode);\n\n\n // helper functions\n void initTree();\n void rebuildTree();\n void initTreeNode(tree_node* node);\n tree_node* traverseTree(tree_node* node, const std::vector<float> &input);\n tree_node* getCorrectChild(tree_node* node, const std::vector<float> &input);\n bool passTest(int dim, float val, const std::vector<float> &input);\n float calcER(int dim, float val, \n const std::vector<tree_experience*> &instances, float error,\n std::vector<tree_experience*> &left,\n std::vector<tree_experience*> &right,\n float *leftError, float *rightError);\n float* sortOnDim(int dim, const std::vector<tree_experience*> &instances);\n std::set<float> getUniques(int dim, const std::vector<tree_experience*> &instances, float & minVal, float& maxVal);\n void deleteTree(tree_node* node);\n float calcAvgErrorforSet(const std::vector<tree_experience*> &instances);\n void printTree(tree_node *t, int level);\n void testPossibleSplits(float avgError, const std::vector<tree_experience*> &instances, \n float *bestER, int *bestDim, \n float *bestVal, \n std::vector<tree_experience*> *bestLeft, \n std::vector<tree_experience*> *bestRight,\n float *bestLeftError, float *bestRightError);\n void implementSplit(tree_node* node, const std::vector<tree_experience*> &instances, \n float bestER, int bestDim,\n float bestVal, \n const std::vector<tree_experience*> &left, \n const std::vector<tree_experience*> &right,\n bool changed, float leftError, float rightError);\n void compareSplits(float er, int dim, float val, \n const std::vector<tree_experience*> &left, \n const std::vector<tree_experience*> &right,\n int *nties, float leftError, float rightError,\n float *bestER, int *bestDim, \n float *bestVal, \n std::vector<tree_experience*> *bestLeft, \n std::vector<tree_experience*> *bestRight,\n float *bestLeftError, float *bestRightError);\n void leafPrediction(tree_node *t, const std::vector<float> &in, std::map<float, float>* retval);\n void makeLeaf(tree_node* node, const std::vector<tree_experience*> &instances);\n\n float fitSimpleLinearModel(const std::vector<tree_experience*> &instances,\n float* constant, std::vector<float> *coeff);\n float fitMultiLinearModel(const std::vector<tree_experience*> &instances,\n float* constant, std::vector<float> * coeff);\n\n tree_node* allocateNode();\n void deallocateNode(tree_node* node);\n void initNodes();\n\n bool INCDEBUG;\n bool DTDEBUG;\n bool LMDEBUG;\n bool SPLITDEBUG;\n bool STOCH_DEBUG;\n bool NODEDEBUG;\n int nExperiences;\n bool COPYDEBUG;\n\n float SPLIT_MARGIN;\n\nprivate:\n\n const int id;\n \n const int mode;\n const int freq;\n const int M;\n const float featPct; \n const bool SIMPLE;\n const float MIN_ER;\n\n Random rng;\n\n int nOutput;\n int nnodes;\n bool hadError;\n int totalnodes;\n int maxnodes;\n\n // INSTANCES\n std::vector<tree_experience*> experiences;\n tree_experience allExp[N_LST_EXP];\n tree_node allNodes[N_LS_NODES];\n std::vector<int> freeNodes;\n\n // TREE\n tree_node* root;\n tree_node* lastNode;\n\n};\n\n\n#endif\n \n"}],"string":"[\n {\n \"alpha_fraction\": 0.5413343906402588,\n \"alphanum_fraction\": 0.546482264995575,\n \"avg_line_length\": 24.399999618530273,\n \"blob_id\": \"471f415a1a75a40ea734d12c453c00ad569f2c60\",\n \"content_id\": \"40815c68682e4dfa352f4ab4e554441cd5b85599\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 19814,\n \"license_type\": \"no_license\",\n \"max_line_length\": 102,\n \"num_lines\": 780,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/PrioritizedSweeping.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include \\\"PrioritizedSweeping.hh\\\"\\n#include <algorithm>\\n\\n//#include <time.h>\\n#include <sys/time.h>\\n\\n\\nPrioritizedSweeping::PrioritizedSweeping(int numactions, float gamma,\\n float MAX_TIME, bool onlyAddLastSA, int modelType,\\n const std::vector<float> &fmax, \\n const std::vector<float> &fmin, \\n Random r):\\n numactions(numactions), gamma(gamma), MAX_TIME(MAX_TIME),\\n onlyAddLastSA(onlyAddLastSA), modelType(modelType)\\n{\\n rng = r;\\n nstates = 0;\\n nactions = 0;\\n\\n timingType = false; //true;\\n\\n model = NULL;\\n planTime = getSeconds();\\n\\n // algorithm options\\n MAX_STEPS = 10; //50; //60; //80; //0; //5; //10;\\n\\n lastModelUpdate = -1;\\n\\n PLANNERDEBUG = false;\\n POLICYDEBUG = false; //true; //false;\\n ACTDEBUG = false; //true;\\n MODELDEBUG = false; //true;\\n LISTDEBUG = false; // true; //false;\\n\\n featmax = fmax;\\n featmin = fmin;\\n\\n}\\n\\nPrioritizedSweeping::~PrioritizedSweeping() {}\\n\\nvoid PrioritizedSweeping::setModel(MDPModel* m){\\n\\n model = m;\\n\\n}\\n\\n\\n/////////////////////////////\\n// Functional functions :) //\\n/////////////////////////////\\n\\n\\nvoid PrioritizedSweeping::initNewState(state_t s){\\n if (PLANNERDEBUG) cout << \\\"initNewState(s = \\\" << s\\n << \\\") size = \\\" << s->size() << endl;\\n\\n if (MODELDEBUG) cout << \\\"New State: \\\" << endl;\\n\\n // create state info and add to hash map\\n state_info* info = &(statedata[s]);\\n initStateInfo(info);\\n\\n // init these from model\\n for (int i = 0; i < numactions; i++){\\n model->getStateActionInfo(*s, i, &(info->modelInfo[i]));\\n }\\n\\n // we have to make sure q-values are initialized properly\\n // or we'll get bizarre results (if these aren't swept over)\\n for (int j = 0; j < numactions; j++){\\n // update q values\\n updateQValues(*s, j);\\n info->Q[j] += rng.uniform(0,0.01);\\n }\\n\\n if (PLANNERDEBUG) cout << \\\"done with initNewState()\\\" << endl;\\n\\n}\\n\\n/** Use the latest experience to update state info and the model. */\\nbool PrioritizedSweeping::updateModelWithExperience(const std::vector<float> &laststate,\\n int lastact,\\n const std::vector<float> &currstate,\\n float reward, bool term){\\n if (PLANNERDEBUG) cout << \\\"updateModelWithExperience(last = \\\" << &laststate\\n << \\\", curr = \\\" << &currstate\\n << \\\", lastact = \\\" << lastact\\n << \\\", r = \\\" << reward\\n << \\\")\\\" << endl;\\n\\n if (!timingType)\\n planTime = getSeconds();\\n\\n // canonicalize these things\\n state_t last = canonicalize(laststate);\\n state_t curr = canonicalize(currstate);\\n\\n prevstate = laststate;\\n prevact = lastact;\\n\\n // if not transition to terminal\\n if (curr == NULL)\\n return false;\\n\\n // get state info\\n state_info* info = &(statedata[last]);\\n\\n // update the state visit count\\n info->visits[lastact]++;\\n\\n // init model?\\n if (model == NULL){\\n cout << \\\"ERROR IN MODEL OR MODEL SIZE\\\" << endl;\\n exit(-1);\\n }\\n\\n experience e;\\n e.s = *last;\\n e.next = *curr;\\n e.act = lastact;\\n e.reward = reward;\\n e.terminal = term;\\n bool modelChanged = model->updateWithExperience(e);\\n\\n if (PLANNERDEBUG) cout << \\\"Added exp: \\\" << modelChanged << endl;\\n if (timingType)\\n planTime = getSeconds();\\n\\n return modelChanged;\\n\\n}\\n\\n\\n\\n/** Update our state info's from the model by calling the model function */\\nvoid PrioritizedSweeping::updateStatesFromModel(){\\n if (PLANNERDEBUG || LISTDEBUG) cout << \\\"updateStatesFromModel()\\\" << endl;\\n\\n // for each state\\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n for (int j = 0; j < numactions; j++){\\n updateStateActionFromModel(*i, j);\\n }\\n\\n }\\n\\n}\\n\\n\\n\\n\\n/** Choose the next action */\\nint PrioritizedSweeping::getBestAction(const std::vector<float> &state){\\n if (PLANNERDEBUG) cout << \\\"getBestAction(s = \\\" << &state\\n << \\\")\\\" << endl;\\n\\n state_t s = canonicalize(state);\\n\\n // get state info\\n state_info* info = &(statedata[s]);\\n\\n // Get Q values\\n std::vector<float> &Q = info->Q;\\n\\n // Choose an action\\n const std::vector<float>::iterator a =\\n random_max_element(Q.begin(), Q.end()); // Choose maximum\\n\\n int act = a - Q.begin();\\n float val = *a;\\n\\n if (ACTDEBUG){\\n cout << endl << \\\"chooseAction State \\\" << (*s)[0] << \\\",\\\" << (*s)[1]\\n << \\\" act: \\\" << act << \\\" val: \\\" << val << endl;\\n for (int iAct = 0; iAct < numactions; iAct++){\\n cout << \\\" Action: \\\" << iAct\\n << \\\" val: \\\" << Q[iAct]\\n << \\\" visits: \\\" << info->visits[iAct]\\n << \\\" modelsAgree: \\\" << info->modelInfo[iAct].known << endl;\\n }\\n }\\n\\n nactions++;\\n\\n // return index of action\\n return act;\\n}\\n\\n\\n\\nvoid PrioritizedSweeping::planOnNewModel(){\\n\\n // update model info\\n\\n // print state\\n if (PLANNERDEBUG){\\n cout << endl << endl << \\\"Before update\\\" << endl << endl;\\n printStates();\\n }\\n\\n // tabular - can just update last state-action from model.\\n if (false && modelType == RMAX){\\n updateStateActionFromModel(prevstate, prevact);\\n }\\n else {\\n updateStatesFromModel();\\n }\\n\\n // just add last state action (this is normal prioritized sweeping).\\n // if bool was false, will have checked for differences and added them in update above\\n if (onlyAddLastSA || modelType == RMAX){\\n float diff = updateQValues(prevstate, prevact);\\n addSAToList(prevstate, prevact, diff);\\n }\\n\\n if (PLANNERDEBUG){\\n cout << endl << endl << \\\"After update\\\" << endl << endl;\\n printStates();\\n }\\n\\n // run value iteration\\n createPolicy();\\n\\n}\\n\\n\\n////////////////////////////\\n// Helper Functions //\\n////////////////////////////\\n\\nPrioritizedSweeping::state_t PrioritizedSweeping::canonicalize(const std::vector<float> &s) {\\n if (PLANNERDEBUG) cout << \\\"canonicalize(s = \\\" << s[0] << \\\", \\\"\\n << s[1] << \\\")\\\" << endl;\\n\\n // get state_t for pointer if its in statespace\\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\\n statespace.insert(s);\\n state_t retval = &*result.first; // Dereference iterator then get pointer\\n\\n if (PLANNERDEBUG) cout << \\\" returns \\\" << retval\\n << \\\" New: \\\" << result.second << endl;\\n\\n // if not, init this new state\\n if (result.second) { // s is new, so initialize Q(s,a) for all a\\n initNewState(retval);\\n if (PLANNERDEBUG) cout << \\\" New state initialized\\\" << endl;\\n }\\n\\n\\n return retval;\\n}\\n\\n// init state info\\nvoid PrioritizedSweeping::initStateInfo(state_info* info){\\n if (PLANNERDEBUG) cout << \\\"initStateInfo()\\\";\\n\\n info->id = nstates++;\\n if (PLANNERDEBUG) cout << \\\" id = \\\" << info->id << endl;\\n\\n info->fresh = true;\\n\\n // model data (transition, reward, known)\\n info->modelInfo = new StateActionInfo[numactions];\\n\\n // model q values, visit counts\\n info->visits.resize(numactions, 0);\\n info->Q.resize(numactions, 0);\\n info->lastUpdate.resize(numactions, nactions);\\n\\n for (int i = 0; i < numactions; i++){\\n info->Q[i] = rng.uniform(0,1);\\n }\\n\\n if (PLANNERDEBUG) cout << \\\"done with initStateInfo()\\\" << endl;\\n\\n}\\n\\n\\n/** Print state info for debugging. */\\nvoid PrioritizedSweeping::printStates(){\\n\\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n state_t s = canonicalize(*i);\\n\\n state_info* info = &(statedata[s]);\\n\\n cout << endl << \\\"State \\\" << info->id << \\\": \\\";\\n for (unsigned j = 0; j < s->size(); j++){\\n cout << (*s)[j] << \\\", \\\";\\n }\\n cout << endl;\\n\\n for (int act = 0; act < numactions; act++){\\n cout << \\\" visits[\\\" << act << \\\"] = \\\" << info->visits[act]\\n << \\\" Q: \\\" << info->Q[act]\\n << \\\" R: \\\" << info->modelInfo[act].reward << endl;\\n\\n cout << \\\" Next states: \\\" << endl;\\n for (std::map<std::vector<float>, float>::iterator outIt\\n = info->modelInfo[act].transitionProbs.begin();\\n outIt != info->modelInfo[act].transitionProbs.end(); outIt++){\\n\\n std::vector<float> nextstate = (*outIt).first;\\n float prob = (*outIt).second;\\n\\n cout << \\\" State \\\";\\n for (unsigned k = 0; k < nextstate.size(); k++){\\n cout << nextstate[k] << \\\", \\\";\\n }\\n cout << \\\" prob: \\\" << prob << endl;\\n\\n } // end of next states\\n\\n } // end of actions\\n\\n // print predecessors\\n for (std::list<saqPair>::iterator x = info->pred.begin();\\n x != info->pred.end(); x++){\\n\\n std::vector<float> s = (*x).s;\\n int a = (*x).a;\\n\\n cout << \\\"Has predecessor state: \\\";\\n for (unsigned k = 0; k < s.size(); k++){\\n cout << s[k] << \\\", \\\";\\n }\\n cout << \\\" action: \\\" << a << endl;\\n }\\n\\n }\\n}\\n\\n\\n\\n\\n\\nvoid PrioritizedSweeping::deleteInfo(state_info* info){\\n\\n delete [] info->modelInfo;\\n\\n}\\n\\n\\ndouble PrioritizedSweeping::getSeconds(){\\n struct timezone tz;\\n timeval timeT;\\n gettimeofday(&timeT, &tz);\\n return timeT.tv_sec + (timeT.tv_usec / 1000000.0);\\n}\\n\\n\\n/** Create policy through prioritized sweeping. */\\nvoid PrioritizedSweeping::createPolicy(){\\n if (POLICYDEBUG) cout << endl << \\\"createPolicy()\\\" << endl;\\n\\n /*\\n // loop through all states, add them all to queue with some high value.\\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n saqPair saq;\\n saq.s = *i;\\n saq.q = 100.0;\\n\\n for (int j = 0; j < numactions; j++){\\n saq.a = j;\\n\\n if (LISTDEBUG){\\n cout << \\\"Added state \\\";\\n for (unsigned k = 0; k < saq.s.size(); k++){\\n cout << saq.s[k] << \\\", \\\";\\n }\\n cout << \\\" action: \\\" << saq.a << endl;\\n }\\n\\n priorityList.push_front(saq);\\n }\\n }\\n */\\n\\n // add last state-action to priority list\\n //addSAToList(prevstate, prevact, 100.0);\\n\\n int updates = 0;\\n\\n // go through queue, doing prioritized sweeping. until nothing left on queue.\\n while (!priorityList.empty()){\\n\\n if ((getSeconds() - planTime) > MAX_TIME)\\n break;\\n\\n // print list!\\n if (LISTDEBUG){\\n cout << endl << \\\"Current List (\\\" << updates << \\\"):\\\" << endl;\\n for (std::list<saqPair>::iterator k = priorityList.begin(); k != priorityList.end(); k++){\\n cout << \\\"State: \\\";\\n for (unsigned l = 0; l < (*k).s.size(); l++){\\n cout << (*k).s[l] << \\\", \\\";\\n }\\n cout << \\\" act: \\\" << (*k).a << \\\" Q: \\\" << (*k).q << endl;\\n }\\n }\\n\\n updates++;\\n\\n // pull off first item\\n saqPair currUpdate = priorityList.front();\\n priorityList.pop_front();\\n\\n state_t s = canonicalize(currUpdate.s);\\n\\n // get state's info\\n state_info* info = &(statedata[s]);\\n\\n updatePriorityList(info, *s);\\n\\n } // is list empty loop\\n\\n\\n priorityList.clear();\\n\\n if (LISTDEBUG)\\n cout << \\\"priority list complete after updates to \\\"\\n << updates << \\\" states.\\\" <<endl;\\n\\n}\\n\\n\\nvoid PrioritizedSweeping::updatePriorityList(state_info* info,\\n const std::vector<float> &next){\\n if (LISTDEBUG) cout << \\\"update priority list\\\" << endl;\\n\\n float MIN_ERROR = 0.01;\\n\\n // find maxq at this state\\n std::vector<float>::iterator maxAct =\\n std::max_element(info->Q.begin(),\\n info->Q.end());\\n float maxval = *maxAct;\\n\\n if (LISTDEBUG) cout << \\\" maxQ at this state: \\\" << maxval << endl;\\n\\n // loop through all s,a predicted to lead to this state\\n for (std::list<saqPair>::iterator i = info->pred.begin();\\n i != info->pred.end(); i++){\\n\\n if ((getSeconds() - planTime) > MAX_TIME)\\n break;\\n\\n std::vector<float> s = (*i).s;\\n int a = (*i).a;\\n\\n if (LISTDEBUG) {\\n cout << endl << \\\" For predecessor state: \\\";\\n for (unsigned j = 0; j < s.size(); j++){\\n cout << s[j] << \\\", \\\";\\n }\\n cout << \\\" action: \\\" << a << endl;\\n }\\n\\n // figure out amount of update\\n float diff = updateQValues(s, a);\\n\\n if (LISTDEBUG) {\\n cout << \\\" diff: \\\" << diff << endl;\\n }\\n // possibly add to queue\\n if (diff > MIN_ERROR){\\n saqPair saq;\\n saq.s = s;\\n saq.a = a;\\n saq.q = diff;\\n\\n // find spot for it in queue\\n if (priorityList.empty()){\\n if (LISTDEBUG) cout << \\\" empty list\\\" << endl;\\n priorityList.push_front(saq);\\n }\\n else {\\n\\n // check that its not already in queue\\n for (std::list<saqPair>::iterator k = priorityList.begin(); k != priorityList.end(); k++){\\n // matched\\n if (saqPairMatch(saq, *k)){\\n if (LISTDEBUG)\\n cout << \\\" found matching element already in list\\\" << endl;\\n\\n priorityList.erase(k);\\n break;\\n }\\n\\n }\\n\\n int l = 0;\\n std::list<saqPair>::iterator k;\\n for (k = priorityList.begin(); k != priorityList.end(); k++){\\n if (LISTDEBUG)\\n cout << \\\" Element \\\" << l << \\\" has q value \\\" << (*k).q << endl;\\n if (diff > (*k).q){\\n if (LISTDEBUG)\\n cout << \\\" insert at \\\" << l << endl;\\n priorityList.insert(k, saq);\\n break;\\n }\\n l++;\\n }\\n // put this at the end\\n if (k == priorityList.end()){\\n if (LISTDEBUG)\\n cout << \\\" insert at end\\\" << endl;\\n priorityList.push_back(saq);\\n }\\n\\n } // not empty\\n\\n\\n } else {\\n if (LISTDEBUG){\\n cout << \\\" Error \\\" << diff << \\\" not big enough to put on list.\\\" << endl;\\n }\\n }\\n }\\n}\\n\\n\\nbool PrioritizedSweeping::saqPairMatch(saqPair a, saqPair b){\\n if (a.a != b.a)\\n return false;\\n\\n for (unsigned i = 0; i < a.s.size(); i++){\\n if (a.s[i] != b.s[i])\\n return false;\\n }\\n\\n return true;\\n}\\n\\n\\n\\nfloat PrioritizedSweeping::updateQValues(const std::vector<float> &state, int act){\\n\\n state_t s = canonicalize(state);\\n\\n // get state's info\\n state_info* info = &(statedata[s]);\\n\\n // see if we should update mode for this state,action\\n /*\\n if (info->lastUpdate[act] < lastModelUpdate){\\n if (LISTDEBUG) {\\n cout << \\\"Updating this state action. Last updated at \\\"\\n << info->lastUpdate[act]\\n << \\\" last model update: \\\" << lastModelUpdate << endl;\\n }\\n updateStateActionFromModel(state, act);\\n }\\n */\\n\\n if (LISTDEBUG || POLICYDEBUG){\\n cout << endl << \\\" State: id: \\\" << info->id << \\\": \\\" ;\\n for (unsigned si = 0; si < s->size(); si++){\\n cout << (*s)[si] << \\\",\\\";\\n }\\n }\\n\\n // get state action info for this action\\n StateActionInfo *modelInfo = &(info->modelInfo[act]);\\n\\n if (LISTDEBUG || POLICYDEBUG)\\n cout << \\\" Action: \\\" << act\\n << \\\" State visits: \\\" << info->visits[act] << endl;\\n\\n // Q = R + discounted val of next state\\n // this is the R part :)\\n float newQ = modelInfo->reward;\\n\\n float probSum = modelInfo->termProb;\\n\\n // for all next states, add discounted value appropriately\\n // loop through next state's that are in this state-actions list\\n for (std::map<std::vector<float>, float>::iterator outIt\\n = modelInfo->transitionProbs.begin();\\n outIt != modelInfo->transitionProbs.end(); outIt++){\\n\\n std::vector<float> nextstate = (*outIt).first;\\n\\n if (POLICYDEBUG){\\n cout << \\\" Next state was: \\\";\\n for (unsigned oi = 0; oi < nextstate.size(); oi++){\\n cout << nextstate[oi] << \\\",\\\";\\n }\\n cout << endl;\\n }\\n\\n // get transition probability\\n float transitionProb = (1.0-modelInfo->termProb) *\\n modelInfo->transitionProbs[nextstate];\\n\\n probSum += transitionProb;\\n\\n if (POLICYDEBUG)\\n cout << \\\" prob: \\\" << transitionProb << endl;\\n\\n if (transitionProb < 0 || transitionProb > 1.0001){\\n cout << \\\"Error with transitionProb: \\\" << transitionProb << endl;\\n exit(-1);\\n }\\n\\n // if there is some probability of this transition\\n if (transitionProb > 0.0){\\n\\n // assume maxval of qmax if we don't know the state\\n float maxval = 0.0;\\n\\n // make sure its a real state\\n bool realState = true;\\n\\n\\n for (unsigned b = 0; b < nextstate.size(); b++){\\n if (nextstate[b] < (featmin[b]-EPSILON)\\n || nextstate[b] > (featmax[b]+EPSILON)){\\n realState = false;\\n if (POLICYDEBUG)\\n cout << \\\" Next state is not valid (feature \\\"\\n << b << \\\" out of range)\\\" << endl;\\n break;\\n }\\n }\\n\\n\\n\\n // update q values for any states within MAX_STEPS of visited states\\n if (realState){\\n\\n state_t next = canonicalize(nextstate);\\n\\n state_info* nextinfo = &(statedata[next]);\\n //nextinfo->fresh = false;\\n\\n // find the max value of this next state\\n std::vector<float>::iterator maxAct =\\n std::max_element(nextinfo->Q.begin(),\\n nextinfo->Q.end());\\n maxval = *maxAct;\\n\\n } // within max steps\\n else {\\n maxval = 0.0;\\n if (POLICYDEBUG){\\n cout << \\\"This state is too far away, state: \\\";\\n for (unsigned si = 0; si < s->size(); si++){\\n cout << (*s)[si] << \\\",\\\";\\n }\\n cout << \\\" Action: \\\" << act << endl;\\n }\\n }\\n\\n nextstate.clear();\\n\\n if (POLICYDEBUG) cout << \\\" Max value: \\\" << maxval << endl;\\n\\n // update q value with this value\\n newQ += (gamma * transitionProb * maxval);\\n\\n } // transition probability > 0\\n\\n } // outcome loop\\n\\n\\n if (probSum < 0.9999 || probSum > 1.0001){\\n cout << \\\"Error: transition probabilities do not add to 1: Sum: \\\"\\n << probSum << endl;\\n exit(-1);\\n }\\n\\n\\n // set q value\\n float tdError = fabs(info->Q[act] - newQ);\\n if (LISTDEBUG || POLICYDEBUG) cout << \\\" NewQ: \\\" << newQ\\n << \\\" OldQ: \\\" << info->Q[act] << endl;\\n info->Q[act] = newQ;\\n\\n return tdError;\\n}\\n\\nvoid PrioritizedSweeping::addSAToList(const std::vector<float> &s, int act, float q){\\n\\n saqPair saq;\\n saq.s = s;\\n saq.a = act;\\n saq.q = q;\\n\\n if (LISTDEBUG){\\n cout << \\\"Added state \\\";\\n for (unsigned k = 0; k < saq.s.size(); k++){\\n cout << saq.s[k] << \\\", \\\";\\n }\\n cout << \\\" action: \\\" << saq.a\\n << \\\" value: \\\" << saq.q << endl;\\n }\\n\\n priorityList.push_front(saq);\\n\\n}\\n\\n\\n\\n/** Update a single state-action from the model */\\nvoid PrioritizedSweeping::updateStateActionFromModel(const std::vector<float> &state, int a){\\n\\n if ((getSeconds() - planTime) > MAX_TIME)\\n return;\\n\\n state_t s = canonicalize(state);\\n\\n // get state's info\\n state_info* info = &(statedata[s]);\\n\\n int j = a;\\n\\n // get updated model\\n model->getStateActionInfo(*s, j, &(info->modelInfo[j]));\\n info->lastUpdate[j] = nactions;\\n\\n if (info->modelInfo[j].termProb >= 1.0)\\n return;\\n\\n // go through next states, for each one, add self to predecessor list\\n for (std::map<std::vector<float>, float>::iterator outIt\\n = info->modelInfo[j].transitionProbs.begin();\\n outIt != info->modelInfo[j].transitionProbs.end(); outIt++){\\n\\n std::vector<float> nextstate = (*outIt).first;\\n state_t next = canonicalize(nextstate);\\n state_info* nextinfo = &(statedata[next]);\\n //float prob = (*outIt).second;\\n\\n if (LISTDEBUG){\\n cout << \\\"State \\\";\\n for (unsigned k = 0; k < nextstate.size(); k++){\\n cout << nextstate[k] << \\\", \\\";\\n }\\n cout << \\\" has predecessor: \\\";\\n for (unsigned k = 0; k < nextstate.size(); k++){\\n cout << (*s)[k] << \\\", \\\";\\n }\\n cout << \\\" action: \\\" << j << endl;\\n }\\n\\n saqPair saq;\\n saq.s = *s;\\n saq.a = j;\\n saq.q = 0.0;\\n\\n // add to list\\n // check that its not already here\\n bool nothere = true;\\n for (std::list<saqPair>::iterator k = nextinfo->pred.begin();\\n k != nextinfo->pred.end(); k++){\\n if (saqPairMatch(saq, *k)){\\n nothere = false;\\n break;\\n }\\n }\\n if (nothere)\\n nextinfo->pred.push_front(saq);\\n\\n }\\n\\n info->fresh = false;\\n\\n //if (PLANNERDEBUG || LISTDEBUG) cout << \\\" updateStatesFromModel i = \\\" << &i << \\\" complete\\\" << endl;\\n\\n}\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7811634540557861,\n \"alphanum_fraction\": 0.7811634540557861,\n \"avg_line_length\": 35.099998474121094,\n \"blob_id\": \"87306360657ef24f3ab2f5b64ad9cbb0c2099d16\",\n 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\"avg_line_length\": 21.051780700683594,\n \"blob_id\": \"b7bf56aea894f634851a620ab1848b299a9c81cc\",\n \"content_id\": \"4432f10a2f929c15b3062c31583f76e9666015a4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 6814,\n \"license_type\": \"no_license\",\n \"max_line_length\": 106,\n \"num_lines\": 309,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/taxi.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file taxi.cc\\n Implements the taxi domain, from:\\n Dietterich, \\\"The MAXQ method for hierarchical reinforcement learning,\\\" ICML 1998.\\n \\\\author Todd Hester\\n \\\\author Nick Jong\\n*/\\n\\n#include <rl_env/taxi.hh>\\n\\nconst Taxi::DefaultLandmarks Taxi::defaultlandmarks;\\n\\nTaxi::DefaultLandmarks::DefaultLandmarks() {\\n push_back(value_type(4.,0.));\\n push_back(value_type(0.,3.));\\n push_back(value_type(4.,4.));\\n push_back(value_type(0.,0.));\\n}\\n\\nTaxi::Taxi(Random &rand, const Gridworld *gridworld, bool stochastic):\\n grid(gridworld), landmarks(4), noisy(stochastic), rng(rand),\\n s(4),\\n ns(s[0]),\\n ew(s[1]),\\n pass(s[2]),\\n dest(s[3])\\n{\\n randomize_landmarks_to_corners();\\n reset();\\n}\\n\\nTaxi::Taxi(Random &rand):\\n grid(create_default_map()),\\n landmarks(defaultlandmarks),\\n noisy(false),\\n rng(rand),\\n s(4),\\n ns(s[0]),\\n ew(s[1]),\\n pass(s[2]),\\n dest(s[3])\\n{\\n reset();\\n}\\n\\nTaxi::Taxi(Random &rand, bool stochastic):\\n grid(create_default_map()),\\n landmarks(defaultlandmarks),\\n noisy(stochastic),\\n rng(rand),\\n s(4),\\n ns(s[0]),\\n ew(s[1]),\\n pass(s[2]),\\n dest(s[3])\\n{\\n reset();\\n}\\n\\nTaxi::Taxi(Random &rand, unsigned width, unsigned height, bool stochastic):\\n grid(new Gridworld(height, width, rand)),\\n landmarks(4), noisy(stochastic), rng(rand),\\n s(4),\\n ns(s[0]),\\n ew(s[1]),\\n pass(s[2]),\\n dest(s[3])\\n{\\n randomize_landmarks_to_corners();\\n reset();\\n}\\n\\nTaxi::~Taxi() { delete grid; }\\n\\nconst std::vector<float> &Taxi::sensation() const { return s; }\\n\\nfloat Taxi::apply(int action) {\\n const taxi_action_t effect =\\n noisy\\n ? add_noise(static_cast<taxi_action_t>(action))\\n : static_cast<taxi_action_t>(action);\\n switch(effect) {\\n case NORTH:\\n if (!grid->wall(static_cast<unsigned>(ns),\\n static_cast<unsigned>(ew),\\n effect))\\n {\\n ++ns;\\n apply_fickle_passenger();\\n }\\n return -1;\\n case SOUTH:\\n if (!grid->wall(static_cast<unsigned>(ns),\\n static_cast<unsigned>(ew),\\n effect))\\n {\\n --ns;\\n apply_fickle_passenger();\\n }\\n return -1;\\n case EAST:\\n if (!grid->wall(static_cast<unsigned>(ns),\\n static_cast<unsigned>(ew),\\n effect))\\n {\\n ++ew;\\n apply_fickle_passenger();\\n }\\n return -1;\\n case WEST:\\n if (!grid->wall(static_cast<unsigned>(ns),\\n static_cast<unsigned>(ew),\\n effect))\\n {\\n --ew;\\n apply_fickle_passenger();\\n }\\n return -1;\\n case PICKUP: {\\n if (pass < landmarks.size()\\n && coord_t(ns,ew) == landmarks[static_cast<unsigned>(pass)])\\n {\\n pass = landmarks.size();\\n fickle = noisy;\\n return -1;\\n } else\\n return -10;\\n }\\n case PUTDOWN:\\n if (pass == landmarks.size()\\n && coord_t(ns,ew) == landmarks[static_cast<unsigned>(dest)]) {\\n pass = dest;\\n return 20;\\n } else\\n return -10;\\n }\\n std::cerr << \\\"Unreachable point reached in Taxi::apply!!!\\\\n\\\";\\n return 0; // unreachable, I hope\\n}\\n\\nbool Taxi::terminal() const {\\n return pass == dest;\\n}\\n\\nvoid Taxi::reset() {\\n ns = rng.uniformDiscrete(1, grid->height()) - 1;\\n ew = rng.uniformDiscrete(1, grid->width()) - 1;\\n pass = rng.uniformDiscrete(1, landmarks.size()) - 1;\\n do dest = rng.uniformDiscrete(1, landmarks.size()) - 1;\\n while (dest == pass);\\n fickle = false;\\n}\\n\\n\\n\\nint Taxi::getNumActions() {\\n return 6;\\n}\\n\\n\\nconst Gridworld *Taxi::create_default_map() {\\n std::vector<std::vector<bool> > nsv(5, std::vector<bool>(4,false));\\n std::vector<std::vector<bool> > ewv(5, std::vector<bool>(4,false));\\n ewv[0][0] = true;\\n ewv[0][2] = true;\\n ewv[1][0] = true;\\n ewv[1][2] = true;\\n ewv[3][1] = true;\\n ewv[4][1] = true;\\n return new Gridworld(5,5,nsv,ewv);\\n}\\n\\nTaxi::taxi_action_t Taxi::add_noise(taxi_action_t action) {\\n switch(action) {\\n case NORTH:\\n case SOUTH:\\n return rng.bernoulli(0.8) ? action : (rng.bernoulli(0.5) ? EAST : WEST);\\n case EAST:\\n case WEST:\\n return rng.bernoulli(0.8) ? action : (rng.bernoulli(0.5) ? NORTH : SOUTH);\\n default:\\n return action;\\n }\\n}\\n\\nvoid Taxi::apply_fickle_passenger() {\\n\\n if (fickle) {\\n fickle = false;\\n if (rng.bernoulli(0.3)) {\\n dest += rng.uniformDiscrete(1, landmarks.size() - 1);\\n dest = static_cast<int>(dest) % landmarks.size();\\n }\\n }\\n\\n}\\n\\nvoid Taxi::randomize_landmarks() {\\n std::vector<unsigned> indices(landmarks.size());\\n const unsigned n = grid->height() * grid->width();\\n for (unsigned i = 0; i < indices.size(); ++i) {\\n unsigned index;\\n bool duplicate;\\n do {\\n index = rng.uniformDiscrete(1,n) - 1;\\n duplicate = false;\\n for (unsigned j = 0; j < i; ++j)\\n if (index == indices[j])\\n duplicate = true;\\n } while (duplicate);\\n indices[i] = index;\\n }\\n for (unsigned i = 0; i < indices.size(); ++i)\\n landmarks[i] = coord_t(indices[i] / grid->width(),\\n indices[i] % grid->width());\\n}\\n\\nvoid Taxi::randomize_landmarks_to_corners() {\\n for (unsigned i = 0; i < landmarks.size(); ++i) {\\n int ns = rng.uniformDiscrete(0,1);\\n int ew = rng.uniformDiscrete(0,1);\\n if (1 == i/2)\\n ns = grid->height() - ns - 1;\\n if (1 == i%2)\\n ew = grid->width() - ew - 1;\\n landmarks[i] = coord_t(ns,ew);\\n }\\n}\\n\\n\\nvoid Taxi::setSensation(std::vector<float> newS){\\n if (s.size() != newS.size()){\\n cerr << \\\"Error in sensation sizes\\\" << endl;\\n }\\n\\n for (unsigned i = 0; i < newS.size(); i++){\\n s[i] = (int)newS[i];\\n }\\n}\\n\\nstd::vector<experience> Taxi::getSeedings() {\\n\\n // return seedings\\n std::vector<experience> seeds;\\n\\n if (true)\\n return seeds;\\n // REMOVE THIS TO USE SEEDINGS\\n\\n // single seed for each of 4 drop off and pickup cases\\n for (int i = 0; i < 4; i++){\\n // drop off\\n seeds.push_back(getExp(landmarks[i].first, landmarks[i].second, 4, i, PUTDOWN));\\n // pick up\\n seeds.push_back(getExp(landmarks[i].first, landmarks[i].second, i, rng.uniformDiscrete(0,3), PICKUP));\\n }\\n\\n reset();\\n\\n return seeds;\\n\\n}\\n\\nexperience Taxi::getExp(float s0, float s1, float s2, float s3, int a){\\n\\n experience e;\\n\\n e.s.resize(4, 0.0);\\n e.next.resize(4, 0.0);\\n\\n ns = s0;\\n ew = s1;\\n pass = s2;\\n dest = s3;\\n\\n e.act = a;\\n e.s = sensation();\\n e.reward = apply(e.act);\\n\\n e.terminal = terminal();\\n e.next = sensation();\\n\\n return e;\\n}\\n\\n\\nvoid Taxi::getMinMaxFeatures(std::vector<float> *minFeat,\\n std::vector<float> *maxFeat){\\n\\n minFeat->resize(s.size(), 0.0);\\n maxFeat->resize(s.size(), 1.0);\\n\\n (*minFeat)[0] = 0.0;\\n (*maxFeat)[0] = 4.0;\\n (*minFeat)[1] = 0.0;\\n (*maxFeat)[1] = 4.0;\\n (*minFeat)[2] = 0.0;\\n (*maxFeat)[2] = 4.0;\\n (*minFeat)[3] = 0.0;\\n (*maxFeat)[3] = 3.0;\\n\\n}\\n\\nvoid Taxi::getMinMaxReward(float *minR,\\n float *maxR){\\n\\n *minR = -10.0;\\n *maxR = 20.0;\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7567567825317383,\n \"alphanum_fraction\": 0.7567567825317383,\n \"avg_line_length\": 32.29999923706055,\n \"blob_id\": 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CPATH=\\\"/home/jman/ros/src/ugv_course/hector_example/devel/include:$CPATH\\\"\\nexport LD_LIBRARY_PATH=\\\"/home/jman/ros/src/ugv_course/hector_example/devel/lib:/home/jman/ros/src/ugv_course/hector_example/devel/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH\\\"\\nexport PATH=\\\"/home/jman/ros/src/ugv_course/hector_example/devel/bin:$PATH\\\"\\nexport PKG_CONFIG_PATH=\\\"/home/jman/ros/src/ugv_course/hector_example/devel/lib/pkgconfig:/home/jman/ros/src/ugv_course/hector_example/devel/lib/x86_64-linux-gnu/pkgconfig:$PKG_CONFIG_PATH\\\"\\nexport PYTHONPATH=\\\"/home/jman/ros/src/ugv_course/hector_example/devel/lib/python2.7/dist-packages:$PYTHONPATH\\\"\\nexport ROSLISP_PACKAGE_DIRECTORIES=\\\"/home/jman/ros/src/ugv_course/hector_example/devel/share/common-lisp:$ROSLISP_PACKAGE_DIRECTORIES\\\"\\nexport ROS_PACKAGE_PATH=\\\"/home/jman/ros/src/ugv_course/hector_example:$ROS_PACKAGE_PATH\\\"\"\n },\n {\n \"alpha_fraction\": 0.5320783853530884,\n \"alphanum_fraction\": 0.5386093258857727,\n \"avg_line_length\": 24.513071060180664,\n \"blob_id\": \"28b17c3283fb34e1704f4e1890e244e45e9c6686\",\n \"content_id\": \"07a400ed2d86ebf50b74d52f1df9936fdaa6f027\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 7809,\n \"license_type\": \"no_license\",\n \"max_line_length\": 144,\n \"num_lines\": 306,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Agent/Sarsa.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include <rl_agent/Sarsa.hh>\\n#include <algorithm>\\n\\nSarsa::Sarsa(int numactions, float gamma,\\n float initialvalue, float alpha, float ep, float lambda,\\n Random rng):\\n numactions(numactions), gamma(gamma),\\n initialvalue(initialvalue), alpha(alpha),\\n epsilon(ep), lambda(lambda),\\n rng(rng)\\n{\\n\\n currentq = NULL;\\n ACTDEBUG = false; //true; //false;\\n ELIGDEBUG = false;\\n\\n}\\n\\nSarsa::~Sarsa() {}\\n\\nint Sarsa::first_action(const std::vector<float> &s) {\\n\\n if (ACTDEBUG){\\n cout << \\\"First - in state: \\\";\\n printState(s);\\n cout << endl;\\n }\\n\\n // clear all eligibility traces\\n for (std::map<state_t, std::vector<float> >::iterator i = eligibility.begin();\\n i != eligibility.end(); i++){\\n\\n std::vector<float> & elig_s = (*i).second;\\n for (int j = 0; j < numactions; j++){\\n elig_s[j] = 0.0;\\n }\\n }\\n\\n // Get action values\\n state_t si = canonicalize(s);\\n std::vector<float> &Q_s = Q[si];\\n\\n // Choose an action\\n const std::vector<float>::iterator a =\\n rng.uniform() < epsilon\\n ? Q_s.begin() + rng.uniformDiscrete(0, numactions - 1) // Choose randomly\\n : random_max_element(Q_s.begin(), Q_s.end()); // Choose maximum\\n\\n // set eligiblity to 1\\n std::vector<float> &elig_s = eligibility[si];\\n elig_s[a-Q_s.begin()] = 1.0;\\n\\n if (ACTDEBUG){\\n cout << \\\" act: \\\" << (a-Q_s.begin()) << \\\" val: \\\" << *a << endl;\\n for (int iAct = 0; iAct < numactions; iAct++){\\n cout << \\\" Action: \\\" << iAct \\n\\t << \\\" val: \\\" << Q_s[iAct] << endl;\\n }\\n cout << \\\"Took action \\\" << (a-Q_s.begin()) << \\\" from state \\\";\\n printState(s);\\n cout << endl;\\n }\\n\\n return a - Q_s.begin();\\n}\\n\\nint Sarsa::next_action(float r, const std::vector<float> &s) {\\n\\n if (ACTDEBUG){\\n cout << \\\"Next: got reward \\\" << r << \\\" in state: \\\";\\n printState(s);\\n cout << endl;\\n }\\n\\n // Get action values\\n state_t st = canonicalize(s);\\n std::vector<float> &Q_s = Q[st];\\n const std::vector<float>::iterator max =\\n random_max_element(Q_s.begin(), Q_s.end());\\n\\n // Choose an action\\n const std::vector<float>::iterator a =\\n rng.uniform() < epsilon\\n ? Q_s.begin() + rng.uniformDiscrete(0, numactions - 1)\\n : max;\\n\\n // Update value for all with positive eligibility\\n for (std::map<state_t, std::vector<float> >::iterator i = eligibility.begin();\\n i != eligibility.end(); i++){\\n\\n state_t si = (*i).first;\\n std::vector<float> & elig_s = (*i).second;\\n for (int j = 0; j < numactions; j++){\\n if (elig_s[j] > 0.0){\\n if (ELIGDEBUG) {\\n cout << \\\"updating state \\\" << (*((*i).first))[0] << \\\", \\\" << (*((*i).first))[1] << \\\" act: \\\" << j << \\\" with elig: \\\" << elig_s[j] << endl;\\n }\\n // update\\n Q[si][j] += alpha * elig_s[j] * (r + gamma * (*a) - Q[si][j]);\\n elig_s[j] *= lambda;\\n }\\n }\\n \\n }\\n\\n // Set elig to 1\\n eligibility[st][a-Q_s.begin()] = 1.0;\\n\\n if (ACTDEBUG){\\n cout << \\\" act: \\\" << (a-Q_s.begin()) << \\\" val: \\\" << *a << endl;\\n for (int iAct = 0; iAct < numactions; iAct++){\\n cout << \\\" Action: \\\" << iAct \\n\\t << \\\" val: \\\" << Q_s[iAct] << endl;\\n }\\n cout << \\\"Took action \\\" << (a-Q_s.begin()) << \\\" from state \\\";\\n printState(s);\\n cout << endl;\\n }\\n\\n return a - Q_s.begin();\\n}\\n\\nvoid Sarsa::last_action(float r) {\\n\\n if (ACTDEBUG){\\n cout << \\\"Last: got reward \\\" << r << endl;\\n }\\n\\n // Update value for all with positive eligibility\\n for (std::map<state_t, std::vector<float> >::iterator i = eligibility.begin();\\n i != eligibility.end(); i++){\\n \\n state_t si = (*i).first;\\n std::vector<float> & elig_s = (*i).second;\\n for (int j = 0; j < numactions; j++){\\n if (elig_s[j] > 0.0){\\n if (ELIGDEBUG){\\n cout << \\\"updating state \\\" << (*((*i).first))[0] << \\\", \\\" << (*((*i).first))[1] << \\\" act: \\\" << j << \\\" with elig: \\\" << elig_s[j] << endl;\\n }\\n // update\\n Q[si][j] += alpha * elig_s[j] * (r - Q[si][j]);\\n elig_s[j] = 0.0;\\n }\\n } \\n }\\n \\n}\\n\\nSarsa::state_t Sarsa::canonicalize(const std::vector<float> &s) {\\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\\n statespace.insert(s);\\n state_t retval = &*result.first; // Dereference iterator then get pointer \\n if (result.second) { // s is new, so initialize Q(s,a) for all a\\n std::vector<float> &Q_s = Q[retval];\\n Q_s.resize(numactions,initialvalue);\\n std::vector<float> &elig = eligibility[retval];\\n elig.resize(numactions,0);\\n }\\n return retval; \\n}\\n\\n\\n\\n std::vector<float>::iterator\\nSarsa::random_max_element(\\n\\t\\t\\t std::vector<float>::iterator start,\\n\\t\\t\\t std::vector<float>::iterator end) {\\n\\n std::vector<float>::iterator max =\\n std::max_element(start, end);\\n int n = std::count(max, end, *max);\\n if (n > 1) {\\n n = rng.uniformDiscrete(1, n);\\n while (n > 1) {\\n max = std::find(max + 1, end, *max);\\n --n;\\n }\\n }\\n return max;\\n}\\n\\n\\n\\n\\nvoid Sarsa::setDebug(bool d){\\n ACTDEBUG = d;\\n}\\n\\n\\nvoid Sarsa::printState(const std::vector<float> &s){\\n for (unsigned j = 0; j < s.size(); j++){\\n cout << s[j] << \\\", \\\";\\n }\\n}\\n\\n\\n\\nvoid Sarsa::seedExp(std::vector<experience> seeds){\\n\\n // for each seeding experience, update our model\\n for (unsigned i = 0; i < seeds.size(); i++){\\n experience e = seeds[i];\\n \\n std::vector<float> &Q_s = Q[canonicalize(e.s)];\\n \\n // Get q value for action taken\\n const std::vector<float>::iterator a = Q_s.begin() + e.act;\\n\\n // Update value of action just executed\\n Q_s[e.act] += alpha * (e.reward + gamma * (*a) - Q_s[e.act]);\\n \\n \\n /*\\n cout << \\\"Seeding with experience \\\" << i << endl;\\n cout << \\\"last: \\\" << (e.s)[0] << \\\", \\\" << (e.s)[1] << \\\", \\\" \\n\\t << (e.s)[2] << endl;\\n cout << \\\"act: \\\" << e.act << \\\" r: \\\" << e.reward << endl;\\n cout << \\\"next: \\\" << (e.next)[0] << \\\", \\\" << (e.next)[1] << \\\", \\\" \\n\\t << (e.next)[2] << \\\", \\\" << e.terminal << endl;\\n cout << \\\"Q: \\\" << *currentq << \\\" max: \\\" << *max << endl;\\n */\\n\\n }\\n\\n\\n}\\n\\nvoid Sarsa::logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax){\\n std::vector<float> s;\\n s.resize(2, 0.0);\\n for (int i = xmin ; i < xmax; i++){\\n for (int j = ymin; j < ymax; j++){\\n s[0] = j;\\n s[1] = i;\\n std::vector<float> &Q_s = Q[canonicalize(s)];\\n const std::vector<float>::iterator max =\\n random_max_element(Q_s.begin(), Q_s.end());\\n *of << (*max) << \\\",\\\";\\n }\\n }\\n}\\n\\n\\nfloat Sarsa::getValue(std::vector<float> state){\\n\\n state_t s = canonicalize(state);\\n\\n // Get Q values\\n std::vector<float> &Q_s = Q[s];\\n\\n // Choose an action\\n const std::vector<float>::iterator a =\\n random_max_element(Q_s.begin(), Q_s.end()); // Choose maximum\\n\\n // Get avg value\\n float valSum = 0.0;\\n float cnt = 0;\\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n state_t s = canonicalize(*i);\\n\\n // get state's info\\n std::vector<float> &Q_s = Q[s];\\n \\n for (int j = 0; j < numactions; j++){\\n valSum += Q_s[j];\\n cnt++;\\n }\\n }\\n\\n cout << \\\"Avg Value: \\\" << (valSum / cnt) << endl;\\n\\n return *a;\\n}\\n\\n\\nvoid Sarsa::savePolicy(const char* filename){\\n\\n ofstream policyFile(filename, ios::out | ios::binary | ios::trunc);\\n\\n // first part, save the vector size\\n std::set< std::vector<float> >::iterator i = statespace.begin();\\n int fsize = (*i).size();\\n policyFile.write((char*)&fsize, sizeof(int));\\n\\n // save numactions\\n policyFile.write((char*)&numactions, sizeof(int));\\n\\n // go through all states, and save Q values\\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n state_t s = canonicalize(*i);\\n std::vector<float> *Q_s = &(Q[s]);\\n\\n // save state\\n policyFile.write((char*)&((*i)[0]), sizeof(float)*fsize);\\n\\n // save q-values\\n policyFile.write((char*)&((*Q_s)[0]), sizeof(float)*numactions);\\n\\n }\\n\\n policyFile.close();\\n}\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7650602459907532,\n \"alphanum_fraction\": 0.7710843086242676,\n \"avg_line_length\": 40.5625,\n \"blob_id\": \"33551b996deb4a0055d4f4f4d1bb37c33bb8da23\",\n \"content_id\": \"ff838ccb4b73a5486cd557e81063004350d34a23\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 664,\n \"license_type\": \"no_license\",\n \"max_line_length\": 49,\n \"num_lines\": 16,\n \"path\": \"/build/ugv_course_gazebo/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"ugv_course_gazebo\\\")\\nset(ugv_course_gazebo_VERSION \\\"0.0.0\\\")\\nset(ugv_course_gazebo_MAINTAINER \\\"abc <abc@todo.todo>\\\")\\nset(ugv_course_gazebo_PACKAGE_FORMAT \\\"1\\\")\\nset(ugv_course_gazebo_BUILD_DEPENDS )\\nset(ugv_course_gazebo_BUILD_EXPORT_DEPENDS )\\nset(ugv_course_gazebo_BUILDTOOL_DEPENDS \\\"catkin\\\")\\nset(ugv_course_gazebo_BUILDTOOL_EXPORT_DEPENDS )\\nset(ugv_course_gazebo_EXEC_DEPENDS )\\nset(ugv_course_gazebo_RUN_DEPENDS )\\nset(ugv_course_gazebo_TEST_DEPENDS )\\nset(ugv_course_gazebo_DOC_DEPENDS )\\nset(ugv_course_gazebo_URL_WEBSITE \\\"\\\")\\nset(ugv_course_gazebo_URL_BUGTRACKER \\\"\\\")\\nset(ugv_course_gazebo_URL_REPOSITORY \\\"\\\")\\nset(ugv_course_gazebo_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.7676348686218262,\n \"alphanum_fraction\": 0.7676348686218262,\n \"avg_line_length\": 31.066667556762695,\n \"blob_id\": \"e7708c1545a29cb65ed5528b1b09049f2715b18a\",\n \"content_id\": \"ccdc9331e4d934900885a391b12188b1e12be3ad\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 482,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 15,\n \"path\": \"/devel/share/gennodejs/ros/rl_msgs/msg/_index.js\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\\"use strict\\\";\\n\\nlet RLStateReward = require('./RLStateReward.js');\\nlet RLAction = require('./RLAction.js');\\nlet RLEnvDescription = require('./RLEnvDescription.js');\\nlet RLExperimentInfo = require('./RLExperimentInfo.js');\\nlet RLEnvSeedExperience = require('./RLEnvSeedExperience.js');\\n\\nmodule.exports = {\\n RLStateReward: RLStateReward,\\n RLAction: RLAction,\\n RLEnvDescription: RLEnvDescription,\\n RLExperimentInfo: RLExperimentInfo,\\n RLEnvSeedExperience: RLEnvSeedExperience,\\n};\\n\"\n },\n {\n \"alpha_fraction\": 0.7051349878311157,\n \"alphanum_fraction\": 0.7051349878311157,\n \"avg_line_length\": 27.621212005615234,\n \"blob_id\": \"66eac39e7f2043d27c101c9a846d8fb6e3280955\",\n \"content_id\": \"299a671d821f68cf22409c0d891128a517a4fcf1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 1889,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 66,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/include/rl_agent/SavedPolicy.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file\\n Interface for an implementation of the saved policy\\n algorithm. */\\n\\n#ifndef _SAVEDPOLICY_HH_\\n#define _SAVEDPOLICY_HH_\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n\\n#include <map>\\n#include <set>\\n#include <vector>\\n\\n/** Agent that uses a saved policy from a file. */\\nclass SavedPolicy: public Agent {\\npublic:\\n /** Standard constructor\\n \\\\param numactions The number of possible actions\\n */\\n SavedPolicy(int numactions, const char* filename);\\n\\n virtual ~SavedPolicy();\\n\\n virtual int first_action(const std::vector<float> &s);\\n virtual int next_action(float r, const std::vector<float> &s);\\n virtual void last_action(float r);\\n virtual void setDebug(bool d) {};\\n virtual void seedExp(std::vector<experience>);\\n\\n void loadPolicy(const char* filename);\\n\\nprotected:\\n /** The implementation maps all sensations to a set of canonical\\n pointers, which serve as the internal representation of\\n environment state. */\\n typedef const std::vector<float> *state_t;\\n\\n /** Produces a canonical representation of the given sensation.\\n \\\\param s The current sensation from the environment.\\n \\\\return A pointer to an equivalent state in statespace. */\\n state_t canonicalize(const std::vector<float> &s);\\n void printState(const std::vector<float> &s);\\n\\n\\nprivate:\\n /** Set of all distinct sensations seen. Pointers to elements of\\n this set serve as the internal representation of the environment\\n state. */\\n std::set<std::vector<float> > statespace;\\n\\n /** The primary data structure of the learning algorithm, the value\\n function Q. For state_t s and int a, Q[s][a] gives the\\n learned maximum expected future discounted reward conditional on\\n executing action a in state s. */\\n std::map<state_t, std::vector<float> > Q;\\n\\n const int numactions;\\n\\n bool ACTDEBUG;\\n bool LOADDEBUG;\\n bool loaded;\\n\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.6567164063453674,\n \"alphanum_fraction\": 0.6641790866851807,\n \"avg_line_length\": 15.75,\n \"blob_id\": \"c77a61c084810b6bf527c08a803d2a3904d65f3d\",\n \"content_id\": \"e8ecaf547f34d5f8538b16c639340ec8f0832f97\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 134,\n \"license_type\": \"no_license\",\n \"max_line_length\": 30,\n \"num_lines\": 8,\n \"path\": \"/src/turtlebot3_ddpg/nodes/test.py\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import rospy\\n \\nrospy.init_node('test')\\nrate = rospy.Rate(1)\\n \\nwhile not rospy.is_shutdown():\\n print \\\"Hello there\\\"\\n rate.sleep()\\n\"\n },\n {\n \"alpha_fraction\": 0.6922787427902222,\n \"alphanum_fraction\": 0.6952918767929077,\n \"avg_line_length\": 22.469026565551758,\n \"blob_id\": \"44d481bd7625faf70828cee5213d2cc701a90370\",\n \"content_id\": \"915294e5436094fd7bfae97c82bbdc19d3259520\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 2655,\n \"license_type\": \"no_license\",\n \"max_line_length\": 89,\n \"num_lines\": 113,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/Stump.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#ifndef _STUMP_HH_\\n#define _STUMP_HH_\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n#include <vector>\\n#include <set>\\n#include <map>\\n\\n#define N_STUMP_EXP 250000\\n\\n/** C4.5 decision stump class */\\nclass Stump: public Classifier {\\n\\npublic:\\n\\n // mode - re-build stump every step? \\n // re-build only on misclassifications? or rebuild every 'trainFreq' steps\\n Stump(int id, int trainMode, int trainFreq, int m, float featPct, Random rng);\\n\\n Stump(const Stump&);\\n virtual Stump* getCopy();\\n\\n ~Stump();\\n\\n // structs to be defined\\n struct stump_experience;\\n \\n struct stump_experience {\\n std::vector<float> input;\\n float output;\\n int id;\\n };\\n \\n enum splitTypes{\\n ONLY,\\n CUT\\n };\\n\\n bool trainInstances(std::vector<classPair> &instances);\\n bool trainInstance(classPair &instance);\\n void testInstance(const std::vector<float> &input, std::map<float, float>* retval);\\n float getConf(const std::vector<float> &input);\\n\\n void buildStump();\\n\\n // helper functions\\n void initStump();\\n bool passTest(int dim, float val, int type, const std::vector<float> &input);\\n float calcGainRatio(int dim, float val, int type,float I);\\n float* sortOnDim(int dim);\\n float calcIofP(float* P, int size);\\n float calcIforSet(const std::vector<stump_experience*> &instances);\\n void printStump();\\n void testPossibleSplits(float *bestGainRatio, int *bestDim, \\n\\t\\t\\t float *bestVal, int *bestType);\\n void implementSplit(float bestGainRatio, int bestDim,\\n\\t\\t float bestVal, int bestType);\\n void compareSplits(float gainRatio, int dim, float val, int type, \\n\\t\\t int *nties, float *bestGainRatio, int *bestDim, \\n\\t\\t float *bestVal, int *bestType);\\n //std::vector<float> calcChiSquare(std::vector<stump_experience*> instances);\\n void outputProbabilities(std::multiset<float> outputs, std::map<float, float>* retval);\\n int findMatching(const std::vector<stump_experience*> &instances, int dim, \\n\\t\\t int val, int minConf);\\n\\n void setParams(float margin, float forestPct, float minRatio);\\n\\n bool ALLOW_ONLY_SPLITS;\\n\\n bool STDEBUG;\\n bool SPLITDEBUG;\\n int nExperiences;\\n\\n float SPLIT_MARGIN;\\n float MIN_GAIN_RATIO; \\n float 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CartPole.cc\\n Implements the Cart-Pole balancing domain, with possible noise.\\n \\\\author Todd Hester\\n*/\\n\\n#include <rl_env/CartPole.hh>\\n\\n \\nCartPole::CartPole(Random &rand):\\n noisy(false),\\n rng(rand),\\n s(4),\\n cartPos(s[0]),\\n cartVel(s[1]),\\n poleAngle(s[2]),\\n poleVel(s[3])\\n{\\n reset();\\n //cout << *this << endl;\\n}\\n \\n\\nCartPole::CartPole(Random &rand, bool stochastic):\\n noisy(stochastic),\\n rng(rand),\\n s(4),\\n cartPos(s[0]),\\n cartVel(s[1]),\\n poleAngle(s[2]),\\n poleVel(s[3])\\n{\\n reset();\\n}\\n\\n\\nCartPole::~CartPole() { }\\n\\nconst std::vector<float> &CartPole::sensation() const { \\n //cout << \\\"At state \\\" << s[0] << \\\", \\\" << s[1] << endl;\\n\\n return s; \\n}\\n\\n\\nfloat CartPole::transition(float force){\\n\\n // transition\\n\\n float xacc;\\n float thetaacc;\\n float costheta;\\n float sintheta;\\n float temp;\\n \\n //Noise of 1.0 means possibly halfway to opposite action\\n if (noisy){\\n float thisNoise=1.0*FORCE_MAG*(rng.uniform(-0.5, 0.5)); \\n force+=thisNoise;\\n }\\n\\n costheta = cos(poleAngle);\\n sintheta = sin(poleAngle);\\n\\n temp = (force + POLEMASS_LENGTH * poleVel * poleVel * sintheta) / TOTAL_MASS;\\n\\n thetaacc = (GRAVITY * sintheta - costheta * temp) / (LENGTH * (FOURTHIRDS - MASSPOLE * costheta * costheta / TOTAL_MASS));\\n\\n xacc = temp - POLEMASS_LENGTH * thetaacc * costheta / TOTAL_MASS;\\n\\n // Update the four state variables, using Euler's method. \\n cartPos += TAU * cartVel;\\n cartVel += TAU * xacc;\\n poleAngle += TAU * poleVel;\\n poleVel += TAU * thetaacc;\\n \\n // These probably never happen because the pole would crash \\n while (poleAngle >= M_PI) {\\n poleAngle -= 2.0 * M_PI;\\n }\\n while (poleAngle < -M_PI) {\\n poleAngle += 2.0 * M_PI;\\n }\\n\\n // dont velocities go past ranges\\n if (fabs(cartVel) > 3){\\n // cout << \\\"cart velocity out of range: \\\" << cartVel << endl;\\n if (cartVel > 0)\\n cartVel = 3;\\n else\\n cartVel = -3;\\n }\\n if (fabs(poleVel) > M_PI){\\n // cout << \\\"pole velocity out of range: \\\" << poleVel << endl;\\n if (poleVel > 0)\\n poleVel = M_PI;\\n else\\n poleVel = -M_PI;\\n }\\n\\n return reward();\\n\\n}\\n\\n\\nfloat CartPole::apply(int action) {\\n\\n float force = 0;\\n if (action == 1) {\\n force = FORCE_MAG;\\n } else {\\n force = -FORCE_MAG;\\n }\\n\\n return transition(force);\\n}\\n\\n \\n\\nfloat CartPole::reward() {\\n\\n // normally +1 and 0 on goal\\n if (terminal())\\n return 0.0;\\n else\\n return 1.0;\\n}\\n\\n\\n\\nbool CartPole::terminal() const {\\n // current position past termination conditions (off track, pole angle)\\n return (fabs(poleAngle) > (DEG_T_RAD*12.0) || fabs(cartPos) > 2.4);\\n}\\n\\n\\n\\nvoid CartPole::reset() {\\n\\n GRAVITY = 9.8;\\n MASSCART = 1.0;\\n MASSPOLE = 0.1;\\n TOTAL_MASS = (MASSPOLE + MASSCART);\\n LENGTH = 0.5;\\t // actually half the pole's length \\n \\n POLEMASS_LENGTH = (MASSPOLE * LENGTH);\\n FORCE_MAG = 10.0;\\n TAU = 0.02;\\t // seconds between state updates \\n \\n FOURTHIRDS = 4.0 / 3.0;\\n DEG_T_RAD = 0.01745329;\\n RAD_T_DEG = 1.0/DEG_T_RAD;\\n\\n if (noisy){\\n cartPos = rng.uniform(-0.5, 0.5);\\n cartVel = rng.uniform(-0.5, 0.5);\\n poleAngle = rng.uniform(-0.0625, 0.0625);\\n poleVel = rng.uniform(-0.0625, 0.0625);\\n } else {\\n cartPos = 0.0;\\n cartVel = 0.0;\\n poleAngle = 0.0;\\n poleVel = 0.0;\\n }\\n\\n}\\n\\n\\n\\nint CartPole::getNumActions(){\\n return 2;\\n}\\n\\n\\nvoid CartPole::setSensation(std::vector<float> newS){\\n if (s.size() != newS.size()){\\n cerr << \\\"Error in sensation sizes\\\" << endl;\\n }\\n\\n for (unsigned i = 0; i < newS.size(); i++){\\n s[i] = newS[i];\\n }\\n}\\n\\nstd::vector<experience> CartPole::getSeedings() {\\n\\n // return seedings\\n std::vector<experience> seeds;\\n\\n // single seed of each 4 terminal cases\\n seeds.push_back(getExp(-2.4, -0.1, 0, 0, 0));\\n seeds.push_back(getExp(2.4, 0.2, 0.1, 0.2, 1));\\n seeds.push_back(getExp(0.4, 0.3, 0.2, 0.3, 0));\\n seeds.push_back(getExp(-.3, 0.05, -0.2, -0.4, 1));\\n\\n reset();\\n\\n return seeds;\\n\\n}\\n\\nexperience CartPole::getExp(float s0, float s1, float s2, float s3, int a){\\n\\n experience e;\\n\\n e.s.resize(4, 0.0);\\n e.next.resize(4, 0.0);\\n\\n cartPos = s0;\\n cartVel = s1;\\n poleAngle = s2;\\n poleVel = s3;\\n\\n e.act = a;\\n e.s = sensation();\\n e.reward = apply(e.act);\\n\\n e.terminal = terminal();\\n e.next = sensation();\\n\\n return e;\\n}\\n\\nvoid CartPole::getMinMaxFeatures(std::vector<float> *minFeat,\\n std::vector<float> *maxFeat){\\n \\n minFeat->resize(s.size(), 0.0);\\n maxFeat->resize(s.size(), 1.0);\\n\\n (*minFeat)[0] = -2.5;//3;\\n (*maxFeat)[0] = 2.5;//3;\\n\\n (*minFeat)[1] = -3.0;\\n (*maxFeat)[1] = 3.0;\\n\\n (*minFeat)[2] = -12.0 * DEG_T_RAD;\\n (*maxFeat)[2] = 12.0 * DEG_T_RAD;\\n \\n (*minFeat)[3] = -M_PI;\\n (*maxFeat)[3] = M_PI;\\n\\n}\\n\\nvoid CartPole::getMinMaxReward(float *minR,\\n float *maxR){\\n \\n *minR = 0.0;\\n *maxR = 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Agent\\\", ICDL 2012.\\n \\\\author Todd Hester\\n*/\\n\\n#include \\\"ExplorationModel.hh\\\"\\n\\n\\n\\n\\nExplorationModel::ExplorationModel(MDPModel* innermodel, int modelType, int exploreType,\\n int predType, int nModels,\\n float m, int numactions,\\n float rmax, float qmax, float rrange,\\n int nfactors, float v, float n,\\n const std::vector<float> &fmax,\\n const std::vector<float> &fmin, Random rng):\\n modelType(modelType), exploreType(exploreType), predType(predType),\\n nModels(nModels),\\n M(m), numactions(numactions), rmax(rmax), qmax(qmax), rrange(rrange),\\n nfactors(nfactors), v(v), n(n), rng(rng)\\n{\\n\\n model = innermodel;\\n\\n MODEL_DEBUG = false; //true;\\n\\n cout << \\\"Exploration Model \\\" << exploreType << \\\", v: \\\" << v << \\\", n: \\\" << n << endl;\\n\\n featmax = fmax;\\n featmin = fmin;\\n\\n}\\n\\nExplorationModel::ExplorationModel(const ExplorationModel &em):\\nmodelType(em.modelType), exploreType(em.exploreType), predType(em.predType),\\n nModels(em.nModels),\\n M(em.M), numactions(em.numactions), rmax(em.rmax), qmax(em.qmax), rrange(em.rrange),\\nnfactors(em.nfactors), v(em.v), n(em.n), rng(em.rng)\\n{\\n model = em.model->getCopy();\\n MODEL_DEBUG = em.MODEL_DEBUG;\\n featmax = em.featmax;\\n featmin = em.featmin;\\n statespace = em.statespace;\\n}\\n\\nExplorationModel* ExplorationModel::getCopy(){\\n ExplorationModel* copy = new ExplorationModel(*this);\\n return copy;\\n}\\n\\n\\nExplorationModel::~ExplorationModel() {\\n delete model;\\n}\\n\\n\\n\\nbool ExplorationModel::updateWithExperiences(std::vector<experience> &instances){\\n bool changed = model->updateWithExperiences(instances);\\n bool visitChange = false;\\n\\n // keep track of which states we've been to for this mode\\n for (unsigned i = 0; i < instances.size(); i++){\\n if (exploreType == UNVISITED_BONUS){\\n bool retval = addStateToSet(instances[i].s);\\n visitChange = visitChange || retval;\\n }\\n\\n if (exploreType == UNVISITED_ACT_BONUS || exploreType == DIFF_AND_VISIT_BONUS || exploreType == NOVEL_STATE_BONUS || exploreType == DIFF_AND_NOVEL_BONUS){\\n std::vector<float> last2 = instances[i].s;\\n last2.push_back(instances[i].act);\\n bool retval = addStateToSet(last2);\\n visitChange = visitChange || retval;\\n }\\n }\\n\\n return (changed || visitChange);\\n}\\n\\n\\n// update all the counts, check if model has changed\\n// stop counting at M\\nbool ExplorationModel::updateWithExperience(experience &e){\\n //if (MODEL_DEBUG) cout << \\\"updateWithExperience \\\" << &last << \\\", \\\" << act\\n // << \\\", \\\" << &curr << \\\", \\\" << reward << endl;\\n\\n bool changed = model->updateWithExperience(e);\\n bool visitChange = false;\\n\\n // keep track of which states we've been to for this mode\\n if (exploreType == UNVISITED_BONUS){\\n bool retval = addStateToSet(e.s);\\n visitChange = visitChange || retval;\\n }\\n\\n if (exploreType == UNVISITED_ACT_BONUS || exploreType == DIFF_AND_VISIT_BONUS || exploreType == NOVEL_STATE_BONUS || exploreType == DIFF_AND_NOVEL_BONUS){\\n std::vector<float> last2 = e.s;\\n last2.push_back(e.act);\\n bool retval = addStateToSet(last2);\\n visitChange = visitChange || retval;\\n }\\n\\n return (changed || visitChange);\\n}\\n\\n\\n// calculate state info such as transition probs, known/unknown, reward prediction\\nfloat ExplorationModel::getStateActionInfo(const std::vector<float> &state, int act, StateActionInfo* retval){\\n //if (MODEL_DEBUG) cout << \\\"getStateActionInfo, \\\" << &state << \\\", \\\" << act << endl;\\n\\n retval->transitionProbs.clear();\\n\\n float conf = model->getStateActionInfo(state, act, retval);\\n\\n\\n //cout << \\\"state: \\\" << state[0] << \\\" act: \\\" << act;\\n\\n if (MODEL_DEBUG)// || (conf > 0.0 && conf < 1.0))\\n cout << \\\"reward: \\\" << retval->reward << \\\" conf: \\\" << conf << endl;\\n\\n // check exploration bonuses\\n\\n // use qmax if state is unknown\\n if (exploreType == EXPLORE_UNKNOWN){\\n if (!retval->known){\\n if (MODEL_DEBUG){\\n cout << \\\"State-Action Unknown in model: conf: \\\" << conf << \\\" \\\";\\n for (unsigned si = 0; si < state.size(); si++){\\n cout << (state)[si] << \\\",\\\";\\n }\\n cout << \\\" Action: \\\" << act << endl;\\n }\\n retval->reward = qmax;\\n retval->termProb = 1.0;\\n if (MODEL_DEBUG || MODEL_DEBUG)\\n cout << \\\" State-Action Unknown in model, using qmax \\\"\\n << qmax << endl;\\n }\\n }\\n\\n // small bonus for unvisited states\\n if (exploreType == UNVISITED_BONUS){\\n if (!checkForState(state)){\\n // modify reward with a bonus of n\\n float newQ =retval->reward + n;\\n if (MODEL_DEBUG){\\n cout << \\\" State unvisited bonus, orig R: \\\"\\n << retval->reward\\n << \\\" adding n: \\\" << n\\n << \\\" new value : \\\" << newQ\\n << endl;\\n }\\n retval->reward = newQ;\\n }\\n }\\n\\n // small bonus for unvisited state-actions\\n if (exploreType == UNVISITED_ACT_BONUS || exploreType == DIFF_AND_VISIT_BONUS){\\n std::vector<float> state2 = state;\\n state2.push_back(act);\\n if (!checkForState(state2)){\\n // modify reward with a bonus of n\\n float newQ =retval->reward + n;\\n if (MODEL_DEBUG){\\n cout << \\\" State-Action unvisited bonus, orig R: \\\"\\n << retval->reward\\n << \\\" adding n: \\\" << n\\n << \\\" new value : \\\" << newQ\\n << endl;\\n }\\n retval->reward = newQ;\\n }\\n }\\n\\n // small bonus for states far from visited states with same action\\n if (exploreType == NOVEL_STATE_BONUS || exploreType == DIFF_AND_NOVEL_BONUS){\\n std::vector<float> state2 = state;\\n state2.push_back(act);\\n float featDist = getFeatDistToVisitedSA(state2);\\n if (featDist > 0){\\n // modify reward with proportional bonus of n\\n float bonus = featDist * n;\\n if (MODEL_DEBUG){\\n cout << \\\" State-Action novel state bonus, dist: \\\" << featDist\\n << \\\" n: \\\" << n << \\\", bonus, \\\" << bonus << endl;\\n }\\n retval->reward += bonus;\\n }\\n }\\n\\n // use some % of v if we're doing continuous terminal bonus\\n if (exploreType == CONTINUOUS_BONUS){\\n if (conf < 1.0){\\n // percent of conf\\n float bonus = (1.0-conf)*v;\\n if (MODEL_DEBUG){\\n cout << \\\" State-Action continuous bonus conf: \\\"\\n << conf\\n << \\\", using v*(1-conf): \\\"\\n << bonus << endl;\\n }\\n retval->reward = bonus;\\n retval->termProb = 1.0;\\n }\\n }\\n\\n // use some % of v if we're doing continuous bonus\\n if (exploreType == CONTINUOUS_BONUS_R || exploreType == DIFF_AND_VISIT_BONUS || exploreType == DIFF_AND_NOVEL_BONUS){\\n if (conf < 1.0){\\n // percent of conf\\n float bonus = (1.0-conf)*v;\\n retval->reward += bonus;\\n if (MODEL_DEBUG){\\n cout << \\\" State-Action continuous bonus conf: \\\"\\n << conf\\n << \\\", using v*(1-conf): \\\"\\n << bonus << endl;\\n }\\n }\\n }\\n\\n // use qmax if we're doing threshold terminal bonus and conf under threshold\\n if (exploreType == THRESHOLD_BONUS){\\n if (conf < 0.5){\\n float bonus = v;\\n if (MODEL_DEBUG){\\n cout << \\\" State-Action conf< thresh: \\\"\\n << conf\\n << \\\" M: \\\" << M\\n << \\\", using v \\\"\\n << v << endl;\\n }\\n retval->reward = bonus;\\n retval->termProb = 1.0;\\n }\\n }\\n\\n // use rmax for additional thresh bonus and conf under thresh\\n if (exploreType == THRESHOLD_BONUS_R){\\n if (conf < 0.9){\\n float bonus = v;\\n retval->reward += bonus;\\n if (MODEL_DEBUG){\\n cout << \\\" State-Action conf< thresh: \\\"\\n << conf\\n << \\\" M: \\\" << M\\n << \\\", using v \\\"\\n << v << endl;\\n }\\n }\\n }\\n\\n // visits conf\\n if (exploreType == VISITS_CONF){\\n if (conf < 0.5){\\n float bonus = qmax;\\n retval->reward += bonus;\\n if (MODEL_DEBUG){\\n cout << \\\" State-Action conf< thresh or 0 visits: \\\"\\n << conf\\n << \\\" M: \\\" << M\\n << \\\", using qmax \\\"\\n << qmax << endl;\\n }\\n retval->reward = bonus;\\n retval->termProb = 1.0;\\n }\\n }\\n\\n\\n if (MODEL_DEBUG)\\n cout << \\\" Conf: \\\" << conf << \\\" Avg reward: \\\" << retval->reward << endl;\\n if (isnan(retval->reward))\\n cout << \\\"ERROR: Model returned reward of NaN\\\" << endl;\\n\\n return true;\\n\\n}\\n\\n// add state to set (if its not already in it)\\nbool ExplorationModel::addStateToSet(const std::vector<float> &s){\\n std::pair<std::set<std::vector<float> >::iterator, bool> retval;\\n retval = statespace.insert(s);\\n return retval.second;\\n}\\n\\n\\n// check if state is in set (so we know if we've visited it)\\nbool ExplorationModel::checkForState(const std::vector<float> &s){\\n return (statespace.count(s) == 1);\\n}\\n\\n// get distance in feature space from this state to one we've visited\\nfloat ExplorationModel::getFeatDistToVisitedSA(const std::vector<float> &s){\\n\\n // if we've visited this exact s,a then dist is 0\\n if (checkForState(s)){\\n return 0;\\n }\\n\\n // otherwise go through all states and find minimum distance\\n float maxDist = 0;\\n unsigned nfeats = s.size()-1;\\n std::vector<float> featRange(nfeats, 0);\\n for (unsigned i = 0; i < nfeats; i++){\\n featRange[i] = featmax[i] - featmin[i];\\n maxDist += 1.0;//featmax[i] - featmin[i];\\n\\n //cout << \\\"feat \\\" << i << \\\" diff: \\\" << (featmax[i] - featmin[i]) << \\\" max: \\\" << maxDist << endl;\\n }\\n\\n float minDist = maxDist;//nfeats;\\n unsigned actionIndex = nfeats;\\n\\n for (std::set<std::vector<float> >::iterator i = statespace.begin(); i != statespace.end(); i++){\\n // ignore if not the same action\\n if (s[actionIndex] != (*i)[actionIndex]) continue;\\n\\n // otherwise, sum all features that are different\\n float count = 0;\\n for (unsigned j = 0; j < nfeats; j++){\\n // distance based on magnitude of feature difference\\n // normalize by feature range\\n count += fabs(s[j] - (*i)[j]) / featRange[j];\\n }\\n if (count < minDist) minDist = count;\\n\\n }\\n\\n return (float)minDist/(float)nfeats;\\n\\n}\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7662650346755981,\n \"alphanum_fraction\": 0.7939758896827698,\n \"avg_line_length\": 50.9375,\n \"blob_id\": \"67c6afa2982132708d8115dc24f100c85e7dc04a\",\n \"content_id\": \"8d1df2765f6af397c976f371b1f494ba65bbc8fa\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 830,\n \"license_type\": \"no_license\",\n \"max_line_length\": 103,\n \"num_lines\": 16,\n \"path\": \"/build/turtlebot3_ddpg/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"turtlebot3_ddpg\\\")\\nset(turtlebot3_ddpg_VERSION \\\"1.0.0\\\")\\nset(turtlebot3_ddpg_MAINTAINER \\\"Pyo <pyo@robotis.com>\\\")\\nset(turtlebot3_ddpg_PACKAGE_FORMAT \\\"2\\\")\\nset(turtlebot3_ddpg_BUILD_DEPENDS \\\"rospy\\\")\\nset(turtlebot3_ddpg_BUILD_EXPORT_DEPENDS \\\"rospy\\\")\\nset(turtlebot3_ddpg_BUILDTOOL_DEPENDS \\\"catkin\\\")\\nset(turtlebot3_ddpg_BUILDTOOL_EXPORT_DEPENDS )\\nset(turtlebot3_ddpg_EXEC_DEPENDS \\\"rospy\\\")\\nset(turtlebot3_ddpg_RUN_DEPENDS \\\"rospy\\\")\\nset(turtlebot3_ddpg_TEST_DEPENDS )\\nset(turtlebot3_ddpg_DOC_DEPENDS )\\nset(turtlebot3_ddpg_URL_WEBSITE \\\"http://wiki.ros.org/turtlebot3_machine_learning\\\")\\nset(turtlebot3_ddpg_URL_BUGTRACKER \\\"https://github.com/ROBOTIS-GIT/turtlebot3_machine_learning/issues\\\")\\nset(turtlebot3_ddpg_URL_REPOSITORY \\\"https://github.com/ROBOTIS-GIT/turtlebot3_machine_learning\\\")\\nset(turtlebot3_ddpg_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.5780776739120483,\n \"alphanum_fraction\": 0.5962544679641724,\n \"avg_line_length\": 32.724456787109375,\n \"blob_id\": \"68e7ec697b6895e8d2f2297c43308ca779fc7465\",\n \"content_id\": \"1501fe8090f97fb4f45f2a3ad5568927a52b6c39\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 10893,\n \"license_type\": \"no_license\",\n \"max_line_length\": 110,\n \"num_lines\": 323,\n \"path\": \"/build/turtlebot3_ddpg/catkin_generated/installspace/turtlebot3_ddpg_stage_1\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python2\\n#################################################################################\\n# Copyright 2018 ROBOTIS CO., LTD.\\n#\\n# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n# you may not use this file except in compliance with the License.\\n# You may obtain a copy of the License at\\n#\\n# http://www.apache.org/licenses/LICENSE-2.0\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n# See the License for the specific language governing permissions and\\n# limitations under the License.\\n#################################################################################\\n\\n# Authors: Gilbert #\\n\\nimport rospy\\nimport os\\nimport json\\nimport numpy as np\\nimport random\\nimport time\\nimport sys\\nsys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))\\nfrom collections import deque\\nfrom std_msgs.msg import Float32MultiArray\\nfrom src.turtlebot3_ddpg.environment_stage_1 import Env\\nimport tensorflow as tf\\nfrom tensorflow.keras.models import Sequential, load_model\\nfrom tensorflow.keras.optimizers import RMSprop\\nfrom tensorflow.keras.layers import Dense, Dropout, Activation, Input\\n\\nfrom tensorflow import keras\\n\\nfrom tensorflow.keras.models import Model\\n\\n\\nEPISODES = 3000\\nGAMMA = 0.99\\nTAU = 0.001\\n\\nclass ReinforceAgent():\\n def __init__(self, state_size, action_size):\\n self.pub_result = rospy.Publisher('result', Float32MultiArray, queue_size=5)\\n self.dirPath = os.path.dirname(os.path.realpath(__file__))\\n self.dirPath = self.dirPath.replace('turtlebot3_ddpg/nodes', 'turtlebot3_ddpg/save_model/stage_1_')\\n self.result = Float32MultiArray()\\n self.load_model = False\\n self.load_episode = 0\\n self.state_size = state_size\\n self.action_size = action_size\\n self.episode_step = 6000\\n self.target_update = 2000\\n self.learning_rate = 0.00025\\n self.epsilon = 1.0\\n self.epsilon_decay = 0.99\\n self.epsilon_min = 0.05\\n self.batch_size = 32\\n self.train_start = 32\\n self.memory = deque(maxlen=1000000)\\n\\n\\n\\tself.TAU = 0.001 #Target Network HyperParameters\\n \\tself.LRA = 0.0001 #Learning rate for Actor\\n\\tself.LRC = 0.001 #Lerning rate for Critic\\n\\n self.model, self.weights, self.state2 = self.buildActor()\\n self.target_model, self.target_weights, self.target_state = self.buildActor()\\n\\n self.critic, self.state, self.action = self.buildCritic()\\n self.target_critic, _, _ = self.buildCritic()\\n\\n self.updateTargetModel()\\n\\n#added\\n\\n self.sess = tf.Session()\\n \\n self.action_gradient = tf.placeholder(tf.float32,[None, 2])\\n self.params_grad = tf.gradients(self.model.output, self.model.weights, -self.action_gradient)\\n grads = zip(self.params_grad, self.model.weights)\\n self.optimize = tf.train.AdamOptimizer(self.learning_rate).apply_gradients(grads)\\n self.sess.run(tf.global_variables_initializer())\\n\\n self.action_grads = tf.gradients(self.critic.output, self.action) #GRADIENTS for policy update\\n#end add\\n\\n\\n\\n def buildCritic(self):\\n s = keras.layers.Input(shape=(14,), name='state')\\n\\n d1 = Dense(512, activation='relu')(s)\\n\\n\\n a = keras.layers.Input(shape=(2,), name='action')\\n x = keras.layers.concatenate([d1, a])\\n\\n x = Dense(512, activation='relu')(x)\\n x = Dense(512, activation='relu')(x)\\n\\n\\n\\n q_val= Dense(1, activation='sigmoid', name='main_output')(x)\\n\\n critic = Model(inputs=[s, a], outputs=[q_val])\\n #critic.summary()\\n\\n critic.compile(optimizer=tf.train.AdamOptimizer(), \\n loss='mse',\\n metrics=['accuracy'])\\n return critic, s, a\\n\\n def buildActor(self):\\n state = keras.layers.Input(shape=(14,), name='main_input')\\n\\n\\n x = Dense(512, activation='relu')(state)\\n x = Dense(512, activation='relu')(x)\\n x = Dense(512, activation='relu')(x)\\n\\n\\n lin_vel = Dense(1, activation='sigmoid', name='lin_vel')(x)\\n ang_vel = Dense(1, activation='tanh', name='ang_vel')(x)\\n output = keras.layers.concatenate([lin_vel, ang_vel])\\n\\n actor = Model(inputs=[state], outputs=[output])\\n\\t#actor.summary()\\n \\n actor.compile(optimizer=tf.train.AdamOptimizer(), \\n loss='mse',\\n metrics=['accuracy'])\\n\\n return actor, actor.trainable_weights, state\\n\\n\\n#added\\n def train(self, states, action_grads):\\n self.sess.run(self.optimize, feed_dict={\\n self.state2: states,\\n self.action_gradient: action_grads\\n })\\n\\n def target_train(self):\\n actor_weights = self.model.get_weights()\\n actor_target_weights = self.target_model.get_weights()\\n for i in xrange(len(actor_weights)):\\n actor_target_weights[i] = self.TAU * actor_weights[i] + (1 - self.TAU)* actor_target_weights[i]\\n self.target_model.set_weights(actor_target_weights)\\n\\n\\n def target_train_c(self):\\n critic_weights = self.target_critic.get_weights()\\n critic_target_weights = self.target_critic.get_weights()\\n for i in xrange(len(critic_weights)):\\n critic_target_weights[i] = self.TAU * critic_weights[i] + (1 - self.TAU)* critic_target_weights[i]\\n\\tself.target_critic.set_weights(critic_target_weights)\\n\\n def gradients(self, states, actions):\\n grad = self.sess.run(self.action_grads, feed_dict={\\n self.state: states,\\n self.action: actions\\n\\t})[0]\\n\\treturn grad\\n\\n#end add\\n\\n#Need to update\\n\\n#should be OK\\n def updateTargetModel(self):\\n #self.target_model.set_weights(self.model.get_weights())\\n #self.target_critic.set_weights(self.critic.get_weights())\\n\\tself.target_train()\\n\\tself.target_train_c()\\n\\n\\n#should be OK\\n def getAction(self, state):\\n if np.random.rand() <= self.epsilon:\\n self.q_value = [random.uniform(0, 1.0), random.uniform(-1.0, 1.0)]\\n return self.q_value\\n else:\\n q_value = self.target_model.predict(state.reshape(1, len(state)))\\n\\t q_value= q_value.flatten()\\n self.q_value = q_value\\n return q_value\\n\\n\\n#should be OK\\n def appendMemory(self, state, action, reward, next_state, done):\\n self.memory.append((state, action, reward, next_state, done))\\n\\n\\n#need to analyze\\n####################### Put the new train and stuff in here\\n###################### also make sure you actually understand what you are doing...\\n\\n def trainModel(self, target=False):\\n env.pause_proxy()\\n mini_batch = random.sample(self.memory, self.batch_size)\\n X_batch = np.empty(([0,14]), dtype=np.float64)\\n X_batch2 = np.empty(([0,2]), dtype=np.float64)\\n Y_batch = np.empty((0), dtype=np.float64)\\n\\n for i in range(self.batch_size):\\n states = mini_batch[i][0]\\n actions = mini_batch[i][1]\\n rewards = mini_batch[i][2]\\n next_states = mini_batch[i][3]\\n dones = mini_batch[i][4]\\n\\t #print len(states), \\\"\\\\n\\\"\\n q_value = self.model.predict(states.reshape(1, len(states)))\\n\\n\\n next_target = self.target_model.predict(next_states.reshape(1, len(next_states)))\\n\\n\\t next_target = next_target[0][:]\\n next_target = next_target.reshape(1, len())\\n\\n \\n\\t next_q = self.target_critic.predict([next_states.reshape(1, len(next_states)), next_target ])\\n\\n Y_sample = rewards+GAMMA*next_q\\n\\n\\t test = states.reshape(1, len(next_states))\\n X_batch = np.append(X_batch, test, axis = 0)\\n X_batch2 = np.append(X_batch2, np.asarray(actions).reshape(1,2), axis = 0) \\n\\n\\n Y_batch = np.append(Y_batch, Y_sample)\\n\\n\\n\\n self.critic.fit([X_batch, X_batch2], Y_batch, batch_size=self.batch_size, epochs=1, verbose=0)\\n\\twith tf.Session() as sess:\\n\\t #grads=self.action_grads, feed_dict={\\n \\t#self.state: states,\\n \\t#self.action: actions}\\t\\n\\t\\tgrads =self.gradients(X_batch, X_batch2)\\n\\t #print X_batch.shape\\n\\t #print grads.shape\\n\\t\\tself.train(X_batch, grads)\\n\\n env.unpause_proxy()\\n\\n\\n#need to analyze\\nif __name__ == '__main__':\\n rospy.init_node('turtlebot3_dqn_stage_1')\\n pub_result = rospy.Publisher('result', Float32MultiArray, queue_size=5)\\n pub_get_action = rospy.Publisher('get_action', Float32MultiArray, queue_size=5)\\n result = Float32MultiArray()\\n get_action = Float32MultiArray()\\n past_action = [0.0,0.0]\\n state_size = 12\\n action_size = 5\\n\\n env = Env(action_size)\\n\\n agent = ReinforceAgent(state_size, action_size)\\n scores, episodes = [], []\\n global_step = 0\\n start_time = time.time()\\n\\n for e in range(agent.load_episode + 1, EPISODES):\\n done = False\\n state = env.reset()\\n score = 0\\n\\t\\n for t in range(agent.episode_step):\\n action = agent.getAction(state)\\n\\t #print action\\n\\t #print \\\"ACTION IS PRINTED\\\"\\n next_state, reward, done = env.step(action, past_action)\\n\\t #print len(state), \\\"\\\\n\\\"\\n agent.appendMemory(state, action, reward, next_state, done)\\n\\n if len(agent.memory) >= agent.train_start:\\n if global_step <= agent.target_update:\\n agent.trainModel()\\n else:\\n agent.trainModel(True)\\n\\n score += reward\\n state = next_state\\n get_action.data = [action, score, reward]\\n pub_get_action.publish(get_action)\\n\\t past_action = action\\n #if e % 10 == 0:#\\n # agent.model.save(agent.dirPath + str(e) + '.h5')#\\n # with open(agent.dirPath + str(e) + '.json', 'w') as outfile:\\n # json.dump(param_dictionary, outfile)\\n\\n if t >= 400:\\n rospy.loginfo(\\\"Time out!!\\\")\\n done = True\\n\\n if done:\\n result.data = [score, np.max(agent.q_value)]\\n pub_result.publish(result)\\n agent.updateTargetModel()\\n scores.append(score)\\n episodes.append(e)\\n m, s = divmod(int(time.time() - start_time), 60)\\n h, m = divmod(m, 60)\\n\\n rospy.loginfo('Ep: %d score: %.2f memory: %d epsilon: %.2f time: %d:%02d:%02d',\\n e, score, len(agent.memory), agent.epsilon, h, m, s)\\n param_keys = ['epsilon']\\n param_values = [agent.epsilon]\\n param_dictionary = dict(zip(param_keys, param_values))\\n break\\n\\n global_step += 1\\n # if global_step % agent.target_update == 0:\\n # rospy.loginfo(\\\"UPDATE TARGET NETWORK\\\")\\n\\n if agent.epsilon > agent.epsilon_min:\\n agent.epsilon *= agent.epsilon_decay\\n\"\n },\n {\n \"alpha_fraction\": 0.7103658318519592,\n \"alphanum_fraction\": 0.7198932766914368,\n \"avg_line_length\": 29.160919189453125,\n \"blob_id\": \"13fa647f1b807f2edb5580f61e8dec29ff5c0cbd\",\n \"content_id\": \"9a1f83f36d4a8b7ffc43f4e597924f425008c2b4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 2624,\n \"license_type\": \"no_license\",\n \"max_line_length\": 135,\n \"num_lines\": 87,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/include/rl_env/RobotCarVel.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file RobotCarVel.hh\\n This domain is a simulation of velocity control for the Austin Robot \\n Technology autonomous vehicle. \\n This vehicle is described in:\\n Beeson et al, \\\"Multiagent Interactions in Urban Driving,\\\" Journal of Physical Agents, March 2008.\\n The velocity control task is described in:\\n Hester, Quinlan, and Stone, \\\"A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control\\\", arXiv 1105.1749, 2011.\\n \\\\author Todd Hester\\n*/\\n\\n#ifndef _ROBOTCAR_H_\\n#define _ROBOTCAR_H_\\n\\n#include <set>\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n\\n/** This class defines a domain that is a simulation of velocity control for the Austin Robot \\n Technology autonomous vehicle. */\\nclass RobotCarVel: public Environment {\\npublic:\\n\\n /** Creates a RobotCarVel domain.\\n \\\\param rand Random number generator \\n \\\\param randomVel Use random starting and target velocities\\n \\\\param upVel For specific velocity pair (not random), do target > starting vel\\n \\\\param tenToSix Use 10 and 6 m/s for specific velocity pair, rather than 7 and 2\\n \\\\param lag Implements lag on the brake actuator to fully model the real vehicle.\\n */\\n RobotCarVel(Random &rand, bool randomVel, bool upVel, bool tenToSix, bool lag);\\n\\n virtual ~RobotCarVel();\\n\\n virtual const std::vector<float> &sensation() const;\\n virtual float apply(int action);\\n\\n virtual bool terminal() const;\\n virtual void reset();\\n\\n virtual int getNumActions();\\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\\n virtual void getMinMaxReward(float* minR, float* maxR);\\n\\n /** Set the state vector for debug purposes. */\\n void setSensation(std::vector<float> newS);\\n\\n virtual std::vector<experience> getSeedings();\\n\\n /** Get an experience seed at a random velocity for the given target velocity. */\\n experience getRandomVelSeed(float target);\\n\\n /** Get an example experience for the given state-action. */\\n experience getExp(float s0, float s1, float s2, float s3, int a);\\n\\n //virtual bool isEpisodic() { return false; };\\n\\n /** Bound a value between the given min and max. */\\n float bound(float val, float min, float max);\\n\\nprotected:\\n enum car_action_t {NOTHING, THROTTLE_UP, THROTTLE_DOWN, BRAKE_UP, BRAKE_DOWN};\\n\\nprivate:\\n\\n Random &rng;\\n\\n std::vector<float> s;\\n std::vector<float> hidden;\\n \\n float &targetVel;\\n float &currVel;\\n float &trueThrottle;\\n float &trueBrake;\\n float &throttleTarget;\\n float &brakeTarget;\\n\\n const bool randomVel;\\n const bool upVel;\\n const bool tenToSix;\\n const bool lag;\\n\\n float brakePosVel;\\n int actNum;\\n\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.6919922828674316,\n \"alphanum_fraction\": 0.694298505783081,\n \"avg_line_length\": 32.35470199584961,\n \"blob_id\": \"3a364e4d421c6bb303de9a185230e9f0e7f4acb1\",\n \"content_id\": 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Kocsis and C. Szepesv´ari, \\\"Bandit based monte-carlo planning,\\\" in\\n ECML-06. Number 4212 in LNCS. Springer, 2006, pp. 282-293.\\n \\\\author Todd Hester\\n*/\\n\\n#ifndef _PO_ETUCT_HH_\\n#define _PO_ETUCT_HH_\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n\\n#include \\\"../Models/FactoredModel.hh\\\"\\n\\n#include <set>\\n#include <vector>\\n#include <map>\\n#include <deque>\\n\\n/** This class defines a modified version of UCT, which plans on a model using Monte Carlo rollouts. Unlike the original UCT, it does not separate values by tree depth, and it incorporates eligibility traces. This version plans over states augmented with k-action histories, for delayed or partially observable domains. */\\nclass PO_ETUCT: public Planner {\\npublic:\\n\\n /** Standard constructor\\n \\\\param numactions, numactions in the domain\\n \\\\param gamma discount factor\\n \\\\param rrange range of one-step rewards in the domain\\n \\\\param lambda for use with eligibility traces\\n \\\\param MAX_ITER maximum number of MC rollouts to perform\\n \\\\param MAX_TIME maximum amount of time to run Monte Carlo rollouts\\n \\\\param MAX_DEPTH maximum depth to perform rollout to\\n \\\\param modelType specifies model type\\n \\\\param featmax maximum value of each feature\\n \\\\param featmin minimum value of each feature\\n \\\\param statesPerDim # of values to discretize each feature into\\n \\\\param trackActual track actual real-valued states (or just discrete states)\\n \\\\param history # of previous actions to use for delayed domains\\n \\\\param rng random number generator\\n */\\n PO_ETUCT(int numactions, float gamma, float rrange, float lambda,\\n int MAX_ITER, float MAX_TIME, int MAX_DEPTH, int modelType,\\n const std::vector<float> &featmax, const std::vector<float> &featmin,\\n const std::vector<int> &statesPerDim, bool trackActual, \\n int history, Random rng = Random());\\n \\n /** Unimplemented copy constructor: internal state cannot be simply\\n copied. */\\n PO_ETUCT(const PO_ETUCT &);\\n\\n virtual ~PO_ETUCT();\\n\\n virtual void setModel(MDPModel* model);\\n virtual bool updateModelWithExperience(const std::vector<float> &last, \\n int act, \\n const std::vector<float> &curr, \\n float reward, bool term);\\n virtual void planOnNewModel();\\n virtual int getBestAction(const std::vector<float> &s);\\n\\n virtual void setSeeding(bool seed);\\n virtual void setFirst();\\n\\n /** Output value function to a file */\\n void logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax);\\n \\n /** Return a discretized version of the input state. */\\n std::vector<float> discretizeState(const std::vector<float> &s);\\n\\n bool PLANNERDEBUG;\\n bool MODELDEBUG;\\n bool ACTDEBUG;\\n bool UCTDEBUG;\\n bool REALSTATEDEBUG;\\n bool HISTORYDEBUG;\\n\\n /** MDPModel that we're using with planning */\\n MDPModel* model;\\n\\n /** The implementation maps all sensations to a set of canonical\\n pointers, which serve as the internal representation of\\n environment state. */\\n typedef const std::vector<float> *state_t;\\n\\n\\nprotected:\\n\\n\\n struct state_info;\\n struct model_info;\\n\\n /** A struct that contains a vector of possible next state samples, weighted by their probabilities. */\\n struct state_samples {\\n std::vector<state_t> samples;\\n };\\n\\n /** State info struct. Maintains visit counts, models, and q-values for state-actions. */\\n struct state_info {\\n\\n // data filled in from models\\n StateActionInfo* model;\\n\\n // q values from policy creation\\n std::vector<float> Q;\\n\\n // uct experience data\\n int uctVisits;\\n std::vector<int> uctActions;\\n short unsigned int visited;\\n short unsigned int id;\\n\\n // needs update\\n bool needsUpdate;\\n\\n };\\n\\n\\n /** Initialize state info struct */\\n void initStateInfo(state_t s, state_info* info);\\n \\n /** Produces a canonical representation of the given sensation.\\n \\\\param s The current sensation from the environment.\\n \\\\return A pointer to an equivalent state in statespace. */\\n state_t canonicalize(const std::vector<float> &s);\\n\\n /** Delete a state_info struct */\\n void deleteInfo(state_info* info);\\n \\n /** Initialize a new state */\\n void initNewState(state_t s);\\n \\n /** Compuate a policy from a model */\\n void createPolicy();\\n \\n /** Print information for each state. */\\n void printStates();\\n \\n /** Calculate which states are reachable from states the agent has actually visited. */\\n void calculateReachableStates();\\n \\n /** Remove states from set that were deemed unreachable. */\\n void removeUnreachableStates();\\n\\n /** Update the state_info copy of the model for the given state-action from the MDPModel */\\n void updateStateActionFromModel(state_t s, int a, state_info* info);\\n\\n /** Update the state_info copy of the model for the given state-action and k-action history from the MDPModel. */\\n void updateStateActionHistoryFromModel(const std::vector<float> modState, int a, StateActionInfo *newModel);\\n\\n /** Get the current time in seconds */\\n double getSeconds();\\n\\n /** Reset UCT visit counts to some baseline level (to decrease our confidence in q-values because model has changed. */\\n void resetUCTCounts();\\n \\n /** Perform UCT/Monte Carlo rollout from the given state.\\n If terminal or at depth, return some reward.\\n Otherwise, select an action based on UCB.\\n Simulate action to get reward and next state.\\n Call search on next state at depth+1 to get reward return from there on.\\n Update q value towards new value: reward + gamma * searchReturn\\n Update visit counts for confidence bounds\\n Return q\\n \\n From \\\"Bandit Based Monte Carlo Planning\\\" by Kocsis and Szepesv´ari.\\n */\\n float uctSearch(const std::vector<float> &actualS, state_t state, int depth);\\n \\n /** Return a sampled state from the next state distribution of the model. \\n Simulate the next state from the given state, action, and possibly history of past actions. */\\n std::vector<float> simulateNextState(const std::vector<float> &actualState, state_t discState, state_info* info, int action, float* reward, bool* term);\\n \\n /** Select UCT action based on UCB1 algorithm. */\\n int selectUCTAction(state_info* info);\\n \\n /** Canonicalize all the next states predicted by this model. */\\n void canonNextStates(StateActionInfo* modelInfo);\\n \\n virtual void savePolicy(const char* filename);\\n \\n /** Add two vectors together. */\\n std::vector<float> addVec(const std::vector<float> &a, const std::vector<float> &b);\\n \\n /** Subtract two vectors. */\\n std::vector<float> subVec(const std::vector<float> &a, const std::vector<float> &b);\\n\\nprivate:\\n\\n /** Set of all distinct sensations seen. 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std::string(\\\"left_wheel\\\"));\\n n.param(\\\"right_joint\\\", right_joint_name_, std::string(\\\"right_wheel\\\"));\\n n.param(\\\"control_timeout\\\", timeout_, 0.25);\\n\\n double max_speed, max_accel, max_decel;\\n n.param(\\\"max_speed\\\", max_speed, 10.0);\\n n.param(\\\"max_accel\\\", max_accel, 1.0);\\n n.param(\\\"max_decel\\\", max_decel, 1.0);\\n\\n left_motor_ = new MotorSim(max_speed, max_accel, max_decel);\\n right_motor_ = new MotorSim(max_speed, max_accel, max_decel);\\n}\\n\\nvoid WheelSpeedController::recvLeftCommand(const std_msgs::Float64::ConstPtr& msg)\\n{\\n left_command_stamp_ = ros::Time::now();\\n left_target_speed_ = msg->data;\\n}\\n\\nvoid WheelSpeedController::recvRightCommand(const std_msgs::Float64::ConstPtr& msg)\\n{\\n right_command_stamp_ = ros::Time::now();\\n right_target_speed_ = msg->data;\\n}\\n\\nvoid WheelSpeedController::update(const ros::Time& time, const ros::Duration& period)\\n{\\n double left_actual;\\n double right_actual;\\n\\n if 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\"length_bytes\": 365,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 10,\n \"path\": \"/build/gmapping/CTestTestfile.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# CMake generated Testfile for \\n# Source directory: /home/justin/ros_test/src/gmapping\\n# Build directory: /home/justin/ros_test/build/gmapping\\n# \\n# This file includes the relevant testing commands required for \\n# testing this directory and lists subdirectories to be tested as well.\\nsubdirs(\\\"gridfastslam\\\")\\nsubdirs(\\\"scanmatcher\\\")\\nsubdirs(\\\"sensor\\\")\\nsubdirs(\\\"utils\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.7284998297691345,\n \"alphanum_fraction\": 0.7331276535987854,\n \"avg_line_length\": 34.52054977416992,\n \"blob_id\": \"fc9d4550f5f485d404bf30491be2b7e4c6fdab36\",\n \"content_id\": \"b10e3656928c9416bc3fb06a83e64909d8ed748e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 2593,\n \"license_type\": \"no_license\",\n \"max_line_length\": 110,\n \"num_lines\": 73,\n \"path\": \"/src/state_space_example/src/state_space_example.cpp\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include <ros/ros.h>\\n#include <geometry_msgs/TwistStamped.h>\\n#include <geometry_msgs/Pose.h>\\n#include <tf/tf.h>\\n#include <tf/transform_broadcaster.h>\\n#include <nav_msgs/Odometry.h>\\n\\nros::Publisher pub_odom;\\ngeometry_msgs::Twist current_twist;\\ngeometry_msgs::Pose current_estimate;\\ndouble sample_time;\\nstd::string parent_frame;\\nstd::string child_frame;\\n\\nvoid recvTwist(const geometry_msgs::TwistStamped::ConstPtr& msg)\\n{\\n current_twist = msg->twist;\\n}\\n\\nvoid timerCallback(const ros::TimerEvent& event)\\n{\\n static tf::TransformBroadcaster broadcaster;\\n\\n // State space equations\\n double v = current_twist.linear.x;\\n double pdot_c = current_twist.angular.z;\\n\\n double cos_psi = current_estimate.orientation.w * current_estimate.orientation.w\\n - current_estimate.orientation.z * current_estimate.orientation.z;\\n double sin_psi = 2 * current_estimate.orientation.w * current_estimate.orientation.z;\\n double psi = atan2(sin_psi, cos_psi);\\n\\n current_estimate.position.x += sample_time * v * cos_psi;\\n current_estimate.position.y += sample_time * v * sin_psi;\\n double new_psi = psi + sample_time * pdot_c;\\n current_estimate.orientation = tf::createQuaternionMsgFromYaw(new_psi);\\n\\n // Populate TF transform\\n tf::StampedTransform transform;\\n transform.frame_id_ = parent_frame;\\n transform.child_frame_id_ = child_frame;\\n transform.stamp_ = event.current_real;\\n transform.setOrigin(tf::Vector3(current_estimate.position.x, current_estimate.position.y, 0));\\n transform.setRotation(tf::Quaternion(0, 0, current_estimate.orientation.z, current_estimate.orientation.w));\\n broadcaster.sendTransform(transform);\\n\\n // Populate and publish odometry\\n nav_msgs::Odometry odom_msg;\\n odom_msg.header.stamp = event.current_real;\\n odom_msg.header.frame_id = parent_frame;\\n odom_msg.child_frame_id = child_frame;\\n odom_msg.pose.pose = current_estimate;\\n odom_msg.twist.twist = current_twist;\\n pub_odom.publish(odom_msg);\\n}\\n\\nint main(int argc, char** argv)\\n{\\n ros::init(argc, argv, \\\"state_space_example\\\");\\n ros::NodeHandle node;\\n ros::NodeHandle private_node(\\\"~\\\");\\n\\n ros::Subscriber sub_twist = node.subscribe(\\\"twist\\\", 1, recvTwist);\\n pub_odom = node.advertise<nav_msgs::Odometry>(\\\"odom\\\", 1);\\n\\n private_node.param(\\\"sample_time\\\", sample_time, 0.02);\\n private_node.param(\\\"parent_frame\\\", parent_frame, std::string(\\\"odom\\\"));\\n private_node.param(\\\"child_frame\\\", child_frame, std::string(\\\"base_footprint\\\"));\\n ros::Timer state_space_timer = node.createTimer(ros::Duration(sample_time), timerCallback);\\n\\n current_estimate.orientation = tf::createQuaternionMsgFromYaw(0);\\n ros::spin();\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.531692385673523,\n \"alphanum_fraction\": 0.5388245582580566,\n \"avg_line_length\": 24.54520606994629,\n \"blob_id\": \"a8a8e68488a6a5e54f38114fcbaca24be788fad0\",\n \"content_id\": \"f902bf198b0d76ef20704b13d0c52919cf204b98\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 18648,\n \"license_type\": \"no_license\",\n \"max_line_length\": 143,\n \"num_lines\": 730,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/ValueIteration.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file ValueIteration.cc\\n Implements the ValueIteration class\\n \\\\author Todd Hester\\n*/\\n\\n#include \\\"ValueIteration.hh\\\"\\n#include <algorithm>\\n\\n#include <sys/time.h>\\n\\n\\nValueIteration::ValueIteration(int numactions, float gamma,\\n int MAX_LOOPS, float MAX_TIME, int modelType,\\n const std::vector<float> &fmax, \\n const std::vector<float> &fmin, \\n const std::vector<int> &n, Random newRng):\\n numactions(numactions), gamma(gamma),\\n MAX_LOOPS(MAX_LOOPS), MAX_TIME(MAX_TIME), modelType(modelType),\\n statesPerDim(n)\\n{\\n rng = newRng;\\n\\n nstates = 0;\\n nactions = 0;\\n\\n timingType = false; //true;\\n\\n model = NULL;\\n planTime = getSeconds();\\n\\n // algorithm options\\n MAX_STEPS = 100; //50; //60; //80; //0; //5; //10;\\n\\n PLANNERDEBUG = false;\\n POLICYDEBUG = false;\\n ACTDEBUG = false;\\n MODELDEBUG = false;\\n\\n featmax = fmax;\\n featmin = fmin;\\n\\n if (statesPerDim[0] > 0){\\n cout << \\\"Planner VI using discretization of \\\" << statesPerDim[0] << endl;\\n }\\n\\n\\n}\\n\\nValueIteration::~ValueIteration() {\\n for (std::map<state_t, state_info>::iterator i = statedata.begin();\\n i != statedata.end(); i++){\\n \\n // get state's info\\n //cout << \\\" planner got info\\\" << endl;\\n state_info* info = &((*i).second);\\n\\n deleteInfo(info);\\n }\\n\\n statedata.clear();\\n \\n}\\n\\nvoid ValueIteration::setModel(MDPModel* m){\\n\\n model = m;\\n\\n // initStates();\\n\\n}\\n\\n\\n// canonicalize all the states so we already have them in our statespace\\nvoid ValueIteration::initStates(){\\n cout << \\\"init states\\\" << endl;\\n std::vector<float> s(featmin.size());\\n\\n fillInState(s,0);\\n cout << \\\"init states complete\\\" << endl;\\n}\\n\\nvoid ValueIteration::fillInState(std::vector<float>s, int depth){\\n\\n // if depth == size, canonicalize and return\\n if (depth == (int)featmin.size()){\\n canonicalize(s);\\n return;\\n }\\n\\n // go through all features at depth\\n for (float i = featmin[depth]; i < featmax[depth]+1; i++){\\n s[depth] = i;\\n fillInState(s, depth+1);\\n }\\n}\\n\\n\\n/////////////////////////////\\n// Functional functions :) //\\n/////////////////////////////\\n\\n\\nvoid ValueIteration::initNewState(state_t s){\\n if (PLANNERDEBUG) cout << \\\"initNewState(s = \\\" << s\\n << \\\") size = \\\" << s->size() << endl;\\n\\n if (MODELDEBUG) cout << \\\"New State: \\\" << endl;\\n\\n // create state info and add to hash map\\n state_info* info = &(statedata[s]);\\n initStateInfo(info);\\n\\n // init these from model\\n for (int i = 0; i < numactions; i++){\\n model->getStateActionInfo(*s, i, &(info->modelInfo[i]));\\n }\\n\\n\\n if (PLANNERDEBUG) cout << \\\"done with initNewState()\\\" << endl;\\n\\n}\\n\\nbool ValueIteration::updateModelWithExperience(const std::vector<float> &laststate,\\n int lastact,\\n const std::vector<float> &currstate,\\n float reward, bool term){\\n if (PLANNERDEBUG) cout << \\\"updateModelWithExperience(last = \\\" << &laststate\\n << \\\", curr = \\\" << &currstate\\n << \\\", lastact = \\\" << lastact\\n << \\\", r = \\\" << reward\\n << \\\")\\\" << endl;\\n\\n if (!timingType)\\n planTime = getSeconds();\\n\\n // canonicalize these things\\n state_t last = canonicalize(laststate);\\n state_t curr = canonicalize(currstate);\\n\\n prevstate = laststate;\\n prevact = lastact;\\n\\n // if not transition to terminal\\n if (curr == NULL)\\n return false;\\n\\n // get state info\\n state_info* info = &(statedata[last]);\\n\\n // update the state visit count\\n info->visits[lastact]++;\\n\\n // init model?\\n if (model == NULL){\\n cout << \\\"ERROR IN MODEL OR MODEL SIZE\\\" << endl;\\n exit(-1);\\n }\\n\\n experience e;\\n e.s = laststate;\\n e.next = currstate;\\n e.act = lastact;\\n e.reward = reward;\\n e.terminal = term;\\n bool modelChanged = model->updateWithExperience(e);\\n\\n if (PLANNERDEBUG) cout << \\\"VI Added exp: \\\" << modelChanged << endl;\\n if (timingType)\\n planTime = getSeconds();\\n\\n return modelChanged;\\n\\n}\\n\\n\\nvoid ValueIteration::updateStateActionFromModel(const std::vector<float> &state, int a){\\n if (PLANNERDEBUG) cout << \\\"updateStateActionFromModel()\\\" << endl;\\n\\n state_t s = canonicalize(state);\\n\\n // get state's info\\n state_info* info = &(statedata[s]);\\n\\n // update state info\\n // get state action info for each action\\n model->getStateActionInfo(state, a, &(info->modelInfo[a]));\\n\\n info->fresh = false;\\n\\n}\\n\\n\\nvoid ValueIteration::updateStatesFromModel(){\\n if (PLANNERDEBUG) cout << \\\"updateStatesFromModel()\\\" << endl;\\n\\n // for each state\\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n if (PLANNERDEBUG){\\n cout << \\\"updateStatesFromModel i = \\\" << &(*i) << endl;\\n cout << \\\"State is \\\";\\n for (unsigned j = 0; j < (*i).size(); j++){\\n cout << (*i)[j] << \\\", \\\";\\n }\\n cout << endl;\\n }\\n\\n state_t s = canonicalize(*i);\\n\\n // get state's info\\n state_info* info = &(statedata[s]);\\n\\n // update state info\\n // get state action info for each action\\n for (int j = 0; j < numactions; j++){\\n model->getStateActionInfo(*s, j, &(info->modelInfo[j]));\\n }\\n\\n //s2.clear();\\n\\n info->fresh = false;\\n\\n if (PLANNERDEBUG) cout << \\\"updateStatesFromModel i = \\\" << &i << \\\" complete\\\" << endl;\\n\\n }\\n\\n if (PLANNERDEBUG) cout << \\\"updateStatesFromModel \\\" << \\\" totally complete\\\" << endl;\\n\\n}\\n\\n\\nint ValueIteration::getBestAction(const std::vector<float> &state){\\n if (PLANNERDEBUG) cout << \\\"getBestAction(s = \\\" << &state\\n << \\\")\\\" << endl;\\n\\n state_t s = canonicalize(state);\\n\\n // get state info\\n state_info* info = &(statedata[s]);\\n\\n // Get Q values\\n std::vector<float> &Q = info->Q;\\n\\n // Choose an action\\n const std::vector<float>::iterator a =\\n random_max_element(Q.begin(), Q.end()); // Choose maximum\\n\\n int act = a - Q.begin();\\n float val = *a;\\n if (ACTDEBUG){\\n cout << endl << \\\"chooseAction State \\\" << (*s)[0] << \\\",\\\" << (*s)[1]\\n << \\\" act: \\\" << act << \\\" val: \\\" << val << endl;\\n for (int iAct = 0; iAct < numactions; iAct++){\\n cout << \\\" Action: \\\" << iAct\\n << \\\" val: \\\" << Q[iAct]\\n << \\\" visits: \\\" << info->visits[iAct]\\n << \\\" modelsAgree: \\\" << info->modelInfo[iAct].known << endl;\\n }\\n }\\n\\n nactions++;\\n\\n // return index of action\\n return act;\\n}\\n\\n\\n\\n\\n// use VI to compute new policy using model\\nvoid ValueIteration::createPolicy(){\\n if (PLANNERDEBUG || POLICYDEBUG) cout << endl << \\\"createPolicy()\\\" << endl;\\n\\n float maxError = 5000;\\n int nloops = 0;\\n\\n calculateReachableStates();\\n\\n float MIN_ERROR = 0.0001;\\n //float initTime = getSeconds();\\n //cout << \\\"max time \\\" << MAX_TIME << \\\" max loops: \\\" << MAX_LOOPS << endl;\\n int statesUpdated = 0;\\n\\n // until convergence (always at least MIN_LOOPS)\\n while (maxError > MIN_ERROR){ // && nloops < MAX_LOOPS){\\n\\n //if ((getSeconds() - initTime) > MAX_TIME)\\n // break;\\n\\n // if ((getSeconds() - planTime) > MAX_TIME)\\n //break;\\n\\n if (POLICYDEBUG)\\n cout << \\\"max error: \\\" << maxError << \\\" nloops: \\\" << nloops\\n << endl;\\n\\n maxError = 0;\\n nloops++;\\n\\n // for all states\\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n // if ((getSeconds() - planTime) > MAX_TIME)\\n //break;\\n\\n statesUpdated++;\\n state_t s = canonicalize(*i);\\n\\n // get state's info\\n state_info* info = &(statedata[s]);\\n\\n if (POLICYDEBUG){\\n cout << endl << \\\" State: id: \\\" << info->id << \\\": \\\" ;\\n for (unsigned si = 0; si < s->size(); si++){\\n cout << (*s)[si] << \\\",\\\";\\n }\\n cout << \\\" Steps: \\\" << info->stepsAway << endl;\\n\\n }\\n\\n // skip states we can't reach\\n if (info->stepsAway > 99999){\\n if (POLICYDEBUG)\\n cout << \\\"State not reachable, ignoring.\\\" << endl;\\n continue;\\n }\\n\\n // for each action\\n for (int act = 0; act < numactions; act++){\\n\\n\\n\\n // get state action info for this action\\n StateActionInfo *modelInfo = &(info->modelInfo[act]);\\n\\n if (POLICYDEBUG)\\n cout << \\\" Action: \\\" << act\\n << \\\" State visits: \\\" << info->visits[act]\\n << \\\" reward: \\\" << modelInfo->reward \\n\\t << \\\" term: \\\" << modelInfo->termProb << endl;\\n\\n // Q = R + discounted val of next state\\n // this is the R part :)\\n float newQ = modelInfo->reward;\\n\\n float probSum = modelInfo->termProb;\\n\\n // for all next states, add discounted value appropriately\\n // loop through next state's that are in this state-actions list\\n for (std::map<std::vector<float>, float>::iterator outIt\\n = modelInfo->transitionProbs.begin();\\n outIt != modelInfo->transitionProbs.end(); outIt++){\\n\\n std::vector<float> nextstate = (*outIt).first;\\n\\n if (POLICYDEBUG){\\n cout << \\\" Next state was: \\\";\\n for (unsigned oi = 0; oi < nextstate.size(); oi++){\\n cout << nextstate[oi] << \\\",\\\";\\n }\\n cout << endl;\\n }\\n\\n // get transition probability\\n float transitionProb = (1.0-modelInfo->termProb) *\\n modelInfo->transitionProbs[nextstate];\\n\\n probSum += transitionProb;\\n\\n if (POLICYDEBUG)\\n cout << \\\" prob: \\\" << transitionProb << endl;\\n\\n if (transitionProb < 0 || transitionProb > 1.0001){\\n cout << \\\"Error with transitionProb: \\\" << transitionProb << endl;\\n exit(-1);\\n }\\n\\n // if there is some probability of this transition\\n if (transitionProb > 0.0){\\n\\n float maxval = 0.0;\\n\\n // make sure its a real state\\n bool realState = true;\\n\\n for (unsigned b = 0; b < nextstate.size(); b++){\\n if (nextstate[b] < (featmin[b]-EPSILON)\\n || nextstate[b] > (featmax[b]+EPSILON)){\\n realState = false;\\n if (POLICYDEBUG)\\n cout << \\\" Next state is not valid (feature \\\"\\n << b << \\\" out of range)\\\" << endl;\\n break;\\n }\\n }\\n\\n state_t next;\\n \\n // update q values for any states within MAX_STEPS of visited states\\n if (info->stepsAway >= MAX_STEPS || !realState){\\n next = s;\\n } else {\\n next = canonicalize(nextstate);\\n }\\n \\n state_info* nextinfo = &(statedata[next]);\\n //nextinfo->fresh = false;\\n \\n int newSteps = info->stepsAway + 1;\\n if (newSteps < nextinfo->stepsAway){\\n if (POLICYDEBUG) {\\n cout << \\\" Setting state to \\\"\\n << newSteps << \\\" steps away.\\\" << endl;\\n }\\n nextinfo->stepsAway = newSteps;\\n }\\n \\n // find the max value of this next state\\n std::vector<float>::iterator maxAct =\\n std::max_element(nextinfo->Q.begin(),\\n nextinfo->Q.end());\\n maxval = *maxAct;\\n \\n nextstate.clear();\\n \\n if (POLICYDEBUG) cout << \\\" Max value: \\\" << maxval << endl;\\n\\n // update q value with this value\\n newQ += (gamma * transitionProb * maxval);\\n\\n } // transition probability > 0\\n\\n } // outcome loop\\n\\n\\n if (probSum < 0.9999 || probSum > 1.0001){\\n cout << \\\"Error: transition probabilities do not add to 1: Sum: \\\"\\n << probSum << endl;\\n exit(-1);\\n }\\n\\n\\n // set q value\\n float tdError = fabs(info->Q[act] - newQ);\\n if (POLICYDEBUG) cout << \\\" NewQ: \\\" << newQ\\n << \\\" OldQ: \\\" << info->Q[act] << endl;\\n info->Q[act] = newQ;\\n\\n // check max error\\n if (tdError > maxError)\\n maxError = tdError;\\n\\n if (POLICYDEBUG)\\n cout << \\\" TD error: \\\" << tdError\\n << \\\" Max error: \\\" << maxError << endl;\\n\\n } // action loop\\n\\n } // state loop\\n\\n } // while not converged loop\\n\\n if (false || nloops >= MAX_LOOPS){\\n cout << nactions << \\\" Policy creation ended with maxError: \\\" << maxError\\n << \\\" nloops: \\\" << nloops << \\\" time: \\\" << (getSeconds()-planTime)\\n << \\\" states: \\\" << statesUpdated\\n << endl;\\n }\\n\\n // remove unreachable states\\n removeUnreachableStates();\\n\\n if (POLICYDEBUG) cout << nactions\\n << \\\" policy creation complete: maxError: \\\"\\n << maxError << \\\" nloops: \\\" << nloops\\n << endl;\\n\\n\\n}\\n\\n\\nvoid ValueIteration::planOnNewModel(){\\n\\n // update model info\\n // can just update one for tabular model\\n if (modelType == RMAX){\\n updateStateActionFromModel(prevstate, prevact);\\n }\\n else {\\n updateStatesFromModel();\\n }\\n\\n // run value iteration\\n createPolicy();\\n\\n}\\n\\n\\n////////////////////////////\\n// Helper Functions //\\n////////////////////////////\\n\\nValueIteration::state_t ValueIteration::canonicalize(const std::vector<float> &s) {\\n if (PLANNERDEBUG) cout << \\\"canonicalize(s = \\\" << s[0] << \\\", \\\"\\n << s[1] << \\\")\\\" << endl;\\n\\n std::vector<float> s2;\\n if (statesPerDim[0] > 0){\\n s2 = discretizeState(s);\\n } else {\\n s2 = s;\\n }\\n\\n if (PLANNERDEBUG) cout << \\\"discretized(\\\" << s2[0] << \\\", \\\" << s2[1] << \\\")\\\" << endl;\\n\\n // get state_t for pointer if its in statespace\\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\\n statespace.insert(s2);\\n state_t retval = &*result.first; // Dereference iterator then get pointer\\n\\n if (PLANNERDEBUG) cout << \\\" returns \\\" << retval\\n << \\\" New: \\\" << result.second << endl;\\n\\n // if not, init this new state\\n if (result.second) { // s is new, so initialize Q(s,a) for all a\\n initNewState(retval);\\n if (PLANNERDEBUG) cout << \\\" New state initialized\\\" << endl;\\n }\\n\\n return retval;\\n}\\n\\n\\n\\n// init state info\\nvoid ValueIteration::initStateInfo(state_info* info){\\n if (PLANNERDEBUG) cout << \\\"initStateInfo()\\\";\\n\\n info->id = nstates++;\\n if (PLANNERDEBUG) cout << \\\" id = \\\" << info->id << endl;\\n\\n info->fresh = true;\\n info->stepsAway = 100000;\\n\\n // model data (transition, reward, known)\\n info->modelInfo = new StateActionInfo[numactions];\\n\\n // model q values, visit counts\\n info->visits.resize(numactions, 0);\\n info->Q.resize(numactions, 0);\\n\\n for (int i = 0; i < numactions; i++){\\n info->Q[i] = rng.uniform(0,0.01);\\n }\\n\\n if (PLANNERDEBUG) cout << \\\"done with initStateInfo()\\\" << endl;\\n\\n}\\n\\n\\nvoid ValueIteration::printStates(){\\n\\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n state_t s = canonicalize(*i);\\n\\n state_info* info = &(statedata[s]);\\n\\n cout << \\\"State \\\" << info->id << \\\": \\\";\\n for (unsigned j = 0; j < s->size(); j++){\\n cout << (*s)[j] << \\\", \\\";\\n }\\n cout << endl;\\n\\n for (int act = 0; act < numactions; act++){\\n cout << \\\" visits[\\\" << act << \\\"] = \\\" << info->visits[act]\\n << \\\" Q: \\\" << info->Q[act]\\n << \\\" R: \\\" << info->modelInfo[act].reward << endl;\\n }\\n\\n }\\n}\\n\\n\\n\\n\\nvoid ValueIteration::calculateReachableStates(){\\n // for all states\\n // set plausible flag to see if we need to calc q value for this state\\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n state_t s = canonicalize(*i);\\n\\n // get state's info\\n state_info* info = &(statedata[s]);\\n\\n info->stepsAway = 100000;\\n\\n //if (info->fresh){\\n // info->stepsAway = 0;\\n // continue;\\n //}\\n\\n for (int j = 0; j < numactions; j++){\\n if (info->visits[j] > 0){\\n info->stepsAway = 0;\\n break;\\n }\\n }\\n }\\n}\\n\\nvoid ValueIteration::removeUnreachableStates(){\\n\\n return;\\n\\n // for all states\\n // set plausible flag to see if we need to calc q value for this state\\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n state_t s = canonicalize(*i);\\n\\n // get state's info\\n state_info* info = &(statedata[s]);\\n\\n // state is unreachable\\n if (info->stepsAway > MAX_STEPS){\\n\\n if (POLICYDEBUG){\\n cout << \\\"Removing unreachable state: \\\" << (*s)[0];\\n for (unsigned i = 1; i < s->size(); i++){\\n cout << \\\", \\\" << (*s)[i];\\n }\\n cout << endl;\\n }\\n\\n // delete state\\n deleteInfo(info);\\n\\n // remove from statespace\\n statespace.erase(*s);\\n\\n // remove from statedata hashmap\\n statedata.erase(s);\\n\\n }\\n }\\n}\\n\\n\\n\\n\\n\\n\\nvoid ValueIteration::deleteInfo(state_info* info){\\n\\n delete [] info->modelInfo;\\n\\n}\\n\\n\\ndouble ValueIteration::getSeconds(){\\n struct timezone tz;\\n timeval timeT;\\n gettimeofday(&timeT, &tz);\\n return timeT.tv_sec + (timeT.tv_usec / 1000000.0);\\n}\\n\\n\\nvoid ValueIteration::savePolicy(const char* filename){\\n\\n ofstream policyFile(filename, ios::out | ios::binary | ios::trunc);\\n\\n // first part, save the vector size\\n int fsize = featmin.size();\\n policyFile.write((char*)&fsize, sizeof(int));\\n\\n // save numactions\\n policyFile.write((char*)&numactions, sizeof(int));\\n\\n // go through all states, and save Q values\\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n state_t s = canonicalize(*i);\\n state_info* info = &(statedata[s]);\\n\\n // save state\\n policyFile.write((char*)&((*i)[0]), sizeof(float)*fsize);\\n\\n // save q-values\\n policyFile.write((char*)&(info->Q[0]), sizeof(float)*numactions);\\n\\n }\\n\\n policyFile.close();\\n}\\n\\n\\n// should do it such that an already discretized state stays the same\\n// mainly the numerical value of each bin should be the average of that bin\\nstd::vector<float> ValueIteration::discretizeState(const std::vector<float> &s){\\n std::vector<float> ds(s.size());\\n\\n for (unsigned i = 0; i < s.size(); i++){\\n \\n // since i'm sometimes doing this for discrete domains\\n // want to center bins on 0, not edge on 0\\n //cout << \\\"feat \\\" << i << \\\" range: \\\" << featmax[i] << \\\" \\\" << featmin[i] << \\\" \\\" << (featmax[i]-featmin[i]) << \\\" n: \\\" << (float)statesPerDim;\\n\\n float factor = (featmax[i] - featmin[i]) / (float)statesPerDim[i];\\n int bin = 0;\\n if (s[i] > 0){\\n bin = (int)((s[i]+factor/2) / factor);\\n } else {\\n bin = (int)((s[i]-factor/2) / factor);\\n }\\n\\n ds[i] = factor*bin;\\n //cout << \\\" factor: \\\" << factor << \\\" bin: \\\" << bin;\\n //cout << \\\" Original: \\\" << s[i] << \\\" Discrete: \\\" << ds[i] << endl;\\n }\\n\\n return ds;\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7742573618888855,\n \"alphanum_fraction\": 0.7850959300994873,\n \"avg_line_length\": 45.371219635009766,\n \"blob_id\": \"841a176ec0389077fc42c28677181d8895aaf558\",\n \"content_id\": \"e680d3b454b3f7d7da39932c62411eaa31057565\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Makefile\",\n \"length_bytes\": 44471,\n \"license_type\": \"no_license\",\n \"max_line_length\": 202,\n \"num_lines\": 959,\n \"path\": \"/build/homework4/Makefile\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# CMAKE generated file: DO NOT EDIT!\\n# Generated by \\\"Unix Makefiles\\\" Generator, CMake Version 3.10\\n\\n# Default target executed when no arguments are given to make.\\ndefault_target: all\\n\\n.PHONY : default_target\\n\\n# Allow only one \\\"make -f 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actionlib_generate_messages_py\\\"\\n\\t@echo \\\"... install/local\\\"\\n\\t@echo \\\"... sensor_msgs_generate_messages_lisp\\\"\\n\\t@echo \\\"... actionlib_generate_messages_eus\\\"\\n\\t@echo \\\"... tf_generate_messages_py\\\"\\n\\t@echo \\\"... sensor_msgs_generate_messages_py\\\"\\n\\t@echo \\\"... rebuild_cache\\\"\\n\\t@echo \\\"... sensor_msgs_generate_messages_nodejs\\\"\\n\\t@echo \\\"... actionlib_generate_messages_cpp\\\"\\n\\t@echo \\\"... actionlib_generate_messages_lisp\\\"\\n\\t@echo \\\"... test\\\"\\n\\t@echo \\\"... homework4_gencfg\\\"\\n\\t@echo \\\"... actionlib_generate_messages_nodejs\\\"\\n\\t@echo \\\"... actionlib_msgs_generate_messages_eus\\\"\\n\\t@echo \\\"... actionlib_msgs_generate_messages_lisp\\\"\\n\\t@echo \\\"... actionlib_msgs_generate_messages_py\\\"\\n\\t@echo \\\"... tf2_msgs_generate_messages_cpp\\\"\\n\\t@echo \\\"... install/strip\\\"\\n\\t@echo \\\"... tf2_msgs_generate_messages_py\\\"\\n\\t@echo \\\"... 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\"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include <rl_env/energyrooms.hh>\\n\\n/*\\n EnergyRooms::EnergyRooms(Random &rand, const Gridworld *gridworld, bool stochastic):\\n grid(gridworld), goal(coord_t(2.,2.)), noisy(stochastic), rng(rand),\\n s(2),\\n ns(s[0]),\\n ew(s[1])\\n {\\n randomize_goal();\\n reset();\\n }\\n*/\\n\\nEnergyRooms::EnergyRooms(Random &rand, bool negReward):\\n grid(create_default_map()),\\n goal(coord_t(1.,10.)),\\n negReward(negReward),\\n noisy(false),\\n rng(rand),\\n doorway(coord_t(2.,4.)),\\n s(3),\\n ns(s[0]),\\n ew(s[1]),\\n energy(s[2]),\\n goalOption(false),\\n fuel(false)\\n{\\n reset();\\n //cout << *this << endl;\\n}\\n\\nEnergyRooms::EnergyRooms(Random &rand, bool stochastic, bool negReward,\\n bool goalOption):\\n grid(create_default_map()),\\n goal(coord_t(1.,10.)),\\n negReward(negReward),\\n noisy(stochastic),\\n rng(rand),\\n doorway(coord_t(2.,4.)),\\n s(3),\\n ns(s[0]),\\n ew(s[1]),\\n energy(s[2]),\\n goalOption(goalOption),\\n fuel(false)\\n{\\n reset();\\n}\\n\\nEnergyRooms::EnergyRooms(Random &rand, bool stochastic, bool negReward,\\n bool goalOption, bool fuel):\\n grid(create_default_map()),\\n goal(coord_t(1.,10.)),\\n negReward(negReward),\\n noisy(stochastic),\\n rng(rand),\\n doorway(coord_t(2.,4.)),\\n s(3),\\n ns(s[0]),\\n ew(s[1]),\\n energy(s[2]),\\n goalOption(goalOption),\\n fuel(fuel)\\n{\\n reset();\\n}\\n\\n\\n/*\\n EnergyRooms::EnergyRooms(Random &rand, unsigned width, unsigned height, bool stochastic):\\n grid(new Gridworld(height, width, rand)),\\n goal(coord_t(2.,2.)),\\n noisy(stochastic), rng(rand),\\n doorway(NULL),\\n s(2),\\n ns(s[0]),\\n ew(s[1])\\n {\\n randomize_goal();\\n reset();\\n }\\n*/\\n\\nEnergyRooms::~EnergyRooms() { delete grid; }\\n\\nconst std::vector<float> &EnergyRooms::sensation() const {\\n //cout << \\\"At state \\\" << s[0] << \\\", \\\" << s[1] << endl;\\n\\n return s;\\n}\\n\\nfloat EnergyRooms::apply(int action) {\\n\\n //cout << \\\"Taking action \\\" << static_cast<room_action_t>(action) << endl;\\n\\n // 80% of the time, lose one energy\\n if (!noisy || rng.bernoulli(0.8))\\n energy--;\\n\\n // many fuel squares, with varying amounts of fuel and reward\\n if (fuel){\\n if ((int)ns % 3 == 0 && (int)ew % 3 == 0){\\n energy += (int)(5 + ns/3 + (11-ew)/3);\\n }\\n if (energy > 20.0)\\n energy = 20.0;\\n }\\n else {\\n // certain squares reset you to 10\\n if (ns == 7 && ew == 3)\\n energy = 10;\\n if (ns == 7 && ew == 7)\\n energy = 10;\\n if (ns == 3 && ew == 3)\\n energy = 10;\\n if (ns == 7 && ew == 7)\\n energy = 10;\\n }\\n\\n // never go below 0\\n if (energy < 0.0)\\n energy = 0;\\n\\n\\n const room_action_t effect =\\n noisy\\n ? add_noise(static_cast<room_action_t>(action))\\n : static_cast<room_action_t>(action);\\n switch(effect) {\\n case NORTH:\\n if (!grid->wall(static_cast<unsigned>(ns),\\n static_cast<unsigned>(ew),\\n effect))\\n {\\n ++ns;\\n }\\n return reward();\\n case SOUTH:\\n if (!grid->wall(static_cast<unsigned>(ns),\\n static_cast<unsigned>(ew),\\n effect))\\n {\\n --ns;\\n }\\n return reward();\\n case EAST:\\n if (!grid->wall(static_cast<unsigned>(ns),\\n static_cast<unsigned>(ew),\\n effect))\\n {\\n ++ew;\\n }\\n return reward();\\n case WEST:\\n if (!grid->wall(static_cast<unsigned>(ns),\\n static_cast<unsigned>(ew),\\n effect))\\n {\\n --ew;\\n }\\n return reward();\\n }\\n\\n std::cerr << \\\"Unreachable point reached in EnergyRooms::apply!!!\\\\n\\\";\\n return 0; // unreachable, I hope\\n}\\n\\n\\nfloat EnergyRooms::reward() {\\n\\n if (negReward){\\n // normally -1 and 0 on goal\\n if (terminal())\\n return 0;\\n else if (energy <= 0.1){\\n if (fuel)\\n return -10;\\n else\\n return -2;\\n }\\n // normal square and normal energy\\n else{\\n // many squares of random cost\\n if (fuel){\\n if ((int)ns % 3 == 0 && (int)ew % 3 == 0)\\n return -2 + (int)(-ew/5 -((int)ns%4));\\n\\n else\\n return -1;\\n }\\n else\\n return -1;\\n }\\n\\n } // not negReward\\n else{\\n\\n // or we could do 0 and 1 on goal\\n if (terminal())\\n return 1;\\n else if (energy <= 0.1){\\n if (fuel)\\n return -10;\\n else\\n return -2;\\n }else\\n return 0;\\n }\\n}\\n\\n\\nbool EnergyRooms::terminal() const {\\n // current position equal to goal??\\n return coord_t(ns,ew) == goal;\\n}\\n\\n\\nvoid EnergyRooms::reset() {\\n // start randomly in upper left room (goal is lower right)\\n ns = rng.uniformDiscrete(6, grid->height()-1);\\n ew = rng.uniformDiscrete(0, 4);\\n energy = 10;\\n\\n // if fuel, start with random amount of fuel\\n if (fuel) {\\n energy = rng.uniformDiscrete(1, 20);\\n }\\n\\n //ns = 8;\\n //ew = 2;\\n\\n //ns = 4;\\n //ew = 9;\\n}\\n\\n\\nstd::vector<std::vector<float> > EnergyRooms::getSubgoals(){\\n\\n //cout << \\\"Getting room subgoals \\\" << endl;\\n\\n // Create vector of state representations, each is a subgoal\\n std::vector<std::vector<float> > subgoals;\\n\\n\\n std::vector<float> subgoal(2);\\n\\n // between two left rooms\\n subgoal[0] = 5;\\n subgoal[1] = 1;\\n subgoals.push_back(subgoal);\\n\\n // between two right rooms\\n subgoal[0] = 4;\\n subgoal[1] = 8;\\n subgoals.push_back(subgoal);\\n\\n // between two top rooms\\n subgoal[0] = 8;\\n subgoal[1] = 5;\\n subgoals.push_back(subgoal);\\n\\n // between two lower rooms\\n subgoal[0] = 1;\\n subgoal[1] = 5;\\n subgoals.push_back(subgoal);\\n\\n if (goalOption){\\n // actual goal\\n subgoal[0] = 1;\\n subgoal[1] = 10;\\n subgoals.push_back(subgoal);\\n }\\n\\n return subgoals;\\n\\n}\\n\\n\\nint EnergyRooms::getNumActions(){\\n return 4;\\n}\\n\\n\\nstd::ostream &operator<<(std::ostream &out, const EnergyRooms &rooms) {\\n out << \\\"Map:\\\\n\\\" << *rooms.grid;\\n\\n // print goal\\n out << \\\"Goal: row \\\" << rooms.goal.first\\n << \\\", column \\\" << rooms.goal.second << \\\"\\\\n\\\";\\n\\n // print doorway\\n out << \\\"Doorway: row \\\" << rooms.doorway.first\\n << \\\", column \\\" << rooms.doorway.second << \\\"\\\\n\\\";\\n\\n return out;\\n}\\n\\nconst Gridworld *EnergyRooms::create_default_map() {\\n int width = 11;\\n int height = 11;\\n std::vector<std::vector<bool> > nsv(width, std::vector<bool>(height-1,false));\\n std::vector<std::vector<bool> > ewv(height, std::vector<bool>(width-1,false));\\n\\n // put the vertical wall between the two rooms\\n for (int j = 0; j < height; j++){\\n // skip doorways at 1 and 8\\n if (j == 1 || j == 8)\\n continue;\\n ewv[j][4] = true;\\n ewv[j][5] = true;\\n }\\n\\n nsv[5][0] = true;\\n nsv[5][1] = true;\\n nsv[5][7] = true;\\n nsv[5][8] = true;\\n\\n // put the horizontal wall for the left room\\n for (int i = 0; i < 6; i++){\\n // skip doorway at 1\\n if (i == 1)\\n continue;\\n nsv[i][4] = true;\\n nsv[i][5] = true;\\n }\\n\\n ewv[5][0] = true;\\n ewv[5][1] = true;\\n\\n // put the horizontal wall for the right room\\n for (int i = 5; i < width; i++){\\n // skip doorway at 8\\n if (i == 8)\\n continue;\\n nsv[i][3] = true;\\n nsv[i][4] = true;\\n }\\n\\n ewv[4][7] = true;\\n ewv[4][8] = true;\\n\\n return new Gridworld(height, width, nsv, ewv);\\n}\\n\\nEnergyRooms::room_action_t EnergyRooms::add_noise(room_action_t action) {\\n switch(action) {\\n case NORTH:\\n case SOUTH:\\n return rng.bernoulli(0.8) ? action : (rng.bernoulli(0.5) ? EAST : WEST);\\n case EAST:\\n case WEST:\\n return rng.bernoulli(0.8) ? action : (rng.bernoulli(0.5) ? NORTH : SOUTH);\\n default:\\n return action;\\n }\\n}\\n\\n\\nvoid EnergyRooms::randomize_goal() {\\n const unsigned n = grid->height() * grid->width();\\n unsigned index = rng.uniformDiscrete(1,n) - 1;\\n goal = coord_t(index / grid->width(), index % grid->width());\\n}\\n\\n\\nvoid EnergyRooms::getMinMaxFeatures(std::vector<float> *minFeat,\\n std::vector<float> *maxFeat){\\n\\n minFeat->resize(s.size(), 0.0);\\n maxFeat->resize(s.size(), 10.0);\\n\\n (*maxFeat)[2] = 20.0;\\n\\n}\\n\\nvoid EnergyRooms::getMinMaxReward(float *minR,\\n float *maxR){\\n\\n *minR = -10.0;\\n *maxR = 1.0;\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6167809367179871,\n \"alphanum_fraction\": 0.6268656849861145,\n \"avg_line_length\": 22.580951690673828,\n \"blob_id\": \"cfe324d342edf7457aa92ff2b7aca683e094449b\",\n \"content_id\": \"8a05de0980eec228cf625940be46bd2d6711f542\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 2479,\n \"license_type\": \"no_license\",\n \"max_line_length\": 138,\n \"num_lines\": 105,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/MultipleModels.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include \\\"MultipleModels.hh\\\"\\n#include \\\"NeuralNetwork.hh\\\"\\n#include \\\"DecisionTree.hh\\\"\\n#include \\\"StochDecisionTree.hh\\\"\\n#include \\\"KNN.hh\\\"\\n\\n\\nconst bool MM_DEBUG = false; //true;\\n\\n\\nMultipleModels::MultipleModels(int id, int nIn, int nOut, int modeltype, Random rng):\\n id(id), nInput(nIn), nOutput(nOut), type(modeltype), rng(rng)\\n{\\n\\n \\n if (MM_DEBUG) \\n cout << \\\"nIn: \\\" << nInput << \\\" nOut: \\\" << nOutput << endl;\\n\\n // create a model for each output variable\\n createModels();\\n\\n if (MM_DEBUG)\\n cout << \\\"multiple models created.\\\" << endl;\\n\\n}\\n\\nMultipleModels::~MultipleModels() {}\\n\\n\\n\\nbool MultipleModels::trainInstance(std::vector<float> input, \\n\\t\\t\\t\\t std::vector<float> targetOutput){\\n if (MM_DEBUG) cout << \\\"Multimodel trainInstance()\\\" << endl;\\n bool modelChanged = false;\\n\\n // for each model\\n for (int i = 0; i < nOutput; i++){\\n\\n std::vector<float> output;\\n output.push_back(targetOutput[i]);\\n\\n // call train instance, with just its output variable\\n bool singleChange = models[i]->trainInstance(input, output);\\n if (singleChange)\\n modelChanged = true;\\n }\\n\\n return modelChanged;\\n\\n}\\n\\n\\n\\n// TODO: figure out how this will work. for each output feature, we'll actually want a vector with the probability of each possible value\\n// TODO: for now, just assume deterministic and return the only outcome\\nstd::vector<float> MultipleModels::testInstance(std::vector<float> input){\\n if (MM_DEBUG) cout << \\\"Multimodel testInstance()\\\" << endl;\\n\\n std::vector<float> outputs;\\n outputs.resize(nOutput);\\n\\n // call each model\\n for (int i = 0; i < nOutput; i++){\\n\\n // combine output variables into one vector\\n outputs[i] = models[i]->testInstance(input)[0];\\n\\n }\\n\\n return outputs;\\n\\n}\\n\\nvoid MultipleModels::createModels(){\\n if (MM_DEBUG) cout << \\\"createModels\\\" << endl;\\n\\n // create a model for each output variable\\n models.resize(nOutput);\\n\\n for (int i = 0; i < nOutput; i++){\\n\\n // NN\\n if (type == 0){\\n //models[i] = new NeuralNetwork(id*nOutput + i, nInput, \\n //\\t\\t\\t\\t 10, 3, 0.3, 0.7, rng);\\n }\\n\\n // DT\\n else if (type == 1){\\n if (MM_DEBUG) cout << \\\"Creating DT for output \\\" << i << endl;\\n models[i] = new DecisionTree(id*nOutput + i, 1, 1000, rng);\\n //models[i] = new StochDecisionTree(id*nOutput + i, 1, 1000, rng);\\n }\\n\\n // KNN\\n else {\\n if (MM_DEBUG) cout << \\\"Creating KNN for output \\\" << i \\n\\t\\t\\t << \\\" with k \\\" << id+1 << endl;\\n models[i] = new KNN(id*nOutput + i, id+2, rng);\\n\\n }\\n\\n\\n }\\n}\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7873753905296326,\n \"alphanum_fraction\": 0.7873753905296326,\n \"avg_line_length\": 49.16666793823242,\n \"blob_id\": \"4c36993ff777d3ce4e79aece77a18e85d54a1ff9\",\n \"content_id\": \"7c15fb3428ea3ad17db45e1d043d5929d34dbe0e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 301,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 6,\n \"path\": \"/build/ugv_course_launch/CTestTestfile.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# CMake generated Testfile for \\n# Source directory: /home/justin/ros_test/src/ugv_course_launch\\n# Build directory: /home/justin/ros_test/build/ugv_course_launch\\n# \\n# This file includes the relevant testing commands required for \\n# testing this directory and lists subdirectories to be tested as well.\\n\"\n },\n {\n \"alpha_fraction\": 0.755033552646637,\n \"alphanum_fraction\": 0.7651006579399109,\n \"avg_line_length\": 41.71428680419922,\n \"blob_id\": \"4f8316850ddc50d3874571545cd6a699f4117c6b\",\n \"content_id\": \"00343379606cc9d93e89af9a9c706858219a79a8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 298,\n \"license_type\": \"no_license\",\n \"max_line_length\": 49,\n \"num_lines\": 7,\n \"path\": \"/src/ugv_course_launch/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"ugv_course_launch\\\")\\nset(ugv_course_launch_MAINTAINER \\\"abc <abc@todo.todo>\\\")\\nset(ugv_course_launch_DEPRECATED \\\"\\\")\\nset(ugv_course_launch_VERSION \\\"0.0.0\\\")\\nset(ugv_course_launch_BUILD_DEPENDS )\\nset(ugv_course_launch_RUN_DEPENDS )\\nset(ugv_course_launch_BUILDTOOL_DEPENDS \\\"catkin\\\")\"\n },\n {\n \"alpha_fraction\": 0.6607614755630493,\n \"alphanum_fraction\": 0.6607614755630493,\n \"avg_line_length\": 23.71895408630371,\n \"blob_id\": \"d2bb4862772a99f0b0125da1e113932cf6479e09\",\n \"content_id\": \"64c835c0bafd6119e9c1108e5d1e23a8cdde7971\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 3782,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 153,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/PolicyIteration.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#ifndef _POLICYITERATION_HH_\\n#define _POLICYITERATION_HH_\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n\\n#include <set>\\n#include <vector>\\n#include <map>\\n\\n\\nclass PolicyIteration: public Planner {\\npublic:\\n\\n /** The implementation maps all sensations to a set of canonical\\n pointers, which serve as the internal representation of\\n environment state. */\\n typedef const std::vector<float> *state_t;\\n\\n\\n /** Standard constructor\\n \\\\param numactions, numactions in the domain\\n \\\\param gamma discount factor\\n \\\\param maxloops\\n \\\\param max time\\n \\\\param rng random\\n */\\n PolicyIteration(int numactions, float gamma,\\n int MAX_LOOPS, float MAX_TIME, int modelType,\\n const std::vector<float> &featmax, \\n const std::vector<float> &featmin,\\n const std::vector<int> &statesPerDim,\\n Random rng = Random());\\n\\n /** Unimplemented copy constructor: internal state cannot be simply\\n copied. */\\n PolicyIteration(const PolicyIteration &);\\n\\n virtual ~PolicyIteration();\\n\\n virtual void setModel(MDPModel* model);\\n virtual bool updateModelWithExperience(const std::vector<float> &last, \\n int act, \\n const std::vector<float> &curr, \\n float reward, bool term);\\n virtual void planOnNewModel();\\n virtual int getBestAction(const std::vector<float> &s);\\n virtual void savePolicy(const char* filename);\\n\\n bool PLANNERDEBUG;\\n bool POLICYDEBUG; //= false; //true;\\n bool MODELDEBUG;\\n bool ACTDEBUG;\\n\\n /** Model that we're using */\\n MDPModel* model;\\n\\n\\n\\nprotected:\\n\\n\\n struct state_info;\\n struct model_info;\\n\\n /** State info struct */\\n struct state_info {\\n int id;\\n\\n int stepsAway;\\n bool fresh;\\n\\n // experience data\\n std::vector<int> visits;\\n\\n // data filled in from models\\n StateActionInfo* modelInfo;\\n\\n //std::map<state_t, std::vector<float> > P;\\n //std::vector<float> R;\\n //std::vector<bool> known;\\n\\n // q values from policy creation\\n\\n float value;\\n int bestAction;\\n\\n };\\n\\n\\n\\n // various helper functions that we need\\n void initStateInfo(state_info* info);\\n \\n /** Produces a canonical representation of the given sensation.\\n \\\\param s The current sensation from the environment.\\n \\\\return A pointer to an equivalent state in statespace. */\\n state_t canonicalize(const std::vector<float> &s);\\n\\n // Operational functions\\n void deleteInfo(state_info* info);\\n void initNewState(state_t s);\\n void createPolicy();\\n void printStates();\\n void calculateReachableStates();\\n void removeUnreachableStates();\\n\\n // functions to update our models and get info from them\\n void updateStatesFromModel();\\n void updateStateActionFromModel(const std::vector<float> &state, int j);\\n\\n double getSeconds();\\n\\n // for policy iter\\n void policyEvaluation();\\n float getActionValue(state_t s, state_info* info, int act);\\n bool policyImprovement();\\n std::vector<float> discretizeState(const std::vector<float> &s);\\n\\nprivate:\\n\\n /** Set of all distinct sensations seen. Pointers to elements of\\n this set serve as the internal representation of the environment\\n state. */\\n std::set<std::vector<float> > statespace;\\n\\n /** Hashmap mapping state vectors to their state_info structs. */\\n std::map<state_t, state_info> statedata;\\n\\n std::vector<float> featmax;\\n std::vector<float> featmin;\\n\\n std::vector<float> prevstate;\\n int prevact;\\n\\n double planTime;\\n int nstates;\\n int nactions; \\n \\n int MAX_STEPS;\\n bool timingType;\\n\\n const int numactions;\\n const float gamma;\\n\\n const int MAX_LOOPS;\\n const float MAX_TIME;\\n const int modelType;\\n const std::vector<int> &statesPerDim;\\n\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.6837209463119507,\n \"alphanum_fraction\": 0.6837209463119507,\n \"avg_line_length\": 85,\n \"blob_id\": \"6cd22510bdc385ab3cf4c0c1f1280ee68ccff94d\",\n \"content_id\": \"7c3624e86d1ff63e685ecc3e032be5729f5a1993\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 430,\n \"license_type\": \"no_license\",\n \"max_line_length\": 201,\n \"num_lines\": 5,\n \"path\": \"/build/catkin_generated/order_packages.py\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# generated from catkin/cmake/template/order_packages.context.py.in\\nsource_root_dir = \\\"/home/justin/ros_test/src\\\"\\nwhitelisted_packages = \\\"\\\".split(';') if \\\"\\\" != \\\"\\\" else []\\nblacklisted_packages = \\\"\\\".split(';') if \\\"\\\" != \\\"\\\" else []\\nunderlay_workspaces = \\\"/home/justin/catkin_ws/devel;/home/justin/ros_test/devel;/opt/ros/melodic\\\".split(';') if \\\"/home/justin/catkin_ws/devel;/home/justin/ros_test/devel;/opt/ros/melodic\\\" != \\\"\\\" else []\\n\"\n },\n {\n \"alpha_fraction\": 0.6606461405754089,\n \"alphanum_fraction\": 0.6606461405754089,\n \"avg_line_length\": 24.03821563720703,\n \"blob_id\": \"66b8c1e0fd3a6abc75011934d41e0d539ac541e8\",\n \"content_id\": \"0b055ba257424d44b4d66e4bbf49c7a2819e19bd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 3931,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 157,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/PrioritizedSweeping.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#ifndef _PRIORITIZEDSWEEPING_HH_\\n#define _PRIORITIZEDSWEEPING_HH_\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n\\n#include <set>\\n#include <vector>\\n#include <map>\\n\\n\\nclass PrioritizedSweeping: public Planner {\\npublic:\\n\\n /** The implementation maps all sensations to a set of canonical\\n pointers, which serve as the internal representation of\\n environment state. */\\n typedef const std::vector<float> *state_t;\\n\\n\\n /** Standard constructor\\n \\\\param numactions, numactions in the domain\\n \\\\param gamma discount factor\\n \\\\param rng random\\n */\\n PrioritizedSweeping(int numactions, float gamma, float MAX_TIME,\\n bool onlyAddLastSA, int modelType,\\n const std::vector<float> &featmax, \\n const std::vector<float> &featmin,\\n Random rng = Random());\\n\\n /** Unimplemented copy constructor: internal state cannot be simply\\n copied. */\\n PrioritizedSweeping(const PrioritizedSweeping &);\\n\\n virtual ~PrioritizedSweeping();\\n\\n virtual void setModel(MDPModel* model);\\n virtual bool updateModelWithExperience(const std::vector<float> &last, \\n int act, \\n const std::vector<float> &curr, \\n float reward, bool term);\\n virtual void planOnNewModel();\\n virtual int getBestAction(const std::vector<float> &s);\\n\\n bool PLANNERDEBUG;\\n bool POLICYDEBUG; //= false; //true;\\n bool MODELDEBUG;\\n bool ACTDEBUG;\\n bool LISTDEBUG;\\n\\n /** Model that we're using */\\n MDPModel* model;\\n\\n\\n\\nprotected:\\n\\n\\n struct state_info;\\n struct model_info;\\n\\n struct saqPair {\\n std::vector<float> s;\\n int a;\\n float q;\\n };\\n\\n /** State info struct */\\n struct state_info {\\n int id;\\n\\n bool fresh;\\n\\n // experience data\\n std::vector<int> visits;\\n\\n // data filled in from models\\n StateActionInfo* modelInfo;\\n\\n //std::map<state_t, std::vector<float> > P;\\n //std::vector<float> R;\\n //std::vector<bool> known;\\n\\n // q values from policy creation\\n std::vector<float> Q;\\n \\n // which states lead to this state?\\n std::list<saqPair> pred;\\n std::vector<int> lastUpdate;\\n\\n };\\n\\n\\n\\n // various helper functions that we need\\n void initStateInfo(state_info* info);\\n \\n /** Produces a canonical representation of the given sensation.\\n \\\\param s The current sensation from the environment.\\n \\\\return A pointer to an equivalent state in statespace. */\\n state_t canonicalize(const std::vector<float> &s);\\n\\n // Operational functions\\n void deleteInfo(state_info* info);\\n void initNewState(state_t s);\\n void createPolicy();\\n void printStates();\\n\\n // functions to update our models and get info from them\\n void updateStatesFromModel();\\n\\n double getSeconds();\\n\\n // for prioritized sweeping\\n void updatePriorityList(state_info* info, const std::vector<float> &next);\\n bool saqPairMatch(saqPair a, saqPair b);\\n float updateQValues(const std::vector<float> &state, int act);\\n void addSAToList(const std::vector<float> &s, int act, float q);\\n void updateStateActionFromModel(const std::vector<float> &state, int a);\\n\\nprivate:\\n\\n /** Set of all distinct sensations seen. Pointers to elements of\\n this set serve as the internal representation of the environment\\n state. */\\n std::set<std::vector<float> > statespace;\\n\\n /** Hashmap mapping state vectors to their state_info structs. */\\n std::map<state_t, state_info> statedata;\\n\\n /** priority list for prioritized sweeping */\\n std::list< saqPair> priorityList;\\n\\n std::vector<float> featmax;\\n std::vector<float> featmin;\\n\\n std::vector<float> prevstate;\\n int prevact;\\n \\n double planTime;\\n int nstates;\\n int nactions; \\n int lastModelUpdate;\\n\\n int MAX_STEPS;\\n bool timingType;\\n\\n const int numactions;\\n const float gamma;\\n const float MAX_TIME;\\n const bool onlyAddLastSA;\\n const int modelType;\\n\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.7082785964012146,\n \"alphanum_fraction\": 0.7135348320007324,\n \"avg_line_length\": 46.625,\n \"blob_id\": \"145aa3e3baa4e35992769277b2a5e5b62c666696\",\n \"content_id\": \"dae4f113dca76867e6da1924c9e391ee06e7cb21\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 761,\n \"license_type\": \"no_license\",\n \"max_line_length\": 99,\n \"num_lines\": 16,\n \"path\": \"/build/rl-texplore-ros-pkg-master/src/rl_agent/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"rl_agent\\\")\\nset(rl_agent_VERSION \\\"0.0.0\\\")\\nset(rl_agent_MAINTAINER \\\"Todd Hester <todd.hester@gmail.com>\\\")\\nset(rl_agent_PACKAGE_FORMAT \\\"1\\\")\\nset(rl_agent_BUILD_DEPENDS \\\"roscpp\\\" \\\"rl_msgs\\\" \\\"rl_common\\\" \\\"tf\\\" \\\"std_msgs\\\")\\nset(rl_agent_BUILD_EXPORT_DEPENDS \\\"message_runtime\\\" \\\"roscpp\\\" \\\"rl_msgs\\\" \\\"tf\\\" \\\"rl_common\\\" \\\"std_msgs\\\")\\nset(rl_agent_BUILDTOOL_DEPENDS \\\"catkin\\\")\\nset(rl_agent_BUILDTOOL_EXPORT_DEPENDS )\\nset(rl_agent_EXEC_DEPENDS \\\"message_runtime\\\" \\\"roscpp\\\" \\\"rl_msgs\\\" \\\"tf\\\" \\\"rl_common\\\" \\\"std_msgs\\\")\\nset(rl_agent_RUN_DEPENDS \\\"message_runtime\\\" \\\"roscpp\\\" \\\"rl_msgs\\\" \\\"tf\\\" \\\"rl_common\\\" \\\"std_msgs\\\")\\nset(rl_agent_TEST_DEPENDS )\\nset(rl_agent_DOC_DEPENDS )\\nset(rl_agent_URL_WEBSITE \\\"\\\")\\nset(rl_agent_URL_BUGTRACKER \\\"\\\")\\nset(rl_agent_URL_REPOSITORY \\\"\\\")\\nset(rl_agent_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.6536796689033508,\n \"alphanum_fraction\": 0.6536796689033508,\n \"avg_line_length\": 27.875,\n \"blob_id\": \"cb461d47731eb47a3d77a9a1eba234f37730f728\",\n \"content_id\": \"f9e24f517592b5b715ff8a827d8cd00dab112bf4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 1155,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 40,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/include/rl_agent/DiscretizationAgent.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#ifndef _DISCAGENT_HH_\\n#define _DISCAGENT_HH_\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n\\n#include <vector>\\n\\n\\nclass DiscretizationAgent: public Agent {\\npublic:\\n\\n DiscretizationAgent(int statesPerDim, Agent* a, \\n std::vector<float> featmin, std::vector<float> featmax,\\n bool d);\\n DiscretizationAgent(std::vector<int> statesPerDim, Agent* a, \\n std::vector<float> featmin, std::vector<float> featmax,\\n bool d);\\n ~DiscretizationAgent();\\n \\n void initEverything(Agent* a, std::vector<float> fmin,\\n std::vector<float> fmax, bool d);\\n virtual int first_action(const std::vector<float> &s);\\n virtual int next_action(float r, const std::vector<float> &s);\\n virtual void last_action(float r);\\n virtual void setDebug(bool b);\\n virtual void seedExp(std::vector<experience> seeds);\\n virtual void savePolicy(const char* filename);\\n\\n \\n std::vector<float> discretizeState(const std::vector<float> &s);\\n std::vector<int> statesPerDim;\\n Agent* agent;\\n std::vector<float> featmin;\\n std::vector<float> featmax;\\n bool DEBUG;\\n\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.5563433766365051,\n \"alphanum_fraction\": 0.5607748627662659,\n \"avg_line_length\": 23.752351760864258,\n \"blob_id\": \"519c443e326d9c1e154525d2c44f6788d6098bc8\",\n \"content_id\": \"fb72ccdcfb44094b0e42d60e500d911e953ada8a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 7898,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 319,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Agent/QLearner.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include <rl_agent/QLearner.hh>\\n#include <algorithm>\\n\\nQLearner::QLearner(int numactions, float gamma,\\n float initialvalue, float alpha, float ep,\\n Random rng):\\n numactions(numactions), gamma(gamma),\\n initialvalue(initialvalue), alpha(alpha),\\n rng(rng), currentq(NULL)\\n{\\n\\n epsilon = ep;\\n ACTDEBUG = false; //true; //false;\\n\\n}\\n\\nQLearner::~QLearner() {}\\n\\nint QLearner::first_action(const std::vector<float> &s) {\\n\\n if (ACTDEBUG){\\n cout << \\\"First - in state: \\\";\\n printState(s);\\n cout << endl;\\n }\\n\\n // Get action values\\n std::vector<float> &Q_s = Q[canonicalize(s)];\\n\\n // Choose an action\\n const std::vector<float>::iterator a =\\n rng.uniform() < epsilon\\n ? Q_s.begin() + rng.uniformDiscrete(0, numactions - 1) // Choose randomly\\n : random_max_element(Q_s.begin(), Q_s.end()); // Choose maximum\\n\\n // Store location to update value later\\n currentq = &*a;\\n\\n if (ACTDEBUG){\\n cout << \\\" act: \\\" << (a-Q_s.begin()) << \\\" val: \\\" << *a << endl;\\n for (int iAct = 0; iAct < numactions; iAct++){\\n cout << \\\" Action: \\\" << iAct\\n << \\\" val: \\\" << Q_s[iAct] << endl;\\n }\\n cout << \\\"Took action \\\" << (a-Q_s.begin()) << \\\" from state \\\";\\n printState(s);\\n cout << endl;\\n }\\n\\n return a - Q_s.begin();\\n}\\n\\nint QLearner::next_action(float r, const std::vector<float> &s) {\\n\\n if (ACTDEBUG){\\n cout << \\\"Next: got reward \\\" << r << \\\" in state: \\\";\\n printState(s);\\n cout << endl;\\n }\\n\\n // Get action values\\n std::vector<float> &Q_s = Q[canonicalize(s)];\\n const std::vector<float>::iterator max =\\n random_max_element(Q_s.begin(), Q_s.end());\\n\\n // Update value of action just executed\\n *currentq += alpha * (r + gamma * (*max) - *currentq);\\n\\n // Choose an action\\n const std::vector<float>::iterator a =\\n rng.uniform() < epsilon\\n ? Q_s.begin() + rng.uniformDiscrete(0, numactions - 1)\\n : max;\\n\\n // Store location to update value later\\n currentq = &*a;\\n\\n if (ACTDEBUG){\\n cout << \\\" act: \\\" << (a-Q_s.begin()) << \\\" val: \\\" << *a << endl;\\n for (int iAct = 0; iAct < numactions; iAct++){\\n cout << \\\" Action: \\\" << iAct\\n << \\\" val: \\\" << Q_s[iAct] << endl;\\n }\\n cout << \\\"Took action \\\" << (a-Q_s.begin()) << \\\" from state \\\";\\n printState(s);\\n cout << endl;\\n }\\n\\n return a - Q_s.begin();\\n}\\n\\nvoid QLearner::last_action(float r) {\\n\\n if (ACTDEBUG){\\n cout << \\\"Last: got reward \\\" << r << endl;\\n }\\n\\n *currentq += alpha * (r - *currentq);\\n currentq = NULL;\\n}\\n\\nQLearner::state_t QLearner::canonicalize(const std::vector<float> &s) {\\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\\n statespace.insert(s);\\n state_t retval = &*result.first; // Dereference iterator then get pointer\\n if (result.second) { // s is new, so initialize Q(s,a) for all a\\n std::vector<float> &Q_s = Q[retval];\\n Q_s.resize(numactions,initialvalue);\\n }\\n return retval;\\n}\\n\\n\\n\\nstd::vector<float>::iterator\\nQLearner::random_max_element(\\n std::vector<float>::iterator start,\\n std::vector<float>::iterator end) {\\n\\n std::vector<float>::iterator max =\\n std::max_element(start, end);\\n int n = std::count(max, end, *max);\\n if (n > 1) {\\n n = rng.uniformDiscrete(1, n);\\n while (n > 1) {\\n max = std::find(max + 1, end, *max);\\n --n;\\n }\\n }\\n return max;\\n}\\n\\n\\n\\n\\nvoid QLearner::setDebug(bool d){\\n ACTDEBUG = d;\\n}\\n\\n\\nvoid QLearner::printState(const std::vector<float> &s){\\n for (unsigned j = 0; j < s.size(); j++){\\n cout << s[j] << \\\", \\\";\\n }\\n}\\n\\n\\n\\nvoid QLearner::seedExp(std::vector<experience> seeds){\\n\\n // for each seeding experience, update our model\\n for (unsigned i = 0; i < seeds.size(); i++){\\n experience e = seeds[i];\\n\\n std::vector<float> &Q_s = Q[canonicalize(e.s)];\\n std::vector<float> &Q_next = Q[canonicalize(e.next)];\\n\\n // get max value of next state\\n const std::vector<float>::iterator max =\\n random_max_element(Q_next.begin(), Q_next.end());\\n\\n // Get q value for action taken\\n const std::vector<float>::iterator a = Q_s.begin() + e.act;\\n currentq = &*a;\\n\\n // Update value of action just executed\\n *currentq += alpha * (e.reward + gamma * (*max) - *currentq);\\n\\n\\n /*\\n cout << \\\"Seeding with experience \\\" << i << endl;\\n cout << \\\"last: \\\" << (e.s)[0] << \\\", \\\" << (e.s)[1] << \\\", \\\"\\n << (e.s)[2] << endl;\\n cout << \\\"act: \\\" << e.act << \\\" r: \\\" << e.reward << endl;\\n cout << \\\"next: \\\" << (e.next)[0] << \\\", \\\" << (e.next)[1] << \\\", \\\"\\n << (e.next)[2] << \\\", \\\" << e.terminal << endl;\\n cout << \\\"Q: \\\" << *currentq << \\\" max: \\\" << *max << endl;\\n */\\n\\n }\\n\\n\\n}\\n\\nvoid QLearner::logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax){\\n std::vector<float> s;\\n s.resize(2, 0.0);\\n for (int i = xmin ; i < xmax; i++){\\n for (int j = ymin; j < ymax; j++){\\n s[0] = j;\\n s[1] = i;\\n std::vector<float> &Q_s = Q[canonicalize(s)];\\n const std::vector<float>::iterator max =\\n random_max_element(Q_s.begin(), Q_s.end());\\n *of << (*max) << \\\",\\\";\\n }\\n }\\n}\\n\\n\\nfloat QLearner::getValue(std::vector<float> state){\\n\\n state_t s = canonicalize(state);\\n\\n // Get Q values\\n std::vector<float> &Q_s = Q[s];\\n\\n // Choose an action\\n const std::vector<float>::iterator a =\\n random_max_element(Q_s.begin(), Q_s.end()); // Choose maximum\\n\\n // Get avg value\\n float valSum = 0.0;\\n float cnt = 0;\\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n state_t s = canonicalize(*i);\\n\\n // get state's info\\n std::vector<float> &Q_s = Q[s];\\n\\n for (int j = 0; j < numactions; j++){\\n valSum += Q_s[j];\\n cnt++;\\n }\\n }\\n\\n cout << \\\"Avg Value: \\\" << (valSum / cnt) << endl;\\n\\n return *a;\\n}\\n\\n\\nvoid QLearner::savePolicy(const char* filename){\\n\\n ofstream policyFile(filename, ios::out | ios::binary | ios::trunc);\\n\\n // first part, save the vector size\\n std::set< std::vector<float> >::iterator i = statespace.begin();\\n int fsize = (*i).size();\\n policyFile.write((char*)&fsize, sizeof(int));\\n\\n // save numactions\\n policyFile.write((char*)&numactions, sizeof(int));\\n\\n // go through all states, and save Q values\\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n state_t s = canonicalize(*i);\\n std::vector<float> *Q_s = &(Q[s]);\\n\\n // save state\\n policyFile.write((char*)&((*i)[0]), sizeof(float)*fsize);\\n\\n // save q-values\\n policyFile.write((char*)&((*Q_s)[0]), sizeof(float)*numactions);\\n\\n }\\n\\n policyFile.close();\\n}\\n\\n\\nvoid QLearner::loadPolicy(const char* filename){\\n bool LOADDEBUG = false;\\n\\n ifstream policyFile(filename, ios::in | ios::binary);\\n if (!policyFile.is_open())\\n return;\\n\\n // first part, save the vector size\\n int fsize;\\n policyFile.read((char*)&fsize, sizeof(int));\\n if (LOADDEBUG) cout << \\\"Numfeats loaded: \\\" << fsize << endl;\\n\\n // save numactions\\n int nact;\\n policyFile.read((char*)&nact, sizeof(int));\\n\\n if (nact != numactions){\\n cout << \\\"this policy is not valid loaded nact: \\\" << nact\\n << \\\" was told: \\\" << numactions << endl;\\n exit(-1);\\n }\\n\\n // go through all states, loading q values\\n while(!policyFile.eof()){\\n std::vector<float> state;\\n state.resize(fsize, 0.0);\\n\\n // load state\\n policyFile.read((char*)&(state[0]), sizeof(float)*fsize);\\n if (LOADDEBUG){\\n cout << \\\"load policy for state: \\\";\\n printState(state);\\n }\\n\\n state_t s = canonicalize(state);\\n std::vector<float> *Q_s = &(Q[s]);\\n\\n if (policyFile.eof()) break;\\n\\n // load q values\\n policyFile.read((char*)&((*Q_s)[0]), sizeof(float)*numactions);\\n\\n if (LOADDEBUG){\\n cout << \\\"Q values: \\\" << endl;\\n for (int iAct = 0; iAct < numactions; iAct++){\\n cout << \\\" Action: \\\" << iAct << \\\" val: \\\" << (*Q_s)[iAct] << endl;\\n }\\n }\\n }\\n\\n policyFile.close();\\n cout << \\\"Policy loaded!!!\\\" << endl;\\n //loaded = true;\\n}\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7370242476463318,\n \"alphanum_fraction\": 0.7474048733711243,\n \"avg_line_length\": 40.42856979370117,\n \"blob_id\": \"262e9904ea28ff03f7daf34421e5846ed3dc9a66\",\n \"content_id\": \"eb60c4f29be1f16bb28f588e6202e630fe70b686\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 289,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 7,\n \"path\": \"/src/mantis_model/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE 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\"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/ExplorationModel.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file ExplorationModel.hh\\n Defines the ExplorationModel class.\\n Reward bonuses based on the variance in model predictions are described in: Hester and Stone, \\\"Real Time Targeted Exploration in Large Domains\\\", ICDL 2010.\\n And intrinsic reward bonuses based on variance novelty as described in:\\n Hester and Stone, \\\"Intinrisically Motivated Model Learning for a Developing Curious Agent\\\", AAMAS ALA 2012.\\n \\\\author Todd Hester\\n*/\\n\\n#ifndef _EXPLOREMODEL_HH_\\n#define _EXPLOREMODEL_HH_\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n#include <vector>\\n#include <map>\\n#include <set>\\n\\n\\n/** This model wraps an another model and adds reward\\n bonuses based on model confidence, # of visits, or other metrics. */\\nclass ExplorationModel: public MDPModel {\\n\\npublic:\\n\\n /** Default contstructor\\n \\\\param model The underlying MDP Model being used.\\n \\\\param modelType the type of model being used.\\n \\\\param exploreType type of reward bonuses to be added on top of model\\n \\\\param predType the way in which ensemble models combine their models\\n \\\\param nModels # of models to use for ensemble models (i.e. random forests)\\n \\\\param m # of visits for a given state-action to be considered known\\n \\\\param numactions # of actions in the domain\\n \\\\param rmax maximum one-step reward in the domain\\n \\\\param qmax maximum possible q-value in a domain\\n \\\\param rrange range of one-step rewards in the domain\\n \\\\param nfactors # of state features in the domain\\n \\\\param b/v coefficient to determine magnitude of variance reward \\n \\\\param n coefficient to determine magnitude of novelty reward\\n \\\\param featmax the maximum value of each state feature\\n \\\\param featmin the minimum value of each state feature\\n \\\\param rng Random Number Generator\\n */\\n ExplorationModel(MDPModel * model, int modelType, \\n int exploreType, int predType, int nModels,\\n float m, int numactions, float rmax, \\n float qmax, float rrange, int nfactors, float v, float n,\\n const std::vector<float> &featmax, \\n const std::vector<float> &featmin, \\n Random rng); \\n\\n /** Copy constructor */\\n ExplorationModel(const ExplorationModel&);\\n\\n virtual ~ExplorationModel();\\n virtual ExplorationModel* getCopy();\\n\\n virtual bool updateWithExperiences(std::vector<experience> &instances);\\n virtual bool updateWithExperience(experience &e);\\n virtual float getStateActionInfo(const std::vector<float> &state, int act, StateActionInfo* retval);\\n\\n /** Add state to a set of visited states */\\n bool addStateToSet(const std::vector<float> &s);\\n\\n /** Check if the given state is in the set of visited states */\\n bool checkForState(const std::vector<float> &s);\\n\\n /** Find distance in feature space to nearest visited state-action */\\n float getFeatDistToVisitedSA(const std::vector<float> &s);\\n\\n\\n bool MODEL_DEBUG;\\n\\nprotected:\\n\\n\\n\\nprivate:\\n \\n /** Set of all distinct sensations seen. \\n This way we can know what we've visited. */\\n std::set<std::vector<float> > statespace;\\n\\n /** Underlying MDP model that we've wrapped and that we add bonus rewards onto. */\\n MDPModel* model;\\n\\n std::vector<float> featmax;\\n std::vector<float> featmin;\\n\\n int modelType; \\n int exploreType; \\n int predType;\\n int nModels;\\n float M; \\n int numactions; float rmax; float qmax; float rrange;\\n int nfactors; \\n const float v;\\n const float n;\\n\\n Random rng;\\n \\n};\\n\\n\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.7820512652397156,\n \"alphanum_fraction\": 0.7897436022758484,\n \"avg_line_length\": 64.16666412353516,\n \"blob_id\": \"b0a85ff62a77cf891b5bf1261bc776a3a6571f14\",\n \"content_id\": \"1cf3f8adba814379e227e153c737de5cf1db151e\",\n 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\"d0578ffd421d58f693e792d73a41a8e1301f7942\",\n \"content_id\": \"bca3a96902f77125d7e56935e42fa5fbb44d2019\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 2840,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 91,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/include/rl_agent/Sarsa.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\File\\n Interface for an implementation of the canonical Sarsa lambda\\n algorithm. */\\n\\n#ifndef _SARSA_HH_\\n#define _SARSA_HH_\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n\\n#include <map>\\n#include <set>\\n#include <vector>\\n\\n/** Agent that uses straight Sarsa Lambda, with no generalization and\\n epsilon-greedy exploration. */\\nclass Sarsa: public Agent {\\npublic:\\n /** Standard constructor\\n \\\\param numactions The number of possible actions\\n \\\\param gamma The discount factor\\n \\\\param initialvalue The initial value of each Q(s,a)\\n \\\\param alpha The learning rate\\n \\\\param epsilon The probability of taking a random action\\n \\\\param rng Initial state of the random number generator to use */\\n Sarsa(int numactions, float gamma,\\n float initialvalue, float alpha, float epsilon, float lambda, \\n Random rng = Random());\\n\\n /** Unimplemented copy constructor: internal state cannot be simply\\n copied. */\\n Sarsa(const Sarsa &);\\n\\n virtual ~Sarsa();\\n\\n virtual int first_action(const std::vector<float> &s);\\n virtual int next_action(float r, const std::vector<float> &s);\\n virtual void last_action(float r);\\n virtual void setDebug(bool d);\\n virtual void seedExp(std::vector<experience>);\\n virtual void savePolicy(const char* filename);\\n\\n void printState(const std::vector<float> &s);\\n float getValue(std::vector<float> state);\\n \\n std::vector<float>::iterator random_max_element(\\n\\t\\t\\t\\t\\t\\t std::vector<float>::iterator start,\\n\\t\\t\\t\\t\\t\\t std::vector<float>::iterator end);\\n\\n void logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax);\\n\\nprotected:\\n /** The implementation maps all sensations to a set of canonical\\n pointers, which serve as the internal representation of\\n environment state. */\\n typedef const std::vector<float> *state_t;\\n\\n /** Produces a canonical representation of the given sensation.\\n \\\\param s The current sensation from the environment.\\n \\\\return A pointer to an equivalent state in statespace. */\\n state_t canonicalize(const std::vector<float> &s);\\n\\nprivate:\\n /** Set of all distinct sensations seen. Pointers to elements of\\n this set serve as the internal representation of the environment\\n state. */\\n std::set<std::vector<float> > statespace;\\n\\n /** The primary data structure of the learning algorithm, the value\\n function Q. For state_t s and int a, Q[s][a] gives the\\n learned maximum expected future discounted reward conditional on\\n executing action a in state s. */\\n std::map<state_t, std::vector<float> > Q;\\n std::map<state_t, std::vector<float> > eligibility;\\n\\n const int numactions;\\n const float gamma;\\n\\n const float initialvalue;\\n const float alpha;\\n const float epsilon;\\n const float lambda;\\n\\n Random rng;\\n float *currentq;\\n\\n bool ACTDEBUG;\\n bool ELIGDEBUG;\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.7648809552192688,\n \"alphanum_fraction\": 0.7648809552192688,\n \"avg_line_length\": 32.599998474121094,\n \"blob_id\": \"6120e4166c1ec91d795c74fb66ee519d410d525c\",\n \"content_id\": \"3d3c8cb53d9e4edc0c26107897b04145628bec13\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 336,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 10,\n \"path\": \"/build/ugv_course_gazebo_plugins/CMakeFiles/gps_plugin.dir/cmake_clean.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"file(REMOVE_RECURSE\\n \\\"CMakeFiles/gps_plugin.dir/src/GpsPlugin.cpp.o\\\"\\n \\\"/home/justin/ros_test/devel/lib/libgps_plugin.pdb\\\"\\n \\\"/home/justin/ros_test/devel/lib/libgps_plugin.so\\\"\\n)\\n\\n# Per-language clean rules from dependency scanning.\\nforeach(lang CXX)\\n include(CMakeFiles/gps_plugin.dir/cmake_clean_${lang}.cmake OPTIONAL)\\nendforeach()\\n\"\n },\n {\n \"alpha_fraction\": 0.57459557056427,\n \"alphanum_fraction\": 0.5775913596153259,\n \"avg_line_length\": 25.492063522338867,\n \"blob_id\": \"a9a00a8edb6278bcf8134697d6f1a3ca0f1648c4\",\n \"content_id\": \"64a6bdb43f52534d51e955dcc2486b5e3e59fa58\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 3338,\n \"license_type\": \"no_license\",\n \"max_line_length\": 142,\n \"num_lines\": 126,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/MBS.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include \\\"MBS.hh\\\"\\n#include <algorithm>\\n\\n//#include <time.h>\\n#include <sys/time.h>\\n\\n\\nMBS::MBS(int numactions, float gamma,\\n int MAX_LOOPS, float MAX_TIME, int modelType,\\n const std::vector<float> &fmax, \\n const std::vector<float> &fmin, \\n const std::vector<int> &n, \\n const int k, Random newRng):\\n k(k)\\n{\\n \\n vi = new ValueIteration(numactions, gamma, MAX_LOOPS, MAX_TIME, modelType,\\n fmax, fmin, n, newRng);\\n DELAYDEBUG = false; //true;\\n seedMode = false;\\n\\n if (DELAYDEBUG) cout << \\\"MBS delay planner with k = \\\" << k << endl;\\n}\\n\\nMBS::~MBS() {\\n delete vi;\\n}\\n\\nvoid MBS::setModel(MDPModel* m){\\n vi->setModel(m);\\n model = m;\\n}\\n\\n\\n/** Use the latest experience to update state info and the model. */\\nbool MBS::updateModelWithExperience(const std::vector<float> &laststate,\\n int lastact,\\n const std::vector<float> &currstate,\\n float reward, bool term){\\n\\n if (seedMode) return false;\\n \\n // add this action to our history list\\n if (DELAYDEBUG) cout << \\\"add new action \\\" << lastact << \\\" to list\\\" << endl;\\n actHistory.push_back(lastact);\\n \\n if (actHistory.size() > k){\\n int effectiveAction = actHistory.front();\\n actHistory.pop_front();\\n \\n // if history size is >= k\\n // then we can add this experience\\n if (DELAYDEBUG){\\n cout << \\\"update with old act: \\\" << effectiveAction << endl;\\n cout << \\\"from: \\\" << laststate[0] << \\\", \\\" << laststate[1];\\n cout << \\\" to: \\\" << currstate[0] << \\\", \\\" << currstate[1];\\n cout << \\\" reward: \\\" << reward << \\\" term: \\\" << term << endl;\\n }\\n \\n\\n return vi->updateModelWithExperience(laststate, effectiveAction,\\n currstate, reward, term);\\n }\\n\\n return false;\\n\\n}\\n\\n\\n/** Choose the next action */\\nint MBS::getBestAction(const std::vector<float> &state){\\n std::vector<float> statePred = state;\\n\\n // figure out what state we think we're in\\n for (unsigned i = 0; i < actHistory.size(); i++){\\n if (DELAYDEBUG) cout << i << \\\" prediction: \\\" \\n << statePred[0] << \\\", \\\" << statePred[1] \\n << \\\" pred for act: \\\" << actHistory[i] << endl;\\n StateActionInfo prediction;\\n model->getStateActionInfo(statePred, actHistory[i], &prediction);\\n\\n // find most likely next state\\n std::vector<float> possibleNext;\\n float maxProb = -1;\\n for (std::map<std::vector<float>, float>::iterator it = prediction.transitionProbs.begin(); it != prediction.transitionProbs.end(); it++){\\n \\n float prob = (*it).second;\\n if (prob > maxProb){\\n possibleNext = (*it).first;\\n maxProb = prob;\\n }\\n }\\n statePred = possibleNext;\\n\\n }\\n if (DELAYDEBUG) cout << \\\"predict current state is \\\" << statePred[0] << \\\", \\\" << statePred[1] << endl;\\n \\n // call get best action for that state\\n int act = vi->getBestAction(statePred);\\n\\n if (DELAYDEBUG) cout << \\\"best action is \\\" << act << endl << endl;\\n\\n return act;\\n\\n}\\n\\n\\nvoid MBS::planOnNewModel(){\\n vi->planOnNewModel();\\n}\\n\\nvoid MBS::savePolicy(const char* filename){\\n vi->savePolicy(filename);\\n}\\n\\nvoid MBS::setSeeding(bool seeding){\\n\\n seedMode = seeding;\\n\\n}\\n\\n\\nvoid MBS::setFirst(){ \\n // first action, reset history vector\\n actHistory.clear();\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6966953277587891,\n \"alphanum_fraction\": 0.6996831297874451,\n \"avg_line_length\": 33.089508056640625,\n \"blob_id\": \"ed4d1a7c1887cc41114eaadb6866fe67cf07b606\",\n \"content_id\": \"2190557f3ffcf6799c6d8315d6b01f461075933f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 11046,\n \"license_type\": \"no_license\",\n \"max_line_length\": 345,\n \"num_lines\": 324,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/ParallelETUCT.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file ParallelETUCT.hh\\n Defines my real-time model-based RL architecture which uses UCT with eligiblity traces for planning. \\n The modified version of UCT used is presented in:\\n L. Kocsis and C. Szepesv´ari, \\\"Bandit based monte-carlo planning,\\\" in\\n ECML-06. Number 4212 in LNCS. Springer, 2006, pp. 282-293.\\n The real-time architecture is presented in: \\n Hester, Quinlan, and Stone, \\\"A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control\\\", arXiv 1105.1749, 2011.\\n \\\\author Todd Hester\\n*/\\n\\n#ifndef _ParallelETUCT_HH_\\n#define _ParallelETUCT_HH_\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n#include <rl_common/ExperienceFile.hh>\\n\\n#include \\\"../Models/FactoredModel.hh\\\"\\n#include \\\"../Models/C45Tree.hh\\\"\\n\\n#include <set>\\n#include <vector>\\n#include <map>\\n#include <sstream>\\n#include <deque>\\n\\n/** Parallel thread that continually does uct search from agent's current state. */\\nvoid* parallelSearchStart(void* arg);\\n\\n/** Thread that loops, continually updating model with new experiences. */\\nvoid* parallelModelLearningStart(void* arg);\\n\\n/** This class defines my real-time model-based RL architecture, which does model learning, planning, and acting on 3 separate threads. It uses a modified version of UCT for planning, which plans on a model using Monte Carlo rollouts. Unlike the original UCT, it does not separate values by tree depth, and it incorporates eligibility traces. */\\nclass ParallelETUCT: public Planner {\\npublic:\\n\\n\\n /** Standard constructor\\n \\\\param numactions, numactions in the domain\\n \\\\param gamma discount factor\\n \\\\param rrange range of one-step rewards in the domain\\n \\\\param lambda for use with eligibility traces\\n \\\\param MAX_ITER maximum number of MC rollouts to perform\\n \\\\param MAX_TIME maximum amount of time to run Monte Carlo rollouts\\n \\\\param MAX_DEPTH maximum depth to perform rollout to\\n \\\\param modelType specifies model type\\n \\\\param featmax maximum value of each feature\\n \\\\param featmin minimum value of each feature\\n \\\\param statesPerDim # of values to discretize each feature into\\n \\\\param trackActual track actual real-valued states (or just discrete states)\\n \\\\param historySize # of previous actions to use for delayed domains\\n \\\\param rng random number generator\\n */\\n ParallelETUCT(int numactions, float gamma, float rrange, float lambda,\\n int MAX_ITER, float MAX_TIME, int MAX_DEPTH, int modelType,\\n const std::vector<float> &featmax, const std::vector<float> &featmin,\\n const std::vector<int> &statesPerDim, bool trackActual, int historySize, Random rng = Random());\\n \\n /** Unimplemented copy constructor: internal state cannot be simply\\n copied. */\\n ParallelETUCT(const ParallelETUCT &);\\n\\n virtual ~ParallelETUCT();\\n\\n virtual void setModel(MDPModel* model);\\n virtual bool updateModelWithExperience(const std::vector<float> &last, \\n int act, \\n const std::vector<float> &curr, \\n float reward, bool term);\\n virtual void planOnNewModel();\\n virtual int getBestAction(const std::vector<float> &s);\\n\\n virtual void setSeeding(bool seed);\\n virtual void setFirst();\\n\\n bool PLANNERDEBUG;\\n bool POLICYDEBUG; //= false; //true;\\n bool MODELDEBUG;\\n bool ACTDEBUG;\\n bool UCTDEBUG;\\n bool PTHREADDEBUG;\\n bool ATHREADDEBUG;\\n bool MTHREADDEBUG;\\n bool TIMINGDEBUG;\\n bool REALSTATEDEBUG;\\n bool HISTORYDEBUG;\\n\\n /** MDPModel that we're using with planning */\\n MDPModel* model;\\n\\n /** Copy of our model used when updating the model, so the original can still be queried by the other threads. */\\n MDPModel* modelcopy;\\n\\n /** The implementation maps all sensations to a set of canonical\\n pointers, which serve as the internal representation of\\n environment state. */\\n typedef const std::vector<float> *state_t;\\n\\n ///////////////////////////////\\n // parallel stuff\\n ///////////////////////////////\\n\\n // tell if thread started\\n bool modelThreadStarted;\\n bool planThreadStarted;\\n\\n /** Thread that performs planning using UCT. */\\n pthread_t planThread;\\n\\n /** Thread that performs model updates. */\\n pthread_t modelThread;\\n\\n // some variables that are locked\\n /** List of experiences from action thread to be added into model */\\n std::vector<experience> expList;\\n\\n /** Agent's current discrete state that UCT rollouts should start from. */\\n state_t discPlanState;\\n \\n /** Agent's current actual real-valued state that UCT rollouts should start from. */\\n std::vector<float> actualPlanState;\\n\\n /** Canonical pointer to agent's current state that UCT rollouts should start from. */\\n state_t startState;\\n\\n // lock over simple objects\\n /** Mutex around the last frame that the model was updated. */\\n pthread_mutex_t update_mutex;\\n /** Mutex around the agent's current state to plan from. */\\n pthread_mutex_t plan_state_mutex;\\n /** Mutex around the model. */\\n pthread_mutex_t model_mutex;\\n /** Mutex around the list of experiences to be added to the model. */\\n pthread_mutex_t list_mutex;\\n /** Mutex around the history of previous actions of the agent. */\\n pthread_mutex_t history_mutex;\\n /** Mutex around the counter of how many actions the agent has taken. */\\n pthread_mutex_t nactions_mutex;\\n\\n /** Mutex around the set of states. */\\n pthread_mutex_t statespace_mutex;\\n\\n // condition for when list is updated\\n pthread_cond_t list_cond; \\n\\n /** Perform UCT/Monte Carlo rollout from the given state.\\n If terminal or at depth, return some reward.\\n Otherwise, select an action based on UCB.\\n Simulate action to get reward and next state.\\n Call search on next state at depth+1 to get reward return from there on.\\n Update q value towards new value: reward + gamma * searchReturn\\n Update visit counts for confidence bounds\\n Return q\\n \\n From \\\"Bandit Based Monte Carlo Planning\\\" by Kocsis and Szepesv´ari.\\n */\\n float uctSearch(const std::vector<float> &actS, state_t state, int depth, std::deque<float> &history);\\n\\n /** Select a random previously visited state. */\\n std::vector<float> selectRandomState();\\n\\n /** Start the parallel model learning thread. */\\n void parallelModelLearning();\\n\\n /** Start the parallel UCT planning thread. */\\n void parallelSearch();\\n\\n /** Load a policy from a file. */\\n void loadPolicy(const char* filename);\\n\\n /** Output value function to a file */\\n void logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax);\\n\\n /** Add two vectors together. */\\n std::vector<float> addVec(const std::vector<float> &a, const std::vector<float> &b);\\n\\n /** Subtract two vectors. */\\n std::vector<float> subVec(const std::vector<float> &a, const std::vector<float> &b);\\n\\nprotected:\\n\\n\\n struct state_info;\\n struct model_info;\\n\\n /** A struct that contains a vector of possible next state samples, weighted by their probabilities. */\\n struct state_samples {\\n std::vector<state_t> samples;\\n };\\n\\n /** State info struct. Maintains visit counts, models, and q-values for state-actions. */\\n struct state_info {\\n\\n // data filled in from models\\n std::map< std::deque<float>, StateActionInfo>* historyModel;\\n\\n // q values from policy creation\\n std::vector<float> Q;\\n\\n // uct experience data\\n int uctVisits;\\n std::vector<int> uctActions;\\n short unsigned int visited;\\n short unsigned int id;\\n\\n // needs update\\n bool needsUpdate;\\n\\n // mutex for model info, samples, everything else\\n pthread_mutex_t statemodel_mutex;\\n pthread_mutex_t stateinfo_mutex;\\n\\n };\\n\\n\\n\\n /** Initialize state info struct */\\n void initStateInfo(state_t s,state_info* info, int id);\\n \\n /** Produces a canonical representation of the given sensation.\\n \\\\param s The current sensation from the environment.\\n \\\\return A pointer to an equivalent state in statespace. */\\n state_t canonicalize(const std::vector<float> &s);\\n\\n /** Delete a state_info struct */\\n void deleteInfo(state_info* info);\\n \\n /** Compuate a policy from a model */\\n void createPolicy();\\n \\n /** Print information for each state. */\\n void printStates();\\n \\n /** Calculate which states are reachable from states the agent has actually visited. */\\n void calculateReachableStates();\\n \\n /** Remove states from set that were deemed unreachable. */\\n void removeUnreachableStates();\\n\\n /** Update the state_info copy of the model for the given state-action from the MDPModel */\\n void updateStateActionFromModel(state_t s, int a, state_info* info);\\n \\n /** Update the state_info copy of the model for the given state-action and k-action history from the MDPModel. */\\n void updateStateActionHistoryFromModel(const std::vector<float> modState, int a, StateActionInfo *newModel);\\n\\n /** Get the current time in seconds */\\n double getSeconds();\\n\\n // uct stuff\\n /** Reset UCT visit counts to some baseline level (to decrease our confidence in q-values because model has changed. */\\n void resetAndUpdateStateActions();\\n \\n /** Return a sampled state from the next state distribution of the model. \\n Simulate the next state from the given state, action, and possibly history of past actions. */\\n std::vector<float> simulateNextState(const std::vector<float> &actS, state_t state, state_info* info, const std::deque<float> &history, int action, float* reward, bool* term);\\n \\n /** Select UCT action based on UCB1 algorithm. */\\n int selectUCTAction(state_info* info);\\n \\n /** Canonicalize all the next states predicted by this model. */\\n void canonNextStates(StateActionInfo* modelInfo);\\n\\n /** Initialize the states for this domain (based on featmin and featmax) */\\n void initStates();\\n \\n /** Fill in a state based on featmin and featmax */\\n void fillInState(std::vector<float>s, int depth);\\n\\n virtual void savePolicy(const char* filename);\\n \\n /** Return a discretized version of the input state. */\\n std::vector<float> discretizeState(const std::vector<float> &s);\\n\\nprivate:\\n\\n /** Set of all distinct sensations seen. Pointers to elements of\\n this set serve as the internal representation of the environment\\n state. */\\n std::set<std::vector<float> > statespace;\\n\\n /** Hashmap mapping state vectors to their state_info structs. */\\n std::map<state_t, state_info> statedata;\\n\\n std::vector<float> featmax;\\n std::vector<float> featmin;\\n\\n /** Current history of previous actions. */\\n std::deque<float> saHistory;\\n\\n state_t prevstate;\\n int prevact;\\n state_info* previnfo;\\n\\n double planTime;\\n double initTime;\\n double setTime;\\n bool seedMode;\\n\\n int nstates;\\n int nsaved;\\n int nactions;\\n int lastUpdate;\\n\\n bool timingType;\\n\\n const int numactions;\\n const float gamma;\\n const float rrange;\\n const float lambda;\\n\\n const int MAX_ITER;\\n const float MAX_TIME;\\n const int MAX_DEPTH;\\n const int modelType;\\n const std::vector<int> &statesPerDim;\\n const bool trackActual;\\n const int HISTORY_SIZE;\\n const int HISTORY_FL_SIZE;\\n\\n const unsigned CLEAR_SIZE;\\n ExperienceFile expfile;\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.6664590835571289,\n \"alphanum_fraction\": 0.6772947907447815,\n \"avg_line_length\": 28.302919387817383,\n \"blob_id\": \"dbcd0028e3d7daaf31dfe877586eb43391a59103\",\n \"content_id\": \"0e7aada902d45bd77880e42561ee4c2103727513\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 8029,\n \"license_type\": \"no_license\",\n \"max_line_length\": 441,\n \"num_lines\": 274,\n \"path\": \"/devel/include/rl_msgs/RLEnvDescription.h\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Generated by gencpp from file rl_msgs/RLEnvDescription.msg\\n// DO NOT EDIT!\\n\\n\\n#ifndef RL_MSGS_MESSAGE_RLENVDESCRIPTION_H\\n#define RL_MSGS_MESSAGE_RLENVDESCRIPTION_H\\n\\n\\n#include <string>\\n#include <vector>\\n#include <map>\\n\\n#include <ros/types.h>\\n#include <ros/serialization.h>\\n#include <ros/builtin_message_traits.h>\\n#include <ros/message_operations.h>\\n\\n\\nnamespace rl_msgs\\n{\\ntemplate <class ContainerAllocator>\\nstruct RLEnvDescription_\\n{\\n typedef RLEnvDescription_<ContainerAllocator> Type;\\n\\n RLEnvDescription_()\\n : num_actions(0.0)\\n , num_states(0.0)\\n , min_state_range()\\n , max_state_range()\\n , max_reward(0.0)\\n , reward_range(0.0)\\n , stochastic(false)\\n , episodic(false)\\n , title() {\\n }\\n RLEnvDescription_(const ContainerAllocator& _alloc)\\n : num_actions(0.0)\\n , num_states(0.0)\\n , min_state_range(_alloc)\\n , max_state_range(_alloc)\\n , max_reward(0.0)\\n , reward_range(0.0)\\n , stochastic(false)\\n , episodic(false)\\n , title(_alloc) {\\n (void)_alloc;\\n }\\n\\n\\n\\n typedef float _num_actions_type;\\n _num_actions_type num_actions;\\n\\n typedef float _num_states_type;\\n _num_states_type num_states;\\n\\n typedef std::vector<float, typename ContainerAllocator::template rebind<float>::other > _min_state_range_type;\\n _min_state_range_type min_state_range;\\n\\n typedef std::vector<float, typename ContainerAllocator::template rebind<float>::other > _max_state_range_type;\\n _max_state_range_type max_state_range;\\n\\n typedef float _max_reward_type;\\n _max_reward_type max_reward;\\n\\n typedef float _reward_range_type;\\n _reward_range_type reward_range;\\n\\n typedef uint8_t _stochastic_type;\\n _stochastic_type stochastic;\\n\\n typedef uint8_t _episodic_type;\\n _episodic_type episodic;\\n\\n typedef std::basic_string<char, std::char_traits<char>, typename ContainerAllocator::template rebind<char>::other > _title_type;\\n _title_type title;\\n\\n\\n\\n\\n\\n typedef boost::shared_ptr< ::rl_msgs::RLEnvDescription_<ContainerAllocator> > Ptr;\\n typedef boost::shared_ptr< ::rl_msgs::RLEnvDescription_<ContainerAllocator> const> ConstPtr;\\n\\n}; // struct RLEnvDescription_\\n\\ntypedef ::rl_msgs::RLEnvDescription_<std::allocator<void> > RLEnvDescription;\\n\\ntypedef boost::shared_ptr< ::rl_msgs::RLEnvDescription > RLEnvDescriptionPtr;\\ntypedef boost::shared_ptr< ::rl_msgs::RLEnvDescription const> RLEnvDescriptionConstPtr;\\n\\n// constants requiring out of line definition\\n\\n\\n\\ntemplate<typename ContainerAllocator>\\nstd::ostream& operator<<(std::ostream& s, const ::rl_msgs::RLEnvDescription_<ContainerAllocator> & v)\\n{\\nros::message_operations::Printer< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >::stream(s, \\\"\\\", v);\\nreturn s;\\n}\\n\\n} // namespace rl_msgs\\n\\nnamespace ros\\n{\\nnamespace message_traits\\n{\\n\\n\\n\\n// BOOLTRAITS {'IsFixedSize': False, 'IsMessage': True, 'HasHeader': False}\\n// {'rl_msgs': ['/home/justin/ros_test/src/rl-texplore-ros-pkg-master/src/rl_msgs/msg'], 'std_msgs': ['/opt/ros/melodic/share/std_msgs/cmake/../msg']}\\n\\n// !!!!!!!!!!! ['__class__', '__delattr__', '__dict__', '__doc__', '__eq__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_parsed_fields', 'constants', 'fields', 'full_name', 'has_header', 'header_present', 'names', 'package', 'parsed_fields', 'short_name', 'text', 'types']\\n\\n\\n\\n\\ntemplate <class ContainerAllocator>\\nstruct IsFixedSize< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\\n : FalseType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct IsFixedSize< ::rl_msgs::RLEnvDescription_<ContainerAllocator> const>\\n : FalseType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct IsMessage< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\\n : TrueType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct IsMessage< ::rl_msgs::RLEnvDescription_<ContainerAllocator> const>\\n : TrueType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct HasHeader< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\\n : FalseType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct HasHeader< ::rl_msgs::RLEnvDescription_<ContainerAllocator> const>\\n : FalseType\\n { };\\n\\n\\ntemplate<class ContainerAllocator>\\nstruct MD5Sum< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\\n{\\n static const char* value()\\n {\\n return \\\"41ee0d621b8031a958ff6d1f587a3860\\\";\\n }\\n\\n static const char* value(const ::rl_msgs::RLEnvDescription_<ContainerAllocator>&) { return value(); }\\n static const uint64_t static_value1 = 0x41ee0d621b8031a9ULL;\\n static const uint64_t static_value2 = 0x58ff6d1f587a3860ULL;\\n};\\n\\ntemplate<class ContainerAllocator>\\nstruct DataType< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\\n{\\n static const char* value()\\n {\\n return \\\"rl_msgs/RLEnvDescription\\\";\\n }\\n\\n static const char* value(const ::rl_msgs::RLEnvDescription_<ContainerAllocator>&) { return value(); }\\n};\\n\\ntemplate<class ContainerAllocator>\\nstruct Definition< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\\n{\\n static const char* value()\\n {\\n return \\\"# Message that contains details about an RL enviroment/task\\\\n\\\\\\nfloat32 num_actions\\\\n\\\\\\nfloat32 num_states\\\\n\\\\\\n\\\\n\\\\\\n#Optional information to describe the range of a continous state\\\\n\\\\\\n#Some RL algorithms may need this to discretize the state space\\\\n\\\\\\nfloat32[] min_state_range\\\\n\\\\\\nfloat32[] max_state_range\\\\n\\\\\\n\\\\n\\\\\\n#Info needed for r-max some other methods\\\\n\\\\\\nfloat32 max_reward\\\\n\\\\\\nfloat32 reward_range\\\\n\\\\\\n\\\\n\\\\\\nbool stochastic\\\\n\\\\\\nbool episodic\\\\n\\\\\\nstring title\\\\n\\\\\\n\\\";\\n }\\n\\n static const char* value(const ::rl_msgs::RLEnvDescription_<ContainerAllocator>&) { return value(); }\\n};\\n\\n} // namespace message_traits\\n} // namespace ros\\n\\nnamespace ros\\n{\\nnamespace serialization\\n{\\n\\n template<class ContainerAllocator> struct Serializer< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\\n {\\n template<typename Stream, typename T> inline static void allInOne(Stream& stream, T m)\\n {\\n stream.next(m.num_actions);\\n stream.next(m.num_states);\\n stream.next(m.min_state_range);\\n stream.next(m.max_state_range);\\n stream.next(m.max_reward);\\n stream.next(m.reward_range);\\n stream.next(m.stochastic);\\n stream.next(m.episodic);\\n stream.next(m.title);\\n }\\n\\n ROS_DECLARE_ALLINONE_SERIALIZER\\n }; // struct RLEnvDescription_\\n\\n} // namespace serialization\\n} // namespace ros\\n\\nnamespace ros\\n{\\nnamespace message_operations\\n{\\n\\ntemplate<class ContainerAllocator>\\nstruct Printer< ::rl_msgs::RLEnvDescription_<ContainerAllocator> >\\n{\\n template<typename Stream> static void stream(Stream& s, const std::string& indent, const ::rl_msgs::RLEnvDescription_<ContainerAllocator>& v)\\n {\\n s << indent << \\\"num_actions: \\\";\\n Printer<float>::stream(s, indent + \\\" \\\", v.num_actions);\\n s << indent << \\\"num_states: \\\";\\n Printer<float>::stream(s, indent + \\\" \\\", v.num_states);\\n s << indent << \\\"min_state_range[]\\\" << std::endl;\\n for (size_t i = 0; i < v.min_state_range.size(); ++i)\\n {\\n s << indent << \\\" min_state_range[\\\" << i << \\\"]: \\\";\\n Printer<float>::stream(s, indent + \\\" \\\", v.min_state_range[i]);\\n }\\n s << indent << \\\"max_state_range[]\\\" << std::endl;\\n for (size_t i = 0; i < v.max_state_range.size(); ++i)\\n {\\n s << indent << \\\" max_state_range[\\\" << i << \\\"]: \\\";\\n Printer<float>::stream(s, indent + \\\" \\\", v.max_state_range[i]);\\n }\\n s << indent << \\\"max_reward: \\\";\\n Printer<float>::stream(s, indent + \\\" \\\", v.max_reward);\\n s << indent << \\\"reward_range: \\\";\\n Printer<float>::stream(s, indent + \\\" \\\", v.reward_range);\\n s << indent << \\\"stochastic: \\\";\\n Printer<uint8_t>::stream(s, indent + \\\" \\\", v.stochastic);\\n s << indent << \\\"episodic: \\\";\\n Printer<uint8_t>::stream(s, indent + \\\" \\\", v.episodic);\\n s << indent << \\\"title: \\\";\\n Printer<std::basic_string<char, std::char_traits<char>, typename ContainerAllocator::template rebind<char>::other > >::stream(s, indent + \\\" \\\", v.title);\\n }\\n};\\n\\n} // namespace message_operations\\n} // namespace ros\\n\\n#endif // RL_MSGS_MESSAGE_RLENVDESCRIPTION_H\\n\"\n },\n {\n \"alpha_fraction\": 0.699404776096344,\n \"alphanum_fraction\": 0.699404776096344,\n \"avg_line_length\": 24.627119064331055,\n \"blob_id\": \"fff286fa5a74767a9e0516e8aebc92df60e3addd\",\n \"content_id\": \"680b0953284cb08534df0ddf4fa43eb944b51307\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 3024,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 118,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/include/rl_env/fourrooms.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#ifndef _FOURROOMS_H_\\n#define _FOURROOMS_H_\\n\\n#include <set>\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n#include \\\"gridworld.hh\\\"\\n\\n/*\\ninline ostream &operator<<(ostream &out, const room_action_t &a) {\\n switch(a) {\\n case NORTH: return out << \\\"NORTH\\\";\\n case SOUTH: return out << \\\"SOUTH\\\";\\n case EAST: return out << \\\"EAST\\\";\\n case WEST: return out << \\\"WEST\\\";\\n }\\n return out;\\n}\\n*/\\n\\nclass FourRooms: public Environment {\\npublic:\\n /** Creates a FourRooms domain using the specified map.\\n \\\\param rand Random number generator to use.\\n \\\\param gridworld The map to use.\\n \\\\param stochastic Whether to use nondeterministic actions. */\\n FourRooms(Random &rand, const Gridworld *gridworld, bool stochastic);\\n\\n /** Creates a deterministic FourRooms domain.\\n \\\\param rand Random number generator used solely for random\\n initial states. \\n \\\\param negReward Whether negative rewards are on/off.\\n */\\n FourRooms(Random &rand);\\n\\n /** Creates a possibly noisy FourRooms domain. */\\n FourRooms(Random &rand, bool stochastic, bool negReward, bool exReward);\\n\\n /** Creates a possibly noisy FourRooms domain with distances to walls. */\\n FourRooms(Random &rand, bool stochastic, bool negReward);\\n\\n /** Creates a Four Rooms domain with distance and reward distances */\\n FourRooms(Random &rand, bool stochastic);\\n\\n /** Creates a random FourRooms domain of the given size. */\\n FourRooms(Random &rand, unsigned width, unsigned height, bool stochastic);\\n\\n virtual ~FourRooms();\\n\\n virtual const std::vector<float> &sensation() const;\\n virtual float apply(int action);\\n\\n virtual bool terminal() const;\\n virtual void reset();\\n\\n virtual int getNumActions();\\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\\n virtual void getMinMaxReward(float* minR, float* maxR);\\n\\n const Gridworld &gridworld() const { return *grid; }\\n\\n friend std::ostream &operator<<(std::ostream &out, const FourRooms &rooms);\\n\\n std::vector<std::vector<float> > getSubgoals();\\n void calcWallDistances();\\n\\n void setSensation(std::vector<float> newS);\\n\\nprotected:\\n typedef std::pair<float,float> coord_t;\\n enum room_action_t {NORTH, SOUTH, EAST, WEST};\\n\\nprivate:\\n const Gridworld *const grid;\\n coord_t goal;\\n\\n const bool negReward;\\n const bool noisy;\\n const bool extraReward;\\n const bool rewardSensor;\\n\\n Random &rng;\\n\\n coord_t doorway;\\n\\n std::vector<float> s;\\n std::vector<float> unused;\\n\\n float &ns;\\n float &ew;\\n\\n float &distN;\\n float &distS;\\n float &distE;\\n float &distW;\\n\\n float &rewardEW;\\n float &rewardNS;\\n\\n const bool goalOption;\\n\\n const Gridworld *create_default_map();\\n\\n /** Corrupts a movement action.\\n \\\\param action The intended action\\n \\\\return The action actually executed */\\n room_action_t add_noise(room_action_t action);\\n\\n /** Randomly assigns the goal to any random \\n position in the world. */\\n void randomize_goal();\\n\\n /** Return the correct reward based on the current state. */\\n float reward(int effect);\\n\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.7690058350563049,\n \"alphanum_fraction\": 0.7777777910232544,\n \"avg_line_length\": 17.052631378173828,\n \"blob_id\": \"83f94f19fedef334c928a152cb1d32ba6167c8fd\",\n \"content_id\": \"0d699c0b65c0d795fa3805f6dbff7b17bee6ee04\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 342,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 19,\n \"path\": \"/src/state_space_example/CMakeLists.txt\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"cmake_minimum_required(VERSION 2.8.3)\\nproject(state_space_example)\\n\\nfind_package(catkin REQUIRED COMPONENTS\\n roscpp\\n tf\\n visualization_msgs\\n)\\n\\ncatkin_package()\\n\\ninclude_directories(\\n ${catkin_INCLUDE_DIRS}\\n)\\n\\nadd_executable(state_space_example src/state_space_example.cpp)\\ntarget_link_libraries(state_space_example\\n ${catkin_LIBRARIES}\\n)\"\n },\n {\n \"alpha_fraction\": 0.5288340449333191,\n \"alphanum_fraction\": 0.5650969743728638,\n \"avg_line_length\": 16.727678298950195,\n \"blob_id\": \"dc226f28144a528a96707845e72b55a676617826\",\n \"content_id\": \"27b0d631bc902b407e1a48bed5140e8e8ba626d8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 3971,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 224,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/MountainCar.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file MountainCar.cc\\n Implements the Mountain Car domain, with possible action delays or linearized \\n transition dynamics.\\n \\\\author Todd Hester\\n*/\\n\\n#include <rl_env/MountainCar.hh>\\n\\n \\nMountainCar::MountainCar(Random &rand):\\n noisy(false),\\n rng(rand),\\n s(2),\\n pos(s[0]),\\n vel(s[1]),\\n linear(false),\\n delay(0)\\n{\\n reset();\\n //cout << *this << endl;\\n}\\n \\n\\nMountainCar::MountainCar(Random &rand, bool stochastic, bool lin, int delay):\\n noisy(stochastic),\\n rng(rand),\\n s(2),\\n pos(s[0]),\\n vel(s[1]),\\n linear(lin),\\n delay(delay)\\n{\\n reset();\\n}\\n\\n\\nMountainCar::~MountainCar() { }\\n\\nconst std::vector<float> &MountainCar::sensation() const { \\n //cout << \\\"At state \\\" << s[0] << \\\", \\\" << s[1] << endl;\\n\\n return s; \\n}\\n\\nfloat MountainCar::apply(int action) {\\n\\n //cout << \\\"Taking action \\\" << action << endl;\\n\\n float actVal = ((float)action-1.0);\\n if (noisy){\\n actVal += rng.uniform(-0.5, 0.5);\\n }\\n\\n float newVel = vel;\\n if (linear){\\n // for now, make this linear\\n newVel = vel + 0.001 * actVal + -0.0075*pos;\\n } else {\\n newVel = vel + 0.001 * actVal + -0.0025*cos(3.0*pos);\\n }\\n\\n newVel = bound(newVel, -0.07, 0.07);\\n\\n float newPos = pos + vel;\\n if (newPos < -1.2f && newVel < 0.0f)\\n newVel = 0.0;\\n newPos = bound(newPos, -1.2, 0.6);\\n\\n pos = newPos;\\n vel = newVel;\\n\\n if (delay > 0){\\n posHistory.push_back(newPos);\\n pos = posHistory.front();\\n posHistory.pop_front();\\n velHistory.push_back(newVel);\\n vel = velHistory.front();\\n velHistory.pop_front();\\n // cout << \\\"new pos: \\\" << newPos << \\\" observed: \\\" << pos << endl;\\n //cout << \\\"new vel: \\\" << newVel << \\\" observed: \\\" << vel << endl;\\n }\\n\\n return reward();\\n\\n}\\n\\nfloat MountainCar::bound(float val, float min, float max){\\n if (val < min)\\n return min;\\n if (val > max)\\n return max;\\n return val;\\n}\\n\\n\\nfloat MountainCar::reward() {\\n \\n // normally -1 and 0 on goal\\n if (terminal())\\n return 0;\\n else \\n return -1;\\n \\n}\\n\\n\\nbool MountainCar::terminal() const {\\n // current position equal to goal??\\n return (pos >= 0.6); \\n}\\n\\n\\n\\nvoid MountainCar::reset() {\\n\\n if (noisy){\\n pos = rng.uniform(-1.2, 0.59);\\n vel = rng.uniform(-0.07, 0.07);\\n } else {\\n pos = 0;\\n vel = 0;\\n }\\n\\n pos = rng.uniform(-1.2, 0.59);\\n vel = rng.uniform(-0.07, 0.07);\\n\\n if (delay > 0){\\n posHistory.clear();\\n velHistory.clear();\\n for (int i = 0; i < delay; i++){\\n posHistory.push_back(pos);\\n velHistory.push_back(vel);\\n }\\n }\\n\\n}\\n\\n\\nint MountainCar::getNumActions(){\\n return 3;\\n}\\n\\n\\nvoid MountainCar::setSensation(std::vector<float> newS){\\n if (s.size() != newS.size()){\\n cerr << \\\"Error in sensation sizes\\\" << endl;\\n }\\n\\n for (unsigned i = 0; i < newS.size(); i++){\\n s[i] = newS[i];\\n }\\n}\\n\\nstd::vector<experience> MountainCar::getSeedings() {\\n\\n int origDelay = delay;\\n delay = 0;\\n\\n // return seedings\\n std::vector<experience> seeds;\\n\\n // two seeds of terminal state\\n seeds.push_back(getExp(0.58, 0.03, 2));\\n //seeds.push_back(getExp(0.57, 0.06, 2));\\n\\n // random seed of each action\\n for (int i = 0; i < getNumActions(); i++){\\n float p = rng.uniform(-1.2, 0.6);\\n float v = rng.uniform(-0.07, 0.07);\\n seeds.push_back(getExp(p, v, i));\\n }\\n\\n delay = origDelay;\\n\\n reset();\\n\\n return seeds;\\n\\n}\\n\\nexperience MountainCar::getExp(float s0, float s1, int a){\\n\\n experience e;\\n\\n e.s.resize(2, 0.0);\\n e.next.resize(2, 0.0);\\n\\n pos = s0;\\n vel = s1;\\n\\n e.act = a;\\n e.s = sensation();\\n e.reward = apply(e.act);\\n\\n e.terminal = terminal();\\n e.next = sensation();\\n\\n reset();\\n\\n return e;\\n}\\n\\n\\nvoid 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\"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 85,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/include/rl_env/CartPole.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file CartPole.hh\\n Defines the Cart-Pole balancing domain, with possible noise.\\n \\\\author Todd Hester\\n*/\\n\\n#ifndef _CARTPOLE_H_\\n#define _CARTPOLE_H_\\n\\n#include <set>\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n\\n/** This class defines the Cart-Pole balancing domain. */\\nclass CartPole: public Environment {\\npublic:\\n\\n /** Creates a deterministic CartPole domain.\\n \\\\param rand Random number generator used solely for random\\n initial states. \\n */\\n CartPole(Random &rand);\\n\\n /** Creates a Cart-Pole domain, possibly with noise.\\n \\\\param rand Random number generator\\n \\\\param stochastic noisy transitions? \\n */\\n CartPole(Random &rand, bool stochastic);\\n\\n virtual ~CartPole();\\n\\n virtual const std::vector<float> &sensation() const;\\n virtual float apply(int action);\\n\\n /** Calculate the new state and reward for the given force */\\n float transition(float force);\\n\\n virtual bool terminal() const;\\n virtual void reset();\\n\\n virtual int getNumActions();\\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\\n virtual void getMinMaxReward(float* minR, float* maxR);\\n\\n /** Set the state vector (for debug purposes) */\\n void setSensation(std::vector<float> newS);\\n\\n virtual std::vector<experience> getSeedings();\\n\\n /** Get an experience for the given state-action */\\n experience getExp(float s0, float s1, float s2, float s3, int a);\\n \\nprotected:\\n enum car_action_t {LEFT, RIGHT};\\n\\nprivate:\\n\\n float GRAVITY;\\n float MASSCART;\\n float MASSPOLE;\\n float TOTAL_MASS;\\n float LENGTH;\\n \\n float POLEMASS_LENGTH;\\n float FORCE_MAG;\\n float TAU;\\n \\n float FOURTHIRDS;\\n float DEG_T_RAD;\\n float RAD_T_DEG;\\n\\n const bool noisy;\\n Random &rng;\\n\\n std::vector<float> s;\\n \\n float &cartPos;\\n float &cartVel;\\n float &poleAngle;\\n float &poleVel;\\n\\n float reward();\\n\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.7238317728042603,\n \"alphanum_fraction\": 0.7254672646522522,\n \"avg_line_length\": 34.96638488769531,\n \"blob_id\": \"60f64263a842d0b0917c8c855d05deb554633e23\",\n \"content_id\": \"5e531cb236a5257837126028cbee49b949dd02cd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 4280,\n \"license_type\": \"no_license\",\n \"max_line_length\": 185,\n \"num_lines\": 119,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/FactoredModel.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file FactoredModel.hh\\n Defines the FactoredModel class\\n Please cite: Hester and Stone, \\\"Real Time Targeted Exploration in Large Domains\\\", ICDL 2010.\\n \\\\author Todd Hester\\n*/\\n\\n#ifndef _FACTOREDMODEL_HH_\\n#define _FACTOREDMODEL_HH_\\n\\n#include \\\"../Models/C45Tree.hh\\\"\\n#include \\\"../Models/M5Tree.hh\\\"\\n#include \\\"../Models/LinearSplitsTree.hh\\\"\\n#include \\\"../Models/Stump.hh\\\"\\n#include \\\"../Models/MultipleClassifiers.hh\\\"\\n#include \\\"../Models/SepPlanExplore.hh\\\"\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n#include <vector>\\n\\n\\n/** Builds an mdp model consisting of a tree (or ensemble of trees) to predict each feature, reward, and termination probability. Thus forming a complete model of the MDP. */\\nclass FactoredModel: public MDPModel {\\npublic:\\n\\n /** Default constructor\\n \\\\param id identify the model\\n \\\\param numactions # of actions in the domain\\n \\\\param M # of visits for a given state-action to be considered known\\n \\\\param modelType identifies which type of model to use\\n \\\\param predType identifies how to combine multiple models\\n \\\\param nModels # of models to use for ensemble models (i.e. random forests)\\n \\\\param treeThreshold determines the amount of error to be tolerated in the tree (prevents over-fitting with larger and larger trees)\\n \\\\param featRange range of each feature in the domain\\n \\\\param rRange range of reward values in domain\\n \\\\param needConf do we need confidence measures?\\n \\\\param dep assume dependent transitions between features (or indep)\\n \\\\param relTrans model relative transitions of features (or absolute)\\n \\\\param featPct pct of features to remove from set used for each tree split\\n \\\\param stoch if the domain is stochastic or deterministic\\n \\\\param episodic if the domain is episodic\\n \\\\param rng Random Number Generator \\n */\\n FactoredModel(int id, int numactions, int M, int modelType, \\n int predType, int nModels, float treeThreshold,\\n const std::vector<float> &featRange, float rRange,\\n bool needConf, bool dep, bool relTrans, float featPct, \\n\\t bool stoch, bool episodic, Random rng = Random());\\n\\n /** Copy Constructor for MDP Tree */\\n FactoredModel(const FactoredModel &);\\n\\n virtual ~FactoredModel();\\n\\n virtual bool updateWithExperiences(std::vector<experience> &instances);\\n virtual bool updateWithExperience(experience &e);\\n\\n /** Initialize the MDP model with the given # of state features */\\n bool initMDPModel(int nfactors);\\n virtual float getStateActionInfo(const std::vector<float> &state, int act, StateActionInfo* retval);\\n virtual FactoredModel* getCopy();\\n\\n /** Method to get a single sample of the predicted next state for the given state-action, rather than the full distribution given by getStateActionInfo */\\n float getSingleSAInfo(const std::vector<float> &state, int act, StateActionInfo* retval);\\n\\n /** Combines predictions for each separate state feature into probabilities of the overall state vector */\\n void addFactorProb(float* probs, std::vector<float>* next, std::vector<float> x, StateActionInfo* retval, int index, std::vector< std::map<float,float> > predictions, float* confSum);\\n\\n /** Set some parameters of the subtrees */\\n void setTreeParams(float margin, float forestPct, float minRatio);\\n\\n /** Helper function to add two vectors together */\\n std::vector<float> addVec(const std::vector<float> &a, const std::vector<float> &b);\\n\\n /** Helper function to subtract two vectors */\\n std::vector<float> subVec(const std::vector<float> &a, const std::vector<float> &b);\\n \\nprivate:\\n \\n /** Classifier to predict each feature */\\n std::vector<Classifier*> outputModels;\\n\\n /** Classifier to predict reward */\\n Classifier* rewardModel;\\n\\n /** Classifier to prediction termination probability */\\n Classifier* terminalModel;\\n\\n int id;\\n int nfactors;\\n const int nact;\\n const int M;\\n const int modelType;\\n const int predType;\\n const int nModels;\\n const int treeBuildType;\\n const float treeThresh;\\n\\n const std::vector<float> featRange;\\n const float rRange;\\n\\n const bool needConf;\\n const bool dep;\\n const bool relTrans;\\n const float FEAT_PCT;\\n const bool stoch;\\n const bool episodic;\\n Random rng;\\n \\n float EXP_PCT;\\n\\n bool MODEL_DEBUG;\\n bool COPYDEBUG;\\n\\n};\\n\\n\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.6179324984550476,\n \"alphanum_fraction\": 0.6217840313911438,\n \"avg_line_length\": 26.388185501098633,\n \"blob_id\": \"96ae44dc8a3f9ec544a1c988f3782a7fe54b00ec\",\n \"content_id\": \"d05c1eba14fb397d604b2586f10d2bb71ca4b36e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 6491,\n \"license_type\": \"no_license\",\n \"max_line_length\": 106,\n \"num_lines\": 237,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/RMaxModel.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file RMaxModel.cc\\n Implements the RMaxModel class.\\n \\\\author Todd Hester\\n*/\\n\\n#include \\\"RMaxModel.hh\\\"\\n\\n\\n\\n\\nRMaxModel::RMaxModel(int m, int nact, Random rng):\\n M(m), nact(nact), rng(rng)\\n{\\n\\n nstates = 0;\\n RMAX_DEBUG = false; //true;\\n //initMDPModel(nfactors); \\n}\\n\\nRMaxModel::RMaxModel(const RMaxModel &rm):\\nM(rm.M), nact(rm.nact), rng(rm.rng)\\n{\\n\\n nstates = rm.nstates;\\n RMAX_DEBUG = rm.RMAX_DEBUG;\\n\\n statespace = rm.statespace;\\n\\n // copy info into map for each of these\\n // because map is by address, must get correct address for each\\n for (std::map<state_t, state_info>::const_iterator i = rm.statedata.begin();\\n i != rm.statedata.end(); i++){\\n\\n state_t s = canonicalize(*((*i).first));\\n statedata[s] = (*i).second;\\n\\n }\\n\\n}\\n\\nRMaxModel* RMaxModel::getCopy(){\\n RMaxModel* copy = new RMaxModel(*this);\\n return copy;\\n}\\n\\nRMaxModel::~RMaxModel() {}\\n\\n\\nbool RMaxModel::updateWithExperiences(std::vector<experience> &instances){\\n\\n bool changed = false;\\n \\n for (unsigned i = 0; i < instances.size(); i++){\\n bool singleChange = updateWithExperience(instances[i]);\\n changed = changed || singleChange;\\n }\\n return changed;\\n}\\n\\n\\n// update all the counts, check if model has changed\\n// stop counting at M\\nbool RMaxModel::updateWithExperience(experience &e){\\n if (RMAX_DEBUG) cout << \\\"updateWithExperience \\\" << &(e.s) << \\\", \\\" << e.act \\n << \\\", \\\" << &(e.next) << \\\", \\\" << e.reward << endl;\\n\\n // get state info for last state\\n state_t l = canonicalize(e.s);\\n state_info* info = &(statedata[l]);\\n\\n bool modelChanged = false;\\n\\n // stop at M\\n //if (info->visits[e.act] == M)\\n if (info->known[e.act])\\n return false;\\n\\n // update visit count for action just executed\\n info->visits[e.act]++;\\n\\n // update termination count\\n if (e.terminal) info->terminations[e.act]++;\\n\\n // update reward sum for this action\\n info->Rsum[e.act] += e.reward;\\n\\n // update transition count for outcome that occured\\n std::vector<int> &transCounts = info->outCounts[e.next];\\n\\n // first, check that we have resized this counter\\n checkTransitionCountSize(&transCounts);\\n\\n // only update state transition counts for non-terminal transitions\\n if (!e.terminal){\\n // then update transition count for this action/outcome\\n transCounts[e.act]++;\\n } \\n\\n // check if count becomes known \\n if (!info->known[e.act] && info->visits[e.act] >= M){\\n info->known[e.act] = true;\\n modelChanged = true;\\n }\\n\\n if (RMAX_DEBUG) cout << \\\"s\\\" << info->id << \\\" act: \\\" << e.act \\n << \\\" transCounts[act] = \\\" << transCounts[e.act] \\n << \\\" visits[act] = \\\" << info->visits[e.act] << endl;\\n \\n // anything that got past the 'return false' above is a change in conf or predictions\\n return true; //modelChanged;\\n\\n}\\n\\n\\n// calculate state info such as transition probs, known/unknown, reward prediction\\nfloat RMaxModel::getStateActionInfo(const std::vector<float> &state, int act, StateActionInfo* retval){\\n if (RMAX_DEBUG) cout << \\\"getStateActionInfo, \\\" << &state << \\\", \\\" << act << endl;\\n\\n\\n retval->transitionProbs.clear();\\n\\n // get state-action info for this state\\n state_t l = canonicalize(state);\\n state_info* info = &(statedata[l]);\\n\\n \\n // see if it has any visits (could still be unknown)\\n if (info->visits[act] == 0){\\n if (RMAX_DEBUG) cout << \\\"This outcome is unknown\\\" << endl;\\n retval->reward = -0.001;\\n\\n // add to transition map\\n retval->transitionProbs[state] = 1.0;\\n retval->known = false;\\n retval->termProb = 0.0;\\n return 0;\\n }\\n \\n \\n // fill in transition probs\\n for (std::map<std::vector<float>, std::vector<int> >::iterator it = info->outCounts.begin(); \\n it != info->outCounts.end(); it++){\\n\\n // get key from iterator\\n std::vector<float> next = (*it).first;\\n int count = ((*it).second)[act];\\n\\n // add to transition map\\n if (count > 0.0){\\n retval->transitionProbs[next] = (float)count / (float)(info->visits[act] - info->terminations[act]);\\n if (RMAX_DEBUG) cout << \\\"Outcome \\\" << &next << \\\" has prob \\\" << retval->transitionProbs[next]\\n\\t\\t\\t << \\\" from count of \\\" << count << \\\" on \\\" \\n\\t\\t\\t << info->visits[act] << \\\" visits.\\\" << endl;\\n }\\n }\\n \\n\\n // add in avg rewrad\\n retval->reward = (float)info->Rsum[act] / (float)info->visits[act];\\n if (RMAX_DEBUG) cout << \\\"Avg Reward of \\\" << retval->reward << \\\" from reward sum of \\\" \\n\\t\\t << info->Rsum[act] \\n\\t\\t << \\\" on \\\" << info->visits[act] << \\\" visits.\\\" << endl;\\n\\n // termination probability\\n retval->termProb = (float)info->terminations[act] / (float)info->visits[act];\\n if (RMAX_DEBUG) cout << \\\"termProb: \\\" << retval->termProb << endl;\\n if (retval->termProb < 0 || retval->termProb > 1){\\n cout << \\\"Problem with termination probability: \\\" << retval->termProb << endl;\\n }\\n\\n\\n retval->known = info->known[act];\\n // conf as a pct of float m (so 0.5 is exactly M)\\n float conf = (float)info->visits[act]/ (2.0 * (float)M);\\n\\n return conf;\\n\\n}\\n\\n\\n\\n\\n\\nRMaxModel::state_t RMaxModel::canonicalize(const std::vector<float> &s) {\\n if (RMAX_DEBUG) cout << \\\"canonicalize, s = \\\" << &s << endl;\\n\\n // get state_t for pointer if its in statespace\\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\\n statespace.insert(s);\\n state_t retval = &*result.first; // Dereference iterator then get pointer \\n\\n if (RMAX_DEBUG) cout << \\\" returns \\\" << retval << endl;\\n\\n // if not, init this new state\\n if (result.second) { // s is new, so initialize Q(s,a) for all a\\n initNewState(retval);\\n }\\n\\n return retval; \\n}\\n\\nvoid RMaxModel::initNewState(state_t s){\\n if (RMAX_DEBUG) cout << \\\"initNewState(s = \\\" << s \\n\\t\\t << \\\")\\\" << endl;\\n \\n // create state info and add to hash map\\n state_info* info = &(statedata[s]);\\n initStateInfo(info);\\n\\n}\\n\\n\\n// init state info\\nvoid RMaxModel::initStateInfo(state_info* info){\\n if (RMAX_DEBUG) cout << \\\"initStateInfo()\\\";\\n\\n info->id = nstates++;\\n if (RMAX_DEBUG) cout << \\\" id = \\\" << info->id << endl;\\n\\n // model data (q values, state-action counts, transition, etc)\\n info->visits.resize(nact, 0);\\n info->Rsum.resize(nact, 0);\\n info->known.resize(nact, false);\\n info->terminations.resize(nact, 0);\\n\\n}\\n\\n\\nvoid RMaxModel::checkTransitionCountSize(std::vector<int>* transCounts){\\n if (RMAX_DEBUG) cout << \\\"checkTransitionCountSize(transCounts) \\\" \\n\\t\\t << \\\"size: \\\" << transCounts->size() << endl;\\n\\n // resize to numactions if not initialized for this outcome yet\\n if (transCounts->size() == 0)\\n transCounts->resize(nact, 0);\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7881137132644653,\n \"alphanum_fraction\": 0.7932816743850708,\n \"avg_line_length\": 47.4375,\n \"blob_id\": \"c4085ed04f7c5fc5456315414b25c63ebbcef847\",\n \"content_id\": \"cfef3dfd824b9134640b05cc88e35c389a9ea695\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 774,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 16,\n \"path\": \"/build/roundbot_control/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"roundbot_control\\\")\\nset(roundbot_control_VERSION \\\"0.0.0\\\")\\nset(roundbot_control_MAINTAINER \\\"micho <micho@todo.todo>\\\")\\nset(roundbot_control_PACKAGE_FORMAT \\\"1\\\")\\nset(roundbot_control_BUILD_DEPENDS \\\"controller_interface\\\")\\nset(roundbot_control_BUILD_EXPORT_DEPENDS \\\"controller_interface\\\" \\\"roscpp\\\")\\nset(roundbot_control_BUILDTOOL_DEPENDS \\\"catkin\\\" \\\"roscpp\\\")\\nset(roundbot_control_BUILDTOOL_EXPORT_DEPENDS )\\nset(roundbot_control_EXEC_DEPENDS \\\"controller_interface\\\" \\\"roscpp\\\")\\nset(roundbot_control_RUN_DEPENDS \\\"controller_interface\\\" \\\"roscpp\\\")\\nset(roundbot_control_TEST_DEPENDS )\\nset(roundbot_control_DOC_DEPENDS )\\nset(roundbot_control_URL_WEBSITE \\\"\\\")\\nset(roundbot_control_URL_BUGTRACKER \\\"\\\")\\nset(roundbot_control_URL_REPOSITORY \\\"\\\")\\nset(roundbot_control_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.7630208134651184,\n \"alphanum_fraction\": 0.7708333134651184,\n \"avg_line_length\": 54,\n \"blob_id\": \"48d1a3f6b4ed1166e33d4a97209921d2f3a64f21\",\n \"content_id\": \"5c8574684bc3ad9441d8300a1627fc86f76f8883\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 384,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 7,\n \"path\": \"/src/state_space_example/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"state_space_example\\\")\\nset(state_space_example_MAINTAINER \\\"student <student@todo.todo>\\\")\\nset(state_space_example_DEPRECATED \\\"\\\")\\nset(state_space_example_VERSION \\\"0.0.0\\\")\\nset(state_space_example_BUILD_DEPENDS \\\"roscpp\\\" \\\"tf\\\" \\\"visualization_msgs\\\")\\nset(state_space_example_RUN_DEPENDS \\\"roscpp\\\" \\\"tf\\\" \\\"visualization_msgs\\\")\\nset(state_space_example_BUILDTOOL_DEPENDS \\\"catkin\\\")\"\n },\n {\n \"alpha_fraction\": 0.5211640000343323,\n \"alphanum_fraction\": 0.529629647731781,\n \"avg_line_length\": 18.6875,\n \"blob_id\": \"c75cc5357c1696d4a63cac010fda443fbdc422ee\",\n \"content_id\": \"f0badfbc2c8094ad659c731cbd21d725d381fe70\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 3780,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 192,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/stocks.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include <rl_env/stocks.hh>\\n\\n\\nStocks::Stocks(Random &rand, bool stochastic, int nsectors, int nstocks):\\n nsectors(nsectors),\\n nstocks(nstocks),\\n noisy(stochastic),\\n rng(rand),\\n s(nsectors*nstocks + nsectors)\\n{\\n STOCK_DEBUG = false; //true;\\n\\n initStocks();\\n reset();\\n}\\n\\n\\nStocks::Stocks(Random &rand, bool stochastic):\\n nsectors(3),\\n nstocks(2),\\n noisy(stochastic),\\n rng(rand),\\n s(nsectors*nstocks + nsectors)\\n{\\n STOCK_DEBUG = false; //true;\\n\\n initStocks();\\n reset();\\n}\\n\\n\\nStocks::~Stocks() {\\n delete [] owners;\\n for (int i = 0; i < nsectors; i++){\\n delete [] rising[i];\\n }\\n delete [] rising;\\n}\\n\\nconst std::vector<float> &Stocks::sensation() const { return s; }\\n\\nfloat Stocks::apply(int action) {\\n\\n // figure out ownership of stocks with this action\\n if (action < nsectors){\\n // flip that bit of sensation array\\n s[owners[action]] = !s[owners[action]];\\n }\\n else if (action == nsectors){\\n // do nothing\\n }\\n else {\\n cout << \\\"Invalid action!\\\" << endl;\\n }\\n\\n if (STOCK_DEBUG){\\n cout << \\\"Action: \\\" << action << \\\" Ownership now: \\\";\\n for (int i = 0; i < nsectors; i++){\\n cout << s[owners[i]] << \\\", \\\";\\n }\\n cout << endl;\\n }\\n\\n float r = reward();\\n\\n calcStockRising();\\n\\n return r;\\n\\n}\\n\\nvoid Stocks::calcStockRising() {\\n\\n // for each sector\\n for (int i = 0; i < nsectors; i++){\\n // get average of stocks\\n float sum = 0;\\n for (int j = 0; j < nstocks; j++){\\n sum += s[rising[i][j]];\\n }\\n float riseProb = 0.1 + 0.8 * (sum / (float)nstocks);\\n\\n // set value for each stock\\n for (int j = 0; j < nstocks; j++){\\n if (noisy) {\\n s[rising[i][j]] = rng.bernoulli(riseProb);\\n } else {\\n s[rising[i][j]] = (riseProb > 0.5);\\n }\\n }\\n\\n if (STOCK_DEBUG){\\n cout << \\\"S\\\" << i << \\\" Prob: \\\" << riseProb << \\\" stocks now: \\\";\\n for (int j = 0; j < nstocks; j++){\\n cout << s[rising[i][j]] << \\\", \\\";\\n }\\n cout << endl;\\n }\\n\\n }\\n\\n}\\n\\nfloat Stocks::reward() {\\n\\n float sum = 0.0;\\n\\n // for each sector\\n for (int i = 0; i < nsectors; i++){\\n // skip if we don't own it\\n if (!s[owners[i]])\\n continue;\\n\\n if (STOCK_DEBUG) cout << \\\"Own sector \\\" << i << \\\": \\\";\\n\\n\\n // add 1 for each rising, sub 1 for each not\\n for (int j = 0; j < nstocks; j++){\\n if (s[rising[i][j]]){\\n sum++;\\n if (STOCK_DEBUG) cout << \\\"rising, \\\";\\n } else {\\n sum--;\\n if (STOCK_DEBUG) cout << \\\"falling, \\\";\\n }\\n }\\n\\n if (STOCK_DEBUG) cout << endl;\\n }\\n\\n if (STOCK_DEBUG)\\n cout << \\\"Reward is \\\" << sum << endl;\\n\\n return sum;\\n\\n}\\n\\nbool Stocks::terminal() const {\\n return false;\\n}\\n\\nvoid Stocks::initStocks(){\\n // set owners and rising variables\\n owners = new int[nsectors];\\n rising = new int*[nsectors];\\n for (int i = 0; i < nsectors; i++){\\n rising[i] = new int[nstocks];\\n owners[i] = i;\\n if (STOCK_DEBUG)\\n cout << \\\"Owners[\\\" << i << \\\"] is \\\" << owners[i] << endl;\\n for (int j = 0; j < nstocks; j++){\\n rising[i][j] = nsectors + (i*nstocks) + j;\\n if (STOCK_DEBUG)\\n cout << \\\"Rising[\\\" << i << \\\"][\\\" << j << \\\"] is \\\" << rising[i][j] << endl;\\n }\\n }\\n}\\n\\nvoid Stocks::reset() {\\n\\n // random values for each variable\\n for (unsigned i = 0; i < s.size(); i++){\\n s[i] = rng.uniformDiscrete(0,1);\\n }\\n}\\n\\n\\nint Stocks::getNumActions() {\\n return nsectors+1;\\n}\\n\\nvoid Stocks::setSensation(std::vector<float> sIn){\\n for (unsigned i = 0; i < s.size(); i++){\\n s[i] = sIn[i];\\n }\\n}\\n\\n\\nvoid Stocks::getMinMaxFeatures(std::vector<float> *minFeat,\\n std::vector<float> *maxFeat){\\n\\n minFeat->resize(s.size(), 0.0);\\n maxFeat->resize(s.size(), 1.0);\\n\\n}\\n\\nvoid Stocks::getMinMaxReward(float *minR,\\n float *maxR){\\n\\n *minR = -(nsectors*nstocks);\\n *maxR = (nsectors*nstocks);\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.74842768907547,\n \"alphanum_fraction\": 0.7547169923782349,\n \"avg_line_length\": 38.8125,\n \"blob_id\": \"23fb2095d804446a816fcd243e04c6c2d9b9258b\",\n \"content_id\": \"d0991a771cccb8cebd8d331cc653fdee790873c6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 636,\n \"license_type\": \"no_license\",\n \"max_line_length\": 52,\n \"num_lines\": 16,\n \"path\": \"/build/mantis_model/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"mantis_model\\\")\\nset(mantis_model_VERSION \\\"0.0.0\\\")\\nset(mantis_model_MAINTAINER \\\"abc <abc@todo.todo>\\\")\\nset(mantis_model_PACKAGE_FORMAT \\\"1\\\")\\nset(mantis_model_BUILD_DEPENDS \\\"roscpp\\\" \\\"tf\\\")\\nset(mantis_model_BUILD_EXPORT_DEPENDS \\\"roscpp\\\" \\\"tf\\\")\\nset(mantis_model_BUILDTOOL_DEPENDS \\\"catkin\\\")\\nset(mantis_model_BUILDTOOL_EXPORT_DEPENDS )\\nset(mantis_model_EXEC_DEPENDS \\\"roscpp\\\" \\\"tf\\\")\\nset(mantis_model_RUN_DEPENDS \\\"roscpp\\\" \\\"tf\\\")\\nset(mantis_model_TEST_DEPENDS )\\nset(mantis_model_DOC_DEPENDS )\\nset(mantis_model_URL_WEBSITE \\\"\\\")\\nset(mantis_model_URL_BUGTRACKER \\\"\\\")\\nset(mantis_model_URL_REPOSITORY \\\"\\\")\\nset(mantis_model_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.7963836193084717,\n \"alphanum_fraction\": 0.8042452931404114,\n \"avg_line_length\": 73.88235473632812,\n \"blob_id\": \"8b8d7133e3269cfff55be05cbb6fa586d53ddd68\",\n \"content_id\": \"9861eed83234d81cf7cec85abb9074573955163e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 1272,\n \"license_type\": \"no_license\",\n \"max_line_length\": 192,\n \"num_lines\": 17,\n \"path\": \"/src/gmapping_example/catkin_generated/setup_cached.sh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env sh\\n# generated from catkin/python/catkin/environment_cache.py\\n\\n# based on a snapshot of the environment before and after calling the setup script\\n# it emulates the modifications of the setup script without recurring computations\\n\\n# new environment variables\\n\\n# modified environment variables\\nexport CMAKE_PREFIX_PATH=\\\"/home/jman/ros/src/ugv_course/gmapping_example/devel:$CMAKE_PREFIX_PATH\\\"\\nexport CPATH=\\\"/home/jman/ros/src/ugv_course/gmapping_example/devel/include:$CPATH\\\"\\nexport LD_LIBRARY_PATH=\\\"/home/jman/ros/src/ugv_course/gmapping_example/devel/lib:/home/jman/ros/src/ugv_course/gmapping_example/devel/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH\\\"\\nexport PATH=\\\"/home/jman/ros/src/ugv_course/gmapping_example/devel/bin:$PATH\\\"\\nexport PKG_CONFIG_PATH=\\\"/home/jman/ros/src/ugv_course/gmapping_example/devel/lib/pkgconfig:/home/jman/ros/src/ugv_course/gmapping_example/devel/lib/x86_64-linux-gnu/pkgconfig:$PKG_CONFIG_PATH\\\"\\nexport PYTHONPATH=\\\"/home/jman/ros/src/ugv_course/gmapping_example/devel/lib/python2.7/dist-packages:$PYTHONPATH\\\"\\nexport ROSLISP_PACKAGE_DIRECTORIES=\\\"/home/jman/ros/src/ugv_course/gmapping_example/devel/share/common-lisp:$ROSLISP_PACKAGE_DIRECTORIES\\\"\\nexport ROS_PACKAGE_PATH=\\\"/home/jman/ros/src/ugv_course/gmapping_example:$ROS_PACKAGE_PATH\\\"\"\n },\n {\n \"alpha_fraction\": 0.6051876544952393,\n \"alphanum_fraction\": 0.6110495924949646,\n \"avg_line_length\": 26.971851348876953,\n \"blob_id\": \"4d28e1fe68d102ff7df5a3952c6df8208299b07e\",\n \"content_id\": \"64c6a4db0ecb62eda0c53b02daaf261857cc3547\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 39748,\n \"license_type\": \"no_license\",\n \"max_line_length\": 201,\n \"num_lines\": 1421,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/ParallelETUCT.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file ParallelETUCT.cc\\n Implements my real-time model-based RL architecture which uses UCT with eligiblity traces for planning.\\n The modified version of UCT used is presented in:\\n L. Kocsis and C. Szepesv´ari, \\\"Bandit based monte-carlo planning,\\\" in\\n ECML-06. Number 4212 in LNCS. Springer, 2006, pp. 282-293.\\n The real-time architecture is presented in:\\n Hester, Quinlan, and Stone, \\\"A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control\\\", arXiv 1105.1749, 2011.\\n \\\\author Todd Hester\\n*/\\n\\n#include \\\"ParallelETUCT.hh\\\"\\n#include <algorithm>\\n\\n#include <sys/time.h>\\n\\n\\nParallelETUCT::ParallelETUCT(int numactions, float gamma, float rrange, float lambda,\\n int MAX_ITER, float MAX_TIME, int MAX_DEPTH, int modelType,\\n const std::vector<float> &fmax, const std::vector<float> &fmin,\\n const std::vector<int> &nstatesPerDim, bool trackActual, int historySize, Random r):\\n numactions(numactions), gamma(gamma), rrange(rrange), lambda(lambda),\\n MAX_ITER(MAX_ITER), MAX_TIME(MAX_TIME),\\n MAX_DEPTH(MAX_DEPTH), modelType(modelType), statesPerDim(nstatesPerDim),\\n trackActual(trackActual), HISTORY_SIZE(historySize),\\n HISTORY_FL_SIZE(historySize*numactions),\\n CLEAR_SIZE(25)\\n{\\n rng = r;\\n\\n nstates = 0;\\n nsaved = 0;\\n nactions = 0;\\n lastUpdate = -1;\\n\\n seedMode = false;\\n timingType = true;\\n\\n previnfo = NULL;\\n model = NULL;\\n planTime = getSeconds();\\n initTime = getSeconds();\\n setTime = getSeconds();\\n\\n PLANNERDEBUG = false;\\n POLICYDEBUG = false; //true; //false; //true; //false;\\n ACTDEBUG = false; //true;\\n MODELDEBUG = false;\\n UCTDEBUG = false;\\n PTHREADDEBUG = false;\\n ATHREADDEBUG = false;//true;\\n MTHREADDEBUG = false; //true;\\n TIMINGDEBUG = false;\\n REALSTATEDEBUG = false;\\n HISTORYDEBUG = false; //true;\\n\\n if (statesPerDim[0] > 0){\\n cout << \\\"Planner Parallel ETUCT using discretization of \\\" << statesPerDim[0] << endl;\\n }\\n if (trackActual){\\n cout << \\\"Parallel ETUCT tracking real state values\\\" << endl;\\n }\\n cout << \\\"Planner using history size: \\\" << HISTORY_SIZE << endl;\\n\\n featmax = fmax;\\n featmin = fmin;\\n\\n pthread_mutex_init(&update_mutex, NULL);\\n pthread_mutex_init(&history_mutex, NULL);\\n pthread_mutex_init(&nactions_mutex, NULL);\\n pthread_mutex_init(&plan_state_mutex, NULL);\\n pthread_mutex_init(&statespace_mutex, NULL);\\n pthread_mutex_init(&model_mutex, NULL);\\n pthread_mutex_init(&list_mutex, NULL);\\n pthread_cond_init(&list_cond, NULL);\\n\\n // start parallel search thread\\n actualPlanState = std::vector<float>(featmax.size());\\n discPlanState = NULL;\\n modelThreadStarted = false;\\n planThreadStarted = false;\\n expList.clear();\\n\\n if (HISTORY_SIZE == 0){\\n saHistory.push_back(0.0);\\n }\\n else {\\n if (HISTORYDEBUG) {\\n cout << \\\"History size of \\\" << HISTORY_SIZE\\n << \\\" float size of \\\" << HISTORY_FL_SIZE\\n << \\\" with state size: \\\" << fmin.size()\\n << \\\" and numact: \\\" << numactions << endl;\\n }\\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\\n saHistory.push_back(0.0);\\n }\\n }\\n\\n // initStates();\\n expfile.initFile(\\\"experiences.bin\\\", featmax.size());\\n}\\n\\nParallelETUCT::~ParallelETUCT() {\\n // join threads\\n\\n //pthread_kill(planThread);\\n //pthread_kill(modelThread);\\n\\n pthread_detach(planThread);//, NULL);\\n pthread_detach(modelThread);//, NULL);\\n\\n pthread_cancel(planThread);//, NULL);\\n pthread_cancel(modelThread);//, NULL);\\n\\n //pthread_join(planThread, NULL);\\n //pthread_join(modelThread, NULL);\\n\\n //pthread_detach(planThread);//, NULL);\\n //pthread_detach(modelThread);//, NULL);\\n\\n\\n pthread_mutex_lock(&plan_state_mutex);\\n pthread_mutex_lock(&statespace_mutex);\\n pthread_mutex_lock(&model_mutex);\\n pthread_mutex_lock(&list_mutex);\\n\\n // delete exp list\\n expList.clear();\\n\\n for (std::map<state_t, state_info>::iterator i = statedata.begin();\\n i != statedata.end(); i++){\\n\\n // get state's info\\n //cout << \\\" planner got info\\\" << endl;\\n state_info* info = &((*i).second);\\n\\n deleteInfo(info);\\n }\\n\\n featmax.clear();\\n featmin.clear();\\n\\n statespace.clear();\\n statedata.clear();\\n\\n pthread_mutex_unlock(&plan_state_mutex);\\n pthread_mutex_unlock(&statespace_mutex);\\n pthread_mutex_unlock(&model_mutex);\\n pthread_mutex_unlock(&list_mutex);\\n\\n}\\n\\nvoid ParallelETUCT::setModel(MDPModel* m){\\n\\n model = m;\\n\\n}\\n\\n\\n/////////////////////////////\\n// Functional functions :) //\\n/////////////////////////////\\n\\n\\n\\n/** Use the latest experience to update state info and the model. */\\nbool ParallelETUCT::updateModelWithExperience(const std::vector<float> &laststate,\\n int lastact,\\n const std::vector<float> &currstate,\\n float reward, bool term){\\n // if (PLANNERDEBUG) cout << \\\"updateModelWithExperience(last = \\\" << &laststate\\n // << \\\", curr = \\\" << &currstate\\n // << \\\", lastact = \\\" << lastact\\n // << \\\", r = \\\" << reward\\n // << \\\", term = \\\" << term\\n // << \\\")\\\" << endl;\\n\\n //cout << \\\"updateModel\\\" << endl << flush;\\n\\n if (!timingType)\\n planTime = getSeconds();\\n initTime = getSeconds();\\n\\n // canonicalize these things\\n state_t last = canonicalize(laststate);\\n\\n prevstate = last;\\n prevact = lastact;\\n\\n // get state info\\n pthread_mutex_lock(&statespace_mutex);\\n previnfo = &(statedata[last]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n if (MODELDEBUG){\\n cout << \\\"Update with exp from state: \\\";\\n for (unsigned i = 0; i < last->size(); i++){\\n cout << (laststate)[i] << \\\", \\\";\\n }\\n cout << \\\" action: \\\" << lastact;\\n cout << \\\" to state: \\\";\\n for (unsigned i = 0; i < currstate.size(); i++){\\n cout << (currstate)[i] << \\\", \\\";\\n }\\n cout << \\\" and reward: \\\" << reward << endl;\\n }\\n\\n // add experiences to list to later be updated into model\\n if (ATHREADDEBUG)\\n cout << \\\"*** Action thread wants list lock ***\\\" << endl << flush;\\n if (TIMINGDEBUG) cout << \\\"Want list mutex, time: \\\" << (getSeconds()-initTime) << endl;\\n pthread_mutex_lock(&list_mutex);\\n if (TIMINGDEBUG) cout << \\\"got list mutex, time: \\\" << (getSeconds()-initTime) << endl;\\n experience e;\\n e.s = laststate;\\n e.next = currstate;\\n e.act = lastact;\\n e.reward = reward;\\n e.terminal = term;\\n\\n if (HISTORY_SIZE > 0){\\n if (HISTORYDEBUG) {\\n cout << \\\"Original state vector (size \\\" << e.s.size() << \\\": \\\" << e.s[0];\\n for (unsigned i = 1; i < e.s.size(); i++){\\n cout << \\\",\\\" << e.s[i];\\n }\\n cout << endl;\\n }\\n // add history onto e.s\\n pthread_mutex_lock(&history_mutex);\\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\\n e.s.push_back(saHistory[i]);\\n }\\n pthread_mutex_unlock(&history_mutex);\\n\\n if (HISTORYDEBUG) {\\n cout << \\\"New state vector (size \\\" << e.s.size() << \\\": \\\" << e.s[0];\\n for (unsigned i = 1; i < e.s.size(); i++){\\n cout << \\\",\\\" << e.s[i];\\n }\\n cout << endl;\\n }\\n\\n if (!seedMode){\\n pthread_mutex_lock(&history_mutex);\\n // push this state and action onto the history vector\\n /*\\n for (unsigned i = 0; i < last->size(); i++){\\n saHistory.push_back((*last)[i]);\\n saHistory.pop_front();\\n }\\n\\n saHistory.push_back((*last)[3]);\\n saHistory.pop_front();\\n */\\n for (int i = 0; i < numactions; i++){\\n if (i == lastact)\\n saHistory.push_back(1.0);\\n else\\n saHistory.push_back(0.0);\\n saHistory.pop_front();\\n }\\n\\n // saHistory.push_back(lastact);\\n //saHistory.pop_front();\\n if (HISTORYDEBUG) {\\n cout << \\\"New history vector (size \\\" << saHistory.size() << \\\": \\\" << saHistory[0];\\n for (unsigned i = 1; i < saHistory.size(); i++){\\n cout << \\\",\\\" << saHistory[i];\\n }\\n cout << endl;\\n }\\n pthread_mutex_unlock(&history_mutex);\\n }\\n }\\n\\n expList.push_back(e);\\n //expfile.saveExperience(e);\\n if (ATHREADDEBUG || MTHREADDEBUG)\\n cout << \\\"added exp to list, size: \\\" << expList.size() << endl << flush;\\n if (TIMINGDEBUG) cout << \\\"list updated, time: \\\" << (getSeconds()-initTime) << endl;\\n pthread_cond_signal(&list_cond);\\n pthread_mutex_unlock(&list_mutex);\\n\\n /*\\n if (e.reward > -0.5 && e.reward < 0){\\n expfile.saveExperience(e);\\n nsaved++;\\n cout << \\\"Saved Experience \\\" << e.reward << endl;\\n }\\n */\\n\\n if (timingType)\\n planTime = getSeconds();\\n\\n if (TIMINGDEBUG) cout << \\\"leaving updateModel, time: \\\" << (getSeconds()-initTime) << endl;\\n\\n\\n return false;\\n\\n}\\n\\n/** Update a single state-action from the model */\\nvoid ParallelETUCT::updateStateActionFromModel(state_t s, int a, state_info* info){\\n\\n if (HISTORY_SIZE == 0){\\n pthread_mutex_lock(&info->statemodel_mutex);\\n\\n std::deque<float> history(1,0.0);\\n StateActionInfo* newModel = NULL;\\n newModel = &(info->historyModel[a][history]);\\n\\n updateStateActionHistoryFromModel(*s, a, newModel);\\n pthread_mutex_unlock(&info->statemodel_mutex);\\n\\n }\\n\\n else {\\n pthread_mutex_lock(&info->statemodel_mutex);\\n\\n // clear large ones\\n if (info->historyModel[a].size() > CLEAR_SIZE){\\n\\n // cout << \\\"clearing model map of size \\\" << info->historyModel[a].size() << endl;\\n\\n // instead, clear because these take too much memory to keep around\\n info->historyModel[a].clear();\\n\\n } else {\\n\\n // fill in for all histories???\\n for (std::map< std::deque<float>, StateActionInfo>::iterator it = info->historyModel[a].begin();\\n it != info->historyModel[a].end(); it++){\\n\\n std::deque<float> oneHist = (*it).first;\\n StateActionInfo* newModel = &((*it).second);\\n\\n // add history to vector\\n std::vector<float> modState = *s;\\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\\n modState.push_back(oneHist[i]);\\n }\\n updateStateActionHistoryFromModel(modState, a, newModel);\\n }\\n }\\n\\n pthread_mutex_unlock(&info->statemodel_mutex);\\n }\\n\\n}\\n\\n/** Update a single state-action from the model */\\nvoid ParallelETUCT::updateStateActionHistoryFromModel(const std::vector<float> modState, int a, StateActionInfo *newModel){\\n\\n // update state info\\n // get state action info for each action\\n pthread_mutex_lock(&model_mutex);\\n\\n model->getStateActionInfo(modState, a, newModel);\\n\\n pthread_mutex_lock(&nactions_mutex);\\n newModel->frameUpdated = nactions;\\n pthread_mutex_unlock(&nactions_mutex);\\n\\n pthread_mutex_unlock(&model_mutex);\\n\\n //canonNextStates(newModel);\\n\\n}\\n\\nvoid ParallelETUCT::canonNextStates(StateActionInfo* modelInfo){\\n\\n\\n // loop through all next states\\n for (std::map<std::vector<float>, float>::iterator outIt\\n = modelInfo->transitionProbs.begin();\\n outIt != modelInfo->transitionProbs.end(); outIt++){\\n\\n std::vector<float> nextstate = (*outIt).first;\\n bool badState = false;\\n\\n // check that it is valid, otherwise replace with current\\n for (unsigned j = 0; j < nextstate.size(); j++){\\n float factor = EPSILON;\\n if (statesPerDim[j] > 0)\\n factor = (featmax[j] - featmin[j]) / (float)statesPerDim[j];\\n if (nextstate[j] < (featmin[j]-factor)\\n || nextstate[j] > (featmax[j]+factor)){\\n //cout << \\\"next state out of range \\\" << nextstate[j] << endl;\\n badState = true;\\n break;\\n }\\n }\\n\\n if (!badState){\\n\\n canonicalize(nextstate);\\n }\\n }\\n}\\n\\n\\n\\n\\n/** Choose the next action */\\nint ParallelETUCT::getBestAction(const std::vector<float> &state){\\n // if (PLANNERDEBUG) cout << \\\"getBestAction(s = \\\" << &state << \\\")\\\" << endl;\\n\\n pthread_mutex_lock(&nactions_mutex);\\n nactions++;\\n pthread_mutex_unlock(&nactions_mutex);\\n\\n if (TIMINGDEBUG) cout << \\\"getBestAction, time: \\\" << (getSeconds()-initTime) << endl;\\n\\n\\n state_t s = canonicalize(state);\\n\\n // set plan state so uct will search from here\\n if (ATHREADDEBUG)\\n cout << \\\"*** Action thread wants plan state lock ***\\\" << endl << flush;\\n if (TIMINGDEBUG) cout << \\\"want planStateMut, time: \\\" << (getSeconds()-initTime) << endl;\\n\\n pthread_mutex_lock(&(plan_state_mutex));\\n if (TIMINGDEBUG) cout << \\\"got planStateMut, time: \\\" << (getSeconds()-initTime) << endl;\\n\\n actualPlanState = state;\\n discPlanState = s;\\n setTime = getSeconds();\\n\\n if (ATHREADDEBUG){\\n cout << \\\"Set planning state as: \\\";\\n for (unsigned i = 0; i < state.size(); i++){\\n cout << state[i] << \\\", \\\";\\n }\\n cout << endl << flush;\\n }\\n\\n // call uct search on it\\n pthread_mutex_unlock(&(plan_state_mutex));\\n if (TIMINGDEBUG) cout << \\\"set planState, time: \\\" << (getSeconds()-initTime) << endl;\\n\\n // get state info\\n pthread_mutex_lock(&statespace_mutex);\\n state_info* info = &(statedata[s]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n // wait a bit for some planning from this state\\n\\n // depending on how you run the code, this has to be setup differently\\n // if someone else calls this method at the appropriate rate, do nothing here\\n\\n // or this can be where we wait to ensure we run at some rate:\\n while (((getSeconds()- initTime) < MAX_TIME)){\\n if (TIMINGDEBUG)\\n cout << \\\"waiting for time: \\\" << (getSeconds()-initTime) << endl;\\n\\n pthread_yield();\\n }\\n\\n if (TIMINGDEBUG) cout << \\\"time up: \\\" << (getSeconds()-initTime) << endl;\\n\\n if (TIMINGDEBUG && (getSeconds()-initTime) > 0.15) cout << \\\"**********\\\" << endl;\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n // Get Q values\\n std::vector<float> &Q = info->Q;\\n\\n\\n if (ATHREADDEBUG) {\\n if (previnfo != NULL)\\n cout << \\\" ... now \\\" << previnfo->uctVisits << \\\" times.\\\" << endl;\\n cout << \\\"Getting best action from state \\\";\\n for (unsigned i = 0; i < s->size(); i++){\\n cout << (*s)[i] << \\\", \\\";\\n }\\n cout << \\\" sampled \\\" << info->uctVisits << \\\" times.\\\";// << endl << flush;\\n }\\n\\n // Choose an action\\n const std::vector<float>::iterator a =\\n random_max_element(Q.begin(), Q.end()); // Choose maximum\\n int act = a - Q.begin();\\n\\n if (TIMINGDEBUG) cout << \\\"got action: \\\" << (getSeconds()-initTime) << endl;\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n // return index of action\\n return act;\\n}\\n\\n\\n\\n\\n\\n\\nvoid ParallelETUCT::planOnNewModel(){\\n //return;\\n // cout << \\\"planOnNewModel\\\" << endl << flush;\\n // start model learning thread here\\n if (!modelThreadStarted){\\n modelThreadStarted = true;\\n pthread_create(&modelThread, NULL, parallelModelLearningStart, this);\\n }\\n\\n if (!planThreadStarted){\\n planThreadStarted = true;\\n pthread_create(&(planThread), NULL, parallelSearchStart, this);\\n }\\n\\n}\\n\\n\\nvoid* parallelModelLearningStart(void* arg){\\n cout << \\\"Start model learning thread\\\" << endl << flush;\\n ParallelETUCT* pe = reinterpret_cast<ParallelETUCT*>(arg);\\n while(true){\\n pe->parallelModelLearning();\\n /*\\n if (!pe->planThreadStarted){\\n pe->planThreadStarted = true;\\n pthread_create(&(pe->planThread), NULL, parallelSearchStart, pe);\\n }\\n */\\n }\\n return NULL;\\n}\\n\\nvoid ParallelETUCT::parallelModelLearning(){\\n //while(true){\\n\\n // wait for experience list to be non-empty\\n pthread_mutex_lock(&list_mutex);\\n while (expList.size() == 0){\\n pthread_cond_wait(&list_cond,&list_mutex);\\n }\\n pthread_mutex_unlock(&list_mutex);\\n\\n // copy over experience list\\n std::vector<experience> updateList;\\n if (MTHREADDEBUG) cout << \\\" *** Model thread wants list lock ***\\\" << endl << flush;\\n pthread_mutex_lock(&list_mutex);\\n updateList = expList;\\n expList.clear();\\n if (MTHREADDEBUG) cout << \\\" *** Model thread done with list lock ***\\\" << endl << flush;\\n pthread_mutex_unlock(&list_mutex);\\n\\n /*\\n // update model\\n //cout << \\\"*** Model thread wants tree lock ***\\\" << endl << flush;\\n pthread_mutex_lock(&model_mutex);\\n if (MTHREADDEBUG) cout << \\\" Model thread: going to update model with \\\" << updateList.size() << \\\" new experiences\\\" << endl << flush;\\n //cout << \\\"****update tree with \\\" << updateList.size() << endl << flush;\\n bool modelChanged = model->updateWithExperiences(updateList);\\n if (MTHREADDEBUG) cout << \\\" Model updated\\\" << endl << flush;\\n pthread_mutex_unlock(&model_mutex);\\n */\\n\\n modelcopy = model->getCopy();\\n //if (COPYDEBUG) cout << \\\"*** PO: model copied\\\" << endl;\\n\\n // update model copy with new experience\\n bool modelChanged = modelcopy->updateWithExperiences(updateList);\\n\\n // set model pointer to point at copy, delete original model cout << \\\"acquire model_mutex for update\\\" << endl;\\n pthread_mutex_lock(&model_mutex);\\n //cout << \\\"model_mutex acquired for update\\\" << endl;\\n //if (COPYDEBUG) cout << \\\"*** PO: delete original model and change pointer\\\" << endl;\\n delete model;\\n model = modelcopy;\\n if (MTHREADDEBUG) cout << \\\" Model updated\\\" << endl << flush;\\n //if (COPYDEBUG) cout << \\\"*** PO: pointer set to updated model copy\\\" << endl;\\n pthread_mutex_unlock(&model_mutex);\\n\\n\\n\\n // if it changed, reset counts, update state actions\\n if (modelChanged) resetAndUpdateStateActions();\\n\\n pthread_yield();\\n\\n //}// while loop\\n} // method\\n\\n\\n\\n\\nvoid ParallelETUCT::resetAndUpdateStateActions(){\\n //cout << \\\"*** Model changed, updating state actions ***\\\" << endl << flush;\\n const int MIN_VISITS = 10;\\n\\n pthread_mutex_lock(&nactions_mutex);\\n int updateTime = nactions;\\n pthread_mutex_unlock(&nactions_mutex);\\n\\n // loop through here\\n\\n pthread_mutex_lock(&statespace_mutex);\\n\\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n state_t s = canonicalize(*i);\\n\\n if (MTHREADDEBUG) cout << \\\" *** Model thread wants search lock ***\\\" << endl;\\n\\n if (MTHREADDEBUG) cout << \\\" *** Model thread got search lock \\\" << endl;\\n\\n pthread_mutex_lock(&statespace_mutex);\\n state_info* info = &(statedata[s]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n if (info->uctVisits > (MIN_VISITS * numactions))\\n info->uctVisits = MIN_VISITS * numactions;\\n\\n for (int j = 0; j < numactions; j++){\\n if (info->uctActions[j] > MIN_VISITS)\\n info->uctActions[j] = MIN_VISITS;\\n if (info->needsUpdate || info->historyModel[j].size() > CLEAR_SIZE){\\n updateStateActionFromModel(s, j, info);\\n }\\n }\\n info->needsUpdate = false;\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n pthread_yield();\\n\\n pthread_mutex_lock(&statespace_mutex);\\n\\n }\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n pthread_mutex_lock(&update_mutex);\\n lastUpdate = updateTime;\\n pthread_mutex_unlock(&update_mutex);\\n\\n}\\n\\n\\n\\n\\n////////////////////////////\\n// Helper Functions //\\n////////////////////////////\\n\\nParallelETUCT::state_t ParallelETUCT::canonicalize(const std::vector<float> &s) {\\n if (PLANNERDEBUG) cout << \\\"canonicalize(s = \\\" << s[0] << \\\", \\\"\\n << s[1] << \\\")\\\" << endl;\\n\\n // discretize it\\n std::vector<float> s2;\\n if (statesPerDim[0] > 0){\\n s2 = discretizeState(s);\\n } else {\\n s2 = s;\\n }\\n\\n pthread_mutex_lock(&statespace_mutex);\\n\\n // get state_t for pointer if its in statespace\\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\\n statespace.insert(s2);\\n state_t retval = &*result.first; // Dereference iterator then get pointer\\n\\n\\n // if not, init this new state\\n if (result.second) { // s is new, so initialize Q(s,a) for all a\\n state_info* info = &(statedata[retval]);\\n int id = nstates++;\\n pthread_mutex_unlock(&statespace_mutex);\\n initStateInfo(retval, info, id);\\n } else {\\n pthread_mutex_unlock(&statespace_mutex);\\n }\\n\\n return retval;\\n}\\n\\n\\n// init state info\\nvoid ParallelETUCT::initStateInfo(state_t s, state_info* info, int id){\\n //if (PLANNERDEBUG) cout << \\\"initStateInfo()\\\";\\n\\n // init mutex's for this state info\\n pthread_mutex_init(&info->statemodel_mutex, NULL);\\n pthread_mutex_init(&info->stateinfo_mutex, NULL);\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n // model data (transition, reward, known)\\n\\n pthread_mutex_lock(&info->statemodel_mutex);\\n info->historyModel = new std::map< std::deque<float>, StateActionInfo>[numactions];\\n pthread_mutex_unlock(&info->statemodel_mutex);\\n\\n info->id = id;\\n if (PLANNERDEBUG) cout << \\\" id = \\\" << info->id << endl;\\n\\n // model q values, visit counts\\n info->Q.resize(numactions, 0);\\n info->uctActions.resize(numactions, 1);\\n info->uctVisits = 1;\\n info->visited = 0; //false;\\n\\n for (int i = 0; i < numactions; i++){\\n info->Q[i] = rng.uniform(0,0.01);\\n }\\n\\n info->needsUpdate = true;\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n //if (PLANNERDEBUG) cout << \\\"done with initStateInfo()\\\" << endl;\\n\\n}\\n\\n\\n/** Print state info for debugging. */\\nvoid ParallelETUCT::printStates(){\\n\\n pthread_mutex_lock(&statespace_mutex);\\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n state_t s = canonicalize(*i);\\n\\n pthread_mutex_lock(&statespace_mutex);\\n state_info* info = &(statedata[s]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n cout << \\\"State \\\" << info->id << \\\": \\\";\\n for (unsigned j = 0; j < s->size(); j++){\\n cout << (*s)[j] << \\\", \\\";\\n }\\n cout << endl;\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n //pthread_mutex_lock(&info->statemodel_mutex);\\n for (int act = 0; act < numactions; act++){\\n cout << \\\" Q: \\\" << info->Q[act] << endl;\\n // << \\\" R: \\\" << info->modelInfo[act].reward << endl;\\n }\\n // pthread_mutex_unlock(&info->statemodel_mutex);\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n pthread_mutex_lock(&statespace_mutex);\\n\\n }\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n}\\n\\n\\nvoid ParallelETUCT::deleteInfo(state_info* info){\\n\\n delete [] info->historyModel;\\n\\n}\\n\\n\\n\\ndouble ParallelETUCT::getSeconds(){\\n struct timezone tz;\\n timeval timeT;\\n gettimeofday(&timeT, &tz);\\n return timeT.tv_sec + (timeT.tv_usec / 1000000.0);\\n}\\n\\n\\n/** Execute the uct search from state state at depth depth.\\n If terminal or at depth, return some reward.\\n Otherwise, select an action based on UCB.\\n Simulate action to get reward and next state.\\n Call search on next state at depth+1 to get reward return from there on.\\n Update q value towards new value: reward + gamma * searchReturn\\n Update visit counts for confidence bounds\\n Return q\\n\\n From \\\"Bandit Based Monte Carlo Planning\\\" by Kocsis and Csaba.\\n*/\\nfloat ParallelETUCT::uctSearch(const std::vector<float> &actS, state_t discS, int depth, std::deque<float> &searchHistory){\\n if (UCTDEBUG){\\n cout << \\\" uctSearch state \\\";\\n for (unsigned i = 0; i < actS.size(); i++){\\n cout << actS[i] << \\\", \\\";\\n }\\n cout << \\\" at depth \\\" << depth << endl;\\n }\\n\\n pthread_mutex_lock(&statespace_mutex);\\n state_info* info = &(statedata[discS]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n // if max depth\\n // iterative deepening (probability inversely proportional to visits)\\n //float terminateProb = 1.0/(2.0+(float)info->uctVisits);\\n\\n // already visited, stop here\\n if (depth > MAX_DEPTH){\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n // return max q value here\\n std::vector<float>::iterator maxAct =\\n std::max_element(info->Q.begin(),\\n info->Q.end());\\n float maxval = *maxAct;\\n\\n if (UCTDEBUG)\\n cout << \\\"Terminated after depth: \\\" << depth\\n // << \\\" prob: \\\" << terminateProb\\n << \\\" Q: \\\" << maxval\\n << \\\" visited: \\\" << info->visited << endl;\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n return maxval;\\n }\\n\\n // select action\\n int action = selectUCTAction(info);\\n\\n // simulate action to get next state and reward\\n // depending on exploration, may also terminate us\\n float reward = 0;\\n bool term = false;\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n float learnRate;\\n //float learnRate = 0.001;\\n //float learnRate = 1.0 / info->uctActions[action];\\n // learnRate = 10.0 / (info->uctActions[action] + 100.0);\\n learnRate = 10.0 / (info->uctActions[action] + 10.0);\\n //if (learnRate < 0.001 && MAX_TIME < 0.5)\\n //learnRate = 0.001;\\n //learnRate = 0.05;\\n //learnRate = 1.0;\\n\\n // tell model learning thread to update this state since we've visited it\\n info->needsUpdate = true;\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n std::vector<float> actualNext = simulateNextState(actS, discS, info, searchHistory, action, &reward, &term);\\n\\n // simulate reward from this action\\n if (term){\\n // this one terminated\\n if (UCTDEBUG) cout << \\\" Terminated on exploration condition\\\" << endl;\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n info->Q[action] += learnRate * (reward - info->Q[action]);\\n info->uctVisits++;\\n info->uctActions[action]++;\\n\\n if (UCTDEBUG)\\n cout << \\\" Depth: \\\" << depth << \\\" Selected action \\\" << action\\n << \\\" r: \\\" << reward\\n << \\\" StateVisits: \\\" << info->uctVisits\\n << \\\" ActionVisits: \\\" << info->uctActions[action] << endl;\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n return reward;\\n }\\n\\n // simulate next state from this action\\n state_t discNext = canonicalize(actualNext);\\n\\n if (UCTDEBUG)\\n cout << \\\" Depth: \\\" << depth << \\\" Selected action \\\" << action\\n << \\\" r: \\\" << reward << endl;\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n info->visited++; // = true;\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n if (HISTORY_SIZE > 0){\\n // update history vector for this state\\n /*\\n for (unsigned i = 0; i < (*discS).size(); i++){\\n searchHistory.push_back((*discS)[i]);\\n searchHistory.pop_front();\\n }\\n\\n searchHistory.push_back((*discS)[3]);\\n searchHistory.pop_front();\\n */\\n for (int i = 0; i < numactions; i++){\\n if (i == action)\\n searchHistory.push_back(1.0);\\n else\\n searchHistory.push_back(0.0);\\n searchHistory.pop_front();\\n }\\n\\n // searchHistory.push_back(action);\\n //searchHistory.pop_front();\\n if (HISTORYDEBUG) {\\n cout << \\\"New planning history vector (size \\\" << searchHistory.size() << \\\": \\\" << searchHistory[0];\\n for (unsigned i = 1; i < searchHistory.size(); i++){\\n cout << \\\",\\\" << searchHistory[i];\\n }\\n cout << endl;\\n }\\n }\\n\\n // new q value\\n float newQ = reward + gamma * uctSearch(actualNext, discNext, depth+1, searchHistory);\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n if (info->visited == 1){\\n\\n // update q and visit counts\\n info->Q[action] += learnRate * (newQ - info->Q[action]);\\n info->uctVisits++;\\n info->uctActions[action]++;\\n\\n if (UCTDEBUG)\\n cout << \\\" Depth: \\\" << depth << \\\" newQ: \\\" << newQ\\n << \\\" StateVisits: \\\" << info->uctVisits\\n << \\\" ActionVisits: \\\" << info->uctActions[action] << endl;\\n\\n if (lambda < 1.0){\\n\\n // new idea, return max of Q or new q\\n std::vector<float>::iterator maxAct =\\n std::max_element(info->Q.begin(),\\n info->Q.end());\\n float maxval = *maxAct;\\n\\n if (UCTDEBUG)\\n cout << \\\" Replacing newQ: \\\" << newQ;\\n\\n // replace with w avg of maxq and new val\\n newQ = (lambda * newQ) + ((1.0-lambda) * maxval);\\n\\n if (UCTDEBUG)\\n cout << \\\" with wAvg: \\\" << newQ << endl;\\n }\\n\\n }\\n\\n info->visited--;\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n // return q\\n return newQ;\\n\\n}\\n\\n\\nint ParallelETUCT::selectUCTAction(state_info* info){\\n // if (UCTDEBUG) cout << \\\" selectUCTAction\\\" << endl;\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n std::vector<float> &Q = info->Q;\\n\\n if (info->uctActions.size() < (unsigned)numactions){\\n cout << \\\"ERROR: uctActions has size \\\" << info->uctActions.size() << endl << flush;\\n info->uctActions.resize(numactions);\\n }\\n\\n // loop through\\n float rewardBound = rrange;\\n if (rewardBound < 1.0)\\n rewardBound = 1.0;\\n rewardBound /= (1.0 - gamma);\\n if (UCTDEBUG) cout << \\\"Reward bound: \\\" << rewardBound << endl;\\n\\n std::vector<float> uctQ(numactions, 0.0);\\n\\n for (int i = 0; i < numactions; i++){\\n\\n // this actions value is Q + rMax * 2 sqrt (log N(s) / N(s,a))\\n uctQ[i] = Q[i] +\\n rewardBound * 2.0 * sqrt(log((float)info->uctVisits) /\\n (float)info->uctActions[i]);\\n\\n if (UCTDEBUG)\\n cout << \\\" Action: \\\" << i << \\\" Q: \\\" << Q[i]\\n << \\\" visits: \\\" << info->uctActions[i]\\n << \\\" value: \\\" << uctQ[i] << endl;\\n }\\n\\n // max element of uctQ\\n std::vector<float>::iterator maxAct =\\n max_element(uctQ.begin(), uctQ.end());\\n float maxval = *maxAct;\\n int act = maxAct - uctQ.begin();\\n\\n if (UCTDEBUG)\\n cout << \\\" Selected \\\" << act << \\\" val: \\\" << maxval << endl;\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n return act;\\n\\n}\\n\\n/** sample from next state distribution */\\nstd::vector<float> ParallelETUCT::simulateNextState(const std::vector<float> &actualState, state_t discState, state_info* info, const std::deque<float> &history, int action, float* reward, bool* term){\\n //if (UCTDEBUG) cout << \\\" simulateNextState\\\" << endl;\\n\\n\\n // check if its up to date\\n pthread_mutex_lock(&info->statemodel_mutex);\\n StateActionInfo* modelInfo = NULL;\\n modelInfo = &(info->historyModel[action][history]);\\n\\n pthread_mutex_lock(&update_mutex);\\n bool upToDate = modelInfo->frameUpdated >= lastUpdate;\\n pthread_mutex_unlock(&update_mutex);\\n\\n if (!upToDate){\\n // must put in appropriate history\\n if (HISTORY_SIZE > 0){\\n std::vector<float> modState = *discState;\\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\\n modState.push_back(history[i]);\\n }\\n updateStateActionHistoryFromModel(modState, action, modelInfo);\\n } else {\\n updateStateActionHistoryFromModel(*discState, action, modelInfo);\\n }\\n }\\n\\n *reward = modelInfo->reward;\\n *term = (rng.uniform() < modelInfo->termProb);\\n\\n if (*term){\\n pthread_mutex_unlock(&info->statemodel_mutex);\\n return actualState;\\n }\\n\\n float randProb = rng.uniform();\\n\\n float probSum = 0.0;\\n std::vector<float> nextstate;\\n\\n if (REALSTATEDEBUG) cout << \\\"randProb: \\\" << randProb << \\\" numNext: \\\" << modelInfo->transitionProbs.size() << endl;\\n\\n //if (modelInfo->transitionProbs.size() == 0)\\n nextstate = actualState;\\n\\n for (std::map<std::vector<float>, float>::iterator outIt\\n = modelInfo->transitionProbs.begin();\\n outIt != modelInfo->transitionProbs.end(); outIt++){\\n\\n float prob = (*outIt).second;\\n probSum += prob;\\n if (REALSTATEDEBUG) cout << randProb << \\\", \\\" << probSum << \\\", \\\" << prob << endl;\\n\\n if (randProb <= probSum){\\n nextstate = (*outIt).first;\\n if (REALSTATEDEBUG) cout << \\\"selected state \\\" << randProb << \\\", \\\" << probSum << \\\", \\\" << prob << endl;\\n break;\\n }\\n }\\n\\n pthread_mutex_unlock(&info->statemodel_mutex);\\n\\n if (trackActual){\\n\\n // find the relative change from discrete center\\n std::vector<float> relChange = subVec(nextstate, *discState);\\n\\n // add that on to actual current state value\\n nextstate = addVec(actualState, relChange);\\n\\n }\\n\\n // check that next state is valid\\n for (unsigned j = 0; j < nextstate.size(); j++){\\n float factor = EPSILON;\\n if (statesPerDim[j] > 0)\\n factor = (featmax[j] - featmin[j]) / (float)statesPerDim[j];\\n if (nextstate[j] < (featmin[j]-factor)\\n || nextstate[j] > (featmax[j]+factor)){\\n return actualState;\\n }\\n }\\n\\n // return new actual state\\n return nextstate;\\n\\n}\\n\\nstd::vector<float> ParallelETUCT::selectRandomState(){\\n\\n pthread_mutex_lock(&statespace_mutex);\\n if (statespace.size() == 0){\\n pthread_mutex_unlock(&statespace_mutex);\\n return std::vector<float>(featmax.size());\\n }\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n // take a random state from the space of ones we've visited\\n int index = 0;\\n std::vector<float> state;\\n\\n pthread_mutex_lock(&statespace_mutex);\\n if (statespace.size() > 1){\\n index = rng.uniformDiscrete(0, statespace.size()-1);\\n }\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n int cnt = 0;\\n\\n if (PTHREADDEBUG) cout << \\\"*** Planning thread wants search lock (randomstate) ***\\\" << endl << flush;\\n\\n pthread_mutex_lock(&statespace_mutex);\\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++, cnt++){\\n if (cnt == index){\\n state = *i;\\n break;\\n }\\n }\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n return state;\\n}\\n\\n\\nvoid* parallelSearchStart(void* arg){\\n ParallelETUCT* pe = reinterpret_cast<ParallelETUCT*>(arg);\\n\\n cout << \\\"start parallel uct planning search thread\\\" << endl << flush;\\n\\n while(true){\\n pe->parallelSearch();\\n }\\n\\n return NULL;\\n}\\n\\nvoid ParallelETUCT::parallelSearch(){\\n\\n std::vector<float> actS;\\n state_t discS;\\n std::deque<float> searchHistory;\\n\\n // get new planning state\\n if (PTHREADDEBUG) {\\n cout << \\\"*** Planning thread wants planning state lock ***\\\" << endl << flush;\\n }\\n pthread_mutex_lock(&(plan_state_mutex));\\n if (HISTORY_SIZE > 0) pthread_mutex_lock(&history_mutex);\\n\\n // take the state we're in \\n actS = actualPlanState;\\n discS = discPlanState;\\n searchHistory = saHistory;\\n\\n\\n // wait for non-null\\n if (discS == NULL){\\n pthread_mutex_unlock(&(plan_state_mutex));\\n if (HISTORY_SIZE > 0) pthread_mutex_unlock(&history_mutex);\\n return;\\n }\\n\\n if (PTHREADDEBUG){\\n pthread_mutex_lock(&statespace_mutex);\\n cout << \\\" uct search from state s (\\\"\\n << statedata[discS].uctVisits <<\\\"): \\\";\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n for (unsigned i = 0; i < discS->size(); i++){\\n cout << (*discS)[i] << \\\", \\\";\\n }\\n cout << endl << flush;\\n }\\n\\n // call uct search on it\\n pthread_mutex_unlock(&(plan_state_mutex));\\n if (HISTORY_SIZE > 0) pthread_mutex_unlock(&history_mutex);\\n\\n if (PTHREADDEBUG) cout << \\\"*** Planning thread wants search lock ***\\\" << endl;\\n uctSearch(actS, discS, 0, searchHistory);\\n\\n pthread_yield();\\n\\n}\\n\\n\\n// canonicalize all the states so we already have them in our statespace\\nvoid ParallelETUCT::initStates(){\\n cout << \\\"init states\\\" << endl;\\n std::vector<float> s(featmin.size());\\n\\n fillInState(s,0);\\n}\\n\\nvoid ParallelETUCT::fillInState(std::vector<float>s, int depth){\\n\\n // if depth == size, canonicalize and return\\n if (depth == (int)featmin.size()){\\n canonicalize(s);\\n return;\\n }\\n\\n // go through all features at depth\\n for (float i = featmin[depth]; i < featmax[depth]+1; i++){\\n s[depth] = i;\\n fillInState(s, depth+1);\\n }\\n}\\n\\n\\n\\nvoid ParallelETUCT::savePolicy(const char* filename){\\n\\n ofstream policyFile(filename, ios::out | ios::binary | ios::trunc);\\n\\n // first part, save the vector size\\n int fsize = featmin.size();\\n policyFile.write((char*)&fsize, sizeof(int));\\n\\n // save numactions\\n policyFile.write((char*)&numactions, sizeof(int));\\n\\n // go through all states, and save Q values\\n pthread_mutex_lock(&statespace_mutex);\\n\\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n state_t s = canonicalize(*i);\\n\\n pthread_mutex_lock(&statespace_mutex);\\n state_info* info = &(statedata[s]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n // save state\\n policyFile.write((char*)&((*i)[0]), sizeof(float)*fsize);\\n\\n // save q-values\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n policyFile.write((char*)&(info->Q[0]), sizeof(float)*numactions);\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n pthread_mutex_lock(&statespace_mutex);\\n }\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n policyFile.close();\\n}\\n\\n\\n\\nvoid ParallelETUCT::loadPolicy(const char* filename){\\n\\n ifstream policyFile(filename, ios::in | ios::binary);\\n\\n // first part, save the vector size\\n int fsize;\\n policyFile.read((char*)&fsize, sizeof(int));\\n cout << \\\"Numfeats loaded: \\\" << fsize << endl << flush;\\n\\n // save numactions\\n int nact;\\n policyFile.read((char*)&nact, sizeof(int));\\n cout << \\\"nact loaded: \\\" << nact << endl << flush;\\n cout << \\\" numactions: \\\" << numactions << endl << flush;\\n\\n if (nact != numactions){\\n cout << \\\"this policy is not valid loaded nact: \\\" << nact\\n << \\\" was told: \\\" << numactions << endl << flush;\\n exit(-1);\\n }\\n\\n // go through all states, loading q values\\n while(!policyFile.eof()){\\n std::vector<float> state(fsize, 0.0);\\n\\n // load state\\n policyFile.read((char*)&(state[0]), sizeof(float)*fsize);\\n //if (LOADDEBUG){\\n //cout << \\\"load policy for state: \\\";\\n // printState(state);\\n //}\\n\\n state_t s = canonicalize(state);\\n\\n pthread_mutex_lock(&statespace_mutex);\\n state_info* info = &(statedata[s]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n if (policyFile.eof()) break;\\n\\n // load q values\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n policyFile.read((char*)&(info->Q[0]), sizeof(float)*numactions);\\n\\n info->uctVisits = numactions * 100;\\n\\n for (int j = 0; j < numactions; j++){\\n info->uctActions[j] = 100;\\n }\\n\\n info->needsUpdate = true;\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n //if (LOADDEBUG){\\n //cout << \\\"Q values: \\\" << endl;\\n //for (int iAct = 0; iAct < numactions; iAct++){\\n // cout << \\\" Action: \\\" << iAct << \\\" val: \\\" << info->Q[iAct] << endl;\\n //}\\n //}\\n }\\n\\n policyFile.close();\\n cout << \\\"Policy loaded!!!\\\" << endl << flush;\\n}\\n\\nvoid ParallelETUCT::logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax){\\n std::vector<float> state(2, 0.0);\\n for (int i = xmin ; i < xmax; i++){\\n for (int j = ymin; j < ymax; j++){\\n state[0] = j;\\n state[1] = i;\\n state_t s = canonicalize(state);\\n\\n pthread_mutex_lock(&statespace_mutex);\\n state_info* info = &(statedata[s]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n std::vector<float> &Q_s = info->Q;\\n const std::vector<float>::iterator max =\\n random_max_element(Q_s.begin(), Q_s.end());\\n *of << (*max) << \\\",\\\";\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n }\\n }\\n}\\n\\n\\n// should do it such that an already discretized state stays the same\\n// mainly the numerical value of each bin should be the average of that bin\\nstd::vector<float> ParallelETUCT::discretizeState(const std::vector<float> &s){\\n std::vector<float> ds(s.size());\\n\\n for (unsigned i = 0; i < s.size(); i++){\\n\\n // since i'm sometimes doing this for discrete domains\\n // want to center bins on 0, not edge on 0\\n //cout << \\\"feat \\\" << i << \\\" range: \\\" << featmax[i] << \\\" \\\" << featmin[i] << \\\" \\\" << (featmax[i]-featmin[i]) << \\\" n: \\\" << (float)statesPerDim;\\n\\n float factor = (featmax[i] - featmin[i]) / (float)statesPerDim[i];\\n int bin = 0;\\n if (s[i] > 0){\\n bin = (int)((s[i]+factor/2) / factor);\\n } else {\\n bin = (int)((s[i]-factor/2) / factor);\\n }\\n\\n ds[i] = factor*bin;\\n //cout << \\\" factor: \\\" << factor << \\\" bin: \\\" << bin;\\n //cout << \\\" Original: \\\" << s[i] << \\\" Discrete: \\\" << ds[i] << endl;\\n }\\n\\n return ds;\\n}\\n\\n\\nstd::vector<float> ParallelETUCT::addVec(const std::vector<float> &a, const std::vector<float> &b){\\n if (a.size() != b.size())\\n cout << \\\"ERROR: add vector sizes wrong \\\" << a.size() << \\\", \\\" << b.size() << endl;\\n\\n std::vector<float> c(a.size(), 0.0);\\n for (unsigned i = 0; i < a.size(); i++){\\n c[i] = a[i] + b[i];\\n }\\n\\n return c;\\n}\\n\\nstd::vector<float> ParallelETUCT::subVec(const std::vector<float> &a, const std::vector<float> &b){\\n if (a.size() != b.size())\\n cout << \\\"ERROR: sub vector sizes wrong \\\" << a.size() << \\\", \\\" << b.size() << endl;\\n\\n std::vector<float> c(a.size(), 0.0);\\n for (unsigned i = 0; i < a.size(); i++){\\n c[i] = a[i] - b[i];\\n }\\n\\n return c;\\n}\\n\\nvoid ParallelETUCT::setFirst(){\\n if (HISTORY_SIZE == 0) return;\\n\\n if (HISTORYDEBUG) cout << \\\"first action, set sahistory to 0s\\\" << endl;\\n\\n pthread_mutex_lock(&(history_mutex));\\n // first action, reset history vector\\n saHistory.resize(saHistory.size(), 0.0);\\n pthread_mutex_unlock(&(history_mutex));\\n}\\n\\nvoid ParallelETUCT::setSeeding(bool seeding){\\n\\n if (HISTORYDEBUG) cout << \\\"set seed mode to \\\" << seeding << endl;\\n seedMode = seeding;\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7761467695236206,\n \"alphanum_fraction\": 0.7798165082931519,\n \"avg_line_length\": 67.1875,\n \"blob_id\": \"6bf312cbc00773f2ed4d16be2aabe4f1b121cfbe\",\n \"content_id\": \"966a0b05bb13ca4ab6622ac6810a50ea5ce1b1db\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 1090,\n \"license_type\": \"no_license\",\n \"max_line_length\": 126,\n \"num_lines\": 16,\n \"path\": \"/build/ugv_course_gazebo_plugins/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"ugv_course_gazebo_plugins\\\")\\nset(ugv_course_gazebo_plugins_VERSION \\\"0.0.0\\\")\\nset(ugv_course_gazebo_plugins_MAINTAINER \\\"micho <micho@todo.todo>\\\")\\nset(ugv_course_gazebo_plugins_PACKAGE_FORMAT \\\"1\\\")\\nset(ugv_course_gazebo_plugins_BUILD_DEPENDS \\\"dynamic_reconfigure\\\" \\\"roscpp\\\" \\\"tf\\\" \\\"ugv_course_libs\\\" \\\"visualization_msgs\\\")\\nset(ugv_course_gazebo_plugins_BUILD_EXPORT_DEPENDS \\\"dynamic_reconfigure\\\" \\\"roscpp\\\" 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\"/**\\n The LightWorld domain from \\n \\\"Building Portable Options: Skill Transfer in Reinforcement Learning\\\"\\n by Konidaris and Barto\\n*/\\n\\n#ifndef _LIGHTWORLD_H_\\n#define _LIGHTWORLD_H_\\n\\n#include <set>\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n\\n\\nclass LightWorld: public Environment {\\npublic:\\n /** Creates a PlayRoom domain using the specified map.\\n Puts objects in random locations.\\n \\\\param rand Random number generator to use.\\n \\\\param stochastic Whether to use nondeterministic actions \\n \\\\param nrooms Number of rooms to have */\\n LightWorld(Random &rand, bool stochastic, int nrooms);\\n\\n virtual ~LightWorld();\\n\\n virtual const std::vector<float> &sensation() const;\\n virtual float apply(int action);\\n\\n virtual bool terminal() const;\\n virtual void reset();\\n virtual int getNumActions();\\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\\n virtual bool isEpisodic() { return false; };\\n virtual void getMinMaxReward(float* minR, float* maxR);\\n\\n\\n friend std::ostream &operator<<(std::ostream &out, const LightWorld &playroom);\\n\\n\\n void resetKey();\\n void setKey(std::vector<float> testS);\\n\\n struct room_info {\\n int height;\\n int width;\\n int key_ns;\\n int key_ew;\\n int lock_ns;\\n int lock_ew;\\n int door_ns;\\n int door_ew;\\n };\\n\\n typedef std::pair<float,float> coord_t;\\n enum lightworld_action_t {NORTH, EAST, WEST, SOUTH, PICKUP, PRESS, NUM_ACTIONS};\\n\\n bool LWDEBUG;\\n\\n const bool noisy;\\n int nrooms;\\n Random &rng;\\n\\n std::vector<float> s;\\n float& ns;\\n float& ew;\\n float& have_key;\\n float& door_open;\\n float& room_id;\\n float& key_n;\\n float& key_e;\\n float& key_w;\\n float& key_s;\\n float& lock_n;\\n float& lock_e;\\n float& lock_w;\\n float& lock_s;\\n float& door_n;\\n float& door_e;\\n float& door_w; \\n float& door_s;\\n\\n std::vector<room_info> rooms;\\n\\n void updateSensors();\\n int applyNoise(int action);\\n\\n /** Prints the current state */\\n void print_state() const;\\n\\n /** Prints the current map. */\\n void print_map() const;\\n\\n void updateVisits();\\n\\n int totalVisited;\\n int keyVisited;\\n int lockVisited;\\n int doorVisited;\\n int haveKey;\\n int doorOpen;\\n int leaveRoom;\\n int pressKey;\\n int pressLockCorrect;\\n int pressLockIncorrect;\\n int pressDoor;\\n int pressOther;\\n int pickupKeyCorrect;\\n int pickupKeyIncorrect;\\n int pickupLock;\\n int pickupDoor;\\n int pickupOther;\\n\\n int MAX_SENSE;\\n\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.7731958627700806,\n \"alphanum_fraction\": 0.7835051417350769,\n \"avg_line_length\": 40.71428680419922,\n \"blob_id\": \"07d8f8b86749ebd521e34478b2aa3da947826c17\",\n \"content_id\": \"72cdcb7431673dd607837cc5d0479e46be951909\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 291,\n \"license_type\": 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\\\"\\\")\\nset(rl_msgs_URL_REPOSITORY \\\"\\\")\\nset(rl_msgs_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.6399772763252258,\n \"alphanum_fraction\": 0.6439523100852966,\n \"avg_line_length\": 33.52941131591797,\n \"blob_id\": \"c335bba2360d99db9167bc8e6026550be4e9d116\",\n \"content_id\": \"10e04c1b28f20d807f8bd6bedf47162b824719a6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 3522,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 102,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/include/rl_env/gridworld.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#ifndef _GRIDWORLD_H_\\n#define _GRIDWORLD_H_\\n\\n#include <rl_common/Random.h>\\n\\n#include <iostream>\\n#include <vector>\\n\\n/** \\\\file\\n Declarations supporting Gridworld */\\n\\n/** A representation of a rectangular grid with interior walls that\\n allows users easily to determine available directions of travel in\\n each cell. */\\nclass Gridworld {\\npublic:\\n /** Creates a gridworld using the given wall occupancy matrices.\\n \\\\param height Height of the gridworld.\\n \\\\param width Width of the gridworld.\\n \\\\param northsouth Whether each interior wall blocking NS\\n movement exists, organized first by [w]\\n columns and then by [h-1] rows.\\n \\\\param eastwest Whether each interior wall blocking EW movement\\n exists, organized first by [h] rows and then by\\n [w-1] columns. */\\n Gridworld(unsigned height, unsigned width,\\n\\t const std::vector<std::vector<bool> > &northsouth,\\n\\t const std::vector<std::vector<bool> > &eastwest);\\n\\n /** Creates a random gridworld with the desired dimensions. */\\n Gridworld(unsigned width, unsigned height, Random &rng);\\n\\n unsigned height() const { return h; }\\n unsigned width() const { return w; }\\n\\n /** Checks if a wall blocks movement in a given direction from a\\n given coordinate.\\n \\\\param nsCoord The coordinate along the NS direction.\\n \\\\param ewCoord The coordinate along the EW direction.\\n \\\\param dir The direction in which to check movement. 0 is\\n north, 1 is south, 2 is east, 3 is west. */\\n bool wall(unsigned nsCoord, unsigned ewCoord, unsigned dir) const;\\n\\n friend std::ostream &operator<<(std::ostream &out, const Gridworld &g);\\n\\nprotected:\\n /** Attempts to add a random wall that must not touch any other\\n interior wall. */\\n void add_obstacle(Random &rng);\\n\\n /** Given a segment of wall that could be built, builds a subset of\\n it of random length. Always builds from one of the two ends. */\\n void chooseSegment(unsigned first,\\n\\t\\t unsigned last,\\n\\t\\t unsigned j,\\n\\t\\t std::vector<std::vector<bool> > ¶llel,\\n\\t\\t Random &rng);\\n\\nprivate:\\n /** Determines if the \\\"smaller\\\" endpoint of the given line segment\\n is clear: none of the four possible walls using that endpoint\\n exist.\\n \\\\param i An index into parallel.\\n \\\\param j An index into parallel[i].\\n \\\\param parallel Occupancy matrix for walls parallel to the wall\\n under consiration.\\n \\\\param perpendicular Occupancy matrix for walls perpendicular to\\n the wall under consiration. */\\n bool isClear(unsigned i, unsigned j,\\n\\t std::vector<std::vector<bool> > ¶llel,\\n\\t std::vector<std::vector<bool> > &perpendicular) const\\n {\\n if (i > parallel.size())\\n return false;\\n if (i < parallel.size() && parallel[i][j])\\n return false;\\n if (i > 0 && parallel[i - 1][j])\\n return false;\\n if (i > 0\\n\\t&& i <= perpendicular[j].size()\\n\\t&& perpendicular[j][i - 1])\\n return false;\\n if (i > 0\\n\\t&& i <= perpendicular[j + 1].size()\\n\\t&& perpendicular[j + 1][i - 1])\\n return false;\\n return true;\\n }\\n\\n const unsigned h;\\n const unsigned w;\\n\\n /** The occupancy matrix for the walls that obstruct NS\\n movement. Element i,j is true if in the ith column the jth\\n east-west wall from the bottom is present. */\\n std::vector<std::vector<bool> > ns;\\n\\n /** The same as for ns but different. */\\n std::vector<std::vector<bool> > ew;\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.6655017733573914,\n \"alphanum_fraction\": 0.6789815425872803,\n \"avg_line_length\": 28.895523071289062,\n \"blob_id\": \"ec0a3ece92261ba5e80e0d15202bdb9e3554eca1\",\n \"content_id\": \"4f51b51ab4eac6e21d0bdfb84e53588e11e7799c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 2003,\n \"license_type\": \"no_license\",\n \"max_line_length\": 101,\n \"num_lines\": 67,\n \"path\": \"/src/roundbot_control/src/DiffController.cpp\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include <roundbot_control/DiffController.h>\\n\\nnamespace roundbot_control\\n{\\n\\nbool DiffController::init(hardware_interface::VelocityJointInterface* hw, ros::NodeHandle &n)\\n{\\n sub_twist_ = n.subscribe(\\\"/roundbot/cmd_vel\\\", 1, &DiffController::recvTwist, this);\\n loadParams(n);\\n left_target_speed_ = 0.0;\\n right_target_speed_ = 0.0;\\n\\n left_joint_ = hw->getHandle(left_joint_name_);\\n right_joint_ = hw->getHandle(right_joint_name_);\\n return true;\\n}\\n\\nvoid DiffController::loadParams(ros::NodeHandle& n)\\n{\\n n.param(\\\"left_joint\\\", left_joint_name_, std::string(\\\"left_wheel\\\"));\\n n.param(\\\"right_joint\\\", right_joint_name_, std::string(\\\"right_wheel\\\"));\\n n.param(\\\"control_timeout\\\", timeout_, 0.25);\\n n.param(\\\"wheel_radius\\\", wheel_radius_, 0.2);\\n n.param(\\\"wheel_separation\\\", wheel_separation_, 0.9);\\n\\n double max_speed, max_accel, max_decel;\\n n.param(\\\"max_speed\\\", max_speed, 10.0);\\n n.param(\\\"max_accel\\\", max_accel, 1.0);\\n n.param(\\\"max_decel\\\", max_decel, 1.0);\\n\\n left_motor_ = new MotorSim(max_speed, max_accel, max_decel);\\n right_motor_ = new MotorSim(max_speed, max_accel, max_decel);\\n}\\n\\nvoid DiffController::recvTwist(const geometry_msgs::Twist::ConstPtr& msg)\\n{\\n command_stamp_ = ros::Time::now();\\n cmd_ = *msg;\\n}\\n\\nvoid DiffController::update(const ros::Time& time, const ros::Duration& period)\\n{\\n if ((time - command_stamp_).toSec() >= timeout_) {\\n left_target_speed_ = 0.0;\\n right_target_speed_ = 0.0;\\n }else{\\n left_target_speed_ = (cmd_.linear.x - 0.5 * wheel_separation_ * cmd_.angular.z) / wheel_radius_;\\n right_target_speed_ = (cmd_.linear.x + 0.5 * wheel_separation_ * cmd_.angular.z) / wheel_radius_;\\n }\\n\\n double left_actual = left_motor_->iterate(left_target_speed_, period.toSec());\\n double right_actual = right_motor_->iterate(right_target_speed_, period.toSec());\\n left_joint_.setCommand(left_actual);\\n right_joint_.setCommand(right_actual);\\n}\\n\\nvoid DiffController::starting(const ros::Time& 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_rl_msgs_generate_messages_check_deps_RLExperimentInfo\\\"\\n\\t@echo \\\"... _rl_msgs_generate_messages_check_deps_RLStateReward\\\"\\n\\t@echo \\\"... rl_msgs_generate_messages\\\"\\n\\t@echo \\\"... _rl_msgs_generate_messages_check_deps_RLEnvSeedExperience\\\"\\n\\t@echo \\\"... rl_msgs_generate_messages_cpp\\\"\\n\\t@echo \\\"... rl_msgs_generate_messages_eus\\\"\\n\\t@echo \\\"... rl_msgs_genpy\\\"\\n\\t@echo \\\"... rl_msgs_gencpp\\\"\\n\\t@echo \\\"... rl_msgs_geneus\\\"\\n\\t@echo \\\"... rl_msgs_generate_messages_py\\\"\\n\\t@echo \\\"... rl_msgs_generate_messages_lisp\\\"\\n\\t@echo \\\"... _rl_msgs_generate_messages_check_deps_RLEnvDescription\\\"\\n\\t@echo \\\"... rl_msgs_generate_messages_nodejs\\\"\\n\\t@echo \\\"... rl_msgs_genlisp\\\"\\n\\t@echo \\\"... rl_msgs_gennodejs\\\"\\n.PHONY : help\\n\\n\\n\\n#=============================================================================\\n# Special targets to cleanup operation of make.\\n\\n# Special rule to run CMake to check the build system integrity.\\n# No rule that depends on this can have commands that come from listfiles\\n# because they might be regenerated.\\ncmake_check_build_system:\\n\\tcd /home/justin/ros_test/build && $(CMAKE_COMMAND) -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR) --check-build-system CMakeFiles/Makefile.cmake 0\\n.PHONY : cmake_check_build_system\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5431063771247864,\n \"alphanum_fraction\": 0.5507646799087524,\n \"avg_line_length\": 27.5848445892334,\n \"blob_id\": \"1009eca8749bb61d9b6b09eb741611195a8246b6\",\n \"content_id\": \"7654504738dadf367eca00237533819604770fcf\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 44135,\n \"license_type\": \"no_license\",\n \"max_line_length\": 132,\n \"num_lines\": 1544,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/LinearSplitsTree.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include \\\"LinearSplitsTree.hh\\\"\\n\\n// LinearSplitsTree, from the following sources:\\n\\n// Include stuff for newmat matrix libraries\\n\\n#define WANT_MATH // include.h will get math fns\\n // newmatap.h will get include.h\\n#include \\\"../newmat/newmatap.h\\\" // need matrix applications\\n#ifdef use_namespace\\nusing namespace NEWMAT; // access NEWMAT namespace\\n#endif\\n\\n// TODO:\\n// - save regression from split testing rather than re-building it later\\n\\n\\nLinearSplitsTree::LinearSplitsTree(int id, int trainMode, int trainFreq, int m,\\n float featPct, bool simple, float min_er,\\n Random rng):\\n id(id), mode(trainMode), \\n freq(trainFreq), M(m),\\n featPct(featPct), SIMPLE(simple), \\n MIN_ER(min_er), rng(rng)\\n{\\n\\n nnodes = 0;\\n nOutput = 0;\\n nExperiences = 0;\\n hadError = false;\\n maxnodes = N_LS_NODES;\\n totalnodes = 0;\\n\\n // how close a split has to be to be randomly selected\\n SPLIT_MARGIN = 0.0; //0.02; //5; //01; //0.05; //0.2; //0.05;\\n\\n LMDEBUG = false;// true;\\n DTDEBUG = false;//true;\\n SPLITDEBUG = false; //true;\\n STOCH_DEBUG = false; //true; //false; //true;\\n INCDEBUG = false; //true; //false; //true;\\n NODEDEBUG = false;\\n COPYDEBUG = false; //true;\\n\\n cout << \\\"Created linear splits decision tree \\\" << id;\\n if (SIMPLE) cout << \\\" simple regression\\\";\\n else cout << \\\" multivariate regrssion\\\";\\n if (DTDEBUG){\\n cout << \\\" mode: \\\" << mode << \\\" freq: \\\" << freq << endl;\\n }\\n cout << \\\" MIN_ER: \\\" << MIN_ER << endl;\\n\\n\\n initNodes();\\n initTree();\\n\\n}\\n\\nLinearSplitsTree::LinearSplitsTree(const LinearSplitsTree& ls):\\n id(ls.id), mode(ls.mode), \\n freq(ls.freq), M(ls.M),\\n featPct(ls.featPct), SIMPLE(ls.SIMPLE), \\n MIN_ER(ls.MIN_ER), rng(ls.rng)\\n{\\n COPYDEBUG = ls.COPYDEBUG;\\n if (COPYDEBUG) cout << \\\"LS copy \\\" << id << endl;\\n nnodes = 0;\\n nOutput = ls.nOutput;\\n nExperiences = ls.nExperiences;\\n hadError = ls.hadError;\\n totalnodes = 0;\\n maxnodes = ls.maxnodes;\\n SPLIT_MARGIN = ls.SPLIT_MARGIN; \\n LMDEBUG = ls.LMDEBUG;\\n DTDEBUG = ls.DTDEBUG;\\n SPLITDEBUG = ls.SPLITDEBUG;\\n STOCH_DEBUG = ls.STOCH_DEBUG; \\n INCDEBUG = ls.INCDEBUG; \\n NODEDEBUG = ls.NODEDEBUG;\\n\\n if (COPYDEBUG) cout << \\\" LS copy nodes, experiences, root, etc\\\" << endl;\\n // copy all experiences\\n for (int i = 0; i < N_LST_EXP; i++){\\n allExp[i] = ls.allExp[i];\\n }\\n if (COPYDEBUG) cout << \\\" LS copied exp array\\\" << endl;\\n\\n // set experience pointers\\n experiences.resize(ls.experiences.size());\\n for (unsigned i = 0; i < ls.experiences.size(); i++){\\n experiences[i] = &(allExp[i]);\\n }\\n if (COPYDEBUG) cout << \\\" LS set experience pointers\\\" << endl;\\n\\n // now the tricky part, set the pointers inside the tree nodes correctly\\n initNodes();\\n\\n if (COPYDEBUG) cout << \\\" LS copy tree \\\" << endl;\\n root = allocateNode();\\n lastNode = root;\\n copyTree(root, ls.root);\\n if (COPYDEBUG) cout << \\\" LS tree copy done\\\" << endl;\\n \\n if (COPYDEBUG) {\\n cout << endl << \\\"New tree: \\\" << endl;\\n printTree(root, 0);\\n cout << endl;\\n cout << \\\" LS copy done\\\" << endl;\\n }\\n\\n}\\n\\nvoid LinearSplitsTree::copyTree(tree_node* newNode, tree_node* origNode){\\n\\n int nodeId = newNode->id;\\n\\n if (COPYDEBUG) {\\n cout << \\\" Copy node \\\" << newNode->id << \\\" from node \\\" << origNode->id << endl;\\n cout << \\\" NewAddy \\\" << newNode << \\\", old: \\\" << origNode << endl;\\n }\\n\\n // copy node from t\\n *newNode = *origNode;\\n newNode->id = nodeId;\\n\\n // if it has children, allocate and copy them too\\n if (origNode->l != NULL && !newNode->leaf){\\n newNode->l = allocateNode();\\n if (COPYDEBUG) cout << \\\" Copy left node \\\" << newNode->l->id << \\\" from \\\" << origNode->l->id << endl;\\n copyTree(newNode->l, origNode->l);\\n } else {\\n newNode->l = NULL;\\n }\\n\\n if (origNode->r != NULL && !newNode->leaf){\\n newNode->r = allocateNode();\\n if (COPYDEBUG) cout << \\\" Copy right node \\\" << newNode->r->id << \\\" from \\\" << origNode->r->id << endl;\\n copyTree(newNode->r, origNode->r);\\n } else {\\n newNode->r = NULL;\\n }\\n}\\n\\nLinearSplitsTree* LinearSplitsTree::getCopy(){\\n LinearSplitsTree* copy = new LinearSplitsTree(*this);\\n return copy;\\n}\\n\\nLinearSplitsTree::~LinearSplitsTree() {\\n deleteTree(root);\\n for (unsigned i = N_LST_EXP; i < experiences.size(); i++){\\n delete experiences[i];\\n }\\n experiences.clear();\\n}\\n\\n// here the target output will be a single value\\nbool LinearSplitsTree::trainInstance(classPair &instance){\\n\\n if (DTDEBUG) cout << id << \\\" trainInstance\\\" << endl;\\n\\n bool modelChanged = false;\\n\\n // simply add this instance to the set of experiences\\n\\n // take from static array until we run out\\n tree_experience *e;\\n if (nExperiences < N_LST_EXP){\\n // from statically created set of experiences\\n e = &(allExp[nExperiences]);\\n\\n } else {\\n // dynamically create experience\\n e = new tree_experience;\\n }\\n\\n\\n e->input = instance.in;\\n e->output = instance.out;\\n experiences.push_back(e);\\n nExperiences++;\\n\\n if (nExperiences == 1000000){\\n cout << \\\"Reached limit of # experiences allowed.\\\" << endl;\\n return false;\\n }\\n\\n if (nExperiences != (int)experiences.size())\\n cout << \\\"ERROR: experience size mismatch: \\\" << nExperiences << \\\", \\\" << experiences.size() << endl;\\n\\n //cout << nExperiences << endl << flush;\\n //if (nExperiences == 503 && id == 10){\\n // DTDEBUG = true;\\n // SPLITDEBUG = true;\\n // INCDEBUG = true;\\n //}\\n\\n if ( DTDEBUG) {\\n cout << \\\"Original input: \\\";\\n for (unsigned i = 0; i < instance.in.size(); i++){\\n cout << instance.in[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Original output: \\\" << instance.out << endl;\\n cout << \\\"Added exp id: \\\" << nExperiences << \\\" output: \\\" << e->output << endl;\\n cout << \\\"Address: \\\" << e << \\\" Input : \\\";\\n for (unsigned i = 0; i < e->input.size(); i++){\\n cout << e->input[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Now have \\\" << nExperiences << \\\" experiences.\\\" << endl;\\n }\\n\\n // mode 0: re-build every step\\n if (mode == BUILD_EVERY || nExperiences <= 1){\\n rebuildTree();\\n modelChanged = true;\\n }\\n\\n // mode 1: re-build on error only\\n else if (mode == BUILD_ON_ERROR){\\n\\n // build on misclassification\\n // check for misclassify\\n std::map<float, float> answer;\\n testInstance(e->input, &answer);\\n float val = answer.begin()->first;\\n float error = fabs(val - e->output);\\n\\n if (error > 0.0){\\n rebuildTree();\\n modelChanged = true;\\n } \\n }\\n\\n // mode 2: re-build every FREQ steps\\n else if (mode == BUILD_EVERY_N){\\n // build every freq steps\\n if (!modelChanged && (nExperiences % freq) == 0){\\n rebuildTree();\\n modelChanged = true;\\n }\\n }\\n\\n if (modelChanged){\\n if (DTDEBUG || SPLITDEBUG) cout << \\\"DT \\\" << id << \\\" tree re-built.\\\" << endl;\\n\\n if (DTDEBUG || SPLITDEBUG){\\n cout << endl << \\\"DT: \\\" << id << endl;\\n printTree(root, 0);\\n cout << \\\"Done printing tree\\\" << endl;\\n }\\n }\\n\\n /*\\n if (nExperiences % 100 == 0){\\n cout << endl << \\\"DT: \\\" << id << endl;\\n printTree(root, 0);\\n cout << \\\"Done printing tree\\\" << endl;\\n \\n // look at error\\n float errorSum = 0.0;\\n for (int i = 0; i < nExperiences; i++){\\n e = experiences[i];\\n std::map<float, float> answer;\\n testInstance(e->input, &answer);\\n float val = answer.begin()->first;\\n float error = fabs(val - e->output);\\n errorSum += error;\\n }\\n float avgError = errorSum / (float)nExperiences;\\n cout << \\\"avgError: \\\" << avgError << endl << endl;\\n }\\n */\\n\\n return modelChanged;\\n\\n}\\n\\n\\n// here the target output will be a single value\\nbool LinearSplitsTree::trainInstances(std::vector<classPair> &instances){\\n if (DTDEBUG) cout << \\\"DT trainInstances: \\\" << instances.size() << endl;\\n\\n bool modelChanged = false;\\n\\n bool doBuild = false;\\n\\n // loop through instances, possibly checking for errors\\n for (unsigned a = 0; a < instances.size(); a++){\\n classPair instance = instances[a];\\n\\n // simply add this instance to the set of experiences\\n\\n // take from static array until we run out\\n tree_experience *e;\\n if (nExperiences < N_LST_EXP){\\n // from statically created set of experiences\\n e = &(allExp[nExperiences]);\\n\\n } else {\\n // dynamically create experience\\n e = new tree_experience;\\n }\\n\\n\\n e->input = instance.in;\\n e->output = instance.out;\\n experiences.push_back(e);\\n nExperiences++;\\n\\n if (nExperiences == 1000000){\\n cout << \\\"Reached limit of # experiences allowed.\\\" << endl;\\n return false;\\n }\\n\\n if (nExperiences != (int)experiences.size())\\n cout << \\\"ERROR: experience size mismatch: \\\" << nExperiences << \\\", \\\" << experiences.size() << endl;\\n\\n if (DTDEBUG) {\\n cout << \\\"Original input: \\\";\\n for (unsigned i = 0; i < instance.in.size(); i++){\\n cout << instance.in[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Original output: \\\" << instance.out << endl;\\n cout << \\\"Added exp id: \\\" << nExperiences << \\\" output: \\\" << e->output << endl;\\n cout << \\\"Address: \\\" << e << \\\" Input : \\\";\\n for (unsigned i = 0; i < e->input.size(); i++){\\n cout << e->input[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Now have \\\" << nExperiences << \\\" experiences.\\\" << endl;\\n }\\n\\n /*\\n if (nExperiences % 100 == 0){\\n cout << endl << \\\"DT: \\\" << id << endl;\\n printTree(root, 0);\\n cout << \\\"Done printing tree\\\" << endl;\\n \\n // look at error\\n float errorSum = 0.0;\\n for (int i = 0; i < nExperiences; i++){\\n e = experiences[i];\\n std::map<float, float> answer;\\n testInstance(e->input, &answer);\\n float val = answer.begin()->first;\\n float error = fabs(val - e->output);\\n errorSum += error;\\n }\\n float avgError = errorSum / (float)nExperiences;\\n cout << \\\"avgError: \\\" << avgError << endl << endl;\\n }\\n */\\n\\n // depending on mode/etc, maybe re-build tree\\n\\n // don't need to check if we've already decided\\n if (doBuild) continue;\\n\\n // mode 0: re-build every step\\n if (mode == BUILD_EVERY || nExperiences <= 1){\\n doBuild = true;\\n }\\n\\n // mode 1: re-build on error only\\n else if (mode == BUILD_ON_ERROR){\\n\\n // build on misclassification\\n // check for misclassify\\n std::map<float, float> answer;\\n testInstance(e->input, &answer);\\n float val = answer.begin()->first;\\n float error = fabs(val - e->output);\\n\\n if (error > 0.0){\\n doBuild = true;\\n }\\n\\n }\\n\\n // mode 2: re-build every FREQ steps\\n else if (mode == BUILD_EVERY_N){\\n // build every freq steps\\n if (!modelChanged && (nExperiences % freq) == 0){\\n doBuild = true;\\n }\\n }\\n\\n } // loop of new instances\\n\\n if (DTDEBUG) cout << \\\"Added \\\" << instances.size() << \\\" new instances. doBuild = \\\" << doBuild << endl;\\n\\n if (doBuild){\\n rebuildTree();\\n modelChanged = true;\\n }\\n\\n if (modelChanged){\\n if (DTDEBUG || SPLITDEBUG) cout << \\\"DT \\\" << id << \\\" tree re-built.\\\" << endl;\\n\\n if (DTDEBUG || SPLITDEBUG){\\n cout << endl << \\\"DT: \\\" << id << endl;\\n printTree(root, 0);\\n cout << \\\"Done printing tree\\\" << endl;\\n }\\n }\\n\\n return modelChanged;\\n\\n}\\n\\n\\nvoid LinearSplitsTree::rebuildTree(){\\n //cout << \\\"rebuild tree \\\" << id << \\\" on exp: \\\" << nExperiences << endl;\\n //deleteTree(root);\\n\\n // re-calculate avg error for root\\n root->avgError = calcAvgErrorforSet(experiences);\\n\\n buildTree(root, experiences, false);\\n //cout << \\\"tree \\\" << id << \\\" rebuilt. \\\" << endl;\\n}\\n\\n\\n// TODO: here we want to return the probability of the output value being each of the possible values, in the stochastic case\\nvoid LinearSplitsTree::testInstance(const std::vector<float> &input, std::map<float, float>* retval){\\n if (DTDEBUG) cout << \\\"testInstance on tree \\\" << id << endl;\\n\\n retval->clear();\\n\\n // in case the tree is empty\\n if (experiences.size() == 0){\\n (*retval)[0.0] = 1.0;\\n return;\\n }\\n\\n // follow through tree to leaf\\n tree_node* leaf = traverseTree(root, input);\\n lastNode = leaf;\\n\\n // and return mapping of outputs and their probabilities\\n leafPrediction(leaf, input, retval);\\n\\n}\\n\\nfloat LinearSplitsTree::getConf(const std::vector<float> &input){\\n if (DTDEBUG) cout << \\\"numVisits\\\" << endl;\\n\\n // in case the tree is empty\\n if (experiences.size() == 0){\\n return 0;\\n }\\n\\n if (lastNode == NULL)\\n return 0;\\n\\n // follow through tree to leaf\\n //tree_node* leaf = traverseTree(root, input);\\n\\n // and return # in this leaf\\n float conf = (float)lastNode->nInstances / (float)(2.0*M);\\n if (conf > 1.0)\\n return 1.0;\\n else\\n return conf;\\n\\n}\\n\\n// check to see if this state is one we should explore\\n// to get more info on potential splits\\n\\n\\n\\n// init the tree\\nvoid LinearSplitsTree::initTree(){\\n if (DTDEBUG) cout << \\\"initTree()\\\" << endl;\\n root = allocateNode();\\n\\n if (DTDEBUG) cout << \\\" root id = \\\" << root->id << endl;\\n\\n // just to ensure the diff models are on different random values\\n for (int i = 0; i < id; i++){\\n rng.uniform(0, 1);\\n }\\n\\n}\\n\\n\\n\\n// init a tree node\\nvoid LinearSplitsTree::initTreeNode(tree_node* node){\\n if (DTDEBUG) cout << \\\"initTreeNode()\\\";\\n\\n node->id = nnodes++;\\n if (DTDEBUG) cout << \\\" id = \\\" << node->id << endl;\\n\\n totalnodes++;\\n if (totalnodes > maxnodes){\\n maxnodes = totalnodes;\\n if (DTDEBUG) cout << id << \\\" LS MAX nodes: \\\" << maxnodes << endl;\\n }\\n\\n // split criterion\\n node->dim = -1;\\n node->val = -1;\\n\\n // current data\\n node->nInstances = 0;\\n\\n // coefficients\\n node->constant = 0.0;\\n\\n // coefficients will get resized later\\n // node->coefficients.resize(2,0);\\n\\n // next nodes in tree\\n node->l = NULL;\\n node->r = NULL;\\n\\n node->leaf = true;\\n node->avgError = 10000;\\n\\n}\\n\\n/** delete current tree */\\nvoid LinearSplitsTree::deleteTree(tree_node* node){\\n if (DTDEBUG) cout << \\\"deleteTree, node=\\\" << node->id << endl;\\n\\n if (node==NULL)\\n return;\\n\\n totalnodes--;\\n\\n node->nInstances = 0;\\n node->coefficients.clear();\\n \\n //recursively call deleteTree on children\\n // then delete them\\n if (!node->leaf){\\n // left child\\n if (node->l != NULL){\\n deleteTree(node->l);\\n deallocateNode(node->l);\\n node->l = NULL;\\n }\\n\\n // right child\\n if (node->r != NULL){\\n deleteTree(node->r);\\n deallocateNode(node->r);\\n node->r = NULL;\\n }\\n }\\n\\n node->dim = -1;\\n node->leaf = true;\\n node->avgError = 10000;\\n node->val = -1;\\n node->constant = 0;\\n}\\n\\n\\n/** Get the correct child of this node based on the input */\\nLinearSplitsTree::tree_node* LinearSplitsTree::getCorrectChild(tree_node* node,\\n const std::vector<float> &input){\\n\\n if (DTDEBUG) cout << \\\"getCorrectChild, node=\\\" << node->id << endl;\\n\\n if (passTest(node->dim, node->val, input))\\n return node->l;\\n else\\n return node->r;\\n\\n}\\n\\n/** Traverse the tree to the leaf for this input. */\\nLinearSplitsTree::tree_node* LinearSplitsTree::traverseTree(tree_node* node,\\n const std::vector<float> &input){\\n\\n if (DTDEBUG) cout << \\\"traverseTree, node=\\\" << node->id << endl;\\n\\n while (!node->leaf){\\n node = getCorrectChild(node, input);\\n }\\n\\n return node;\\n}\\n\\n\\n/** Decide if this passes the test */\\nbool LinearSplitsTree::passTest(int dim, float val, const std::vector<float> &input){\\n if (DTDEBUG) cout << \\\"passTest, dim=\\\" << dim << \\\",val=\\\" << val \\n << \\\",input[\\\"<<dim<<\\\"]=\\\" << input[dim] <<endl;\\n\\n \\n if (input[dim] > val)\\n return false;\\n else\\n return true;\\n\\n}\\n\\n\\n/** Build the tree from this node down using this set of experiences. */\\nvoid LinearSplitsTree::buildTree(tree_node *node,\\n const std::vector<tree_experience*> &instances,\\n bool changed){\\n if(DTDEBUG) cout << \\\"buildTree, node=\\\" << node->id\\n << \\\",nInstances:\\\" << instances.size()\\n << \\\",chg:\\\" << changed << endl;\\n\\n if (instances.size() == 0){\\n cout << \\\"Error: buildTree called on tree \\\" << id << \\\" node \\\" << node->id << \\\" with no instances.\\\" << endl;\\n exit(-1);\\n }\\n\\n\\n // TODO: what about stochastic data?\\n //std::vector<float> chiSquare = calcChiSquare(instances);\\n\\n // first, add instances to tree\\n node->nInstances = instances.size();\\n\\n // add each output to this node\\n bool allSame = true;\\n float val0 = instances[0]->output;\\n for (unsigned i = 1; i < instances.size(); i++){\\n if (instances[i]->output != val0){\\n allSame = false;\\n break;\\n }\\n }\\n\\n // see if they're all the same\\n if (allSame){\\n makeLeaf(node, instances);\\n if (DTDEBUG){\\n cout << \\\"Tree \\\" << id << \\\" node \\\" << node->id \\n << \\\" All \\\" << node->nInstances\\n << \\\" classified with output \\\"\\n << instances[0]->output << \\\", \\\" << node->constant << endl;\\n }\\n return;\\n }\\n\\n // check if linear model has no error\\n if (node != root && node->avgError < MIN_ER){\\n makeLeaf(node, instances);\\n if (node->avgError < MIN_ER){\\n if (DTDEBUG) {\\n cout << \\\"Tree \\\" << id << \\\" node \\\" << node->id\\n << \\\" has low error \\\" << node->avgError << \\\" keeping as leaf\\\" << endl;\\n }\\n return;\\n }\\n }\\n \\n // if not, calculate ER to determine best split\\n if (SPLITDEBUG) cout << endl << \\\"Creating new decision node \\\" << id << \\\"-\\\" << node->id << endl;\\n\\n node->leaf = false;\\n //node->nInstances++;\\n\\n float bestER = -1.0;\\n int bestDim = -1;\\n float bestVal = -1;\\n std::vector<tree_experience*> bestLeft;\\n std::vector<tree_experience*> bestRight;\\n float leftError = 10000;\\n float rightError = 10000;\\n\\n testPossibleSplits(node->avgError, instances, &bestER, &bestDim, &bestVal, &bestLeft, &bestRight, &leftError, &rightError);\\n\\n implementSplit(node, instances, bestER, bestDim, bestVal, bestLeft, bestRight, changed, leftError, rightError);\\n\\n}\\n\\n\\nvoid LinearSplitsTree::makeLeaf(tree_node* node, const std::vector<tree_experience*> &instances){\\n\\n // check on children\\n if (node->l != NULL){\\n deleteTree(node->l);\\n deallocateNode(node->l);\\n node->l = NULL;\\n }\\n\\n if (node->r != NULL){\\n deleteTree(node->r);\\n deallocateNode(node->r);\\n node->r = NULL;\\n }\\n\\n node->leaf = true;\\n\\n // fit linear model\\n if (SIMPLE)\\n node->avgError = fitSimpleLinearModel(instances, &(node->constant), &(node->coefficients));\\n else \\n node->avgError = fitMultiLinearModel(instances, &(node->constant), &(node->coefficients));\\n\\n if (DTDEBUG || LMDEBUG){\\n cout << \\\"make leaf, fit linear model with constant \\\" << node->constant \\n << \\\" error: \\\" << node->avgError << endl;\\n }\\n\\n}\\n\\n\\nfloat LinearSplitsTree::fitMultiLinearModel(const std::vector<tree_experience*> &instances,\\n float *bestConstant, \\n std::vector<float> *bestCoefficients){\\n if(DTDEBUG || LMDEBUG) cout << \\\"fitMultiLinearModel\\\"\\n << \\\",nInstances:\\\" << instances.size() << endl;\\n\\n // make sure there are enough coefficients for all the features\\n if (bestCoefficients->size() != instances[0]->input.size()){\\n bestCoefficients->resize(instances[0]->input.size(), 0); \\n }\\n\\n // in case of size 1\\n if (instances.size() == 1){\\n *bestConstant = instances[0]->output;\\n for (unsigned i = 0; i < bestCoefficients->size(); i++){\\n (*bestCoefficients)[i] = 0.0;\\n }\\n if (LMDEBUG || SPLITDEBUG){\\n cout << \\\" Singleton constant: \\\" \\n << *bestConstant << \\\" avgE: \\\" << 0 << endl;\\n }\\n return 0;\\n }\\n\\n // loop through all features, try simple single variable regression\\n // keep track of error/coefficient of each\\n float bestError = 100000;\\n float avgError = 100000;\\n bool doRegression = true;\\n int ntimes = 0;\\n\\n std::vector<bool> featureMask(bestCoefficients->size(), true);\\n\\n while (doRegression){\\n //cout << id << \\\" Attempt linear model \\\" << ntimes << endl;\\n ntimes++;\\n\\n int nObs = (int)instances.size();\\n\\n int nFeats = 0;\\n for (unsigned i = 0; i < featureMask.size(); i++){\\n if (featureMask[i]) nFeats++;\\n }\\n if (nFeats < 1)\\n break;\\n\\n // no feats or obs, no model to build\\n if (nObs == 0 || nFeats == 0)\\n break;\\n\\n Matrix X(nObs, nFeats);\\n ColumnVector Y(nObs);\\n\\n std::vector<int> featIndices(nFeats);\\n\\n std::vector<bool> constants(nFeats,true);\\n bool foundProblem = false;\\n \\n // load up matrices\\n for (int i = 0; i < nObs; i++){\\n tree_experience *e = instances[i];\\n if (LMDEBUG) cout << \\\"Obs: \\\" << i;\\n\\n // go through all features\\n int featIndex = 1;\\n for (unsigned j = 0; j < featureMask.size(); j++){\\n (*bestCoefficients)[j] = 0;\\n if (!featureMask[j])\\n continue;\\n\\n if (constants[j] && e->input[j] != instances[0]->input[j]){\\n constants[j] = false;\\n }\\n\\n if (i == nObs-1 && constants[j]){\\n //cout << \\\"PROBLEM: feat \\\" << j << \\\" is constant!\\\" << endl;\\n foundProblem = true;\\n featureMask[j] = false;\\n nFeats--;\\n if (nFeats > 0)\\n continue;\\n else\\n break;\\n }\\n\\n featIndices[featIndex-1] = j;\\n // HACK: I'm adding random noise here to prevent colinear features\\n X(i+1,featIndex) = e->input[j]; // + rng.uniform(-0.00001, 0.00001);\\n if (LMDEBUG){\\n cout << \\\" Feat \\\" << featIndex << \\\" index \\\" << j\\n << \\\" val \\\" << X(i+1,featIndex) << \\\",\\\";\\n }\\n featIndex++;\\n }\\n\\n Y(i+1) = e->output;\\n if (LMDEBUG) cout << \\\" out: \\\" << e->output << endl;\\n }\\n\\n if (foundProblem)\\n continue;\\n\\n // make vector of 1s\\n ColumnVector Ones(nObs); Ones = 1.0;\\n\\n // calculate means (averages) of x1 and x2 [ .t() takes transpose]\\n RowVector Mrow = Ones.t() * X / nObs;\\n\\n // and subtract means from x1 and x1\\n Matrix XC(nObs,nFeats);\\n XC = X - Ones * Mrow;\\n\\n // do the same to Y [use Sum to get sum of elements]\\n ColumnVector YC(nObs);\\n Real mval = Sum(Y) / nObs;\\n YC = Y - Ones * mval;\\n\\n // form sum of squares and product matrix\\n // [use << rather than = for copying Matrix into SymmetricMatrix]\\n SymmetricMatrix SSQ;\\n SSQ << XC.t() * XC;\\n\\n Try {\\n\\n ///////////////////////////\\n // Cholesky Method\\n LowerTriangularMatrix L = Cholesky(SSQ);\\n\\n // calculate estimate\\n ColumnVector A = L.t().i() * (L.i() * (XC.t() * YC));\\n ///////////////////////////\\n \\n //////////////////////////\\n // Least Squares Method\\n /*\\n // calculate estimate\\n // [bracket last two terms to force this multiplication first]\\n // [ .i() means inverse, but inverse is not explicity calculated]\\n ColumnVector A = SSQ.i() * (XC.t() * YC);\\n //////////////////////////\\n */\\n \\n // calculate estimate of constant term\\n // [AsScalar converts 1x1 matrix to Real]\\n Real a = mval - (Mrow * A).AsScalar();\\n\\n // Calculate fitted values and residuals\\n //int npred1 = nFeats+1;\\n ColumnVector Fitted = X * A + a;\\n ColumnVector Residual = Y - Fitted;\\n //Real ResVar = Residual.SumSquare() / (nObs-npred1);\\n\\n // print out answers\\n // for each instance\\n bestError = 0;\\n for (int i = 0; i < nObs; i++){\\n if (DTDEBUG || LMDEBUG){\\n cout << \\\"instance \\\" << i << \\\" linear model predicted: \\\" << Fitted(i+1)\\n << \\\" actual: \\\" << instances[i]->output\\n << \\\" error: \\\" << Residual(i+1) << endl;\\n }\\n bestError += fabs(Residual(i+1));\\n }\\n avgError = bestError / (float)nObs;\\n\\n // coeff\\n for (int i = 0; i < nFeats; i++){\\n if (DTDEBUG || LMDEBUG) cout << \\\"Coeff \\\" << i << \\\" on feat: \\\" << featIndices[i] << \\\" is \\\" << A(i+1) << endl;\\n (*bestCoefficients)[featIndices[i]] = A(i+1);\\n }\\n if (DTDEBUG || LMDEBUG) cout << \\\"constant is \\\" << a << endl;\\n *bestConstant = a;\\n\\n }\\n\\n CatchAll {\\n // had an error trying the linear regression.\\n // HACK TODO: for now, turn off one variable\\n //cout << ntimes << \\\" linear regression had exception\\\" << endl;\\n //<< BaseException::what() <<endl;\\n\\n /*\\n for (int i = 0; i < nObs; i++){\\n tree_experience *e = instances[i];\\n cout << \\\"Obs: \\\" << i;\\n \\n // go through all features\\n int featIndex = 1;\\n for (unsigned j = 0; j < featureMask.size(); j++){\\n node->coefficients[j] = 0;\\n if (!featureMask[j])\\n continue;\\n \\n \\n cout << \\\" Feat \\\" << featIndex << \\\" index \\\" << j\\n << \\\" val \\\" << X(i+1,featIndex) << \\\",\\\";\\n \\n featIndex++;\\n }\\n \\n cout << \\\" out: \\\" << e->output << endl;\\n }\\n */\\n\\n // tried this already, stop now\\n if (ntimes > 2 || nFeats < 2){\\n //cout << \\\"max regression\\\" << endl;\\n doRegression = false;\\n break;\\n }\\n for (unsigned j = 0; j < featureMask.size(); j++){\\n if (featureMask[j]){\\n //cout << \\\"remove feature \\\" << j << endl;\\n featureMask[j] = false;\\n nFeats--;\\n break;\\n }\\n }\\n continue;\\n } // catch\\n\\n // it worked, dont need to do it again\\n doRegression = false;\\n\\n }\\n\\n // return error\\n return avgError;\\n\\n}\\n\\n\\nfloat LinearSplitsTree::fitSimpleLinearModel(const std::vector<tree_experience*> &instances,\\n float *bestConstant, std::vector<float> *bestCoefficients){\\n if(DTDEBUG || LMDEBUG || SPLITDEBUG) cout << \\\" fitSimpleLinearModel, \\\"\\n << \\\",nInstances:\\\" << instances.size() << endl;\\n \\n // make sure there are enough coefficients for all the features\\n if (bestCoefficients->size() != instances[0]->input.size()){\\n bestCoefficients->resize(instances[0]->input.size(), 0); \\n }\\n\\n // loop through all features, try simple single variable regression\\n // keep track of error/coefficient of each\\n int bestFeat = -1;\\n float bestCoeff = -1;\\n float bestError = 100000;\\n *bestConstant = -1;\\n\\n // in case of size 1\\n if (instances.size() == 1){\\n bestFeat = 0;\\n *bestConstant = instances[0]->output;\\n for (unsigned i = 0; i < bestCoefficients->size(); i++){\\n (*bestCoefficients)[i] = 0.0;\\n }\\n bestError = 0;\\n if (LMDEBUG || SPLITDEBUG){\\n cout << \\\" Singleton constant: \\\" \\n << *bestConstant << \\\" avgE: \\\" << bestError << endl;\\n }\\n return 0;\\n }\\n\\n std::vector<float> xsum(instances[0]->input.size(), 0);\\n std::vector<float> xysum(instances[0]->input.size(), 0);\\n std::vector<float> x2sum(instances[0]->input.size(), 0);\\n float ysum = 0;\\n\\n int nObs = (int)instances.size();\\n for (int i = 0; i < nObs; i++){\\n tree_experience *e = instances[i];\\n if (LMDEBUG) cout << \\\"Obs: \\\" << i;\\n \\n // go through all features\\n for (unsigned j = 0; j < instances[0]->input.size(); j++){\\n if (LMDEBUG) cout << \\\", F\\\" << j << \\\": \\\" << e->input[j];\\n xsum[j] += e->input[j];\\n xysum[j] += (e->input[j] * e->output);\\n x2sum[j] += (e->input[j] * e->input[j]);\\n }\\n ysum += e->output;\\n if (LMDEBUG) cout << \\\", out: \\\" << e->output << endl;\\n }\\n\\n // now go through all features and calc coeff and constant\\n for (unsigned j = 0; j < instances[0]->input.size(); j++){\\n float coeff = (xysum[j] - xsum[j]*ysum/nObs)/(x2sum[j]-(xsum[j]*xsum[j])/nObs);\\n float constant = (ysum/nObs) - coeff*(xsum[j]/nObs);\\n\\n // so we don't get absurd coefficients that get rounded off earlier\\n if (fabs(coeff) < 1e-5){\\n coeff = 0.0;\\n }\\n if (fabs(constant) < 1e-5){\\n constant = 0.0;\\n }\\n\\n if (LMDEBUG) {\\n cout << \\\"Feat \\\" << j << \\\" coeff: \\\" << coeff << \\\", constant: \\\" \\n << constant << endl;\\n }\\n\\n // now try to make predictions and see what error is\\n float errorSum = 0;\\n for (int i = 0; i < nObs; i++){\\n tree_experience *e = instances[i];\\n float pred = constant + coeff * e->input[j];\\n float error = fabs(pred - e->output);\\n if (LMDEBUG) cout << \\\"Instance \\\" << i << \\\" error: \\\" << error << endl;\\n errorSum += error;\\n }\\n float avgError = errorSum / (float)nObs;\\n if (LMDEBUG) cout << \\\"avgError: \\\" << avgError << endl;\\n\\n // check if this is the best\\n if (avgError < bestError){\\n bestError = avgError;\\n bestFeat = j;\\n *bestConstant = constant;\\n bestCoeff = coeff;\\n }\\n }\\n \\n if (LMDEBUG || SPLITDEBUG){\\n cout << \\\" LST feat: \\\" << bestFeat << \\\" coeff: \\\" \\n << bestCoeff << \\\" constant: \\\" \\n << *bestConstant << \\\" avgE: \\\" << bestError << endl;\\n }\\n\\n if (bestFeat < 0 || bestFeat > (int)instances[0]->input.size()){\\n for (unsigned i = 0; i < bestCoefficients->size(); i++){\\n (*bestCoefficients)[i] = 0.0;\\n }\\n *bestConstant = 0.0;\\n return 100000;\\n }\\n\\n // fill in coeff vector\\n for (unsigned i = 0; i < bestCoefficients->size(); i++){\\n if (i == (unsigned)bestFeat)\\n (*bestCoefficients)[i] = bestCoeff;\\n else\\n (*bestCoefficients)[i] = 0.0; \\n }\\n\\n return bestError;\\n \\n}\\n\\n\\n\\n\\nvoid LinearSplitsTree::implementSplit(tree_node* node, \\n const std::vector<tree_experience*> &instances,\\n float bestER, int bestDim,\\n float bestVal, \\n const std::vector<tree_experience*> &bestLeft,\\n const std::vector<tree_experience*> &bestRight,\\n bool changed, float leftError, float rightError){\\n if (DTDEBUG) cout << \\\"implementSplit node=\\\" << node->id\\n << \\\",er=\\\" << bestER\\n << \\\",dim=\\\" << bestDim\\n << \\\",val=\\\" << bestVal \\n << \\\",chg=\\\" << changed << endl;\\n\\n\\n // see if this should still be a leaf node\\n if (bestER < MIN_ER){\\n makeLeaf(node, instances);\\n\\n if (SPLITDEBUG || STOCH_DEBUG){\\n cout << \\\" DT \\\" << id << \\\" Node \\\" << node->id << \\\" Poor er \\\"\\n << node->nInstances\\n << \\\" instances classified at leaf \\\" << node->id\\n << \\\" with er \\\" << bestER << \\\" constant: \\\" << node->constant << endl;\\n }\\n return;\\n }\\n\\n //cout << id << \\\" implement split with er \\\" << bestER << endl;\\n\\n // see if this split changed or not\\n // assuming no changes above\\n if (!changed && node->dim == bestDim && node->val == bestVal\\n && !node->leaf && node->l != NULL && node->r != NULL){\\n // same split as before.\\n if (DTDEBUG || SPLITDEBUG) cout << \\\"Same split as before \\\" << node->id << endl;\\n node->l->avgError = leftError;\\n node->r->avgError = rightError;\\n\\n\\n // see which leaf changed\\n if (bestLeft.size() > (unsigned)node->l->nInstances){\\n // redo left side\\n if (DTDEBUG) cout << \\\"Rebuild left side of tree\\\" << endl;\\n buildTree(node->l, bestLeft, changed);\\n }\\n\\n if (bestRight.size() > (unsigned)node->r->nInstances){\\n // redo right side\\n if (DTDEBUG) cout << \\\"Rebuild right side of tree\\\" << endl;\\n buildTree(node->r, bestRight, changed);\\n }\\n return;\\n }\\n\\n\\n // totally new\\n // set the best split here\\n node->leaf = false;\\n node->dim = bestDim;\\n node->val = bestVal;\\n\\n if (SPLITDEBUG) cout << \\\"Best split was cut with val \\\" << node->val\\n << \\\" on dim \\\" << node->dim\\n << \\\" with er: \\\" << bestER << endl;\\n\\n if (DTDEBUG) cout << \\\"Left has \\\" << bestLeft.size()\\n << \\\", right has \\\" << bestRight.size() << endl;\\n\\n // make sure both instances\\n if (bestLeft.size() == 0 || bestRight.size() == 0){\\n cout << \\\"ERROR: DT \\\" << id << \\\" node \\\" << node->id << \\\" has 0 instances: left: \\\" << bestLeft.size()\\n << \\\" right: \\\" << bestRight.size() << endl;\\n cout << \\\"Split was cut with val \\\" << node->val\\n << \\\" on dim \\\" << node->dim\\n << \\\" with er: \\\" << bestER << endl;\\n exit(-1);\\n }\\n\\n\\n // check if these already exist\\n if (node->l == NULL){\\n if (DTDEBUG) cout << \\\"Init new left tree nodes \\\" << endl;\\n node->l = allocateNode();\\n }\\n if (node->r == NULL){\\n if (DTDEBUG) cout << \\\"Init new right tree nodes \\\" << endl;\\n node->r = allocateNode();\\n }\\n\\n // recursively build the sub-trees to this one\\n if (DTDEBUG) cout << \\\"Building left tree for node \\\" << node->id << endl;\\n node->l->avgError = leftError;\\n buildTree(node->l, bestLeft, true);\\n if (DTDEBUG) cout << \\\"Building right tree for node \\\" << node->id << endl;\\n node->r->avgError = rightError;\\n buildTree(node->r, bestRight, true);\\n\\n}\\n\\n\\nvoid LinearSplitsTree::testPossibleSplits(float avgError, const std::vector<tree_experience*> &instances,\\n float *bestER, int *bestDim,\\n float *bestVal, \\n std::vector<tree_experience*> *bestLeft,\\n std::vector<tree_experience*> *bestRight,\\n float *bestLeftError, float *bestRightError) {\\n if (DTDEBUG || SPLITDEBUG) cout << \\\"testPossibleSplits, error=\\\" << avgError << endl;\\n\\n int nties = 0;\\n\\n // for each possible split, calc standard deviation reduction\\n for (unsigned idim = 0; idim < instances[0]->input.size(); idim++){\\n\\n //float* sorted = sortOnDim(idim, instances);\\n float minVal, maxVal;\\n std::set<float> uniques = getUniques(idim, instances, minVal, maxVal);\\n\\n for (std::set<float>::iterator j = uniques.begin(); j != uniques.end(); j++){\\n\\n // skip max val, not a valid cut for either\\n if ((*j) == maxVal)\\n continue;\\n\\n // if this is a random forest, we eliminate some random number of splits\\n // here (decision is taken from the random set that are left)\\n if (rng.uniform() < featPct)\\n continue;\\n \\n std::vector<tree_experience*> left;\\n std::vector<tree_experience*> right;\\n float leftError = 10000;\\n float rightError = 10000;\\n\\n // splits that are cuts\\n float splitval = (*j);\\n float er = calcER(idim, splitval, instances, avgError, left, right, &leftError, &rightError);\\n\\n if (SPLITDEBUG){\\n cout << id << \\\" CUT split val \\\" << splitval\\n << \\\" on dim: \\\" << idim << \\\" had er \\\"\\n << er << endl;\\n }\\n\\n // see if this is the new best er\\n compareSplits(er, idim, splitval, left, right, &nties, leftError, rightError,\\n bestER, bestDim, bestVal,bestLeft, bestRight, bestLeftError, bestRightError);\\n\\n\\n } // j loop\\n }\\n}\\n\\n\\n\\n/** Decide if this split is better. */\\nvoid LinearSplitsTree::compareSplits(float er, int dim, float val, \\n const std::vector<tree_experience*> &left, \\n const std::vector<tree_experience*> &right,\\n int *nties, float leftError, float rightError,\\n float *bestER, int *bestDim,\\n float *bestVal, \\n std::vector<tree_experience*> *bestLeft,\\n std::vector<tree_experience*> *bestRight,\\n float *bestLeftError, float *bestRightError){\\n if (DTDEBUG) cout << \\\"compareSplits er=\\\" << er << \\\",dim=\\\" << dim\\n << \\\",val=\\\" << val <<endl;\\n\\n\\n bool newBest = false;\\n\\n // if its a virtual tie, break it randomly\\n if (fabs(*bestER - er) < SPLIT_MARGIN){\\n //cout << \\\"Split tie, making random decision\\\" << endl;\\n\\n (*nties)++;\\n float randomval = rng.uniform(0,1);\\n float newsplitprob = (1.0 / (float)*nties);\\n\\n if (randomval < newsplitprob){\\n newBest = true;\\n if (SPLITDEBUG) cout << \\\" Tie on split. DT: \\\" << id << \\\" rand: \\\" << randomval\\n << \\\" splitProb: \\\" << newsplitprob << \\\", selecting new split \\\" << endl;\\n }\\n else\\n if (SPLITDEBUG) cout << \\\" Tie on split. DT: \\\" << id << \\\" rand: \\\" << randomval\\n << \\\" splitProb: \\\" << newsplitprob << \\\", staying with old split \\\" << endl;\\n }\\n\\n // if its clearly better, set this as the best split\\n else if (er > *bestER){\\n newBest = true;\\n *nties = 1;\\n }\\n\\n\\n // set the split features\\n if (newBest){\\n *bestER = er;\\n *bestDim = dim;\\n *bestVal = val;\\n *bestLeft = left;\\n *bestRight = right;\\n *bestLeftError = leftError;\\n *bestRightError = rightError;\\n if (SPLITDEBUG){\\n cout << \\\" New best er: \\\" << *bestER\\n << \\\" with val \\\" << *bestVal\\n << \\\" on dim \\\" << *bestDim << endl;\\n }\\n } // newbest\\n}\\n\\n/** Calculate error reduction for this possible split. */\\nfloat LinearSplitsTree::calcER(int dim, float val,\\n const std::vector<tree_experience*> &instances,\\n float avgError,\\n std::vector<tree_experience*> &left,\\n std::vector<tree_experience*> &right,\\n float *leftError, float *rightError){\\n if (DTDEBUG || SPLITDEBUG) cout << \\\"calcER, dim=\\\" << dim\\n << \\\" val=\\\" << val\\n << \\\" err=\\\" << avgError\\n << \\\" nInstances= \\\" << instances.size() << endl;\\n\\n left.clear();\\n right.clear();\\n\\n // split into two sides\\n for (unsigned i = 0; i < instances.size(); i++){\\n if (DTDEBUG) cout << \\\" calcER - Classify instance \\\" << i << \\\" on new split \\\" << endl;\\n\\n if (passTest(dim, val, instances[i]->input)){\\n left.push_back(instances[i]);\\n }\\n else{\\n right.push_back(instances[i]);\\n }\\n }\\n\\n if (DTDEBUG) cout << \\\"Left has \\\" << left.size()\\n << \\\", right has \\\" << right.size() << endl;\\n\\n // get sd for both sides\\n *leftError = calcAvgErrorforSet(left);\\n *rightError = calcAvgErrorforSet(right);\\n\\n float leftRatio = (float)left.size() / (float)instances.size();\\n float rightRatio = (float)right.size() / (float)instances.size();\\n float newError = (leftRatio * (*leftError) + rightRatio * (*rightError));\\n\\n float er = avgError - newError;\\n\\n if (DTDEBUG || SPLITDEBUG){\\n cout << \\\"LeftError: \\\" << *leftError\\n << \\\" RightError: \\\" << *rightError\\n << \\\" NewError: \\\" << newError\\n << \\\" NodeError: \\\" << avgError\\n << \\\" ER: \\\" << er\\n << endl;\\n }\\n\\n return er;\\n\\n}\\n\\n/** Calculate std deviation for set. */\\nfloat LinearSplitsTree::calcAvgErrorforSet(const std::vector<tree_experience*> &instances){\\n if (DTDEBUG) cout << \\\"calcAvgErrorforSet\\\" << endl;\\n\\n int n = instances.size();\\n\\n if (n == 0)\\n return 0;\\n\\n // fit a linear model to instances\\n // and figure out avg error of it\\n float constant;\\n float avgError = 0.0;\\n std::vector<float> coeff;\\n if (SIMPLE) \\n avgError = fitSimpleLinearModel(instances, &constant, &coeff);\\n else \\n avgError = fitMultiLinearModel(instances, &constant, &coeff);\\n\\n return avgError;\\n}\\n\\n\\n/** Returns the unique elements at this index */\\nstd::set<float> LinearSplitsTree::getUniques(int dim, const std::vector<tree_experience*> &instances, float& minVal, float& maxVal){\\n if (DTDEBUG) cout << \\\"getUniques,dim = \\\" << dim;\\n\\n std::set<float> uniques;\\n\\n for (int i = 0; i < (int)instances.size(); i++){\\n if (i == 0 || instances[i]->input[dim] < minVal)\\n minVal = instances[i]->input[dim];\\n if (i == 0 || instances[i]->input[dim] > maxVal)\\n maxVal = instances[i]->input[dim];\\n\\n uniques.insert(instances[i]->input[dim]);\\n }\\n\\n \\n // lets not try more than 100 possible splits per dimension\\n if (uniques.size() > 100){\\n float rangeInc = (maxVal - minVal) / 100.0;\\n uniques.clear();\\n for (float i = minVal; i < maxVal; i+= rangeInc){\\n uniques.insert(i);\\n }\\n }\\n uniques.insert(maxVal);\\n \\n\\n if (DTDEBUG) cout << \\\" #: \\\" << uniques.size() << endl;\\n return uniques;\\n}\\n\\n\\n/** Returns a list of the attributes in this dimension sorted\\n from lowest to highest. */\\nfloat* LinearSplitsTree::sortOnDim(int dim, const std::vector<tree_experience*> &instances){\\n if (DTDEBUG) cout << \\\"sortOnDim,dim = \\\" << dim << endl;\\n\\n float* values = new float[instances.size()];\\n\\n for (int i = 0; i < (int)instances.size(); i++){\\n //cout << \\\"Instance \\\" << i << endl;\\n\\n float val = instances[i]->input[dim];\\n //cout << \\\" val: \\\" << val << endl;\\n\\n // find where this should go\\n for (int j = 0; j <= i; j++){\\n //cout << \\\" j: \\\" << j << endl;\\n\\n // get to i, this is the spot then\\n if (j==i){\\n values[j] = val;\\n //cout << \\\" At i, putting value in slot j: \\\" << j << endl;\\n }\\n\\n // if this is the spot\\n else if (val < values[j]){\\n //cout << \\\" Found slot at j: \\\" << j << endl;\\n\\n // slide everything forward to make room\\n for (int k = i; k > j; k--){\\n //cout << \\\" k = \\\" << k << \\\" Sliding value from k-1 to k\\\" << endl;\\n values[k] = values[k-1];\\n }\\n\\n // put value in its spot at j\\n //cout << \\\" Putting value at slot j: \\\" << j << endl;\\n values[j] = val;\\n\\n // break\\n break;\\n }\\n\\n }\\n }\\n\\n if (DTDEBUG){\\n cout << \\\"Sorted array: \\\" << values[0];\\n for (int i = 1; i < (int)instances.size(); i++){\\n cout << \\\", \\\" << values[i];\\n }\\n cout << endl;\\n }\\n\\n return values;\\n\\n}\\n\\n\\n/** Print the tree for debug purposes. */\\nvoid LinearSplitsTree::printTree(tree_node *t, int level){\\n\\n for (int i = 0; i < level; i++){\\n cout << \\\".\\\";\\n }\\n\\n cout << \\\"Node \\\" << t->id << \\\" nInstances: \\\" << t->nInstances ;\\n\\n // Leaf, print regression stuff\\n if (t->leaf){\\n cout << \\\" Constant: \\\" << t->constant << \\\" coeff: \\\";\\n for (unsigned j = 0; j < t->coefficients.size(); j++){\\n if (!SIMPLE)\\n cout << t->coefficients[j] << \\\", \\\";\\n if (SIMPLE && t->coefficients[j] != 0)\\n cout << \\\" on feat \\\" << j << \\\": \\\" << t->coefficients[j];\\n }\\n cout << endl;\\n return;\\n }\\n\\n // otherwise, print split info, next nodes\\n\\n cout << \\\" Type: CUT\\\";\\n cout << \\\" Dim: \\\" << t->dim << \\\" Val: \\\" << t->val;\\n cout << \\\" Left: \\\" << t->l->id << \\\" Right: \\\" << t->r->id << endl;\\n\\n // print children\\n if (t->dim != -1 && !t->leaf){\\n printTree(t->l, level+1);\\n printTree(t->r, level+1);\\n }\\n\\n}\\n\\n\\n// output a map of outcomes and their probabilities for this leaf node\\nvoid LinearSplitsTree::leafPrediction(tree_node* leaf, const std::vector<float> &input,\\n std::map<float, float>* retval){\\n if (DTDEBUG)\\n cout << \\\"Calculating output for leaf \\\" << leaf->id << endl;\\n\\n float prediction = leaf->constant;\\n if (DTDEBUG) cout << \\\"leaf constant: \\\" << leaf->constant << endl;\\n\\n // plus each coefficient\\n for (unsigned i = 0; i < input.size(); i++){\\n prediction += leaf->coefficients[i] * input[i];\\n if (DTDEBUG) {\\n cout << \\\"feat \\\" << i << \\\" coeff: \\\" << leaf->coefficients[i]\\n << \\\" on input \\\" << input[i] << endl;\\n }\\n }\\n\\n if (DTDEBUG) cout << \\\" prediction: \\\" << prediction << endl;\\n\\n (*retval)[prediction] = 1.0;\\n \\n}\\n\\n\\nvoid LinearSplitsTree::initNodes(){\\n\\n for (int i = 0; i < N_LS_NODES; i++){\\n initTreeNode(&(allNodes[i]));\\n freeNodes.push_back(i);\\n if (NODEDEBUG) \\n cout << \\\"init node \\\" << i << \\\" with id \\\" << allNodes[i].id \\n << \\\", now \\\" << freeNodes.size() << \\\" free nodes.\\\" << endl;\\n }\\n\\n}\\n\\nLinearSplitsTree::tree_node* LinearSplitsTree::allocateNode(){\\n if (freeNodes.empty()){\\n tree_node* newNode = new tree_node;\\n initTreeNode(newNode);\\n if (NODEDEBUG) \\n cout << id << \\\" PROBLEM: No more pre-allocated nodes!!!\\\" << endl\\n << \\\"return new node \\\" << newNode->id \\n << \\\", now \\\" << freeNodes.size() << \\\" free nodes.\\\" << endl;\\n return newNode;\\n }\\n\\n int i = freeNodes.back();\\n freeNodes.pop_back();\\n if (NODEDEBUG) \\n cout << id << \\\" allocate node \\\" << i << \\\" with id \\\" << allNodes[i].id \\n << \\\", now \\\" << freeNodes.size() << \\\" free nodes.\\\" << endl;\\n return &(allNodes[i]);\\n}\\n\\nvoid LinearSplitsTree::deallocateNode(tree_node* node){\\n if (node->id >= N_LS_NODES){\\n if (NODEDEBUG) \\n cout << id << \\\" dealloc extra node id \\\" << node->id \\n << \\\", now \\\" << freeNodes.size() << \\\" free nodes.\\\" << endl;\\n delete node;\\n return;\\n }\\n\\n freeNodes.push_back(node->id);\\n if (NODEDEBUG) \\n cout << id << \\\" dealloc node \\\" << node->id \\n << \\\", now \\\" << freeNodes.size() << \\\" free nodes.\\\" << endl;\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7025181651115417,\n \"alphanum_fraction\": 0.7059325575828552,\n \"avg_line_length\": 24.74725341796875,\n \"blob_id\": \"bfc0e8ea30c9298b7af45cf4ef577f4849c871c3\",\n \"content_id\": \"574a9e124b11a26708b7c73625431411482254d2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 2343,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 91,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/include/rl_env/tworooms.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file tworooms.hh\\n Defines a two room gridworld domain, with possible action delays or \\n multiple goals (with partial observability). \\n \\\\author Todd Hester\\n*/\\n\\n#ifndef _TWOROOMS_H_\\n#define _TWOROOMS_H_\\n\\n#include <set>\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n#include \\\"gridworld.hh\\\"\\n\\n#include <deque>\\n\\n\\n/** This class defines a two room gridworld domain. It can optionally be stochastic, have action\\n delays, or multiple goals (with partial observability). */\\nclass TwoRooms: public Environment {\\npublic:\\n\\n /** Standard Constructor\\n \\\\param rand Random Number generator\\n \\\\param stochastic Make the domain stochastic \\n \\\\param rewardType Create -1 per step and 0 on termination (vs 0 and 1)\\n \\\\param actDelay # of steps to delay actions\\n \\\\param multiGoal create mulitple goals that are randomly selected from each episode\\n */\\n TwoRooms(Random &rand, bool stochastic, bool rewardType, int actDelay, bool multiGoal);\\n\\n virtual ~TwoRooms();\\n\\n virtual const std::vector<float> &sensation() const;\\n virtual float apply(int action);\\n\\n virtual bool terminal() const;\\n virtual void reset();\\n\\n virtual int getNumActions();\\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\\n virtual void getMinMaxReward(float* minR, float* maxR);\\n\\n /** Create an experience tuple for the given state-action. */\\n experience getExp(float s0, float s1, int a);\\n\\n virtual std::vector<experience> getSeedings();\\n\\nprotected:\\n typedef std::pair<float,float> coord_t;\\n enum room_action_t {NORTH, SOUTH, EAST, WEST};\\n\\nprivate:\\n const Gridworld *const grid;\\n coord_t goal;\\n coord_t goal2;\\n std::deque<int> actHistory;\\n bool useGoal2;\\n\\n const bool negReward;\\n const bool noisy;\\n const int actDelay;\\n const bool multiGoal;\\n\\n Random &rng;\\n\\n coord_t doorway;\\n\\n std::vector<float> s;\\n\\n float &ns;\\n float &ew;\\n\\n /** Create default two room gridworld */\\n const Gridworld *create_default_map();\\n\\n /** Corrupts a movement action.\\n \\\\param action The intended action\\n \\\\return The action actually executed */\\n room_action_t add_noise(room_action_t action);\\n\\n /** Randomly assigns the goal to any random \\n position in the world. */\\n void randomize_goal();\\n\\n /** Return the correct reward based on the current state. */\\n float reward();\\n\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.5695687532424927,\n \"alphanum_fraction\": 0.5732302665710449,\n \"avg_line_length\": 20.373912811279297,\n \"blob_id\": \"be23ec9b3ab12f8a319d206fc716b16c9d0e14c7\",\n \"content_id\": \"6f31c73dc46e1164426dec166846cba60a8f6e72\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 2458,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 115,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_common/include/rl_common/ExperienceFile.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#ifndef _EXPFILE_HH_\\n#define _EXPFILE_HH_\\n\\n#include \\\"Random.h\\\"\\n#include \\\"core.hh\\\"\\n\\n#include <vector>\\n#include <algorithm>\\n\\n#include <sys/time.h>\\n\\nclass ExperienceFile {\\npublic:\\n /** Standard constructor\\n */\\n\\n ofstream vectorFile;\\n int expNum;\\n\\n ExperienceFile(){\\n expNum = 0;\\n }\\n\\n ~ExperienceFile(){\\n if (vectorFile.is_open())\\n vectorFile.close();\\n }\\n\\n void initFile(const char* filename, int nfeats){\\n vectorFile.open(filename, ios::out | ios::binary);\\n\\n // first part, save the vector size\\n vectorFile.write((char*)&nfeats, sizeof(int));\\n }\\n\\n void saveExperience(experience e){\\n if (!vectorFile.is_open())\\n return;\\n\\n /*\\n if (expNum == 50){\\n vectorFile.close();\\n return;\\n }\\n */\\n\\n vectorFile.write((char*)&(e.s[0]), e.s.size()*sizeof(float));\\n vectorFile.write((char*)&(e.next[0]), e.next.size()*sizeof(float));\\n vectorFile.write((char*)&e.act, sizeof(int));\\n vectorFile.write((char*)&e.reward, sizeof(float));\\n vectorFile.write((char*)&e.terminal, sizeof(bool));\\n\\n //cout << \\\"Experience \\\" << expNum << endl;\\n expNum++;\\n //printExperience(e);\\n }\\n\\n void printExperience(experience e){\\n\\n cout << \\\"State s: \\\";\\n for(unsigned i = 0; i < e.s.size(); i++){\\n cout << e.s[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Next: \\\";\\n for(unsigned i = 0; i < e.next.size(); i++){\\n cout << e.next[i] << \\\", \\\";\\n }\\n cout << endl;\\n cout << \\\"action: \\\" << e.act << \\\" reward: \\\" << e.reward << endl;\\n\\n }\\n\\n\\n\\n std::vector<experience> loadExperiences(const char* filename){\\n ifstream inFile (filename, ios::in | ios::binary);\\n\\n int numFeats;\\n inFile.read((char*)&numFeats, sizeof(int));\\n\\n std::vector<experience> seeds;\\n\\n // while file is not empty\\n while(!inFile.eof()){\\n experience e;\\n e.s.resize(numFeats);\\n e.next.resize(numFeats);\\n\\n inFile.read((char*)&(e.s[0]), e.s.size()*sizeof(float));\\n if (inFile.eof()) break;\\n inFile.read((char*)&(e.next[0]), e.next.size()*sizeof(float));\\n if (inFile.eof()) break;\\n inFile.read((char*)&e.act, sizeof(int));\\n inFile.read((char*)&e.reward, sizeof(float));\\n inFile.read((char*)&e.terminal, sizeof(bool));\\n\\n //cout << \\\"Experience \\\" << seeds.size() << endl;\\n //printExperience(e);\\n\\n seeds.push_back(e);\\n }\\n\\n inFile.close();\\n\\n return seeds;\\n }\\n\\n void closeFile(){\\n if (vectorFile.is_open())\\n vectorFile.close();\\n }\\n\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.6877940893173218,\n \"alphanum_fraction\": 0.6877940893173218,\n \"avg_line_length\": 34.248779296875,\n \"blob_id\": \"c5f25c065a5281cb2dd0ef44fe7744b9117b7443\",\n \"content_id\": \"489a8083197c9970ca751a5805177340f20a2f6d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 7226,\n \"license_type\": \"no_license\",\n \"max_line_length\": 108,\n \"num_lines\": 205,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/include/rl_agent/ModelBasedAgent.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file ModelBasedAgent.hh\\n Defines the ModelBasedAgent class\\n \\\\author Todd Hester\\n*/\\n\\n#ifndef _MODELBASED_HH_\\n#define _MODELBASED_HH_\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n\\n#include <set>\\n#include <vector>\\n#include <map>\\n\\n/** Code for a model based agent, that can use any model and planner that meet the interface requirements */\\nclass ModelBasedAgent: public Agent {\\npublic:\\n /** Standard constructor\\n \\\\param numactions The number of possible actions\\n \\\\param gamma The discount factor\\n \\\\param rmax max reward value, given out for unknown states\\n \\\\param rrange range between max and min reward in domain\\n \\\\param modelType specifies model type\\n \\\\param exploreType specifies exploration type\\n \\\\param predType specifies how to combine multiple models\\n \\\\param nModels number of models in ensemble\\n \\\\param plannerType specifies planner type\\n \\\\param epsilon used for epsilon-greedy action selection\\n \\\\param lambda used for eligibility traces in uct planning\\n \\\\param MAX_TIME amount of time the uct planners are given for planning\\n \\\\param m # visits required for a state to become known when doing rmax exploration\\n \\\\param featmin min values of each feature\\n \\\\param featmax max values of each feature\\n \\\\param statesPerDim # of values to discretize each feature into\\n \\\\param history # of previous actions to use for delayed domains\\n \\\\param b\\\\v bonus reward used when models disagree\\n \\\\param n bonus reward used for novel states\\n \\\\param depTrans assume dependent or indep. feature transitions\\n \\\\param relTrans model transitions relatively vs absolutely\\n \\\\param featPct pct of feature to remove from set used for each split in tree\\n \\\\param stoch is the domain stochastic?\\n \\\\param episodic is the domain episodic?\\n \\\\param rng Initial state of the random number generator to use */\\n ModelBasedAgent(int numactions, float gamma, float rmax, float rrange, \\n int modelType, int exploreType, \\n int predType, int nModels, int plannerType,\\n float epsilon, float lambda, float MAX_TIME,\\n float m, const std::vector<float> &featmin, \\n const std::vector<float> &featmax,\\n int statesPerDim, int history, float v, float n,\\n bool depTrans, bool relTrans, float featPct,\\n bool stoch, bool episodic, Random rng = Random());\\n\\n /** Standard constructor \\n \\\\param numactions The number of possible actions\\n \\\\param gamma The discount factor\\n \\\\param rmax max reward value, given out for unknown states\\n \\\\param rrange range between max and min reward in domain\\n \\\\param modelType specifies model type\\n \\\\param exploreType specifies exploration type\\n \\\\param predType specifies how to combine multiple models\\n \\\\param nModels number of models in ensemble\\n \\\\param plannerType specifies planner type\\n \\\\param epsilon used for epsilon-greedy action selection\\n \\\\param lambda used for eligibility traces in uct planning\\n \\\\param MAX_TIME amount of time the uct planners are given for planning\\n \\\\param m # visits required for a state to become known when doing rmax exploration\\n \\\\param featmin min values of each feature\\n \\\\param featmax max values of each feature\\n \\\\param statesPerDim # of values to discretize each feature into\\n \\\\param history # of previous actions to use for delayed domains\\n \\\\param b bonus reward used when models disagree\\n \\\\param depTrans assume dependent or indep. feature transitions\\n \\\\param relTrans model transitions relatively vs absolutely\\n \\\\param featPct pct of feature to remove from set used for each split in tree\\n \\\\param stoch is the domain stochastic?\\n \\\\param episodic is the domain episodic?\\n \\\\param rng Initial state of the random number generator to use*/\\n ModelBasedAgent(int numactions, float gamma, float rmax, float rrange, \\n int modelType, int exploreType, \\n int predType, int nModels, int plannerType,\\n float epsilon, float lambda, float MAX_TIME,\\n float m, const std::vector<float> &featmin, \\n const std::vector<float> &featmax,\\n std::vector<int> statesPerDim, int history, float v, float n,\\n bool depTrans, bool relTrans, float featPct,\\n bool stoch, bool episodic, Random rng = Random());\\n \\n /** Init params for both constructors */\\n void initParams();\\n\\n /** Unimplemented copy constructor: internal state cannot be simply\\n copied. */\\n ModelBasedAgent(const ModelBasedAgent &);\\n\\n virtual ~ModelBasedAgent();\\n\\n virtual int first_action(const std::vector<float> &s);\\n virtual int next_action(float r, const std::vector<float> &s);\\n virtual void last_action(float r);\\n virtual void seedExp(std::vector<experience> seeds);\\n virtual void setDebug(bool d);\\n virtual void savePolicy(const char* filename);\\n\\n /** Output value function to a file */\\n void logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax);\\n\\n bool AGENTDEBUG;\\n 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keep pkgConfig\\n# self contained\\nmacro(_list_append_deduplicate listname)\\n if(NOT \\\"${ARGN}\\\" STREQUAL \\\"\\\")\\n if(${listname})\\n list(REMOVE_ITEM ${listname} ${ARGN})\\n endif()\\n list(APPEND ${listname} ${ARGN})\\n endif()\\nendmacro()\\n\\n# append elements to a list if they are not already in the list\\n# copied from catkin/cmake/list_append_unique.cmake to keep pkgConfig\\n# self contained\\nmacro(_list_append_unique listname)\\n foreach(_item ${ARGN})\\n list(FIND ${listname} ${_item} _index)\\n if(_index EQUAL -1)\\n list(APPEND ${listname} ${_item})\\n endif()\\n endforeach()\\nendmacro()\\n\\n# pack a list of libraries with optional build configuration keywords\\n# copied from catkin/cmake/catkin_libraries.cmake to keep pkgConfig\\n# self contained\\nmacro(_pack_libraries_with_build_configuration VAR)\\n set(${VAR} \\\"\\\")\\n set(_argn ${ARGN})\\n list(LENGTH _argn _count)\\n set(_index 0)\\n while(${_index} LESS ${_count})\\n list(GET _argn ${_index} lib)\\n if(\\\"${lib}\\\" MATCHES \\\"^(debug|optimized|general)$\\\")\\n math(EXPR _index \\\"${_index} + 1\\\")\\n if(${_index} EQUAL ${_count})\\n message(FATAL_ERROR \\\"_pack_libraries_with_build_configuration() the list of libraries '${ARGN}' ends with '${lib}' which is a build configuration keyword and must be followed by a library\\\")\\n endif()\\n list(GET _argn ${_index} library)\\n list(APPEND ${VAR} \\\"${lib}${CATKIN_BUILD_CONFIGURATION_KEYWORD_SEPARATOR}${library}\\\")\\n else()\\n list(APPEND ${VAR} \\\"${lib}\\\")\\n endif()\\n math(EXPR _index \\\"${_index} + 1\\\")\\n endwhile()\\nendmacro()\\n\\n# unpack a list of libraries with optional build configuration keyword prefixes\\n# copied from catkin/cmake/catkin_libraries.cmake to keep pkgConfig\\n# self contained\\nmacro(_unpack_libraries_with_build_configuration VAR)\\n set(${VAR} \\\"\\\")\\n foreach(lib ${ARGN})\\n string(REGEX REPLACE \\\"^(debug|optimized|general)${CATKIN_BUILD_CONFIGURATION_KEYWORD_SEPARATOR}(.+)$\\\" \\\"\\\\\\\\1;\\\\\\\\2\\\" lib \\\"${lib}\\\")\\n list(APPEND ${VAR} \\\"${lib}\\\")\\n endforeach()\\nendmacro()\\n\\n\\nif(boost_python_catkin_example_CONFIG_INCLUDED)\\n return()\\nendif()\\nset(boost_python_catkin_example_CONFIG_INCLUDED TRUE)\\n\\n# set variables for source/devel/install prefixes\\nif(\\\"TRUE\\\" STREQUAL \\\"TRUE\\\")\\n set(boost_python_catkin_example_SOURCE_PREFIX /home/justin/ros_test/src/boost-python-catkin-example-master)\\n set(boost_python_catkin_example_DEVEL_PREFIX /home/justin/ros_test/devel)\\n set(boost_python_catkin_example_INSTALL_PREFIX \\\"\\\")\\n set(boost_python_catkin_example_PREFIX ${boost_python_catkin_example_DEVEL_PREFIX})\\nelse()\\n set(boost_python_catkin_example_SOURCE_PREFIX \\\"\\\")\\n set(boost_python_catkin_example_DEVEL_PREFIX \\\"\\\")\\n set(boost_python_catkin_example_INSTALL_PREFIX /home/justin/ros_test/install)\\n set(boost_python_catkin_example_PREFIX ${boost_python_catkin_example_INSTALL_PREFIX})\\nendif()\\n\\n# warn when using a deprecated package\\nif(NOT \\\"\\\" STREQUAL \\\"\\\")\\n set(_msg \\\"WARNING: package 'boost_python_catkin_example' is deprecated\\\")\\n # append custom deprecation text if available\\n if(NOT \\\"\\\" STREQUAL \\\"TRUE\\\")\\n set(_msg \\\"${_msg} ()\\\")\\n endif()\\n message(\\\"${_msg}\\\")\\nendif()\\n\\n# flag project as catkin-based to distinguish if a find_package()-ed project is a catkin project\\nset(boost_python_catkin_example_FOUND_CATKIN_PROJECT TRUE)\\n\\nif(NOT \\\" \\\" STREQUAL \\\" \\\")\\n set(boost_python_catkin_example_INCLUDE_DIRS \\\"\\\")\\n set(_include_dirs \\\"\\\")\\n if(NOT \\\" \\\" STREQUAL \\\" \\\")\\n set(_report \\\"Check the issue tracker '' and consider creating a ticket if the problem has not been reported yet.\\\")\\n elseif(NOT \\\"https://github.com/luator/ros-boost-python \\\" STREQUAL \\\" \\\")\\n set(_report \\\"Check the website 'https://github.com/luator/ros-boost-python' for information and consider reporting the problem.\\\")\\n else()\\n set(_report \\\"Report the problem to the maintainer 'Felix Widmaier <felix@todo.todo>' and request to fix the problem.\\\")\\n endif()\\n foreach(idir ${_include_dirs})\\n if(IS_ABSOLUTE ${idir} AND IS_DIRECTORY ${idir})\\n set(include ${idir})\\n elseif(\\\"${idir} \\\" STREQUAL \\\"include \\\")\\n get_filename_component(include \\\"${boost_python_catkin_example_DIR}/../../../include\\\" ABSOLUTE)\\n if(NOT IS_DIRECTORY ${include})\\n message(FATAL_ERROR \\\"Project 'boost_python_catkin_example' specifies '${idir}' as an include dir, which is not found. It does not exist in '${include}'. ${_report}\\\")\\n endif()\\n else()\\n message(FATAL_ERROR \\\"Project 'boost_python_catkin_example' specifies '${idir}' as an include dir, which is not found. It does neither exist as an absolute directory nor in '/home/justin/ros_test/src/boost-python-catkin-example-master/${idir}'. ${_report}\\\")\\n endif()\\n _list_append_unique(boost_python_catkin_example_INCLUDE_DIRS ${include})\\n endforeach()\\nendif()\\n\\nset(libraries \\\"\\\")\\nforeach(library ${libraries})\\n # keep build configuration keywords, target names and absolute libraries as-is\\n if(\\\"${library}\\\" MATCHES \\\"^(debug|optimized|general)$\\\")\\n list(APPEND boost_python_catkin_example_LIBRARIES ${library})\\n elseif(TARGET ${library})\\n list(APPEND boost_python_catkin_example_LIBRARIES ${library})\\n elseif(IS_ABSOLUTE ${library})\\n list(APPEND boost_python_catkin_example_LIBRARIES ${library})\\n else()\\n set(lib_path \\\"\\\")\\n set(lib \\\"${library}-NOTFOUND\\\")\\n # since the path where the library is found is returned we have to iterate over the paths manually\\n foreach(path /home/justin/ros_test/devel/lib;/home/justin/catkin_ws/devel/lib;/home/justin/ros_test/devel/lib;/opt/ros/melodic/lib)\\n find_library(lib ${library}\\n PATHS ${path}\\n NO_DEFAULT_PATH NO_CMAKE_FIND_ROOT_PATH)\\n if(lib)\\n set(lib_path ${path})\\n break()\\n endif()\\n endforeach()\\n if(lib)\\n _list_append_unique(boost_python_catkin_example_LIBRARY_DIRS ${lib_path})\\n list(APPEND boost_python_catkin_example_LIBRARIES ${lib})\\n else()\\n # as a fall back for non-catkin libraries try to search globally\\n find_library(lib ${library})\\n if(NOT lib)\\n message(FATAL_ERROR \\\"Project '${PROJECT_NAME}' tried to find library '${library}'. The library is neither a target nor built/installed properly. Did you compile project 'boost_python_catkin_example'? Did you find_package() it before the subdirectory containing its code is included?\\\")\\n endif()\\n list(APPEND boost_python_catkin_example_LIBRARIES ${lib})\\n endif()\\n endif()\\nendforeach()\\n\\nset(boost_python_catkin_example_EXPORTED_TARGETS \\\"\\\")\\n# create dummy targets for exported code generation targets to make life of users easier\\nforeach(t ${boost_python_catkin_example_EXPORTED_TARGETS})\\n if(NOT TARGET ${t})\\n add_custom_target(${t})\\n endif()\\nendforeach()\\n\\nset(depends \\\"\\\")\\nforeach(depend ${depends})\\n string(REPLACE \\\" \\\" \\\";\\\" depend_list ${depend})\\n # the package name of the dependency must be kept in a unique variable so that it is not overwritten in recursive calls\\n list(GET depend_list 0 boost_python_catkin_example_dep)\\n list(LENGTH depend_list count)\\n if(${count} EQUAL 1)\\n # simple dependencies must only be find_package()-ed once\\n if(NOT ${boost_python_catkin_example_dep}_FOUND)\\n find_package(${boost_python_catkin_example_dep} REQUIRED NO_MODULE)\\n endif()\\n else()\\n # dependencies with components must be find_package()-ed again\\n list(REMOVE_AT depend_list 0)\\n find_package(${boost_python_catkin_example_dep} REQUIRED NO_MODULE ${depend_list})\\n endif()\\n _list_append_unique(boost_python_catkin_example_INCLUDE_DIRS ${${boost_python_catkin_example_dep}_INCLUDE_DIRS})\\n\\n # merge build configuration keywords with library names to correctly deduplicate\\n _pack_libraries_with_build_configuration(boost_python_catkin_example_LIBRARIES ${boost_python_catkin_example_LIBRARIES})\\n _pack_libraries_with_build_configuration(_libraries ${${boost_python_catkin_example_dep}_LIBRARIES})\\n _list_append_deduplicate(boost_python_catkin_example_LIBRARIES ${_libraries})\\n # undo build configuration keyword merging after deduplication\\n _unpack_libraries_with_build_configuration(boost_python_catkin_example_LIBRARIES ${boost_python_catkin_example_LIBRARIES})\\n\\n _list_append_unique(boost_python_catkin_example_LIBRARY_DIRS ${${boost_python_catkin_example_dep}_LIBRARY_DIRS})\\n list(APPEND boost_python_catkin_example_EXPORTED_TARGETS ${${boost_python_catkin_example_dep}_EXPORTED_TARGETS})\\nendforeach()\\n\\nset(pkg_cfg_extras \\\"\\\")\\nforeach(extra ${pkg_cfg_extras})\\n if(NOT IS_ABSOLUTE ${extra})\\n set(extra ${boost_python_catkin_example_DIR}/${extra})\\n endif()\\n include(${extra})\\nendforeach()\\n\"\n },\n {\n \"alpha_fraction\": 0.7450425028800964,\n \"alphanum_fraction\": 0.7507082223892212,\n \"avg_line_length\": 43.1875,\n \"blob_id\": \"b3be00ce5aa2c5cb42bbd0deec5b4014e7cd50f7\",\n \"content_id\": \"fc042528e9c28902fbc18bf280400df3ed614497\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 706,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 16,\n \"path\": \"/build/ugv_course_libs/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"ugv_course_libs\\\")\\nset(ugv_course_libs_VERSION \\\"0.0.0\\\")\\nset(ugv_course_libs_MAINTAINER \\\"micho <micho@todo.todo>\\\")\\nset(ugv_course_libs_PACKAGE_FORMAT \\\"1\\\")\\nset(ugv_course_libs_BUILD_DEPENDS \\\"sensor_msgs\\\" \\\"tf\\\")\\nset(ugv_course_libs_BUILD_EXPORT_DEPENDS \\\"sensor_msgs\\\" \\\"tf\\\")\\nset(ugv_course_libs_BUILDTOOL_DEPENDS \\\"catkin\\\")\\nset(ugv_course_libs_BUILDTOOL_EXPORT_DEPENDS )\\nset(ugv_course_libs_EXEC_DEPENDS \\\"sensor_msgs\\\" \\\"tf\\\")\\nset(ugv_course_libs_RUN_DEPENDS \\\"sensor_msgs\\\" \\\"tf\\\")\\nset(ugv_course_libs_TEST_DEPENDS )\\nset(ugv_course_libs_DOC_DEPENDS )\\nset(ugv_course_libs_URL_WEBSITE \\\"\\\")\\nset(ugv_course_libs_URL_BUGTRACKER \\\"\\\")\\nset(ugv_course_libs_URL_REPOSITORY \\\"\\\")\\nset(ugv_course_libs_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.7905638813972473,\n 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)\\nset(boost_python_catkin_example_BUILDTOOL_DEPENDS \\\"catkin\\\")\\nset(boost_python_catkin_example_BUILDTOOL_EXPORT_DEPENDS )\\nset(boost_python_catkin_example_EXEC_DEPENDS )\\nset(boost_python_catkin_example_RUN_DEPENDS )\\nset(boost_python_catkin_example_TEST_DEPENDS )\\nset(boost_python_catkin_example_DOC_DEPENDS )\\nset(boost_python_catkin_example_URL_WEBSITE \\\"https://github.com/luator/ros-boost-python\\\")\\nset(boost_python_catkin_example_URL_BUGTRACKER \\\"\\\")\\nset(boost_python_catkin_example_URL_REPOSITORY \\\"\\\")\\nset(boost_python_catkin_example_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.5498554706573486,\n \"alphanum_fraction\": 0.5698802471160889,\n \"avg_line_length\": 23.14214515686035,\n \"blob_id\": \"4110c4d347e596a05cb20334e6b5d7e436da25d5\",\n \"content_id\": \"22ce8f04dd66435de0cb2371ab4a4c22914444d5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 9688,\n \"license_type\": \"no_license\",\n \"max_line_length\": 138,\n \"num_lines\": 401,\n \"path\": \"/src/boost-python-catkin-example-master/src/mycpplib.cpp\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\n\\n\\n\\n\\n\\n#include <iostream>\\n#include <boost/python.hpp>\\n#include <boost/python/list.hpp>\\n#include <boost/python/extract.hpp>\\n#include <string>\\n#include <sstream>\\n#include <vector>\\n#include <cmath>\\n#include <ros/ros.h>\\n#include <std_msgs/Float64.h>\\n#include <geometry_msgs/Twist.h>\\n#include <geometry_msgs/Pose.h>\\n#include <std_srvs/Empty.h>\\n#include <gazebo_msgs/SpawnModel.h>\\n#include <gazebo_msgs/DeleteModel.h>\\n#include <tf/tf.h>\\n#include <geometry_msgs/Pose.h>\\n#include <nav_msgs/Odometry.h>\\n#include <vector>\\n#include <boost/python/suite/indexing/vector_indexing_suite.hpp>\\n#include <boost/python/numpy.hpp>\\n#include <sensor_msgs/LaserScan.h>\\n#include <std_msgs/String.h>\\n#include <algorithm> \\n#include <cstdlib>\\n#include <fstream>\\n#include <tf2/LinearMath/Quaternion.h>\\n\\n//#include <condition_variable> // std::condition_variable\\n\\n\\n\\n//using namespace std;\\n //using namespace std::placeholders; \\n//namespace p = boost::python;\\n//namespace np = boost::python::numpy;\\ntypedef std::vector<float> MyList;\\nclass World\\n{\\nprivate:\\n\\t\\n float goal_angle =0.0;\\n float heading = 0.0;\\n float min_range = 0.15;\\n float goal_x = 0.0;\\n float goal_y = 0.0;\\n std::string mMsg;\\n int count = 0;\\n float goal_distance = 0.0;\\n int old_count = 0;\\n float past_distance = 0.0;\\n float pos_x = 0.0;\\n float pos_y = 0.0;\\n geometry_msgs::Twist msg;\\n geometry_msgs::Twist msg_empty;\\n ros::NodeHandle n;\\n MyList action = MyList(2);\\n MyList past_action = MyList(2);\\n MyList state = MyList(15);\\n bool done = false;\\n bool get_goalbox = false;\\n float distance = 0.0;\\n float reward = 0;\\n //float past_action = 0;\\n geometry_msgs::Pose post;\\n MyList goal_x_list = {0.6, 1.9, 0.5, 0.2, -0.8, -1, -1.9, 0.5, 2, 0.5, 0, -0.1, -2};\\n MyList goal_y_list = {0, -0.5, -1.9, 1.5, -0.9, 1, 1.1, -1.5, 1.5, 1.8, -1, 1.6, -0.8};\\n // int rand();\\n double yaw = 0;\\n bool init_goal = true;\\n \\n\\n\\n\\n\\npublic: \\n\\n //nav_msgs::Odometry::ConstPtr msg;\\n\\n ros::Publisher chatter_pub = n.advertise<geometry_msgs::Twist>(\\\"cmd_vel\\\", 5); \\n //ros::Subscriber odom_sub = n.subscribe(\\\"odom\\\", 1, std::bind(getOdometry, &msg));\\n ros::Subscriber sub_t = n.subscribe(\\\"odom\\\", 1, &World::getOdometry, this);\\n //ros::Subscriber scan = n.subscribe(\\\"scan\\\", 1, &World::getScan, this);\\nsensor_msgs::LaserScanConstPtr test3 = ros::topic::waitForMessage<sensor_msgs::LaserScan>(\\\"scan\\\");\\n\\n\\n\\n//SOMETHING HERE \\n //self.respawn_goal = Respawn()\\n\\n\\n ros::ServiceClient reset_proxy = n.serviceClient<std_srvs::Empty>(\\\"gazebo/reset_simulation\\\"); \\n ros::ServiceClient pauseGazebo = n.serviceClient<std_srvs::Empty>(\\\"/gazebo/pause_physics\\\");\\n ros::ServiceClient unpauseGazebo = n.serviceClient<std_srvs::Empty>(\\\"/gazebo/unpause_physics\\\");\\n std_srvs::Empty emptySrv;\\n //pauseGazebo.call(emptySrv);\\n //msg_empty.linear.x = 0.0;\\n // msg_empty.angular.z = 0.0;\\n\\n\\n\\n\\n//unpause_proxy\\n void unpause_proxy() {\\n unpauseGazebo.call(emptySrv);\\n }\\n\\n//pause_proxy\\n void pause_proxy() {\\n pauseGazebo.call(emptySrv);\\n }\\n\\n\\n//getGoaldistance\\n float getGoalDistance() {\\n\\tgoal_distance = hypot(post.position.x - pos_x, post.position.y-pos_y);\\n\\tstate[13] = goal_distance;\\n\\n }\\n\\n//getOdometry\\n\\n void getOdometry(const nav_msgs::Odometry::ConstPtr& msg) {\\n\\tpos_x = msg->pose.pose.position.x;\\n\\tpos_y = msg->pose.pose.position.y;\\n\\n\\tgeometry_msgs::Quaternion q = msg->pose.pose.orientation;\\n\\tdouble siny_cosp = +2.0 * (q.w * q.z + q.x * q.y);\\n\\tdouble cosy_cosp = +1.0 - 2.0 * (q.y * q.y + q.z * q.z); \\n\\tyaw = atan2(siny_cosp, cosy_cosp);\\n\\tgoal_angle = atan2(post.position.x - pos_x, post.position.y-pos_y);\\n\\theading = goal_angle - yaw;\\n\\n\\tif (heading > 3.14)\\n heading -= 2 * 3.14;\\n\\n else if (heading < -3.14)\\n heading += 2 * 3.14;\\n\\tstate[12] = heading;\\n }\\n\\n//getScan\\n void getScan(const sensor_msgs::LaserScan::ConstPtr& msg) {\\n\\tfor (int i =0; i <10; ++i) {\\n\\t state[i] = msg->ranges[i];\\n\\n\\t if (state[i] > 3.5)\\n\\t\\tstate[i] = 3.5;\\t \\t \\t\\n\\t}\\n\\t count = msg->header.seq;\\n }\\n\\n//getstate\\n void getState() {\\n\\n\\tfor (int i =0; i<10; ++i) {\\n\\t\\t//std::min_element(state.begin(), state.end());\\n\\t if (state[i] < min_range) \\n\\t\\t{done = true;\\n\\t\\t//state[12] = state[i];\\n\\t\\t//state[13] = 5;\\n\\t\\tbreak;\\n\\t\\t}\\n\\t}\\n\\tgetGoalDistance();\\n\\t//distance = hypot(goal_x - pos_x, goal_y-pos_y);\\t \\n\\tif (goal_distance < 0.2) \\n\\t get_goalbox = true;\\n }\\n\\n//setreward\\n void setReward() {\\n\\t\\n float distance_rate = past_distance - goal_distance;\\n\\treward = (500 * distance_rate);\\n\\tpast_distance = goal_distance;\\n\\t\\n\\tif (done) {\\n\\t ROS_INFO(\\\"Collision!\\\");\\n\\t reward = -150;\\n\\t chatter_pub.publish(msg_empty);\\n\\t reset();\\n\\t done = false;\\n\\t}\\n\\tif (get_goalbox) {\\n\\t ROS_INFO(\\\"Goal!\\\");\\n\\t reward = 200;\\n\\t deleteGoal();\\n\\t spawnGoal();\\n\\n\\t ///// new goal and get position?\\n\\t /////////////////////////////////\\n\\t /////////////////////////////////\\n\\t getGoalDistance();\\n\\t get_goalbox = false;\\n\\t}\\n\\n\\t//current_distance = \\n\\n }\\n\\n//step\\n MyList step(boost::python::list msgs, boost::python::list msgs2) {\\n long l = len(msgs);\\n long l2 = len(msgs2);\\n\\n for (long i = 0; i<l; ++i) {\\n\\n action[i] = boost::python::extract<float>(msgs[i]);\\n\\t}\\t\\n for (long i = 0; i<l2; ++i) {\\n\\n past_action[i] = boost::python::extract<float>(msgs2[i]);\\n\\t}\\t\\n if (action[1] > 1.0) {\\n action[1] = 1.0;\\n\\t }\\n\\t else if (action[1] < -1.0) {\\n\\t\\taction[1] = -1.0;\\n\\t }\\n\\tstate[11] = past_action[0];\\n\\tstate[12] = past_action[1];\\n\\tmsg.linear.x = action[0]*0.3;\\n\\tmsg.angular.z = action[1];\\n\\twhile (test3 == NULL)\\n\\t{\\n\\t} \\n\\t\\n\\tfor (int i =0; i <10; ++i) {\\n\\t state[i] = test3->ranges[i];\\n\\n\\t if (state[i] > 3.5)\\n\\t\\tstate[i] = 3.5;\\t \\t \\t\\n\\t}\\n\\t//state[13] = test3->header.seq;\\n\\n\\n\\ttest3 = ros::topic::waitForMessage<sensor_msgs::LaserScan>(\\\"scan\\\");\\n\\n\\tchatter_pub.publish(msg);\\n\\t//////////////////////////////////////////\\n //need to make so this function can only run once per laser scan...\\n\\t//currently it will not publis until there is a new laser scan, I need it to run only once per scan \\n\\t///////////////////////////////////////////\\n\\t//////////////////////////////////////////\\n\\t/////////////////////////////////////////\\n\\n\\told_count = count;\\n\\tgetState();\\n\\tsetReward();\\n\\tstate[14] = reward;\\n\\n\\treturn state;\\n\\n }\\n\\n//reset\\n void reset() {\\n\\tros::service::waitForService(\\\"gazebo/reset_simulation\\\");\\n\\ttry {\\n\\t reset_proxy.call(emptySrv);\\n\\t}\\n\\tcatch (int e) {\\n\\t ROS_INFO(\\\"Unable to reset Gazebo. Service call failed\\\");\\n\\t}\\n\\t\\n\\tif (init_goal == true) {\\n\\t spawnGoal();\\n\\t init_goal = false;\\n\\t}\\n\\t\\n\\tgetGoalDistance();\\n\\t\\n }\\n\\n void spawnGoal() {\\n\\n \\tros::service::waitForService(\\\"gazebo/spawn_sdf_model\\\");\\n \\tgazebo_msgs::SpawnModel model;\\n \\n \\tros::ServiceClient spawn_model = n.serviceClient<gazebo_msgs::SpawnModel>(\\\"gazebo/spawn_sdf_model\\\");\\n \\tstd::ifstream file(\\\"/home/justin/ros_test/src/turtlebot3_simulations/turtlebot3_gazebo/models/turtlebot3_square/goal_box/model.sdf\\\");\\n\\n \\tstd::string line;\\n \\n \\twhile(!file.eof()) // Parse the contents of the given urdf in a string\\n \\t{\\n \\t std::getline(file,line);\\n model.request.model_xml+=line;\\n \\t}\\n \\tfile.close();\\n\\t//int temp = rand();\\n \\tpost.position.x = goal_x_list[rand()%13];\\n \\tpost.position.y = goal_y_list[rand()%13];\\n \\n \\tmodel.request.model_name=\\\"goal\\\";\\n \\tmodel.request.reference_frame=\\\"world\\\";\\n \\tmodel.request.initial_pose = post;\\n \\tspawn_model.call(model); //Call the service\\n\\n\\tgetGoalDistance();\\n\\n }\\n\\n\\n void deleteGoal() {\\n\\t\\n\\tros::service::waitForService(\\\"gazebo/delete_model\\\");\\n \\tgazebo_msgs::DeleteModel model;\\n\\tros::ServiceClient delete_model = n.serviceClient<gazebo_msgs::DeleteModel>(\\\"gazebo/delete_model\\\");\\n\\tmodel.request.model_name = \\\"goal\\\";\\n\\tdelete_model.call(model);\\n\\n\\t//send an 0 cmd_vel command\\n\\t//chatter_pub.publish(msg_empty);\\n\\n }\\n\\n\\n\\n/*\\n\\n\\n\\n get position\\ncheck model\\n def deleteModel(self):\\n while True:\\n if self.check_model:\\n rospy.wait_for_service('gazebo/delete_model')\\n del_model_prox = rospy.ServiceProxy('gazebo/delete_model', DeleteModel)\\n del_model_prox(self.modelName)\\n break\\n else:\\n pass\\n\\n\\n*/\\n\\n\\n//used for learning\\n void many(boost::python::list msgs) {\\n long l = len(msgs);\\n std::stringstream ss;\\n for (long i = 0; i<l; ++i) {\\n if (i>0) ss << \\\", \\\";\\n std::string s = boost::python::extract<std::string>(msgs[i]);\\n ss << s;\\n }\\n mMsg = ss.str();\\n }\\n\\n//used for learing\\n void chat() {\\n \\tmsg.linear.x = pos_x;\\n \\tmsg.angular.z = 5; \\n chatter_pub.publish(msg);\\n }\\n\\n\\n//returns value\\n MyList greet() { return state; }\\n\\n\\n\\n\\n};\\n\\n\\n///////////////////////////////////////////////////////////////////////////////////\\n\\n//namespace bn = boost::python::numpy;\\n\\n\\n///////////////////////////////////////////////////////////////////////////////////////////\\nusing namespace boost::python;\\n\\nBOOST_PYTHON_MODULE(mycpplib)\\n{\\n\\n //boost::python::numpy::initialize();\\n\\n //def(\\\"testf\\\", testf);\\n\\n class_<MyList>(\\\"MyList\\\")\\n .def(vector_indexing_suite<MyList>() );\\n\\n class_<World>(\\\"World\\\")\\n\\n\\n .def(\\\"greet\\\", &World::greet)\\n .def(\\\"pause_proxy\\\", &World::pause_proxy)\\n .def(\\\"unpause_proxy\\\", &World::unpause_proxy)\\n .def(\\\"reset\\\", &World::reset) \\n .def(\\\"step\\\", &World::step)\\n .def(\\\"many\\\", &World::many)\\n .def(\\\"chat\\\", &World::chat)\\n .def(\\\"spawnGoal\\\", &World::spawnGoal)\\n .def(\\\"deleteGoal\\\", &World::deleteGoal)\\n ;\\n};\\n\"\n },\n {\n \"alpha_fraction\": 0.7618767619132996,\n \"alphanum_fraction\": 0.7736359238624573,\n \"avg_line_length\": 33.01599884033203,\n \"blob_id\": \"f4db42256adbd2023e74e267103cf49c276c2a98\",\n \"content_id\": \"5aa201566dc316c7b034479e99bb80c4408c297d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 8504,\n \"license_type\": \"no_license\",\n \"max_line_length\": 144,\n \"num_lines\": 250,\n \"path\": \"/build/homework2/cmake/homework2-genmsg.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# generated from genmsg/cmake/pkg-genmsg.cmake.em\\n\\nmessage(STATUS \\\"homework2: 0 messages, 1 services\\\")\\n\\nset(MSG_I_FLAGS \\\"-Istd_msgs:/opt/ros/melodic/share/std_msgs/cmake/../msg\\\")\\n\\n# Find all generators\\nfind_package(gencpp REQUIRED)\\nfind_package(geneus REQUIRED)\\nfind_package(genlisp REQUIRED)\\nfind_package(gennodejs REQUIRED)\\nfind_package(genpy REQUIRED)\\n\\nadd_custom_target(homework2_generate_messages ALL)\\n\\n# verify that message/service dependencies have not changed since configure\\n\\n\\n\\nget_filename_component(_filename \\\"/home/justin/ros_test/src/homework2/srv/string_cat.srv\\\" NAME_WE)\\nadd_custom_target(_homework2_generate_messages_check_deps_${_filename}\\n COMMAND ${CATKIN_ENV} ${PYTHON_EXECUTABLE} ${GENMSG_CHECK_DEPS_SCRIPT} \\\"homework2\\\" \\\"/home/justin/ros_test/src/homework2/srv/string_cat.srv\\\" \\\"\\\"\\n)\\n\\n#\\n# langs = gencpp;geneus;genlisp;gennodejs;genpy\\n#\\n\\n### Section generating for lang: gencpp\\n### Generating Messages\\n\\n### Generating Services\\n_generate_srv_cpp(homework2\\n \\\"/home/justin/ros_test/src/homework2/srv/string_cat.srv\\\"\\n \\\"${MSG_I_FLAGS}\\\"\\n \\\"\\\"\\n ${CATKIN_DEVEL_PREFIX}/${gencpp_INSTALL_DIR}/homework2\\n)\\n\\n### Generating Module File\\n_generate_module_cpp(homework2\\n ${CATKIN_DEVEL_PREFIX}/${gencpp_INSTALL_DIR}/homework2\\n \\\"${ALL_GEN_OUTPUT_FILES_cpp}\\\"\\n)\\n\\nadd_custom_target(homework2_generate_messages_cpp\\n DEPENDS ${ALL_GEN_OUTPUT_FILES_cpp}\\n)\\nadd_dependencies(homework2_generate_messages homework2_generate_messages_cpp)\\n\\n# add dependencies to all check dependencies targets\\nget_filename_component(_filename \\\"/home/justin/ros_test/src/homework2/srv/string_cat.srv\\\" NAME_WE)\\nadd_dependencies(homework2_generate_messages_cpp _homework2_generate_messages_check_deps_${_filename})\\n\\n# target for backward compatibility\\nadd_custom_target(homework2_gencpp)\\nadd_dependencies(homework2_gencpp homework2_generate_messages_cpp)\\n\\n# register target for catkin_package(EXPORTED_TARGETS)\\nlist(APPEND ${PROJECT_NAME}_EXPORTED_TARGETS homework2_generate_messages_cpp)\\n\\n### Section generating for lang: geneus\\n### Generating Messages\\n\\n### Generating Services\\n_generate_srv_eus(homework2\\n \\\"/home/justin/ros_test/src/homework2/srv/string_cat.srv\\\"\\n \\\"${MSG_I_FLAGS}\\\"\\n \\\"\\\"\\n ${CATKIN_DEVEL_PREFIX}/${geneus_INSTALL_DIR}/homework2\\n)\\n\\n### Generating Module File\\n_generate_module_eus(homework2\\n ${CATKIN_DEVEL_PREFIX}/${geneus_INSTALL_DIR}/homework2\\n \\\"${ALL_GEN_OUTPUT_FILES_eus}\\\"\\n)\\n\\nadd_custom_target(homework2_generate_messages_eus\\n DEPENDS ${ALL_GEN_OUTPUT_FILES_eus}\\n)\\nadd_dependencies(homework2_generate_messages homework2_generate_messages_eus)\\n\\n# add dependencies to all check dependencies targets\\nget_filename_component(_filename \\\"/home/justin/ros_test/src/homework2/srv/string_cat.srv\\\" NAME_WE)\\nadd_dependencies(homework2_generate_messages_eus _homework2_generate_messages_check_deps_${_filename})\\n\\n# target for backward compatibility\\nadd_custom_target(homework2_geneus)\\nadd_dependencies(homework2_geneus homework2_generate_messages_eus)\\n\\n# register target for catkin_package(EXPORTED_TARGETS)\\nlist(APPEND ${PROJECT_NAME}_EXPORTED_TARGETS homework2_generate_messages_eus)\\n\\n### Section generating for lang: genlisp\\n### Generating Messages\\n\\n### Generating Services\\n_generate_srv_lisp(homework2\\n \\\"/home/justin/ros_test/src/homework2/srv/string_cat.srv\\\"\\n \\\"${MSG_I_FLAGS}\\\"\\n \\\"\\\"\\n ${CATKIN_DEVEL_PREFIX}/${genlisp_INSTALL_DIR}/homework2\\n)\\n\\n### Generating Module File\\n_generate_module_lisp(homework2\\n ${CATKIN_DEVEL_PREFIX}/${genlisp_INSTALL_DIR}/homework2\\n \\\"${ALL_GEN_OUTPUT_FILES_lisp}\\\"\\n)\\n\\nadd_custom_target(homework2_generate_messages_lisp\\n DEPENDS ${ALL_GEN_OUTPUT_FILES_lisp}\\n)\\nadd_dependencies(homework2_generate_messages homework2_generate_messages_lisp)\\n\\n# add dependencies to all check dependencies targets\\nget_filename_component(_filename \\\"/home/justin/ros_test/src/homework2/srv/string_cat.srv\\\" NAME_WE)\\nadd_dependencies(homework2_generate_messages_lisp _homework2_generate_messages_check_deps_${_filename})\\n\\n# target for backward compatibility\\nadd_custom_target(homework2_genlisp)\\nadd_dependencies(homework2_genlisp homework2_generate_messages_lisp)\\n\\n# register target for catkin_package(EXPORTED_TARGETS)\\nlist(APPEND ${PROJECT_NAME}_EXPORTED_TARGETS homework2_generate_messages_lisp)\\n\\n### Section generating for lang: gennodejs\\n### Generating Messages\\n\\n### Generating Services\\n_generate_srv_nodejs(homework2\\n \\\"/home/justin/ros_test/src/homework2/srv/string_cat.srv\\\"\\n \\\"${MSG_I_FLAGS}\\\"\\n \\\"\\\"\\n ${CATKIN_DEVEL_PREFIX}/${gennodejs_INSTALL_DIR}/homework2\\n)\\n\\n### Generating Module File\\n_generate_module_nodejs(homework2\\n ${CATKIN_DEVEL_PREFIX}/${gennodejs_INSTALL_DIR}/homework2\\n \\\"${ALL_GEN_OUTPUT_FILES_nodejs}\\\"\\n)\\n\\nadd_custom_target(homework2_generate_messages_nodejs\\n DEPENDS ${ALL_GEN_OUTPUT_FILES_nodejs}\\n)\\nadd_dependencies(homework2_generate_messages homework2_generate_messages_nodejs)\\n\\n# add dependencies to all check dependencies targets\\nget_filename_component(_filename \\\"/home/justin/ros_test/src/homework2/srv/string_cat.srv\\\" NAME_WE)\\nadd_dependencies(homework2_generate_messages_nodejs _homework2_generate_messages_check_deps_${_filename})\\n\\n# target for backward compatibility\\nadd_custom_target(homework2_gennodejs)\\nadd_dependencies(homework2_gennodejs homework2_generate_messages_nodejs)\\n\\n# register target for catkin_package(EXPORTED_TARGETS)\\nlist(APPEND ${PROJECT_NAME}_EXPORTED_TARGETS homework2_generate_messages_nodejs)\\n\\n### Section generating for lang: genpy\\n### Generating Messages\\n\\n### Generating Services\\n_generate_srv_py(homework2\\n \\\"/home/justin/ros_test/src/homework2/srv/string_cat.srv\\\"\\n \\\"${MSG_I_FLAGS}\\\"\\n \\\"\\\"\\n ${CATKIN_DEVEL_PREFIX}/${genpy_INSTALL_DIR}/homework2\\n)\\n\\n### Generating Module File\\n_generate_module_py(homework2\\n ${CATKIN_DEVEL_PREFIX}/${genpy_INSTALL_DIR}/homework2\\n \\\"${ALL_GEN_OUTPUT_FILES_py}\\\"\\n)\\n\\nadd_custom_target(homework2_generate_messages_py\\n DEPENDS ${ALL_GEN_OUTPUT_FILES_py}\\n)\\nadd_dependencies(homework2_generate_messages homework2_generate_messages_py)\\n\\n# add dependencies to all check dependencies targets\\nget_filename_component(_filename \\\"/home/justin/ros_test/src/homework2/srv/string_cat.srv\\\" NAME_WE)\\nadd_dependencies(homework2_generate_messages_py _homework2_generate_messages_check_deps_${_filename})\\n\\n# target for backward compatibility\\nadd_custom_target(homework2_genpy)\\nadd_dependencies(homework2_genpy homework2_generate_messages_py)\\n\\n# register target for catkin_package(EXPORTED_TARGETS)\\nlist(APPEND ${PROJECT_NAME}_EXPORTED_TARGETS homework2_generate_messages_py)\\n\\n\\n\\nif(gencpp_INSTALL_DIR AND EXISTS ${CATKIN_DEVEL_PREFIX}/${gencpp_INSTALL_DIR}/homework2)\\n # install generated code\\n install(\\n DIRECTORY ${CATKIN_DEVEL_PREFIX}/${gencpp_INSTALL_DIR}/homework2\\n DESTINATION ${gencpp_INSTALL_DIR}\\n )\\nendif()\\nif(TARGET std_msgs_generate_messages_cpp)\\n add_dependencies(homework2_generate_messages_cpp std_msgs_generate_messages_cpp)\\nendif()\\n\\nif(geneus_INSTALL_DIR AND EXISTS ${CATKIN_DEVEL_PREFIX}/${geneus_INSTALL_DIR}/homework2)\\n # install generated code\\n install(\\n DIRECTORY ${CATKIN_DEVEL_PREFIX}/${geneus_INSTALL_DIR}/homework2\\n DESTINATION ${geneus_INSTALL_DIR}\\n )\\nendif()\\nif(TARGET std_msgs_generate_messages_eus)\\n add_dependencies(homework2_generate_messages_eus std_msgs_generate_messages_eus)\\nendif()\\n\\nif(genlisp_INSTALL_DIR AND EXISTS ${CATKIN_DEVEL_PREFIX}/${genlisp_INSTALL_DIR}/homework2)\\n # install generated code\\n install(\\n DIRECTORY ${CATKIN_DEVEL_PREFIX}/${genlisp_INSTALL_DIR}/homework2\\n DESTINATION ${genlisp_INSTALL_DIR}\\n )\\nendif()\\nif(TARGET std_msgs_generate_messages_lisp)\\n add_dependencies(homework2_generate_messages_lisp std_msgs_generate_messages_lisp)\\nendif()\\n\\nif(gennodejs_INSTALL_DIR AND EXISTS ${CATKIN_DEVEL_PREFIX}/${gennodejs_INSTALL_DIR}/homework2)\\n # install generated code\\n install(\\n DIRECTORY ${CATKIN_DEVEL_PREFIX}/${gennodejs_INSTALL_DIR}/homework2\\n DESTINATION ${gennodejs_INSTALL_DIR}\\n )\\nendif()\\nif(TARGET std_msgs_generate_messages_nodejs)\\n add_dependencies(homework2_generate_messages_nodejs std_msgs_generate_messages_nodejs)\\nendif()\\n\\nif(genpy_INSTALL_DIR AND EXISTS ${CATKIN_DEVEL_PREFIX}/${genpy_INSTALL_DIR}/homework2)\\n install(CODE \\\"execute_process(COMMAND \\\\\\\"/usr/bin/python2\\\\\\\" -m compileall \\\\\\\"${CATKIN_DEVEL_PREFIX}/${genpy_INSTALL_DIR}/homework2\\\\\\\")\\\")\\n # install generated code\\n install(\\n DIRECTORY ${CATKIN_DEVEL_PREFIX}/${genpy_INSTALL_DIR}/homework2\\n DESTINATION ${genpy_INSTALL_DIR}\\n )\\nendif()\\nif(TARGET std_msgs_generate_messages_py)\\n add_dependencies(homework2_generate_messages_py std_msgs_generate_messages_py)\\nendif()\\n\"\n },\n {\n \"alpha_fraction\": 0.6954732537269592,\n \"alphanum_fraction\": 0.7009602189064026,\n \"avg_line_length\": 44.625,\n \"blob_id\": \"f60d9f38236e2599d2da9ae8f186f587110570d8\",\n \"content_id\": \"1fff917c90556dd400dc735ec835ad15645ffa84\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 729,\n \"license_type\": \"no_license\",\n \"max_line_length\": 97,\n \"num_lines\": 16,\n \"path\": \"/build/rl-texplore-ros-pkg-master/src/rl_env/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"rl_env\\\")\\nset(rl_env_VERSION \\\"0.0.0\\\")\\nset(rl_env_MAINTAINER \\\"Todd Hester <todd.hester@gmail.com>\\\")\\nset(rl_env_PACKAGE_FORMAT \\\"1\\\")\\nset(rl_env_BUILD_DEPENDS \\\"roscpp\\\" \\\"std_msgs\\\" \\\"rl_msgs\\\" \\\"tf\\\" \\\"rl_common\\\")\\nset(rl_env_BUILD_EXPORT_DEPENDS \\\"message_runtime\\\" \\\"roscpp\\\" \\\"std_msgs\\\" \\\"rl_msgs\\\" \\\"tf\\\" \\\"rl_common\\\")\\nset(rl_env_BUILDTOOL_DEPENDS \\\"catkin\\\")\\nset(rl_env_BUILDTOOL_EXPORT_DEPENDS )\\nset(rl_env_EXEC_DEPENDS \\\"message_runtime\\\" \\\"roscpp\\\" \\\"std_msgs\\\" \\\"rl_msgs\\\" \\\"tf\\\" \\\"rl_common\\\")\\nset(rl_env_RUN_DEPENDS \\\"message_runtime\\\" \\\"roscpp\\\" \\\"std_msgs\\\" \\\"rl_msgs\\\" \\\"tf\\\" \\\"rl_common\\\")\\nset(rl_env_TEST_DEPENDS )\\nset(rl_env_DOC_DEPENDS )\\nset(rl_env_URL_WEBSITE \\\"\\\")\\nset(rl_env_URL_BUGTRACKER \\\"\\\")\\nset(rl_env_URL_REPOSITORY \\\"\\\")\\nset(rl_env_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.7657300233840942,\n \"alphanum_fraction\": 0.7690607309341431,\n \"avg_line_length\": 44.93185043334961,\n \"blob_id\": \"2cdcf8ebc147e59aee10fd359fa6f902a983e03a\",\n \"content_id\": \"3c58a88cbde3904dedd924717c9bcb82834138cc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Makefile\",\n \"length_bytes\": 33026,\n \"license_type\": \"no_license\",\n \"max_line_length\": 216,\n \"num_lines\": 719,\n \"path\": \"/build/nav_stack_example/Makefile\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# CMAKE generated file: DO NOT EDIT!\\n# Generated by \\\"Unix Makefiles\\\" Generator, CMake Version 3.10\\n\\n# Default target executed when no arguments are given to make.\\ndefault_target: all\\n\\n.PHONY : default_target\\n\\n# Allow only one \\\"make -f Makefile2\\\" at a time, but pass parallelism.\\n.NOTPARALLEL:\\n\\n\\n#=============================================================================\\n# Special targets provided by cmake.\\n\\n# Disable implicit rules so canonical targets will work.\\n.SUFFIXES:\\n\\n\\n# Remove some rules from gmake that .SUFFIXES does not remove.\\nSUFFIXES =\\n\\n.SUFFIXES: .hpux_make_needs_suffix_list\\n\\n\\n# Suppress display of executed commands.\\n$(VERBOSE).SILENT:\\n\\n\\n# A target that is always out of date.\\ncmake_force:\\n\\n.PHONY : cmake_force\\n\\n#=============================================================================\\n# Set environment variables for the build.\\n\\n# The shell in which to execute make rules.\\nSHELL = /bin/sh\\n\\n# The CMake executable.\\nCMAKE_COMMAND = /usr/bin/cmake\\n\\n# The command to remove a file.\\nRM = /usr/bin/cmake -E remove -f\\n\\n# Escaping for special characters.\\nEQUALS = =\\n\\n# The top-level source directory on which CMake was run.\\nCMAKE_SOURCE_DIR = /home/justin/ros_test/src\\n\\n# The top-level build directory on which CMake was run.\\nCMAKE_BINARY_DIR = /home/justin/ros_test/build\\n\\n#=============================================================================\\n# Targets provided globally by CMake.\\n\\n# Special rule for the target install/strip\\ninstall/strip: preinstall\\n\\t@$(CMAKE_COMMAND) -E cmake_echo_color --switch=$(COLOR) --cyan \\\"Installing the project stripped...\\\"\\n\\t/usr/bin/cmake -DCMAKE_INSTALL_DO_STRIP=1 -P cmake_install.cmake\\n.PHONY : install/strip\\n\\n# Special rule for the target install/strip\\ninstall/strip/fast: preinstall/fast\\n\\t@$(CMAKE_COMMAND) -E cmake_echo_color --switch=$(COLOR) --cyan \\\"Installing the project stripped...\\\"\\n\\t/usr/bin/cmake -DCMAKE_INSTALL_DO_STRIP=1 -P cmake_install.cmake\\n.PHONY : install/strip/fast\\n\\n# Special rule for the target install\\ninstall: preinstall\\n\\t@$(CMAKE_COMMAND) -E cmake_echo_color --switch=$(COLOR) --cyan \\\"Install the project...\\\"\\n\\t/usr/bin/cmake -P cmake_install.cmake\\n.PHONY : install\\n\\n# Special rule for the target install\\ninstall/fast: preinstall/fast\\n\\t@$(CMAKE_COMMAND) -E cmake_echo_color --switch=$(COLOR) --cyan \\\"Install the project...\\\"\\n\\t/usr/bin/cmake -P cmake_install.cmake\\n.PHONY : install/fast\\n\\n# Special rule for the target rebuild_cache\\nrebuild_cache:\\n\\t@$(CMAKE_COMMAND) -E cmake_echo_color --switch=$(COLOR) --cyan \\\"Running CMake to regenerate build system...\\\"\\n\\t/usr/bin/cmake -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR)\\n.PHONY : rebuild_cache\\n\\n# Special rule for the target rebuild_cache\\nrebuild_cache/fast: rebuild_cache\\n\\n.PHONY : rebuild_cache/fast\\n\\n# Special rule for the target test\\ntest:\\n\\t@$(CMAKE_COMMAND) -E cmake_echo_color --switch=$(COLOR) --cyan \\\"Running tests...\\\"\\n\\t/usr/bin/ctest --force-new-ctest-process $(ARGS)\\n.PHONY : test\\n\\n# Special rule for the target test\\ntest/fast: test\\n\\n.PHONY : test/fast\\n\\n# Special rule for the target list_install_components\\nlist_install_components:\\n\\t@$(CMAKE_COMMAND) 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\"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file C45Tree.hh\\n Defines the C4.5 decision tree class.\\n This is an implementation of C4.5 decision trees described in:\\n J. R. Quinlan, \\\"Induction of decision trees,\\\" Machine Learning, vol 1. pp 81-106, 1986.\\n \\\\author Todd Hester\\n*/\\n\\n\\n#ifndef _C45TREE_HH_\\n#define _C45TREE_HH_\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n#include <vector>\\n#include <set>\\n#include <map>\\n\\n#define N_C45_EXP 200000\\n#define N_C45_NODES 2500\\n\\n#define BUILD_EVERY 0\\n#define BUILD_ON_ERROR 1\\n#define BUILD_EVERY_N 2\\n#define BUILD_ON_TERMINAL 3\\n#define BUILD_ON_TERMINAL_AND_ERROR 4\\n\\n\\n/** C4.5 decision tree class. */\\nclass C45Tree: public Classifier {\\n\\npublic:\\n\\n /** Default constructor \\n \\\\param id id of the tree for debug\\n \\\\param trainMode build every step? only on errors? every freq steps?\\n \\\\param trainFreq frequency of model building if using latter mode\\n \\\\param m # of visits for a given state-action to be considered known\\n \\\\param featPct pct of features to remove from set used for each tree split\\n \\\\param rng Random Number Generator \\n */\\n C45Tree(int id, int trainMode, int trainFreq, int m, \\n\\t float featPct, Random rng);\\n\\n /** Copy constructor */\\n C45Tree(const C45Tree&);\\n\\n ~C45Tree();\\n\\n // structs to be defined\\n struct tree_node;\\n struct tree_experience;\\n \\n /** Make a copy of the subtree from origNode to newNode */\\n void copyTree(tree_node* newNode, tree_node* origNode);\\n\\n virtual C45Tree* getCopy();\\n\\n /** Tree node struct. For decision nodes, it contains split information and pointers to child nodes. For leaf nodes, it contains all outputs that went into this leaf during trainiing. */\\n struct tree_node {\\n int id;\\n\\n // split criterion\\n int dim;\\n float val;\\n bool type;\\n\\n // set of all outputs seen at this leaf/node\\n std::map<float,int> outputs;\\n int nInstances;\\n\\n // next nodes in tree\\n tree_node *l;\\n tree_node *r;\\n\\n bool leaf;\\n };\\n\\n /** Experiences the tree is trained on. A vector of inputs and one float output to predict */\\n struct tree_experience {\\n std::vector<float> input;\\n float output;\\n };\\n \\n /** The types of splits. Split on ONLY meaning is input == x, or CUT meaning is input > x */\\n enum splitTypes{\\n ONLY,\\n CUT\\n };\\n\\n virtual bool trainInstance(classPair &instance);\\n virtual bool trainInstances(std::vector<classPair> &instances);\\n virtual void testInstance(const std::vector<float> &input, std::map<float, float>* retval);\\n virtual float getConf(const std::vector<float> &input);\\n\\n /** Build the tree with the given instances from the given tree node */\\n bool buildTree(tree_node* node, const std::vector<tree_experience*> &instances, bool changed);\\n\\n // helper functions\\n /** Initialize the tree */\\n void initTree();\\n\\n /** Rebuild the tree */\\n bool rebuildTree();\\n\\n /** Initialize the tree_node struct */\\n void initTreeNode(tree_node* node);\\n\\n /** Traverse the tree to a leaf for the given input */\\n tree_node* traverseTree(tree_node* node, const std::vector<float> &input);\\n\\n /** Get the correct child of this node for a given input */\\n tree_node* getCorrectChild(tree_node* node, const std::vector<float> &input);\\n\\n /** Determine if the input passes the test defined by dim, val, type */\\n bool passTest(int dim, float val, bool type, const std::vector<float> &input);\\n\\n /** Calculate the gain ratio for the given split of instances */\\n float calcGainRatio(int dim, float val, bool type,\\n\\t\\t const std::vector<tree_experience*> &instances, float I,\\n\\t\\t std::vector<tree_experience*> &left,\\n\\t\\t std::vector<tree_experience*> &right);\\n\\n /** Returns an array of the values of features at the index dim, sorted from lowest to highest */\\n float* sortOnDim(int dim, const std::vector<tree_experience*> &instances);\\n\\n /** Get all the unique values of the features on dimension dim */\\n std::set<float> getUniques(int dim, const std::vector<tree_experience*> &instances, float & minVal, float& maxVal);\\n\\n /** Delete this tree node and all nodes below it in the tree. */\\n void deleteTree(tree_node* node);\\n\\n /** Calculate I(P) */\\n float calcIofP(float* P, int size);\\n\\n /** Calculate I(P) for set. */\\n float calcIforSet(const std::vector<tree_experience*> &instances);\\n\\n /** Print the tree for debug purposes. */\\n void printTree(tree_node *t, int level);\\n\\n /** Test the possible splits for the given set of instances */\\n void testPossibleSplits(const std::vector<tree_experience*> &instances, float *bestGainRatio, int *bestDim, \\n float *bestVal, bool *bestType,\\n std::vector<tree_experience*> *bestLeft, std::vector<tree_experience*> *bestRight);\\n\\n /** Implement the given split at the given node */\\n bool implementSplit(tree_node* node, float bestGainRatio, int bestDim,\\n float bestVal, bool bestType, \\n const std::vector<tree_experience*> &left, \\n const std::vector<tree_experience*> &right, bool changed);\\n\\n /** Compare the current split to determine if it is the best split. */\\n void compareSplits(float gainRatio, int dim, float val, bool type, \\n const std::vector<tree_experience*> &left, \\n const std::vector<tree_experience*> &right,\\n int *nties, float *bestGainRatio, int *bestDim, \\n float *bestVal, bool *bestType,\\n std::vector<tree_experience*> *bestLeft, \\n std::vector<tree_experience*> *bestRight);\\n\\n /** Get the probability distribution for the given leaf node. */\\n void outputProbabilities(tree_node *t, std::map<float, float>* retval);\\n\\n /** Make the given node into a leaf node. */\\n bool makeLeaf(tree_node* node);\\n\\n /** Allocate a new node from our pre-allocated store of tree nodes */\\n tree_node* allocateNode();\\n\\n /** Return tree node back to store of nodes */\\n void deallocateNode(tree_node* node);\\n\\n /** Initialize our store of tree nodes */\\n void initNodes();\\n\\n\\n bool INCDEBUG;\\n bool DTDEBUG;\\n bool SPLITDEBUG;\\n bool STOCH_DEBUG;\\n int nExperiences;\\n bool NODEDEBUG;\\n bool COPYDEBUG;\\n\\n float SPLIT_MARGIN;\\n float MIN_GAIN_RATIO; \\n\\nprivate:\\n\\n const int id;\\n \\n const int mode;\\n const int freq;\\n const int M;\\n const float featPct; \\n const bool ALLOW_ONLY_SPLITS;\\n\\n Random rng;\\n\\n int nOutput;\\n int nnodes;\\n bool hadError;\\n int maxnodes;\\n int totalnodes;\\n\\n /** Vector of all experiences used to train the tree */\\n std::vector<tree_experience*> experiences;\\n\\n /** Pre-allocated array of experiences to be filled during training. */\\n tree_experience allExp[N_C45_EXP];\\n\\n /** Pre-allocated array of tree nodes to be used for tree */\\n tree_node allNodes[N_C45_NODES];\\n std::vector<int> freeNodes;\\n\\n // TREE\\n /** Pointer to root node of tree. */\\n tree_node* root;\\n /** Pointer to last node of tree used (leaf used in last prediction made). */\\n tree_node* lastNode;\\n\\n};\\n\\n\\n#endif\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.7613292932510376,\n \"alphanum_fraction\": 0.7613292932510376,\n \"avg_line_length\": 32.099998474121094,\n \"blob_id\": \"6810efd9682e8a6d915f8f08dc52fd25057e19d6\",\n \"content_id\": \"742b1d943a88b7a9c12a899c4d89e5ede17264a1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 331,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 10,\n \"path\": \"/build/roundbot_control/CMakeFiles/motor_sim.dir/cmake_clean.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"file(REMOVE_RECURSE\\n \\\"CMakeFiles/motor_sim.dir/src/MotorSim.cpp.o\\\"\\n \\\"/home/justin/ros_test/devel/lib/libmotor_sim.pdb\\\"\\n \\\"/home/justin/ros_test/devel/lib/libmotor_sim.so\\\"\\n)\\n\\n# Per-language clean rules from dependency scanning.\\nforeach(lang CXX)\\n include(CMakeFiles/motor_sim.dir/cmake_clean_${lang}.cmake OPTIONAL)\\nendforeach()\\n\"\n },\n {\n \"alpha_fraction\": 0.5917937755584717,\n \"alphanum_fraction\": 0.6034324169158936,\n \"avg_line_length\": 29.355289459228516,\n \"blob_id\": \"c47f363e4bfe11dd51bb903df77e448916578205\",\n \"content_id\": \"8a60033420c398c4f1e57f096b4c77cfa55d13d4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 15208,\n \"license_type\": \"no_license\",\n \"max_line_length\": 171,\n \"num_lines\": 501,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Agent/ModelBasedAgent.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file ModelBasedAgent.cc\\n Implements the ModelBasedAgent class\\n \\\\author Todd Hester\\n*/\\n\\n#include <rl_agent/ModelBasedAgent.hh>\\n#include <algorithm>\\n\\n#include <sys/time.h>\\n\\n// planners\\n#include \\\"../Planners/ValueIteration.hh\\\"\\n#include \\\"../Planners/PolicyIteration.hh\\\"\\n#include \\\"../Planners/PrioritizedSweeping.hh\\\"\\n#include \\\"../Planners/ETUCT.hh\\\"\\n#include \\\"../Planners/ParallelETUCT.hh\\\"\\n#include \\\"../Planners/PO_ETUCT.hh\\\"\\n#include \\\"../Planners/PO_ParallelETUCT.hh\\\"\\n#include \\\"../Planners/MBS.hh\\\"\\n\\n// models\\n#include \\\"../Models/RMaxModel.hh\\\"\\n#include \\\"../Models/FactoredModel.hh\\\"\\n#include \\\"../Models/ExplorationModel.hh\\\"\\n\\nModelBasedAgent::ModelBasedAgent(int numactions, float gamma, \\n float rmax, float rrange,\\n int modelType,\\tint exploreType, \\n int predType, int nModels, int plannerType, \\n float epsilon, float lambda, float MAX_TIME,\\n float m, const std::vector<float> &featmin,\\n const std::vector<float> &featmax, \\n std::vector<int> nstatesPerDim, int history, float v, float n,\\n bool depTrans, bool relTrans, float featPct, bool stoch, bool episodic,\\n Random rng):\\n featmin(featmin), featmax(featmax),\\n numactions(numactions), gamma(gamma), rmax(rmax), rrange(rrange),\\n qmax(rmax/(1.0-gamma)), \\n modelType(modelType), exploreType(exploreType), \\n predType(predType), nModels(nModels), plannerType(plannerType),\\n epsilon(epsilon), lambda(lambda), MAX_TIME(MAX_TIME),\\n M(m), statesPerDim(nstatesPerDim), history(history), v(v), n(n),\\n depTrans(depTrans), relTrans(relTrans), featPct(featPct),\\n stoch(stoch), episodic(episodic), rng(rng)\\n{\\n\\n if (statesPerDim[0] > 0){\\n cout << \\\"MBA: Planner will use states discretized by various amounts per dim with continuous model\\\" << endl;\\n }\\n\\n initParams();\\n\\n}\\n\\n\\n\\nModelBasedAgent::ModelBasedAgent(int numactions, float gamma, \\n float rmax, float rrange,\\n int modelType,\\tint exploreType, \\n int predType, int nModels, int plannerType, \\n float epsilon, float lambda, float MAX_TIME,\\n float m, const std::vector<float> &featmin,\\n const std::vector<float> &featmax, \\n int nstatesPerDim, int history, float v, float n,\\n bool depTrans, bool relTrans, float featPct,\\n\\t\\t\\t\\t bool stoch, bool episodic, Random rng):\\n featmin(featmin), featmax(featmax),\\n numactions(numactions), gamma(gamma), rmax(rmax), rrange(rrange),\\n qmax(rmax/(1.0-gamma)), \\n modelType(modelType), exploreType(exploreType), \\n predType(predType), nModels(nModels), plannerType(plannerType),\\n epsilon(epsilon), lambda(lambda), MAX_TIME(MAX_TIME),\\n M(m), statesPerDim(featmin.size(),nstatesPerDim), history(history), v(v), n(n),\\n depTrans(depTrans), relTrans(relTrans), featPct(featPct),\\n stoch(stoch), episodic(episodic), rng(rng)\\n{\\n\\n if (statesPerDim[0] > 0){\\n cout << \\\"MBA: Planner will use states discretized by \\\" << statesPerDim[0] << \\\" with continuous model\\\" << endl;\\n }\\n\\n initParams();\\n\\n}\\n\\n\\nvoid ModelBasedAgent::initParams(){\\n\\n nstates = 0;\\n nactions = 0;\\n\\n model = NULL;\\n planner = NULL;\\n\\n modelUpdateTime = 0.0;\\n planningTime = 0.0;\\n actionTime = 0.0;\\n \\n modelChanged = false;\\n\\n \\n BATCH_FREQ = 1; //50;\\n\\n TIMEDEBUG = false; //true;\\n AGENTDEBUG = false;\\n ACTDEBUG = false;//true;\\n SIMPLEDEBUG = false; //true; //false; //true;\\n\\n // check\\n if (qmax <= 0.1 && (exploreType == TWO_MODE_PLUS_R || \\n\\t\\t exploreType == CONTINUOUS_BONUS_R || \\n\\t\\t exploreType == CONTINUOUS_BONUS ||\\n\\t\\t exploreType == THRESHOLD_BONUS_R)) {\\n std::cerr << \\\"For this exploration type, rmax needs to be an additional positive bonus value, not a replacement for the q-value\\\" << endl;\\n exit(-1);\\n }\\n\\n if (exploreType == TWO_MODE || exploreType == TWO_MODE_PLUS_R){\\n std::cerr << \\\"This exploration type does not work in this agent.\\\" << endl;\\n exit(-1);\\n }\\n\\n seeding = false;\\n \\n if (SIMPLEDEBUG)\\n cout << \\\"qmax: \\\" << qmax << endl;\\n\\n}\\n\\nModelBasedAgent::~ModelBasedAgent() {\\n delete planner;\\n delete model;\\n featmin.clear();\\n featmax.clear();\\n prevstate.clear();\\n}\\n\\nint ModelBasedAgent::first_action(const std::vector<float> &s) {\\n if (AGENTDEBUG) cout << \\\"first_action(s)\\\" << endl;\\n\\n if (model == NULL)\\n initModel(s.size());\\n\\n planner->setFirst();\\n\\n // in case we didn't do it after seeding\\n if (plannerType == PARALLEL_ET_UCT || plannerType == PAR_ETUCT_ACTUAL)\\n planner->planOnNewModel();\\n\\n // choose an action\\n int act = chooseAction(s);\\n\\n // save curr state/action for next time\\n saveStateAndAction(s, act);\\n\\n if (ACTDEBUG)\\n cout << \\\"Took action \\\" << act << \\\" from state \\\" \\n\\t << s[0] << \\\",\\\" << s[1] \\n\\t << endl;\\n\\n // return that action\\n return act;\\n\\n}\\n\\nint ModelBasedAgent::next_action(float r, const std::vector<float> &s) {\\n if (AGENTDEBUG) {\\n cout << \\\"next_action(r = \\\" << r \\n\\t << \\\", s = \\\" << &s << \\\")\\\" << endl;\\n }\\n \\n if (SIMPLEDEBUG) cout << \\\"Got Reward \\\" << r;\\n \\n // update our models\\n // this is where we possibly plan again if model changes\\n updateWithNewExperience(prevstate, s, prevact, r, false);\\n\\n // choose an action\\n int act = chooseAction(s);\\n \\n // save curr state/action for next time\\n saveStateAndAction(s, act);\\n\\n if (ACTDEBUG){\\n cout << \\\"Took action \\\" << act << \\\" from state \\\" \\n\\t << (s)[0];\\n for (unsigned i = 1; i < s.size(); i++){\\n cout << \\\",\\\" << (s)[i];\\n }\\n cout << endl;\\n }\\n\\n // return that action\\n return act;\\n\\n}\\n\\nvoid ModelBasedAgent::last_action(float r) {\\n if (AGENTDEBUG) cout << \\\"last_action(r = \\\" << r\\n\\t\\t << \\\")\\\" << endl;\\n\\n if (AGENTDEBUG) cout << \\\"Got Reward \\\" << r;\\n\\n // update our models\\n // this is where we possibly plan again if model changes\\n updateWithNewExperience(prevstate, prevstate, prevact, r, true);\\n\\n}\\n\\n\\n\\n/////////////////////////////\\n// Functional functions :) //\\n/////////////////////////////\\n\\n\\nvoid ModelBasedAgent::initModel(int nfactors){\\n if ( AGENTDEBUG) cout << \\\"initModel nfactors: \\\" << nfactors << endl;\\n \\n bool needConf = \\n (exploreType != NO_EXPLORE && exploreType != EXPLORE_UNKNOWN && \\n exploreType != EPSILONGREEDY && exploreType != UNVISITED_BONUS &&\\n exploreType != UNVISITED_ACT_BONUS);\\n\\n std::vector<float> featRange(featmax.size(), 0);\\n for (unsigned i = 0; i < featmax.size(); i++){\\n featRange[i] = featmax[i] - featmin[i];\\n cout << \\\"feature \\\" << i << \\\" has range \\\" << featRange[i] << endl;\\n }\\n cout << \\\"reward range: \\\" << rrange << endl;\\n\\n float treeRangePct = 0.0001;\\n \\n // m5 tree\\n if (modelType == M5MULTI || modelType == M5SINGLE ||\\n modelType == M5ALLMULTI || modelType == M5ALLSINGLE ||\\n modelType == ALLM5TYPES){\\n treeRangePct = 0.0001;\\n }\\n \\n // just to ensure the diff models are on different random values\\n for (int i = 0; i < modelType; i++){\\n rng.uniform(0, 1);\\n }\\n \\n // 0 - traditional rmax model (unknown until m visits, then ML)\\n if (modelType == RMAX) {\\n model = new RMaxModel(M, numactions, rng);\\n }\\n \\n // any tree or stump will be mdptree\\n else if (modelType == C45TREE || modelType == STUMP ||\\n modelType == M5MULTI || modelType == M5SINGLE ||\\n modelType == M5ALLMULTI || modelType == M5ALLSINGLE ||\\n modelType == ALLM5TYPES ||\\n modelType == LSTMULTI || modelType == LSTSINGLE ||\\n modelType == GPREGRESS || modelType == GPTREE){\\n\\n model = new FactoredModel(0,numactions, M, modelType, predType, nModels, treeRangePct, featRange, rrange, needConf, depTrans, relTrans, featPct, stoch, episodic, rng);\\n }\\n \\n /*\\n else if (modelType == GPREGRESS){\\n model = new GPmdp(0, numactions, relTrans, rng);\\n }\\n */\\n\\n // pass model into exploration model wrapper model\\n if (exploreType != NO_EXPLORE && exploreType != EPSILONGREEDY){\\n MDPModel* m2 = model;\\n\\n model = new ExplorationModel(m2, modelType, exploreType,\\n predType, nModels, M, numactions,\\n rmax, qmax, rrange, nfactors, v, n,\\n featmax, featmin, rng);\\n \\n }\\n \\n initPlanner();\\n planner->setModel(model);\\n\\n}\\n\\nvoid ModelBasedAgent::initPlanner(){\\n if (AGENTDEBUG) cout << \\\"InitPlanner type: \\\" << plannerType << endl;\\n\\n int max_path = 200; //500;\\n\\n // init planner based on type\\n if (plannerType == VALUE_ITERATION){\\n planner = new ValueIteration(numactions, gamma, 500000, 10.0, modelType, featmax, featmin, statesPerDim, rng);\\n }\\n else if (plannerType == MBS_VI){\\n planner = new MBS(numactions, gamma, 500000, 10.0, modelType, featmax, featmin, statesPerDim, history, rng);\\n }\\n else if (plannerType == POLICY_ITERATION){\\n planner = new PolicyIteration(numactions, gamma, 500000, 10.0, modelType, featmax, featmin, statesPerDim, rng);\\n }\\n else if (plannerType == PRI_SWEEPING){\\n planner = new PrioritizedSweeping(numactions, gamma, 10.0, true, modelType, featmax, featmin, rng);\\n }\\n else if (plannerType == MOD_PRI_SWEEPING){\\n planner = new PrioritizedSweeping(numactions, gamma, 10.0, false, modelType, featmax, featmin, rng);\\n }\\n else if (plannerType == ET_UCT){\\n planner = new ETUCT(numactions, gamma, rrange, lambda, 500000, MAX_TIME, max_path, modelType, featmax, featmin, statesPerDim, false, history, rng);\\n }\\n else if (plannerType == POMDP_ETUCT){\\n planner = new PO_ETUCT(numactions, gamma, rrange, lambda, 500000, MAX_TIME, max_path, modelType, featmax, featmin, statesPerDim, true, history, rng);\\n }\\n else if (plannerType == POMDP_PAR_ETUCT){\\n planner = new PO_ParallelETUCT(numactions, gamma, rrange, lambda, 500000, MAX_TIME, max_path, modelType, featmax, featmin, statesPerDim, true, history, rng);\\n }\\n else if (plannerType == ET_UCT_ACTUAL){\\n planner = new ETUCT(numactions, gamma, rrange, lambda, 500000, MAX_TIME, max_path, modelType, featmax, featmin, statesPerDim, true, history, rng);\\n }\\n else if (plannerType == PARALLEL_ET_UCT){\\n planner = new ParallelETUCT(numactions, gamma, rrange, lambda, 500000, MAX_TIME, max_path, modelType, featmax, featmin, statesPerDim, false, history, rng);\\n }\\n else if (plannerType == PAR_ETUCT_ACTUAL){\\n planner = new ParallelETUCT(numactions, gamma, rrange, lambda, 500000, MAX_TIME, max_path, modelType, featmax, featmin, statesPerDim, true, history, rng);\\n }\\n else if (plannerType == ET_UCT_L1){\\n planner = new ETUCT(numactions, gamma, rrange, 1.0, 500000, MAX_TIME, max_path, modelType, featmax, featmin, statesPerDim, false, history, rng);\\n }\\n else {\\n std::cerr << \\\"ERROR: invalid planner type: \\\" << plannerType << endl;\\n exit(-1);\\n }\\n\\n}\\n\\nvoid ModelBasedAgent::updateWithNewExperience(const std::vector<float> &last, \\n const std::vector<float> &curr, \\n int lastact, float reward, \\n bool terminal){\\n if (AGENTDEBUG) cout << \\\"updateWithNewExperience(last = \\\" << &last \\n << \\\", curr = \\\" << &curr\\n << \\\", lastact = \\\" << lastact \\n << \\\", r = \\\" << reward\\n << \\\", t = \\\" << terminal\\n << \\\")\\\" << endl;\\n \\n double initTime = 0;\\n double timeTwo = 0;\\n double timeThree = 0;\\n\\n if (model == NULL)\\n initModel(last.size());\\n\\n // update our models and see if they change\\n if (false || TIMEDEBUG) initTime = getSeconds();\\n\\n modelChanged = planner->updateModelWithExperience(last, lastact, curr, reward, terminal) || modelChanged;\\n\\n if (false || TIMEDEBUG) timeTwo = getSeconds();\\n\\n if (AGENTDEBUG) cout << \\\"Agent Added exp: \\\" << modelChanged << endl;\\n\\n // tell the planner to update with the updated model\\n if ((modelChanged && (!seeding || modelType == RMAX) \\n && (nactions % BATCH_FREQ == 0))){\\n planner->planOnNewModel();\\n modelChanged = false;\\n }\\n\\n if (TIMEDEBUG){\\n\\n timeThree = getSeconds();\\n \\n planningTime += (timeThree-timeTwo);\\n modelUpdateTime += (timeTwo - initTime);\\n \\n if (nactions % 10 == 0){\\n cout << nactions \\n\\t << \\\" UpdateModel \\\" << modelUpdateTime/ (float)nactions\\n\\t << \\\" createPolicy \\\" << planningTime/(float)nactions << endl;\\n \\n }\\n }\\n\\n\\n}\\n\\n\\nint ModelBasedAgent::chooseAction(const std::vector<float> &s){\\n if (AGENTDEBUG) cout << \\\"chooseAction(s = \\\" << &s \\n\\t\\t << \\\")\\\" << endl;\\n\\n double initTime = 0;\\n double timeTwo = 0;\\n\\n // get action to take from planner\\n if (TIMEDEBUG) initTime = getSeconds();\\n int act = planner->getBestAction(s);\\n if (TIMEDEBUG) {\\n timeTwo = getSeconds();\\n planningTime += (timeTwo - initTime);\\n }\\n\\n if (exploreType == EPSILONGREEDY && rng.bernoulli(epsilon)){\\n //if (true) cout << \\\"Random action\\\" << endl;\\n act = rng.uniformDiscrete(0, numactions-1);\\n }\\n\\n if (SIMPLEDEBUG){\\n cout << endl << \\\"Action \\\" << nactions\\n\\t << \\\": State \\\" << (s)[0];\\n for (unsigned i = 1; i < s.size(); i++){\\n cout << \\\",\\\" << (s)[i];\\n }\\n cout << \\\", Took action \\\" << act << \\\", \\\";\\n }\\n\\n nactions++;\\n\\n // return index of action\\n return act;\\n}\\n\\nvoid ModelBasedAgent::saveStateAndAction(const std::vector<float> &s, int act){\\n if (AGENTDEBUG) cout << \\\"saveStateAndAction(s = \\\" << &s \\n\\t\\t << \\\", act = \\\" << act\\n\\t\\t << \\\")\\\" << endl;\\n prevstate = s;\\n prevact = act;\\n\\n}\\n\\n\\n\\n\\n\\ndouble ModelBasedAgent::getSeconds(){\\n struct timezone tz;\\n timeval timeT;\\n gettimeofday(&timeT, &tz);\\n return timeT.tv_sec + (timeT.tv_usec / 1000000.0);\\n}\\n\\n\\nvoid ModelBasedAgent::seedExp(std::vector<experience> seeds){\\n if (AGENTDEBUG) cout << \\\"seed experiences\\\" << endl;\\n\\n if (seeds.size() == 0) return;\\n\\n if (model == NULL)\\n initModel(seeds[0].s.size());\\n\\n seeding = true;\\n planner->setSeeding(true);\\n\\n // for each seeding experience, update our model\\n for (unsigned i = 0; i < seeds.size(); i++){\\n experience e = seeds[i];\\n\\n // update our models\\n // this is where we possibly run qmax again if model(s) change\\n updateWithNewExperience(e.s, e.next, e.act, e.reward, e.terminal);\\n\\n /*\\n cout << \\\"Seeding with experience \\\" << i << endl;\\n cout << \\\"last: \\\" << (*curr)[0] << \\\", \\\" << (*curr)[1] << \\\", \\\" \\n\\t << (*curr)[2] << endl;\\n cout << \\\"act: \\\" << e.act << \\\" r: \\\" << e.reward << endl;\\n cout << \\\"next: \\\" << (*next)[0] << \\\", \\\" << (*next)[1] << \\\", \\\" \\n\\t << (*next)[2] << \\\", \\\" << e.terminal << endl;\\n */\\n\\n }\\n\\n seeding = false;\\n planner->setSeeding(false);\\n\\n if (seeds.size() > 0)\\n planner->planOnNewModel();\\n\\n}\\n\\n\\n void ModelBasedAgent::setDebug(bool d){\\n AGENTDEBUG = d;\\n }\\n\\nvoid ModelBasedAgent::savePolicy(const char* filename){\\n planner->savePolicy(filename);\\n}\\n\\n\\n\\nvoid ModelBasedAgent::logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax){\\n\\n // call planner\\n if (plannerType == PARALLEL_ET_UCT){\\n ((ParallelETUCT*)planner)->logValues(of, xmin, xmax, ymin, ymax);\\n }\\n if (plannerType == ET_UCT){\\n ((ETUCT*)planner)->logValues(of, xmin, xmax, ymin, ymax);\\n }\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7727272510528564,\n \"alphanum_fraction\": 0.7792207598686218,\n \"avg_line_length\": 37.5625,\n \"blob_id\": \"79c1da0170b91951169d82c1095eb04c36e5e444\",\n \"content_id\": \"68e128ef79310769af70a796ca9966df8af3654f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 616,\n \"license_type\": \"no_license\",\n \"max_line_length\": 46,\n \"num_lines\": 16,\n \"path\": \"/build/hector_example/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"hector_example\\\")\\nset(hector_example_VERSION \\\"0.0.0\\\")\\nset(hector_example_MAINTAINER \\\"abc <abc@todo.todo>\\\")\\nset(hector_example_PACKAGE_FORMAT \\\"1\\\")\\nset(hector_example_BUILD_DEPENDS )\\nset(hector_example_BUILD_EXPORT_DEPENDS )\\nset(hector_example_BUILDTOOL_DEPENDS \\\"catkin\\\")\\nset(hector_example_BUILDTOOL_EXPORT_DEPENDS )\\nset(hector_example_EXEC_DEPENDS )\\nset(hector_example_RUN_DEPENDS )\\nset(hector_example_TEST_DEPENDS )\\nset(hector_example_DOC_DEPENDS )\\nset(hector_example_URL_WEBSITE \\\"\\\")\\nset(hector_example_URL_BUGTRACKER \\\"\\\")\\nset(hector_example_URL_REPOSITORY \\\"\\\")\\nset(hector_example_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.6762574315071106,\n \"alphanum_fraction\": 0.6888320446014404,\n \"avg_line_length\": 23.825397491455078,\n \"blob_id\": \"11278b6e630af56415bb4bd2be2cc0efb98a51af\",\n \"content_id\": \"d86f037000d0514cdd240a3f73ab317ff4b3f50c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 4692,\n \"license_type\": \"no_license\",\n \"max_line_length\": 441,\n \"num_lines\": 189,\n \"path\": \"/devel/include/rl_msgs/RLAction.h\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Generated by gencpp from file rl_msgs/RLAction.msg\\n// DO NOT EDIT!\\n\\n\\n#ifndef RL_MSGS_MESSAGE_RLACTION_H\\n#define RL_MSGS_MESSAGE_RLACTION_H\\n\\n\\n#include <string>\\n#include <vector>\\n#include <map>\\n\\n#include <ros/types.h>\\n#include <ros/serialization.h>\\n#include <ros/builtin_message_traits.h>\\n#include <ros/message_operations.h>\\n\\n\\nnamespace rl_msgs\\n{\\ntemplate <class ContainerAllocator>\\nstruct RLAction_\\n{\\n typedef RLAction_<ContainerAllocator> Type;\\n\\n RLAction_()\\n : action(0) {\\n }\\n RLAction_(const ContainerAllocator& _alloc)\\n : action(0) {\\n (void)_alloc;\\n }\\n\\n\\n\\n typedef int32_t _action_type;\\n _action_type action;\\n\\n\\n\\n\\n\\n typedef boost::shared_ptr< ::rl_msgs::RLAction_<ContainerAllocator> > Ptr;\\n typedef boost::shared_ptr< ::rl_msgs::RLAction_<ContainerAllocator> const> ConstPtr;\\n\\n}; // struct RLAction_\\n\\ntypedef ::rl_msgs::RLAction_<std::allocator<void> > RLAction;\\n\\ntypedef boost::shared_ptr< ::rl_msgs::RLAction > RLActionPtr;\\ntypedef boost::shared_ptr< ::rl_msgs::RLAction const> RLActionConstPtr;\\n\\n// constants requiring out of line definition\\n\\n\\n\\ntemplate<typename ContainerAllocator>\\nstd::ostream& operator<<(std::ostream& s, const ::rl_msgs::RLAction_<ContainerAllocator> & v)\\n{\\nros::message_operations::Printer< ::rl_msgs::RLAction_<ContainerAllocator> >::stream(s, \\\"\\\", v);\\nreturn s;\\n}\\n\\n} // namespace rl_msgs\\n\\nnamespace ros\\n{\\nnamespace message_traits\\n{\\n\\n\\n\\n// BOOLTRAITS {'IsFixedSize': True, 'IsMessage': True, 'HasHeader': False}\\n// {'rl_msgs': ['/home/justin/ros_test/src/rl-texplore-ros-pkg-master/src/rl_msgs/msg'], 'std_msgs': ['/opt/ros/melodic/share/std_msgs/cmake/../msg']}\\n\\n// !!!!!!!!!!! ['__class__', '__delattr__', '__dict__', '__doc__', '__eq__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_parsed_fields', 'constants', 'fields', 'full_name', 'has_header', 'header_present', 'names', 'package', 'parsed_fields', 'short_name', 'text', 'types']\\n\\n\\n\\n\\ntemplate <class ContainerAllocator>\\nstruct IsFixedSize< ::rl_msgs::RLAction_<ContainerAllocator> >\\n : TrueType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct IsFixedSize< ::rl_msgs::RLAction_<ContainerAllocator> const>\\n : TrueType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct IsMessage< ::rl_msgs::RLAction_<ContainerAllocator> >\\n : TrueType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct IsMessage< ::rl_msgs::RLAction_<ContainerAllocator> const>\\n : TrueType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct HasHeader< ::rl_msgs::RLAction_<ContainerAllocator> >\\n : FalseType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct HasHeader< ::rl_msgs::RLAction_<ContainerAllocator> const>\\n : FalseType\\n { };\\n\\n\\ntemplate<class ContainerAllocator>\\nstruct MD5Sum< ::rl_msgs::RLAction_<ContainerAllocator> >\\n{\\n static const char* value()\\n {\\n return \\\"b028501ac85c840a01d50342b4cc9b6e\\\";\\n }\\n\\n static const char* value(const ::rl_msgs::RLAction_<ContainerAllocator>&) { return value(); }\\n static const uint64_t static_value1 = 0xb028501ac85c840aULL;\\n static const uint64_t static_value2 = 0x01d50342b4cc9b6eULL;\\n};\\n\\ntemplate<class ContainerAllocator>\\nstruct DataType< ::rl_msgs::RLAction_<ContainerAllocator> >\\n{\\n static const char* value()\\n {\\n return \\\"rl_msgs/RLAction\\\";\\n }\\n\\n static const char* value(const ::rl_msgs::RLAction_<ContainerAllocator>&) { return value(); }\\n};\\n\\ntemplate<class ContainerAllocator>\\nstruct Definition< ::rl_msgs::RLAction_<ContainerAllocator> >\\n{\\n static const char* value()\\n {\\n return \\\"# Message for describing an action in RL\\\\n\\\\\\n\\\\n\\\\\\nint32 action\\\\n\\\\\\n\\\";\\n }\\n\\n static const char* value(const ::rl_msgs::RLAction_<ContainerAllocator>&) { return value(); }\\n};\\n\\n} // namespace message_traits\\n} // namespace ros\\n\\nnamespace ros\\n{\\nnamespace serialization\\n{\\n\\n template<class ContainerAllocator> struct Serializer< ::rl_msgs::RLAction_<ContainerAllocator> >\\n {\\n template<typename Stream, typename T> inline static void allInOne(Stream& stream, T m)\\n {\\n stream.next(m.action);\\n }\\n\\n ROS_DECLARE_ALLINONE_SERIALIZER\\n }; // struct RLAction_\\n\\n} // namespace serialization\\n} // namespace ros\\n\\nnamespace ros\\n{\\nnamespace message_operations\\n{\\n\\ntemplate<class ContainerAllocator>\\nstruct Printer< ::rl_msgs::RLAction_<ContainerAllocator> >\\n{\\n template<typename Stream> static void stream(Stream& s, const std::string& indent, const ::rl_msgs::RLAction_<ContainerAllocator>& v)\\n {\\n s << indent << \\\"action: \\\";\\n Printer<int32_t>::stream(s, indent + \\\" \\\", v.action);\\n }\\n};\\n\\n} // namespace message_operations\\n} // namespace ros\\n\\n#endif // RL_MSGS_MESSAGE_RLACTION_H\\n\"\n },\n {\n \"alpha_fraction\": 0.7804877758026123,\n \"alphanum_fraction\": 0.7804877758026123,\n \"avg_line_length\": 50.25,\n \"blob_id\": \"7b811105ddeb5d6231bb8b5acde4376c4b23d510\",\n \"content_id\": \"bdcbb9fd3b2e2a954ad8d18580368520701e5c90\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 205,\n \"license_type\": \"no_license\",\n \"max_line_length\": 100,\n \"num_lines\": 4,\n \"path\": \"/devel/share/rl_msgs/cmake/rl_msgs-msg-paths.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# generated from genmsg/cmake/pkg-msg-paths.cmake.develspace.in\\n\\nset(rl_msgs_MSG_INCLUDE_DIRS \\\"/home/justin/ros_test/src/rl-texplore-ros-pkg-master/src/rl_msgs/msg\\\")\\nset(rl_msgs_MSG_DEPENDENCIES std_msgs)\\n\"\n },\n {\n \"alpha_fraction\": 0.5638216733932495,\n \"alphanum_fraction\": 0.5668789744377136,\n \"avg_line_length\": 22.363094329833984,\n \"blob_id\": \"c5dbc6fad2536e5b928959066f3e9a453e1c0561\",\n \"content_id\": \"fff02928d22460c17087cf1bd56c3f38c89a8736\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 3925,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 168,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Agent/SavedPolicy.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include <rl_agent/SavedPolicy.hh>\\n#include <algorithm>\\n\\nSavedPolicy::SavedPolicy(int numactions, const char* filename):\\n numactions(numactions)\\n{\\n\\n ACTDEBUG = false;\\n LOADDEBUG = false;\\n loaded = false;\\n\\n loadPolicy(filename);\\n\\n \\n}\\n\\nSavedPolicy::~SavedPolicy() {}\\n\\nint SavedPolicy::first_action(const std::vector<float> &s) {\\n\\n if (ACTDEBUG){\\n cout << \\\"First - in state: \\\";\\n printState(s);\\n cout << endl;\\n }\\n\\n // Get action values\\n std::vector<float> &Q_s = Q[canonicalize(s)];\\n\\n // Choose an action\\n const std::vector<float>::iterator a =\\n std::max_element(Q_s.begin(), Q_s.end()); // Choose maximum\\n\\n if (ACTDEBUG){\\n cout << \\\" act: \\\" << (a-Q_s.begin()) << \\\" val: \\\" << *a << endl;\\n for (int iAct = 0; iAct < numactions; iAct++){\\n cout << \\\" Action: \\\" << iAct \\n\\t << \\\" val: \\\" << Q_s[iAct] << endl;\\n }\\n cout << \\\"Took action \\\" << (a-Q_s.begin()) << \\\" from state \\\";\\n printState(s);\\n cout << endl;\\n }\\n\\n return a - Q_s.begin();\\n}\\n\\nint SavedPolicy::next_action(float r, const std::vector<float> &s) {\\n\\n if (ACTDEBUG){\\n cout << \\\"Next: got reward \\\" << r << \\\" in state: \\\";\\n printState(s);\\n cout << endl;\\n }\\n\\n // Get action values\\n std::vector<float> &Q_s = Q[canonicalize(s)];\\n const std::vector<float>::iterator max =\\n std::max_element(Q_s.begin(), Q_s.end());\\n\\n // Choose an action\\n const std::vector<float>::iterator a = max;\\n\\n if (ACTDEBUG){\\n cout << \\\" act: \\\" << (a-Q_s.begin()) << \\\" val: \\\" << *a << endl;\\n for (int iAct = 0; iAct < numactions; iAct++){\\n cout << \\\" Action: \\\" << iAct \\n\\t << \\\" val: \\\" << Q_s[iAct] << endl;\\n }\\n cout << \\\"Took action \\\" << (a-Q_s.begin()) << \\\" from state \\\";\\n printState(s);\\n cout << endl;\\n }\\n\\n return a - Q_s.begin();\\n}\\n\\nvoid SavedPolicy::last_action(float r) {\\n\\n if (ACTDEBUG){\\n cout << \\\"Last: got reward \\\" << r << endl;\\n }\\n\\n}\\n\\nSavedPolicy::state_t SavedPolicy::canonicalize(const std::vector<float> &s) {\\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\\n statespace.insert(s);\\n state_t retval = &*result.first; // Dereference iterator then get pointer \\n if (result.second) { // s is new, so initialize Q(s,a) for all a\\n if (loaded){\\n cout << \\\"State unknown in policy!!!\\\" << endl;\\n for (unsigned i = 0; i < s.size(); i++){\\n cout << s[i] << \\\", \\\";\\n }\\n cout << endl;\\n }\\n std::vector<float> &Q_s = Q[retval];\\n Q_s.resize(numactions,0.0);\\n }\\n return retval; \\n}\\n\\n\\n\\nvoid SavedPolicy::printState(const std::vector<float> &s){\\n for (unsigned j = 0; j < s.size(); j++){\\n cout << s[j] << \\\", \\\";\\n }\\n}\\n\\n\\n\\nvoid SavedPolicy::seedExp(std::vector<experience> seeds){\\n return;\\n}\\n\\n\\nvoid SavedPolicy::loadPolicy(const char* filename){\\n\\n ifstream policyFile(filename, ios::in | ios::binary);\\n\\n // first part, save the vector size\\n int fsize;\\n policyFile.read((char*)&fsize, sizeof(int));\\n if (LOADDEBUG) cout << \\\"Numfeats loaded: \\\" << fsize << endl;\\n\\n // save numactions\\n int nact;\\n policyFile.read((char*)&nact, sizeof(int));\\n\\n if (nact != numactions){\\n cout << \\\"this policy is not valid loaded nact: \\\" << nact \\n << \\\" was told: \\\" << numactions << endl;\\n exit(-1);\\n }\\n\\n // go through all states, loading q values\\n while(!policyFile.eof()){\\n std::vector<float> state;\\n state.resize(fsize, 0.0);\\n\\n // load state\\n policyFile.read((char*)&(state[0]), sizeof(float)*fsize);\\n if (LOADDEBUG){\\n cout << \\\"load policy for state: \\\";\\n printState(state);\\n }\\n\\n state_t s = canonicalize(state);\\n\\n if (policyFile.eof()) break;\\n\\n // load q values\\n policyFile.read((char*)&(Q[s][0]), sizeof(float)*numactions);\\n \\n if (LOADDEBUG){\\n cout << \\\"Q values: \\\" << endl;\\n for (int iAct = 0; iAct < numactions; iAct++){\\n cout << \\\" Action: \\\" << iAct << \\\" val: \\\" << Q[s][iAct] << endl;\\n }\\n }\\n }\\n \\n policyFile.close();\\n cout << \\\"Policy loaded!!!\\\" << endl;\\n loaded = true;\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6638965606689453,\n \"alphanum_fraction\": 0.6906740665435791,\n \"avg_line_length\": 24.785715103149414,\n \"blob_id\": \"e702eaa916d6f6a4d7a106cbe85cdbcdb4554489\",\n \"content_id\": \"d8a8e98c902b3a5aef1fc70d6a3de30d6ab26368\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 1083,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 42,\n \"path\": \"/src/mantis_model/src/joint_state_pub.cpp\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include <ros/ros.h>\\n#include <sensor_msgs/JointState.h>\\n\\nsensor_msgs::JointState joint_state;\\nros::Publisher pub_joints;\\n\\nvoid timerCallback(const ros::TimerEvent& event)\\n{\\n double constant_rate = 2.0;\\n\\n joint_state.header.stamp = event.current_real;\\n joint_state.position[0] += 0.02 * constant_rate;\\n joint_state.position[1] += 0.02 * constant_rate;\\n\\n joint_state.velocity[0] = constant_rate;\\n joint_state.velocity[1] = constant_rate;\\n\\n joint_state.effort[0] = 5;\\n joint_state.effort[1] = 5;\\n pub_joints.publish(joint_state);\\n}\\n\\nint main(int argc, char** argv)\\n{\\n ros::init(argc, argv, \\\"joint_state_pub\\\");\\n ros::NodeHandle n;\\n\\n joint_state.header.frame_id = \\\"base_footprint\\\";\\n joint_state.name.resize(2);\\n joint_state.position.resize(2, 0);\\n joint_state.velocity.resize(2, 0);\\n joint_state.effort.resize(2, 0);\\n\\n joint_state.name[0] = \\\"left_wheel_joint\\\";\\n joint_state.name[1] = \\\"right_wheel_joint\\\";\\n\\n pub_joints = n.advertise<sensor_msgs::JointState>(\\\"joint_states\\\", 1);\\n\\n ros::Timer timer = n.createTimer(ros::Duration(0.02), timerCallback);\\n\\n ros::spin();\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7407407164573669,\n \"alphanum_fraction\": 0.7460317611694336,\n \"avg_line_length\": 46.3125,\n \"blob_id\": \"f1a756eb0d25827008d9222cb8b30d57cfdc4781\",\n \"content_id\": \"d9c7ae1e805ed0d7a4183d9695ca56abdbd386c3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 756,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 16,\n \"path\": \"/build/gps_sim_project/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"gps_sim_project\\\")\\nset(gps_sim_project_VERSION \\\"0.0.0\\\")\\nset(gps_sim_project_MAINTAINER \\\"abc <abc@todo.todo>\\\")\\nset(gps_sim_project_PACKAGE_FORMAT \\\"1\\\")\\nset(gps_sim_project_BUILD_DEPENDS \\\"roscpp\\\" \\\"ugv_course_libs\\\" \\\"tf\\\")\\nset(gps_sim_project_BUILD_EXPORT_DEPENDS \\\"roscpp\\\" \\\"ugv_course_libs\\\" \\\"tf\\\")\\nset(gps_sim_project_BUILDTOOL_DEPENDS \\\"catkin\\\")\\nset(gps_sim_project_BUILDTOOL_EXPORT_DEPENDS )\\nset(gps_sim_project_EXEC_DEPENDS \\\"roscpp\\\" \\\"ugv_course_libs\\\" \\\"tf\\\")\\nset(gps_sim_project_RUN_DEPENDS \\\"roscpp\\\" \\\"ugv_course_libs\\\" \\\"tf\\\")\\nset(gps_sim_project_TEST_DEPENDS )\\nset(gps_sim_project_DOC_DEPENDS )\\nset(gps_sim_project_URL_WEBSITE \\\"\\\")\\nset(gps_sim_project_URL_BUGTRACKER \\\"\\\")\\nset(gps_sim_project_URL_REPOSITORY \\\"\\\")\\nset(gps_sim_project_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.7014492750167847,\n \"alphanum_fraction\": 0.7014492750167847,\n \"avg_line_length\": 13.333333015441895,\n \"blob_id\": \"6f0e32cf109a665c44f632961b5263ad1ef028d0\",\n \"content_id\": \"7d8abd0c053a6a6079cd9cfcdd0442c8b697311d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 345,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 24,\n \"path\": \"/src/roundbot_control/include/roundbot_control/MotorSim.h\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n#ifndef MOTORSIM_H_\\n#define MOTORSIM_H_\\n\\nnamespace roundbot_control{\\n\\nclass MotorSim{\\npublic:\\n MotorSim(double max_speed, double max_accel, double max_decel);\\n\\n double iterate(double target_speed, double dt);\\nprivate:\\n\\n double current_speed_;\\n\\n double max_speed_;\\n double max_accel_;\\n double max_decel_;\\n};\\n\\n}\\n\\n\\n\\n#endif /* MOTORSIM_H_ */\\n\"\n },\n {\n \"alpha_fraction\": 0.7580246925354004,\n \"alphanum_fraction\": 0.7580246925354004,\n \"avg_line_length\": 39.5,\n \"blob_id\": \"df097f6c7921ca55195230b4ba15cfba619d3635\",\n \"content_id\": \"92ad028630719f8f91fb3fed010e323fc3d2a75a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 810,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 20,\n \"path\": \"/build/rl-texplore-ros-pkg-master/src/rl_env/CMakeFiles/envlib.dir/cmake_clean.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"file(REMOVE_RECURSE\\n \\\"CMakeFiles/envlib.dir/src/Env/tworooms.cc.o\\\"\\n \\\"CMakeFiles/envlib.dir/src/Env/taxi.cc.o\\\"\\n \\\"CMakeFiles/envlib.dir/src/Env/MountainCar.cc.o\\\"\\n \\\"CMakeFiles/envlib.dir/src/Env/FuelRooms.cc.o\\\"\\n \\\"CMakeFiles/envlib.dir/src/Env/CartPole.cc.o\\\"\\n \\\"CMakeFiles/envlib.dir/src/Env/fourrooms.cc.o\\\"\\n \\\"CMakeFiles/envlib.dir/src/Env/RobotCarVel.cc.o\\\"\\n \\\"CMakeFiles/envlib.dir/src/Env/energyrooms.cc.o\\\"\\n \\\"CMakeFiles/envlib.dir/src/Env/gridworld.cc.o\\\"\\n \\\"CMakeFiles/envlib.dir/src/Env/stocks.cc.o\\\"\\n \\\"CMakeFiles/envlib.dir/src/Env/LightWorld.cc.o\\\"\\n \\\"/home/justin/ros_test/devel/lib/libenvlib.pdb\\\"\\n \\\"/home/justin/ros_test/devel/lib/libenvlib.so\\\"\\n)\\n\\n# Per-language clean rules from dependency scanning.\\nforeach(lang CXX)\\n include(CMakeFiles/envlib.dir/cmake_clean_${lang}.cmake OPTIONAL)\\nendforeach()\\n\"\n },\n {\n \"alpha_fraction\": 0.6715205311775208,\n \"alphanum_fraction\": 0.6820968985557556,\n \"avg_line_length\": 27,\n \"blob_id\": \"211038a99efd15d79b0429eeed637e3a7a1d306a\",\n \"content_id\": \"bed02599c6ddd2ce8370ce4ea7368cc21c3305d0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 6524,\n \"license_type\": \"no_license\",\n \"max_line_length\": 441,\n \"num_lines\": 233,\n \"path\": \"/devel/include/rl_msgs/RLEnvSeedExperience.h\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Generated by gencpp from file rl_msgs/RLEnvSeedExperience.msg\\n// DO NOT EDIT!\\n\\n\\n#ifndef RL_MSGS_MESSAGE_RLENVSEEDEXPERIENCE_H\\n#define RL_MSGS_MESSAGE_RLENVSEEDEXPERIENCE_H\\n\\n\\n#include <string>\\n#include <vector>\\n#include <map>\\n\\n#include <ros/types.h>\\n#include <ros/serialization.h>\\n#include <ros/builtin_message_traits.h>\\n#include <ros/message_operations.h>\\n\\n\\nnamespace rl_msgs\\n{\\ntemplate <class ContainerAllocator>\\nstruct RLEnvSeedExperience_\\n{\\n typedef RLEnvSeedExperience_<ContainerAllocator> Type;\\n\\n RLEnvSeedExperience_()\\n : from_state()\\n , action(0)\\n , to_state()\\n , reward(0.0)\\n , terminal(false) {\\n }\\n RLEnvSeedExperience_(const ContainerAllocator& _alloc)\\n : from_state(_alloc)\\n , action(0)\\n , to_state(_alloc)\\n , reward(0.0)\\n , terminal(false) {\\n (void)_alloc;\\n }\\n\\n\\n\\n typedef std::vector<float, typename ContainerAllocator::template rebind<float>::other > _from_state_type;\\n _from_state_type from_state;\\n\\n typedef int32_t _action_type;\\n _action_type action;\\n\\n typedef std::vector<float, typename ContainerAllocator::template rebind<float>::other > _to_state_type;\\n _to_state_type to_state;\\n\\n typedef float _reward_type;\\n _reward_type reward;\\n\\n typedef uint8_t _terminal_type;\\n _terminal_type terminal;\\n\\n\\n\\n\\n\\n typedef boost::shared_ptr< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> > Ptr;\\n typedef boost::shared_ptr< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> const> ConstPtr;\\n\\n}; // struct RLEnvSeedExperience_\\n\\ntypedef ::rl_msgs::RLEnvSeedExperience_<std::allocator<void> > RLEnvSeedExperience;\\n\\ntypedef boost::shared_ptr< ::rl_msgs::RLEnvSeedExperience > RLEnvSeedExperiencePtr;\\ntypedef boost::shared_ptr< ::rl_msgs::RLEnvSeedExperience const> RLEnvSeedExperienceConstPtr;\\n\\n// constants requiring out of line definition\\n\\n\\n\\ntemplate<typename ContainerAllocator>\\nstd::ostream& operator<<(std::ostream& s, const ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> & v)\\n{\\nros::message_operations::Printer< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >::stream(s, \\\"\\\", v);\\nreturn s;\\n}\\n\\n} // namespace rl_msgs\\n\\nnamespace ros\\n{\\nnamespace message_traits\\n{\\n\\n\\n\\n// BOOLTRAITS {'IsFixedSize': False, 'IsMessage': True, 'HasHeader': False}\\n// {'rl_msgs': ['/home/justin/ros_test/src/rl-texplore-ros-pkg-master/src/rl_msgs/msg'], 'std_msgs': ['/opt/ros/melodic/share/std_msgs/cmake/../msg']}\\n\\n// !!!!!!!!!!! ['__class__', '__delattr__', '__dict__', '__doc__', '__eq__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_parsed_fields', 'constants', 'fields', 'full_name', 'has_header', 'header_present', 'names', 'package', 'parsed_fields', 'short_name', 'text', 'types']\\n\\n\\n\\n\\ntemplate <class ContainerAllocator>\\nstruct IsFixedSize< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\\n : FalseType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct IsFixedSize< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> const>\\n : FalseType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct IsMessage< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\\n : TrueType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct IsMessage< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> const>\\n : TrueType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct HasHeader< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\\n : FalseType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct HasHeader< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> const>\\n : FalseType\\n { };\\n\\n\\ntemplate<class ContainerAllocator>\\nstruct MD5Sum< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\\n{\\n static const char* value()\\n {\\n return \\\"e195f7b7c1a41138d96a4af2de05b1b1\\\";\\n }\\n\\n static const char* value(const ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator>&) { return value(); }\\n static const uint64_t static_value1 = 0xe195f7b7c1a41138ULL;\\n static const uint64_t static_value2 = 0xd96a4af2de05b1b1ULL;\\n};\\n\\ntemplate<class ContainerAllocator>\\nstruct DataType< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\\n{\\n static const char* value()\\n {\\n return \\\"rl_msgs/RLEnvSeedExperience\\\";\\n }\\n\\n static const char* value(const ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator>&) { return value(); }\\n};\\n\\ntemplate<class ContainerAllocator>\\nstruct Definition< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\\n{\\n static const char* value()\\n {\\n return \\\"# Message that contains a seed experience to initialize the model\\\\n\\\\\\nfloat32[] from_state\\\\n\\\\\\nint32 action\\\\n\\\\\\nfloat32[] to_state\\\\n\\\\\\nfloat32 reward\\\\n\\\\\\nbool terminal\\\\n\\\\\\n\\\\n\\\\\\n\\\";\\n }\\n\\n static const char* value(const ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator>&) { return value(); }\\n};\\n\\n} // namespace message_traits\\n} // namespace ros\\n\\nnamespace ros\\n{\\nnamespace serialization\\n{\\n\\n template<class ContainerAllocator> struct Serializer< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\\n {\\n template<typename Stream, typename T> inline static void allInOne(Stream& stream, T m)\\n {\\n stream.next(m.from_state);\\n stream.next(m.action);\\n stream.next(m.to_state);\\n stream.next(m.reward);\\n stream.next(m.terminal);\\n }\\n\\n ROS_DECLARE_ALLINONE_SERIALIZER\\n }; // struct RLEnvSeedExperience_\\n\\n} // namespace serialization\\n} // namespace ros\\n\\nnamespace ros\\n{\\nnamespace message_operations\\n{\\n\\ntemplate<class ContainerAllocator>\\nstruct Printer< ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator> >\\n{\\n template<typename Stream> static void stream(Stream& s, const std::string& indent, const ::rl_msgs::RLEnvSeedExperience_<ContainerAllocator>& v)\\n {\\n s << indent << \\\"from_state[]\\\" << std::endl;\\n for (size_t i = 0; i < v.from_state.size(); ++i)\\n {\\n s << indent << \\\" from_state[\\\" << i << \\\"]: \\\";\\n Printer<float>::stream(s, indent + \\\" \\\", v.from_state[i]);\\n }\\n s << indent << \\\"action: \\\";\\n Printer<int32_t>::stream(s, indent + \\\" \\\", v.action);\\n s << indent << \\\"to_state[]\\\" << std::endl;\\n for (size_t i = 0; i < v.to_state.size(); ++i)\\n {\\n s << indent << \\\" to_state[\\\" << i << \\\"]: \\\";\\n Printer<float>::stream(s, indent + \\\" \\\", v.to_state[i]);\\n }\\n s << indent << \\\"reward: \\\";\\n Printer<float>::stream(s, indent + \\\" \\\", v.reward);\\n s << indent << \\\"terminal: \\\";\\n Printer<uint8_t>::stream(s, indent + \\\" \\\", v.terminal);\\n }\\n};\\n\\n} // namespace message_operations\\n} // namespace ros\\n\\n#endif // RL_MSGS_MESSAGE_RLENVSEEDEXPERIENCE_H\\n\"\n },\n {\n \"alpha_fraction\": 0.5189234018325806,\n \"alphanum_fraction\": 0.5412456393241882,\n \"avg_line_length\": 25.163461685180664,\n \"blob_id\": \"0947109f50aebad0cf0de8b3eb2eaf8f008b009e\",\n \"content_id\": \"d7a775a5ff5ca15d149bdf76aa035d2ad65f2311\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 10886,\n \"license_type\": \"no_license\",\n \"max_line_length\": 290,\n \"num_lines\": 416,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/LightWorld.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n The LightWorld domain from\\n \\\"Building Portable Options: Skill Transfer in Reinforcement Learning\\\"\\n by Konidaris and Barto\\n*/\\n\\n#include <rl_env/LightWorld.hh>\\n\\n\\nLightWorld::LightWorld(Random &rand, bool stochastic, int nrooms):\\n noisy(stochastic),\\n nrooms(nrooms),\\n rng(rand),\\n s(17),\\n ns(s[0]),\\n ew(s[1]),\\n have_key(s[2]),\\n door_open(s[3]),\\n room_id(s[4]),\\n key_n(s[5]),\\n key_e(s[6]),\\n key_w(s[7]),\\n key_s(s[8]),\\n lock_n(s[9]),\\n lock_e(s[10]),\\n lock_w(s[11]),\\n lock_s(s[12]),\\n door_n(s[13]),\\n door_e(s[14]),\\n door_w(s[15]),\\n door_s(s[16])\\n{\\n\\n LWDEBUG = false;\\n MAX_SENSE = 10;\\n\\n\\n totalVisited = 0;\\n keyVisited = 0;\\n lockVisited = 0;\\n doorVisited = 0;\\n haveKey = 0;\\n doorOpen = 0;\\n leaveRoom = 0;\\n pressKey = 0;\\n pressLockCorrect = 0;\\n pressLockIncorrect = 0;\\n pressDoor = 0;\\n pressOther = 0;\\n pickupKeyCorrect = 0;\\n pickupKeyIncorrect = 0;\\n pickupLock = 0;\\n pickupDoor = 0;\\n pickupOther = 0;\\n\\n reset();\\n}\\n\\n\\n\\nLightWorld::~LightWorld() { }\\n\\n\\nconst std::vector<float> &LightWorld::sensation() const {\\n if (LWDEBUG) print_map();\\n return s;\\n}\\n\\n\\nint LightWorld::applyNoise(int action){\\n switch(action) {\\n case NORTH:\\n case SOUTH:\\n return rng.bernoulli(0.9) ? action : (rng.bernoulli(0.5) ? EAST : WEST);\\n case EAST:\\n case WEST:\\n return rng.bernoulli(0.9) ? action : (rng.bernoulli(0.5) ? NORTH : SOUTH);\\n case PRESS:\\n case PICKUP:\\n return rng.bernoulli(0.9) ? action : -1;\\n default:\\n return action;\\n }\\n}\\n\\nfloat LightWorld::apply(int origAction) {\\n\\n int reward = 0;\\n\\n int action = origAction;\\n if (noisy)\\n action = applyNoise(origAction);\\n\\n if (action == NORTH){\\n if (((ns < rooms[room_id].height-2) || (rooms[room_id].lock_ns == rooms[room_id].height-1 && ew == rooms[room_id].lock_ew) || (rooms[room_id].door_ns == rooms[room_id].height-1 && ew == rooms[room_id].door_ew)) && ew > 0 && ew < rooms[room_id].width-1 && ns < rooms[room_id].height-1) {\\n ns++;\\n } else if (door_open && ns == rooms[room_id].door_ns && ew == rooms[room_id].door_ew && rooms[room_id].door_ns == rooms[room_id].height-1){\\n leaveRoom++;\\n room_id++;\\n if (room_id >= nrooms) room_id = 0;\\n have_key = false;\\n door_open = false;\\n if (rooms[room_id].key_ns < 0) have_key = true;\\n ns = 0;\\n resetKey();\\n reward+=10;\\n }\\n }\\n if (action == EAST){\\n if (((ew < rooms[room_id].width-2) || (rooms[room_id].lock_ew == rooms[room_id].width-1 && ns == rooms[room_id].lock_ns) || (rooms[room_id].door_ew == rooms[room_id].width-1 && ns == rooms[room_id].door_ns)) && ns > 0 && ns < rooms[room_id].height-1 && ew < rooms[room_id].width-1) {\\n ew++;\\n } else if (door_open && ew == rooms[room_id].door_ew && ns == rooms[room_id].door_ns && rooms[room_id].door_ew == rooms[room_id].width-1){\\n leaveRoom++;\\n room_id++;\\n if (room_id >= nrooms) room_id = 0;\\n have_key = false;\\n door_open = false;\\n if (rooms[room_id].key_ns < 0) have_key = true;\\n ew = 0;\\n if (room_id == 1) ns = 3;\\n resetKey();\\n reward+=10;\\n }\\n }\\n if (action == SOUTH){\\n if (((ns > 1) || (rooms[room_id].lock_ns == 0 && ew == rooms[room_id].lock_ew) || (rooms[room_id].door_ns == 0 && ew == rooms[room_id].door_ew)) && ew > 0 && ew < rooms[room_id].width-1 && ns > 0) {\\n ns--;\\n } else if (door_open && ns == rooms[room_id].door_ns && ew == rooms[room_id].door_ew && rooms[room_id].door_ns == 0){\\n leaveRoom++;\\n room_id++;\\n if (room_id >= nrooms) room_id = 0;\\n have_key = false;\\n door_open = false;\\n if (rooms[room_id].key_ns < 0) have_key = true;\\n ns = rooms[room_id].height-1;\\n resetKey();\\n reward+=10;\\n }\\n }\\n if (action == WEST){\\n if (((ew > 1) || (rooms[room_id].lock_ew == 0 && ns == rooms[room_id].lock_ns) || (rooms[room_id].door_ew == 0 && ns == rooms[room_id].door_ns)) && ns > 0 && ns < rooms[room_id].height-1 && ew > 0) {\\n ew--;\\n } else if (door_open && ew == rooms[room_id].door_ew && ns == rooms[room_id].door_ns && rooms[room_id].door_ew == 0){\\n leaveRoom++;\\n room_id++;\\n if (room_id >= nrooms) room_id = 0;\\n have_key = false;\\n door_open = false;\\n if (rooms[room_id].key_ns < 0) have_key = true;\\n ew = rooms[room_id].width-1;\\n resetKey();\\n reward+=10;\\n }\\n }\\n\\n\\n if (action == PICKUP){\\n if (ns == rooms[room_id].key_ns && ew == rooms[room_id].key_ew){\\n if (!have_key) pickupKeyCorrect++;\\n else pickupKeyIncorrect++;\\n have_key = true;\\n }\\n else if (ns == rooms[room_id].lock_ns && ew == rooms[room_id].lock_ew)\\n pickupLock++;\\n else if (ns == rooms[room_id].door_ns && ew == rooms[room_id].door_ew)\\n pickupDoor++;\\n else\\n pickupOther++;\\n }\\n\\n if (action == PRESS){\\n if (ns == rooms[room_id].lock_ns && ew == rooms[room_id].lock_ew){\\n if (have_key) {\\n if (!door_open) pressLockCorrect++;\\n else pressLockIncorrect++;\\n door_open = true;\\n } else {\\n pressLockIncorrect++;\\n }\\n }\\n else if (ns == rooms[room_id].key_ns && ew == rooms[room_id].key_ew)\\n pressKey++;\\n else if (ns == rooms[room_id].door_ns && ew == rooms[room_id].door_ew)\\n pressDoor++;\\n else\\n pressOther++;\\n }\\n\\n updateSensors();\\n\\n return reward;\\n\\n}\\n\\nvoid LightWorld::updateSensors() {\\n\\n // set all to 0\\n key_n = 0;\\n key_e = 0;\\n key_w = 0;\\n key_s = 0;\\n lock_n = 0;\\n lock_e = 0;\\n lock_w = 0;\\n lock_s = 0;\\n door_n = 0;\\n door_e = 0;\\n door_w = 0;\\n door_s = 0;\\n\\n if (!have_key){\\n if (rooms[room_id].key_ns <= ns){\\n key_s = MAX_SENSE - (ns - rooms[room_id].key_ns);\\n }\\n if (rooms[room_id].key_ns >= ns){\\n key_n = MAX_SENSE - (rooms[room_id].key_ns - ns);\\n }\\n if (rooms[room_id].key_ew <= ew){\\n key_w = MAX_SENSE - (ew - rooms[room_id].key_ew);\\n }\\n if (rooms[room_id].key_ew >= ew){\\n key_e = MAX_SENSE - (rooms[room_id].key_ew - ew);\\n }\\n }\\n\\n if (door_open){\\n if (rooms[room_id].door_ns <= ns){\\n door_s = MAX_SENSE - (ns - rooms[room_id].door_ns);\\n }\\n if (rooms[room_id].door_ns >= ns){\\n door_n = MAX_SENSE - (rooms[room_id].door_ns - ns);\\n }\\n if (rooms[room_id].door_ew <= ew){\\n door_w = MAX_SENSE - (ew - rooms[room_id].door_ew);\\n }\\n if (rooms[room_id].door_ew >= ew){\\n door_e = MAX_SENSE - (rooms[room_id].door_ew - ew);\\n }\\n }\\n\\n if (rooms[room_id].lock_ns <= ns){\\n lock_s = MAX_SENSE - (ns - rooms[room_id].lock_ns);\\n }\\n if (rooms[room_id].lock_ns >= ns){\\n lock_n = MAX_SENSE - (rooms[room_id].lock_ns - ns);\\n }\\n if (rooms[room_id].lock_ew <= ew){\\n lock_w = MAX_SENSE - (ew - rooms[room_id].lock_ew);\\n }\\n if (rooms[room_id].lock_ew >= ew){\\n lock_e = MAX_SENSE - (rooms[room_id].lock_ew - ew);\\n }\\n}\\n\\n\\nvoid LightWorld::resetKey() {\\n if (!have_key && rooms[room_id].key_ns > -1){\\n rooms[room_id].key_ns = rng.uniformDiscrete(1, rooms[room_id].height-2);\\n rooms[room_id].key_ew = rng.uniformDiscrete(1, rooms[room_id].width-2);\\n }\\n}\\n\\nvoid LightWorld::setKey(std::vector<float> testS){\\n if (!have_key){\\n float nsDist = 0;\\n if (testS[5] > 0)\\n nsDist = MAX_SENSE - testS[5];\\n else\\n nsDist = -MAX_SENSE + testS[8];\\n rooms[room_id].key_ns = testS[0] + nsDist;\\n float ewDist = 0;\\n if (testS[6] > 0)\\n ewDist = MAX_SENSE - testS[6];\\n else\\n ewDist = -MAX_SENSE + testS[7];\\n rooms[room_id].key_ew = testS[1] + ewDist;\\n }\\n}\\n\\n\\n\\nbool LightWorld::terminal() const {\\n // TODO: different terminal condition\\n //return scream || (dangerous && (under_eye == 6 || under_hand == 6));\\n return false;\\n}\\n\\nvoid LightWorld::reset() {\\n\\n // init rooms\\n rooms.resize(nrooms);\\n\\n rooms[0].height = 8;\\n rooms[0].width = 7;\\n rooms[0].key_ns = 3;\\n rooms[0].key_ew = 1;\\n rooms[0].lock_ns = 2;\\n rooms[0].lock_ew = 6;\\n rooms[0].door_ns = 5;\\n rooms[0].door_ew = 6;\\n\\n rooms[1].height = 6;\\n rooms[1].width = 5;\\n rooms[1].key_ns = 3;\\n rooms[1].key_ew = 2;\\n rooms[1].lock_ns = 0;\\n rooms[1].lock_ew = 3;\\n rooms[1].door_ns = 0;\\n rooms[1].door_ew = 1;\\n\\n rooms[2].height = 8;\\n rooms[2].width = 5;\\n rooms[2].key_ns = -1;\\n rooms[2].key_ew = -1;\\n rooms[2].lock_ns = 3;\\n rooms[2].lock_ew = 4;\\n rooms[2].door_ns = 3;\\n rooms[2].door_ew = 0;\\n\\n rooms[3].height = 6;\\n rooms[3].width = 7;\\n rooms[3].key_ns = 1;\\n rooms[3].key_ew = 1;\\n rooms[3].lock_ns = 0;\\n rooms[3].lock_ew = 4;\\n rooms[3].door_ns = 5;\\n rooms[3].door_ew = 2;\\n\\n for (int i = 4; i < nrooms; i++){\\n rooms[i].height = i+3;\\n rooms[i].width = i+2;\\n rooms[i].key_ns = i;\\n rooms[i].key_ew = i-1;\\n rooms[i].lock_ns = 0;\\n rooms[i].lock_ew = i-2;\\n rooms[i].door_ns = 0;\\n rooms[i].door_ew = i;\\n }\\n\\n // random spot in first room\\n room_id = 0;\\n ns = rng.uniformDiscrete(1, rooms[0].height-2);\\n ew = rng.uniformDiscrete(1, rooms[0].width-2);\\n have_key = false;\\n door_open = false;\\n resetKey();\\n updateSensors();\\n\\n if (LWDEBUG) print_map();\\n\\n}\\n\\n\\nint LightWorld::getNumActions() {\\n if (LWDEBUG) cout << \\\"Return number of actions: \\\" << NUM_ACTIONS << endl;\\n return NUM_ACTIONS; //num_actions;\\n}\\n\\n\\nvoid LightWorld::print_map() const{\\n // TODO: print map in rows, including symbols for all objects\\n\\n cout << \\\"\\\\nLightWorld, Room \\\" << room_id << endl;\\n\\n // for each row\\n for (int j = rooms[room_id].height-1; j >= 0; --j){\\n // for each column\\n for (int i = 0; i < rooms[room_id].width; i++){\\n if (ns == j && ew == i) cout << \\\"A\\\";\\n else if (j == rooms[room_id].key_ns && i == rooms[room_id].key_ew && !have_key) cout << \\\"K\\\";\\n else if (j == rooms[room_id].lock_ns && i == rooms[room_id].lock_ew) cout << \\\"L\\\";\\n else if (j == rooms[room_id].door_ns && i == rooms[room_id].door_ew) cout << \\\"D\\\";\\n else if (j == 0 || i == 0 || j == rooms[room_id].height-1 || i == rooms[room_id].width-1) cout << \\\"X\\\";\\n else cout << \\\".\\\";\\n } // last col of row\\n cout << endl;\\n } // last row\\n\\n cout << \\\"at \\\" << ns << \\\", \\\" << ew << endl;\\n cout << \\\"Key: \\\" << have_key << \\\" door: \\\"<< door_open << endl;\\n cout << \\\"NORTH: key: \\\" << key_n << \\\", door: \\\" << door_n << \\\", lock: \\\" << lock_n << endl;\\n cout << \\\"EAST: key: \\\" << key_e << \\\", door: \\\" << door_e << \\\", lock: \\\" << lock_e << endl;\\n cout << \\\"SOUTH: key: \\\" << key_s << \\\", door: \\\" << door_s << \\\", lock: \\\" << lock_s << endl;\\n cout << \\\"WEST: key: \\\" << key_w << \\\", door: \\\" << door_w << \\\", lock: \\\" << lock_w << endl;\\n\\n\\n}\\n\\n\\n\\nvoid LightWorld::getMinMaxFeatures(std::vector<float> *minFeat,\\n std::vector<float> *maxFeat){\\n\\n minFeat->resize(s.size(), 0.0);\\n maxFeat->resize(s.size(), MAX_SENSE);\\n\\n // room id only goes to 2\\n (*maxFeat)[4] = 2;\\n\\n // have_key and door_open are boolean\\n (*maxFeat)[2] = 1;\\n (*maxFeat)[3] = 1;\\n\\n // room sizes only go to 8\\n (*maxFeat)[0] = 8;\\n (*maxFeat)[1] = 8;\\n\\n}\\n\\nvoid LightWorld::getMinMaxReward(float *minR,\\n float *maxR){\\n\\n *minR = 0.0;\\n *maxR = 10.0;\\n\\n}\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6338916420936584,\n \"alphanum_fraction\": 0.6452034115791321,\n \"avg_line_length\": 29.633333206176758,\n \"blob_id\": \"4303907cf941162532c3e7695a8b06a2b35f1bf7\",\n \"content_id\": \"bb2fcd891c1a3ed0cbee1d9ecbdc268e3de76179\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 4597,\n \"license_type\": \"no_license\",\n \"max_line_length\": 113,\n \"num_lines\": 150,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/SepPlanExplore.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include \\\"SepPlanExplore.hh\\\"\\n\\n\\n\\nSepPlanExplore::SepPlanExplore(int id, int modelType, int predType,\\n int nModels, int trainMode,\\n int trainFreq,\\n float featPct, float expPct,\\n float treeThreshold, bool stoch,\\n float featRange, Random rng):\\n id(id), modelType(modelType), predType(predType), nModels(nModels),\\n mode(trainMode), freq(trainFreq),\\n featPct(featPct), expPct(expPct),\\n treeThresh(treeThreshold), stoch(stoch),\\n featRange(featRange), rng(rng)\\n{\\n SPEDEBUG = false;//true;\\n\\n cout << \\\"Created Sep Plan & Explore models \\\" << id << \\\" with nModels: \\\" << nModels << endl;\\n\\n for (int i = 0; i < id; i++)\\n rng.uniform(0,1);\\n\\n initModels();\\n\\n}\\n\\nSepPlanExplore::SepPlanExplore(const SepPlanExplore& spe):\\n id(spe.id), modelType(spe.modelType), \\n predType(spe.predType), nModels(spe.nModels),\\n mode(spe.mode), freq(spe.freq),\\n featPct(spe.featPct), expPct(spe.expPct),\\n treeThresh(spe.treeThresh), stoch(spe.stoch),\\n featRange(spe.featRange), rng(spe.rng)\\n{\\n cout << \\\"spe get copy\\\" << endl;\\n SPEDEBUG = spe.SPEDEBUG;\\n expModel = spe.expModel->getCopy();\\n planModel = spe.planModel->getCopy();\\n}\\n\\nSepPlanExplore* SepPlanExplore::getCopy(){\\n SepPlanExplore* copy = new SepPlanExplore(*this);\\n return copy;\\n}\\n\\nSepPlanExplore::~SepPlanExplore() {\\n delete expModel;\\n delete planModel;\\n}\\n\\n\\nbool SepPlanExplore::trainInstances(std::vector<classPair> &instances){\\n if (SPEDEBUG) cout << id << \\\"SPE trainInstances: \\\" << instances.size() << endl;\\n\\n // train both\\n bool expChanged = expModel->trainInstances(instances);\\n bool planChanged = planModel->trainInstances(instances);\\n\\n return (expChanged || planChanged);\\n\\n}\\n\\n\\n\\n// here the target output will be a single value\\nbool SepPlanExplore::trainInstance(classPair &instance){\\n if (SPEDEBUG) cout << id << \\\"SPE trainInstance: \\\" << endl;\\n\\n // train both\\n bool expChanged = expModel->trainInstance(instance);\\n bool planChanged = planModel->trainInstance(instance);\\n\\n return (expChanged || planChanged);\\n\\n}\\n\\n// get all the models outputs and combine them somehow\\nvoid SepPlanExplore::testInstance(const std::vector<float> &input, std::map<float, float>* retval){\\n if (SPEDEBUG) cout << id << \\\" testInstance\\\" << endl;\\n\\n retval->clear();\\n\\n // use planning model to get predictions\\n planModel->testInstance(input, retval);\\n\\n}\\n\\n\\n\\nfloat SepPlanExplore::getConf(const std::vector<float> &input){\\n if (SPEDEBUG) cout << \\\"getConf\\\" << endl;\\n\\n // use exploreation model for confidence measure\\n return expModel->getConf(input);\\n\\n}\\n\\n\\n// init models\\nvoid SepPlanExplore::initModels(){\\n if (SPEDEBUG) cout << \\\"initModels()\\\" << endl;\\n\\n if (nModels < 2){\\n cout << \\\"Should really use Sep plan & explore models with multiple models\\\" << endl;\\n exit(-1);\\n }\\n\\n // explore model should be of type MultipleClassifiers\\n expModel = new MultipleClassifiers(id, modelType, predType,\\n nModels, mode, freq,\\n featPct, expPct, treeThresh, stoch, featRange, rng);\\n\\n // init the trees or stumps\\n if (modelType == C45TREE){\\n planModel = new C45Tree(id, mode, freq, 0, 0.0, rng);\\n }\\n else if (modelType == M5MULTI){\\n planModel = new M5Tree(id, mode, freq, 0, 0.0, false, false, treeThresh, rng);\\n }\\n else if (modelType == M5ALLMULTI){\\n planModel = new M5Tree(id, mode, freq, 0, 0.0, false, true, treeThresh, rng);\\n }\\n else if (modelType == M5ALLSINGLE){\\n planModel = new M5Tree(id, mode, freq, 0, 0.0, true, true, treeThresh, rng);\\n }\\n else if (modelType == M5SINGLE){\\n planModel = new M5Tree(id, mode, freq, 0, 0.0, true, false, treeThresh, rng);\\n }\\n else if (modelType == LSTSINGLE){\\n planModel = new LinearSplitsTree(id, mode, freq, 0, 0.0, true, treeThresh, rng);\\n }\\n else if (modelType == LSTMULTI){\\n planModel = new LinearSplitsTree(id, mode, freq, 0, 0.0, false, treeThresh, rng);\\n }\\n else if (modelType == STUMP){\\n planModel = new Stump(id, mode, freq, 0, 0.0, rng);\\n }\\n else if (modelType == ALLM5TYPES){\\n // select an m5 type randomly. so multivariate v single and allfeats v subtree feats\\n bool simple = rng.bernoulli(0.5);\\n bool allFeats = rng.bernoulli(0.5);\\n //cout << \\\"ALL types init tree \\\" << i << \\\" with simple: \\\" << simple << \\\" and allFeats: \\\" << allFeats << endl;\\n planModel = new M5Tree(id, mode, freq, 0, 0.0, simple, allFeats, treeThresh, rng);\\n }\\n else {\\n cout << \\\"Invalid model type for this committee\\\" << endl;\\n exit(-1);\\n }\\n}\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7492711544036865,\n \"alphanum_fraction\": 0.7492711544036865,\n \"avg_line_length\": 25.365385055541992,\n \"blob_id\": \"20da0718e5928f3fa6086f758e4ef33ff5f96017\",\n \"content_id\": \"24a9c8b1c1eb126b75ce408f5897a51a49815607\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 1372,\n \"license_type\": \"no_license\",\n \"max_line_length\": 106,\n \"num_lines\": 52,\n \"path\": \"/src/roundbot_control/include/roundbot_control/DiffController.h\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n#ifndef DIFFCONTROLLER_H_\\n#define DIFFCONTROLLER_H_\\n\\n#include <controller_interface/controller.h>\\n#include <hardware_interface/joint_command_interface.h>\\n#include <pluginlib/class_list_macros.h>\\n\\n#include <geometry_msgs/Twist.h>\\n#include <roundbot_control/MotorSim.h>\\n\\nnamespace roundbot_control\\n{\\n\\nclass DiffController : public controller_interface::Controller<hardware_interface::VelocityJointInterface>\\n{\\npublic:\\n bool init(hardware_interface::VelocityJointInterface* hw, ros::NodeHandle &n);\\n void update(const ros::Time& time, const ros::Duration& period);\\n void starting(const ros::Time& time);\\n void stopping(const ros::Time& time);\\nprivate:\\n void recvTwist(const geometry_msgs::Twist::ConstPtr& msg);\\n void loadParams(ros::NodeHandle& n);\\n\\n ros::Subscriber sub_twist_;\\n\\n hardware_interface::JointHandle left_joint_;\\n hardware_interface::JointHandle right_joint_;\\n\\n MotorSim* left_motor_;\\n MotorSim* right_motor_;\\n geometry_msgs::Twist cmd_;\\n double left_target_speed_;\\n double right_target_speed_;\\n ros::Time left_command_stamp_;\\n ros::Time command_stamp_;\\n\\n // Parameters\\n std::string left_joint_name_;\\n std::string right_joint_name_;\\n double timeout_;\\n double wheel_radius_;\\n double wheel_separation_;\\n};\\n\\nPLUGINLIB_EXPORT_CLASS(roundbot_control::DiffController, controller_interface::ControllerBase);\\n\\n}\\n\\n\\n\\n#endif /* DIFFCONTROLLER_H_ */\\n\"\n },\n {\n \"alpha_fraction\": 0.5477648377418518,\n \"alphanum_fraction\": 0.5517452359199524,\n \"avg_line_length\": 27.399999618530273,\n \"blob_id\": \"fde370e2a8e232497e33b03a8838aedd6ba75238\",\n \"content_id\": \"9481a8e158ab6eca3c544cd069bfbef2f0a980fe\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 3266,\n \"license_type\": \"no_license\",\n \"max_line_length\": 141,\n \"num_lines\": 115,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Agent/DiscretizationAgent.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include <rl_agent/DiscretizationAgent.hh>\\n#include <algorithm>\\n\\nDiscretizationAgent::DiscretizationAgent(int n, Agent* a, \\n std::vector<float> fmin,\\n std::vector<float> fmax,\\n bool d){\\n\\n statesPerDim.resize(fmin.size(), n);\\n initEverything(a, fmin, fmax, d);\\n\\n}\\n\\nDiscretizationAgent::DiscretizationAgent(std::vector<int> n, Agent* a, \\n std::vector<float> fmin,\\n std::vector<float> fmax,\\n bool d){\\n\\n if (n.size() != fmin.size()){\\n cout << \\\"ERROR: discretition vector size is different than # features\\\" << endl;\\n exit(-1);\\n }\\n\\n statesPerDim = n;\\n initEverything(a, fmin, fmax, d);\\n\\n}\\n\\nvoid DiscretizationAgent::initEverything(Agent* a, \\n std::vector<float> fmin,\\n std::vector<float> fmax,\\n bool d){\\n agent = a;\\n featmin = fmin;\\n featmax = fmax;\\n DEBUG = d;\\n\\n // print number of features\\n int totalFeatures = 1;\\n for (unsigned i = 0; i < featmin.size(); i++){\\n if (DEBUG) cout << \\\"Dim \\\" << i << \\\" has \\\" \\n << (1+statesPerDim[i]) << \\\" values.\\\" << endl;\\n totalFeatures *= (1+statesPerDim[i]);\\n }\\n if (DEBUG) cout << \\\"Total # States: \\\" << totalFeatures << endl;\\n\\n}\\n\\nDiscretizationAgent::~DiscretizationAgent(){\\n delete agent;\\n}\\n\\nint DiscretizationAgent::first_action(const std::vector<float> &s) {\\n std::vector<float> ds = discretizeState(s);\\n\\n return agent->first_action(ds);\\n}\\n\\nint DiscretizationAgent::next_action(float r, const std::vector<float> &s) {\\n std::vector<float> ds = discretizeState(s);\\n\\n return agent->next_action((int)r, ds);\\n}\\n\\nvoid DiscretizationAgent::last_action(float r) {\\n return agent->last_action((int)r);\\n}\\n\\n\\nvoid DiscretizationAgent::setDebug(bool b){}\\n\\nstd::vector<float> DiscretizationAgent::discretizeState(const std::vector<float> &s){\\n std::vector<float> ds;\\n ds.resize(s.size());\\n \\n // since i'm sometimes doing this for discrete domains\\n // want to center bins on 0, not edge on 0\\n //cout << \\\"feat \\\" << i << \\\" range: \\\" << featmax[i] << \\\" \\\" << featmin[i] << \\\" \\\" << (featmax[i]-featmin[i]) << \\\" n: \\\" << (float)statesPerDim;\\n for (unsigned i = 0; i < s.size(); i++){\\n float factor = (featmax[i] - featmin[i]) / (float)statesPerDim[i];\\n int bin = 0;\\n if (s[i] > 0){\\n bin = (int)((s[i]+factor/2) / factor);\\n } else {\\n bin = (int)((s[i]-factor/2) / factor);\\n }\\n \\n ds[i] = factor*bin;\\n //cout << \\\"DA factor: \\\" << factor << \\\" bin: \\\" << bin;\\n //cout << \\\" Original: \\\" << s[i] << \\\" Discrete: \\\" << ds[i] << endl;\\n }\\n\\n return ds;\\n\\n}\\n\\n\\nvoid DiscretizationAgent::seedExp(std::vector<experience> seedings){\\n // discretize each experience\\n for (unsigned i = 0; i < seedings.size(); i++){\\n experience* e = &(seedings[i]);\\n\\n e->next = discretizeState(e->next);\\n e->s = discretizeState(e->s);\\n e->reward = ((int)e->reward);\\n\\n }\\n\\n // and pass to internal agent\\n agent->seedExp(seedings);\\n}\\n\\nvoid DiscretizationAgent::savePolicy(const char* filename){\\n agent->savePolicy(filename);\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5541346073150635,\n \"alphanum_fraction\": 0.5619643926620483,\n \"avg_line_length\": 25.303203582763672,\n \"blob_id\": \"42357a8bb1fb9fdacef95df45ab8ec3823e44a3d\",\n \"content_id\": \"ae2bde877e7cb95398a2e904102b78b251f3a64e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 22989,\n \"license_type\": \"no_license\",\n \"max_line_length\": 125,\n \"num_lines\": 874,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/Stump.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include \\\"Stump.hh\\\"\\n\\n\\n\\nStump::Stump(int id, int trainMode, int trainFreq, int m, float featPct, Random rng):\\n id(id), mode(trainMode), freq(trainFreq), M(m), featPct(featPct), rng(rng)\\n{\\n\\n nOutput = 0;\\n nExperiences = 0;\\n\\n // how close a split has to be to be randomly selected\\n SPLIT_MARGIN = 0.02; //5; //01; //0.05; //0.2; //0.05;\\n\\n LOSS_MARGIN = 0.0; //0.02;\\n REBUILD_RATIO = 0.05;\\n\\n MIN_GAIN_RATIO = 0.0001; //0.0004; //0.001; //0.0002; //0.001;\\n\\n ALLOW_ONLY_SPLITS = true; //false;\\n\\n STDEBUG = false;\\n SPLITDEBUG = false; //true;\\n\\n if (STDEBUG) {\\n cout << \\\"Created decision stump \\\" << id << endl;\\n cout << \\\" mode: \\\" << mode\\n << \\\" freq: \\\" << freq << endl;\\n }\\n\\n initStump();\\n\\n}\\n\\nStump::Stump(const Stump& s):\\nid(s.id), mode(s.mode), freq(s.freq), M(s.M), featPct(s.featPct), rng(s.rng)\\n{\\n nOutput = s.nOutput;\\n nExperiences = s.nExperiences;\\n SPLIT_MARGIN = s.SPLIT_MARGIN;\\n LOSS_MARGIN = s.LOSS_MARGIN;\\n REBUILD_RATIO = s.REBUILD_RATIO;\\n MIN_GAIN_RATIO = s.MIN_GAIN_RATIO;\\n ALLOW_ONLY_SPLITS = s.ALLOW_ONLY_SPLITS;\\n STDEBUG = s.STDEBUG;\\n SPLITDEBUG = s.SPLITDEBUG;\\n\\n for (int i = 0; i < N_STUMP_EXP; i++){\\n allExp[i] = s.allExp[i];\\n }\\n\\n // set experience pointers\\n experiences.resize(s.experiences.size());\\n for (unsigned i = 0; i < s.experiences.size(); i++){\\n experiences[i] = &(allExp[i]);\\n }\\n\\n // actual split\\n dim = s.dim;\\n val = s.val;\\n type = s.type;\\n gainRatio = s.gainRatio;\\n leftOutputs = s.leftOutputs;\\n rightOutputs = s.rightOutputs;\\n}\\n\\nStump* Stump::getCopy(){\\n Stump* copy = new Stump(*this);\\n return copy;\\n}\\n\\nStump::~Stump() {\\n for (unsigned i = N_STUMP_EXP; i < experiences.size(); i++){\\n delete experiences[i];\\n }\\n experiences.clear();\\n}\\n\\n\\n// multiple instances at once\\nbool Stump::trainInstances(std::vector<classPair> &instances){\\n if (STDEBUG) cout << \\\"trainInstances: \\\" << instances.size() << endl;\\n\\n bool modelChanged = false;\\n bool doBuild = false;\\n\\n // loop through instances, possibly checking for errors\\n for (unsigned a = 0; a < instances.size(); a++){\\n classPair instance = instances[a];\\n\\n // take from static array until we run out\\n stump_experience *e;\\n if (nExperiences < N_STUMP_EXP){\\n // from statically created set of experiences\\n e = &(allExp[nExperiences]);\\n\\n } else {\\n // dynamically create experience\\n e = new stump_experience;\\n }\\n\\n e->input = instance.in;\\n e->output = instance.out;\\n e->id = nExperiences;\\n experiences.push_back(e);\\n nExperiences++;\\n\\n if (nExperiences == 1000000){\\n cout << \\\"Reached limit of # experiences allowed.\\\" << endl;\\n return false;\\n }\\n\\n if (nExperiences != (int)experiences.size())\\n cout << \\\"ERROR: experience size mismatch: \\\" << nExperiences << \\\", \\\" << experiences.size() << endl;\\n\\n //cout << nExperiences << endl << flush;\\n //if (nExperiences == 503 && id == 10){\\n // STDEBUG = true;\\n // SPLITDEBUG = true;\\n // INCDEBUG = true;\\n //}\\n\\n if (STDEBUG) {\\n cout << \\\"Original input: \\\";\\n for (unsigned i = 0; i < instance.in.size(); i++){\\n cout << instance.in[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Original output: \\\" << instance.out << endl;\\n cout << \\\"Added exp id: \\\" << e->id << \\\" output: \\\" << e->output << endl;\\n cout << \\\"Address: \\\" << e << \\\" Input : \\\";\\n for (unsigned i = 0; i < e->input.size(); i++){\\n cout << e->input[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Now have \\\" << nExperiences << \\\" experiences.\\\" << endl;\\n }\\n\\n // mode 0: re-build every step\\n if (mode == 0 || mode == 1 || nExperiences <= 1){\\n // build every step\\n doBuild = true;\\n }\\n\\n // mode 2: re-build every FREQ steps\\n else if (mode == 2){\\n // build every freq steps\\n if (!modelChanged && (nExperiences % freq) == 0){\\n doBuild = true;\\n }\\n }\\n\\n } // end instance loop\\n\\n if (doBuild){\\n buildStump();\\n modelChanged = true;\\n }\\n\\n if (modelChanged){\\n if (STDEBUG) cout << \\\"ST \\\" << id << \\\" stump re-built.\\\" << endl;\\n\\n if (STDEBUG){\\n cout << endl << \\\"ST: \\\" << id << endl;\\n printStump();\\n cout << \\\"Done printing stump\\\" << endl;\\n }\\n }\\n\\n return modelChanged;\\n}\\n\\n\\n// here the target output will be a single value\\nbool Stump::trainInstance(classPair &instance){\\n if (STDEBUG) cout << \\\"trainInstance\\\" << endl;\\n\\n bool modelChanged = false;\\n\\n // simply add this instance to the set of experiences\\n\\n // take from static array until we run out\\n stump_experience *e;\\n if (nExperiences < N_STUMP_EXP){\\n // from statically created set of experiences\\n e = &(allExp[nExperiences]);\\n\\n } else {\\n // dynamically create experience\\n e = new stump_experience;\\n }\\n\\n\\n e->input = instance.in;\\n e->output = instance.out;\\n e->id = nExperiences;\\n experiences.push_back(e);\\n nExperiences++;\\n\\n if (nExperiences == 1000000){\\n cout << \\\"Reached limit of # experiences allowed.\\\" << endl;\\n return false;\\n }\\n\\n if (nExperiences != (int)experiences.size())\\n cout << \\\"ERROR: experience size mismatch: \\\" << nExperiences << \\\", \\\" << experiences.size() << endl;\\n\\n //cout << nExperiences << endl << flush;\\n //if (nExperiences == 503 && id == 10){\\n // STDEBUG = true;\\n // SPLITDEBUG = true;\\n // INCDEBUG = true;\\n //}\\n\\n if (STDEBUG) {\\n cout << \\\"Original input: \\\";\\n for (unsigned i = 0; i < instance.in.size(); i++){\\n cout << instance.in[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Original output: \\\" << instance.out << endl;\\n cout << \\\"Added exp id: \\\" << e->id << \\\" output: \\\" << e->output << endl;\\n cout << \\\"Address: \\\" << e << \\\" Input : \\\";\\n for (unsigned i = 0; i < e->input.size(); i++){\\n cout << e->input[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Now have \\\" << nExperiences << \\\" experiences.\\\" << endl;\\n }\\n\\n // depending on mode/etc, maybe re-build stump\\n\\n // mode 0: re-build every step\\n if (mode == 0 || mode == 1 || nExperiences <= 1){\\n\\n // build every step\\n buildStump();\\n modelChanged = true;\\n\\n }\\n\\n // mode 2: re-build every FREQ steps\\n else if (mode == 2){\\n // build every freq steps\\n if (!modelChanged && (nExperiences % freq) == 0){\\n buildStump();\\n modelChanged = true;\\n\\n }\\n }\\n\\n if (modelChanged){\\n if (STDEBUG) cout << \\\"ST \\\" << id << \\\" stump re-built.\\\" << endl;\\n\\n if (STDEBUG){\\n cout << endl << \\\"ST: \\\" << id << endl;\\n printStump();\\n cout << \\\"Done printing stump\\\" << endl;\\n }\\n }\\n\\n return modelChanged;\\n\\n}\\n\\n\\n\\n// TODO: here we want to return the probability of the output value being each of the possible values, in the stochastic case\\nvoid Stump::testInstance(const std::vector<float> &input, std::map<float, float>* retval){\\n if (STDEBUG) cout << \\\"testInstance ST: \\\" << id << endl;\\n\\n retval->clear();\\n\\n // in case the stump is empty\\n if (experiences.size() == 0){\\n (*retval)[0.0] = 1.0;\\n if (STDEBUG) cout << \\\"no experiences, return 1.0 prob of 0.0\\\" << endl;\\n return; \\n }\\n\\n // and return mapping of outputs and their probabilities\\n if (passTest(dim, val, type, input))\\n outputProbabilities(leftOutputs, retval);\\n else\\n outputProbabilities(rightOutputs, retval);\\n}\\n\\nfloat Stump::getConf(const std::vector<float> &input){\\n if (STDEBUG) cout << \\\"numVisits\\\" << endl;\\n\\n // in case the stump is empty\\n if (experiences.size() == 0){\\n return 0;\\n }\\n\\n // and return #\\n float conf = (float)nExperiences / (float)(2.0 *M);\\n if (conf > 1.0)\\n return 1.0;\\n else\\n return conf;\\n\\n}\\n\\n\\nint Stump::findMatching(const std::vector<stump_experience*> &instances, int dim,\\n int val, int minConf){\\n\\n int count = 0;\\n\\n for (unsigned i = 0; i < instances.size(); i++){\\n if (!passTest(dim, val, ONLY, instances[i]->input)){\\n count++;\\n // no need to continue if this won't be the new min\\n if (count >= minConf)\\n return count;\\n }\\n }\\n return count;\\n}\\n\\n\\n\\n\\n// init the stump\\nvoid Stump::initStump(){\\n if (STDEBUG) cout << \\\"initStump()\\\" << endl;\\n\\n dim = -1;\\n val = -1;\\n type = -1;\\n\\n // current data\\n leftOutputs.clear();\\n rightOutputs.clear();\\n\\n // just to ensure the diff models are on different random values\\n for (int i = 0; i < id; i++){\\n rng.uniform(0, 1);\\n }\\n\\n}\\n\\n\\n/** Decide if this passes the test */\\nbool Stump::passTest(int dim, float val, int type, const std::vector<float> &input){\\n if (STDEBUG) cout << \\\"passTest, dim=\\\" << dim << \\\",val=\\\" << val << \\\",type=\\\" << type\\n << \\\",input[\\\"<<dim<<\\\"]=\\\" << input[dim] <<endl;\\n\\n // TODO: deal with categorical attributes in addition to continuous ones\\n\\n if (type == CUT){\\n if (input[dim] > val)\\n return false;\\n else\\n return true;\\n } else if (type == ONLY){\\n if (input[dim] == val)\\n return false;\\n else\\n return true;\\n } else {\\n return false;\\n }\\n\\n}\\n\\n\\n/** Build the stump from this node down using this set of experiences. */\\nvoid Stump::buildStump(){\\n if(STDEBUG) cout << \\\"buildStump\\\" << endl;\\n\\n if (experiences.size() == 0){\\n cout << \\\"Error: buildStump called on stump \\\" << id << \\\" with no instances.\\\" << endl;\\n exit(-1);\\n }\\n\\n if (SPLITDEBUG) cout << endl << \\\"Creating new decision node\\\" << endl;\\n\\n //node->nInstances++;\\n\\n float bestGainRatio = -1.0;\\n int bestDim = -1;\\n float bestVal = -1;\\n int bestType = -1;\\n\\n testPossibleSplits(&bestGainRatio, &bestDim, &bestVal, &bestType);\\n\\n implementSplit(bestGainRatio, bestDim, bestVal, bestType);\\n\\n}\\n\\n\\nvoid Stump::implementSplit(float bestGainRatio, int bestDim,\\n float bestVal, int bestType){\\n if (STDEBUG) cout << \\\"implementSplit gainRatio=\\\" << bestGainRatio\\n << \\\",dim=\\\" << bestDim\\n << \\\",val=\\\" << bestVal << \\\",type=\\\" << bestType << endl;\\n\\n\\n // set the best split here\\n dim = bestDim;\\n val = bestVal;\\n type = bestType;\\n gainRatio = bestGainRatio;\\n if (SPLITDEBUG) cout << \\\"Best split was type \\\" << type\\n << \\\" with val \\\" << val\\n << \\\" on dim \\\" << dim\\n << \\\" with gainratio: \\\" << bestGainRatio << endl;\\n\\n\\n // split up the instances based on this split\\n std::vector<stump_experience*> left;\\n std::vector<stump_experience*> right;\\n leftOutputs.clear();\\n rightOutputs.clear();\\n for (unsigned i = 0; i < experiences.size(); i++){\\n if (STDEBUG) cout << \\\"implmentSplit - Classify instance \\\" << i << \\\" id: \\\"\\n << experiences[i]->id << \\\" on new split \\\" << endl;\\n if (passTest(dim, val, type, experiences[i]->input)){\\n left.push_back(experiences[i]);\\n leftOutputs.insert(experiences[i]->output);\\n }\\n else{\\n right.push_back(experiences[i]);\\n rightOutputs.insert(experiences[i]->output);\\n }\\n }\\n\\n if (STDEBUG) cout << \\\"Left has \\\" << left.size()\\n << \\\", right has \\\" << right.size() << endl;\\n\\n}\\n\\n\\n\\nvoid Stump::testPossibleSplits(float *bestGainRatio, int *bestDim,\\n float *bestVal, int *bestType) {\\n if (STDEBUG) cout << \\\"testPossibleSplits\\\" << endl;\\n\\n\\n // pre-calculate some stuff for these splits (namely I, P, C)\\n float I = calcIforSet(experiences);\\n //if (STDEBUG) cout << \\\"I: \\\" << I << endl;\\n\\n int nties = 0;\\n\\n // for each possible split, calc gain ratio\\n for (unsigned idim = 0; idim < experiences[0]->input.size(); idim++){\\n\\n // we eliminate some random number of splits\\n // here (decision is taken from the random set that are left)\\n if (rng.uniform() < featPct)\\n continue;\\n\\n\\n float* sorted = sortOnDim(idim);\\n\\n for (int j = 0; j < (int)experiences.size(); j++){\\n\\n // splits that are cuts\\n // if different from last value, try split in between\\n if (j > 0 && sorted[j] != sorted[j-1]){\\n float splitval = (sorted[j] + sorted[j-1]) / 2.0;\\n float gainRatio = calcGainRatio(idim, splitval, CUT, I);\\n\\n if (SPLITDEBUG) cout << \\\" CUT split val \\\" << splitval\\n << \\\" on dim: \\\" << idim << \\\" had gain ratio \\\"\\n << gainRatio << endl;\\n\\n // see if this is the new best gain ratio\\n compareSplits(gainRatio, idim, splitval, CUT, &nties,\\n bestGainRatio, bestDim, bestVal, bestType);\\n\\n\\n } // if its a possible cut\\n\\n if (ALLOW_ONLY_SPLITS){\\n // splits that are true only if this value is equal\\n if (j == 0 || sorted[j] != sorted[j-1]){\\n float splitval = sorted[j];\\n\\n float gainRatio = calcGainRatio(idim, splitval, ONLY, I);\\n\\n if (SPLITDEBUG) cout << \\\" ONLY split val \\\" << splitval\\n << \\\" on dim: \\\" << idim << \\\" had gain ratio \\\"\\n << gainRatio << endl;\\n\\n // see if this is the new best gain ratio\\n compareSplits(gainRatio, idim, splitval, ONLY, &nties,\\n bestGainRatio, bestDim, bestVal, bestType);\\n\\n } // splits with only\\n }\\n\\n } // j loop\\n delete[] sorted;\\n\\n }\\n}\\n\\n\\n/** Decide if this split is better. */\\nvoid Stump::compareSplits(float gainRatio, int dim, float val, int type,\\n int *nties, float *bestGainRatio, int *bestDim,\\n float *bestVal, int *bestType){\\n if (STDEBUG) cout << \\\"compareSplits gainRatio=\\\" << gainRatio << \\\",dim=\\\" << dim\\n << \\\",val=\\\" << val << \\\",type= \\\" << type <<endl;\\n\\n\\n bool newBest = false;\\n\\n // if its a virtual tie, break it randomly\\n if (fabs(*bestGainRatio - gainRatio) < SPLIT_MARGIN){\\n //cout << \\\"Split tie, making random decision\\\" << endl;\\n\\n (*nties)++;\\n float randomval = rng.uniform(0,1);\\n float newsplitprob = (1.0 / (float)*nties);\\n\\n if (randomval < newsplitprob){\\n newBest = true;\\n if (SPLITDEBUG) cout << \\\" Tie on split. ST: \\\" << id << \\\" rand: \\\" << randomval\\n << \\\" splitProb: \\\" << newsplitprob << \\\", selecting new split \\\" << endl;\\n }\\n else\\n if (SPLITDEBUG) cout << \\\" Tie on split. ST: \\\" << id << \\\" rand: \\\" << randomval\\n << \\\" splitProb: \\\" << newsplitprob << \\\", staying with old split \\\" << endl;\\n }\\n\\n // if its clearly better, set this as the best split\\n else if (gainRatio > *bestGainRatio){\\n newBest = true;\\n *nties = 1;\\n }\\n\\n\\n // set the split features\\n if (newBest){\\n *bestGainRatio = gainRatio;\\n *bestDim = dim;\\n *bestVal = val;\\n *bestType = type;\\n if (SPLITDEBUG){\\n cout << \\\" New best gain ratio: \\\" << *bestGainRatio\\n << \\\": type \\\" << *bestType\\n << \\\" with val \\\" << *bestVal\\n << \\\" on dim \\\" << *bestDim << endl;\\n }\\n } // newbest\\n}\\n\\n/** Calculate gain ratio for this possible split. */\\nfloat Stump::calcGainRatio(int dim, float val, int type,\\n float I){\\n if (STDEBUG) cout << \\\"calcGainRatio, dim=\\\" << dim\\n << \\\" val=\\\" << val\\n << \\\" I=\\\" << I\\n << \\\" nInstances= \\\" << experiences.size() << endl;\\n\\n std::vector<stump_experience*> left;\\n std::vector<stump_experience*> right;\\n\\n\\n // array with percentage positive and negative for this test\\n float D[2];\\n\\n // info(T) = I(P): float I;\\n\\n // Info for this split = Info(X,T)\\n float Info;\\n\\n // Gain for this split = Gain(X,T)\\n float Gain;\\n\\n // SplitInfo for this split = I(|pos|/|T|, |neg|/|T|)\\n float SplitInfo;\\n\\n // GainRatio for this split = GainRatio(X,T) = Gain(X,T) / SplitInfo(X,T)\\n float GainRatio;\\n\\n // see where the instances would go with this split\\n for (unsigned i = 0; i < experiences.size(); i++){\\n if (STDEBUG) cout << \\\"calcGainRatio - Classify instance \\\" << i << \\\" id: \\\"\\n << experiences[i]->id << \\\" on new split \\\" << endl;\\n\\n if (passTest(dim, val, type, experiences[i]->input)){\\n left.push_back(experiences[i]);\\n }\\n else{\\n right.push_back(experiences[i]);\\n }\\n }\\n\\n if (STDEBUG) cout << \\\"Left has \\\" << left.size()\\n << \\\", right has \\\" << right.size() << endl;\\n\\n D[0] = (float)left.size() / (float)experiences.size();\\n D[1] = (float)right.size() / (float)experiences.size();\\n float leftInfo = calcIforSet(left);\\n float rightInfo = calcIforSet(right);\\n Info = D[0] * leftInfo + D[1] * rightInfo;\\n Gain = I - Info;\\n SplitInfo = calcIofP((float*)&D, 2);\\n GainRatio = Gain / SplitInfo;\\n\\n if (STDEBUG){\\n cout << \\\"LeftInfo: \\\" << leftInfo\\n << \\\" RightInfo: \\\" << rightInfo\\n << \\\" Info: \\\" << Info\\n << \\\" Gain: \\\" << Gain\\n << \\\" SplitInfo: \\\" << SplitInfo\\n << \\\" GainRatio: \\\" << GainRatio\\n << endl;\\n }\\n\\n return GainRatio;\\n\\n}\\n\\nfloat Stump::calcIofP(float* P, int size){\\n if (STDEBUG) cout << \\\"calcIofP, size=\\\" << size << endl;\\n float I = 0;\\n for (int i = 0; i < size; i++){\\n I -= P[i] * log(P[i]);\\n }\\n return I;\\n}\\n\\n/** Calculate I(P) for set. */\\nfloat Stump::calcIforSet(const std::vector<stump_experience*> &instances){\\n if (STDEBUG) cout << \\\"calcIforSet\\\" << endl;\\n\\n std::vector<float> classes;\\n std::vector<int> classCounts;\\n\\n // go through instances and figure count of each type\\n for (unsigned i = 0; i < instances.size(); i++){\\n\\n float val = instances[i]->output;\\n bool newValue = true;\\n // see if this is a new val\\n for (unsigned j = 0; j < classes.size(); j++){\\n // not new, increment count for this class\\n if (val == classes[j]){\\n newValue = false;\\n classCounts[j]++;\\n break;\\n }\\n }\\n\\n // it is a new value\\n if (newValue){\\n classes.push_back(val);\\n classCounts.push_back(1);\\n if (STDEBUG) cout << \\\"found new class with val \\\" << val << endl;\\n }\\n }\\n\\n // now calculate P\\n float *P = new float[classes.size()];\\n for (int i = 0; i < (int)classCounts.size(); i++){\\n P[i] = (float)classCounts[i] / (float)instances.size();\\n }\\n\\n // calculate I(P)\\n float I = calcIofP(P, classes.size());\\n delete [] P;\\n\\n return I;\\n\\n}\\n\\n/** Returns a list of the attributes in this dimension sorted\\n from lowest to highest. */\\nfloat* Stump::sortOnDim(int dim){\\n if (STDEBUG) cout << \\\"sortOnDim,dim = \\\" << dim << endl;\\n\\n float* values = new float[experiences.size()];\\n\\n for (int i = 0; i < (int)experiences.size(); i++){\\n //cout << \\\"Instance \\\" << i << endl;\\n\\n float val = experiences[i]->input[dim];\\n //cout << \\\" val: \\\" << val << endl;\\n\\n // find where this should go\\n for (int j = 0; j <= i; j++){\\n //cout << \\\" j: \\\" << j << endl;\\n\\n // get to i, this is the spot then\\n if (j==i){\\n values[j] = val;\\n //cout << \\\" At i, putting value in slot j: \\\" << j << endl;\\n }\\n\\n // if this is the spot\\n else if (val < values[j]){\\n //cout << \\\" Found slot at j: \\\" << j << endl;\\n\\n // slide everything forward to make room\\n for (int k = i; k > j; k--){\\n //cout << \\\" k = \\\" << k << \\\" Sliding value from k-1 to k\\\" << endl;\\n values[k] = values[k-1];\\n }\\n\\n // put value in its spot at j\\n //cout << \\\" Putting value at slot j: \\\" << j << endl;\\n values[j] = val;\\n\\n // break\\n break;\\n }\\n\\n }\\n }\\n\\n if (STDEBUG){\\n cout << \\\"Sorted array: \\\" << values[0];\\n for (int i = 1; i < (int)experiences.size(); i++){\\n cout << \\\", \\\" << values[i];\\n }\\n cout << endl;\\n }\\n\\n return values;\\n\\n}\\n\\n\\n/** Print the stump for debug purposes. */\\nvoid Stump::printStump(){\\n\\n cout << \\\"Type: \\\" << type\\n << \\\" Dim: \\\" << dim << \\\" Val: \\\" << val\\n << \\\" nExperiences: \\\" << nExperiences ;\\n\\n cout << \\\" Left Outputs: \\\";\\n for (std::multiset<float>::iterator j = leftOutputs.begin();\\n j != leftOutputs.end(); j++){\\n cout << *j << \\\", \\\";\\n }\\n cout << endl;\\n cout << \\\" Right Outputs: \\\";\\n for (std::multiset<float>::iterator j = rightOutputs.begin();\\n j != rightOutputs.end(); j++){\\n cout << *j << \\\", \\\";\\n }\\n cout << endl;\\n\\n}\\n\\n/** See if the attributes are independent of the output class\\n If they are all independent, we have no more inputs to split on */\\n/*\\n std::vector<float> Stump::calcChiSquare(std::vector<experience> experiences){\\n\\n std::vector<float> chiSquare;\\n chiSquare.resize(experiences[0].input.size());\\n\\n // for each input attribute\\n for (int i = 0; i < (int)experiences[0].input.size(); i++){\\n\\n // attribute counts\\n std::vector<float> attribValue;\\n std::vector<int> attribCount;\\n\\n // outcome counts\\n std::vector<float> outcomeValue;\\n std::vector<int> outcomeCount;\\n\\n for (int k = 0; k < (int)experiences.size(); k++){\\n\\n experience* exp = &(experiences[k]);\\n\\n // match attribute value\\n bool attribMatch = false;\\n for (int l = 0; l < (int)attribValue.size(); l++){\\n if (attribValue[l] == exp->input[i]){\\n attribMatch = true;\\n attribCount[l]++;\\n break;\\n }\\n }\\n // no match\\n attribValue.push_back(exp->input[i]);\\n attribCount.push_back(1);\\n\\n // match outcome value\\n bool outcomeMatch = false;\\n for (int l = 0; l < (int)outcomeValue.size(); l++){\\n if (outcomeValue[l] == exp->output){\\n outcomeMatch = true;\\n outcomeCount[l]++;\\n break;\\n }\\n }\\n // no match\\n outcomeValue.push_back(exp->output);\\n outcomeCount.push_back(1);\\n\\n }\\n\\n // table\\n // TODO\\n\\n float** actual = new float*[attribValue.size()];//[outcomeValue.size()];\\n float** expected = new float*[attribValue.size()];//[outcomeValue.size()];\\n for (int k = 0; k < (int)attribValue.size(); k++){\\n actual[k] = new float[outcomeValue.size()];\\n expected[k] = new float[outcomeValue.size()];\\n }\\n\\n // calculate actual table\\n for (int k = 0; k < (int)experiences.size(); k++){\\n experience* exp = &(experiences[k]);\\n\\n // match\\n for (int l = 0; l < (int)attribValue.size(); l++){\\n if (attribValue[l] != exp->input[i])\\n continue;\\n for (int j = 0; j < (int)outcomeValue.size(); j++){\\n if (outcomeValue[j] != exp->output)\\n continue;\\n actual[l][j]++;\\n }\\n }\\n }\\n\\n // calculate expected table and compare\\n float x2 = 0.0;\\n for (int l = 0; l < (int)attribValue.size(); l++){\\n for (int j = 0; j < (int)outcomeValue.size(); j++){\\n\\n // calculate expected\\n expected[l][j] = attribCount[l] * outcomeCount[j] /\\n (float)experiences.size();\\n\\n float cell = (actual[l][j] - expected[l][j]) *\\n (actual[l][j] - expected[l][j]) / expected[i][j];\\n\\n x2 += cell;\\n }\\n }\\n\\n delete actual;\\n delete expected;\\n\\n chiSquare[i] = x2;\\n cout << \\\" Feature \\\" << i << \\\" Independence: \\\" << chiSquare[i] << endl;\\n\\n }\\n return chiSquare;\\n\\n }\\n*/\\n\\n// output a map of outcomes and their probabilities for this leaf node\\nvoid Stump::outputProbabilities(std::multiset<float> outputs, std::map<float, float>* retval){\\n if (STDEBUG) cout << \\\" Calculating output probs\\\" << endl;\\n\\n // go through all possible output values\\n for (std::multiset<float>::iterator it = outputs.begin();\\n it != outputs.end(); it = outputs.upper_bound(*it)){\\n\\n // get count for this value\\n float val = *it;\\n int count = outputs.count(val);\\n if (count > 0){\\n (*retval)[val] = (float) count / (float)outputs.size();\\n if (STDEBUG) cout << \\\" Output value \\\" << val << \\\" had count of \\\" << count\\n << \\\" on \\\"\\n << outputs.size() << \\\" experiences and prob of \\\"\\n << (*retval)[val] << endl;\\n }\\n }\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.701309323310852,\n \"alphanum_fraction\": 0.7045826315879822,\n \"avg_line_length\": 25,\n \"blob_id\": \"389f8e8a1342dbabb3e19223487deb68250a7358\",\n \"content_id\": \"ab07c880416b45c476f1a821dfd62b10871feedf\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 2444,\n \"license_type\": \"no_license\",\n \"max_line_length\": 102,\n \"num_lines\": 94,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/include/rl_env/FuelRooms.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file FuelRooms.hh\\n Defines the Fuel World domain, with possible noise.\\n From the paper:\\n Hester and Stone, \\\"Real Time Targeted Exploration in Large Domains\\\", ICDL 2010.\\n \\\\author Todd Hester\\n*/\\n\\n#ifndef _FUELROOMS_H_\\n#define _FUELROOMS_H_\\n\\n#include <set>\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n#include \\\"gridworld.hh\\\"\\n\\n\\n/** This class defines the Fuel World gridworld domain */\\nclass FuelRooms: public Environment {\\npublic:\\n\\n /** Creates a deterministic FuelRooms domain.\\n \\\\param rand Random number generator.\\n \\\\param extraVariation the costs of fuel stations vary even more\\n \\\\param stoch Stochastic or deterministic\\n */\\n FuelRooms(Random &rand, bool extraVariation, bool stoch);\\n\\n virtual ~FuelRooms();\\n\\n virtual const std::vector<float> &sensation() const;\\n virtual float apply(int action);\\n\\n virtual bool terminal() const;\\n virtual void reset();\\n\\n virtual int getNumActions();\\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\\n virtual void getMinMaxReward(float* minR, float* maxR);\\n\\n virtual std::vector<experience> getSeedings();\\n\\n /** Get an example experience for this state-action. */\\n experience getExp(int s0, int s1, int s2, int a);\\n\\n // stuff to check on exploration\\n /** For heatmap plots: Keep track of visits to each state, total states, and fuel station states. */\\n void checkVisits();\\n\\n /** For heatmap plots: Reset visit counts for states to 0. */\\n void resetVisits();\\n\\n /** For heatmap plots: Print % of visits to different state types */\\n void printVisits();\\n\\n /** For heatmap plots: Print map of visits to each state cell */\\n void printVisitMap(string filename);\\n\\nprotected:\\n typedef std::pair<float,float> coord_t;\\n enum room_action_t {NORTH, NORTHEAST, EAST, SOUTHEAST, SOUTH, SOUTHWEST, WEST, NORTHWEST};\\n\\nprivate:\\n const int height;\\n const int width;\\n coord_t goal;\\n\\n const bool extraVar;\\n const bool noisy;\\n Random &rng;\\n\\n\\n std::vector<float> s;\\n\\n float &ns;\\n float &ew;\\n float &energy;\\n\\n // keep track of the agent's exploration so we can look at it later\\n int fuelVisited;\\n int totalVisited;\\n \\n int** stateVisits;\\n\\n /** Corrupts a movement action.\\n \\\\param action The intended action\\n \\\\return The action actually executed */\\n room_action_t add_noise(room_action_t action);\\n\\n /** Return the correct reward based on the current state. */\\n float reward(int effect);\\n\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.7230769395828247,\n \"alphanum_fraction\": 0.7576923370361328,\n \"avg_line_length\": 42.5,\n \"blob_id\": \"884ac901e5dc07d5ceedb806c6f1723f53fc140e\",\n \"content_id\": \"b32447b7fe6a87301ff882295918b84306d5104d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 260,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 6,\n \"path\": \"/build/turtlebot3_ddpg/catkin_generated/setup_py_interrogation.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(turtlebot3_ddpg_SETUP_PY_VERSION \\\"1.0.0\\\")\\nset(turtlebot3_ddpg_SETUP_PY_SCRIPTS \\\"\\\")\\nset(turtlebot3_ddpg_SETUP_PY_PACKAGES \\\"\\\")\\nset(turtlebot3_ddpg_SETUP_PY_PACKAGE_DIRS \\\"\\\")\\nset(turtlebot3_ddpg_SETUP_PY_MODULES \\\"\\\")\\nset(turtlebot3_ddpg_SETUP_PY_MODULE_DIRS \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.5495814085006714,\n \"alphanum_fraction\": 0.5591315627098083,\n \"avg_line_length\": 26.578577041625977,\n \"blob_id\": \"695ee97efa81e1917e386cedca5054a126317c06\",\n \"content_id\": \"729e49a1470b5c7a5b5d94d58d82aee21a5f9dbb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 31413,\n \"license_type\": \"no_license\",\n \"max_line_length\": 125,\n \"num_lines\": 1139,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/C45Tree.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file C45Tree.cc\\n Implements the C4.5 decision tree class.\\n This is an implementation of C4.5 decision trees described in:\\n J. R. Quinlan, \\\"Induction of decision trees,\\\" Machine Learning, vol 1. pp 81-106, 1986.\\n \\\\author Todd Hester\\n*/\\n\\n#include \\\"C45Tree.hh\\\"\\n\\n\\n\\nC45Tree::C45Tree(int id, int trainMode, int trainFreq, int m,\\n float featPct, Random rng):\\n id(id), mode(trainMode), freq(trainFreq), M(m),\\n featPct(featPct), ALLOW_ONLY_SPLITS(true), rng(rng)\\n{\\n\\n nnodes = 0;\\n nOutput = 0;\\n nExperiences = 0;\\n hadError = false;\\n maxnodes = N_C45_NODES;\\n totalnodes = 0;\\n\\n // how close a split has to be to be randomly selected\\n SPLIT_MARGIN = 0.0; //0.02; //5; //01; //0.05; //0.2; //0.05;\\n\\n MIN_GAIN_RATIO = 0.0001; //0.0004; //0.001; //0.0002; //0.001;\\n\\n DTDEBUG = false; //true;\\n SPLITDEBUG = false; //true;\\n STOCH_DEBUG = false; //true; //false; //true;\\n INCDEBUG = false; //true; //false; //true;\\n NODEDEBUG = false;\\n COPYDEBUG = false;\\n\\n cout << \\\"Created C4.5 decision tree \\\" << id;\\n if (ALLOW_ONLY_SPLITS) cout << \\\" with == and > splits\\\" << endl;\\n else cout << \\\" with > splits only\\\" << endl;\\n if (DTDEBUG) {\\n cout << \\\" mode: \\\" << mode << \\\" freq: \\\" << freq << endl;\\n }\\n\\n initNodes();\\n initTree();\\n\\n}\\n\\nC45Tree::C45Tree(const C45Tree &t):\\n id(t.id), mode(t.mode), freq(t.freq), M(t.M),\\n featPct(t.featPct), ALLOW_ONLY_SPLITS(t.ALLOW_ONLY_SPLITS), rng(t.rng)\\n{\\n COPYDEBUG = t.COPYDEBUG;\\n if (COPYDEBUG) cout << \\\" C4.5 tree copy constructor id \\\" << id << endl;\\n nnodes = 0;\\n nOutput = t.nOutput;\\n nExperiences = t.nExperiences;\\n hadError = t.hadError;\\n maxnodes = t.maxnodes;\\n totalnodes = 0;\\n\\n SPLIT_MARGIN = t.SPLIT_MARGIN;\\n MIN_GAIN_RATIO = t.MIN_GAIN_RATIO;\\n DTDEBUG = t.DTDEBUG;\\n SPLITDEBUG = t.SPLITDEBUG;\\n STOCH_DEBUG = t.STOCH_DEBUG;\\n INCDEBUG = t.INCDEBUG;\\n NODEDEBUG = t.NODEDEBUG;\\n \\n \\n if (COPYDEBUG) cout << \\\" C4.5 copy nodes, experiences, root, etc\\\" << endl;\\n // copy all experiences\\n for (int i = 0; i < N_C45_EXP; i++){\\n allExp[i] = t.allExp[i];\\n }\\n if (COPYDEBUG) cout << \\\" C4.5 copied exp array\\\" << endl;\\n\\n // set experience pointers\\n experiences.resize(t.experiences.size());\\n for (unsigned i = 0; i < t.experiences.size(); i++){\\n experiences[i] = &(allExp[i]);\\n }\\n if (COPYDEBUG) cout << \\\" C4.5 set experience pointers\\\" << endl;\\n\\n // now the tricky part, set the pointers inside the tree nodes correctly\\n initNodes();\\n\\n if (COPYDEBUG) cout << \\\" C4.5 copy tree \\\" << endl;\\n root = allocateNode();\\n lastNode = root;\\n copyTree(root, t.root);\\n if (COPYDEBUG) cout << \\\" C4.5 tree copy done\\\" << endl;\\n \\n if (COPYDEBUG) {\\n cout << endl << \\\"New tree: \\\" << endl;\\n printTree(root, 0);\\n cout << endl;\\n cout << \\\" c4.5 copy done\\\" << endl;\\n }\\n\\n}\\n\\nC45Tree* C45Tree::getCopy(){\\n \\n C45Tree* copy = new C45Tree(*this);\\n return copy;\\n\\n}\\n\\nvoid C45Tree::copyTree(tree_node* newNode, tree_node* origNode){\\n\\n int nodeId = newNode->id;\\n\\n if (COPYDEBUG) {\\n cout << \\\" Copy node \\\" << newNode->id << \\\" from node \\\" << origNode->id << endl;\\n cout << \\\" NewAddy \\\" << newNode << \\\", old: \\\" << origNode << endl;\\n }\\n\\n // copy node from t\\n *newNode = *origNode;\\n newNode->id = nodeId;\\n\\n // if it has children, allocate and copy them too\\n if (origNode->l != NULL && !newNode->leaf){\\n newNode->l = allocateNode();\\n if (COPYDEBUG) cout << \\\" Copy left node \\\" << newNode->l->id << \\\" from \\\" << origNode->l->id << endl;\\n copyTree(newNode->l, origNode->l);\\n } else {\\n newNode->l = NULL;\\n }\\n\\n if (origNode->r != NULL && !newNode->leaf){\\n newNode->r = allocateNode();\\n if (COPYDEBUG) cout << \\\" Copy right node \\\" << newNode->r->id << \\\" from \\\" << origNode->r->id << endl;\\n copyTree(newNode->r, origNode->r);\\n } else {\\n newNode->r = NULL;\\n }\\n}\\n\\n\\nC45Tree::~C45Tree() {\\n deleteTree(root);\\n for (unsigned i = N_C45_EXP; i < experiences.size(); i++){\\n delete experiences[i];\\n }\\n experiences.clear();\\n}\\n\\n// here the target output will be a single value\\nbool C45Tree::trainInstance(classPair &instance){\\n\\n if (DTDEBUG) cout << \\\"trainInstance\\\" << endl;\\n\\n bool modelChanged = false;\\n\\n // simply add this instance to the set of experiences\\n\\n // take from static array until we run out\\n tree_experience *e;\\n if (nExperiences < N_C45_EXP){\\n // from statically created set of experiences\\n e = &(allExp[nExperiences]);\\n\\n } else {\\n // dynamically create experience\\n e = new tree_experience;\\n }\\n\\n\\n e->input = instance.in;\\n e->output = instance.out;\\n experiences.push_back(e);\\n nExperiences++;\\n\\n if (nExperiences == 1000000){\\n cout << \\\"Reached limit of # experiences allowed.\\\" << endl;\\n return false;\\n }\\n\\n if (nExperiences != (int)experiences.size())\\n cout << \\\"ERROR: experience size mismatch: \\\" << nExperiences << \\\", \\\" << experiences.size() << endl;\\n\\n //cout << nExperiences << endl << flush;\\n //if (nExperiences == 503 && id == 10){\\n // DTDEBUG = true;\\n // SPLITDEBUG = true;\\n // INCDEBUG = true;\\n //}\\n\\n if (DTDEBUG) {\\n cout << \\\"Original input: \\\";\\n for (unsigned i = 0; i < instance.in.size(); i++){\\n cout << instance.in[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Original output: \\\" << instance.out << endl;\\n cout << \\\"Added exp id: \\\" << nExperiences << \\\" output: \\\" << e->output << endl;\\n cout << \\\"Address: \\\" << e << \\\" Input : \\\";\\n for (unsigned i = 0; i < e->input.size(); i++){\\n cout << e->input[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Now have \\\" << nExperiences << \\\" experiences.\\\" << endl;\\n }\\n\\n // depending on mode/etc, maybe re-build tree\\n\\n // mode 0: re-build every step\\n if (mode == BUILD_EVERY || nExperiences <= 1){\\n modelChanged = rebuildTree();\\n }\\n\\n // mode 1: re-build on error only\\n else if (mode == BUILD_ON_ERROR){\\n\\n // build on misclassification\\n // check for misclassify\\n // get leaf\\n tree_node* leaf = traverseTree(root, e->input);\\n // find probability for this output\\n float count = (float)leaf->outputs[e->output];\\n float outputProb = count / (float)leaf->nInstances;\\n\\n if (outputProb < 0.75){\\n modelChanged = rebuildTree();\\n modelChanged = true;\\n }\\n }\\n\\n // mode 2: re-build every FREQ steps\\n else if (mode == BUILD_EVERY_N){\\n // build every freq steps\\n if (!modelChanged && (nExperiences % freq) == 0){\\n modelChanged = rebuildTree();\\n }\\n }\\n\\n if (modelChanged){\\n if (DTDEBUG) cout << \\\"DT \\\" << id << \\\" tree re-built.\\\" << endl;\\n\\n if (DTDEBUG){\\n cout << endl << \\\"DT: \\\" << id << endl;\\n printTree(root, 0);\\n cout << \\\"Done printing tree\\\" << endl;\\n }\\n }\\n\\n /*\\n if (nExperiences % 50 == 0){\\n cout << endl << \\\"DT: \\\" << id << endl;\\n printTree(root, 0);\\n cout << \\\"Done printing tree\\\" << endl;\\n }\\n */\\n\\n return modelChanged;\\n\\n}\\n\\n\\n// here the target output will be a single value\\nbool C45Tree::trainInstances(std::vector<classPair> &instances){\\n if (DTDEBUG) cout << \\\"DT \\\" << id << \\\" trainInstances: \\\" << instances.size() << endl;\\n\\n bool modelChanged = false;\\n\\n bool doBuild = false;\\n\\n bool buildOnError = false;\\n\\n // loop through instances, possibly checking for errors\\n for (unsigned a = 0; a < instances.size(); a++){\\n classPair instance = instances[a];\\n\\n // simply add this instance to the set of experiences\\n\\n // take from static array until we run out\\n tree_experience *e;\\n if (nExperiences < N_C45_EXP){\\n // from statically created set of experiences\\n e = &(allExp[nExperiences]);\\n\\n } else {\\n // dynamically create experience\\n e = new tree_experience;\\n }\\n\\n\\n e->input = instance.in;\\n e->output = instance.out;\\n experiences.push_back(e);\\n nExperiences++;\\n\\n if (nExperiences == 1000000){\\n cout << \\\"Reached limit of # experiences allowed.\\\" << endl;\\n return false;\\n }\\n\\n if (nExperiences != (int)experiences.size())\\n cout << \\\"ERROR: experience size mismatch: \\\" << nExperiences << \\\", \\\" << experiences.size() << endl;\\n\\n if (DTDEBUG) {\\n cout << \\\"Original input: \\\";\\n for (unsigned i = 0; i < instance.in.size(); i++){\\n cout << instance.in[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Original output: \\\" << instance.out << endl;\\n cout << \\\"Added exp id: \\\" << nExperiences << \\\" output: \\\" << e->output << endl;\\n cout << \\\"Address: \\\" << e << \\\" Input : \\\";\\n for (unsigned i = 0; i < e->input.size(); i++){\\n cout << e->input[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Now have \\\" << nExperiences << \\\" experiences.\\\" << endl;\\n }\\n\\n // depending on mode/etc, maybe re-build tree\\n\\n // don't need to check if we've already decided\\n if (doBuild) continue;\\n\\n // mode 0: re-build every step\\n if (mode == BUILD_EVERY || nExperiences <= 1){\\n doBuild = true;\\n }\\n\\n // mode 1: re-build on error only\\n else if (mode == BUILD_ON_ERROR){\\n\\n // build on misclassification\\n // check for misclassify\\n // get leaf\\n tree_node* leaf = traverseTree(root, e->input);\\n // find probability for this output\\n float count = (float)leaf->outputs[e->output];\\n float outputProb = count / (float)leaf->nInstances;\\n\\n if (outputProb < 0.75){\\n doBuild = true;\\n buildOnError = true;\\n }\\n }\\n\\n // mode 2: re-build every FREQ steps\\n else if (mode == BUILD_EVERY_N){\\n // build every freq steps\\n if (!modelChanged && (nExperiences % freq) == 0){\\n doBuild = true;\\n }\\n }\\n\\n } // loop of new instances\\n\\n if (DTDEBUG) cout << \\\"Added \\\" << instances.size() << \\\" new instances. doBuild = \\\" << doBuild << endl;\\n\\n if (doBuild){\\n modelChanged = rebuildTree();\\n }\\n\\n if (modelChanged){\\n if (DTDEBUG) cout << \\\"DT \\\" << id << \\\" tree re-built.\\\" << endl;\\n\\n if (DTDEBUG){\\n cout << endl << \\\"DT: \\\" << id << endl;\\n printTree(root, 0);\\n cout << \\\"Done printing tree\\\" << endl;\\n }\\n }\\n\\n return (modelChanged || buildOnError);\\n\\n}\\n\\n\\nbool C45Tree::rebuildTree(){\\n return buildTree(root, experiences, false);\\n}\\n\\n\\n// TODO: here we want to return the probability of the output value being each of the possible values, in the stochastic case\\nvoid C45Tree::testInstance(const std::vector<float> &input, std::map<float, float>* retval){\\n if (DTDEBUG) cout << \\\"testInstance\\\" << endl;\\n\\n retval->clear();\\n\\n // in case the tree is empty\\n if (experiences.size() == 0){\\n (*retval)[0.0] = 1.0;\\n return;\\n }\\n\\n // follow through tree to leaf\\n tree_node* leaf = traverseTree(root, input);\\n lastNode = leaf;\\n\\n // and return mapping of outputs and their probabilities\\n outputProbabilities(leaf, retval);\\n\\n}\\n\\nfloat C45Tree::getConf(const std::vector<float> &input){\\n if (DTDEBUG) cout << \\\"numVisits\\\" << endl;\\n\\n // in case the tree is empty\\n if (experiences.size() == 0){\\n return 0;\\n }\\n\\n // follow through tree to leaf\\n //tree_node* leaf = traverseTree(root, input);\\n\\n // and return # in this leaf\\n float conf = (float)lastNode->nInstances / (float)(2.0*M);\\n if (conf > 1.0)\\n return 1.0;\\n else\\n return conf;\\n\\n}\\n\\n// check to see if this state is one we should explore\\n// to get more info on potential splits\\n\\n\\n// init the tree\\nvoid C45Tree::initTree(){\\n if (DTDEBUG) cout << \\\"initTree()\\\" << endl;\\n root = allocateNode();\\n\\n if (DTDEBUG) cout << \\\" root id = \\\" << root->id << endl;\\n\\n // just to ensure the diff models are on different random values\\n for (int i = 0; i < id; i++){\\n rng.uniform(0, 1);\\n }\\n\\n}\\n\\n\\n\\n// init a tree node\\nvoid C45Tree::initTreeNode(tree_node* node){\\n if (DTDEBUG) cout << \\\"initTreeNode()\\\";\\n\\n node->id = nnodes++;\\n if (DTDEBUG) cout << \\\" id = \\\" << node->id << endl;\\n\\n totalnodes++;\\n if (totalnodes > maxnodes){\\n maxnodes = totalnodes;\\n if (DTDEBUG) cout << id << \\\" C4.5 MAX nodes: \\\" << maxnodes << endl;\\n }\\n\\n // split criterion\\n node->dim = -1;\\n node->val = -1;\\n node->type = -1;\\n\\n // current data\\n node->nInstances = 0;\\n node->outputs.clear();\\n\\n // next nodes in tree\\n node->l = NULL;\\n node->r = NULL;\\n\\n node->leaf = true;\\n\\n}\\n\\nvoid C45Tree::deleteTree(tree_node* node){\\n if (DTDEBUG) cout << \\\"deleteTree, node=\\\" << node->id << endl;\\n\\n if (node==NULL)\\n return;\\n\\n totalnodes--;\\n\\n node->nInstances = 0;\\n node->outputs.clear();\\n\\n //recursively call deleteTree on children\\n // then delete them\\n if (!node->leaf){\\n // left child\\n if (node->l != NULL){\\n deleteTree(node->l);\\n deallocateNode(node->l);\\n node->l = NULL;\\n }\\n\\n // right child\\n if (node->r != NULL){\\n deleteTree(node->r);\\n deallocateNode(node->r);\\n node->r = NULL;\\n }\\n }\\n\\n node->leaf = true;\\n node->dim = -1;\\n\\n}\\n\\n\\nC45Tree::tree_node* C45Tree::getCorrectChild(tree_node* node,\\n const std::vector<float> &input){\\n\\n if (DTDEBUG) cout << \\\"getCorrectChild, node=\\\" << node->id << endl;\\n\\n if (passTest(node->dim, node->val, node->type, input))\\n return node->l;\\n else\\n return node->r;\\n\\n}\\n\\nC45Tree::tree_node* C45Tree::traverseTree(tree_node* node,\\n const std::vector<float> &input){\\n\\n if (DTDEBUG) cout << \\\"traverseTree, node=\\\" << node->id << endl;\\n\\n while (!node->leaf){\\n node = getCorrectChild(node, input);\\n }\\n\\n return node;\\n}\\n\\n\\nbool C45Tree::passTest(int dim, float val, bool type, const std::vector<float> &input){\\n if (DTDEBUG) cout << \\\"passTest, dim=\\\" << dim << \\\",val=\\\" << val << \\\",type=\\\" << type\\n << \\\",input[\\\"<<dim<<\\\"]=\\\" << input[dim] <<endl;\\n\\n if (type == CUT){\\n if (input[dim] > val)\\n return false;\\n else\\n return true;\\n } else if (type == ONLY){\\n if (input[dim] == val)\\n return false;\\n else\\n return true;\\n } else {\\n return false;\\n }\\n\\n}\\n\\n\\nbool C45Tree::buildTree(tree_node *node,\\n const std::vector<tree_experience*> &instances,\\n bool changed){\\n if(DTDEBUG) cout << \\\"buildTree, node=\\\" << node->id\\n << \\\",nInstances:\\\" << instances.size() \\n << \\\",chg:\\\" << changed << endl;\\n\\n if (instances.size() == 0){\\n cout << \\\"Error: buildTree called on tree \\\" << id << \\\" node \\\" << node->id << \\\" with no instances.\\\" << endl;\\n exit(-1);\\n }\\n\\n\\n // TODO: what about stochastic data?\\n //std::vector<float> chiSquare = calcChiSquare(instances);\\n\\n // first, add instances to tree\\n node->nInstances = instances.size();\\n\\n // add each output to this node\\n node->outputs.clear();\\n for (unsigned i = 0; i < instances.size(); i++){\\n node->outputs[instances[i]->output]++;\\n }\\n\\n // see if they're all the same\\n if (node->outputs.size() == 1){\\n bool change = makeLeaf(node);\\n if (DTDEBUG){\\n cout << \\\"All \\\" << node->nInstances\\n << \\\" classified with output \\\"\\n << instances[0]->output << endl;\\n }\\n return change;\\n }\\n\\n // if not, calculate gain ratio to determine best split\\n else {\\n\\n if (SPLITDEBUG) cout << endl << \\\"Creating new decision node\\\" << endl;\\n\\n //node->leaf = false;\\n //node->nInstances++;\\n\\n float bestGainRatio = -1.0;\\n int bestDim = -1;\\n float bestVal = -1;\\n bool bestType = 0;\\n std::vector<tree_experience*> bestLeft;\\n std::vector<tree_experience*> bestRight;\\n\\n testPossibleSplits(instances, &bestGainRatio, &bestDim, &bestVal, &bestType, &bestLeft, &bestRight);\\n\\n return implementSplit(node, bestGainRatio, bestDim, bestVal, bestType, bestLeft, bestRight, changed);\\n\\n }\\n\\n}\\n\\n\\nbool C45Tree::makeLeaf(tree_node* node){\\n\\n // check on children\\n if (node->l != NULL){\\n deleteTree(node->l);\\n deallocateNode(node->l);\\n node->l = NULL;\\n }\\n\\n if (node->r != NULL){\\n deleteTree(node->r);\\n deallocateNode(node->r);\\n node->r = NULL;\\n }\\n\\n // changed from not leaf to leaf, or just init'd\\n bool change = (!node->leaf || node->type < 0);\\n\\n node->leaf = true;\\n node->type = 0;\\n\\n return change;\\n}\\n\\nbool C45Tree::implementSplit(tree_node* node, float bestGainRatio, int bestDim,\\n float bestVal, bool bestType,\\n const std::vector<tree_experience*> &bestLeft, \\n const std::vector<tree_experience*> &bestRight,\\n bool changed){\\n if (DTDEBUG) cout << \\\"implementSplit node=\\\" << node->id << \\\",gainRatio=\\\" << bestGainRatio\\n << \\\",dim=\\\" << bestDim\\n << \\\",val=\\\" << bestVal << \\\",type=\\\" << bestType \\n << \\\",chg=\\\" << changed << endl;\\n\\n\\n // see if this should still be a leaf node\\n if (bestGainRatio < MIN_GAIN_RATIO){\\n bool change = makeLeaf(node);\\n if (SPLITDEBUG || STOCH_DEBUG){\\n cout << \\\"DT \\\" << id << \\\" Node \\\" << node->id << \\\" Poor gain ratio: \\\"\\n\\t << bestGainRatio << \\\", \\\" << node->nInstances\\n << \\\" instances classified at leaf \\\" << node->id\\n << \\\" with multiple outputs \\\" << endl;\\n }\\n return change;\\n }\\n\\n // see if this split changed or not\\n // assuming no changes above\\n if (!changed && node->dim == bestDim && node->val == bestVal\\n && node->type == bestType && !node->leaf\\n && node->l != NULL && node->r != NULL){\\n // same split as before.\\n if (DTDEBUG || SPLITDEBUG) cout << \\\"Same split as before\\\" << endl;\\n bool changeL = false;\\n bool changeR = false;\\n\\n // see which leaf changed\\n if (bestLeft.size() > (unsigned)node->l->nInstances){\\n // redo left side\\n if (DTDEBUG) cout << \\\"Rebuild left side of tree\\\" << endl;\\n changeL = buildTree(node->l, bestLeft, changed);\\n }\\n\\n if (bestRight.size() > (unsigned)node->r->nInstances){\\n // redo right side\\n if (DTDEBUG) cout << \\\"Rebuild right side of tree\\\" << endl;\\n changeR = buildTree(node->r, bestRight, changed);\\n }\\n\\n // everything up to here is the same, check if there were changes below\\n return (changeL || changeR);\\n }\\n\\n // totally new\\n // set the best split here\\n node->leaf = false;\\n node->dim = bestDim;\\n node->val = bestVal;\\n node->type = bestType;\\n\\n if (SPLITDEBUG) cout << \\\"Best split was type \\\" << node->type\\n << \\\" with val \\\" << node->val\\n << \\\" on dim \\\" << node->dim\\n << \\\" with gainratio: \\\" << bestGainRatio << endl;\\n\\n if (DTDEBUG) cout << \\\"Left has \\\" << bestLeft.size()\\n << \\\", right has \\\" << bestRight.size() << endl;\\n\\n // make sure both instances\\n if (bestLeft.size() == 0 || bestRight.size() == 0){\\n cout << \\\"ERROR: DT \\\" << id << \\\" node \\\" << node->id << \\\" has 0 instances: left: \\\" << bestLeft.size()\\n << \\\" right: \\\" << bestRight.size() << endl;\\n cout << \\\"Split was type \\\" << node->type\\n << \\\" with val \\\" << node->val\\n << \\\" on dim \\\" << node->dim\\n << \\\" with gainratio: \\\" << bestGainRatio << endl;\\n exit(-1);\\n }\\n\\n\\n // check if these already exist\\n if (node->l == NULL){\\n if (DTDEBUG) cout << \\\"Init new left tree nodes \\\" << endl;\\n node->l = allocateNode();\\n }\\n if (node->r == NULL){\\n if (DTDEBUG) cout << \\\"Init new right tree nodes \\\" << endl;\\n node->r = allocateNode();\\n }\\n\\n // recursively build the sub-trees to this one\\n if (DTDEBUG) cout << \\\"Building left tree for node \\\" << node->id << endl;\\n buildTree(node->l, bestLeft, true);\\n if (DTDEBUG) cout << \\\"Building right tree for node \\\" << node->id << endl;\\n buildTree(node->r, bestRight, true);\\n\\n // this one changed, or above changed, no reason to check change of lower parts\\n return true;\\n \\n}\\n\\n\\n\\nvoid C45Tree::testPossibleSplits(const std::vector<tree_experience*> &instances, \\n float *bestGainRatio, int *bestDim,\\n float *bestVal, bool *bestType, \\n std::vector<tree_experience*> *bestLeft,\\n std::vector<tree_experience*> *bestRight) {\\n if (DTDEBUG) cout << \\\"testPossibleSplits\\\" << endl;\\n\\n\\n // pre-calculate some stuff for these splits (namely I, P, C)\\n float I = calcIforSet(instances);\\n //if (DTDEBUG) cout << \\\"I: \\\" << I << endl;\\n\\n int nties = 0;\\n\\n // for each possible split, calc gain ratio\\n for (unsigned idim = 0; idim < instances[0]->input.size(); idim++){\\n\\n //float* sorted = sortOnDim(idim, instances);\\n float minVal, maxVal;\\n std::set<float> uniques = getUniques(idim, instances, minVal, maxVal);\\n\\n for (std::set<float>::iterator j = uniques.begin(); j != uniques.end(); j++){\\n\\n // skip max val, not a valid cut for either\\n if ((*j) == maxVal)\\n continue;\\n \\n // if this is a random forest, we eliminate some random number of splits\\n // here (decision is taken from the random set that are left)\\n if (rng.uniform() < featPct)\\n continue;\\n\\n std::vector<tree_experience*> left;\\n std::vector<tree_experience*> right;\\n\\n // splits that are cuts\\n float splitval = (*j);\\n float gainRatio = calcGainRatio(idim, splitval, CUT, instances, I, left, right);\\n\\n if (SPLITDEBUG) cout << \\\" CUT split val \\\" << splitval\\n << \\\" on dim: \\\" << idim << \\\" had gain ratio \\\"\\n << gainRatio << endl;\\n\\n // see if this is the new best gain ratio\\n compareSplits(gainRatio, idim, splitval, CUT, left, right, &nties,\\n bestGainRatio, bestDim, bestVal, bestType, bestLeft, bestRight);\\n\\n\\n // no minval here, it would be the same as the cut split on minval\\n if (ALLOW_ONLY_SPLITS && (*j) != minVal){\\n // splits that are true only if this value is equal\\n float splitval = (*j);\\n\\n float gainRatio = calcGainRatio(idim, splitval, ONLY, instances, I, left, right);\\n\\n if (SPLITDEBUG) cout << \\\" ONLY split val \\\" << splitval\\n << \\\" on dim: \\\" << idim << \\\" had gain ratio \\\"\\n << gainRatio << endl;\\n\\n // see if this is the new best gain ratio\\n compareSplits(gainRatio, idim, splitval, ONLY, left, right, &nties,\\n bestGainRatio, bestDim, bestVal, bestType, bestLeft, bestRight);\\n\\n } // splits with only\\n\\n } // j loop\\n }\\n}\\n\\n\\n\\nvoid C45Tree::compareSplits(float gainRatio, int dim, float val, bool type,\\n const std::vector<tree_experience*> &left, \\n const std::vector<tree_experience*> &right,\\n int *nties, float *bestGainRatio, int *bestDim,\\n float *bestVal, bool *bestType,\\n std::vector<tree_experience*> *bestLeft, std::vector<tree_experience*> *bestRight){\\n if (DTDEBUG) cout << \\\"compareSplits gainRatio=\\\" << gainRatio << \\\",dim=\\\" << dim\\n << \\\",val=\\\" << val << \\\",type= \\\" << type <<endl;\\n\\n\\n bool newBest = false;\\n\\n // if its a virtual tie, break it randomly\\n if (fabs(*bestGainRatio - gainRatio) < SPLIT_MARGIN){\\n //cout << \\\"Split tie, making random decision\\\" << endl;\\n\\n (*nties)++;\\n float randomval = rng.uniform(0,1);\\n float newsplitprob = (1.0 / (float)*nties);\\n\\n if (randomval < newsplitprob){\\n newBest = true;\\n if (SPLITDEBUG) cout << \\\" Tie on split. DT: \\\" << id << \\\" rand: \\\" << randomval\\n << \\\" splitProb: \\\" << newsplitprob << \\\", selecting new split \\\" << endl;\\n }\\n else\\n if (SPLITDEBUG) cout << \\\" Tie on split. DT: \\\" << id << \\\" rand: \\\" << randomval\\n << \\\" splitProb: \\\" << newsplitprob << \\\", staying with old split \\\" << endl;\\n }\\n\\n // if its clearly better, set this as the best split\\n else if (gainRatio > *bestGainRatio){\\n newBest = true;\\n *nties = 1;\\n }\\n\\n\\n // set the split features\\n if (newBest){\\n *bestGainRatio = gainRatio;\\n *bestDim = dim;\\n *bestVal = val;\\n *bestType = type;\\n *bestLeft = left;\\n *bestRight = right;\\n if (SPLITDEBUG){\\n cout << \\\" New best gain ratio: \\\" << *bestGainRatio\\n << \\\": type \\\" << *bestType\\n << \\\" with val \\\" << *bestVal\\n << \\\" on dim \\\" << *bestDim << endl;\\n }\\n } // newbest\\n}\\n\\nfloat C45Tree::calcGainRatio(int dim, float val, bool type,\\n const std::vector<tree_experience*> &instances,\\n float I,\\n std::vector<tree_experience*> &left,\\n std::vector<tree_experience*> &right){\\n if (DTDEBUG) cout << \\\"calcGainRatio, dim=\\\" << dim\\n << \\\" val=\\\" << val\\n << \\\" I=\\\" << I\\n << \\\" nInstances= \\\" << instances.size() << endl;\\n\\n left.clear();\\n right.clear();\\n\\n // array with percentage positive and negative for this test\\n float D[2];\\n\\n // info(T) = I(P): float I;\\n\\n // Info for this split = Info(X,T)\\n float Info;\\n\\n // Gain for this split = Gain(X,T)\\n float Gain;\\n\\n // SplitInfo for this split = I(|pos|/|T|, |neg|/|T|)\\n float SplitInfo;\\n\\n // GainRatio for this split = GainRatio(X,T) = Gain(X,T) / SplitInfo(X,T)\\n float GainRatio;\\n\\n // see where the instances would go with this split\\n for (unsigned i = 0; i < instances.size(); i++){\\n if (DTDEBUG) cout << \\\"calcGainRatio - Classify instance \\\" << i \\n << \\\" on new split \\\" << endl;\\n\\n if (passTest(dim, val, type, instances[i]->input)){\\n left.push_back(instances[i]);\\n }\\n else{\\n right.push_back(instances[i]);\\n }\\n }\\n\\n if (DTDEBUG) cout << \\\"Left has \\\" << left.size()\\n << \\\", right has \\\" << right.size() << endl;\\n\\n D[0] = (float)left.size() / (float)instances.size();\\n D[1] = (float)right.size() / (float)instances.size();\\n float leftInfo = calcIforSet(left);\\n float rightInfo = calcIforSet(right);\\n Info = D[0] * leftInfo + D[1] * rightInfo;\\n Gain = I - Info;\\n SplitInfo = calcIofP((float*)&D, 2);\\n GainRatio = Gain / SplitInfo;\\n\\n if (DTDEBUG){\\n cout << \\\"LeftInfo: \\\" << leftInfo\\n << \\\" RightInfo: \\\" << rightInfo\\n << \\\" Info: \\\" << Info\\n << \\\" Gain: \\\" << Gain\\n << \\\" SplitInfo: \\\" << SplitInfo\\n << \\\" GainRatio: \\\" << GainRatio\\n << endl;\\n }\\n\\n return GainRatio;\\n\\n}\\n\\nfloat C45Tree::calcIofP(float* P, int size){\\n if (DTDEBUG) cout << \\\"calcIofP, size=\\\" << size << endl;\\n float I = 0;\\n for (int i = 0; i < size; i++){\\n I -= P[i] * log(P[i]);\\n }\\n return I;\\n}\\n\\nfloat C45Tree::calcIforSet(const std::vector<tree_experience*> &instances){\\n if (DTDEBUG) cout << \\\"calcIforSet\\\" << endl;\\n\\n std::map<float, int> classes;\\n\\n // go through instances and figure count of each type\\n for (unsigned i = 0; i < instances.size(); i++){\\n // increment count for this value\\n float val = instances[i]->output;\\n classes[val]++;\\n }\\n\\n // now calculate P\\n float Pval;\\n float I = 0;\\n for (std::map<float, int>::iterator i = classes.begin(); i != classes.end(); i++){\\n Pval = (float)(*i).second / (float)instances.size();\\n // calc I of P\\n I -= Pval * log(Pval);\\n }\\n\\n return I;\\n\\n}\\n\\nstd::set<float> C45Tree::getUniques(int dim, const std::vector<tree_experience*> &instances, float& minVal, float& maxVal){\\n if (DTDEBUG) cout << \\\"getUniques,dim = \\\" << dim;\\n\\n std::set<float> uniques;\\n\\n for (int i = 0; i < (int)instances.size(); i++){\\n if (i == 0 || instances[i]->input[dim] < minVal)\\n minVal = instances[i]->input[dim];\\n if (i == 0 || instances[i]->input[dim] > maxVal)\\n maxVal = instances[i]->input[dim];\\n\\n uniques.insert(instances[i]->input[dim]);\\n }\\n\\n if (DTDEBUG) cout << \\\" #: \\\" << uniques.size() << endl;\\n return uniques;\\n}\\n\\n\\nfloat* C45Tree::sortOnDim(int dim, const std::vector<tree_experience*> &instances){\\n if (DTDEBUG) cout << \\\"sortOnDim,dim = \\\" << dim << endl;\\n\\n float* values = new float[instances.size()];\\n\\n for (int i = 0; i < (int)instances.size(); i++){\\n //cout << \\\"Instance \\\" << i << endl;\\n\\n float val = instances[i]->input[dim];\\n //cout << \\\" val: \\\" << val << endl;\\n\\n // find where this should go\\n for (int j = 0; j <= i; j++){\\n //cout << \\\" j: \\\" << j << endl;\\n\\n // get to i, this is the spot then\\n if (j==i){\\n values[j] = val;\\n //cout << \\\" At i, putting value in slot j: \\\" << j << endl;\\n }\\n\\n // if this is the spot\\n else if (val < values[j]){\\n //cout << \\\" Found slot at j: \\\" << j << endl;\\n\\n // slide everything forward to make room\\n for (int k = i; k > j; k--){\\n //cout << \\\" k = \\\" << k << \\\" Sliding value from k-1 to k\\\" << endl;\\n values[k] = values[k-1];\\n }\\n\\n // put value in its spot at j\\n //cout << \\\" Putting value at slot j: \\\" << j << endl;\\n values[j] = val;\\n\\n // break\\n break;\\n }\\n\\n }\\n }\\n\\n if (DTDEBUG){\\n cout << \\\"Sorted array: \\\" << values[0];\\n for (int i = 1; i < (int)instances.size(); i++){\\n cout << \\\", \\\" << values[i];\\n }\\n cout << endl;\\n }\\n\\n return values;\\n\\n}\\n\\n\\nvoid C45Tree::printTree(tree_node *t, int level){\\n\\n for (int i = 0; i < level; i++){\\n cout << \\\".\\\";\\n }\\n\\n cout << \\\"Node \\\" << t->id;\\n if (t->type == C45Tree::CUT) cout << \\\" Type: CUT\\\"; \\n else cout << \\\" Type: ONLY\\\";\\n cout << \\\" Dim: \\\" << t->dim << \\\" Val: \\\" << t->val\\n << \\\" nInstances: \\\" << t->nInstances ;\\n \\n if (t->leaf){\\n cout << \\\" Outputs: \\\";\\n for (std::map<float, int>::iterator j = t->outputs.begin();\\n j != t->outputs.end(); j++){\\n cout << (*j).first << \\\": \\\" << (*j).second << \\\", \\\";\\n }\\n cout << endl;\\n }\\n else\\n cout << \\\" Left: \\\" << t->l->id << \\\" Right: \\\" << t->r->id << endl;\\n\\n\\n // print children\\n if (t->dim != -1 && !t->leaf){\\n printTree(t->l, level+1);\\n printTree(t->r, level+1);\\n }\\n\\n}\\n\\n\\n\\n\\n\\n// output a map of outcomes and their probabilities for this leaf node\\nvoid C45Tree::outputProbabilities(tree_node* leaf, std::map<float, float>* retval){\\n if (STOCH_DEBUG) cout << \\\"Calculating output probs for leaf \\\" << leaf->id << endl;\\n\\n // go through all output values at this leaf, turn into probabilities\\n for (std::map<float, int>::iterator it = leaf->outputs.begin();\\n it != leaf->outputs.end(); it++){\\n\\n float val = (*it).first;\\n float count = (float)(*it).second;\\n if (count > 0)\\n (*retval)[val] = count / (float)leaf->nInstances;\\n\\n if (STOCH_DEBUG) \\n cout << \\\"Output value \\\" << val << \\\" had count of \\\" << count << \\\" on \\\"\\n << leaf->nInstances <<\\\" instances and prob of \\\" \\n << (*retval)[val] << endl;\\n }\\n\\n}\\n\\n\\nvoid C45Tree::initNodes(){\\n\\n for (int i = 0; i < N_C45_NODES; i++){\\n initTreeNode(&(allNodes[i]));\\n freeNodes.push_back(i);\\n if (NODEDEBUG) \\n cout << \\\"init node \\\" << i << \\\" with id \\\" << allNodes[i].id \\n << \\\", now \\\" << freeNodes.size() << \\\" free nodes.\\\" << endl;\\n }\\n\\n}\\n\\nC45Tree::tree_node* C45Tree::allocateNode(){\\n if (freeNodes.empty()){\\n tree_node* newNode = new tree_node;\\n initTreeNode(newNode);\\n if (NODEDEBUG) \\n cout << id << \\\" PROBLEM: No more pre-allocated nodes!!!\\\" << endl\\n << \\\"return new node \\\" << newNode->id \\n << \\\", now \\\" << freeNodes.size() << \\\" free nodes.\\\" << endl;\\n return newNode;\\n }\\n\\n int i = freeNodes.back();\\n freeNodes.pop_back();\\n if (NODEDEBUG) \\n cout << id << \\\" allocate node \\\" << i << \\\" with id \\\" << allNodes[i].id \\n << \\\", now \\\" << freeNodes.size() << \\\" free nodes.\\\" << endl;\\n return &(allNodes[i]);\\n}\\n\\nvoid C45Tree::deallocateNode(tree_node* node){\\n\\n if (node->id >= N_C45_NODES){\\n if (NODEDEBUG) \\n cout << id << \\\" dealloc extra node id \\\" << node->id \\n << \\\", now \\\" << freeNodes.size() << \\\" free nodes.\\\" << endl;\\n delete node;\\n return;\\n }\\n\\n freeNodes.push_back(node->id);\\n if (NODEDEBUG) \\n cout << id << \\\" dealloc node \\\" << node->id \\n << \\\", now \\\" << freeNodes.size() << \\\" free nodes.\\\" << endl;\\n}\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7576243877410889,\n \"alphanum_fraction\": 0.7654207944869995,\n \"avg_line_length\": 47.988765716552734,\n \"blob_id\": \"e8b9b102c53e3ba0e977eae0dd1fd3fb9bde4796\",\n \"content_id\": \"62f84013087c68290cd986b3335e3f793e635e38\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 4361,\n \"license_type\": \"no_license\",\n \"max_line_length\": 188,\n \"num_lines\": 89,\n \"path\": 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<map>\\n\\n#include <ros/types.h>\\n#include <ros/serialization.h>\\n#include <ros/builtin_message_traits.h>\\n#include <ros/message_operations.h>\\n\\n\\nnamespace rl_msgs\\n{\\ntemplate <class ContainerAllocator>\\nstruct RLExperimentInfo_\\n{\\n typedef RLExperimentInfo_<ContainerAllocator> Type;\\n\\n RLExperimentInfo_()\\n : episode_number(0)\\n , episode_reward(0.0)\\n , number_actions(0) {\\n }\\n RLExperimentInfo_(const ContainerAllocator& _alloc)\\n : episode_number(0)\\n , episode_reward(0.0)\\n , number_actions(0) {\\n (void)_alloc;\\n }\\n\\n\\n\\n typedef int32_t _episode_number_type;\\n _episode_number_type episode_number;\\n\\n typedef float _episode_reward_type;\\n _episode_reward_type episode_reward;\\n\\n typedef int32_t _number_actions_type;\\n _number_actions_type number_actions;\\n\\n\\n\\n\\n\\n typedef boost::shared_ptr< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> > Ptr;\\n typedef boost::shared_ptr< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> const> ConstPtr;\\n\\n}; // struct RLExperimentInfo_\\n\\ntypedef ::rl_msgs::RLExperimentInfo_<std::allocator<void> > RLExperimentInfo;\\n\\ntypedef boost::shared_ptr< ::rl_msgs::RLExperimentInfo > RLExperimentInfoPtr;\\ntypedef boost::shared_ptr< ::rl_msgs::RLExperimentInfo const> RLExperimentInfoConstPtr;\\n\\n// constants requiring out of line definition\\n\\n\\n\\ntemplate<typename ContainerAllocator>\\nstd::ostream& operator<<(std::ostream& s, const ::rl_msgs::RLExperimentInfo_<ContainerAllocator> & v)\\n{\\nros::message_operations::Printer< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >::stream(s, \\\"\\\", v);\\nreturn s;\\n}\\n\\n} // namespace rl_msgs\\n\\nnamespace ros\\n{\\nnamespace message_traits\\n{\\n\\n\\n\\n// BOOLTRAITS {'IsFixedSize': True, 'IsMessage': True, 'HasHeader': False}\\n// {'rl_msgs': ['/home/justin/ros_test/src/rl-texplore-ros-pkg-master/src/rl_msgs/msg'], 'std_msgs': ['/opt/ros/melodic/share/std_msgs/cmake/../msg']}\\n\\n// !!!!!!!!!!! ['__class__', '__delattr__', '__dict__', '__doc__', '__eq__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_parsed_fields', 'constants', 'fields', 'full_name', 'has_header', 'header_present', 'names', 'package', 'parsed_fields', 'short_name', 'text', 'types']\\n\\n\\n\\n\\ntemplate <class ContainerAllocator>\\nstruct IsFixedSize< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\\n : TrueType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct IsFixedSize< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> const>\\n : TrueType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct IsMessage< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\\n : TrueType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct IsMessage< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> const>\\n : TrueType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct HasHeader< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\\n : FalseType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct HasHeader< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> const>\\n : FalseType\\n { };\\n\\n\\ntemplate<class ContainerAllocator>\\nstruct MD5Sum< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\\n{\\n static const char* value()\\n {\\n return \\\"df389dab0f017dc2a66e6cedd44b7a1e\\\";\\n }\\n\\n static const char* value(const ::rl_msgs::RLExperimentInfo_<ContainerAllocator>&) { return value(); }\\n static const uint64_t static_value1 = 0xdf389dab0f017dc2ULL;\\n static const uint64_t static_value2 = 0xa66e6cedd44b7a1eULL;\\n};\\n\\ntemplate<class ContainerAllocator>\\nstruct DataType< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\\n{\\n static const char* value()\\n {\\n return \\\"rl_msgs/RLExperimentInfo\\\";\\n }\\n\\n static const char* value(const ::rl_msgs::RLExperimentInfo_<ContainerAllocator>&) { return value(); }\\n};\\n\\ntemplate<class ContainerAllocator>\\nstruct Definition< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\\n{\\n static const char* value()\\n {\\n return \\\"# Message displaying / plotting / printing info about experiments\\\\n\\\\\\n\\\\n\\\\\\nint32 episode_number\\\\n\\\\\\nfloat32 episode_reward\\\\n\\\\\\n\\\\n\\\\\\nint32 number_actions\\\\n\\\\\\n\\\";\\n }\\n\\n static const char* value(const ::rl_msgs::RLExperimentInfo_<ContainerAllocator>&) { return value(); }\\n};\\n\\n} // namespace message_traits\\n} // namespace ros\\n\\nnamespace ros\\n{\\nnamespace serialization\\n{\\n\\n template<class ContainerAllocator> struct Serializer< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\\n {\\n template<typename Stream, typename T> inline static void allInOne(Stream& stream, T m)\\n {\\n stream.next(m.episode_number);\\n stream.next(m.episode_reward);\\n stream.next(m.number_actions);\\n }\\n\\n ROS_DECLARE_ALLINONE_SERIALIZER\\n }; // struct RLExperimentInfo_\\n\\n} // namespace serialization\\n} // namespace ros\\n\\nnamespace ros\\n{\\nnamespace message_operations\\n{\\n\\ntemplate<class ContainerAllocator>\\nstruct Printer< ::rl_msgs::RLExperimentInfo_<ContainerAllocator> >\\n{\\n template<typename Stream> static void stream(Stream& s, const std::string& indent, const ::rl_msgs::RLExperimentInfo_<ContainerAllocator>& v)\\n {\\n s << indent << \\\"episode_number: \\\";\\n Printer<int32_t>::stream(s, indent + \\\" \\\", v.episode_number);\\n s << indent << \\\"episode_reward: \\\";\\n Printer<float>::stream(s, indent + \\\" \\\", v.episode_reward);\\n s << indent << \\\"number_actions: \\\";\\n Printer<int32_t>::stream(s, indent + \\\" \\\", v.number_actions);\\n }\\n};\\n\\n} // namespace message_operations\\n} // namespace ros\\n\\n#endif // RL_MSGS_MESSAGE_RLEXPERIMENTINFO_H\\n\"\n },\n {\n \"alpha_fraction\": 0.7677419185638428,\n \"alphanum_fraction\": 0.7774193286895752,\n \"avg_line_length\": 43.42856979370117,\n 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predType(predType), nModels(nModels),\\n mode(trainMode), freq(trainFreq),\\n featPct(featPct), expPct(expPct), \\n treeThresh(treeThreshold), stoch(stoch), \\n addNoise(!stoch && (modelType == M5MULTI || modelType == M5SINGLE || modelType == M5ALLMULTI || modelType == M5ALLSINGLE || modelType == LSTMULTI || modelType == LSTSINGLE)),\\n featRange(featRange),\\n rng(rng)\\n{\\n STDEBUG = false;//true;\\n ACC_DEBUG = false;//true;\\n PRED_DEBUG = false; //true;\\n CONF_DEBUG = false;\\n COPYDEBUG = false;\\n nsteps = 0;\\n \\n cout << \\\"Created MultClass \\\" << id << \\\" with nModels: \\\" << nModels << \\\", addNoise: \\\" << addNoise << endl;\\n\\n for (int i = -1; i < id; i++)\\n rng.uniform(0,1);\\n\\n\\n initModels();\\n\\n}\\n\\nMultipleClassifiers::MultipleClassifiers(const MultipleClassifiers &t):\\n id(t.id), modelType(t.modelType), predType(t.predType), nModels(t.nModels),\\n mode(t.mode), freq(t.freq),\\n featPct(t.featPct), expPct(t.expPct), \\n treeThresh(t.treeThresh), stoch(t.stoch), addNoise(t.addNoise),\\n featRange(t.featRange), rng(t.rng)\\n{\\n COPYDEBUG = t.COPYDEBUG;\\n if (COPYDEBUG) cout << \\\" MC copy constructor id \\\" << id << endl;\\n STDEBUG = t.STDEBUG;\\n ACC_DEBUG = t.ACC_DEBUG;\\n PRED_DEBUG = t.PRED_DEBUG;\\n CONF_DEBUG = t.CONF_DEBUG;\\n nsteps = t.nsteps;\\n\\n accuracy = t.accuracy;\\n\\n infos.resize(nModels);\\n models.resize(nModels);\\n if (COPYDEBUG) cout << \\\"models size now \\\" << models.size() << \\\" nModels: \\\" << nModels << endl;\\n // copy each model\\n for (unsigned i = 0; i < models.size(); i++){\\n if (COPYDEBUG)cout << \\\"MC copy model \\\" << i << endl;\\n models[i] = t.models[i]->getCopy();\\n }\\n\\n if (COPYDEBUG) cout << \\\"MC copy complete models size: \\\" << models.size() << endl;\\n}\\n\\nMultipleClassifiers* MultipleClassifiers::getCopy(){\\n \\n MultipleClassifiers* copy = new MultipleClassifiers(*this);\\n return copy;\\n\\n}\\n\\nMultipleClassifiers::~MultipleClassifiers() {\\n for (unsigned i = 0; i < models.size(); i++){\\n delete models[i];\\n }\\n models.clear();\\n accuracy.clear();\\n infos.clear();\\n}\\n\\n\\nbool MultipleClassifiers::trainInstances(std::vector<classPair> &instances){\\n if (STDEBUG) cout << id << \\\" MultClass trainInstances: \\\" << instances.size() << endl;\\n\\n bool changed = false;\\n \\n std::vector< std::vector<classPair> >subsets(nModels);\\n for (int i = 0; i < nModels; i++){\\n subsets[i].reserve(instances.size());\\n }\\n\\n for (unsigned j = 0; j < instances.size(); j++){\\n bool didUpdate = false;\\n \\n // train each model\\n for (int i = 0; i < nModels; i++){\\n \\n // check accuracy of this model\\n if (predType == BEST || predType == WEIGHTAVG){\\n\\tfor (unsigned j = 0; j < instances.size(); j++){\\n\\t updateModelAccuracy(i, instances[j].in, instances[j].out);\\n\\t}\\n }\\n\\n // create new vector with some random subset of experiences\\n if (rng.uniform() < expPct){\\n\\tfloat origOutput = instances[j].out;\\n\\tif (addNoise) instances[j].out += rng.uniform(-0.2,0.2)*treeThresh;\\n subsets[i].push_back(instances[j]);\\n\\tif (addNoise) instances[j].out = origOutput;\\n\\tdidUpdate = true;\\n }\\n } // model loop\\n \\n // make sure someone got this instance\\n if (!didUpdate){\\n int model = rng.uniformDiscrete(0,nModels-1);\\n //cout << \\\"none got instance, update model \\\" << model << endl;\\n if (addNoise) instances[j].out += rng.uniform(-0.2,0.2)*treeThresh;\\n subsets[model].push_back(instances[j]);\\n }\\n } // instances loop\\n \\n // now update models\\n for (int i = 0; i < nModels; i++){\\n if (subsets[i].size() > 0){\\n if (STDEBUG) cout << id << \\\" train model \\\" << i << \\\" on subset of size \\\"\\n << subsets[i].size() << endl;\\n bool singleChange = models[i]->trainInstances(subsets[i]);\\n changed = changed || singleChange;\\n }\\n }\\n \\n nsteps += instances.size();\\n\\n return changed;\\n}\\n\\n// here the target output will be a single value\\nbool MultipleClassifiers::trainInstance(classPair &instance){\\n if (STDEBUG) cout << id << \\\" trainInstance\\\" << endl;\\n\\n bool changed = false;\\n\\n // train each model\\n bool didUpdate = false;\\n for (int i = 0; i < nModels; i++){\\n\\n // check accuracy of this model\\n if (predType == BEST || predType == WEIGHTAVG){\\n updateModelAccuracy(i, instance.in, instance.out);\\n }\\n\\n // update with new experience\\n if (rng.uniform() < expPct){\\n didUpdate = true;\\n float origOutput = instance.out;\\n if (addNoise) instance.out += rng.uniform(-0.2,0.2)*treeThresh;\\n bool singleChange = models[i]->trainInstance(instance);\\n if (addNoise) instance.out = origOutput;\\n changed = changed || singleChange;\\n }\\n }\\n\\n // make sure some model got the transition\\n if (!didUpdate){\\n int model = rng.uniformDiscrete(0,nModels-1);\\n if (addNoise) instance.out += rng.uniform(-0.2,0.2)*treeThresh;\\n bool singleChange = models[model]->trainInstance(instance);\\n changed = singleChange || changed;\\n }\\n\\n nsteps++;\\n\\n return changed;\\n\\n}\\n\\n\\n// get all the models outputs and combine them somehow\\nvoid MultipleClassifiers::testInstance(const std::vector<float> &input, std::map<float, float>* retval){\\n if (STDEBUG) cout << id << \\\" testInstance\\\" << endl;\\n\\n if ((int)infos.size() != nModels){\\n infos.resize(nModels);\\n }\\n\\n for (unsigned i = 0; i < infos.size(); i++){\\n infos[i].clear();\\n }\\n\\n retval->clear();\\n\\n // possibly have to init our model\\n if ((int)models.size() != nModels)\\n initModels();\\n\\n ///////////////////////////////////\\n // return best\\n if (predType == BEST){\\n float acc = -1.0;\\n int best = -1;\\n for (int i = 0; i < nModels; i++){\\n if (PRED_DEBUG) cout << \\\"Model \\\" << i << \\\" has acc \\\"\\n << accuracy[i] << endl;\\n if (accuracy[i] > acc){\\n acc = accuracy[i];\\n best = i;\\n }\\n }\\n if (PRED_DEBUG) cout << id << \\\" Returning model \\\" << best\\n << \\\" with accuracy \\\" << acc << endl;\\n models[best]->testInstance(input, retval);\\n return;\\n }\\n //////////////////////////////////////\\n\\n\\n /////////////////////////////////////////\\n // calculate weights for weighted avg\\n std::vector<float> weights(nModels, 1.0);\\n if (predType == WEIGHTAVG){\\n float accSum = 0.0;\\n \\n for (int j = 0; j < nModels; j++){\\n accSum += accuracy[j];\\n }\\n\\n if (accSum > 0.0){\\n for (int j = 0; j < nModels; j++){\\n weights[j] = (float)nModels * accuracy[j] / accSum;\\n if (PRED_DEBUG || ACC_DEBUG) cout << \\\"Model \\\" << j << \\\" acc: \\\"\\n << accuracy[j] << \\\" weight: \\\"\\n << weights[j] << endl;\\n }\\n }\\n }\\n /////////////////////////////////////////\\n\\n // get state action info from each tree in our set\\n for (int j = 0; j < nModels; j++){\\n models[j]->testInstance(input, &(infos[j]));\\n }\\n\\n // make a list of all outcomes any model predicted\\n for (int i = 0; i < nModels; i++){\\n for (std::map<float, float>::iterator it = infos[i].begin();\\n it != infos[i].end(); it++){\\n\\n float outcome = (*it).first;\\n float prob = (*it).second;\\n\\n // only if prob > 0\\n if (prob > 0){\\n\\n // see if its not already in there\\n if (!retval->count(outcome)){\\n if (PRED_DEBUG) cout << \\\" new outcome \\\" << outcome << endl;\\n // check if it really is a new one\\n for (std::map<float, float>::iterator j = retval->begin();\\n j != retval->end(); j++){\\n if (fabs(outcome - j->first) < treeThresh){\\n if (PRED_DEBUG) cout << \\\"within treeThresh of \\\" << j->first << endl;\\n outcome = j->first;\\n break;\\n }\\n }\\n }\\n\\n (*retval)[outcome] += (prob * weights[i] / (float)nModels);\\n \\n if (PRED_DEBUG) cout << \\\"inserting outcome \\\" << outcome\\n << \\\" from model \\\" << i\\n << \\\" with prob: \\\" << prob\\n << \\\" and weight: \\\" << weights[i]\\n << \\\" now total prob: \\\" << (*retval)[outcome] << endl;\\n \\n }\\n }\\n }\\n}\\n\\n\\n\\nfloat MultipleClassifiers::getConf(const std::vector<float> &input){\\n if (STDEBUG || CONF_DEBUG) cout << id << \\\" getConf\\\" << endl;\\n\\n if ((int)infos.size() != nModels){\\n infos.resize(nModels);\\n }\\n\\n // get predictions if we haven't\\n if (predType == BEST || predType == SEPARATE){\\n if (CONF_DEBUG) cout << \\\"conf: getting predictions again\\\" << endl;\\n for (int j = 0; j < nModels; j++){\\n infos[j].clear();\\n models[j]->testInstance(input, &(infos[j]));\\n }\\n }\\n\\n float conf = 0;\\n\\n // for deterministic trees, calculating a distribution of outcomes\\n // calculate kl divergence\\n if (modelType == RMAX || modelType == SLF || modelType == C45TREE ||\\n modelType == STUMP ){ \\n conf = 1.0 - klDivergence(input);\\n }\\n\\n // for continuous trees, providing a single continuous prediction\\n // calcluate variance\\n else {\\n // use scaled sd\\n conf = 1.0 - sqrt(variance(input)) / featRange;\\n }\\n\\n if (CONF_DEBUG) cout << \\\"return conf: \\\" << conf << endl;\\n\\n return conf;\\n\\n}\\n\\n// calculate kl divergence of these predictions\\nfloat MultipleClassifiers::klDivergence(const std::vector<float> &input){\\n\\n // KL divergence equation\\n // D_KL(P||Q) = SUM_i P(i) log (P(i)/Q(i))\\n float totalKL = 0.0;\\n\\n // I guess let's take the avg KL divergence of comparisons \\n // between all pairs of models\\n for (int i = 0; i < nModels; i++){\\n for (int j = 0; j < nModels; j++){\\n if (i == j) continue;\\n float singleKL = 0.0;\\n\\n for (std::map<float, float>::iterator it = infos[i].begin();\\n it != infos[i].end(); it++){\\n \\n float outcome = (*it).first;\\n float prob = (*it).second;\\n \\n if (prob == 0){\\n continue;\\n }\\n\\n if (CONF_DEBUG) \\n cout << \\\"model \\\" << i << \\\" predicts \\\" << outcome << \\\" with prob \\\" \\n << prob << \\\", model \\\" << j << \\\" has prob \\\" \\n << infos[j][outcome] << endl;\\n\\n float jProb = infos[j][outcome];\\n if (jProb == 0) jProb = 0.01;\\n\\n singleKL += prob * log(prob / jProb);\\n \\n }\\n totalKL += singleKL;\\n\\n if (CONF_DEBUG) \\n cout << \\\"D_KL(\\\" << i << \\\"||\\\" << j << \\\") = \\\" << singleKL\\n << \\\" klsum: \\\" << totalKL << endl;\\n\\n } // outcomes of model i\\n } // models\\n\\n int npairs = nModels * (nModels - 1);\\n float avgKL = totalKL / (float)npairs;\\n\\n // normalize. since my 0 prob is really 0.01, and log 1/0.01 = 4.60517, we'll divide by that\\n avgKL /= 4.60517;\\n\\n if (CONF_DEBUG) cout << \\\"AvgKL: \\\" << avgKL << endl << endl;\\n\\n return avgKL;\\n\\n}\\n\\n// calculate the variance of these predictions\\nfloat MultipleClassifiers::variance(const std::vector<float> &input){\\n\\n float sum = 0.0;\\n float sumSqr = 0.0;\\n\\n for (int i = 0; i < nModels; i++){\\n float val = infos[i].begin()->first;\\n sum += val;\\n sumSqr += (val*val);\\n }\\n\\n float mean = sum / (float)nModels;\\n float variance = (sumSqr - sum*mean) / (float)(nModels-1.0);\\n\\n if (CONF_DEBUG) cout << \\\"variance of predictions is \\\" << variance << endl;\\n\\n return variance;\\n\\n}\\n\\n// init models\\nvoid MultipleClassifiers::initModels(){\\n if (STDEBUG) cout << \\\"initModels()\\\" << endl;\\n\\n models.resize(nModels);\\n accuracy.resize(nModels, 0.0);\\n\\n // init the trees or stumps\\n for (int i = 0; i < nModels; i++){\\n\\n if (modelType == C45TREE){\\n models[i] = new C45Tree(id + i*(1+nModels), mode, freq, 0, featPct, rng);\\n }\\n else if (modelType == M5MULTI){\\n models[i] = new M5Tree(id + i*(1+nModels), mode, freq, 0, featPct, false, false, treeThresh, rng);\\n }\\n else if (modelType == M5ALLMULTI){\\n models[i] = new M5Tree(id + i*(1+nModels), mode, freq, 0, featPct, false, true, treeThresh, rng);\\n }\\n else if (modelType == M5ALLSINGLE){\\n models[i] = new M5Tree(id + i*(1+nModels), mode, freq, 0, featPct, true, true, treeThresh, rng);\\n }\\n else if (modelType == M5SINGLE){\\n models[i] = new M5Tree(id + i*(1+nModels), mode, freq, 0, featPct, true, false, treeThresh, rng);\\n }\\n else if (modelType == LSTSINGLE){\\n models[i] = new LinearSplitsTree(id + i*(1+nModels), mode, freq, 0, featPct, true, treeThresh, rng);\\n }\\n else if (modelType == LSTMULTI){\\n models[i] = new LinearSplitsTree(id + i*(1+nModels), mode, freq, 0, featPct, false, treeThresh, rng);\\n }\\n else if (modelType == STUMP){\\n models[i] = new Stump(id + 1*(1+nModels), mode, freq, 0, featPct, rng);\\n }\\n else if (modelType == ALLM5TYPES){\\n // select an m5 type randomly. so multivariate v single and allfeats v subtree feats\\n bool simple = rng.bernoulli(0.5);\\n bool allFeats = rng.bernoulli(0.5);\\n //cout << \\\"ALL types init tree \\\" << i << \\\" with simple: \\\" << simple << \\\" and allFeats: \\\" << allFeats << endl;\\n models[i] = new M5Tree(id + i*(1+nModels), mode, freq, 0, featPct, simple, allFeats, treeThresh, rng);\\n }\\n else {\\n cout << \\\"Invalid model type for this committee\\\" << endl;\\n exit(-1);\\n }\\n }\\n\\n}\\n\\n\\nvoid MultipleClassifiers::updateModelAccuracy(int i, const std::vector<float> &in,\\n float output){\\n\\n if (ACC_DEBUG) cout << \\\"Check model accuracy \\\" << i << \\\" curr: \\\" << accuracy[i] << endl;\\n\\n // get prediction\\n std::map<float, float> pred;\\n models[i]->testInstance(in, &pred);\\n\\n // see if it was correct\\n bool correct = true;\\n // check trans prob\\n // for now, just see if prob of next was ML from this model\\n float prob = pred[output];\\n if (ACC_DEBUG) cout << \\\"Model \\\" << i << \\\" predicted outcome \\\"\\n << output << \\\" with prob: \\\" << prob << endl;\\n if (prob <= 0.5)\\n correct = false;\\n\\n // update accuracy\\n accuracy[i] = accuracy[i] * (float)nsteps/(float)(nsteps+1.0);\\n if (correct)\\n accuracy[i] += 1.0/(float)(nsteps+1.0);\\n\\n if (ACC_DEBUG) cout << \\\"Model \\\" << i << \\\" new accuracy: \\\" << accuracy[i] << endl;\\n\\n}\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7858880758285522,\n \"alphanum_fraction\": 0.7858880758285522,\n \"avg_line_length\": 33.25,\n \"blob_id\": \"4deac065ec057d27f29837a46c4c40fe61ae0f1d\",\n \"content_id\": \"b383b9cd383342c634227cde67936c23ee8c4dbc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 411,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 12,\n \"path\": \"/src/roundbot_control/CMakeFiles/diff_controller.dir/cmake_clean.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"FILE(REMOVE_RECURSE\\n \\\"CMakeFiles/diff_controller.dir/src/DiffController.cpp.o\\\"\\n \\\"devel/lib/libdiff_controller.pdb\\\"\\n \\\"devel/lib/libdiff_controller.so\\\"\\n \\\"devel/lib/libdiff_controller.pdb\\\"\\n \\\"CMakeFiles/CMakeRelink.dir/libdiff_controller.so\\\"\\n)\\n\\n# Per-language clean rules from dependency scanning.\\nFOREACH(lang CXX)\\n INCLUDE(CMakeFiles/diff_controller.dir/cmake_clean_${lang}.cmake OPTIONAL)\\nENDFOREACH(lang)\\n\"\n },\n {\n \"alpha_fraction\": 0.5141873359680176,\n \"alphanum_fraction\": 0.5293720364570618,\n \"avg_line_length\": 30.08300018310547,\n \"blob_id\": \"fe4ef2e1979648dcaebf1e1991dcdbb6abe74df9\",\n \"content_id\": \"1a71eb674bfb3afe0dc392e25a91d7426e0d9204\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 31084,\n \"license_type\": \"no_license\",\n \"max_line_length\": 237,\n \"num_lines\": 1000,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_experiment/src/rl.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file Main file that starts agents and environments\\n \\\\author Todd Hester\\n*/\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n\\n#include <stdio.h>\\n#include <string.h>\\n#include <sys/time.h>\\n\\n//////////////////\\n// Environments //\\n//////////////////\\n#include <rl_env/RobotCarVel.hh>\\n#include <rl_env/fourrooms.hh>\\n#include <rl_env/tworooms.hh>\\n#include <rl_env/taxi.hh>\\n#include <rl_env/FuelRooms.hh>\\n#include <rl_env/stocks.hh>\\n#include <rl_env/energyrooms.hh>\\n#include <rl_env/MountainCar.hh>\\n#include <rl_env/CartPole.hh>\\n#include <rl_env/LightWorld.hh>\\n\\n\\n////////////\\n// Agents //\\n////////////\\n#include <rl_agent/QLearner.hh>\\n#include <rl_agent/ModelBasedAgent.hh>\\n#include <rl_agent/DiscretizationAgent.hh>\\n#include <rl_agent/SavedPolicy.hh>\\n#include <rl_agent/Dyna.hh>\\n#include <rl_agent/Sarsa.hh>\\n\\n\\n\\n#include <vector>\\n#include <sstream>\\n#include <iostream>\\n\\n#include <getopt.h>\\n#include <stdlib.h>\\n\\nunsigned NUMEPISODES = 1000; //10; //200; //500; //200;\\nconst unsigned NUMTRIALS = 1; //30; //30; //5; //30; //30; //50\\nunsigned MAXSTEPS = 1000; // per episode\\nbool PRINTS = false;\\n\\n\\nvoid displayHelp(){\\n cout << \\\"\\\\n Call experiment --agent type --env type [options]\\\\n\\\";\\n cout << \\\"Agent types: qlearner sarsa modelbased rmax texplore dyna savedpolicy\\\\n\\\";\\n cout << \\\"Env types: taxi tworooms fourrooms energy fuelworld mcar cartpole car2to7 car7to2 carrandom stocks lightworld\\\\n\\\";\\n\\n cout << \\\"\\\\n Agent Options:\\\\n\\\";\\n cout << \\\"--gamma value (discount factor between 0 and 1)\\\\n\\\";\\n cout << \\\"--epsilon value (epsilon for epsilon-greedy exploration)\\\\n\\\";\\n cout << \\\"--alpha value (learning rate alpha)\\\\n\\\";\\n cout << \\\"--initialvalue value (initial q values)\\\\n\\\";\\n cout << \\\"--actrate value (action selection rate (Hz))\\\\n\\\";\\n cout << \\\"--lamba value (lamba for eligibility traces)\\\\n\\\";\\n cout << \\\"--m value (parameter for R-Max)\\\\n\\\";\\n cout << \\\"--k value (For Dyna: # of model based updates to do between each real world update)\\\\n\\\";\\n cout << \\\"--history value (# steps of history to use for planning with delay)\\\\n\\\";\\n cout << \\\"--filename file (file to load saved policy from for savedpolicy agent)\\\\n\\\";\\n cout << \\\"--model type (tabular,tree,m5tree)\\\\n\\\";\\n cout << \\\"--planner type (vi,pi,sweeping,uct,parallel-uct,delayed-uct,delayed-parallel-uct)\\\\n\\\";\\n cout << \\\"--explore type (unknown,greedy,epsilongreedy,variancenovelty)\\\\n\\\";\\n cout << \\\"--combo type (average,best,separate)\\\\n\\\";\\n cout << \\\"--nmodels value (# of models)\\\\n\\\";\\n cout << \\\"--nstates value (optionally discretize domain into value # of states on each feature)\\\\n\\\";\\n cout << \\\"--reltrans (learn relative transitions)\\\\n\\\";\\n cout << \\\"--abstrans (learn absolute transitions)\\\\n\\\";\\n cout << \\\"--v value (For TEXPLORE: b/v coefficient for rewarding state-actions where models disagree)\\\\n\\\";\\n cout << \\\"--n value (For TEXPLORE: n coefficient for rewarding state-actions which are novel)\\\\n\\\";\\n\\n cout << \\\"\\\\n Env Options:\\\\n\\\";\\n cout << \\\"--deterministic (deterministic version of domain)\\\\n\\\";\\n cout << \\\"--stochastic (stochastic version of domain)\\\\n\\\";\\n cout << \\\"--delay value (# steps of action delay (for mcar and tworooms)\\\\n\\\";\\n cout << \\\"--lag (turn on brake lag for car driving domain)\\\\n\\\";\\n cout << \\\"--highvar (have variation fuel costs in Fuel World)\\\\n\\\";\\n cout << \\\"--nsectors value (# sectors for stocks domain)\\\\n\\\";\\n cout << \\\"--nstocks value (# stocks for stocks domain)\\\\n\\\";\\n\\n cout << \\\"\\\\n--prints (turn on debug printing of actions/rewards)\\\\n\\\";\\n cout << \\\"--nepisodes value (# of episodes to run (1000 default)\\\\n\\\";\\n cout << \\\"--seed value (integer seed for random number generator)\\\\n\\\";\\n\\n cout << \\\"\\\\n For more info, see: http://www.ros.org/wiki/rl_experiment\\\\n\\\";\\n\\n exit(-1);\\n\\n}\\n\\n\\nint main(int argc, char **argv) {\\n\\n // default params for env and agent\\n char* agentType = NULL;\\n char* envType = NULL;\\n float discountfactor = 0.99;\\n float epsilon = 0.1;\\n float alpha = 0.3;\\n float initialvalue = 0.0;\\n float actrate = 10.0;\\n float lambda = 0.1;\\n int M = 5;\\n int modelType = C45TREE;\\n int exploreType = GREEDY;\\n int predType = BEST;\\n int plannerType = PAR_ETUCT_ACTUAL;\\n int nmodels = 1;\\n bool reltrans = true;\\n bool deptrans = false;\\n float v = 0;\\n float n = 0;\\n float featPct = 0.2;\\n int nstates = 0;\\n int k = 1000;\\n char *filename = NULL;\\n bool stochastic = true;\\n int nstocks = 3;\\n int nsectors = 3;\\n int delay = 0;\\n bool lag = false;\\n bool highvar = false;\\n int history = 0;\\n int seed = 1;\\n // change some of these parameters based on command line args\\n\\n // parse agent type\\n bool gotAgent = false;\\n for (int i = 1; i < argc-1; i++){\\n if (strcmp(argv[i], \\\"--agent\\\") == 0){\\n gotAgent = true;\\n agentType = argv[i+1];\\n }\\n }\\n if (!gotAgent) {\\n cout << \\\"--agent type option is required\\\" << endl;\\n displayHelp();\\n }\\n\\n // set some default options for rmax or texplore\\n if (strcmp(agentType, \\\"rmax\\\") == 0){\\n modelType = RMAX;\\n exploreType = EXPLORE_UNKNOWN;\\n predType = BEST;\\n plannerType = VALUE_ITERATION;\\n nmodels = 1;\\n reltrans = false;\\n M = 5;\\n history = 0;\\n } else if (strcmp(agentType, \\\"texplore\\\") == 0){\\n modelType = C45TREE;\\n exploreType = DIFF_AND_NOVEL_BONUS;\\n v = 0;\\n n = 0;\\n predType = AVERAGE;\\n plannerType = PAR_ETUCT_ACTUAL;\\n nmodels = 5;\\n reltrans = true;\\n M = 0;\\n history = 0;\\n }\\n\\n // parse env type\\n bool gotEnv = false;\\n for (int i = 1; i < argc-1; i++){\\n if (strcmp(argv[i], \\\"--env\\\") == 0){\\n gotEnv = true;\\n envType = argv[i+1];\\n }\\n }\\n if (!gotEnv) {\\n cout << \\\"--env type option is required\\\" << endl;\\n displayHelp();\\n }\\n\\n // parse other arguments\\n char ch;\\n const char* optflags = \\\"geairlmoxpcn:\\\";\\n int option_index = 0;\\n static struct option long_options[] = {\\n {\\\"gamma\\\", 1, 0, 'g'},\\n {\\\"discountfactor\\\", 1, 0, 'g'},\\n {\\\"epsilon\\\", 1, 0, 'e'},\\n {\\\"alpha\\\", 1, 0, 'a'},\\n {\\\"initialvalue\\\", 1, 0, 'i'},\\n {\\\"actrate\\\", 1, 0, 'r'},\\n {\\\"lambda\\\", 1, 0, 'l'},\\n {\\\"m\\\", 1, 0, 'm'},\\n {\\\"model\\\", 1, 0, 'o'},\\n {\\\"explore\\\", 1, 0, 'x'},\\n {\\\"planner\\\", 1, 0, 'p'},\\n {\\\"combo\\\", 1, 0, 'c'},\\n {\\\"nmodels\\\", 1, 0, '#'},\\n {\\\"reltrans\\\", 0, 0, 't'},\\n {\\\"abstrans\\\", 0, 0, '0'},\\n {\\\"seed\\\", 1, 0, 's'},\\n {\\\"agent\\\", 1, 0, 'q'},\\n {\\\"prints\\\", 0, 0, 'd'},\\n {\\\"nstates\\\", 1, 0, 'w'},\\n {\\\"k\\\", 1, 0, 'k'},\\n {\\\"filename\\\", 1, 0, 'f'},\\n {\\\"history\\\", 1, 0, 'y'},\\n {\\\"b\\\", 1, 0, 'b'},\\n {\\\"v\\\", 1, 0, 'v'},\\n {\\\"n\\\", 1, 0, 'n'},\\n\\n {\\\"env\\\", 1, 0, 1},\\n {\\\"deterministic\\\", 0, 0, 2},\\n {\\\"stochastic\\\", 0, 0, 3},\\n {\\\"delay\\\", 1, 0, 4},\\n {\\\"nsectors\\\", 1, 0, 5},\\n {\\\"nstocks\\\", 1, 0, 6},\\n {\\\"lag\\\", 0, 0, 7},\\n {\\\"nolag\\\", 0, 0, 8},\\n {\\\"highvar\\\", 0, 0, 11},\\n {\\\"nepisodes\\\", 1, 0, 12}\\n\\n };\\n\\n bool epsilonChanged = false;\\n bool actrateChanged = false;\\n bool mChanged = false;\\n bool bvnChanged = false;\\n bool lambdaChanged = false;\\n\\n while(-1 != (ch = getopt_long_only(argc, argv, optflags, long_options, &option_index))) {\\n switch(ch) {\\n\\n case 'g':\\n discountfactor = std::atof(optarg);\\n cout << \\\"discountfactor: \\\" << discountfactor << endl;\\n break;\\n\\n case 'e':\\n epsilonChanged = true;\\n epsilon = std::atof(optarg);\\n cout << \\\"epsilon: \\\" << epsilon << endl;\\n break;\\n\\n case 'y':\\n {\\n if (strcmp(agentType, \\\"texplore\\\") == 0 || strcmp(agentType, \\\"modelbased\\\") == 0){\\n history = std::atoi(optarg);\\n cout << \\\"history: \\\" << history << endl;\\n } else {\\n cout << \\\"--history is not a valid option for agent: \\\" << agentType << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 'k':\\n {\\n if (strcmp(agentType, \\\"dyna\\\") == 0){\\n k = std::atoi(optarg);\\n cout << \\\"k: \\\" << k << endl;\\n } else {\\n cout << \\\"--k is only a valid option for the Dyna agent\\\" << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 'f':\\n filename = optarg;\\n cout << \\\"policy filename: \\\" << filename << endl;\\n break;\\n\\n case 'a':\\n {\\n if (strcmp(agentType, \\\"qlearner\\\") == 0 || strcmp(agentType, \\\"dyna\\\") == 0 || strcmp(agentType, \\\"sarsa\\\") == 0){\\n alpha = std::atof(optarg);\\n cout << \\\"alpha: \\\" << alpha << endl;\\n } else {\\n cout << \\\"--alpha option is only valid for Q-Learning, Dyna, and Sarsa\\\" << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 'i':\\n {\\n if (strcmp(agentType, \\\"qlearner\\\") == 0 || strcmp(agentType, \\\"dyna\\\") == 0 || strcmp(agentType, \\\"sarsa\\\") == 0){\\n initialvalue = std::atof(optarg);\\n cout << \\\"initialvalue: \\\" << initialvalue << endl;\\n } else {\\n cout << \\\"--initialvalue option is only valid for Q-Learning, Dyna, and Sarsa\\\" << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 'r':\\n {\\n actrateChanged = true;\\n if (strcmp(agentType, \\\"texplore\\\") == 0 || strcmp(agentType, \\\"modelbased\\\") == 0 || strcmp(agentType, \\\"rmax\\\") == 0){\\n actrate = std::atof(optarg);\\n cout << \\\"actrate: \\\" << actrate << endl;\\n } else {\\n cout << \\\"Model-free methods do not require an action rate\\\" << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 'l':\\n {\\n lambdaChanged = true;\\n if (strcmp(agentType, \\\"texplore\\\") == 0 || strcmp(agentType, \\\"modelbased\\\") == 0 || strcmp(agentType, \\\"rmax\\\") == 0 || strcmp(agentType, \\\"sarsa\\\") == 0){\\n lambda = std::atof(optarg);\\n cout << \\\"lambda: \\\" << lambda << endl;\\n } else {\\n cout << \\\"--lambda option is invalid for this agent: \\\" << agentType << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 'm':\\n {\\n mChanged = true;\\n if (strcmp(agentType, \\\"texplore\\\") == 0 || strcmp(agentType, \\\"modelbased\\\") == 0 || strcmp(agentType, \\\"rmax\\\") == 0){\\n M = std::atoi(optarg);\\n cout << \\\"M: \\\" << M << endl;\\n } else {\\n cout << \\\"--M option only useful for model-based agents, not \\\" << agentType << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 'o':\\n {\\n if (strcmp(agentType, \\\"texplore\\\") == 0 || strcmp(agentType, \\\"modelbased\\\") == 0 || strcmp(agentType, \\\"rmax\\\") == 0){\\n if (strcmp(optarg, \\\"tabular\\\") == 0) modelType = RMAX;\\n else if (strcmp(optarg, \\\"tree\\\") == 0) modelType = C45TREE;\\n else if (strcmp(optarg, \\\"texplore\\\") == 0) modelType = C45TREE;\\n else if (strcmp(optarg, \\\"c45tree\\\") == 0) modelType = C45TREE;\\n else if (strcmp(optarg, \\\"m5tree\\\") == 0) modelType = M5ALLMULTI;\\n if (strcmp(agentType, \\\"rmax\\\") == 0 && modelType != RMAX){\\n cout << \\\"R-Max should use tabular model\\\" << endl;\\n exit(-1);\\n }\\n } else {\\n cout << \\\"Model-free methods do not need a model, --model option does nothing for this agent type\\\" << endl;\\n exit(-1);\\n }\\n cout << \\\"model: \\\" << modelNames[modelType] << endl;\\n break;\\n }\\n\\n case 'x':\\n {\\n if (strcmp(optarg, \\\"unknown\\\") == 0) exploreType = EXPLORE_UNKNOWN;\\n else if (strcmp(optarg, \\\"greedy\\\") == 0) exploreType = GREEDY;\\n else if (strcmp(optarg, \\\"epsilongreedy\\\") == 0) exploreType = EPSILONGREEDY;\\n else if (strcmp(optarg, \\\"unvisitedstates\\\") == 0) exploreType = UNVISITED_BONUS;\\n else if (strcmp(optarg, \\\"unvisitedactions\\\") == 0) exploreType = UNVISITED_ACT_BONUS;\\n else if (strcmp(optarg, \\\"variancenovelty\\\") == 0) exploreType = DIFF_AND_NOVEL_BONUS;\\n if (strcmp(agentType, \\\"rmax\\\") == 0 && exploreType != EXPLORE_UNKNOWN){\\n cout << \\\"R-Max should use \\\\\\\"--explore unknown\\\\\\\" exploration\\\" << endl;\\n exit(-1);\\n }\\n else if (strcmp(agentType, \\\"texplore\\\") != 0 && strcmp(agentType, \\\"modelbased\\\") != 0 && strcmp(agentType, \\\"rmax\\\") != 0 && (exploreType != GREEDY && exploreType != EPSILONGREEDY)) {\\n cout << \\\"Model free methods must use either greedy or epsilon-greedy exploration!\\\" << endl;\\n exploreType = EPSILONGREEDY;\\n exit(-1);\\n }\\n cout << \\\"explore: \\\" << exploreNames[exploreType] << endl;\\n break;\\n }\\n\\n case 'p':\\n {\\n if (strcmp(optarg, \\\"vi\\\") == 0) plannerType = VALUE_ITERATION;\\n else if (strcmp(optarg, \\\"valueiteration\\\") == 0) plannerType = VALUE_ITERATION;\\n else if (strcmp(optarg, \\\"policyiteration\\\") == 0) plannerType = POLICY_ITERATION;\\n else if (strcmp(optarg, \\\"pi\\\") == 0) plannerType = POLICY_ITERATION;\\n else if (strcmp(optarg, \\\"sweeping\\\") == 0) plannerType = PRI_SWEEPING;\\n else if (strcmp(optarg, \\\"prioritizedsweeping\\\") == 0) plannerType = PRI_SWEEPING;\\n else if (strcmp(optarg, \\\"uct\\\") == 0) plannerType = ET_UCT_ACTUAL;\\n else if (strcmp(optarg, \\\"paralleluct\\\") == 0) plannerType = PAR_ETUCT_ACTUAL;\\n else if (strcmp(optarg, \\\"realtimeuct\\\") == 0) plannerType = PAR_ETUCT_ACTUAL;\\n else if (strcmp(optarg, \\\"realtime-uct\\\") == 0) plannerType = PAR_ETUCT_ACTUAL;\\n else if (strcmp(optarg, \\\"parallel-uct\\\") == 0) plannerType = PAR_ETUCT_ACTUAL;\\n else if (strcmp(optarg, \\\"delayeduct\\\") == 0) plannerType = POMDP_ETUCT;\\n else if (strcmp(optarg, \\\"delayed-uct\\\") == 0) plannerType = POMDP_ETUCT;\\n else if (strcmp(optarg, \\\"delayedparalleluct\\\") == 0) plannerType = POMDP_PAR_ETUCT;\\n else if (strcmp(optarg, \\\"delayed-parallel-uct\\\") == 0) plannerType = POMDP_PAR_ETUCT;\\n if (strcmp(agentType, \\\"texplore\\\") != 0 && strcmp(agentType, \\\"modelbased\\\") != 0 && strcmp(agentType, \\\"rmax\\\") != 0){\\n cout << \\\"Model-free methods do not require planners, --planner option does nothing with this agent\\\" << endl;\\n exit(-1);\\n }\\n if (strcmp(agentType, \\\"rmax\\\") == 0 && plannerType != VALUE_ITERATION){\\n cout << \\\"Typical implementation of R-Max would use value iteration, but another planner type is ok\\\" << endl;\\n }\\n cout << \\\"planner: \\\" << plannerNames[plannerType] << endl;\\n break;\\n }\\n\\n case 'c':\\n {\\n if (strcmp(agentType, \\\"texplore\\\") == 0 || strcmp(agentType, \\\"modelbased\\\") == 0){\\n if (strcmp(optarg, \\\"average\\\") == 0) predType = AVERAGE;\\n else if (strcmp(optarg, \\\"weighted\\\") == 0) predType = WEIGHTAVG;\\n else if (strcmp(optarg, \\\"best\\\") == 0) predType = BEST;\\n else if (strcmp(optarg, \\\"separate\\\") == 0) predType = SEPARATE;\\n cout << \\\"predType: \\\" << comboNames[predType] << endl;\\n } else {\\n cout << \\\"--combo is an invalid option for agent: \\\" << agentType << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case '#':\\n {\\n if (strcmp(agentType, \\\"texplore\\\") == 0 || strcmp(agentType, \\\"modelbased\\\") == 0){\\n nmodels = std::atoi(optarg);\\n cout << \\\"nmodels: \\\" << nmodels << endl;\\n } else {\\n cout << \\\"--nmodels is an invalid option for agent: \\\" << agentType << endl;\\n exit(-1);\\n }\\n if (nmodels < 1){\\n cout << \\\"nmodels must be > 0\\\" << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 't':\\n {\\n if (strcmp(agentType, \\\"texplore\\\") == 0 || strcmp(agentType, \\\"modelbased\\\") == 0){\\n reltrans = true;\\n cout << \\\"reltrans: \\\" << reltrans << endl;\\n } else {\\n cout << \\\"--reltrans is an invalid option for agent: \\\" << agentType << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case '0':\\n {\\n if (strcmp(agentType, \\\"texplore\\\") == 0 || strcmp(agentType, \\\"modelbased\\\") == 0){\\n reltrans = false;\\n cout << \\\"reltrans: \\\" << reltrans << endl;\\n } else {\\n cout << \\\"--abstrans is an invalid option for agent: \\\" << agentType << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 's':\\n seed = std::atoi(optarg);\\n cout << \\\"seed: \\\" << seed << endl;\\n break;\\n\\n case 'q':\\n // already processed this one\\n cout << \\\"agent: \\\" << agentType << endl;\\n break;\\n\\n case 'd':\\n PRINTS = true;\\n break;\\n\\n case 'w':\\n nstates = std::atoi(optarg);\\n cout << \\\"nstates for discretization: \\\" << nstates << endl;\\n break;\\n\\n case 'v':\\n case 'b':\\n {\\n bvnChanged = true;\\n if (strcmp(agentType, \\\"texplore\\\") == 0){\\n v = std::atof(optarg);\\n cout << \\\"v coefficient (variance bonus): \\\" << v << endl;\\n }\\n else {\\n cout << \\\"--v and --b are invalid options for agent: \\\" << agentType << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 'n':\\n {\\n bvnChanged = true;\\n if (strcmp(agentType, \\\"texplore\\\") == 0){\\n n = std::atof(optarg);\\n cout << \\\"n coefficient (novelty bonus): \\\" << n << endl;\\n }\\n else {\\n cout << \\\"--n is an invalid option for agent: \\\" << agentType << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 2:\\n stochastic = false;\\n cout << \\\"stochastic: \\\" << stochastic << endl;\\n break;\\n\\n case 11:\\n {\\n if (strcmp(envType, \\\"fuelworld\\\") == 0){\\n highvar = true;\\n cout << \\\"fuel world fuel cost variation: \\\" << highvar << endl;\\n } else {\\n cout << \\\"--highvar is only a valid option for the fuelworld domain.\\\" << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 3:\\n stochastic = true;\\n cout << \\\"stochastic: \\\" << stochastic << endl;\\n break;\\n\\n case 4:\\n {\\n if (strcmp(envType, \\\"mcar\\\") == 0 || strcmp(envType, \\\"tworooms\\\") == 0){\\n delay = std::atoi(optarg);\\n cout << \\\"delay steps: \\\" << delay << endl;\\n } else {\\n cout << \\\"--delay option is only valid for the mcar and tworooms domains\\\" << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 5:\\n {\\n if (strcmp(envType, \\\"stocks\\\") == 0){\\n nsectors = std::atoi(optarg);\\n cout << \\\"nsectors: \\\" << nsectors << endl;\\n } else {\\n cout << \\\"--nsectors option is only valid for the stocks domain\\\" << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 6:\\n {\\n if (strcmp(envType, \\\"stocks\\\") == 0){\\n nstocks = std::atoi(optarg);\\n cout << \\\"nstocks: \\\" << nstocks << endl;\\n } else {\\n cout << \\\"--nstocks option is only valid for the stocks domain\\\" << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 7:\\n {\\n if (strcmp(envType, \\\"car2to7\\\") == 0 || strcmp(envType, \\\"car7to2\\\") == 0 || strcmp(envType, \\\"carrandom\\\") == 0){\\n lag = true;\\n cout << \\\"lag: \\\" << lag << endl;\\n } else {\\n cout << \\\"--lag option is only valid for car velocity tasks\\\" << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 8:\\n {\\n if (strcmp(envType, \\\"car2to7\\\") == 0 || strcmp(envType, \\\"car7to2\\\") == 0 || strcmp(envType, \\\"carrandom\\\") == 0){\\n lag = false;\\n cout << \\\"lag: \\\" << lag << endl;\\n } else {\\n cout << \\\"--nolag option is only valid for car velocity tasks\\\" << endl;\\n exit(-1);\\n }\\n break;\\n }\\n\\n case 1:\\n // already processed this one\\n cout << \\\"env: \\\" << envType << endl;\\n break;\\n\\n case 12:\\n NUMEPISODES = std::atoi(optarg);\\n cout << \\\"Num Episodes: \\\" << NUMEPISODES << endl;\\n break;\\n\\n case 'h':\\n case '?':\\n case 0:\\n default:\\n displayHelp();\\n break;\\n }\\n }\\n\\n // default back to greedy if no coefficients\\n if (exploreType == DIFF_AND_NOVEL_BONUS && v == 0 && n == 0)\\n exploreType = GREEDY;\\n\\n // check for conflicting options\\n // changed epsilon but not doing epsilon greedy exploration\\n if (epsilonChanged && exploreType != EPSILONGREEDY){\\n cout << \\\"No reason to change epsilon when not using epsilon-greedy exploration\\\" << endl;\\n exit(-1);\\n }\\n\\n // set history value but not doing uct w/history planner\\n if (history > 0 && (plannerType == VALUE_ITERATION || plannerType == POLICY_ITERATION || plannerType == PRI_SWEEPING)){\\n cout << \\\"No reason to set history higher than 0 if not using a UCT planner\\\" << endl;\\n exit(-1);\\n }\\n\\n // set action rate but not doing real-time planner\\n if (actrateChanged && (plannerType == VALUE_ITERATION || plannerType == POLICY_ITERATION || plannerType == PRI_SWEEPING)){\\n cout << \\\"No reason to set actrate if not using a UCT planner\\\" << endl;\\n exit(-1);\\n }\\n\\n // set lambda but not doing uct (lambda)\\n if (lambdaChanged && (strcmp(agentType, \\\"texplore\\\") == 0 || strcmp(agentType, \\\"modelbased\\\") == 0 || strcmp(agentType, \\\"rmax\\\") == 0) && (plannerType == VALUE_ITERATION || plannerType == POLICY_ITERATION || plannerType == PRI_SWEEPING)){\\n cout << \\\"No reason to set actrate if not using a UCT planner\\\" << endl;\\n exit(-1);\\n }\\n\\n // set n/v/b but not doing that diff_novel exploration\\n if (bvnChanged && exploreType != DIFF_AND_NOVEL_BONUS){\\n cout << \\\"No reason to set n or v if not doing variance & novelty exploration\\\" << endl;\\n exit(-1);\\n }\\n\\n // set combo other than best but only doing 1 model\\n if (predType != BEST && nmodels == 1){\\n cout << \\\"No reason to have model combo other than best with nmodels = 1\\\" << endl;\\n exit(-1);\\n }\\n\\n // set M but not doing explore unknown\\n if (mChanged && exploreType != EXPLORE_UNKNOWN){\\n cout << \\\"No reason to set M if not doing R-max style Explore Unknown exploration\\\" << endl;\\n exit(-1);\\n }\\n\\n if (PRINTS){\\n if (stochastic)\\n cout << \\\"Stohastic\\\\n\\\";\\n else\\n cout << \\\"Deterministic\\\\n\\\";\\n }\\n\\n Random rng(1 + seed);\\n\\n std::vector<int> statesPerDim;\\n\\n // Construct environment here.\\n Environment* e;\\n\\n if (strcmp(envType, \\\"cartpole\\\") == 0){\\n if (PRINTS) cout << \\\"Environment: Cart Pole\\\\n\\\";\\n e = new CartPole(rng, stochastic);\\n }\\n\\n else if (strcmp(envType, \\\"mcar\\\") == 0){\\n if (PRINTS) cout << \\\"Environment: Mountain Car\\\\n\\\";\\n e = new MountainCar(rng, stochastic, false, delay);\\n }\\n\\n // taxi\\n else if (strcmp(envType, \\\"taxi\\\") == 0){\\n if (PRINTS) cout << \\\"Environment: Taxi\\\\n\\\";\\n e = new Taxi(rng, stochastic);\\n }\\n\\n // Light World\\n else if (strcmp(envType, \\\"lightworld\\\") == 0){\\n if (PRINTS) cout << \\\"Environment: Light World\\\\n\\\";\\n e = new LightWorld(rng, stochastic, 4);\\n }\\n\\n // two rooms\\n else if (strcmp(envType, \\\"tworooms\\\") == 0){\\n if (PRINTS) cout << \\\"Environment: TwoRooms\\\\n\\\";\\n e = new TwoRooms(rng, stochastic, true, delay, false);\\n }\\n\\n // car vel, 2 to 7\\n else if (strcmp(envType, \\\"car2to7\\\") == 0){\\n if (PRINTS) cout << \\\"Environment: Car Velocity 2 to 7 m/s\\\\n\\\";\\n e = new RobotCarVel(rng, false, true, false, lag);\\n statesPerDim.resize(4,0);\\n statesPerDim[0] = 12;\\n statesPerDim[1] = 120;\\n statesPerDim[2] = 4;\\n statesPerDim[3] = 10;\\n MAXSTEPS = 100;\\n }\\n // car vel, 7 to 2\\n else if (strcmp(envType, \\\"car7to2\\\") == 0){\\n if (PRINTS) cout << \\\"Environment: Car Velocity 7 to 2 m/s\\\\n\\\";\\n e = new RobotCarVel(rng, false, false, false, lag);\\n statesPerDim.resize(4,0);\\n statesPerDim[0] = 12;\\n statesPerDim[1] = 120;\\n statesPerDim[2] = 4;\\n statesPerDim[3] = 10;\\n MAXSTEPS = 100;\\n }\\n // car vel, random vels\\n else if (strcmp(envType, \\\"carrandom\\\") == 0){\\n if (PRINTS) cout << \\\"Environment: Car Velocity Random Velocities\\\\n\\\";\\n e = new RobotCarVel(rng, true, false, false, lag);\\n statesPerDim.resize(4,0);\\n statesPerDim[0] = 12;\\n statesPerDim[1] = 48;\\n statesPerDim[2] = 4;\\n statesPerDim[3] = 10;\\n MAXSTEPS = 100;\\n }\\n\\n // four rooms\\n else if (strcmp(envType, \\\"fourrooms\\\") == 0){\\n if (PRINTS) cout << \\\"Environment: FourRooms\\\\n\\\";\\n e = new FourRooms(rng, stochastic, true, false);\\n }\\n\\n // four rooms with energy level\\n else if (strcmp(envType, \\\"energy\\\") == 0){\\n if (PRINTS) cout << \\\"Environment: EnergyRooms\\\\n\\\";\\n e = new EnergyRooms(rng, stochastic, true, false);\\n }\\n\\n // gridworld with fuel (fuel stations on top and bottom with random costs)\\n else if (strcmp(envType, \\\"fuelworld\\\") == 0){\\n if (PRINTS) cout << \\\"Environment: FuelWorld\\\\n\\\";\\n e = new FuelRooms(rng, highvar, stochastic);\\n }\\n\\n // stocks\\n else if (strcmp(envType, \\\"stocks\\\") == 0){\\n if (PRINTS) cout << \\\"Enironment: Stocks with \\\" << nsectors\\n << \\\" sectors and \\\" << nstocks << \\\" stocks\\\\n\\\";\\n e = new Stocks(rng, stochastic, nsectors, nstocks);\\n }\\n\\n else {\\n std::cerr << \\\"Invalid env type\\\" << endl;\\n exit(-1);\\n }\\n\\n const int numactions = e->getNumActions(); // Most agents will need this?\\n\\n std::vector<float> minValues;\\n std::vector<float> maxValues;\\n e->getMinMaxFeatures(&minValues, &maxValues);\\n bool episodic = e->isEpisodic();\\n\\n cout << \\\"Environment is \\\";\\n if (!episodic) cout << \\\"NOT \\\";\\n cout << \\\"episodic.\\\" << endl;\\n\\n // lets just check this for now\\n for (unsigned i = 0; i < minValues.size(); i++){\\n if (PRINTS) cout << \\\"Feat \\\" << i << \\\" min: \\\" << minValues[i]\\n << \\\" max: \\\" << maxValues[i] << endl;\\n }\\n\\n // get max/min reward for the domain\\n float rMax = 0.0;\\n float rMin = -1.0;\\n\\n e->getMinMaxReward(&rMin, &rMax);\\n float rRange = rMax - rMin;\\n if (PRINTS) cout << \\\"Min Reward: \\\" << rMin\\n << \\\", Max Reward: \\\" << rMax << endl;\\n\\n // set rmax as a bonus for certain exploration types\\n if (rMax <= 0.0 && (exploreType == TWO_MODE_PLUS_R ||\\n exploreType == CONTINUOUS_BONUS_R ||\\n exploreType == CONTINUOUS_BONUS ||\\n exploreType == THRESHOLD_BONUS_R)){\\n rMax = 1.0;\\n }\\n\\n\\n float rsum = 0;\\n\\n if (statesPerDim.size() == 0){\\n cout << \\\"set statesPerDim to \\\" << nstates << \\\" for all dim\\\" << endl;\\n statesPerDim.resize(minValues.size(), nstates);\\n }\\n\\n for (unsigned j = 0; j < NUMTRIALS; ++j) {\\n\\n // Construct agent here.\\n Agent* agent;\\n\\n if (strcmp(agentType, \\\"qlearner\\\") == 0){\\n if (PRINTS) cout << \\\"Agent: QLearner\\\" << endl;\\n agent = new QLearner(numactions,\\n discountfactor,\\n initialvalue, //0.0, // initialvalue\\n alpha, // alpha\\n epsilon, // epsilon\\n rng);\\n }\\n\\n else if (strcmp(agentType, \\\"dyna\\\") == 0){\\n if (PRINTS) cout << \\\"Agent: Dyna\\\" << endl;\\n agent = new Dyna(numactions,\\n discountfactor,\\n initialvalue, //0.0, // initialvalue\\n alpha, // alpha\\n k, // k\\n epsilon, // epsilon\\n rng);\\n }\\n\\n else if (strcmp(agentType, \\\"sarsa\\\") == 0){\\n if (PRINTS) cout << \\\"Agent: SARSA\\\" << endl;\\n agent = new Sarsa(numactions,\\n discountfactor,\\n initialvalue, //0.0, // initialvalue\\n alpha, // alpha\\n epsilon, // epsilon\\n lambda,\\n rng);\\n }\\n\\n else if (strcmp(agentType, \\\"modelbased\\\") == 0 || strcmp(agentType, \\\"rmax\\\") || strcmp(agentType, \\\"texplore\\\")){\\n if (PRINTS) cout << \\\"Agent: Model Based\\\" << endl;\\n agent = new ModelBasedAgent(numactions,\\n discountfactor,\\n rMax, rRange,\\n modelType,\\n exploreType,\\n predType,\\n nmodels,\\n plannerType,\\n epsilon, // epsilon\\n lambda,\\n (1.0/actrate), //0.1, //0.1, //0.01, // max time\\n M,\\n minValues, maxValues,\\n statesPerDim,//0,\\n history, v, n,\\n deptrans, reltrans, featPct, stochastic, episodic,\\n rng);\\n }\\n\\n else if (strcmp(agentType, \\\"savedpolicy\\\") == 0){\\n if (PRINTS) cout << \\\"Agent: Saved Policy\\\" << endl;\\n agent = new SavedPolicy(numactions,filename);\\n }\\n\\n else {\\n std::cerr << \\\"ERROR: Invalid agent type\\\" << endl;\\n exit(-1);\\n }\\n\\n // start discrete agent if we're discretizing (if nstates > 0 and not agent type 'c')\\n int totalStates = 1;\\n Agent* a2 = agent;\\n // not for model based when doing continuous model\\n if (nstates > 0 && (modelType != M5ALLMULTI || strcmp(agentType, \\\"qlearner\\\") == 0)){\\n int totalStates = powf(nstates,minValues.size());\\n if (PRINTS) cout << \\\"Discretize with \\\" << nstates << \\\", total: \\\" << totalStates << endl;\\n agent = new DiscretizationAgent(nstates, a2,\\n minValues, maxValues, PRINTS);\\n }\\n else {\\n totalStates = 1;\\n for (unsigned i = 0; i < minValues.size(); i++){\\n int range = 1+maxValues[i] - minValues[i];\\n totalStates *= range;\\n }\\n if (PRINTS) cout << \\\"No discretization, total: \\\" << totalStates << endl;\\n }\\n\\n // before we start, seed the agent with some experiences\\n agent->seedExp(e->getSeedings());\\n\\n // STEP BY STEP DOMAIN\\n if (!episodic){\\n\\n // performance tracking\\n float sum = 0;\\n int steps = 0;\\n float trialSum = 0.0;\\n\\n int a = 0;\\n float r = 0;\\n\\n //////////////////////////////////\\n // non-episodic\\n //////////////////////////////////\\n for (unsigned i = 0; i < NUMEPISODES; ++i){\\n\\n std::vector<float> es = e->sensation();\\n\\n // first step\\n if (i == 0){\\n\\n // first action\\n a = agent->first_action(es);\\n r = e->apply(a);\\n\\n } else {\\n // next action\\n a = agent->next_action(r, es);\\n r = e->apply(a);\\n }\\n\\n // update performance\\n sum += r;\\n ++steps;\\n\\n std::cerr << r << endl;\\n\\n }\\n ///////////////////////////////////\\n\\n rsum += sum;\\n trialSum += sum;\\n if (PRINTS) cout << \\\"Rsum(trial \\\" << j << \\\"): \\\" << trialSum << \\\" Avg: \\\"\\n << (rsum / (float)(j+1))<< endl;\\n\\n }\\n\\n // EPISODIC DOMAINS\\n else {\\n\\n //////////////////////////////////\\n // episodic\\n //////////////////////////////////\\n for (unsigned i = 0; i < NUMEPISODES; ++i) {\\n\\n // performance tracking\\n float sum = 0;\\n int steps = 0;\\n\\n // first action\\n std::vector<float> es = e->sensation();\\n int a = agent->first_action(es);\\n float r = e->apply(a);\\n\\n // update performance\\n sum += r;\\n ++steps;\\n\\n while (!e->terminal() && steps < MAXSTEPS) {\\n\\n // perform an action\\n es = e->sensation();\\n a = agent->next_action(r, es);\\n r = e->apply(a);\\n\\n // update performance info\\n sum += r;\\n ++steps;\\n\\n }\\n\\n // terminal/last state\\n if (e->terminal()){\\n agent->last_action(r);\\n }else{\\n agent->next_action(r, e->sensation());\\n }\\n\\n e->reset();\\n std::cerr << sum << endl;\\n\\n rsum += sum;\\n\\n }\\n\\n }\\n\\n if (NUMTRIALS > 1) delete agent;\\n\\n }\\n\\n if (PRINTS) cout << \\\"Avg Rsum: \\\" << (rsum / (float)NUMTRIALS) << endl;\\n\\n} // end main\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7760140895843506,\n \"alphanum_fraction\": 0.7760140895843506,\n \"avg_line_length\": 42.61538314819336,\n \"blob_id\": \"20bb18408ba86202ee274bd4811ec3400c045948\",\n \"content_id\": \"6e731157013cfbae7994d4936bc0117d2417673a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 567,\n \"license_type\": \"no_license\",\n \"max_line_length\": 90,\n \"num_lines\": 13,\n \"path\": \"/build/rl-texplore-ros-pkg-master/src/rl_msgs/CMakeFiles/rl_msgs_generate_messages_cpp.dir/cmake_clean.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"file(REMOVE_RECURSE\\n \\\"CMakeFiles/rl_msgs_generate_messages_cpp\\\"\\n \\\"/home/justin/ros_test/devel/include/rl_msgs/RLEnvSeedExperience.h\\\"\\n \\\"/home/justin/ros_test/devel/include/rl_msgs/RLExperimentInfo.h\\\"\\n \\\"/home/justin/ros_test/devel/include/rl_msgs/RLEnvDescription.h\\\"\\n \\\"/home/justin/ros_test/devel/include/rl_msgs/RLStateReward.h\\\"\\n \\\"/home/justin/ros_test/devel/include/rl_msgs/RLAction.h\\\"\\n)\\n\\n# Per-language clean rules from dependency scanning.\\nforeach(lang )\\n include(CMakeFiles/rl_msgs_generate_messages_cpp.dir/cmake_clean_${lang}.cmake OPTIONAL)\\nendforeach()\\n\"\n },\n {\n \"alpha_fraction\": 0.5642428398132324,\n \"alphanum_fraction\": 0.5697131156921387,\n \"avg_line_length\": 22.276397705078125,\n \"blob_id\": \"a67412fc4e1ee8aa822993c1fc396599337c022e\",\n \"content_id\": \"4ae503c12eca89664f4a8e78a75e6ca94c30ec4b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 7495,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 322,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Agent/Dyna.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include <rl_agent/Dyna.hh>\\n#include <algorithm>\\n\\n#include <sys/time.h>\\n\\n\\nDyna::Dyna(int numactions, float gamma,\\n float initialvalue, float alpha, int k, float ep,\\n\\t\\t Random rng):\\n numactions(numactions), gamma(gamma),\\n initialvalue(initialvalue), alpha(alpha), k(k),\\n rng(rng), currentq(NULL), laststate(NULL), lastact(0)\\n{\\n\\n epsilon = ep;\\n ACTDEBUG = false; //true; //false;\\n cout << \\\"Dyna agent with k:\\\" << k << endl;\\n\\n}\\n\\nDyna::~Dyna() {}\\n\\nint Dyna::first_action(const std::vector<float> &s) {\\n\\n if (ACTDEBUG){\\n cout << \\\"First - in state: \\\";\\n printState(s);\\n cout << endl;\\n }\\n\\n return getBestAction(s);\\n\\n}\\n\\nint Dyna::getBestAction(const std::vector<float> &s){\\n //cout << \\\"get best action\\\" << endl;\\n\\n // for some amount of time, update based on randomly sampled experiences\\n int numExp = (int)experiences.size();\\n for (int i = 0; i < k && numExp > 0; i++){\\n \\n // update from randoml sampled action\\n int exp = 0;\\n if (numExp > 1)\\n exp = rng.uniformDiscrete(0, numExp-1);\\n //cout << count << \\\" Update exp \\\" << exp << endl;\\n\\n dynaExperience e = experiences[exp];\\n\\n std::vector<float> &Q_s = Q[e.s];\\n if (e.term){\\n Q_s[e.a] += alpha * (e.r - Q_s[e.a]);\\n } else {\\n std::vector<float> &Q_next = Q[e.next];\\n const std::vector<float>::iterator max =\\n random_max_element(Q_next.begin(), Q_next.end());\\n Q_s[e.a] += alpha * (e.r + (gamma * *max) - Q_s[e.a]);\\n }\\n\\n }\\n\\n\\n // then do normal action selection\\n // Get action values\\n state_t st = canonicalize(s);\\n std::vector<float> &Q_s = Q[st];\\n\\n // Choose an action\\n const std::vector<float>::iterator a =\\n rng.uniform() < epsilon\\n ? Q_s.begin() + rng.uniformDiscrete(0, numactions - 1) // Choose randomly\\n : random_max_element(Q_s.begin(), Q_s.end()); // Choose maximum\\n\\n // Store location to update value later\\n currentq = &*a;\\n laststate = st;\\n lastact = a - Q_s.begin();\\n\\n if (ACTDEBUG){\\n cout << \\\" act: \\\" << (a-Q_s.begin()) << \\\" val: \\\" << *a << endl;\\n for (int iAct = 0; iAct < numactions; iAct++){\\n cout << \\\" Action: \\\" << iAct \\n\\t << \\\" val: \\\" << Q_s[iAct] << endl;\\n }\\n cout << \\\"Took action \\\" << (a-Q_s.begin()) << \\\" from state \\\";\\n printState(s);\\n cout << endl;\\n }\\n\\n return a - Q_s.begin();\\n}\\n\\nvoid Dyna::addExperience(float r, state_t s, bool term){\\n\\n dynaExperience e;\\n e.s = laststate;\\n e.a = lastact;\\n e.next = s;\\n e.r = r;\\n e.term = term;\\n\\n experiences.push_back(e);\\n\\n}\\n\\nint Dyna::next_action(float r, const std::vector<float> &s) {\\n\\n if (ACTDEBUG){\\n cout << \\\"Next: got reward \\\" << r << \\\" in state: \\\";\\n printState(s);\\n cout << endl;\\n }\\n\\n state_t st = canonicalize(s);\\n\\n addExperience(r,st,false);\\n\\n // Get action values\\n std::vector<float> &Q_s = Q[st];\\n const std::vector<float>::iterator max =\\n random_max_element(Q_s.begin(), Q_s.end());\\n\\n // Update value of action just executed\\n *currentq += alpha * (r + gamma * (*max) - *currentq);\\n\\n return getBestAction(s);\\n\\n}\\n\\n\\n\\n\\nvoid Dyna::last_action(float r) {\\n\\n if (ACTDEBUG){\\n cout << \\\"Last: got reward \\\" << r << endl;\\n }\\n\\n addExperience(r,NULL,true);\\n\\n *currentq += alpha * (r - *currentq);\\n currentq = NULL;\\n laststate = NULL;\\n}\\n\\nDyna::state_t Dyna::canonicalize(const std::vector<float> &s) {\\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\\n statespace.insert(s);\\n state_t retval = &*result.first; // Dereference iterator then get pointer \\n if (result.second) { // s is new, so initialize Q(s,a) for all a\\n std::vector<float> &Q_s = Q[retval];\\n Q_s.resize(numactions,initialvalue);\\n }\\n return retval; \\n}\\n\\n\\n\\n std::vector<float>::iterator\\nDyna::random_max_element(\\n\\t\\t\\t std::vector<float>::iterator start,\\n\\t\\t\\t std::vector<float>::iterator end) {\\n\\n std::vector<float>::iterator max =\\n std::max_element(start, end);\\n int n = std::count(max, end, *max);\\n if (n > 1) {\\n n = rng.uniformDiscrete(1, n);\\n while (n > 1) {\\n max = std::find(max + 1, end, *max);\\n --n;\\n }\\n }\\n return max;\\n}\\n\\n\\n\\n\\nvoid Dyna::setDebug(bool d){\\n ACTDEBUG = d;\\n}\\n\\n\\nvoid Dyna::printState(const std::vector<float> &s){\\n for (unsigned j = 0; j < s.size(); j++){\\n cout << s[j] << \\\", \\\";\\n }\\n}\\n\\n\\n\\nvoid Dyna::seedExp(std::vector<experience> seeds){\\n\\n // for each seeding experience, update our model\\n for (unsigned i = 0; i < seeds.size(); i++){\\n experience e = seeds[i];\\n \\n laststate = canonicalize(e.s);\\n lastact = e.act;\\n state_t st = canonicalize(e.next);\\n std::vector<float> &Q_s = Q[laststate];\\n std::vector<float> &Q_next = Q[st];\\n \\n // add experience\\n addExperience(e.reward,st,e.terminal);\\n\\n // get max value of next state\\n const std::vector<float>::iterator max =\\n random_max_element(Q_next.begin(), Q_next.end());\\n\\n // Get q value for action taken\\n const std::vector<float>::iterator a = Q_s.begin() + e.act;\\n currentq = &*a;\\n\\n // Update value of action just executed\\n *currentq += alpha * (e.reward + gamma * (*max) - *currentq);\\n\\n \\n /*\\n cout << \\\"Seeding with experience \\\" << i << endl;\\n cout << \\\"last: \\\" << (e.s)[0] << \\\", \\\" << (e.s)[1] << \\\", \\\" \\n\\t << (e.s)[2] << endl;\\n cout << \\\"act: \\\" << e.act << \\\" r: \\\" << e.reward << endl;\\n cout << \\\"next: \\\" << (e.next)[0] << \\\", \\\" << (e.next)[1] << \\\", \\\" \\n\\t << (e.next)[2] << \\\", \\\" << e.terminal << endl;\\n cout << \\\"Q: \\\" << *currentq << \\\" max: \\\" << *max << endl;\\n */\\n\\n }\\n\\n\\n}\\n\\nvoid Dyna::logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax){\\n std::vector<float> s;\\n s.resize(2, 0.0);\\n for (int i = xmin ; i < xmax; i++){\\n for (int j = ymin; j < ymax; j++){\\n s[0] = j;\\n s[1] = i;\\n std::vector<float> &Q_s = Q[canonicalize(s)];\\n const std::vector<float>::iterator max =\\n random_max_element(Q_s.begin(), Q_s.end());\\n *of << (*max) << \\\",\\\";\\n }\\n }\\n}\\n\\n\\nfloat Dyna::getValue(std::vector<float> state){\\n\\n state_t s = canonicalize(state);\\n\\n // Get Q values\\n std::vector<float> &Q_s = Q[s];\\n\\n // Choose an action\\n const std::vector<float>::iterator a =\\n random_max_element(Q_s.begin(), Q_s.end()); // Choose maximum\\n\\n // Get avg value\\n float valSum = 0.0;\\n float cnt = 0;\\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n state_t s = canonicalize(*i);\\n\\n // get state's info\\n std::vector<float> &Q_s = Q[s];\\n \\n for (int j = 0; j < numactions; j++){\\n valSum += Q_s[j];\\n cnt++;\\n }\\n }\\n\\n cout << \\\"Avg Value: \\\" << (valSum / cnt) << endl;\\n\\n return *a;\\n}\\n\\n\\nvoid Dyna::savePolicy(const char* filename){\\n\\n ofstream policyFile(filename, ios::out | ios::binary | ios::trunc);\\n\\n // first part, save the vector size\\n std::set< std::vector<float> >::iterator i = statespace.begin();\\n int fsize = (*i).size();\\n policyFile.write((char*)&fsize, sizeof(int));\\n\\n // save numactions\\n policyFile.write((char*)&numactions, sizeof(int));\\n\\n // go through all states, and save Q values\\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n state_t s = canonicalize(*i);\\n std::vector<float> *Q_s = &(Q[s]);\\n\\n // save state\\n policyFile.write((char*)&((*i)[0]), sizeof(float)*fsize);\\n\\n // save q-values\\n policyFile.write((char*)&((*Q_s)[0]), sizeof(float)*numactions);\\n\\n }\\n\\n policyFile.close();\\n}\\n\\n\\n\\ndouble Dyna::getSeconds(){\\n struct timezone tz;\\n timeval timeT;\\n gettimeofday(&timeT, &tz);\\n return timeT.tv_sec + (timeT.tv_usec / 1000000.0);\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7935222387313843,\n \"alphanum_fraction\": 0.7935222387313843,\n \"avg_line_length\": 29.875,\n \"blob_id\": \"7afb77fcfc7ecd248f0b1ace3a00fd25bb65757b\",\n \"content_id\": \"44e47469bf420c3692ddd7adbecf4ccea2a23e05\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 247,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 8,\n \"path\": \"/src/mantis_model/CMakeFiles/clean_test_results_mantis_model.dir/cmake_clean.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"FILE(REMOVE_RECURSE\\n \\\"CMakeFiles/clean_test_results_mantis_model\\\"\\n)\\n\\n# Per-language clean rules from dependency scanning.\\nFOREACH(lang)\\n INCLUDE(CMakeFiles/clean_test_results_mantis_model.dir/cmake_clean_${lang}.cmake OPTIONAL)\\nENDFOREACH(lang)\\n\"\n },\n {\n \"alpha_fraction\": 0.7027310729026794,\n \"alphanum_fraction\": 0.7037814855575562,\n \"avg_line_length\": 23.10126495361328,\n \"blob_id\": \"060cfbd92583345e52d7504116e2260159243c8f\",\n \"content_id\": \"90f04235c922975597781d613af1a9a55ec30dc4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 1904,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 79,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/include/rl_env/MountainCar.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file MountainCar.hh\\n Defines the Mountain Car domain, with possible action delays or linearized \\n transition dynamics.\\n \\\\author Todd Hester\\n*/\\n\\n#ifndef _MOUNTAINCAR_H_\\n#define _MOUNTAINCAR_H_\\n\\n#include <set>\\n#include <deque>\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n\\n/** This class defines the Mountain Car domain, with possible action delays or linearized \\n transition dynamics.\\n*/\\nclass MountainCar: public Environment {\\npublic:\\n\\n /** Creates a deterministic MountainCar domain.\\n \\\\param rand Random number generator used solely for random\\n initial states. \\n */\\n MountainCar(Random &rand);\\n\\n /** Creates a Mountain Car domain.\\n \\\\param rand Random number generator\\n \\\\param stochastic if transitions are noisy\\n \\\\param lin create linearized transition dynamics\\n \\\\param delay # of steps to delay state observations\\n */\\n MountainCar(Random &rand, bool stochastic, bool lin, int delay);\\n\\n virtual ~MountainCar();\\n\\n virtual const std::vector<float> &sensation() const;\\n virtual float apply(int action);\\n\\n virtual bool terminal() const;\\n virtual void reset();\\n\\n virtual int getNumActions();\\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\\n virtual void getMinMaxReward(float* minR, float* maxR);\\n\\n /** Set the state vector (for debug purposes) */\\n void setSensation(std::vector<float> newS);\\n\\n virtual std::vector<experience> getSeedings();\\n\\n /** Get an experience for the given state-action */\\n experience getExp(float s0, float s1, int a);\\n \\nprotected:\\n enum car_action_t {REVERSE, ZERO, FORWARD};\\n\\nprivate:\\n\\n std::deque<float> posHistory;\\n std::deque<float> velHistory;\\n\\n const bool noisy;\\n Random &rng;\\n\\n std::vector<float> s;\\n \\n float &pos;\\n float &vel;\\n const bool linear;\\n int delay;\\n\\n float reward();\\n\\n float bound(float val, float min, float max);\\n \\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.6909680962562561,\n \"alphanum_fraction\": 0.693329393863678,\n \"avg_line_length\": 28.982301712036133,\n \"blob_id\": \"8210ce515ff45afaf7800739bfd4c73463dce4e2\",\n \"content_id\": \"13644f1556a7d2388bb79d14ec0fdfdf00e2e1fa\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 3388,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 113,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/include/rl_env/taxi.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file taxi.hh\\n Defines the taxi domain, from:\\n Dietterich, \\\"The MAXQ method for hierarchical reinforcement learning,\\\" ICML 1998. \\n \\\\author Todd Hester\\n \\\\author Nick Jong\\n*/\\n\\n#ifndef _TAXI_H_\\n#define _TAXI_H_\\n\\n#include <set>\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n#include \\\"gridworld.hh\\\"\\n\\n/** This class defines the Taxi domain. */\\nclass Taxi: public Environment {\\npublic:\\n /** Creates a Taxi domain using the specified map.\\n \\\\param rand Random number generator to use.\\n \\\\param gridworld The map to use.\\n \\\\param stochastic Whether to use nondeterministic actions and\\n fickle passenger. */\\n Taxi(Random &rand, const Gridworld *gridworld, bool stochastic);\\n\\n /** Creates a deterministic Taxi domain.\\n \\\\param rand Random number generator used solely for random\\n initial states. */\\n Taxi(Random &rand);\\n\\n /** Creates a possibly noisy Taxi domain. \\n \\\\param rand Random number generator to use.\\n \\\\param stochastic Whether to use nondeterministic actions and\\n fickle passenger. \\n */\\n Taxi(Random &rand, bool stochastic);\\n\\n /** Creates a random Taxi domain of the given size. \\n \\\\param rand Random number generator to use.\\n \\\\param width width of grid\\n \\\\param height height of grid\\n \\\\param stochastic Whether to use nondeterministic actions and\\n fickle passenger. \\n */ \\n Taxi(Random &rand, unsigned width, unsigned height, bool stochastic);\\n\\n virtual ~Taxi();\\n\\n virtual const std::vector<float> &sensation() const;\\n virtual float apply(int action);\\n\\n virtual bool terminal() const;\\n virtual void reset();\\n virtual int getNumActions();\\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\\n virtual void getMinMaxReward(float* minR, float* maxR);\\n virtual std::vector<experience> getSeedings();\\n\\n /** Get an example experience for the given state-action. */\\n experience getExp(float s0, float s1, float s2, float s3, int a);\\n\\n /** Set the current state (for debug purposes) */\\n void setSensation(std::vector<float> newS);\\n\\nprotected:\\n typedef std::pair<float,float> coord_t;\\n enum taxi_action_t {NORTH, SOUTH, EAST, WEST, PICKUP, PUTDOWN};\\n\\n /** The default landmarks for the Taxi domain */\\n class DefaultLandmarks: public std::vector<coord_t> {\\n public:\\n DefaultLandmarks();\\n };\\n\\nprivate:\\n const Gridworld *const grid;\\n std::vector<coord_t> landmarks; // not const because of randomize_landmarks\\n const bool noisy;\\n Random &rng;\\n\\n std::vector<float> s;\\n bool fickle;\\n\\n float &ns;\\n float &ew;\\n float &pass;\\n float &dest;\\n\\n /** Create the default gridworld */\\n static const Gridworld *create_default_map();\\n\\n static const DefaultLandmarks defaultlandmarks;\\n\\n /** Corrupts a movement action.\\n \\\\param action The intended action\\n \\\\return The action actually executed */\\n taxi_action_t add_noise(taxi_action_t action);\\n\\n /** If the domain is noisy and the taxi has just taken its first\\n step since picking up the passenger, then potentially change the\\n destination. */\\n void apply_fickle_passenger();\\n\\n /** Randomly assigns the four landmarks to any four distinct\\n positions in the world. */\\n void randomize_landmarks();\\n\\n /** Randomly assigns the landmarks to positions near the corners of\\n the map. */\\n void randomize_landmarks_to_corners();\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.780281662940979,\n \"alphanum_fraction\": 0.7887324094772339,\n \"avg_line_length\": 49.85714340209961,\n \"blob_id\": \"73ef949951bbf549a8827dd540d945076e642dbc\",\n \"content_id\": \"0fcefe86ede41a869ec5bf01e39a61aaac40acff\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 355,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 7,\n \"path\": \"/src/roundbot_control/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"roundbot_control\\\")\\nset(roundbot_control_MAINTAINER \\\"micho <micho@todo.todo>\\\")\\nset(roundbot_control_DEPRECATED \\\"\\\")\\nset(roundbot_control_VERSION \\\"0.0.0\\\")\\nset(roundbot_control_BUILD_DEPENDS \\\"controller_interface\\\")\\nset(roundbot_control_RUN_DEPENDS \\\"controller_interface\\\" \\\"roscpp\\\")\\nset(roundbot_control_BUILDTOOL_DEPENDS \\\"catkin\\\" \\\"roscpp\\\")\"\n },\n {\n \"alpha_fraction\": 0.7864372730255127,\n \"alphanum_fraction\": 0.7894737124443054,\n \"avg_line_length\": 21.953489303588867,\n \"blob_id\": \"f40102b51609c4bb77de1cf551756045bd9477cc\",\n \"content_id\": \"969c03997aa2582936a87122295036b5140bb712\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 988,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 43,\n \"path\": \"/src/roundbot_control/CMakeLists.txt\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"cmake_minimum_required(VERSION 2.8.3)\\nproject(roundbot_control)\\n\\nfind_package(catkin REQUIRED COMPONENTS\\n roscpp\\n controller_interface\\n)\\n\\ncatkin_package(\\n INCLUDE_DIRS include\\n LIBRARIES wheel_speed_controller diff_controller\\n)\\n\\ninclude_directories(\\n include\\n ${catkin_INCLUDE_DIRS}\\n)\\n\\nadd_library(motor_sim src/MotorSim.cpp)\\ntarget_link_libraries(motor_sim\\n ${catkin_LIBRARIES}\\n)\\n\\nadd_library(wheel_speed_controller src/WheelSpeedController.cpp)\\ntarget_link_libraries(wheel_speed_controller\\n ${catkin_LIBRARIES}\\n motor_sim\\n)\\n\\nadd_library(diff_controller src/DiffController.cpp)\\ntarget_link_libraries(diff_controller\\n ${catkin_LIBRARIES}\\n motor_sim\\n)\\n\\ninstall(TARGETS wheel_speed_controller diff_controller\\n ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}\\n LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}\\n RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}\\n )\\n\\ninstall(FILES roundbot_control_plugins.xml\\n DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION})\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5598581433296204,\n \"alphanum_fraction\": 0.5926241278648376,\n \"avg_line_length\": 21.889610290527344,\n \"blob_id\": \"c5647db6f8ec35b5daa66efaf7e9a341c0ba2f56\",\n \"content_id\": \"c5b1032fd53f03afcc38767375a340a911efde58\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 7050,\n \"license_type\": \"no_license\",\n \"max_line_length\": 135,\n \"num_lines\": 308,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/RobotCarVel.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file RobotCarVel.cc\\n This domain is a simulation of velocity control for the Austin Robot \\n Technology autonomous vehicle. \\n This vehicle is described in:\\n Beeson et al, \\\"Multiagent Interactions in Urban Driving,\\\" Journal of Physical Agents, March 2008.\\n The velocity control task is described in:\\n Hester, Quinlan, and Stone, \\\"A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control\\\", arXiv 1105.1749, 2011.\\n \\\\author Todd Hester\\n*/\\n\\n#include <rl_env/RobotCarVel.hh>\\n\\n// normal: true values of each\\nRobotCarVel::RobotCarVel(Random &rand, bool randomVel, bool upVel, bool tenToSix, bool lag):\\n rng(rand),\\n s(4),\\n hidden(2),\\n targetVel(s[0]),\\n currVel(s[1]),\\n trueThrottle(hidden[0]),\\n trueBrake(hidden[1]),\\n throttleTarget(s[2]),\\n brakeTarget(s[3]),\\n randomVel(randomVel),\\n upVel(upVel),\\n tenToSix(tenToSix),\\n lag(lag)\\n{\\n reset();\\n}\\n \\n\\n\\nRobotCarVel::~RobotCarVel() { }\\n\\nconst std::vector<float> &RobotCarVel::sensation() const { \\n return s; \\n}\\n\\nfloat RobotCarVel::apply(int action) {\\n\\n float HZ = 10.0;\\n\\n actNum++;\\n\\n // figure out reward based on target/curr vel\\n float reward = -10.0 * fabs(currVel - targetVel);\\n\\n float throttleChangePct = 1.0;//0.9; //1.0;\\n float brakeChangePct = 1.0;//0.9; //1.0;\\n if (lag){\\n brakeChangePct = brakePosVel / HZ;\\n float brakeVelTarget = 3.0*(brakeTarget - trueBrake);\\n brakePosVel += (brakeVelTarget - brakePosVel) * 3.0 / HZ;\\n trueBrake += brakeChangePct;\\n } else {\\n trueBrake += (brakeTarget-trueBrake) * brakeChangePct;\\n }\\n trueBrake = bound(trueBrake, 0.0, 1.0);\\n\\n // figure out the change of true brake/throttle position based on last targets\\n trueThrottle += (throttleTarget-trueThrottle) * throttleChangePct;\\n trueThrottle = bound(trueThrottle, 0.0, 0.4);\\n\\n // figure out new velocity based on those positions \\n // from the stage simulation\\n float g = 9.81; // acceleration due to gravity\\n float throttle_accel = g;\\n float brake_decel = g;\\n float rolling_resistance = 0.01 * g;\\n float drag_coeff = 0.01;\\n float idle_accel = (rolling_resistance\\n + drag_coeff * 3.1 * 3.1);\\n float wind_resistance = drag_coeff * currVel * currVel;\\n float accel = (idle_accel\\n + trueThrottle * throttle_accel\\n - trueBrake * brake_decel\\n - rolling_resistance\\n - wind_resistance);\\n currVel += (accel / HZ);\\n currVel = bound(currVel, 0.0, 12.0);\\n \\n\\n // figure out action's adjustment to throttle/brake targets\\n if (action == THROTTLE_UP){\\n brakeTarget = 0.0;\\n if (throttleTarget < 0.4)\\n throttleTarget += 0.1;\\n }\\n else if (action == THROTTLE_DOWN){\\n brakeTarget = 0.0;\\n if (throttleTarget > 0.0)\\n throttleTarget -= 0.1;\\n }\\n else if (action == BRAKE_UP){\\n throttleTarget = 0.0;\\n if (brakeTarget < 1.0)\\n brakeTarget += 0.1;\\n }\\n else if (action == BRAKE_DOWN){\\n throttleTarget = 0.0;\\n if (brakeTarget > 0.0)\\n brakeTarget -= 0.1;\\n }\\n else if (action != NOTHING){\\n cout << \\\"invalid action \\\" << action << endl;\\n }\\n throttleTarget = bound(throttleTarget, 0.0, 0.4);\\n brakeTarget = bound(brakeTarget, 0.0, 1.0);\\n throttleTarget = 0.1 * (float)((int)(throttleTarget*10.0));\\n brakeTarget = 0.1 * (float)((int)(brakeTarget*10.0));\\n \\n /*\\n cout << action << \\\", throt: \\\" << throttleTarget << \\\", brake: \\\" << brakeTarget\\n << \\\", trueBrake: \\\" << trueBrake \\n << \\\", currVel: \\\" << currVel << \\\", reward: \\\" << reward << endl;\\n */\\n\\n return reward;\\n\\n}\\n\\n\\nbool RobotCarVel::terminal() const {\\n return false;\\n}\\n\\n\\n\\nvoid RobotCarVel::reset() {\\n\\n // for now\\n if (randomVel){\\n targetVel = rng.uniformDiscrete(0, 11);\\n currVel = rng.uniformDiscrete(0, 11);\\n } else {\\n if (tenToSix){ // 10 to 6\\n if (upVel){\\n targetVel = 10.0;\\n currVel = 6.0;\\n } else {\\n targetVel = 6.0;\\n currVel = 10.0;\\n } \\n } else { // 7 to 2\\n if (upVel){\\n targetVel = 7.0;\\n currVel = 2.0;\\n } else {\\n targetVel = 2.0;\\n currVel = 7.0;\\n } \\n }\\n }\\n\\n actNum = 0;\\n throttleTarget = rng.uniformDiscrete(0,4) * 0.1;\\n brakeTarget = 0.0;\\n trueThrottle = throttleTarget;\\n trueBrake = brakeTarget;\\n brakePosVel = 0.0;\\n\\n}\\n\\n\\n\\nint RobotCarVel::getNumActions(){\\n return 5;\\n}\\n\\n\\nvoid RobotCarVel::setSensation(std::vector<float> newS){\\n if (s.size() != newS.size()){\\n cerr << \\\"Error in sensation sizes\\\" << endl;\\n }\\n\\n for (unsigned i = 0; i < newS.size(); i++){\\n s[i] = newS[i];\\n }\\n}\\n\\nstd::vector<experience> RobotCarVel::getSeedings() {\\n\\n // return seedings\\n std::vector<experience> seeds;\\n //return seeds;\\n\\n \\n reset();\\n\\n /*\\n // seeds of perfect velocity\\n if (randomVel){\\n for (int i = 0; i < 12; i++){\\n // 3 random of each\\n for (int j = 0; j < 3; j++)\\n seeds.push_back(getRandomVelSeed(i));\\n } \\n } else {\\n // just for target velocity\\n // 5 random seeds\\n for (int j = 0; j < 5; j++){\\n seeds.push_back(getRandomVelSeed(targetVel));\\n }\\n }\\n */\\n\\n \\n // some completely random (non target)\\n for (int i = 0; i < 25; i++){\\n float vel = rng.uniform(0,11);\\n\\n float throt = 0;\\n float brake = 0;\\n if (rng.bernoulli(0.5)){\\n throt = rng.uniformDiscrete(0,4)*0.1;\\n } else {\\n brake = rng.uniformDiscrete(0,9)*0.1;\\n } \\n int act = i%getNumActions();\\n seeds.push_back(getExp(targetVel,vel,throt,brake,act));\\n }\\n\\n reset();\\n\\n return seeds;\\n}\\n\\nexperience RobotCarVel::getRandomVelSeed(float target){\\n float throt = 0;\\n float brake = 0;\\n if (rng.bernoulli(0.5)){\\n throt = rng.uniformDiscrete(0,4)*0.1;\\n } else {\\n brake = rng.uniformDiscrete(0,4)*0.1;\\n } \\n\\n return getExp(target, target, throt, brake, rng.uniformDiscrete(0,4));\\n}\\n\\nexperience RobotCarVel::getExp(float s0, float s1, float s2, float s3, int a){\\n\\n if (!randomVel) s0 = targetVel;\\n\\n experience e;\\n\\n e.s.resize(4, 0.0);\\n e.next.resize(4, 0.0);\\n\\n targetVel = s0;\\n currVel = s1;\\n throttleTarget = s2;\\n brakeTarget = s3;\\n trueThrottle = throttleTarget;\\n trueBrake = brakeTarget;\\n brakePosVel = 0.0;\\n\\n e.act = a;\\n e.s = sensation();\\n e.reward = apply(e.act);\\n\\n e.terminal = terminal();\\n e.next = sensation();\\n\\n /*\\n cout << \\\"seed from state: \\\";\\n for (unsigned i = 0; i < e.s.size(); i++){\\n cout << e.s[i] << \\\", \\\";\\n } \\n cout << \\\"act: \\\" << e.act << \\\" reward: \\\" << e.reward << \\\" next: \\\";\\n for (unsigned i = 0; i < e.next.size(); i++){\\n cout << e.next[i] << \\\", \\\";\\n } \\n cout << endl;\\n */\\n\\n reset();\\n\\n return e;\\n}\\n\\n\\nvoid RobotCarVel::getMinMaxFeatures(std::vector<float> *minFeat,\\n std::vector<float> *maxFeat){\\n \\n minFeat->resize(s.size(), 0.0);\\n maxFeat->resize(s.size(), 12.0);\\n\\n (*maxFeat)[2] = 0.4;\\n (*maxFeat)[3] = 1.0;\\n\\n}\\n\\nvoid RobotCarVel::getMinMaxReward(float *minR,\\n float *maxR){\\n \\n *minR = -120.0;\\n *maxR = 0.0; \\n \\n}\\n\\nfloat RobotCarVel::bound(float val, float min, float max){\\n if (val < min)\\n return min;\\n if (val > max)\\n return max;\\n return val;\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7822349667549133,\n \"alphanum_fraction\": 0.7908309698104858,\n \"avg_line_length\": 49,\n \"blob_id\": \"c58c6de0972a2ef6b80a3a649aa85176391da3f0\",\n \"content_id\": \"4e553c30fd0f94deae2c74a30f94bbb2da801cd9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 349,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 7,\n \"path\": \"/src/roundbot_description/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"roundbot_description\\\")\\nset(roundbot_description_MAINTAINER \\\"micho <micho@todo.todo>\\\")\\nset(roundbot_description_DEPRECATED \\\"\\\")\\nset(roundbot_description_VERSION \\\"0.0.0\\\")\\nset(roundbot_description_BUILD_DEPENDS \\\"urdf\\\" \\\"xacro\\\")\\nset(roundbot_description_RUN_DEPENDS \\\"urdf\\\" \\\"xacro\\\")\\nset(roundbot_description_BUILDTOOL_DEPENDS \\\"catkin\\\")\"\n },\n {\n \"alpha_fraction\": 0.6486238241195679,\n \"alphanum_fraction\": 0.660717248916626,\n \"avg_line_length\": 27.34515380859375,\n \"blob_id\": \"6387bbe93445b4b98e1a394acaaea628b37cc46d\",\n \"content_id\": \"458e3021bedd9604acc9a177719abb7856f01ef1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 11990,\n \"license_type\": \"no_license\",\n \"max_line_length\": 118,\n \"num_lines\": 423,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_common/include/rl_common/core.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#ifndef _RLCORE_H_\\n#define _RLCORE_H_\\n\\n#include \\\"Random.h\\\"\\n#include <vector>\\n#include <map>\\n\\n\\n/** \\\\file\\n Fundamental declarations for the universal concepts in the\\n reinforcement learning framework.\\n \\\\author Nick Jong\\n \\\\author Todd Hester\\n*/\\n\\n\\n// types of models\\n#define RMAX 0\\n#define TABULAR 0\\n#define SLF 1\\n#define C45TREE 2\\n#define SINGLETREE 3\\n#define SVM 4\\n#define STUMP 5\\n#define M5MULTI 6\\n#define M5SINGLE 7\\n#define M5ALLMULTI 8\\n#define M5ALLSINGLE 9 \\n#define LSTMULTI 10\\n#define LSTSINGLE 11\\n#define ALLM5TYPES 12\\n#define GPREGRESS 13\\n#define GPTREE 14\\n\\nconst std::string modelNames[] = {\\n \\\"Tabular\\\",\\n \\\"SLF\\\",\\n \\\"C4.5 Tree\\\",\\n \\\"Single Tree\\\",\\n \\\"SVM\\\",\\n \\\"Stump\\\",\\n \\\"M5 Tree\\\",\\n \\\"M5 Tree\\\",\\n \\\"M5 Tree\\\",\\n \\\"M5 Tree\\\",\\n \\\"LS Tree\\\",\\n \\\"LS Tree\\\",\\n \\\"M5 Combo\\\",\\n \\\"GP Regression\\\",\\n \\\"GP Tree\\\"\\n};\\n\\n// types of model combos\\n#define AVERAGE 1\\n#define WEIGHTAVG 2\\n#define BEST 3\\n#define SEPARATE 4 // sep model for planning, and forest for uncertainty\\n\\nconst std::string comboNames[] = {\\n \\\"Average\\\",\\n \\\"Weighted Average\\\",\\n \\\"Best\\\",\\n \\\"Separate\\\"\\n};\\n\\n// types of exploration\\n#define EXPLORE_UNKNOWN 0\\n#define TWO_MODE 1\\n#define TWO_MODE_PLUS_R 2\\n#define CONTINUOUS_BONUS 3\\n#define THRESHOLD_BONUS 4\\n#define CONTINUOUS_BONUS_R 5\\n#define THRESHOLD_BONUS_R 6\\n#define NO_EXPLORE 7\\n#define GREEDY 7\\n#define EPSILONGREEDY 8\\n#define VISITS_CONF 9\\n#define UNVISITED_BONUS 11\\n#define UNVISITED_ACT_BONUS 13\\n#define DIFF_AND_VISIT_BONUS 16\\n#define NOVEL_STATE_BONUS 18\\n#define DIFF_AND_NOVEL_BONUS 19\\n\\nconst std::string exploreNames[] = {\\n \\\"Explore Unknowns\\\",\\n \\\"Two Modes\\\",\\n \\\"Two Models +R\\\",\\n \\\"Continuous Bonus\\\",\\n \\\"Threshold Bonus\\\",\\n \\\"Continuous Bonus +R\\\",\\n \\\"Threshold Bonus +R\\\",\\n \\\"Greedy\\\",\\n \\\"Epsilon-Greedy\\\",\\n \\\"Visits Confidence\\\",\\n \\\"Type 10\\\",\\n \\\"Unvisited State Bonus\\\",\\n \\\"Type 12\\\", \\n \\\"Unvisited Action Bonus\\\",\\n \\\"Type 14\\\",\\n \\\"Type 15\\\",\\n \\\"Model Diff & Visit Bonus\\\",\\n \\\"Type 17\\\",\\n \\\"FeatDist Bonus\\\",\\n \\\"Model Diff & FeatDist Bonus\\\"\\n};\\n\\n// types of planners\\n#define VALUE_ITERATION 0\\n#define POLICY_ITERATION 1\\n#define PRI_SWEEPING 2\\n#define UCT 3\\n#define ET_UCT 4\\n#define ET_UCT_WITH_ENV 5\\n#define SWEEPING_UCT_HYBRID 6\\n#define CMAC_PLANNER 7\\n#define NN_PLANNER 8\\n#define MOD_PRI_SWEEPING 9\\n#define ET_UCT_L1 10\\n#define UCT_WITH_L 11\\n#define UCT_WITH_ENV 12\\n#define PARALLEL_ET_UCT 13\\n#define ET_UCT_ACTUAL 14\\n#define ET_UCT_CORNERS 15\\n#define PAR_ETUCT_ACTUAL 16\\n#define PAR_ETUCT_CORNERS 17\\n#define POMDP_ETUCT 18\\n#define POMDP_PAR_ETUCT 19\\n#define MBS_VI 20\\n\\nconst std::string plannerNames[] = {\\n \\\"Value Iteration\\\",\\n \\\"Policy Iteration\\\",\\n \\\"Prioritized Sweeping\\\",\\n \\\"UCT\\\",\\n \\\"UCT\\\",\\n \\\"UCT\\\",\\n \\\"Sweeping UCT Hybrid\\\",\\n \\\"CMACs\\\",\\n \\\"NN\\\",\\n \\\"Mod. Pri Sweeping\\\",\\n \\\"UCT L=1\\\",\\n \\\"UCT L\\\",\\n \\\"UCT Env\\\",\\n \\\"Parallel UCT\\\",\\n \\\"Real-Valued UCT\\\",\\n \\\"Corner UCT\\\",\\n \\\"Parallel Real-Valued UCT\\\",\\n \\\"Parallel Corner UCT\\\",\\n \\\"Delayed UCT\\\",\\n \\\"Parallel Delayed UCT\\\",\\n \\\"Model Based Simulation - VI\\\"\\n};\\n \\n\\n\\n#define EPSILON 1e-5\\n\\n/** Experience <s,a,s',r> struct */\\nstruct experience {\\n std::vector<float> s;\\n int act;\\n float reward;\\n std::vector<float> next;\\n bool terminal;\\n};\\n\\n/** Training instances for prediction models */\\nstruct modelPair {\\n std::vector<float> in;\\n std::vector<float> out;\\n};\\n\\n/** Training instances for classification models */\\nstruct classPair {\\n std::vector<float> in;\\n float out;\\n};\\n\\n/** Interface for an environment, whose states can be represented as\\n vectors of floats and whose actions can be represented as ints.\\n Implementations of the Environment interface determine how actions\\n influence sensations. Note that this design assumes only one\\n agent: it would be more accurate to name this interface\\n EnvironmentAsPerceivedByOneParticularAgent. */\\nclass Environment {\\npublic:\\n /** Provides access to the current sensation that the environment\\n gives to the agent.\\n \\\\return The current sensation. */\\n virtual const std::vector<float> &sensation() const = 0;\\n\\n /** Allows an agent to affect its environment.\\n \\\\param action The action the agent wishes to apply.\\n \\\\return The immediate one-step reward caused by the action. */\\n virtual float apply(int action) = 0;\\n\\n /** Determines whether the environment has reached a terminal state.\\n \\\\return true iff the task is episodic and the present episode\\n has ended. Nonepisodic tasks should simply always\\n return false. */\\n virtual bool terminal() const = 0;\\n\\n /** Resets the internal state of the environment according to some\\n initial state distribution. Typically the user calls this only\\n for episodic tasks that have reached terminal states, but this\\n usage is not required. */\\n virtual void reset() = 0;\\n\\n /** Returns the number of actions available in this environment.\\n \\\\return The number of actions available */\\n virtual int getNumActions() = 0;\\n\\n /** Gets the minimum and maximum of the features in the environment.\\n */\\n virtual void getMinMaxFeatures(std::vector<float> *minFeat,\\n std::vector<float> *maxFeat) = 0;\\n\\n /** Gets the minimum and maximum one-step reward in the domain. */\\n virtual void getMinMaxReward(float *minR, float *maxR) = 0;\\n\\n /** Returns if the domain is episodic (true by default). */\\n virtual bool isEpisodic(){ return true; };\\n\\n /** Get seeding experiences for agent. */\\n virtual std::vector<experience> getSeedings()\\n {\\n std::vector<experience> e;\\n return e;\\n } ;\\n\\n /** Set the current state for testing purposes. */\\n virtual void setSensation(std::vector<float> s){};\\n\\n virtual ~Environment() {};\\n\\n};\\n\\n/** Interface for an agent. Implementations of the Agent interface\\n determine the choice of actions given previous sensations and\\n rewards. */\\nclass Agent {\\npublic:\\n /** Determines the first action that an agent takes in an\\n environment. This method implies that the environment is\\n currently in an initial state.\\n \\\\param s The initial sensation from the environment.\\n \\\\return The action the agent wishes to take first. */\\n virtual int first_action(const std::vector<float> &s) = 0;\\n\\n /** Determines the next action that an agent takes in an environment\\n and gives feedback for the previous action. This method may\\n only be called if the last method called was first_action or\\n next_action.\\n \\\\param r The one-step reward resulting from the previous action.\\n \\\\param s The current sensation from the environment.\\n \\\\return The action the agent wishes to take next. */\\n virtual int next_action(float r, const std::vector<float> &s) = 0;\\n\\n /** Gives feedback for the last action taken. This method may only\\n be called if the last method called was first_action or\\n next_action. It implies that the task is episodic and has just\\n terminated. Note that terminal sensations (states) are not\\n represented.\\n \\\\param r The one-step reward resulting from the previous action. */\\n virtual void last_action(float r) = 0;\\n\\n /** Set some debug flags on/off */\\n virtual void setDebug(bool d) = 0;\\n\\n /** Use the model seeds from the environment to initialize the agent or its model */\\n virtual void seedExp(std::vector<experience> seeds) {};\\n\\n /** Save the current policy to a file */\\n virtual void savePolicy(const char* filename) {};\\n\\n virtual ~Agent() {};\\n};\\n\\n/** Interface for a model that predicts a vector of floats given a vector of floats as input. */\\nclass Model {\\npublic:\\n /** Train the model on a vector of training instances */\\n virtual bool trainInstances(std::vector<modelPair> &instances) = 0;\\n\\n /** Train the model on a single training instance */\\n virtual bool trainInstance(modelPair &instance) = 0;\\n\\n /** Get the model's prediction for a given input */\\n virtual std::vector<float> testInstance(const std::vector<float> &in) = 0;\\n\\n virtual ~Model() {};\\n};\\n\\n/** Interface for a classification model that predicts a class given a vector of floats as input. */\\nclass Classifier {\\npublic:\\n /** Train the model on a vector of training instances */\\n virtual bool trainInstances(std::vector<classPair> &instances) = 0;\\n\\n /** Train the model on a single training instance */\\n virtual bool trainInstance(classPair &instance) = 0;\\n\\n /** Get the model's prediction for a given input */\\n virtual void testInstance(const std::vector<float> &in, std::map<float, float>* retval) = 0;\\n\\n /** Get the model's confidence in its predictions for a given input. */\\n virtual float getConf(const std::vector<float> &in) = 0;\\n\\n /** Get a copy of the model */\\n virtual Classifier* getCopy() = 0;\\n\\n virtual ~Classifier() {};\\n};\\n\\n/** All the relevant information predicted by a model for a given state-action.\\n This includes predicted reward, next state probabilities, probability of episod termination, and model confidence.\\n*/\\nstruct StateActionInfo {\\n bool known;\\n float reward;\\n float termProb;\\n int frameUpdated;\\n\\n // map from outcome state to probability\\n std::map< std::vector<float> , float> transitionProbs;\\n\\n StateActionInfo(){\\n known = false;\\n reward = 0.0;\\n termProb = 0.0;\\n frameUpdated = -1;\\n };\\n};\\n\\n\\n/** Interface for a model of an MDP. */\\nclass MDPModel {\\npublic:\\n /** Update the MDP model with a vector of experiences. */\\n virtual bool updateWithExperiences(std::vector<experience> &instances) = 0;\\n\\n /** Update the MDP model with a single experience. */\\n virtual bool updateWithExperience(experience &instance) = 0;\\n\\n /** Get the predictions of the MDP model for a given state action */\\n virtual float getStateActionInfo(const std::vector<float> &state, int action, StateActionInfo* retval) = 0;\\n\\n /** Get a copy of the MDP Model */\\n virtual MDPModel* getCopy() = 0;\\n virtual ~MDPModel() {};\\n};\\n\\n/** Interface for planners */\\nclass Planner {\\npublic:\\n /** Give the planner the model being used with the agent */\\n virtual void setModel(MDPModel* model) = 0;\\n\\n /** Update the given model with an experience <s,a,s',r>. */\\n virtual bool updateModelWithExperience(const std::vector<float>& last,\\n int act,\\n const std::vector<float>& curr,\\n float reward, bool terminal) = 0;\\n\\n /** Plan a new policy suing the current model. */\\n virtual void planOnNewModel() = 0;\\n\\n /** Return the best action for a given state. */\\n virtual int getBestAction(const std::vector<float> &s) = 0;\\n\\n /** Save the policy to a file. */\\n virtual void savePolicy(const char* filename) {};\\n\\n /** Set whether the next experiences are seeds or actual experiences from the agent. */\\n virtual void setSeeding(bool seeding) {};\\n\\n /** Set if this is the first experience of the agent. */\\n virtual void setFirst() {};\\n\\n /** A method to return at random one of the maximum values in the vector. \\n Such that when more than one actions are optimal, we select one of them at random.\\n */\\n std::vector<float>::iterator\\n random_max_element(std::vector<float>::iterator start,\\n\\t\\t std::vector<float>::iterator end) {\\n const float Q_EPSILON = 1e-4;\\n \\n std::vector<float>::iterator max =\\n std::max_element(start, end);\\n\\n // find # within epsilon of max\\n int nfound = 0;\\n for (std::vector<float>::iterator it = start; it != end; it++){\\n if (fabs(*it - *max) < Q_EPSILON){\\n nfound++;\\n }\\n }\\n \\n // only 1: take it\\n if (nfound == 1)\\n return max;\\n\\n // take one of close to max at random\\n for (std::vector<float>::iterator it = start; it != end; it++){\\n if (fabs(*it - *max) < Q_EPSILON){\\n if (rng.uniform() < (1.0 / (float)nfound)){\\n return it;\\n }\\n nfound--;\\n }\\n }\\n \\n return max;\\n };\\n\\n virtual ~Planner() {};\\n \\n Random rng;\\n\\n};\\n\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.6849710941314697,\n \"alphanum_fraction\": 0.6849710941314697,\n \"avg_line_length\": 14.311111450195312,\n \"blob_id\": \"acc207bf7ddeebfd354dc4b2f5d93d902a72130a\",\n \"content_id\": \"7b8aba35ac041d179a1b7ffb592c3dc30454694a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 692,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 45,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/MultipleModels.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#ifndef _MULTIPLEMODELS_HH_\\n#define _MULTIPLEMODELS_HH_\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n#include <vector>\\n\\n\\n\\n/** Multiple Models */\\nclass MultipleModels: public Model {\\n\\npublic:\\n\\n MultipleModels(int id, int nInput, int nOutput, int modelType, Random rng);\\n\\n virtual ~MultipleModels();\\n\\n\\n\\n\\n\\n virtual bool trainInstance(std::vector<float> input, \\n\\t\\t\\t std::vector<float> output);\\n virtual std::vector<float> testInstance(std::vector<float> input);\\n \\n // helper functions\\n void createModels();\\n\\nprivate:\\n\\n const int id;\\n const int nInput; \\n const int nOutput;\\n const int type;\\n \\n Random rng;\\n\\n // MODELS\\n std::vector<Model*> models;\\n\\n};\\n\\n\\n#endif\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.781899094581604,\n \"alphanum_fraction\": 0.781899094581604,\n \"avg_line_length\": 50.846153259277344,\n \"blob_id\": \"db771a1f56a940624ccd0256fe9865d768b65f50\",\n \"content_id\": \"91292f22f1dba7f06d60b08f0a1ec22f8f2e7ed9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 674,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 13,\n \"path\": \"/build/rl-texplore-ros-pkg-master/src/rl_msgs/CMakeFiles/rl_msgs_generate_messages_lisp.dir/cmake_clean.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"file(REMOVE_RECURSE\\n \\\"CMakeFiles/rl_msgs_generate_messages_lisp\\\"\\n \\\"/home/justin/ros_test/devel/share/common-lisp/ros/rl_msgs/msg/RLEnvSeedExperience.lisp\\\"\\n \\\"/home/justin/ros_test/devel/share/common-lisp/ros/rl_msgs/msg/RLExperimentInfo.lisp\\\"\\n \\\"/home/justin/ros_test/devel/share/common-lisp/ros/rl_msgs/msg/RLEnvDescription.lisp\\\"\\n \\\"/home/justin/ros_test/devel/share/common-lisp/ros/rl_msgs/msg/RLStateReward.lisp\\\"\\n \\\"/home/justin/ros_test/devel/share/common-lisp/ros/rl_msgs/msg/RLAction.lisp\\\"\\n)\\n\\n# Per-language clean rules from dependency scanning.\\nforeach(lang )\\n include(CMakeFiles/rl_msgs_generate_messages_lisp.dir/cmake_clean_${lang}.cmake OPTIONAL)\\nendforeach()\\n\"\n },\n {\n \"alpha_fraction\": 0.7771260738372803,\n \"alphanum_fraction\": 0.7829912304878235,\n \"avg_line_length\": 41.6875,\n \"blob_id\": \"33a8cd993beb49e092b9720fd3ad755e3357cff5\",\n \"content_id\": \"16425e57ecbd64f042cd8088102d8e40e86ff2e7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 682,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 16,\n \"path\": \"/build/roundbot_gazebo/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"roundbot_gazebo\\\")\\nset(roundbot_gazebo_VERSION \\\"0.0.0\\\")\\nset(roundbot_gazebo_MAINTAINER \\\"micho <micho@todo.todo>\\\")\\nset(roundbot_gazebo_PACKAGE_FORMAT \\\"1\\\")\\nset(roundbot_gazebo_BUILD_DEPENDS \\\"gazebo_ros\\\")\\nset(roundbot_gazebo_BUILD_EXPORT_DEPENDS \\\"gazebo_ros\\\")\\nset(roundbot_gazebo_BUILDTOOL_DEPENDS \\\"catkin\\\")\\nset(roundbot_gazebo_BUILDTOOL_EXPORT_DEPENDS )\\nset(roundbot_gazebo_EXEC_DEPENDS \\\"gazebo_ros\\\")\\nset(roundbot_gazebo_RUN_DEPENDS \\\"gazebo_ros\\\")\\nset(roundbot_gazebo_TEST_DEPENDS )\\nset(roundbot_gazebo_DOC_DEPENDS )\\nset(roundbot_gazebo_URL_WEBSITE \\\"\\\")\\nset(roundbot_gazebo_URL_BUGTRACKER \\\"\\\")\\nset(roundbot_gazebo_URL_REPOSITORY \\\"\\\")\\nset(roundbot_gazebo_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.7801418304443359,\n \"alphanum_fraction\": 0.7943262457847595,\n \"avg_line_length\": 34.25,\n \"blob_id\": \"3c7d5f23f783d4ab2c58abbbaaf086772de0c155\",\n \"content_id\": \"4bd5cd89a281da4e00508780581d1b50c82a08d6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 141,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 4,\n \"path\": \"/devel/share/homework2/cmake/homework2-msg-paths.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# generated from genmsg/cmake/pkg-msg-paths.cmake.develspace.in\\n\\nset(homework2_MSG_INCLUDE_DIRS \\\"\\\")\\nset(homework2_MSG_DEPENDENCIES std_msgs)\\n\"\n },\n {\n \"alpha_fraction\": 0.7114960551261902,\n \"alphanum_fraction\": 0.7124409675598145,\n \"avg_line_length\": 30.719999313354492,\n \"blob_id\": \"fbe45b152ad1b750e1f48a46d4590d914a0b303f\",\n \"content_id\": \"abb88e6f44fcb68ada66a54b7177ee266bb2ae4e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 3175,\n \"license_type\": \"no_license\",\n \"max_line_length\": 138,\n \"num_lines\": 100,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/MultipleClassifiers.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file MultipleClassifiers.hh\\n Defines the Multiple Classifiers class, which uses an ensemble of classifiers, such as a set of decision trees in a random forest.\\n \\\\author Todd Hester\\n*/\\n\\n#ifndef _MULTCLASS_HH_\\n#define _MULTCLASS_HH_\\n\\n#include \\\"../Models/C45Tree.hh\\\"\\n#include \\\"../Models/M5Tree.hh\\\"\\n#include \\\"../Models/LinearSplitsTree.hh\\\"\\n\\n#include \\\"../Models/Stump.hh\\\"\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n#include <vector>\\n#include <set>\\n#include <map>\\n\\n/** Multiple Classifiers class: uses an ensemble of classifiers, such as a set of decision trees in a random forest */\\nclass MultipleClassifiers: public Classifier {\\n\\npublic:\\n\\n /** Default Constructor\\n \\\\param id identify the model\\n \\\\param modelType identifies which type of model to use\\n \\\\param predType identifies how to combine multiple models\\n \\\\param nModels # of models to use for ensemble models (i.e. random forests)\\n \\\\param trainMode build every step? only on errors? every freq steps?\\n \\\\param trainFreq frequency of model building if using latter mode\\n \\\\param featPct pct of features to remove from set used for each tree split\\n \\\\param expPct pct of experiences to give to each model\\n \\\\param treeThreshold determines the amount of error to be tolerated in the tree (prevents over-fitting with larger and larger trees)\\n \\\\param stoch if the domain is stochastic or deterministic\\n \\\\param rng Random Number Generator \\n */\\n MultipleClassifiers(int id, int modelType, int predType, int nModels, \\n int trainMode, int trainFreq,\\n float featPct, float expPct, float treeThreshold,\\n bool stoch, float featRange, Random rng);\\n\\n /** Copy constructor */\\n MultipleClassifiers(const MultipleClassifiers&);\\n virtual MultipleClassifiers* getCopy();\\n\\n ~MultipleClassifiers();\\n\\n virtual bool trainInstances(std::vector<classPair> &instances);\\n virtual bool trainInstance(classPair &instance);\\n virtual void testInstance(const std::vector<float> &input, std::map<float, float>* retval);\\n virtual float getConf(const std::vector<float> &s);\\n \\n /** Update measure of accuracy for model if we're using best model only */\\n void updateModelAccuracy(int i, const std::vector<float> &input, float out);\\n\\n /** Initialize models */\\n void initModels();\\n\\n /** Calculate kl divergence of the model's predicted probability distributions */\\n float klDivergence(const std::vector<float> &input);\\n\\n /** Calculate the variance of the model's predictions of continuous values */\\n float variance(const std::vector<float> &input);\\n\\n bool STDEBUG;\\n bool PRED_DEBUG;\\n bool ACC_DEBUG;\\n bool CONF_DEBUG;\\n bool COPYDEBUG;\\n\\n /** The ensemble of Classifier Models used. */\\n std::vector<Classifier*> models;\\n\\nprivate:\\n\\n const int id;\\n const int modelType;\\n const int predType;\\n const int nModels;\\n const int mode;\\n const int freq;\\n float featPct;\\n float expPct;\\n const float treeThresh;\\n const bool stoch;\\n const bool addNoise;\\n const float featRange;\\n \\n Random rng;\\n\\n std::vector<float> accuracy;\\n int nsteps;\\n std::vector<std::map<float, float> >infos;\\n\\n};\\n\\n\\n#endif\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.5587970614433289,\n \"alphanum_fraction\": 0.5700989365577698,\n \"avg_line_length\": 31.204030990600586,\n \"blob_id\": \"e23d61000708c8fbd7a561360d17875d8b5be507\",\n \"content_id\": \"5fbe8d8af54e1328c6515df6a77750ab9def5c78\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 25571,\n \"license_type\": \"no_license\",\n \"max_line_length\": 198,\n \"num_lines\": 794,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/FactoredModel.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** FactoredModel.cc\\n Implements the FactoredModel class, which uses separate supervised learners to model each feature of an MDP.\\n Please cite: Hester and Stone, \\\"Real Time Targeted Exploration in Large Domains\\\", ICDL 2010.\\n \\\\author Todd Hester\\n*/\\n\\n#include \\\"FactoredModel.hh\\\"\\n\\n\\nFactoredModel::FactoredModel(int id, int numactions, int M, int modelType,\\n int predType, int nModels, float treeThreshold,\\n const std::vector<float> &featRange, float rRange,\\n bool needConf, bool dep, bool relTrans, float featPct, \\n\\t\\t bool stoch, bool episodic, Random rng):\\n rewardModel(NULL), terminalModel(NULL), \\n id(id), nact(numactions), M(M), modelType(modelType),\\n predType(predType), nModels(nModels),\\n treeBuildType(BUILD_ON_ERROR), // build tree after prediction error\\n treeThresh(treeThreshold), featRange(featRange), rRange(rRange),\\n needConf(needConf), dep(dep), relTrans(relTrans), FEAT_PCT(featPct), \\n stoch(stoch), episodic(episodic), rng(rng)\\n{\\n\\n //cout << \\\"MDP Tree explore type: \\\" << predType << endl;\\n MODEL_DEBUG = false; //true;\\n COPYDEBUG = false;\\n\\n // percent of experiences to use for each model\\n EXP_PCT = 0.55;//6; //0.4;\\n\\n // just to ensure the diff models are on different random values\\n for (int i = 0; i < id; i++){\\n rng.uniform(0, 1);\\n }\\n\\n}\\n\\n\\nFactoredModel::FactoredModel(const FactoredModel & m):\\n rewardModel(NULL), terminalModel(NULL), \\n id(m.id), nact(m.nact), M(m.M), modelType(m.modelType),\\n predType(m.predType), nModels(m.nModels),\\n treeBuildType(m.treeBuildType),\\n treeThresh(m.treeThresh), featRange(m.featRange), rRange(m.rRange),\\n needConf(m.needConf), dep(m.dep), relTrans(m.relTrans), FEAT_PCT(m.FEAT_PCT),\\n stoch(m.stoch), episodic(m.episodic), rng(m.rng)\\n{\\n COPYDEBUG = m.COPYDEBUG;\\n\\n if (COPYDEBUG) cout << \\\"MDP Tree copy constructor\\\" << endl;\\n MODEL_DEBUG = m.MODEL_DEBUG;\\n EXP_PCT = m.EXP_PCT;\\n nfactors = m.nfactors;\\n\\n\\n if (m.outputModels.size() > 0){\\n if (COPYDEBUG) cout << \\\" FactoredModel copy trees\\\" << endl;\\n rewardModel = m.rewardModel->getCopy();\\n if (m.terminalModel != NULL) terminalModel = m.terminalModel->getCopy();\\n if (COPYDEBUG) cout << \\\" copy output trees\\\" << endl;\\n outputModels.resize(m.outputModels.size());\\n for (unsigned i = 0; i < m.outputModels.size(); i++){\\n outputModels[i] = m.outputModels[i]->getCopy();\\n }\\n if (COPYDEBUG) cout << \\\" FactoredModel trees copied\\\" << endl;\\n }\\n if (COPYDEBUG) cout << \\\"FactoredModel copy complete \\\" << endl;\\n}\\n\\nFactoredModel* FactoredModel::getCopy(){\\n\\n FactoredModel* copy = new FactoredModel(*this);\\n return copy;\\n\\n}\\n\\n\\nFactoredModel::~FactoredModel() {\\n if (rewardModel != NULL) delete rewardModel;\\n if (terminalModel != NULL) delete terminalModel;\\n for (unsigned i = 0; i < outputModels.size(); i++){\\n delete outputModels[i];\\n }\\n outputModels.clear();\\n}\\n\\n\\n\\n// init the trees\\nbool FactoredModel::initMDPModel(int nfactors){\\n if (MODEL_DEBUG) cout << \\\"Init trees for each state factor and reward\\\" << endl;\\n\\n outputModels.resize(nfactors);\\n\\n bool simpleRegress = false;\\n if (modelType == M5SINGLE || modelType == M5ALLSINGLE || modelType == LSTSINGLE)\\n simpleRegress = true;\\n\\n // institute a model for each state factor, depending on model type\\n for (int i = 0; i < nfactors; i++){\\n if (modelType == C45TREE && nModels == 1){\\n outputModels[i] = new C45Tree((id * (nfactors+1)) + i, treeBuildType, 5, M, 0, rng);\\n if (i == 0){\\n rewardModel = new C45Tree((id*(nfactors+1))+nfactors, treeBuildType,5, M, 0, rng);\\n if (episodic) terminalModel = new C45Tree((id*(nfactors+1))+nfactors+1, treeBuildType,5, M, 0, rng);\\n }\\n }\\n else if ((modelType == M5MULTI || modelType == M5SINGLE) && nModels == 1){\\n outputModels[i] = new M5Tree((id * (nfactors+1)) + i, treeBuildType, 5, M, 0, simpleRegress, false, treeThresh *featRange[i], rng);\\n if (i == 0){\\n rewardModel = new M5Tree((id * (nfactors+1)) + nfactors, treeBuildType, 5, M, 0, simpleRegress, false, treeThresh *rRange, rng);\\n if (episodic) terminalModel = new M5Tree((id * (nfactors+1)) + 1+nfactors, treeBuildType, 5, M, 0, simpleRegress, false, treeThresh, rng);\\n }\\n }\\n else if ((modelType == M5ALLMULTI || modelType == M5ALLSINGLE) && nModels == 1){\\n outputModels[i] = new M5Tree((id * (nfactors+1)) + i, treeBuildType, 5, M, 0, simpleRegress, true, treeThresh *featRange[i], rng);\\n if (i == 0){\\n rewardModel = new M5Tree((id * (nfactors+1)) + nfactors, treeBuildType, 5, M, 0, simpleRegress, true, treeThresh *rRange, rng);\\n if (episodic) terminalModel = new M5Tree((id * (nfactors+1)) + 1+nfactors, treeBuildType, 5, M, 0, simpleRegress, true, treeThresh, rng);\\n }\\n }\\n else if ((modelType == LSTMULTI || modelType == LSTSINGLE) && nModels == 1){\\n outputModels[i] = new LinearSplitsTree((id * (nfactors+1)) + i, treeBuildType, 5, M, 0, simpleRegress, treeThresh *featRange[i], rng);\\n if (i == 0){\\n rewardModel = new LinearSplitsTree((id * (nfactors+1)) + nfactors, treeBuildType, 5, M, 0, simpleRegress, treeThresh *rRange, rng);\\n if (episodic) terminalModel = new LinearSplitsTree((id * (nfactors+1)) + 1+nfactors, treeBuildType, 5, M, 0, simpleRegress, treeThresh, rng);\\n }\\n }\\n else if (modelType == STUMP && nModels == 1){\\n outputModels[i] = new Stump((id * (nfactors+1)) + i, 1, 5, M, 0, rng);\\n if (i == 0){\\n rewardModel = new Stump((id * (nfactors+1)) + nfactors, 1, 5, M, 0, rng);\\n if (episodic) terminalModel = new Stump((id * (nfactors+1)) +1+ nfactors, 1, 5, M, 0, rng);\\n }\\n }\\n else if (predType == SEPARATE && nModels > 1){\\n outputModels[i] = new SepPlanExplore((id * (nfactors+1)) + i,\\n modelType, predType,\\n nModels, treeBuildType, 5,\\n FEAT_PCT,\\n EXP_PCT,\\n treeThresh *featRange[i], stoch, featRange[i], rng);\\n if (i == 0){\\n rewardModel = new SepPlanExplore((id * (nfactors+1)) + nfactors,\\n modelType, predType,\\n nModels, treeBuildType, 5,\\n FEAT_PCT, // remove this pct of feats\\n EXP_PCT, treeThresh *rRange, stoch, rRange, rng);\\n\\tif (episodic){\\n\\t terminalModel = new SepPlanExplore((id * (nfactors+1)) +1+ nfactors,\\n modelType, predType,\\n nModels, treeBuildType, 5,\\n FEAT_PCT, // remove this pct of feats\\n EXP_PCT, treeThresh, stoch, 1.0, rng);\\n\\t}\\n }\\n }\\n else if (nModels > 1 || modelType == ALLM5TYPES){\\n outputModels[i] = new MultipleClassifiers((id * (nfactors+1)) + i,\\n modelType, predType,\\n nModels, treeBuildType, 5,\\n FEAT_PCT,\\n EXP_PCT,\\n treeThresh *featRange[i], stoch, featRange[i], rng);\\n if (i == 0){\\n rewardModel = new MultipleClassifiers((id * (nfactors+1)) + nfactors,\\n modelType, predType,\\n nModels, treeBuildType, 5,\\n FEAT_PCT, // remove this pct of feats\\n EXP_PCT, treeThresh *rRange, stoch, rRange, rng);\\n\\tif (episodic){\\n\\t terminalModel = new MultipleClassifiers((id * (nfactors+1)) +1+ nfactors,\\n modelType, predType,\\n nModels, treeBuildType, 5,\\n FEAT_PCT, // remove this pct of feats\\n EXP_PCT, treeThresh, stoch, 1.0, rng);\\n\\t}\\n }\\n } else {\\n cout << \\\"Invalid model type for MDP TREE\\\" << endl;\\n exit(-1);\\n }\\n\\n }\\n\\n return true;\\n\\n}\\n\\n\\n// update all trees with multiple experiences\\nbool FactoredModel::updateWithExperiences(std::vector<experience> &instances){\\n if (MODEL_DEBUG) cout << \\\"FactoredModel updateWithExperiences : \\\" << instances.size() << endl;\\n\\n bool changed = false;\\n if (outputModels.size() == 0){\\n nfactors = instances[0].next.size();\\n initMDPModel(instances[0].next.size());\\n }\\n\\n // make sure size is right\\n if (outputModels.size() != instances[0].next.size()){\\n if (MODEL_DEBUG)\\n cout << \\\"ERROR: size mismatch between input vector and # trees \\\"\\n << outputModels.size() << \\\", \\\" << instances[0].next.size() << endl;\\n return false;\\n exit(-1);\\n }\\n\\n // separate these experience instances into classPairs\\n std::vector<std::vector<classPair> > stateData(outputModels.size());\\n std::vector<classPair> rewardData(instances.size());\\n std::vector<classPair> termData(instances.size());\\n\\n // count non-terminal experiences\\n int nonTerm = 0;\\n for (unsigned i = 0; i < instances.size(); i++){\\n if (!instances[i].terminal)\\n nonTerm++;\\n }\\n for (unsigned i = 0; i < outputModels.size(); i++){\\n stateData[i].resize(nonTerm);\\n }\\n int nonTermIndex = 0;\\n\\n for (unsigned i = 0; i < instances.size(); i++){\\n experience e = instances[i];\\n\\n std::vector<float> inputs(e.s.size() + nact);\\n\\n for (unsigned k = 0; k < e.s.size(); k++){\\n inputs[k] = e.s[k];\\n }\\n // convert to binary vector of length nact\\n for (int k = 0; k < nact; k++){\\n if (e.act == k)\\n inputs[e.s.size()+k] = 1;\\n else\\n inputs[e.s.size()+k] = 0;\\n }\\n\\n // convert to rel\\n if (relTrans)\\n e.next = subVec(e.next, e.s);\\n\\n // reward and terminal models\\n classPair cp;\\n cp.in = inputs;\\n cp.out = e.reward;\\n rewardData[i] = cp;\\n\\n cp.out = e.terminal;\\n termData[i] = cp;\\n\\n // add to each vector\\n if (!e.terminal){\\n for (unsigned j = 0; j < outputModels.size(); j++){\\n classPair cp;\\n cp.in = inputs;\\n\\n // split the outcome and rewards up\\n // into each vector\\n cp.out = e.next[j];\\n stateData[j][nonTermIndex] = cp;\\n\\n // for dep trees, add this models target to next model's input\\n if (dep){\\n inputs.push_back(e.next[j]);\\n }\\n }\\n nonTermIndex++;\\n }\\n\\n }\\n\\n // build trees on all data\\n for (unsigned k = 0; k < stateData.size(); k++){\\n if (stateData[k].size() > 0){\\n bool singleChange = outputModels[k]->trainInstances(stateData[k]);\\n changed = changed || singleChange;\\n }\\n }\\n\\n bool singleChange = rewardModel->trainInstances(rewardData);\\n changed = changed || singleChange;\\n\\n if (episodic){\\n singleChange = terminalModel->trainInstances(termData);\\n changed = changed || singleChange;\\n }\\n\\n return changed;\\n}\\n\\n\\n// update all the trees, check if model has changed\\nbool FactoredModel::updateWithExperience(experience &e){\\n if (MODEL_DEBUG) cout << \\\"updateWithExperience \\\" << &(e.s) << \\\", \\\" << e.act\\n << \\\", \\\" << &(e.next) << \\\", \\\" << e.reward << endl;\\n\\n if (MODEL_DEBUG){\\n cout << \\\"From: \\\";\\n for (unsigned i = 0; i < e.s.size(); i++){\\n cout << e.s[i] << \\\", \\\";\\n }\\n cout << \\\"Action: \\\" << e.act << endl;;\\n cout << \\\"To: \\\";\\n for (unsigned i = 0; i < e.next.size(); i++){\\n cout << e.next[i] << \\\", \\\";\\n }\\n cout << \\\"Reward: \\\" << e.reward \\n\\t << \\\" term: \\\" << e.terminal << endl;\\n }\\n\\n bool changed = false;\\n\\n if (outputModels.size() == 0){\\n nfactors = e.next.size();\\n initMDPModel(e.next.size());\\n }\\n\\n // make sure size is right\\n if (outputModels.size() != e.next.size()){\\n if (MODEL_DEBUG) cout << \\\"ERROR: size mismatch between input vector and # trees \\\"\\n << outputModels.size() << \\\", \\\" << e.next.size() << endl;\\n return false;\\n exit(-1);\\n }\\n\\n std::vector<float> inputs(e.s.size() + nact);\\n for (unsigned i = 0; i < e.s.size(); i++){\\n inputs[i] = e.s[i];\\n }\\n // convert to binary vector of length nact\\n for (int k = 0; k < nact; k++){\\n if (e.act == k)\\n inputs[e.s.size()+k] = 1;\\n else\\n inputs[e.s.size()+k] = 0;\\n }\\n\\n // convert to rel\\n if (relTrans)\\n e.next = subVec(e.next, e.s);\\n\\n // split the outcome and rewards up\\n // and train the trees\\n classPair cp;\\n cp.in = inputs;\\n\\n // reward model\\n cp.out = e.reward;\\n bool singleChange = rewardModel->trainInstance(cp);\\n changed = changed || singleChange;\\n\\n // termination model\\n if (episodic){\\n cp.out = e.terminal;\\n singleChange = terminalModel->trainInstance(cp);\\n changed = changed || singleChange;\\n }\\n\\n // if not a terminal transition\\n if (!e.terminal){\\n for (unsigned i = 0; i < e.next.size(); i++){\\n cp.in = inputs;\\n cp.out = e.next[i];\\n\\n bool singleChange = outputModels[i]->trainInstance(cp);\\n changed = changed || singleChange;\\n\\n // add this model's target to input for next model\\n if (dep){\\n inputs.push_back(e.next[i]);\\n }\\n }\\n }\\n\\n if (MODEL_DEBUG) cout << \\\"Model updated, changed: \\\" << changed << endl;\\n return changed;\\n\\n}\\n\\n\\nfloat FactoredModel::getSingleSAInfo(const std::vector<float> &state, int act, StateActionInfo* retval){\\n\\n retval->transitionProbs.clear();\\n\\n if (outputModels.size() == 0){\\n retval->reward = -0.001;\\n\\n // add to transition map\\n retval->transitionProbs[state] = 1.0;\\n retval->known = false;\\n retval->termProb = 0.0;\\n return 0;\\n }\\n\\n // input we want predictions for\\n std::vector<float> inputs(state.size() + nact);\\n for (unsigned i = 0; i < state.size(); i++){\\n inputs[i] = state[i];\\n }\\n // convert to binary vector of length nact\\n for (int k = 0; k < nact; k++){\\n if (act == k)\\n inputs[state.size()+k] = 1;\\n else\\n inputs[state.size()+k] = 0;\\n }\\n\\n // just pick one sample from each feature prediction\\n std::vector<float>output(nfactors);\\n for (int i = 0; i < nfactors; i++){\\n\\n // get prediction\\n std::map<float, float> outputPreds;\\n outputModels[i]->testInstance(inputs, &outputPreds);\\n\\n // sample a value\\n float randProb = rng.uniform();\\n float probSum = 0;\\n for (std::map<float, float>::iterator it1 = outputPreds.begin(); it1 != outputPreds.end(); it1++){\\n\\n // get prob\\n probSum += (*it1).second;\\n\\n if (randProb <= probSum){\\n\\toutput[i] = (*it1).first;\\n\\tbreak;\\n }\\n }\\n }\\n\\n retval->transitionProbs[output] = 1.0;\\n\\n // calculate reward and terminal probabilities\\n // calculate expected reward\\n float rewardSum = 0.0;\\n // each value\\n std::map<float, float> rewardPreds;\\n rewardModel->testInstance(inputs, &rewardPreds);\\n\\n float totalVisits = 0.0;\\n for (std::map<float, float>::iterator it = rewardPreds.begin(); it != rewardPreds.end(); it++){\\n // get key from iterator\\n float val = (*it).first;\\n float prob = (*it).second;\\n totalVisits += prob;\\n if (MODEL_DEBUG) cout << \\\"Reward value \\\" << val << \\\" had prob of \\\" << prob << endl;\\n rewardSum += (prob * val);\\n }\\n\\n retval->reward = rewardSum / totalVisits;\\n if (MODEL_DEBUG) cout << \\\"Average reward was \\\" << retval->reward << endl;\\n\\n if (isnan(retval->reward))\\n cout << \\\"FactoredModel setting model reward to NaN\\\" << endl;\\n\\n\\n // get termination prob\\n std::map<float, float> termProbs;\\n if (!episodic){\\n termProbs[0.0] = 1.0;\\n } else {\\n terminalModel->testInstance(inputs, &termProbs);\\n }\\n // this needs to be a weighted sum.\\n // discrete trees will give some probabilty of termination (outcome 1)\\n // where continuous ones will give some value between 0 and 1\\n float termSum = 0;\\n float probSum = 0;\\n for (std::map<float, float>::iterator it = termProbs.begin(); it != termProbs.end(); it++){\\n // get key from iterator\\n float val = (*it).first;\\n if (val > 1.0) val = 1.0;\\n if (val < 0.0) val = 0.0;\\n float prob = (*it).second;\\n if (MODEL_DEBUG) cout << \\\"Term value \\\" << val << \\\" had prob of \\\" << prob << endl;\\n termSum += (prob * val);\\n probSum += prob;\\n }\\n\\n retval->termProb = termSum / probSum;\\n if (retval->termProb < 0 || retval->termProb > 1){\\n cout << \\\"Invalid termination probability!!! \\\" << retval->termProb << endl;\\n }\\n if (MODEL_DEBUG) cout << \\\"Termination prob is \\\" << retval->termProb << endl;\\n\\n return 1.0;\\n\\n}\\n\\n\\n// fill in StateActionInfo struct and return it\\nfloat FactoredModel::getStateActionInfo(const std::vector<float> &state, int act, StateActionInfo* retval){\\n if (MODEL_DEBUG) cout << \\\"getStateActionInfo, \\\" << &state << \\\", \\\" << act << endl;\\n\\n\\n\\n if (MODEL_DEBUG){\\n for (unsigned i = 0; i < state.size(); i++){\\n cout << state[i] << \\\", \\\";\\n }\\n // cout << endl;\\n cout << \\\"a: \\\" << act << \\\" has \\\" << retval->transitionProbs.size() << \\\" outcomes already\\\" << endl;\\n }\\n\\n retval->transitionProbs.clear();\\n\\n if (outputModels.size() == 0){\\n retval->reward = -0.001;\\n\\n // add to transition map\\n retval->transitionProbs[state] = 1.0;\\n retval->known = false;\\n retval->termProb = 0.0;\\n return 0;\\n }\\n\\n // input we want predictions for\\n std::vector<float> inputs(state.size() + nact);\\n for (unsigned i = 0; i < state.size(); i++){\\n inputs[i] = state[i];\\n }\\n // convert to binary vector of length nact\\n for (int k = 0; k < nact; k++){\\n if (act == k)\\n inputs[state.size()+k] = 1;\\n else\\n inputs[state.size()+k] = 0;\\n }\\n\\n\\n // get the separate predictions for each outcome variable from the respective trees\\n // combine together for outcome predictions\\n\\n // combine together and put into StateActionInfo struct\\n retval->known = true;\\n float confSum = 0.0;\\n\\n if (nModels == 1 &&\\n (modelType == M5MULTI || modelType == M5SINGLE ||\\n modelType == M5ALLMULTI || modelType == M5ALLSINGLE ||\\n modelType == LSTMULTI || modelType == LSTSINGLE ||\\n modelType == ALLM5TYPES)){\\n //cout << \\\"mdptree, combine deterministic outputs for each feature\\\" << endl;\\n ///////////////////////////////////////////\\n // alternate version -> assuming one model that gives one prediction\\n ///////////////////////////////////////////\\n std::vector<float> MLnext(nfactors);\\n std::vector<float> inputCopy = inputs;\\n for (int i = 0; i < nfactors; i++){\\n // get single outcome for this factor\\n std::map<float, float> outputPreds;\\n outputModels[i]->testInstance(inputCopy, &outputPreds);\\n if (needConf && dep) confSum += outputModels[i]->getConf(inputCopy);\\n float val = outputPreds.begin()->first;\\n if (relTrans) val = val + inputs[i];\\n MLnext[i] = val;\\n if (dep){\\n inputCopy.push_back(val);\\n }\\n }\\n //add this one\\n retval->transitionProbs[MLnext] = 1.0;\\n if (MODEL_DEBUG){\\n cout << \\\"Final prob of outcome: \\\";\\n for (int i = 0; i < nfactors; i++){\\n cout << MLnext[i] << \\\", \\\";\\n }\\n cout << \\\" is \\\" << 1.0 << endl;\\n }\\n ////////////////////////////////////////////\\n }\\n\\n else {\\n //cout << \\\"mdp tree, combine stochastic predictions for each feature\\\" << endl;\\n //////////////////////////////////////////////////////////////////////\\n // Full version: assume possibly stochastic prediction for each model\\n //////////////////////////////////////////////////////////////////////\\n // grab predicted transition probs just once\\n std::vector< std::map<float,float> > predictions(nfactors);\\n if (!dep){\\n for (int i = 0; i < nfactors; i++){\\n outputModels[i]->testInstance(inputs, &(predictions[i]));\\n }\\n }\\n\\n ///////////////////////////////////////\\n // get probability of each transition\\n float* probs = new float[nfactors];\\n std::vector<float> next(nfactors, 0);\\n addFactorProb(probs, &next, inputs, retval, 0, predictions, &confSum);\\n delete[] probs;\\n\\n /////////////////////////////////////////////////\\n }\\n\\n // calculate expected reward\\n float rewardSum = 0.0;\\n // each value\\n std::map<float, float> rewardPreds;\\n rewardModel->testInstance(inputs, &rewardPreds);\\n\\n if (rewardPreds.size() == 0){\\n //cout << \\\"FactoredModel setting state known false\\\" << endl;\\n retval->known = false;\\n return 0;\\n }\\n\\n float totalVisits = 0.0;\\n for (std::map<float, float>::iterator it = rewardPreds.begin(); it != rewardPreds.end(); it++){\\n // get key from iterator\\n float val = (*it).first;\\n float prob = (*it).second;\\n totalVisits += prob;\\n if (MODEL_DEBUG) cout << \\\"Reward value \\\" << val << \\\" had prob of \\\" << prob << endl;\\n rewardSum += (prob * val);\\n\\n }\\n\\n retval->reward = rewardSum / totalVisits;\\n if (MODEL_DEBUG) cout << \\\"Average reward was \\\" << retval->reward << endl;\\n\\n if (isnan(retval->reward))\\n cout << \\\"FactoredModel setting model reward to NaN\\\" << endl;\\n\\n\\n // get termination prob\\n std::map<float, float> termProbs;\\n if (!episodic){\\n termProbs[0.0] = 1.0;\\n } else {\\n terminalModel->testInstance(inputs, &termProbs);\\n }\\n // this needs to be a weighted sum.\\n // discrete trees will give some probabilty of termination (outcome 1)\\n // where continuous ones will give some value between 0 and 1\\n float termSum = 0;\\n float probSum = 0;\\n for (std::map<float, float>::iterator it = termProbs.begin(); it != termProbs.end(); it++){\\n // get key from iterator\\n float val = (*it).first;\\n if (val > 1.0) val = 1.0;\\n if (val < 0.0) val = 0.0;\\n float prob = (*it).second;\\n if (MODEL_DEBUG) cout << \\\"Term value \\\" << val << \\\" had prob of \\\" << prob << endl;\\n termSum += (prob * val);\\n probSum += prob;\\n }\\n\\n retval->termProb = termSum / probSum;\\n if (retval->termProb < 0 || retval->termProb > 1){\\n cout << \\\"Invalid termination probability!!! \\\" << retval->termProb << endl;\\n }\\n if (MODEL_DEBUG) cout << \\\"Termination prob is \\\" << retval->termProb << endl;\\n\\n // if we need confidence measure\\n if (needConf){\\n // conf is avg of each variable's model's confidence\\n float rConf = rewardModel->getConf(inputs);\\n float tConf = 1.0;\\n if (episodic)\\n tConf = terminalModel->getConf(inputs);\\n\\n //cout << \\\"conf is \\\" << confSum << \\\", r: \\\" << rConf << \\\", \\\" << tConf << endl;\\n\\n confSum += rConf + tConf;\\n\\n if (!dep){\\n for (int i = 0; i < nfactors; i++){\\n float featConf = outputModels[i]->getConf(inputs);\\n confSum += featConf;\\n //cout << \\\"indep, conf for \\\" << i << \\\": \\\" << featConf << endl;\\n }\\n }\\n confSum /= (float)(state.size() + 2.0);\\n } else {\\n confSum = 1.0;\\n }\\n\\n if (MODEL_DEBUG) cout << \\\"avg conf returned \\\" << confSum << endl;\\n\\n //cout << \\\"now has \\\" << retval->transitionProbs.size() << \\\" outcomes\\\" << endl;\\n\\n // return filled-in struct\\n retval->known = true;\\n return confSum;\\n\\n}\\n\\n\\n\\n// gets the values/probs for index and adds them to the appropriate spot in the array\\nvoid FactoredModel::addFactorProb(float* probs, std::vector<float>* next, std::vector<float> x, StateActionInfo* retval, int index, std::vector< std::map<float,float> > predictions, float* confSum){\\n\\n // get values, probs etc for this index\\n std::map<float, float> outputPreds = predictions[index];\\n\\n // get prediction each time for dep\\n if (dep){\\n outputModels[index]->testInstance(x, &outputPreds);\\n }\\n\\n // sum up confidences\\n if (dep && needConf){\\n float conf = outputModels[index]->getConf(x);\\n if (index > 0)\\n (*confSum) += conf * probs[index-1];\\n else\\n (*confSum) += conf;\\n }\\n\\n for (std::map<float, float>::iterator it1 = outputPreds.begin(); it1 != outputPreds.end(); it1++){\\n // get key from iterator\\n float val = (*it1).first;\\n\\n if (MODEL_DEBUG) cout << \\\"Prob of outcome \\\" << val << \\\" on factor \\\" << index << \\\" is \\\" << (*it1).second << endl;\\n\\n // ignore it if it has prob 0\\n if ((*it1).second == 0){\\n if (MODEL_DEBUG) cout << \\\"Prob 0, ignore\\\" << endl;\\n continue;\\n }\\n\\n if (dep){\\n x.push_back(val);\\n }\\n\\n if (relTrans)\\n val = val + x[index];\\n\\n (*next)[index] = val;\\n if (index == 0)\\n probs[index] = (*it1).second;\\n else\\n probs[index] = probs[index-1] * (*it1).second;\\n\\n // if last one, lets set it in our transition prob map\\n if (index == nfactors - 1 && probs[index] > 0.0){\\n\\n if (MODEL_DEBUG){\\n cout << \\\"Final prob of outcome: \\\";\\n for (int i = 0; i < nfactors; i++){\\n cout << (*next)[i] << \\\", \\\";\\n }\\n cout << \\\" is \\\" << probs[index] << endl;\\n cout << \\\" was \\\" << retval->transitionProbs[*next] << endl;\\n cout << \\\" now \\\" << (retval->transitionProbs[*next]+probs[index]) << endl;\\n }\\n\\n retval->transitionProbs[*next] += probs[index];\\n continue;\\n }\\n\\n // next factors\\n addFactorProb(probs, next, x, retval, index+1, predictions, confSum);\\n\\n }\\n}\\n\\nstd::vector<float> FactoredModel::addVec(const std::vector<float> &a, const std::vector<float> &b){\\n //if (a.size() != b.size())\\n // cout << \\\"ERROR: vector sizes wrong\\\" << endl;\\n\\n\\n int smaller = a.size();\\n if (b.size() < a.size())\\n smaller = b.size();\\n\\n std::vector<float> c(smaller, 0.0);\\n for (int i = 0; i < smaller; i++){\\n c[i] = a[i] + b[i];\\n }\\n\\n return c;\\n}\\n\\nstd::vector<float> FactoredModel::subVec(const std::vector<float> &a, const std::vector<float> &b){\\n //if (a.size() != b.size())\\n // cout << \\\"ERROR: vector sizes wrong\\\" << endl;\\n\\n int smaller = a.size();\\n if (b.size() < a.size())\\n smaller = b.size();\\n\\n std::vector<float> c(smaller, 0.0);\\n for (int i = 0; i < smaller; i++){\\n c[i] = a[i] - b[i];\\n }\\n\\n return c;\\n}\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7770700454711914,\n \"alphanum_fraction\": 0.7770700454711914,\n \"avg_line_length\": 30.399999618530273,\n \"blob_id\": \"291c25972a004c919db900f051e489445028423a\",\n \"content_id\": \"1bbe3b10a8739f39da0fb088278be984b60e4414\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 157,\n \"license_type\": \"no_license\",\n \"max_line_length\": 35,\n \"num_lines\": 5,\n \"path\": \"/devel/lib/python2.7/dist-packages/rl_msgs/msg/__init__.py\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from ._RLAction import *\\nfrom ._RLEnvDescription import *\\nfrom ._RLEnvSeedExperience import *\\nfrom ._RLExperimentInfo import *\\nfrom ._RLStateReward import *\\n\"\n },\n {\n \"alpha_fraction\": 0.5590455532073975,\n \"alphanum_fraction\": 0.567432701587677,\n \"avg_line_length\": 24.696121215820312,\n \"blob_id\": \"259de638584a6b0c8e7d359c63278e810268546d\",\n \"content_id\": \"df9e7d56aa3f765053cb76593087ced7517281b1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 23846,\n \"license_type\": \"no_license\",\n \"max_line_length\": 193,\n \"num_lines\": 928,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/ETUCT.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file ETUCT.cc\\n Implements UCT with eligiblity traces. A modified version of UCT \\n as presented in:\\n L. Kocsis and C. Szepesv´ari, \\\"Bandit based monte-carlo planning,\\\" in\\n ECML-06. Number 4212 in LNCS. Springer, 2006, pp. 282-293.\\n \\\\author Todd Hester\\n*/\\n\\n#include \\\"ETUCT.hh\\\"\\n#include <algorithm>\\n\\n#include <sys/time.h>\\n\\n\\nETUCT::ETUCT(int numactions, float gamma, float rrange, float lambda,\\n int MAX_ITER, float MAX_TIME, int MAX_DEPTH, int modelType,\\n const std::vector<float> &fmax, const std::vector<float> &fmin,\\n const std::vector<int> &nstatesPerDim, bool trackActual,\\n int historySize, Random r):\\n numactions(numactions), gamma(gamma), rrange(rrange), lambda(lambda),\\n MAX_ITER(MAX_ITER), MAX_TIME(MAX_TIME),\\n MAX_DEPTH(MAX_DEPTH), modelType(modelType), statesPerDim(nstatesPerDim),\\n trackActual(trackActual), HISTORY_SIZE(historySize),\\n HISTORY_FL_SIZE(historySize*numactions)//fmax.size())\\n{\\n rng = r;\\n\\n nstates = 0;\\n nactions = 0;\\n lastUpdate = -1;\\n seedMode = false;\\n\\n timingType = true;\\n\\n model = NULL;\\n planTime = getSeconds();\\n\\n PLANNERDEBUG = false;//true;\\n ACTDEBUG = false; //true;\\n MODELDEBUG = false;//true;//false;\\n UCTDEBUG = false; //true;//false;\\n REALSTATEDEBUG = false;\\n HISTORYDEBUG = false; //true; //false;\\n\\n featmax = fmax;\\n featmin = fmin;\\n\\n if (statesPerDim[0] > 0){\\n cout << \\\"Planner ETUCT using discretization of \\\" << statesPerDim[0] << endl;\\n }\\n if (trackActual){\\n cout << \\\"ETUCT tracking real state values\\\" << endl;\\n }\\n cout << \\\"Planner using history size: \\\" << HISTORY_SIZE << endl;\\n\\n if (HISTORY_SIZE == 0){\\n saHistory.push_back(0.0);\\n }\\n else {\\n if (HISTORYDEBUG) {\\n cout << \\\"History size of \\\" << HISTORY_SIZE\\n << \\\" float size of \\\" << HISTORY_FL_SIZE\\n << \\\" with state size: \\\" << fmin.size()\\n << \\\" and numact: \\\" << numactions << endl;\\n }\\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\\n saHistory.push_back(0.0);\\n }\\n }\\n\\n // initStates();\\n\\n}\\n\\nETUCT::~ETUCT() {\\n //cout << \\\"planner delete\\\" << endl;\\n // clear all state info\\n\\n for (std::map<state_t, state_info>::iterator i = statedata.begin();\\n i != statedata.end(); i++){\\n\\n // get state's info\\n //cout << \\\" planner got info\\\" << endl;\\n state_info* info = &((*i).second);\\n\\n deleteInfo(info);\\n }\\n\\n featmax.clear();\\n featmin.clear();\\n\\n statespace.clear();\\n statedata.clear();\\n //cout << \\\"planner done\\\" << endl;\\n}\\n\\nvoid ETUCT::setModel(MDPModel* m){\\n\\n model = m;\\n\\n}\\n\\n// canonicalize all the states so we already have them in our statespace\\nvoid ETUCT::initStates(){\\n cout << \\\"init states\\\" << endl;\\n std::vector<float> s(featmin.size());\\n\\n fillInState(s,0);\\n}\\n\\nvoid ETUCT::fillInState(std::vector<float>s, int depth){\\n\\n // if depth == size, canonicalize and return\\n if (depth == (int)featmin.size()){\\n canonicalize(s);\\n return;\\n }\\n\\n // go through all features at depth\\n for (float i = featmin[depth]; i < featmax[depth]+1; i++){\\n s[depth] = i;\\n fillInState(s, depth+1);\\n }\\n}\\n\\n\\n/////////////////////////////\\n// Functional functions :) //\\n/////////////////////////////\\n\\n\\nvoid ETUCT::initNewState(state_t s){\\n //if (PLANNERDEBUG) cout << \\\"initNewState(s = \\\" << s\\n // << \\\") size = \\\" << s->size() << endl;\\n\\n // create state info and add to hash map\\n state_info* info = &(statedata[s]);\\n initStateInfo(s, info);\\n\\n // dont init any model info\\n // we'll get it during search if we have to\\n\\n}\\n\\nbool ETUCT::updateModelWithExperience(const std::vector<float> &laststate,\\n int lastact,\\n const std::vector<float> &currstate,\\n float reward, bool term){\\n // if (PLANNERDEBUG) cout << \\\"updateModelWithExperience(last = \\\" << &laststate\\n // << \\\", curr = \\\" << &currstate\\n // << \\\", lastact = \\\" << lastact\\n // << \\\", r = \\\" << reward\\n // << \\\", term = \\\" << term\\n // << \\\")\\\" << endl;\\n\\n if (!timingType)\\n planTime = getSeconds();\\n\\n // canonicalize these things\\n state_t last = canonicalize(laststate);\\n\\n prevstate = last;\\n prevact = lastact;\\n\\n // get state info\\n previnfo = &(statedata[last]);\\n\\n // init model?\\n if (model == NULL){\\n cout << \\\"ERROR IN MODEL OR MODEL SIZE\\\" << endl;\\n exit(-1);\\n }\\n\\n if (MODELDEBUG){\\n cout << \\\"Update with exp from state: \\\";\\n for (unsigned i = 0; i < last->size(); i++){\\n cout << (laststate)[i] << \\\", \\\";\\n }\\n cout << \\\" action: \\\" << lastact;\\n cout << \\\" to state: \\\";\\n for (unsigned i = 0; i < currstate.size(); i++){\\n cout << currstate[i] << \\\", \\\";\\n }\\n cout << \\\" and reward: \\\" << reward << endl;\\n }\\n\\n experience e;\\n e.s = laststate;\\n e.next = currstate;\\n e.act = lastact;\\n e.reward = reward;\\n e.terminal = term;\\n\\n if (HISTORY_SIZE > 0){\\n if (HISTORYDEBUG) {\\n cout << \\\"Original state vector (size \\\" << e.s.size() << \\\": \\\" << e.s[0];\\n for (unsigned i = 1; i < e.s.size(); i++){\\n cout << \\\",\\\" << e.s[i];\\n }\\n cout << endl;\\n }\\n // add history onto e.s\\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\\n e.s.push_back(saHistory[i]);\\n }\\n\\n if (HISTORYDEBUG) {\\n cout << \\\"New state vector (size \\\" << e.s.size() << \\\": \\\" << e.s[0];\\n for (unsigned i = 1; i < e.s.size(); i++){\\n cout << \\\",\\\" << e.s[i];\\n }\\n cout << endl;\\n }\\n\\n if (!seedMode){\\n // push this state and action onto the history vector\\n /*\\n for (unsigned i = 0; i < last->size(); i++){\\n saHistory.push_back((*last)[i]);\\n saHistory.pop_front();\\n }\\n */\\n \\n for (int i = 0; i < numactions; i++){\\n if (i == lastact)\\n saHistory.push_back(1.0);\\n else\\n saHistory.push_back(0.0);\\n saHistory.pop_front();\\n }\\n \\n if (HISTORYDEBUG) {\\n cout << \\\"New history vector (size \\\" << saHistory.size() << \\\": \\\" << saHistory[0];\\n for (unsigned i = 1; i < saHistory.size(); i++){\\n cout << \\\",\\\" << saHistory[i];\\n }\\n cout << endl;\\n }\\n }\\n }\\n\\n bool modelChanged = model->updateWithExperience(e);\\n\\n if (timingType)\\n planTime = getSeconds();\\n\\n return modelChanged;\\n\\n}\\n\\nvoid ETUCT::updateStateActionFromModel(state_t s, int a, state_info* info){\\n\\n if (HISTORY_SIZE == 0){\\n\\n std::deque<float> history(1,0.0);\\n StateActionInfo* newModel = NULL;\\n newModel = &(info->historyModel[a][history]);\\n\\n updateStateActionHistoryFromModel(*s, a, newModel);\\n\\n }\\n\\n else {\\n\\n // fill in for all histories???\\n for (std::map< std::deque<float>, StateActionInfo>::iterator it = info->historyModel[a].begin(); it != info->historyModel[a].end(); it++){\\n\\n std::deque<float> oneHist = (*it).first;\\n StateActionInfo* newModel = &((*it).second);\\n\\n // add history to vector\\n std::vector<float> modState = *s;\\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\\n modState.push_back(oneHist[i]);\\n }\\n updateStateActionHistoryFromModel(modState, a, newModel);\\n }\\n\\n }\\n\\n}\\n\\nvoid ETUCT::updateStateActionHistoryFromModel(const std::vector<float> modState, int a, StateActionInfo *newModel){\\n\\n // update state info\\n // get state action info for each action\\n model->getStateActionInfo(modState, a, newModel);\\n newModel->frameUpdated = nactions;\\n\\n //canonNextStates(newModel);\\n\\n}\\n\\n\\n\\nvoid ETUCT::canonNextStates(StateActionInfo* modelInfo){\\n\\n // loop through all next states\\n for (std::map<std::vector<float>, float>::iterator outIt\\n = modelInfo->transitionProbs.begin();\\n outIt != modelInfo->transitionProbs.end(); outIt++){\\n\\n std::vector<float> nextstate = (*outIt).first;\\n\\n // check that it is valid, otherwise replace with current\\n bool badState = false;\\n for (unsigned j = 0; j < nextstate.size(); j++){\\n if (nextstate[j] < (featmin[j]-EPSILON)\\n || nextstate[j] > (featmax[j]+EPSILON)){\\n //cout << \\\"next state out of range \\\" << nextstate[j] << endl;\\n badState = true;\\n break;\\n }\\n }\\n\\n if (!badState){\\n canonicalize(nextstate);\\n }\\n }\\n}\\n\\n\\n\\n\\nint ETUCT::getBestAction(const std::vector<float> &state){\\n // if (PLANNERDEBUG) cout << \\\"getBestAction(s = \\\" << &state << \\\")\\\" << endl;\\n\\n // resetUCTCounts();\\n\\n state_t s = canonicalize(state);\\n\\n int i = 0;\\n for (i = 0; i < MAX_ITER; i++){\\n\\n std::deque<float> searchHistory = saHistory;\\n uctSearch(state, s, 0, searchHistory);\\n\\n // break after some max time\\n if ((getSeconds() - planTime) > MAX_TIME){ // && i > 500){\\n break;\\n }\\n\\n }\\n double currTime = getSeconds();\\n if (false || UCTDEBUG){\\n cout << \\\"Search complete after \\\" << (currTime-planTime) << \\\" seconds and \\\"\\n << i << \\\" iterations.\\\" << endl;\\n }\\n\\n // get state info\\n state_info* info = &(statedata[s]);\\n\\n // Get Q values\\n std::vector<float> &Q = info->Q;\\n\\n // Choose an action\\n const std::vector<float>::iterator a =\\n random_max_element(Q.begin(), Q.end()); // Choose maximum\\n\\n int act = a - Q.begin();\\n nactions++;\\n\\n if (false){\\n cout << \\\"State \\\" << (*s)[0];\\n for (unsigned i = 1; i < s->size(); i++){\\n cout << \\\",\\\" << (*s)[i];\\n }\\n cout << \\\", Took action \\\" << act << \\\", \\\"\\n << \\\"value: \\\" << *a << endl;\\n }\\n\\n // return index of action\\n return act;\\n}\\n\\n\\n\\n\\n\\n\\nvoid ETUCT::planOnNewModel(){\\n\\n // reset visit counts/q values\\n resetUCTCounts();\\n\\n // for rmax, only s-a's prediction has changed\\n if (modelType == RMAX){\\n updateStateActionFromModel(prevstate, prevact, previnfo);\\n }\\n\\n // for other model types, it all could change, clear all cached model predictions\\n else {\\n\\n // still update flagged s-a's\\n // then less stuff to query while planning\\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n state_t s = canonicalize(*i);\\n state_info* info = &(statedata[s]);\\n if (info->needsUpdate){\\n for (int j = 0; j < numactions; j++){\\n updateStateActionFromModel(s, j, info);\\n }\\n info->needsUpdate = false;\\n }\\n }\\n lastUpdate = nactions;\\n }\\n\\n}\\n\\n\\nvoid ETUCT::resetUCTCounts(){\\n // if (PLANNERDEBUG) cout << \\\"Reset UCT Counts\\\" << endl;\\n const int MIN_VISITS = 10;\\n\\n // loop through here\\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n state_t s = canonicalize(*i);\\n\\n state_info* info = &(statedata[s]);\\n\\n if (info->uctVisits > (MIN_VISITS * numactions))\\n info->uctVisits = MIN_VISITS * numactions;\\n\\n for (int j = 0; j < numactions; j++){\\n if (info->uctActions[j] > MIN_VISITS)\\n info->uctActions[j] = MIN_VISITS;\\n }\\n\\n }\\n\\n}\\n\\n\\n\\n\\n////////////////////////////\\n// Helper Functions //\\n////////////////////////////\\n\\nETUCT::state_t ETUCT::canonicalize(const std::vector<float> &s) {\\n //if (PLANNERDEBUG) cout << \\\"canonicalize(s = \\\" << s[0] << \\\", \\\"\\n // << s[1] << \\\")\\\" << endl;\\n\\n // discretize it\\n std::vector<float> s2;\\n if (statesPerDim[0] > 0){\\n s2 = discretizeState(s);\\n } else {\\n s2 = s;\\n }\\n\\n // get state_t for pointer if its in statespace\\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\\n statespace.insert(s2);\\n state_t retval = &*result.first; // Dereference iterator then get pointer\\n\\n //if (PLANNERDEBUG) cout << \\\" returns \\\" << retval\\n // << \\\" New: \\\" << result.second << endl;\\n\\n // if not, init this new state\\n if (result.second) { // s is new, so initialize Q(s,a) for all a\\n initNewState(retval);\\n if (PLANNERDEBUG) {\\n cout << \\\" New state initialized \\\"\\n << \\\" orig:(\\\" << s[0] << \\\",\\\" << s[1] << \\\")\\\"\\n << \\\" disc:(\\\" << s2[0] << \\\",\\\" << s2[1] << \\\")\\\" << endl;\\n }\\n }\\n\\n\\n return retval;\\n}\\n\\n\\n// init state info\\nvoid ETUCT::initStateInfo(state_t s, state_info* info){\\n //if (PLANNERDEBUG) cout << \\\"initStateInfo()\\\";\\n\\n info->id = nstates++;\\n if (PLANNERDEBUG){\\n cout << \\\" id = \\\" << info->id;\\n cout << \\\", (\\\" << (*s)[0] << \\\",\\\" << (*s)[1] << \\\")\\\" << endl;\\n }\\n\\n info->historyModel = new std::map< std::deque<float>, StateActionInfo>[numactions];\\n\\n // model q values, visit counts\\n info->Q.resize(numactions, 0);\\n info->uctActions.resize(numactions, 1);\\n info->uctVisits = 1;\\n info->visited = 0; //false;\\n info->needsUpdate = true;\\n\\n for (int i = 0; i < numactions; i++){\\n info->Q[i] = rng.uniform(0,0.01);\\n }\\n\\n //if (PLANNERDEBUG) cout << \\\"done with initStateInfo()\\\" << endl;\\n\\n}\\n\\n\\nvoid ETUCT::printStates(){\\n\\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n state_t s = canonicalize(*i);\\n\\n state_info* info = &(statedata[s]);\\n\\n cout << \\\"State \\\" << info->id << \\\": \\\";\\n for (unsigned j = 0; j < s->size(); j++){\\n cout << (*s)[j] << \\\", \\\";\\n }\\n cout << endl;\\n\\n for (int act = 0; act < numactions; act++){\\n cout << \\\" Q: \\\" << info->Q[act] << endl;\\n }\\n\\n }\\n}\\n\\n\\nvoid ETUCT::deleteInfo(state_info* info){\\n\\n delete [] info->historyModel;\\n\\n}\\n\\n\\n\\ndouble ETUCT::getSeconds(){\\n struct timezone tz;\\n timeval timeT;\\n gettimeofday(&timeT, &tz);\\n return timeT.tv_sec + (timeT.tv_usec / 1000000.0);\\n}\\n\\n\\nfloat ETUCT::uctSearch(const std::vector<float> &actS, state_t discS, int depth,std::deque<float> searchHistory){\\n if (UCTDEBUG){\\n cout << \\\" uctSearch state \\\";\\n for (unsigned i = 0; i < actS.size(); i++){\\n cout << actS[i] << \\\", \\\";\\n }\\n cout << \\\" at depth \\\" << depth << endl;\\n }\\n\\n state_info* info = &(statedata[discS]);\\n\\n // if max depth\\n // iterative deepening (probability inversely proportional to visits)\\n //float terminateProb = 1.0/(2.0+(float)info->uctVisits);\\n\\n // already visited, stop here\\n if (depth > MAX_DEPTH){\\n // return max q value here\\n std::vector<float>::iterator maxAct =\\n std::max_element(info->Q.begin(),\\n info->Q.end());\\n float maxval = *maxAct;\\n\\n if (UCTDEBUG)\\n cout << \\\"Terminated after depth: \\\" << depth\\n // << \\\" prob: \\\" << terminateProb\\n << \\\" Q: \\\" << maxval\\n << \\\" visited: \\\" << info->visited << endl;\\n\\n return maxval;\\n }\\n\\n // select action\\n int action = selectUCTAction(info);\\n\\n // simulate action to get next state and reward\\n // depending on exploration, may also terminate us\\n float reward = 0;\\n bool term = false;\\n\\n float learnRate;\\n //float learnRate = 0.001;\\n //float learnRate = 1.0 / info->uctActions[action];\\n // learnRate = 10.0 / (info->uctActions[action] + 100.0);\\n learnRate = 10.0 / (info->uctActions[action] + 10.0);\\n //if (learnRate < 0.001 && MAX_TIME < 0.5)\\n //learnRate = 0.001;\\n //learnRate = 0.05;\\n //learnRate = 1.0;\\n\\n // tell model learning thread to update this state since we've visited it\\n info->needsUpdate = true;\\n\\n // simulate next state, reward, terminal\\n std::vector<float> actualNext = simulateNextState(actS, discS, info, searchHistory, action, &reward, &term);\\n\\n // simulate reward from this action\\n if (term){\\n // this one terminated\\n if (UCTDEBUG) cout << \\\" Terminated on exploration condition\\\" << endl;\\n info->Q[action] += learnRate * (reward - info->Q[action]);\\n info->uctVisits++;\\n info->uctActions[action]++;\\n if (UCTDEBUG)\\n cout << \\\" Depth: \\\" << depth << \\\" Selected action \\\" << action\\n << \\\" r: \\\" << reward\\n << \\\" StateVisits: \\\" << info->uctVisits\\n << \\\" ActionVisits: \\\" << info->uctActions[action] << endl;\\n\\n return reward;\\n }\\n\\n // get discretized version of next\\n state_t discNext = canonicalize(actualNext);\\n\\n if (UCTDEBUG)\\n cout << \\\" Depth: \\\" << depth << \\\" Selected action \\\" << action\\n << \\\" r: \\\" << reward << endl;\\n\\n info->visited++; // = true;\\n\\n if (HISTORY_SIZE > 0){\\n // update history vector for this state\\n /*\\n for (unsigned i = 0; i < (*discS).size(); i++){\\n searchHistory.push_back((*discS)[i]);\\n searchHistory.pop_front();\\n }\\n */\\n for (int i = 0; i < numactions; i++){\\n if (i == action)\\n searchHistory.push_back(1.0);\\n else\\n searchHistory.push_back(0.0);\\n searchHistory.pop_front();\\n }\\n \\n if (HISTORYDEBUG) {\\n cout << \\\"New planning history vector (size \\\" << searchHistory.size() << \\\": \\\" << searchHistory[0];\\n for (unsigned i = 1; i < searchHistory.size(); i++){\\n cout << \\\",\\\" << searchHistory[i];\\n }\\n cout << endl;\\n }\\n }\\n\\n\\n // new q value\\n float newQ = reward + gamma * uctSearch(actualNext, discNext, depth+1, searchHistory);\\n\\n if (info->visited == 1){\\n\\n // update q and visit counts\\n info->Q[action] += learnRate * (newQ - info->Q[action]);\\n info->uctVisits++;\\n info->uctActions[action]++;\\n\\n if (UCTDEBUG)\\n cout << \\\" Depth: \\\" << depth << \\\" newQ: \\\" << newQ\\n << \\\" StateVisits: \\\" << info->uctVisits\\n << \\\" ActionVisits: \\\" << info->uctActions[action] << endl;\\n\\n if (lambda < 1.0){\\n\\n // new idea, return max of Q or new q\\n std::vector<float>::iterator maxAct =\\n std::max_element(info->Q.begin(),\\n info->Q.end());\\n float maxval = *maxAct;\\n\\n if (UCTDEBUG)\\n cout << \\\" Replacing newQ: \\\" << newQ;\\n\\n // replace with w avg of maxq and new val\\n newQ = (lambda * newQ) + ((1.0-lambda) * maxval);\\n\\n if (UCTDEBUG)\\n cout << \\\" with wAvg: \\\" << newQ << endl;\\n }\\n\\n }\\n\\n info->visited--;\\n\\n // return q\\n return newQ;\\n\\n}\\n\\n\\nint ETUCT::selectUCTAction(state_info* info){\\n // if (UCTDEBUG) cout << \\\" selectUCTAction\\\" << endl;\\n\\n std::vector<float> &Q = info->Q;\\n\\n // loop through\\n float rewardBound = rrange;\\n if (rewardBound < 1.0)\\n rewardBound = 1.0;\\n rewardBound /= (1.0 - gamma);\\n if (UCTDEBUG) cout << \\\"Reward bound: \\\" << rewardBound << endl;\\n\\n std::vector<float> uctQ(numactions, 0.0);\\n\\n for (int i = 0; i < numactions; i++){\\n\\n // this actions value is Q + rMax * 2 sqrt (log N(s) / N(s,a))\\n uctQ[i] = Q[i] +\\n rewardBound * 2.0 * sqrt(log((float)info->uctVisits) /\\n (float)info->uctActions[i]);\\n\\n if (UCTDEBUG)\\n cout << \\\" Action: \\\" << i << \\\" Q: \\\" << Q[i]\\n << \\\" visits: \\\" << info->uctActions[i]\\n << \\\" value: \\\" << uctQ[i] << endl;\\n }\\n\\n // max element of uctQ\\n std::vector<float>::iterator maxAct =\\n max_element(uctQ.begin(), uctQ.end());\\n float maxval = *maxAct;\\n int act = maxAct - uctQ.begin();\\n\\n if (UCTDEBUG)\\n cout << \\\" Selected \\\" << act << \\\" val: \\\" << maxval << endl;\\n\\n return act;\\n\\n}\\n\\n\\n\\nstd::vector<float> ETUCT::simulateNextState(const std::vector<float> &actualState, state_t discState, state_info* info, const std::deque<float> &history, int action, float* reward, bool* term){\\n\\n StateActionInfo* modelInfo = &(info->historyModel[action][history]);\\n bool upToDate = modelInfo->frameUpdated >= lastUpdate;\\n\\n if (!upToDate){\\n // must put in appropriate history\\n if (HISTORY_SIZE > 0){\\n std::vector<float> modState = *discState;\\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\\n modState.push_back(history[i]);\\n }\\n updateStateActionHistoryFromModel(modState, action, modelInfo);\\n } else {\\n updateStateActionHistoryFromModel(*discState, action, modelInfo);\\n }\\n }\\n\\n\\n *reward = modelInfo->reward;\\n *term = (rng.uniform() < modelInfo->termProb);\\n\\n if (*term){\\n return actualState;\\n }\\n\\n float randProb = rng.uniform();\\n\\n float probSum = 0.0;\\n std::vector<float> nextstate;\\n\\n if (REALSTATEDEBUG) cout << \\\"randProb: \\\" << randProb << \\\" numNext: \\\" << modelInfo->transitionProbs.size() << endl;\\n\\n if (modelInfo->transitionProbs.size() == 0)\\n nextstate = actualState;\\n\\n for (std::map<std::vector<float>, float>::iterator outIt\\n = modelInfo->transitionProbs.begin();\\n outIt != modelInfo->transitionProbs.end(); outIt++){\\n\\n float prob = (*outIt).second;\\n probSum += prob;\\n if (REALSTATEDEBUG) cout << randProb << \\\", \\\" << probSum << \\\", \\\" << prob << endl;\\n\\n if (randProb <= probSum){\\n nextstate = (*outIt).first;\\n if (REALSTATEDEBUG) cout << \\\"selected state \\\" << randProb << \\\", \\\" << probSum << \\\", \\\" << prob << endl;\\n break;\\n }\\n }\\n\\n if (trackActual){\\n\\n\\n // find the relative change from discrete center\\n std::vector<float> relChange = subVec(nextstate, *discState);\\n\\n // add that on to actual current state value\\n nextstate = addVec(actualState, relChange);\\n\\n\\n }\\n\\n // check that next state is valid\\n for (unsigned j = 0; j < nextstate.size(); j++){\\n if (nextstate[j] < (featmin[j]-EPSILON)\\n || nextstate[j] > (featmax[j]+EPSILON)){\\n return actualState;\\n }\\n }\\n\\n if (UCTDEBUG) cout << \\\"predicted next state: \\\" << nextstate[0] << \\\", \\\" << nextstate[1] << endl;\\n\\n // return new actual state\\n return nextstate;\\n\\n}\\n\\n\\nvoid ETUCT::savePolicy(const char* filename){\\n\\n ofstream policyFile(filename, ios::out | ios::binary | ios::trunc);\\n\\n // first part, save the vector size\\n int fsize = featmin.size();\\n policyFile.write((char*)&fsize, sizeof(int));\\n\\n // save numactions\\n policyFile.write((char*)&numactions, sizeof(int));\\n\\n // go through all states, and save Q values\\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n\\n state_t s = canonicalize(*i);\\n state_info* info = &(statedata[s]);\\n\\n // save state\\n policyFile.write((char*)&((*i)[0]), sizeof(float)*fsize);\\n\\n // save q-values\\n policyFile.write((char*)&(info->Q[0]), sizeof(float)*numactions);\\n\\n }\\n\\n policyFile.close();\\n}\\n\\nvoid ETUCT::logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax){\\n std::vector<float> state(2, 0.0);\\n for (int i = xmin ; i < xmax; i++){\\n for (int j = ymin; j < ymax; j++){\\n state[0] = j;\\n state[1] = i;\\n state_t s = canonicalize(state);\\n state_info* info = &(statedata[s]);\\n std::vector<float> &Q_s = info->Q;\\n const std::vector<float>::iterator max =\\n random_max_element(Q_s.begin(), Q_s.end());\\n *of << (*max) << \\\",\\\";\\n }\\n }\\n}\\n\\n\\n// should do it such that an already discretized state stays the same\\n// mainly the numerical value of each bin should be the average of that bin\\nstd::vector<float> ETUCT::discretizeState(const std::vector<float> &s){\\n std::vector<float> ds(s.size());\\n\\n for (unsigned i = 0; i < s.size(); i++){\\n\\n // since i'm sometimes doing this for discrete domains\\n // want to center bins on 0, not edge on 0\\n //cout << \\\"feat \\\" << i << \\\" range: \\\" << featmax[i] << \\\" \\\" << featmin[i] << \\\" \\\" << (featmax[i]-featmin[i]) << \\\" n: \\\" << (float)statesPerDim;\\n\\n float factor = (featmax[i] - featmin[i]) / (float)statesPerDim[i];\\n int bin = 0;\\n if (s[i] > 0){\\n bin = (int)((s[i]+factor/2) / factor);\\n } else {\\n bin = (int)((s[i]-factor/2) / factor);\\n }\\n\\n ds[i] = factor*bin;\\n //cout << \\\"P factor: \\\" << factor << \\\" bin: \\\" << bin;\\n //cout << \\\" Original: \\\" << s[i] << \\\" Discrete: \\\" << ds[i] << endl;\\n }\\n\\n return ds;\\n}\\n\\nstd::vector<float> ETUCT::addVec(const std::vector<float> &a, const std::vector<float> &b){\\n if (a.size() != b.size())\\n cout << \\\"ERROR: add vector sizes wrong \\\" << a.size() << \\\", \\\" << b.size() << endl;\\n\\n std::vector<float> c(a.size(), 0.0);\\n for (unsigned i = 0; i < a.size(); i++){\\n c[i] = a[i] + b[i];\\n }\\n\\n return c;\\n}\\n\\nstd::vector<float> ETUCT::subVec(const std::vector<float> &a, const std::vector<float> &b){\\n if (a.size() != b.size())\\n cout << \\\"ERROR: sub vector sizes wrong \\\" << a.size() << \\\", \\\" << b.size() << endl;\\n\\n std::vector<float> c(a.size(), 0.0);\\n for (unsigned i = 0; i < a.size(); i++){\\n c[i] = a[i] - b[i];\\n 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Special targets to cleanup operation of make.\\n\\n# Special rule to run CMake to check the build system integrity.\\n# No rule that depends on this can have commands that come from listfiles\\n# because they might be regenerated.\\ncmake_check_build_system:\\n\\tcd /home/justin/ros_test/build && $(CMAKE_COMMAND) -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR) --check-build-system CMakeFiles/Makefile.cmake 0\\n.PHONY : cmake_check_build_system\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.540432333946228,\n \"alphanum_fraction\": 0.5986789464950562,\n \"avg_line_length\": 23.73267364501953,\n \"blob_id\": \"45a15d13f598ebfadcf228ed97be5c5874cb1830\",\n \"content_id\": \"cc149f5d20573ff8a493affc48a3b64e1c331172\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 9992,\n \"license_type\": \"no_license\",\n \"max_line_length\": 133,\n \"num_lines\": 404,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/FuelRooms.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file FuelRooms.cc\\n Implements the Fuel World domain, with possible noise.\\n From the paper:\\n Hester and Stone, \\\"Real Time Targeted Exploration in Large Domains\\\", ICDL 2010.\\n \\\\author Todd Hester\\n*/\\n\\n#include <rl_env/FuelRooms.hh>\\n\\n\\nFuelRooms::FuelRooms(Random &rand, bool extraVariation, bool stoch):\\n height(20), width(30),\\n goal(coord_t(11.0,24.0)), \\n extraVar(extraVariation),\\n noisy(stoch),\\n rng(rand),\\n s(3),\\n ns(s[0]),\\n ew(s[1]),\\n energy(s[2])\\n{\\n\\n fuelVisited = 0;\\n totalVisited = 0;\\n\\n stateVisits = new int*[21];\\n for (int i = 0; i < 21; i++){\\n stateVisits[i] = new int[31];\\n }\\n\\n reset();\\n resetVisits();\\n}\\n\\n\\n\\nFuelRooms::~FuelRooms() { \\n for (int i = 0; i < 21; i++){\\n delete [] stateVisits[i];\\n }\\n delete [] stateVisits;\\n}\\n\\nconst std::vector<float> &FuelRooms::sensation() const { \\n return s; \\n}\\n\\nfloat FuelRooms::apply(int action) {\\n\\n checkVisits();\\n\\n if (terminal())\\n return 0.0;\\n\\n //cout << \\\"Taking action \\\" << static_cast<room_action_t>(action) << endl;\\n\\n //cout << \\\"state: \\\" << s[0] << \\\", \\\" << s[1] << \\\", \\\" << s[2] \\n // << \\\" act: \\\" << action << endl;\\n\\n // 20% lose none\\n // 20% lose two\\n // 80% of the time, lose one energy\\n // if (!noisy || rng.bernoulli(0.8))\\n energy--;\\n\\n // many fuel squares, with varying amounts reward\\n if ((int)ns == 0 || (int)ns == height){\\n energy += 20.0;\\n }\\n \\n\\n if (energy > 60.0) \\n energy = 60.0; \\n\\n const room_action_t effect =\\n noisy\\n ? add_noise(static_cast<room_action_t>(action)) \\n : static_cast<room_action_t>(action);\\n\\n float r = reward(effect);\\n\\n if (effect == NORTH || effect == NORTHWEST || effect == NORTHEAST)\\n if (ns < height)\\n ns++;\\n\\n if (effect == SOUTH || effect == SOUTHWEST || effect == SOUTHEAST)\\n if (ns > 0)\\n ns--;\\n\\n if (effect == EAST || effect == SOUTHEAST || effect == NORTHEAST)\\n if (ew < width)\\n ew++;\\n\\n if (effect == WEST || effect == SOUTHWEST || effect == NORTHWEST)\\n if (ew > 0)\\n ew--;\\n \\n return r;\\n \\n std::cerr << \\\"Unreachable point reached in FuelRooms::apply!!!\\\\n\\\";\\n return 0; // unreachable, I hope\\n}\\n\\n\\n\\nfloat FuelRooms::reward(int effect) {\\n \\n if (energy < 0.0){\\n return -400.0;\\n }\\n\\n if (terminal()){\\n //cout << \\\"Found goal!!!!\\\" << endl;\\n return 0.0;\\n }\\n\\n // extra cost at fuel stations\\n /*\\n if (ns == 0 || ns == height){\\n return -5.0;\\n }\\n */\\n\\n if (ns == 0 || ns == height){\\n float base = -10.0;\\n if (ns == 0)\\n base = -13.0;\\n \\n // extra variation\\n float var = 1.0;\\n if (extraVar)\\n var = 5.0;\\n else\\n base -= 8.0;\\n\\n return base - (((int)ew % 5) * var);\\n \\n }\\n \\n if (effect == NORTH || effect == SOUTH || effect == EAST || effect == WEST)\\n return -1.0;\\n else\\n return -1.4;\\n \\n}\\n\\n\\nbool FuelRooms::terminal() const {\\n // current position equal to goal??\\n // or out of fuel\\n return (coord_t(ns,ew) == goal) || (energy < 0.0);\\n}\\n\\n\\nvoid FuelRooms::reset() {\\n // start randomly in left region\\n ns = rng.uniformDiscrete(7, 12);\\n ew = rng.uniformDiscrete(0, 4);\\n\\n // start with random amount of fuel\\n // enough to get to gas stations, not enough to get to goal\\n // gas stations up to 9 steps away, goal at least 20 steps away\\n energy = rng.uniformDiscrete(14, 18);\\n\\n}\\n\\n\\nvoid FuelRooms::resetVisits(){\\n fuelVisited = 0;\\n totalVisited = 0;\\n\\n for (int i = 0; i < 21; i++)\\n for (int j = 0; j < 31; j++)\\n stateVisits[i][j] = 0;\\n}\\n\\nvoid FuelRooms::checkVisits(){\\n stateVisits[(int)ns][(int)ew]++;\\n // first visit to a state\\n if (stateVisits[(int)ns][(int)ew] == 1){\\n totalVisited++;\\n if (ns == 0 || ns == 20)\\n fuelVisited++;\\n }\\n}\\n\\nvoid FuelRooms::printVisits(){\\n float totalStates = 31.0 * 21.0;\\n float fuelStates = 31.0 * 2.0;\\n float otherStates = totalStates - fuelStates;\\n cout << (fuelVisited/fuelStates) << endl << ((totalVisited-fuelVisited)/otherStates) << endl << (totalVisited/totalStates) << endl;\\n}\\n\\nvoid FuelRooms::printVisitMap(string filename){\\n ofstream fout(filename.c_str());\\n for (int i = 0; i < 21; i++){\\n for (int j = 0; j < 31; j++){\\n fout << stateVisits[i][j] << \\\"\\\\t\\\";\\n }\\n fout << endl;\\n }\\n fout.close();\\n}\\n\\n\\nint FuelRooms::getNumActions(){\\n return 8;\\n}\\n\\n\\nFuelRooms::room_action_t FuelRooms::add_noise(room_action_t action) {\\n\\n int newAct = rng.bernoulli(0.8) ? action : (rng.bernoulli(0.5) ? action+1 : action-1);\\n\\n if (newAct < 0)\\n newAct = getNumActions()-1;\\n if (newAct >= getNumActions())\\n newAct = 0;\\n\\n return (room_action_t)newAct;\\n}\\n\\n\\n\\nstd::vector<experience> FuelRooms::getSeedings() {\\n\\n // return seedings\\n std::vector<experience> seeds;\\n\\n // how many copies of each?\\n for (int i = 0; i < 1; i++){\\n\\n // single seed of terminal state\\n seeds.push_back(getExp(11,24,rng.uniformDiscrete(2,40),rng.uniformDiscrete(0,7)));\\n \\n // one seed from each fuel row\\n seeds.push_back(getExp(0, rng.uniformDiscrete(1,29),rng.uniformDiscrete(2,40),rng.uniformDiscrete(0,7))); \\n seeds.push_back(getExp(20,rng.uniformDiscrete(1,29),rng.uniformDiscrete(2,40),rng.uniformDiscrete(0,7)));\\n\\n // seed of terminal\\n seeds.push_back(getExp(rng.uniformDiscrete(1,19),rng.uniformDiscrete(1,22),0,rng.uniformDiscrete(0,7)));\\n }\\n\\n /*\\n // two seeds around the goal state\\n seeds.push_back(getExp(10,24,4,NORTH));\\n seeds.push_back(getExp(11,25,42,EAST));\\n\\n // one of death\\n seeds.push_back(getExp(9,15,0,SOUTHEAST));\\n */\\n\\n // lots of seeds of various shit\\n /*\\n for (int i = 0; i < 3; i++){\\n\\n // each wall\\n //seeds.push_back(getExp(0,11,10,SOUTH));\\n //seeds.push_back(getExp(0,27,14,SOUTH));\\n //seeds.push_back(getExp(0,17,10,NORTH));\\n \\n //seeds.push_back(getExp(20,12,20,SOUTH));\\n //seeds.push_back(getExp(20,28,24,NORTH));\\n //seeds.push_back(getExp(20,18,20,NORTH));\\n \\n //seeds.push_back(getExp(10,30,30,EAST));\\n //seeds.push_back(getExp(12,30,34,EAST));\\n //seeds.push_back(getExp(10,30,30,WEST));\\n \\n //seeds.push_back(getExp(13,0,2,WEST));\\n //seeds.push_back(getExp(17,0,4,WEST));\\n //seeds.push_back(getExp(13,0,2,EAST));\\n \\n // experiences showing where the goal state is (11,24)\\n seeds.push_back(getExp(10,24,20,NORTH));\\n seeds.push_back(getExp(12,24,44,SOUTH));\\n seeds.push_back(getExp(11,23,52,EAST));\\n seeds.push_back(getExp(11,25,7,WEST));\\n seeds.push_back(getExp(10,23,11,NORTHEAST));\\n seeds.push_back(getExp(10,25,16,NORTHWEST));\\n seeds.push_back(getExp(12,23,21,SOUTHEAST));\\n seeds.push_back(getExp(12,25,36,SOUTHWEST));\\n\\n // near the goal state\\n seeds.push_back(getExp(11,23,45,NORTH));\\n seeds.push_back(getExp(10,24,31,NORTHEAST));\\n seeds.push_back(getExp(11,25,11,SOUTH));\\n seeds.push_back(getExp(12,24,18,WEST));\\n seeds.push_back(getExp(10,23,18,SOUTHWEST));\\n\\n // a few normal\\n seeds.push_back(getExp(17,14,52,SOUTH));\\n seeds.push_back(getExp(1,6,43,EAST));\\n seeds.push_back(getExp(9,18,24,NORTH));\\n seeds.push_back(getExp(12,8,3,WEST));\\n seeds.push_back(getExp(7,1,42,SOUTHEAST));\\n seeds.push_back(getExp(6,9,7,NORTHEAST));\\n seeds.push_back(getExp(19,28,28,NORTHWEST));\\n seeds.push_back(getExp(2,18,33,SOUTHWEST));\\n\\n // actions do different things from one state!\\n seeds.push_back(getExp(9,3,19,SOUTHEAST));\\n seeds.push_back(getExp(9,3,19,EAST));\\n seeds.push_back(getExp(9,3,19,NORTHWEST));\\n seeds.push_back(getExp(9,3,19,WEST));\\n seeds.push_back(getExp(9,3,19,NORTH));\\n seeds.push_back(getExp(9,3,19,SOUTHWEST));\\n\\n // and fuel running out\\n seeds.push_back(getExp(7,4,0,NORTH));\\n seeds.push_back(getExp(19,21,0,SOUTHWEST));\\n seeds.push_back(getExp(3,16,0,NORTHEAST));\\n seeds.push_back(getExp(13,1,0,SOUTH));\\n \\n // general gas stations \\n seeds.push_back(getExp(0,5,12,EAST));\\n seeds.push_back(getExp(0,23,9,WEST));\\n seeds.push_back(getExp(0,26,3,NORTHWEST));\\n seeds.push_back(getExp(20,7,7,SOUTHEAST));\\n seeds.push_back(getExp(20,18,4,SOUTH));\\n seeds.push_back(getExp(20,25,4,SOUTHWEST));\\n\\n // terminal states\\n seeds.push_back(getExp(3,24,-1,WEST));\\n seeds.push_back(getExp(9,14,-1,SOUTH));\\n seeds.push_back(getExp(7,4,-1,EAST));\\n seeds.push_back(getExp(14,18,-1,NORTH));\\n seeds.push_back(getExp(7,23,-1,NORTHWEST));\\n seeds.push_back(getExp(16,5,-1,SOUTHWEST));\\n seeds.push_back(getExp(17,14,-1,NORTHEAST));\\n seeds.push_back(getExp(4,28,-1,SOUTHEAST));\\n\\n seeds.push_back(getExp(11,24,12,NORTH));\\n seeds.push_back(getExp(11,24,22,WEST));\\n seeds.push_back(getExp(11,24,32,EAST));\\n seeds.push_back(getExp(11,24,2,SOUTH));\\n seeds.push_back(getExp(11,24,17,NORTHWEST));\\n seeds.push_back(getExp(11,24,27,SOUTHWEST));\\n seeds.push_back(getExp(11,24,37,NORTHEAST));\\n seeds.push_back(getExp(11,24,7,SOUTHEAST));\\n }\\n */\\n\\n /*\\n // a bunch of random seeds\\n for (int j = 0; j < 1000; j++){\\n seeds.push_back(getExp(rng.uniformDiscrete(0,20),rng.uniformDiscrete(0,30),rng.uniformDiscrete(-1,60),rng.uniformDiscrete(0,3)));\\n }\\n */\\n\\n reset();\\n resetVisits();\\n\\n return seeds;\\n\\n}\\n\\n\\nexperience FuelRooms::getExp(int s0, int s1, int s2, int a){\\n\\n experience e;\\n\\n e.s.resize(3, 0.0);\\n e.next.resize(3, 0.0);\\n\\n ns = s0;\\n ew = s1;\\n energy = s2;\\n e.act = a;\\n e.s = sensation();\\n e.reward = apply(e.act);\\n\\n e.terminal = terminal();\\n e.next = sensation();\\n\\n /*\\n cout << \\\"Seed experience from state (\\\" << e.s[0] << \\\", \\\"\\n << e.s[1] << \\\", \\\" << e.s[2] << \\\") action: \\\" << e.act\\n << \\\" to (\\\" << e.next[0] << \\\", \\\" << e.next[1] << \\\", \\\" << e.next[2] \\n << \\\") with reward \\\" << e.reward << \\\" and term: \\\" << e.terminal << endl;\\n */\\n\\n return e;\\n}\\n\\nvoid FuelRooms::getMinMaxFeatures(std::vector<float> *minFeat,\\n std::vector<float> *maxFeat){\\n \\n minFeat->resize(s.size(), 0.0);\\n maxFeat->resize(s.size(), 10.0);\\n\\n (*maxFeat)[0] = 20.0;\\n (*maxFeat)[1] = 30.0;\\n (*maxFeat)[2] = 60.0;\\n (*minFeat)[2] = -1.0;\\n}\\n\\nvoid FuelRooms::getMinMaxReward(float *minR,\\n float *maxR){\\n \\n *minR = -400.0;\\n *maxR = 0.0; \\n \\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7339312434196472,\n \"alphanum_fraction\": 0.7399103045463562,\n \"avg_line_length\": 40.875,\n \"blob_id\": \"2914dd387aa701808de524ffccfd5a98b4fd0b9f\",\n \"content_id\": \"5d0f3f5a0ffa50d405a9e60abe77b0f245ecc4ae\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 669,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 16,\n \"path\": \"/build/rl-texplore-ros-pkg-master/src/rl_common/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"rl_common\\\")\\nset(rl_common_VERSION \\\"0.0.0\\\")\\nset(rl_common_MAINTAINER \\\"Todd Hester <todd.hester@gmail.com>\\\")\\nset(rl_common_PACKAGE_FORMAT \\\"1\\\")\\nset(rl_common_BUILD_DEPENDS \\\"roscpp\\\" \\\"std_msgs\\\")\\nset(rl_common_BUILD_EXPORT_DEPENDS \\\"message_runtime\\\" \\\"roscpp\\\" \\\"std_msgs\\\")\\nset(rl_common_BUILDTOOL_DEPENDS \\\"catkin\\\")\\nset(rl_common_BUILDTOOL_EXPORT_DEPENDS )\\nset(rl_common_EXEC_DEPENDS \\\"message_runtime\\\" \\\"roscpp\\\" \\\"std_msgs\\\")\\nset(rl_common_RUN_DEPENDS \\\"message_runtime\\\" \\\"roscpp\\\" \\\"std_msgs\\\")\\nset(rl_common_TEST_DEPENDS )\\nset(rl_common_DOC_DEPENDS )\\nset(rl_common_URL_WEBSITE \\\"\\\")\\nset(rl_common_URL_BUGTRACKER \\\"\\\")\\nset(rl_common_URL_REPOSITORY \\\"\\\")\\nset(rl_common_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.5432450175285339,\n \"alphanum_fraction\": 0.5525165796279907,\n \"avg_line_length\": 26.980234146118164,\n \"blob_id\": \"b2b09959625543b5798ad5402e286b2e44c75b06\",\n \"content_id\": \"9fca810f21c104eac6907dcacf49e66744b490df\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 45300,\n \"license_type\": \"no_license\",\n \"max_line_length\": 125,\n \"num_lines\": 1619,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/M5Tree.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file M5Tree.cc\\n Implements the M5 Decision tree, as described in:\\n \\\"Learning with Continuous Classes\\\" by J.R. Quinlan\\n \\\"Inducing Model Trees for Continuous Classes\\\" by Y. Wang and I.H. Witten\\n \\\\author Todd Hester\\n*/\\n\\n#include \\\"M5Tree.hh\\\"\\n\\n\\n// Include stuff for newmat matrix libraries\\n\\n#define WANT_MATH // include.h will get math fns\\n // newmatap.h will get include.h\\n#include \\\"../newmat/newmatap.h\\\" // need matrix applications\\n#ifdef use_namespace\\nusing namespace NEWMAT; // access NEWMAT namespace\\n#endif\\n\\n\\n\\nM5Tree::M5Tree(int id, int trainMode, int trainFreq, int m,\\n float featPct, bool simple, bool allowAllFeats, \\n\\t float min_sdr, Random rng):\\n id(id), mode(trainMode), freq(trainFreq), M(m),\\n featPct(featPct), SIMPLE(simple), ALLOW_ALL_FEATS(allowAllFeats),\\n MIN_SDR(min_sdr), rng(rng)\\n{\\n\\n nnodes = 0;\\n nOutput = 0;\\n nExperiences = 0;\\n hadError = false;\\n totalnodes = 0;\\n maxnodes = N_M5_NODES;\\n\\n\\n // how close a split has to be to be randomly selected\\n SPLIT_MARGIN = 0.0; //0.02; //5; //01; //0.05; //0.2; //0.05;\\n\\n LMDEBUG = false;\\n DTDEBUG = false;///true;\\n SPLITDEBUG = false;//true;\\n STOCH_DEBUG = false; //true; //false; //true;\\n INCDEBUG = false; //true; //false; //true;\\n NODEDEBUG = false;\\n COPYDEBUG = false; //true;\\n nfeat = 4;\\n\\n cout << \\\"Created m5 decision tree \\\" << id;\\n if (SIMPLE) cout << \\\" simple regression\\\";\\n else cout << \\\" multivariate regression\\\";\\n if (ALLOW_ALL_FEATS) cout << \\\" (all feats)\\\";\\n else cout << \\\" (subtree feats)\\\";\\n if (DTDEBUG){\\n cout << \\\" mode: \\\" << mode << \\\" freq: \\\" << freq << endl;\\n }\\n cout << \\\" MIN_SDR: \\\" << MIN_SDR << endl;\\n\\n initNodes();\\n initTree();\\n\\n\\n}\\n\\nM5Tree::M5Tree(const M5Tree& m5):\\n id(m5.id), mode(m5.mode), freq(m5.freq), M(m5.M),\\n featPct(m5.featPct), SIMPLE(m5.SIMPLE), ALLOW_ALL_FEATS(m5.ALLOW_ALL_FEATS),\\n MIN_SDR(m5.MIN_SDR), rng(m5.rng)\\n{\\n COPYDEBUG = m5.COPYDEBUG;\\n if (COPYDEBUG) cout << \\\"m5 copy \\\" << id << endl;\\n nnodes = 0;\\n nOutput = m5.nOutput;\\n nExperiences = m5.nExperiences;\\n hadError = m5.hadError;\\n totalnodes = 0;\\n maxnodes = m5.maxnodes;\\n SPLIT_MARGIN = m5.SPLIT_MARGIN; \\n LMDEBUG = m5.LMDEBUG;\\n DTDEBUG = m5.DTDEBUG;\\n SPLITDEBUG = m5.SPLITDEBUG;\\n STOCH_DEBUG = m5.STOCH_DEBUG; \\n INCDEBUG = m5.INCDEBUG; \\n NODEDEBUG = m5.NODEDEBUG;\\n nfeat = m5.nfeat;\\n\\n if (COPYDEBUG) cout << \\\" M5 copy nodes, experiences, root, etc\\\" << endl;\\n // copy all experiences\\n for (int i = 0; i < N_M5_EXP; i++){\\n allExp[i] = m5.allExp[i];\\n }\\n if (COPYDEBUG) cout << \\\" M5 copied exp array\\\" << endl;\\n\\n // set experience pointers\\n experiences.resize(m5.experiences.size());\\n for (unsigned i = 0; i < m5.experiences.size(); i++){\\n experiences[i] = &(allExp[i]);\\n }\\n if (COPYDEBUG) cout << \\\" M5 set experience pointers\\\" << endl;\\n\\n // now the tricky part, set the pointers inside the tree nodes correctly\\n initNodes();\\n\\n if (COPYDEBUG) cout << \\\" M5 copy tree \\\" << endl;\\n root = allocateNode();\\n lastNode = root;\\n copyTree(root, m5.root);\\n if (COPYDEBUG) cout << \\\" M5 tree copy done\\\" << endl;\\n \\n if (COPYDEBUG) {\\n cout << endl << \\\"New tree: \\\" << endl;\\n printTree(root, 0);\\n cout << endl;\\n cout << \\\" m5 copy done\\\" << endl;\\n }\\n\\n}\\n\\nvoid M5Tree::copyTree(tree_node* newNode, tree_node* origNode){\\n\\n int nodeId = newNode->id;\\n\\n if (COPYDEBUG) {\\n cout << \\\" Copy node \\\" << newNode->id << \\\" from node \\\" << origNode->id << endl;\\n cout << \\\" NewAddy \\\" << newNode << \\\", old: \\\" << origNode << endl;\\n }\\n\\n // copy node from t\\n *newNode = *origNode;\\n newNode->id = nodeId;\\n\\n // if it has children, allocate and copy them too\\n if (origNode->l != NULL && !newNode->leaf){\\n newNode->l = allocateNode();\\n if (COPYDEBUG) cout << \\\" Copy left node \\\" << newNode->l->id << \\\" from \\\" << origNode->l->id << endl;\\n copyTree(newNode->l, origNode->l);\\n } else {\\n newNode->l = NULL;\\n }\\n\\n if (origNode->r != NULL && !newNode->leaf){\\n newNode->r = allocateNode();\\n if (COPYDEBUG) cout << \\\" Copy right node \\\" << newNode->r->id << \\\" from \\\" << origNode->r->id << endl;\\n copyTree(newNode->r, origNode->r);\\n } else {\\n newNode->r = NULL;\\n }\\n}\\n\\nM5Tree* M5Tree::getCopy(){\\n M5Tree* copy = new M5Tree(*this);\\n return copy;\\n}\\n\\nM5Tree::~M5Tree() {\\n deleteTree(root);\\n for (unsigned i = N_M5_EXP; i < experiences.size(); i++){\\n delete experiences[i];\\n }\\n experiences.clear();\\n}\\n\\n// here the target output will be a single value\\nbool M5Tree::trainInstance(classPair &instance){\\n\\n if (DTDEBUG) cout << id << \\\" trainInstance\\\" << endl;\\n\\n // featPct *= 0.99;\\n\\n nfeat = instance.in.size();\\n\\n bool modelChanged = false;\\n\\n // simply add this instance to the set of experiences\\n\\n // take from static array until we run out\\n tree_experience *e;\\n if (nExperiences < N_M5_EXP){\\n // from statically created set of experiences\\n e = &(allExp[nExperiences]);\\n\\n } else {\\n // dynamically create experience\\n e = new tree_experience;\\n }\\n\\n\\n e->input = instance.in;\\n e->output = instance.out;\\n experiences.push_back(e);\\n nExperiences++;\\n\\n if (nExperiences == 1000000){\\n cout << \\\"Reached limit of # experiences allowed.\\\" << endl;\\n return false;\\n }\\n\\n if (nExperiences != (int)experiences.size())\\n cout << \\\"ERROR: experience size mismatch: \\\" << nExperiences << \\\", \\\" << experiences.size() << endl;\\n\\n //cout << nExperiences << endl << flush;\\n //if (nExperiences == 503 && id == 10){\\n // DTDEBUG = true;\\n // SPLITDEBUG = true;\\n // INCDEBUG = true;\\n //}\\n\\n if ( DTDEBUG) {\\n cout << \\\"Original input: \\\";\\n for (unsigned i = 0; i < instance.in.size(); i++){\\n cout << instance.in[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Original output: \\\" << instance.out << endl;\\n cout << \\\"Added exp id: \\\" << nExperiences << \\\" output: \\\" << e->output << endl;\\n cout << \\\"Address: \\\" << e << \\\" Input : \\\";\\n for (unsigned i = 0; i < e->input.size(); i++){\\n cout << e->input[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Now have \\\" << nExperiences << \\\" experiences.\\\" << endl;\\n }\\n\\n // depending on mode/etc, maybe re-build tree\\n\\n // mode 0: re-build every step\\n if (mode == BUILD_EVERY || nExperiences <= 1){\\n rebuildTree();\\n modelChanged = true;\\n }\\n\\n // mode 1: re-build on error only\\n else if (mode == BUILD_ON_ERROR){\\n\\n // build on misclassification\\n // check for misclassify\\n std::map<float, float> answer;\\n testInstance(e->input, &answer);\\n float val = answer.begin()->first;\\n float error = fabs(val - e->output);\\n\\n if (error > 0.0){\\n rebuildTree();\\n modelChanged = true;\\n }\\n }\\n\\n // mode 2: re-build every FREQ steps\\n else if (mode == BUILD_EVERY_N){\\n // build every freq steps\\n if (!modelChanged && (nExperiences % freq) == 0){\\n rebuildTree();\\n modelChanged = true;\\n }\\n }\\n\\n if (modelChanged){\\n if (DTDEBUG) cout << \\\"DT \\\" << id << \\\" tree re-built.\\\" << endl;\\n\\n if (DTDEBUG){\\n cout << endl << \\\"DT: \\\" << id << endl;\\n printTree(root, 0);\\n cout << \\\"Done printing tree\\\" << endl;\\n }\\n }\\n\\n return modelChanged;\\n\\n}\\n\\n\\n// here the target output will be a single value\\nbool M5Tree::trainInstances(std::vector<classPair> &instances){\\n if (DTDEBUG) cout << id << \\\" DT trainInstances: \\\" \\n << instances.size() \\n << \\\" nExp: \\\" << nExperiences << endl;\\n nfeat = instances[0].in.size();\\n \\n // featPct *= 0.99;\\n \\n bool modelChanged = false;\\n\\n bool doBuild = false;\\n\\n // loop through instances, possibly checking for errors\\n for (unsigned a = 0; a < instances.size(); a++){\\n classPair instance = instances[a];\\n\\n // simply add this instance to the set of experiences\\n\\n // take from static array until we run out\\n tree_experience *e;\\n if (nExperiences < N_M5_EXP){\\n // from statically created set of experiences\\n e = &(allExp[nExperiences]);\\n\\n } else {\\n // dynamically create experience\\n e = new tree_experience;\\n }\\n\\n\\n e->input = instance.in;\\n e->output = instance.out;\\n experiences.push_back(e);\\n nExperiences++;\\n\\n if (nExperiences == 1000000){\\n cout << \\\"Reached limit of # experiences allowed.\\\" << endl;\\n return false;\\n }\\n\\n if (nExperiences != (int)experiences.size())\\n cout << \\\"ERROR: experience size mismatch: \\\" << nExperiences << \\\", \\\" << experiences.size() << endl;\\n\\n if (DTDEBUG) {\\n cout << \\\"Original input: \\\";\\n for (unsigned i = 0; i < instance.in.size(); i++){\\n cout << instance.in[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Original output: \\\" << instance.out << endl;\\n cout << \\\"Added exp id: \\\" << nExperiences << \\\" output: \\\" << e->output << endl;\\n cout << \\\"Address: \\\" << e << \\\" Input : \\\";\\n for (unsigned i = 0; i < e->input.size(); i++){\\n cout << e->input[i] << \\\", \\\";\\n }\\n cout << endl << \\\" Now have \\\" << nExperiences << \\\" experiences.\\\" << endl;\\n }\\n\\n // depending on mode/etc, maybe re-build tree\\n\\n // don't need to check if we've already decided\\n if (doBuild) continue;\\n\\n // mode 0: re-build every step\\n if (mode == BUILD_EVERY || nExperiences <= 1){\\n doBuild = true;\\n }\\n\\n // mode 1: re-build on error only\\n else if (mode == BUILD_ON_ERROR){\\n\\n // build on misclassification\\n // check for misclassify\\n std::map<float, float> answer;\\n testInstance(e->input, &answer);\\n float val = answer.begin()->first;\\n float error = fabs(val - e->output);\\n\\n if (error > 0.0){\\n doBuild = true;\\n }\\n\\n }\\n\\n // mode 2: re-build every FREQ steps\\n else if (mode == BUILD_EVERY_N){\\n // build every freq steps\\n if (!modelChanged && (nExperiences % freq) == 0){\\n doBuild = true;\\n }\\n }\\n\\n } // loop of new instances\\n\\n if (DTDEBUG) cout << \\\"Added \\\" << instances.size() << \\\" new instances. doBuild = \\\" << doBuild << endl;\\n\\n if (doBuild){\\n rebuildTree();\\n modelChanged = true;\\n }\\n\\n if (modelChanged){\\n if (DTDEBUG) cout << \\\"DT \\\" << id << \\\" tree re-built.\\\" << endl;\\n\\n if (DTDEBUG){\\n cout << endl << \\\"DT: \\\" << id << endl;\\n printTree(root, 0);\\n cout << \\\"Done printing tree\\\" << endl;\\n }\\n }\\n\\n return modelChanged;\\n\\n}\\n\\n\\nvoid M5Tree::rebuildTree(){\\n //cout << \\\"rebuild tree \\\" << id << \\\" on exp: \\\" << nExperiences << endl;\\n // deleteTree(root);\\n buildTree(root, experiences, false);\\n //cout << \\\"tree \\\" << id << \\\" rebuilt. \\\" << endl;\\n}\\n\\n\\n// TODO: here we want to return the probability of the output value being each of the possible values, in the stochastic case\\nvoid M5Tree::testInstance(const std::vector<float> &input, std::map<float, float>* retval){\\n if (DTDEBUG) cout << \\\"testInstance\\\" << endl;\\n\\n retval->clear();\\n\\n // in case the tree is empty\\n if (experiences.size() == 0){\\n (*retval)[0.0] = 1.0;\\n return;\\n }\\n\\n // follow through tree to leaf\\n tree_node* leaf = traverseTree(root, input);\\n lastNode = leaf;\\n\\n // and return mapping of outputs and their probabilities\\n leafPrediction(leaf, input, retval);\\n\\n}\\n\\nfloat M5Tree::getConf(const std::vector<float> &input){\\n if (DTDEBUG) cout << \\\"numVisits\\\" << endl;\\n\\n // in case the tree is empty\\n if (experiences.size() == 0){\\n return 0;\\n }\\n\\n if (lastNode == NULL){\\n return 0;\\n }\\n\\n // follow through tree to leaf\\n //tree_node* leaf = traverseTree(root, input);\\n\\n // and return # in this leaf\\n float conf = (float)lastNode->nInstances / (float)(2.0*M);\\n if (conf > 1.0)\\n return 1.0;\\n else\\n return conf;\\n\\n}\\n\\n// check to see if this state is one we should explore\\n// to get more info on potential splits\\n\\n\\n// init the tree\\nvoid M5Tree::initTree(){\\n if (DTDEBUG) cout << \\\"initTree()\\\" << endl;\\n root = allocateNode();\\n\\n if (DTDEBUG) cout << \\\" root id = \\\" << root->id << endl;\\n\\n // just to ensure the diff models are on different random values\\n for (int i = 0; i < id; i++){\\n rng.uniform(0, 1);\\n }\\n\\n}\\n\\n\\n\\n// init a tree node\\nvoid M5Tree::initTreeNode(tree_node* node){\\n if (DTDEBUG) cout << \\\"initTreeNode()\\\";\\n\\n node->id = nnodes++;\\n if (DTDEBUG) cout << \\\" id = \\\" << node->id << endl;\\n\\n totalnodes++;\\n if (totalnodes > maxnodes){\\n maxnodes = totalnodes;\\n if (DTDEBUG) cout << id << \\\" M5 MAX nodes: \\\" << maxnodes << endl;\\n }\\n\\n // split criterion\\n node->dim = -1;\\n node->val = -1;\\n\\n // current data\\n node->nInstances = 0;\\n node->constant = 0;\\n\\n // coefficients will get resized later\\n // node->coefficients.resize(2,0);\\n\\n // next nodes in tree\\n node->l = NULL;\\n node->r = NULL;\\n\\n node->leaf = true;\\n\\n}\\n\\nvoid M5Tree::deleteTree(tree_node* node){\\n if (DTDEBUG) cout << \\\"deleteTree, node=\\\" << node->id << endl;\\n\\n if (node==NULL)\\n return;\\n\\n totalnodes--;\\n\\n node->nInstances = 0;\\n node->coefficients.clear();\\n\\n //recursively call deleteTree on children\\n // then delete them\\n if (!node->leaf){\\n // left child\\n if (node->l != NULL){\\n deleteTree(node->l);\\n deallocateNode(node->l);\\n node->l = NULL;\\n }\\n\\n // right child\\n if (node->r != NULL){\\n deleteTree(node->r);\\n deallocateNode(node->r);\\n node->r = NULL;\\n }\\n }\\n\\n node->leaf = true;\\n node->dim = -1;\\n node->val = -1;\\n node->constant = 0;\\n}\\n\\n\\nM5Tree::tree_node* M5Tree::getCorrectChild(tree_node* node,\\n const std::vector<float> &input){\\n\\n if (DTDEBUG) cout << \\\"getCorrectChild, node=\\\" << node->id << endl;\\n\\n if (passTest(node->dim, node->val, input))\\n return node->l;\\n else\\n return node->r;\\n\\n}\\n\\nM5Tree::tree_node* M5Tree::traverseTree(tree_node* node,\\n const std::vector<float> &input){\\n\\n if (DTDEBUG) cout << \\\"traverseTree, node=\\\" << node->id << endl;\\n\\n while (!node->leaf){\\n node = getCorrectChild(node, input);\\n }\\n\\n return node;\\n}\\n\\n\\nbool M5Tree::passTest(int dim, float val, const std::vector<float> &input){\\n if (DTDEBUG) cout << \\\"passTest, dim=\\\" << dim << \\\",val=\\\" << val \\n << \\\",input[\\\"<<dim<<\\\"]=\\\" << input[dim] <<endl;\\n\\n if (input[dim] > val)\\n return false;\\n else\\n return true;\\n\\n}\\n\\n\\nvoid M5Tree::buildTree(tree_node *node,\\n const std::vector<tree_experience*> &instances,\\n bool changed){\\n if(DTDEBUG) cout << \\\"buildTree, node=\\\" << node->id\\n << \\\",nInstances:\\\" << instances.size()\\n << \\\",chg:\\\" << changed << endl;\\n\\n if (instances.size() == 0){\\n cout << \\\"Error: buildTree called on tree \\\" << id << \\\" node \\\" << node->id << \\\" with no instances.\\\" << endl;\\n exit(-1);\\n }\\n\\n\\n // TODO: what about stochastic data?\\n //std::vector<float> chiSquare = calcChiSquare(instances);\\n\\n // first, add instances to tree\\n node->nInstances = instances.size();\\n\\n bool allSame = true;\\n float val0 = instances[0]->output;\\n for (unsigned i = 1; i < instances.size(); i++){\\n if (instances[i]->output != val0){\\n allSame = false;\\n break;\\n }\\n }\\n\\n // see if they're all the same\\n if (allSame){\\n makeLeaf(node);\\n node->constant = instances[0]->output;\\n if (DTDEBUG){\\n cout << \\\"Tree \\\" << id << \\\" node \\\" << node->id \\n << \\\" All \\\" << node->nInstances\\n << \\\" classified with output \\\"\\n << instances[0]->output << endl;\\n }\\n return;\\n }\\n\\n // check if this is a leaf and linear model has no error\\n if (node->leaf && node->coefficients.size() > 0) {\\n //cout << \\\"Node \\\" << node->id << \\\" is leaf, checking lm\\\" << endl;\\n float errorSum = 0;\\n for (unsigned i = 0; i < instances.size(); i++){\\n // get prediction for instance and compare with actual output\\n tree_node* leaf = traverseTree(node, instances[i]->input);\\n std::map<float, float> retval;\\n leafPrediction(leaf, instances[i]->input, &retval);\\n float prediction = retval.begin()->first;\\n float absError = fabs(prediction - instances[i]->output);\\n errorSum += absError;\\n //cout << \\\"instance \\\" << i << \\\" leaf predicted: \\\" << prediction\\n // << \\\" actual: \\\" << instances[i]->output\\n // << \\\" error: \\\" << absError << endl;\\n }\\n float avgError = errorSum / (float)instances.size();\\n if (avgError < 0.001){\\n // cout << \\\"stick with linear model\\\" << endl;\\n return;\\n }\\n }\\n\\n // if not, calculate SDR to determine best split\\n if (SPLITDEBUG) cout << endl << \\\"Creating new decision node\\\" << endl;\\n\\n node->leaf = false;\\n //node->nInstances++;\\n\\n float bestSDR = -1.0;\\n int bestDim = -1;\\n float bestVal = -1;\\n std::vector<tree_experience*> bestLeft;\\n std::vector<tree_experience*> bestRight;\\n\\n testPossibleSplits(instances, &bestSDR, &bestDim, &bestVal, &bestLeft, &bestRight);\\n\\n implementSplit(node, instances, bestSDR, bestDim, bestVal, bestLeft, bestRight, changed);\\n\\n // possibly replace split node with linear regression model\\n pruneTree(node, instances);\\n\\n}\\n\\n\\nvoid M5Tree::makeLeaf(tree_node* node){\\n\\n removeChildren(node);\\n\\n // make sure there are enough coefficients for all the features\\n // and that they are 0\\n if (node->coefficients.size() != (unsigned)nfeat){\\n node->coefficients.resize(nfeat, 0);\\n }\\n for (unsigned i = 0; i < node->coefficients.size(); i++){\\n node->coefficients[i] = 0;\\n }\\n\\n}\\n\\n\\nvoid M5Tree::removeChildren(tree_node* node){\\n // check on children\\n if (node->l != NULL){\\n deleteTree(node->l);\\n deallocateNode(node->l);\\n node->l = NULL;\\n }\\n\\n if (node->r != NULL){\\n deleteTree(node->r);\\n deallocateNode(node->r);\\n node->r = NULL;\\n }\\n\\n node->leaf = true;\\n}\\n\\n\\nvoid M5Tree::pruneTree(tree_node *node,\\n const std::vector<tree_experience*> &instances){\\n if (LMDEBUG || DTDEBUG){\\n printTree(root, 0);\\n cout << \\\"pruneTree, node=\\\" << node->id\\n << \\\",nInstances:\\\" << instances.size() << endl;\\n }\\n\\n // TODO: no pruning right now\\n // return;\\n\\n // calculate error of current subtree\\n float subtreeErrorSum = 0;\\n for (unsigned i = 0; i < instances.size(); i++){\\n \\n // get prediction for instance and compare with actual output\\n tree_node* leaf = traverseTree(node, instances[i]->input);\\n std::map<float, float> retval;\\n leafPrediction(leaf, instances[i]->input, &retval);\\n float prediction = retval.begin()->first;\\n float absError = fabs(prediction - instances[i]->output);\\n subtreeErrorSum += absError;\\n if (LMDEBUG || DTDEBUG){\\n cout << \\\"instance \\\" << i << \\\" subtree predicted: \\\" << prediction\\n << \\\" actual: \\\" << instances[i]->output\\n << \\\" error: \\\" << absError << endl;\\n }\\n }\\n if (instances.size() < 3){\\n if (LMDEBUG || DTDEBUG) cout << \\\"instances size <= 2!!!\\\" << endl;\\n return;\\n }\\n\\n float avgTreeError = subtreeErrorSum / (float)instances.size();\\n\\n // if this is zero error, we're not going to replace it\\n if (false && avgTreeError <= 0.0001){\\n if (LMDEBUG || DTDEBUG) \\n cout << \\\"Sub-tree is perfect (\\\" << avgTreeError\\n << \\\"), do not attempt LM\\\" << endl;\\n return;\\n }\\n \\n // figure out tree feats used\\n std::vector<bool> treeFeatsUsed(instances[0]->input.size(), false);\\n getFeatsUsed(node, &treeFeatsUsed);\\n int nTreeFeatsUsed = 0;\\n for (unsigned i = 0; i < treeFeatsUsed.size(); i++){\\n if (treeFeatsUsed[i])\\n nTreeFeatsUsed++;\\n }\\n\\n // Just use them all... otherwise we ignore some that weren't good enough\\n // for splitting, but are good here\\n std::vector<bool> featsUsed(instances[0]->input.size(), true);\\n int nFeatsUsed = featsUsed.size();\\n\\n // or just use ones allowed by subtree\\n if (!ALLOW_ALL_FEATS){\\n featsUsed = treeFeatsUsed;\\n nFeatsUsed = nTreeFeatsUsed;\\n }\\n\\n // no features to build linear model on\\n if (nFeatsUsed == 0){\\n if (LMDEBUG || DTDEBUG) cout << \\\"No features for LM\\\" << endl;\\n return;\\n }\\n\\n // add on error bonus based on nInstances and nParams\\n float treeErrorEst = avgTreeError;\\n float denom = (instances.size() - nTreeFeatsUsed);\\n if (denom < 1){\\n denom = 0.5;\\n if (LMDEBUG) {\\n cout << \\\"denom of tree error factor is \\\" << denom \\n << \\\" with nInst \\\" << instances.size() \\n << \\\" nfeats: \\\" << nTreeFeatsUsed << endl;\\n }\\n }\\n treeErrorEst *= (instances.size() + nTreeFeatsUsed) / denom;\\n\\n // fit linear model to this set of instances\\n float lmErrorSum = 0;\\n int nlmFeats = 0;\\n if (SIMPLE)\\n nlmFeats = fitSimpleLinearModel(node, instances, featsUsed, nFeatsUsed, &lmErrorSum);\\n else \\n nlmFeats = fitLinearModel(node, instances, featsUsed, nFeatsUsed, &lmErrorSum);\\n\\n float avgLMError = lmErrorSum / (float)instances.size();\\n\\n float lmErrorEst = avgLMError;\\n float denom2 = (instances.size() - nlmFeats);\\n if (denom2 < 1){\\n denom2 = 0.5;\\n if (LMDEBUG) {\\n cout << \\\"denom2 of lm error factor is \\\" << denom2\\n << \\\" with nInst \\\" << instances.size() \\n << \\\" nfeats: \\\" << nlmFeats << endl;\\n }\\n }\\n lmErrorEst *= (instances.size() + nlmFeats) / denom2; \\n\\n // replace subtree with linear model?\\n if (LMDEBUG || DTDEBUG) {\\n cout << \\\"Sub-Tree Error: \\\" << treeErrorEst << \\\", lm Error: \\\"\\n << lmErrorEst << endl;\\n }\\n // if (lmErrorEst < (treeErrorEst + 0.0001)){\\n if (lmErrorEst < (treeErrorEst + 0.1*MIN_SDR)){\\n //if (lmErrorEst < treeErrorEst){\\n if (LMDEBUG || DTDEBUG)\\n cout << node->id << \\\" replace tree with linear model\\\" << endl;\\n removeChildren(node);\\n } else {\\n // remove coefficients again, for memory\\n // node->coefficients.clear();\\n }\\n\\n}\\n\\n\\nint M5Tree::fitLinearModel(tree_node *node,\\n const std::vector<tree_experience*> &instances,\\n std::vector<bool> featureMask,\\n int nFeats, float* resSum){\\n \\n if(DTDEBUG || LMDEBUG) cout << \\\"fitLinearModel, node=\\\" << node->id\\n << \\\",nInstances:\\\" << instances.size() << endl;\\n\\n // make sure there are enough coefficients for all the features\\n if (node->coefficients.size() != instances[0]->input.size()){\\n node->coefficients.resize(instances[0]->input.size(), 0);\\n }\\n\\n node->constant = 0.0;\\n bool doRegression = true;\\n int ntimes = 0;\\n int nlmFeats = 10;\\n (*resSum) = 1000000;\\n\\n while (doRegression){\\n //cout << id << \\\" Attempt linear model \\\" << ntimes << endl;\\n ntimes++;\\n\\n int nObs = (int)instances.size();\\n\\n //cout << \\\"with nObs: \\\" << nObs << \\\" and nFeats: \\\" << nFeats << endl;\\n\\n // no feats or obs, no model to build\\n if (nObs == 0 || nFeats == 0)\\n break;\\n\\n Matrix X(nObs, nFeats);\\n ColumnVector Y(nObs);\\n\\n std::vector<int> featIndices(nFeats);\\n\\n std::vector<bool> constants(nFeats,true);\\n bool foundProblem = false;\\n \\n // load up matrices\\n for (int i = 0; i < nObs; i++){\\n tree_experience *e = instances[i];\\n if (LMDEBUG) cout << \\\"Obs: \\\" << i;\\n\\n // go through all features\\n int featIndex = 1;\\n for (unsigned j = 0; j < featureMask.size(); j++){\\n node->coefficients[j] = 0;\\n if (!featureMask[j])\\n continue;\\n \\n if (constants[j] && e->input[j] != instances[0]->input[j]){\\n constants[j] = false;\\n }\\n\\n\\t/*\\n if (rng.uniform() < featPct){\\n featureMask[j] = false;\\n nFeats--;\\n if (nFeats > 0)\\n continue;\\n else\\n break;\\n }\\n\\t*/\\n\\n if (i == nObs-1 && constants[j]){\\n //cout << \\\"PROBLEM: feat \\\" << j << \\\" is constant!\\\" << endl;\\n foundProblem = true;\\n featureMask[j] = false;\\n nFeats--;\\n if (nFeats > 0)\\n continue;\\n else\\n break;\\n }\\n\\n featIndices[featIndex-1] = j;\\n // HACK: I'm adding random noise here to prevent colinear features\\n X(i+1,featIndex) = e->input[j]; // + rng.uniform(-0.00001, 0.00001);\\n if (LMDEBUG){\\n cout << \\\" Feat \\\" << featIndex << \\\" index \\\" << j\\n << \\\" val \\\" << X(i+1,featIndex) << \\\",\\\";\\n }\\n featIndex++;\\n }\\n\\n Y(i+1) = e->output;\\n if (LMDEBUG) cout << \\\" out: \\\" << e->output << endl;\\n }\\n\\n if (foundProblem || nFeats == 0)\\n continue;\\n\\n // make vector of 1s\\n ColumnVector Ones(nObs); Ones = 1.0;\\n\\n // calculate means (averages) of x1 and x2 [ .t() takes transpose]\\n RowVector Mrow = Ones.t() * X / nObs;\\n\\n // and subtract means from x1 and x1\\n Matrix XC(nObs,nFeats);\\n XC = X - Ones * Mrow;\\n\\n // do the same to Y [use Sum to get sum of elements]\\n ColumnVector YC(nObs);\\n Real mval = Sum(Y) / nObs;\\n YC = Y - Ones * mval;\\n\\n Try {\\n\\n // form sum of squares and product matrix\\n // [use << rather than = for copying Matrix into SymmetricMatrix]\\n SymmetricMatrix SSQ;\\n SSQ << XC.t() * XC;\\n \\n ///////////////////////////\\n // Cholesky Method\\n LowerTriangularMatrix L = Cholesky(SSQ);\\n\\n // calculate estimate\\n ColumnVector A = L.t().i() * (L.i() * (XC.t() * YC));\\n ///////////////////////////\\n \\n //////////////////////////\\n // Least Squares Method\\n // calculate estimate\\n // [bracket last two terms to force this multiplication first]\\n // [ .i() means inverse, but inverse is not explicity calculated]\\n //ColumnVector A = SSQ.i() * (XC.t() * YC);\\n //////////////////////////\\n \\n \\n // calculate estimate of constant term\\n // [AsScalar converts 1x1 matrix to Real]\\n Real a = mval - (Mrow * A).AsScalar();\\n\\n // Calculate fitted values and residuals\\n //int npred1 = nFeats+1;\\n ColumnVector Fitted = X * A + a;\\n ColumnVector Residual = Y - Fitted;\\n //Real ResVar = Residual.SumSquare() / (nObs-npred1);\\n\\n // print out answers\\n // for each instance\\n (*resSum) = 0;\\n for (int i = 0; i < nObs; i++){\\n if (DTDEBUG || LMDEBUG){\\n cout << \\\"instance \\\" << i << \\\" linear model predicted: \\\" << Fitted(i+1)\\n << \\\" actual: \\\" << instances[i]->output\\n << \\\" error: \\\" << Residual(i+1) << endl;\\n }\\n (*resSum) += fabs(Residual(i+1));\\n }\\n\\n\\n // coeff\\n nlmFeats = 0;\\n for (int i = 0; i < nFeats; i++){\\n if (DTDEBUG || LMDEBUG) cout << \\\"Coeff \\\" << i << \\\" on feat: \\\" << featIndices[i] << \\\" is \\\" << A(i+1) << endl;\\n node->coefficients[featIndices[i]] = A(i+1);\\n if (A(i+1) != 0)\\n nlmFeats++;\\n }\\n if (DTDEBUG || LMDEBUG) cout << \\\"constant is \\\" << a << endl;\\n node->constant = a;\\n\\n\\n }\\n\\n CatchAll {\\n // had an error trying the linear regression.\\n // HACK TODO: for now, turn off one variable\\n //cout << ntimes << \\\" linear regression had exception\\\" << endl;\\n //<< BaseException::what() <<endl;\\n\\n // tried this already, stop now\\n if (ntimes > 1 || nFeats < 2){\\n //cout << \\\"max regression\\\" << endl;\\n doRegression = false;\\n break;\\n }\\n for (unsigned j = 0; j < featureMask.size(); j++){\\n if (featureMask[j]){\\n //cout << \\\"remove feature \\\" << j << endl;\\n featureMask[j] = false;\\n nFeats--;\\n break;\\n }\\n }\\n continue;\\n }\\n\\n // it worked, dont need to do it again\\n doRegression = false;\\n\\n }\\n\\n // return # features used\\n return nlmFeats;\\n\\n}\\n\\nint M5Tree::fitSimpleLinearModel(tree_node *node,\\n const std::vector<tree_experience*> &instances,\\n std::vector<bool> featureMask,\\n int nFeats, float* resSum){\\n if(DTDEBUG || LMDEBUG) cout << \\\"fitSimpleLinearModel, node=\\\" << node->id\\n << \\\",nInstances:\\\" << instances.size() << endl;\\n \\n // make sure there are enough coefficients for all the features\\n if (node->coefficients.size() != instances[0]->input.size()){\\n node->coefficients.resize(instances[0]->input.size(), 0);\\n }\\n\\n // loop through all features, try simple single variable regression\\n // keep track of error/coefficient of each\\n int bestFeat = -1;\\n float bestCoeff = -1;\\n float bestError = 1000000;\\n float bestConstant = -1;\\n\\n std::vector<float> xsum(instances[0]->input.size(), 0);\\n std::vector<float> xysum(instances[0]->input.size(), 0);\\n std::vector<float> x2sum(instances[0]->input.size(), 0);\\n float ysum = 0;\\n\\n int nObs = (int)instances.size();\\n for (int i = 0; i < nObs; i++){\\n tree_experience *e = instances[i];\\n if (LMDEBUG) cout << \\\"Obs: \\\" << i;\\n \\n // go through all features\\n for (unsigned j = 0; j < instances[0]->input.size(); j++){\\n if (!featureMask[j]) continue;\\n if (LMDEBUG) cout << \\\", F\\\" << j << \\\": \\\" << e->input[j];\\n xsum[j] += e->input[j];\\n xysum[j] += (e->input[j] * e->output);\\n x2sum[j] += (e->input[j] * e->input[j]);\\n }\\n ysum += e->output;\\n if (LMDEBUG) cout << \\\", out: \\\" << e->output << endl;\\n }\\n \\n // now go through all features and calc coeff and constant\\n for (unsigned j = 0; j < instances[0]->input.size(); j++){\\n if (!featureMask[j]) continue;\\n /*\\n if (rng.uniform() < featPct){\\n continue;\\n }\\n */\\n float coeff = (xysum[j] - xsum[j]*ysum/nObs)/(x2sum[j]-(xsum[j]*xsum[j])/nObs);\\n float constant = (ysum/nObs) - coeff*(xsum[j]/nObs);\\n \\n if (LMDEBUG) {\\n cout << \\\"Feat \\\" << j << \\\" coeff: \\\" << coeff << \\\", constant: \\\" \\n << constant << \\\" mask: \\\" << featureMask[j] << endl;\\n }\\n\\n // now try to make predictions and see what error is\\n float errorSum = 0;\\n for (int i = 0; i < nObs; i++){\\n tree_experience *e = instances[i];\\n float pred = constant + coeff * e->input[j];\\n float error = fabs(pred - e->output);\\n if (LMDEBUG) cout << \\\"Instance \\\" << i << \\\" error: \\\" << error << endl;\\n errorSum += error;\\n }\\n if (LMDEBUG) cout << \\\"eSum: \\\" << errorSum << endl;\\n\\n // check if this is the best\\n if (errorSum < bestError){\\n bestError = errorSum;\\n bestFeat = j;\\n bestConstant = constant;\\n bestCoeff = coeff;\\n }\\n }\\n \\n if (LMDEBUG){\\n cout << \\\"SLM feat: \\\" << bestFeat << \\\" coeff: \\\" \\n << bestCoeff << \\\" constant: \\\" \\n << bestConstant << \\\" avgE: \\\" << (bestError/nObs) << endl;\\n }\\n\\n if (bestFeat < 0 || bestFeat > (int)instances[0]->input.size()){\\n node->constant = 0.0;\\n for (unsigned i = 0; i < node->coefficients.size(); i++){\\n node->coefficients[i] = 0.0;\\n }\\n (*resSum) = 1000000;\\n return 10;\\n }\\n\\n // pick best feature somehow\\n node->constant = bestConstant;\\n for (unsigned i = 0; i < node->coefficients.size(); i++){\\n if (i == (unsigned)bestFeat){\\n node->coefficients[i] = bestCoeff;\\n if (LMDEBUG) cout << \\\"Set coefficient on feat \\\" << i << \\\" to \\\" << bestCoeff << endl;\\n }\\n else {\\n node->coefficients[i] = 0.0;\\n }\\n }\\n\\n (*resSum) = bestError;\\n\\n // only use 1 input feature this way\\n return 1;\\n \\n}\\n\\n\\n\\n\\nvoid M5Tree::implementSplit(tree_node* node, \\n const std::vector<tree_experience*> &instances,\\n float bestSDR, int bestDim,\\n float bestVal, \\n const std::vector<tree_experience*> &bestLeft,\\n const std::vector<tree_experience*> &bestRight,\\n bool changed){\\n if (DTDEBUG) cout << \\\"implementSplit node=\\\" << node->id\\n << \\\",sdr=\\\" << bestSDR\\n << \\\",dim=\\\" << bestDim\\n << \\\",val=\\\" << bestVal \\n << \\\",chg=\\\" << changed << endl;\\n\\n\\n // see if this should still be a leaf node\\n if (bestSDR < MIN_SDR){\\n makeLeaf(node);\\n float valSum = 0;\\n for (unsigned i = 0; i < instances.size(); i++){\\n valSum += instances[i]->output;\\n }\\n float avg = valSum / (float)(instances.size());\\n node->constant = avg;\\n if (SPLITDEBUG || STOCH_DEBUG){\\n cout << \\\"DT \\\" << id << \\\" Node \\\" << node->id << \\\" Poor sdr \\\"\\n << node->nInstances\\n << \\\" instances classified at leaf \\\" << node->id\\n << \\\" with multiple outputs \\\" << endl;\\n }\\n return;\\n }\\n\\n // see if this split changed or not\\n // assuming no changes above\\n if (!changed && node->dim == bestDim && node->val == bestVal\\n && !node->leaf && node->l != NULL && node->r != NULL){\\n // same split as before.\\n if (DTDEBUG || SPLITDEBUG) cout << \\\"Same split as before\\\" << endl;\\n\\n // see which leaf changed\\n if (bestLeft.size() > (unsigned)node->l->nInstances){\\n // redo left side\\n if (DTDEBUG) cout << \\\"Rebuild left side of tree\\\" << endl;\\n buildTree(node->l, bestLeft, changed);\\n }\\n\\n if (bestRight.size() > (unsigned)node->r->nInstances){\\n // redo right side\\n if (DTDEBUG) cout << \\\"Rebuild right side of tree\\\" << endl;\\n buildTree(node->r, bestRight, changed);\\n }\\n return;\\n }\\n\\n\\n // totally new\\n // set the best split here\\n node->leaf = false;\\n node->dim = bestDim;\\n node->val = bestVal;\\n\\n if (SPLITDEBUG) cout << \\\"Best split was cut with val \\\" << node->val\\n << \\\" on dim \\\" << node->dim\\n << \\\" with sdr: \\\" << bestSDR << endl;\\n\\n if (DTDEBUG) cout << \\\"Left has \\\" << bestLeft.size()\\n << \\\", right has \\\" << bestRight.size() << endl;\\n\\n // make sure both instances\\n if (bestLeft.size() == 0 || bestRight.size() == 0){\\n cout << \\\"ERROR: DT \\\" << id << \\\" node \\\" << node->id << \\\" has 0 instances: left: \\\" << bestLeft.size()\\n << \\\" right: \\\" << bestRight.size() << endl;\\n cout << \\\"Split was cut with val \\\" << node->val\\n << \\\" on dim \\\" << node->dim\\n << \\\" with sdr: \\\" << bestSDR << endl;\\n exit(-1);\\n }\\n\\n\\n // check if these already exist\\n if (node->l == NULL){\\n if (DTDEBUG) cout << \\\"Init new left tree nodes \\\" << endl;\\n node->l = allocateNode();\\n }\\n if (node->r == NULL){\\n if (DTDEBUG) cout << \\\"Init new right tree nodes \\\" << endl;\\n node->r = allocateNode();\\n }\\n\\n // recursively build the sub-trees to this one\\n if (DTDEBUG) cout << \\\"Building left tree for node \\\" << node->id << endl;\\n buildTree(node->l, bestLeft, true);\\n if (DTDEBUG) cout << \\\"Building right tree for node \\\" << node->id << endl;\\n buildTree(node->r, bestRight, true);\\n\\n}\\n\\n\\nvoid M5Tree::getFeatsUsed(tree_node* node, std::vector<bool> *featsUsed){\\n\\n // if leaf, ones from linear model\\n if (node->leaf){\\n //cout << \\\"coeff size: \\\" << node->coefficients.size() << endl;\\n for (unsigned i = 0; i < node->coefficients.size(); i++){\\n if (node->coefficients[i] != 0){\\n if (LMDEBUG || DTDEBUG) cout << \\\"Leaf node, used coeff \\\" << i << endl;\\n (*featsUsed)[i] = true;\\n }\\n }\\n return;\\n }\\n\\n // otherwise see what split was used\\n // and call for left and right sub-trees\\n (*featsUsed)[node->dim] = true;\\n if (LMDEBUG || DTDEBUG) cout << \\\"Split node, used feat \\\" << node->dim << endl;\\n\\n getFeatsUsed(node->l, featsUsed);\\n getFeatsUsed(node->r, featsUsed);\\n\\n return;\\n}\\n\\n\\nvoid M5Tree::testPossibleSplits(const std::vector<tree_experience*> &instances,\\n float *bestSDR, int *bestDim,\\n float *bestVal, \\n std::vector<tree_experience*> *bestLeft,\\n std::vector<tree_experience*> *bestRight) {\\n if (DTDEBUG) cout << \\\"testPossibleSplits\\\" << endl;\\n\\n\\n // calculate sd for the set\\n float sd = calcSDforSet(instances);\\n //if (DTDEBUG) cout << \\\"I: \\\" << I << endl;\\n\\n int nties = 0;\\n\\n // for each possible split, calc standard deviation reduction\\n for (unsigned idim = 0; idim < instances[0]->input.size(); idim++){\\n\\n //float* sorted = sortOnDim(idim, instances);\\n float minVal, maxVal;\\n std::set<float> uniques = getUniques(idim, instances, minVal, maxVal);\\n\\n for (std::set<float>::iterator j = uniques.begin(); j != uniques.end(); j++){\\n\\n // skip max val, not a valid cut for either\\n if ((*j) == maxVal)\\n continue;\\n\\n // if this is a random forest, we eliminate some random number of splits\\n // here (decision is taken from the random set that are left)\\n if (rng.uniform() < featPct)\\n continue;\\n\\n std::vector<tree_experience*> left;\\n std::vector<tree_experience*> right;\\n\\n // splits that are cuts\\n float splitval = (*j);\\n float sdr = calcSDR(idim, splitval, instances, sd, left, right);\\n\\n if (SPLITDEBUG) cout << \\\" CUT split val \\\" << splitval\\n << \\\" on dim: \\\" << idim << \\\" had sdr \\\"\\n << sdr << endl;\\n\\n // see if this is the new best sdr\\n compareSplits(sdr, idim, splitval, left, right, &nties,\\n bestSDR, bestDim, bestVal, bestLeft, bestRight);\\n\\n\\n } // j loop\\n }\\n}\\n\\n\\n\\nvoid M5Tree::compareSplits(float sdr, int dim, float val, \\n const std::vector<tree_experience*> &left, \\n const std::vector<tree_experience*> &right,\\n int *nties, float *bestSDR, int *bestDim,\\n float *bestVal, \\n std::vector<tree_experience*> *bestLeft,\\n std::vector<tree_experience*> *bestRight){\\n if (DTDEBUG) cout << \\\"compareSplits sdr=\\\" << sdr << \\\",dim=\\\" << dim\\n << \\\",val=\\\" << val <<endl;\\n\\n\\n bool newBest = false;\\n\\n // if its a virtual tie, break it randomly\\n if (fabs(*bestSDR - sdr) < SPLIT_MARGIN){\\n //cout << \\\"Split tie, making random decision\\\" << endl;\\n\\n (*nties)++;\\n float randomval = rng.uniform(0,1);\\n float newsplitprob = (1.0 / (float)*nties);\\n\\n if (randomval < newsplitprob){\\n newBest = true;\\n if (SPLITDEBUG) cout << \\\" Tie on split. DT: \\\" << id << \\\" rand: \\\" << randomval\\n << \\\" splitProb: \\\" << newsplitprob << \\\", selecting new split \\\" << endl;\\n }\\n else\\n if (SPLITDEBUG) cout << \\\" Tie on split. DT: \\\" << id << \\\" rand: \\\" << randomval\\n << \\\" splitProb: \\\" << newsplitprob << \\\", staying with old split \\\" << endl;\\n }\\n\\n // if its clearly better, set this as the best split\\n else if (sdr > *bestSDR){\\n newBest = true;\\n *nties = 1;\\n }\\n\\n\\n // set the split features\\n if (newBest){\\n *bestSDR = sdr;\\n *bestDim = dim;\\n *bestVal = val;\\n *bestLeft = left;\\n *bestRight = right;\\n if (SPLITDEBUG){\\n cout << \\\" New best sdr: \\\" << *bestSDR\\n << \\\" with val \\\" << *bestVal\\n << \\\" on dim \\\" << *bestDim << endl;\\n }\\n } // newbest\\n}\\n\\nfloat M5Tree::calcSDR(int dim, float val, \\n const std::vector<tree_experience*> &instances,\\n float sd,\\n std::vector<tree_experience*> &left,\\n std::vector<tree_experience*> &right){\\n if (DTDEBUG) cout << \\\"calcSDR, dim=\\\" << dim\\n << \\\" val=\\\" << val\\n << \\\" sd=\\\" << sd\\n << \\\" nInstances= \\\" << instances.size() << endl;\\n\\n left.clear();\\n right.clear();\\n\\n // split into two sides\\n for (unsigned i = 0; i < instances.size(); i++){\\n if (DTDEBUG) cout << \\\"calcSDR - Classify instance \\\" << i \\n << \\\" on new split \\\" << endl;\\n\\n if (passTest(dim, val, instances[i]->input)){\\n left.push_back(instances[i]);\\n }\\n else{\\n right.push_back(instances[i]);\\n }\\n }\\n\\n if (DTDEBUG) cout << \\\"Left has \\\" << left.size()\\n << \\\", right has \\\" << right.size() << endl;\\n\\n // get sd for both sides\\n float sdLeft = calcSDforSet(left);\\n float sdRight = calcSDforSet(right);\\n\\n float leftRatio = (float)left.size() / (float)instances.size();\\n float rightRatio = (float)right.size() / (float)instances.size();\\n\\n float sdr = sd - (leftRatio * sdLeft + rightRatio * sdRight);\\n\\n if (DTDEBUG){\\n cout << \\\"LeftSD: \\\" << sdLeft\\n << \\\" RightSD: \\\" << sdRight\\n << \\\" SD: \\\" << sd\\n << \\\" SDR: \\\" << sdr\\n << endl;\\n }\\n\\n return sdr;\\n\\n}\\n\\nfloat M5Tree::calcSDforSet(const std::vector<tree_experience*> &instances){\\n if (DTDEBUG) cout << \\\"calcSDforSet\\\" << endl;\\n\\n int n = instances.size();\\n\\n if (n == 0)\\n return 0;\\n\\n double sum = 0;\\n double sumSqr = 0;\\n\\n // go through instances and calculate sums, sum of squares\\n for (unsigned i = 0; i < instances.size(); i++){\\n float val = instances[i]->output;\\n sum += val;\\n sumSqr += (val * val);\\n }\\n\\n double mean = sum / (double)n;\\n double variance = (sumSqr - sum*mean)/(double)n;\\n float sd = sqrt(variance);\\n\\n return sd;\\n\\n}\\n\\nstd::set<float> M5Tree::getUniques(int dim, const std::vector<tree_experience*> &instances, float& minVal, float& maxVal){\\n if (DTDEBUG) cout << \\\"getUniques,dim = \\\" << dim;\\n\\n std::set<float> uniques;\\n\\n for (int i = 0; i < (int)instances.size(); i++){\\n if (i == 0 || instances[i]->input[dim] < minVal)\\n minVal = instances[i]->input[dim];\\n if (i == 0 || instances[i]->input[dim] > maxVal)\\n maxVal = instances[i]->input[dim];\\n\\n uniques.insert(instances[i]->input[dim]);\\n }\\n\\n // lets not try more than 100 possible splits per dimension\\n if (uniques.size() > 100){\\n float rangeInc = (maxVal - minVal) / 100.0;\\n uniques.clear();\\n for (float i = minVal; i < maxVal; i+= rangeInc){\\n uniques.insert(i);\\n }\\n }\\n uniques.insert(maxVal);\\n\\n if (DTDEBUG) cout << \\\" #: \\\" << uniques.size() << endl;\\n return uniques;\\n}\\n\\n\\nfloat* M5Tree::sortOnDim(int dim, const std::vector<tree_experience*> &instances){\\n if (DTDEBUG) cout << \\\"sortOnDim,dim = \\\" << dim << endl;\\n\\n float* values = new float[instances.size()];\\n\\n for (int i = 0; i < (int)instances.size(); i++){\\n //cout << \\\"Instance \\\" << i << endl;\\n\\n float val = instances[i]->input[dim];\\n //cout << \\\" val: \\\" << val << endl;\\n\\n // find where this should go\\n for (int j = 0; j <= i; j++){\\n //cout << \\\" j: \\\" << j << endl;\\n\\n // get to i, this is the spot then\\n if (j==i){\\n values[j] = val;\\n //cout << \\\" At i, putting value in slot j: \\\" << j << endl;\\n }\\n\\n // if this is the spot\\n else if (val < values[j]){\\n //cout << \\\" Found slot at j: \\\" << j << endl;\\n\\n // slide everything forward to make room\\n for (int k = i; k > j; k--){\\n //cout << \\\" k = \\\" << k << \\\" Sliding value from k-1 to k\\\" << endl;\\n values[k] = values[k-1];\\n }\\n\\n // put value in its spot at j\\n //cout << \\\" Putting value at slot j: \\\" << j << endl;\\n values[j] = val;\\n\\n // break\\n break;\\n }\\n\\n }\\n }\\n\\n if (DTDEBUG){\\n cout << \\\"Sorted array: \\\" << values[0];\\n for (int i = 1; i < (int)instances.size(); i++){\\n cout << \\\", \\\" << values[i];\\n }\\n cout << endl;\\n }\\n\\n return values;\\n\\n}\\n\\n\\nvoid M5Tree::printTree(tree_node *t, int level){\\n\\n for (int i = 0; i < level; i++){\\n cout << \\\".\\\";\\n }\\n\\n cout << \\\"Node \\\" << t->id << \\\" nInstances: \\\" << t->nInstances ;\\n\\n // Leaf, print regression stuff\\n if (t->leaf){\\n cout << \\\" Constant: \\\" << t->constant << \\\" coeff: \\\";\\n for (unsigned j = 0; j < t->coefficients.size(); j++){\\n if (!SIMPLE)\\n cout << t->coefficients[j] << \\\", \\\";\\n if (SIMPLE && t->coefficients[j] != 0)\\n cout << \\\" on feat \\\" << j << \\\": \\\" << t->coefficients[j];\\n }\\n cout << endl;\\n return;\\n }\\n\\n // otherwise, print split info, next nodes\\n\\n cout << \\\" Type: CUT\\\";\\n cout << \\\" Dim: \\\" << t->dim << \\\" Val: \\\" << t->val;\\n cout << \\\" Left: \\\" << t->l->id << \\\" Right: \\\" << t->r->id << endl;\\n\\n // print children\\n if (t->dim != -1 && !t->leaf){\\n printTree(t->l, level+1);\\n printTree(t->r, level+1);\\n }\\n\\n}\\n\\n\\n// output a map of outcomes and their probabilities for this leaf node\\nvoid M5Tree::leafPrediction(tree_node* leaf, const std::vector<float> &input, std::map<float, float>* retval){\\n if (DTDEBUG)\\n cout << \\\"Calculating output for leaf \\\" << leaf->id << endl;\\n\\n float prediction = leaf->constant;\\n if (DTDEBUG) cout << \\\"leaf constant: \\\" << leaf->constant << endl;\\n\\n // plus each coefficient\\n for (unsigned i = 0; i < input.size(); i++){\\n prediction += leaf->coefficients[i] * input[i];\\n if (DTDEBUG) {\\n cout << \\\"feat \\\" << i << \\\" coeff: \\\" << leaf->coefficients[i]\\n << \\\" on input \\\" << input[i] << endl;\\n }\\n }\\n\\n if (DTDEBUG) cout << \\\" prediction: \\\" << prediction << endl;\\n\\n (*retval)[prediction] = 1.0;\\n}\\n\\n\\nvoid M5Tree::initNodes(){\\n\\n for (int i = 0; i < N_M5_NODES; i++){\\n initTreeNode(&(allNodes[i]));\\n freeNodes.push_back(i);\\n if (NODEDEBUG) \\n cout << \\\"init node \\\" << i << \\\" with id \\\" << allNodes[i].id \\n << \\\", now \\\" << freeNodes.size() << \\\" free nodes.\\\" << endl;\\n }\\n\\n}\\n\\nM5Tree::tree_node* M5Tree::allocateNode(){\\n if (freeNodes.empty()){\\n tree_node* newNode = new tree_node;\\n initTreeNode(newNode);\\n if (NODEDEBUG) \\n cout << \\\"PROBLEM: No more pre-allocated nodes!!!\\\" << endl\\n << \\\"return new node \\\" << newNode->id \\n << \\\", now \\\" << freeNodes.size() << \\\" free nodes.\\\" << endl;\\n return newNode;\\n }\\n\\n int i = freeNodes.back();\\n freeNodes.pop_back();\\n if (NODEDEBUG) \\n cout << \\\"allocate node \\\" << i << \\\" with id \\\" << allNodes[i].id \\n << \\\", now \\\" << freeNodes.size() << \\\" free nodes.\\\" << endl;\\n return &(allNodes[i]);\\n}\\n\\nvoid M5Tree::deallocateNode(tree_node* node){\\n if (node->id >= N_M5_NODES){\\n if (NODEDEBUG) \\n cout << \\\"dealloc extra node id \\\" << node->id \\n << \\\", now \\\" << freeNodes.size() << \\\" free nodes.\\\" << endl;\\n delete node;\\n return;\\n }\\n\\n freeNodes.push_back(node->id);\\n if (NODEDEBUG) \\n cout << \\\"dealloc node \\\" << node->id \\n << \\\", now \\\" << freeNodes.size() << \\\" free nodes.\\\" << endl;\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6485013365745544,\n \"alphanum_fraction\": 0.6498637795448303,\n \"avg_line_length\": 21.24242401123047,\n \"blob_id\": \"78bd435b874e44fd0a7f84414ec989ee0bbf845c\",\n \"content_id\": \"69e34b24d7b1882c6e1c684c23df466d5f7e7164\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 734,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 33,\n \"path\": \"/src/roundbot_control/src/MotorSim.cpp\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include <roundbot_control/MotorSim.h>\\n\\nnamespace roundbot_control{\\n\\nMotorSim::MotorSim(double max_speed, double max_accel, double max_decel)\\n{\\n current_speed_ = 0;\\n max_speed_ = max_speed;\\n max_accel_ = max_accel;\\n max_decel_ = max_decel;\\n}\\n\\ndouble MotorSim::iterate(double target_speed, double dt)\\n{\\n double target_accel = (target_speed - current_speed_) / dt;\\n\\n if (target_accel > max_accel_){\\n target_accel = max_accel_;\\n }else if (target_accel < -max_decel_){\\n target_accel = -max_decel_;\\n }\\n\\n current_speed_ += (dt * target_accel);\\n if (current_speed_ > max_speed_){\\n current_speed_ = max_speed_;\\n }else if (current_speed_ < -max_speed_){\\n current_speed_ = -max_speed_;\\n }\\n\\n return current_speed_;\\n}\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7360248565673828,\n \"alphanum_fraction\": 0.7453415989875793,\n \"avg_line_length\": 45.14285659790039,\n \"blob_id\": \"7db568eb77b8805a09cc7d163c40a14f8703acd1\",\n \"content_id\": \"70d4af0e785a671025579586d5080238ae9c701b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 322,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 7,\n \"path\": \"/src/ugv_course_libs/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"ugv_course_libs\\\")\\nset(ugv_course_libs_MAINTAINER \\\"micho <micho@todo.todo>\\\")\\nset(ugv_course_libs_DEPRECATED \\\"\\\")\\nset(ugv_course_libs_VERSION \\\"0.0.0\\\")\\nset(ugv_course_libs_BUILD_DEPENDS \\\"sensor_msgs\\\" \\\"tf\\\")\\nset(ugv_course_libs_RUN_DEPENDS \\\"sensor_msgs\\\" \\\"tf\\\")\\nset(ugv_course_libs_BUILDTOOL_DEPENDS \\\"catkin\\\")\"\n },\n {\n \"alpha_fraction\": 0.602712869644165,\n \"alphanum_fraction\": 0.6084104180335999,\n \"avg_line_length\": 26.7864933013916,\n \"blob_id\": \"8aea4675025d25e599395537e1fa27da4e1f7486\",\n \"content_id\": \"7151fd6171cc77a782e97d03293f8b1106aa2b38\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 38262,\n \"license_type\": \"no_license\",\n \"max_line_length\": 180,\n \"num_lines\": 1377,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Planners/PO_ParallelETUCT.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file PO_ParallelETUCT.cc\\n Implements my real-time model-based RL architecture which uses UCT with eligiblity traces for planning. This version of UCT plans over states augmented with k-action histories.\\n The modified version of UCT used is presented in:\\n L. Kocsis and C. Szepesv´ari, \\\"Bandit based monte-carlo planning,\\\" in\\n ECML-06. Number 4212 in LNCS. Springer, 2006, pp. 282-293.\\n The real-time architecture is presented in:\\n Hester, Quinlan, and Stone, \\\"A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control\\\", arXiv 1105.1749, 2011.\\n \\\\author Todd Hester\\n*/\\n\\n#include \\\"PO_ParallelETUCT.hh\\\"\\n#include <algorithm>\\n\\n#include <sys/time.h>\\n\\n\\nPO_ParallelETUCT::PO_ParallelETUCT(int numactions, float gamma, float rrange, float lambda,\\n int MAX_ITER, float MAX_TIME, int MAX_DEPTH, int modelType,\\n const std::vector<float> &fmax, const std::vector<float> &fmin,\\n const std::vector<int> &nstatesPerDim, bool trackActual, int historySize, Random r):\\n numactions(numactions), gamma(gamma), rrange(rrange), lambda(lambda),\\n MAX_ITER(MAX_ITER), MAX_TIME(MAX_TIME),\\n MAX_DEPTH(MAX_DEPTH), modelType(modelType), statesPerDim(nstatesPerDim),\\n trackActual(trackActual), HISTORY_SIZE(historySize),\\n HISTORY_FL_SIZE(historySize*numactions)\\n{\\n rng = r;\\n\\n nstates = 0;\\n nsaved = 0;\\n nactions = 0;\\n lastUpdate = -1;\\n\\n seedMode = false;\\n timingType = true;\\n\\n previnfo = NULL;\\n model = NULL;\\n planTime = getSeconds();\\n initTime = getSeconds();\\n setTime = getSeconds();\\n\\n PLANNERDEBUG = false;\\n POLICYDEBUG = false; //true; //false; //true; //false;\\n ACTDEBUG = false; //true;\\n MODELDEBUG = false;\\n UCTDEBUG = false;//true; //false;\\n PTHREADDEBUG = false;\\n ATHREADDEBUG = false;// true;\\n MTHREADDEBUG = false;//true;\\n TIMINGDEBUG = false;\\n REALSTATEDEBUG = false;\\n HISTORYDEBUG = false;//true;\\n\\n if (statesPerDim[0] > 0){\\n cout << \\\"Planner Parallel ETUCT using discretization of \\\" << statesPerDim[0] << endl;\\n }\\n if (trackActual){\\n cout << \\\"Parallel ETUCT tracking real state values\\\" << endl;\\n }\\n\\n featmax = fmax;\\n featmin = fmin;\\n\\n pthread_mutex_init(&update_mutex, NULL);\\n pthread_mutex_init(&nactions_mutex, NULL);\\n pthread_mutex_init(&history_mutex, NULL);\\n pthread_mutex_init(&plan_state_mutex, NULL);\\n pthread_mutex_init(&statespace_mutex, NULL);\\n pthread_mutex_init(&model_mutex, NULL);\\n pthread_mutex_init(&list_mutex, NULL);\\n pthread_cond_init(&list_cond, NULL);\\n\\n // start parallel search thread\\n actualPlanState = std::vector<float>(featmax.size());\\n discPlanState = NULL;\\n modelThreadStarted = false;\\n planThreadStarted = false;\\n expList.clear();\\n\\n if (HISTORY_SIZE == 0){\\n saHistory.push_back(0.0);\\n }\\n else {\\n if (HISTORYDEBUG) {\\n cout << \\\"History size of \\\" << HISTORY_SIZE\\n << \\\" float size of \\\" << HISTORY_FL_SIZE\\n << \\\" with state size: \\\" << fmin.size()\\n << \\\" and numact: \\\" << numactions << endl;\\n }\\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\\n saHistory.push_back(0.0);\\n }\\n }\\n\\n // initStates();\\n expfile.initFile(\\\"experiences.bin\\\", featmax.size());\\n}\\n\\nPO_ParallelETUCT::~PO_ParallelETUCT() {\\n // join threads\\n\\n //pthread_kill(planThread);\\n //pthread_kill(modelThread);\\n\\n pthread_detach(planThread);//, NULL);\\n pthread_detach(modelThread);//, NULL);\\n\\n pthread_cancel(planThread);//, NULL);\\n pthread_cancel(modelThread);//, NULL);\\n\\n //pthread_join(planThread, NULL);\\n //pthread_join(modelThread, NULL);\\n\\n //pthread_detach(planThread);//, NULL);\\n //pthread_detach(modelThread);//, NULL);\\n\\n\\n pthread_mutex_lock(&plan_state_mutex);\\n pthread_mutex_lock(&statespace_mutex);\\n pthread_mutex_lock(&model_mutex);\\n pthread_mutex_lock(&list_mutex);\\n pthread_mutex_lock(&history_mutex);\\n\\n // delete exp list\\n expList.clear();\\n\\n for (std::map<state_t, state_info>::iterator i = statedata.begin();\\n i != statedata.end(); i++){\\n\\n // get state's info\\n //cout << \\\" planner got info\\\" << endl;\\n state_info* info = &((*i).second);\\n\\n deleteInfo(info);\\n }\\n\\n featmax.clear();\\n featmin.clear();\\n\\n statespace.clear();\\n statedata.clear();\\n\\n pthread_mutex_unlock(&plan_state_mutex);\\n pthread_mutex_unlock(&statespace_mutex);\\n pthread_mutex_unlock(&model_mutex);\\n pthread_mutex_unlock(&list_mutex);\\n pthread_mutex_unlock(&history_mutex);\\n\\n}\\n\\nvoid PO_ParallelETUCT::setModel(MDPModel* m){\\n\\n model = m;\\n\\n}\\n\\n\\n/////////////////////////////\\n// Functional functions :) //\\n/////////////////////////////\\n\\n\\n\\nbool PO_ParallelETUCT::updateModelWithExperience(const std::vector<float> &laststate,\\n int lastact,\\n const std::vector<float> &currstate,\\n float reward, bool term){\\n // if (PLANNERDEBUG) cout << \\\"updateModelWithExperience(last = \\\" << &laststate\\n // << \\\", curr = \\\" << &currstate\\n // << \\\", lastact = \\\" << lastact\\n // << \\\", r = \\\" << reward\\n // << \\\", term = \\\" << term\\n // << \\\")\\\" << endl;\\n\\n //cout << \\\"updateModel\\\" << endl << flush;\\n\\n if (!timingType)\\n planTime = getSeconds();\\n initTime = getSeconds();\\n\\n // canonicalize these things\\n state_t last = NULL;\\n\\n // add one history to last state\\n if (HISTORY_SIZE > 0){\\n std::vector<float> modState = laststate;\\n if (HISTORYDEBUG) {\\n cout << \\\"Original state vector (size \\\" << modState.size() << \\\": \\\" << modState[0];\\n for (unsigned i = 1; i < modState.size(); i++){\\n cout << \\\",\\\" << modState[i];\\n }\\n cout << endl;\\n }\\n // add history onto modState\\n pthread_mutex_lock(&history_mutex);\\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\\n modState.push_back(saHistory[i]);\\n }\\n pthread_mutex_unlock(&history_mutex);\\n\\n if (HISTORYDEBUG) {\\n cout << \\\"New state vector (size \\\" << modState.size() << \\\": \\\" << modState[0];\\n for (unsigned i = 1; i < modState.size(); i++){\\n cout << \\\",\\\" << modState[i];\\n }\\n cout << endl;\\n }\\n\\n last = canonicalize(modState);\\n\\n if (!seedMode){\\n // push this state and action onto the history vector\\n /*\\n for (unsigned i = 0; i < last->size(); i++){\\n saHistory.push_back((*last)[i]);\\n saHistory.pop_front();\\n }\\n */\\n pthread_mutex_lock(&history_mutex);\\n for (int i = 0; i < numactions; i++){\\n if (i == lastact)\\n saHistory.push_back(1.0);\\n else\\n saHistory.push_back(0.0);\\n saHistory.pop_front();\\n }\\n if (HISTORYDEBUG) {\\n cout << \\\"New history vector (size \\\" << saHistory.size() << \\\": \\\" << saHistory[0];\\n for (unsigned i = 1; i < saHistory.size(); i++){\\n cout << \\\",\\\" << saHistory[i];\\n }\\n cout << endl;\\n }\\n pthread_mutex_unlock(&history_mutex);\\n }\\n }\\n\\n // no history\\n else {\\n\\n // canonicalize these things\\n last = canonicalize(laststate);\\n }\\n\\n prevstate = last;\\n prevact = lastact;\\n\\n // get state info\\n pthread_mutex_lock(&statespace_mutex);\\n previnfo = &(statedata[last]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n if (MODELDEBUG){\\n cout << \\\"Update with exp from state: \\\";\\n for (unsigned i = 0; i < last->size(); i++){\\n cout << (laststate)[i] << \\\", \\\";\\n }\\n cout << \\\" action: \\\" << lastact;\\n cout << \\\" to state: \\\";\\n for (unsigned i = 0; i < currstate.size(); i++){\\n cout << (currstate)[i] << \\\", \\\";\\n }\\n cout << \\\" and reward: \\\" << reward << endl;\\n }\\n\\n // add experiences to list to later be updated into model\\n if (ATHREADDEBUG)\\n cout << \\\"*** Action thread wants list lock ***\\\" << endl << flush;\\n if (TIMINGDEBUG) cout << \\\"Want list mutex, time: \\\" << (getSeconds()-initTime) << endl;\\n pthread_mutex_lock(&list_mutex);\\n if (TIMINGDEBUG) cout << \\\"got list mutex, time: \\\" << (getSeconds()-initTime) << endl;\\n experience e;\\n e.s = *last;\\n e.next = currstate;\\n e.act = lastact;\\n e.reward = reward;\\n e.terminal = term;\\n\\n expList.push_back(e);\\n //expfile.saveExperience(e);\\n if (ATHREADDEBUG || MTHREADDEBUG)\\n cout << \\\"added exp to list, size: \\\" << expList.size() << endl << flush;\\n if (TIMINGDEBUG) cout << \\\"list updated, time: \\\" << (getSeconds()-initTime) << endl;\\n pthread_cond_signal(&list_cond);\\n pthread_mutex_unlock(&list_mutex);\\n\\n /*\\n if (e.reward > -0.5 && e.reward < 0){\\n expfile.saveExperience(e);\\n nsaved++;\\n cout << \\\"Saved Experience \\\" << e.reward << endl;\\n }\\n */\\n\\n if (timingType)\\n planTime = getSeconds();\\n\\n if (TIMINGDEBUG) cout << \\\"leaving updateModel, time: \\\" << (getSeconds()-initTime) << endl;\\n\\n\\n return false;\\n\\n}\\n\\nvoid PO_ParallelETUCT::updateStateActionFromModel(state_t s, int a, state_info* info){\\n\\n pthread_mutex_lock(&info->statemodel_mutex);\\n StateActionInfo* newModel = &(info->model[a]);\\n updateStateActionHistoryFromModel(*s, a, newModel);\\n pthread_mutex_unlock(&info->statemodel_mutex);\\n\\n}\\n\\nvoid PO_ParallelETUCT::updateStateActionHistoryFromModel(const std::vector<float> modState, int a, StateActionInfo *newModel){\\n\\n // update state info\\n // get state action info for each action\\n pthread_mutex_lock(&model_mutex);\\n\\n model->getStateActionInfo(modState, a, newModel);\\n\\n pthread_mutex_lock(&nactions_mutex);\\n newModel->frameUpdated = nactions;\\n pthread_mutex_unlock(&nactions_mutex);\\n\\n pthread_mutex_unlock(&model_mutex);\\n\\n if (HISTORY_SIZE > 0){\\n\\n // figure out new history\\n std::deque<float> newHistory;\\n int stateSize = modState.size() - HISTORY_FL_SIZE;\\n\\n if (HISTORYDEBUG) cout << \\\"input history was: \\\";\\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\\n newHistory.push_back(modState[i+stateSize]);\\n if (HISTORYDEBUG) cout << modState[i+stateSize] << \\\", \\\";\\n }\\n if (HISTORYDEBUG) cout << endl;\\n\\n // now add on for action\\n for (int i = 0; i < numactions; i++){\\n if (i == a)\\n newHistory.push_back(1.0);\\n else\\n newHistory.push_back(0.0);\\n newHistory.pop_front();\\n }\\n\\n if (HISTORYDEBUG){\\n cout << \\\"act: \\\" << a << \\\", new history:\\\";\\n for (unsigned i = 0; i < newHistory.size(); i++){\\n cout << newHistory[i] << \\\", \\\";\\n }\\n cout << endl;\\n }\\n\\n // add outcome histories onto newModel predictions\\n std::map< std::vector<float>, float> oldProbs = newModel->transitionProbs;\\n newModel->transitionProbs.clear();\\n\\n for (std::map<std::vector<float>, float>::iterator outIt\\n = oldProbs.begin();\\n outIt != oldProbs.end(); outIt++){\\n\\n float prob = (*outIt).second;\\n std::vector<float> next = (*outIt).first;\\n\\n for (unsigned i = 0; i < newHistory.size(); i++){\\n next.push_back(newHistory[i]);\\n }\\n\\n if (HISTORYDEBUG){\\n cout << \\\"add history onto prediction of state: \\\";\\n for (unsigned i = 0; i < next.size(); i++){\\n cout << next[i] << \\\", \\\";\\n }\\n cout << \\\" with prob \\\" << prob << endl;\\n }\\n\\n newModel->transitionProbs[next] = prob;\\n }\\n }\\n\\n\\n //canonNextStates(newModel);\\n\\n}\\n\\nvoid PO_ParallelETUCT::canonNextStates(StateActionInfo* modelInfo){\\n\\n\\n // loop through all next states\\n for (std::map<std::vector<float>, float>::iterator outIt\\n = modelInfo->transitionProbs.begin();\\n outIt != modelInfo->transitionProbs.end(); outIt++){\\n\\n std::vector<float> nextstate = (*outIt).first;\\n bool badState = false;\\n\\n // check that it is valid, otherwise replace with current\\n for (unsigned j = 0; j < featmax.size(); j++){\\n if (nextstate[j] < (featmin[j]-EPSILON)\\n || nextstate[j] > (featmax[j]+EPSILON)){\\n //cout << \\\"next state out of range \\\" << nextstate[j] << endl;\\n badState = true;\\n break;\\n }\\n }\\n\\n if (!badState){\\n canonicalize(nextstate);\\n }\\n }\\n}\\n\\nint PO_ParallelETUCT::getBestAction(const std::vector<float> &state){\\n // if (PLANNERDEBUG) cout << \\\"getBestAction(s = \\\" << &state << \\\")\\\" << endl;\\n\\n pthread_mutex_lock(&nactions_mutex);\\n nactions++;\\n pthread_mutex_unlock(&nactions_mutex);\\n\\n\\n if (TIMINGDEBUG) cout << \\\"getBestAction, time: \\\" << (getSeconds()-initTime) << endl;\\n\\n // add current history on top\\n pthread_mutex_lock(&history_mutex);\\n std::vector<float> modState = state;\\n for (int i = 0; i < HISTORY_FL_SIZE; i++){\\n modState.push_back(saHistory[i]);\\n }\\n pthread_mutex_unlock(&history_mutex);\\n\\n state_t s = canonicalize(modState);\\n\\n // set plan state so uct will search from here\\n if (ATHREADDEBUG)\\n cout << \\\"*** Action thread wants plan state lock ***\\\" << endl << flush;\\n if (TIMINGDEBUG) cout << \\\"want planStateMut, time: \\\" << (getSeconds()-initTime) << endl;\\n\\n pthread_mutex_lock(&(plan_state_mutex));\\n if (TIMINGDEBUG) cout << \\\"got planStateMut, time: \\\" << (getSeconds()-initTime) << endl;\\n\\n actualPlanState = modState;\\n discPlanState = s;\\n setTime = getSeconds();\\n\\n if (ATHREADDEBUG){\\n cout << \\\"Set planning state as: \\\";\\n for (unsigned i = 0; i < modState.size(); i++){\\n cout << modState[i] << \\\", \\\";\\n }\\n cout << endl << flush;\\n }\\n\\n // call uct search on it\\n pthread_mutex_unlock(&(plan_state_mutex));\\n if (TIMINGDEBUG) cout << \\\"set planState, time: \\\" << (getSeconds()-initTime) << endl;\\n\\n // get state info\\n pthread_mutex_lock(&statespace_mutex);\\n state_info* info = &(statedata[s]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n // wait a bit for some planning from this state\\n\\n // depending on how you run the code, this has to be setup differently\\n // if someone else calls this method at the appropriate rate, do nothing here\\n\\n // or this can be where we wait to ensure we run at some rate:\\n while (((getSeconds()- initTime) < MAX_TIME)){\\n if (TIMINGDEBUG)\\n cout << \\\"waiting for time: \\\" << (getSeconds()-initTime) << endl;\\n\\n pthread_yield();\\n }\\n\\n if (TIMINGDEBUG) cout << \\\"time up: \\\" << (getSeconds()-initTime) << endl;\\n\\n if (TIMINGDEBUG && (getSeconds()-initTime) > 0.15) cout << \\\"**********\\\" << endl;\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n // Get Q values\\n std::vector<float> &Q = info->Q;\\n\\n\\n if (ATHREADDEBUG) {\\n if (previnfo != NULL)\\n cout << \\\" ... now \\\" << previnfo->uctVisits << \\\" times.\\\" << endl;\\n cout << \\\"Getting best action from state \\\";\\n for (unsigned i = 0; i < s->size(); i++){\\n cout << (*s)[i] << \\\", \\\";\\n }\\n cout << \\\" sampled \\\" << info->uctVisits << \\\" times.\\\";// << endl << flush;\\n }\\n\\n // Choose an action\\n const std::vector<float>::iterator a =\\n random_max_element(Q.begin(), Q.end()); // Choose maximum\\n int act = a - Q.begin();\\n\\n if (TIMINGDEBUG) cout << \\\"got action: \\\" << (getSeconds()-initTime) << endl;\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n // return index of action\\n return act;\\n}\\n\\n\\n\\n\\n\\n\\nvoid PO_ParallelETUCT::planOnNewModel(){\\n //return;\\n // cout << \\\"planOnNewModel\\\" << endl << flush;\\n // start model learning thread here\\n if (!modelThreadStarted){\\n modelThreadStarted = true;\\n pthread_create(&modelThread, NULL, poParallelModelLearningStart, this);\\n }\\n\\n if (!planThreadStarted){\\n planThreadStarted = true;\\n pthread_create(&(planThread), NULL, poParallelSearchStart, this);\\n }\\n\\n}\\n\\nvoid* poParallelModelLearningStart(void* arg){\\n cout << \\\"Start model learning thread\\\" << endl << flush;\\n PO_ParallelETUCT* pe = reinterpret_cast<PO_ParallelETUCT*>(arg);\\n while(true){\\n pe->parallelModelLearning();\\n /*\\n if (!pe->planThreadStarted){\\n pe->planThreadStarted = true;\\n pthread_create(&(pe->planThread), NULL, poParallelSearchStart, pe);\\n }\\n */\\n }\\n return NULL;\\n}\\n\\nvoid PO_ParallelETUCT::parallelModelLearning(){\\n //while(true){\\n\\n // wait for experience list to be non-empty\\n pthread_mutex_lock(&list_mutex);\\n while (expList.size() == 0){\\n pthread_cond_wait(&list_cond,&list_mutex);\\n }\\n pthread_mutex_unlock(&list_mutex);\\n\\n // copy over experience list\\n std::vector<experience> updateList;\\n if (MTHREADDEBUG) cout << \\\" *** Model thread wants list lock ***\\\" << endl << flush;\\n pthread_mutex_lock(&list_mutex);\\n updateList = expList;\\n expList.clear();\\n if (MTHREADDEBUG) cout << \\\" *** Model thread done with list lock ***\\\" << endl << flush;\\n pthread_mutex_unlock(&list_mutex);\\n\\n /*\\n // update model\\n //cout << \\\"*** Model thread wants tree lock ***\\\" << endl << flush;\\n pthread_mutex_lock(&model_mutex);\\n if (MTHREADDEBUG) cout << \\\" Model thread: going to update model with \\\" << updateList.size() << \\\" new experiences\\\" << endl << flush;\\n //cout << \\\"****update tree with \\\" << updateList.size() << endl << flush;\\n bool modelChanged = model->updateWithExperiences(updateList);\\n if (MTHREADDEBUG) cout << \\\" Model updated\\\" << endl << flush;\\n pthread_mutex_unlock(&model_mutex);\\n */\\n\\n modelcopy = model->getCopy();\\n //if (COPYDEBUG) cout << \\\"*** PO: model copied\\\" << endl;\\n\\n // update model copy with new experience\\n bool modelChanged = modelcopy->updateWithExperiences(updateList);\\n\\n // set model pointer to point at copy, delete original model cout << \\\"acquire model_mutex for update\\\" << endl;\\n pthread_mutex_lock(&model_mutex);\\n //cout << \\\"model_mutex acquired for update\\\" << endl;\\n //if (COPYDEBUG) cout << \\\"*** PO: delete original model and change pointer\\\" << endl;\\n delete model;\\n model = modelcopy;\\n if (MTHREADDEBUG) cout << \\\" Model updated\\\" << endl << flush;\\n //if (COPYDEBUG) cout << \\\"*** PO: pointer set to updated model copy\\\" << endl;\\n pthread_mutex_unlock(&model_mutex);\\n\\n\\n\\n // if it changed, reset counts, update state actions\\n if (modelChanged) resetAndUpdateStateActions();\\n\\n pthread_yield();\\n\\n //}// while loop\\n} // method\\n\\n\\n\\n\\nvoid PO_ParallelETUCT::resetAndUpdateStateActions(){\\n //cout << \\\"*** Model changed, updating state actions ***\\\" << endl << flush;\\n const int MIN_VISITS = 10;\\n\\n pthread_mutex_lock(&nactions_mutex);\\n int updateTime = nactions;\\n pthread_mutex_unlock(&nactions_mutex);\\n\\n // loop through here\\n\\n pthread_mutex_lock(&statespace_mutex);\\n\\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n state_t s = canonicalize(*i);\\n\\n if (MTHREADDEBUG) cout << \\\" *** Model thread wants search lock ***\\\" << endl;\\n\\n if (MTHREADDEBUG) cout << \\\" *** Model thread got search lock \\\" << endl;\\n\\n pthread_mutex_lock(&statespace_mutex);\\n state_info* info = &(statedata[s]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n if (info->uctVisits > (MIN_VISITS * numactions))\\n info->uctVisits = MIN_VISITS * numactions;\\n\\n for (int j = 0; j < numactions; j++){\\n if (info->needsUpdate){\\n updateStateActionFromModel(s, j, info);\\n }\\n if (info->uctActions[j] > MIN_VISITS)\\n info->uctActions[j] = MIN_VISITS;\\n }\\n info->needsUpdate = false;\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n pthread_yield();\\n\\n pthread_mutex_lock(&statespace_mutex);\\n\\n }\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n pthread_mutex_lock(&update_mutex);\\n lastUpdate = updateTime;\\n pthread_mutex_unlock(&update_mutex);\\n\\n}\\n\\n\\n\\n\\n////////////////////////////\\n// Helper Functions //\\n////////////////////////////\\n\\nPO_ParallelETUCT::state_t PO_ParallelETUCT::canonicalize(const std::vector<float> &s) {\\n if (PLANNERDEBUG) cout << \\\"canonicalize(s = \\\" << s[0] << \\\", \\\"\\n << s[1] << \\\")\\\" << endl;\\n\\n // discretize it\\n std::vector<float> s2;\\n if (statesPerDim[0] > 0){\\n s2 = discretizeState(s);\\n } else {\\n s2 = s;\\n }\\n\\n pthread_mutex_lock(&statespace_mutex);\\n\\n // get state_t for pointer if its in statespace\\n const std::pair<std::set<std::vector<float> >::iterator, bool> result =\\n statespace.insert(s2);\\n state_t retval = &*result.first; // Dereference iterator then get pointer\\n\\n // if not, init this new state\\n if (result.second) { // s is new, so initialize Q(s,a) for all a\\n state_info* info = &(statedata[retval]);\\n int id = nstates++;\\n pthread_mutex_unlock(&statespace_mutex);\\n initStateInfo(retval, info, id);\\n } else {\\n pthread_mutex_unlock(&statespace_mutex);\\n }\\n\\n return retval;\\n}\\n\\n\\n// init state info\\nvoid PO_ParallelETUCT::initStateInfo(state_t s, state_info* info, int id){\\n //if (PLANNERDEBUG) cout << \\\"initStateInfo()\\\";\\n\\n // init mutex's for this state info\\n pthread_mutex_init(&info->statemodel_mutex, NULL);\\n pthread_mutex_init(&info->stateinfo_mutex, NULL);\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n // model data (transition, reward, known)\\n\\n pthread_mutex_lock(&info->statemodel_mutex);\\n info->model = new StateActionInfo[numactions];\\n pthread_mutex_unlock(&info->statemodel_mutex);\\n\\n\\n\\n info->id = id;\\n if (PLANNERDEBUG) cout << \\\" id = \\\" << info->id << endl;\\n\\n // model q values, visit counts\\n info->Q.resize(numactions, 0);\\n info->uctActions.resize(numactions, 1);\\n info->uctVisits = 1;\\n info->visited = 0; //false;\\n\\n for (int i = 0; i < numactions; i++){\\n info->Q[i] = rng.uniform(0, 0.01);\\n }\\n\\n info->needsUpdate = true;\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n //if (PLANNERDEBUG) cout << \\\"done with initStateInfo()\\\" << endl;\\n\\n}\\n\\n\\nvoid PO_ParallelETUCT::printStates(){\\n\\n pthread_mutex_lock(&statespace_mutex);\\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n state_t s = canonicalize(*i);\\n\\n pthread_mutex_lock(&statespace_mutex);\\n state_info* info = &(statedata[s]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n cout << \\\"State \\\" << info->id << \\\": \\\";\\n for (unsigned j = 0; j < s->size(); j++){\\n cout << (*s)[j] << \\\", \\\";\\n }\\n cout << endl;\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n //pthread_mutex_lock(&info->statemodel_mutex);\\n for (int act = 0; act < numactions; act++){\\n cout << \\\" Q: \\\" << info->Q[act] << endl;\\n // << \\\" R: \\\" << info->modelInfo[act].reward << endl;\\n }\\n // pthread_mutex_unlock(&info->statemodel_mutex);\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n pthread_mutex_lock(&statespace_mutex);\\n\\n }\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n}\\n\\n\\nvoid PO_ParallelETUCT::deleteInfo(state_info* info){\\n\\n pthread_mutex_lock(&info->statemodel_mutex);\\n delete [] info->model;\\n pthread_mutex_unlock(&info->statemodel_mutex);\\n\\n}\\n\\n\\n\\ndouble PO_ParallelETUCT::getSeconds(){\\n struct timezone tz;\\n timeval timeT;\\n gettimeofday(&timeT, &tz);\\n return timeT.tv_sec + (timeT.tv_usec / 1000000.0);\\n}\\n\\n\\nfloat PO_ParallelETUCT::uctSearch(const std::vector<float> &actS, state_t discS, int depth){\\n if (UCTDEBUG){\\n cout << \\\" uctSearch state \\\";\\n for (unsigned i = 0; i < actS.size(); i++){\\n cout << actS[i] << \\\", \\\";\\n }\\n cout << \\\" at depth \\\" << depth << endl;\\n }\\n\\n pthread_mutex_lock(&statespace_mutex);\\n state_info* info = &(statedata[discS]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n // if max depth\\n // iterative deepening (probability inversely proportional to visits)\\n //float terminateProb = 1.0/(2.0+(float)info->uctVisits);\\n\\n // already visited, stop here\\n if (depth > MAX_DEPTH){\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n // return max q value here\\n std::vector<float>::iterator maxAct =\\n std::max_element(info->Q.begin(),\\n info->Q.end());\\n float maxval = *maxAct;\\n\\n if (UCTDEBUG)\\n cout << \\\"Terminated after depth: \\\" << depth\\n // << \\\" prob: \\\" << terminateProb\\n << \\\" Q: \\\" << maxval\\n << \\\" visited: \\\" << info->visited << endl;\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n return maxval;\\n }\\n\\n // select action\\n int action = selectUCTAction(info);\\n\\n // simulate action to get next state and reward\\n // depending on exploration, may also terminate us\\n float reward = 0;\\n bool term = false;\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n float learnRate;\\n //float learnRate = 0.001;\\n //float learnRate = 1.0 / info->uctActions[action];\\n // learnRate = 10.0 / (info->uctActions[action] + 100.0);\\n learnRate = 10.0 / (info->uctActions[action] + 10.0);\\n //if (learnRate < 0.001 && MAX_TIME < 0.5)\\n //learnRate = 0.001;\\n //learnRate = 0.05;\\n //learnRate = 1.0;\\n\\n // tell model learning thread to update this state since we've visited it\\n info->needsUpdate = true;\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n std::vector<float> actualNext = simulateNextState(actS, discS, info, action, &reward, &term);\\n\\n // simulate reward from this action\\n if (term){\\n // this one terminated\\n if (UCTDEBUG) cout << \\\" Terminated on exploration condition\\\" << endl;\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n info->Q[action] += learnRate * (reward - info->Q[action]);\\n info->uctVisits++;\\n info->uctActions[action]++;\\n\\n if (UCTDEBUG)\\n cout << \\\" Depth: \\\" << depth << \\\" Selected action \\\" << action\\n << \\\" r: \\\" << reward\\n << \\\" StateVisits: \\\" << info->uctVisits\\n << \\\" ActionVisits: \\\" << info->uctActions[action] << endl;\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n return reward;\\n }\\n\\n // simulate next state from this action\\n state_t discNext = canonicalize(actualNext);\\n\\n if (UCTDEBUG)\\n cout << \\\" Depth: \\\" << depth << \\\" Selected action \\\" << action\\n << \\\" r: \\\" << reward << endl;\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n info->visited++; // = true;\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n // new q value\\n float newQ = reward + gamma * uctSearch(actualNext, discNext, depth+1);\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n if (info->visited == 1){\\n\\n // update q and visit counts\\n info->Q[action] += learnRate * (newQ - info->Q[action]);\\n info->uctVisits++;\\n info->uctActions[action]++;\\n\\n if (UCTDEBUG)\\n cout << \\\" Depth: \\\" << depth << \\\" newQ: \\\" << newQ\\n << \\\" StateVisits: \\\" << info->uctVisits\\n << \\\" ActionVisits: \\\" << info->uctActions[action] << endl;\\n\\n if (lambda < 1.0){\\n\\n // new idea, return max of Q or new q\\n std::vector<float>::iterator maxAct =\\n std::max_element(info->Q.begin(),\\n info->Q.end());\\n float maxval = *maxAct;\\n\\n if (UCTDEBUG)\\n cout << \\\" Replacing newQ: \\\" << newQ;\\n\\n // replace with w avg of maxq and new val\\n newQ = (lambda * newQ) + ((1.0-lambda) * maxval);\\n\\n if (UCTDEBUG)\\n cout << \\\" with wAvg: \\\" << newQ << endl;\\n }\\n\\n }\\n\\n info->visited--;\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n // return q\\n return newQ;\\n\\n}\\n\\n\\nint PO_ParallelETUCT::selectUCTAction(state_info* info){\\n // if (UCTDEBUG) cout << \\\" selectUCTAction\\\" << endl;\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n std::vector<float> &Q = info->Q;\\n\\n if (info->uctActions.size() < (unsigned)numactions){\\n cout << \\\"ERROR: uctActions has size \\\" << info->uctActions.size() << endl << flush;\\n info->uctActions.resize(numactions);\\n }\\n\\n // loop through\\n float rewardBound = rrange;\\n if (rewardBound < 1.0)\\n rewardBound = 1.0;\\n rewardBound /= (1.0 - gamma);\\n if (UCTDEBUG) cout << \\\"Reward bound: \\\" << rewardBound << endl;\\n\\n std::vector<float> uctQ(numactions, 0.0);\\n\\n for (int i = 0; i < numactions; i++){\\n\\n // this actions value is Q + rMax * 2 sqrt (log N(s) / N(s,a))\\n uctQ[i] = Q[i] +\\n rewardBound * 2.0 * sqrt(log((float)info->uctVisits) /\\n (float)info->uctActions[i]);\\n\\n if (UCTDEBUG)\\n cout << \\\" Action: \\\" << i << \\\" Q: \\\" << Q[i]\\n << \\\" visits: \\\" << info->uctActions[i]\\n << \\\" value: \\\" << uctQ[i] << endl;\\n }\\n\\n // max element of uctQ\\n std::vector<float>::iterator maxAct =\\n max_element(uctQ.begin(), uctQ.end());\\n float maxval = *maxAct;\\n int act = maxAct - uctQ.begin();\\n\\n if (UCTDEBUG)\\n cout << \\\" Selected \\\" << act << \\\" val: \\\" << maxval << endl;\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n return act;\\n\\n}\\n\\nstd::vector<float> PO_ParallelETUCT::simulateNextState(const std::vector<float> &actualState, state_t discState, state_info* info, int action, float* reward, bool* term){\\n //if (UCTDEBUG) cout << \\\" simulateNextState\\\" << endl;\\n\\n\\n // check if its up to date\\n pthread_mutex_lock(&info->statemodel_mutex);\\n StateActionInfo* modelInfo = NULL;\\n modelInfo = &(info->model[action]);\\n pthread_mutex_lock(&update_mutex);\\n bool upToDate = modelInfo->frameUpdated >= lastUpdate;\\n pthread_mutex_unlock(&update_mutex);\\n\\n if (!upToDate){\\n updateStateActionHistoryFromModel(*discState, action, modelInfo);\\n }\\n\\n *reward = modelInfo->reward;\\n *term = (rng.uniform() < modelInfo->termProb);\\n\\n if (*term){\\n pthread_mutex_unlock(&info->statemodel_mutex);\\n return actualState;\\n }\\n\\n float randProb = rng.uniform();\\n\\n float probSum = 0.0;\\n std::vector<float> nextstate;\\n\\n if (REALSTATEDEBUG) cout << \\\"randProb: \\\" << randProb << \\\" numNext: \\\" << modelInfo->transitionProbs.size() << endl;\\n\\n if (modelInfo->transitionProbs.size() == 0)\\n nextstate = actualState;\\n\\n for (std::map<std::vector<float>, float>::iterator outIt\\n = modelInfo->transitionProbs.begin();\\n outIt != modelInfo->transitionProbs.end(); outIt++){\\n\\n float prob = (*outIt).second;\\n probSum += prob;\\n if (REALSTATEDEBUG) cout << randProb << \\\", \\\" << probSum << \\\", \\\" << prob << endl;\\n\\n if (randProb <= probSum){\\n nextstate = (*outIt).first;\\n if (REALSTATEDEBUG) cout << \\\"selected state \\\" << randProb << \\\", \\\" << probSum << \\\", \\\" << prob << endl;\\n break;\\n }\\n }\\n\\n pthread_mutex_unlock(&info->statemodel_mutex);\\n\\n if (trackActual){\\n\\n\\n // find the relative change from discrete center\\n std::vector<float> relChange = subVec(nextstate, *discState);\\n\\n // add that on to actual current state value\\n nextstate = addVec(actualState, relChange);\\n\\n\\n }\\n\\n // check that next state is valid\\n for (unsigned j = 0; j < featmin.size(); j++){\\n if (nextstate[j] < (featmin[j]-EPSILON)\\n || nextstate[j] > (featmax[j]+EPSILON)){\\n\\n if (HISTORY_SIZE == 0) return actualState;\\n\\n // still tack on correct history\\n std::vector<float> modState = actualState;\\n int stateOnlySize = modState.size()-HISTORY_FL_SIZE;\\n for (int i = stateOnlySize; i < (int)modState.size(); i++){\\n if (action == (i - stateOnlySize))\\n modState[i] = 1;\\n else\\n modState[i] = 0;\\n }\\n return modState;\\n\\n }\\n }\\n\\n // return new actual state\\n return nextstate;\\n\\n}\\n\\nstd::vector<float> PO_ParallelETUCT::selectRandomState(){\\n\\n pthread_mutex_lock(&statespace_mutex);\\n if (statespace.size() == 0){\\n pthread_mutex_unlock(&statespace_mutex);\\n return std::vector<float>(featmax.size());\\n }\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n // take a random state from the space of ones we've visited\\n int index = 0;\\n std::vector<float> state;\\n\\n pthread_mutex_lock(&statespace_mutex);\\n if (statespace.size() > 1){\\n index = rng.uniformDiscrete(0, statespace.size()-1);\\n }\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n int cnt = 0;\\n\\n if (PTHREADDEBUG) cout << \\\"*** Planning thread wants search lock (randomstate) ***\\\" << endl << flush;\\n\\n pthread_mutex_lock(&statespace_mutex);\\n for (std::set<std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++, cnt++){\\n if (cnt == index){\\n state = *i;\\n break;\\n }\\n }\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n return state;\\n}\\n\\n\\nvoid* poParallelSearchStart(void* arg){\\n PO_ParallelETUCT* pe = reinterpret_cast<PO_ParallelETUCT*>(arg);\\n\\n cout << \\\"start parallel uct planning search thread\\\" << endl << flush;\\n\\n while(true){\\n pe->parallelSearch();\\n }\\n\\n return NULL;\\n}\\n\\nvoid PO_ParallelETUCT::parallelSearch(){\\n\\n std::vector<float> actS;\\n state_t discS;\\n\\n // get new planning state\\n if (PTHREADDEBUG) {\\n cout << \\\"*** Planning thread wants planning state lock ***\\\" << endl << flush;\\n }\\n pthread_mutex_lock(&(plan_state_mutex));\\n\\n // take the state we're in (during episodes)\\n actS = actualPlanState;\\n discS = discPlanState;\\n\\n // wait for non-null\\n if (discS == NULL){\\n pthread_mutex_unlock(&(plan_state_mutex));\\n return;\\n }\\n\\n if (PTHREADDEBUG){\\n pthread_mutex_lock(&statespace_mutex);\\n cout << \\\" uct search from state s (\\\"\\n << statedata[discS].uctVisits <<\\\"): \\\";\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n for (unsigned i = 0; i < discS->size(); i++){\\n cout << (*discS)[i] << \\\", \\\";\\n }\\n cout << endl << flush;\\n }\\n\\n // call uct search on it\\n pthread_mutex_unlock(&(plan_state_mutex));\\n\\n if (PTHREADDEBUG) cout << \\\"*** Planning thread wants search lock ***\\\" << endl;\\n uctSearch(actS, discS, 0);\\n\\n pthread_yield();\\n\\n}\\n\\n\\n\\nvoid PO_ParallelETUCT::savePolicy(const char* filename){\\n\\n ofstream policyFile(filename, ios::out | ios::binary | ios::trunc);\\n\\n // first part, save the vector size\\n int fsize = featmin.size();\\n policyFile.write((char*)&fsize, sizeof(int));\\n\\n // save numactions\\n policyFile.write((char*)&numactions, sizeof(int));\\n\\n // go through all states, and save Q values\\n pthread_mutex_lock(&statespace_mutex);\\n\\n for (std::set< std::vector<float> >::iterator i = statespace.begin();\\n i != statespace.end(); i++){\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n state_t s = canonicalize(*i);\\n\\n pthread_mutex_lock(&statespace_mutex);\\n state_info* info = &(statedata[s]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n // save state\\n policyFile.write((char*)&((*i)[0]), sizeof(float)*fsize);\\n\\n // save q-values\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n policyFile.write((char*)&(info->Q[0]), sizeof(float)*numactions);\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n pthread_mutex_lock(&statespace_mutex);\\n }\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n policyFile.close();\\n}\\n\\n\\n\\nvoid PO_ParallelETUCT::loadPolicy(const char* filename){\\n\\n ifstream policyFile(filename, ios::in | ios::binary);\\n\\n // first part, save the vector size\\n int fsize;\\n policyFile.read((char*)&fsize, sizeof(int));\\n cout << \\\"Numfeats loaded: \\\" << fsize << endl << flush;\\n\\n // save numactions\\n int nact;\\n policyFile.read((char*)&nact, sizeof(int));\\n cout << \\\"nact loaded: \\\" << nact << endl << flush;\\n cout << \\\" numactions: \\\" << numactions << endl << flush;\\n\\n if (nact != numactions){\\n cout << \\\"this policy is not valid loaded nact: \\\" << nact\\n << \\\" was told: \\\" << numactions << endl << flush;\\n exit(-1);\\n }\\n\\n // go through all states, loading q values\\n while(!policyFile.eof()){\\n std::vector<float> state(fsize, 0.0);\\n\\n // load state\\n policyFile.read((char*)&(state[0]), sizeof(float)*fsize);\\n //if (LOADDEBUG){\\n //cout << \\\"load policy for state: \\\";\\n // printState(state);\\n //}\\n\\n state_t s = canonicalize(state);\\n\\n pthread_mutex_lock(&statespace_mutex);\\n state_info* info = &(statedata[s]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n if (policyFile.eof()) break;\\n\\n // load q values\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n policyFile.read((char*)&(info->Q[0]), sizeof(float)*numactions);\\n\\n info->uctVisits = numactions * 100;\\n\\n for (int j = 0; j < numactions; j++){\\n info->uctActions[j] = 100;\\n }\\n\\n info->needsUpdate = true;\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n //if (LOADDEBUG){\\n //cout << \\\"Q values: \\\" << endl;\\n //for (int iAct = 0; iAct < numactions; iAct++){\\n // cout << \\\" Action: \\\" << iAct << \\\" val: \\\" << info->Q[iAct] << endl;\\n //}\\n //}\\n }\\n\\n policyFile.close();\\n cout << \\\"Policy loaded!!!\\\" << endl << flush;\\n}\\n\\nvoid PO_ParallelETUCT::logValues(ofstream *of, int xmin, int xmax, int ymin, int ymax){\\n std::vector<float> state(2, 0.0);\\n for (int i = xmin ; i < xmax; i++){\\n for (int j = ymin; j < ymax; j++){\\n state[0] = j;\\n state[1] = i;\\n state_t s = canonicalize(state);\\n\\n pthread_mutex_lock(&statespace_mutex);\\n state_info* info = &(statedata[s]);\\n pthread_mutex_unlock(&statespace_mutex);\\n\\n pthread_mutex_lock(&info->stateinfo_mutex);\\n\\n std::vector<float> &Q_s = info->Q;\\n const std::vector<float>::iterator max =\\n random_max_element(Q_s.begin(), Q_s.end());\\n *of << (*max) << \\\",\\\";\\n\\n pthread_mutex_unlock(&info->stateinfo_mutex);\\n\\n }\\n }\\n}\\n\\n\\n// should do it such that an already discretized state stays the same\\n// mainly the numerical value of each bin should be the average of that bin\\nstd::vector<float> PO_ParallelETUCT::discretizeState(const std::vector<float> &s){\\n std::vector<float> ds(s.size());\\n\\n for (unsigned i = 0; i < statesPerDim.size(); i++){\\n\\n // since i'm sometimes doing this for discrete domains\\n // want to center bins on 0, not edge on 0\\n //cout << \\\"feat \\\" << i << \\\" range: \\\" << featmax[i] << \\\" \\\" << featmin[i] << \\\" \\\" << (featmax[i]-featmin[i]) << \\\" n: \\\" << (float)statesPerDim;\\n\\n float factor = (featmax[i] - featmin[i]) / (float)statesPerDim[i];\\n int bin = 0;\\n if (s[i] > 0){\\n bin = (int)((s[i]+factor/2) / factor);\\n } else {\\n bin = (int)((s[i]-factor/2) / factor);\\n }\\n\\n ds[i] = factor*bin;\\n //cout << \\\" factor: \\\" << factor << \\\" bin: \\\" << bin;\\n //cout << \\\" Original: \\\" << s[i] << \\\" Discrete: \\\" << ds[i] << endl;\\n }\\n\\n for (unsigned i = statesPerDim.size(); i < s.size(); i++){\\n ds[i] = s[i];\\n }\\n\\n return ds;\\n}\\n\\n\\nstd::vector<float> PO_ParallelETUCT::addVec(const std::vector<float> &a, const std::vector<float> &b){\\n if (a.size() != b.size())\\n cout << \\\"ERROR: add vector sizes wrong \\\" << a.size() << \\\", \\\" << b.size() << endl;\\n\\n std::vector<float> c(a.size(), 0.0);\\n for (unsigned i = 0; i < a.size(); i++){\\n c[i] = a[i] + b[i];\\n }\\n\\n return c;\\n}\\n\\nstd::vector<float> PO_ParallelETUCT::subVec(const std::vector<float> &a, const std::vector<float> &b){\\n if (a.size() != b.size())\\n cout << \\\"ERROR: sub vector sizes wrong \\\" << a.size() << \\\", \\\" << b.size() << endl;\\n\\n std::vector<float> c(a.size(), 0.0);\\n for (unsigned i = 0; i < a.size(); i++){\\n c[i] = a[i] - b[i];\\n }\\n\\n return c;\\n}\\n\\nvoid PO_ParallelETUCT::setFirst(){\\n if (HISTORY_SIZE == 0) return;\\n\\n if (HISTORYDEBUG) cout << \\\"first action, set sahistory to 0s\\\" << endl;\\n\\n pthread_mutex_lock(&(history_mutex));\\n // first action, reset history vector\\n saHistory.resize(saHistory.size(), 0.0);\\n pthread_mutex_unlock(&(history_mutex));\\n}\\n\\nvoid PO_ParallelETUCT::setSeeding(bool seeding){\\n\\n if (HISTORYDEBUG) cout << \\\"set seed mode to \\\" << seeding << endl;\\n seedMode = seeding;\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7757256031036377,\n \"alphanum_fraction\": 0.7757256031036377,\n \"avg_line_length\": 36.900001525878906,\n \"blob_id\": \"2540ff83bdc7e30c83bdd9097e27e75cdcb16d9a\",\n \"content_id\": \"754bc976d1ad50a87af8c41b198721bab185c2b0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 379,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 10,\n \"path\": \"/build/nav_stack_example/CMakeFiles/local_planner.dir/cmake_clean.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"file(REMOVE_RECURSE\\n \\\"CMakeFiles/local_planner.dir/src/local_planner.cpp.o\\\"\\n \\\"/home/justin/ros_test/devel/lib/nav_stack_example/local_planner.pdb\\\"\\n \\\"/home/justin/ros_test/devel/lib/nav_stack_example/local_planner\\\"\\n)\\n\\n# Per-language clean rules from dependency scanning.\\nforeach(lang CXX)\\n include(CMakeFiles/local_planner.dir/cmake_clean_${lang}.cmake OPTIONAL)\\nendforeach()\\n\"\n },\n {\n \"alpha_fraction\": 0.5256774425506592,\n \"alphanum_fraction\": 0.5504074096679688,\n \"avg_line_length\": 17.982013702392578,\n \"blob_id\": \"6aeb26ff4bc01f5398987712d3d7c237d25bdfa8\",\n \"content_id\": \"65528181b98eed38a2c1be1b0c28c38f263275ff\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 10554,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 556,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/fourrooms.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include <rl_env/fourrooms.hh>\\n\\n/*\\nFourRooms::FourRooms(Random &rand, const Gridworld *gridworld, bool stochastic):\\n grid(gridworld), goal(coord_t(2.,2.)), noisy(stochastic), rng(rand),\\n s(2),\\n ns(s[0]),\\n ew(s[1])\\n{\\n randomize_goal();\\n reset();\\n}\\n*/\\n\\n \\nFourRooms::FourRooms(Random &rand):\\n grid(create_default_map()),\\n goal(coord_t(1.,10.)), \\n negReward(true),\\n noisy(false),\\n extraReward(false),\\n rewardSensor(false),\\n rng(rand),\\n doorway(coord_t(2.,4.)),\\n s(2),\\n unused(6),\\n ns(s[0]),\\n ew(s[1]),\\n distN(unused[0]),\\n distS(unused[1]),\\n distE(unused[2]),\\n distW(unused[3]),\\n rewardEW(unused[4]),\\n rewardNS(unused[5]),\\n goalOption(false)\\n{\\n reset();\\n //cout << *this << endl;\\n}\\n \\n\\nFourRooms::FourRooms(Random &rand, bool stochastic, bool negReward, \\n\\t\\t bool exReward):\\n grid(create_default_map()),\\n goal(coord_t(1.,10.)), \\n negReward(negReward),\\n noisy(stochastic),\\n extraReward(exReward),\\n rewardSensor(false),\\n rng(rand),\\n doorway(coord_t(2.,4.)),\\n s(2),\\n unused(6),\\n ns(s[0]),\\n ew(s[1]),\\n distN(unused[0]),\\n distS(unused[1]),\\n distE(unused[2]),\\n distW(unused[3]),\\n rewardEW(unused[4]),\\n rewardNS(unused[5]),\\n goalOption(goalOption)\\n{\\n reset();\\n}\\n\\n// Create the version with extra state features for wall distances\\nFourRooms::FourRooms(Random &rand, bool stochastic, bool negReward):\\n grid(create_default_map()),\\n goal(coord_t(1.,10.)), \\n negReward(negReward),\\n noisy(stochastic),\\n extraReward(true), //false),\\n rewardSensor(false),\\n rng(rand),\\n doorway(coord_t(2.,4.)),\\n s(6),\\n unused(2),\\n ns(s[0]),\\n ew(s[1]),\\n distN(s[2]),\\n distS(s[3]),\\n distE(s[4]),\\n distW(s[5]),\\n rewardEW(unused[0]),\\n rewardNS(unused[1]),\\n goalOption(false)\\n{\\n reset();\\n}\\n\\n\\n// Create the version with extra state features for wall distances and \\n// reward distance\\nFourRooms::FourRooms(Random &rand, bool stochastic):\\n grid(create_default_map()),\\n goal(coord_t(1.,10.)),\\n negReward(true),\\n noisy(stochastic),\\n extraReward(false),\\n rewardSensor(false),\\n rng(rand),\\n doorway(coord_t(2.,4.)),\\n s(8),\\n unused(0),\\n ns(s[0]),\\n ew(s[1]),\\n distN(s[2]),\\n distS(s[3]),\\n distE(s[4]),\\n distW(s[5]),\\n rewardEW(s[6]),\\n rewardNS(s[7]),\\n goalOption(false)\\n{\\n // cout << \\\"Four room with wall dist and reward sensor\\\" << endl;\\n reset();\\n}\\n\\n\\n\\n/*\\nFourRooms::FourRooms(Random &rand, unsigned width, unsigned height, bool stochastic):\\n grid(new Gridworld(height, width, rand)),\\n goal(coord_t(2.,2.)), \\n noisy(stochastic), rng(rand),\\n doorway(NULL), \\n s(2),\\n ns(s[0]),\\n ew(s[1])\\n{\\n randomize_goal();\\n reset();\\n}\\n*/\\n\\nFourRooms::~FourRooms() { delete grid; }\\n\\nconst std::vector<float> &FourRooms::sensation() const { \\n //cout << \\\"At state \\\" << s[0] << \\\", \\\" << s[1] << endl;\\n\\n return s; \\n}\\n\\nfloat FourRooms::apply(int action) {\\n\\n //cout << \\\"Taking action \\\" << static_cast<room_action_t>(action) << endl;\\n\\n const room_action_t effect =\\n noisy\\n ? add_noise(static_cast<room_action_t>(action)) \\n : static_cast<room_action_t>(action);\\n switch(effect) {\\n case NORTH:\\n if (!grid->wall(static_cast<unsigned>(ns),\\n\\t\\t static_cast<unsigned>(ew),\\n\\t\\t effect))\\n {\\n\\t++ns;\\n\\tcalcWallDistances();\\n }\\n return reward(effect);\\n case SOUTH:\\n if (!grid->wall(static_cast<unsigned>(ns),\\n\\t\\t static_cast<unsigned>(ew),\\n\\t\\t effect))\\n {\\n\\t--ns;\\n\\tcalcWallDistances();\\n }\\n return reward(effect);\\n case EAST:\\n if (!grid->wall(static_cast<unsigned>(ns),\\n\\t\\t static_cast<unsigned>(ew),\\n\\t\\t effect))\\n {\\n\\t++ew;\\n\\tcalcWallDistances();\\n }\\n return reward(effect);\\n case WEST:\\n if (!grid->wall(static_cast<unsigned>(ns),\\n\\t\\t static_cast<unsigned>(ew),\\n\\t\\t effect))\\n {\\n\\t--ew;\\n\\tcalcWallDistances();\\n }\\n return reward(effect);\\n }\\n std::cerr << \\\"Unreachable point reached in FourRooms::apply!!!\\\\n\\\";\\n return 0; // unreachable, I hope\\n}\\n\\n\\nfloat FourRooms::reward(int effect) {\\n \\n if (extraReward){\\n // 0 on goal\\n if (terminal())\\n return 0;\\n\\n // 0 when heading right dir towards door\\n // towards top middle door\\n if (ew < 6 && ns == 8 && effect == EAST){\\n return -1;\\n }\\n\\n // towards left door\\n if (ew == 1 && ns > 4 && effect == SOUTH){\\n return -1;\\n }\\n\\n // towards right door\\n if (ew == 8 && ns > 3 && effect == SOUTH){\\n return -1;\\n }\\n\\n // towrads bottom door\\n if (ew < 6 && ns == 1 && effect == EAST){\\n return -1;\\n }\\n\\n // 0 when heading towards goal\\n if (ns == 1 && effect == EAST){\\n return -1;\\n }\\n if (ew == 10 && ns < 4 && effect == SOUTH){\\n return -1;\\n }\\n\\n // normally -2\\n return -2;\\n\\n }\\n\\n\\n if (negReward){\\n // normally -1 and 0 on goal\\n if (terminal())\\n return 0;\\n else \\n return -1;\\n \\n }else{\\n\\n // or we could do 0 and 1 on goal\\n if (terminal())\\n return 1;\\n else \\n return 0;\\n }\\n}\\n\\n\\nbool FourRooms::terminal() const {\\n // current position equal to goal??\\n return coord_t(ns,ew) == goal;\\n}\\n\\n\\nvoid FourRooms::calcWallDistances(){\\n\\n // calculate distances East and West\\n // if we're not in the same row as a doorway\\n if (ns != 1 && ns != 8){\\n // left side of wall\\n if (ew < 5){\\n distW = ew;\\n distE = 4 - ew;\\n }\\n // right side of wall\\n else {\\n distW = ew - 6;\\n distE = 10 - ew;\\n }\\n } \\n // doorway\\n else {\\n distW = ew;\\n distE = 10 - ew;\\n }\\n\\n // in a vertical doorway\\n if (ns == 5 && ew == 1){\\n distW = 0;\\n distE = 0;\\n }\\n if (ns == 4 && ew == 8){\\n distW = 0;\\n distE = 0;\\n }\\n\\n // calculate NS\\n // left side\\n if (ew < 5){\\n // not in doorway column\\n if (ew != 1){\\n // top room\\n if (ns > 5){\\n\\tdistN = 10 - ns;\\n\\tdistS = ns - 6;\\n }\\n // bottom room\\n else {\\n\\tdistN = 4 - ns;\\n\\tdistS = ns;\\n }\\n }\\n // doorway column\\n else {\\n distN = 10 - ns;\\n distS = ns;\\n }\\n }\\n // right side\\n else {\\n // not in doorway column\\n if (ew != 8){\\n // top room\\n if (ns > 4){\\n\\tdistN = 10-ns;\\n\\tdistS = ns - 5;\\n }\\n // bottom room\\n else {\\n\\tdistN = 3 - ns;\\n\\tdistS = ns;\\n }\\n }\\n // doorway column\\n else {\\n distN = 10-ns;\\n distS = ns;\\n }\\n }\\n\\n // in horiz doorway\\n if (ew == 5 && (ns == 1 || ns == 8)){\\n distN = 0;\\n distS = 0;\\n }\\n\\n\\n // calculate reward distances\\n // can see it e/w\\n if (ns == 1){\\n rewardEW = 10 - ew;\\n }\\n else {\\n rewardEW = 100;\\n }\\n\\n // can see ns\\n if (ew == 10 && ns < 4){\\n rewardNS = 1 - ns;\\n }\\n else {\\n rewardNS = 100;\\n }\\n \\n /*\\n cout << \\\"x,y: \\\" << ew << \\\", \\\" << ns << \\\" N,S,E,W: \\\" \\n << distN << \\\", \\\" << distS << \\\", \\\" \\n << distE << \\\", \\\" << distW << \\\" reward EW, NS: \\\" \\n << rewardEW << \\\", \\\" << rewardNS << endl;\\n */\\n}\\n\\n\\nvoid FourRooms::reset() {\\n // start randomly in upper left room (goal is lower right)\\n ns = rng.uniformDiscrete(6, grid->height()-1);\\n ew = rng.uniformDiscrete(0, 4);\\n\\n //ns = 8;\\n //ew = 2;\\n\\n //ns = 4;\\n //ew = 9;\\n\\n calcWallDistances();\\n}\\n\\n\\nstd::vector<std::vector<float> > FourRooms::getSubgoals(){\\n\\n //cout << \\\"Getting room subgoals \\\" << endl;\\n\\n // Create vector of state representations, each is a subgoal\\n std::vector<std::vector<float> > subgoals;\\n\\n \\n std::vector<float> subgoal(2);\\n\\n // between two left rooms\\n subgoal[0] = 5;\\n subgoal[1] = 1;\\n subgoals.push_back(subgoal);\\n \\n // between two right rooms\\n subgoal[0] = 4;\\n subgoal[1] = 8;\\n subgoals.push_back(subgoal);\\n \\n // between two top rooms\\n subgoal[0] = 8;\\n subgoal[1] = 5;\\n subgoals.push_back(subgoal);\\n \\n // between two lower rooms\\n subgoal[0] = 1;\\n subgoal[1] = 5;\\n subgoals.push_back(subgoal);\\n\\n if (goalOption){\\n // actual goal\\n subgoal[0] = 1;\\n subgoal[1] = 10;\\n subgoals.push_back(subgoal);\\n }\\n\\n return subgoals;\\n\\n}\\n\\n\\nint FourRooms::getNumActions(){\\n return 4;\\n}\\n\\n\\nstd::ostream &operator<<(std::ostream &out, const FourRooms &rooms) {\\n out << \\\"Map:\\\\n\\\" << *rooms.grid;\\n\\n // print goal\\n out << \\\"Goal: row \\\" << rooms.goal.first\\n << \\\", column \\\" << rooms.goal.second << \\\"\\\\n\\\";\\n\\n // print doorway\\n out << \\\"Doorway: row \\\" << rooms.doorway.first\\n << \\\", column \\\" << rooms.doorway.second << \\\"\\\\n\\\";\\n\\n return out;\\n}\\n\\nconst Gridworld *FourRooms::create_default_map() {\\n int width = 11;\\n int height = 11;\\n std::vector<std::vector<bool> > nsv(width, std::vector<bool>(height-1,false));\\n std::vector<std::vector<bool> > ewv(height, std::vector<bool>(width-1,false));\\n\\n // put the vertical wall between the two rooms\\n for (int j = 0; j < height; j++){\\n \\t// skip doorways at 1 and 8\\n \\tif (j == 1 || j == 8)\\n \\t\\tcontinue;\\n ewv[j][4] = true;\\n ewv[j][5] = true;\\n }\\n \\n nsv[5][0] = true;\\n nsv[5][1] = true;\\n nsv[5][7] = true;\\n nsv[5][8] = true;\\n\\n // put the horizontal wall for the left room\\n for (int i = 0; i < 6; i++){\\n \\t// skip doorway at 1\\n \\tif (i == 1)\\n \\t\\tcontinue;\\n \\tnsv[i][4] = true;\\n \\tnsv[i][5] = true;\\n }\\n\\n ewv[5][0] = true;\\n ewv[5][1] = true;\\n\\n // put the horizontal wall for the right room\\n for (int i = 5; i < width; i++){\\n \\t// skip doorway at 8\\n \\tif (i == 8)\\n \\t\\tcontinue;\\n \\tnsv[i][3] = true;\\n \\tnsv[i][4] = true;\\n } \\t\\n \\n ewv[4][7] = true;\\n ewv[4][8] = true;\\n\\n return new Gridworld(height, width, nsv, ewv);\\n}\\n\\nFourRooms::room_action_t FourRooms::add_noise(room_action_t action) {\\n switch(action) {\\n case NORTH:\\n case SOUTH:\\n return rng.bernoulli(0.8) ? action : (rng.bernoulli(0.5) ? EAST : WEST);\\n case EAST:\\n case WEST:\\n return rng.bernoulli(0.8) ? action : (rng.bernoulli(0.5) ? NORTH : SOUTH);\\n default:\\n return action;\\n }\\n}\\n\\n\\nvoid FourRooms::randomize_goal() {\\n const unsigned n = grid->height() * grid->width();\\n unsigned index = rng.uniformDiscrete(1,n) - 1;\\n goal = coord_t(index / grid->width(), index % grid->width());\\n}\\n\\n\\n/** For special use to test true transitions */\\nvoid FourRooms::setSensation(std::vector<float> newS){\\n if (s.size() != newS.size()){\\n cerr << \\\"Error in sensation sizes\\\" << endl;\\n }\\n\\n for (unsigned i = 0; i < newS.size(); i++){\\n s[i] = (int)newS[i];\\n }\\n}\\n\\n\\nvoid FourRooms::getMinMaxFeatures(std::vector<float> *minFeat,\\n std::vector<float> *maxFeat){\\n \\n minFeat->resize(s.size(), 0.0);\\n maxFeat->resize(s.size(), 10.0);\\n \\n if (s.size() > 2) {\\n for (unsigned i = 2; i < s.size(); i++){\\n (*minFeat)[i] = -10.0;\\n }\\n }\\n\\n}\\n\\nvoid FourRooms::getMinMaxReward(float *minR,\\n float *maxR){\\n \\n if (extraReward){\\n *minR = -2.0;\\n *maxR = 0.0;\\n }\\n else if (negReward){\\n *minR = -1.0;\\n *maxR = 0.0; \\n }else{\\n *minR = 0.0;\\n *maxR = 1.0;\\n }\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6913043260574341,\n \"alphanum_fraction\": 0.6913043260574341,\n \"avg_line_length\": 23.80392074584961,\n \"blob_id\": \"22d9dcfca76df4b857d7553b0202f5b335a3e5d8\",\n \"content_id\": \"1a9e3732b99b18d9dfcba465256dd6149be12956\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 2530,\n \"license_type\": \"no_license\",\n \"max_line_length\": 168,\n \"num_lines\": 102,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/RMaxModel.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/** \\\\file RMaxModel.hh\\n Defines the RMaxModel class.\\n \\\\author Todd Hester\\n*/\\n\\n#ifndef _RMAXMODEL_HH_\\n#define _RMAXMODEL_HH_\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n#include <vector>\\n#include <map>\\n#include <set>\\n\\n\\n/** MDPModel used for RMax. Tabular model with Maximum Likelihood model for each state-action. */\\nclass RMaxModel: public MDPModel {\\n\\npublic:\\n\\n /** Default constructor\\n \\\\param m # of visits before a state-actions becomes known.\\n \\\\param nact # of actions in the domain\\n \\\\param rng Random Number Generator \\n */\\n RMaxModel(int m, int nact, Random rng);\\n \\n /** Copy constructor */\\n RMaxModel(const RMaxModel&);\\n\\n virtual ~RMaxModel();\\n virtual RMaxModel* getCopy();\\n\\n virtual bool updateWithExperiences(std::vector<experience> &instances);\\n virtual bool updateWithExperience(experience &e);\\n virtual float getStateActionInfo(const std::vector<float> &state, int act, StateActionInfo* retval);\\n\\n\\n // structs to be defined\\n struct state_info;\\n\\n\\n /** State info struct. Maintaints visit counts, outcome counts,\\n reward sums, terminal transitions, and whether the state-action is \\n considered 'known' (>= m visits) \\n */\\n struct state_info {\\n int id;\\n\\n // model data (visit counts, outcome counts, reward sums, known)\\n std::vector<int> visits;\\n\\n std::map< std::vector<float> , std::vector<int> > outCounts;\\n std::vector<float> Rsum;\\n std::vector<int> terminations;\\n\\n std::vector<bool> known;\\n\\n };\\n\\nprotected:\\n typedef const std::vector<float> *state_t;\\n\\n // various helper functions that we need\\n\\n /** Initialize a state_info struct */\\n void initStateInfo(state_info* info);\\n \\n /** Add the given state to the state set. initializes state info for new states not yet in set. The pointer to the state in the set is used for map of state_info's */\\n state_t canonicalize(const std::vector<float> &s);\\n\\n /** Make sure the transition count vector is sized properly before indexing into it. */\\n void checkTransitionCountSize(std::vector<int> *transCounts);\\n\\n /** Initialize a new state */\\n void initNewState(state_t s);\\n\\n\\n\\nprivate:\\n \\n /** Set of all distinct sensations seen. Pointers to elements of\\n this set serve as the internal representation of the environment\\n state. */\\n std::set<std::vector<float> > statespace;\\n\\n /** Hashmap mapping state vectors to their state_info structs. */\\n std::map<state_t, state_info> statedata;\\n\\n int nstates;\\n\\n int M;\\n int nact;\\n Random rng;\\n\\n bool RMAX_DEBUG;\\n \\n};\\n\\n\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.7666666507720947,\n \"alphanum_fraction\": 0.7733333110809326,\n \"avg_line_length\": 36.5625,\n \"blob_id\": \"5567893295f62d937d92095678d347ae8798a147\",\n \"content_id\": \"2f814463d190520e414be125ca71e6a61f77ecca\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 600,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 16,\n \"path\": \"/build/robot_sensors/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"robot_sensors\\\")\\nset(robot_sensors_VERSION \\\"0.0.0\\\")\\nset(robot_sensors_MAINTAINER \\\"abc <abc@todo.todo>\\\")\\nset(robot_sensors_PACKAGE_FORMAT \\\"1\\\")\\nset(robot_sensors_BUILD_DEPENDS )\\nset(robot_sensors_BUILD_EXPORT_DEPENDS )\\nset(robot_sensors_BUILDTOOL_DEPENDS \\\"catkin\\\")\\nset(robot_sensors_BUILDTOOL_EXPORT_DEPENDS )\\nset(robot_sensors_EXEC_DEPENDS )\\nset(robot_sensors_RUN_DEPENDS )\\nset(robot_sensors_TEST_DEPENDS )\\nset(robot_sensors_DOC_DEPENDS )\\nset(robot_sensors_URL_WEBSITE \\\"\\\")\\nset(robot_sensors_URL_BUGTRACKER \\\"\\\")\\nset(robot_sensors_URL_REPOSITORY \\\"\\\")\\nset(robot_sensors_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.7524752616882324,\n \"alphanum_fraction\": 0.7623762488365173,\n \"avg_line_length\": 15.833333015441895,\n \"blob_id\": \"191d96f5b4416e0571a154349d706a544d021b0f\",\n \"content_id\": \"a1507ebaa6dce73425530098c0014763adfd194d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 303,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 18,\n \"path\": \"/src/mantis_model/CMakeLists.txt\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"cmake_minimum_required(VERSION 2.8.3)\\nproject(mantis_model)\\n\\nfind_package(catkin REQUIRED COMPONENTS\\n roscpp\\n tf\\n)\\n\\ncatkin_package()\\n\\ninclude_directories(\\n ${catkin_INCLUDE_DIRS}\\n)\\n\\nadd_executable(joint_state_pub src/joint_state_pub.cpp)\\ntarget_link_libraries(joint_state_pub\\n ${catkin_LIBRARIES}\\n)\\n\"\n },\n {\n \"alpha_fraction\": 0.5678496956825256,\n \"alphanum_fraction\": 0.581071674823761,\n \"avg_line_length\": 28.030303955078125,\n \"blob_id\": \"86a85e34ed2b215469db7e7a9619667bdf1cd555\",\n \"content_id\": \"f618c5253ee28912ae7e8056a92456ae898f5c4f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 2874,\n \"license_type\": \"no_license\",\n \"max_line_length\": 78,\n \"num_lines\": 99,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_env/src/Env/gridworld.cc\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include <rl_env/gridworld.hh>\\n#include <cmath>\\n\\nstd::ostream &operator<<(std::ostream &out, const Gridworld &g) {\\n for (unsigned i = 0; i < g.ns.size(); ++i)\\n out << \\\" -\\\";\\n out << \\\" \\\\n\\\";\\n\\n for (unsigned h = g.ew.size() - 1; h > 0; --h) {\\n out << \\\"| \\\";\\n for (unsigned w = 0; w < g.ew[h].size(); ++w)\\n out << (g.ew[h][w] ? \\\"| \\\" : \\\" \\\");\\n out << \\\"|\\\\n\\\";\\n\\n for (unsigned w = 0; w < g.ns.size(); ++w)\\n out << (g.ns[w][h-1] ? \\\" -\\\" : \\\" \\\");\\n out << \\\" \\\\n\\\";\\n }\\n\\n out << \\\"| \\\";\\n for (unsigned w = 0; w < g.ew[0].size(); ++w)\\n out << (g.ew[0][w] ? \\\"| \\\" : \\\" \\\");\\n out << \\\"|\\\\n\\\";\\n\\n for (unsigned i = 0; i < g.ns.size(); ++i)\\n out << \\\" -\\\";\\n out << \\\" \\\\n\\\";\\n\\n return out;\\n}\\n\\nGridworld::Gridworld(unsigned height, unsigned width, \\n\\t\\t const std::vector<std::vector<bool> > &northsouth,\\n\\t\\t const std::vector<std::vector<bool> > &eastwest):\\n h(height), w(width), ns(northsouth), ew(eastwest)\\n{}\\n\\nGridworld::Gridworld(unsigned height, unsigned width, Random &rng):\\n h(height), w(width),\\n ns(w, std::vector<bool>(h - 1, false)),\\n ew(h, std::vector<bool>(w - 1, false))\\n{\\n const unsigned n = static_cast<unsigned>(sqrt(static_cast<float>(w*h)));\\n for (unsigned i = 0; i < n; ++i)\\n add_obstacle(rng);\\n}\\n\\nbool Gridworld::wall(unsigned nsCoord, unsigned ewCoord, unsigned dir) const {\\n const bool isNS = 0 == dir/2;\\n const bool isIncr = 0 == dir%2;\\n const std::vector<std::vector<bool> > &walls = isNS ? ns : ew;\\n unsigned major = isNS ? ewCoord : nsCoord;\\n unsigned minor = isNS ? nsCoord : ewCoord;\\n if (!isIncr) {\\n if (minor == 0)\\n return true;\\n --minor;\\n }\\n if (minor >= walls[major].size())\\n return true;\\n return walls[major][minor];\\n}\\n\\nvoid Gridworld::add_obstacle(Random &rng) {\\n bool direction = rng.bernoulli(0.5);\\n std::vector<std::vector<bool> > ¶llel = direction ? ns : ew;\\n std::vector<std::vector<bool> > &perpendicular = direction ? ew : ns;\\n\\n unsigned seedi = rng.uniformDiscrete(1, parallel.size()) - 1;\\n unsigned seedj = rng.uniformDiscrete(1, parallel[seedi].size()) - 1;\\n\\n unsigned first = seedi + 1;\\n while (isClear(first - 1, seedj, parallel, perpendicular))\\n --first;\\n unsigned last = seedi;\\n while (isClear(last + 1, seedj, parallel, perpendicular))\\n ++last;\\n\\n chooseSegment(first, last, seedj, parallel, rng);\\n}\\n\\nvoid Gridworld::chooseSegment(unsigned first,\\n\\t\\t\\t unsigned last,\\n\\t\\t\\t unsigned j,\\n\\t\\t\\t std::vector<std::vector<bool> > ¶llel,\\n\\t\\t\\t Random &rng)\\n{\\n if (last <= first)\\n return;\\n unsigned maxLength = last - first;\\n if (maxLength >= parallel.size())\\n maxLength = parallel.size() - 1;\\n unsigned length = maxLength > 1 ? rng.uniformDiscrete(1,maxLength) : 1;\\n int dir = 1 - 2*rng.uniformDiscrete(0,1);\\n unsigned start = (dir > 0) ? first : (last - 1);\\n\\n for (unsigned i = 0; i < length; ++i)\\n parallel[start + i*dir][j] = true;\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5573731064796448,\n \"alphanum_fraction\": 0.5785422325134277,\n \"avg_line_length\": 33.599998474121094,\n \"blob_id\": \"78a8f915381ef3907b755ee29d061df3915b5c6d\",\n \"content_id\": \"9e08ece872e495d00477d4435c94643477a62e71\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4157,\n \"license_type\": \"no_license\",\n \"max_line_length\": 110,\n \"num_lines\": 120,\n \"path\": \"/src/run_model.py\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n#!/usr/bin/env python\\nimport rospy\\nimport tensorflow as tf\\nfrom nav_msgs.msg import Odometry\\nfrom move_base_msgs.msg import *\\nfrom geometry_msgs.msg import Twist\\nimport math\\nfrom tensorflow import keras\\nimport numpy as np\\n\\ngoal = np.empty([1,1,2])\\nML_in = np.empty([1,1,5])\\npredictions = np.empty([2])\\n\\nvel_msg = Twist()\\n\\ndef create_model():\\n model = keras.Sequential([\\n keras.layers.Flatten(input_shape=(1, 5)),\\n keras.layers.Dense(64, activation=tf.nn.tanh),\\n keras.layers.Dense(32, activation=tf.nn.tanh),\\n keras.layers.Dense(16, activation=tf.nn.tanh),\\n keras.layers.Dense(2, activation=tf.nn.tanh)\\n ])\\n\\n\\n \\n model.compile(optimizer='adam', loss=tf.keras.losses.sparse_categorical_crossentropy, metrics=['accuracy'])\\n \\n return model\\n\\n\\ndef goal_got(msg):\\n rospy.loginfo(rospy.get_caller_id() + \\\"I heard %s\\\", msg.goal.target_pose.pose)\\n goal[0][0][0] = msg.goal.target_pose.pose.position.x\\n goal[0][0][1] = msg.goal.target_pose.pose.position.y\\n #rospy.loginfo(rospy.get_caller_id() + \\\"I heard %s\\\", goal[0][0][0])\\n\\n\\ndef odom_got(msg):\\n #rospy.loginfo(rospy.get_caller_id() + \\\"I heard %s\\\", msg.pose.pose)\\n #should do this as an NP array.\\n x_loc = msg.pose.pose.position.x\\n y_loc = msg.pose.pose.position.y\\n #ML_in[0][0][0]=math.sqrt((goal[0][0][0]-x_loc)**2+(goal[0][0][1]-y_loc)**2)\\n #ML_in[0][0][1] = math.atan2((goal[0][0][1]-y_loc),(goal[0][0][0]-x_loc))\\n ML_in[0][0][2]=msg.pose.pose.orientation.z\\n qz = msg.pose.pose.orientation.z\\n qw = msg.pose.pose.orientation.w\\n qx = msg.pose.pose.orientation.x\\n qy = msg.pose.pose.orientation.y\\n\\n #rospy.loginfo(rospy.get_caller_id() + \\\"I heard %s\\\", ML_in)\\n siny_cosp = +2.0 * (qw * qz + qx * qy);\\n cosy_cosp = +1.0 - 2.0 * (qy * qy + qz * qz); \\n yaw = math.atan2(siny_cosp, cosy_cosp);\\n rospy.loginfo(\\\"Distance %s\\\", yaw)\\n ML_in[0][0][0]=math.sqrt((5-x_loc)**2+(2-y_loc)**2)\\n ML_in[0][0][1] = math.atan2((2-y_loc),(5-x_loc))\\n #rospy.loginfo(\\\"Distance %s\\\", (5-x_loc)**2+(2-y_loc)**2)\\n #rospy.loginfo(rospy.get_caller_id() + \\\"I heard %s\\\", ML_in)\\n\\n\\n\\n#####################################################\\n#####################################################\\n############################################\\ndef prep_ml():\\n predictions = model.predict(ML_in)\\n rospy.loginfo(\\\"I heard %s\\\", predictions)\\n \\n\\n###########need to do basic math then call model with it, then send cmd##########################\\n################ ########################\\n########################################\\n########################################\\ndef listener():\\n\\n # In ROS, nodes are uniquely named. If two nodes with the same\\n # name are launched, the previous one is kicked off. The\\n # anonymous=True flag means that rospy will choose a unique\\n # name for our 'listener' node so that multiple listeners can\\n # run simultaneously.\\n\\n # subscribe to odom\\n #rospy.init_node('odometry', anonymous=True) #make node \\n rospy.Subscriber('odom',Odometry,odom_got)\\n\\n # subscribe to goal\\n #rospy.init_node('goal', anonymous=True) #make node \\n rospy.Subscriber('move_base/goal',MoveBaseActionGoal,goal_got)\\n # spin() simply keeps python from exiting until this node is stopped\\n #rospy.spin()\\n\\ndef talker():\\n pub = rospy.Publisher('cmd_vel', Twist, queue_size=10)\\n #vel_msg = Twist()\\n #prep_ml()\\n \\n ML_in[0][0][3]=vel_msg.linear.x\\n ML_in[0][0][4]= vel_msg.angular.z\\n predictions = model.predict(ML_in)\\n vel_msg.linear.x = predictions[0][0]\\n vel_msg.angular.z =predictions[0][1]\\n #rospy.loginfo(\\\"Sent %s\\\", predictions)\\n #rospy.loginfo(\\\"Distance %s\\\", ML_in)\\n #hello_str = \\\"hello world %s\\\" % rospy.get_time()\\n #rospy.loginfo(rospy.get_caller_id() + \\\"I heard %s\\\")\\n #rospy.loginfo(hello_str)\\n pub.publish(vel_msg)\\n\\nif __name__ == '__main__':\\n rospy.init_node('talker', anonymous=True) \\n rate = rospy.Rate(1) # 10hz\\n model = create_model()\\n model.load_weights('./checkpoints/my_checkpoint') \\n while not rospy.is_shutdown(): \\n\\ttalker() \\n \\tlistener()\\n rate.sleep()\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.767123281955719,\n \"alphanum_fraction\": 0.767123281955719,\n \"avg_line_length\": 35.5,\n \"blob_id\": \"6c9c363aa9be41f49f462099010d1aabba5a61f2\",\n \"content_id\": \"dbcae5e6f6175ac357447dfbd6c76d088796dca0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 365,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 10,\n \"path\": \"/build/gps_sim_project/CMakeFiles/gps_project.dir/cmake_clean.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n 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\"/src/rl-texplore-ros-pkg-master/src/rl_env/include/rl_env/stocks.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#ifndef _STOCKS_H_\\n#define _STOCKS_H_\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n#include <set>\\n\\n\\nclass Stocks: public Environment {\\npublic:\\n /** Creates a Stocks domain using the specified map.\\n \\\\param rand Random number generator to use.\\n \\\\param stochastic Whether to use nondeterministism. \\n \\\\param nsectors number of stock sectors\\n \\\\param nstocks number of stocks per sector */\\n Stocks(Random &rand, bool stochastic, int nsectors, int nstocks);\\n\\n /** Creates a deterministic Stocks domain.\\n \\\\param rand Random number generator used.\\n \\\\param stochastic Whether to use nondeterministism. */\\n Stocks(Random &rand, bool stochastic);\\n\\n virtual ~Stocks();\\n\\n virtual const std::vector<float> &sensation() const;\\n virtual float apply(int action);\\n\\n virtual bool terminal() const;\\n virtual void reset();\\n virtual int getNumActions();\\n virtual void getMinMaxFeatures(std::vector<float> *minFeat, std::vector<float> *maxFeat);\\n virtual bool isEpisodic() { return false; };\\n virtual void getMinMaxReward(float* minR, float* maxR);\\n\\n void calcStockRising();\\n float reward();\\n void setSensation(std::vector<float> s);\\n void initStocks();\\n \\nprotected:\\n\\n\\n\\nprivate:\\n\\n const int nsectors;\\n const int nstocks;\\n const bool noisy;\\n Random &rng;\\n\\n std::vector<float> s;\\n\\n // lets just have an index into the array\\n int** rising;\\n int* owners;\\n\\n bool STOCK_DEBUG;\\n\\n};\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.7703348994255066,\n \"alphanum_fraction\": 0.7751196026802063,\n \"avg_line_length\": 51.3125,\n \"blob_id\": \"ac295681c01b340ed18c723505778797843f68b4\",\n \"content_id\": \"d7e9bed00d53e104688836f1f6ea2df27e735140\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n 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\\\"roscpp\\\" \\\"tf\\\" \\\"visualization_msgs\\\")\\nset(state_space_example_TEST_DEPENDS )\\nset(state_space_example_DOC_DEPENDS )\\nset(state_space_example_URL_WEBSITE \\\"\\\")\\nset(state_space_example_URL_BUGTRACKER \\\"\\\")\\nset(state_space_example_URL_REPOSITORY \\\"\\\")\\nset(state_space_example_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.7922077775001526,\n \"alphanum_fraction\": 0.7974026203155518,\n \"avg_line_length\": 47.1875,\n \"blob_id\": \"4f6a57efb914d57c18dc2cb72ed412816a351bb0\",\n \"content_id\": \"96a77923f05c0e9fc4efe20b53dee98143b045d1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 770,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 16,\n \"path\": \"/build/roundbot_description/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"roundbot_description\\\")\\nset(roundbot_description_VERSION \\\"0.0.0\\\")\\nset(roundbot_description_MAINTAINER \\\"micho <micho@todo.todo>\\\")\\nset(roundbot_description_PACKAGE_FORMAT \\\"1\\\")\\nset(roundbot_description_BUILD_DEPENDS \\\"urdf\\\" \\\"xacro\\\")\\nset(roundbot_description_BUILD_EXPORT_DEPENDS \\\"urdf\\\" \\\"xacro\\\")\\nset(roundbot_description_BUILDTOOL_DEPENDS \\\"catkin\\\")\\nset(roundbot_description_BUILDTOOL_EXPORT_DEPENDS )\\nset(roundbot_description_EXEC_DEPENDS \\\"urdf\\\" \\\"xacro\\\")\\nset(roundbot_description_RUN_DEPENDS \\\"urdf\\\" \\\"xacro\\\")\\nset(roundbot_description_TEST_DEPENDS )\\nset(roundbot_description_DOC_DEPENDS )\\nset(roundbot_description_URL_WEBSITE \\\"\\\")\\nset(roundbot_description_URL_BUGTRACKER \\\"\\\")\\nset(roundbot_description_URL_REPOSITORY \\\"\\\")\\nset(roundbot_description_DEPRECATED \\\"\\\")\"\n },\n {\n \"alpha_fraction\": 0.7934451103210449,\n \"alphanum_fraction\": 0.8018292784690857,\n 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state(_alloc)\\n , reward(0.0)\\n , terminal(false) {\\n (void)_alloc;\\n }\\n\\n\\n\\n typedef std::vector<float, typename ContainerAllocator::template rebind<float>::other > _state_type;\\n _state_type state;\\n\\n typedef float _reward_type;\\n _reward_type reward;\\n\\n typedef uint8_t _terminal_type;\\n _terminal_type terminal;\\n\\n\\n\\n\\n\\n typedef boost::shared_ptr< ::rl_msgs::RLStateReward_<ContainerAllocator> > Ptr;\\n typedef boost::shared_ptr< ::rl_msgs::RLStateReward_<ContainerAllocator> const> ConstPtr;\\n\\n}; // struct RLStateReward_\\n\\ntypedef ::rl_msgs::RLStateReward_<std::allocator<void> > RLStateReward;\\n\\ntypedef boost::shared_ptr< ::rl_msgs::RLStateReward > RLStateRewardPtr;\\ntypedef boost::shared_ptr< ::rl_msgs::RLStateReward const> RLStateRewardConstPtr;\\n\\n// constants requiring out of line definition\\n\\n\\n\\ntemplate<typename ContainerAllocator>\\nstd::ostream& operator<<(std::ostream& s, const ::rl_msgs::RLStateReward_<ContainerAllocator> & v)\\n{\\nros::message_operations::Printer< ::rl_msgs::RLStateReward_<ContainerAllocator> >::stream(s, \\\"\\\", v);\\nreturn s;\\n}\\n\\n} // namespace rl_msgs\\n\\nnamespace ros\\n{\\nnamespace message_traits\\n{\\n\\n\\n\\n// BOOLTRAITS {'IsFixedSize': False, 'IsMessage': True, 'HasHeader': False}\\n// {'rl_msgs': ['/home/justin/ros_test/src/rl-texplore-ros-pkg-master/src/rl_msgs/msg'], 'std_msgs': ['/opt/ros/melodic/share/std_msgs/cmake/../msg']}\\n\\n// !!!!!!!!!!! ['__class__', '__delattr__', '__dict__', '__doc__', '__eq__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_parsed_fields', 'constants', 'fields', 'full_name', 'has_header', 'header_present', 'names', 'package', 'parsed_fields', 'short_name', 'text', 'types']\\n\\n\\n\\n\\ntemplate <class ContainerAllocator>\\nstruct IsFixedSize< ::rl_msgs::RLStateReward_<ContainerAllocator> >\\n : FalseType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct IsFixedSize< ::rl_msgs::RLStateReward_<ContainerAllocator> const>\\n : FalseType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct IsMessage< ::rl_msgs::RLStateReward_<ContainerAllocator> >\\n : TrueType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct IsMessage< ::rl_msgs::RLStateReward_<ContainerAllocator> const>\\n : TrueType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct HasHeader< ::rl_msgs::RLStateReward_<ContainerAllocator> >\\n : FalseType\\n { };\\n\\ntemplate <class ContainerAllocator>\\nstruct HasHeader< ::rl_msgs::RLStateReward_<ContainerAllocator> const>\\n : FalseType\\n { };\\n\\n\\ntemplate<class ContainerAllocator>\\nstruct MD5Sum< ::rl_msgs::RLStateReward_<ContainerAllocator> >\\n{\\n static const char* value()\\n {\\n return \\\"d7e0cc8b9cf2889f09d7f096a11a2873\\\";\\n }\\n\\n static const char* value(const ::rl_msgs::RLStateReward_<ContainerAllocator>&) { return value(); }\\n static const uint64_t static_value1 = 0xd7e0cc8b9cf2889fULL;\\n static const uint64_t static_value2 = 0x09d7f096a11a2873ULL;\\n};\\n\\ntemplate<class ContainerAllocator>\\nstruct DataType< ::rl_msgs::RLStateReward_<ContainerAllocator> >\\n{\\n static const char* value()\\n {\\n return \\\"rl_msgs/RLStateReward\\\";\\n }\\n\\n static const char* value(const ::rl_msgs::RLStateReward_<ContainerAllocator>&) { return value(); }\\n};\\n\\ntemplate<class ContainerAllocator>\\nstruct Definition< ::rl_msgs::RLStateReward_<ContainerAllocator> >\\n{\\n static const char* value()\\n {\\n return \\\"# Message for returning the current sensation vector \\\\n\\\\\\n# (i.e. state or observation or sensor readings) and a\\\\n\\\\\\n# reward from an environment\\\\n\\\\\\n\\\\n\\\\\\nfloat32[] state\\\\n\\\\\\nfloat32 reward\\\\n\\\\\\nbool terminal\\\\n\\\\\\n\\\";\\n }\\n\\n static const char* value(const ::rl_msgs::RLStateReward_<ContainerAllocator>&) { return value(); }\\n};\\n\\n} // namespace message_traits\\n} // namespace ros\\n\\nnamespace ros\\n{\\nnamespace serialization\\n{\\n\\n template<class ContainerAllocator> struct Serializer< ::rl_msgs::RLStateReward_<ContainerAllocator> >\\n {\\n template<typename Stream, typename T> inline static void allInOne(Stream& stream, T m)\\n {\\n stream.next(m.state);\\n stream.next(m.reward);\\n stream.next(m.terminal);\\n }\\n\\n ROS_DECLARE_ALLINONE_SERIALIZER\\n }; // struct RLStateReward_\\n\\n} // namespace serialization\\n} // namespace ros\\n\\nnamespace ros\\n{\\nnamespace message_operations\\n{\\n\\ntemplate<class ContainerAllocator>\\nstruct Printer< ::rl_msgs::RLStateReward_<ContainerAllocator> >\\n{\\n template<typename Stream> static void stream(Stream& s, const std::string& indent, const ::rl_msgs::RLStateReward_<ContainerAllocator>& v)\\n {\\n s << indent << \\\"state[]\\\" << std::endl;\\n for (size_t i = 0; i < v.state.size(); ++i)\\n {\\n s << indent << \\\" state[\\\" << i << \\\"]: \\\";\\n Printer<float>::stream(s, indent + \\\" \\\", v.state[i]);\\n }\\n s << indent << \\\"reward: \\\";\\n Printer<float>::stream(s, indent + \\\" \\\", v.reward);\\n s << indent << \\\"terminal: \\\";\\n Printer<uint8_t>::stream(s, indent + \\\" \\\", v.terminal);\\n }\\n};\\n\\n} // namespace message_operations\\n} // namespace ros\\n\\n#endif // RL_MSGS_MESSAGE_RLSTATEREWARD_H\\n\"\n },\n {\n \"alpha_fraction\": 0.6990163922309875,\n 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<map>\\n\\n/** C4.5 decision stump class */\\nclass SepPlanExplore: public Classifier {\\n\\npublic:\\n\\n // mode - re-build stump every step? \\n // re-build only on misclassifications? or rebuild every 'trainFreq' steps\\n SepPlanExplore(int id, int modelType, int predType, int nModels, \\n int trainMode, int trainFreq,\\n float featPct, float expPct, float treeThreshold, bool stoch,\\n float featRange, Random rng);\\n\\n SepPlanExplore(const SepPlanExplore&);\\n virtual SepPlanExplore* getCopy();\\n\\n ~SepPlanExplore();\\n\\n virtual bool trainInstances(std::vector<classPair> &instances);\\n virtual bool trainInstance(classPair &instance);\\n virtual void testInstance(const std::vector<float> &input, std::map<float, float>* retval);\\n virtual float getConf(const std::vector<float> &s);\\n \\n void initModels();\\n\\n bool SPEDEBUG;\\n\\nprivate:\\n\\n const int id;\\n const int modelType;\\n const int predType;\\n const int nModels;\\n const int mode;\\n const int freq;\\n const float featPct;\\n const float expPct;\\n const float treeThresh;\\n const bool stoch;\\n const float featRange; \\n\\n Random rng;\\n\\n Classifier* expModel;\\n Classifier* planModel;\\n\\n};\\n\\n\\n#endif\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.7555555701255798,\n \"alphanum_fraction\": 0.7666666507720947,\n \"avg_line_length\": 37.71428680419922,\n \"blob_id\": \"57ae7c12fef2dcd2c309e6bde8bdb3880a62be33\",\n \"content_id\": \"417afc43b48b0de0098a7f8009791fe289170f7b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"CMake\",\n \"length_bytes\": 270,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 7,\n \"path\": \"/src/robot_sensors/catkin_generated/package.cmake\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"set(_CATKIN_CURRENT_PACKAGE \\\"robot_sensors\\\")\\nset(robot_sensors_MAINTAINER \\\"abc <abc@todo.todo>\\\")\\nset(robot_sensors_DEPRECATED \\\"\\\")\\nset(robot_sensors_VERSION \\\"0.0.0\\\")\\nset(robot_sensors_BUILD_DEPENDS )\\nset(robot_sensors_RUN_DEPENDS )\\nset(robot_sensors_BUILDTOOL_DEPENDS \\\"catkin\\\")\"\n },\n {\n \"alpha_fraction\": 0.6356500387191772,\n \"alphanum_fraction\": 0.6387133598327637,\n \"avg_line_length\": 29.526315689086914,\n \"blob_id\": \"148d882c82c04048493cd33bf6e277f8be6cf12f\",\n \"content_id\": \"8d79a798f312842ee89ad72e88b04f7c99953e7b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 5223,\n \"license_type\": \"no_license\",\n \"max_line_length\": 117,\n \"num_lines\": 171,\n \"path\": \"/src/rl-texplore-ros-pkg-master/src/rl_agent/src/Models/LinearSplitsTree.hh\",\n \"repo_name\": \"jstestsuite/ros-test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#ifndef _LINEARSPLITS_HH_\\n#define _LINEARSPLITS_HH_\\n\\n#include <rl_common/Random.h>\\n#include <rl_common/core.hh>\\n#include <vector>\\n#include <set>\\n#include <map>\\n\\n#define N_LST_EXP 200000\\n#define N_LS_NODES 2500\\n\\n#define BUILD_EVERY 0\\n#define BUILD_ON_ERROR 1\\n#define BUILD_EVERY_N 2\\n#define BUILD_ON_TERMINAL 3\\n#define BUILD_ON_TERMINAL_AND_ERROR 4\\n\\n/** M5 decision tree class */\\nclass LinearSplitsTree: public Classifier {\\n\\npublic:\\n\\n // mode - re-build tree every step? \\n // re-build only on misclassifications? or rebuild every 'trainFreq' steps\\n LinearSplitsTree(int id, int trainMode, int trainFreq, int m, \\n float featPct, bool simple, float min_er, Random rng);\\n\\n LinearSplitsTree(const LinearSplitsTree&);\\n virtual LinearSplitsTree* getCopy();\\n\\n ~LinearSplitsTree();\\n\\n // structs to be defined\\n struct tree_node;\\n struct tree_experience;\\n \\n \\n /** Tree node struct */\\n struct tree_node {\\n int id;\\n\\n // split criterion\\n int dim;\\n float val;\\n float avgError;\\n\\n bool leaf;\\n \\n // for regression model\\n float constant;\\n std::vector<float> coefficients;\\n\\n // next nodes in tree\\n tree_node *l;\\n tree_node *r;\\n\\n // set of all outputs seen at this leaf/node\\n int nInstances;\\n\\n };\\n\\n struct tree_experience {\\n std::vector<float> input;\\n float output;\\n };\\n\\n bool trainInstance(classPair &instance);\\n bool trainInstances(std::vector<classPair> &instances);\\n void testInstance(const std::vector<float> &input, std::map<float, float>* retval);\\n float getConf(const std::vector<float> &input);\\n\\n void buildTree(tree_node* node, const std::vector<tree_experience*> &instances,\\n bool changed);\\n void copyTree(tree_node* newNode, tree_node* origNode);\\n\\n\\n // helper functions\\n void initTree();\\n void rebuildTree();\\n void initTreeNode(tree_node* node);\\n tree_node* traverseTree(tree_node* node, const std::vector<float> &input);\\n tree_node* getCorrectChild(tree_node* node, const std::vector<float> &input);\\n bool passTest(int dim, float val, const std::vector<float> &input);\\n float calcER(int dim, float val, \\n const std::vector<tree_experience*> &instances, float error,\\n std::vector<tree_experience*> &left,\\n std::vector<tree_experience*> &right,\\n float *leftError, float *rightError);\\n float* sortOnDim(int dim, const std::vector<tree_experience*> &instances);\\n std::set<float> getUniques(int dim, const std::vector<tree_experience*> &instances, float & minVal, float& maxVal);\\n void deleteTree(tree_node* node);\\n float calcAvgErrorforSet(const std::vector<tree_experience*> &instances);\\n void printTree(tree_node *t, int level);\\n void testPossibleSplits(float avgError, const std::vector<tree_experience*> &instances, \\n float *bestER, int *bestDim, \\n float *bestVal, \\n std::vector<tree_experience*> *bestLeft, \\n std::vector<tree_experience*> *bestRight,\\n float *bestLeftError, float *bestRightError);\\n void implementSplit(tree_node* node, const std::vector<tree_experience*> &instances, \\n float bestER, int bestDim,\\n float bestVal, \\n const std::vector<tree_experience*> &left, \\n const std::vector<tree_experience*> &right,\\n bool changed, float leftError, float rightError);\\n void compareSplits(float er, int dim, float val, \\n const std::vector<tree_experience*> &left, \\n const std::vector<tree_experience*> &right,\\n int *nties, float leftError, float rightError,\\n float *bestER, int *bestDim, \\n float *bestVal, \\n std::vector<tree_experience*> *bestLeft, \\n std::vector<tree_experience*> *bestRight,\\n float *bestLeftError, float *bestRightError);\\n void leafPrediction(tree_node *t, const std::vector<float> &in, std::map<float, float>* retval);\\n void makeLeaf(tree_node* node, const std::vector<tree_experience*> &instances);\\n\\n float fitSimpleLinearModel(const std::vector<tree_experience*> &instances,\\n float* constant, std::vector<float> *coeff);\\n float fitMultiLinearModel(const std::vector<tree_experience*> &instances,\\n float* constant, std::vector<float> * coeff);\\n\\n tree_node* allocateNode();\\n void deallocateNode(tree_node* node);\\n void initNodes();\\n\\n bool INCDEBUG;\\n bool DTDEBUG;\\n bool LMDEBUG;\\n bool SPLITDEBUG;\\n bool STOCH_DEBUG;\\n bool NODEDEBUG;\\n int nExperiences;\\n bool COPYDEBUG;\\n\\n float SPLIT_MARGIN;\\n\\nprivate:\\n\\n const int id;\\n \\n const int mode;\\n const int freq;\\n const int M;\\n const float featPct; \\n const bool SIMPLE;\\n const float MIN_ER;\\n\\n Random rng;\\n\\n int nOutput;\\n int nnodes;\\n bool hadError;\\n int totalnodes;\\n int maxnodes;\\n\\n // INSTANCES\\n std::vector<tree_experience*> experiences;\\n tree_experience allExp[N_LST_EXP];\\n tree_node allNodes[N_LS_NODES];\\n std::vector<int> freeNodes;\\n\\n // TREE\\n tree_node* root;\\n tree_node* lastNode;\\n\\n};\\n\\n\\n#endif\\n \\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":185,"string":"185"}}},{"rowIdx":2983082,"cells":{"repo_name":{"kind":"string","value":"my-poi/machina"},"repo_url":{"kind":"string","value":"https://github.com/my-poi/machina"},"snapshot_id":{"kind":"string","value":"05104812d7bc32c7eba7fa2567498754164ee32a"},"revision_id":{"kind":"string","value":"a84e5f530a03acc36cadcf8002c40c25dfeb41d8"},"directory_id":{"kind":"string","value":"a9cba816576883e7d679f85bbf7166c8b8250fc5"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-10T06:53:55.307126","string":"2021-01-10T06:53:55.307126"},"revision_date":{"kind":"timestamp","value":"2018-02-16T09:24:00","string":"2018-02-16T09:24:00"},"committer_date":{"kind":"timestamp","value":"2018-02-16T09:24:00","string":"2018-02-16T09:24:00"},"github_id":{"kind":"number","value":69585258,"string":"69,585,258"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5708245038986206,"alphanum_fraction":0.6109936833381653,"avg_line_length":25.33333396911621,"blob_id":"0690e73f84dea84000924d34575abc8037f348b0","content_id":"cccb0497d3de87c80d60107c6812f38253bd4542","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":473,"license_type":"no_license","max_line_length":119,"num_lines":18,"path":"/machina.prv/server-test.js","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"var spawn = require('child_process').spawn\nvar child = spawn('/opt/vc/bin/raspivid', ['-t', '0', '-w', '300', '-h', '168', '-hf', '-vf', '-fps', '30', '-o', '-'])\nvar http = require('http')\n\nvar server = http.createServer(function (request, response) {\n\n response.writeHead(200, {\n 'Content-Type': 'video/h-264',\n 'Connection': 'keep-alive',\n 'Accept-Ranges': 'bytes'\n })\n\n child.stdout.pipe(response)\n\n})\n\nserver.listen(8003)\nconsole.log('Server is listening')"},{"alpha_fraction":0.704375684261322,"alphanum_fraction":0.7235859036445618,"avg_line_length":35.07692337036133,"blob_id":"cc854e4167c2a79f565e06b95795473466bfbddc","content_id":"93a33ee564026298a0d880c58faf7806e7252bb2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":941,"license_type":"no_license","max_line_length":94,"num_lines":26,"path":"/machina.api/server.js","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"var express = require('express')\n// var request = require('request')\nvar pwmController = require('./controllers/pwmController.js')\nvar moveController = require('./controllers/moveController.js')\nvar cameraController = require('./controllers/cameraController.js')\nvar app = express()\n\nvar pwmCtrl = new pwmController()\nvar moveCtrl = new moveController(pwmCtrl.pwm)\nvar cameraCtrl = new cameraController(pwmCtrl.pwm)\n// cameraCtrl.setDefault()\n\napp.use(function(req, res, next) {\n res.header('Access-Control-Allow-Origin', '*')\n res.header('Access-Control-Allow-Headers', 'Origin, X-Requested-With, Content-Type, Accept')\n next()\n})\n\napp.post('/:axis0/:axis1/:a/:b/:x/:y', function (req, res) {\n moveCtrl.setMotors(req.params.axis0, req.params.axis1)\n cameraCtrl.setMotors(req.params.a, req.params.b, req.params.x, req.params.y)\n res.end()\n})\n\n// request.post('http://127.0.0.1:8001/\\'Cześć! Machina już działa.\\'')\napp.listen(8000)"},{"alpha_fraction":0.4693877696990967,"alphanum_fraction":0.48469388484954834,"avg_line_length":16.840909957885742,"blob_id":"9953e71cd3c0d92eeac7289174d4f2725a5614b0","content_id":"8cda72bbeb6d49db0747323d2e4e05b8566601da","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":784,"license_type":"no_license","max_line_length":44,"num_lines":44,"path":"/machina.api/test.py","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n\nimport serial, time\n# sudo chmod a+rw /dev/ttyS0\nser = serial.Serial('/dev/ttyS0', 9600)\n\nif(ser.isOpen() == False):\n ser.open()\n\nwhile 1:\n serial_line = ser.readline()\n print serial_line\n\nser.close()\n\n# import serial\n\n# port = serial.Serial(\"/dev/ttyAMA0\", 9600)\n# if(port.isOpen() == False):\n# port.open()\n\n# line = []\n\n# try:\n# while True:\n# for c in port.read():\n# line.append(c)\n# if c == '\\n':\n# print(\"Line: \" + line)\n# line = []\n# break\n\n# # try:\n# # while True:\n# # print \"OK!!!!!\"\n# # line = port.read()\n# # print line\n\n# except Exception as ex:\n# print (ex)\n# pass\n\n# # if(port.isOpen() == True):\n# # port.close()"},{"alpha_fraction":0.6425504088401794,"alphanum_fraction":0.6653452515602112,"avg_line_length":31.913043975830078,"blob_id":"b2dfb19729f4d6d7ec4463780211208b85819cbd","content_id":"448c0a94a74e8431f426d9fb7361aa6bf85309c3","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":3031,"license_type":"permissive","max_line_length":137,"num_lines":92,"path":"/machina.app - old/www/js/app.js","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"var haveEvents = 'ongamepadconnected' in window;\nvar gamepad = null;\nvar timestampValue = 1;\nvar timestamp = document.getElementById('timestamp');\nvar axis0 = document.getElementById('axis0');\nvar axis1 = document.getElementById('axis1');\nvar a = document.getElementById('a');\nvar b = document.getElementById('b');\nvar c = document.getElementById('x');\nvar d = document.getElementById('y');\nvar lb = document.getElementById('lb');\nvar rb = document.getElementById('rb');\n\nfunction connectHandler(e) {\n addGamepad(e);\n}\n\nfunction disconnectHandler() {\n gamepad = null;\n // Jakaś metoda zabezpieczająca pojazd na wypadek utraty połączenia z gamepadem\n}\n\nfunction addGamepad(device) {\n // gamepad = device;\n // document.getElementById('gamepad').innerText =\n // String.format('Gamepad {0} {1}.',\n // gamepad.index,\n // gamepad.id);\n // requestAnimationFrame(updateStatus);\n}\n\nfunction updateStatus() {\n\n // if (!haveEvents) scanGamepads();\n\n // document.getElementById('timestamp').innerText = gamepad.timestamp;\n // document.getElementById('axis0').innerText = gamepad.axes[0].toFixed(6);\n // document.getElementById('axis1').innerText = gamepad.axes[1].toFixed(6);\n // document.getElementById('a').innerText = gamepad.buttons[0].value;\n // document.getElementById('b').innerText = gamepad.buttons[1].value;\n // document.getElementById('x').innerText = gamepad.buttons[2].value;\n // document.getElementById('y').innerText = gamepad.buttons[3].value;\n // document.getElementById('lb').innerText = gamepad.buttons[6].value;\n // document.getElementById('rb').innerText = gamepad.buttons[7].value;\n\n // if (timestampValue < gamepad.timestamp) {\n // sendData();\n // timestampValue = gamepad.timestamp;\n // }\n\n // requestAnimationFrame(updateStatus);\n}\n\nfunction sendData() {\n\n // $.ajax({\n // url: String.format('http://192.168.1.57:8000/{0}/{1}/{2}/{3}/{4}/{5}', \n // gamepad.axes[0].toFixed(6), \n // gamepad.axes[1].toFixed(6),\n // gamepad.buttons[0].value,\n // gamepad.buttons[1].value,\n // gamepad.buttons[2].value,\n // gamepad.buttons[3].value),\n // type: 'POST',\n // success: function(result) {\n // document.getElementById('message').innerText = result;\n // },\n // error: function(error) {\n // document.getElementById('message').innerText = error;\n // },\n // timeout: 1000\n // });\n}\n\nfunction scanGamepads() {\n // var gamepads = navigator.getGamepads ? navigator.getGamepads() : (navigator.webkitGetGamepads ? navigator.webkitGetGamepads() : []);\n // if (gamepads[0] == undefined || gamepad != null) return;\n // for (var i = 0; i < gamepads.length; i++) {\n // if (gamepads[i]) {\n // addGamepad(gamepads[i]);\n // break;\n // }\n // }\n}\n\n// window.addEventListener('gamepadconnected', connectHandler);\n// window.addEventListener('gamepaddisconnected', disconnectHandler);\n\n// if (!haveEvents) setInterval(scanGamepads, 1000);\n\nvar player = new MJPEG.Player('player', 'http://192.168.1.57:8001');\nplayer.start();"},{"alpha_fraction":0.5229591727256775,"alphanum_fraction":0.6071428656578064,"avg_line_length":20.77777862548828,"blob_id":"066c900303620fb011ec9cd1b6e6028c709744c1","content_id":"fa71a687283151b39b0f83a7695493bb7b5fa659","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":392,"license_type":"no_license","max_line_length":55,"num_lines":18,"path":"/machina.api/controllers/pwm.controller.js","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"module.exports = function () {\n var i2cBus = require('i2c-bus');\n var Pca9685Driver = require('pca9685').Pca9685Driver;\n\n var options = {\n i2c: i2cBus.openSync(1),\n address: 0x60,\n frequency: 1000,\n debug: false\n };\n\n this.pwm = new Pca9685Driver(options, (error) => {\n if (error) {\n console.error(\"Error initializing PCA9685\");\n process.exit(-1);\n }\n });\n}\n"},{"alpha_fraction":0.7300613522529602,"alphanum_fraction":0.803680956363678,"avg_line_length":17.22222137451172,"blob_id":"31472ae9cd37e68ef8de815977fb0cdd0b3aec0a","content_id":"5b1d6873b44f01bae6d2d26fa3dbaafc20db0041","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":165,"license_type":"no_license","max_line_length":38,"num_lines":9,"path":"/docs/android.md","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"## Uruchomienie projektu na urządzeniu\ncd machina.app\nadb tcpip 5555\nadb connect 192.168.1.4\ncordova run android\nadb devices\n\n## W przeglądarce\ncordova run browser"},{"alpha_fraction":0.623258650302887,"alphanum_fraction":0.6711084246635437,"avg_line_length":31.372549057006836,"blob_id":"92e584661444cc618d262e7623456fb5c99d5c71","content_id":"7244d12b60426810185e7ba83b7695ad069d7c59","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":1651,"license_type":"no_license","max_line_length":124,"num_lines":51,"path":"/machina.api/controllers/move.controller.js","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"module.exports = function (pwm) {\n var motorsController = require('../controllers/motors.controller.js')\n var motors = new motorsController(pwm)\n\n this.setMotors = function (data) {\n // 1070 1480 1905\n\n const movement = { forward: 0, backward: 0, left: 0, right: 0 };\n\n const speedMin = 0;\n const speedMax = 1100;\n\n const forwardMin = 1550;\n const forwardMax = 1905;\n\n const backwardMin = 1410;\n const backwardMax = 1070;\n\n const leftMin = 1410;\n const leftMax = 1070;\n\n const rightMin = 1550;\n const rightMax = 1905;\n\n if (data.throttle >= 1410 && data.throttle <= 1550) {\n pwm.allChannelsOff();\n return;\n }\n\n if (data.throttle > 1500) movement.forward = scaleBetween(data.throttle, forwardMin, forwardMax, speedMin, speedMax);\n if (data.throttle < 1410) movement.backward = scaleBetween(data.throttle, backwardMin, backwardMax, speedMin, speedMax);\n if (data.yaw < 1410) movement.left = scaleBetween(data.yaw, leftMin, leftMax, speedMin, speedMax);\n if (data.yaw > 1500) movement.right = scaleBetween(data.yaw, rightMin, rightMax, speedMin, speedMax);\n\n if (movement.forward > 0) {\n motors.forwardLeft(movement.forward - movement.left);\n motors.forwardRight(movement.forward - movement.right);\n return;\n }\n\n if (movement.backward > 0) {\n motors.backwardLeft(movement.backward - movement.left);\n motors.backwardRight(movement.backward - movement.right);\n return;\n }\n }\n\n function scaleBetween(valueIn, baseMin, baseMax, limitMin, limitMax) {\n return ((limitMax - limitMin) * (valueIn - baseMin) / (baseMax - baseMin)) + limitMin;\n }\n}\n"},{"alpha_fraction":0.5835843086242676,"alphanum_fraction":0.6378012299537659,"avg_line_length":21.508474349975586,"blob_id":"82d7b8d026dd4d896db3faeff1580179bcce296d","content_id":"3929a63f041e1b555d311442758bd6e66d17704e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":1328,"license_type":"no_license","max_line_length":41,"num_lines":59,"path":"/machina.api/controllers/motors.controller.js","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"module.exports = function (pwm) {\n this.forwardLeft = function (speed) {\n // Tylne\n pwm.setPulseLength(2, speed);\n pwm.setPulseLength(4, 0);\n pwm.setPulseLength(3, 4096);\n\n // Przednie\n pwm.setPulseLength(7, speed);\n pwm.setPulseLength(5, 0);\n pwm.setPulseLength(6, 4096);\n }\n\n this.forwardRight = function (speed) {\n // Przednie\n pwm.setPulseLength(8, speed);\n pwm.setPulseLength(10, 0);\n pwm.setPulseLength(9, 4096);\n\n // Tylne\n pwm.setPulseLength(13, speed);\n pwm.setPulseLength(11, 0);\n pwm.setPulseLength(12, 4096);\n }\n\n this.forward = function (speed) {\n this.forwardLeft(speed);\n this.forwardRight(speed);\n }\n\n this.backwardLeft = function (speed) {\n // Tylne\n pwm.setPulseLength(2, speed);\n pwm.setPulseLength(4, 4096);\n pwm.setPulseLength(3, 0);\n\n // Przednie\n pwm.setPulseLength(7, speed);\n pwm.setPulseLength(5, 4096);\n pwm.setPulseLength(6, 0);\n }\n\n this.backwardRight = function (speed) {\n // Przednie\n pwm.setPulseLength(8, speed);\n pwm.setPulseLength(10, 4096);\n pwm.setPulseLength(9, 0);\n\n // Tylne\n pwm.setPulseLength(13, speed);\n pwm.setPulseLength(11, 4096);\n pwm.setPulseLength(12, 0);\n }\n\n this.backward = function (speed) {\n this.backwardsLeft(speed);\n this.backwardsRight(speed);\n }\n}\n"},{"alpha_fraction":0.6050955653190613,"alphanum_fraction":0.6050955653190613,"avg_line_length":18.75,"blob_id":"177d4d22bd5c85b316ce3261c4e6a9c7d78143e4","content_id":"80885fa994263a2be6c4e41f0ff147672f55b456","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":157,"license_type":"no_license","max_line_length":42,"num_lines":8,"path":"/machina.api/objects/rc-data.js","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"class RcData {\n constructor(throttle, yaw, pith, roll) {\n this.throttle = throttle;\n this.yaw = yaw;\n this.pith = pith;\n this.roll = roll;\n }\n}"},{"alpha_fraction":0.6205016374588013,"alphanum_fraction":0.6314067840576172,"avg_line_length":23.157894134521484,"blob_id":"c11c90f6f10f5075482434a6428b619989031416","content_id":"57d701e74756490b40e61684dfd3abd12ca882eb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":918,"license_type":"no_license","max_line_length":71,"num_lines":38,"path":"/machina.prv/server-old.js","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"var http = require('http')\nvar fs = require('fs')\n\nvar server = http.createServer(function (request, response) {\n\n response.writeHead(200, {\n 'Content-Type': 'multipart/x-mixed-replace; boundary=myboundary',\n 'Cache-Control': 'no-cache',\n 'Connection': 'close',\n 'Pragma': 'no-cache'\n })\n\n var stop = false;\n\n response.connection.on('close', function () { stop = true })\n\n var sendPicture = function () {\n\n if (stop) return\n\n fs.readFile('/apps/stream/picture.jpg', function (error, content) {\n response.write(\"--myboundary\\r\\n\")\n response.write(\"Content-Type: image/jpeg\\r\\n\")\n response.write(\"Content-Length: \" + content.length + \"\\r\\n\")\n response.write(\"\\r\\n\")\n response.write(content, 'binary')\n response.write(\"\\r\\n\")\n\n setTimeout(sendPicture, 200)\n })\n }\n\n sendPicture()\n})\n\nserver.listen(8003, function () {\n console.log('Podgląd uruchomiony')\n})"},{"alpha_fraction":0.779046356678009,"alphanum_fraction":0.7991940975189209,"avg_line_length":39.135135650634766,"blob_id":"eaeac5dccff5c482cf666eba1f70e597a4dd5a99","content_id":"42764a131fbf4668e6ce822b8d224f18d118db31","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1544,"license_type":"no_license","max_line_length":165,"num_lines":37,"path":"/README.md","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":" \n# Machina\n\nZdalne sterowanie robotem na bazie Raspberry Pi.\n\n \n\nProjekt przede wszystkim ma na celu bawiąc/uczyć stosowania technologii JavaScript: Node.js i Cordova. Aby zacząć prace należy zaopatrzyć się w niezbędne podzespoły:\n\n- komputer Raspberry Pi\n- sterownik silników Motor Hat lub inny\n- czterokołowe podwozie z silnikami\n- kamerę\n- taśmę 15 żył 30cm do kamery\n- uchwyt do serw micro Pan/Tilt\n- dwa micro serwa TowerPro SG-90 lub inne\n- ultradźwiękowy czujnik odległości HC-SR04\n- uchwyt montażowy do czujnika odległości HC-SR04\n- akumulator/pakiet Li-Pol\n- głośnik 0.1W\n\nUWAGA: Podłączenie akumulatora przy zworce Motor Hat-a ustawionej na 5V spali Raspberry oraz sam sterownik!\n\n[Proces instalacji oprogramowania](./docs/instalacja.md)\n\nZałożenia:\n\n- pojazd ma poruszać się sterowany aplikacją na Androida z wykorzystaniem kontrolera do gier podłączonego przez USB OTG do telefonu\n- w aplikacji na telefonie ma być dostępny podgląd obrazu z kamery\n- czujnik odległości ma zabezpieczyć pojazd przed uderzeniem w przeszkodę uruchamiając na około 20cm przed nią automatyczne hamowanie\n- bardziej zaawansowane funkcje do opracowania to np. skanowanie kamerą pomieszczenia w celu autonomicznego omijania przeszkód\n- Raspberry docelowo ma być Access Pointem (hostapd?) aby uzyskać bezpośrednią łączność.\n\n[Dyskusja na Facebook-u](https://www.facebook.com/groups/malinowepi/permalink/503939639776690/)\n\n___\nMaciej Tokarz © My-Poi!\n\n\n\n\n"},{"alpha_fraction":0.6194690465927124,"alphanum_fraction":0.6415929198265076,"avg_line_length":21.700000762939453,"blob_id":"351d8bacaee4091da9b089efc8a24e4fa4e759af","content_id":"405572a1db14f0ec9995ac9f9485fd87dc86983a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":226,"license_type":"no_license","max_line_length":62,"num_lines":10,"path":"/machina.spk/server.js","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"var express = require('express')\nvar exec = require('child_process').exec\nvar app = express()\n\napp.post('/:text', function (req) {\n var cmd = 'espeak -v pl ' + req.params.text + ' 2>/dev/null'\n exec(cmd)\n})\n\napp.listen(8001)"},{"alpha_fraction":0.5548060536384583,"alphanum_fraction":0.5843170285224915,"avg_line_length":27.95121955871582,"blob_id":"418a6b7105b40c8cf915f5d4460652b02b088af9","content_id":"7a62ed541d775999b63cfd6db597be2f0b1297c8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":1187,"license_type":"no_license","max_line_length":135,"num_lines":41,"path":"/machina.prv/server.js","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"var http = require('http');\nvar fs = require('fs');\nvar spawn = require('child_process').spawn;\n\nvar server = http.createServer(function (request, response) {\n response.writeHead(200, {\n 'Content-Type': 'multipart/x-mixed-replace; boundary=myboundary',\n 'Cache-Control': 'no-cache',\n 'Connection': 'close',\n 'Pragma': 'no-cache'\n });\n\n var args = ['--nopreview', '-w', '559', '-h', '283', '-q', '50', '-o', 'preview.jpg', '-t', '9999999', '-th', '0:0:0', '-tl', '100'];\n var proc = spawn('raspistill', args);\n var stop = false;\n\n response.connection.on('close', () => { \n stop = true;\n if (proc) proc.kill();\n });\n\n var sendPicture = () => {\n if (stop) return;\n\n fs.readFile('/home/pi/machina.prv/preview.jpg', (error, content) => {\n response.write('--myboundary\\r\\n');\n response.write('Content-Type: image/jpeg\\r\\n');\n response.write('Content-Length: ' + content.length + '\\r\\n');\n response.write('\\r\\n');\n response.write(content, 'binary');\n response.write('\\r\\n');\n setTimeout(sendPicture, 200);\n });\n };\n\n sendPicture();\n});\n\nserver.listen(8001, () => {\n console.log('Podgląd uruchomiony na porcie 8001');\n});"},{"alpha_fraction":0.6914731860160828,"alphanum_fraction":0.7363913059234619,"avg_line_length":19.289575576782227,"blob_id":"fb247a674a58a158d78829a39f7c4b8f382fd4bb","content_id":"81290a7e413853bd1b9023589c011a8bb8e11fa4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":5339,"license_type":"no_license","max_line_length":225,"num_lines":259,"path":"/docs/instalacja.md","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"# Scenariusz przygotowania Raspberry Pi od strony programowej\n\n## Instalacja Raspbiana Jessie\n\nJako podstawę działania posłuży wersja:\n\n[Minimal image based on Debian Jessie](https://www.raspberrypi.org/downloads/raspbian/)\n\nPo zainstalowaniu należy przejść do ustawień:\n```\nsudo raspi-config\n```\noraz\n\n- zmienić hasło administratora-\n- ustawić autologowanie do konsoli\n- rozszerzyć partycję na całą objętość karty\n- włączyć kamerę\n- właczyć audio na jack-a\n- włączyć SSH\n- włączyć i2c\n- włączyć GPIO Server\n- włączyć Serial (jak po UART PuTTY)\n\n## Instalacja karty WiFi\n\nAby można było połączyć się z Raspberry Pi warto skorzystać z karty WiFi \ni sprawić aby przy starcie następowało automatyczne łączenie z naszą siecią:\n```\nsudo nano /etc/wpa_supplicant/wpa_supplicant.conf\n\nnetwork={\n ssid=\"nazwa_sieci\"\n psk=\"hasło\"\n}\n\n## Zmiana ustawień na PL\n\n\n```\n## Instalacja Node.js\n```\nwget -O - https://raw.githubusercontent.com/sdesalas/node-pi-zero/master/install-node-v.last.sh | bash\nnode -v\n```\n\n## Uruchomienie api\nsudo nano /etc/systemd/system/machina.service\n\n[Service]\nWorkingDirectory=/home/pi/machina.api\nExecStart=/home/pi/machina.api/index.js\nRestart=always\nStandardOutput=syslog\nStandardError=syslog\nSyslogIdentifier=machina\nUser=root\nGroup=root\nEnvironment=NODE_ENV=production\n[Install]\nWantedBy=multi-user.target\n\n\nsudo chmod u+rwx /etc/systemd/system/machina.service\nsudo systemctl enable machina\n\n---\nsudo nano /etc/rc.local\ncd /home/pi/machina.api\nnode index.js\n\n\n\n\n\n## Przygotowanie miejsca na aplikacje serwera\n\nDo pracy z plikami polecam: [WinSCP](https://winscp.net)\n```\nsudo mkdir /apps\nsudo mkdir /apps/machina.api\nsudo mkdir /apps/machina.spk\n```\nmachina.api (sterowanie pojazdem, kamerą)\n```\ncd /apps/machina.api\nsudo -s\nsudo npm install i2c-bus\nsudo npm install pca9685\nsudo npm install express\nsudo npm install request\n```\nmachina.spk (odtwarzanie tekstów komunikatów)\n```\ncd ..\ncd machina.spk\nsudo npm install express\n\nsudo chown -R pi:pi /apps\n```\n\n## Ma mówić\n```\nsudo apt-get install espeak\namixer sset PCM,0 100%\nespeak \"Hello, I am Espeak, the voice synthesizer\" 2>/dev/null\n```\n## Cordova (https://cordova.apache.org/docs/pl/latest/guide/cli/)\n```\nnpm install -g cordova\ncordova create machina.app nazwa_wydawcy Machina\ncd machina.app\ncordova platform add android --save\n\nd:\\Projekty\\Machina\\machina.app>cordova requirements\n\ncordova build\ncordova emulate android\ncordova run android\n```\n# Uruchomienie podglądu z kamery:\nsudo nano /etc/rc.local\nraspistill --nopreview -w 300 -h 168 -vf -hf -q 50 -o /apps/stream/picture.jpg -tl 50 -t 9999999 -th 0:0:0 &\n\nexit 0\n\nmachina.prv (preview)\nsudo node /apps/machina.prv/server.js\nhttp://192.168.1.7:8003\n```\n## Linki\n```\nFilmy https://1drv.ms/f/s!AoVId-NDPgmihPZR0ijOLWl7qOKYgQ\nhttps://github.com/fivdi/pigpio\n```\n## Akumulatory\n```\nNapięcie minimalne bez obciążenia: 11,15V\n\n```\n\nW opracowaniu...\n\nz\n\nz\n\nz\n\nz\n\n## Serwa SG90\n```\nbrązowy przewód: GND (masa zasilania)\nczerwony przewód: Zasilanie 5 V\npomarańczowy przewód: sygnał PWM\n```\n## Czujnik odległosci SR04\n```\nhttp://uczymy.edu.pl/wp/blog/2016/01/20/hc-sr04/\nUWAGA! Echo trzeba obniżyć do 3,3V\n```\n\n```\n## Kolory\n```\nWiodący rgb 0 114 198 hex 0072C6\n```\n## Różności\n```\nUprawnienia roota\nsudo -s\n\nUsunięcie katalogu\nsudo rm -r /foo/foo\n\nAktualizacja\nsudo apt-get update # Fetches the list of available updates\nsudo apt-get upgrade # Strictly upgrades the current packages\nsudo apt-get dist-upgrade # Installs updates (new ones)\n\nSprzątanie\nsudo apt-get autoclean\nsudo apt-get clean\nsudo apt-get autoremove\n\nUsuwanie pakietu\nsudo apt-get remove <package> && sudo apt-get autoremove\n```\n\n\n## Podgląd stare\n\n\n\nmkfifo fifo.500\n/opt/vc/bin/raspivid -o fifo.500 -t 0 -b 20000\n\n\n\n\ncat fifo.500 | nc.traditional 192.168.1.7 5000 &\n\n\n```\n???\nsudo apt-get install libav-tools\n???\n\nsudo -s\nsudo apt-get install git\n\ncd /usr/src\ngit clone git://git.videolan.org/x264\ncd x264\n./configure --host=arm-unknown-linux-gnueabi --enable-static --disable-opencl\nmake\nsudo make install\n\ncd /usr/src\ngit clone https://github.com/ffmpeg/ffmpeg.git\ncd ffmpeg\nsudo ./configure --arch=armel --target-os=linux --enable-gpl --enable-libx264 --enable-nonfree\nmake\nsudo make install\n\nraspivid -o - -t 0 -vf -hf -fps 30 -b 6000000 | ffmpeg -re -ar 44100 -ac 2 -acodec pcm_s16le -f s16le -ac 2 -i /apps -f h264 -i -\n\nraspivid -o - -t 0 -vf -hf -fps 30 -b 6000000 | ffmpeg -re -ar 44100 -ac 2 -acodec pcm_s16le -f s16le -ac 2 -i /dev/zero -f h264 -i - -vcodec copy -acodec aac -ab 128k -g 50 -strict experimental -f flv rtmp://192.168.1.7:8003\n\nffmpeg raspivid\n\nTODO: dostroić podgląd aby nie było lagów!\n\nOpcja z zapisem sekwencyjnym zdjęć:\n\nsudo mkdir /apps/stream\n\nsudo nano /etc/rc.local\n\nraspistill --nopreview -w 400 -h 225 -vf -q 30 -o /apps/stream/picture.jpg -tl 50 -t 9999999 -th 0:0:0 &\n\nraspistill --nopreview -w 300 -h 168 -vf -hf -q 50 -o /apps/stream/picture.jpg -tl 50 -t 9999999 -th 0:0:0 &\n\nexit 0\n\nPodgląd\nsudo nano /etc/apt/sources.list\ndodać\ndeb http://vontaene.de/raspbian-updates/ . main\n\nsudo apt-get install debian-keyring\ngpg --keyserver pgp.mit.edu --recv-keys 1F41B907\ngpg --armor --export 1F41B907 | sudo apt-key add -\n\nsudo apt-get update\nsudo apt-get install gstreamer1.0\n\napt-get install gstreamer-tools"},{"alpha_fraction":0.4603399336338043,"alphanum_fraction":0.4886685609817505,"avg_line_length":14.3695650100708,"blob_id":"00740366673dedbc6e9aeb443261bcd32168e57b","content_id":"b5ab998273ddde24655b6b5f072505ecb53bf431","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":706,"license_type":"no_license","max_line_length":42,"num_lines":46,"path":"/machina.api/controllers/camera.controller.js","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"module.exports = function (pwm) {\n var self = this\n\n this.a\n this.b\n this.x\n this.y\n\n // var positionV = 0\n // var positionH = 0\n \n\n // this.setDefault = function () {\n // pwm.setPulseLength(0, positionV)\n // pwm.setPulseLength(1, positionH)\n // pwm.allChannelsOff()\n // }\n\n this.setMotors = function (a, b, x, y) {\n\n var i = 0\n\n self.a = a\n self.b = b\n self.x = x\n self.y = y\n\n if (self.y == 1) {\n pwm.setPulseLength(0, 1)\n }\n\n if (self.a == 1) {\n pwm.setPulseLength(0, -4)\n }\n\n if (self.x == 1) {\n for (i = 0; i < 10; i++) {\n pwm.setPulseLength(1, -8)\n }\n }\n\n if (self.b == 1) {\n pwm.setPulseLength(1, 1)\n }\n }\n}"},{"alpha_fraction":0.6351970434188843,"alphanum_fraction":0.6535288691520691,"avg_line_length":26.274999618530273,"blob_id":"0af332adfb1662c4e927ded3309c6c03f45f1ac7","content_id":"42da1a898af49f1dbb6275bb3883989cf7211dd1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"TypeScript","length_bytes":1091,"license_type":"no_license","max_line_length":62,"num_lines":40,"path":"/machina.app/src/app/services/gamepad.service.ts","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"import { Injectable } from '@angular/core';\n\n@Injectable()\nexport class GamepadService {\n gamepad: Gamepad;\n timestamp: number;\n axis0: number;\n axis1: number;\n axis2: number;\n axis3: number;\n a: number;\n b: number;\n x: number;\n y: number;\n lb: number;\n rb: number;\n\n constructor() {\n // this.gamepad = navigator.getGamepads()[1];\n // console.log(this.gamepad);\n // this.setGamepadValues();\n }\n\n setGamepadValues() {\n this.gamepad = navigator.getGamepads()[1];\n this.timestamp = this.gamepad.timestamp;\n this.axis0 = this.gamepad.axes[0];\n this.axis1 = this.gamepad.axes[1];\n this.axis2 = this.gamepad.axes[2];\n this.axis3 = this.gamepad.axes[3];\n // this.gamepadValues.a = this.gamepad.buttons[0];\n // this.gamepadValues.b = this.gamepad.buttons[1];\n // this.gamepadValues.x = this.gamepad.buttons[2];\n // this.gamepadValues.y = this.gamepad.buttons[3];\n // this.gamepadValues.lb = this.gamepad.buttons[6];\n // this.gamepadValues.rb = this.gamepad.buttons[7];\n\n requestAnimationFrame(() => { this.setGamepadValues(); });\n }\n}\n"},{"alpha_fraction":0.5131348371505737,"alphanum_fraction":0.5499124526977539,"avg_line_length":17.419355392456055,"blob_id":"162e28c90dac404d457be66fc49cf349976e70dd","content_id":"3348f36f705160b107de8d7f20689339d6f00f9b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":571,"license_type":"no_license","max_line_length":32,"num_lines":31,"path":"/machina.ino/machina.ino","repo_name":"my-poi/machina","src_encoding":"UTF-8","text":"void setup()\n{\n pinMode(2, INPUT);\n pinMode(3, INPUT);\n pinMode(4, INPUT);\n pinMode(5, INPUT);\n Serial.begin(9600);\n}\n\nvoid loop()\n{\n double channel[4];\n String result = \"\";\n\n channel[0] = pulseIn(2, HIGH);\n channel[1] = pulseIn(3, HIGH);\n channel[2] = pulseIn(4, HIGH);\n channel[3] = pulseIn(5, HIGH);\n\n result += \"{\\\"throttle\\\": \";\n result += channel[0];\n result += \", \\\"yaw\\\": \";\n result += channel[1];\n result += \", \\\"pith\\\": \";\n result += channel[2];\n result += \", \\\"roll\\\": \";\n result += channel[3];\n result += \"}\";\n\n Serial.println(result);\n}\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5708245038986206,\n \"alphanum_fraction\": 0.6109936833381653,\n \"avg_line_length\": 25.33333396911621,\n \"blob_id\": \"0690e73f84dea84000924d34575abc8037f348b0\",\n \"content_id\": \"cccb0497d3de87c80d60107c6812f38253bd4542\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 473,\n \"license_type\": \"no_license\",\n \"max_line_length\": 119,\n \"num_lines\": 18,\n \"path\": \"/machina.prv/server-test.js\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"var spawn = require('child_process').spawn\\nvar child = spawn('/opt/vc/bin/raspivid', ['-t', '0', '-w', '300', '-h', '168', '-hf', '-vf', '-fps', '30', '-o', '-'])\\nvar http = require('http')\\n\\nvar server = http.createServer(function (request, response) {\\n\\n response.writeHead(200, {\\n 'Content-Type': 'video/h-264',\\n 'Connection': 'keep-alive',\\n 'Accept-Ranges': 'bytes'\\n })\\n\\n child.stdout.pipe(response)\\n\\n})\\n\\nserver.listen(8003)\\nconsole.log('Server is listening')\"\n },\n {\n \"alpha_fraction\": 0.704375684261322,\n \"alphanum_fraction\": 0.7235859036445618,\n \"avg_line_length\": 35.07692337036133,\n \"blob_id\": \"cc854e4167c2a79f565e06b95795473466bfbddc\",\n \"content_id\": \"93a33ee564026298a0d880c58faf7806e7252bb2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 941,\n \"license_type\": \"no_license\",\n \"max_line_length\": 94,\n \"num_lines\": 26,\n \"path\": \"/machina.api/server.js\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"var express = require('express')\\n// var request = require('request')\\nvar pwmController = require('./controllers/pwmController.js')\\nvar moveController = require('./controllers/moveController.js')\\nvar cameraController = require('./controllers/cameraController.js')\\nvar app = express()\\n\\nvar pwmCtrl = new pwmController()\\nvar moveCtrl = new moveController(pwmCtrl.pwm)\\nvar cameraCtrl = new cameraController(pwmCtrl.pwm)\\n// cameraCtrl.setDefault()\\n\\napp.use(function(req, res, next) {\\n res.header('Access-Control-Allow-Origin', '*')\\n res.header('Access-Control-Allow-Headers', 'Origin, X-Requested-With, Content-Type, Accept')\\n next()\\n})\\n\\napp.post('/:axis0/:axis1/:a/:b/:x/:y', function (req, res) {\\n moveCtrl.setMotors(req.params.axis0, req.params.axis1)\\n cameraCtrl.setMotors(req.params.a, req.params.b, req.params.x, req.params.y)\\n res.end()\\n})\\n\\n// request.post('http://127.0.0.1:8001/\\\\'Cześć! Machina już działa.\\\\'')\\napp.listen(8000)\"\n },\n {\n \"alpha_fraction\": 0.4693877696990967,\n \"alphanum_fraction\": 0.48469388484954834,\n \"avg_line_length\": 16.840909957885742,\n \"blob_id\": \"9953e71cd3c0d92eeac7289174d4f2725a5614b0\",\n \"content_id\": \"8cda72bbeb6d49db0747323d2e4e05b8566601da\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 784,\n \"license_type\": \"no_license\",\n \"max_line_length\": 44,\n \"num_lines\": 44,\n \"path\": \"/machina.api/test.py\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n\\nimport serial, time\\n# sudo chmod a+rw /dev/ttyS0\\nser = serial.Serial('/dev/ttyS0', 9600)\\n\\nif(ser.isOpen() == False):\\n ser.open()\\n\\nwhile 1:\\n serial_line = ser.readline()\\n print serial_line\\n\\nser.close()\\n\\n# import serial\\n\\n# port = serial.Serial(\\\"/dev/ttyAMA0\\\", 9600)\\n# if(port.isOpen() == False):\\n# port.open()\\n\\n# line = []\\n\\n# try:\\n# while True:\\n# for c in port.read():\\n# line.append(c)\\n# if c == '\\\\n':\\n# print(\\\"Line: \\\" + line)\\n# line = []\\n# break\\n\\n# # try:\\n# # while True:\\n# # print \\\"OK!!!!!\\\"\\n# # line = port.read()\\n# # print line\\n\\n# except Exception as ex:\\n# print (ex)\\n# pass\\n\\n# # if(port.isOpen() == True):\\n# # port.close()\"\n },\n {\n \"alpha_fraction\": 0.6425504088401794,\n \"alphanum_fraction\": 0.6653452515602112,\n \"avg_line_length\": 31.913043975830078,\n \"blob_id\": \"b2dfb19729f4d6d7ec4463780211208b85819cbd\",\n \"content_id\": \"448c0a94a74e8431f426d9fb7361aa6bf85309c3\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 3031,\n \"license_type\": \"permissive\",\n \"max_line_length\": 137,\n \"num_lines\": 92,\n \"path\": \"/machina.app - old/www/js/app.js\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"var haveEvents = 'ongamepadconnected' in window;\\nvar gamepad = null;\\nvar timestampValue = 1;\\nvar timestamp = document.getElementById('timestamp');\\nvar axis0 = document.getElementById('axis0');\\nvar axis1 = document.getElementById('axis1');\\nvar a = document.getElementById('a');\\nvar b = document.getElementById('b');\\nvar c = document.getElementById('x');\\nvar d = document.getElementById('y');\\nvar lb = document.getElementById('lb');\\nvar rb = document.getElementById('rb');\\n\\nfunction connectHandler(e) {\\n addGamepad(e);\\n}\\n\\nfunction disconnectHandler() {\\n gamepad = null;\\n // Jakaś metoda zabezpieczająca pojazd na wypadek utraty połączenia z gamepadem\\n}\\n\\nfunction addGamepad(device) {\\n // gamepad = device;\\n // document.getElementById('gamepad').innerText =\\n // String.format('Gamepad {0} {1}.',\\n // gamepad.index,\\n // gamepad.id);\\n // requestAnimationFrame(updateStatus);\\n}\\n\\nfunction updateStatus() {\\n\\n // if (!haveEvents) scanGamepads();\\n\\n // document.getElementById('timestamp').innerText = gamepad.timestamp;\\n // document.getElementById('axis0').innerText = gamepad.axes[0].toFixed(6);\\n // document.getElementById('axis1').innerText = gamepad.axes[1].toFixed(6);\\n // document.getElementById('a').innerText = gamepad.buttons[0].value;\\n // document.getElementById('b').innerText = gamepad.buttons[1].value;\\n // document.getElementById('x').innerText = gamepad.buttons[2].value;\\n // document.getElementById('y').innerText = gamepad.buttons[3].value;\\n // document.getElementById('lb').innerText = gamepad.buttons[6].value;\\n // document.getElementById('rb').innerText = gamepad.buttons[7].value;\\n\\n // if (timestampValue < gamepad.timestamp) {\\n // sendData();\\n // timestampValue = gamepad.timestamp;\\n // }\\n\\n // requestAnimationFrame(updateStatus);\\n}\\n\\nfunction sendData() {\\n\\n // $.ajax({\\n // url: String.format('http://192.168.1.57:8000/{0}/{1}/{2}/{3}/{4}/{5}', \\n // gamepad.axes[0].toFixed(6), \\n // gamepad.axes[1].toFixed(6),\\n // gamepad.buttons[0].value,\\n // gamepad.buttons[1].value,\\n // gamepad.buttons[2].value,\\n // gamepad.buttons[3].value),\\n // type: 'POST',\\n // success: function(result) {\\n // document.getElementById('message').innerText = result;\\n // },\\n // error: function(error) {\\n // document.getElementById('message').innerText = error;\\n // },\\n // timeout: 1000\\n // });\\n}\\n\\nfunction scanGamepads() {\\n // var gamepads = navigator.getGamepads ? navigator.getGamepads() : (navigator.webkitGetGamepads ? navigator.webkitGetGamepads() : []);\\n // if (gamepads[0] == undefined || gamepad != null) return;\\n // for (var i = 0; i < gamepads.length; i++) {\\n // if (gamepads[i]) {\\n // addGamepad(gamepads[i]);\\n // break;\\n // }\\n // }\\n}\\n\\n// window.addEventListener('gamepadconnected', connectHandler);\\n// window.addEventListener('gamepaddisconnected', disconnectHandler);\\n\\n// if (!haveEvents) setInterval(scanGamepads, 1000);\\n\\nvar player = new MJPEG.Player('player', 'http://192.168.1.57:8001');\\nplayer.start();\"\n },\n {\n \"alpha_fraction\": 0.5229591727256775,\n \"alphanum_fraction\": 0.6071428656578064,\n \"avg_line_length\": 20.77777862548828,\n \"blob_id\": \"066c900303620fb011ec9cd1b6e6028c709744c1\",\n \"content_id\": \"fa71a687283151b39b0f83a7695493bb7b5fa659\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 392,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 18,\n \"path\": \"/machina.api/controllers/pwm.controller.js\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"module.exports = function () {\\n var i2cBus = require('i2c-bus');\\n var Pca9685Driver = require('pca9685').Pca9685Driver;\\n\\n var options = {\\n i2c: i2cBus.openSync(1),\\n address: 0x60,\\n frequency: 1000,\\n debug: false\\n };\\n\\n this.pwm = new Pca9685Driver(options, (error) => {\\n if (error) {\\n console.error(\\\"Error initializing PCA9685\\\");\\n process.exit(-1);\\n }\\n });\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7300613522529602,\n \"alphanum_fraction\": 0.803680956363678,\n \"avg_line_length\": 17.22222137451172,\n \"blob_id\": \"31472ae9cd37e68ef8de815977fb0cdd0b3aec0a\",\n \"content_id\": \"5b1d6873b44f01bae6d2d26fa3dbaafc20db0041\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 165,\n \"license_type\": \"no_license\",\n \"max_line_length\": 38,\n \"num_lines\": 9,\n \"path\": \"/docs/android.md\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"## Uruchomienie projektu na urządzeniu\\ncd machina.app\\nadb tcpip 5555\\nadb connect 192.168.1.4\\ncordova run android\\nadb devices\\n\\n## W przeglądarce\\ncordova run browser\"\n },\n {\n \"alpha_fraction\": 0.623258650302887,\n \"alphanum_fraction\": 0.6711084246635437,\n \"avg_line_length\": 31.372549057006836,\n \"blob_id\": \"92e584661444cc618d262e7623456fb5c99d5c71\",\n \"content_id\": \"7244d12b60426810185e7ba83b7695ad069d7c59\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 1651,\n \"license_type\": \"no_license\",\n \"max_line_length\": 124,\n \"num_lines\": 51,\n \"path\": \"/machina.api/controllers/move.controller.js\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"module.exports = function (pwm) {\\n var motorsController = require('../controllers/motors.controller.js')\\n var motors = new motorsController(pwm)\\n\\n this.setMotors = function (data) {\\n // 1070 1480 1905\\n\\n const movement = { forward: 0, backward: 0, left: 0, right: 0 };\\n\\n const speedMin = 0;\\n const speedMax = 1100;\\n\\n const forwardMin = 1550;\\n const forwardMax = 1905;\\n\\n const backwardMin = 1410;\\n const backwardMax = 1070;\\n\\n const leftMin = 1410;\\n const leftMax = 1070;\\n\\n const rightMin = 1550;\\n const rightMax = 1905;\\n\\n if (data.throttle >= 1410 && data.throttle <= 1550) {\\n pwm.allChannelsOff();\\n return;\\n }\\n\\n if (data.throttle > 1500) movement.forward = scaleBetween(data.throttle, forwardMin, forwardMax, speedMin, speedMax);\\n if (data.throttle < 1410) movement.backward = scaleBetween(data.throttle, backwardMin, backwardMax, speedMin, speedMax);\\n if (data.yaw < 1410) movement.left = scaleBetween(data.yaw, leftMin, leftMax, speedMin, speedMax);\\n if (data.yaw > 1500) movement.right = scaleBetween(data.yaw, rightMin, rightMax, speedMin, speedMax);\\n\\n if (movement.forward > 0) {\\n motors.forwardLeft(movement.forward - movement.left);\\n motors.forwardRight(movement.forward - movement.right);\\n return;\\n }\\n\\n if (movement.backward > 0) {\\n motors.backwardLeft(movement.backward - movement.left);\\n motors.backwardRight(movement.backward - movement.right);\\n return;\\n }\\n }\\n\\n function scaleBetween(valueIn, baseMin, baseMax, limitMin, limitMax) {\\n return ((limitMax - limitMin) * (valueIn - baseMin) / (baseMax - baseMin)) + limitMin;\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5835843086242676,\n \"alphanum_fraction\": 0.6378012299537659,\n \"avg_line_length\": 21.508474349975586,\n \"blob_id\": \"82d7b8d026dd4d896db3faeff1580179bcce296d\",\n \"content_id\": \"3929a63f041e1b555d311442758bd6e66d17704e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 1328,\n \"license_type\": \"no_license\",\n \"max_line_length\": 41,\n \"num_lines\": 59,\n \"path\": \"/machina.api/controllers/motors.controller.js\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"module.exports = function (pwm) {\\n this.forwardLeft = function (speed) {\\n // Tylne\\n pwm.setPulseLength(2, speed);\\n pwm.setPulseLength(4, 0);\\n pwm.setPulseLength(3, 4096);\\n\\n // Przednie\\n pwm.setPulseLength(7, speed);\\n pwm.setPulseLength(5, 0);\\n pwm.setPulseLength(6, 4096);\\n }\\n\\n this.forwardRight = function (speed) {\\n // Przednie\\n pwm.setPulseLength(8, speed);\\n pwm.setPulseLength(10, 0);\\n pwm.setPulseLength(9, 4096);\\n\\n // Tylne\\n pwm.setPulseLength(13, speed);\\n pwm.setPulseLength(11, 0);\\n pwm.setPulseLength(12, 4096);\\n }\\n\\n this.forward = function (speed) {\\n this.forwardLeft(speed);\\n this.forwardRight(speed);\\n }\\n\\n this.backwardLeft = function (speed) {\\n // Tylne\\n pwm.setPulseLength(2, speed);\\n pwm.setPulseLength(4, 4096);\\n pwm.setPulseLength(3, 0);\\n\\n // Przednie\\n pwm.setPulseLength(7, speed);\\n pwm.setPulseLength(5, 4096);\\n pwm.setPulseLength(6, 0);\\n }\\n\\n this.backwardRight = function (speed) {\\n // Przednie\\n pwm.setPulseLength(8, speed);\\n pwm.setPulseLength(10, 4096);\\n pwm.setPulseLength(9, 0);\\n\\n // Tylne\\n pwm.setPulseLength(13, speed);\\n pwm.setPulseLength(11, 4096);\\n pwm.setPulseLength(12, 0);\\n }\\n\\n this.backward = function (speed) {\\n this.backwardsLeft(speed);\\n this.backwardsRight(speed);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6050955653190613,\n \"alphanum_fraction\": 0.6050955653190613,\n \"avg_line_length\": 18.75,\n \"blob_id\": \"177d4d22bd5c85b316ce3261c4e6a9c7d78143e4\",\n \"content_id\": \"80885fa994263a2be6c4e41f0ff147672f55b456\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 157,\n \"license_type\": \"no_license\",\n \"max_line_length\": 42,\n \"num_lines\": 8,\n \"path\": \"/machina.api/objects/rc-data.js\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class RcData {\\n constructor(throttle, yaw, pith, roll) {\\n this.throttle = throttle;\\n this.yaw = yaw;\\n this.pith = pith;\\n this.roll = roll;\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6205016374588013,\n \"alphanum_fraction\": 0.6314067840576172,\n \"avg_line_length\": 23.157894134521484,\n \"blob_id\": \"c11c90f6f10f5075482434a6428b619989031416\",\n \"content_id\": \"57d701e74756490b40e61684dfd3abd12ca882eb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 918,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 38,\n \"path\": \"/machina.prv/server-old.js\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"var http = require('http')\\nvar fs = require('fs')\\n\\nvar server = http.createServer(function (request, response) {\\n\\n response.writeHead(200, {\\n 'Content-Type': 'multipart/x-mixed-replace; boundary=myboundary',\\n 'Cache-Control': 'no-cache',\\n 'Connection': 'close',\\n 'Pragma': 'no-cache'\\n })\\n\\n var stop = false;\\n\\n response.connection.on('close', function () { stop = true })\\n\\n var sendPicture = function () {\\n\\n if (stop) return\\n\\n fs.readFile('/apps/stream/picture.jpg', function (error, content) {\\n response.write(\\\"--myboundary\\\\r\\\\n\\\")\\n response.write(\\\"Content-Type: image/jpeg\\\\r\\\\n\\\")\\n response.write(\\\"Content-Length: \\\" + content.length + \\\"\\\\r\\\\n\\\")\\n response.write(\\\"\\\\r\\\\n\\\")\\n response.write(content, 'binary')\\n response.write(\\\"\\\\r\\\\n\\\")\\n\\n setTimeout(sendPicture, 200)\\n })\\n }\\n\\n sendPicture()\\n})\\n\\nserver.listen(8003, function () {\\n console.log('Podgląd uruchomiony')\\n})\"\n },\n {\n \"alpha_fraction\": 0.779046356678009,\n \"alphanum_fraction\": 0.7991940975189209,\n \"avg_line_length\": 39.135135650634766,\n \"blob_id\": \"eaeac5dccff5c482cf666eba1f70e597a4dd5a99\",\n \"content_id\": \"42764a131fbf4668e6ce822b8d224f18d118db31\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1544,\n \"license_type\": \"no_license\",\n \"max_line_length\": 165,\n \"num_lines\": 37,\n \"path\": \"/README.md\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \" \\n# Machina\\n\\nZdalne sterowanie robotem na bazie Raspberry Pi.\\n\\n \\n\\nProjekt przede wszystkim ma na celu bawiąc/uczyć stosowania technologii JavaScript: Node.js i Cordova. Aby zacząć prace należy zaopatrzyć się w niezbędne podzespoły:\\n\\n- komputer Raspberry Pi\\n- sterownik silników Motor Hat lub inny\\n- czterokołowe podwozie z silnikami\\n- kamerę\\n- taśmę 15 żył 30cm do kamery\\n- uchwyt do serw micro Pan/Tilt\\n- dwa micro serwa TowerPro SG-90 lub inne\\n- ultradźwiękowy czujnik odległości HC-SR04\\n- uchwyt montażowy do czujnika odległości HC-SR04\\n- akumulator/pakiet Li-Pol\\n- głośnik 0.1W\\n\\nUWAGA: Podłączenie akumulatora przy zworce Motor Hat-a ustawionej na 5V spali Raspberry oraz sam sterownik!\\n\\n[Proces instalacji oprogramowania](./docs/instalacja.md)\\n\\nZałożenia:\\n\\n- pojazd ma poruszać się sterowany aplikacją na Androida z wykorzystaniem kontrolera do gier podłączonego przez USB OTG do telefonu\\n- w aplikacji na telefonie ma być dostępny podgląd obrazu z kamery\\n- czujnik odległości ma zabezpieczyć pojazd przed uderzeniem w przeszkodę uruchamiając na około 20cm przed nią automatyczne hamowanie\\n- bardziej zaawansowane funkcje do opracowania to np. skanowanie kamerą pomieszczenia w celu autonomicznego omijania przeszkód\\n- Raspberry docelowo ma być Access Pointem (hostapd?) aby uzyskać bezpośrednią łączność.\\n\\n[Dyskusja na Facebook-u](https://www.facebook.com/groups/malinowepi/permalink/503939639776690/)\\n\\n___\\nMaciej Tokarz © My-Poi!\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6194690465927124,\n \"alphanum_fraction\": 0.6415929198265076,\n \"avg_line_length\": 21.700000762939453,\n \"blob_id\": \"351d8bacaee4091da9b089efc8a24e4fa4e759af\",\n \"content_id\": \"405572a1db14f0ec9995ac9f9485fd87dc86983a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 226,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 10,\n \"path\": \"/machina.spk/server.js\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"var express = require('express')\\nvar exec = require('child_process').exec\\nvar app = express()\\n\\napp.post('/:text', function (req) {\\n var cmd = 'espeak -v pl ' + req.params.text + ' 2>/dev/null'\\n exec(cmd)\\n})\\n\\napp.listen(8001)\"\n },\n {\n \"alpha_fraction\": 0.5548060536384583,\n \"alphanum_fraction\": 0.5843170285224915,\n \"avg_line_length\": 27.95121955871582,\n \"blob_id\": \"418a6b7105b40c8cf915f5d4460652b02b088af9\",\n \"content_id\": \"7a62ed541d775999b63cfd6db597be2f0b1297c8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 1187,\n \"license_type\": \"no_license\",\n \"max_line_length\": 135,\n \"num_lines\": 41,\n \"path\": \"/machina.prv/server.js\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"var http = require('http');\\nvar fs = require('fs');\\nvar spawn = require('child_process').spawn;\\n\\nvar server = http.createServer(function (request, response) {\\n response.writeHead(200, {\\n 'Content-Type': 'multipart/x-mixed-replace; boundary=myboundary',\\n 'Cache-Control': 'no-cache',\\n 'Connection': 'close',\\n 'Pragma': 'no-cache'\\n });\\n\\n var args = ['--nopreview', '-w', '559', '-h', '283', '-q', '50', '-o', 'preview.jpg', '-t', '9999999', '-th', '0:0:0', '-tl', '100'];\\n var proc = spawn('raspistill', args);\\n var stop = false;\\n\\n response.connection.on('close', () => { \\n stop = true;\\n if (proc) proc.kill();\\n });\\n\\n var sendPicture = () => {\\n if (stop) return;\\n\\n fs.readFile('/home/pi/machina.prv/preview.jpg', (error, content) => {\\n response.write('--myboundary\\\\r\\\\n');\\n response.write('Content-Type: image/jpeg\\\\r\\\\n');\\n response.write('Content-Length: ' + content.length + '\\\\r\\\\n');\\n response.write('\\\\r\\\\n');\\n response.write(content, 'binary');\\n response.write('\\\\r\\\\n');\\n setTimeout(sendPicture, 200);\\n });\\n };\\n\\n sendPicture();\\n});\\n\\nserver.listen(8001, () => {\\n console.log('Podgląd uruchomiony na porcie 8001');\\n});\"\n },\n {\n \"alpha_fraction\": 0.6914731860160828,\n \"alphanum_fraction\": 0.7363913059234619,\n \"avg_line_length\": 19.289575576782227,\n \"blob_id\": \"fb247a674a58a158d78829a39f7c4b8f382fd4bb\",\n \"content_id\": \"81290a7e413853bd1b9023589c011a8bb8e11fa4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 5339,\n \"license_type\": \"no_license\",\n \"max_line_length\": 225,\n \"num_lines\": 259,\n \"path\": \"/docs/instalacja.md\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Scenariusz przygotowania Raspberry Pi od strony programowej\\n\\n## Instalacja Raspbiana Jessie\\n\\nJako podstawę działania posłuży wersja:\\n\\n[Minimal image based on Debian Jessie](https://www.raspberrypi.org/downloads/raspbian/)\\n\\nPo zainstalowaniu należy przejść do ustawień:\\n```\\nsudo raspi-config\\n```\\noraz\\n\\n- zmienić hasło administratora-\\n- ustawić autologowanie do konsoli\\n- rozszerzyć partycję na całą objętość karty\\n- włączyć kamerę\\n- właczyć audio na jack-a\\n- włączyć SSH\\n- włączyć i2c\\n- włączyć GPIO Server\\n- włączyć Serial (jak po UART PuTTY)\\n\\n## Instalacja karty WiFi\\n\\nAby można było połączyć się z Raspberry Pi warto skorzystać z karty WiFi \\ni sprawić aby przy starcie następowało automatyczne łączenie z naszą siecią:\\n```\\nsudo nano /etc/wpa_supplicant/wpa_supplicant.conf\\n\\nnetwork={\\n ssid=\\\"nazwa_sieci\\\"\\n psk=\\\"hasło\\\"\\n}\\n\\n## Zmiana ustawień na PL\\n\\n\\n```\\n## Instalacja Node.js\\n```\\nwget -O - https://raw.githubusercontent.com/sdesalas/node-pi-zero/master/install-node-v.last.sh | bash\\nnode -v\\n```\\n\\n## Uruchomienie api\\nsudo nano /etc/systemd/system/machina.service\\n\\n[Service]\\nWorkingDirectory=/home/pi/machina.api\\nExecStart=/home/pi/machina.api/index.js\\nRestart=always\\nStandardOutput=syslog\\nStandardError=syslog\\nSyslogIdentifier=machina\\nUser=root\\nGroup=root\\nEnvironment=NODE_ENV=production\\n[Install]\\nWantedBy=multi-user.target\\n\\n\\nsudo chmod u+rwx /etc/systemd/system/machina.service\\nsudo systemctl enable machina\\n\\n---\\nsudo nano /etc/rc.local\\ncd /home/pi/machina.api\\nnode index.js\\n\\n\\n\\n\\n\\n## Przygotowanie miejsca na aplikacje serwera\\n\\nDo pracy z plikami polecam: [WinSCP](https://winscp.net)\\n```\\nsudo mkdir /apps\\nsudo mkdir /apps/machina.api\\nsudo mkdir /apps/machina.spk\\n```\\nmachina.api (sterowanie pojazdem, kamerą)\\n```\\ncd /apps/machina.api\\nsudo -s\\nsudo npm install i2c-bus\\nsudo npm install pca9685\\nsudo npm install express\\nsudo npm install request\\n```\\nmachina.spk (odtwarzanie tekstów komunikatów)\\n```\\ncd ..\\ncd machina.spk\\nsudo npm install express\\n\\nsudo chown -R pi:pi /apps\\n```\\n\\n## Ma mówić\\n```\\nsudo apt-get install espeak\\namixer sset PCM,0 100%\\nespeak \\\"Hello, I am Espeak, the voice synthesizer\\\" 2>/dev/null\\n```\\n## Cordova (https://cordova.apache.org/docs/pl/latest/guide/cli/)\\n```\\nnpm install -g cordova\\ncordova create machina.app nazwa_wydawcy Machina\\ncd machina.app\\ncordova platform add android --save\\n\\nd:\\\\Projekty\\\\Machina\\\\machina.app>cordova requirements\\n\\ncordova build\\ncordova emulate android\\ncordova run android\\n```\\n# Uruchomienie podglądu z kamery:\\nsudo nano /etc/rc.local\\nraspistill --nopreview -w 300 -h 168 -vf -hf -q 50 -o /apps/stream/picture.jpg -tl 50 -t 9999999 -th 0:0:0 &\\n\\nexit 0\\n\\nmachina.prv (preview)\\nsudo node /apps/machina.prv/server.js\\nhttp://192.168.1.7:8003\\n```\\n## Linki\\n```\\nFilmy https://1drv.ms/f/s!AoVId-NDPgmihPZR0ijOLWl7qOKYgQ\\nhttps://github.com/fivdi/pigpio\\n```\\n## Akumulatory\\n```\\nNapięcie minimalne bez obciążenia: 11,15V\\n\\n```\\n\\nW opracowaniu...\\n\\nz\\n\\nz\\n\\nz\\n\\nz\\n\\n## Serwa SG90\\n```\\nbrązowy przewód: GND (masa zasilania)\\nczerwony przewód: Zasilanie 5 V\\npomarańczowy przewód: sygnał PWM\\n```\\n## Czujnik odległosci SR04\\n```\\nhttp://uczymy.edu.pl/wp/blog/2016/01/20/hc-sr04/\\nUWAGA! Echo trzeba obniżyć do 3,3V\\n```\\n\\n```\\n## Kolory\\n```\\nWiodący rgb 0 114 198 hex 0072C6\\n```\\n## Różności\\n```\\nUprawnienia roota\\nsudo -s\\n\\nUsunięcie katalogu\\nsudo rm -r /foo/foo\\n\\nAktualizacja\\nsudo apt-get update # Fetches the list of available updates\\nsudo apt-get upgrade # Strictly upgrades the current packages\\nsudo apt-get dist-upgrade # Installs updates (new ones)\\n\\nSprzątanie\\nsudo apt-get autoclean\\nsudo apt-get clean\\nsudo apt-get autoremove\\n\\nUsuwanie pakietu\\nsudo apt-get remove <package> && sudo apt-get autoremove\\n```\\n\\n\\n## Podgląd stare\\n\\n\\n\\nmkfifo fifo.500\\n/opt/vc/bin/raspivid -o fifo.500 -t 0 -b 20000\\n\\n\\n\\n\\ncat fifo.500 | nc.traditional 192.168.1.7 5000 &\\n\\n\\n```\\n???\\nsudo apt-get install libav-tools\\n???\\n\\nsudo -s\\nsudo apt-get install git\\n\\ncd /usr/src\\ngit clone git://git.videolan.org/x264\\ncd x264\\n./configure --host=arm-unknown-linux-gnueabi --enable-static --disable-opencl\\nmake\\nsudo make install\\n\\ncd /usr/src\\ngit clone https://github.com/ffmpeg/ffmpeg.git\\ncd ffmpeg\\nsudo ./configure --arch=armel --target-os=linux --enable-gpl --enable-libx264 --enable-nonfree\\nmake\\nsudo make install\\n\\nraspivid -o - -t 0 -vf -hf -fps 30 -b 6000000 | ffmpeg -re -ar 44100 -ac 2 -acodec pcm_s16le -f s16le -ac 2 -i /apps -f h264 -i -\\n\\nraspivid -o - -t 0 -vf -hf -fps 30 -b 6000000 | ffmpeg -re -ar 44100 -ac 2 -acodec pcm_s16le -f s16le -ac 2 -i /dev/zero -f h264 -i - -vcodec copy -acodec aac -ab 128k -g 50 -strict experimental -f flv rtmp://192.168.1.7:8003\\n\\nffmpeg raspivid\\n\\nTODO: dostroić podgląd aby nie było lagów!\\n\\nOpcja z zapisem sekwencyjnym zdjęć:\\n\\nsudo mkdir /apps/stream\\n\\nsudo nano /etc/rc.local\\n\\nraspistill --nopreview -w 400 -h 225 -vf -q 30 -o /apps/stream/picture.jpg -tl 50 -t 9999999 -th 0:0:0 &\\n\\nraspistill --nopreview -w 300 -h 168 -vf -hf -q 50 -o /apps/stream/picture.jpg -tl 50 -t 9999999 -th 0:0:0 &\\n\\nexit 0\\n\\nPodgląd\\nsudo nano /etc/apt/sources.list\\ndodać\\ndeb http://vontaene.de/raspbian-updates/ . main\\n\\nsudo apt-get install debian-keyring\\ngpg --keyserver pgp.mit.edu --recv-keys 1F41B907\\ngpg --armor --export 1F41B907 | sudo apt-key add -\\n\\nsudo apt-get update\\nsudo apt-get install gstreamer1.0\\n\\napt-get install gstreamer-tools\"\n },\n {\n \"alpha_fraction\": 0.4603399336338043,\n \"alphanum_fraction\": 0.4886685609817505,\n \"avg_line_length\": 14.3695650100708,\n \"blob_id\": \"00740366673dedbc6e9aeb443261bcd32168e57b\",\n \"content_id\": \"b5ab998273ddde24655b6b5f072505ecb53bf431\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 706,\n \"license_type\": \"no_license\",\n \"max_line_length\": 42,\n \"num_lines\": 46,\n \"path\": \"/machina.api/controllers/camera.controller.js\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"module.exports = function (pwm) {\\n var self = this\\n\\n this.a\\n this.b\\n this.x\\n this.y\\n\\n // var positionV = 0\\n // var positionH = 0\\n \\n\\n // this.setDefault = function () {\\n // pwm.setPulseLength(0, positionV)\\n // pwm.setPulseLength(1, positionH)\\n // pwm.allChannelsOff()\\n // }\\n\\n this.setMotors = function (a, b, x, y) {\\n\\n var i = 0\\n\\n self.a = a\\n self.b = b\\n self.x = x\\n self.y = y\\n\\n if (self.y == 1) {\\n pwm.setPulseLength(0, 1)\\n }\\n\\n if (self.a == 1) {\\n pwm.setPulseLength(0, -4)\\n }\\n\\n if (self.x == 1) {\\n for (i = 0; i < 10; i++) {\\n pwm.setPulseLength(1, -8)\\n }\\n }\\n\\n if (self.b == 1) {\\n pwm.setPulseLength(1, 1)\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6351970434188843,\n \"alphanum_fraction\": 0.6535288691520691,\n \"avg_line_length\": 26.274999618530273,\n \"blob_id\": \"0af332adfb1662c4e927ded3309c6c03f45f1ac7\",\n \"content_id\": \"42da1a898af49f1dbb6275bb3883989cf7211dd1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"TypeScript\",\n \"length_bytes\": 1091,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 40,\n \"path\": \"/machina.app/src/app/services/gamepad.service.ts\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import { Injectable } from '@angular/core';\\n\\n@Injectable()\\nexport class GamepadService {\\n gamepad: Gamepad;\\n timestamp: number;\\n axis0: number;\\n axis1: number;\\n axis2: number;\\n axis3: number;\\n a: number;\\n b: number;\\n x: number;\\n y: number;\\n lb: number;\\n rb: number;\\n\\n constructor() {\\n // this.gamepad = navigator.getGamepads()[1];\\n // console.log(this.gamepad);\\n // this.setGamepadValues();\\n }\\n\\n setGamepadValues() {\\n this.gamepad = navigator.getGamepads()[1];\\n this.timestamp = this.gamepad.timestamp;\\n this.axis0 = this.gamepad.axes[0];\\n this.axis1 = this.gamepad.axes[1];\\n this.axis2 = this.gamepad.axes[2];\\n this.axis3 = this.gamepad.axes[3];\\n // this.gamepadValues.a = this.gamepad.buttons[0];\\n // this.gamepadValues.b = this.gamepad.buttons[1];\\n // this.gamepadValues.x = this.gamepad.buttons[2];\\n // this.gamepadValues.y = this.gamepad.buttons[3];\\n // this.gamepadValues.lb = this.gamepad.buttons[6];\\n // this.gamepadValues.rb = this.gamepad.buttons[7];\\n\\n requestAnimationFrame(() => { this.setGamepadValues(); });\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5131348371505737,\n \"alphanum_fraction\": 0.5499124526977539,\n \"avg_line_length\": 17.419355392456055,\n \"blob_id\": \"162e28c90dac404d457be66fc49cf349976e70dd\",\n \"content_id\": \"3348f36f705160b107de8d7f20689339d6f00f9b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 571,\n \"license_type\": \"no_license\",\n \"max_line_length\": 32,\n \"num_lines\": 31,\n \"path\": \"/machina.ino/machina.ino\",\n \"repo_name\": \"my-poi/machina\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"void setup()\\n{\\n pinMode(2, INPUT);\\n pinMode(3, INPUT);\\n pinMode(4, INPUT);\\n pinMode(5, INPUT);\\n Serial.begin(9600);\\n}\\n\\nvoid loop()\\n{\\n double channel[4];\\n String result = \\\"\\\";\\n\\n channel[0] = pulseIn(2, HIGH);\\n channel[1] = pulseIn(3, HIGH);\\n channel[2] = pulseIn(4, HIGH);\\n channel[3] = pulseIn(5, HIGH);\\n\\n result += \\\"{\\\\\\\"throttle\\\\\\\": \\\";\\n result += channel[0];\\n result += \\\", \\\\\\\"yaw\\\\\\\": \\\";\\n result += channel[1];\\n result += \\\", \\\\\\\"pith\\\\\\\": \\\";\\n result += channel[2];\\n result += \\\", \\\\\\\"roll\\\\\\\": \\\";\\n result += channel[3];\\n result += \\\"}\\\";\\n\\n Serial.println(result);\\n}\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":17,"string":"17"}}},{"rowIdx":2983083,"cells":{"repo_name":{"kind":"string","value":"mike7105/RenameFiles"},"repo_url":{"kind":"string","value":"https://github.com/mike7105/RenameFiles"},"snapshot_id":{"kind":"string","value":"e6cc50b9150ea83df7c2a9ecb584157b34bd987d"},"revision_id":{"kind":"string","value":"5dc1aefea90ccd5ce667f577b8c254fd6f783cc8"},"directory_id":{"kind":"string","value":"0a0e701c406ad4b30aa055b8e83a38b59d5f1aa0"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-08-15T14:52:48.591776","string":"2023-08-15T14:52:48.591776"},"revision_date":{"kind":"timestamp","value":"2021-10-06T20:59:11","string":"2021-10-06T20:59:11"},"committer_date":{"kind":"timestamp","value":"2021-10-06T20:59:11","string":"2021-10-06T20:59:11"},"github_id":{"kind":"number","value":414353345,"string":"414,353,345"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6688963174819946,"alphanum_fraction":0.6845039129257202,"avg_line_length":25.382352828979492,"blob_id":"68f05c121679cee6b145319373312cb878ae2fb1","content_id":"03e6d9b9d82bf1b5b232b43928db0f3405a56c50","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":946,"license_type":"no_license","max_line_length":76,"num_lines":34,"path":"/renameFiles.py","repo_name":"mike7105/RenameFiles","src_encoding":"UTF-8","text":"\"\"\"Rename files for current folder\nMihail Chesnokov 04.10.2021\"\"\"\n__version__ = 'Version:1.0'\n\nimport os\nimport sys\nfrom PyQt5 import QtGui, QtWidgets\n\nfrom modules.mainwindow import MainWindow\n\n\ndef resource_path(relative_path: str) -> str:\n \"\"\" Get absolute path to resource, works for dev and for PyInstaller\n\n :param relative_path: относительный путь к файлу\n :return: реальный путь для теста и прода\n \"\"\"\n if getattr(sys, 'frozen', False):\n # noinspection PyUnresolvedReferences,PyProtectedMember\n base_path = sys._MEIPASS\n else:\n base_path = os.getcwd()\n return os.path.join(base_path, relative_path)\n\n\ntry:\n app = QtWidgets.QApplication(sys.argv)\n app.setWindowIcon(QtGui.QIcon(resource_path(r\"modules\\ico\\ico256.png\")))\n window = MainWindow(__version__)\n window.show()\n sys.exit(app.exec_())\nexcept Exception as ex:\n print(ex)\n input()\n"},{"alpha_fraction":0.5970199108123779,"alphanum_fraction":0.6002416014671326,"avg_line_length":37.007652282714844,"blob_id":"9fa77329002ab7ddb6a071590a847f5a898e2916","content_id":"b6c0cd06e21c5a7b3cff2bc033ff072fb2c7832e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":15713,"license_type":"no_license","max_line_length":120,"num_lines":392,"path":"/modules/MyWindow.py","repo_name":"mike7105/RenameFiles","src_encoding":"UTF-8","text":"\"\"\"Основной компонент приложения\"\"\"\nimport os\nimport re\nimport time\nimport glob\nimport shutil\n\nfrom PyQt5 import QtCore, QtWidgets, QtGui\n\nclass ClickedLabel(QtWidgets.QLabel):\n \"\"\"Кдикабельная по двойному нажатию Label\"\"\"\n doubleclicked = QtCore.pyqtSignal()\n\n def mouseDoubleClickEvent(self, event):\n \"\"\"\n Событие двойногонажатия\n :param event: событие\n \"\"\"\n super().mouseDoubleClickEvent(event)\n\n self.doubleclicked.emit()\n\nclass MyStandardItemModel(QtGui.QStandardItemModel):\n \"\"\"\n Класс, поддерживающий подкраску ячейки\n \"\"\"\n _colorize = False\n\n def __init__(self, parent=None):\n super(MyStandardItemModel, self).__init__(parent)\n\n def setColorized(self, state):\n \"\"\"\n Подкрашивать или нет\n :param bool state: True or False\n \"\"\"\n self._colorize = state\n\n def data(self, index, role=QtCore.Qt.DisplayRole):\n \"\"\"\n апдейтит данные\n :param index: индекс\n :param role: роль\n :return:\n \"\"\"\n if role == QtCore.Qt.BackgroundColorRole and not self._colorize:\n return QtGui.QBrush()\n\n return super(MyStandardItemModel, self).data(index, role)\n\n# noinspection PyAttributeOutsideInit\nclass MyWindow(QtWidgets.QWidget):\n \"\"\"\n Класс приложения\n \"\"\"\n\n def __init__(self, parent=None):\n\n QtWidgets.QWidget.__init__(self, parent)\n self.statusBar: QtWidgets.QStatusBar = parent.statusBar()\n\n self.vbox = QtWidgets.QVBoxLayout()\n\n # блок для папки с результатом\n self.label1 = QtWidgets.QLabel(\"Current folder\")\n self.vbox.addWidget(self.label1)\n self.hbox = QtWidgets.QHBoxLayout()\n self.lePathImg = QtWidgets.QLineEdit()\n self.lePathImg.setReadOnly(True)\n self.hbox.addWidget(self.lePathImg)\n self.btnPathImg = QtWidgets.QPushButton(\"...\")\n self.btnPathImg.clicked.connect(self.on_open_result)\n self.btnPathImg.setToolTip(\"Press open folder for rename files\")\n self.hbox.addWidget(self.btnPathImg)\n self.vbox.addLayout(self.hbox)\n\n # блок фильтрации\n self.hboxIDSet = QtWidgets.QHBoxLayout()\n self.hbox = QtWidgets.QHBoxLayout()\n self.gbFilters = QtWidgets.QGroupBox(\"Filters\")\n self.label2 = QtWidgets.QLabel(\"Extensions:\")\n self.label2.setToolTip(\"File extensions devided by | (ex. png|jpg|gif)\")\n self.hbox.addWidget(self.label2)\n self.leExt = QtWidgets.QLineEdit()\n self.leExt.setText(\"\")\n self.hbox.addWidget(self.leExt)\n self.gbFilters.setLayout(self.hbox)\n self.hboxIDSet.addWidget(self.gbFilters)\n\n # блок по регулярке\n self.hbox = QtWidgets.QHBoxLayout()\n self.gbRegEx = QtWidgets.QGroupBox(\"RegExp\")\n self.gbRegEx.setCheckable(True)\n self.gbRegEx.toggled.connect(lambda flag: self.gbSep.setChecked(not flag) if flag else flag)\n self.label3 = QtWidgets.QLabel(\"Find what:\")\n self.label3.setToolTip(\"Search part of the file name to replace\")\n self.hbox.addWidget(self.label3)\n self.leFind = QtWidgets.QLineEdit()\n self.leFind.setText(\"\")\n self.hbox.addWidget(self.leFind)\n self.label4 = QtWidgets.QLabel(\"Replace with:\")\n self.hbox.addWidget(self.label4)\n self.leReplace = QtWidgets.QLineEdit()\n self.leReplace.setText(\"\")\n self.hbox.addWidget(self.leReplace)\n self.gbRegEx.setLayout(self.hbox)\n self.hboxIDSet.addWidget(self.gbRegEx)\n\n # блок по разделителю\n self.hbox = QtWidgets.QHBoxLayout()\n self.gbSep = QtWidgets.QGroupBox(\"Separator\")\n self.gbSep.setCheckable(True)\n self.gbSep.setChecked(False)\n self.gbSep.toggled.connect(lambda flag: self.gbRegEx.setChecked(not flag) if flag else flag)\n self.label5 = QtWidgets.QLabel(\"Type separator\")\n self.label5.setToolTip(\"Split file name by separator and choose first part\")\n self.hbox.addWidget(self.label5)\n self.leSep = QtWidgets.QLineEdit()\n self.hbox.addWidget(self.leSep)\n self.gbSep.setLayout(self.hbox)\n self.hboxIDSet.addWidget(self.gbSep)\n\n self.vbox.addLayout(self.hboxIDSet)\n\n # блок с табличкой и моделью для данных из Экселя\n self.tv = QtWidgets.QTableView()\n self.sti = MyStandardItemModel()\n self.tv.setModel(self.sti)\n self.vbox.addWidget(self.tv)\n # self.vbox.addStretch(1)\n\n # блок с кнопками\n self.hbox = QtWidgets.QHBoxLayout()\n self.btnGet: QtWidgets.QPushButton = QtWidgets.QPushButton(\"&Get\")\n self.btnGet.clicked.connect(self.on_get)\n self.btnGet.setShortcut(\"Ctrl+G\")\n self.btnGet.setToolTip(\"Press to get file names (Ctrl+G)\")\n self.hbox.addWidget(self.btnGet)\n\n self.btnTry: QtWidgets.QPushButton = QtWidgets.QPushButton(\"&Try\")\n self.btnTry.clicked.connect(self.on_try)\n self.btnTry.setShortcut(\"Ctrl+T\")\n self.btnTry.setToolTip(\"Press to try (Ctrl+T)\")\n self.hbox.addWidget(self.btnTry)\n\n self.btnRun: QtWidgets.QPushButton = QtWidgets.QPushButton(\"&Run\")\n self.btnRun.clicked.connect(self.on_run)\n self.btnRun.setShortcut(\"Ctrl+R\")\n self.btnRun.setToolTip(\"Press to rename (Ctrl+R)\")\n self.hbox.addWidget(self.btnRun)\n\n self.btnQuit = QtWidgets.QPushButton(\"&Quit\")\n self.btnQuit.clicked.connect(self.parent().quitApp)\n # self.btnRun.setShortcut(\"Ctrl+Q\")\n self.hbox.addWidget(self.btnQuit)\n self.vbox.addLayout(self.hbox)\n\n # блок со статусами\n self.progressBar = QtWidgets.QProgressBar()\n self.progressBar.setMinimum(0)\n self.progressBar.setFormat(\"%v from %m %p%\")\n # self.vbox.addWidget(self.progressBar)\n\n self.lblStatus = QtWidgets.QLabel()\n self.resPath = \"\"\n self.lblPerStatus = ClickedLabel()\n self.lblPerStatus.doubleclicked.connect(self.open_folder)\n self.lblPerStatus.setToolTip(\"Double click to open folder\")\n\n self.statusBar.addWidget(self.progressBar, 2)\n self.statusBar.addWidget(self.lblStatus, 1)\n self.statusBar.addPermanentWidget(self.lblPerStatus, 1)\n\n self.setLayout(self.vbox)\n\n # Создаем кнопку панели задач\n # Получаем индикатор процесса, задаем его параметры\n # и делаем его видимым\n self.progress = self.parent().progress\n\n self.rens = set()\n\n @QtCore.pyqtSlot()\n def open_folder(self):\n \"\"\"\n Открывает папку с переименованными файлами\n \"\"\"\n if self.resPath:\n os.startfile(self.resPath)\n\n @QtCore.pyqtSlot()\n def on_open_result(self):\n \"\"\"\n Метод открывает папку с файлами\n \"\"\"\n dirName = QtWidgets.QFileDialog.getExistingDirectory(\n caption='Folder for rename',\n directory=self.lePathImg.text() if self.lePathImg.text() else QtCore.QDir.currentPath())\n if dirName:\n self.lePathImg.setText(dirName)\n self.sti.clear()\n\n @QtCore.pyqtSlot()\n def create_resPath(self):\n \"\"\"\n создает папку для переименованных файлов\n \"\"\"\n self.resPath = os.path.normpath(os.path.join(\n self.lePathImg.text(), \"Renamed \" + time.strftime(\"%Y%m%d_%H%M%S\", time.localtime(self.startTime))))\n os.makedirs(self.resPath, exist_ok=True)\n self.lblPerStatus.setText(f\"{self.resPath}\")\n\n @QtCore.pyqtSlot()\n def on_get(self):\n \"\"\"\n Собирает файлы с фильтрами\n \"\"\"\n if not self.lePathImg.text():\n QtWidgets.QMessageBox.critical(self, \"Error\", \"Choose the folder for rename files\",\n defaultButton=QtWidgets.QMessageBox.Ok)\n return\n exts: list\n if self.leExt.text():\n exts = self.leExt.text().split(\"|\")\n else:\n exts = [\"*\"]\n files: list = []\n for ext in exts:\n files.extend(glob.glob(os.path.join(self.lePathImg.text(), f\"*.{ext}\")))\n # files = glob.glob(join(fPath, \"**\", \"*.txt\"), recursive=True)\n self.lblStatus.setText(f\"Get files from folders\")\n self.sti.clear()\n self.progressBar.reset()\n self.progress.reset()\n self.progressBar.setValue(0)\n self.progress.setValue(0)\n\n cols = [\"Name\", \"Ext\", \"Full Name\", \"Rename\"]\n\n for file in files:\n colsCurr = [QtGui.QStandardItem(os.path.splitext(os.path.basename(file))[0]),\n QtGui.QStandardItem(os.path.splitext(os.path.basename(file))[1]),\n QtGui.QStandardItem(os.path.basename(file)), QtGui.QStandardItem(\"\")]\n self.sti.appendRow(colsCurr)\n\n self.sti.setHorizontalHeaderLabels(cols)\n self.tv.resizeColumnsToContents()\n self.tv.setColumnWidth(3, self.tv.columnWidth(2))\n self.progressBar.setMaximum(self.sti.rowCount())\n self.progress.setMaximum(self.sti.rowCount())\n # self.btnRun.setDisabled(False)\n\n @QtCore.pyqtSlot()\n def on_try(self):\n \"\"\"\n Проставляет новые имена файлов, чтоыб посмотреть все ли в порядке\n \"\"\"\n self.lblStatus.setText(f\"Try and revise column Rename\")\n self.rens.clear()\n if self.isValid(\"try\"):\n self.sti.setColorized(True)\n for i in range(0, self.sti.rowCount()):\n self.sti.item(i, 3).setText(\"\")\n txt = self.sti.item(i, 0).text()\n ren = \"\"\n\n if self.gbRegEx.isChecked():\n what = re.compile(self.leFind.text())\n res = self.leReplace.text()\n if what.search(txt):\n ren = what.sub(res, txt)\n\n if self.gbSep.isChecked():\n ren = txt.split(self.leSep.text())[0]\n\n ren = ren if ren else txt\n\n self.sti.item(i, 3).setText(ren)\n self.color_dup(self.sti.item(i, 3))\n\n QtWidgets.qApp.processEvents()\n # self.sti.endResetModel()\n\n @QtCore.pyqtSlot()\n def on_run(self):\n \"\"\"\n Переименовывает файлы\n \"\"\"\n\n if self.isValid():\n self.setAllDisabled()\n self.startTime = time.time()\n self.create_resPath()\n\n for i in range(0, self.sti.rowCount()):\n if self.sti.item(i, 3).text():\n curr = self.sti.item(i, 2).text()\n ren = self.sti.item(i, 3).text() + self.sti.item(i, 1).text()\n\n shutil.copy2(os.path.join(self.lePathImg.text(), curr), os.path.join(self.resPath, ren))\n\n self.tv.scrollTo(self.sti.index(i, 0), QtWidgets.QAbstractItemView.EnsureVisible)\n self.progressBar.setValue(self.progressBar.value() + 1)\n self.progress.setValue(self.progress.value() + 1)\n\n QtWidgets.qApp.processEvents()\n\n self.progressBar.setValue(self.progressBar.value() + 1)\n self.progress.reset()\n self.finishTime = time.time()\n self.lblStatus.setText(f\"Done! duration: {(self.finishTime - self.startTime) / 60:.4f} min\")\n self.setAllDisabled(False)\n\n os.startfile(self.resPath)\n\n def setAllDisabled(self, flag=True):\n \"\"\"\n метод все кнопки переключает с актвиных на неактвиные и наоборот\n :param bool flag: True если не активно нужно сделать\n \"\"\"\n self.lePathImg.setDisabled(flag)\n self.leExt.setDisabled(flag)\n if self.gbRegEx.isChecked():\n self.leFind.setDisabled(flag)\n self.leReplace.setDisabled(flag)\n self.gbRegEx.setDisabled(flag)\n\n if self.gbSep.isChecked():\n self.leSep.setDisabled(flag)\n self.gbSep.setDisabled(flag)\n\n self.btnPathImg.setDisabled(flag)\n self.btnTry.setDisabled(flag)\n self.btnRun.setDisabled(flag)\n self.btnQuit.setDisabled(flag)\n\n self.parent().menuBar.setDisabled(flag)\n\n def isValid(self, method=\"run\"):\n \"\"\"\n Проверяет валидность введённых параметров\n :param str method: какой метод вызывает валидацию\n :return bool:\n \"\"\"\n if not self.lePathImg.text():\n QtWidgets.QMessageBox.critical(self, \"Error\", \"Choose the folder for rename files\",\n defaultButton=QtWidgets.QMessageBox.Ok)\n return False\n if not os.path.exists(self.lePathImg.text()):\n QtWidgets.QMessageBox.critical(self, \"Error\", f\"Folder {self.lePathImg.text()} doesn't exist!\",\n defaultButton=QtWidgets.QMessageBox.Ok)\n return False\n if self.gbRegEx.isChecked() and self.gbSep.isChecked():\n QtWidgets.QMessageBox.critical(self, \"Error\", \"You must choose something one: RegExp or Separator\",\n defaultButton=QtWidgets.QMessageBox.Ok)\n return False\n if self.gbRegEx.isChecked():\n if not self.leFind.text():\n QtWidgets.QMessageBox.critical(self, \"Error\", \"Type what to replace in file names\",\n defaultButton=QtWidgets.QMessageBox.Ok)\n return False\n if self.gbSep.isChecked():\n if not self.leSep.text():\n QtWidgets.QMessageBox.critical(self, \"Error\", \"Type separator to devide file names\",\n defaultButton=QtWidgets.QMessageBox.Ok)\n return False\n if self.sti.rowCount() == 0:\n QtWidgets.QMessageBox.critical(self, \"Error\", f\"Push button Get\",\n defaultButton=QtWidgets.QMessageBox.Ok)\n return False\n\n if method == \"run\":\n self.rens.clear()\n for i in range(0, self.sti.rowCount()):\n self.color_dup(self.sti.item(i, 3))\n\n if len(self.rens) > 0 and len(self.rens) != self.sti.rowCount():\n QtWidgets.QMessageBox.critical(self, \"Error\", f\"You have duplicate names in Rename columns!\",\n defaultButton=QtWidgets.QMessageBox.Ok)\n return False\n return True\n\n def color_dup(self, sind):\n \"\"\"\n Смотрит является ли текущий элемент дубликатом и подкрашивает дубли\n :param MyStandardItemModel sind: индекс элемента\n \"\"\"\n if sind.text() in self.rens:\n self.sti.setData(self.sti.indexFromItem(sind), QtGui.QColor(QtCore.Qt.red), QtCore.Qt.BackgroundColorRole)\n else:\n self.sti.setData(self.sti.indexFromItem(sind), QtGui.QColor(QtCore.Qt.white), QtCore.Qt.BackgroundColorRole)\n self.rens.add(sind.text())\n"},{"alpha_fraction":0.6166258454322815,"alphanum_fraction":0.6200980544090271,"avg_line_length":36.661537170410156,"blob_id":"dde7d815650dbe6ec082b065b13ab27e9e7bd1ba","content_id":"2aee4c1b20bf631416139fc5a70ba350bde93636","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5174,"license_type":"no_license","max_line_length":99,"num_lines":130,"path":"/modules/mainwindow.py","repo_name":"mike7105/RenameFiles","src_encoding":"UTF-8","text":"\"\"\"Главное окно приложения\"\"\"\nimport os\n\nfrom PyQt5 import QtCore, QtWidgets, QtWinExtras\n\nfrom modules.MyWindow import MyWindow\n\n\nclass MainWindow(QtWidgets.QMainWindow):\n \"\"\"\n Главное окно\n \"\"\"\n\n def __init__(self, version, parent=None):\n QtWidgets.QMainWindow.__init__(self, parent)\n self.version = version\n self.setWindowTitle(\"Rename Files\")\n\n # Создаем кнопку панели задач\n self.taskbarButton = QtWinExtras.QWinTaskbarButton(parent=self)\n # Получаем индикатор процесса, задаем его параметры\n # и делаем его видимым\n self.progress = self.taskbarButton.progress()\n self.progress.setMaximum(0)\n self.progress.show()\n\n self.dtGeo = QtWidgets.QApplication.desktop().availableGeometry()\n self.setMinimumSize(600, 600)\n self.resize(self.dtGeo.width()//2, self.dtGeo.height()-30)\n # self.showMaximized()\n self.move(self.dtGeo.width()//2, 0)\n\n QtCore.QCoreApplication.setOrganizationName(\"Mihail Chesnokov\")\n QtCore.QCoreApplication.setApplicationName(\"renameFiles\")\n self.settings = QtCore.QSettings()\n\n self.mywindow = MyWindow(self)\n self.setCentralWidget(self.mywindow)\n\n self.menuBar = self.menuBar()\n # toolBar = QtWidgets.QToolBar()\n\n self.myMenuFile = self.menuBar.addMenu(\"&File\")\n\n self.myMenuFile.addSeparator()\n\n action = self.myMenuFile.addAction(\"&Quit\", self.quitApp, QtCore.Qt.CTRL + QtCore.Qt.Key_Q)\n action.setStatusTip(\"Quit program\")\n\n self.myMenuAbout = self.menuBar.addMenu(\"&About\")\n\n action = self.myMenuAbout.addAction(\"About...\", self.aboutInfo)\n action.setStatusTip(\"Get info about program\")\n\n action = self.myMenuAbout.addAction(\"about &Qt...\", QtWidgets.qApp.aboutQt)\n action.setStatusTip(\"Get info about Qt\")\n\n self.restoreSettings()\n\n def closeEvent(self, evt):\n \"\"\"\n событие при закрытии приложения\n :param evt: событие\n \"\"\"\n self.saveSettings()\n\n def saveSettings(self):\n \"\"\"\n Сохраняет настройки\n \"\"\"\n self.settings.beginGroup(\"Window\")\n self.settings.setValue(\"Geometry\", self.geometry())\n self.settings.endGroup()\n\n self.settings.beginGroup(\"Data\")\n self.settings.setValue(\"lePathImg\", self.mywindow.lePathImg.text())\n self.settings.setValue(\"leExt\", self.mywindow.leExt.text())\n self.settings.setValue(\"gbRegEx\", int(self.mywindow.gbRegEx.isChecked()))\n self.settings.setValue(\"leFind\", self.mywindow.leFind.text())\n self.settings.setValue(\"leReplace\", self.mywindow.leReplace.text())\n self.settings.setValue(\"gbSep\", int(self.mywindow.gbSep.isChecked()))\n self.settings.setValue(\"leSep\", self.mywindow.leSep.text())\n self.settings.endGroup()\n\n def restoreSettings(self):\n \"\"\"\n Восстанавливает сохраненные настройки\n \"\"\"\n if self.settings.contains(\"Window/Geometry\"):\n self.setGeometry(self.settings.value(\"Window/Geometry\"))\n if self.settings.contains(\"Data/lePathImg\"):\n if os.path.exists(self.settings.value(\"Data/lePathImg\")):\n self.mywindow.lePathImg.setText(self.settings.value(\"Data/lePathImg\"))\n if self.settings.contains(\"Data/leExt\"):\n self.mywindow.leExt.setText(self.settings.value(\"Data/leExt\"))\n if self.settings.contains(\"Data/gbRegEx\"):\n self.mywindow.gbRegEx.setChecked(bool(self.settings.value(\"Data/gbRegEx\")))\n if self.settings.value(\"Data/gbRegEx\"):\n if self.settings.contains(\"Data/leFind\"):\n self.mywindow.leFind.setText(self.settings.value(\"Data/leFind\"))\n if self.settings.contains(\"Data/leReplace\"):\n self.mywindow.leReplace.setText(self.settings.value(\"Data/leReplace\"))\n if self.settings.contains(\"Data/gbSep\"):\n self.mywindow.gbSep.setChecked(bool(self.settings.value(\"Data/gbSep\")))\n if self.settings.value(\"Data/gbSep\"):\n if self.settings.contains(\"Data/leSep\"):\n self.mywindow.leSep.setText(self.settings.value(\"Data/leSep\"))\n\n def quitApp(self):\n \"\"\"\n ЗАкрывает приложение\n \"\"\"\n self.saveSettings()\n QtWidgets.qApp.quit()\n\n def aboutInfo(self):\n \"\"\"\n Показывает описание программы\n \"\"\"\n QtWidgets.QMessageBox.about(self, \"About program\",\n f\"<center>\\\"renameFiles\\\" {self.version}<br><br>\"\n f\"Rename files for current folder<br><br> \"\n f\"(c) Mihail Chesnokov 2021\")\n\n def showEvent(self, evt):\n \"\"\"\n событие при показе окна на экране\n :param evt:\n \"\"\"\n self.taskbarButton.setWindow(self.windowHandle())\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6688963174819946,\n \"alphanum_fraction\": 0.6845039129257202,\n \"avg_line_length\": 25.382352828979492,\n \"blob_id\": \"68f05c121679cee6b145319373312cb878ae2fb1\",\n \"content_id\": \"03e6d9b9d82bf1b5b232b43928db0f3405a56c50\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 946,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 34,\n \"path\": \"/renameFiles.py\",\n \"repo_name\": \"mike7105/RenameFiles\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Rename files for current folder\\nMihail Chesnokov 04.10.2021\\\"\\\"\\\"\\n__version__ = 'Version:1.0'\\n\\nimport os\\nimport sys\\nfrom PyQt5 import QtGui, QtWidgets\\n\\nfrom modules.mainwindow import MainWindow\\n\\n\\ndef resource_path(relative_path: str) -> str:\\n \\\"\\\"\\\" Get absolute path to resource, works for dev and for PyInstaller\\n\\n :param relative_path: относительный путь к файлу\\n :return: реальный путь для теста и прода\\n \\\"\\\"\\\"\\n if getattr(sys, 'frozen', False):\\n # noinspection PyUnresolvedReferences,PyProtectedMember\\n base_path = sys._MEIPASS\\n else:\\n base_path = os.getcwd()\\n return os.path.join(base_path, relative_path)\\n\\n\\ntry:\\n app = QtWidgets.QApplication(sys.argv)\\n app.setWindowIcon(QtGui.QIcon(resource_path(r\\\"modules\\\\ico\\\\ico256.png\\\")))\\n window = MainWindow(__version__)\\n window.show()\\n sys.exit(app.exec_())\\nexcept Exception as ex:\\n print(ex)\\n input()\\n\"\n },\n {\n \"alpha_fraction\": 0.5970199108123779,\n \"alphanum_fraction\": 0.6002416014671326,\n \"avg_line_length\": 37.007652282714844,\n \"blob_id\": \"9fa77329002ab7ddb6a071590a847f5a898e2916\",\n \"content_id\": \"b6c0cd06e21c5a7b3cff2bc033ff072fb2c7832e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 15713,\n \"license_type\": \"no_license\",\n \"max_line_length\": 120,\n \"num_lines\": 392,\n \"path\": \"/modules/MyWindow.py\",\n \"repo_name\": \"mike7105/RenameFiles\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Основной компонент приложения\\\"\\\"\\\"\\nimport os\\nimport re\\nimport time\\nimport glob\\nimport shutil\\n\\nfrom PyQt5 import QtCore, QtWidgets, QtGui\\n\\nclass ClickedLabel(QtWidgets.QLabel):\\n \\\"\\\"\\\"Кдикабельная по двойному нажатию Label\\\"\\\"\\\"\\n doubleclicked = QtCore.pyqtSignal()\\n\\n def mouseDoubleClickEvent(self, event):\\n \\\"\\\"\\\"\\n Событие двойногонажатия\\n :param event: событие\\n \\\"\\\"\\\"\\n super().mouseDoubleClickEvent(event)\\n\\n self.doubleclicked.emit()\\n\\nclass MyStandardItemModel(QtGui.QStandardItemModel):\\n \\\"\\\"\\\"\\n Класс, поддерживающий подкраску ячейки\\n \\\"\\\"\\\"\\n _colorize = False\\n\\n def __init__(self, parent=None):\\n super(MyStandardItemModel, self).__init__(parent)\\n\\n def setColorized(self, state):\\n \\\"\\\"\\\"\\n Подкрашивать или нет\\n :param bool state: True or False\\n \\\"\\\"\\\"\\n self._colorize = state\\n\\n def data(self, index, role=QtCore.Qt.DisplayRole):\\n \\\"\\\"\\\"\\n апдейтит данные\\n :param index: индекс\\n :param role: роль\\n :return:\\n \\\"\\\"\\\"\\n if role == QtCore.Qt.BackgroundColorRole and not self._colorize:\\n return QtGui.QBrush()\\n\\n return super(MyStandardItemModel, self).data(index, role)\\n\\n# noinspection PyAttributeOutsideInit\\nclass MyWindow(QtWidgets.QWidget):\\n \\\"\\\"\\\"\\n Класс приложения\\n \\\"\\\"\\\"\\n\\n def __init__(self, parent=None):\\n\\n QtWidgets.QWidget.__init__(self, parent)\\n self.statusBar: QtWidgets.QStatusBar = parent.statusBar()\\n\\n self.vbox = QtWidgets.QVBoxLayout()\\n\\n # блок для папки с результатом\\n self.label1 = QtWidgets.QLabel(\\\"Current folder\\\")\\n self.vbox.addWidget(self.label1)\\n self.hbox = QtWidgets.QHBoxLayout()\\n self.lePathImg = QtWidgets.QLineEdit()\\n self.lePathImg.setReadOnly(True)\\n self.hbox.addWidget(self.lePathImg)\\n self.btnPathImg = QtWidgets.QPushButton(\\\"...\\\")\\n self.btnPathImg.clicked.connect(self.on_open_result)\\n self.btnPathImg.setToolTip(\\\"Press open folder for rename files\\\")\\n self.hbox.addWidget(self.btnPathImg)\\n self.vbox.addLayout(self.hbox)\\n\\n # блок фильтрации\\n self.hboxIDSet = QtWidgets.QHBoxLayout()\\n self.hbox = QtWidgets.QHBoxLayout()\\n self.gbFilters = QtWidgets.QGroupBox(\\\"Filters\\\")\\n self.label2 = QtWidgets.QLabel(\\\"Extensions:\\\")\\n self.label2.setToolTip(\\\"File extensions devided by | (ex. png|jpg|gif)\\\")\\n self.hbox.addWidget(self.label2)\\n self.leExt = QtWidgets.QLineEdit()\\n self.leExt.setText(\\\"\\\")\\n self.hbox.addWidget(self.leExt)\\n self.gbFilters.setLayout(self.hbox)\\n self.hboxIDSet.addWidget(self.gbFilters)\\n\\n # блок по регулярке\\n self.hbox = QtWidgets.QHBoxLayout()\\n self.gbRegEx = QtWidgets.QGroupBox(\\\"RegExp\\\")\\n self.gbRegEx.setCheckable(True)\\n self.gbRegEx.toggled.connect(lambda flag: self.gbSep.setChecked(not flag) if flag else flag)\\n self.label3 = QtWidgets.QLabel(\\\"Find what:\\\")\\n self.label3.setToolTip(\\\"Search part of the file name to replace\\\")\\n self.hbox.addWidget(self.label3)\\n self.leFind = QtWidgets.QLineEdit()\\n self.leFind.setText(\\\"\\\")\\n self.hbox.addWidget(self.leFind)\\n self.label4 = QtWidgets.QLabel(\\\"Replace with:\\\")\\n self.hbox.addWidget(self.label4)\\n self.leReplace = QtWidgets.QLineEdit()\\n self.leReplace.setText(\\\"\\\")\\n self.hbox.addWidget(self.leReplace)\\n self.gbRegEx.setLayout(self.hbox)\\n self.hboxIDSet.addWidget(self.gbRegEx)\\n\\n # блок по разделителю\\n self.hbox = QtWidgets.QHBoxLayout()\\n self.gbSep = QtWidgets.QGroupBox(\\\"Separator\\\")\\n self.gbSep.setCheckable(True)\\n self.gbSep.setChecked(False)\\n self.gbSep.toggled.connect(lambda flag: self.gbRegEx.setChecked(not flag) if flag else flag)\\n self.label5 = QtWidgets.QLabel(\\\"Type separator\\\")\\n self.label5.setToolTip(\\\"Split file name by separator and choose first part\\\")\\n self.hbox.addWidget(self.label5)\\n self.leSep = QtWidgets.QLineEdit()\\n self.hbox.addWidget(self.leSep)\\n self.gbSep.setLayout(self.hbox)\\n self.hboxIDSet.addWidget(self.gbSep)\\n\\n self.vbox.addLayout(self.hboxIDSet)\\n\\n # блок с табличкой и моделью для данных из Экселя\\n self.tv = QtWidgets.QTableView()\\n self.sti = MyStandardItemModel()\\n self.tv.setModel(self.sti)\\n self.vbox.addWidget(self.tv)\\n # self.vbox.addStretch(1)\\n\\n # блок с кнопками\\n self.hbox = QtWidgets.QHBoxLayout()\\n self.btnGet: QtWidgets.QPushButton = QtWidgets.QPushButton(\\\"&Get\\\")\\n self.btnGet.clicked.connect(self.on_get)\\n self.btnGet.setShortcut(\\\"Ctrl+G\\\")\\n self.btnGet.setToolTip(\\\"Press to get file names (Ctrl+G)\\\")\\n self.hbox.addWidget(self.btnGet)\\n\\n self.btnTry: QtWidgets.QPushButton = QtWidgets.QPushButton(\\\"&Try\\\")\\n self.btnTry.clicked.connect(self.on_try)\\n self.btnTry.setShortcut(\\\"Ctrl+T\\\")\\n self.btnTry.setToolTip(\\\"Press to try (Ctrl+T)\\\")\\n self.hbox.addWidget(self.btnTry)\\n\\n self.btnRun: QtWidgets.QPushButton = QtWidgets.QPushButton(\\\"&Run\\\")\\n self.btnRun.clicked.connect(self.on_run)\\n self.btnRun.setShortcut(\\\"Ctrl+R\\\")\\n self.btnRun.setToolTip(\\\"Press to rename (Ctrl+R)\\\")\\n self.hbox.addWidget(self.btnRun)\\n\\n self.btnQuit = QtWidgets.QPushButton(\\\"&Quit\\\")\\n self.btnQuit.clicked.connect(self.parent().quitApp)\\n # self.btnRun.setShortcut(\\\"Ctrl+Q\\\")\\n self.hbox.addWidget(self.btnQuit)\\n self.vbox.addLayout(self.hbox)\\n\\n # блок со статусами\\n self.progressBar = QtWidgets.QProgressBar()\\n self.progressBar.setMinimum(0)\\n self.progressBar.setFormat(\\\"%v from %m %p%\\\")\\n # self.vbox.addWidget(self.progressBar)\\n\\n self.lblStatus = QtWidgets.QLabel()\\n self.resPath = \\\"\\\"\\n self.lblPerStatus = ClickedLabel()\\n self.lblPerStatus.doubleclicked.connect(self.open_folder)\\n self.lblPerStatus.setToolTip(\\\"Double click to open folder\\\")\\n\\n self.statusBar.addWidget(self.progressBar, 2)\\n self.statusBar.addWidget(self.lblStatus, 1)\\n self.statusBar.addPermanentWidget(self.lblPerStatus, 1)\\n\\n self.setLayout(self.vbox)\\n\\n # Создаем кнопку панели задач\\n # Получаем индикатор процесса, задаем его параметры\\n # и делаем его видимым\\n self.progress = self.parent().progress\\n\\n self.rens = set()\\n\\n @QtCore.pyqtSlot()\\n def open_folder(self):\\n \\\"\\\"\\\"\\n Открывает папку с переименованными файлами\\n \\\"\\\"\\\"\\n if self.resPath:\\n os.startfile(self.resPath)\\n\\n @QtCore.pyqtSlot()\\n def on_open_result(self):\\n \\\"\\\"\\\"\\n Метод открывает папку с файлами\\n \\\"\\\"\\\"\\n dirName = QtWidgets.QFileDialog.getExistingDirectory(\\n caption='Folder for rename',\\n directory=self.lePathImg.text() if self.lePathImg.text() else QtCore.QDir.currentPath())\\n if dirName:\\n self.lePathImg.setText(dirName)\\n self.sti.clear()\\n\\n @QtCore.pyqtSlot()\\n def create_resPath(self):\\n \\\"\\\"\\\"\\n создает папку для переименованных файлов\\n \\\"\\\"\\\"\\n self.resPath = os.path.normpath(os.path.join(\\n self.lePathImg.text(), \\\"Renamed \\\" + time.strftime(\\\"%Y%m%d_%H%M%S\\\", time.localtime(self.startTime))))\\n os.makedirs(self.resPath, exist_ok=True)\\n self.lblPerStatus.setText(f\\\"{self.resPath}\\\")\\n\\n @QtCore.pyqtSlot()\\n def on_get(self):\\n \\\"\\\"\\\"\\n Собирает файлы с фильтрами\\n \\\"\\\"\\\"\\n if not self.lePathImg.text():\\n QtWidgets.QMessageBox.critical(self, \\\"Error\\\", \\\"Choose the folder for rename files\\\",\\n defaultButton=QtWidgets.QMessageBox.Ok)\\n return\\n exts: list\\n if self.leExt.text():\\n exts = self.leExt.text().split(\\\"|\\\")\\n else:\\n exts = [\\\"*\\\"]\\n files: list = []\\n for ext in exts:\\n files.extend(glob.glob(os.path.join(self.lePathImg.text(), f\\\"*.{ext}\\\")))\\n # files = glob.glob(join(fPath, \\\"**\\\", \\\"*.txt\\\"), recursive=True)\\n self.lblStatus.setText(f\\\"Get files from folders\\\")\\n self.sti.clear()\\n self.progressBar.reset()\\n self.progress.reset()\\n self.progressBar.setValue(0)\\n self.progress.setValue(0)\\n\\n cols = [\\\"Name\\\", \\\"Ext\\\", \\\"Full Name\\\", \\\"Rename\\\"]\\n\\n for file in files:\\n colsCurr = [QtGui.QStandardItem(os.path.splitext(os.path.basename(file))[0]),\\n QtGui.QStandardItem(os.path.splitext(os.path.basename(file))[1]),\\n QtGui.QStandardItem(os.path.basename(file)), QtGui.QStandardItem(\\\"\\\")]\\n self.sti.appendRow(colsCurr)\\n\\n self.sti.setHorizontalHeaderLabels(cols)\\n self.tv.resizeColumnsToContents()\\n self.tv.setColumnWidth(3, self.tv.columnWidth(2))\\n self.progressBar.setMaximum(self.sti.rowCount())\\n self.progress.setMaximum(self.sti.rowCount())\\n # self.btnRun.setDisabled(False)\\n\\n @QtCore.pyqtSlot()\\n def on_try(self):\\n \\\"\\\"\\\"\\n Проставляет новые имена файлов, чтоыб посмотреть все ли в порядке\\n \\\"\\\"\\\"\\n self.lblStatus.setText(f\\\"Try and revise column Rename\\\")\\n self.rens.clear()\\n if self.isValid(\\\"try\\\"):\\n self.sti.setColorized(True)\\n for i in range(0, self.sti.rowCount()):\\n self.sti.item(i, 3).setText(\\\"\\\")\\n txt = self.sti.item(i, 0).text()\\n ren = \\\"\\\"\\n\\n if self.gbRegEx.isChecked():\\n what = re.compile(self.leFind.text())\\n res = self.leReplace.text()\\n if what.search(txt):\\n ren = what.sub(res, txt)\\n\\n if self.gbSep.isChecked():\\n ren = txt.split(self.leSep.text())[0]\\n\\n ren = ren if ren else txt\\n\\n self.sti.item(i, 3).setText(ren)\\n self.color_dup(self.sti.item(i, 3))\\n\\n QtWidgets.qApp.processEvents()\\n # self.sti.endResetModel()\\n\\n @QtCore.pyqtSlot()\\n def on_run(self):\\n \\\"\\\"\\\"\\n Переименовывает файлы\\n \\\"\\\"\\\"\\n\\n if self.isValid():\\n self.setAllDisabled()\\n self.startTime = time.time()\\n self.create_resPath()\\n\\n for i in range(0, self.sti.rowCount()):\\n if self.sti.item(i, 3).text():\\n curr = self.sti.item(i, 2).text()\\n ren = self.sti.item(i, 3).text() + self.sti.item(i, 1).text()\\n\\n shutil.copy2(os.path.join(self.lePathImg.text(), curr), os.path.join(self.resPath, ren))\\n\\n self.tv.scrollTo(self.sti.index(i, 0), QtWidgets.QAbstractItemView.EnsureVisible)\\n self.progressBar.setValue(self.progressBar.value() + 1)\\n self.progress.setValue(self.progress.value() + 1)\\n\\n QtWidgets.qApp.processEvents()\\n\\n self.progressBar.setValue(self.progressBar.value() + 1)\\n self.progress.reset()\\n self.finishTime = time.time()\\n self.lblStatus.setText(f\\\"Done! duration: {(self.finishTime - self.startTime) / 60:.4f} min\\\")\\n self.setAllDisabled(False)\\n\\n os.startfile(self.resPath)\\n\\n def setAllDisabled(self, flag=True):\\n \\\"\\\"\\\"\\n метод все кнопки переключает с актвиных на неактвиные и наоборот\\n :param bool flag: True если не активно нужно сделать\\n \\\"\\\"\\\"\\n self.lePathImg.setDisabled(flag)\\n self.leExt.setDisabled(flag)\\n if self.gbRegEx.isChecked():\\n self.leFind.setDisabled(flag)\\n self.leReplace.setDisabled(flag)\\n self.gbRegEx.setDisabled(flag)\\n\\n if self.gbSep.isChecked():\\n self.leSep.setDisabled(flag)\\n self.gbSep.setDisabled(flag)\\n\\n self.btnPathImg.setDisabled(flag)\\n self.btnTry.setDisabled(flag)\\n self.btnRun.setDisabled(flag)\\n self.btnQuit.setDisabled(flag)\\n\\n self.parent().menuBar.setDisabled(flag)\\n\\n def isValid(self, method=\\\"run\\\"):\\n \\\"\\\"\\\"\\n Проверяет валидность введённых параметров\\n :param str method: какой метод вызывает валидацию\\n :return bool:\\n \\\"\\\"\\\"\\n if not self.lePathImg.text():\\n QtWidgets.QMessageBox.critical(self, \\\"Error\\\", \\\"Choose the folder for rename files\\\",\\n defaultButton=QtWidgets.QMessageBox.Ok)\\n return False\\n if not os.path.exists(self.lePathImg.text()):\\n QtWidgets.QMessageBox.critical(self, \\\"Error\\\", f\\\"Folder {self.lePathImg.text()} doesn't exist!\\\",\\n defaultButton=QtWidgets.QMessageBox.Ok)\\n return False\\n if self.gbRegEx.isChecked() and self.gbSep.isChecked():\\n QtWidgets.QMessageBox.critical(self, \\\"Error\\\", \\\"You must choose something one: RegExp or Separator\\\",\\n defaultButton=QtWidgets.QMessageBox.Ok)\\n return False\\n if self.gbRegEx.isChecked():\\n if not self.leFind.text():\\n QtWidgets.QMessageBox.critical(self, \\\"Error\\\", \\\"Type what to replace in file names\\\",\\n defaultButton=QtWidgets.QMessageBox.Ok)\\n return False\\n if self.gbSep.isChecked():\\n if not self.leSep.text():\\n QtWidgets.QMessageBox.critical(self, \\\"Error\\\", \\\"Type separator to devide file names\\\",\\n defaultButton=QtWidgets.QMessageBox.Ok)\\n return False\\n if self.sti.rowCount() == 0:\\n QtWidgets.QMessageBox.critical(self, \\\"Error\\\", f\\\"Push button Get\\\",\\n defaultButton=QtWidgets.QMessageBox.Ok)\\n return False\\n\\n if method == \\\"run\\\":\\n self.rens.clear()\\n for i in range(0, self.sti.rowCount()):\\n self.color_dup(self.sti.item(i, 3))\\n\\n if len(self.rens) > 0 and len(self.rens) != self.sti.rowCount():\\n QtWidgets.QMessageBox.critical(self, \\\"Error\\\", f\\\"You have duplicate names in Rename columns!\\\",\\n defaultButton=QtWidgets.QMessageBox.Ok)\\n return False\\n return True\\n\\n def color_dup(self, sind):\\n \\\"\\\"\\\"\\n Смотрит является ли текущий элемент дубликатом и подкрашивает дубли\\n :param MyStandardItemModel sind: индекс элемента\\n \\\"\\\"\\\"\\n if sind.text() in self.rens:\\n self.sti.setData(self.sti.indexFromItem(sind), QtGui.QColor(QtCore.Qt.red), QtCore.Qt.BackgroundColorRole)\\n else:\\n self.sti.setData(self.sti.indexFromItem(sind), QtGui.QColor(QtCore.Qt.white), QtCore.Qt.BackgroundColorRole)\\n self.rens.add(sind.text())\\n\"\n },\n {\n \"alpha_fraction\": 0.6166258454322815,\n \"alphanum_fraction\": 0.6200980544090271,\n \"avg_line_length\": 36.661537170410156,\n \"blob_id\": \"dde7d815650dbe6ec082b065b13ab27e9e7bd1ba\",\n \"content_id\": \"2aee4c1b20bf631416139fc5a70ba350bde93636\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5174,\n \"license_type\": \"no_license\",\n \"max_line_length\": 99,\n \"num_lines\": 130,\n \"path\": \"/modules/mainwindow.py\",\n \"repo_name\": \"mike7105/RenameFiles\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Главное окно приложения\\\"\\\"\\\"\\nimport os\\n\\nfrom PyQt5 import QtCore, QtWidgets, QtWinExtras\\n\\nfrom modules.MyWindow import MyWindow\\n\\n\\nclass MainWindow(QtWidgets.QMainWindow):\\n \\\"\\\"\\\"\\n Главное окно\\n \\\"\\\"\\\"\\n\\n def __init__(self, version, parent=None):\\n QtWidgets.QMainWindow.__init__(self, parent)\\n self.version = version\\n self.setWindowTitle(\\\"Rename Files\\\")\\n\\n # Создаем кнопку панели задач\\n self.taskbarButton = QtWinExtras.QWinTaskbarButton(parent=self)\\n # Получаем индикатор процесса, задаем его параметры\\n # и делаем его видимым\\n self.progress = self.taskbarButton.progress()\\n self.progress.setMaximum(0)\\n self.progress.show()\\n\\n self.dtGeo = QtWidgets.QApplication.desktop().availableGeometry()\\n self.setMinimumSize(600, 600)\\n self.resize(self.dtGeo.width()//2, self.dtGeo.height()-30)\\n # self.showMaximized()\\n self.move(self.dtGeo.width()//2, 0)\\n\\n QtCore.QCoreApplication.setOrganizationName(\\\"Mihail Chesnokov\\\")\\n QtCore.QCoreApplication.setApplicationName(\\\"renameFiles\\\")\\n self.settings = QtCore.QSettings()\\n\\n self.mywindow = MyWindow(self)\\n self.setCentralWidget(self.mywindow)\\n\\n self.menuBar = self.menuBar()\\n # toolBar = QtWidgets.QToolBar()\\n\\n self.myMenuFile = self.menuBar.addMenu(\\\"&File\\\")\\n\\n self.myMenuFile.addSeparator()\\n\\n action = self.myMenuFile.addAction(\\\"&Quit\\\", self.quitApp, QtCore.Qt.CTRL + QtCore.Qt.Key_Q)\\n action.setStatusTip(\\\"Quit program\\\")\\n\\n self.myMenuAbout = self.menuBar.addMenu(\\\"&About\\\")\\n\\n action = self.myMenuAbout.addAction(\\\"About...\\\", self.aboutInfo)\\n action.setStatusTip(\\\"Get info about program\\\")\\n\\n action = self.myMenuAbout.addAction(\\\"about &Qt...\\\", QtWidgets.qApp.aboutQt)\\n action.setStatusTip(\\\"Get info about Qt\\\")\\n\\n self.restoreSettings()\\n\\n def closeEvent(self, evt):\\n \\\"\\\"\\\"\\n событие при закрытии приложения\\n :param evt: событие\\n \\\"\\\"\\\"\\n self.saveSettings()\\n\\n def saveSettings(self):\\n \\\"\\\"\\\"\\n Сохраняет настройки\\n \\\"\\\"\\\"\\n self.settings.beginGroup(\\\"Window\\\")\\n self.settings.setValue(\\\"Geometry\\\", self.geometry())\\n self.settings.endGroup()\\n\\n self.settings.beginGroup(\\\"Data\\\")\\n self.settings.setValue(\\\"lePathImg\\\", self.mywindow.lePathImg.text())\\n self.settings.setValue(\\\"leExt\\\", self.mywindow.leExt.text())\\n self.settings.setValue(\\\"gbRegEx\\\", int(self.mywindow.gbRegEx.isChecked()))\\n self.settings.setValue(\\\"leFind\\\", self.mywindow.leFind.text())\\n self.settings.setValue(\\\"leReplace\\\", self.mywindow.leReplace.text())\\n self.settings.setValue(\\\"gbSep\\\", int(self.mywindow.gbSep.isChecked()))\\n self.settings.setValue(\\\"leSep\\\", self.mywindow.leSep.text())\\n self.settings.endGroup()\\n\\n def restoreSettings(self):\\n \\\"\\\"\\\"\\n Восстанавливает сохраненные настройки\\n \\\"\\\"\\\"\\n if self.settings.contains(\\\"Window/Geometry\\\"):\\n self.setGeometry(self.settings.value(\\\"Window/Geometry\\\"))\\n if self.settings.contains(\\\"Data/lePathImg\\\"):\\n if os.path.exists(self.settings.value(\\\"Data/lePathImg\\\")):\\n self.mywindow.lePathImg.setText(self.settings.value(\\\"Data/lePathImg\\\"))\\n if self.settings.contains(\\\"Data/leExt\\\"):\\n self.mywindow.leExt.setText(self.settings.value(\\\"Data/leExt\\\"))\\n if self.settings.contains(\\\"Data/gbRegEx\\\"):\\n self.mywindow.gbRegEx.setChecked(bool(self.settings.value(\\\"Data/gbRegEx\\\")))\\n if self.settings.value(\\\"Data/gbRegEx\\\"):\\n if self.settings.contains(\\\"Data/leFind\\\"):\\n self.mywindow.leFind.setText(self.settings.value(\\\"Data/leFind\\\"))\\n if self.settings.contains(\\\"Data/leReplace\\\"):\\n self.mywindow.leReplace.setText(self.settings.value(\\\"Data/leReplace\\\"))\\n if self.settings.contains(\\\"Data/gbSep\\\"):\\n self.mywindow.gbSep.setChecked(bool(self.settings.value(\\\"Data/gbSep\\\")))\\n if self.settings.value(\\\"Data/gbSep\\\"):\\n if self.settings.contains(\\\"Data/leSep\\\"):\\n self.mywindow.leSep.setText(self.settings.value(\\\"Data/leSep\\\"))\\n\\n def quitApp(self):\\n \\\"\\\"\\\"\\n ЗАкрывает приложение\\n \\\"\\\"\\\"\\n self.saveSettings()\\n QtWidgets.qApp.quit()\\n\\n def aboutInfo(self):\\n \\\"\\\"\\\"\\n Показывает описание программы\\n \\\"\\\"\\\"\\n QtWidgets.QMessageBox.about(self, \\\"About program\\\",\\n f\\\"<center>\\\\\\\"renameFiles\\\\\\\" {self.version}<br><br>\\\"\\n f\\\"Rename files for current folder<br><br> \\\"\\n f\\\"(c) Mihail Chesnokov 2021\\\")\\n\\n def showEvent(self, evt):\\n \\\"\\\"\\\"\\n событие при показе окна на экране\\n :param evt:\\n \\\"\\\"\\\"\\n self.taskbarButton.setWindow(self.windowHandle())\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2983084,"cells":{"repo_name":{"kind":"string","value":"yuyurun/note_tag"},"repo_url":{"kind":"string","value":"https://github.com/yuyurun/note_tag"},"snapshot_id":{"kind":"string","value":"0ab5dda70de803118b374b76e66f3ce4c3f0acea"},"revision_id":{"kind":"string","value":"d5d7dd5cc4939af8bcfb4d30b2a2a0b94aecb527"},"directory_id":{"kind":"string","value":"96ccd9b0b76657b4f7a06d65d4c9fe9fcc29a0ec"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-05-09T18:15:42.767498","string":"2020-05-09T18:15:42.767498"},"revision_date":{"kind":"timestamp","value":"2019-08-25T14:19:08","string":"2019-08-25T14:19:08"},"committer_date":{"kind":"timestamp","value":"2019-08-25T14:19:08","string":"2019-08-25T14:19:08"},"github_id":{"kind":"number","value":181334733,"string":"181,334,733"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.807692289352417,"alphanum_fraction":0.807692289352417,"avg_line_length":12,"blob_id":"f4a20d4fb3eb3a3956291533f3a50e537166d774","content_id":"bcad7cf924758cc4455d99f28b299c866bad3327","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":46,"license_type":"no_license","max_line_length":14,"num_lines":2,"path":"/README.md","repo_name":"yuyurun/note_tag","src_encoding":"UTF-8","text":"# note_tag\nnoteのタグ自動生成したい\n"},{"alpha_fraction":0.5339747667312622,"alphanum_fraction":0.5511124730110168,"avg_line_length":19.280487060546875,"blob_id":"fbf8d52910f55272cd0f890240988c178fcb9eec","content_id":"9201cfcbbf694d3318215fff6c3e1f165b787da1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3364,"license_type":"no_license","max_line_length":80,"num_lines":164,"path":"/Crawling.py","repo_name":"yuyurun/note_tag","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n#-*- coding:utf-8 -*-\n\nimport sys\nimport argparse\nimport os\nimport glob\n\nimport time\nimport MeCab\n\nimport urllib3\n#import urllib.request, urllib.error\nfrom bs4 import BeautifulSoup\n\n\ndef url_open(url):\n http = urllib3.PoolManager()\n r = http.request('GET',url)\n #print(r.data)\n #print(r.status)\n \n #html = urllib3.urlopen(url)\n soup = BeautifulSoup(r.data,\"lxml\")\n\n return soup\n \ndef html2data(soup):\n return soup.search('keywords')\n\n\ndef html2link(soup):\n #atag = soup.find_all(\"a\")\n #print(atag)\n #f1 = open(filename,\"w\")\n num = 0\n http = urllib3.PoolManager()\n link = []\n for line in soup.find_all(\"a\"):\n\n\n htag = line.get(\"href\")\n if \"/rb/\" in str(htag):\n print(htag)\n link.append(htag)\n\n print(link)\n l = []\n link2 = list(set(link))\n print(link2)\n \n for l in link2:\n if num > 20:\n break \n f1 = open(filename,\"a\")\n num += 1\n print(l)\n http = urllib3.PoolManager()\n r = http.request('GET',l)\n soup = BeautifulSoup(r.data,\"lxml\")\n print(\"書き込み\")\n f1.write(\"num:::::\" + str(num) + \"\\n\")\n f1.write(\"url:::::\" + l + \"\\n\")\n f1.write(str(soup) + \"\\n\")\n time.sleep(2)\n\ndef link2rec(filename,soup):\n f2 = open(filename)\n lines = f2.readlines()\n c = 5\n sentence = {}\n for line in lines:\n if \"url:::::\" in line:\n l = line.replace(\"url:::::\",\"\")\n l = line.replace(\"\\n\",\"\")\n url = l\n elif \"<h2>商品説明\" in line:\n c = 0\n elif \"<p>\" in line:\n c = c\n else:\n c += 1\n if c == 2:\n sen = line\n sentence[url] = sen\n sp = []\n \n sp = str(soup).split(\"\\n\")\n c = 5\n for l in sp:\n if \"<h2>商品説明\" in l:\n c = 0\n elif \"<p>\" in l:\n c = c\n else:\n c += 1\n if c == 2:\n sentence[\"master\"] = l\n\n return sentence\n\ndef jaccard(list_a,list_b):\n set_intersection = set.intersection(set(list_a), set(list_b))\n num_intersection = len(set_intersection)\n \n set_union = set.union(set(list_a), set(list_b))\n num_union = len(set_union)\n return float(num_intersection) / num_union\n\ndef calc_jaccard(sentence):\n mecab = MeCab.Tagger('-d /usr/local/lib/mecab/dic/mecab-ipadic-neologd')\n mecab.parse('')\n word_d = {}\n for u,s in sentence.items():\n node = mecab.parseToNode(s)\n wordlist = []\n while node:\n if node.feature.split(\",\")[6] == '*':\n word = node.surface\n else:\n word = node.feature.split(\",\")[6] \n\n part = node.feature.split(\",\")[0]\n\n if part in [\"名詞\", \"形容詞\", \"動詞\"] and word != \"BR\":\n wordlist.append(word)\n node = node.next\n #print(wordlist)\n\n word_d[u] = wordlist\n master = word_d[\"master\"]\n j1 = 0\n j2 = 0\n j3 = 0\n for u,w in word_d.items():\n if not u == \"master\":\n u = u.replace(\"url:::::\",\"\")\n j = jaccard(master,w)\n if j1 < j:\n j1 = j\n j1_url = u\n elif j2 < j:\n j2 = j\n j2_url = u\n elif j3 < j:\n j3 = j\n j3_url = u\n return j1_url, j2_url, j3_url\n\n\n\n return word_d\n#def target()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('-i', '--input_url', action='store', nargs='?', type=str)\n parser.add_argument('-o', '--output_dir', action='store', nargs='?', type=str)\n args = parser.parse_args()\n\n soup = url_open(args.input_url)\n print(soup)\n print(html2data)\n"}],"string":"[\n {\n \"alpha_fraction\": 0.807692289352417,\n \"alphanum_fraction\": 0.807692289352417,\n \"avg_line_length\": 12,\n \"blob_id\": \"f4a20d4fb3eb3a3956291533f3a50e537166d774\",\n \"content_id\": \"bcad7cf924758cc4455d99f28b299c866bad3327\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 46,\n \"license_type\": \"no_license\",\n \"max_line_length\": 14,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"yuyurun/note_tag\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# note_tag\\nnoteのタグ自動生成したい\\n\"\n },\n {\n \"alpha_fraction\": 0.5339747667312622,\n \"alphanum_fraction\": 0.5511124730110168,\n \"avg_line_length\": 19.280487060546875,\n \"blob_id\": \"fbf8d52910f55272cd0f890240988c178fcb9eec\",\n \"content_id\": \"9201cfcbbf694d3318215fff6c3e1f165b787da1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3364,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 164,\n \"path\": \"/Crawling.py\",\n \"repo_name\": \"yuyurun/note_tag\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n#-*- coding:utf-8 -*-\\n\\nimport sys\\nimport argparse\\nimport os\\nimport glob\\n\\nimport time\\nimport MeCab\\n\\nimport urllib3\\n#import urllib.request, urllib.error\\nfrom bs4 import BeautifulSoup\\n\\n\\ndef url_open(url):\\n http = urllib3.PoolManager()\\n r = http.request('GET',url)\\n #print(r.data)\\n #print(r.status)\\n \\n #html = urllib3.urlopen(url)\\n soup = BeautifulSoup(r.data,\\\"lxml\\\")\\n\\n return soup\\n \\ndef html2data(soup):\\n return soup.search('keywords')\\n\\n\\ndef html2link(soup):\\n #atag = soup.find_all(\\\"a\\\")\\n #print(atag)\\n #f1 = open(filename,\\\"w\\\")\\n num = 0\\n http = urllib3.PoolManager()\\n link = []\\n for line in soup.find_all(\\\"a\\\"):\\n\\n\\n htag = line.get(\\\"href\\\")\\n if \\\"/rb/\\\" in str(htag):\\n print(htag)\\n link.append(htag)\\n\\n print(link)\\n l = []\\n link2 = list(set(link))\\n print(link2)\\n \\n for l in link2:\\n if num > 20:\\n break \\n f1 = open(filename,\\\"a\\\")\\n num += 1\\n print(l)\\n http = urllib3.PoolManager()\\n r = http.request('GET',l)\\n soup = BeautifulSoup(r.data,\\\"lxml\\\")\\n print(\\\"書き込み\\\")\\n f1.write(\\\"num:::::\\\" + str(num) + \\\"\\\\n\\\")\\n f1.write(\\\"url:::::\\\" + l + \\\"\\\\n\\\")\\n f1.write(str(soup) + \\\"\\\\n\\\")\\n time.sleep(2)\\n\\ndef link2rec(filename,soup):\\n f2 = open(filename)\\n lines = f2.readlines()\\n c = 5\\n sentence = {}\\n for line in lines:\\n if \\\"url:::::\\\" in line:\\n l = line.replace(\\\"url:::::\\\",\\\"\\\")\\n l = line.replace(\\\"\\\\n\\\",\\\"\\\")\\n url = l\\n elif \\\"<h2>商品説明\\\" in line:\\n c = 0\\n elif \\\"<p>\\\" in line:\\n c = c\\n else:\\n c += 1\\n if c == 2:\\n sen = line\\n sentence[url] = sen\\n sp = []\\n \\n sp = str(soup).split(\\\"\\\\n\\\")\\n c = 5\\n for l in sp:\\n if \\\"<h2>商品説明\\\" in l:\\n c = 0\\n elif \\\"<p>\\\" in l:\\n c = c\\n else:\\n c += 1\\n if c == 2:\\n sentence[\\\"master\\\"] = l\\n\\n return sentence\\n\\ndef jaccard(list_a,list_b):\\n set_intersection = set.intersection(set(list_a), set(list_b))\\n num_intersection = len(set_intersection)\\n \\n set_union = set.union(set(list_a), set(list_b))\\n num_union = len(set_union)\\n return float(num_intersection) / num_union\\n\\ndef calc_jaccard(sentence):\\n mecab = MeCab.Tagger('-d /usr/local/lib/mecab/dic/mecab-ipadic-neologd')\\n mecab.parse('')\\n word_d = {}\\n for u,s in sentence.items():\\n node = mecab.parseToNode(s)\\n wordlist = []\\n while node:\\n if node.feature.split(\\\",\\\")[6] == '*':\\n word = node.surface\\n else:\\n word = node.feature.split(\\\",\\\")[6] \\n\\n part = node.feature.split(\\\",\\\")[0]\\n\\n if part in [\\\"名詞\\\", \\\"形容詞\\\", \\\"動詞\\\"] and word != \\\"BR\\\":\\n wordlist.append(word)\\n node = node.next\\n #print(wordlist)\\n\\n word_d[u] = wordlist\\n master = word_d[\\\"master\\\"]\\n j1 = 0\\n j2 = 0\\n j3 = 0\\n for u,w in word_d.items():\\n if not u == \\\"master\\\":\\n u = u.replace(\\\"url:::::\\\",\\\"\\\")\\n j = jaccard(master,w)\\n if j1 < j:\\n j1 = j\\n j1_url = u\\n elif j2 < j:\\n j2 = j\\n j2_url = u\\n elif j3 < j:\\n j3 = j\\n j3_url = u\\n return j1_url, j2_url, j3_url\\n\\n\\n\\n return word_d\\n#def target()\\n\\n\\nif __name__ == '__main__':\\n parser = argparse.ArgumentParser()\\n parser.add_argument('-i', '--input_url', action='store', nargs='?', type=str)\\n parser.add_argument('-o', '--output_dir', action='store', nargs='?', type=str)\\n args = parser.parse_args()\\n\\n soup = url_open(args.input_url)\\n print(soup)\\n print(html2data)\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2983085,"cells":{"repo_name":{"kind":"string","value":"srinicoder035/Temperature-Predictor"},"repo_url":{"kind":"string","value":"https://github.com/srinicoder035/Temperature-Predictor"},"snapshot_id":{"kind":"string","value":"ea431022ebd640cddcfc5107bc94df17167c4db5"},"revision_id":{"kind":"string","value":"858bafcd018c7a6a7b82f33f3585e2f81c56e98d"},"directory_id":{"kind":"string","value":"7db1cb71b85313786f422f4eea7a08b9ae85eb08"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-09-23T07:25:04.923881","string":"2020-09-23T07:25:04.923881"},"revision_date":{"kind":"timestamp","value":"2019-12-02T18:09:31","string":"2019-12-02T18:09:31"},"committer_date":{"kind":"timestamp","value":"2019-12-02T18:09:31","string":"2019-12-02T18:09:31"},"github_id":{"kind":"number","value":225438555,"string":"225,438,555"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6727879643440247,"alphanum_fraction":0.6883695125579834,"avg_line_length":31.089284896850586,"blob_id":"6b3bcfd26c1b3ab262aaffa8b42567caa58c8abb","content_id":"a555a3535fb5488fc14eb9498d963b72d942f48e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1797,"license_type":"no_license","max_line_length":130,"num_lines":56,"path":"/assignment.py","repo_name":"srinicoder035/Temperature-Predictor","src_encoding":"UTF-8","text":"import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestRegressor\n\nfeatures = pd.read_csv('temps.csv')\n\nprint('The shape of the dataset is:', features.shape)\n\n#statistics on the dataset\n#print(features.describe())\n\n# One-hot encode the data \nfeatures = pd.get_dummies(features)\n#print(features.iloc[:,5:].head(5))\n\nlabels = np.array(features['actual'])\n\nfeatures= features.drop('actual', axis = 1)\nfeature_list = list(features.columns)\n\nfeatures = np.array(features)\n#print(features)\n\ntrain_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.25, random_state = 42)\n\n#train_features, test_features = train_test_split(features, test_size = 0.25, random_state = 42)\n\nprint('Training Features Shape:', train_features.shape)\nprint('Training Labels Shape:', train_labels.shape)\nprint('Testing Features Shape:', test_features.shape)\nprint('Testing Labels Shape:', test_labels.shape)\n\n\nbaseline_preds = test_features[:, feature_list.index('average')]\nprint(\"----------------------------Actual Values------------------------------------\")\nprint(test_labels)\n\nrf = RandomForestRegressor(n_estimators = 1000, random_state = 42)\nrf.fit(train_features, train_labels)\n\npredictions = rf.predict(test_features)\n\nprint(\"----------------------------Predicted Values---------------------------------\")\nprint(predictions)\nerrors = abs(predictions - test_labels)\n\n#baseline_errors = abs(baseline_preds - test_labels)\n#print('Average baseline error: ', round(np.mean(baseline_errors), 2))\n\n#print(errors)\nprint('\\nMean Absolute Error:', round(np.mean(errors), 2), 'degrees.')\n\nmape = 100 * (errors / test_labels)\naccuracy = 100 - np.mean(mape)\nprint('\\nAccuracy:', round(accuracy, 2), '%.')\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6727879643440247,\n \"alphanum_fraction\": 0.6883695125579834,\n \"avg_line_length\": 31.089284896850586,\n \"blob_id\": \"6b3bcfd26c1b3ab262aaffa8b42567caa58c8abb\",\n \"content_id\": \"a555a3535fb5488fc14eb9498d963b72d942f48e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1797,\n \"license_type\": \"no_license\",\n \"max_line_length\": 130,\n \"num_lines\": 56,\n \"path\": \"/assignment.py\",\n \"repo_name\": \"srinicoder035/Temperature-Predictor\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import pandas as pd\\nimport numpy as np\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.ensemble import RandomForestRegressor\\n\\nfeatures = pd.read_csv('temps.csv')\\n\\nprint('The shape of the dataset is:', features.shape)\\n\\n#statistics on the dataset\\n#print(features.describe())\\n\\n# One-hot encode the data \\nfeatures = pd.get_dummies(features)\\n#print(features.iloc[:,5:].head(5))\\n\\nlabels = np.array(features['actual'])\\n\\nfeatures= features.drop('actual', axis = 1)\\nfeature_list = list(features.columns)\\n\\nfeatures = np.array(features)\\n#print(features)\\n\\ntrain_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.25, random_state = 42)\\n\\n#train_features, test_features = train_test_split(features, test_size = 0.25, random_state = 42)\\n\\nprint('Training Features Shape:', train_features.shape)\\nprint('Training Labels Shape:', train_labels.shape)\\nprint('Testing Features Shape:', test_features.shape)\\nprint('Testing Labels Shape:', test_labels.shape)\\n\\n\\nbaseline_preds = test_features[:, feature_list.index('average')]\\nprint(\\\"----------------------------Actual Values------------------------------------\\\")\\nprint(test_labels)\\n\\nrf = RandomForestRegressor(n_estimators = 1000, random_state = 42)\\nrf.fit(train_features, train_labels)\\n\\npredictions = rf.predict(test_features)\\n\\nprint(\\\"----------------------------Predicted Values---------------------------------\\\")\\nprint(predictions)\\nerrors = abs(predictions - test_labels)\\n\\n#baseline_errors = abs(baseline_preds - test_labels)\\n#print('Average baseline error: ', round(np.mean(baseline_errors), 2))\\n\\n#print(errors)\\nprint('\\\\nMean Absolute Error:', round(np.mean(errors), 2), 'degrees.')\\n\\nmape = 100 * (errors / test_labels)\\naccuracy = 100 - np.mean(mape)\\nprint('\\\\nAccuracy:', round(accuracy, 2), '%.')\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2983086,"cells":{"repo_name":{"kind":"string","value":"Soundpruf/block_party-server"},"repo_url":{"kind":"string","value":"https://github.com/Soundpruf/block_party-server"},"snapshot_id":{"kind":"string","value":"7ea703b17326612b8ac4ac894f10e475be9756eb"},"revision_id":{"kind":"string","value":"bf180a8b83b460b902d751bfaa4143f3b5e8bb5b"},"directory_id":{"kind":"string","value":"924181628e23c92fc275f8fd880be21a5faafada"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-05-11T00:32:04.177366","string":"2023-05-11T00:32:04.177366"},"revision_date":{"kind":"timestamp","value":"2018-01-14T05:18:01","string":"2018-01-14T05:18:01"},"committer_date":{"kind":"timestamp","value":"2018-01-14T05:18:01","string":"2018-01-14T05:18:01"},"github_id":{"kind":"number","value":116161921,"string":"116,161,921"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6465422511100769,"alphanum_fraction":0.6465422511100769,"avg_line_length":34.03845977783203,"blob_id":"e701383a8dd9272ccff82a081ddd89f7eba515ed","content_id":"9eaea6bc14c9187f3e8287f58272ce6080c24109","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":911,"license_type":"no_license","max_line_length":106,"num_lines":26,"path":"/app/models/artist.py","repo_name":"Soundpruf/block_party-server","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom app import db\n\n\nclass Artist(db.Model):\n __tablename__ = 'artists'\n __table_args__ = {'extend_existing': True}\n\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.Text)\n date_joined = db.Column(db.DateTime, default=datetime.now())\n profile_image = db.Column(db.Text, default=None)\n email_address = db.Column(db.Text)\n spotify_id = db.Column(db.Text, default=None)\n wallet_address = db.Column(db.Text)\n # genres = db.Column(db.Text, default=None)\n\n def __init__(self, id, name, email, password, date_joined, profile_image, wallet_address, spotify_id):\n self.id = id\n self.spotify_id = spotify_id\n self.name = name\n self.date_joined = date_joined\n self.profile_image = profile_image\n self.wallet_address = wallet_address\n self.email_address = email\n # self.genres = genres\n"},{"alpha_fraction":0.49140894412994385,"alphanum_fraction":0.69243985414505,"avg_line_length":15.628571510314941,"blob_id":"5d00ec2fd63fb28d3547edd3844cb201fe5fe538","content_id":"c4ec6d2a475a54f29be9e48140b40d0b3db89096","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":582,"license_type":"no_license","max_line_length":24,"num_lines":35,"path":"/requirements.txt","repo_name":"Soundpruf/block_party-server","src_encoding":"UTF-8","text":"alembic==0.9.6\nastroid==1.6.0\nautoenv==1.0.0\ncertifi==2017.11.5\nchardet==3.0.4\nclick==6.7\nf==0.0.1\nFlask==0.12.2\nFlask-Cors==3.0.3\nFlask-Migrate==2.1.1\nFlask-Script==2.0.6\nFlask-SocketIO==2.9.3\nFlask-SQLAlchemy==2.3.2\ngunicorn==19.7.1\nidna==2.6\nisort==4.2.15\nitsdangerous==0.24\nJinja2==2.10\nlazy-object-proxy==1.3.1\nMako==1.0.7\nMarkupSafe==1.0\nmccabe==0.6.1\npsycopg2==2.7.3.2\npylint==1.8.1\npython-dateutil==2.6.1\npython-editor==1.0.3\npython-engineio==2.0.1\npython-socketio==1.8.4\nrequests==2.18.4\nsix==1.11.0\nSQLAlchemy==1.2.0\nurllib3==1.22\nuuid==1.30\nWerkzeug==0.13\nwrapt==1.10.11\n"},{"alpha_fraction":0.6508333086967468,"alphanum_fraction":0.7149999737739563,"avg_line_length":28.268293380737305,"blob_id":"f0132d5b2f35abca620df83c776e240462bc5d1e","content_id":"093425b2a5f7eac7722090a9534ee90943460241","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1200,"license_type":"no_license","max_line_length":183,"num_lines":41,"path":"/app/config.py","repo_name":"Soundpruf/block_party-server","src_encoding":"UTF-8","text":"import os\nbasedir = os.path.abspath(os.path.dirname(__file__))\n\n\nclass Config(object):\n DEBUG = True\n TESTING = False\n CSRF_ENABLED = True\n SECRET_KEY = 'this-really-needs-to-be-changed'\n SQLALCHEMY_DATABASE_URI = os.environ.get('DATABASE_URL') or 'postgresql://localhost/block_party'\n\n\nclass ProductionConfig(Config):\n DEBUG = True\n SQLALCHEMY_DATABASE_URI = 'postgres://vyifvrvzhxfqjr:6abb99931b0b754a6cef72d4e5c09218e8329d29fcb079527fa86739eff363c2@ec2-23-21-246-25.compute-1.amazonaws.com:5432/d7kdus8jfnjbhj'\n\n\nclass StagingConfig(Config):\n DEVELOPMENT = True\n DEBUG = True\n SQLALCHEMY_DATABASE_URI = 'postgres://thcnzodhdmfmci:f444914121c12078f50b98a0183dbfa38385d2f1165a520fb9dac8856b71b69e@ec2-107-21-95-70.compute-1.amazonaws.com:5432/d8otav6il3klb1'\n\n\nclass DevelopmentConfig(Config):\n DEVELOPMENT = True\n # SQLALCHEMY_DATABASE_URI = 'postgresql://localhost/block_party'\n DEBUG = True\n\n\ndef apply_config(app):\n\n \"\"\" Apply configuration for application based on OS environ \"\"\"\n \n if os.environ.get('productionConfig', False):\n config = ProductionConfig\n elif os.environ.get('stagingConfig', False):\n config = StagingConfig\n else:\n config = DevelopmentConfig\n\n app.config.from_object(config)\n"},{"alpha_fraction":0.6296851634979248,"alphanum_fraction":0.6806596517562866,"avg_line_length":22.821428298950195,"blob_id":"cb23733629aa3c64630743b1ed40de0ce585b424","content_id":"e1e39c49526cb23ae6e82ac8353fa76cbe6fd313","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":667,"license_type":"no_license","max_line_length":80,"num_lines":28,"path":"/migrations/versions/18f1d056ac5e_.py","repo_name":"Soundpruf/block_party-server","src_encoding":"UTF-8","text":"\"\"\"empty message\n\nRevision ID: 18f1d056ac5e\nRevises: f226666b8429\nCreate Date: 2018-01-13 19:15:22.290823\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '18f1d056ac5e'\ndown_revision = 'f226666b8429'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('songs', sa.Column('popularity', sa.Integer(), nullable=True))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('songs', 'popularity')\n # ### end Alembic commands ###\n"},{"alpha_fraction":0.6171954870223999,"alphanum_fraction":0.6171954870223999,"avg_line_length":32.68965530395508,"blob_id":"ba99e93910de7bd4b4af76a21c8a4f8dd7291a12","content_id":"9a50d56e40f56ddf3dec32229cf84340632b4538","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":977,"license_type":"no_license","max_line_length":85,"num_lines":29,"path":"/app/models/album.py","repo_name":"Soundpruf/block_party-server","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom app import db\n\nclass Album(db.Model):\n __tablename__ = 'albums'\n __table_args__ = {'extend_existing': True} \n\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.Text)\n artist_id = db.Column(db.Integer, db.ForeignKey(\n 'artists.id'), primary_key=True)\n created_at = db.Column(db.DateTime, default=datetime.now())\n photo = db.Column(db.Text, default=None)\n claps = db.Column(db.Integer, default=None)\n shares = db.Column(db.Integer, default=None)\n value = db.Column(db.Integer, default=None)\n\n def __init__(self, id, name, artist_id, created_at, photo, claps, shares, value):\n self.id = id\n self.name = name\n self.artist_id = artist_id\n self.created_at = created_at\n self.photo = photo\n self.claps = claps\n self.shares = shares\n self.value = value\n\n def __repr__(self):\n return '<Album {}, {}>'.format(self.id, self.name)\n"},{"alpha_fraction":0.7177242636680603,"alphanum_fraction":0.7177242636680603,"avg_line_length":17.31999969482422,"blob_id":"af0c8476a4dc1baaf7a3098baf802f8cb1b1ad35","content_id":"c5532a8d6d1f922f9d4396a24339388d9d997204","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":457,"license_type":"no_license","max_line_length":40,"num_lines":25,"path":"/app/__init__.py","repo_name":"Soundpruf/block_party-server","src_encoding":"UTF-8","text":"import os\nfrom flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate\nfrom .config import Config\n\napp = Flask(__name__)\napp.config.from_object(Config)\ndb = SQLAlchemy(app)\nmigrate = Migrate(app, db)\n\n\ndef create_app(config_class=Config):\n app = Flask(__name__)\n app.config.from_object(config_class)\n\n db.init_app(app)\n migrate.init_app(app, db)\n\n return app\n\n\n\nfrom .models import *\nfrom .routes import *"},{"alpha_fraction":0.6019716858863831,"alphanum_fraction":0.6019716858863831,"avg_line_length":37.64285659790039,"blob_id":"609e337527f24f9dbf57fea4fd52897b06220514","content_id":"abf3d2b3a7cb6d9c41cdc914f365e122c43a50d6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1623,"license_type":"no_license","max_line_length":128,"num_lines":42,"path":"/app/models/user.py","repo_name":"Soundpruf/block_party-server","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom app import db\n# from .stream import Stream\n\n# user_stream_table = db.Table('user_streams', db.Model.metadata,\n# db.Column('id', db.Integer, primary_key=True),\n# db.Column('stream_id', db.Integer, db.ForeignKey('stream.id')),\n# db.Column('user_id', db.Integer, db.ForeignKey('user.id'))\n# )\n\n\nclass User(db.Model):\n __tablename__ = 'users'\n __table_args__ = {'extend_existing': True} \n\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.Text)\n email = db.Column(db.Text)\n password = db.Column(db.Text)\n date_joined = db.Column(db.DateTime, default=datetime.now())\n profile_image = db.Column(db.Text, default=None)\n spotify_id = db.Column(db.Integer, default=None)\n followers = db.Column(db.Integer, default=None)\n platforms = db.Column(db.JSON)\n wallet_address = db.Column(db.Text)\n # streams = db.relationship('Stream', secondary='streams', backref='stream')\n\n\n def __init__(self, id, name, email, password, date_joined, profile_image, spotify_id, platforms, wallet_address, followers):\n self.id = id\n self.name = name\n self.email = email\n self.password = password\n self.date_joined = date_joined\n self.profile_image = profile_image\n self.spotify_id = spotify_id\n self.platforms = platforms\n self.followers = followers\n self.wallet_address = wallet_address\n \n def __repr__(self):\n return '<User {}, {}>'.format(self.id, self.name)\n"},{"alpha_fraction":0.6310679316520691,"alphanum_fraction":0.6310679316520691,"avg_line_length":34.517242431640625,"blob_id":"08bfa2c8a60d3a21e8b912df0ddd48a6f3f83fe7","content_id":"38f88d182637757942b9f18c64fbac7efe5d0082","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1030,"license_type":"no_license","max_line_length":96,"num_lines":29,"path":"/app/models/stream.py","repo_name":"Soundpruf/block_party-server","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom app import db\n\n\nclass Stream(db.Model):\n __tablename__ = 'streams'\n __table_args__ = {'extend_existing': True} \n\n id = db.Column(db.Integer, primary_key=True)\n song_id = db.Column(db.Integer, db.ForeignKey('songs.id'))\n artist_id = db.Column(db.Integer, db.ForeignKey('artists.id'))\n played_at = db.Column(db.DateTime, default=None)\n user_id = db.Column(db.Integer, db.ForeignKey('users.id'))\n created_at = db.Column(db.DateTime, default=datetime.now())\n duration = db.Column(db.Integer, default=None)\n value = db.Column(db.Integer, default=None)\n\n def __init__(self, id, song_id ,user_id ,artist_id ,created_at ,played_at ,duration ,value):\n self.id = id\n self.song_id = song_id\n self.user_id = user_id\n self.artist_id = artist_id\n self.created_at = created_at\n self.played_at = played_at\n self.duration = duration\n self.value = value\n\n def __repr__(self):\n return '<Stream {}>'.format(self.id)\n"},{"alpha_fraction":0.5733333230018616,"alphanum_fraction":0.5975757837295532,"avg_line_length":23.264705657958984,"blob_id":"b82154519fc62ce66d58a73b5f520eaa9c807540","content_id":"ef4bf19f56441b572d3bd82080f0c8c7a92201a6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":825,"license_type":"no_license","max_line_length":65,"num_lines":34,"path":"/migrations/versions/f226666b8429_.py","repo_name":"Soundpruf/block_party-server","src_encoding":"UTF-8","text":"\"\"\"empty message\n\nRevision ID: f226666b8429\nRevises: 228bba4edc6f\nCreate Date: 2018-01-13 19:13:24.086418\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'f226666b8429'\ndown_revision = '228bba4edc6f'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.alter_column('artists', 'spotify_id',\n existing_type=sa.INTEGER(),\n type_=sa.Text(),\n existing_nullable=True)\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.alter_column('artists', 'spotify_id',\n existing_type=sa.Text(),\n type_=sa.INTEGER(),\n existing_nullable=True)\n # ### end Alembic commands ###\n"},{"alpha_fraction":0.6460055112838745,"alphanum_fraction":0.6593204736709595,"avg_line_length":40.09434127807617,"blob_id":"1abd74f005696ca93b5ff874bd10c06d2420dfde","content_id":"42685613b3edc50db4070d217f4739e8e4a1e50a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2178,"license_type":"no_license","max_line_length":110,"num_lines":53,"path":"/migrations/versions/07c44256b8d8_.py","repo_name":"Soundpruf/block_party-server","src_encoding":"UTF-8","text":"\"\"\"empty message\n\nRevision ID: 07c44256b8d8\nRevises: 18f1d056ac5e\nCreate Date: 2018-01-13 19:26:08.892437\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = '07c44256b8d8'\ndown_revision = '18f1d056ac5e'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('streams',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('song_id', sa.Integer(), nullable=True),\n sa.Column('artist_id', sa.Integer(), nullable=True),\n sa.Column('played_at', sa.DateTime(), nullable=True),\n sa.Column('user_id', sa.Integer(), nullable=True),\n sa.Column('created_at', sa.DateTime(), nullable=True),\n sa.Column('duration', sa.Integer(), nullable=True),\n sa.Column('value', sa.Integer(), nullable=True),\n sa.ForeignKeyConstraint(['artist_id'], ['artists.id'], ),\n sa.ForeignKeyConstraint(['song_id'], ['songs.id'], ),\n sa.ForeignKeyConstraint(['user_id'], ['users.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n op.drop_constraint('songs_song_id_fkey', 'songs', type_='foreignkey')\n op.drop_constraint('songs_user_id_fkey', 'songs', type_='foreignkey')\n op.drop_column('songs', 'played_at')\n op.drop_column('songs', 'song_id')\n op.drop_column('songs', 'duration')\n op.drop_column('songs', 'user_id')\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('songs', sa.Column('user_id', sa.INTEGER(), autoincrement=False, nullable=True))\n op.add_column('songs', sa.Column('duration', sa.INTEGER(), autoincrement=False, nullable=True))\n op.add_column('songs', sa.Column('song_id', sa.INTEGER(), autoincrement=False, nullable=True))\n op.add_column('songs', sa.Column('played_at', postgresql.TIMESTAMP(), autoincrement=False, nullable=True))\n op.create_foreign_key('songs_user_id_fkey', 'songs', 'users', ['user_id'], ['id'])\n op.create_foreign_key('songs_song_id_fkey', 'songs', 'songs', ['song_id'], ['id'])\n op.drop_table('streams')\n # ### end Alembic commands ###\n"},{"alpha_fraction":0.6253776550292969,"alphanum_fraction":0.6253776550292969,"avg_line_length":34.783782958984375,"blob_id":"1aec6607ef9bb4e43975e74d1ba62e694c8e43f5","content_id":"4fd9e21f708ec0d7b5db2fc98e2a8dbe071e0ea2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1324,"license_type":"no_license","max_line_length":109,"num_lines":37,"path":"/app/models/song.py","repo_name":"Soundpruf/block_party-server","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom app import db\n\n\nclass Song(db.Model):\n __tablename__ = 'songs'\n __table_args__ = {'extend_existing': True} \n\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.Text)\n artist_id = db.Column(db.Integer, db.ForeignKey('artists.id'))\n # album_id = db.Column(db.Integer, db.ForeignKey('albums.id'))\n spotify_id = db.Column(db.Text, default=None)\n created_at = db.Column(db.DateTime, default=datetime.now())\n photo = db.Column(db.Text, default=None)\n claps = db.Column(db.Integer, default=None)\n shares = db.Column(db.Integer, default=None)\n popularity = db.Column(db.Integer, default=None)\n value = db.Column(db.Integer, default=None)\n # genres = db.Column(db.Text, default=None)\n\n def __init__(self, id, name, artist_id, created_at, photo, claps, shares, value, spotify_id, popularity):\n self.id = id\n self.name = name\n self.popularity = popularity\n self.artist_id = artist_id\n # self.album = album\n self.spotify_id = spotify_id\n self.created_at = created_at\n self.photo = photo\n self.claps = claps\n self.shares = shares\n self.value = value\n # self.genres = genres\n\n def __repr__(self):\n return '<Song {}, {}>'.format(self.id, self.name)\n"},{"alpha_fraction":0.516879141330719,"alphanum_fraction":0.5208884477615356,"avg_line_length":27.735023498535156,"blob_id":"3df0907b14f3860696d29f4dafd08a3a8c645e9c","content_id":"2fe3557c9b3e704b1e61c7ec411654270a931a37","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":12471,"license_type":"no_license","max_line_length":123,"num_lines":434,"path":"/app/routes.py","repo_name":"Soundpruf/block_party-server","src_encoding":"UTF-8","text":"import os\nfrom datetime import datetime\nfrom json import JSONEncoder\nfrom app import app, db\nfrom flask import Flask, jsonify, request, json, redirect, render_template, send_from_directory, make_response, current_app\nfrom flask_cors import CORS, cross_origin\nfrom uuid import uuid4\nfrom textwrap import dedent\nfrom .models.blockchain import Blockchain\nfrom .models.user import User\nfrom .models.artist import Artist\nfrom .models.album import Album\nfrom .models.song import Song\nfrom .models.stream import Stream\n\n\nclass MyEncoder(JSONEncoder):\n def default(self, o):\n return o.__dict__\n\n\nCORS(app, origins=['*', 'https://block-party-client.herokuapp.com'])\n\nnode_identifier = str(uuid4()).replace('-', '')\nblockchain = Blockchain()\n\n\n@app.route('/mine', methods=['POST'])\n@cross_origin()\ndef mine():\n\n data = request.get_json()\n print(data)\n\n artist_id = data['artist_id']\n user_id = data['user_id']\n\n print(artist_id)\n print(user_id)\n\n artist = Artist.query.filter_by(id=int(artist_id)).first()\n listener = User.query.filter_by(id=int(user_id)).first()\n\n print(artist)\n print(listener)\n print(listener.name + ' is listening to ' + artist.name +\n '. 25% of this BlockNote attributed to ' + listener.name + ', 50% to ' + artist.name)\n\n last_block = blockchain.last_block\n last_proof = last_block['proof']\n proof = blockchain.proof_of_work(last_proof)\n\n # Receive a reward for finding this proof\n blockchain.new_transaction(\n sender=\"0\",\n recipient=node_identifier,\n amount=1\n )\n\n # Forge new block by adding it to the chain\n previous_hash = blockchain.hash(last_block)\n block = blockchain.new_block(proof, previous_hash)\n\n response = {\n 'message': 'New Block Forged',\n 'index': block['index'],\n 'transactions': block['transactions'],\n 'proof': block['proof'],\n 'previous_hash': block['previous_hash']\n }\n\n return jsonify(response), 200\n\n\n@app.route('/transactions/new', methods=['POST'])\ndef new_transaction():\n\n values = request.get_json()\n\n # Check that the required fields are in the POST'ed data\n required = ['sender', 'recipient', 'amount']\n if not all(k in values for k in required):\n return 'Missing values', 400\n\n # Create a new Transaction\n index = blockchain.new_transaction(\n values['sender'], values['recipient'], values['amount'])\n\n response = {'message': f'Transaction will be added to Block {index}'}\n\n return jsonify(response), 201\n\n\n@app.route('/chain', methods=['GET'])\ndef full_chain():\n\n response = {\n 'chain': blockchain.chain,\n 'length': len(blockchain.chain)\n }\n\n return jsonify(response), 200\n\n\n@app.route('/users/signup', methods=['POST'])\ndef signup():\n\n data = request.get_json()\n address = str(uuid4())\n\n if data['platform'] == True:\n\n incoming_user = data['platform_user']\n print(incoming_user)\n\n user_name = incoming_user.get('user_name')\n profile_photo = incoming_user.get('profile_photo')\n email = incoming_user.get('email')\n followers = incoming_user.get('followers')\n platforms = incoming_user.get('platforms')\n account_tier = incoming_user.get('account_tier')\n accessToken = incoming_user.get('accessToken')\n\n new_user = User(id=None,\n password=accessToken,\n date_joined=None,\n spotify_id=None,\n name=user_name,\n followers=followers,\n email=email,\n profile_image=profile_photo,\n platforms=platforms,\n wallet_address=address\n )\n else:\n user_name = data['user_name']\n profile_photo = data['profile_photo']\n email = data['email']\n platforms = data['platforms']\n\n print(user_name)\n print(profile_photo)\n print(email)\n print(platforms)\n print(address)\n\n new_user = User(id=None,\n password=None,\n date_joined=None,\n spotify_id=None,\n name=user_name,\n email=email,\n followers=None,\n profile_image=profile_photo,\n platforms=platforms,\n wallet_address=address\n )\n\n db.session.add(new_user)\n db.session.commit()\n print(new_user)\n\n resp_data = {\n 'success': True,\n 'new_user': {\n 'user_name': new_user.name,\n 'profile_photo': new_user.profile_image,\n 'email': new_user.email,\n 'followers': new_user.followers,\n 'platforms': new_user.platforms,\n 'accessToken': new_user.password,\n \"id\": new_user.id\n }\n }\n\n resp = make_response(jsonify(resp_data), 200)\n resp.headers['Access-Control-Allow-Origin'] = '*'\n\n return resp\n\n\n@app.route('/artists/signup', methods=['POST'])\n# @cross_origin()\ndef artist_signup():\n\n data = request.get_json()\n print(data)\n\n artist_name = data['artist_name']\n password = data['password']\n email = data['email']\n address = str(uuid4())\n\n new_artist = Artist(id=None,\n password=password,\n date_joined=None,\n name=artist_name,\n spotify_id=None,\n email=email,\n profile_image=None,\n wallet_address=address\n )\n\n db.session.add(new_artist)\n db.session.commit()\n\n resp_data = {\n 'artist': {\n 'artist_name': artist_name,\n 'email': email,\n 'wallet_address': address,\n 'id': new_artist.id\n }\n }\n\n resp = make_response(jsonify(resp_data), 200)\n resp.headers['Access-Control-Allow-Origin'] = '*'\n\n return resp\n\n\n@app.route('/login', methods=['POST'])\ndef login():\n req = request.get_json()\n data = req['data']\n\n print(data)\n\n if data.get('isArtist'):\n artist = Artist.query.filter_by(email_address=data['email']).first()\n print(artist)\n\n if artist:\n print(artist)\n resp_data = {\n 'artist': {\n 'artist_name': artist.name,\n 'email': artist.email_address,\n 'wallet_address': artist.wallet_address,\n 'id': artist.id\n }\n }\n\n resp = make_response(jsonify(resp_data), 200)\n resp.headers['Access-Control-Allow-Origin'] = '*'\n\n return resp\n elif data.get('spotify_login'):\n email = data['platform_user']['email']\n print(email)\n user = User.query.filter_by(email=email).first()\n\n if user:\n resp_data = {\n 'user': {\n 'user_name': user.name,\n 'email': user.email,\n 'password': user.password,\n 'wallet_address': user.wallet_address,\n 'id': user.id\n }\n }\n\n resp = make_response(jsonify(resp_data), 200)\n resp.headers['Access-Control-Allow-Origin'] = '*'\n\n return resp\n\n\n@app.route('/artists/<string:artist_id>/onboard', methods=['GET', 'POST'])\n@cross_origin()\ndef artist_onboard(artist_id):\n if request.method == \"GET\":\n print(artist_id)\n print(int(artist_id))\n new_artist = Artist.query.filter_by(id=int(artist_id)).first()\n\n if new_artist:\n name = new_artist.name\n wallet_address = new_artist.wallet_address\n\n resp = {\n 'name': name,\n 'wallet_address': wallet_address\n }\n\n return jsonify(resp), 200\n else:\n return 'Error'\n\n # elif request.method == 'POST':\n\n\n@app.route('/users/<string:user_id>/stream/add-artists', methods=['POST'])\ndef add_artists_stream(user_id):\n\n req = request.get_json()\n stream_data = req['data']\n recent_tracks = None\n if 'recent_tracks' in stream_data:\n recent_tracks = stream_data['recent_tracks']\n\n print(recent_tracks)\n user = User.query.filter_by(id=int(user_id)).first()\n\n for stream in recent_tracks:\n streamed_artist = stream['artist']\n streamed_song = stream\n existing_song = Song.query.filter_by(\n name=streamed_song.get('name')).first()\n existing_artist = Artist.query.filter_by(\n name=streamed_artist.get('name')).first()\n\n song = existing_song\n artist = existing_artist\n\n if not existing_artist:\n address = str(uuid4())\n new_artist = Artist(id=None,\n password=None,\n date_joined=None,\n name=streamed_artist.get('name'),\n spotify_id=streamed_artist.get('spotify_id'),\n email=None,\n profile_image=streamed_artist.get(\n 'photo').get('url'),\n wallet_address=address\n )\n db.session.add(new_artist)\n db.session.commit()\n artist = new_artist\n\n if not existing_song:\n new_song = Song(id=None,\n name=streamed_song.get('name'),\n artist_id=artist.id,\n popularity=streamed_song.get('popularity'),\n spotify_id=streamed_song.get('spotify_id'),\n created_at=None,\n photo=None,\n claps=None,\n shares=None,\n value=None\n )\n db.session.add(new_song)\n db.session.commit()\n song = new_song\n\n new_stream = Stream(id=None,\n song_id=song.id,\n user_id=user.id,\n artist_id=artist.id,\n created_at=None,\n played_at=streamed_song.get('played_at'),\n duration=streamed_song.get('duration'),\n value=None\n )\n\n db.session.add(new_stream)\n db.session.commit()\n\n all_user_streams = Stream.query.filter_by(user_id=user.id).all()\n streams = []\n\n for stream in all_user_streams:\n artist = Artist.query.filter_by(id=int(stream.artist_id)).first()\n song = Song.query.filter_by(id=int(stream.song_id)).first()\n\n print(song)\n print(artist)\n \n new_stream = {\n 'id': stream.id,\n 'song': {\n 'name': song.name,\n 'popularity': song.popularity\n },\n 'artist':{\n 'name': artist.name,\n 'photo': artist.profile_image\n },\n 'played_at': stream.played_at,\n 'duration': stream.duration,\n 'value': stream.value\n }\n streams.append(new_stream)\n\n resp = make_response(jsonify(streams), 200)\n resp.headers['Access-Control-Allow-Origin'] = '*'\n\n return resp\n\n\n@app.route('/nodes/register/', methods=['POST'])\n@cross_origin()\ndef register_nodes():\n\n values = request.get_json()\n nodes = values.get('nodes')\n\n if nodes is None:\n return 'Error Please supply a valid list of nodes', 400\n\n for node in nodes:\n blockchain.register_node(node)\n\n response = {\n 'message': 'New nodes have been added',\n 'total_nodes': list(blockchain.nodes)\n }\n\n return jsonify(response), 201\n\n\n@app.route('/nodes/resolve', methods=['GET'])\ndef consensus():\n\n replaced = blockchain.resolve_conflicts()\n\n if replaced:\n response = {\n 'message': 'Our chain was replaced',\n 'new_chain': blockchain.chain\n }\n else:\n response = {\n 'message': 'Our chain is authoritative',\n 'chain': blockchain.chain\n }\n return jsonify(response), 200\n\n\nif __name__ == \"__main__\":\n app.run()\n"},{"alpha_fraction":0.6901408433914185,"alphanum_fraction":0.6901408433914185,"avg_line_length":16.75,"blob_id":"b122ce625385dc0b9a07d895c455e058a1ccbaf5","content_id":"2257b2850907e790b9a26c4f571ca5fc6fd9ea90","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":71,"license_type":"no_license","max_line_length":26,"num_lines":4,"path":"/main.py","repo_name":"Soundpruf/block_party-server","src_encoding":"UTF-8","text":"\nfrom app import app, db\n# from app.models import *\n\n# app.create_app()"},{"alpha_fraction":0.6278735399246216,"alphanum_fraction":0.6530172228813171,"avg_line_length":33.79999923706055,"blob_id":"7a832ba8ea77bd6d4b22d4c516ce0c5a5cb18e05","content_id":"5a0b8e087898d20e36ee26297ebbbbee22195c90","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1392,"license_type":"no_license","max_line_length":82,"num_lines":40,"path":"/migrations/versions/92ddede47972_.py","repo_name":"Soundpruf/block_party-server","src_encoding":"UTF-8","text":"\"\"\"empty message\n\nRevision ID: 92ddede47972\nRevises: 2cf7de52d9de\nCreate Date: 2018-01-13 18:51:31.047662\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '92ddede47972'\ndown_revision = '2cf7de52d9de'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('artists', sa.Column('spotify_id', sa.Integer(), nullable=True))\n op.add_column('songs', sa.Column('duration', sa.Integer(), nullable=True))\n op.add_column('songs', sa.Column('played_at', sa.DateTime(), nullable=True))\n op.add_column('songs', sa.Column('song_id', sa.Integer(), nullable=True))\n op.add_column('songs', sa.Column('user_id', sa.Integer(), nullable=True))\n op.create_foreign_key(None, 'songs', 'songs', ['song_id'], ['id'])\n op.create_foreign_key(None, 'songs', 'users', ['user_id'], ['id'])\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_constraint(None, 'songs', type_='foreignkey')\n op.drop_constraint(None, 'songs', type_='foreignkey')\n op.drop_column('songs', 'user_id')\n op.drop_column('songs', 'song_id')\n op.drop_column('songs', 'played_at')\n op.drop_column('songs', 'duration')\n op.drop_column('artists', 'spotify_id')\n # ### end Alembic commands ###\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6465422511100769,\n \"alphanum_fraction\": 0.6465422511100769,\n \"avg_line_length\": 34.03845977783203,\n \"blob_id\": \"e701383a8dd9272ccff82a081ddd89f7eba515ed\",\n \"content_id\": \"9eaea6bc14c9187f3e8287f58272ce6080c24109\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 911,\n \"license_type\": \"no_license\",\n \"max_line_length\": 106,\n \"num_lines\": 26,\n \"path\": \"/app/models/artist.py\",\n \"repo_name\": \"Soundpruf/block_party-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom app import db\\n\\n\\nclass Artist(db.Model):\\n __tablename__ = 'artists'\\n __table_args__ = {'extend_existing': True}\\n\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.Text)\\n date_joined = db.Column(db.DateTime, default=datetime.now())\\n profile_image = db.Column(db.Text, default=None)\\n email_address = db.Column(db.Text)\\n spotify_id = db.Column(db.Text, default=None)\\n wallet_address = db.Column(db.Text)\\n # genres = db.Column(db.Text, default=None)\\n\\n def __init__(self, id, name, email, password, date_joined, profile_image, wallet_address, spotify_id):\\n self.id = id\\n self.spotify_id = spotify_id\\n self.name = name\\n self.date_joined = date_joined\\n self.profile_image = profile_image\\n self.wallet_address = wallet_address\\n self.email_address = email\\n # self.genres = genres\\n\"\n },\n {\n \"alpha_fraction\": 0.49140894412994385,\n \"alphanum_fraction\": 0.69243985414505,\n \"avg_line_length\": 15.628571510314941,\n \"blob_id\": \"5d00ec2fd63fb28d3547edd3844cb201fe5fe538\",\n \"content_id\": \"c4ec6d2a475a54f29be9e48140b40d0b3db89096\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 582,\n \"license_type\": \"no_license\",\n \"max_line_length\": 24,\n \"num_lines\": 35,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"Soundpruf/block_party-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"alembic==0.9.6\\nastroid==1.6.0\\nautoenv==1.0.0\\ncertifi==2017.11.5\\nchardet==3.0.4\\nclick==6.7\\nf==0.0.1\\nFlask==0.12.2\\nFlask-Cors==3.0.3\\nFlask-Migrate==2.1.1\\nFlask-Script==2.0.6\\nFlask-SocketIO==2.9.3\\nFlask-SQLAlchemy==2.3.2\\ngunicorn==19.7.1\\nidna==2.6\\nisort==4.2.15\\nitsdangerous==0.24\\nJinja2==2.10\\nlazy-object-proxy==1.3.1\\nMako==1.0.7\\nMarkupSafe==1.0\\nmccabe==0.6.1\\npsycopg2==2.7.3.2\\npylint==1.8.1\\npython-dateutil==2.6.1\\npython-editor==1.0.3\\npython-engineio==2.0.1\\npython-socketio==1.8.4\\nrequests==2.18.4\\nsix==1.11.0\\nSQLAlchemy==1.2.0\\nurllib3==1.22\\nuuid==1.30\\nWerkzeug==0.13\\nwrapt==1.10.11\\n\"\n },\n {\n \"alpha_fraction\": 0.6508333086967468,\n \"alphanum_fraction\": 0.7149999737739563,\n \"avg_line_length\": 28.268293380737305,\n \"blob_id\": \"f0132d5b2f35abca620df83c776e240462bc5d1e\",\n \"content_id\": \"093425b2a5f7eac7722090a9534ee90943460241\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1200,\n \"license_type\": \"no_license\",\n \"max_line_length\": 183,\n \"num_lines\": 41,\n \"path\": \"/app/config.py\",\n \"repo_name\": \"Soundpruf/block_party-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nbasedir = os.path.abspath(os.path.dirname(__file__))\\n\\n\\nclass Config(object):\\n DEBUG = True\\n TESTING = False\\n CSRF_ENABLED = True\\n SECRET_KEY = 'this-really-needs-to-be-changed'\\n SQLALCHEMY_DATABASE_URI = os.environ.get('DATABASE_URL') or 'postgresql://localhost/block_party'\\n\\n\\nclass ProductionConfig(Config):\\n DEBUG = True\\n SQLALCHEMY_DATABASE_URI = 'postgres://vyifvrvzhxfqjr:6abb99931b0b754a6cef72d4e5c09218e8329d29fcb079527fa86739eff363c2@ec2-23-21-246-25.compute-1.amazonaws.com:5432/d7kdus8jfnjbhj'\\n\\n\\nclass StagingConfig(Config):\\n DEVELOPMENT = True\\n DEBUG = True\\n SQLALCHEMY_DATABASE_URI = 'postgres://thcnzodhdmfmci:f444914121c12078f50b98a0183dbfa38385d2f1165a520fb9dac8856b71b69e@ec2-107-21-95-70.compute-1.amazonaws.com:5432/d8otav6il3klb1'\\n\\n\\nclass DevelopmentConfig(Config):\\n DEVELOPMENT = True\\n # SQLALCHEMY_DATABASE_URI = 'postgresql://localhost/block_party'\\n DEBUG = True\\n\\n\\ndef apply_config(app):\\n\\n \\\"\\\"\\\" Apply configuration for application based on OS environ \\\"\\\"\\\"\\n \\n if os.environ.get('productionConfig', False):\\n config = ProductionConfig\\n elif os.environ.get('stagingConfig', False):\\n config = StagingConfig\\n else:\\n config = DevelopmentConfig\\n\\n app.config.from_object(config)\\n\"\n },\n {\n \"alpha_fraction\": 0.6296851634979248,\n \"alphanum_fraction\": 0.6806596517562866,\n \"avg_line_length\": 22.821428298950195,\n \"blob_id\": \"cb23733629aa3c64630743b1ed40de0ce585b424\",\n \"content_id\": \"e1e39c49526cb23ae6e82ac8353fa76cbe6fd313\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 667,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 28,\n \"path\": \"/migrations/versions/18f1d056ac5e_.py\",\n \"repo_name\": \"Soundpruf/block_party-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"empty message\\n\\nRevision ID: 18f1d056ac5e\\nRevises: f226666b8429\\nCreate Date: 2018-01-13 19:15:22.290823\\n\\n\\\"\\\"\\\"\\nfrom alembic import op\\nimport sqlalchemy as sa\\n\\n\\n# revision identifiers, used by Alembic.\\nrevision = '18f1d056ac5e'\\ndown_revision = 'f226666b8429'\\nbranch_labels = None\\ndepends_on = None\\n\\n\\ndef upgrade():\\n # ### commands auto generated by Alembic - please adjust! ###\\n op.add_column('songs', sa.Column('popularity', sa.Integer(), nullable=True))\\n # ### end Alembic commands ###\\n\\n\\ndef downgrade():\\n # ### commands auto generated by Alembic - please adjust! ###\\n op.drop_column('songs', 'popularity')\\n # ### end Alembic commands ###\\n\"\n },\n {\n \"alpha_fraction\": 0.6171954870223999,\n \"alphanum_fraction\": 0.6171954870223999,\n \"avg_line_length\": 32.68965530395508,\n \"blob_id\": \"ba99e93910de7bd4b4af76a21c8a4f8dd7291a12\",\n \"content_id\": \"9a50d56e40f56ddf3dec32229cf84340632b4538\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 977,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 29,\n \"path\": \"/app/models/album.py\",\n \"repo_name\": \"Soundpruf/block_party-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom app import db\\n\\nclass Album(db.Model):\\n __tablename__ = 'albums'\\n __table_args__ = {'extend_existing': True} \\n\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.Text)\\n artist_id = db.Column(db.Integer, db.ForeignKey(\\n 'artists.id'), primary_key=True)\\n created_at = db.Column(db.DateTime, default=datetime.now())\\n photo = db.Column(db.Text, default=None)\\n claps = db.Column(db.Integer, default=None)\\n shares = db.Column(db.Integer, default=None)\\n value = db.Column(db.Integer, default=None)\\n\\n def __init__(self, id, name, artist_id, created_at, photo, claps, shares, value):\\n self.id = id\\n self.name = name\\n self.artist_id = artist_id\\n self.created_at = created_at\\n self.photo = photo\\n self.claps = claps\\n self.shares = shares\\n self.value = value\\n\\n def __repr__(self):\\n return '<Album {}, {}>'.format(self.id, self.name)\\n\"\n },\n {\n \"alpha_fraction\": 0.7177242636680603,\n \"alphanum_fraction\": 0.7177242636680603,\n \"avg_line_length\": 17.31999969482422,\n \"blob_id\": \"af0c8476a4dc1baaf7a3098baf802f8cb1b1ad35\",\n \"content_id\": \"c5532a8d6d1f922f9d4396a24339388d9d997204\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 457,\n \"license_type\": \"no_license\",\n \"max_line_length\": 40,\n \"num_lines\": 25,\n \"path\": \"/app/__init__.py\",\n \"repo_name\": \"Soundpruf/block_party-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nfrom flask import Flask\\nfrom flask_sqlalchemy import SQLAlchemy\\nfrom flask_migrate import Migrate\\nfrom .config import Config\\n\\napp = Flask(__name__)\\napp.config.from_object(Config)\\ndb = SQLAlchemy(app)\\nmigrate = Migrate(app, db)\\n\\n\\ndef create_app(config_class=Config):\\n app = Flask(__name__)\\n app.config.from_object(config_class)\\n\\n db.init_app(app)\\n migrate.init_app(app, db)\\n\\n return app\\n\\n\\n\\nfrom .models import *\\nfrom .routes import *\"\n },\n {\n \"alpha_fraction\": 0.6019716858863831,\n \"alphanum_fraction\": 0.6019716858863831,\n \"avg_line_length\": 37.64285659790039,\n \"blob_id\": \"609e337527f24f9dbf57fea4fd52897b06220514\",\n \"content_id\": \"abf3d2b3a7cb6d9c41cdc914f365e122c43a50d6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1623,\n \"license_type\": \"no_license\",\n \"max_line_length\": 128,\n \"num_lines\": 42,\n \"path\": \"/app/models/user.py\",\n \"repo_name\": \"Soundpruf/block_party-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom app import db\\n# from .stream import Stream\\n\\n# user_stream_table = db.Table('user_streams', db.Model.metadata,\\n# db.Column('id', db.Integer, primary_key=True),\\n# db.Column('stream_id', db.Integer, db.ForeignKey('stream.id')),\\n# db.Column('user_id', db.Integer, db.ForeignKey('user.id'))\\n# )\\n\\n\\nclass User(db.Model):\\n __tablename__ = 'users'\\n __table_args__ = {'extend_existing': True} \\n\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.Text)\\n email = db.Column(db.Text)\\n password = db.Column(db.Text)\\n date_joined = db.Column(db.DateTime, default=datetime.now())\\n profile_image = db.Column(db.Text, default=None)\\n spotify_id = db.Column(db.Integer, default=None)\\n followers = db.Column(db.Integer, default=None)\\n platforms = db.Column(db.JSON)\\n wallet_address = db.Column(db.Text)\\n # streams = db.relationship('Stream', secondary='streams', backref='stream')\\n\\n\\n def __init__(self, id, name, email, password, date_joined, profile_image, spotify_id, platforms, wallet_address, followers):\\n self.id = id\\n self.name = name\\n self.email = email\\n self.password = password\\n self.date_joined = date_joined\\n self.profile_image = profile_image\\n self.spotify_id = spotify_id\\n self.platforms = platforms\\n self.followers = followers\\n self.wallet_address = wallet_address\\n \\n def __repr__(self):\\n return '<User {}, {}>'.format(self.id, self.name)\\n\"\n },\n {\n \"alpha_fraction\": 0.6310679316520691,\n \"alphanum_fraction\": 0.6310679316520691,\n \"avg_line_length\": 34.517242431640625,\n \"blob_id\": \"08bfa2c8a60d3a21e8b912df0ddd48a6f3f83fe7\",\n \"content_id\": \"38f88d182637757942b9f18c64fbac7efe5d0082\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1030,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 29,\n \"path\": \"/app/models/stream.py\",\n \"repo_name\": \"Soundpruf/block_party-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom app import db\\n\\n\\nclass Stream(db.Model):\\n __tablename__ = 'streams'\\n __table_args__ = {'extend_existing': True} \\n\\n id = db.Column(db.Integer, primary_key=True)\\n song_id = db.Column(db.Integer, db.ForeignKey('songs.id'))\\n artist_id = db.Column(db.Integer, db.ForeignKey('artists.id'))\\n played_at = db.Column(db.DateTime, default=None)\\n user_id = db.Column(db.Integer, db.ForeignKey('users.id'))\\n created_at = db.Column(db.DateTime, default=datetime.now())\\n duration = db.Column(db.Integer, default=None)\\n value = db.Column(db.Integer, default=None)\\n\\n def __init__(self, id, song_id ,user_id ,artist_id ,created_at ,played_at ,duration ,value):\\n self.id = id\\n self.song_id = song_id\\n self.user_id = user_id\\n self.artist_id = artist_id\\n self.created_at = created_at\\n self.played_at = played_at\\n self.duration = duration\\n self.value = value\\n\\n def __repr__(self):\\n return '<Stream {}>'.format(self.id)\\n\"\n },\n {\n \"alpha_fraction\": 0.5733333230018616,\n \"alphanum_fraction\": 0.5975757837295532,\n \"avg_line_length\": 23.264705657958984,\n \"blob_id\": \"b82154519fc62ce66d58a73b5f520eaa9c807540\",\n \"content_id\": \"ef4bf19f56441b572d3bd82080f0c8c7a92201a6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 825,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 34,\n \"path\": \"/migrations/versions/f226666b8429_.py\",\n \"repo_name\": \"Soundpruf/block_party-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"empty message\\n\\nRevision ID: f226666b8429\\nRevises: 228bba4edc6f\\nCreate Date: 2018-01-13 19:13:24.086418\\n\\n\\\"\\\"\\\"\\nfrom alembic import op\\nimport sqlalchemy as sa\\n\\n\\n# revision identifiers, used by Alembic.\\nrevision = 'f226666b8429'\\ndown_revision = '228bba4edc6f'\\nbranch_labels = None\\ndepends_on = None\\n\\n\\ndef upgrade():\\n # ### commands auto generated by Alembic - please adjust! ###\\n op.alter_column('artists', 'spotify_id',\\n existing_type=sa.INTEGER(),\\n type_=sa.Text(),\\n existing_nullable=True)\\n # ### end Alembic commands ###\\n\\n\\ndef downgrade():\\n # ### commands auto generated by Alembic - please adjust! ###\\n op.alter_column('artists', 'spotify_id',\\n existing_type=sa.Text(),\\n type_=sa.INTEGER(),\\n existing_nullable=True)\\n # ### end Alembic commands ###\\n\"\n },\n {\n \"alpha_fraction\": 0.6460055112838745,\n \"alphanum_fraction\": 0.6593204736709595,\n \"avg_line_length\": 40.09434127807617,\n \"blob_id\": \"1abd74f005696ca93b5ff874bd10c06d2420dfde\",\n \"content_id\": \"42685613b3edc50db4070d217f4739e8e4a1e50a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2178,\n \"license_type\": \"no_license\",\n \"max_line_length\": 110,\n \"num_lines\": 53,\n \"path\": \"/migrations/versions/07c44256b8d8_.py\",\n \"repo_name\": \"Soundpruf/block_party-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"empty message\\n\\nRevision ID: 07c44256b8d8\\nRevises: 18f1d056ac5e\\nCreate Date: 2018-01-13 19:26:08.892437\\n\\n\\\"\\\"\\\"\\nfrom alembic import op\\nimport sqlalchemy as sa\\nfrom sqlalchemy.dialects import postgresql\\n\\n# revision identifiers, used by Alembic.\\nrevision = '07c44256b8d8'\\ndown_revision = '18f1d056ac5e'\\nbranch_labels = None\\ndepends_on = None\\n\\n\\ndef upgrade():\\n # ### commands auto generated by Alembic - please adjust! ###\\n op.create_table('streams',\\n sa.Column('id', sa.Integer(), nullable=False),\\n sa.Column('song_id', sa.Integer(), nullable=True),\\n sa.Column('artist_id', sa.Integer(), nullable=True),\\n sa.Column('played_at', sa.DateTime(), nullable=True),\\n sa.Column('user_id', sa.Integer(), nullable=True),\\n sa.Column('created_at', sa.DateTime(), nullable=True),\\n sa.Column('duration', sa.Integer(), nullable=True),\\n sa.Column('value', sa.Integer(), nullable=True),\\n sa.ForeignKeyConstraint(['artist_id'], ['artists.id'], ),\\n sa.ForeignKeyConstraint(['song_id'], ['songs.id'], ),\\n sa.ForeignKeyConstraint(['user_id'], ['users.id'], ),\\n sa.PrimaryKeyConstraint('id')\\n )\\n op.drop_constraint('songs_song_id_fkey', 'songs', type_='foreignkey')\\n op.drop_constraint('songs_user_id_fkey', 'songs', type_='foreignkey')\\n op.drop_column('songs', 'played_at')\\n op.drop_column('songs', 'song_id')\\n op.drop_column('songs', 'duration')\\n op.drop_column('songs', 'user_id')\\n # ### end Alembic commands ###\\n\\n\\ndef downgrade():\\n # ### commands auto generated by Alembic - please adjust! ###\\n op.add_column('songs', sa.Column('user_id', sa.INTEGER(), autoincrement=False, nullable=True))\\n op.add_column('songs', sa.Column('duration', sa.INTEGER(), autoincrement=False, nullable=True))\\n op.add_column('songs', sa.Column('song_id', sa.INTEGER(), autoincrement=False, nullable=True))\\n op.add_column('songs', sa.Column('played_at', postgresql.TIMESTAMP(), autoincrement=False, nullable=True))\\n op.create_foreign_key('songs_user_id_fkey', 'songs', 'users', ['user_id'], ['id'])\\n op.create_foreign_key('songs_song_id_fkey', 'songs', 'songs', ['song_id'], ['id'])\\n op.drop_table('streams')\\n # ### end Alembic commands ###\\n\"\n },\n {\n \"alpha_fraction\": 0.6253776550292969,\n \"alphanum_fraction\": 0.6253776550292969,\n \"avg_line_length\": 34.783782958984375,\n \"blob_id\": \"1aec6607ef9bb4e43975e74d1ba62e694c8e43f5\",\n \"content_id\": \"4fd9e21f708ec0d7b5db2fc98e2a8dbe071e0ea2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1324,\n \"license_type\": \"no_license\",\n \"max_line_length\": 109,\n \"num_lines\": 37,\n \"path\": \"/app/models/song.py\",\n \"repo_name\": \"Soundpruf/block_party-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom app import db\\n\\n\\nclass Song(db.Model):\\n __tablename__ = 'songs'\\n __table_args__ = {'extend_existing': True} \\n\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.Text)\\n artist_id = db.Column(db.Integer, db.ForeignKey('artists.id'))\\n # album_id = db.Column(db.Integer, db.ForeignKey('albums.id'))\\n spotify_id = db.Column(db.Text, default=None)\\n created_at = db.Column(db.DateTime, default=datetime.now())\\n photo = db.Column(db.Text, default=None)\\n claps = db.Column(db.Integer, default=None)\\n shares = db.Column(db.Integer, default=None)\\n popularity = db.Column(db.Integer, default=None)\\n value = db.Column(db.Integer, default=None)\\n # genres = db.Column(db.Text, default=None)\\n\\n def __init__(self, id, name, artist_id, created_at, photo, claps, shares, value, spotify_id, popularity):\\n self.id = id\\n self.name = name\\n self.popularity = popularity\\n self.artist_id = artist_id\\n # self.album = album\\n self.spotify_id = spotify_id\\n self.created_at = created_at\\n self.photo = photo\\n self.claps = claps\\n self.shares = shares\\n self.value = value\\n # self.genres = genres\\n\\n def __repr__(self):\\n return '<Song {}, {}>'.format(self.id, self.name)\\n\"\n },\n {\n \"alpha_fraction\": 0.516879141330719,\n \"alphanum_fraction\": 0.5208884477615356,\n \"avg_line_length\": 27.735023498535156,\n \"blob_id\": \"3df0907b14f3860696d29f4dafd08a3a8c645e9c\",\n \"content_id\": \"2fe3557c9b3e704b1e61c7ec411654270a931a37\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 12471,\n \"license_type\": \"no_license\",\n \"max_line_length\": 123,\n \"num_lines\": 434,\n \"path\": \"/app/routes.py\",\n \"repo_name\": \"Soundpruf/block_party-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nfrom datetime import datetime\\nfrom json import JSONEncoder\\nfrom app import app, db\\nfrom flask import Flask, jsonify, request, json, redirect, render_template, send_from_directory, make_response, current_app\\nfrom flask_cors import CORS, cross_origin\\nfrom uuid import uuid4\\nfrom textwrap import dedent\\nfrom .models.blockchain import Blockchain\\nfrom .models.user import User\\nfrom .models.artist import Artist\\nfrom .models.album import Album\\nfrom .models.song import Song\\nfrom .models.stream import Stream\\n\\n\\nclass MyEncoder(JSONEncoder):\\n def default(self, o):\\n return o.__dict__\\n\\n\\nCORS(app, origins=['*', 'https://block-party-client.herokuapp.com'])\\n\\nnode_identifier = str(uuid4()).replace('-', '')\\nblockchain = Blockchain()\\n\\n\\n@app.route('/mine', methods=['POST'])\\n@cross_origin()\\ndef mine():\\n\\n data = request.get_json()\\n print(data)\\n\\n artist_id = data['artist_id']\\n user_id = data['user_id']\\n\\n print(artist_id)\\n print(user_id)\\n\\n artist = Artist.query.filter_by(id=int(artist_id)).first()\\n listener = User.query.filter_by(id=int(user_id)).first()\\n\\n print(artist)\\n print(listener)\\n print(listener.name + ' is listening to ' + artist.name +\\n '. 25% of this BlockNote attributed to ' + listener.name + ', 50% to ' + artist.name)\\n\\n last_block = blockchain.last_block\\n last_proof = last_block['proof']\\n proof = blockchain.proof_of_work(last_proof)\\n\\n # Receive a reward for finding this proof\\n blockchain.new_transaction(\\n sender=\\\"0\\\",\\n recipient=node_identifier,\\n amount=1\\n )\\n\\n # Forge new block by adding it to the chain\\n previous_hash = blockchain.hash(last_block)\\n block = blockchain.new_block(proof, previous_hash)\\n\\n response = {\\n 'message': 'New Block Forged',\\n 'index': block['index'],\\n 'transactions': block['transactions'],\\n 'proof': block['proof'],\\n 'previous_hash': block['previous_hash']\\n }\\n\\n return jsonify(response), 200\\n\\n\\n@app.route('/transactions/new', methods=['POST'])\\ndef new_transaction():\\n\\n values = request.get_json()\\n\\n # Check that the required fields are in the POST'ed data\\n required = ['sender', 'recipient', 'amount']\\n if not all(k in values for k in required):\\n return 'Missing values', 400\\n\\n # Create a new Transaction\\n index = blockchain.new_transaction(\\n values['sender'], values['recipient'], values['amount'])\\n\\n response = {'message': f'Transaction will be added to Block {index}'}\\n\\n return jsonify(response), 201\\n\\n\\n@app.route('/chain', methods=['GET'])\\ndef full_chain():\\n\\n response = {\\n 'chain': blockchain.chain,\\n 'length': len(blockchain.chain)\\n }\\n\\n return jsonify(response), 200\\n\\n\\n@app.route('/users/signup', methods=['POST'])\\ndef signup():\\n\\n data = request.get_json()\\n address = str(uuid4())\\n\\n if data['platform'] == True:\\n\\n incoming_user = data['platform_user']\\n print(incoming_user)\\n\\n user_name = incoming_user.get('user_name')\\n profile_photo = incoming_user.get('profile_photo')\\n email = incoming_user.get('email')\\n followers = incoming_user.get('followers')\\n platforms = incoming_user.get('platforms')\\n account_tier = incoming_user.get('account_tier')\\n accessToken = incoming_user.get('accessToken')\\n\\n new_user = User(id=None,\\n password=accessToken,\\n date_joined=None,\\n spotify_id=None,\\n name=user_name,\\n followers=followers,\\n email=email,\\n profile_image=profile_photo,\\n platforms=platforms,\\n wallet_address=address\\n )\\n else:\\n user_name = data['user_name']\\n profile_photo = data['profile_photo']\\n email = data['email']\\n platforms = data['platforms']\\n\\n print(user_name)\\n print(profile_photo)\\n print(email)\\n print(platforms)\\n print(address)\\n\\n new_user = User(id=None,\\n password=None,\\n date_joined=None,\\n spotify_id=None,\\n name=user_name,\\n email=email,\\n followers=None,\\n profile_image=profile_photo,\\n platforms=platforms,\\n wallet_address=address\\n )\\n\\n db.session.add(new_user)\\n db.session.commit()\\n print(new_user)\\n\\n resp_data = {\\n 'success': True,\\n 'new_user': {\\n 'user_name': new_user.name,\\n 'profile_photo': new_user.profile_image,\\n 'email': new_user.email,\\n 'followers': new_user.followers,\\n 'platforms': new_user.platforms,\\n 'accessToken': new_user.password,\\n \\\"id\\\": new_user.id\\n }\\n }\\n\\n resp = make_response(jsonify(resp_data), 200)\\n resp.headers['Access-Control-Allow-Origin'] = '*'\\n\\n return resp\\n\\n\\n@app.route('/artists/signup', methods=['POST'])\\n# @cross_origin()\\ndef artist_signup():\\n\\n data = request.get_json()\\n print(data)\\n\\n artist_name = data['artist_name']\\n password = data['password']\\n email = data['email']\\n address = str(uuid4())\\n\\n new_artist = Artist(id=None,\\n password=password,\\n date_joined=None,\\n name=artist_name,\\n spotify_id=None,\\n email=email,\\n profile_image=None,\\n wallet_address=address\\n )\\n\\n db.session.add(new_artist)\\n db.session.commit()\\n\\n resp_data = {\\n 'artist': {\\n 'artist_name': artist_name,\\n 'email': email,\\n 'wallet_address': address,\\n 'id': new_artist.id\\n }\\n }\\n\\n resp = make_response(jsonify(resp_data), 200)\\n resp.headers['Access-Control-Allow-Origin'] = '*'\\n\\n return resp\\n\\n\\n@app.route('/login', methods=['POST'])\\ndef login():\\n req = request.get_json()\\n data = req['data']\\n\\n print(data)\\n\\n if data.get('isArtist'):\\n artist = Artist.query.filter_by(email_address=data['email']).first()\\n print(artist)\\n\\n if artist:\\n print(artist)\\n resp_data = {\\n 'artist': {\\n 'artist_name': artist.name,\\n 'email': artist.email_address,\\n 'wallet_address': artist.wallet_address,\\n 'id': artist.id\\n }\\n }\\n\\n resp = make_response(jsonify(resp_data), 200)\\n resp.headers['Access-Control-Allow-Origin'] = '*'\\n\\n return resp\\n elif data.get('spotify_login'):\\n email = data['platform_user']['email']\\n print(email)\\n user = User.query.filter_by(email=email).first()\\n\\n if user:\\n resp_data = {\\n 'user': {\\n 'user_name': user.name,\\n 'email': user.email,\\n 'password': user.password,\\n 'wallet_address': user.wallet_address,\\n 'id': user.id\\n }\\n }\\n\\n resp = make_response(jsonify(resp_data), 200)\\n resp.headers['Access-Control-Allow-Origin'] = '*'\\n\\n return resp\\n\\n\\n@app.route('/artists/<string:artist_id>/onboard', methods=['GET', 'POST'])\\n@cross_origin()\\ndef artist_onboard(artist_id):\\n if request.method == \\\"GET\\\":\\n print(artist_id)\\n print(int(artist_id))\\n new_artist = Artist.query.filter_by(id=int(artist_id)).first()\\n\\n if new_artist:\\n name = new_artist.name\\n wallet_address = new_artist.wallet_address\\n\\n resp = {\\n 'name': name,\\n 'wallet_address': wallet_address\\n }\\n\\n return jsonify(resp), 200\\n else:\\n return 'Error'\\n\\n # elif request.method == 'POST':\\n\\n\\n@app.route('/users/<string:user_id>/stream/add-artists', methods=['POST'])\\ndef add_artists_stream(user_id):\\n\\n req = request.get_json()\\n stream_data = req['data']\\n recent_tracks = None\\n if 'recent_tracks' in stream_data:\\n recent_tracks = stream_data['recent_tracks']\\n\\n print(recent_tracks)\\n user = User.query.filter_by(id=int(user_id)).first()\\n\\n for stream in recent_tracks:\\n streamed_artist = stream['artist']\\n streamed_song = stream\\n existing_song = Song.query.filter_by(\\n name=streamed_song.get('name')).first()\\n existing_artist = Artist.query.filter_by(\\n name=streamed_artist.get('name')).first()\\n\\n song = existing_song\\n artist = existing_artist\\n\\n if not existing_artist:\\n address = str(uuid4())\\n new_artist = Artist(id=None,\\n password=None,\\n date_joined=None,\\n name=streamed_artist.get('name'),\\n spotify_id=streamed_artist.get('spotify_id'),\\n email=None,\\n profile_image=streamed_artist.get(\\n 'photo').get('url'),\\n wallet_address=address\\n )\\n db.session.add(new_artist)\\n db.session.commit()\\n artist = new_artist\\n\\n if not existing_song:\\n new_song = Song(id=None,\\n name=streamed_song.get('name'),\\n artist_id=artist.id,\\n popularity=streamed_song.get('popularity'),\\n spotify_id=streamed_song.get('spotify_id'),\\n created_at=None,\\n photo=None,\\n claps=None,\\n shares=None,\\n value=None\\n )\\n db.session.add(new_song)\\n db.session.commit()\\n song = new_song\\n\\n new_stream = Stream(id=None,\\n song_id=song.id,\\n user_id=user.id,\\n artist_id=artist.id,\\n created_at=None,\\n played_at=streamed_song.get('played_at'),\\n duration=streamed_song.get('duration'),\\n value=None\\n )\\n\\n db.session.add(new_stream)\\n db.session.commit()\\n\\n all_user_streams = Stream.query.filter_by(user_id=user.id).all()\\n streams = []\\n\\n for stream in all_user_streams:\\n artist = Artist.query.filter_by(id=int(stream.artist_id)).first()\\n song = Song.query.filter_by(id=int(stream.song_id)).first()\\n\\n print(song)\\n print(artist)\\n \\n new_stream = {\\n 'id': stream.id,\\n 'song': {\\n 'name': song.name,\\n 'popularity': song.popularity\\n },\\n 'artist':{\\n 'name': artist.name,\\n 'photo': artist.profile_image\\n },\\n 'played_at': stream.played_at,\\n 'duration': stream.duration,\\n 'value': stream.value\\n }\\n streams.append(new_stream)\\n\\n resp = make_response(jsonify(streams), 200)\\n resp.headers['Access-Control-Allow-Origin'] = '*'\\n\\n return resp\\n\\n\\n@app.route('/nodes/register/', methods=['POST'])\\n@cross_origin()\\ndef register_nodes():\\n\\n values = request.get_json()\\n nodes = values.get('nodes')\\n\\n if nodes is None:\\n return 'Error Please supply a valid list of nodes', 400\\n\\n for node in nodes:\\n blockchain.register_node(node)\\n\\n response = {\\n 'message': 'New nodes have been added',\\n 'total_nodes': list(blockchain.nodes)\\n }\\n\\n return jsonify(response), 201\\n\\n\\n@app.route('/nodes/resolve', methods=['GET'])\\ndef consensus():\\n\\n replaced = blockchain.resolve_conflicts()\\n\\n if replaced:\\n response = {\\n 'message': 'Our chain was replaced',\\n 'new_chain': blockchain.chain\\n }\\n else:\\n response = {\\n 'message': 'Our chain is authoritative',\\n 'chain': blockchain.chain\\n }\\n return jsonify(response), 200\\n\\n\\nif __name__ == \\\"__main__\\\":\\n app.run()\\n\"\n },\n {\n \"alpha_fraction\": 0.6901408433914185,\n \"alphanum_fraction\": 0.6901408433914185,\n \"avg_line_length\": 16.75,\n \"blob_id\": \"b122ce625385dc0b9a07d895c455e058a1ccbaf5\",\n \"content_id\": \"2257b2850907e790b9a26c4f571ca5fc6fd9ea90\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 71,\n \"license_type\": \"no_license\",\n \"max_line_length\": 26,\n \"num_lines\": 4,\n \"path\": \"/main.py\",\n \"repo_name\": \"Soundpruf/block_party-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nfrom app import app, db\\n# from app.models import *\\n\\n# app.create_app()\"\n },\n {\n \"alpha_fraction\": 0.6278735399246216,\n \"alphanum_fraction\": 0.6530172228813171,\n \"avg_line_length\": 33.79999923706055,\n \"blob_id\": \"7a832ba8ea77bd6d4b22d4c516ce0c5a5cb18e05\",\n \"content_id\": \"5a0b8e087898d20e36ee26297ebbbbee22195c90\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1392,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 40,\n \"path\": \"/migrations/versions/92ddede47972_.py\",\n \"repo_name\": \"Soundpruf/block_party-server\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"empty message\\n\\nRevision ID: 92ddede47972\\nRevises: 2cf7de52d9de\\nCreate Date: 2018-01-13 18:51:31.047662\\n\\n\\\"\\\"\\\"\\nfrom alembic import op\\nimport sqlalchemy as sa\\n\\n\\n# revision identifiers, used by Alembic.\\nrevision = '92ddede47972'\\ndown_revision = '2cf7de52d9de'\\nbranch_labels = None\\ndepends_on = None\\n\\n\\ndef upgrade():\\n # ### commands auto generated by Alembic - please adjust! ###\\n op.add_column('artists', sa.Column('spotify_id', sa.Integer(), nullable=True))\\n op.add_column('songs', sa.Column('duration', sa.Integer(), nullable=True))\\n op.add_column('songs', sa.Column('played_at', sa.DateTime(), nullable=True))\\n op.add_column('songs', sa.Column('song_id', sa.Integer(), nullable=True))\\n op.add_column('songs', sa.Column('user_id', sa.Integer(), nullable=True))\\n op.create_foreign_key(None, 'songs', 'songs', ['song_id'], ['id'])\\n op.create_foreign_key(None, 'songs', 'users', ['user_id'], ['id'])\\n # ### end Alembic commands ###\\n\\n\\ndef downgrade():\\n # ### commands auto generated by Alembic - please adjust! ###\\n op.drop_constraint(None, 'songs', type_='foreignkey')\\n op.drop_constraint(None, 'songs', type_='foreignkey')\\n op.drop_column('songs', 'user_id')\\n op.drop_column('songs', 'song_id')\\n op.drop_column('songs', 'played_at')\\n op.drop_column('songs', 'duration')\\n op.drop_column('artists', 'spotify_id')\\n # ### end Alembic commands ###\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":14,"string":"14"}}},{"rowIdx":2983087,"cells":{"repo_name":{"kind":"string","value":"vcar/vcar"},"repo_url":{"kind":"string","value":"https://github.com/vcar/vcar"},"snapshot_id":{"kind":"string","value":"3635bab9032baab1616c7b722b2640cb087bc0d5"},"revision_id":{"kind":"string","value":"cfdb105f7cc290a60d9f3ddc21608bf6c351811e"},"directory_id":{"kind":"string","value":"00c379007e67c5b2e19891e6d80177df5fe651b5"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-02-02T17:54:31.007578","string":"2022-02-02T17:54:31.007578"},"revision_date":{"kind":"timestamp","value":"2018-01-03T11:40:00","string":"2018-01-03T11:40:00"},"committer_date":{"kind":"timestamp","value":"2018-01-03T11:40:00","string":"2018-01-03T11:40:00"},"github_id":{"kind":"number","value":90060759,"string":"90,060,759"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6157371997833252,"alphanum_fraction":0.6294881701469421,"avg_line_length":35.36111068725586,"blob_id":"461dc76e0988e646166619a62d0b4de18c322026","content_id":"1556a006b8f01a46d4cd784b64b3f98bcd3be476","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1309,"license_type":"no_license","max_line_length":135,"num_lines":36,"path":"/app/carboard/models/article.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom ...extensions import db\n\n# -------------------------------- Brand Model ------------------------------ #\n\n\nclass Article(db.Model):\n \"\"\" Article Model \"\"\"\n __tablename__ = 'articles'\n\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(255))\n authors = db.Column(db.String(255))\n abstract = db.Column(db.String(255), nullable=True)\n publication_date = db.Column(db.DateTime(), nullable=True)\n keywords = db.Column(db.String(255), nullable=True)\n reference = db.Column(db.String(255), nullable=True)\n link = db.Column(db.String(255), nullable=True)\n\n dataset_id = db.Column(db.Integer, db.ForeignKey('datasets.id'), nullable=True)\n dataset = db.relationship('Dataset', back_populates=\"article\")\n\n created = db.Column(db.DateTime(), default=datetime.utcnow())\n\n def __init__(self, name, authors, dataset_id=None, abstract=None, publication_date=None, keywords=None, reference=None, link=None):\n self.dataset_id = dataset_id\n self.name = name\n self.authors = authors\n self.abstract = abstract\n self.publication_date = publication_date\n self.keywords = keywords\n self.reference = reference\n self.link = link\n\n def __repr__(self):\n return self.name\n"},{"alpha_fraction":0.5966640114784241,"alphanum_fraction":0.6004765629768372,"avg_line_length":33.21195602416992,"blob_id":"d0c829b5c5ffc58e4b9690eefa3a841611799dab","content_id":"a853a6ee4b85c1ce13ee2291612327d91b403edf","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6295,"license_type":"no_license","max_line_length":113,"num_lines":184,"path":"/app/carboard/views/dataset.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import json\nfrom os.path import basename\nfrom importlib import import_module\nfrom threading import Thread\nimport traceback\nfrom flask import (\n render_template, request, flash, redirect, url_for\n)\nfrom flask_login import login_required, current_user\n\nfrom . import carboard\nfrom ..models.dataset import Dataset\nfrom ..models.file import File\nfrom ..forms.dataset import DatasetForm, FeedDatasetForm\nfrom ..helpers import upload_dsfile\nfrom ..constants import PER_PAGE\nfrom ...extensions import db\n\n# -------------------- /carboard/dataset/ : List of datasets ---------------- #\n\n\n@carboard.route('/dataset/')\n@login_required\ndef indexDataset():\n datasets = Dataset.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/dataset/index.html', datasets=datasets)\n\n# -------------------- /carboard/dataset/id : Show dataset ------------------ #\n\n\n@carboard.route('/dataset/<int:id>', methods=['GET'])\n@login_required\ndef showDataset(id):\n dataset = Dataset.query.get_or_404(id)\n return render_template('carboard/dataset/show.html', dataset=dataset)\n\n# -------------------- /carboard/dataset/new : Add dataset ------------------ #\n\n\n@carboard.route('/dataset/new', methods=['GET', 'POST'])\n@login_required\ndef newDataset():\n \"\"\" Add new dataset \"\"\"\n\n form = DatasetForm()\n\n if form.validate_on_submit():\n dataset = Dataset(\n name=form.name.data,\n slug=form.slug.data,\n description=form.description.data,\n author=form.author.data,\n lab=form.lab.data,\n website=form.website.data,\n )\n db.session.add(dataset)\n db.session.commit()\n flash('Dataset {}, added successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.indexDataset'))\n\n return render_template('carboard/dataset/new.html', form=form)\n\n# -------------------- /carboard/dataset/id/edit : Edit dataset ------------- #\n\n\n@carboard.route('/dataset/feed/<int:id>', methods=['GET', 'POST'])\n@login_required\ndef feedDataset(id):\n \"\"\" Fill dataset with data that may come from diff sources \"\"\"\n\n dataset = Dataset.query.get_or_404(id)\n\n form = FeedDatasetForm()\n\n if form.validate_on_submit():\n files = request.form.getlist('files')\n try:\n # Dataset slug a.k.a module name\n slug = dataset.slug\n # Import the module\n # mod = import_module(\". elastic\", 'app.importer.datasets.' + slug)\n mod = import_module('app.importer.datasets.' + slug + '.elastic')\n # Indexing function\n index_function = 'index_bulk' # 'index'\n # get a reference to the init function\n init = getattr(mod, index_function)\n # create a threaded job to index uploaded data according to it's dataset\n thread = Thread(target=init, args=[files, dataset, current_user.id])\n thread.daemon = True\n thread.start()\n # thread.join(1)\n # print(active_count())\n flash('Files added to Dataset \"{}\" are being indexed in background.'.format(dataset.name), 'success')\n except ImportError:\n flash('No dataset indexer is provided!', 'error')\n except Exception:\n flash('Something went wrong while trying to indexed dataset files!', 'error')\n print('\\n\\n\\n')\n traceback.print_exc()\n print('\\n\\n\\n')\n # raise\n # raise\n return redirect(url_for('carboard.indexDataset'))\n else:\n for field, errors in form.errors.items():\n for error in errors:\n flash(u\"Error in the %s field - %s\" % (\n getattr(form, field).label.text,\n error\n ), 'error')\n\n return render_template(\n 'carboard/dataset/feed.html',\n form=form,\n dataset=dataset\n )\n\n# -------------------- /carboard/dataset/id/edit : Edit dataset ------------- #\n\n\n@carboard.route('/dataset/getfile/<int:id>', methods=['POST'])\n@login_required\ndef getFileDataset(id):\n \"\"\" Handle multiple dataset file uploads \"\"\"\n\n dataset = Dataset.query.get_or_404(id)\n if request.method == 'POST':\n trace = upload_dsfile('file', dataset.slug)\n if trace['status'] is True:\n file = File(\n user_id=current_user.id,\n filename=basename(trace['message']),\n path=trace['message']\n )\n db.session.add(file)\n db.session.commit()\n return json.dumps(trace)\n\n# -------------------- /carboard/dataset/id/edit : Edit dataset ------------- #\n\n\n@carboard.route('/dataset/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editDataset(id):\n \"\"\" Edit existing dataset \"\"\"\n dataset = Dataset.query.get_or_404(id)\n form = DatasetForm(obj=dataset)\n if form.validate_on_submit():\n form.populate_obj(dataset)\n db.session.commit()\n flash('Dataset {}, updated successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.showDataset', id=id))\n\n return render_template('carboard/dataset/edit.html', form=form, id=id)\n\n# -------------------- /carboard/dataset/id/delete : Delete dataset --------- #\n\n\n@carboard.route('/dataset/<int:id>/toggle', methods=['GET'])\n@login_required\ndef toggleDataset(id):\n dataset = Dataset.query.get_or_404(id)\n # getattr(dataset, 'status', 0)\n status = dataset.status if dataset.status is not None else 0\n dataset.status = 1 - status\n db.session.commit()\n msg = 'activated' if dataset.status is 1 else 'deactivated'\n flash('Dataset {}, {} successfully.'.format(dataset.name, msg), 'success')\n return redirect(url_for('carboard.indexDataset'))\n\n# -------------------- /carboard/dataset/id/delete : Delete dataset --------- #\n\n\n@carboard.route('/dataset/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteDataset(id):\n dataset = Dataset.query.get_or_404(id)\n db.session.delete(dataset)\n db.session.commit()\n flash('Dataset {}, deleted successfully.'.format(dataset.name), 'success')\n return redirect(url_for('carboard.indexDataset'))\n"},{"alpha_fraction":0.8387096524238586,"alphanum_fraction":0.8387096524238586,"avg_line_length":30,"blob_id":"ebe86a3bb58a41314c90239dea85344ebf82cbf4","content_id":"16f29bc21be6a34f76dbbf694186c1a3f5aa326f","detected_licenses":["MIT","BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":31,"license_type":"permissive","max_line_length":30,"num_lines":1,"path":"/app/plugins/driver_graph/src/__init__.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from .views import driverGraph\n"},{"alpha_fraction":0.41207748651504517,"alphanum_fraction":0.4204329550266266,"avg_line_length":28.255556106567383,"blob_id":"0e645f9c58623de3f243be025c23c112d3b4e6a8","content_id":"d76c2bc9d4a72449a45509bfb1a1bc5946e5d91c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"HTML","length_bytes":2633,"license_type":"no_license","max_line_length":113,"num_lines":90,"path":"/templates/carboard/signal/bulk.html","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"{% extends \"layout.html\" %}\n\n{% from \"carboard/macros.html\" import render_field, box_header %}\n\n{% block title %}\n Bulk Add Signals\n{% endblock %}\n{% block styles %}\n {{ super() }}\n <style>\n .box-footer {\n background-color: #2A2A2A;\n border-color: #2A2A2A;\n }\n\n .box-body {\n padding: 40px 50px;\n }\n\n .drop-file {\n width: 100%;\n height: 200px;\n }\n\n </style>\n{% endblock %}\n{% block content_header %}\n <section class=\"content-header\">\n <h1>\n Add Signals\n </h1>\n <ol class=\"breadcrumb\">\n <li><a href=\"{{ url_for('carboard.index') }}\"><i class=\"fa fa-dashboard\"></i>Home</a></li>\n <li><a href=\"{{ url_for('carboard.indexSignal') }}\">Signals</a></li>\n <li class=\"active\">Bulk Add Signals</li>\n </ol>\n </section>\n{% endblock content_header %}\n\n{% block content %}\n {% if errors %}\n <div class=\"row\">\n <div class=\"col-md-12\">\n <div class=\"well-error\">\n <ul>\n {% for error in errors %}\n <li>{{ error }}</li>\n {% endfor %}\n </ul>\n </div>\n </div>\n </div>\n {% endif %}\n <div class=\"row\">\n <div class=\"col-md-12\">\n <div class=\"box box-solid vcar-box\">\n\n {{ box_header('Bulk Add Signals') }}\n\n <div class=\"box-body\">\n <form action=\"{{ url_for('carboard.bulkAdd') }}\" method=\"POST\" enctype=\"multipart/form-data\">\n <div class=\"drop-file\">\n <span id=\"file-info\" class=\"animated-bounce\">Drop Your file Here</span>\n {% for field in form %}\n {{ render_field(field) }}\n {% endfor %}\n </div>\n </form>\n </div>\n </div>\n <div class=\"box-footer form-footer\">\n <button type=\"submit\" id=\"submit\" class=\"btn btn-success\">Submit</button>\n <a href=\"{{ url_for('carboard.indexSignal') }}\" class=\"btn btn-default pull-right\">Back to\n list\n </a>\n </div>\n </div>\n </div>\n{% endblock %}\n\n{% block scripts %}\n {{ super() }}\n <script type=\"text/javascript\">\n $('#submit').click(function (e) {\n if (fileInput.value) {\n $('form').submit();\n }\n })\n </script>\n{% endblock %}\n"},{"alpha_fraction":0.7157894968986511,"alphanum_fraction":0.7178947329521179,"avg_line_length":42.181819915771484,"blob_id":"8f23986eeb88a61a52b9c4013b0c68c70af783fa","content_id":"10ad93bfdf9957adb1eb7a694d60879e7b8bd79a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Dockerfile","length_bytes":475,"license_type":"no_license","max_line_length":92,"num_lines":11,"path":"/tests/Dockerfile","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"FROM alpine\nLABEL maintainer=\"boubouh.karim@gmail.com\"\nRUN apk update && apk add nodejs \nRUN mkdir average\nADD average.js average/\nWORKDIR average\n# ADD # copies new files, directories or remote file to container.\n# CMD [ \"node\", \"average.js\" ] # You can overide the cmd (bin/sh -c) \n# CMD ping google.com\nENTRYPOINT [ \"node\", \"average.js\" ] # You can't (no shell && pid 1 && passed are arguments)\n# ONBUILD # adds a trigger instruction when the image is used as the base for another build.\n"},{"alpha_fraction":0.6984572410583496,"alphanum_fraction":0.6984572410583496,"avg_line_length":26.960784912109375,"blob_id":"83432121e38a78f16e7c532f4a7f6c827f4d1dfa","content_id":"cec2b807b8df75982a1228b612c0e6d35dbdec0d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1426,"license_type":"no_license","max_line_length":81,"num_lines":51,"path":"/app/carboard/views/__init__.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import Blueprint, render_template\nfrom flask_login import login_required, current_user\nfrom flask import g\n\nfrom ..models.record import Record\nfrom ..models.vehicle import Vehicle\nfrom ..models.driver import Driver\nfrom ..constants import PER_HOME_PAGE\n\ncarboard = Blueprint('carboard', __name__, url_prefix='/carboard')\n\nfrom .article import *\nfrom .brand import *\nfrom .country import *\nfrom .dataset import *\nfrom .driver import *\nfrom .drivetype import *\nfrom .extrasignal import *\nfrom .model import *\nfrom .platform import *\nfrom .record import *\nfrom .signal import *\nfrom .signalclass import *\nfrom .signalsource import *\nfrom .status import *\nfrom .user import *\nfrom .vehicle import *\nfrom .plugin import *\n# --------------------- /carboard/index : User home page -------------------- #\n\n\n@carboard.route('/')\n@login_required\ndef index():\n records = Record.query.order_by(Record.created).limit(PER_HOME_PAGE).all()\n vehicles = Vehicle.query.order_by(Vehicle.created).limit(PER_HOME_PAGE).all()\n drivers = Driver.query.order_by(Driver.created).limit(PER_HOME_PAGE).all()\n return render_template(\n 'carboard/index.html',\n user=current_user,\n records=records,\n vehicles=vehicles,\n drivers=drivers,\n )\n\n\n@carboard.route('/configuration')\n@login_required\ndef configuration():\n # may be some logic !\n return render_template('elements/configuration.html')\n"},{"alpha_fraction":0.388141393661499,"alphanum_fraction":0.39042189717292786,"avg_line_length":44.67708206176758,"blob_id":"14c6bc6d8e8f2cb93573410134c4b4a96bcf0228","content_id":"e423188ab373213fe1ccd771e228079c35a44e2d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"HTML","length_bytes":4385,"license_type":"no_license","max_line_length":201,"num_lines":96,"path":"/templates/carboard/user/index.html","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"{% extends \"layout.html\" %}\n\n{% from \"carboard/macros.html\" import render_pagination %}\n\n{% block title %}\n List of users | {{ super() }}\n{% endblock %}\n\n{% block content_header %}\n\t<section class=\"content-header\">\n\t\t<h1>\n\t\tList of users\n\t\t</h1>\n\t\t<ol class=\"breadcrumb\">\n\t\t\t<li><a href=\"{{ url_for('carboard.index') }}\"><i class=\"fa fa-dashboard\"></i>Home</a></li>\n\t\t\t<li><a href=\"{{ url_for('carboard.indexUser') }}\">User</a></li>\n\t\t\t<li class=\"active\">List of users</li>\n\t\t</ol>\n\t</section>\n{% endblock content_header %}\n\n{% block content %}\n\n <div class=\"row\">\n <div class=\"col-md-12\">\n <div class=\"box\">\n <div class=\"box-header\">\n <div class=\"box-actions\">\n <a href=\"{{ url_for('carboard.newUser') }}\" class=\"btn btn-primary\">\n Add new user\n </a>\n </div>\n <div class=\"box-tools\">\n <div class=\"input-group input-group-sm\" style=\"width: 150px;\">\n <input type=\"text\" name=\"table_search\" class=\"form-control pull-right\" placeholder=\"Search\">\n\n <div class=\"input-group-btn\">\n <button type=\"submit\" class=\"btn btn-default\">\n <i class=\"fa fa-search\"></i>\n </button>\n </div>\n </div>\n </div>\n </div>\n <div class=\"box-body no-padding\">\n <table class=\"table table-hover\">\n <tr>\n <th>ID</th>\n <th>Avatar</th>\n <th>Username</th>\n <th>Email</th>\n <th>Role</th>\n <th>Status</th>\n <th>Actions</th>\n </tr>\n\n {% for user in users.items %}\n <tr>\n <td><a href=\"{{ url_for('carboard.showUser', id=user.id) }}\">{{ user.id }}</a></td>\n <td><a href=\"{{ url_for('carboard.showUser', id=user.id) }}\">\n {% if user.avatar %}\n <img class=\"avatar table-avatar\" src=\"{{ resized_img_src(config.UPLOAD_USER + user.avatar, width=60) }}\" alt=\"{{ user.username }}\">\n {% else %}\n <img class=\"avatar table-avatar\" src=\"/static{{ config.UPLOAD_USER}}default.png\">\n {% endif %}\n </a></td>\n <td>{{ user.username }}</td>\n <td>{{ user.email }}</td>\n <td>{{ user.role }}</td>\n <td>\n {% if user.status == 1 %}\n <span class=\"label bg-green\">Activated</span>\n {% else %}\n <span class=\"label bg-red\">Disabled</span>\n {% endif %}\n </td>\n <td>\n <a href=\"{{ url_for('carboard.showUser', id=user.id) }}\" class=\"btn btn-default btn-sm\" data-toggle=\"tooltip\" title=\"Show\"><i class=\"fa fa-eye\"></i></a>\n\n <a href=\"{{ url_for('carboard.toggleUser', id=user.id) }}\" class=\"btn btn-warning btn-sm\" data-toggle=\"tooltip\" title=\"Toggle status\"><i class=\"fa fa-toggle-on\"></i></a>\n\n <a href=\"{{ url_for('carboard.editUser', id=user.id) }}\" class=\"btn btn-success btn-sm\" data-toggle=\"tooltip\" title=\"Edit\"><i class=\"fa fa-pencil\"></i></a>\n\n <a href=\"{{ url_for('carboard.deleteUser', id=user.id) }}\" class=\"btn btn-danger btn-sm\" data-toggle=\"tooltip\" title=\"Remove\"><i class=\"fa fa-trash\"></i></a>\n </td>\n </tr>\n {% endfor %}\n </table>\n </div>\n <div class=\"box-footer clearfix\">\n {{render_pagination(users)}}\n </div>\n </div>\n </div>\n </div>\n{% endblock %}\n"},{"alpha_fraction":0.4556135833263397,"alphanum_fraction":0.4595300257205963,"avg_line_length":21.52941131591797,"blob_id":"d036480697f30c5133b49bece98ff0351b07e318","content_id":"4574c26fcce7df4206f90cd7e8a26058c4ddfbd5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":766,"license_type":"no_license","max_line_length":79,"num_lines":34,"path":"/app/explorer/forms.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import Form\nfrom wtforms import StringField, SelectField\nfrom wtforms.validators import DataRequired, Optional, Length\n\n\n# ------------------------ custom validation methods ------------------------ #\n\n\n# ---------------------------- Chart form class ----------------------------- #\n\nclass ChartForm(Form):\n \"\"\" Chart add/edit Form \"\"\"\n\n name = StringField(\n 'Name',\n validators=[\n DataRequired(),\n Length(min=3, max=50),\n ]\n )\n driver_id = SelectField(\n 'Driver',\n coerce=int, # str\n validators=[\n Optional(),\n ]\n )\n vehicle_id = SelectField(\n 'Driver',\n coerce=int, # str\n validators=[\n Optional(),\n ]\n )\n"},{"alpha_fraction":0.4919908344745636,"alphanum_fraction":0.4988558292388916,"avg_line_length":22,"blob_id":"996c6444071241be227f2e1263a6a6af26136277","content_id":"4612600d61e3f3ad77d93159a6edb590859cc785","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":437,"license_type":"no_license","max_line_length":81,"num_lines":19,"path":"/app/carboard/models/drivetype.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from ...extensions import db\n\n# -------------------------------- Status Model ------------------------------- #\n\n\nclass DriveType(db.Model):\n \"\"\" DriveType Model:\n The type of driving records.\n \"\"\"\n __tablename__ = 'drivetypes'\n\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(255))\n\n def __init__(self, name):\n self.name = name\n\n def __repr__(self):\n return self.name\n"},{"alpha_fraction":0.8064516186714172,"alphanum_fraction":0.8064516186714172,"avg_line_length":30,"blob_id":"9d11738f2fbcd05618486030fbac0af072b02910","content_id":"a6ac6c26ee7da60b0f54f4effd4d3ac285d9d672","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":31,"license_type":"no_license","max_line_length":30,"num_lines":1,"path":"/app/importer/platforms/__init__.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from .openxc.views import help\n"},{"alpha_fraction":0.5874316692352295,"alphanum_fraction":0.5997267961502075,"avg_line_length":28.280000686645508,"blob_id":"bb1cd81e8a882868e1b1e78ae671a1263804b451","content_id":"4d21ee47ec9f468b41b0e295224641bdd5cf5386","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":732,"license_type":"no_license","max_line_length":79,"num_lines":25,"path":"/app/carboard/models/signalsource.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom ...extensions import db\n\n# -------------------------------- Signalsource Model ----------------------- #\n\n\nclass Signalsource(db.Model):\n \"\"\" Signalsource Model: \"\"\"\n __tablename__ = 'signalsources'\n\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(255))\n description = db.Column(db.String(1000))\n signals = db.relationship(\"Signal\")\n\n status = db.Column(db.SmallInteger, default=1)\n created = db.Column(db.DateTime(), default=datetime.utcnow())\n\n def __init__(self, name, description, status=1):\n self.name = name\n self.status = status\n self.description = description\n\n def __repr__(self):\n return str(self.name)\n"},{"alpha_fraction":0.557232677936554,"alphanum_fraction":0.5735849142074585,"avg_line_length":28.44444465637207,"blob_id":"d593037cd43f09719f1ba9e2ae801617ce6eed15","content_id":"cb4574865351f96398427f85a7b0c82adb47588a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":795,"license_type":"no_license","max_line_length":80,"num_lines":27,"path":"/app/carboard/models/brand.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom ..helpers import slugify\nfrom ...extensions import db\n\n# -------------------------------- Brand Model ------------------------------- #\n\n\nclass Brand(db.Model):\n \"\"\" Brand (Marque) Model \"\"\"\n __tablename__ = 'brands'\n\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(255))\n slug = db.Column(db.String(255))\n logo = db.Column(db.String(255), nullable=True)\n code = db.Column(db.String(255))\n status = db.Column(db.SmallInteger, default=1)\n created = db.Column(db.DateTime(), default=datetime.utcnow())\n\n def __init__(self, name, code, logo=None):\n self.name = name\n self.slug = slugify(name)\n self.code = code\n self.logo = logo\n\n def __repr__(self):\n return self.name\n"},{"alpha_fraction":0.5753138065338135,"alphanum_fraction":0.5753138065338135,"avg_line_length":19.782608032226562,"blob_id":"50effb48d98da9d4a40dde44536d9ff72b679178","content_id":"290a206943f7ff9b385dcb336fbc9c3df8656376","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":478,"license_type":"permissive","max_line_length":72,"num_lines":23,"path":"/app/plugins/hello_world/src/views.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import Blueprint, render_template\nfrom app.plugins.helpers import render_md\n\nfrom .. import __path__\n\nhello = Blueprint(\n \"hello\",\n __name__,\n url_prefix='/hello',\n template_folder=\"../templates\"\n)\n\n# --------------------- /hello/ : Overview --------------------------- #\n\n\n@hello.route(\"/\")\ndef index():\n\n # Hello world is a one page plugin\n\n html = render_md(__path__, \"README.md\")\n\n return render_template(\"hello_world/index.html\", html=html)\n"},{"alpha_fraction":0.5689900517463684,"alphanum_fraction":0.574679970741272,"avg_line_length":27.1200008392334,"blob_id":"3b75980c970154dd0097ab6480aa1b0184e86d90","content_id":"a98b3b564727e602561088e1f295b27654e92d10","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":703,"license_type":"no_license","max_line_length":81,"num_lines":25,"path":"/app/carboard/models/model.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom ...extensions import db\n\n# -------------------------------- Driver Model ------------------------------- #\n\n\nclass Model(db.Model):\n \"\"\" Vehicle Models Model \"\"\"\n __tablename__ = 'models'\n\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(255))\n\n brand_id = db.Column(db.Integer, db.ForeignKey('brands.id'))\n brand = db.relationship('Brand', backref='model')\n\n status = db.Column(db.SmallInteger, default=1)\n created = db.Column(db.DateTime(), default=datetime.utcnow())\n\n def __init__(self, name, brand_id):\n self.name = name\n self.brand_id = brand_id\n\n def __repr__(self):\n return self.name\n"},{"alpha_fraction":0.7708333134651184,"alphanum_fraction":0.7708333134651184,"avg_line_length":39,"blob_id":"52a0f89c55c3965b638533d8cb572c09046a3ccc","content_id":"7e16a58241225687ec68ea2fd42bd593a5c64682","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":240,"license_type":"no_license","max_line_length":108,"num_lines":6,"path":"/app/carboard/forms/filesignal.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom flask_wtf.file import FileAllowed, FileField, FileRequired\n\n\nclass FileSignalForm(FlaskForm):\n file = FileField(validators=[FileAllowed(['csv'], 'only .csv files are supported now'), FileRequired()])\n"},{"alpha_fraction":0.7516387701034546,"alphanum_fraction":0.7523670792579651,"avg_line_length":23.087718963623047,"blob_id":"9c3598bf4e73feff839cdbe08b24289583ba6c56","content_id":"6352c6f883d8c379b28afe8a06b9040b6aa84b08","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1373,"license_type":"no_license","max_line_length":124,"num_lines":57,"path":"/README.md","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"# vCar\n\n## vCar is a Driving Behaviour Analytics Platfom\n\nvCar Driving Analytics Solution processes the data coming from the connected vehicle and generates advanced driving behavior\nand driving pattern reports. The data is collected and analysed with the ultimate goal to gain a full understanding of\nthe drivers capability to adjust his driving based on driving conditions.\n\n## Instalation\n\n### build static files\n\nInstall dependencies :\n`npm install`\n\n* Production :\n\n`gulp build --prod`\nThen you need to set DEBUG=False in config/config.py\n\n* Development :\n * `gulp build [--dev]` to generate static files.\n * `gulp watch` to watch static files.\n\n### Install Elasticsearch\n\n* Download at [Elasticsearch](https://www.elastic.co/products/elasticsearch)\n* (Optionel) [Kibana](https://www.elastic.co/products/kibana)\n\n### Install Redis\n\n* Download at [Redis](https://redis.io/download)\n\n### Docker integration :\nTo run vcar inside docker please make this changes:\n\nChange `REDIS_HOST` and `ELASTICSEARCH_HOST` in `importer/platforms/openxc/constants.py` to : \n\n`REDIS_HOST = 'redis'`\n\n`ELASTICSEARCH_HOST = 'elastic'`\nthen run :\n`docker-compose up`\n\n## Technologies\n\nPython, FLASK, MYSQL, REDIS, ELASTIC, DOCKER, VISJS, NODEJS, BOOTSTRAP, CSS, JINJA2, HTML\n\n## Platform Insights\n\n* Car data storages\n* Data visualization\n* Driving Behavior\n\n## Team members\n\nboubouhkarim\n"},{"alpha_fraction":0.5985247492790222,"alphanum_fraction":0.6032665967941284,"avg_line_length":24.648649215698242,"blob_id":"28b09138d20015e18ac42bb512dc55908b3fa525","content_id":"737d6882b2a98e5c9bfc84357b65fb690bcd2d95","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1898,"license_type":"no_license","max_line_length":78,"num_lines":74,"path":"/manage.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_script import Manager\nfrom flask_migrate import MigrateCommand\nfrom main import create_app\nfrom app.extensions import db\nfrom app.carboard.models.user import User\n\nimport os\n\napp = create_app()\nmanager = Manager(app)\n\n\n@manager.command\ndef run():\n \"\"\"Run in local machine.\"\"\"\n app.config['TEMPLATES_AUTO_RELOAD'] = True\n\n # Extrafiles are just used to track changer in template folder\n # thus our application is forced to reload.\n extra_dirs = [\n '/home/karim/OpenXC/Dashboard/Flask/vcar/templates',\n ]\n extra_files = extra_dirs[:]\n for extra_dir in extra_dirs:\n for dirname, dirs, files in os.walk(extra_dir):\n for filename in files:\n filename = os.path.join(dirname, filename)\n if os.path.isfile(filename):\n extra_files.append(filename)\n\n \"\"\"\n Get informed : Flask development server can handle only one request :)\n * processes = x : allow x processes (forks) means x clients\n Windows sorry you can't fork :)\n * threaded = Bool : allow or not multiple threads.\n \"\"\"\n # app.run(processes=4, extra_files=extra_files)\n # app.run(threaded=True, host='0.0.0.0', extra_files=extra_files)\n app.run(threaded=True, extra_files=extra_files, host='0.0.0.0')\n # app.run(extra_files=extra_files)\n # app.run()\n\n\n@manager.command\ndef initdb():\n \"\"\"Init/reset database.\"\"\"\n\n # db.drop_all()\n # db.create_all()\n\n admin = User(\n fullname=\"Misaki Yooko\",\n username=\"root\",\n email=\"misaki.yooko@gmail.com\",\n password='root'\n )\n\n db.session.add(admin)\n db.session.commit()\n\n\n# manager.add_option(\n# '-c', '--config',\n# dest=\"config\",\n# required=False,\n# help=\"config file\"\n# )\nmanager.add_command(\n 'db',\n MigrateCommand\n)\n\nif __name__ == \"__main__\":\n manager.run()\n"},{"alpha_fraction":0.42556634545326233,"alphanum_fraction":0.46278315782546997,"avg_line_length":21.814815521240234,"blob_id":"4213f754bad281bc6bf6025e7ed130ebd5a5576a","content_id":"a357429ad5dea4eaa99a5caae5f29b6a2762413b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":618,"license_type":"no_license","max_line_length":79,"num_lines":27,"path":"/app/importer/datasets/drive/constants.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"\n# -------------------- Redis constants -------------------------------------- #\nREDIS_HOST = 'localhost'\nREDIS_PORT = 6379\nREDIS_DB = 0\n\n# -------------------- Elasticsearch constants ------------------------------ #\n\n# basic configuration\nELASTICSEARCH_HOST = 'localhost'\nELASTICSEARCH_PORT = 9200\n\n# indexing configuration\nOPENXC_INDEX = 'openxc'\nBULK_SIZE = 10000\n\n# -------------------- Driver Graph Configuration --------------------------- #\nTRANSFORM = {\n -1: \"reverse\",\n 0: \"neutral\",\n 1: \"first\",\n 2: \"second\",\n 3: \"third\",\n 4: \"fourth\",\n 5: \"fifth\",\n 6: \"sixth\",\n 7: \"seventh\"\n}\n\n"},{"alpha_fraction":0.6167481541633606,"alphanum_fraction":0.6179706454277039,"avg_line_length":24.96825408935547,"blob_id":"d448706db4a7366e9df01bc24a3bcd33b3b2a90e","content_id":"3953f2c03c0555f5d6b9335877a4313c332657f7","detected_licenses":["MIT","BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1636,"license_type":"permissive","max_line_length":79,"num_lines":63,"path":"/app/plugins/driver_graph/src/views.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import Blueprint, render_template\nfrom app.plugins.helpers import render_md\n\nfrom .algorithms.drivergraph import DriverGraph\nfrom .algorithms.settings import DefaultSettings\n\nfrom .. import __path__\n\ndriverGraph = Blueprint(\n \"driverGraph\",\n __name__,\n url_prefix='/driver_graph',\n template_folder=\"../templates\"\n)\n\n# --------------------- /driver_graph/ : Overview --------------------------- #\n\n\n@driverGraph.route(\"/\")\ndef index():\n\n html = render_md(__path__, \"README.md\")\n\n return render_template(\"driver_graph/index.html\", html=html)\n\n# --------------------- /driver_graph/datasets : Show available datasets ---- #\n\n\n@driverGraph.route(\"/datasets\")\ndef datasets():\n # todo\n return render_template(\"driver_graph/datasets.html\")\n\n# --------------------- /driver_graph/run : Main plugin function ------------ #\n\n\n@driverGraph.route(\"/run\")\ndef run():\n driver = DriverGraph(max_nodes=30)\n driver.settings = DefaultSettings\n driver.create_digraph(\"/home/karim/OpenXC/uptown-west.json\")\n graph_js = driver.vis_network(physics=True)\n\n return render_template(\"driver_graph/run.html\", graph_js=graph_js)\n\n# --------------------- /driver_graph/custom : custom functions ------------- #\n\n\n@driverGraph.route(\"/custom\")\ndef custom():\n # some plugin spesific functions ...\n\n return render_template(\"driver_graph/custom.html\")\n\n# --------------------- /driver_graph/docs : Documentation ------------------ #\n\n\n@driverGraph.route(\"/docs\")\ndef docs():\n # documentation in markdown\n html = render_md(__path__, \"DOC.md\")\n\n return render_template(\"driver_graph/docs.html\", html=html)\n"},{"alpha_fraction":0.6293660998344421,"alphanum_fraction":0.6293660998344421,"avg_line_length":27.10909080505371,"blob_id":"25d9ed4e88eaee236d6da0607354aad313505700","content_id":"592e3cbd589cd30e39b9cf5ba48b858c56b88f25","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1546,"license_type":"no_license","max_line_length":79,"num_lines":55,"path":"/app/carboard/views/plugin.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import (\n render_template, flash, redirect, url_for\n)\nimport flask_plugins\nfrom flask_login import login_required\n\nfrom . import carboard\n\n# -------------------- /carboard/plugin/ : List of plugins ------------------ #\n\n\n@carboard.route('/plugin/', methods=['GET'])\n@login_required\ndef indexPlugin():\n plugins = flask_plugins.get_all_plugins()\n try:\n plugins = flask_plugins.get_all_plugins()\n except Exception:\n plugins = None\n\n # raise\n\n return render_template('carboard/plugin/index.html', plugins=plugins)\n\n# -------------------- /carboard/plugin/id : Show plugin -------------------- #\n\n\n@carboard.route('/plugin/<identifier>', methods=['GET'])\n@login_required\ndef showPlugin(identifier):\n try:\n plugin = flask_plugins.get_plugin_from_all(identifier)\n except Exception:\n plugin = None\n\n return render_template('carboard/plugin/show.html', plugin=plugin)\n\n# -------------------- /carboard/plugin/id/enable : Enable plugin ----------- #\n\n\n@carboard.route('/plugin/<identifier>/enable', methods=['GET'])\n@login_required\ndef enablePlugin(identifier):\n try:\n enabled = flask_plugins.get_plugin_from_all(identifier)\n flash('Plugin {}, updated successfully.'.format(identifier), 'success')\n except Exception:\n enabled = None\n\n if enabled:\n flash('Plugin {}, enabled successfully.'.format(identifier), 'success')\n else:\n flash('Plugin {}, not enabled.'.format(identifier), 'error')\n\n return redirect(url_for('carboard.indexPlugin'))\n"},{"alpha_fraction":0.637442946434021,"alphanum_fraction":0.6420091390609741,"avg_line_length":33.761905670166016,"blob_id":"0ee28b09de9bdd0df3db74a7f751fc1f4d996d1b","content_id":"eb51ea394b4b02e03d1fe4d2459b227563865015","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4380,"license_type":"no_license","max_line_length":85,"num_lines":126,"path":"/app/carboard/views/platform.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import (\n render_template, request, flash, redirect, url_for, jsonify\n)\nfrom flask_login import login_required\n\nfrom . import carboard\nfrom ..models.platform import Platform, PlatformSchema\nfrom ..models.extrasignal import ExtrasignalSchema\nfrom ..forms.platform import PlatformForm\nfrom ..helpers import paginate, upload_file\nfrom ..constants import PER_PAGE, PLATFORM_LOGO_DIR\nfrom ...extensions import db\n\n# --------------------- /carboard/platform/ : List of platforms ------------------ #\n\n\n@carboard.route('/platform/')\n@login_required\ndef indexPlatform():\n platforms = Platform.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/platform/index.html', platforms=platforms)\n\n# ----------------------- /carboard/platform/id : Show platform ------------------- #\n\n\n@carboard.route('/platform/<int:id>', methods=['GET'])\n@login_required\ndef showPlatform(id):\n platform = Platform.query.get_or_404(id)\n return render_template('carboard/platform/show.html', platform=platform)\n\n# ----------------------- /carboard/platform/id/json : Json platform object ------- #\n\n\n@carboard.route('/platform/<int:id>/json', methods=['GET'])\n@login_required\ndef jsonPlatform(id):\n platform = Platform.query.get_or_404(id)\n platform_schema = PlatformSchema()\n extrasignal_eschema = ExtrasignalSchema(many=True)\n platform_result = platform_schema.dump(platform)\n extrasignal_result = extrasignal_eschema.dump(platform.signals)\n\n return jsonify({\n 'platform': platform_result.data,\n 'signals': extrasignal_result.data\n })\n\n# ---------------------- /carboard/platform/new : Add platform -------------------- #\n\n\n@carboard.route('/platform/new', methods=['GET', 'POST'])\n@login_required\ndef newPlatform():\n \"\"\" Add new platform \"\"\"\n\n form = PlatformForm()\n\n if form.validate_on_submit():\n logo = upload_file(form.logo.data, PLATFORM_LOGO_DIR)\n platform = Platform(\n name=form.name.data,\n mimetype=form.mimetype.data,\n website=form.website.data,\n description=form.description.data,\n logo=logo\n )\n db.session.add(platform)\n db.session.commit()\n flash('Platform {}, added successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.indexPlatform'))\n\n return render_template('carboard/platform/new.html', form=form)\n\n# -------------------- /carboard/platform/id/edit : Edit platform ----------------- #\n\n\n@carboard.route('/platform/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editPlatform(id):\n \"\"\" Edit existing platform \"\"\"\n platform = Platform.query.get_or_404(id)\n oldLogo = platform.logo\n form = PlatformForm(obj=platform)\n if form.validate_on_submit():\n form.populate_obj(platform)\n logo = upload_file(form.logo.data, PLATFORM_LOGO_DIR)\n if logo is None:\n platform.logo = oldLogo\n else:\n platform.logo = logo\n db.session.commit()\n flash('Platform {}, updated successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.showPlatform', id=id))\n\n return render_template('carboard/platform/edit.html', form=form, id=id)\n\n# ------------------ /carboard/platform/id/delete : Delete platform --------------- #\n\n\n@carboard.route('/platform/<int:id>/toggle', methods=['GET'])\n@login_required\ndef togglePlatform(id):\n platform = Platform.query.get_or_404(id)\n # getattr(platform, 'status', 0)\n status = platform.status if platform.status is not None else 0\n platform.status = 1 - status\n db.session.commit()\n msg = 'activated' if platform.status is 1 else 'deactivated'\n flash('Platform {}, {} successfully.'.format(platform.name, msg), 'success')\n return redirect(url_for('carboard.indexPlatform'))\n\n# ------------------ /carboard/platform/id/delete : Delete platform --------------- #\n\n\n@carboard.route('/platform/<int:id>/delete', methods=['GET'])\n@login_required\ndef deletePlatform(id):\n platform = Platform.query.get_or_404(id)\n db.session.delete(platform)\n db.session.commit()\n flash('Platform {}, deleted successfully.'.format(platform.name), 'success')\n return redirect(url_for('carboard.indexPlatform'))\n"},{"alpha_fraction":0.6414941549301147,"alphanum_fraction":0.6447283029556274,"avg_line_length":37.89308166503906,"blob_id":"270622067e0bc8aefedd5cbc4da6fd466327a255","content_id":"7e2038a0d940ffee6d01c84da3c18c93cb728c07","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6184,"license_type":"no_license","max_line_length":115,"num_lines":159,"path":"/app/carboard/views/signalsource.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import os\n\nfrom flask import (\n render_template, request, flash, redirect, url_for\n)\nfrom flask_login import login_required\nfrom sqlalchemy import or_\n\nfrom . import carboard\nfrom ..models.signalsource import Signalsource\nfrom ..forms.signalsource import SignalsourceForm\nfrom ..helpers import paginate, CSVLoader\nfrom ..constants import PER_PAGE, CSV_TEMP\nfrom ...extensions import db\nfrom ..forms.filesignal import FileSignalForm\nfrom ..helpers import remove_csv, upload_csv\n\n\n# --------------------- /carboard/signalsource/ : List of signalsources ----- #\n\n\n@carboard.route('/signalsource/')\n@login_required\ndef indexSignalsource():\n signalsources = Signalsource.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n start = (request.args.get('page', 1, type=int) - 1) * PER_PAGE + 1\n return render_template('carboard/signalsource/index.html', signalsources=signalsources, start=start)\n\n\n# ----------------------- /carboard/signalsource/id : Show signalsource ----- #\n\n\n@carboard.route('/signalsource/<int:id>', methods=['GET'])\n@login_required\ndef showSignalsource(id):\n signalsource = Signalsource.query.get_or_404(id)\n return render_template('carboard/signalsource/show.html', signalsource=signalsource)\n\n\n# ---------------------- /carboard/signalsource/new : Add signalsource -------------------- #\n\n@carboard.route('/signalsource/new', methods=['GET', 'POST'])\n@login_required\ndef newSignalsource():\n \"\"\" Add new signalsource \"\"\"\n form = SignalsourceForm()\n\n if form.validate_on_submit():\n ret = Signalsource.query.filter_by(name=form.name.data).first()\n if ret:\n flash('Signal source {}, already exists in the database.'.format(form.name.data), 'error')\n return redirect(url_for('carboard.indexSignalsource'))\n signalsource = Signalsource(\n name=form.name.data,\n description=form.description.data\n )\n db.session.add(signalsource)\n db.session.commit()\n flash('Signal source {}, added successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.indexSignalsource'))\n\n return render_template('carboard/signalsource/new.html', form=form)\n\n\n# -------------------------------- /carboard/signalsource/bulk-add add all from file -------------#\n\n@carboard.route('/signalsource/bulk-add', methods=['GET', 'POST'])\n@login_required\ndef bulkAddSource():\n form = FileSignalForm()\n errors = None\n if form.validate_on_submit():\n try:\n f = upload_csv(form.file.data, CSV_TEMP)\n loader = CSVLoader(os.path.join(CSV_TEMP, form.file.data.filename))\n res = loader.load()\n already_there = False\n for row in res:\n ret = Signalsource.query.filter_by(name=row['Signal Source'])\n if ret:\n already_there = True # check to not add already added signal sources\n continue\n signal_source = Signalsource(\n name=row['Signal Source'],\n description=row['Description']\n )\n db.session.add(signal_source)\n db.session.commit()\n remove_csv(form.file.data.filename, CSV_TEMP)\n except KeyError:\n errors = ['Your file is Not well Formated, please review your file structure .']\n if not errors and not already_there:\n flash('Signal sources added Succesfully', 'success')\n if already_there:\n flash('Some Signal sources are not added because they are already in the database', 'warning')\n return render_template('carboard/signalsource/bulk.html', form=form, errors=errors)\n\n\n# -------------------- /carboard/signalsource/id/edit : Edit signalsource ----------------- #\n\n\n@carboard.route('/signalsource/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editSignalsource(id):\n \"\"\" Edit existing signalsource \"\"\"\n signalsource = Signalsource.query.get_or_404(id)\n form = SignalsourceForm(obj=signalsource)\n if form.validate_on_submit():\n form.populate_obj(signalsource)\n db.session.commit()\n flash('Signal source {}, updated successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.showSignalsource', id=id))\n\n return render_template('carboard/signalsource/edit.html', form=form, id=id)\n\n\n# ------------------ /carboard/signalsource/id/delete : Delete signalsource --------------- #\n\n\n@carboard.route('/signalsource/<int:id>/toggle', methods=['GET'])\n@login_required\ndef toggleSignalsource(id):\n signalsource = Signalsource.query.get_or_404(id)\n status = signalsource.status if signalsource.status is not None else 0\n signalsource.status = 1 - status\n db.session.commit()\n msg = 'activated' if signalsource.status is 1 else 'deactivated'\n flash('Signal source {}, {} successfully.'.format(signalsource.name, msg), 'success')\n return redirect(url_for('carboard.indexSignalsource'))\n\n\n# ------------------ /carboard/signalsource/id/delete : Delete signalsource --------------- #\n\n\n@carboard.route('/signalsource/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteSignalsource(id):\n signalsource = Signalsource.query.get_or_404(id)\n db.session.delete(signalsource)\n db.session.commit()\n flash('Signal source {}, deleted successfully.'.format(signalsource.name), 'success')\n return redirect(url_for('carboard.indexSignalsource'))\n\n\n# ------------------ /carboard/signalsource/search?table_seach=? : Search for signal source --------------- #\n\n@carboard.route('/signalsource/search')\n@login_required\ndef searchSignalSource():\n param = request.args.get('table_search')\n signalsources = Signalsource.query.filter(\n or_(Signalsource.name.like('%' + param + '%'), Signalsource.description.like('%' + param + '%'))).paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/signalsource/search.html', signalsources=signalsources)\n"},{"alpha_fraction":0.45069825649261475,"alphanum_fraction":0.4549301862716675,"avg_line_length":30.81944465637207,"blob_id":"4bb6cdb6047aa7f6b765bb8b406c6486977961cd","content_id":"07202559b90a57671c5a3d7fef6bcb573288ada8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"HTML","length_bytes":2363,"license_type":"no_license","max_line_length":121,"num_lines":72,"path":"/templates/importer/records.html","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"{% extends \"layout.html\" %}\r\n\r\n{% from \"importer/macros.html\" import render_field, info_box, box_header %}\r\n\r\n{% block title %}\r\n vCar importer manager :: Records | {{ super() }}\r\n{% endblock %}\r\n\r\n{% block content_header %}\r\n <section class=\"content-header\">\r\n <h1>\r\n Import wizard\r\n </h1>\r\n <ol class=\"breadcrumb\">\r\n <li><a href=\"{{ url_for('carboard.index') }}\"><i class=\"fa fa-dashboard\"></i>Home</a></li>\r\n <li><a href=\"#\">Importer</a></li>\r\n <li class=\"active\">Import wizard</li>\r\n </ol>\r\n </section>\r\n{% endblock content_header %}\r\n\r\n{% block content %}\r\n<div class=\"row\">\r\n <div class=\"col-md-12\">\r\n <div class=\"callout callout-vcar\">\r\n <i class=\"callout-icon\">Step 4 of 5</i>\r\n <h4>Upload trace files</h4>\r\n <p>Upload one or multiple trace files according to the choosed platform.</p>\r\n </div>\r\n </div>\r\n</div>\r\n\r\n<div class=\"row\">\r\n <div class=\"col-xs-12\">\r\n <div class=\"box box-solid vcar-box\">\r\n {{ box_header('Uploader manager') }}\r\n <div class=\"box-body\">\r\n <form id=\"upload\" method=\"post\" action=\"{{ url_for('importer.getfile') }}\" enctype=\"multipart/form-data\">\r\n <div id=\"drop\">\r\n Drop Here<br>\r\n <a>Browse</a>\r\n <input type=\"file\" name=\"openxc\" multiple />\r\n </div>\r\n <ul><!-- The file uploads will be shown here --></ul>\r\n </form>\r\n </div>\r\n <div class=\"box-footer\">\r\n <a class=\"pull-right btn btn-vcar-action\" href=\"#\" id=\"next\">\r\n <i class=\"fa fa-chevron-right\"></i>\r\n Submit\r\n </a>\r\n </div>\r\n <form id=\"send_files\" method=\"post\" action=\"{{ url_for('importer.records') }}\">\r\n <!-- hidden inputs goes here -->\r\n </form>\r\n </div>\r\n </form>\r\n </div>\r\n</div>\r\n{%- endblock content %}\r\n\r\n{% block scripts %}\r\n {{ super() }}\r\n <script type=\"text/javascript\">\r\n $(function() {\r\n $('#next').click(function(e) {\r\n e.preventDefault();\r\n $(\"#send_files\").submit();\r\n });\r\n });\r\n </script>\r\n{% endblock %}\r\n"},{"alpha_fraction":0.6872586607933044,"alphanum_fraction":0.6988416910171509,"avg_line_length":18.923076629638672,"blob_id":"77ffcdfa748c773d589f5dada2b3ffae3c2abebe","content_id":"285c6a8a97f71d8c4207a273aa17d9f5b11b272f","detected_licenses":["MIT","BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":259,"license_type":"permissive","max_line_length":44,"num_lines":13,"path":"/app/plugins/driver_graph/__init__.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from os import getcwd\nfrom app.plugin import AppPlugin\nfrom .src.views import driverGraph\n\n__plugin__ = \"DriverGraph\"\n__version__ = \"1.0.0\"\n__path__ = getcwd()\n\n\nclass DriverGraph(AppPlugin):\n\n def setup(self):\n self.register_blueprint(driverGraph)\n"},{"alpha_fraction":0.5469200015068054,"alphanum_fraction":0.5506620407104492,"avg_line_length":25.571428298950195,"blob_id":"ef8ce1f4d0f2d09ccee076022b19bad8bd357c3c","content_id":"330ced43e4b589d0196eb431fbd7e354d54bd9ea","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3474,"license_type":"no_license","max_line_length":79,"num_lines":126,"path":"/app/importer/platforms/openxc/indexing_org.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import redis\r\nimport json\r\nfrom time import strftime, sleep\r\nfrom datetime import datetime\r\nfrom elasticsearch import Elasticsearch, helpers\r\nfrom .constants import *\r\nfrom .helpers import correct_value, correct_time\r\nfrom ...transformer import Transformer\r\n\r\n# ----------------- Setting up tools --------------------------------------- #\r\nred = redis.StrictRedis(\r\n\thost = REDIS_HOST,\r\n\tport = REDIS_PORT,\r\n\tdb = REDIS_DB\r\n)\r\n\r\ndef task_info(name='info'):\r\n pubsub = red.pubsub()\r\n pubsub.subscribe(name)\r\n for message in pubsub.listen():\r\n # print(\"[+] Message: {}\".format(message))\r\n yield 'data: {}\\n\\n'.format(message['data'])\r\n\r\n# ----------------- Indexing with Bulk : Large files ----------------------- #\r\n\r\ndef index_bulk(traces, metadata, bulk_size=BULK_SIZE):\r\n\tred.publish('info', 'Hello word! ')\r\n\ttry:\r\n\t\tes = Elasticsearch([{\r\n\t\t\t'host': ELASTICSEARCH_HOST,\r\n\t\t\t'port': ELASTICSEARCH_PORT\r\n\t\t}])\r\n\r\n\t\tindex = OPENXC_INDEX\r\n\t\ttype = \"platform\"\r\n\t\tsleep(2)\r\n\t\tred.publish('info', 'Inisializing Elasticsearch ...')\r\n\t\tes.cluster.health(wait_for_status='yellow', request_timeout=30)\r\n\t\tred.publish('info', 'creating index if it dosn\\'t exist ...')\r\n\t\tmapping = {\r\n\t\t\ttype: {\r\n\t\t\t\t\"properties\": {\r\n\t\t\t\t\t\"user\": {\r\n\t\t\t\t\t\t\"type\": \"integer\"\r\n\t\t\t\t\t},\r\n\t\t\t\t\t\"vehicle\": {\r\n\t\t\t\t\t\t\"type\": \"integer\"\r\n\t\t\t\t\t},\r\n\t\t\t\t\t\"driver\": {\r\n\t\t\t\t\t\t\"type\": \"integer\"\r\n\t\t\t\t\t},\r\n\t\t\t\t\t\"class\": {\r\n\t\t\t\t\t\t\"type\": \"string\"\r\n\t\t\t\t\t},\r\n\t\t\t\t\t\"name\": {\r\n\t\t\t\t\t\t\"type\": \"string\",\r\n\t\t\t\t\t\t\"index\": \"not_analyzed\"\r\n\t\t\t\t\t},\r\n\t\t\t\t\t\"value\": {\r\n\t\t\t\t\t\t\"type\": \"string\",\r\n\t\t\t\t\t\t\"index\": \"not_analyzed\"\r\n\t\t\t\t\t},\r\n\t\t\t\t\t\"timestamp\": {\r\n\t\t\t\t\t\t\"type\": \"date\"\r\n\t\t\t\t\t}\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t\t# es.indices.delete(index=index, ignore=404)\r\n\t\tif not es.indices.exists(index=index):\r\n\t\t\tes.indices.create(index=index, ignore=400)\r\n\t\t\tes.indices.put_mapping(index=index, doc_type=type, body=mapping)\r\n\r\n\t\t# inisialise a Transformer instance + set the platform attribute\r\n\t\ttr = Transformer();\r\n\t\ttr.setPlatformById(metadata['platform']);\r\n\t\tfor trace in traces:\r\n\t\t\ti = 1\r\n\t\t\tred.publish('info', 'indexing the file: {}'.format(trace))\r\n\t\t\tactions = []\r\n\t\t\twith open(trace, 'r') as f:\r\n\t\t\t\tfor line in f:\r\n\t\t\t\t\tsource = json.loads(line)\r\n\t\t\t\t\tsignal = tr.getInfo(source.get('name'))\r\n\r\n\t\t\t\t\tif signal is not None:\r\n\t\t\t\t\t\taction = {\r\n\t\t\t\t\t\t\t\"_index\": index,\r\n\t\t\t\t\t\t\t\"_type\": type,\r\n\t\t\t\t\t\t\t\"_id\": i,\r\n\t\t\t\t\t\t\t\"_source\": {\r\n\t\t\t\t\t\t\t\t\"user\": metadata['user'],\r\n\t\t\t\t\t\t\t\t\"vehicle\": metadata['vehicle'],\r\n\t\t\t\t\t\t\t\t\"driver\": metadata['driver'],\r\n\t\t\t\t\t\t\t\t\"class\": signal['class'],\r\n\t\t\t\t\t\t\t\t\"name\": signal['name'],\r\n\t\t\t\t\t\t\t\t\"value\": source.get('value'),\r\n\t\t\t\t\t\t\t\t\"timestamp\": correct_time(source.get('timestamp'))\r\n\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t\tactions.append(action)\r\n\t\t\t\t\t\tif i % bulk_size == 0:\r\n\t\t\t\t\t\t\thelpers.bulk(es, actions)\r\n\t\t\t\t\t\t\tred.publish('info', '{} events indexed'.format(i))\r\n\t\t\t\t\t\t\tactions = []\r\n\t\t\t\t\t\ti += 1\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\t\"\"\" Signal not included, should we remove it !! \"\"\"\r\n\t\t\t\t\t\tpass\r\n\r\n\t\t\t\t\t# ,overwrite_existing=True\r\n\t\t\t\t\t# es.index(index=index, doc_type=type, id=i, body=line)\r\n\t\t\tif i < bulk_size:\r\n\t\t\t\thelpers.bulk(es, actions)\r\n\t\t\tred.publish('info', 'File {} indexed'.format(trace))\r\n\t\t\tred.publish('info', 'end_trace'.format(trace))\r\n\t\tred.publish('info', 'end_all')\r\n\t\tsleep(2)\r\n\t\tred.connection_pool.disconnect()\r\n\t\treturn True\r\n\t\tsys.exit()\r\n\texcept Exception as e:\r\n\t\tprint('Exception: ', e)\r\n\t\tred.publish('info', 'Exception: {}'.format(e))\r\n\t\treturn False\r\n\t\tsys.exit()\r\n"},{"alpha_fraction":0.46082594990730286,"alphanum_fraction":0.5218062400817871,"avg_line_length":22.990739822387695,"blob_id":"e09b993ba147ef6654456bb5106bef315a172456","content_id":"54bebddb8df437f3c9c2f16b5089d89d5018f8c3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5182,"license_type":"no_license","max_line_length":106,"num_lines":216,"path":"/tests/elastic.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from elasticsearch import Elasticsearch, helpers\n\nes = Elasticsearch()\nindex = \"openxc\"\ntype = \"driver_1\"\nes.cluster.health(wait_for_status='yellow', request_timeout=1)\n# count documents\n# count = es.count(index=index, doc_type=type)\n# print(count)\n# get one document\n# data = es.get(index=index, doc_type=type, id=200)\n# print(data)\n# search one signal\nbody = {\n \"query\": {\n \"match\": {\n 'name': 'accelerator_pedal_position'\n }\n }\n}\n# search = es.search(index=index, doc_type=type, body=body, _source=False)\n\n# print(search)\n\n# Query for all results (no matching criteria)\n# res = es.search(index=index, body={\"query\": {\"match_all\": {}}})\n# print (res['hits']['total'])\n# print (res['hits']['hits'][100]['_source']['name'])\n\n# bulk\nbody = {\n '_op_type': 'delete', # values are (index, create, delete, update)\n '_index': 'index-name',\n '_type': 'document',\n '_id': 42,\n}\n\n# if not es.exists(index=index, doc_type=type):\n# print(\"NOOOO\")\n# if es.exists(index=index, doc_type=type):\n# print(\"YEEES\")\n# Mapping\n\nmapping = {\n type: {\n \"properties\": {\n \"name\": {\n \"type\": \"string\"\n },\n \"value\": {\n \"type\": \"double\"\n },\n \"timestamp\": {\n \"type\": \"date\",\n \"format\": \"epoch_millis\"\n }\n }\n }\n}\n# if not es.indices.exists(index=index):\n# es.indices.create(index=index, ignore=400)\n# es.indices.put_mapping(index=index, doc_type=type, body=mapping)\n# x = \"1364323978.53600\"\n# t = x.split('.')\n# data = {\n# \"name\": \"accelerator_pedal_position\",\n# \"timestamp\": '{}{}'.format(t[0], t[1][:3]),\n# \"value\": 53.5\n# }\n# es.index(index=index, doc_type=type, id=1, body=data)\n# print(\"index creared\")\n# else:\n# es.indices.delete(index=index, ignore=404)\n\n\ndef correct_time(timestamp):\n try:\n timestamp = str(timestamp)\n print(timestamp)\n t = timestamp.split('.')\n return '{}{}'.format(t[0], t[1][:3].ljust(3, '0'))\n except:\n return timestamp.split('.')[0]\n\nx = correct_time(1361454774.657980)\nprint(x)\n# 13614547982\n# 136145482189\n# 136145484557\n# 136145486924\n# 136145489296\n# 136145491667\n# 136145494043\n# 136145496416\n# 136145498789\n# 13614550116\n# 13614550353\n# 136145505902\n\n# put_mapping(*args, **kwargs)\n# http://elasticsearch-py.readthedocs.io/en/master/api.html#elasticsearch.client.IndicesClient.put_mapping\n# https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-put-mapping.html\nmapping = '''\n {\n \"mappings\":{\n \"logs_june\":{\n \"_timestamp\":{\n \"enabled\":\"true\"\n },\n \"properties\":{\n \"logdate\":{\n \"type\":\"date\",\n \"format\":\"dd/MM/yyy HH:mm:ss\"\n }\n }\n }\n }\n }\n'''\n# self.elastic_con.indices.create(index='test-index', ignore=400, body=mapping)\n# 2\n# conntect es\n# es = Elasticsearch()\n# delete index if exists\n# if es.indices.exists(config.elastic_urls_index):\n# es.indices.delete(index=config.elastic_urls_index)\n# index settings\nsettings = {\n \"settings\": {\n \"number_of_shards\": 1,\n \"number_of_replicas\": 0\n },\n \"mappings\": {\n \"urls\": {\n \"properties\": {\n \"url\": {\n \"type\": \"string\"\n }\n }\n }\n }\n}\n# create index\n# es.indices.create(index=config.elastic_urls_index, ignore=400, body=settings)\n\n# Notes\n# curl -XGET 'http://localhost:9200/openxc/driver_1/1/_source'\n# curl --head 'http://localhost:9200/openxc/driver_1/1/_source'\n# curl -XGET 'http://localhost:9200/openxc/driver_1/1?fields=name,value'\n# curl -XGET 'http://localhost:9200/openxc/driver_1/1?_source=false'\n# curl -XGET 'http://localhost:9200/openxc/driver_1/_search?pretty&q=name:vehicle_speed'\n# curl -XGET 'http://localhost:9200/openxc/driver_1/_search?pretty&size=10000'\n\n{\n \"size\": 0,\n \"aggs\": {\n \"2\": {\n \"date_histogram\": {\n \"field\": \"timestamp\",\n \"interval\": \"1s\",\n \"time_zone\": \"Europe/London\",\n \"min_doc_count\": 1,\n \"extended_bounds\": {\n \"min\": 1361454733109,\n \"max\": 1361454981775\n }\n },\n \"aggs\": {\n \"1\": {\n \"max\": {\n \"field\": \"value\"\n }\n }\n }\n }\n },\n \"highlight\": {\n \"pre_tags\": [\n \"@kibana-highlighted-field@\"\n ],\n \"post_tags\": [\n \"@/kibana-highlighted-field@\"\n ],\n \"fields\": {\n \"*\": {}\n },\n \"require_field_match\": false,\n \"fragment_size\": 2147483647\n },\n \"query\": {\n \"filtered\": {\n \"query\": {\n \"query_string\": {\n \"analyze_wildcard\": true,\n \"query\": \"*name=\\\"vehicle_speed\\\"\"\n }\n },\n \"filter\": {\n \"bool\": {\n \"must\": [\n {\n \"range\": {\n \"timestamp\": {\n \"gte\": 1361454733109,\n \"lte\": 1361454981775,\n \"format\": \"epoch_millis\"\n }\n }\n }\n ],\n \"must_not\": []\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.5970037579536438,"alphanum_fraction":0.6029962301254272,"avg_line_length":30.785715103149414,"blob_id":"c9a9dac2fa2877d99670a9030937d28fe7235fef","content_id":"2fd03b73fa5c6c89d3812e60fd562d58b5b95342","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1335,"license_type":"no_license","max_line_length":86,"num_lines":42,"path":"/app/carboard/models/extrasignal.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom marshmallow import Schema, fields\nfrom .signal import SignalSchema\nfrom ...extensions import db\n\n# -------------------------------- Extrasignal Model ------------------------------- #\n\n\nclass Extrasignal(db.Model):\n \"\"\" Extrasignal Model: \"\"\"\n __tablename__ = 'extrasignals'\n\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(255))\n storage = db.Column(db.String(255))\n\n signal_id = db.Column(db.Integer, db.ForeignKey('signals.id'))\n signal = db.relationship(\"Signal\", back_populates=\"extrasignals\")\n #platform_id = db.Column(db.Integer, db.ForeignKey('platforms.id'))\n #platform = db.relationship('Platform', back_populates=\"signals\")\n\n status = db.Column(db.SmallInteger, default=1)\n created = db.Column(db.DateTime(), default=datetime.utcnow())\n\n def __init__(self, name, signal_id, storage, status=1):\n self.name = name\n self.signal_id = signal_id\n self.storage = storage\n self.status = status\n\n def __repr__(self):\n return str(self.name)\n\n# -------------------------------- Extrasignal Model ------------------------------- #\n\n\nclass ExtrasignalSchema(Schema):\n \"\"\" Extra signals schema \"\"\"\n\n id = fields.Int(dump_only=True)\n name = fields.Str()\n signal = fields.Nested(SignalSchema)\n"},{"alpha_fraction":0.46034297347068787,"alphanum_fraction":0.4643622636795044,"avg_line_length":27.272727966308594,"blob_id":"8045d3af655218575403ad8202f9ea5444cb9cb9","content_id":"81f4202554a5da1ccc79dc7bc5d6c1194d753d1f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3732,"license_type":"no_license","max_line_length":91,"num_lines":132,"path":"/app/carboard/forms/dataset.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom wtforms import FormField, FieldList, StringField, SelectField\nfrom wtforms.fields.html5 import URLField\nfrom flask_wtf.file import FileAllowed, FileField\nfrom wtforms.validators import ValidationError, Optional, DataRequired, Regexp, Length, url\nfrom ..models.dataset import Dataset\nfrom .article import ArticleForm\n\n# ------------------------ custom validation methods ------------------------ #\n\n\ndef dataset_exist(form, field):\n if Dataset.select().where(Dataset.name == field.data).exists():\n raise ValidationError('Dataset already exists !')\n\n# ---------------------------- Dataset form classes ------------------------- #\n\n\nclass DatasetForm(FlaskForm):\n \"\"\" Dataset add/edit FlaskForm \"\"\"\n name = StringField(\n 'Name',\n validators=[\n DataRequired(),\n Length(min=3, max=50),\n Regexp(\n r'^[a-zA-Z_\\- ]+$',\n message=(\"Dataset name is not correct !\")\n ),\n ]\n )\n slug = StringField(\n 'Slug',\n validators=[\n Optional(),\n Length(min=3, max=50),\n Regexp(\n r'^[a-z_]+$',\n message=(\"Dataset slug is not correct !\")\n ),\n ]\n )\n description = StringField(\n 'Description',\n validators=[\n DataRequired(),\n ]\n )\n author = StringField(\n 'Author',\n )\n lab = StringField(\n 'Lab',\n )\n website = StringField(\n 'Website',\n )\n\n\nclass FeedDatasetForm(FlaskForm):\n \"\"\" Dataset add/edit FlaskForm \"\"\"\n\n where = SelectField(\n 'Where is your dataset files?',\n coerce=int,\n choices=[\n (0, 'Pick a choice ...'),\n (1, 'In my local machine'),\n (2, 'On a remote server'),\n (3, 'In vCar platform server')\n ],\n validators=[DataRequired()]\n )\n\n # local_type = SelectField(\n # 'What is your data?',\n # coerce=int,\n # choices=[\n # (0, 'Pick a choice ...'),\n # (1, 'One file'),\n # (2, 'One zip file'),\n # (3, 'Multiple files')\n # ],\n # validators=[DataRequired()]\n # )\n # local_file = FileField(\n # 'Now select your file', validators=[\n # FileAllowed(['json', 'csv', 'txt'], 'File format not supported !')\n # ]\n # )\n # local_zip = FileField(\n # 'Now select your zip file', validators=[\n # FileAllowed(['zip'], 'only zip files are supported !')\n # ]\n # )\n\n remote = URLField(\n 'Specify file link',\n validators=[\n Optional(),\n url(message='Sorry, this is not a valid URL,')\n ],\n # default='http://example.com/file.json'\n )\n\n vcar = StringField(\n 'Path',\n validators=[Optional()],\n # default='http://example.com/file.json'\n )\n\n \"\"\"\n ---------------------------------------------------\n Dependences :\n from wtforms import FormField, FieldList\n ---------------------------------------------------\n Nested FlaskForm :\n article = FormField(ArticleForm)\n article = FieldList(FormField(ArticleForm))\n ---------------------------------------------------\n Jinja implementation :\n {{ form.hidden_tag() }}\n {{ render_field(form.name) }}\n {{ render_field(form.description) }}\n {{ render_field(form.author) }}\n ...\n\n {% for subform in form.article %}\n {{ render_field(subform) }}\n {% endfor %}\n ---------------------------------------------------\n \"\"\"\n"},{"alpha_fraction":0.4590461254119873,"alphanum_fraction":0.4634525775909424,"avg_line_length":33.39449691772461,"blob_id":"7c2cfffae91ff44abce08eb9426a2004b4f3dcc6","content_id":"0ccfbbfec55ff54c61e62e8edc30202364c62116","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3858,"license_type":"no_license","max_line_length":135,"num_lines":109,"path":"/app/importer/platforms/openxc/indexing.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import redis\r\nimport json\r\nfrom time import strftime, sleep\r\nfrom datetime import datetime\r\nfrom elasticsearch import Elasticsearch, helpers\r\nfrom .constants import *\r\nfrom .helpers import correct_value, correct_time\r\nfrom ...transformer import Transformer\r\n\r\n# ----------------- Setting up tools --------------------------------------- #\r\nred = redis.StrictRedis(\r\n host=REDIS_HOST,\r\n port=REDIS_PORT,\r\n db=REDIS_DB\r\n)\r\n\r\n\r\ndef my_handler(message):\r\n # print('MY HANDLER: {}'.format(message['data']))\r\n yield 'data: {}\\n\\n'.format(message['data'])\r\n\r\n\r\ndef task_info(name='info'):\r\n p = red.pubsub(ignore_subscribe_messages=True)\r\n p.subscribe('info')\r\n # p.subscribe(**{'info': msg_handler})\r\n # thread = p.run_in_thread(sleep_time=0.001)\r\n # thread.stop()\r\n while True:\r\n msg = p.get_message()\r\n if msg:\r\n yield 'data: {}\\n\\n'.format(msg['data'])\r\n sleep(1)\r\n\r\n # pubsub = red.pubsub()\r\n # pubsub.subscribe(name)\r\n # for message in pubsub.listen():\r\n # # print(\"[+] Message: {}\".format(message))\r\n # yield 'data: {}\\n\\n'.format(message['data'])\r\n\r\n# ----------------- Indexing with Bulk : Large files ----------------------- #\r\n\r\n\r\ndef index_bulk(traces, metadata, bulk_size=BULK_SIZE):\r\n red.publish('info', 'Hello word! ')\r\n try:\r\n es = Elasticsearch([{\r\n 'host': ELASTICSEARCH_HOST,\r\n 'port': ELASTICSEARCH_PORT\r\n }])\r\n\r\n index = OPENXC_INDEX\r\n type = \"platform\"\r\n es.cluster.health(wait_for_status='yellow', request_timeout=30)\r\n\r\n # es.indices.delete(index=index, ignore=404)\r\n if not es.indices.exists(index=index):\r\n es.indices.create(index=index, ignore=400)\r\n es.indices.put_mapping(index=index, doc_type=type, body=MAPPING)\r\n\r\n # inisialise a Transformer instance + set the platform attribute\r\n tr = Transformer()\r\n tr.setPlatformById(metadata['platform'])\r\n\r\n for trace in traces:\r\n i = 1\r\n actions = []\r\n with open(trace, 'r') as f:\r\n for line in f:\r\n source = json.loads(line)\r\n signal = tr.getInfo(source.get('name'))\r\n\r\n if signal is not None:\r\n action = {\r\n \"_index\": index,\r\n \"_type\": type,\r\n \"_id\": i,\r\n \"_source\": {\r\n \"user\": metadata['user'],\r\n \"vehicle\": metadata['vehicle'],\r\n \"driver\": metadata['driver'],\r\n \"class\": signal['class'],\r\n \"name\": signal['name'],\r\n \"value\": source.get('value'),\r\n \"timestamp\": correct_time(source.get('timestamp'))\r\n }\r\n }\r\n actions.append(action)\r\n if i % bulk_size == 0:\r\n helpers.bulk(es, actions)\r\n actions = []\r\n i += 1\r\n else:\r\n \"\"\" Signal not included, should we remove it !! \"\"\"\r\n pass\r\n\r\n if i < bulk_size:\r\n helpers.bulk(es, actions)\r\n red.publish(\r\n 'info', 'Background indexing task [<b>{}</b> file<small>(s)</small>] has been completed successfully.'.format(len(traces)))\r\n # sleep(1)\r\n # red.connection_pool.disconnect()\r\n return True\r\n sys.exit()\r\n except Exception as e:\r\n print('Exception: ', e)\r\n red.publish('info', 'Exception: {}'.format(e))\r\n return False\r\n sys.exit()\r\n"},{"alpha_fraction":0.5380747318267822,"alphanum_fraction":0.5380747318267822,"avg_line_length":24.309091567993164,"blob_id":"9f6d39064624b716ccf4cc6c1531cb21eb201497","content_id":"292f2a74fa88fea8b9e16a595393825f949082ff","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1392,"license_type":"no_license","max_line_length":82,"num_lines":55,"path":"/app/carboard/forms/vehicle.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom flask_wtf.file import FileAllowed, FileField\nfrom wtforms import StringField, SelectField\nfrom wtforms.validators import Optional, DataRequired, Regexp, Length\n\nfrom ..models.vehicle import Vehicle\n\n# ------------------------ custom validation methods ------------------------ #\n\n\ndef vehicle_exist(form, field):\n if Vehicle.select().where(Vehicle.name == field.data).exists():\n raise ValidationError('Vehicle already exists !')\n\n# ---------------------------- Vehicle form classes ---------------------------- #\n\nclass VehicleForm(FlaskForm):\n \"\"\" Vehicle add/edit FlaskForm \"\"\"\n user_id = SelectField(\n 'User',\n coerce=int,\n validators=[\n DataRequired(),\n ]\n )\n brand_id = SelectField(\n 'Brand',\n coerce=int,\n validators=[\n DataRequired(),\n ]\n )\n model_id = SelectField(\n 'Model',\n coerce=int,\n )\n driver_id = SelectField(\n 'Driver',\n coerce=int,\n )\n image = FileField(\n 'Picture', validators=[\n FileAllowed(['jpg', 'jpeg', 'png', 'gif'], 'Images only plz :) ')\n ]\n )\n\nclass VehicleStatusForm(FlaskForm):\n \"\"\" Edit vehicle status FlaskForm \"\"\"\n status_id = SelectField(\n 'Status',\n coerce=int,\n validators=[\n DataRequired(),\n ]\n )\n"},{"alpha_fraction":0.8484848737716675,"alphanum_fraction":0.8484848737716675,"avg_line_length":13.666666984558105,"blob_id":"423db6513bd6edc448c832a46bc8a4a510e04e2d","content_id":"568503a3f87728fc7ad6270ca05381eb72a9bcc9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":264,"license_type":"no_license","max_line_length":56,"num_lines":18,"path":"/requirements2.txt","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"elasticsearch\nFlask\nFlask-Bcrypt\nFlask-Cache\nFlask-DebugToolbar\nFlask-Login\nFlask-Mail\nFlask-Migrate\nFlask-Images\nFlask-Redis\nFlask-Script\nFlask-SQLAlchemy\nFlask-WTF\nFlask-Misaka\nmarshmallow\nnetworkx\nPyYAML\nhttps://github.com/vcar/flask-plugins/archive/master.zip\n"},{"alpha_fraction":0.5739071369171143,"alphanum_fraction":0.5795403122901917,"avg_line_length":33.13846206665039,"blob_id":"90ff4b06ef950bd5057bd1d971f0b509dafcdb5f","content_id":"faee1957e8370b75384bd96b551b351b804f2278","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4438,"license_type":"no_license","max_line_length":109,"num_lines":130,"path":"/app/importer/helpers.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import os\nimport re\nfrom unicodedata import normalize\nfrom uuid import uuid4\nfrom time import strftime\nfrom datetime import datetime\n\nfrom flask import request, url_for, session, redirect, flash\nfrom werkzeug import secure_filename\n\nfrom config.config import DefaultConfig\nfrom .constants import UPLOAD_TRACE_DIR, ALLOWED_TRACE_EXTENSIONS\n\n# ------------------------------ View Helpers ------------------------------- #\n\n\ndef upload_file(file_data, upload_dir, oldFile=None):\n \"\"\" Upload Files \"\"\"\n if file_data:\n file = secure_filename(\n str(uuid4()) + os.path.splitext(file_data.filename)[1].lower())\n path = os.path.join(upload_dir, strftime(\"%Y/%m\"))\n # os.makedirs(path, exist_ok=True) Py 3.4+\n make_dir(path)\n file_data.save(\"/\".join([path, file]))\n if oldFile is not None:\n pass\n return \"/\".join([strftime(\"%Y/%m\"), file])\n return None\n\n\ndef slugify(text, delim=u'-'):\n \"\"\"Generates an slightly worse ASCII-only slug.\"\"\"\n if type(text) == str:\n text = text.decode()\n result = []\n _punct_re = re.compile(r'[\\t !\"#$%&\\'()*\\-/<=>?@\\[\\\\\\]^_`{|},.]+')\n for word in _punct_re.split(text.lower()):\n word = normalize('NFKD', word).encode('ascii', 'ignore')\n if word:\n result.append(word)\n return unicode(delim.join(result))\n\n\ndef choices(Model, placeholder=None, cond=1, orderField='name'):\n if cond:\n c = [(b.id, b) for b in Model.query.filter_by(\n status=cond).order_by(orderField).all()]\n else:\n c = [(b.id, b) for b in Model.query.order_by(orderField).all()]\n if placeholder is not None:\n c.insert(0, ('0', placeholder))\n else:\n c.insert(0, ('0', 'Select an option ...'))\n return c\n\n\ndef make_dir(dir_path):\n \"\"\" Make recursive directories \"\"\"\n try:\n if not os.path.exists(dir_path) and not os.path.isdir(dir_path):\n os.makedirs(dir_path)\n except Exception as e:\n raise e\n\n\ndef redirect_back():\n \"\"\" Redirect back to last url \"\"\"\n return request.args.get('next') or request.referrer or url_for('index')\n\n\ndef get_current_time():\n return datetime.utcnow()\n\n\ndef allowed_extensions(filename):\n \"\"\" check if the file extension is allowed \"\"\"\n return '.' in filename and filename.rsplit('.', 1)[1] in DefaultConfig.ALLOWED_AVATAR_EXTENSIONS\n\n\ndef checkSteps(step):\n if step >= 1:\n if 'import' not in session or 'user' not in session['import'] or 'platform' not in session['import']:\n flash('Please choose a platform first.', 'success')\n return redirect(url_for('importer.platform'))\n elif step == 1:\n return True\n if step >= 2:\n if 'vehicle' not in session['import']:\n flash('Please choose a vehicle or select an anonymous one.', 'success')\n return redirect(url_for('importer.vehicle'))\n elif step == 2:\n return True\n if step >= 3:\n if 'driver' not in session['import']:\n flash('Please choose a driver or select an anonymous one.', 'success')\n return redirect(url_for('importer.driver'))\n elif step == 3:\n return True\n if step > 4:\n if 'files' not in session['import']:\n flash('Please upload some files before getting here', 'success')\n return redirect(url_for('importer.records'))\n elif step == 4:\n return True\n\n\ndef upload_trace(input='file', user_id=0):\n \"\"\"Upload a trace file\"\"\"\n result = {'status': False, 'message': 'No input file'}\n if input not in request.files:\n return result\n file = request.files[input]\n if file.filename == '':\n return result\n if '.' not in file.filename:\n result['message'] = 'File without any extension'\n return result\n if file.filename.rsplit('.', 1)[1] not in ALLOWED_TRACE_EXTENSIONS:\n result['message'] = 'Extension not allowed'\n return result\n directory = \"/\".join([UPLOAD_TRACE_DIR, str(user_id)])\n directory = \"/\".join([directory, strftime(\"%Y/%m\")])\n make_dir(directory)\n file_path = \"/\".join([directory, secure_filename(str(uuid4()) +\n os.path.splitext(file.filename)[1].lower())])\n file.save(file_path)\n return {'status': True, 'message': file_path}\n\n# ------------------------------- Form Helpers ------------------------------ #\n"},{"alpha_fraction":0.5741350650787354,"alphanum_fraction":0.5831960439682007,"avg_line_length":29.128204345703125,"blob_id":"ddee825a4d4b3c2de402beef3a15955aa2f89c76","content_id":"819e9cf65ab780aa669e7d935fb01489deda2ce7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1214,"license_type":"no_license","max_line_length":79,"num_lines":39,"path":"/app/carboard/models/file.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from uuid import uuid4\r\nfrom datetime import datetime\r\nfrom ...extensions import db\r\n\r\n\r\n# ------------------------------ File Model -------------------------------- #\r\n\r\n\r\nclass File(db.Model):\r\n\r\n __tablename__ = 'files'\r\n\r\n id = db.Column(db.Integer, primary_key=True)\r\n\r\n user_id = db.Column(db.Integer, db.ForeignKey('users.id'), nullable=False)\r\n user = db.relationship('User', backref=db.backref('files', lazy='dynamic'))\r\n\r\n filename = db.Column(db.String(255))\r\n path = db.Column(db.String(255), nullable=False, unique=True)\r\n processed = db.Column(db.SmallInteger, default=0)\r\n created = db.Column(db.DateTime(), default=datetime.utcnow())\r\n\r\n def __init__(self, user_id, filename=None, path=None, processed=0):\r\n self.filename = filename\r\n self.path = path\r\n self.user_id = user_id\r\n self.processed = processed\r\n\r\n def is_processed(self):\r\n return self.processed == 1\r\n\r\n def __repr__(self):\r\n return '<File {}>'.format(self.filename)\r\n\r\n @classmethod\r\n def get_by_user_id(cls, user_id, processed=0):\r\n return cls.query.filter(\r\n File.user_id == user_id and File.processed == processed\r\n ).all()\r\n"},{"alpha_fraction":0.6300984621047974,"alphanum_fraction":0.6347866654396057,"avg_line_length":33.40322494506836,"blob_id":"799855806acf1aeda8f77202edec9ae77e49bd49","content_id":"4ae27ae6d1f94c1d8a4e202c1ea6c603e057162a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2133,"license_type":"no_license","max_line_length":93,"num_lines":62,"path":"/config/config.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import os\n\n\n# --------------------------- Default Configuration ------------------------- #\nclass DefaultConfig(object):\n # Project name\n PROJECT = \"vCar\"\n # Turns on debugging features in Flask\n DEBUG = True\n # secret key\n SECRET_KEY = \"Zfe&45f4y5fhgse6f4e&@+-%_r4oij5hgfa&#@\"\n # Redis database url\n REDIS_URL = \"redis://:password@localhost:6379/0\"\n # Flask Session type\n SESSION_TYPE = 'filesystem'\n # Configuration for the Flask-Bcrypt extension\n BCRYPT_LEVEL = 12\n # Application root directory\n APP_ROOT = os.path.dirname(os.path.abspath('.'))\n # Application email\n MAIL_FROM_EMAIL = \"misaki.yooko@gmail.com\"\n # Upload directory\n UPLOAD_DIR = \"static/uploads/\"\n # Avater upload directory\n UPLOAD_AVATAR_DIR = os.path.join(UPLOAD_DIR, 'avatars/')\n ALLOWED_AVATAR_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif'])\n # Instance folder path\n INSTANCE_FOLDER_PATH = os.path.join(os.path.abspath('.'), 'instance')\n # INSTANCE_FOLDER_PATH = os.path.join('/home/karim/OpenXC/Dashboard/Flask/instance')\n # INSTANCE_FOLDER_PATH = os.path.join('I:/home/karim/OpenXC/Dashboard/Flask', 'instance')\n UPLOAD_USER = \"/uploads/avatars/\"\n UPLOAD_BRAND = \"/uploads/brands/\"\n UPLOAD_DRIVER = \"/uploads/drivers/\"\n UPLOAD_VEHICLE = \"/uploads/vehicles/\"\n UPLOAD_RECORDS = \"/uploads/records/\"\n UPLOAD_PLATFORM = \"/uploads/platforms/\"\n # Cache configuration\n CACHE_TYPE = 'null'\n CACHE_DEFAULT_TIMEOUT = 1\n TEMPLATES_AUTO_RELOAD = True\n # ToolbarExtention Configuration\n DEBUG_TB_ENABLED = True\n DEBUG_TB_INTERCEPT_REDIRECTS = False\n DEBUG_TB_TEMPLATE_EDITOR_ENABLED = True\n DEBUG_TB_PROFILER_ENABLED = True\n\n\n# ---------------------------- Debug Configuration -------------------------- #\n\n\nclass DebugConfig(DefaultConfig):\n # Turns on debugging features in Flask\n DEBUG = True\n # secret key\n SECRET_KEY = \"devlopement key\"\n # Avater upload directory\n UPLOAD_AVATAR_DIR = \"static/uploads/avatars/\"\n # Instance folder path\n INSTANCE_FOLDER_PATH = os.path.join('/tmp', 'instance')\n # Cache configuration\n CACHE_TYPE = 'null'\n CACHE_DEFAULT_TIMEOUT = 60\n"},{"alpha_fraction":0.5079169869422913,"alphanum_fraction":0.5100281238555908,"avg_line_length":29.395721435546875,"blob_id":"d10b8c890b520e103cf2bc657ab2655e290b0c23","content_id":"c2357f66b54ca503698930d3f55139bc1c30cb1a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5684,"license_type":"no_license","max_line_length":79,"num_lines":187,"path":"/app/dashboard/views.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import os\nfrom os.path import basename\nimport json\nfrom werkzeug import secure_filename\nfrom threading import Thread, active_count\nfrom flask import (\n Blueprint, render_template, request, flash, redirect, url_for, Response\n)\nfrom flask_login import (\n login_user, logout_user, login_required, current_user\n)\n\nfrom .models import Trace\nfrom .drivergraph import DriverGraph\nfrom .helpers import upload_trace, debug\nfrom .tasks import task_info, openxc_task\nfrom ..extensions import db\n\nfrom elasticsearch import Elasticsearch, helpers\n\n\ndashboard = Blueprint('dashboard', __name__, url_prefix='/dashboard')\n\n\n# -------------------- /dashboard : Dashboard home page -------------------- #\n\n# @dashboard.route('/stream')\n# def stream():\n# return Response(task_info(), mimetype=\"text/event-stream\")\n\n\n@dashboard.route('/')\n@login_required\ndef index():\n return render_template('dashboard/index.html', user=current_user)\n\n# ------------------------- /import : Import Addons ------------------------- #\n\n\n@dashboard.route('/import')\n@login_required\ndef import_index():\n return render_template('dashboard/import/index.html', user=current_user)\n\n# __________________________________________________________________________ #\n# | | #\n# | OPENXC | #\n# | | #\n# __________________________________________________________________________ #\n\n# ---------------- /dashboard/import/openxc : User home page --------------- #\n\n\n@dashboard.route('/import/openxc', methods=['GET', 'POST'])\n@login_required\ndef import_openxc():\n if request.method == 'POST':\n openxc = upload_trace('openxc', current_user.id)\n if openxc['status'] is True:\n trace = Trace(\n user_id=current_user.id,\n filename=basename(openxc['message']),\n path=openxc['message']\n )\n db.session.add(trace)\n db.session.commit()\n return json.dumps(openxc)\n return render_template('dashboard/import/openxc.html', user=current_user)\n\n# --------------- /import/openxc/process : Process openxc file ------------- #\n\n\n@dashboard.route('/import/openxc/process', methods=['GET', 'POST'])\n@login_required\ndef process_openxc():\n if request.method == 'POST':\n traces = request.form.getlist('traces')\n thread = Thread(target=openxc_task, args=[traces, current_user.id])\n thread.daemon = True\n thread.start()\n # thread.join(1)\n # print(active_count())\n else:\n return redirect(url_for('dashboard.import_openxc'))\n return render_template(\n 'dashboard/import/openxc_porcess.html',\n user=current_user\n )\n\n# ------------------------- /openxc : Openxc Dashboard ---------------------- #\n\n\n@dashboard.route('/openxc/', methods=['GET'])\n@login_required\ndef openxc():\n info = {\n index: 'openxc',\n type: 'driver_{}'.format(current_user.id)\n }\n return render_template(\n 'dashboard/openxc.html',\n user=current_user,\n elastic=info\n )\n\n# -------------------------- /openxc/map : Openxc Map ----------------------- #\n\n\n@dashboard.route('/openxc/map', methods=['GET'])\n@login_required\ndef openxc_map():\n info = {\n index: 'openxc',\n type: 'driver_{}'.format(current_user.id)\n }\n return render_template(\n 'dashboard/openxc_map.html',\n user=current_user,\n elastic=info\n )\n\n# ------------------------ /openxc/graph : Openxc Graph --------------------- #\n\n\n@dashboard.route('/openxc/graph', methods=['GET'])\n@login_required\ndef openxc_graph():\n index = \"openxc\"\n type = \"driver_{}\".format(current_user.id)\n size = 10000\n es = Elasticsearch([{\n 'host': 'localhost',\n 'port': 9200\n }])\n es.cluster.health(wait_for_status='yellow', request_timeout=1)\n if es.indices.exists(index=index):\n body = {\n \"sort\": [\n {\"timestamp\": {\"order\": \"asc\"}}\n ],\n \"query\": {\n \"bool\": {\n \"should\": [\n {\"term\": {\"name\": 'vehicle_speed'}},\n {\"term\": {\"name\": 'engine_speed'}},\n {\"term\": {\"name\": 'fuel_level'}},\n {\"term\": {\"name\": 'torque_at_transmission'}},\n {\"term\": {\"name\": 'transmission_gear_position'}}\n ]\n }\n }\n }\n res = es.search(index=index, doc_type=type, size=size, body=body)\n # print(\"Got {} Hits:\".format(res['hits']['total']))\n # for hit in res['hits']['hits']:\n # print(\"%(timestamp)s %(name)s: %(value)s\" % hit[\"_source\"])\n if res['hits']['total'] > 0:\n driver = DriverGraph()\n driver.build_digraph(res)\n graph = driver.vis_network(True)\n\n info = {\n index: 'openxc',\n type: 'driver_{}'.format(current_user.id)\n }\n return render_template(\n 'dashboard/openxc_graph.html',\n user=current_user,\n elastic=info,\n graph=graph\n )\n\n# ---------------------------- /kill : kill Server ------------------------- #\n\n\n# @dashboard.route('/kill')\n# def killo():\n# func = request.environ.get('werkzeug.server.shutdown')\n# if func is None:\n# raise RuntimeError('Not running with the Werkzeug Server')\n# func()\n# return \"Flask Development Server killed successfully!\"\n\n\n@dashboard.route('/restart')\ndef restart_server():\n os.kill(os.getpid(), signal.SIGHUP)\n"},{"alpha_fraction":0.47234365344047546,"alphanum_fraction":0.479186475276947,"avg_line_length":22.591928482055664,"blob_id":"d464a6001503075c14ed71d30bd654b1dec4c621","content_id":"a06572ad10ff156f4150fbfa0527122f7a983beb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5261,"license_type":"no_license","max_line_length":86,"num_lines":223,"path":"/app/carboard/forms/user.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField, PasswordField, BooleanField, HiddenField, SelectField\nfrom wtforms.validators import (\n DataRequired, Regexp, ValidationError, Email, Length, EqualTo\n)\nfrom flask_wtf.file import FileAllowed, FileField\nfrom ..models.user import User\nfrom ..constants import ROLES\n\n# ------------------------ custom validation methods ------------------------ #\n\n\ndef name_exist(form, field):\n if User.query.filter_by(username=field.data).exists():\n raise ValidationError('User already exists !')\n\n\ndef email_exist(form, field):\n if User.query.filter_by(email=field.data).exists():\n raise ValidationError('Email already exists !')\n\n\n# ---------------------------- User form classes ---------------------------- #\n\nclass UserForm(FlaskForm):\n \"\"\" User add/edit FlaskForm \"\"\"\n fullname = StringField(\n 'Full name',\n validators=[\n Length(min=3, max=50),\n Regexp(\n r'^[a-zA-Z0-9 ,._-]+$',\n message=(\"Full name is not correct !\")\n )\n ]\n )\n username = StringField(\n 'Username',\n validators=[\n DataRequired(),\n Length(min=3, max=50),\n Regexp(\n r'^[a-zA-A0-9_]+$',\n message=(\"Username is not correct !\")\n ),\n # name_exist\n ]\n )\n email = StringField(\n 'Email',\n validators=[\n DataRequired(),\n Email(),\n # email_exist\n ]\n )\n password = PasswordField(\n 'Password',\n validators=[\n DataRequired(),\n Length(min=4),\n EqualTo('password2', message='Password must much !')\n ]\n )\n password2 = PasswordField(\n 'Confirm Password',\n validators=[\n DataRequired()\n ]\n )\n avatar = FileField(\n 'Avatar', validators=[\n FileAllowed(['jpg', 'png', 'gif'], 'Images only plz :)')\n ]\n )\n role = SelectField(\n 'Role',\n coerce=int,\n choices=ROLES,\n validators=[\n DataRequired(),\n ]\n )\n\n\nclass RegisterForm(FlaskForm):\n \"\"\" RegisterForm FlaskForm \"\"\"\n username = StringField(\n 'Username',\n validators=[\n DataRequired(),\n Length(min=3, max=50),\n Regexp(\n r'^[a-zA-Z0-9_]+$',\n message=(\"Username is not correct !\")\n ),\n # name_exist\n ]\n )\n email = StringField(\n 'Email',\n validators=[\n DataRequired(),\n Email(),\n # email_exist\n ]\n )\n password = PasswordField(\n 'Password',\n validators=[\n DataRequired(),\n Length(min=4),\n EqualTo('password2', message='Password must much !')\n ]\n )\n password2 = PasswordField(\n 'Confirm Password',\n validators=[\n DataRequired()\n ]\n )\n avatar = FileField(\n 'Avatar', validators=[\n FileAllowed(['jpg', 'png', 'gif'], 'Images only plz :) ')\n ]\n )\n\n\nclass LoginForm(FlaskForm):\n \"\"\"Login FlaskForm\"\"\"\n # next = HiddenField()\n login = StringField(\n 'Username or email',\n validators=[\n DataRequired()\n ]\n )\n password = PasswordField(\n 'Password',\n validators=[\n DataRequired()\n ]\n )\n remember = BooleanField('Remember me?')\n # submit = SubmitField('Sign in')\n\n\nclass RecoverPasswordForm(FlaskForm):\n email = StringField(\n 'Email',\n validators=[\n DataRequired(),\n Email()\n ]\n )\n\n\nclass ChangePasswordForm(FlaskForm):\n activation_key = HiddenField()\n password = PasswordField(\n 'Password',\n validators=[\n DataRequired(),\n Length(min=4),\n EqualTo('password2', message='Password must much !')\n ]\n )\n password2 = PasswordField(\n 'Confirm Password',\n validators=[\n DataRequired()\n ]\n )\n\n\nclass ReauthForm(FlaskForm):\n password = PasswordField(\n 'Password',\n validators=[\n DataRequired(),\n Length(min=4),\n EqualTo('password2', message='Password must much !')\n ]\n )\n\n\nclass ProfileForm(FlaskForm):\n fullname = StringField(\n 'Full name',\n validators=[\n Length(min=3, max=50),\n Regexp(\n r'^[a-zA-Z0-9,._-]+$',\n message=(\"Full name is not correct !\")\n ),\n name_exist\n ]\n )\n username = StringField(\n 'Username',\n validators=[\n DataRequired(),\n Length(min=3, max=50),\n Regexp(\n r'^[a-zA-A0-9_]+$',\n message=(\"Username is not correct !\")\n ),\n name_exist\n ]\n )\n email = StringField(\n 'Email',\n validators=[\n DataRequired(),\n Email(),\n email_exist\n ]\n )\n avatar = FileField(\n 'Avatar', validators=[\n FileAllowed(['jpg', 'png', 'gif'], 'Images only plz :)')\n ]\n )\n"},{"alpha_fraction":0.4688202142715454,"alphanum_fraction":0.4733578860759735,"avg_line_length":38.255733489990234,"blob_id":"eecce5a182ec848b3c6c0b13548cea0b17ae94ec","content_id":"a1609c5517c4bbc3f8e4bf61ec1e2dc96fc3f75d","detected_licenses":["MIT","BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":22258,"license_type":"permissive","max_line_length":81,"num_lines":567,"path":"/app/plugins/driver_graph/src/algorithms/drivergraph.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\nimport os\nimport copy\nimport json\nimport yaml\nfrom random import choice\nimport networkx as nx\nfrom networkx.drawing.nx_pydot import write_dot, read_dot\nfrom networkx.readwrite import json_graph\nimport networkx.algorithms.isomorphism as iso\n\nfrom .settings import DefaultSettings\nfrom .settings import DATA, WEIGHT, COLORS\n\n# ___________________________ Start : DriverGraph ___________________________ #\n\n\nclass DriverGraph:\n \"\"\" this class create a driver graph using registred\n driver traces, after an abstraction algorithm wich\n is configured using the settings configuration\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"inisialize the DriverGraph with optional arguments\"\"\"\n self.name = kwargs.get('name', 'Driver Bihavior Graph')\n self.settings = kwargs.get('settings', DefaultSettings)\n self.max_nodes = kwargs.get('max_nodes', None)\n self.node_id = kwargs.get('init_node', 0)\n self.G = kwargs.get('graph', None) # Networkx graph\n self.last_id = 0 # internal param\n # ====================================================================== #\n\n @property\n def settings(self):\n return self._settings\n\n @settings.setter\n def settings(self, settings):\n self._settings = settings\n # ====================================================================== #\n\n def __str__(self):\n return \"<DriverGraph name: {}, time: {}, settings: {}>\".format(\n self.name, self.time, self.settings\n )\n # ====================================================================== #\n\n def show_attributes(self):\n \"\"\"Show DriverGraph attributes\"\"\"\n print(\"\\nATTRIBUTES:\")\n for node_id, attributes in self.G.nodes(data=True):\n print(\"Node {} :: [\".format(node_id), end='')\n for name, value in attributes.items():\n print(\"{} |\".format(value), end=' ')\n print(\"]\")\n\n def show_edges(self):\n \"\"\"Show DriverGraph edges\"\"\"\n print(\"\\nEDGES :\")\n for id1, id2 in self.G.edges():\n # if id1 == 4 and id2 == 6:\n # print(self.G.node[id1])\n # print(self.G.node[id2])\n print(\"({}, {}, {})\".format(\n id1, id2, self.G[id1][id2]['weight']\n ), end=', ')\n print()\n\n def debug(self, id=None):\n if id is None:\n for node_id, attributes in self.G.nodes(data=True):\n print('-------------- Node {} --------------'.format(node_id))\n for att, value in attributes.items():\n print(\">>[[{}]] {} : {}\".format(id, att, value))\n else:\n print('.', end=\"\")\n\n def graph_summary(self):\n print(\"\\nNodes: {}\".format(len(self.G.nodes(data=True))))\n print(\"\\nEdges: {}\".format(len(self.G.edges())))\n # ====================================================================== #\n\n def create_digraph(self, filename=None):\n \"\"\"Create a Directed Graph from the openxc traces\"\"\"\n if filename is None:\n raise ValueError('Plz provide an openxc trace file.')\n\n data = DATA\n self._init_graph()\n with open(filename, 'r') as f:\n for line in f:\n temp = json.loads(line)\n signal = temp.get('name')\n value = self._invoke_ranges(signal, temp.get('value'))\n if value is None:\n continue\n data = copy.deepcopy(data)\n if signal in data:\n data[signal] = value\n # data[signal] = str(value)\n if self._create_node(data) is False:\n break\n print('Done')\n # ====================================================================== #\n\n def _create_node(self, data):\n \"\"\"Create node if doesn't exist, and create or update edges\"\"\"\n if self.max_nodes is not None and self.node_id >= self.max_nodes:\n return False\n # print(\"---------------------------------------------------->\")\n id = self._find_node(data)\n # print(\"---------------------------------------------------->\")\n # self.debug(self.node_id)\n if id is None:\n self.node_id += 1\n self.G.add_node(self.node_id, data)\n self.G.add_edge(self.last_id, self.node_id, weight=WEIGHT)\n # print(self.G.nodes())\n # print(self.G.edges())\n self.last_id = self.node_id\n # print(self.node_id, self.last_id)\n else:\n if self.G.has_edge(self.last_id, id):\n # print(\"Edge exist\")\n self.G[self.last_id][id]['weight'] += WEIGHT\n else:\n pass\n # print(\"New Edge\")\n self.G.add_edge(self.last_id, id, weight=WEIGHT)\n # print(self.G.edges())\n self.last_id = id\n # ====================================================================== #\n\n def _init_graph(self):\n \"\"\" inisialize the graph with the first ZERO node\"\"\"\n # self.G = nx.DiGraph(name=self.name)\n self.G = nx.DiGraph()\n self.G.add_node(self.node_id, DATA)\n # ====================================================================== #\n\n def _find_node(self, data):\n \"\"\"\n Loop throw the whole graph!, to check if a node hase the same\n attributes as the data argument, if so return the node id,\n otherwise return None.\n \"\"\"\n for node_id, attributes in self.G.nodes(data=True):\n # print(\">> {} | {}\".format(node_id, attributes))\n if data == attributes:\n # if node_id >= 5:\n # print(\"data == attributes\")\n # print(node_id, ' -- ' ,end=\"\")\n # for att, value in attributes.items(): print(value ,end=\"\")\n # print(' -- ' ,end=\"\")\n # for att, value in data.items(): print(value ,end=\"\")\n # print()\n return node_id\n else:\n # if node_id >= 5:\n # print(\"data <> attributes\")\n # print(node_id, ' -- ', end=\"\")\n # for att, value in attributes.items(): print(value , end=\"\")\n # print(' -- ', end=\"\")\n # for att, value in data.items(): print(value , end=\"\")\n # print()\n pass\n return None\n # ====================================================================== #\n\n def _invoke_ranges(self, signal, value):\n \"\"\"get the range value from the corret signal\"\"\"\n value = str(value).lower()\n if signal == \"fuel_level\":\n return self._get_range_key(value, self.settings.FUEL_EFFICIENCY)\n elif signal == \"transmission_gear_position\":\n return self._get_range_key(value, self.settings.GEAR_POSITION)\n elif signal == \"vehicle_speed\":\n return self._get_range_key(value, self.settings.VEHICLE_SPEED)\n elif signal == \"engine_speed\":\n return self._get_range_key(value, self.settings.ENGINE_SPEED)\n elif signal == \"torque_at_transmission\":\n return self._get_range_key(value, self.settings.TORQUE)\n # elif signal == \"accelerator_pedal_position\":\n # return self._get_range_key(value, self.settings.ACCEL_PEDAL)\n # elif signal == \"steering_wheel_angle\":\n # return self._get_range_key(value, self.settings.STEERING)\n # elif signal == \"brake_pedal_status\":\n # return self._get_range_key(value, self.settings.PARKING_BRAKE)\n else:\n # print(\"\"\"\n # Error: The signal <<{}>> is not included in the algorithm!\\n\n # Please take a look at the _INVOKE_RANGES function to add it\\n\n # [line 112].\n # \"\"\".format(signal))\n return None\n # ====================================================================== #\n\n def _get_range_key(self, value, d):\n \"\"\"retrun the key from the settings list\"\"\"\n try:\n value = round(float(value), 2)\n debug_key = \"\"\n for key in d:\n if d[key][0] <= value <= d[key][1]:\n # print(\"V({}) =>\".format(key), end=' ')\n return key\n debug_key = key\n print(\"Warning: {} Not Found for {}!!\".format(value, d[debug_key]))\n return -99\n except:\n for key in d:\n if d[key] == value:\n # print(\"G({}) ====>\".format(key), end=' ')\n return key\n print(\"Warning: value {} Not Found in any range !\".format(value))\n return -99\n # ====================================================================== #\n\n def _ranges2real(self, signal, value):\n \"\"\"get the real value from signal range \"\"\"\n value = int(value)\n if value == 0:\n return 0\n if signal == \"fuel_level\":\n return json.dumps(self.settings.FUEL_EFFICIENCY[value])\n elif signal == \"transmission_gear_position\":\n return json.dumps(self.settings.GEAR_POSITION[value])\n elif signal == \"vehicle_speed\":\n return json.dumps(self.settings.VEHICLE_SPEED[value])\n elif signal == \"engine_speed\":\n return json.dumps(self.settings.ENGINE_SPEED[value])\n elif signal == \"torque_at_transmission\":\n return json.dumps(self.settings.TORQUE[value])\n elif signal == \"accelerator_pedal_position\":\n return json.dumps(self.settings.ACCEL_PEDAL[value])\n elif signal == \"steering_wheel_angle\":\n return json.dumps(self.settings.STEERING[value])\n elif signal == \"brake_pedal_status\":\n return json.dumps(self.settings.PARKING_BRAKE[value])\n else:\n return None\n # ====================================================================== #\n\n def factorize_graph(self, factor=1):\n \"\"\"Show DriverGraph attributes\"\"\"\n try:\n for node_id, attributes in self.G.nodes(data=True):\n for name, value in attributes.items():\n self.G.node[node_id][name] = float(value) * factor\n for id1, id2 in self.G.edges():\n self.G[id1][id2]['weight'] *= factor\n except ValueError as e:\n print(\"Errror: \", e)\n # ====================================================================== #\n\n def export_graphml(self, filename=None):\n \"\"\"Export the DriverGraph to graphML format\"\"\"\n if filename is not None:\n nx.write_graphml(self.G, \"{}.graphml\".format(filename))\n\n def import_graphml(self, filename=None):\n \"\"\"Import the DriverGraph from a graphML file\"\"\"\n if filename is not None:\n self.G = nx.read_graphml(filename)\n # ====================================================================== #\n\n def export_json(self, filename=None):\n \"\"\"Export the DriverGraph to Json format\"\"\"\n if filename is not None:\n g_json = json_graph.node_link_data(self.G)\n json.dump(g_json, open(\"{}.json\".format(filename), 'w'), indent=4)\n\n def import_json(self, filename=None):\n \"\"\"Import the DriverGraph from a Json file\"\"\"\n if filename is not None:\n with open(filename) as f:\n js_graph = json.load(f)\n self.G = json_graph.node_link_graph(js_graph)\n # ====================================================================== #\n\n def export_gexf(self, filename=None):\n \"\"\"Export the DriverGraph to GEXF format\"\"\"\n if filename is not None:\n nx.write_gexf(self.G, \"{}.gexf\".format(filename))\n print(\"File exported\")\n\n def import_gexf(self, filename=None):\n \"\"\"Import the DriverGraph from a GEXF file\"\"\"\n if filename is not None:\n self.G = nx.read_gexf(filename)\n # ====================================================================== #\n\n def export_gxl(self, filename=None):\n \"\"\"Export the DriverGraph to GXL format\"\"\"\n if filename is not None:\n write_dot(self.G, \"helpers/_tmp/xfile.dot\")\n os.system(\"dot2gxl helpers/_tmp/xfile.dot -o {}\".format(filename))\n os.system(\"sed -i -e '/name=\\\"name\\\"/ ,+2d' {}\".format(filename))\n\n def import_gxl(self, filename=None, digraph=True):\n \"\"\"Import the DriverGraph from a GXL file\"\"\"\n if filename is not None:\n os.system(\"gxl2dot {} -o helpers/_tmp/ifile.dot\".format(filename))\n self.G = read_dot(\"helpers/_tmp/ifile.dot\")\n # ====================================================================== #\n\n def browse_sigma(self, filename=None):\n \"\"\"View the Graph in borwser using sigmajs and GEXF format\"\"\"\n self.export_gexf(\"helpers/sigma/driver_graph\")\n os.system(\"cd helpers/sigma && python2 -m SimpleHTTPServer 8002\")\n # ====================================================================== #\n\n def _build_vis_network(self, nodes, edges, physics=False):\n # \"barnesHut\": {{\"centralGravity\": 1}},\n html = \"\"\"\n <script type=\"text/javascript\">\n var nodes = new vis.DataSet({nodes});\n var edges = new vis.DataSet({edges});\n var container = document.getElementById(\"driver\");\n var data = {{nodes: nodes, edges: edges}};\n var options = {{\n nodes: {{ size: 25, font: {{size: 14}} }},\n edges: {{\n font: {{size: 10, align: 'middle'}},\n color: 'gray',\n arrows: {{ to: {{enabled: true, scaleFactor: 0.5}} }},\n smooth: {{enabled: true}}\n }},\n layout:{{\n hierarchical: false\n }},\n physics: {{\n enabled: {physics},\n \"timestep\": 1,\n stabilization: true\n }}\n }};\n var network = new vis.Network(container, data, options);\n </script>\n \"\"\"\n html = html.format(\n nodes=json.dumps(nodes),\n edges=json.dumps(edges),\n physics=json.dumps(physics)\n )\n # filename = \"helpers/vis/index.html\"\n # file = open(filename, \"w\")\n # file.write(html)\n # file.close()\n\n return html\n\n def vis_network(self, physics=False):\n nodes = []\n for node_id, attributes in self.G.nodes(data=True):\n tmp = {'id': node_id, 'label': node_id, 'color': choice(COLORS)}\n tmp['title'] = \"<pre>{}</pre>\".format(yaml.dump(\n {k: self._ranges2real(k, v) for k, v in attributes.items()},\n default_flow_style=False\n ))\n nodes.append(tmp)\n edges = []\n for id1, id2 in self.G.edges():\n tmp = {\n 'from': id1,\n 'to': id2,\n 'color': choice(COLORS)\n }\n try:\n tmp['label'] = self.G[id1][id2]['weight']\n except:\n tmp['label'] = self.G[id1][id2][0]['weight']\n edges.append(tmp)\n return self._build_vis_network(nodes, edges, physics)\n # os.system(\"cd helpers/vis && python2 -m SimpleHTTPServer 8001\")\n # ====================================================================== #\n\n @staticmethod\n def build_multiple(files=None, max_nodes=None, export=False):\n if type(files) is list:\n graphs = []\n for file in files:\n graph = DriverGraph(max_nodes=max_nodes, name=\"file\")\n graph.create_digraph(filename=file)\n graphs.append(graph)\n if export is True:\n filename = os.path.splitext(os.path.basename(file))[0]\n graph.export_gxl(\"{}.gxl\".format(filename))\n print(\"[{}]: {} nodes, edges: {}\".format(\n filename, len(graph.G), len(graph.G.edges())\n ))\n return graphs\n else:\n raise ValueError(\"Please provide a list of trace files\")\n # ====================================================================== #\n\n @staticmethod\n def divide_graph(G=None, nparts=2):\n if nparts < 1:\n raise ValueError(\"you cant divide a graph by {}.\".format(nparts))\n elif nparts == 1:\n return self.G\n else:\n parts = [[] for i in range(nparts)]\n # print(zip(G, part))\n # for u, i in zip(G, part):\n # G.remove_node(1)\n # graph_size = len(G)\n # print(graph_size)\n # fist_node = [n for n, d in G.in_degree().items() if d == 0]\n # if len(fist_node) != 1:\n # raise ValueError(\"The graph has\n # {} root nodes.\".format(len(fist_node)))\n\n # for i in nparts:\n # print(i * graph_size / nparts)\n # print(graph_size / nparts)\n # # for i in range(i * graph_size / nparts, (i + 1) *\n # graph_size / nparts):\n # # print(i * graph_size / nparts)\n # # for node_id, attributes in self.G.nodes(data=True):\n # # print(i)\n # pass\n # # parts[i].append(u)\n # # return objval, parts\n # ====================================================================== #\n\n @staticmethod\n def is_isomorphic(G1=None, G2=None, weight=None):\n \"\"\"\n - Structures that are the same except for relabelling\n (can be the same using relabelling technique)\n are called ISOMORPHIC structures.\n - We can match also nodes attributes & edges attributes\n edge attributes are implemented\n - The function return True if G1 & G2 are isomorphic\n False if not\n \"\"\"\n if G1 is None or G2 is None:\n raise ValueError(\"Please provide two graphs.\")\n if weight is None:\n return nx.is_isomorphic(G1, G2)\n else:\n em = iso.numerical_edge_match('weight', weight)\n return nx.is_isomorphic(G1, G2, edge_match=em)\n\n# ____________________________ End : DriverGraph ____________________________ #\n\nif __name__ == \"__main__\":\n try:\n print('working ...')\n Files = [\n # \"traces/usa/downtown-west.json\",\n # \"traces/usa/downtown-west2.json\",\n # \"traces/usa/uptown-west.json\",\n # \"traces/usa/uptown-west2.json\",\n # \"traces/usa/uptown-crossdown.json\",\n # \"traces/usa/uptown-crosstown.json\",\n # \"traces/usa/downtown-crosstown.json\",\n # \"traces/usa/downtown-east.json\",\n # \"traces/scenarios/localwithgps.json\",\n # \"traces/scenarios/highway-speeding.json\",\n ]\n # graphs = DriverGraph.build_multiple(Files, max_nodes=None, export=True)\n # driver = DriverGraph(max_nodes=99)\n driver = DriverGraph()\n # driver.create_digraph(\"traces/usa/downtown-crosstown.json\")\n # driver.export_gxl(\"downtown-crosstown.gxl\")\n driver.create_digraph(\"traces/usa/downtown-east.json\")\n driver.export_gxl(\"downtown-east.gxl\")\n print(len(driver.G))\n print(len(driver.G.edges()))\n # driver.export_json('downtown-crosstown')\n # driver.show_attributes()\n # driver.show_edges()\n # driver.vis_network(True)\n # saya = DriverGraph()\n # driver.import_json(\"saya.json\")\n # saya.import_graphml(\"saya.graphml\")\n # driver.draw_graph('image.png')\n # driver.browse_sigma()\n # driver.export_gxl(\"graph.gxl\")\n # driver.factorize_graph(1.5)\n # driver.export_gxl(\"factorized.gxl\")\n # print(\"-----\")\n # print(len(driver.G))\n # print(len(driver.G.edges()))\n # print(len(driver.G))\n # print(len(driver.G.edges()))\n # driver.vis_network(True)\n # driver.export_gxl(\"graph.gxl\")\n # if DriverGraph.is_isomorphic(driver.G, saya.G):\n # print(\"G1 & G2 are ISOMORPHIC\")\n # else:\n # print(\"G1 & G2 are NOT isomorphic\")\n # driver.export_graphml('downtown-crosstown')\n # driver.export_json('downtown-crosstown')\n #\n # driver.graph_summary()\n # print('---------------')\n # driver.divide_graph(driver.G, 4)\n # print('---------------')\n # driver.graph_summary()\n except ValueError as e:\n print(e)\n # except:\n # print(\"Error !!\")\n\n #\n #\n #\n #\n #\n #\n #\n #\n #\n #\n #\n #\n #\n #\n #\n #\n #\n #\n\n # print(\"{} : {}\".format(temp.get('name'), temp.get('value')))\n # print('***********************')\n\n # print('----')\n # print(\"nam# print(item)\n # print('----')e: {} | value: {}\".format(\n # x.get('name', 'NAME'), x.get('value', 'VALUE')))\n # while True:\n # try:\n # parser = ijsonself. (line)\n # for prefix, event, value in parser:\n # if prefix == \"name\":\n # print(value)\n # if prefix == \"value\":\n # print(value)\n # if prefix == \"timestamp\":\n # print(value)\n # except ValueError:\n # line += next(f)\n\n # print(\"prefix: {} | value: {} | event: {}\".format(prefix, value, event))\n # ret = {'builders': {}}\n # if (prefix, event) == ('builders', 'map_key'):\n # buildername = value\n # ret['builders'][buildername] = {}\n # elif prefix.endswith('.shortname'):\n # ret['builders'][buildername]['shortname'] = value\n #\n # return ret\n\n # driver.create_digraph(\"traces/downtown-crosstown.json\")\n # except Exception as e:\n # print('>> {}'.format(e))\n\n #\n # data_write_file = sys.argv[2]\n # data_read_file = open(sys.argv[1])\n"},{"alpha_fraction":0.8333333134651184,"alphanum_fraction":0.8333333134651184,"avg_line_length":24,"blob_id":"391281fbc8e102385b22556afc3b0b88524a53d4","content_id":"3a5db0490a57a4bf26fbdac5fdab201eafa8677d","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":24,"license_type":"permissive","max_line_length":24,"num_lines":1,"path":"/app/plugins/hello_world/src/__init__.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from .views import hello"},{"alpha_fraction":0.2944444417953491,"alphanum_fraction":0.42679738998413086,"avg_line_length":20.542253494262695,"blob_id":"ab4252cad013d8b796d88d528e7b73aff18059e6","content_id":"8ef5c99912192a59b2826e439c5c75438c6d70b6","detected_licenses":["MIT","BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3060,"license_type":"permissive","max_line_length":56,"num_lines":142,"path":"/app/plugins/driver_graph/src/algorithms/settings.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"\nDATA = {\n 'fuel_level': 0,\n 'transmission_gear_position': 0,\n 'vehicle_speed': 0,\n 'engine_speed': 0,\n 'torque_at_transmission': 0,\n}\n# 'accelerator_pedal_position': 0,\n# 'steering_wheel_angle': 0,\n# 'brake_pedal_status': 0,\n\nWEIGHT = 1\n\n# For key in range d\n# if d[key][0] <= value <= d[key][1]:\n# if d[key][0] <= value < d[key][1]:\n# End For\n\n\nclass DefaultSettings(object):\n # 1 - fuel_level [ 0 => 120] (%)\n FUEL_EFFICIENCY = {\n 1: [0, 20],\n 2: [20, 40],\n 3: [40, 60],\n 4: [60, 80],\n 5: [80, 100],\n 6: [100, 120]\n }\n # 2 - transmission gear position [ -1 => 7 ]\n GEAR_POSITION = {\n -1: \"reverse\",\n 0: \"neutral\",\n 1: \"first\",\n 2: \"second\",\n 3: \"third\",\n 4: \"fourth\",\n 5: \"fifth\",\n 6: \"sixth\",\n 7: \"seventh\"\n }\n # 3 - vehicle speed [ 0 => 800] (km/h)\n VEHICLE_SPEED = {\n 1: [0, 10],\n 2: [10, 20],\n 3: [20, 30],\n 4: [30, 40],\n 5: [40, 50],\n 6: [50, 60],\n 7: [60, 655],\n 8: [655, 800]\n }\n # 4 - engine speed [ 0 => 20000] (RPM)\n ENGINE_SPEED = {\n 1: [0, 500],\n 2: [500, 1000],\n 3: [1000, 1500],\n 4: [1500, 2000],\n 5: [2000, 2500],\n 6: [2500, 3000],\n 7: [3000, 4000],\n 8: [4000, 16382],\n 9: [16382, 20000]\n }\n # 5 - torque at transmission [ -500 => 2000] (Nm)\n TORQUE = {\n 1: [-500, -100],\n 2: [-100, 0],\n 3: [0, 50],\n 4: [50, 100],\n 5: [100, 150],\n 6: [150, 200],\n 7: [200, 500],\n 8: [500, 1500],\n 9: [1500, 2000]\n }\n # 6 - accelerator pedal position [ 0 => 100] (%)\n ACCEL_PEDAL = {\n 1: [0, 10],\n 2: [10, 20],\n 3: [20, 30],\n 4: [30, 40],\n 5: [40, 50],\n 6: [50, 60],\n 7: [60, 100]\n }\n # 7 - steering wheel angle [ -600 => +600] (Degrees)\n STEERING = {\n 1: [-600, -300],\n 2: [-300, 100],\n 3: [100, 300],\n 4: [300, 600]\n }\n # 8 - parking brake status [ True or False]\n PARKING_BRAKE = {\n 1: \"true\",\n 2: \"false\"\n }\n # 9 - parking pedal status [ True or False]\n PARKING_PEDAL = {\n 1: \"true\",\n 2: \"false\"\n }\n\nCOLORS = [\n '#FFFF00',\n '#FB7E81',\n '#7BE141',\n '#6E6EFD',\n '#C2FABC',\n '#FFA807',\n '#FFEB3B',\n '#9C27B0',\n '#FF5722',\n '#673AB7',\n '#00BCD4',\n '#8BC34A',\n '#FF5722',\n '#6E6EFD',\n '#607D8B',\n]\n\n# All openxc data\n# DATA = {\n# 'accelerator_pedal_position': 0,\n# 'brake_pedal_status': 0,\n# 'button_state': 0,\n# 'door_status': 0,\n# 'engine_speed': 0,\n# 'fuel_consumed_since_restart': 0,\n# 'fuel_level': 0,\n# 'headlamp_status': 0,\n# 'ignition_status': 0,\n# 'latitude': 0,\n# 'longitude': 0,\n# 'odometer': 0,\n# 'steering_wheel_angle': 0,\n# 'torque_at_transmission': 0,\n# 'transmission_gear_position': 0,\n# 'vehicle_speed': 0,\n# 'windshield_wiper_status': 0\n# }\n"},{"alpha_fraction":0.6064339280128479,"alphanum_fraction":0.6071184277534485,"avg_line_length":35.525001525878906,"blob_id":"3aae5abf440eceea54ea4f2274ba5c703d199175","content_id":"97ff209ca20c33743de6a45d6a8536b4613f61fc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1461,"license_type":"no_license","max_line_length":83,"num_lines":40,"path":"/app/dashboard/models.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from uuid import uuid4\n\nfrom .constants import STRING_LEN, PROCESSED, NOT_PROCESSED\nfrom .helpers import get_current_time\nfrom ..extensions import db\n\n# -------------------------------- Trace Model ------------------------------ #\n\n\nclass Trace(db.Model):\n\n __tablename__ = 'traces'\n\n id = db.Column(db.Integer, primary_key=True)\n filename = db.Column(db.String(STRING_LEN))\n path = db.Column(db.String(STRING_LEN), nullable=False, unique=True)\n user_id = db.Column(db.Integer, db.ForeignKey('users.id'), nullable=False)\n processed = db.Column(db.SmallInteger, default=NOT_PROCESSED)\n created = db.Column(db.DateTime(), default=get_current_time())\n user = db.relationship('User', backref=db.backref('traces', lazy='dynamic'))\n\n def __init__(self, user_id, filename=None, path=None, processed=NOT_PROCESSED):\n self.filename = filename\n self.path = path\n self.user_id = user_id\n self.processed = processed\n\n def is_processed(self):\n return self.processed == PROCESSED\n\n def __repr__(self):\n return '<Trace {}>'.format(self.filename)\n\n @classmethod\n def get_by_user_id(cls, user_id, processed=NOT_PROCESSED):\n return cls.query.filter(\n Trace.user_id == user_id and Trace.processed == processed\n ).all()\n# (db.session.query(Address).filter_by(person_id=current_user.id).count())\n# -------------------------------- Trace Model ------------------------------ #\n"},{"alpha_fraction":0.3473523259162903,"alphanum_fraction":0.3517491817474365,"avg_line_length":34.83802795410156,"blob_id":"59ecc059a7d7569a69c34a6e911cc3c84047833f","content_id":"d2f49271e79da20d9cfe7f8d397c7deb3c6b0059","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5231,"license_type":"no_license","max_line_length":138,"num_lines":142,"path":"/app/importer/datasets/t-drive/elastic.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import redis\r\nimport csv\r\nimport copy\r\nimport json\r\nfrom time import strftime, sleep\r\nfrom elasticsearch import Elasticsearch, helpers\r\nfrom .constants import *\r\n\r\n# ----------------- Setting up tools --------------------------------------- #\r\nred = redis.StrictRedis(\r\n host=REDIS_HOST,\r\n port=REDIS_PORT,\r\n db=REDIS_DB\r\n)\r\n\r\n\r\ndef task_info(name='info'):\r\n p = red.pubsub(ignore_subscribe_messages=True)\r\n p.subscribe(name)\r\n thread = p.run_in_thread(sleep_time=0.001)\r\n # thread.stop()\r\n\r\n# def task_info(name='info'):\r\n# pubsub = red.pubsub()\r\n# pubsub.subscribe(name)\r\n# for message in pubsub.listen():\r\n# # print(\"[+] Message: {}\".format(message))\r\n# yield 'data: {}\\n\\n'.format(message['data'])\r\n\r\n# ----------------- Indexing with Bulk : Large files ----------------------- #\r\n\r\n\r\ndef index_bulk(traces, dataset, user_id, bulk_size=BULK_SIZE):\r\n try:\r\n es = Elasticsearch([{\r\n 'host': ELASTICSEARCH_HOST,\r\n 'port': ELASTICSEARCH_PORT\r\n }])\r\n\r\n index = dataset.slug\r\n type = \"dataset\"\r\n es.cluster.health(wait_for_status='yellow', request_timeout=30)\r\n mapping = {\r\n type: {\r\n \"properties\": {\r\n \"user\": {\r\n \"type\": \"integer\"\r\n },\r\n \"vehicle\": {\r\n \"type\": \"integer\"\r\n },\r\n \"driver\": {\r\n \"type\": \"integer\"\r\n },\r\n \"class\": {\r\n \"type\": \"string\"\r\n },\r\n \"name\": {\r\n \"type\": \"string\",\r\n \"index\": \"not_analyzed\"\r\n },\r\n \"value\": {\r\n \"type\": \"string\",\r\n \"index\": \"not_analyzed\"\r\n },\r\n \"timestamp\": {\r\n \"type\": \"date\",\r\n \"format\": \"yyyy-MM-dd HH:mm:ss\"\r\n }\r\n }\r\n }\r\n }\r\n\r\n if not es.indices.exists(index=index):\r\n es.indices.create(index=index, ignore=400)\r\n es.indices.put_mapping(index=index, doc_type=type, body=mapping)\r\n\r\n for trace in traces:\r\n actions = []\r\n i = 1\r\n ignored = 0\r\n with open(trace, 'r') as f:\r\n reader = csv.reader(f)\r\n for line in reader:\r\n try:\r\n vehicle = int(line[0])\r\n driver = int(line[0])\r\n lat = float(line[2])\r\n lng = float(line[3])\r\n timestamp = line[1]\r\n # adding Latitude\r\n actions.append({\r\n \"_index\": dataset.slug,\r\n \"_type\": 'dataset',\r\n \"_source\": {\r\n \"user\": user_id,\r\n \"vehicle\": vehicle,\r\n \"driver\": driver,\r\n \"class\": 3,\r\n \"name\": 'latitude',\r\n \"value\": lat,\r\n \"timestamp\": timestamp\r\n }\r\n })\r\n # adding Longitude\r\n actions.append({\r\n \"_index\": dataset.slug,\r\n \"_type\": 'dataset',\r\n \"_source\": {\r\n \"user\": user_id,\r\n \"vehicle\": vehicle,\r\n \"driver\": driver,\r\n \"class\": 3,\r\n \"name\": 'longitude',\r\n \"value\": lng,\r\n \"timestamp\": timestamp\r\n }\r\n })\r\n if i*2 % bulk_size == 0:\r\n helpers.bulk(es, actions)\r\n actions = []\r\n print(\" ============================================> Bulk : i = \" + str(i))\r\n i += 1\r\n except:\r\n ignored += 1\r\n if i < bulk_size:\r\n print(\" ============================================> Bulk Rest : i = \" + str(i))\r\n helpers.bulk(es, actions)\r\n # bulk the actions dict to elastic\r\n helpers.bulk(es, actions)\r\n # End processing files\r\n if ignored > 0:\r\n red.publish('info', 'background indexing completed with {} ignored line(s)'.format(ignored))\r\n else:\r\n red.publish('info', 'Background indexing task [<b>{}</b> file<small>(s)</small>] completed successfully.'.format(len(traces)))\r\n return True\r\n sys.exit()\r\n except Exception as e:\r\n print('Exception OUT: ', e)\r\n red.publish('info', 'Exception: {}'.format(e))\r\n return False\r\n sys.exit()\r\n"},{"alpha_fraction":0.5856115221977234,"alphanum_fraction":0.5856115221977234,"avg_line_length":27.95833396911621,"blob_id":"8db8eff8fda8baf25e278bd728784656ab6d8b78","content_id":"1b1e7748fcbefe61a19745c4c1dc88e50d0365d0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":695,"license_type":"no_license","max_line_length":81,"num_lines":24,"path":"/app/importer/models.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from ..constants import STRING_LEN, ACTIVE\nfrom ..helpers import get_current_time\nfrom ...extensions import db\n\n# -------------------------------- Driver Model ------------------------------- #\n\n\nclass Country(db.Model):\n \"\"\" Country Model \"\"\"\n __tablename__ = 'countries'\n\n id = db.Column(db.Integer, primary_key=True)\n title = db.Column(db.String(STRING_LEN), index=True)\n code = db.Column(db.String(STRING_LEN))\n status = db.Column(db.SmallInteger, default=ACTIVE)\n created = db.Column(db.DateTime(), default=get_current_time())\n\n\n def __init__(self, title, code):\n self.title = title\n self.code = code\n\n def __repr__(self):\n return self.title\n"},{"alpha_fraction":0.5254515409469604,"alphanum_fraction":0.5303776860237122,"avg_line_length":26.68181800842285,"blob_id":"15e1e069fef1e5b94742b03f1a79ae0286769b5e","content_id":"4bc3c861fb7ca510399759853fd535c677eb3d6d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":609,"license_type":"no_license","max_line_length":82,"num_lines":22,"path":"/app/carboard/forms/drivetype.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField\nfrom wtforms.validators import DataRequired, Regexp, Length\n\nfrom ..models.drivetype import DriveType\n\n# ----------------------------- DriveType form classes ---------------------------\n\n\nclass DriveTypeForm(FlaskForm):\n \"\"\" Driver type add/edit FlaskForm \"\"\"\n name = StringField(\n 'Driver type name',\n validators=[\n DataRequired(),\n Length(min=3, max=50),\n Regexp(\n r'^[a-zA-Z ]+$',\n message=(\"Driver type name is not correct !\")\n ),\n ]\n )\n"},{"alpha_fraction":0.40299883484840393,"alphanum_fraction":0.4053056538105011,"avg_line_length":44.6315803527832,"blob_id":"136265614911504d5ed09380f14c16ee58482a17","content_id":"c936ebdfa8bcd12b2093f9329a961454445debb7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"HTML","length_bytes":4335,"license_type":"no_license","max_line_length":209,"num_lines":95,"path":"/templates/carboard/platform/index.html","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"{% extends \"layout.html\" %}\n\n{% from \"carboard/macros.html\" import render_pagination %}\n\n{% block title %}\n List of platforms | {{ super() }}\n{% endblock %}\n\n{% block content_header %}\n\t<section class=\"content-header\">\n\t\t<h1>\n\t\tList of platforms\n\t\t</h1>\n\t\t<ol class=\"breadcrumb\">\n\t\t\t<li><a href=\"#\"><i class=\"fa fa-dashboard\"></i>Home</a></li>\n\t\t\t<li><a href=\"#\">Platform</a></li>\n\t\t\t<li class=\"active\">List of platforms</li>\n\t\t</ol>\n\t</section>\n{% endblock content_header %}\n\n{% block content %}\n\n <div class=\"row\">\n <div class=\"col-md-12\">\n <div class=\"box\">\n <div class=\"box-header\">\n <div class=\"box-actions\">\n <a href=\"{{ url_for('carboard.newPlatform') }}\" class=\"btn btn-primary\">\n Add new platform\n </a>\n </div>\n <div class=\"box-tools\">\n <div class=\"input-group input-group-sm\" style=\"width: 150px;\">\n <input type=\"text\" name=\"table_search\" class=\"form-control pull-right\" placeholder=\"Search\">\n\n <div class=\"input-group-btn\">\n <button type=\"submit\" class=\"btn btn-default\">\n <i class=\"fa fa-search\"></i>\n </button>\n </div>\n </div>\n </div>\n </div>\n <div class=\"box-body no-padding\">\n <table class=\"table table-hover\">\n <tr>\n <th>ID</th>\n <th>Logo</th>\n <th>Title</th>\n <th>Website</th>\n <th>Status</th>\n <th>Actions</th>\n\n </tr>\n\n {% for platform in platforms.items %}\n <tr>\n <td><a href=\"{{ url_for('carboard.showPlatform', id=platform.id) }}\">{{ platform.id }}</a></td>\n <td><a href=\"{{ url_for('carboard.showPlatform', id=platform.id) }}\">\n {% if platform.logo %}\n <img class=\"avatar table-avatar\" src=\"{{ resized_img_src(config.UPLOAD_PLATFORM + platform.logo, width=60) }}\" alt=\"{{ platform.name }}\">\n {% else %}\n <img class=\"avatar table-avatar\" src=\"/static{{ config.UPLOAD_PLATFORM}}/default.png\">\n {% endif %}\n </a></td>\n <td>{{ platform.name }}</td>\n <td>{{ platform.website }}</td>\n <td>\n {% if platform.status == 1 %}\n <span class=\"label bg-green\">Activated</span>\n {% else %}\n <span class=\"label bg-red\">Disabled</span>\n {% endif %}\n </td>\n <td>\n <a href=\"{{ url_for('carboard.showPlatform', id=platform.id) }}\" class=\"btn btn-default btn-sm\" data-toggle=\"tooltip\" title=\"Show\"><i class=\"fa fa-eye\"></i></a>\n\n <a href=\"{{ url_for('carboard.togglePlatform', id=platform.id) }}\" class=\"btn btn-warning btn-sm\" data-toggle=\"tooltip\" title=\"Toggle status\"><i class=\"fa fa-toggle-on\"></i></a>\n\n <a href=\"{{ url_for('carboard.editPlatform', id=platform.id) }}\" class=\"btn btn-success btn-sm\" data-toggle=\"tooltip\" title=\"Edit\"><i class=\"fa fa-pencil\"></i></a>\n\n <a href=\"{{ url_for('carboard.deletePlatform', id=platform.id) }}\" class=\"btn btn-danger btn-sm\" data-toggle=\"tooltip\" title=\"Remove\"><i class=\"fa fa-trash\"></i></a>\n </td>\n </tr>\n {% endfor %}\n </table>\n </div>\n <div class=\"box-footer clearfix\">\n {{render_pagination(platforms)}}\n </div>\n </div>\n </div>\n </div>\n{% endblock %}\n"},{"alpha_fraction":0.7322834730148315,"alphanum_fraction":0.7322834730148315,"avg_line_length":24.399999618530273,"blob_id":"b7a3de9ef988daf5ea457b8480b0ca672c92b7e3","content_id":"5e86556caebe8fb10615a1ae7e2920e86a590d2f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":127,"license_type":"no_license","max_line_length":66,"num_lines":5,"path":"/app/vizboard/__init__.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import Blueprint\n\nvizboard = Blueprint('vizboard', __name__, url_prefix='/vizboard')\n\nfrom .explorer.views import *\n"},{"alpha_fraction":0.8783783912658691,"alphanum_fraction":0.8783783912658691,"avg_line_length":36.5,"blob_id":"6d89b2d0aa3026ec45069585c8a2d1309dd8d1fe","content_id":"706f7d77d89330ef7c1c1b7f6e71794c28f9321c","detected_licenses":["MIT","BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":74,"license_type":"permissive","max_line_length":37,"num_lines":2,"path":"/app/plugins/driver_graph/src/algorithms/__init__.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from .drivergraph import DriverGraph\nfrom .settings import DefaultSettings"},{"alpha_fraction":0.6251664161682129,"alphanum_fraction":0.6284953355789185,"avg_line_length":30.621051788330078,"blob_id":"a325810a5abf9ad45e60a395c8a2d17e78f7b42d","content_id":"53eac5d82e265ddfb0636c5003bcd2d9563d536e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3004,"license_type":"no_license","max_line_length":89,"num_lines":95,"path":"/app/carboard/views/article.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import (\n render_template, request, flash, redirect, url_for\n)\nfrom flask_login import login_required\n\nfrom . import carboard\nfrom ..models.article import Article\nfrom ..forms.article import ArticleForm\nfrom ..constants import PER_PAGE\nfrom ...extensions import db\n\n\n# -------------------- /carboard/article/ : List of articles ---------------- #\n\n\n@carboard.route('/article/')\n@login_required\ndef indexArticle():\n articles = Article.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/article/index.html', articles=articles)\n\n\n# -------------------- /carboard/article/id : Show article ------------------ #\n\n\n@carboard.route('/article/<int:id>', methods=['GET'])\n@login_required\ndef showArticle(id):\n article = Article.query.get_or_404(id)\n return render_template('carboard/article/show.html', article=article)\n\n\n# -------------------- /carboard/article/new : Add article ------------------ #\n\n\n@carboard.route('/article/new/<int:dataset_id>', methods=['GET', 'POST'])\n@carboard.route('/article/new', methods=['GET', 'POST'])\n@login_required\ndef newArticle(dataset_id=None):\n \"\"\" Add new article \"\"\"\n\n form = ArticleForm()\n\n if form.validate_on_submit():\n article = Article(\n dataset_id=dataset_id,\n name=form.name.data,\n authors=form.authors.data,\n abstract=form.abstract.data,\n keywords=form.keywords.data,\n publication_date=form.publication_date.data,\n reference=form.reference.data,\n link=form.link.data,\n )\n db.session.add(article)\n db.session.commit()\n flash('Article {}, added successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.indexArticle'))\n\n return render_template('carboard/article/new.html', form=form, dataset_id=dataset_id)\n\n\n# -------------------- /carboard/article/id/edit : Edit article ------------- #\n\n\n@carboard.route('/article/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editArticle(id):\n \"\"\" Edit existing article \"\"\"\n\n article = Article.query.get_or_404(id)\n form = ArticleForm(obj=article)\n if form.validate_on_submit():\n form.populate_obj(article)\n db.session.commit()\n flash('Article {}, updated successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.showArticle', id=id))\n\n return render_template('carboard/article/edit.html', form=form, id=id)\n\n\n# -------------------- /carboard/article/id/delete : Delete article --------- #\n\n\n@carboard.route('/article/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteArticle(id):\n article = Article.query.get_or_404(id)\n db.session.delete(article)\n db.session.commit()\n flash('Article {}, deleted successfully.'.format(article.name), 'success')\n return redirect(url_for('carboard.indexArticle'))\n"},{"alpha_fraction":0.41769281029701233,"alphanum_fraction":0.42125728726387024,"avg_line_length":40.702701568603516,"blob_id":"7b961ca9fbc450c548ddef2f7ea37ac46969144a","content_id":"634fe2e7d0d8887df2c51e4b149bc319efe39cf2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"HTML","length_bytes":3086,"license_type":"no_license","max_line_length":195,"num_lines":74,"path":"/templates/carboard/article/index.html","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"{% extends \"layout.html\" %}\n\n{% from \"carboard/macros.html\" import render_pagination %}\n\n{% block title %}\n List of articles | {{ super() }}\n{% endblock %}\n\n{% block content_header %}\n\t<section class=\"content-header\">\n\t\t<h1>\n\t\tList of articles\n\t\t</h1>\n\t\t<ol class=\"breadcrumb\">\n\t\t\t<li><a href=\"#\"><i class=\"fa fa-dashboard\"></i>Home</a></li>\n\t\t\t<li><a href=\"#\">Article</a></li>\n\t\t\t<li class=\"active\">List of articles</li>\n\t\t</ol>\n\t</section>\n{% endblock content_header %}\n\n{% block content %}\n\n <div class=\"row\">\n <div class=\"col-md-12\">\n <div class=\"box\">\n <div class=\"box-header\">\n <div class=\"box-tools\">\n <div class=\"input-group input-group-sm\" style=\"width: 150px;\">\n <input type=\"text\" name=\"table_search\" class=\"form-control pull-right\" placeholder=\"Search\">\n\n <div class=\"input-group-btn\">\n <button type=\"submit\" class=\"btn btn-default\">\n <i class=\"fa fa-search\"></i>\n </button>\n </div>\n </div>\n </div>\n </div>\n <br>\n <div class=\"box-body no-padding\">\n <table class=\"table table-hover\">\n <tr>\n <th>ID</th>\n <th>Name</th>\n <th>Authors</th>\n <th>Date of publication</th>\n <th>Actions</th>\n </tr>\n\n {% for article in articles.items %}\n <tr>\n <td><a href=\"{{ url_for('carboard.showArticle', id=article.id) }}\">{{ article.id }}</a></td>\n <td><a href=\"{{ url_for('carboard.showArticle', id=article.id) }}\">{{ article.name | truncate(70) }}</a></td>\n <td>{{ article.authors | truncate(35) }}</td>\n <td>{{ article.publication_date }}</td>\n <td>\n <a href=\"{{ url_for('carboard.showArticle', id=article.id) }}\" class=\"btn btn-default btn-sm\" data-toggle=\"tooltip\" title=\"Show\"><i class=\"fa fa-eye\"></i></a>\n\n <a href=\"{{ url_for('carboard.editArticle', id=article.id) }}\" class=\"btn btn-success btn-sm\" data-toggle=\"tooltip\" title=\"Edit\"><i class=\"fa fa-pencil\"></i></a>\n\n <a href=\"{{ url_for('carboard.deleteArticle', id=article.id) }}\" class=\"btn btn-danger btn-sm\" data-toggle=\"tooltip\" title=\"Remove\"><i class=\"fa fa-trash\"></i></a>\n </td>\n </tr>\n {% endfor %}\n </table>\n </div>\n <div class=\"box-footer clearfix\">\n {{render_pagination(articles)}}\n </div>\n </div>\n </div>\n </div>\n{% endblock %}\n"},{"alpha_fraction":0.49799197912216187,"alphanum_fraction":0.5030120611190796,"avg_line_length":25.91891860961914,"blob_id":"a31bf86b9258cce9438d42cc4505dec1019b7453","content_id":"094e00aae2c95ad68dc6730c25a3d63c40535ec2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":996,"license_type":"no_license","max_line_length":79,"num_lines":37,"path":"/app/carboard/forms/country.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField\nfrom wtforms.validators import DataRequired, Regexp, Length\n\nfrom ..models.country import Country\n\n# ------------------------ custom validation methods ------------------------ #\n\n\ndef country_exist(form, field):\n if Country.select().where(Country.title == field.data).exists():\n raise ValidationError('Country already exists !')\n\n# ---------------------------- User form classes ---------------------------- #\n\n\nclass CountryForm(FlaskForm):\n \"\"\" Country add/edit FlaskForm \"\"\"\n title = StringField(\n 'Name',\n validators=[\n DataRequired(),\n Length(min=3, max=50),\n Regexp(\n r'^[a-zA-Z ]+$',\n message=(\"Country name is not correct !\")\n ),\n # country_exist()\n ]\n )\n code = StringField(\n 'Iso Code',\n validators=[\n DataRequired(),\n Length(min=2, max=2),\n ]\n )\n"},{"alpha_fraction":0.5804204344749451,"alphanum_fraction":0.5857980847358704,"avg_line_length":30.229007720947266,"blob_id":"efc8012ae618975522cd866793c109d5caef7f4f","content_id":"2d125043fd572da29217e861e5fe6556c7597eb1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8182,"license_type":"no_license","max_line_length":113,"num_lines":262,"path":"/app/carboard/helpers.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import os\nimport re\nimport csv\nfrom unicodedata import normalize\nfrom uuid import uuid4\nfrom time import strftime\nfrom datetime import datetime\n\nfrom flask import request, url_for\nfrom werkzeug import secure_filename\n\nfrom app.carboard.models.dataset import Dataset\nfrom app.carboard.models.platform import Platform\nfrom app.carboard.models.signal import Signal\nfrom config.config import DefaultConfig\nfrom .constants import UPLOAD_DATASET_DIR, ALLOWED_DATASET_EXTENSIONS\n\n\n# ------------------------------ View Helpers ------------------------------- #\n\n\ndef paginate(query, max_per_page=25):\n \"\"\" Helper to manage paginate configuration\n it take as argument a query and return a\n paginated query with given parametters\n \"\"\"\n # obtain pagination arguments from the URL's query string\n page = request.args.get('page', 1, type=int)\n per_page = min(\n request.args.get('per_page', max_per_page, type=int),\n max_per_page\n )\n # run the query with Flask-SQLAlchemy's pagination\n p = query.paginate(page, per_page)\n # build the pagination metadata to include in the response\n pages = {\n 'page': page,\n 'per_page': per_page,\n 'total': p.total,\n 'pages': p.pages\n }\n if p.has_prev:\n pages['prev_url'] = url_for(\n request.endpoint, page=p.prev_num, per_page=per_page)\n else:\n pages['prev_url'] = None\n if p.has_next:\n pages['next_url'] = url_for(\n request.endpoint, page=p.next_num, per_page=per_page)\n else:\n pages['next_url'] = None\n pages['first_url'] = url_for(\n request.endpoint, page=1, per_page=per_page)\n pages['last_url'] = url_for(\n request.endpoint, page=p.pages, per_page=per_page)\n # return a dictionary as a response\n return {'items': p.items, 'pages': pages}\n\n\ndef upload_csv(file_data, upload_dir):\n \"\"\" upload csv file (keeping the name cause it will be removed after processing \"\"\"\n if file_data:\n path = os.path.join(upload_dir)\n file_data.save(\"/\".join([path, file_data.filename]))\n return file_data.filename\n\n\ndef remove_csv(filename, upload_dir):\n \"\"\" delet the plugins csv file \"\"\"\n path = os.path.join(upload_dir)\n os.remove(\"/\".join([path, filename]))\n return True\n\n\ndef upload_file(file_data, upload_dir, oldFile=None):\n \"\"\" Upload Files \"\"\"\n if file_data:\n file = secure_filename(str(uuid4()) + os.path.splitext(file_data.filename)[1].lower())\n path = os.path.join(upload_dir, strftime(\"%Y/%m\"))\n # os.makedirs(path, exist_ok=True) Py 3.4+\n make_dir(path)\n file_data.save(\"/\".join([path, file]))\n if oldFile is not None:\n pass\n return \"/\".join([strftime(\"%Y/%m\"), file])\n return None\n\n\ndef upload_dsfile(input='file', dataset='_default'):\n \"\"\"Upload a dataset file\"\"\"\n result = {'status': False, 'message': 'No input file'}\n if input not in request.files:\n return result\n file = request.files[input]\n if file.filename == '':\n return result\n if '.' not in file.filename:\n result['message'] = 'File without any extension'\n return result\n if file.filename.rsplit('.', 1)[1] not in ALLOWED_DATASET_EXTENSIONS:\n result['message'] = 'Extension not allowed'\n return result\n directory = \"/\".join([UPLOAD_DATASET_DIR, str(dataset)])\n make_dir(directory)\n file_path = \"/\".join([directory, secure_filename(str(uuid4()) + os.path.splitext(file.filename)[1].lower())])\n file.save(file_path)\n return {'status': True, 'message': file_path}\n\n\ndef slugify(text, delim=u'-'):\n \"\"\"Generates an slightly worse ASCII-only slug.\"\"\"\n result = []\n _punct_re = re.compile(r'[\\t !\"#$%&\\'()*\\-/<=>?@\\[\\\\\\]^_`{|},.]+')\n for word in _punct_re.split(text.lower()):\n word = normalize('NFKD', word).encode('ascii', 'ignore')\n if word:\n result.append(word)\n try:\n return unicode(delim.join(result))\n except:\n d = d = bytes(delim, 'utf-8')\n return str(d.join(result).decode(\"utf-8\"))\n\n\ndef choices(Model, placeholder=None, cond=1, orderField='name'):\n if cond:\n c = [(b.id, b) for b in Model.query.filter_by(status=cond).order_by(orderField).all()]\n else:\n c = [(b.id, b) for b in Model.query.order_by(orderField).all()]\n if placeholder is not None:\n c.insert(0, ('0', placeholder))\n else:\n c.insert(0, ('0', 'Select an option ...'))\n return c\n\n\ndef platform_choices(placeholder=None, orderField='name'):\n platforms = [(b.name, b) for b in Platform.query.filter_by(status=1).order_by(orderField).all()]\n datasets = [(b.name, b) for b in Dataset.query.filter_by(status=1).order_by(orderField).all()]\n ret = platforms + datasets\n if placeholder:\n ret.insert(0, ('0', placeholder))\n else:\n ret.insert(0, ('0', 'Select A Data Storatge'))\n return ret\n\n\ndef types():\n return [\n ('type', 'Select a Type ...'),\n ('Numerical', 'Numerical'),\n ('String', 'String'),\n ('States', 'States'),\n ('Path', 'Path')\n ]\n\n\ndef get_signal_from_form(form):\n \"\"\" get a signal object from the given form \"\"\"\n signal = Signal(\n name=form.name.data,\n signalclass_id=form.signalclass_id.data,\n signalsource_id=form.signalsource_id.data,\n unit=form.unit.data,\n description=form.unit.description,\n frequency=form.frequency.data,\n type=form.type.data\n )\n if form.type.data == \"States\" or form.type.data == \"Numerical\":\n if form.type.data == \"States\":\n number = form.number.data\n values = form.values.data\n signal.range = values\n else:\n signal.range = \" - \".join([form.min_value.data, form.max_value.data])\n return signal\n else:\n return signal\n\n\ndef make_dir(dir_path):\n \"\"\" Make recursive directories \"\"\"\n try:\n if not os.path.exists(dir_path) and not os.path.isdir(dir_path):\n os.makedirs(dir_path)\n except Exception as e:\n raise e\n\n\ndef redirect_back():\n \"\"\" Redirect back to last url \"\"\"\n return request.args.get('next') or request.referrer or url_for('index')\n\n\ndef get_current_time():\n return datetime.utcnow()\n\n\ndef pretty_date(dt, default=None):\n \"\"\" Returns string representing \"time since\" e.g. 5 hours ago etc.\"\"\"\n\n if default is None:\n default = 'just now'\n now = datetime.utcnow()\n diff = now - dt\n periods = (\n (diff.days / 365, 'year', 'years'),\n (diff.days / 30, 'month', 'months'),\n (diff.days / 7, 'week', 'weeks'),\n (diff.days, 'day', 'days'),\n (diff.seconds / 3600, 'hour', 'hours'),\n (diff.seconds / 60, 'minute', 'minutes'),\n (diff.seconds, 'second', 'seconds'),\n )\n for period, singular, plural in periods:\n if not period:\n continue\n if period == 1:\n return u'%d %s ago' % (period, singular)\n else:\n return u'%d %s ago' % (period, plural)\n return default\n\n\ndef allowed_extensions(filename):\n \"\"\" check if the file extension is allowed \"\"\"\n return '.' in filename and filename.rsplit('.', 1)[1] in DefaultConfig.ALLOWED_AVATAR_EXTENSIONS\n\n\n# ------------------------------- Form Helpers ------------------------------ #\n\n\ndef find_id(model, field_name):\n \"\"\" find the the given model by name, if not found return -1 \"\"\"\n res = model.query.filter_by(name=field_name).first()\n if res:\n return res.id\n return -1\n\n\n# ------------------------------- CSV File Helper --------------------------- #\n\nclass CSVLoader:\n ## initialize with the wanted file path\n def __init__(self, path):\n self.path = path\n\n ## give value in case of Non or empty\n def sanitize(self, val):\n if not val:\n return \"UNDEFINED\"\n return val\n\n ## load the contents of the file an generate a resulting Hash\n def load(self):\n list = []\n with open(self.path, 'rU') as f:\n r = csv.DictReader(f)\n for a in r:\n list.append(a)\n # final result\n return list\n"},{"alpha_fraction":0.5522975921630859,"alphanum_fraction":0.5584245324134827,"avg_line_length":27.92405128479004,"blob_id":"0f9e947e28c3942d307fb0c6656d396efdaac2c9","content_id":"fb0c15712c9b6f65eb5e038a9968828befbfce01","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2285,"license_type":"no_license","max_line_length":79,"num_lines":79,"path":"/app/dashboard/helpers.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import os\nfrom time import strftime\nfrom datetime import datetime\n\nfrom flask import request, url_for\nfrom werkzeug import secure_filename\n\nfrom config.config import DefaultConfig\nfrom .constants import UPLOAD_TRACE_DIR, ALLOWED_TRACE_EXTENSIONS, TRANSFORM\n\n# ------------------------------ View Helpers ------------------------------- #\n\n\ndef upload_trace(input='file', user_id=0):\n \"\"\"Upload a trace file\"\"\"\n result = {'status': False, 'message': 'No input file'}\n if input not in request.files:\n return result\n file = request.files[input]\n if file.filename == '':\n return result\n if '.' not in file.filename:\n result['message'] = 'extension not allowed'\n return result\n if file.filename.rsplit('.', 1)[1] not in ALLOWED_TRACE_EXTENSIONS:\n result['message'] = 'extension not allowed'\n return result\n directory = \"/\".join([UPLOAD_TRACE_DIR, str(user_id)])\n directory = \"/\".join([directory, strftime(\"%Y/%m\")])\n make_dir(directory)\n file_path = \"/\".join([directory, secure_filename(file.filename)])\n file.save(file_path)\n return {'status': True, 'message': file_path}\n\n\ndef correct_value(value):\n try:\n return int(value)\n except:\n for k, v in TRANSFORM.items():\n if value == v:\n return k\n return -99999\n\n\ndef correct_time(timestamp):\n try:\n timestamp = str(timestamp)\n t = timestamp.split('.')\n return '{}{}'.format(t[0], t[1][:3].ljust(3, '0'))\n except:\n return timestamp.split('.')[0]\n\n\ndef debug(obj):\n print(\"\\n------------------ Start DEBUG ------------------\")\n for a, v in obj.__dict__.iteritems():\n print(\"\\t [{}] = {}\".format(a, v))\n print(\"------------------- END DEBUG -------------------\\n\")\n\n\ndef make_dir(dir_path):\n \"\"\" Make recursive directories \"\"\"\n try:\n if not os.path.exists(dir_path) and not os.path.isdir(dir_path):\n os.makedirs(dir_path)\n except Exception as e:\n raise e\n\n\ndef redirect_back():\n \"\"\" Redirect back to last url \"\"\"\n return request.args.get('next') or request.referrer or url_for('index')\n\n\ndef get_current_time():\n return datetime.utcnow()\n\n# ------------------------------- Form Helpers ------------------------------ #\n"},{"alpha_fraction":0.47676992416381836,"alphanum_fraction":0.4800885021686554,"avg_line_length":22.802631378173828,"blob_id":"d793e3d7e15f124c6f2db35d430664a8511ad3e1","content_id":"2ad5fb09526a2bc9c2cb41f000c7a30e94a5c1a6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1808,"license_type":"no_license","max_line_length":80,"num_lines":76,"path":"/app/importer/forms.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom flask_wtf.file import FileAllowed, FileField\nfrom wtforms import StringField, SelectField\nfrom wtforms.validators import DataRequired, Regexp, Length\nfrom .constants import DRIVER_GENDER\n\n\n# ------------------------ custom validation methods ------------------------ #\n\n\n# ---------------------------- Brand form classes ---------------------------- #\n\nclass VehicleForm(FlaskForm):\n \"\"\" Vehicle add/edit FlaskForm \"\"\"\n brand_id = SelectField(\n 'Brand',\n coerce=int,\n validators=[\n DataRequired(),\n ]\n )\n model_id = SelectField(\n 'Model',\n coerce=int,\n )\n image = FileField(\n 'Picture', validators=[\n FileAllowed(['jpg', 'jpeg', 'png', 'gif'], 'Images only plz :) ')\n ]\n )\n\nclass DriverForm(FlaskForm):\n \"\"\" Driver add/edit FlaskForm \"\"\"\n gender = SelectField(\n 'Gender',\n coerce=int,\n choices=DRIVER_GENDER,\n validators=[\n DataRequired(),\n ]\n )\n fullname = StringField(\n 'Fullname',\n validators=[\n Length(min=3, max=50),\n Regexp(\n r'^[a-zA-Z ]+$',\n message=(\"Driver name is not correct !\")\n ),\n ]\n )\n country_id = SelectField(\n 'Country',\n coerce=int,\n validators=[\n DataRequired(),\n ]\n )\n status_id = SelectField(\n 'Status',\n coerce=int,\n validators=[\n DataRequired(),\n ]\n )\n city = StringField(\n 'City',\n validators=[\n Length(min=2, max=50),\n ]\n )\n avatar = FileField(\n 'Avatar', validators=[\n FileAllowed(['jpg', 'jpeg', 'png', 'gif'], 'Images only plz :) ')\n ]\n )"},{"alpha_fraction":0.5849056839942932,"alphanum_fraction":0.5933962464332581,"avg_line_length":30.64179039001465,"blob_id":"63f3cabb0f857adfc4f77f4ef4fc76a447e153bc","content_id":"ac9e6236fe1ae04f95f05a8d95516150a24bb834","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2120,"license_type":"no_license","max_line_length":81,"num_lines":67,"path":"/app/carboard/models/platform.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import re\nfrom datetime import datetime\nfrom unicodedata import normalize\n\nfrom marshmallow import Schema, fields\nfrom .extrasignal import ExtrasignalSchema\nfrom ..constants import DEFAULT_PHOTO\nfrom ...extensions import db\n\n# -------------------------------- Brand Model ------------------------------- #\ndef slugify(text, delim=u'-'):\n \"\"\"Generates an slightly worse ASCII-only slug.\"\"\"\n result = []\n _punct_re = re.compile(r'[\\t !\"#$%&\\'()*\\-/<=>?@\\[\\\\\\]^_`{|},.]+')\n for word in _punct_re.split(text.lower()):\n word = normalize('NFKD', word).encode('ascii', 'ignore')\n if word:\n result.append(word)\n try:\n return unicode(delim.join(result))\n except:\n d = d = bytes(delim, 'utf-8')\n return str(d.join(result).decode(\"utf-8\"))\n\nclass Platform(db.Model):\n \"\"\" Platform Model \"\"\"\n __tablename__ = 'platforms'\n\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(255))\n slug = db.Column(db.String(255))\n mimetype = db.Column(db.SmallInteger)\n logo = db.Column(db.String(255), nullable=True)\n description = db.Column(db.String(255))\n website = db.Column(db.String(255), nullable=True)\n\n #signals = db.relationship(\"Extrasignal\")\n\n status = db.Column(db.SmallInteger, default=1)\n created = db.Column(db.DateTime(), default=datetime.utcnow())\n\n def __init__(self, name, mimetype, description, website, logo=None):\n self.name = name\n self.slug = slugify(name)\n self.mimetype = mimetype\n self.logo = logo or DEFAULT_PHOTO\n self.description = description\n self.website = website\n\n def __repr__(self):\n return self.name\n\n# -------------------------------- Brand Schema ------------------------------- #\n\n\nclass PlatformSchema(Schema):\n \"\"\" Platform Schema \"\"\"\n\n id = fields.Int(dump_only=True)\n name = fields.Str()\n slug = fields.Str()\n mimetype = fields.Str()\n logo = fields.Str()\n signals = fields.Nested(ExtrasignalSchema)\n description = fields.Str()\n website = fields.Str()\n created = fields.DateTime(dump_only=True)\n"},{"alpha_fraction":0.48859935998916626,"alphanum_fraction":0.49837133288383484,"avg_line_length":18.125,"blob_id":"4bde2d6e38e73bdd165915537b349bb1bb722711","content_id":"e2e530c42da59ec8978c412d62d99427434163c6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":307,"license_type":"no_license","max_line_length":79,"num_lines":16,"path":"/config/mail.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"\n# ----------------------------- Mail Configuration -------------------------- #\n\n\nclass MailConfig(object):\n\n MAIL_USERNAME = 'misaki.yooko@gmail.com'\n\n MAIL_DEFAULT_SENDER = 'vCar <noreply@vcar.ma>'\n\n MAIL_SERVER = 'MAIL_SERVER', 'smtp.gmail.com'\n\n MAIL_PORT = 465\n\n MAIL_USE_SSL = True\n\n MAIL_USE_TLS = False\n"},{"alpha_fraction":0.4707895517349243,"alphanum_fraction":0.4707895517349243,"avg_line_length":42.521278381347656,"blob_id":"bab3aeb7e14c4ede4c122de4ec07fb65618ac9bc","content_id":"769f0ff5a5496b38983f1f32af37cb680ddbf253","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"HTML","length_bytes":4091,"license_type":"no_license","max_line_length":187,"num_lines":94,"path":"/templates/elements/sidebar.html","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"{% from \"elements/macros.html\" import is_active %}\n\n<a class=\"user-panel\" href=\"{{ url_for('importer.index') }}\">\n <div class=\"pull-left image import-icon\">\n <i class=\"fa fa-bandcamp\" aria-hidden=\"true\"></i>\n </div>\n <div class=\"pull-left info\">\n <p>IMPORT DATA</p>\n </div>\n</a>\n\n<ul class=\"sidebar-menu\">\n\n <li CLASS=\"header\">V<strong>C</strong>AR DASHBOARD</li>\n <li class=\"{ is_active('explorer.index') }}\">\n <a href=\"{{ url_for('explorer.index') }}\">\n <i class=\"fa fa-dashboard noor\"></i> <span>Explorer</span>\n </a>\n </li>\n <li class=\"{{ is_active('explorer.index') }}\">\n <a href=\"{{ url_for('explorer.index') }}\">\n <i class=\"fa fa-map-o\"></i> <span>Google Map</span>\n </a>\n </li>\n\n {# Plugin links shows here #}\n <li CLASS=\"header\">\n VCAR Extensions\n <span>\n <a class=\"see-all\" href=\"{{ url_for('carboard.indexPlugin') }}\">view all</a>\n </span>\n </li>\n\n {% if plugins_menu is not none %}\n {% for plugin in plugins_menu %}\n {% if plugin.treeview %}\n {% set status = {'treeview': '', 'menu': ''} %}\n {% for item in plugin.menu %}\n {% if request.path == url_for(item.action) %}\n {% if status.update({'treeview': 'active', 'menu': 'menu-open'}) %} {% endif %}\n {% endif %}\n {% endfor %}\n <li class=\"treeview {{ status.treeview }}\">\n <a href=\"#\">\n <i class=\"{{ plugin.iclass | default('fa fa-puzzle-piece')}}\"></i>\n <span>{{ plugin.name }}</span>\n <i class=\"fa fa-chevron-left pull-right\"></i>\n </a>\n <ul class=\"treeview-menu {{ status.menu }}\">\n {% for item in plugin.menu %}\n <li class=\"{{ is_active(item.action) }}\">\n <a href=\"{{ url_for(item.action) }}\">\n <i class=\"{{ item.iclass | default('fa fa-puzzle-piece')}}\"></i>\n {{ item.name }}\n </a>\n </li>\n {% endfor %}\n </ul>\n </li>\n {% else %}\n <li class=\"{{ is_active(plugin.menu.action) }}\">\n <a href=\"{{ url_for(plugin.menu.action | string) }}\">\n <i class=\"{{ plugin.menu.iclass | default('fa fa-puzzle-piece') }}\"></i> <span>{{ plugin.name }}</span>\n </a>\n </li>\n {% endif %}\n {% endfor %}\n {% endif %}\n\n {# Data storage #}\n <li class=\"header\">Data storage</li>\n {# <li><a href=\"{{ url_for('importer.index') }}\"><i class=\"fa fa-bandcamp\"></i> Import Manger</a></li> #}\n\n <li class=\"{{ is_active('carboard.indexPlatform') }}\">\n <a href=\"{{ url_for('carboard.indexPlatform') }}\"><i class=\"fa fa-leaf\"></i> <span>Platforms</span></a>\n </li>\n <li class=\"{{ is_active('carboard.indexDataset') }}\">\n <a href=\"{{ url_for('carboard.indexDataset') }}\"><i class=\"fa fa-cube\"></i> <span>Datasets</span></a>\n </li>\n\n <li class=\"{{ is_active('carboard.configuration') }}\">\n <a href=\"{{ url_for('carboard.configuration') }}\"><i class=\"fa fa-cogs\"></i> <span>Configuration</span></a>\n </li>\n</ul>\n\n\n<ul class=\"sidebar-menu buttom\">\n <li class=\"header big-icon\">\n <span><a href=\"#\" data-toggle=\"tooltip\" title=\"vCar License Policy\"><i class=\"fa fa-copyright\"></i></a></span>\n <span><a href=\"#\" data-toggle=\"tooltip\" title=\"About vCar\"><i class=\"fa fa-info-circle\"></i></a></span>\n <span><a href=\"#\" data-toggle=\"tooltip\" title=\"vCar Documentation\"><i class=\"fa fa-book\"></i></a></span>\n <span><a class=\"power-off\" href=\"/kill\" data-toggle=\"confirmation\" data-container=\"body\" title=\"\" data-original-title=\"Shutdonw server?\"><i class=\"fa fa-power-off\"></i></a></span>\n </li>\n</ul>\n"},{"alpha_fraction":0.5916727781295776,"alphanum_fraction":0.5953250527381897,"avg_line_length":30.471263885498047,"blob_id":"5c55a8adaaa1a077aa66f402cb1befad76c9e6d2","content_id":"6c0b4e43d00fd8e05815268e70ce92126b3fa80d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2738,"license_type":"no_license","max_line_length":84,"num_lines":87,"path":"/app/carboard/views/status.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import (\n render_template, request, flash, redirect, url_for\n)\nfrom flask_login import login_required\n\nfrom . import carboard\nfrom ..models.status import Status\nfrom ..forms.status import StatusForm\nfrom ..helpers import paginate\nfrom ..constants import PER_PAGE\nfrom ...extensions import db\n\n\n# --------------------- /carboard/status/ : List of countries ------------------ #\n\n@carboard.route('/status/')\n@login_required\ndef indexStatus():\n status = Status.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/status/index.html',\n status=status\n )\n\n# ----------------------- /carboard/status/id : Show status ------------------- #\n\n\n@carboard.route('/status/<int:id>', methods=['GET'])\n@login_required\ndef showStatus(id):\n status = Status.query.get_or_404(id)\n return render_template('carboard/status/show.html',\n status=status\n )\n\n# ---------------------- /carboard/status/new : Add status -------------------- #\n\n\n@carboard.route('/status/new', methods=['GET', 'POST'])\n@login_required\ndef newStatus():\n \"\"\" Add new status \"\"\"\n\n form = StatusForm()\n\n if form.validate_on_submit():\n status = Status(\n title=form.title.data,\n color=form.color.data\n )\n db.session.add(status)\n db.session.commit()\n flash('Status {}, added successfully.'.format(form.title.data), 'success')\n return redirect(url_for('carboard.indexStatus'))\n\n return render_template('carboard/status/new.html', form=form)\n\n# -------------------- /carboard/status/id/edit : Edit status ----------------- #\n\n\n@carboard.route('/status/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editStatus(id):\n \"\"\" Edit existing status \"\"\"\n status = Status.query.get_or_404(id)\n form = StatusForm(obj=status)\n if form.validate_on_submit():\n form.populate_obj(status)\n db.session.commit()\n flash('Status {}, updated successfully.'.format(form.title.data), 'success')\n return redirect(url_for('carboard.showStatus', id=id))\n\n return render_template('carboard/status/edit.html', form=form, id=id)\n\n# ------------------ /carboard/status/id/delete : Delete status --------------- #\n\n\n@carboard.route('/status/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteStatus(id):\n status = Status.query.get_or_404(id)\n db.session.delete(status)\n db.session.commit()\n flash('Status {}, deleted successfully.'.format(status.title), 'success')\n return redirect(url_for('carboard.indexStatus'))\n"},{"alpha_fraction":0.6078697443008423,"alphanum_fraction":0.6180461049079895,"avg_line_length":34.095237731933594,"blob_id":"f7ad17644b093d58e5f1e7752838e3537308c9b3","content_id":"cd0353a0236727f2d76d20a580cee129ac1249ed","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1474,"license_type":"no_license","max_line_length":79,"num_lines":42,"path":"/app/explorer/models.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom ..extensions import db\n\n# -------------------------------- Chart Model ------------------------------ #\n\n\nclass Chart(db.Model):\n \"\"\" Chart Model \"\"\"\n __tablename__ = 'charts'\n\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(255), index=True, unique=True)\n token = db.Column(db.String(255), unique=True)\n isValid = db.Column(db.SmallInteger, default=0)\n error = db.Column(db.String(255), nullable=True)\n query = db.Column(db.String(255), nullable=True)\n\n user_id = db.Column(db.Integer, db.ForeignKey('users.id'))\n user = db.relationship('User')\n\n driver_id = db.Column(db.Integer, db.ForeignKey('drivers.id'))\n driver = db.relationship('Driver')\n\n vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'))\n vehicle = db.relationship('Vehicle')\n\n status = db.Column(db.SmallInteger, default=1)\n deleted = db.Column(db.SmallInteger, default=0)\n updated = db.Column(db.DateTime(), default=datetime.utcnow())\n created = db.Column(db.DateTime(), default=datetime.utcnow())\n\n def __init__(self, name, user_id, driver_id, vehicle_id, query, **kwargs):\n self.name = name\n self.user_id = user_id\n self.driver_id = driver_id\n self.vehicle_id = vehicle_id\n self.query = query\n self.isValid = kwargs.get('isValid', True)\n self.error = kwargs.get('error', None)\n\n def __repr__(self):\n return self.name\n"},{"alpha_fraction":0.5019304752349854,"alphanum_fraction":0.5135135054588318,"avg_line_length":23.66666603088379,"blob_id":"9bc4013cd0414ad4e9e8280934202d57e1920c9e","content_id":"62c63a06a0ac565908fadab209c6ad2cd990927e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":518,"license_type":"no_license","max_line_length":81,"num_lines":21,"path":"/app/carboard/models/status.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from ...extensions import db\n\n# -------------------------------- Status Model ------------------------------- #\n\n\nclass Status(db.Model):\n \"\"\" Status Model:\n The status of users and drivers.\n \"\"\"\n __tablename__ = 'status'\n\n id = db.Column(db.Integer, primary_key=True)\n title = db.Column(db.String(255))\n color = db.Column(db.String(255))\n\n def __init__(self, title, color=\"primary\"):\n self.title = title\n self.color = color\n\n def __repr__(self):\n return self.title\n"},{"alpha_fraction":0.6079865097999573,"alphanum_fraction":0.6136108040809631,"avg_line_length":34.560001373291016,"blob_id":"eb5d5b9074a749735c0c2a6519f28e8a4c1addca","content_id":"da4ce5b6751c6fd2768f92bb76ab932c97a0cb22","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1778,"license_type":"no_license","max_line_length":83,"num_lines":50,"path":"/app/carboard/models/record.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom ...extensions import db\n\n# -------------------------------- Record Model ------------------------------- #\n\n\nclass Record(db.Model):\n \"\"\" Record Model: \"\"\"\n __tablename__ = 'records'\n\n id = db.Column(db.Integer, primary_key=True)\n\n name = db.Column(db.String(255))\n\n user_id = db.Column(db.Integer, db.ForeignKey('users.id'))\n user = db.relationship('User')\n\n drivetype_id = db.Column(db.Integer, db.ForeignKey('drivetypes.id'))\n drivetype = db.relationship('DriveType')\n\n driver_id = db.Column(db.Integer, db.ForeignKey('drivers.id'), nullable=True)\n driver = db.relationship('Driver')\n\n vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=True)\n vehicle = db.relationship('Vehicle')\n\n status_id = db.Column(db.Integer, db.ForeignKey('status.id'))\n status = db.relationship('Status')\n\n trace = db.Column(db.String(255))\n start = db.Column(db.DateTime(), nullable=True)\n end = db.Column(db.DateTime(), nullable=True)\n description = db.Column(db.String(255), nullable=True)\n created = db.Column(db.DateTime(), default=datetime.utcnow())\n\n def __init__(self, user_id, drivetype_id, trace, **kwargs):\n self.vars = kwargs\n self.user_id = user_id\n self.drivetype_id = drivetype_id\n self.trace = trace\n self.name = kwargs.get('name', \"Record\")\n self.driver_id = kwargs.get('driver_id', None)\n self.vehicle_id = kwargs.get('vehicle_id', None)\n self.status_id = kwargs.get('status_id', 1)\n self.start = kwargs.get('start', None)\n self.end = kwargs.get('end', None)\n self.description = kwargs.get('description', None)\n\n def __repr__(self):\n return '{} {}'.format(self.name, self.id)\n"},{"alpha_fraction":0.5675675868988037,"alphanum_fraction":0.5675675868988037,"avg_line_length":17,"blob_id":"5d37c636b3fec72ba9442334662758a3e7ffb043","content_id":"30506a458cb50dd513be8e4d135acd575d1b62db","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":37,"license_type":"no_license","max_line_length":23,"num_lines":2,"path":"/app/importer/platforms/openxc/views.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"\n\ndef help():\n\treturn 'Help text ...'"},{"alpha_fraction":0.602672278881073,"alphanum_fraction":0.6054852604866028,"avg_line_length":32.85714340209961,"blob_id":"3292c0c2d96d64b8845b36ab94aa7a0c209864aa","content_id":"f198778ccc2062b6028064691674b4db4435c96a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1422,"license_type":"no_license","max_line_length":82,"num_lines":42,"path":"/app/carboard/models/vehicle.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom ...extensions import db\n\n# -------------------------------- Vehicle Model ------------------------------- #\n\n\nclass Vehicle(db.Model):\n \"\"\" Vehicle Model: \"\"\"\n __tablename__ = 'vehicles'\n\n id = db.Column(db.Integer, primary_key=True)\n\n brand_id = db.Column(db.Integer, db.ForeignKey('brands.id'))\n brand = db.relationship('Brand')\n\n model_id = db.Column(db.Integer, db.ForeignKey('models.id'), nullable=True)\n model = db.relationship('Model')\n\n image = db.Column(db.String(255), nullable=True)\n\n user_id = db.Column(db.Integer, db.ForeignKey('users.id'))\n user = db.relationship('User', backref=db.backref('vehicles', lazy='dynamic'))\n\n driver_id = db.Column(db.Integer, db.ForeignKey('drivers.id'), nullable=True)\n driver = db.relationship('Driver')\n\n status_id = db.Column(db.Integer, db.ForeignKey('status.id'))\n status = db.relationship('Status')\n\n created = db.Column(db.DateTime(), default=datetime.utcnow())\n\n def __init__(self, brand_id, user_id, **kwargs):\n self.vars = kwargs\n self.brand_id = brand_id\n self.user_id = user_id\n self.model_id = kwargs.get('model_id', None)\n self.driver_id = kwargs.get('driver_id', None)\n self.status_id = kwargs.get('status_id', 1)\n self.image = kwargs.get('image')\n\n def __repr__(self):\n return '{} ({})'.format(self.brand, self.model)\n"},{"alpha_fraction":0.5950000286102295,"alphanum_fraction":0.5975000262260437,"avg_line_length":22.52941131591797,"blob_id":"6851764925d54d3196d807e4f3b06687be5550bd","content_id":"64c545439a5f837b642b98d097c0956ec1f579e4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":400,"license_type":"no_license","max_line_length":64,"num_lines":17,"path":"/app/plugins/helpers.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from os import path\nfrom flask_misaka import markdown\n\n\ndef render_md(filepath, file):\n \"\"\"\n Function takes as input a markdown file and generate\n a rendred html file as output\n \"\"\"\n try:\n with open(path.join(str(filepath[0]), str(file))) as fd:\n mkdwn = fd.read()\n html = markdown(mkdwn)\n except Exception:\n html = None\n\n return html\n"},{"alpha_fraction":0.5080527067184448,"alphanum_fraction":0.5090287923812866,"avg_line_length":32.04838562011719,"blob_id":"99144873fd56f978517dfcb43b23c18719ecfd82","content_id":"b7fe20926541a0166600863234b20b9098414e1d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4098,"license_type":"no_license","max_line_length":114,"num_lines":124,"path":"/app/importer/transformer.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\n# import main\nfrom config.config import DefaultConfig\nfrom config.database import DatabaseConfig\nfrom ..extensions import db\nfrom ..carboard.models.platform import Platform\nfrom ..carboard.models.signal import Signal\nfrom ..carboard.models.extrasignal import Extrasignal\nfrom .helpers import slugify\nfrom .constants import ACTIVE\n# -------------------- Transformer Class : Version 0.1 ---------------------- #\n\n\nclass Transformer:\n\n def __init__(self, **kwargs):\n self.registerDB()\n\n def registerDB(self):\n self.app = Flask(__name__)\n self.app.config.from_object(DefaultConfig)\n self.app.config.from_object(DatabaseConfig)\n db = SQLAlchemy(self.app)\n\n def setPlatformById(self, platform_id):\n with self.app.app_context():\n self.platform = Platform.query.filter_by(id=platform_id, status=ACTIVE).first()\n self.signals = self.getAvailable()\n return self\n\n def setPlatformBySlug(self, platform_slug):\n with self.app.app_context():\n self.platform = Platform.query.filter_by(slug=platform_slug, status=ACTIVE).first()\n self.signals = self.getAvailable()\n return self\n \n def getAvailable(self):\n extra = self.platform.signals\n signals = {}\n for x in extra:\n if x.status == 1 and x.signal.status == 1:\n signals[str(x.name)] = {\n 'main': x.signal.name,\n 'class': x.signal.signalclass.name\n }\n return signals\n \n def getInfo(self, name, ignore=True):\n x = {}\n if name in self.signals:\n x['name'] = self.signals[name]['main']\n x['class'] = self.signals[name]['class']\n elif ignore == False:\n x['name'] = name\n x['class'] = 'unknown'\n else:\n x = None\n return x\n\n def getMain(self, extra, ignore=None):\n extra = slugify(extra)\n extrasignal = None\n with self.app.app_context():\n extrasignal = Extrasignal.query.filter_by(name=extra, platform_id=self.platform.id).first()\n ignore = self.ignore if ignore is None else ignore\n\n if extrasignal is not None:\n main = extrasignal.signal\n if main.status == ACTIVE and extrasignal.status == ACTIVE:\n return {\n 'name': main.name,\n 'main': True,\n 'class': main.signalclass.name,\n 'error': None\n }\n elif not ignore:\n return {\n 'name': main.name,\n 'main': True,\n 'class': main.signalclass.name,\n 'error': 'Signal `{}` is disabled but added to database anyway ! '.format(extra)\n }\n else:\n return {\n 'name': None,\n 'main': None,\n 'class': None,\n 'error': 'Signal `{}` is not currently supported'.format(extra)\n }\n else:\n if not ignore:\n msg = extrasignal\n return {\n 'name': extra,\n 'main': False,\n 'class': 'unknown',\n 'error': 'Signal `{}` is not currently supported but added to database anyway !'.format(extra)\n }\n else:\n return {\n 'name': None,\n 'main': None,\n 'class': None,\n 'error': 'Signal `{}` is not currently supported'.format(extra)\n }\n\n def getCorrectValue(self, value):\n return value;\n\n def __str__(self):\n str = \"\"\n for k, v in self.vars.items():\n str = str + \"{} : {} | \".format(k, v)\n return str\n\n def showVars(self):\n print(self.vars)\n\n def setVar(self, k, v):\n self.vars[k] = v\n\n def getVar(self, k):\n return self.vars.get(k, None)\n"},{"alpha_fraction":0.43885281682014465,"alphanum_fraction":0.44426408410072327,"avg_line_length":33.867923736572266,"blob_id":"2113de0f69cd75a955e28ff99dde661211825477","content_id":"5f973634629c2ecd0b0c466091c4030f353cec56","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3696,"license_type":"no_license","max_line_length":79,"num_lines":106,"path":"/app/dashboard/tasks.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import redis\nimport json\nfrom time import strftime, sleep\nfrom datetime import datetime\nfrom elasticsearch import Elasticsearch, helpers\nfrom .constants import BULK_SIZE\nfrom .helpers import correct_value, correct_time\nred = redis.StrictRedis(host='localhost', port=6379, db=0)\n\n\ndef task_info(name='info'):\n pubsub = red.pubsub()\n pubsub.subscribe(name)\n for message in pubsub.listen():\n # print(\"[+] Message: {}\".format(message))\n yield 'data: {}\\n\\n'.format(message['data'])\n# ---------------------------- Process openxc task -------------------------- #\n\n\ndef openxc_task(traces, user_id):\n try:\n es = Elasticsearch([{\n 'host': 'localhost',\n 'port': 9200\n }])\n index = \"openxc\"\n type = \"driver_{}\".format(user_id)\n sleep(2)\n red.publish('info', 'Inisializing Elasticsearch ...')\n es.cluster.health(wait_for_status='yellow', request_timeout=1)\n\n red.publish('info', 'creating index if it dosn\\'t exist ...')\n mapping = {\n type: {\n \"properties\": {\n \"name\": {\n \"type\": \"string\",\n \"index\": \"not_analyzed\"\n },\n \"value\": {\n \"type\": \"string\",\n \"index\": \"not_analyzed\"\n },\n \"timestamp\": {\n \"type\": \"date\"\n }\n }\n }\n }\n # es.indices.delete(index=index, ignore=404)\n if not es.indices.exists(index=index):\n es.indices.create(index=index, ignore=400)\n es.indices.put_mapping(index=index, doc_type=type, body=mapping)\n\n for trace in traces:\n i = 1\n red.publish('info', 'indexing the file: {}'.format(trace))\n actions = []\n with open(trace, 'r') as f:\n for line in f:\n source = json.loads(line)\n action = {\n \"_index\": index,\n \"_type\": type,\n \"_id\": i,\n \"_source\": {\n \"name\": source.get('name'),\n # \"value\": correct_value(source.get('value')),\n \"value\": source.get('value'),\n \"timestamp\": correct_time(source.get('timestamp'))\n }\n }\n actions.append(action)\n if i % BULK_SIZE == 0:\n helpers.bulk(es, actions)\n red.publish('info', '{} events indexed'.format(i))\n actions = []\n i += 1\n # ,overwrite_existing=True\n # es.index(index=index, doc_type=type, id=i, body=line)\n if i < BULK_SIZE:\n helpers.bulk(es, actions)\n red.publish('info', 'File {} indexed'.format(trace))\n red.publish('info', 'end_trace'.format(trace))\n red.publish('info', 'end_all')\n print('[+] DONE ALL FILES')\n red.publish('info', 'end_all')\n red.connection_pool.disconnect()\n return True\n sys.exit()\n except Exception as e:\n print('Exception: ', e)\n red.publish('info', 'Exception: {}'.format(e))\n return False\n sys.exit()\n\n\n# def realtime_info(info=None):\n# socketio.emit(\n# 'send_info',\n# {\n# 'data': info.get('data', 'No data!'),\n# 'status': info.get('status', 'GREEN')\n# },\n# namespace='/import'\n# )\n"},{"alpha_fraction":0.8214285969734192,"alphanum_fraction":0.8214285969734192,"avg_line_length":27,"blob_id":"87a24bff92a898a314b1d8fd4bf159fb97449a5a","content_id":"cbd45b293f8fda71ee62c155e75d5f526886a590","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":28,"license_type":"no_license","max_line_length":27,"num_lines":1,"path":"/app/carboard/__init__.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from .views import carboard\n"},{"alpha_fraction":0.64236980676651,"alphanum_fraction":0.6456981301307678,"avg_line_length":37.519229888916016,"blob_id":"9dd5b5e381c26249014d29b03d609631a742b6f5","content_id":"1812f978380275e9f7d56d5686dd05c78ecadfff","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6009,"license_type":"no_license","max_line_length":124,"num_lines":156,"path":"/app/carboard/views/signalclass.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import os\n\nfrom flask import (\n render_template, request, flash, redirect, url_for\n)\n\nfrom flask_login import login_required\nfrom sqlalchemy import or_\n\nfrom ..forms.filesignal import FileSignalForm\nfrom . import carboard\nfrom ..models.signalclass import Signalclass\nfrom ..forms.signalclass import SignalclassForm\nfrom ..helpers import paginate, CSVLoader, find_id\nfrom ..constants import PER_PAGE, CSV_TEMP\nfrom ...extensions import db\nfrom ..helpers import upload_csv, remove_csv\n\n\n# --------------------- /carboard/signalclass/ : List of signalclasses ----- #\n\n\n@carboard.route('/signalclass/')\n@login_required\ndef indexSignalclass():\n signalclasses = Signalclass.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n start = (request.args.get('page', 1, type=int) - 1) * PER_PAGE + 1 # get the pagination number to render in the template\n return render_template('carboard/signalclass/index.html', signalclasses=signalclasses, start=start)\n\n\n# ----------------------- /carboard/signalclass/id : Show signalclass ----- #\n\n\n@carboard.route('/signalclass/<int:id>', methods=['GET'])\n@login_required\ndef showSignalclass(id):\n signalclass = Signalclass.query.get_or_404(id)\n return render_template('carboard/signalclass/show.html', signalclass=signalclass)\n\n\n# ---------------------- /carboard/signalclass/new : Add signalclass -------------------- #\n\n@carboard.route('/signalclass/bulk-add', methods=['GET', 'POST'])\n@login_required\ndef bulkAddClass():\n form = FileSignalForm()\n errors = None\n if form.validate_on_submit():\n f = upload_csv(form.file.data, CSV_TEMP)\n loader = CSVLoader(os.path.join(CSV_TEMP, form.file.data.filename))\n res = loader.load()\n already_there = False\n try:\n for row in res:\n ret = Signalclass.query.filter_by(name=row['Signal Class'])\n if ret:\n already_there = True\n continue\n signal_class = Signalclass(\n name=row['Signal Class'],\n description=row['Description']\n )\n db.session.add(signal_class)\n db.session.commit()\n remove_csv(form.file.data.filename, CSV_TEMP)\n except KeyError:\n errors = ['Your file is Not well Formated, please review your file structure .']\n if not errors and not already_there:\n flash('Signal classes added Succesfully', 'success')\n if already_there:\n flash('Some Signals are not added because they are already in the database', 'warning')\n return render_template('carboard/signalclass/bulk.html', form=form, errors=errors)\n\n\n@carboard.route('/signalclass/new', methods=['GET', 'POST'])\n@login_required\ndef newSignalclass():\n \"\"\" Add new signalclass \"\"\"\n form = SignalclassForm()\n if form.validate_on_submit():\n ret = Signalclass.query.filter_by(name=form.name.data).first()\n if ret:\n flash('Signal class {}, already exists in the database.'.format(form.name.data), 'error')\n return redirect(url_for('carboard.indexSignalclass'))\n signalclass = Signalclass(\n name=form.name.data,\n description=form.description.data\n )\n db.session.add(signalclass)\n db.session.commit()\n flash('Signal class {}, added successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.indexSignalclass'))\n\n return render_template('carboard/signalclass/new.html', form=form)\n\n\n# -------------------- /carboard/signalclass/id/edit : Edit signalclass ----------------- #\n\n\n@carboard.route('/signalclass/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editSignalclass(id):\n \"\"\" Edit existing signalclass \"\"\"\n signalclass = Signalclass.query.get_or_404(id)\n form = SignalclassForm(obj=signalclass)\n if form.validate_on_submit():\n form.populate_obj(signalclass)\n db.session.commit()\n flash('Signal class {}, updated successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.showSignalclass', id=id))\n\n return render_template('carboard/signalclass/edit.html', form=form, id=id)\n\n\n# ------------------ /carboard/signalclass/id/delete : Delete signalclass --------------- #\n\n\n@carboard.route('/signalclass/<int:id>/toggle', methods=['GET'])\n@login_required\ndef toggleSignalclass(id):\n signalclass = Signalclass.query.get_or_404(id)\n status = signalclass.status if signalclass.status is not None else 0\n signalclass.status = 1 - status\n db.session.commit()\n msg = 'activated' if signalclass.status is 1 else 'deactivated'\n flash('Signal class {}, {} successfully.'.format(signalclass.name, msg), 'success')\n return redirect(url_for('carboard.indexSignalclass'))\n\n\n# ------------------ /carboard/signalclass/id/delete : Delete signalclass --------------- #\n\n\n@carboard.route('/signalclass/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteSignalclass(id):\n signalclass = Signalclass.query.get_or_404(id)\n db.session.delete(signalclass)\n db.session.commit()\n flash('Signal class {}, deleted successfully.'.format(signalclass.name), 'success')\n return redirect(url_for('carboard.indexSignalclass'))\n\n# ------------------ /carboard/signalclass/search?table_seach=?: Search for given signal class --------------- #\n\n@carboard.route('/signalclass/search', methods=['GET'])\n@login_required\ndef searchSignalClass():\n param = request.args.get('table_search')\n signalclasses = Signalclass.query.filter(\n or_(Signalclass.name.like('%' + param + '%'), Signalclass.description.like('%' + param + '%'))).paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/signalclass/search.html', signalclasses=signalclasses)\n"},{"alpha_fraction":0.5967857837677002,"alphanum_fraction":0.6005165576934814,"avg_line_length":37.29121017456055,"blob_id":"baaa5176944e295085e8731dede29716da32b573","content_id":"da1b08c91ab76f503c2693171c986df36aecd042","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6969,"license_type":"no_license","max_line_length":130,"num_lines":182,"path":"/app/carboard/views/signal.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import (\n render_template, request, flash, redirect, url_for\n)\nfrom flask_login import login_required\nimport os\n\nfrom sqlalchemy import or_\n\nfrom . import carboard\nfrom ..models.signal import Signal\nfrom ..models.signalclass import Signalclass\nfrom ..models.signalsource import Signalsource\nfrom ..forms.signal import SignalForm\nfrom ..forms.filesignal import FileSignalForm\nfrom ..helpers import paginate, choices, CSVLoader, find_id, types, get_signal_from_form\nfrom ..constants import PER_PAGE, CSV_TEMP\nfrom ...extensions import db\nfrom ..helpers import upload_csv, remove_csv\n\n\n# --------------------- /carboard/signal/ : List of signals ----- #\n\n\n@carboard.route('/signal/')\n@login_required\ndef indexSignal():\n signals = Signal.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n start = (request.args.get('page', 1, type=int) - 1) * PER_PAGE + 1 # get the pagination start number to render in the template\n return render_template('carboard/signal/index.html', signals=signals, start=start)\n\n\n# ----------------------- /carboard/signal/id : Show signal ----- #\n\n\n@carboard.route('/signal/<int:id>', methods=['GET'])\n@login_required\ndef showSignal(id):\n signal = Signal.query.get_or_404(id)\n return render_template('carboard/signal/show.html', signal=signal)\n\n\n# ---------------------- /carboard/signal/new : Add signal -------------------- #\n\n\n@carboard.route('/signal/new', methods=['GET', 'POST'])\n@login_required\ndef newSignal():\n \"\"\" Add new signal \"\"\"\n form = SignalForm()\n form.signalclass_id.choices = choices(Signalclass, 'Select signal class', 1)\n form.signalsource_id.choices = choices(Signalsource, 'Select signal source', 1)\n form.type.choices = types()\n if form.validate_on_submit():\n signal = get_signal_from_form(form);\n db.session.add(signal)\n db.session.commit()\n flash('Signal {}, added successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.indexSignal'))\n\n return render_template('carboard/signal/new.html', form=form)\n\n\n# ---------------------- /carboard/signal/bulk-add : Add signals from file -------------------- #\n\n@carboard.route('/signal/bulk-add', methods=['GET', 'POST'])\n@login_required\ndef bulkAdd():\n form = FileSignalForm()\n errors = None\n print(request.form.getlist('files'))\n if form.validate_on_submit():\n f = upload_csv(form.file.data, CSV_TEMP)\n loader = CSVLoader(os.path.join(CSV_TEMP, form.file.data.filename))\n res = loader.load()\n already_there = False\n try:\n for row in res:\n print(row)\n signal_class_id = find_id(Signalclass, row['Signal Class'])\n signal_source_id = find_id(Signalsource, row['Signal Source'])\n\n if signal_class_id == -1 or signal_source_id == -1:\n if not errors: errors = [] # lazy instatiation\n errors.append('The Signal ' + row[\n 'Signal Name'] + ' could not be added because of invalid Signal Class or Signal Source ')\n continue\n ret = Signal.query.filter_by(\n name=row['Signal Name']).all() # check if a signal with the same name already exists\n same = False\n for sig in ret:\n if sig.signalsource_id == signal_source_id:\n same = True\n break\n if same:\n already_there = True\n continue\n signal = Signal(\n name=row['Signal Name'],\n signalclass_id=signal_class_id,\n signalsource_id=signal_source_id,\n frequency=row['Frequency'],\n type=row['Type'],\n description=row['Description'],\n unit=row['Unit']\n )\n signal.range = row['Range']\n db.session.add(signal)\n db.session.commit()\n except KeyError:\n errors = ['Your file is Not well Formated, please review your file structure .']\n remove_csv(form.file.data.filename, CSV_TEMP)\n if not errors: flash('Signals Added Successfully !', 'success')\n if already_there:\n flash('Some Signals are not added because they are already in the database', 'warning')\n return render_template('carboard/signal/bulk.html', form=form, errors=errors)\n\n\n# -------------------- /carboard/signal/id/edit : Edit signal ----------------- #\n\n\n@carboard.route('/signal/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editSignal(id):\n \"\"\" Edit existing signal \"\"\"\n signal = Signal.query.get_or_404(id)\n form = SignalForm(obj=signal)\n print(form.data)\n form.signalclass_id.choices = choices(Signalclass, 'Select signal class', 1)\n form.signalsource_id.choices = choices(Signalsource, 'Select signal source', 1)\n form.type.choices = types()\n if form.validate_on_submit():\n form.populate_obj(signal)\n db.session.commit()\n flash('Signal {}, updated successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.showSignal', id=id))\n\n return render_template('carboard/signal/edit.html', form=form, id=id)\n\n\n# ------------------ /carboard/signal/id/delete : Delete signal --------------- #\n\n\n@carboard.route('/signal/<int:id>/toggle', methods=['GET'])\n@login_required\ndef toggleSignal(id):\n signal = Signal.query.get_or_404(id)\n status = signal.status if signal.status is not None else 0\n signal.status = 1 - status\n db.session.commit()\n msg = 'activated' if signal.status is 1 else 'deactivated'\n flash('Signal {}, {} successfully.'.format(signal.name, msg), 'success')\n return redirect(url_for('carboard.indexSignal'))\n\n\n# ------------------ /carboard/signal/id/delete : Delete signal --------------- #\n\n\n@carboard.route('/signal/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteSignal(id):\n signal = Signal.query.get_or_404(id)\n db.session.delete(signal)\n db.session.commit()\n flash('Signal {}, deleted successfully.'.format(signal.name), 'success')\n return redirect(url_for('carboard.indexSignal'))\n\n# ------------------ /carboard/signal/search : signal lookup (by description or name) --------------- #\n\n@carboard.route('/signal/search', methods=['GET'])\n@login_required\ndef searchSignal():\n param = request.args.get('table_search')\n signals = Signal.query.filter(\n or_(Signal.name.like('%' + param + '%'), Signal.description.like('%' + param + '%')))\\\n .paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/signal/search.html', signals=signals)\n"},{"alpha_fraction":0.5780312418937683,"alphanum_fraction":0.5906362533569336,"avg_line_length":33.020408630371094,"blob_id":"9a492fccd9548024ae59843749c070cd8a101cbb","content_id":"9b0963d9b97b7efab423e30b1b62366f383d0beb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1666,"license_type":"no_license","max_line_length":90,"num_lines":49,"path":"/app/carboard/models/dataset.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import re\nfrom datetime import datetime\n#from ..helpers import slugify\nfrom unicodedata import normalize\n\nfrom ...extensions import db\n\n# -------------------------------- Brand Model ------------------------------ #\n\ndef slugify(text, delim=u'-'):\n \"\"\"Generates an slightly worse ASCII-only slug.\"\"\"\n result = []\n _punct_re = re.compile(r'[\\t !\"#$%&\\'()*\\-/<=>?@\\[\\\\\\]^_`{|},.]+')\n for word in _punct_re.split(text.lower()):\n word = normalize('NFKD', word).encode('ascii', 'ignore')\n if word:\n result.append(word)\n try:\n return unicode(delim.join(result))\n except:\n d = d = bytes(delim, 'utf-8')\n return str(d.join(result).decode(\"utf-8\"))\nclass Dataset(db.Model):\n \"\"\" Dataset Model \"\"\"\n __tablename__ = 'datasets'\n\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(255))\n slug = db.Column(db.String(255))\n description = db.Column(db.String(255))\n author = db.Column(db.String(255), nullable=True)\n lab = db.Column(db.String(255), nullable=True)\n website = db.Column(db.String(255), nullable=True)\n\n article = db.relationship(\"Article\", uselist=False, back_populates=\"dataset\")\n\n status = db.Column(db.SmallInteger, default=1)\n created = db.Column(db.DateTime(), default=datetime.utcnow())\n\n def __init__(self, name, description, slug=None, author=None, lab=None, website=None):\n self.name = name\n self.slug = slug if slug != '' else slugify(name, '_')\n self.description = description\n self.author = author\n self.lab = lab\n self.website = website\n\n def __repr__(self):\n return self.name"},{"alpha_fraction":0.419380247592926,"alphanum_fraction":0.42121848464012146,"avg_line_length":44.33333206176758,"blob_id":"f6a37bc13016cf26ff98afb567e3b74f30dbdb84","content_id":"45d8010a6817d397c547b2463a3cc6f80ddaf1d9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"HTML","length_bytes":3808,"license_type":"no_license","max_line_length":205,"num_lines":84,"path":"/templates/carboard/record/index.html","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"{% extends \"layout.html\" %}\n\n{% from \"carboard/macros.html\" import render_pagination %}\n\n{% block title %}\n List of records | {{ super() }}\n{% endblock %}\n\n{% block content_header %}\n\t<section class=\"content-header\">\n\t\t<h1>\n\t\tList of records\n\t\t</h1>\n\t\t<ol class=\"breadcrumb\">\n\t\t\t<li><a href=\"{{ url_for('carboard.index') }}\"><i class=\"fa fa-dashboard\"></i>Home</a></li>\n\t\t\t<li><a href=\"{{ url_for('carboard.indexRecord') }}\">Record</a></li>\n\t\t\t<li class=\"active\">List of records</li>\n\t\t</ol>\n\t</section>\n{% endblock content_header %}\n\n{% block content %}\n\n <div class=\"row\">\n <div class=\"col-md-12\">\n <div class=\"box\">\n <div class=\"box-header\">\n <div class=\"box-actions\">\n <a href=\"{{ url_for('carboard.newRecord') }}\" class=\"btn btn-primary\">\n Add new record\n </a>\n </div>\n <div class=\"box-tools\">\n <div class=\"input-group input-group-sm\" style=\"width: 150px;\">\n <input type=\"text\" name=\"table_search\" class=\"form-control pull-right\" placeholder=\"Search\">\n\n <div class=\"input-group-btn\">\n <button type=\"submit\" class=\"btn btn-default\">\n <i class=\"fa fa-search\"></i>\n </button>\n </div>\n </div>\n </div>\n </div>\n <div class=\"box-body no-padding\">\n <table class=\"table table-hover\">\n <tr>\n <th>ID</th>\n <th>Name</th>\n <th>Record file</th>\n <th>User</th>\n <th>Drive type</th>\n <th>Status</th>\n <th>Actions</th>\n </tr>\n\n {% for record in records.items %}\n <tr>\n <td><a href=\"{{ url_for('carboard.showRecord', id=record.id) }}\">{{ record.id }}</a></td>\n <td>{{ record.name | default('--') }}</td>\n <td><a href=\"/static{{ config.UPLOAD_RECORDS}}{{ record.trace}}\" target=\"_blank\">Get File</a></td>\n <td>{{ record.user.username }}</td>\n <td>{{ record.drivetype.name }}</td>\n <td><span class=\"label bg-{{ record.status.color }}\">{{ record.status.title }}</span></td>\n <td>\n <a href=\"{{ url_for('carboard.showRecord', id=record.id) }}\" class=\"btn btn-default btn-sm\" data-toggle=\"tooltip\" title=\"Show\"><i class=\"fa fa-eye\"></i></a>\n\n <a href=\"{{ url_for('carboard.toggleRecord', id=record.id) }}\" class=\"btn btn-warning btn-sm\" data-toggle=\"tooltip\" title=\"Toggle status\"><i class=\"fa fa-toggle-on\"></i></a>\n\n <a href=\"{{ url_for('carboard.editRecord', id=record.id) }}\" class=\"btn btn-success btn-sm\" data-toggle=\"tooltip\" title=\"Edit\"><i class=\"fa fa-pencil\"></i></a>\n\n <a href=\"{{ url_for('carboard.deleteRecord', id=record.id) }}\" class=\"btn btn-danger btn-sm\" data-toggle=\"tooltip\" title=\"Remove\"><i class=\"fa fa-trash\"></i></a>\n </td>\n </tr>\n {% endfor %}\n </table>\n </div>\n <div class=\"box-footer clearfix\">\n {{render_pagination(records)}}\n </div>\n </div>\n </div>\n </div>\n{% endblock %}\n"},{"alpha_fraction":0.8381876945495605,"alphanum_fraction":0.8414239287376404,"avg_line_length":16.16666603088379,"blob_id":"366ec87651506558b0771b21ad3ba92c65fc169d","content_id":"aac0bdbfb0352de358003fc56c246dca574270eb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":309,"license_type":"no_license","max_line_length":57,"num_lines":18,"path":"/requirements3.txt","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"elasticsearch\nFlask\nFlask-Bcrypt\nFlask-Cache\nFlask-DebugToolbar\nFlask-Login\nFlask-Mail\nFlask-Migrate\nFlask-Redis\nFlask-Script\nFlask-SQLAlchemy\nFlask-WTF\nFlask-Misaka\nmarshmallow\nnetworkx\nPyYAML\nhttps://github.com/mikeboers/Flask-Images/archive/py3.zip\nhttps://github.com/vcar/flask-plugins/archive/master.zip\n"},{"alpha_fraction":0.6060874462127686,"alphanum_fraction":0.6111111044883728,"avg_line_length":32.17647171020508,"blob_id":"eb1e776adb1be26feb555a982ecf5c8e509f2ad1","content_id":"3449cba5979d061a951c708a7126fe322a0c116f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3384,"license_type":"no_license","max_line_length":85,"num_lines":102,"path":"/app/carboard/views/country.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import (\n render_template, request, flash, redirect, url_for\n)\nfrom flask_login import login_required\n\nfrom . import carboard\nfrom ..models.country import Country\nfrom ..forms.country import CountryForm\nfrom ..helpers import paginate\nfrom ..constants import PER_PAGE\nfrom ...extensions import db\n\n\n# --------------------- /carboard/country/ : List of countries ------------------ #\n\n@carboard.route('/country/')\n@login_required\ndef indexCountry():\n countries = Country.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/country/index.html',\n countries=countries\n )\n\n# ----------------------- /carboard/country/id : Show country ------------------- #\n\n\n@carboard.route('/country/<int:id>', methods=['GET'])\n@login_required\ndef showCountry(id):\n country = Country.query.get_or_404(id)\n return render_template('carboard/country/show.html',\n country=country\n )\n\n# ---------------------- /carboard/country/new : Add country -------------------- #\n\n\n@carboard.route('/country/new', methods=['GET', 'POST'])\n@login_required\ndef newCountry():\n \"\"\" Add new country \"\"\"\n\n form = CountryForm()\n\n if form.validate_on_submit():\n country = Country(\n title=form.title.data,\n code=form.code.data\n )\n db.session.add(country)\n db.session.commit()\n flash('Country {}, added successfully.'.format(form.title.data), 'success')\n return redirect(url_for('carboard.indexCountry'))\n\n return render_template('carboard/country/new.html', form=form)\n\n# -------------------- /carboard/country/id/edit : Edit country ----------------- #\n\n\n@carboard.route('/country/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editCountry(id):\n \"\"\" Edit existing country \"\"\"\n country = Country.query.get_or_404(id)\n form = CountryForm(obj=country)\n if form.validate_on_submit():\n form.populate_obj(country)\n db.session.commit()\n flash('Country {}, updated successfully.'.format(form.title.data), 'success')\n return redirect(url_for('carboard.showCountry', id=id))\n\n return render_template('carboard/country/edit.html', form=form, id=id)\n\n# ------------------ /carboard/country/id/delete : Delete country --------------- #\n\n\n@carboard.route('/country/<int:id>/toggle', methods=['GET'])\n@login_required\ndef toggleCountry(id):\n country = Country.query.get_or_404(id)\n # getattr(country, 'status', 0)\n status = country.status if country.status is not None else 0;\n country.status = 1 - status\n db.session.commit()\n msg = 'activated' if country.status is 1 else 'deactivated'\n flash('Country {}, {} successfully.'.format(country.title, msg), 'success')\n return redirect(url_for('carboard.indexCountry'))\n\n# ------------------ /carboard/country/id/delete : Delete country --------------- #\n\n\n@carboard.route('/country/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteCountry(id):\n country = Country.query.get_or_404(id)\n db.session.delete(country)\n db.session.commit()\n flash('Country {}, deleted successfully.'.format(country.title), 'success')\n return redirect(url_for('carboard.indexCountry'))\n"},{"alpha_fraction":0.4561251103878021,"alphanum_fraction":0.4610483646392822,"avg_line_length":26.84677505493164,"blob_id":"e7193cef92a958ea6a0e8739690836f80a468f5d","content_id":"592b9aface79d7c43abfb780f50743e11cf15031","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":3453,"license_type":"no_license","max_line_length":95,"num_lines":124,"path":"/static/js/scripts/main.js","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"/*\n * Main JS application file : app.js\n * =========================\n */\n\n$(document).ready(function() {\n // Switch Theme -------------------------------------------------------------\n // if ($.cookie(\"dark-theme\")) {\n // $('body').removeClass(\"light\").addClass(\"dark\");\n // $('#switch-mode').html('<i class=\"fa fa-sun-o\"></i> Light mode');\n // } else {\n // $('body').removeClass(\"dark\").addClass(\"light\");\n // $('#switch-mode').html('<i class=\"fa fa-moon-o\"></i> Dark mode');\n // }\n $('#switch-mode').click(function() {\n if ($.cookie(\"dark-theme\")) {\n $.removeCookie(\"dark-theme\", {\n path: '/'\n });\n $('#switch-mode').html('<i class=\"fa fa-moon-o\"></i> Dark mode');\n } else {\n $.cookie(\"dark-theme\", 1, {\n expires: 365,\n path: '/'\n });\n $('#switch-mode').html('<i class=\"fa fa-sun-o\"></i> Light mode');\n }\n location.reload();\n });\n\n // Switch Sidebar ---------------------------------------------------------\n // if ($.cookie(\"sidebar-collapse\")) {\n // $('body').removeClass(\"light\").addClass(\"dark\");\n // $('#switch-mode').html('<i class=\"fa fa-sun-o\"></i> Light mode');\n // } else {\n // $('body').removeClass(\"dark\").addClass(\"light\");\n // $('#switch-mode').html('<i class=\"fa fa-moon-o\"></i> Dark mode');\n // }\n $('#switch-sidebar').click(function() {\n if ($.cookie(\"sidebar-collapse\")) {\n $.removeCookie(\"sidebar-collapse\", {\n path: '/'\n });\n $('#switch-sidebar').html('<i class=\"fa fa-minus-square-o\"></i> Collapse sidebar');\n } else {\n $.cookie(\"sidebar-collapse\", 1, {\n expires: 365,\n path: '/'\n });\n $('#switch-sidebar').html('<i class=\"fa fa-plus-square-o\"></i> Expand sidebar');\n }\n location.reload();\n });\n});\n\n\n\n\n\n\n\n\n\nvar toastr = window.toastr;\ntoastr.options = {\n closeButton: false,\n closeEasing: 'swing',\n // showMethod: 'slideUp',\n closeMethod: 'slideDown',\n positionClass: 'toast-bottom-right',\n progressBar: false\n};\n\n/* turning flask flash messages into js popup notifications */\n\nwindow.popupMessages.forEach(function(m, i) {\n var category = m[0];\n var text = m[1];\n setTimeout(function() {\n switch (category) {\n case 'success':\n toastr.success(text);\n break;\n case 'warning':\n toastr.warning(text);\n break;\n case 'error':\n toastr.error(text);\n break;\n default:\n toastr.info(text);\n break;\n }\n }, (1 + i) * 1500);\n});\n\n$(window).load(function() {\n\n $('.full').click(function(e) {\n console.log(\"Clicked\");\n $('.vcar-box').toggleClass('fullscreen');\n });\n\n});\n\n$(function() {\n\n $('input').iCheck({\n checkboxClass: 'icheckbox_square-orange',\n radioClass: 'iradio_square-orange',\n increaseArea: '20%' // optional\n });\n\n // var iradio = $('.iradio_square-orange');\n // if(iradio.hasClass('.checked')){\n // \tiradio.closest('.btn-vcar').addClass('active');\n // }\n\n //Confirmation\n $('[data-toggle=confirmation]').confirmation({\n rootSelector: '[data-confirm=confirmation]',\n });\n\n});\n"},{"alpha_fraction":0.5389671325683594,"alphanum_fraction":0.5389671325683594,"avg_line_length":23.76744270324707,"blob_id":"fb7793fb23684894a6ce6650975d975e87935973","content_id":"447c64f2dcd99670a16a62c77c4a8bbe95994157","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1065,"license_type":"no_license","max_line_length":79,"num_lines":43,"path":"/app/frontend/views.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import os\nimport signal\nfrom flask import Blueprint, request, render_template\n\nfrontend = Blueprint('frontend', __name__, url_prefix='/')\n\n# ------------------------------- /api/ : Api ------------------------------- #\n\n\n@frontend.route('/')\ndef index():\n home = {\n 'title': \"xx\",\n 'sub_title': \"sub title\",\n 'Other': \" bla bla bla ...\"\n }\n return render_template('frontend/index.html')\n\n\n# ---------------------------- /kill : kill Server ------------------------- #\n\n\n@frontend.route('about')\ndef about():\n return \"About : vCar Platform !\"\n\n\n# ---------------------------- /kill : kill Server ------------------------- #\n\n\n@frontend.route('kill')\ndef killo():\n func = request.environ.get('werkzeug.server.shutdown')\n if func is None:\n raise RuntimeError('Not running with the Werkzeug Server')\n func()\n return \"Flask Development Server killed successfully!\"\n\n\n@frontend.route('restart')\ndef restart_server():\n os.kill(os.getpid(), signal.SIGHUP)\n return \"Flask Development Server restarted successfully!\"\n"},{"alpha_fraction":0.5096439123153687,"alphanum_fraction":0.5126112699508667,"avg_line_length":23.962963104248047,"blob_id":"3c5e55c6c34403e5a9ed2be121d7d16c7d48db14","content_id":"ed231a271f985e26e73b1a36217da63584c8b097","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1348,"license_type":"no_license","max_line_length":86,"num_lines":54,"path":"/app/carboard/forms/article.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField, DateField\nfrom wtforms.validators import ValidationError, DataRequired, Optional, Regexp, Length\nfrom ..models.article import Article\n\n# ------------------------ custom validation methods ------------------------ #\n\n\ndef article_exist(form, field):\n if Article.select().where(Article.name == field.data).exists():\n raise ValidationError('Article already exists !')\n\n# ---------------------------- Article form classes ------------------------- #\n\n\nclass ArticleForm(FlaskForm):\n \"\"\" Article add/edit FlaskForm \"\"\"\n name = StringField(\n 'Name',\n validators=[\n DataRequired(),\n Length(min=3, max=255),\n Regexp(\n r'^[a-zA-Z_\\- ]+$',\n message=(\"Article name is not correct !\")\n ),\n ]\n )\n abstract = StringField(\n 'Abstract',\n validators=[\n DataRequired(),\n ]\n )\n authors = StringField(\n 'Authors',\n )\n keywords = StringField(\n 'Keywords',\n )\n publication_date = DateField(\n 'Date of publication',\n format='%d/%m/%Y',\n validators=[\n Optional(),\n ]\n )\n\n reference = StringField(\n 'Reference',\n )\n link = StringField(\n 'Link to article',\n )\n"},{"alpha_fraction":0.3785078823566437,"alphanum_fraction":0.4024640619754791,"avg_line_length":23.74576187133789,"blob_id":"6c91f9db43fd964e40359401a63933f16b11ec99","content_id":"5bf76b0f45028a4428f115f216fb0feea108fdcd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1461,"license_type":"no_license","max_line_length":81,"num_lines":59,"path":"/app/importer/platforms/openxc/constants.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"\n# -------------------- Redis constants -------------------------------------- #\nREDIS_HOST = '127.0.0.1' # default config when runing inside a virtualenv\n# REDIS_HOST = 'redis' # docker links\nREDIS_PORT = 6379\nREDIS_DB = 1\n\n# -------------------- Elasticsearch constants ------------------------------ #\n\n# basic configuration\nELASTICSEARCH_HOST = '127.0.0.1' # default config when runing inside a virtualenv\n# ELASTICSEARCH_HOST = 'elastic' # docker links\nELASTICSEARCH_PORT = 9200\n\n# indexing configuration\nOPENXC_INDEX = 'openxc'\nBULK_SIZE = 10000\n\n# -------------------- Driver Graph Configuration --------------------------- #\nTRANSFORM = {\n -1: \"reverse\",\n 0: \"neutral\",\n 1: \"first\",\n 2: \"second\",\n 3: \"third\",\n 4: \"fourth\",\n 5: \"fifth\",\n 6: \"sixth\",\n 7: \"seventh\"\n}\n\nMAPPING = {\n \"platform\": {\n \"properties\": {\n \"user\": {\n \"type\": \"integer\"\n },\n \"vehicle\": {\n \"type\": \"integer\"\n },\n \"driver\": {\n \"type\": \"integer\"\n },\n \"class\": {\n \"type\": \"string\"\n },\n \"name\": {\n \"type\": \"string\",\n \"index\": \"not_analyzed\"\n },\n \"value\": {\n \"type\": \"string\",\n \"index\": \"not_analyzed\"\n },\n \"timestamp\": {\n \"type\": \"date\"\n }\n }\n }\n}\n"},{"alpha_fraction":0.43503835797309875,"alphanum_fraction":0.4544757008552551,"avg_line_length":33.54545593261719,"blob_id":"bb1f8eaa1602381a1358f7757141fa52962ce10e","content_id":"8859099511b3b8d8ca2eb2366c61e7a91015e2bd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":3910,"license_type":"no_license","max_line_length":205,"num_lines":110,"path":"/static/js/scripts/uploader.js","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"$(function() {\r\n\r\n var ul = $('#upload ul');\r\n var acceptFileTypes = /^(application\\/json|text\\/plain|text\\/csv)$/i;\r\n var extensions = ['json', 'txt', 'csv', 'xml', 'mat', 'zip'];\r\n\r\n $('#drop a').click(function() {\r\n $(this).parent().find('input').click();\r\n });\r\n\r\n // Initialize the jQuery File Upload plugin\r\n $('#upload').fileupload({\r\n\r\n dropZone: $('#drop'),\r\n\r\n add: function(e, data) {\r\n var uploadError = true;\r\n var re = /(?:\\.([^.]+))?$/;\r\n var ext = re.exec(data.files[0]['name'])[1];\r\n // Check whether a file is accepted or not.\r\n\r\n if (ext != undefined && ext.length && (extensions.indexOf(ext) >= 0)) {\r\n uploadError = false;\r\n }\r\n if (!uploadError) {\r\n // if File Accepted :\r\n var tpl = $('<li class=\"working\"><input type=\"text\" value=\"0\" data-width=\"48\" data-height=\"48\" data-fgColor=\"#0788a5\" data-readOnly=\"1\" data-bgColor=\"#3e4043\" /><p></p><span></span></li>');\r\n tpl.find('p').text(data.files[0].name).append('<i>' + formatFileSize(data.files[0].size) + '</i>');\r\n data.context = tpl.appendTo(ul);\r\n tpl.find('input').knob({\r\n fgColor: \"#03A100\"\r\n });\r\n\r\n tpl.find('span').click(function() {\r\n if (tpl.hasClass('working')) {\r\n jqXHR.abort();\r\n }\r\n tpl.fadeOut(function() {\r\n tpl.remove();\r\n });\r\n });\r\n var jqXHR = data.submit();\r\n } else {\r\n // if Error\r\n var tpl = $('<li class=\"error working\"><div class=\"bad-extension\"></div><p></p><span></span></li>');\r\n tpl.find('p').text(data.files[0].name).append('<i>Extension not supported</i>');\r\n data.context = tpl.appendTo(ul);\r\n tpl.find('span').click(function() {\r\n tpl.fadeOut(function() {\r\n tpl.remove();\r\n });\r\n });\r\n }\r\n },\r\n\r\n progress: function(e, data) {\r\n\r\n // Calculate the completion percentage of the upload\r\n var progress = parseInt(data.loaded / data.total * 100, 10);\r\n\r\n // Update the hidden input field and trigger a change\r\n // so that the jQuery knob plugin knows to update the dial\r\n data.context.find('input').val(progress).change();\r\n\r\n if (progress == 100) {\r\n data.context.removeClass('working');\r\n }\r\n },\r\n\r\n fail: function(e, data) {\r\n // Something has gone wrong!\r\n data.context.addClass('error');\r\n\r\n },\r\n\r\n done: function(e, data) {\r\n var result = JSON.parse(data.result);\r\n if (result.status) {\r\n $('#send_files').append('<input type=\"hidden\" name=\"files\" value=\"' + result.message + '\" />');\r\n } else {\r\n data.context.addClass('error');\r\n data.context.find('p').find('i').html(\" \" + result.message).addClass('fa fa-exclamation-circle');\r\n }\r\n }\r\n\r\n });\r\n\r\n // Prevent the default action when a file is dropped on the window\r\n $(document).on('drop dragover', function(e) {\r\n e.preventDefault();\r\n });\r\n\r\n // Helper function that formats the file sizes\r\n function formatFileSize(bytes) {\r\n if (typeof bytes !== 'number') {\r\n return '';\r\n }\r\n\r\n if (bytes >= 1000000000) {\r\n return (bytes / 1000000000).toFixed(2) + ' GB';\r\n }\r\n\r\n if (bytes >= 1000000) {\r\n return (bytes / 1000000).toFixed(2) + ' MB';\r\n }\r\n\r\n return (bytes / 1000).toFixed(2) + ' KB';\r\n }\r\n\r\n});\r\n"},{"alpha_fraction":0.5564159154891968,"alphanum_fraction":0.5652654767036438,"avg_line_length":28.129032135009766,"blob_id":"7b8277514c35a037cdd16c892f4cd6299112855a","content_id":"bbdbcbba5fc6615ccd6ee258ba77bcb40e2510ef","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":904,"license_type":"no_license","max_line_length":81,"num_lines":31,"path":"/app/carboard/forms/signalclass.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField, TextAreaField\nfrom wtforms.validators import DataRequired, Regexp, Length\n\nfrom ..models.signalclass import Signalclass\n# ------------------------ custom validation methods ------------------------ #\n\ndef signalclass_exist(form, field):\n if Signalclass.select().where(Signalclass.name == field.data).exists():\n raise ValidationError('Attribute already exists !')\n\n# ---------------------------- Driver form classes ---------------------------- #\n\n\nclass SignalclassForm(FlaskForm):\n \"\"\" Signalclass add/edit FlaskForm \"\"\"\n name = StringField(\n 'Name',\n validators=[\n DataRequired(),\n Length(min=3, max=50),\n # signalclass_exist()\n ]\n )\n\n description = TextAreaField(\n 'Description',\n validators=[\n Length(min=0,max=1000)\n ]\n )\n\n"},{"alpha_fraction":0.6693877577781677,"alphanum_fraction":0.6816326379776001,"avg_line_length":17.846153259277344,"blob_id":"876283a953a2af4c0c5861ec386eda8e4863641f","content_id":"4657ecce0fa319da8d80618c15d361febb83ef21","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":245,"license_type":"permissive","max_line_length":38,"num_lines":13,"path":"/app/plugins/hello_world/__init__.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from os import getcwd\nfrom app.plugin import AppPlugin\nfrom .src.views import hello\n\n__plugin__ = \"HelloWorld\"\n__version__ = \"1.0.0\"\n__path__ = getcwd()\n\n\nclass HelloWorld(AppPlugin):\n\n def setup(self):\n self.register_blueprint(hello)\n"},{"alpha_fraction":0.5905323624610901,"alphanum_fraction":0.5935607552528381,"avg_line_length":25.927038192749023,"blob_id":"fdb1fc430e47b186d85f412e79f135548463fc66","content_id":"eaa79974d810fec888714824c2eb98fdc9983e8a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6274,"license_type":"no_license","max_line_length":81,"num_lines":233,"path":"/main.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import Flask, render_template, g\nimport flask_plugins\n\nfrom config.config import DefaultConfig\nfrom config.database import DatabaseConfig\nfrom config.mail import MailConfig\n\nfrom app.frontend import frontend\nfrom app.carboard import carboard\nfrom app.importer import importer\nfrom app.explorer import explorer\n# from app.dashboard import dashboard\nfrom app.api import api\nfrom app.filters import filters\n\nfrom app.carboard.models.user import User\nfrom app.extensions import (\n db, migrate, mail, toolbar, images, login_manager, redis,\n plugin_manager, socketio, misaka # , cache , session\n)\n\n\n__all__ = ['create_app']\n\n# ----------------------------- Blueprints List ----------------------------- #\n\nDEFAULT_BLUEPRINTS = (\n api,\n frontend,\n carboard,\n # dashboard,\n importer,\n explorer,\n filters\n)\n\n# ------------------------ Create a flask application ----------------------- #\n\n\ndef create_app(config=None, app_name=None, blueprints=None):\n \"\"\"Create a flask application.\"\"\"\n\n if app_name is None:\n app_name = DefaultConfig.PROJECT\n if blueprints is None:\n blueprints = DEFAULT_BLUEPRINTS\n # Create a flask application instance\n app = Flask(\n app_name,\n # static_url_path='',\n instance_path=DefaultConfig.INSTANCE_FOLDER_PATH,\n instance_relative_config=True\n )\n # jinja configuration\n app.jinja_env.trim_blocks = True\n app.jinja_env.lstrip_blocks = True\n # App configuration\n configure_app(app, config)\n # Static configuration\n static_file_status(app)\n # Before request\n configure_hook(app)\n # Register blueprints\n configure_blueprints(app, blueprints)\n # Inisialize extensions\n configure_extensions(app)\n # Template filters\n configure_template_filters(app)\n # Register plugins menu in g\n register_plugins_menu(app)\n # Error handling\n configure_error_handlers(app)\n\n return app\n\n\n# --------------------------- Minified static files ------------------------ #\n\ndef static_file_status(app, debug=None):\n \"\"\"Use minified static files if debug is True\"\"\"\n if debug is None:\n debug = app.config['DEBUG']\n minified = \".min\" if debug is False else \"\"\n app.jinja_env.globals.update(minified=minified)\n app.jinja_env.add_extension('jinja2.ext.do')\n\n# ----------------------------- App configuration --------------------------- #\n\n\ndef configure_app(app, config=None):\n \"\"\"App configuration.\"\"\"\n\n app.config.from_object(DefaultConfig)\n app.config.from_object(DatabaseConfig)\n app.config.from_object(MailConfig)\n if config:\n app.config.from_object(config)\n\n# ------------------------------- Before & After request -------------------- #\n\n\ndef configure_hook(app):\n @app.before_request\n def before_request():\n pass\n\n @app.after_request\n def apply_caching(response):\n response.headers['Cache-Control'] = 'no-cache, no-store, must-revalidate'\n return response\n# ---------------------------- Register blueprints ------------------------- #\n\n\ndef configure_blueprints(app, blueprints):\n \"\"\"Configure blueprints in views.\"\"\"\n\n for blueprint in blueprints:\n app.register_blueprint(blueprint)\n\n# --------------------------- Inisialize extensions ------------------------ #\n\n\ndef configure_extensions(app):\n\n # flask-sqlalchemy\n db.init_app(app)\n\n # flask-migrate\n migrate.init_app(app, db)\n\n # flask-mail\n mail.init_app(app)\n # flask-cache\n # cache.init_app(app)\n\n # initialize debug toolbar\n # toolbar.init_app(app)\n\n # initialize Images utils\n images.init_app(app)\n\n # initialize FlaskRedis\n redis.init_app(app)\n\n # initialize FlaskRedis\n misaka.init_app(app)\n\n # initialize SocketIO\n socketio.init_app(app)\n\n # initialize Session\n # session.init_app(app)\n\n # initialize pluginManager\n # plugin_manager.init_app(\n # app,\n # base_app_folder='app',\n # plugin_folder=\"app/plugins\"\n # )\n plugin_manager.init_app(\n app,\n plugin_folder=\"app/plugins\",\n plugin_import_path='app.plugins',\n )\n\n # flask-login\n login_manager.login_view = 'carboard.login'\n login_manager.refresh_view = 'carboard.reauth'\n\n @login_manager.user_loader\n def load_user(id):\n return User.query.get(id)\n login_manager.setup_app(app)\n\n# ------------------------------ Template filters -------------------------- #\n\n\ndef configure_template_filters(app):\n\n @app.template_filter()\n def pretty_date(value):\n return pretty_date(value)\n\n @app.template_filter()\n def format_date(value, format='%Y-%m-%d'):\n return value.strftime(format)\n\n# ------------------------------ Plugins menus ----------------------------- #\n\n\ndef convert_keys_to_string(dictionary):\n \"\"\"Recursively converts dictionary keys to strings.\"\"\"\n if not isinstance(dictionary, dict):\n return dictionary\n return dict((str(k), str(convert_keys_to_string(v)))\n for k, v in dictionary.items())\n\n\ndef register_plugins_menu(app):\n\n @app.context_processor\n def configure_plugins_menu():\n plugins = flask_plugins.get_all_plugins()\n\n plugins_menu = []\n for plugin in plugins:\n if plugin.options['favorite']:\n plugins_menu.append({\n 'id': str(plugin.identifier),\n 'name': str(plugin.name),\n 'iclass': plugin.options.get('iclass', 'fa fa-puzzle-piece'),\n 'favorite': plugin.options.get('favorite', False),\n 'treeview': plugin.options.get('treeview', False),\n 'menu': plugin.options.get('menu', None),\n })\n return {'plugins_menu': plugins_menu}\n\n# ------------------------------- Error handling --------------------------- #\n\n\ndef configure_error_handlers(app):\n\n @app.errorhandler(403)\n def forbidden_page(error):\n return render_template(\"errors/forbidden_page.html\"), 403\n\n @app.errorhandler(404)\n def page_not_found(error):\n return render_template(\"errors/page_not_found.html\"), 404\n\n @app.errorhandler(500)\n def server_error_page(error):\n return render_template(\"errors/server_error.html\"), 500\n"},{"alpha_fraction":0.6039374470710754,"alphanum_fraction":0.6137810945510864,"avg_line_length":30.399999618530273,"blob_id":"157e0e127f255feff0af887ff00185a3b85f2b80","content_id":"cb68e824d60ba07b909c2d76b56529f3b9a6e628","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1727,"license_type":"no_license","max_line_length":109,"num_lines":55,"path":"/app/carboard/models/signal.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom marshmallow import Schema, fields\nfrom ...extensions import db\n\n\n# -------------------------------- Signal Model ------------------------------- #\n\n\nclass Signal(db.Model):\n \"\"\" Signal Model: \"\"\"\n __tablename__ = 'signals'\n\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(255))\n description = db.Column(db.Text)\n type = db.Column(db.String(255))\n range = db.Column(db.String(255))\n frequency = db.Column(db.String(255))\n unit = db.Column(db.String(255))\n\n extrasignals = db.relationship(\"Extrasignal\")\n\n signalclass_id = db.Column(db.Integer, db.ForeignKey('signalclasses.id'))\n signalclass = db.relationship(\"Signalclass\", back_populates=\"signals\")\n\n signalsource_id = db.Column(db.Integer, db.ForeignKey('signalsources.id'))\n signalsource = db.relationship(\"Signalsource\", back_populates=\"signals\")\n\n status = db.Column(db.SmallInteger, default=1)\n created = db.Column(db.DateTime(), default=datetime.utcnow())\n\n def __init__(self, name, signalclass_id, signalsource_id, frequency, type, description, unit, status=1 ):\n self.name = name\n self.signalclass_id = signalclass_id\n self.signalsource_id = signalsource_id\n self.status = status\n self.frequency = frequency\n self.type = type\n self.description = description\n self.unit = unit\n\n def __repr__(self):\n return str(self.name)\n\n\n# -------------------------------- Signal Model ----------------------------- #\n\n\nclass SignalSchema(Schema):\n \"\"\" Extra signals schema \"\"\"\n\n id = fields.Int(dump_only=True)\n name = fields.Str()\n signalclass = fields.Str()\n signalsource = fields.Str()\n"},{"alpha_fraction":0.6158388257026672,"alphanum_fraction":0.6193122863769531,"avg_line_length":32.47674560546875,"blob_id":"84bc3f3b98034ed60709f6a6f74c5c12ff015cb2","content_id":"e28bfba94241fb9777b32080ffbc3c27a5b88ef2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2879,"license_type":"no_license","max_line_length":88,"num_lines":86,"path":"/app/carboard/views/drivetype.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import (\n render_template, request, flash, redirect, url_for\n)\nfrom flask_login import login_required\n\nfrom . import carboard\nfrom ..models.drivetype import DriveType\nfrom ..forms.drivetype import DriveTypeForm\nfrom ..helpers import paginate\nfrom ..constants import PER_PAGE\nfrom ...extensions import db\n\n\n# --------------------- /carboard/drivetype/ : List of countries ------------------ #\n\n@carboard.route('/drivetype/')\n@login_required\ndef indexDriveType():\n drivetypes = DriveType.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/drivetype/index.html',\n drivetypes=drivetypes\n )\n\n# ----------------------- /carboard/drivetype/id : Show drivetype ------------------- #\n\n\n@carboard.route('/drivetype/<int:id>', methods=['GET'])\n@login_required\ndef showDriveType(id):\n drivetype = DriveType.query.get_or_404(id)\n return render_template('carboard/drivetype/show.html',\n drivetype=drivetype\n )\n\n# ---------------------- /carboard/drivetype/new : Add drivetype -------------------- #\n\n\n@carboard.route('/drivetype/new', methods=['GET', 'POST'])\n@login_required\ndef newDriveType():\n \"\"\" Add new driver type \"\"\"\n\n form = DriveTypeForm()\n\n if form.validate_on_submit():\n drivetype = DriveType(\n name=form.name.data\n )\n db.session.add(drivetype)\n db.session.commit()\n flash('Driver type {}, added successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.indexDriveType'))\n\n return render_template('carboard/drivetype/new.html', form=form)\n\n# -------------------- /carboard/drivetype/id/edit : Edit drivetype ----------------- #\n\n\n@carboard.route('/drivetype/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editDriveType(id):\n \"\"\" Edit existing driver type \"\"\"\n drivetype = DriveType.query.get_or_404(id)\n form = DriveTypeForm(obj=drivetype)\n if form.validate_on_submit():\n form.populate_obj(drivetype)\n db.session.commit()\n flash('Driver type {}, updated successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.showDriveType', id=id))\n\n return render_template('carboard/drivetype/edit.html', form=form, id=id)\n\n# ------------------ /carboard/drivetype/id/delete : Delete drivetype --------------- #\n\n\n@carboard.route('/drivetype/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteDriveType(id):\n drivetype = DriveType.query.get_or_404(id)\n db.session.delete(drivetype)\n db.session.commit()\n flash('Driver type {}, deleted successfully.'.format(drivetype.name), 'success')\n return redirect(url_for('carboard.indexDriveType'))\n"},{"alpha_fraction":0.4955640137195587,"alphanum_fraction":0.5044360160827637,"avg_line_length":25.299999237060547,"blob_id":"bf7b5431db1e30c928452dfa315dccda9555651d","content_id":"029fbbe989dca0f19259f776646c7f7ca051d49a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":789,"license_type":"no_license","max_line_length":79,"num_lines":30,"path":"/app/filters.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import jinja2\nimport flask\n\nfrom .carboard.constants import ROLES, MIMETYPE\n\nfilters = flask.Blueprint('filters', __name__)\n\n# ---------------------- -- -- -- -- -- -- -- -- -- -- ---------------------- #\n\n\n@jinja2.contextfilter\n@filters.app_template_filter()\ndef roles(context, value):\n \"\"\" Return the Role name from ROLES list.\"\"\"\n return ROLES[value][1]\n# ---------------------- -- -- -- -- -- -- -- -- -- -- ---------------------- #\n\n\n@jinja2.contextfilter\n@filters.app_template_filter()\ndef mimetypes(context, value):\n \"\"\" Return the Format name from MIMETYPE list.\"\"\"\n return MIMETYPE[value][1]\n# ---------------------- -- -- -- -- -- -- -- -- -- -- ---------------------- #\n\n\n@jinja2.contextfilter\n@filters.app_template_filter()\ndef status(context, value):\n return 2\n"},{"alpha_fraction":0.5395408272743225,"alphanum_fraction":0.5497449040412903,"avg_line_length":22.75757598876953,"blob_id":"944d206274d05291e2ae630dde8d5bc5e53fbfbc","content_id":"769c1dcc57cba0760a30819d85dc893f89e943a6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":784,"license_type":"no_license","max_line_length":79,"num_lines":33,"path":"/app/carboard/forms/signalsource.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField, TextAreaField\nfrom wtforms.validators import DataRequired, Regexp, Length\n\nfrom ..models.signalsource import Signalsource\n\n\n# ------------------------ custom validation methods ------------------------ #\n\ndef signalsource_exist(form, field):\n pass\n\n\n# ---------------------------- Signalsource form classes -------------------- #\n\n\nclass SignalsourceForm(FlaskForm):\n \"\"\" Signalsource add/edit FlaskForm \"\"\"\n name = StringField(\n 'Name',\n validators=[\n DataRequired(),\n Length(min=3, max=50),\n # Signalsource_exist()\n ]\n )\n\n description = TextAreaField(\n 'Description',\n validators=[\n Length(min=0, max=1000)\n ]\n )\n"},{"alpha_fraction":0.6364645957946777,"alphanum_fraction":0.63979572057724,"avg_line_length":35.60975646972656,"blob_id":"950a520400fcdb690e3bb8a3666677228cf5fba5","content_id":"aefab2b419eab46a6c67f69531862d361d09c207","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4503,"license_type":"no_license","max_line_length":83,"num_lines":123,"path":"/app/carboard/views/vehicle.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import (\n render_template, request, flash, redirect, url_for\n)\nfrom flask_login import login_required\n\nfrom . import carboard\nfrom ..models.vehicle import Vehicle\nfrom ..models.user import User\nfrom ..models.brand import Brand\nfrom ..models.model import Model\nfrom ..models.driver import Driver\nfrom ..models.status import Status\nfrom ..forms.vehicle import VehicleForm, VehicleStatusForm\nfrom ..helpers import paginate, choices, upload_file\nfrom ..constants import PER_PAGE, VEHICLE_LOGO_DIR\nfrom ...extensions import db\n\n# --------------------- /carboard/vehicle/ : List of vehicles ------------------ #\n\n@carboard.route('/vehicle/')\n@login_required\ndef indexVehicle():\n vehicles = Vehicle.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/vehicle/index.html', vehicles=vehicles)\n\n# ----------------------- /carboard/vehicle/id : Show vehicle ------------------- #\n\n\n@carboard.route('/vehicle/<int:id>', methods=['GET'])\n@login_required\ndef showVehicle(id):\n vehicle = Vehicle.query.get_or_404(id)\n return render_template('carboard/vehicle/show.html', vehicle=vehicle)\n\n# ---------------------- /carboard/vehicle/new : Add vehicle -------------------- #\n\n\n@carboard.route('/vehicle/new', methods=['GET', 'POST'])\n@login_required\ndef newVehicle():\n \"\"\" Add new vehicle \"\"\"\n\n form = VehicleForm()\n form.user_id.choices = choices(User, 'Select a user', 1, 'username')\n form.brand_id.choices = choices(Brand, 'Select a brand')\n form.model_id.choices = choices(Model, 'Select a model')\n form.driver_id.choices = choices(Driver, 'Select a Driver', False, 'fullname')\n\n if form.validate_on_submit():\n image = upload_file(form.image.data, VEHICLE_LOGO_DIR)\n vehicle = Vehicle(\n user_id=form.user_id.data,\n brand_id=form.brand_id.data,\n model_id=form.model_id.data,\n driver_id=form.driver_id.data,\n image=image\n )\n db.session.add(vehicle)\n db.session.commit()\n flash('Vehicle {}, added successfully.'.format(vehicle), 'success')\n return redirect(url_for('carboard.indexVehicle'))\n\n return render_template('carboard/vehicle/new.html', form=form)\n\n# -------------------- /carboard/vehicle/id/edit : Edit vehicle ----------------- #\n\n\n@carboard.route('/vehicle/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editVehicle(id):\n \"\"\" Edit existing vehicle \"\"\"\n vehicle = Vehicle.query.get_or_404(id)\n oldImage = vehicle.image\n\n form = VehicleForm(obj=vehicle)\n form.user_id.choices = choices(User, 'Select a user', 1, 'username')\n form.brand_id.choices = choices(Brand, 'Select a brand')\n form.model_id.choices = choices(Model, 'Select a model')\n form.driver_id.choices = choices(Driver, 'Select a Driver', False, 'fullname')\n\n if form.validate_on_submit():\n form.populate_obj(vehicle)\n image = upload_file(form.image.data, VEHICLE_LOGO_DIR)\n if image is None:\n vehicle.image = oldImage\n else:\n vehicle.image = image\n db.session.commit()\n flash('Vehicle {}, updated successfully.'.format(vehicle), 'success')\n return redirect(url_for('carboard.showVehicle', id=id))\n\n return render_template('carboard/vehicle/edit.html', form=form, id=id)\n\n# ------------------ /carboard/vehicle/id/delete : Delete vehicle --------------- #\n\n\n@carboard.route('/vehicle/<int:id>/toggle', methods=['GET', 'POST'])\n@login_required\ndef toggleVehicle(id):\n vehicle = Vehicle.query.get_or_404(id)\n form = VehicleStatusForm(obj=vehicle)\n form.status_id.choices = [(s.id, s) for s in Status.query.all()]\n if form.validate_on_submit():\n form.populate_obj(vehicle)\n db.session.commit()\n flash('Vehicle {}, updated successfully.'.format(vehicle.brand), 'success')\n return redirect(url_for('carboard.showVehicle', id=id))\n return render_template('carboard/vehicle/status.html', form=form, id=id)\n\n# ------------------ /carboard/vehicle/id/delete : Delete vehicle --------------- #\n\n\n@carboard.route('/vehicle/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteVehicle(id):\n vehicle = Vehicle.query.get_or_404(id)\n db.session.delete(vehicle)\n db.session.commit()\n flash('Vehicle {}, deleted successfully.'.format(vehicle), 'success')\n return redirect(url_for('carboard.indexVehicle'))\n"},{"alpha_fraction":0.6046239137649536,"alphanum_fraction":0.6095990538597107,"avg_line_length":31.542856216430664,"blob_id":"ccb2b87e62a52bbf934cd2f2cfe1ee5835f88e66","content_id":"67bb4fcd076245e6ca931fab9a2b15f71660b7e3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3417,"license_type":"no_license","max_line_length":82,"num_lines":105,"path":"/app/carboard/views/brand.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import (\n render_template, request, flash, redirect, url_for\n)\nfrom flask_login import login_required\n\nfrom . import carboard\nfrom ..models.brand import Brand\nfrom ..forms.brand import BrandForm\nfrom ..helpers import paginate, upload_file\nfrom ..constants import PER_PAGE, BRAND_LOGO_DIR\nfrom ...extensions import db\n\n# --------------------- /carboard/brand/ : List of brands ------------------ #\n\n@carboard.route('/brand/')\n@login_required\ndef indexBrand():\n brands = Brand.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/brand/index.html', brands=brands)\n\n# ----------------------- /carboard/brand/id : Show brand ------------------- #\n\n\n@carboard.route('/brand/<int:id>', methods=['GET'])\n@login_required\ndef showBrand(id):\n brand = Brand.query.get_or_404(id)\n return render_template('carboard/brand/show.html', brand=brand)\n\n# ---------------------- /carboard/brand/new : Add brand -------------------- #\n\n\n@carboard.route('/brand/new', methods=['GET', 'POST'])\n@login_required\ndef newBrand():\n \"\"\" Add new brand \"\"\"\n\n form = BrandForm()\n\n if form.validate_on_submit():\n logo = upload_file(form.logo.data, BRAND_LOGO_DIR)\n brand = Brand(\n name=form.name.data,\n code=form.code.data,\n logo=logo\n )\n db.session.add(brand)\n db.session.commit()\n flash('Brand {}, added successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.indexBrand'))\n\n return render_template('carboard/brand/new.html', form=form)\n\n# -------------------- /carboard/brand/id/edit : Edit brand ----------------- #\n\n\n@carboard.route('/brand/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editBrand(id):\n \"\"\" Edit existing brand \"\"\"\n brand = Brand.query.get_or_404(id)\n oldLogo = brand.logo\n form = BrandForm(obj=brand)\n if form.validate_on_submit():\n form.populate_obj(brand)\n logo = upload_file(form.logo.data, BRAND_LOGO_DIR)\n if logo is None:\n brand.logo = oldLogo\n else:\n brand.logo = logo\n db.session.commit()\n flash('Brand {}, updated successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.showBrand', id=id))\n\n return render_template('carboard/brand/edit.html', form=form, id=id)\n\n# ------------------ /carboard/brand/id/delete : Delete brand --------------- #\n\n\n@carboard.route('/brand/<int:id>/toggle', methods=['GET'])\n@login_required\ndef toggleBrand(id):\n brand = Brand.query.get_or_404(id)\n # getattr(brand, 'status', 0)\n status = brand.status if brand.status is not None else 0\n brand.status = 1 - status\n db.session.commit()\n msg = 'activated' if brand.status is 1 else 'deactivated'\n flash('Brand {}, {} successfully.'.format(brand.name, msg), 'success')\n return redirect(url_for('carboard.indexBrand'))\n\n# ------------------ /carboard/brand/id/delete : Delete brand --------------- #\n\n\n@carboard.route('/brand/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteBrand(id):\n brand = Brand.query.get_or_404(id)\n db.session.delete(brand)\n db.session.commit()\n flash('Brand {}, deleted successfully.'.format(brand.name), 'success')\n return redirect(url_for('carboard.indexBrand'))\n"},{"alpha_fraction":0.6235954761505127,"alphanum_fraction":0.6235954761505127,"avg_line_length":23.920000076293945,"blob_id":"50fb527bb7bea5fba74504e6b7cb3f9ca21078e9","content_id":"4daf4cbad1ed6edf5b865b8c462b24490cf10d6d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1246,"license_type":"no_license","max_line_length":74,"num_lines":50,"path":"/app/plugin.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import current_app\nfrom flask_plugins import Plugin\n\n\nclass AppPlugin(Plugin):\n\n \"\"\" Custom plugin class, it implements some useful methods\n to deal with plugins\n \"\"\"\n\n has_settings = False\n\n def register_blueprint(self, blueprint, **kwargs):\n \"\"\"Register a plugin blueprint\"\"\"\n current_app.register_blueprint(blueprint, **kwargs)\n\n def is_installed(self):\n \"\"\"Check whether the plugin is installed\"\"\"\n return self.has_settings is True\n\n def is_enabled(self):\n \"\"\"Check whether the plugin is enabled\"\"\"\n return self.has_settings is True\n\n def uninstallable(self):\n \"\"\"Check whether the plugin is installed and can be uninstalled\"\"\"\n return self.has_settings is True\n\n def enabled(self):\n \"\"\"Enable plugin\"\"\"\n return self.has_settings is True\n\n def disable(self):\n \"\"\"Disable plugin\"\"\"\n return self.has_settings is True\n\n @staticmethod\n def all(self):\n \"\"\"List all plugin\"\"\"\n return True\n\n @staticmethod\n def all_enabled(self):\n \"\"\"List all enabled plugin\"\"\"\n return True\n\n @staticmethod\n def all_disabled(self):\n \"\"\"List all disabled plugin\"\"\"\n return True\n"},{"alpha_fraction":0.6257582902908325,"alphanum_fraction":0.630657970905304,"avg_line_length":34.71666717529297,"blob_id":"8fc569a2bd8dc77d2650bdb76cc22e9694d4423b","content_id":"53f8908bd573cc395ead1879359207b45f9e51e9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4286,"license_type":"no_license","max_line_length":87,"num_lines":120,"path":"/app/carboard/views/driver.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import (\n render_template, request, flash, redirect, url_for\n)\nfrom flask_login import login_required\n\nfrom . import carboard\nfrom ..models.driver import Driver\nfrom ..models.user import User\nfrom ..models.country import Country\nfrom ..models.status import Status\nfrom ..forms.driver import DriverForm\nfrom ..helpers import paginate, choices, upload_file\nfrom ..constants import PER_PAGE, DRIVER_LOGO_DIR\nfrom ...extensions import db\n\n# --------------------- /carboard/driver/ : List of drivers ------------------ #\n\n@carboard.route('/driver/')\n@login_required\ndef indexDriver():\n drivers = Driver.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/driver/index.html', drivers=drivers)\n\n# ----------------------- /carboard/driver/id : Show driver ------------------- #\n\n\n@carboard.route('/driver/<int:id>', methods=['GET'])\n@login_required\ndef showDriver(id):\n driver = Driver.query.get_or_404(id)\n return render_template('carboard/driver/show.html', driver=driver)\n\n# ---------------------- /carboard/driver/new : Add driver -------------------- #\n\n\n@carboard.route('/driver/new', methods=['GET', 'POST'])\n@login_required\ndef newDriver():\n \"\"\" Add new driver \"\"\"\n\n form = DriverForm()\n form.user_id.choices = choices(User, 'Select a user', 1, 'username')\n form.country_id.choices = choices(Country, 'Select a country', 1, 'title')\n form.status_id.choices = choices(Status, 'Select a status', False, 'title')\n\n if form.validate_on_submit():\n avatar = upload_file(form.avatar.data, DRIVER_LOGO_DIR)\n driver = Driver(\n gender=form.gender.data,\n fullname=form.fullname.data,\n user_id=form.user_id.data,\n status_id=form.status_id.data,\n country_id=form.country_id.data,\n city=form.city.data,\n avatar=avatar\n )\n db.session.add(driver)\n db.session.commit()\n flash('Driver {}, added successfully.'.format(form.fullname.data), 'success')\n return redirect(url_for('carboard.indexDriver'))\n\n return render_template('carboard/driver/new.html', form=form)\n\n# -------------------- /carboard/driver/id/edit : Edit driver ----------------- #\n\n\n@carboard.route('/driver/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editDriver(id):\n \"\"\" Edit existing driver \"\"\"\n driver = Driver.query.get_or_404(id)\n oldAvatar = driver.avatar\n\n form = DriverForm(obj=driver)\n form.user_id.choices = choices(User, 'Select a user', 1, 'username')\n form.country_id.choices = choices(Country, 'Select a country', 1, 'title')\n form.status_id.choices = choices(Status, 'Select a status', False, 'title')\n\n if form.validate_on_submit():\n form.populate_obj(driver)\n avatar = upload_file(form.avatar.data, DRIVER_LOGO_DIR)\n if avatar is None:\n driver.avatar = oldAvatar\n else:\n driver.avatar = avatar\n db.session.commit()\n flash('Driver {}, updated successfully.'.format(form.fullname.data), 'success')\n return redirect(url_for('carboard.showDriver', id=id))\n\n return render_template('carboard/driver/edit.html', form=form, id=id)\n\n# ------------------ /carboard/driver/id/delete : Delete driver --------------- #\n\n\n@carboard.route('/driver/<int:id>/toggle', methods=['GET'])\n@login_required\ndef toggleDriver(id):\n driver = Driver.query.get_or_404(id)\n # getattr(driver, 'status', 0)\n status = driver.status if driver.status is not None else 0\n driver.status = 1 - status\n db.session.commit()\n msg = 'activated' if driver.status is 1 else 'deactivated'\n flash('Driver {}, {} successfully.'.format(driver.fullname, msg), 'success')\n return redirect(url_for('carboard.indexDriver'))\n\n# ------------------ /carboard/driver/id/delete : Delete driver --------------- #\n\n\n@carboard.route('/driver/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteDriver(id):\n driver = Driver.query.get_or_404(id)\n db.session.delete(driver)\n db.session.commit()\n flash('Driver {}, deleted successfully.'.format(driver.fullname), 'success')\n return redirect(url_for('carboard.indexDriver'))\n"},{"alpha_fraction":0.6800000071525574,"alphanum_fraction":0.6800000071525574,"avg_line_length":20.428571701049805,"blob_id":"bfb43119f76439252d4f55db210f1403a8fb5805","content_id":"d0d8ec73d88f57b5fb830fa1c72f0e13fa7a08fb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":150,"license_type":"no_license","max_line_length":59,"num_lines":7,"path":"/app/api/__init__.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import Blueprint\n\n\"\"\"\n This package should represent the vCar API main package\n\"\"\"\n\napi = Blueprint('api', __name__, url_prefix='/api')\n"},{"alpha_fraction":0.4884667694568634,"alphanum_fraction":0.49027588963508606,"avg_line_length":23.032608032226562,"blob_id":"51d0cccc49c50e86a49b6572f29b0fc52104c7f3","content_id":"06b35595815ddad9665e3bcac4a2b7d5a24599f5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2211,"license_type":"no_license","max_line_length":91,"num_lines":92,"path":"/app/carboard/forms/record.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom flask_wtf.file import FileAllowed, FileField\nfrom wtforms import StringField, SelectField, DateTimeField\nfrom wtforms.validators import Optional, DataRequired, Regexp, Length\n\nfrom ..models.record import Record\n\n# ------------------------ custom validation methods ------------------------ #\n\n\ndef vehicle_exist(form, field):\n if Record.select().where(Record.name == field.data).exists():\n raise ValidationError('Record already exists !')\n\n# ---------------------------- Record form classes ---------------------------- #\n\nclass RecordForm(FlaskForm):\n \"\"\" Record add/edit FlaskForm \"\"\"\n trace = FileField(\n 'Record trace file', validators=[\n DataRequired(),\n FileAllowed(['json', 'txt', 'xml', 'csv', 'xsl'], 'File extension not allowed')\n ]\n )\n name = StringField(\n 'Name',\n validators=[\n Optional(),\n Length(min=3, max=50),\n Regexp(\n r'^[a-zA-Z ]+$',\n message=(\"Brand name is not correct !\")\n ),\n ]\n )\n user_id = SelectField(\n 'User',\n coerce=int,\n validators=[\n DataRequired(),\n ]\n )\n drivetype_id = SelectField(\n 'Drive Type',\n coerce=int,\n validators=[\n Optional(),\n ]\n )\n driver_id = SelectField(\n 'Driver',\n coerce=int, # str\n validators=[\n Optional(),\n ]\n )\n vehicle_id = SelectField(\n 'Driver',\n coerce=int, # str\n validators=[\n Optional(),\n ]\n )\n start = DateTimeField(\n 'Starting record at',\n validators=[\n Optional(),\n ]\n )\n end = DateTimeField(\n 'End record at',\n validators=[\n Optional(),\n ]\n )\n description = StringField(\n 'Description',\n validators=[\n Optional(),\n Length(min=3),\n ]\n )\n\nclass RecordStatusForm(FlaskForm):\n \"\"\" Edit vehicle status FlaskForm \"\"\"\n status_id = SelectField(\n 'Status',\n coerce=int,\n validators=[\n DataRequired(),\n ]\n )\n"},{"alpha_fraction":0.6349206566810608,"alphanum_fraction":0.6349206566810608,"avg_line_length":14.25,"blob_id":"17268a0b2139b10abfe52b741d4ae754b9aca411","content_id":"feaea50a20548f560cb48d9bb0a912f992060b9f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":63,"license_type":"no_license","max_line_length":23,"num_lines":4,"path":"/app/importer/platforms/openxc/__init__.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from .views import help\r\n\r\ndef init():\r\n\treturn 'init text ...'"},{"alpha_fraction":0.6328259706497192,"alphanum_fraction":0.6365918517112732,"avg_line_length":39.08176040649414,"blob_id":"702c4ae983c9361fc2e46e6e939957a9a2106eca","content_id":"ce68efdae9e2dabbe07658fa45efb3c341865e52","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6373,"license_type":"no_license","max_line_length":115,"num_lines":159,"path":"/app/carboard/views/extrasignal.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import os\n\nfrom flask import (\n render_template, request, flash, redirect, url_for\n)\nfrom flask_login import login_required\nfrom sqlalchemy import or_\n\nfrom app.carboard.forms.filesignal import FileSignalForm\nfrom . import carboard\nfrom ..models.extrasignal import Extrasignal\nfrom ..models.signal import Signal\nfrom ..models.platform import Platform\nfrom ..forms.extrasignal import ExtrasignalForm\nfrom ..helpers import paginate, choices, remove_csv, find_id, CSVLoader, upload_csv, platform_choices\nfrom ..constants import PER_PAGE, CSV_TEMP\nfrom ...extensions import db\n\n# --------------------- /carboard/extrasignal/ : List of extrasignals ------------- #\n\n@carboard.route('/extrasignal/')\n@login_required\ndef indexExtrasignal():\n extrasignals = Extrasignal.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n start = (request.args.get('page', 1, type=int) - 1) * PER_PAGE + 1\n return render_template('carboard/extrasignal/index.html', extrasignals=extrasignals,start=start)\n\n# ----------------------- /carboard/extrasignal/id : Show extrasignal ------------------- #\n\n\n@carboard.route('/extrasignal/<int:id>', methods=['GET'])\n@login_required\ndef showExtrasignal(id):\n extrasignal = Extrasignal.query.get_or_404(id)\n return render_template('carboard/extrasignal/show.html', extrasignal=extrasignal)\n\n# ---------------------- /carboard/extrasignal/new : Add extrasignal -------------------- #\n\n\n@carboard.route('/extrasignal/new', methods=['GET', 'POST'])\n@login_required\ndef newExtrasignal():\n \"\"\" Add new extrasignal \"\"\"\n\n form = ExtrasignalForm()\n form.signal_id.choices = choices(Signal, 'Select main signal', 1)\n form.storage.choices = platform_choices('Select a Data Storage', 'name')\n\n if form.validate_on_submit():\n extrasignal = Extrasignal(\n name=form.name.data,\n signal_id=form.signal_id.data,\n storage=form.storage.data,\n )\n db.session.add(extrasignal)\n db.session.commit()\n flash('Extra signal {}, added successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.indexExtrasignal'))\n\n return render_template('carboard/extrasignal/new.html', form=form)\n\n#------------------------- /carboard/extrasignal/bulk-add bulk add extra signals from file ------------------- #\n\n@carboard.route('/extrasignal/bulk-add', methods=['GET', 'POST'])\n@login_required\ndef bulkAddExtraSignal():\n \"\"\" Add new extrasignal \"\"\"\n form = FileSignalForm()\n errors = None\n if form.validate_on_submit():\n f = upload_csv(form.file.data, CSV_TEMP)\n loader = CSVLoader(os.path.join(CSV_TEMP, form.file.data.filename))\n res = loader.load()\n try:\n for row in res:\n signal_id = find_id(Signal, row['Main Signal'])\n storage = row['Data Storage']\n if signal_id == -1:\n if not errors: errors = [] # lazy instatiation\n errors.append('The Extra Signal ' + row[\n 'Signal Name'] + ' could not be added because of invalid Main Signal or Data Storage ')\n continue\n extraSignal = Extrasignal(\n name=row['Signal Name'],\n signal_id=signal_id ,\n storage=storage\n )\n db.session.add(extraSignal)\n db.session.commit()\n except KeyError:\n errors = ['Your file is Not well Formated, please review your file structure .']\n remove_csv(form.file.data.filename, CSV_TEMP)\n if not errors: flash('Signals Added Successfully !', 'success')\n return render_template('carboard/extrasignal/bulk.html', form=form, errors=errors)\n\n\n\n# -------------------- /carboard/extrasignal/id/edit : Edit extrasignal ----------------- #\n\n\n@carboard.route('/extrasignal/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editExtrasignal(id):\n \"\"\" Edit existing extrasignal \"\"\"\n extrasignal = Extrasignal.query.get_or_404(id)\n form = ExtrasignalForm(obj=extrasignal)\n form.signal_id.choices = choices(Signal, 'Select main signal', 1)\n form.storage.choices = platform_choices('Select a Data Storage','name')\n\n if form.validate_on_submit():\n form.populate_obj(extrasignal)\n print(form.data)\n db.session.commit()\n flash('Extra signal {}, updated successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.showExtrasignal', id=id))\n\n return render_template('carboard/extrasignal/edit.html', form=form, id=id)\n\n# ------------------ /carboard/extrasignal/id/delete : Delete extrasignal --------------- #\n\n\n@carboard.route('/extrasignal/<int:id>/toggle', methods=['GET'])\n@login_required\ndef toggleExtrasignal(id):\n extrasignal = Extrasignal.query.get_or_404(id)\n # getattr(extrasignal, 'status', 0)\n status = extrasignal.status if extrasignal.status is not None else 0\n extrasignal.status = 1 - status\n db.session.commit()\n msg = 'activated' if extrasignal.status is 1 else 'deactivated'\n flash('Extra signal {}, {} successfully.'.format(extrasignal.name, msg), 'success')\n return redirect(url_for('carboard.indexExtrasignal'))\n\n# ------------------ /carboard/extrasignal/id/delete : Delete extrasignal --------------- #\n\n\n@carboard.route('/extrasignal/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteExtrasignal(id):\n extrasignal = Extrasignal.query.get_or_404(id)\n db.session.delete(extrasignal)\n db.session.commit()\n flash('Extra signal {}, deleted successfully.'.format(extrasignal.name), 'success')\n return redirect(url_for('carboard.indexExtrasignal'))\n\n# ------------------ /carboard/extrasignal/search : extra signal lookup (by description or name) --------------- #\n\n@carboard.route('/extrasignal/search', methods=['GET'])\n@login_required\ndef searchExtraSignal():\n param = request.args.get('table_search')\n extrasignals = Extrasignal.query.filter(Extrasignal.name.like('%' + param + '%')).paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/extrasignal/search.html', extrasignals=extrasignals)\n"},{"alpha_fraction":0.5466330051422119,"alphanum_fraction":0.5480639934539795,"avg_line_length":35.33027648925781,"blob_id":"6cbba07a41afa307137a7af23c94b47ef989dca4","content_id":"5a262b629069bde373b6089ed9c00a1f6d66eaa4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":11880,"license_type":"no_license","max_line_length":85,"num_lines":327,"path":"/app/importer/views.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import json\nfrom os.path import basename\nfrom importlib import import_module\nfrom threading import Thread\nfrom flask import (\n Blueprint, Response, render_template, request, session, flash, redirect, url_for\n)\nfrom flask_login import (\n login_user, logout_user, login_required, current_user\n)\n\n# import .platforms.openxc.views\nfrom ..extensions import db\nfrom ..carboard.models.platform import Platform\nfrom ..carboard.models.vehicle import Vehicle\nfrom ..carboard.models.driver import Driver\nfrom ..carboard.models.brand import Brand\nfrom ..carboard.models.model import Model\nfrom ..carboard.models.country import Country\nfrom ..carboard.models.status import Status\nfrom ..carboard.models.file import File\n\nfrom .helpers import checkSteps, choices, upload_file, upload_trace\nfrom .forms import VehicleForm, DriverForm\nfrom .transformer import Transformer\nfrom .constants import VEHICLE_LOGO_DIR, DRIVER_LOGO_DIR\n\nfrom .platforms.openxc.indexing import task_info\n\nimporter = Blueprint('importer', __name__, url_prefix='/carboard/import')\n\n\n# -------------------- /stream : Redis stream server ------------------------ #\n\n@importer.route('/stream')\ndef stream():\n return Response(task_info(), mimetype=\"text/event-stream\")\n\n# -------------------- / : Introduce import wizard -------------------------- #\n\n\n@importer.route('/')\n@login_required\ndef index():\n \"\"\" Import index page, containe wellcome text \"\"\"\n return render_template('importer/index.html')\n\n# -------------------- /platform : Choose platform -------------------------- #\n\n\n@importer.route('/platform', methods=['GET', 'POST'])\n@login_required\ndef platform():\n \"\"\"Select upload files platform in order to process indexing correctly\"\"\"\n platforms = Platform.query.filter_by(status=1).all()\n if request.method == 'POST':\n wizard_platform = request.form.get('platform', False)\n if wizard_platform is not False:\n session['import'] = {}\n session['import']['user'] = current_user.id\n session['import']['platform'] = int(request.form['platform'])\n session.modified = True\n return redirect(url_for('importer.vehicle'))\n else:\n session['import'] = {}\n session.modified = True\n # session.clear();\n return render_template('importer/platform.html', platforms=platforms)\n\n# -------------------- /vehicle : Choose or add a vehicle ------------------- #\n\n\n@importer.route('/vehicle', methods=['GET', 'POST'])\n@login_required\ndef vehicle():\n \"\"\"Select or Add the vehicle used to generate traces\"\"\"\n check = checkSteps(step=1)\n if check is True:\n # Preparing data\n vehicle_selected = False\n vehicles = Vehicle.query.filter_by(\n user_id=current_user.id, status_id=1).all()\n form = VehicleForm()\n form.brand_id.choices = choices(Brand, 'Select a brand')\n form.model_id.choices = choices(Model, 'Select a model')\n\n # Geting selected vehicle if any\n if request.method == 'POST':\n wizard_vehicle = request.form.get('vehicle', False)\n if wizard_vehicle is not False:\n session['import']['vehicle'] = int(request.form['vehicle'])\n session.modified = True\n vehicle_selected = True\n return redirect(url_for('importer.driver'))\n\n # Adding a new vehicle if any\n if vehicle_selected is False and form.validate_on_submit():\n image = upload_file(form.image.data, VEHICLE_LOGO_DIR)\n vehicle = Vehicle(\n user_id=current_user.id,\n brand_id=form.brand_id.data,\n model_id=form.model_id.data,\n image=image\n )\n db.session.add(vehicle)\n db.session.commit()\n session['import']['vehicle'] = vehicle.id\n session.modified = True\n flash('Vehicle {}, added successfully.'.format(vehicle), 'success')\n return redirect(url_for('importer.driver'))\n\n # Rendring view template\n return render_template(\n 'importer/vehicle.html',\n vehicles=vehicles,\n form=form\n )\n else:\n return check\n\n\n# -------------------- /driver : Choose or add a driver --------------------- #\n\n@importer.route('/driver', methods=['GET', 'POST'])\n@login_required\ndef driver():\n \"\"\"Select or Add the driver\"\"\"\n check = checkSteps(step=2)\n if check is True:\n # preparing data\n driver_selected = False\n drivers = Driver.query.filter_by(\n user_id=current_user.id, status_id=1).all()\n form = DriverForm()\n form.country_id.choices = choices(\n Country, 'Select a country', 1, 'title')\n form.status_id.choices = choices(\n Status, 'Select a status', False, 'title')\n # geting selected vehicle if any\n if request.method == 'POST':\n wizard_driver = request.form.get('driver', False)\n if wizard_driver is not False:\n session['import']['driver'] = int(request.form['driver'])\n session.modified = True\n driver_selected = True\n return redirect(url_for('importer.records'))\n # # adding a new vehicle if any\n if driver_selected is False and form.validate_on_submit():\n avatar = upload_file(form.avatar.data, DRIVER_LOGO_DIR)\n driver = Driver(\n gender=form.gender.data,\n fullname=form.fullname.data,\n user_id=current_user.id,\n status_id=form.status_id.data,\n country_id=form.country_id.data,\n city=form.city.data,\n avatar=avatar\n )\n db.session.add(driver)\n db.session.commit()\n session['import']['driver'] = driver.id\n session.modified = True\n flash('Driver {}, added successfully.'.format(driver), 'success')\n return redirect(url_for('importer.records'))\n\n return render_template(\n 'importer/driver.html',\n drivers=drivers,\n form=form\n )\n else:\n return check\n\n# -------------------- /records : Uplaod and process record files ----------- #\n\n\n@importer.route('/records', methods=['GET', 'POST'])\n@login_required\ndef records():\n check = checkSteps(step=3)\n if check is True:\n if request.method == 'POST':\n files = request.form.getlist('files')\n if files:\n \"\"\"\n Process data indexing using the python module associated\n with the selected platform.\n Every platform folder should containe a module named: indexing.py\n \"\"\"\n try:\n # Get the platform slug a.k.a module name\n slug = Platform.query.get_or_404(\n session['import']['platform']).slug\n # Import the module\n mod = import_module(\n \".indexing\", 'app.importer.platforms.' + slug)\n # Indexing function\n index_function = 'index_bulk' # 'index'\n # get a reference to the init function\n init = getattr(mod, index_function)\n # create a threaded job to index uploaded data according to\n # it's platform\n thread = Thread(target=init, args=[files, session['import']])\n # thread.daemon = True\n thread.start()\n # thread.join(1)\n # print(active_count())\n flash('Your records are being indexed in background.', 'info')\n return redirect(url_for('carboard.index'))\n except:\n raise\n else:\n session['import']['files'] = []\n session.modified = True\n flash('Please upload some files', 'success')\n return redirect(url_for('importer.records'))\n\n return render_template('importer/records.html')\n else:\n return check\n\n# -------------------- /records : Uplaod record files ----------------------- #\n\n\n@importer.route('/getfile', methods=['POST'])\n@login_required\ndef getfile():\n \"\"\" Import trace files\"\"\"\n if request.method == 'POST':\n uploaded_file = upload_trace('openxc', current_user.id)\n if uploaded_file['status'] is True:\n file = File(\n user_id=current_user.id,\n filename=basename(uploaded_file['message']),\n path=uploaded_file['message']\n )\n db.session.add(file)\n db.session.commit()\n\n if \"files\" in session['import']:\n files = session['import']['files']\n else:\n files = []\n files.append(uploaded_file['message'])\n session['import']['files'] = files\n session.modified = True\n return json.dumps(uploaded_file)\n\n# -------------------- /process : index files using elasticsearch ----------- #\n\n\n@importer.route('/records', methods=['GET', 'POST'])\n@login_required\ndef process():\n \"\"\"\n Process data indexing using the python module associated\n with the selected platform.\n Every platform folder should containe a module named: indexing.py\n \"\"\"\n if request.method == 'POST':\n files = request.form.getlist('files')\n if files:\n try:\n # Get the platform slug a.k.a module name\n slug = Platform.query.get_or_404(\n session['import']['platform']).slug\n # Import the module\n mod = import_module(\n \".indexing\", 'app.importer.platforms.' + slug)\n # Indexing function\n index_function = 'index_bulk' # 'index'\n # get a reference to the init function\n init = getattr(mod, index_function)\n # create a threaded job to index uploaded data according to\n # it's platform\n\n thread = Thread(target=init, args=[files, session['import']])\n # thread.daemon = True\n thread.start()\n # thread.join(1)\n # print(active_count())\n except:\n raise\n else:\n session['import']['files'] = []\n session.modified = True\n flash('Please upload some files before getting here', 'success')\n return redirect(url_for('importer.records'))\n\n return render_template('importer/porcess.html')\n\n# -------------------- /carboard/user/id : Show user ------------------------ #\n\n\n@importer.route('/help', methods=['GET'])\ndef help():\n # endpoints = [rule.rule for rule in app.url_map.iter_rules()\n # if rule.endpoint !='static']\n # return jsonify(dict(api_endpoints=endpoints))\n # return render_template('dashboard/import/openxc_porcess.html')\n tr = Transformer()\n tr.setPlatformById(1)\n a = 'Speeda'\n x = tr.getInfo(a, ignore=False)\n raise\n\n\ndef dispatcher(data=None):\n pass\n # if data:\n #\n # else:\n # return None\n #\n # if request.method == 'POST':\n # files = request.form.getlist('files')\n # if files:\n # thread = Thread(target=openxc_task, args=[traces, current_user.id])\n # thread.daemon = True\n # thread.start()\n # # thread.join(1)\n # # print(active_count())\n # raise\n # else:\n # session['import']['files'] = []\n # flash('Please upload some files before getting here', 'success')\n # return redirect(url_for('importer.records'))\n"},{"alpha_fraction":0.5230333209037781,"alphanum_fraction":0.525159478187561,"avg_line_length":25.62264060974121,"blob_id":"0a0096ed919569ef213be445d70a3cd8b0e4ead4","content_id":"d167976bd5667c28778f3ede3be2a62e536a58c6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1411,"license_type":"no_license","max_line_length":83,"num_lines":53,"path":"/app/carboard/forms/platform.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom flask_wtf.file import FileAllowed, FileField\nfrom wtforms import StringField, SelectField\nfrom wtforms.validators import DataRequired, Regexp, Length\n\nfrom ..models.platform import Platform\nfrom ..constants import MIMETYPE\n\n# ------------------------ custom validation methods ------------------------ #\n\n\ndef platform_exist(form, field):\n if Platform.select().where(Platform.name == field.data).exists():\n raise ValidationError('Platform already exists !')\n\n# ---------------------------- Platform form classes ---------------------------- #\n\n\nclass PlatformForm(FlaskForm):\n \"\"\" Platform add/edit FlaskForm \"\"\"\n name = StringField(\n 'Name',\n validators=[\n DataRequired(),\n Length(min=3, max=50),\n Regexp(\n r'^[a-zA-Z_\\- ]+$',\n message=(\"Platform name is not correct !\")\n ),\n ]\n )\n mimetype = SelectField(\n 'Data format',\n coerce=int,\n choices=MIMETYPE,\n validators=[\n DataRequired(),\n ]\n )\n description = StringField(\n 'Description',\n validators=[\n DataRequired(),\n ]\n )\n website = StringField(\n 'Website',\n )\n logo = FileField(\n 'Logo', validators=[\n FileAllowed(['jpg', 'jpeg', 'png', 'gif'], 'Images only plz :) ')\n ]\n )\n"},{"alpha_fraction":0.508369505405426,"alphanum_fraction":0.5133292078971863,"avg_line_length":20.223684310913086,"blob_id":"1afd27ce488394be7509657efff571d51642310f","content_id":"a32614f3b7d93dbc5c64fb2e55cd78783a6257fb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1613,"license_type":"no_license","max_line_length":81,"num_lines":76,"path":"/app/carboard/forms/signal.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField, SelectField, TextAreaField\nfrom wtforms.validators import DataRequired, Regexp, Length, ValidationError\n\nfrom ..models.signal import Signal\n\n\n# ------------------------ custom validation methods ------------------------ #\n\ndef signal_exist(form, field):\n if Signal.select().where(Signal.name == field.data).exists():\n raise ValidationError('Attribute already exists !')\n\n\n# ---------------------------- Driver form classes ---------------------------- #\n\n\nclass SignalForm(FlaskForm):\n \"\"\" Signal add/edit FlaskForm \"\"\"\n name = StringField(\n 'Name',\n validators=[\n DataRequired(),\n Length(min=3, max=50),\n # signal_exist()\n ]\n )\n\n type = SelectField(\n 'Type',\n validators=[\n DataRequired()\n ]\n )\n\n unit = StringField(\n 'Unit'\n )\n\n min_value = StringField(\n 'no_min_value'\n )\n max_value = StringField(\n 'no_max_value'\n )\n\n number = StringField(\n 'no-number'\n )\n\n values = StringField(\n 'no-values'\n )\n frequency = StringField(\n 'Frequency'\n )\n description = TextAreaField(\n 'Description',\n validators=[\n Length(min=0, max=1000)\n ]\n )\n signalclass_id = SelectField(\n 'Signal class',\n coerce=int,\n validators=[\n DataRequired(),\n ]\n )\n signalsource_id = SelectField(\n 'Signal source',\n coerce=int,\n validators=[\n DataRequired(),\n ]\n )\n"},{"alpha_fraction":0.4173261225223541,"alphanum_fraction":0.43315285444259644,"avg_line_length":23.75257682800293,"blob_id":"53142acbb4f33736aa6a2f31295a913cd30179fe","content_id":"b0cbaabd2950d064683dc22e57395835c1f7ac2f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2401,"license_type":"no_license","max_line_length":79,"num_lines":97,"path":"/app/importer/constants.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"import os\nfrom config.config import DefaultConfig\n# ----------------------------- Database constants -------------------------- #\nMAPPING = {\n \"platform\": {\n \"properties\": {\n \"user\": {\n \"type\": \"integer\"\n },\n \"vehicle\": {\n \"type\": \"integer\"\n },\n \"driver\": {\n \"type\": \"integer\"\n },\n \"class\": {\n \"type\": \"string\"\n },\n \"name\": {\n \"type\": \"string\",\n \"index\": \"not_analyzed\"\n },\n \"value\": {\n \"type\": \"string\",\n \"index\": \"not_analyzed\"\n },\n \"timestamp\": {\n \"type\": \"date\"\n }\n }\n }\n}\n\n\n# ----------------------------- Database constants -------------------------- #\nSTRING_LEN = 255\n# ----------------------------- Default constants --------------------------- #\nDEFAULT_AVATAR = \"default.jpg\"\nDEFAULT_YEAR_ID = 1\nDEFAULT_MODEL_ID = 1\n# -------------------------------- User role -------------------------------- #\nUSER = 0\nADMIN = 1\nRESEARCHER = 2\nUSER_ROLES = [\n (0, 'Select an option ...'),\n (1, 'ADMIN'),\n (2, 'USER'),\n (3, 'Driver')\n]\n# ------------------------------- User status ------------------------------- #\nDEFAULT_STATUS = 1\nACTIVE = 1\nINACTIVE = 0\nUSER_STATUS = {\n INACTIVE: 'inactive',\n ACTIVE: 'active',\n}\n# ----------------------- Form validation configuration --------------------- #\nUSERNAME_LEN_MIN = 4\nUSERNAME_LEN_MAX = 25\n\nFULLNAME_LEN_MIN = 4\nFULLNAME_LEN_MAX = 25\n\nPASSWORD_LEN_MIN = 4\nPASSWORD_LEN_MAX = 16\n\nAGE_MIN = 1\nAGE_MAX = 100\n\nMALE = 1\nFEMALE = 2\nSEX_TYPE = {\n MALE: 'Male',\n FEMALE: 'Female',\n}\nDRIVER_GENDER = [\n (0, 'Select a gender'),\n (1, 'Male'),\n (2, 'Female'),\n (3, 'Unknown')\n]\n# ------------------------------ View constants ----------------------------- #\nPER_HOME_PAGE = 10\nPER_PAGE = 20\nUSER_LOGO_DIR = 'static/uploads/avatars/'\nBRAND_LOGO_DIR = 'static/uploads/brands/'\nPLATFORM_LOGO_DIR = 'static/uploads/platforms/'\nDRIVER_LOGO_DIR = 'static/uploads/drivers/'\nVEHICLE_LOGO_DIR = 'static/uploads/vehicles/'\nDRIVE_FILES_DIR = 'static/uploads/records/'\n\n\n# Trace upload directory\nUPLOAD_TRACE_DIR = os.path.join(DefaultConfig.UPLOAD_DIR, 'files')\nALLOWED_TRACE_EXTENSIONS = set(['txt', 'json', 'pdf', 'gif'])\n"},{"alpha_fraction":0.7513368725776672,"alphanum_fraction":0.7700534462928772,"avg_line_length":40.66666793823242,"blob_id":"047079874ee9e6a027cadb528e55b22eed70402e","content_id":"1d6641178a6ae0b5ef4e73330ac1377c98dc1767","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Dockerfile","length_bytes":374,"license_type":"no_license","max_line_length":92,"num_lines":9,"path":"/Dockerfile","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"FROM python:2.7-alpine\nLABEL maintainer=\"boubouh.karim@gmail.com\"\nRUN apk --update add libxml2-dev libxslt-dev libffi-dev gcc musl-dev libgcc openssl-dev curl\nRUN apk add jpeg-dev zlib-dev freetype-dev lcms2-dev openjpeg-dev tiff-dev tk-dev tcl-dev\n# ADD . /vcar\nCOPY ./requirements2.txt /vcar/requirements2.txt\nWORKDIR /vcar\nRUN pip install -r requirements2.txt\nCMD [\"python\", \"manage.py\"]"},{"alpha_fraction":0.5580233931541443,"alphanum_fraction":0.5610461235046387,"avg_line_length":31.517093658447266,"blob_id":"e064d32e8376fb14ee96ff6366567c2f8d5c90c7","content_id":"12a896ab3f837480e33f753aad6f390a829476b6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7609,"license_type":"no_license","max_line_length":85,"num_lines":234,"path":"/app/importer/views_org.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import (\n render_template, request, flash, redirect, url_for\n)\nfrom flask_login import (\n login_user, logout_user, login_required, current_user\n)\n\nfrom . import carboard\nfrom ..models.user import User\nfrom ..forms.user import UserForm, LoginForm, RegisterForm\nfrom ..helpers import paginate, upload_file\nfrom ..constants import PER_PAGE, USER_LOGO_DIR\nfrom ...extensions import db\n\n\n# --------------------- /carboard/user/ : List of users ------------------ #\n\n@carboard.route('/user/')\n@login_required\ndef indexUser():\n users = User.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/user/index.html', users=users)\n\n# ----------------------- /carboard/user/id : Show user ------------------- #\n\n\n@carboard.route('/user/<int:id>', methods=['GET'])\n@login_required\ndef showUser(id):\n user = User.query.get_or_404(id)\n return render_template('carboard/user/show.html', user=user)\n\n# ---------------------- /carboard/user/new : Add user -------------------- #\n\n\n@carboard.route('/user/new', methods=['GET', 'POST'])\n@login_required\ndef newUser():\n \"\"\" Add new user \"\"\"\n\n form = UserForm()\n\n if form.validate_on_submit():\n avatar = upload_file(form.avatar.data, USER_LOGO_DIR)\n user = User(\n fullname=form.fullname.data,\n username=form.username.data,\n email=form.email.data,\n password=form.password.data,\n role=form.role.data,\n avatar=avatar\n )\n db.session.add(user)\n db.session.commit()\n flash('User {}, added successfully.'.format(form.username.data), 'success')\n return redirect(url_for('carboard.indexUser'))\n\n return render_template('carboard/user/new.html', form=form)\n\n# -------------------- /carboard/user/id/edit : Edit user ----------------- #\n\n\n@carboard.route('/user/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editUser(id):\n \"\"\" Edit existing user \"\"\"\n user = User.query.get_or_404(id)\n oldAvatar = user.avatar\n form = UserForm(obj=user)\n if form.validate_on_submit():\n form.populate_obj(user)\n user.set_password(form.password.data)\n avatar = upload_file(form.avatar.data, USER_LOGO_DIR)\n if avatar is None:\n user.avatar = oldAvatar\n else:\n user.avatar = avatar\n db.session.commit()\n flash('User {}, updated successfully.'.format(form.username.data), 'success')\n return redirect(url_for('carboard.showUser', id=id))\n\n return render_template('carboard/user/edit.html', form=form, id=id)\n\n# ------------------ /carboard/user/id/delete : Delete user --------------- #\n\n\n@carboard.route('/user/<int:id>/toggle', methods=['GET'])\n@login_required\ndef toggleUser(id):\n user = User.query.get_or_404(id)\n # getattr(user, 'status', 0)\n status = user.status if user.status is not None else 0\n user.status = 1 - status\n db.session.commit()\n msg = 'activated' if user.status is 1 else 'deactivated'\n flash('User {}, {} successfully.'.format(user.username, msg), 'success')\n return redirect(url_for('carboard.indexUser'))\n\n# ------------------ /carboard/user/id/delete : Delete user --------------- #\n\n\n@carboard.route('/user/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteUser(id):\n user = User.query.get_or_404(id)\n db.session.delete(user)\n db.session.commit()\n flash('User {}, deleted successfully.'.format(user.username), 'success')\n return redirect(url_for('carboard.indexUser'))\n\n# ----------------------- /carboard/login : User login page --------------------- #\n\n\n@carboard.route('/login', methods=['GET', 'POST'])\ndef login():\n \"\"\" User login method \"\"\"\n if current_user.is_authenticated:\n return redirect(url_for('carboard.index'))\n\n form = LoginForm()\n if form.validate_on_submit():\n # request.form.get('login', 'default')\n user, authenticated = User.authenticate(\n form.login.data, form.password.data\n )\n if user and authenticated:\n remember = form.remember.data == 'y'\n if login_user(user, remember=remember):\n flash(\"Logged in successfully\", 'success')\n next = request.args.get('next')\n # if not next_is_valid(next):\n # return abort(400)\n return redirect(next or url_for('carboard.index'))\n else:\n flash('Sorry, invalid login', 'error')\n\n return render_template('carboard/user/login.html', form=form)\n\n# ----------------------- /carboard/logout : User logout page ------------------- #\n\n\n@carboard.route('/logout')\n@login_required\ndef logout():\n logout_user()\n flash('Logged out', 'success')\n return redirect(url_for('frontend.index'))\n\n# ----------------------- /carboard/register : User register page --------------- #\n\n\n@carboard.route('/register', methods=['GET', 'POST'])\ndef register():\n \"\"\" User Sigup method \"\"\"\n if current_user.is_authenticated:\n return redirect(url_for('carboard.index'))\n\n form = RegisterForm()\n\n if form.validate_on_submit():\n avatar = upload_file(form.avatar.data, USER_LOGO_DIR)\n user = User(\n username=form.username.data,\n email=form.email.data,\n avatar=avatar,\n password=form.password.data,\n )\n db.session.add(user)\n db.session.commit()\n flash('Yay, you successfully registered !', 'success')\n if login_user(user):\n return redirect(url_for('carboard.index'))\n\n return render_template('carboard/user/register.html', form=form)\n\n# ----------------------- /carboard/profile : User profile page ----------------- #\n\n\n@carboard.route('/profile')\ndef profile():\n return render_template('carboard/user/profile.html', user=current_user)\n\n# ---------------------- /carboard/profile : Public user profile page ----------- #\n\n\n@carboard.route('/<int:user_id>/profile')\ndef public_profile(user_id):\n user = User.get_by_id(user_id)\n return render_template('carboard/user/profile.html', user=user)\n\n\n# /\\/\\/\\/\\/\\\\/\\/\\/\\/\\\\/\\/\\/\\/\\\\/\\/\\/\\/\\\\/\\/\\/\\/\\\\/\\/\\/\\/\\\\/\\/\\/\\/\\\\/\\/\\\\/\\/\\/ #\n# #\n# ================== Admin Blueprint ===================== #\n# #\n# /\\/\\/\\/\\/\\\\/\\/\\/\\/\\\\/\\/\\/\\/\\\\/\\/\\/\\/\\\\/\\/\\/\\/\\\\/\\/\\/\\/\\\\/\\/\\/\\/\\\\/\\//\\/\\/\\/ #\n\n# admin = Blueprint('admin', __name__, url_prefix='/admin')\n\n\n# ---------------------- /admin/index : Admin home page -------------------- #\n\n\n# @admin.route('/')\n# @login_required\n# @admin_required\n# def index():\n# users = User.query.all()\n# return render_template('admin/index.html', users=users, active='index')\n#\n#\n# @admin.route('/users')\n# @login_required\n# @admin_required\n# def users():\n# users = User.query.all()\n# return render_template('admin/users.html', users=users, active='users')\n#\n#\n# @admin.route('/carboard/<int:user_id>', methods=['GET', 'POST'])\n# @login_required\n# @admin_required\n# def user(user_id):\n# user = User.query.filter_by(id=user_id).first_or_404()\n# form = UserForm(obj=user, next=request.args.get('next'))\n# if form.validate_on_submit():\n# form.populate_obj(user)\n# db.session.add(user)\n# db.session.commit()\n# flash('User updated.', 'success')\n# return render_template('admin/user.html', user=user, form=form)\n"},{"alpha_fraction":0.5292425751686096,"alphanum_fraction":0.5340364575386047,"avg_line_length":25.743589401245117,"blob_id":"1143dc8e3b6adc938e00160fe955789b5a750584","content_id":"ac647c07ed3ec2b2cc9cb50b25fec4bab81d6772","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1043,"license_type":"no_license","max_line_length":80,"num_lines":39,"path":"/app/carboard/forms/model.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField, SelectField\nfrom wtforms.ext.sqlalchemy.fields import QuerySelectField\n\nfrom wtforms.validators import DataRequired, Regexp, Length\n\nfrom ..models.model import Model\nfrom ..models.brand import Brand\n\n# ------------------------ custom validation methods ------------------------ #\n\n\ndef model_exist(form, field):\n if Model.select().where(Model.name == field.data).exists():\n raise ValidationError('Model already exists !')\n\n# ---------------------------- Model form classes ---------------------------- #\n\n\nclass ModelForm(FlaskForm):\n \"\"\" Model add/edit FlaskForm \"\"\"\n name = StringField(\n 'Name',\n validators=[\n DataRequired(),\n Length(min=2, max=50),\n Regexp(\n r'^[a-zA-Z0-9 ]+$',\n message=(\"Model name is not correct !\")\n ),\n ]\n )\n brand_id = SelectField(\n 'Brand',\n coerce=int,\n validators=[\n DataRequired(),\n ]\n )\n"},{"alpha_fraction":0.8214285969734192,"alphanum_fraction":0.8214285969734192,"avg_line_length":27,"blob_id":"b9f50a926da217cfa6dac64f6d7accf9b02f5fe1","content_id":"ee45d2f96588cca8fd00708ee79daaaffcc10431","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":28,"license_type":"no_license","max_line_length":27,"num_lines":1,"path":"/app/importer/__init__.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from .views import importer\n"},{"alpha_fraction":0.540826141834259,"alphanum_fraction":0.5437079668045044,"avg_line_length":25.69230842590332,"blob_id":"ad3968971fa6a6e61da8b37b7111abe07a66d481","content_id":"b3582e749c739930027cd5f434b71faff180ccb3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1041,"license_type":"no_license","max_line_length":79,"num_lines":39,"path":"/app/carboard/forms/extrasignal.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField, SelectField\nfrom wtforms.validators import DataRequired, Regexp, Length\n\nfrom ..models.extrasignal import Extrasignal\n\n# ------------------------ custom validation methods ------------------------ #\n\n\ndef extrasignal_exist(form, field):\n if Extrasignal.select().where(Extrasignal.name == field.data).exists():\n raise ValidationError('Extra signal already exists !')\n\n# ---------------------------- User form classes ---------------------------- #\n\n\nclass ExtrasignalForm(FlaskForm):\n \"\"\" Extrasignal add/edit FlaskForm \"\"\"\n name = StringField(\n 'Name',\n validators=[\n DataRequired(),\n Length(min=3, max=50),\n # extrasignal_exist()\n ]\n )\n signal_id = SelectField(\n 'Main signal',\n coerce=int,\n validators=[\n DataRequired(),\n ]\n )\n storage = SelectField(\n 'Data Storage (Optionel)',\n validators = [\n DataRequired(),\n ]\n )\n"},{"alpha_fraction":0.614625096321106,"alphanum_fraction":0.6195618510246277,"avg_line_length":31.089109420776367,"blob_id":"c2a0073d4107e3993fba80ae6e66bff786908077","content_id":"35dd92d0c0447834bc554f2cc7dc9c78ad266a1d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3241,"license_type":"no_license","max_line_length":82,"num_lines":101,"path":"/app/carboard/views/model.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import (\n render_template, request, flash, redirect, url_for\n)\nfrom flask_login import login_required\n\nfrom . import carboard\nfrom ..models.model import Model\nfrom ..models.brand import Brand\nfrom ..forms.model import ModelForm\nfrom ..helpers import paginate, choices\nfrom ..constants import PER_PAGE, BRAND_LOGO_DIR\nfrom ...extensions import db\n\n# --------------------- /carboard/model/ : List of models ------------------ #\n\n\n@carboard.route('/model/')\n@login_required\ndef indexModel():\n models = Model.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/model/index.html', models=models)\n\n# ----------------------- /carboard/model/id : Show model ------------------- #\n\n\n@carboard.route('/model/<int:id>', methods=['GET'])\n@login_required\ndef showModel(id):\n model = Model.query.get_or_404(id)\n return render_template('carboard/model/show.html', model=model)\n\n# ---------------------- /carboard/model/new : Add model -------------------- #\n\n\n@carboard.route('/model/new', methods=['GET', 'POST'])\n@login_required\ndef newModel():\n \"\"\" Add new model \"\"\"\n\n form = ModelForm()\n form.brand_id.choices = choices(Brand)\n\n if form.validate_on_submit():\n model = Model(\n name=form.name.data,\n brand_id=form.brand_id.data,\n )\n db.session.add(model)\n db.session.commit()\n flash('Model {}, added successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.indexModel'))\n\n return render_template('carboard/model/new.html', form=form)\n\n# -------------------- /carboard/model/id/edit : Edit model ----------------- #\n\n\n@carboard.route('/model/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editModel(id):\n \"\"\" Edit existing model \"\"\"\n model = Model.query.get_or_404(id)\n form = ModelForm(obj=model)\n form.brand_id.choices = choices(Brand)\n\n if form.validate_on_submit():\n form.populate_obj(model)\n db.session.commit()\n flash('Model {}, updated successfully.'.format(form.name.data), 'success')\n return redirect(url_for('carboard.showModel', id=id))\n\n return render_template('carboard/model/edit.html', form=form, id=id)\n\n# ------------------ /carboard/model/id/delete : Delete model --------------- #\n\n\n@carboard.route('/model/<int:id>/toggle', methods=['GET'])\n@login_required\ndef toggleModel(id):\n model = Model.query.get_or_404(id)\n status = model.status if model.status is not None else 0\n model.status = 1 - status\n db.session.commit()\n msg = 'activated' if model.status is 1 else 'deactivated'\n flash('Model {}, {} successfully.'.format(model.name, msg), 'success')\n return redirect(url_for('carboard.indexModel'))\n\n# ------------------ /carboard/model/id/delete : Delete model --------------- #\n\n\n@carboard.route('/model/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteModel(id):\n model = Model.query.get_or_404(id)\n db.session.delete(model)\n db.session.commit()\n flash('Model {}, deleted successfully.'.format(model.name), 'success')\n return redirect(url_for('carboard.indexModel'))\n"},{"alpha_fraction":0.4991212785243988,"alphanum_fraction":0.5035149455070496,"avg_line_length":26.095237731933594,"blob_id":"3656a7e734327f8a3490e4bcc1b2eaf46ee67dda","content_id":"de9e8618a09db26aeaa7561e4deabe15457b0df2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1138,"license_type":"no_license","max_line_length":80,"num_lines":42,"path":"/app/carboard/forms/brand.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom flask_wtf.file import FileAllowed, FileField\nfrom wtforms import StringField\nfrom wtforms.validators import DataRequired, Regexp, Length\n\nfrom ..models.brand import Brand\n\n# ------------------------ custom validation methods ------------------------ #\n\n\ndef brand_exist(form, field):\n if Brand.select().where(Brand.name == field.data).exists():\n raise ValidationError('Brand already exists !')\n\n# ---------------------------- Brand form classes ---------------------------- #\n\n\nclass BrandForm(FlaskForm):\n \"\"\" Brand add/edit FlaskForm \"\"\"\n name = StringField(\n 'Name',\n validators=[\n DataRequired(),\n Length(min=3, max=50),\n Regexp(\n r'^[a-zA-Z ]+$',\n message=(\"Brand name is not correct !\")\n ),\n ]\n )\n code = StringField(\n 'Code',\n validators=[\n DataRequired(),\n Length(min=2, max=2),\n ]\n )\n logo = FileField(\n 'Logo', validators=[\n FileAllowed(['jpg', 'jpeg', 'png', 'gif'], 'Images only plz :) ')\n ]\n )\n"},{"alpha_fraction":0.8518518805503845,"alphanum_fraction":0.8518518805503845,"avg_line_length":27,"blob_id":"a6c9fb121422878aa02fa746ab6bb2ddf99d76b4","content_id":"8c4daf0368a6d3a8dd2df0c96d6026a41c492f70","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":27,"license_type":"no_license","max_line_length":27,"num_lines":1,"path":"/app/explorer/__init__.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from .views import explorer"},{"alpha_fraction":0.6349801421165466,"alphanum_fraction":0.6382884979248047,"avg_line_length":35.86178970336914,"blob_id":"b8e9c6acd70ab85debe1010dbdbdb042d1312a34","content_id":"202c60def46f118a41566b5f8d50312766c46038","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4534,"license_type":"no_license","max_line_length":82,"num_lines":123,"path":"/app/carboard/views/record.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import (\n render_template, request, flash, redirect, url_for\n)\nfrom flask_login import login_required\n\nfrom . import carboard\nfrom ..models.record import Record\nfrom ..models.user import User\nfrom ..models.drivetype import DriveType\nfrom ..models.vehicle import Vehicle\nfrom ..models.driver import Driver\nfrom ..models.status import Status\nfrom ..forms.record import RecordForm, RecordStatusForm\nfrom ..helpers import paginate, choices, upload_file\nfrom ..constants import PER_PAGE, DRIVE_FILES_DIR\nfrom ...extensions import db\n\n# --------------------- /carboard/record/ : List of records ------------------ #\n\n@carboard.route('/record/')\n@login_required\ndef indexRecord():\n records = Record.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', PER_PAGE, type=int),\n )\n return render_template('carboard/record/index.html', records=records)\n\n# ----------------------- /carboard/record/id : Show record ------------------- #\n\n\n@carboard.route('/record/<int:id>', methods=['GET'])\n@login_required\ndef showRecord(id):\n record = Record.query.get_or_404(id)\n return render_template('carboard/record/show.html', record=record)\n\n# ---------------------- /carboard/record/new : Add record -------------------- #\n\n\n@carboard.route('/record/new', methods=['GET', 'POST'])\n@login_required\ndef newRecord():\n \"\"\" Add new record \"\"\"\n\n form = RecordForm()\n form.user_id.choices = choices(User, 'Select a user', 1, 'username')\n form.drivetype_id.choices = choices(DriveType, 'Select a drive type', False)\n form.driver_id.choices = choices(Driver, 'Select a Driver', False, 'fullname')\n form.vehicle_id.choices = choices(Vehicle, 'Select a vehicle', False, 'id')\n\n if form.validate_on_submit():\n trace = upload_file(form.trace.data, DRIVE_FILES_DIR)\n record = Record(\n user_id=form.user_id.data,\n drivetype_id=form.drivetype_id.data,\n driver_id=form.driver_id.data,\n vehicle_id=form.vehicle_id.data,\n name=form.name.data,\n start=form.start.data,\n end=form.end.data,\n description=form.description.data,\n trace=trace\n )\n db.session.add(record)\n db.session.commit()\n flash('Record {}, added successfully.'.format(record.name), 'success')\n return redirect(url_for('carboard.indexRecord'))\n\n return render_template('carboard/record/new.html', form=form)\n\n# -------------------- /carboard/record/id/edit : Edit record ----------------- #\n\n\n@carboard.route('/record/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editRecord(id):\n \"\"\" Edit existing record \"\"\"\n record = Record.query.get_or_404(id)\n oldTrace = record.trace\n\n form = RecordForm(obj=record)\n del form.trace\n form.user_id.choices = choices(User, 'Select a user', 1, 'username')\n form.drivetype_id.choices = choices(DriveType, 'Select a drive type', False)\n form.driver_id.choices = choices(Driver, 'Select a Driver', False, 'fullname')\n form.vehicle_id.choices = choices(Vehicle, 'Select a vehicle', False, 'id')\n\n if form.validate_on_submit():\n form.populate_obj(record)\n db.session.commit()\n flash('Record {}, updated successfully.'.format(record.name), 'success')\n return redirect(url_for('carboard.showRecord', id=id))\n\n return render_template('carboard/record/edit.html', form=form, id=id)\n\n# ------------------ /carboard/record/id/delete : Delete record --------------- #\n\n\n@carboard.route('/record/<int:id>/toggle', methods=['GET', 'POST'])\n@login_required\ndef toggleRecord(id):\n record = Record.query.get_or_404(id)\n form = RecordStatusForm(obj=record)\n form.status_id.choices = [(s.id, s) for s in Status.query.all()]\n if form.validate_on_submit():\n form.populate_obj(record)\n db.session.commit()\n flash('Record {}, updated successfully.'.format(record.name), 'success')\n return redirect(url_for('carboard.showRecord', id=id))\n return render_template('carboard/record/status.html', form=form, id=id)\n\n# ------------------ /carboard/record/id/delete : Delete record --------------- #\n\n\n@carboard.route('/record/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteRecord(id):\n record = Record.query.get_or_404(id)\n db.session.delete(record)\n db.session.commit()\n flash('Record {}, deleted successfully.'.format(record.name), 'success')\n return redirect(url_for('carboard.indexRecord'))\n"},{"alpha_fraction":0.6031966805458069,"alphanum_fraction":0.6115357875823975,"avg_line_length":34.09756088256836,"blob_id":"a97d93e935fd1128e80ee749a002f3d74748188d","content_id":"95ad5c085ab8b2a76f645ed5c51052e5c8402dd3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1439,"license_type":"no_license","max_line_length":81,"num_lines":41,"path":"/app/carboard/models/driver.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom ...extensions import db\n\n# -------------------------------- Driver Model ------------------------------- #\n\n\nclass Driver(db.Model):\n \"\"\" Driver Model:\n The driver is the one who produce data records.\n \"\"\"\n __tablename__ = 'drivers'\n\n id = db.Column(db.Integer, primary_key=True)\n gender = db.Column(db.SmallInteger, default=0)\n fullname = db.Column(db.String(255), default='Anonyme')\n avatar = db.Column(db.String(255), nullable=True)\n city = db.Column(db.String(255), nullable=True)\n\n status_id = db.Column(db.SmallInteger, db.ForeignKey('status.id'))\n status = db.relationship('Status')\n\n user_id = db.Column(db.Integer, db.ForeignKey('users.id'))\n user = db.relationship('User', backref=db.backref('drivers', lazy='dynamic'))\n\n country_id = db.Column(db.Integer, db.ForeignKey('countries.id'))\n country = db.relationship('Country')\n\n created = db.Column(db.DateTime(), default=datetime.utcnow())\n\n def __init__(self, user_id, country_id, **kwargs):\n self.vars = kwargs\n self.gender = kwargs.get('gender', 3)\n self.fullname = kwargs.get('fullname', 'Anonymous driver')\n self.user_id = user_id\n self.country_id = country_id\n self.avatar = kwargs.get('avatar')\n self.status_id = kwargs.get('status_id', 1)\n self.city = kwargs.get('city')\n\n def __repr__(self):\n return self.fullname\n"},{"alpha_fraction":0.5982000827789307,"alphanum_fraction":0.6029645204544067,"avg_line_length":28.984127044677734,"blob_id":"599893240b7f3d51ac757bf1cc6a52fc69b1d8da","content_id":"f5a64d3fb4b2d8999c898d3865c3b46d226438f2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3778,"license_type":"no_license","max_line_length":82,"num_lines":126,"path":"/app/explorer/views.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask import (\n Blueprint, render_template, request, flash, redirect, url_for\n)\nfrom flask_login import (\n login_required, current_user\n)\n\nfrom .models import Chart\nfrom .forms import ChartForm\nfrom .helpers import choices\n\nfrom ..carboard.models.vehicle import Vehicle\nfrom ..carboard.models.driver import Driver\nfrom ..extensions import db\n\n\n# Declaring Explorer blueprint\n\nexplorer = Blueprint('explorer', __name__, url_prefix='/explorer')\n\n# --------------------- /explorer/ : List of charts ------------------- #\n\n\n@explorer.route('/', methods=['GET'])\n@login_required\ndef index():\n\n return render_template('explorer/explorer.html')\n\n# --------------------- /explorer/ : List of charts ------------------- #\n\n\n@explorer.route('/chart', methods=['GET'])\n@login_required\ndef indexChart():\n charts = Chart.query.paginate(\n page=request.args.get('page', 1, type=int),\n per_page=request.args.get('per_page', 10, type=int),\n )\n return render_template('explorer/index.html', charts=charts)\n\n# ----------------------- /explorer/id : Show chart ------------------- #\n\n\n@explorer.route('/chart/<int:id>', methods=['GET'])\n@login_required\ndef showChart(id):\n chart = Chart.query.get_or_404(id)\n return render_template('explorer/show.html', chart=chart)\n\n# ---------------------- /explorer/new : Add new chart ----------- #\n\n\n@explorer.route('/chart/new', methods=['GET', 'POST'])\n@login_required\ndef newChart():\n \"\"\" Add new chart\"\"\"\n\n form = ChartForm()\n form.driver_id.choices = choices(Driver, 'Select a Driver', False, 'fullname')\n form.vehicle_id.choices = choices(Vehicle, 'Select a vehicle')\n\n if form.validate_on_submit():\n chart = Chart(\n name=form.name.data,\n user_id=current_user.id,\n driver_id=form.driver_id.data,\n vehicle_id=form.vehicle_id.data,\n isValid=False,\n error=\"Not initialized yet\"\n )\n db.session.add(chart)\n db.session.commit()\n return redirect(url_for('carboard.explorerChart'))\n\n return render_template('explorer/new.html', form=form)\n\n# -------------------- /explorer/id/edit : Edit chart ----------------- #\n\n\n@explorer.route('/chart/<int:id>/explorer', methods=['GET', 'POST'])\n@login_required\ndef explorerChart():\n \"\"\"Setup chart via explorer \"\"\"\n\n chart = Chart.query.get_or_404(id)\n\n # if form.validate_on_submit():\n # trace = upload_file(form.trace.data, DRIVE_FILES_DIR)\n # chart = Chart(\n # user_id=form.user_id.data,\n # driver_id=form.driver_id.data,\n # vehicle_id=form.vehicle_id.data,\n # name=form.name.data,\n # )\n # db.session.add(chart)\n # db.session.commit()\n # return redirect(url_for('carboard.explorerChart'))\n\n return render_template('explorer/new.html', chart=chart)\n\n# ------------------ /explorer/id/delete : Delete chart --------------- #\n\n\n@explorer.route('/chart/<int:id>/toggle', methods=['GET', 'POST'])\n@login_required\ndef toggleChart(id):\n chart = Chart.query.get_or_404(id)\n status = chart.status if chart.status is not None else 0\n chart.status = 1 - status\n db.session.commit()\n msg = 'activated' if chart.status is 1 else 'deactivated'\n flash('Chart {}, {} successfully.'.format(chart.name, msg), 'info')\n return redirect(url_for('carboard.indexChart'))\n\n# ------------------ /explorer/id/delete : Delete chart --------------- #\n\n\n@explorer.route('/chart/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteChart(id):\n chart = Chart.query.get_or_404(id)\n db.session.delete(chart)\n db.session.commit()\n flash('Chart {}, deleted successfully.'.format(chart.name), 'info')\n return redirect(url_for('carboard.indexChart'))\n"},{"alpha_fraction":0.43623512983322144,"alphanum_fraction":0.4384837746620178,"avg_line_length":37.912498474121094,"blob_id":"e052b46c074823176d1ad39fb8b188bf75e3af7e","content_id":"020e82a41062beb6bfa61b9fa7e4de0e2403b841","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"HTML","length_bytes":3113,"license_type":"no_license","max_line_length":128,"num_lines":80,"path":"/templates/carboard/signalsource/show.html","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"{% extends \"layout.html\" %}\n\n{% from \"carboard/macros.html\" import render_pagination %}\n\n{% block title %}\n Signal source details | {{ super() }}\n{% endblock %}\n\n{% block content_header %}\n <section class=\"content-header\">\n <h1>\n Signal source details\n </h1>\n <ol class=\"breadcrumb\">\n <li><a href=\"{{ url_for('carboard.index') }}\"><i class=\"fa fa-dashboard\"></i>Home</a></li>\n <li><a href=\"{{ url_for('carboard.indexSignalsource') }}\">Signal Sources</a></li>\n <li class=\"active\">Signal Source Details</li>\n </ol>\n </section>\n{% endblock content_header %}\n\n\n{% block content %}\n <div class=\"row\">\n <div class=\"col-md-12\">\n <div class=\"box\">\n\n <div class=\"box-header with-border\">\n <h3 class=\"box-title\">Signal source : {{ signalsource.name }}</h3>\n </div>\n\n <div class=\"box-body\">\n\n <div class=\"line\">\n <strong class=\"line-title\">ID</strong>\n <p class=\"line-body\">{{ signalsource.id }}</p>\n </div>\n\n <div class=\"line\">\n <strong class=\"line-title\">Name</strong>\n <p class=\"line-body\">{{ signalsource.name }}</p>\n </div>\n\n <div class=\"line\">\n <strong class=\"line-title\">Signals</strong>\n <p class=\"line-body\">{{ signalsource.signals }}</p>\n </div>\n <div class=\"line\">\n <strong class=\"line-title\">Description </strong>\n <p class=\"line-body\">{{ signalsource.description or 'No description provided' }}</p>\n </div>\n\n <div class=\"line\">\n <strong class=\"line-title\">Status</strong>\n <p class=\"line-body\">\n {% if signalsource.status == 1 %}\n <span class=\"label bg-green\">Activated</span>\n {% else %}\n <span class=\"label bg-red\">Disabled</span>\n {% endif %}\n </p>\n </div>\n\n <div class=\"line\">\n <strong class=\"line-title\">Creation</strong>\n <p class=\"line-body\">{{ signalsource.created.strftime('%d-%m-%Y') }}</p>\n </div>\n\n </div>\n\n <div class=\"box-footer form-footer\">\n <a href=\"{{ url_for('carboard.editSignalsource', id=signalsource.id) }}\" class=\"btn btn-success\">Edit</a>\n <a href=\"{{ url_for('carboard.deleteSignalsource', id=signalsource.id) }}\" class=\"btn btn-danger\">Delete</a>\n <a href=\"{{ url_for('carboard.indexSignalsource') }}\" class=\"btn btn-default pull-right\">Back to\n list</a>\n </div>\n </div>\n </div>\n </div>\n{% endblock %}\n"},{"alpha_fraction":0.6225000023841858,"alphanum_fraction":0.6303571462631226,"avg_line_length":30.81818199157715,"blob_id":"5093e692ad6551c0523cf38ea01257bf530915b2","content_id":"f344faee678f5be6a58902ab3a1b3d73c2306f71","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2800,"license_type":"no_license","max_line_length":104,"num_lines":88,"path":"/app/carboard/models/user.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from uuid import uuid4\nfrom datetime import datetime\nfrom flask_login import UserMixin\nfrom flask_bcrypt import generate_password_hash, check_password_hash\nfrom ..constants import DEFAULT_AVATAR, ROLE_USER, ROLE_ADMIN\nfrom ...extensions import db\n\n\n# -------------------------------- User Model ------------------------------- #\n\nclass User(db.Model, UserMixin):\n \"\"\" User Model:\n the user can be a person or even a campany, he/it can manage drivers\n and vehicles.\n \"\"\"\n __tablename__ = 'users'\n\n id = db.Column(db.Integer, primary_key=True)\n fullname = db.Column(db.String(255))\n username = db.Column(db.String(255), nullable=False, unique=True)\n email = db.Column(db.String(255), nullable=False, unique=True)\n password = db.Column(db.String(255), nullable=False)\n avatar = db.Column(db.String(255), nullable=True, default=DEFAULT_AVATAR)\n role = db.Column(db.SmallInteger, default=ROLE_USER)\n activation = db.Column(db.String(255))\n status = db.Column(db.SmallInteger, default=1)\n last_login = db.Column(db.DateTime(), default=datetime.utcnow())\n created = db.Column(db.DateTime(), default=datetime.utcnow())\n\n def __init__(self, username, email, password, fullname=None, avatar=None, role=ROLE_USER, status=1):\n self.fullname = fullname or username\n self.username = username\n self.email = email.lower()\n self.password = generate_password_hash(password)\n self.avatar = avatar\n self.role = role\n self.activation = str(uuid4())\n self.status = status\n\n def set_password(self, password):\n self.password = generate_password_hash(password)\n\n def check_password(self, password):\n return check_password_hash(self.password, password)\n\n @property\n def is_authenticated(self):\n return True\n\n def is_active(self):\n return self.status\n\n def is_anonymous(self):\n return False\n\n def is_admin(self):\n return self.role == ROLE_ADMIN\n\n def get_id(self):\n return self.id\n\n def __repr__(self):\n return '{} ({})'.format(self.username, self.email)\n\n def get_role(self):\n return self.role\n\n def get_status(self):\n return self.status\n\n @classmethod\n def check_username(cls, username):\n return cls.query.exists().where(User.username == username).scalar()\n\n @classmethod\n def authenticate(cls, login, password):\n user = cls.query.filter(\n db.or_(User.username == login, User.email == login)\n ).first()\n if user:\n authenticated = user.check_password(password)\n else:\n authenticated = False\n return user, authenticated\n\n @classmethod\n def get_by_id(cls, id):\n return cls.query.filter(User.id == id).first()\n"},{"alpha_fraction":0.5208333134651184,"alphanum_fraction":0.5256410241127014,"avg_line_length":26.130434036254883,"blob_id":"8dcadf72dca6d731fd132e4fd21cd99e596db03c","content_id":"6047eed6924a0c9b6a70a29660ed441c42551f72","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":624,"license_type":"no_license","max_line_length":79,"num_lines":23,"path":"/app/carboard/forms/status.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField\nfrom wtforms.validators import DataRequired, Regexp, Length\n\nfrom ..models.status import Status\n\n# ----------------------------- Status form classes ---------------------------\n\n\nclass StatusForm(FlaskForm):\n \"\"\" Status add/edit FlaskForm \"\"\"\n title = StringField(\n 'Status title',\n validators=[\n DataRequired(),\n Length(min=3, max=50),\n Regexp(\n r'^[a-zA-Z ]+$',\n message=(\"Status title is not correct !\")\n ),\n ]\n )\n color = StringField('Class color')\n"},{"alpha_fraction":0.44807693362236023,"alphanum_fraction":0.4692307710647583,"avg_line_length":22.636363983154297,"blob_id":"f9292e04d390f642a694e208a2cb936716ddd975","content_id":"f6bdd7539507b802cf2190a73b50718e2ac85cb8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":520,"license_type":"no_license","max_line_length":79,"num_lines":22,"path":"/app/importer/datasets/t-drive/helpers.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from .constants import TRANSFORM\n\n# -------------------- OpenXC Helpers --------------------------------------- #\n\n\ndef correct_value(value):\n try:\n return int(value)\n except:\n for k, v in TRANSFORM.items():\n if value == v:\n return k\n return -99999\n\n\ndef correct_time(timestamp):\n try:\n timestamp = str(timestamp)\n t = timestamp.split('.')\n return '{}{}'.format(t[0], t[1][:3].ljust(3, '0'))\n except:\n return timestamp.split('.')[0]\n"},{"alpha_fraction":0.6282051205635071,"alphanum_fraction":0.6307692527770996,"avg_line_length":26.85714340209961,"blob_id":"e7e543acd6ccb8ea03ebce8f6b4203172e067fce","content_id":"aa67bf12987c51d5fdb74111cf1ba05ed65a8789","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":390,"license_type":"no_license","max_line_length":93,"num_lines":14,"path":"/config/database.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from .config import DefaultConfig\n\n# -------------------------- Sqlalchemy Configuration ----------------------- #\n\n\nclass DatabaseConfig(object):\n\n SQLALCHEMY_TRACK_MODIFICATIONS = True\n\n SQLALCHEMY_ECHO = False\n\n SQLALCHEMY_DATABASE_URI = 'sqlite:///' + DefaultConfig.INSTANCE_FOLDER_PATH + '/YooNa.db'\n\n # SQLALCHEMY_DATABASE_URI = 'mysql://user:pass@server/db?charset=utf8'\n"},{"alpha_fraction":0.4947145879268646,"alphanum_fraction":0.4947145879268646,"avg_line_length":28.10769271850586,"blob_id":"16730bab587beb021b21554e1d108249e019a602","content_id":"4101731d530e5a16f961c71ae05d6706da636845","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1892,"license_type":"no_license","max_line_length":79,"num_lines":65,"path":"/app/extensions.py","repo_name":"vcar/vcar","src_encoding":"UTF-8","text":"from flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate\nfrom flask_cache import Cache\nfrom flask_mail import Mail\nfrom flask_login import LoginManager\nfrom flask_debugtoolbar import DebugToolbarExtension\nfrom flask_images import Images\nfrom flask_redis import FlaskRedis\nfrom flask_plugins import PluginManager\nfrom flask_misaka import Misaka\nfrom flask_socketio import SocketIO\n# from flask_session import Session\n\n# ------------------------- Inisialize Database object ---------------------- #\n\ndb = SQLAlchemy()\n\n# ------------------------- Inisialize Database object ---------------------- #\n\nmigrate = Migrate()\n\n# ------------------------- Inisialize Cache object ------------------------- #\n\ncache = Cache()\n\n# ------------------------- Inisialize Mail object -------------------------- #\n\nmail = Mail()\n\n# ------------------------- Inisialize Login object ------------------------- #\n\nlogin_manager = LoginManager()\n\n# ------------------------- Inisialize DebugToolbar object ------------------ #\n\ntoolbar = DebugToolbarExtension()\n\n# ------------------------- Inisialize Images object ------------------------ #\n\nimages = Images()\n\n# ------------------------- Inisialize SocketIO object ---------------------- #\n\nsocketio = SocketIO()\n\n# ------------------------- Inisialize Redis object ------------------------- #\n\nredis = FlaskRedis()\n\n# ------------------------- Inisialize PluginManager object ----------------- #\n\nplugin_manager = PluginManager()\n\n# ------------------------- Inisialize Misaka object ----------------- #\n\nmisaka = Misaka(tables=True, highlight=True)\n\n# ------------------------- Inisialize Session object ----------------------- #\n\"\"\"\n Flask-Session save session data on server with many options: filesystem,\n Redis, Sqlite, ...\n\"\"\"\n# session = Session()\n\n# ------------------------------------ X ------------------------------------ #\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6157371997833252,\n \"alphanum_fraction\": 0.6294881701469421,\n \"avg_line_length\": 35.36111068725586,\n \"blob_id\": \"461dc76e0988e646166619a62d0b4de18c322026\",\n \"content_id\": \"1556a006b8f01a46d4cd784b64b3f98bcd3be476\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1309,\n \"license_type\": \"no_license\",\n \"max_line_length\": 135,\n \"num_lines\": 36,\n \"path\": \"/app/carboard/models/article.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom ...extensions import db\\n\\n# -------------------------------- Brand Model ------------------------------ #\\n\\n\\nclass Article(db.Model):\\n \\\"\\\"\\\" Article Model \\\"\\\"\\\"\\n __tablename__ = 'articles'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.String(255))\\n authors = db.Column(db.String(255))\\n abstract = db.Column(db.String(255), nullable=True)\\n publication_date = db.Column(db.DateTime(), nullable=True)\\n keywords = db.Column(db.String(255), nullable=True)\\n reference = db.Column(db.String(255), nullable=True)\\n link = db.Column(db.String(255), nullable=True)\\n\\n dataset_id = db.Column(db.Integer, db.ForeignKey('datasets.id'), nullable=True)\\n dataset = db.relationship('Dataset', back_populates=\\\"article\\\")\\n\\n created = db.Column(db.DateTime(), default=datetime.utcnow())\\n\\n def __init__(self, name, authors, dataset_id=None, abstract=None, publication_date=None, keywords=None, reference=None, link=None):\\n self.dataset_id = dataset_id\\n self.name = name\\n self.authors = authors\\n self.abstract = abstract\\n self.publication_date = publication_date\\n self.keywords = keywords\\n self.reference = reference\\n self.link = link\\n\\n def __repr__(self):\\n return self.name\\n\"\n },\n {\n \"alpha_fraction\": 0.5966640114784241,\n \"alphanum_fraction\": 0.6004765629768372,\n \"avg_line_length\": 33.21195602416992,\n \"blob_id\": \"d0c829b5c5ffc58e4b9690eefa3a841611799dab\",\n \"content_id\": \"a853a6ee4b85c1ce13ee2291612327d91b403edf\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6295,\n \"license_type\": \"no_license\",\n \"max_line_length\": 113,\n \"num_lines\": 184,\n \"path\": \"/app/carboard/views/dataset.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import json\\nfrom os.path import basename\\nfrom importlib import import_module\\nfrom threading import Thread\\nimport traceback\\nfrom flask import (\\n render_template, request, flash, redirect, url_for\\n)\\nfrom flask_login import login_required, current_user\\n\\nfrom . import carboard\\nfrom ..models.dataset import Dataset\\nfrom ..models.file import File\\nfrom ..forms.dataset import DatasetForm, FeedDatasetForm\\nfrom ..helpers import upload_dsfile\\nfrom ..constants import PER_PAGE\\nfrom ...extensions import db\\n\\n# -------------------- /carboard/dataset/ : List of datasets ---------------- #\\n\\n\\n@carboard.route('/dataset/')\\n@login_required\\ndef indexDataset():\\n datasets = Dataset.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/dataset/index.html', datasets=datasets)\\n\\n# -------------------- /carboard/dataset/id : Show dataset ------------------ #\\n\\n\\n@carboard.route('/dataset/<int:id>', methods=['GET'])\\n@login_required\\ndef showDataset(id):\\n dataset = Dataset.query.get_or_404(id)\\n return render_template('carboard/dataset/show.html', dataset=dataset)\\n\\n# -------------------- /carboard/dataset/new : Add dataset ------------------ #\\n\\n\\n@carboard.route('/dataset/new', methods=['GET', 'POST'])\\n@login_required\\ndef newDataset():\\n \\\"\\\"\\\" Add new dataset \\\"\\\"\\\"\\n\\n form = DatasetForm()\\n\\n if form.validate_on_submit():\\n dataset = Dataset(\\n name=form.name.data,\\n slug=form.slug.data,\\n description=form.description.data,\\n author=form.author.data,\\n lab=form.lab.data,\\n website=form.website.data,\\n )\\n db.session.add(dataset)\\n db.session.commit()\\n flash('Dataset {}, added successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.indexDataset'))\\n\\n return render_template('carboard/dataset/new.html', form=form)\\n\\n# -------------------- /carboard/dataset/id/edit : Edit dataset ------------- #\\n\\n\\n@carboard.route('/dataset/feed/<int:id>', methods=['GET', 'POST'])\\n@login_required\\ndef feedDataset(id):\\n \\\"\\\"\\\" Fill dataset with data that may come from diff sources \\\"\\\"\\\"\\n\\n dataset = Dataset.query.get_or_404(id)\\n\\n form = FeedDatasetForm()\\n\\n if form.validate_on_submit():\\n files = request.form.getlist('files')\\n try:\\n # Dataset slug a.k.a module name\\n slug = dataset.slug\\n # Import the module\\n # mod = import_module(\\\". elastic\\\", 'app.importer.datasets.' + slug)\\n mod = import_module('app.importer.datasets.' + slug + '.elastic')\\n # Indexing function\\n index_function = 'index_bulk' # 'index'\\n # get a reference to the init function\\n init = getattr(mod, index_function)\\n # create a threaded job to index uploaded data according to it's dataset\\n thread = Thread(target=init, args=[files, dataset, current_user.id])\\n thread.daemon = True\\n thread.start()\\n # thread.join(1)\\n # print(active_count())\\n flash('Files added to Dataset \\\"{}\\\" are being indexed in background.'.format(dataset.name), 'success')\\n except ImportError:\\n flash('No dataset indexer is provided!', 'error')\\n except Exception:\\n flash('Something went wrong while trying to indexed dataset files!', 'error')\\n print('\\\\n\\\\n\\\\n')\\n traceback.print_exc()\\n print('\\\\n\\\\n\\\\n')\\n # raise\\n # raise\\n return redirect(url_for('carboard.indexDataset'))\\n else:\\n for field, errors in form.errors.items():\\n for error in errors:\\n flash(u\\\"Error in the %s field - %s\\\" % (\\n getattr(form, field).label.text,\\n error\\n ), 'error')\\n\\n return render_template(\\n 'carboard/dataset/feed.html',\\n form=form,\\n dataset=dataset\\n )\\n\\n# -------------------- /carboard/dataset/id/edit : Edit dataset ------------- #\\n\\n\\n@carboard.route('/dataset/getfile/<int:id>', methods=['POST'])\\n@login_required\\ndef getFileDataset(id):\\n \\\"\\\"\\\" Handle multiple dataset file uploads \\\"\\\"\\\"\\n\\n dataset = Dataset.query.get_or_404(id)\\n if request.method == 'POST':\\n trace = upload_dsfile('file', dataset.slug)\\n if trace['status'] is True:\\n file = File(\\n user_id=current_user.id,\\n filename=basename(trace['message']),\\n path=trace['message']\\n )\\n db.session.add(file)\\n db.session.commit()\\n return json.dumps(trace)\\n\\n# -------------------- /carboard/dataset/id/edit : Edit dataset ------------- #\\n\\n\\n@carboard.route('/dataset/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editDataset(id):\\n \\\"\\\"\\\" Edit existing dataset \\\"\\\"\\\"\\n dataset = Dataset.query.get_or_404(id)\\n form = DatasetForm(obj=dataset)\\n if form.validate_on_submit():\\n form.populate_obj(dataset)\\n db.session.commit()\\n flash('Dataset {}, updated successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.showDataset', id=id))\\n\\n return render_template('carboard/dataset/edit.html', form=form, id=id)\\n\\n# -------------------- /carboard/dataset/id/delete : Delete dataset --------- #\\n\\n\\n@carboard.route('/dataset/<int:id>/toggle', methods=['GET'])\\n@login_required\\ndef toggleDataset(id):\\n dataset = Dataset.query.get_or_404(id)\\n # getattr(dataset, 'status', 0)\\n status = dataset.status if dataset.status is not None else 0\\n dataset.status = 1 - status\\n db.session.commit()\\n msg = 'activated' if dataset.status is 1 else 'deactivated'\\n flash('Dataset {}, {} successfully.'.format(dataset.name, msg), 'success')\\n return redirect(url_for('carboard.indexDataset'))\\n\\n# -------------------- /carboard/dataset/id/delete : Delete dataset --------- #\\n\\n\\n@carboard.route('/dataset/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteDataset(id):\\n dataset = Dataset.query.get_or_404(id)\\n db.session.delete(dataset)\\n db.session.commit()\\n flash('Dataset {}, deleted successfully.'.format(dataset.name), 'success')\\n return redirect(url_for('carboard.indexDataset'))\\n\"\n },\n {\n \"alpha_fraction\": 0.8387096524238586,\n \"alphanum_fraction\": 0.8387096524238586,\n \"avg_line_length\": 30,\n \"blob_id\": \"ebe86a3bb58a41314c90239dea85344ebf82cbf4\",\n \"content_id\": \"16f29bc21be6a34f76dbbf694186c1a3f5aa326f\",\n \"detected_licenses\": [\n \"MIT\",\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 31,\n \"license_type\": \"permissive\",\n \"max_line_length\": 30,\n \"num_lines\": 1,\n \"path\": \"/app/plugins/driver_graph/src/__init__.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from .views import driverGraph\\n\"\n },\n {\n \"alpha_fraction\": 0.41207748651504517,\n \"alphanum_fraction\": 0.4204329550266266,\n \"avg_line_length\": 28.255556106567383,\n \"blob_id\": \"0e645f9c58623de3f243be025c23c112d3b4e6a8\",\n \"content_id\": \"d76c2bc9d4a72449a45509bfb1a1bc5946e5d91c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"HTML\",\n \"length_bytes\": 2633,\n \"license_type\": \"no_license\",\n \"max_line_length\": 113,\n \"num_lines\": 90,\n \"path\": \"/templates/carboard/signal/bulk.html\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"{% extends \\\"layout.html\\\" %}\\n\\n{% from \\\"carboard/macros.html\\\" import render_field, box_header %}\\n\\n{% block title %}\\n Bulk Add Signals\\n{% endblock %}\\n{% block styles %}\\n {{ super() }}\\n <style>\\n .box-footer {\\n background-color: #2A2A2A;\\n border-color: #2A2A2A;\\n }\\n\\n .box-body {\\n padding: 40px 50px;\\n }\\n\\n .drop-file {\\n width: 100%;\\n height: 200px;\\n }\\n\\n </style>\\n{% endblock %}\\n{% block content_header %}\\n <section class=\\\"content-header\\\">\\n <h1>\\n Add Signals\\n </h1>\\n <ol class=\\\"breadcrumb\\\">\\n <li><a href=\\\"{{ url_for('carboard.index') }}\\\"><i class=\\\"fa fa-dashboard\\\"></i>Home</a></li>\\n <li><a href=\\\"{{ url_for('carboard.indexSignal') }}\\\">Signals</a></li>\\n <li class=\\\"active\\\">Bulk Add Signals</li>\\n </ol>\\n </section>\\n{% endblock content_header %}\\n\\n{% block content %}\\n {% if errors %}\\n <div class=\\\"row\\\">\\n <div class=\\\"col-md-12\\\">\\n <div class=\\\"well-error\\\">\\n <ul>\\n {% for error in errors %}\\n <li>{{ error }}</li>\\n {% endfor %}\\n </ul>\\n </div>\\n </div>\\n </div>\\n {% endif %}\\n <div class=\\\"row\\\">\\n <div class=\\\"col-md-12\\\">\\n <div class=\\\"box box-solid vcar-box\\\">\\n\\n {{ box_header('Bulk Add Signals') }}\\n\\n <div class=\\\"box-body\\\">\\n <form action=\\\"{{ url_for('carboard.bulkAdd') }}\\\" method=\\\"POST\\\" enctype=\\\"multipart/form-data\\\">\\n <div class=\\\"drop-file\\\">\\n <span id=\\\"file-info\\\" class=\\\"animated-bounce\\\">Drop Your file Here</span>\\n {% for field in form %}\\n {{ render_field(field) }}\\n {% endfor %}\\n </div>\\n </form>\\n </div>\\n </div>\\n <div class=\\\"box-footer form-footer\\\">\\n <button type=\\\"submit\\\" id=\\\"submit\\\" class=\\\"btn btn-success\\\">Submit</button>\\n <a href=\\\"{{ url_for('carboard.indexSignal') }}\\\" class=\\\"btn btn-default pull-right\\\">Back to\\n list\\n </a>\\n </div>\\n </div>\\n </div>\\n{% endblock %}\\n\\n{% block scripts %}\\n {{ super() }}\\n <script type=\\\"text/javascript\\\">\\n $('#submit').click(function (e) {\\n if (fileInput.value) {\\n $('form').submit();\\n }\\n })\\n </script>\\n{% endblock %}\\n\"\n },\n {\n \"alpha_fraction\": 0.7157894968986511,\n \"alphanum_fraction\": 0.7178947329521179,\n \"avg_line_length\": 42.181819915771484,\n \"blob_id\": \"8f23986eeb88a61a52b9c4013b0c68c70af783fa\",\n \"content_id\": \"10ad93bfdf9957adb1eb7a694d60879e7b8bd79a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Dockerfile\",\n \"length_bytes\": 475,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 11,\n \"path\": \"/tests/Dockerfile\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"FROM alpine\\nLABEL maintainer=\\\"boubouh.karim@gmail.com\\\"\\nRUN apk update && apk add nodejs \\nRUN mkdir average\\nADD average.js average/\\nWORKDIR average\\n# ADD # copies new files, directories or remote file to container.\\n# CMD [ \\\"node\\\", \\\"average.js\\\" ] # You can overide the cmd (bin/sh -c) \\n# CMD ping google.com\\nENTRYPOINT [ \\\"node\\\", \\\"average.js\\\" ] # You can't (no shell && pid 1 && passed are arguments)\\n# ONBUILD # adds a trigger instruction when the image is used as the base for another build.\\n\"\n },\n {\n \"alpha_fraction\": 0.6984572410583496,\n \"alphanum_fraction\": 0.6984572410583496,\n \"avg_line_length\": 26.960784912109375,\n \"blob_id\": \"83432121e38a78f16e7c532f4a7f6c827f4d1dfa\",\n \"content_id\": \"cec2b807b8df75982a1228b612c0e6d35dbdec0d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1426,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 51,\n \"path\": \"/app/carboard/views/__init__.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import Blueprint, render_template\\nfrom flask_login import login_required, current_user\\nfrom flask import g\\n\\nfrom ..models.record import Record\\nfrom ..models.vehicle import Vehicle\\nfrom ..models.driver import Driver\\nfrom ..constants import PER_HOME_PAGE\\n\\ncarboard = Blueprint('carboard', __name__, url_prefix='/carboard')\\n\\nfrom .article import *\\nfrom .brand import *\\nfrom .country import *\\nfrom .dataset import *\\nfrom .driver import *\\nfrom .drivetype import *\\nfrom .extrasignal import *\\nfrom .model import *\\nfrom .platform import *\\nfrom .record import *\\nfrom .signal import *\\nfrom .signalclass import *\\nfrom .signalsource import *\\nfrom .status import *\\nfrom .user import *\\nfrom .vehicle import *\\nfrom .plugin import *\\n# --------------------- /carboard/index : User home page -------------------- #\\n\\n\\n@carboard.route('/')\\n@login_required\\ndef index():\\n records = Record.query.order_by(Record.created).limit(PER_HOME_PAGE).all()\\n vehicles = Vehicle.query.order_by(Vehicle.created).limit(PER_HOME_PAGE).all()\\n drivers = Driver.query.order_by(Driver.created).limit(PER_HOME_PAGE).all()\\n return render_template(\\n 'carboard/index.html',\\n user=current_user,\\n records=records,\\n vehicles=vehicles,\\n drivers=drivers,\\n )\\n\\n\\n@carboard.route('/configuration')\\n@login_required\\ndef configuration():\\n # may be some logic !\\n return render_template('elements/configuration.html')\\n\"\n },\n {\n \"alpha_fraction\": 0.388141393661499,\n \"alphanum_fraction\": 0.39042189717292786,\n \"avg_line_length\": 44.67708206176758,\n \"blob_id\": \"14c6bc6d8e8f2cb93573410134c4b4a96bcf0228\",\n \"content_id\": \"e423188ab373213fe1ccd771e228079c35a44e2d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"HTML\",\n \"length_bytes\": 4385,\n \"license_type\": \"no_license\",\n \"max_line_length\": 201,\n \"num_lines\": 96,\n \"path\": \"/templates/carboard/user/index.html\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"{% extends \\\"layout.html\\\" %}\\n\\n{% from \\\"carboard/macros.html\\\" import render_pagination %}\\n\\n{% block title %}\\n List of users | {{ super() }}\\n{% endblock %}\\n\\n{% block content_header %}\\n\\t<section class=\\\"content-header\\\">\\n\\t\\t<h1>\\n\\t\\tList of users\\n\\t\\t</h1>\\n\\t\\t<ol class=\\\"breadcrumb\\\">\\n\\t\\t\\t<li><a href=\\\"{{ url_for('carboard.index') }}\\\"><i class=\\\"fa fa-dashboard\\\"></i>Home</a></li>\\n\\t\\t\\t<li><a href=\\\"{{ url_for('carboard.indexUser') }}\\\">User</a></li>\\n\\t\\t\\t<li class=\\\"active\\\">List of users</li>\\n\\t\\t</ol>\\n\\t</section>\\n{% endblock content_header %}\\n\\n{% block content %}\\n\\n <div class=\\\"row\\\">\\n <div class=\\\"col-md-12\\\">\\n <div class=\\\"box\\\">\\n <div class=\\\"box-header\\\">\\n <div class=\\\"box-actions\\\">\\n <a href=\\\"{{ url_for('carboard.newUser') }}\\\" class=\\\"btn btn-primary\\\">\\n Add new user\\n </a>\\n </div>\\n <div class=\\\"box-tools\\\">\\n <div class=\\\"input-group input-group-sm\\\" style=\\\"width: 150px;\\\">\\n <input type=\\\"text\\\" name=\\\"table_search\\\" class=\\\"form-control pull-right\\\" placeholder=\\\"Search\\\">\\n\\n <div class=\\\"input-group-btn\\\">\\n <button type=\\\"submit\\\" class=\\\"btn btn-default\\\">\\n <i class=\\\"fa fa-search\\\"></i>\\n </button>\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\\\"box-body no-padding\\\">\\n <table class=\\\"table table-hover\\\">\\n <tr>\\n <th>ID</th>\\n <th>Avatar</th>\\n <th>Username</th>\\n <th>Email</th>\\n <th>Role</th>\\n <th>Status</th>\\n <th>Actions</th>\\n </tr>\\n\\n {% for user in users.items %}\\n <tr>\\n <td><a href=\\\"{{ url_for('carboard.showUser', id=user.id) }}\\\">{{ user.id }}</a></td>\\n <td><a href=\\\"{{ url_for('carboard.showUser', id=user.id) }}\\\">\\n {% if user.avatar %}\\n <img class=\\\"avatar table-avatar\\\" src=\\\"{{ resized_img_src(config.UPLOAD_USER + user.avatar, width=60) }}\\\" alt=\\\"{{ user.username }}\\\">\\n {% else %}\\n <img class=\\\"avatar table-avatar\\\" src=\\\"/static{{ config.UPLOAD_USER}}default.png\\\">\\n {% endif %}\\n </a></td>\\n <td>{{ user.username }}</td>\\n <td>{{ user.email }}</td>\\n <td>{{ user.role }}</td>\\n <td>\\n {% if user.status == 1 %}\\n <span class=\\\"label bg-green\\\">Activated</span>\\n {% else %}\\n <span class=\\\"label bg-red\\\">Disabled</span>\\n {% endif %}\\n </td>\\n <td>\\n <a href=\\\"{{ url_for('carboard.showUser', id=user.id) }}\\\" class=\\\"btn btn-default btn-sm\\\" data-toggle=\\\"tooltip\\\" title=\\\"Show\\\"><i class=\\\"fa fa-eye\\\"></i></a>\\n\\n <a href=\\\"{{ url_for('carboard.toggleUser', id=user.id) }}\\\" class=\\\"btn btn-warning btn-sm\\\" data-toggle=\\\"tooltip\\\" title=\\\"Toggle status\\\"><i class=\\\"fa fa-toggle-on\\\"></i></a>\\n\\n <a href=\\\"{{ url_for('carboard.editUser', id=user.id) }}\\\" class=\\\"btn btn-success btn-sm\\\" data-toggle=\\\"tooltip\\\" title=\\\"Edit\\\"><i class=\\\"fa fa-pencil\\\"></i></a>\\n\\n <a href=\\\"{{ url_for('carboard.deleteUser', id=user.id) }}\\\" class=\\\"btn btn-danger btn-sm\\\" data-toggle=\\\"tooltip\\\" title=\\\"Remove\\\"><i class=\\\"fa fa-trash\\\"></i></a>\\n </td>\\n </tr>\\n {% endfor %}\\n </table>\\n </div>\\n <div class=\\\"box-footer clearfix\\\">\\n {{render_pagination(users)}}\\n </div>\\n </div>\\n </div>\\n </div>\\n{% endblock %}\\n\"\n },\n {\n \"alpha_fraction\": 0.4556135833263397,\n \"alphanum_fraction\": 0.4595300257205963,\n \"avg_line_length\": 21.52941131591797,\n \"blob_id\": \"d036480697f30c5133b49bece98ff0351b07e318\",\n \"content_id\": \"4574c26fcce7df4206f90cd7e8a26058c4ddfbd5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 766,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 34,\n \"path\": \"/app/explorer/forms.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import Form\\nfrom wtforms import StringField, SelectField\\nfrom wtforms.validators import DataRequired, Optional, Length\\n\\n\\n# ------------------------ custom validation methods ------------------------ #\\n\\n\\n# ---------------------------- Chart form class ----------------------------- #\\n\\nclass ChartForm(Form):\\n \\\"\\\"\\\" Chart add/edit Form \\\"\\\"\\\"\\n\\n name = StringField(\\n 'Name',\\n validators=[\\n DataRequired(),\\n Length(min=3, max=50),\\n ]\\n )\\n driver_id = SelectField(\\n 'Driver',\\n coerce=int, # str\\n validators=[\\n Optional(),\\n ]\\n )\\n vehicle_id = SelectField(\\n 'Driver',\\n coerce=int, # str\\n validators=[\\n Optional(),\\n ]\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.4919908344745636,\n \"alphanum_fraction\": 0.4988558292388916,\n \"avg_line_length\": 22,\n \"blob_id\": \"996c6444071241be227f2e1263a6a6af26136277\",\n \"content_id\": \"4612600d61e3f3ad77d93159a6edb590859cc785\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 437,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 19,\n \"path\": \"/app/carboard/models/drivetype.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from ...extensions import db\\n\\n# -------------------------------- Status Model ------------------------------- #\\n\\n\\nclass DriveType(db.Model):\\n \\\"\\\"\\\" DriveType Model:\\n The type of driving records.\\n \\\"\\\"\\\"\\n __tablename__ = 'drivetypes'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.String(255))\\n\\n def __init__(self, name):\\n self.name = name\\n\\n def __repr__(self):\\n return self.name\\n\"\n },\n {\n \"alpha_fraction\": 0.8064516186714172,\n \"alphanum_fraction\": 0.8064516186714172,\n \"avg_line_length\": 30,\n \"blob_id\": \"9d11738f2fbcd05618486030fbac0af072b02910\",\n \"content_id\": \"a6ac6c26ee7da60b0f54f4effd4d3ac285d9d672\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 31,\n \"license_type\": \"no_license\",\n \"max_line_length\": 30,\n \"num_lines\": 1,\n \"path\": \"/app/importer/platforms/__init__.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from .openxc.views import help\\n\"\n },\n {\n \"alpha_fraction\": 0.5874316692352295,\n \"alphanum_fraction\": 0.5997267961502075,\n \"avg_line_length\": 28.280000686645508,\n \"blob_id\": \"bb1cd81e8a882868e1b1e78ae671a1263804b451\",\n \"content_id\": \"4d21ee47ec9f468b41b0e295224641bdd5cf5386\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 732,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 25,\n \"path\": \"/app/carboard/models/signalsource.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom ...extensions import db\\n\\n# -------------------------------- Signalsource Model ----------------------- #\\n\\n\\nclass Signalsource(db.Model):\\n \\\"\\\"\\\" Signalsource Model: \\\"\\\"\\\"\\n __tablename__ = 'signalsources'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.String(255))\\n description = db.Column(db.String(1000))\\n signals = db.relationship(\\\"Signal\\\")\\n\\n status = db.Column(db.SmallInteger, default=1)\\n created = db.Column(db.DateTime(), default=datetime.utcnow())\\n\\n def __init__(self, name, description, status=1):\\n self.name = name\\n self.status = status\\n self.description = description\\n\\n def __repr__(self):\\n return str(self.name)\\n\"\n },\n {\n \"alpha_fraction\": 0.557232677936554,\n \"alphanum_fraction\": 0.5735849142074585,\n \"avg_line_length\": 28.44444465637207,\n \"blob_id\": \"d593037cd43f09719f1ba9e2ae801617ce6eed15\",\n \"content_id\": \"cb4574865351f96398427f85a7b0c82adb47588a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 795,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 27,\n \"path\": \"/app/carboard/models/brand.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom ..helpers import slugify\\nfrom ...extensions import db\\n\\n# -------------------------------- Brand Model ------------------------------- #\\n\\n\\nclass Brand(db.Model):\\n \\\"\\\"\\\" Brand (Marque) Model \\\"\\\"\\\"\\n __tablename__ = 'brands'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.String(255))\\n slug = db.Column(db.String(255))\\n logo = db.Column(db.String(255), nullable=True)\\n code = db.Column(db.String(255))\\n status = db.Column(db.SmallInteger, default=1)\\n created = db.Column(db.DateTime(), default=datetime.utcnow())\\n\\n def __init__(self, name, code, logo=None):\\n self.name = name\\n self.slug = slugify(name)\\n self.code = code\\n self.logo = logo\\n\\n def __repr__(self):\\n return self.name\\n\"\n },\n {\n \"alpha_fraction\": 0.5753138065338135,\n \"alphanum_fraction\": 0.5753138065338135,\n \"avg_line_length\": 19.782608032226562,\n \"blob_id\": \"50effb48d98da9d4a40dde44536d9ff72b679178\",\n \"content_id\": \"290a206943f7ff9b385dcb336fbc9c3df8656376\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 478,\n \"license_type\": \"permissive\",\n \"max_line_length\": 72,\n \"num_lines\": 23,\n \"path\": \"/app/plugins/hello_world/src/views.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import Blueprint, render_template\\nfrom app.plugins.helpers import render_md\\n\\nfrom .. import __path__\\n\\nhello = Blueprint(\\n \\\"hello\\\",\\n __name__,\\n url_prefix='/hello',\\n template_folder=\\\"../templates\\\"\\n)\\n\\n# --------------------- /hello/ : Overview --------------------------- #\\n\\n\\n@hello.route(\\\"/\\\")\\ndef index():\\n\\n # Hello world is a one page plugin\\n\\n html = render_md(__path__, \\\"README.md\\\")\\n\\n return render_template(\\\"hello_world/index.html\\\", html=html)\\n\"\n },\n {\n \"alpha_fraction\": 0.5689900517463684,\n \"alphanum_fraction\": 0.574679970741272,\n \"avg_line_length\": 27.1200008392334,\n \"blob_id\": \"3b75980c970154dd0097ab6480aa1b0184e86d90\",\n \"content_id\": \"a98b3b564727e602561088e1f295b27654e92d10\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 703,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 25,\n \"path\": \"/app/carboard/models/model.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom ...extensions import db\\n\\n# -------------------------------- Driver Model ------------------------------- #\\n\\n\\nclass Model(db.Model):\\n \\\"\\\"\\\" Vehicle Models Model \\\"\\\"\\\"\\n __tablename__ = 'models'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.String(255))\\n\\n brand_id = db.Column(db.Integer, db.ForeignKey('brands.id'))\\n brand = db.relationship('Brand', backref='model')\\n\\n status = db.Column(db.SmallInteger, default=1)\\n created = db.Column(db.DateTime(), default=datetime.utcnow())\\n\\n def __init__(self, name, brand_id):\\n self.name = name\\n self.brand_id = brand_id\\n\\n def __repr__(self):\\n return self.name\\n\"\n },\n {\n \"alpha_fraction\": 0.7708333134651184,\n \"alphanum_fraction\": 0.7708333134651184,\n \"avg_line_length\": 39,\n \"blob_id\": \"52a0f89c55c3965b638533d8cb572c09046a3ccc\",\n \"content_id\": \"7e16a58241225687ec68ea2fd42bd593a5c64682\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 240,\n \"license_type\": \"no_license\",\n \"max_line_length\": 108,\n \"num_lines\": 6,\n \"path\": \"/app/carboard/forms/filesignal.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom flask_wtf.file import FileAllowed, FileField, FileRequired\\n\\n\\nclass FileSignalForm(FlaskForm):\\n file = FileField(validators=[FileAllowed(['csv'], 'only .csv files are supported now'), FileRequired()])\\n\"\n },\n {\n \"alpha_fraction\": 0.7516387701034546,\n \"alphanum_fraction\": 0.7523670792579651,\n \"avg_line_length\": 23.087718963623047,\n \"blob_id\": \"9c3598bf4e73feff839cdbe08b24289583ba6c56\",\n \"content_id\": \"6352c6f883d8c379b28afe8a06b9040b6aa84b08\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1373,\n \"license_type\": \"no_license\",\n \"max_line_length\": 124,\n \"num_lines\": 57,\n \"path\": \"/README.md\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# vCar\\n\\n## vCar is a Driving Behaviour Analytics Platfom\\n\\nvCar Driving Analytics Solution processes the data coming from the connected vehicle and generates advanced driving behavior\\nand driving pattern reports. The data is collected and analysed with the ultimate goal to gain a full understanding of\\nthe drivers capability to adjust his driving based on driving conditions.\\n\\n## Instalation\\n\\n### build static files\\n\\nInstall dependencies :\\n`npm install`\\n\\n* Production :\\n\\n`gulp build --prod`\\nThen you need to set DEBUG=False in config/config.py\\n\\n* Development :\\n * `gulp build [--dev]` to generate static files.\\n * `gulp watch` to watch static files.\\n\\n### Install Elasticsearch\\n\\n* Download at [Elasticsearch](https://www.elastic.co/products/elasticsearch)\\n* (Optionel) [Kibana](https://www.elastic.co/products/kibana)\\n\\n### Install Redis\\n\\n* Download at [Redis](https://redis.io/download)\\n\\n### Docker integration :\\nTo run vcar inside docker please make this changes:\\n\\nChange `REDIS_HOST` and `ELASTICSEARCH_HOST` in `importer/platforms/openxc/constants.py` to : \\n\\n`REDIS_HOST = 'redis'`\\n\\n`ELASTICSEARCH_HOST = 'elastic'`\\nthen run :\\n`docker-compose up`\\n\\n## Technologies\\n\\nPython, FLASK, MYSQL, REDIS, ELASTIC, DOCKER, VISJS, NODEJS, BOOTSTRAP, CSS, JINJA2, HTML\\n\\n## Platform Insights\\n\\n* Car data storages\\n* Data visualization\\n* Driving Behavior\\n\\n## Team members\\n\\nboubouhkarim\\n\"\n },\n {\n \"alpha_fraction\": 0.5985247492790222,\n \"alphanum_fraction\": 0.6032665967941284,\n \"avg_line_length\": 24.648649215698242,\n \"blob_id\": \"28b09138d20015e18ac42bb512dc55908b3fa525\",\n \"content_id\": \"737d6882b2a98e5c9bfc84357b65fb690bcd2d95\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1898,\n \"license_type\": \"no_license\",\n \"max_line_length\": 78,\n \"num_lines\": 74,\n \"path\": \"/manage.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_script import Manager\\nfrom flask_migrate import MigrateCommand\\nfrom main import create_app\\nfrom app.extensions import db\\nfrom app.carboard.models.user import User\\n\\nimport os\\n\\napp = create_app()\\nmanager = Manager(app)\\n\\n\\n@manager.command\\ndef run():\\n \\\"\\\"\\\"Run in local machine.\\\"\\\"\\\"\\n app.config['TEMPLATES_AUTO_RELOAD'] = True\\n\\n # Extrafiles are just used to track changer in template folder\\n # thus our application is forced to reload.\\n extra_dirs = [\\n '/home/karim/OpenXC/Dashboard/Flask/vcar/templates',\\n ]\\n extra_files = extra_dirs[:]\\n for extra_dir in extra_dirs:\\n for dirname, dirs, files in os.walk(extra_dir):\\n for filename in files:\\n filename = os.path.join(dirname, filename)\\n if os.path.isfile(filename):\\n extra_files.append(filename)\\n\\n \\\"\\\"\\\"\\n Get informed : Flask development server can handle only one request :)\\n * processes = x : allow x processes (forks) means x clients\\n Windows sorry you can't fork :)\\n * threaded = Bool : allow or not multiple threads.\\n \\\"\\\"\\\"\\n # app.run(processes=4, extra_files=extra_files)\\n # app.run(threaded=True, host='0.0.0.0', extra_files=extra_files)\\n app.run(threaded=True, extra_files=extra_files, host='0.0.0.0')\\n # app.run(extra_files=extra_files)\\n # app.run()\\n\\n\\n@manager.command\\ndef initdb():\\n \\\"\\\"\\\"Init/reset database.\\\"\\\"\\\"\\n\\n # db.drop_all()\\n # db.create_all()\\n\\n admin = User(\\n fullname=\\\"Misaki Yooko\\\",\\n username=\\\"root\\\",\\n email=\\\"misaki.yooko@gmail.com\\\",\\n password='root'\\n )\\n\\n db.session.add(admin)\\n db.session.commit()\\n\\n\\n# manager.add_option(\\n# '-c', '--config',\\n# dest=\\\"config\\\",\\n# required=False,\\n# help=\\\"config file\\\"\\n# )\\nmanager.add_command(\\n 'db',\\n MigrateCommand\\n)\\n\\nif __name__ == \\\"__main__\\\":\\n manager.run()\\n\"\n },\n {\n \"alpha_fraction\": 0.42556634545326233,\n \"alphanum_fraction\": 0.46278315782546997,\n \"avg_line_length\": 21.814815521240234,\n \"blob_id\": \"4213f754bad281bc6bf6025e7ed130ebd5a5576a\",\n \"content_id\": \"a357429ad5dea4eaa99a5caae5f29b6a2762413b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 618,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 27,\n \"path\": \"/app/importer/datasets/drive/constants.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n# -------------------- Redis constants -------------------------------------- #\\nREDIS_HOST = 'localhost'\\nREDIS_PORT = 6379\\nREDIS_DB = 0\\n\\n# -------------------- Elasticsearch constants ------------------------------ #\\n\\n# basic configuration\\nELASTICSEARCH_HOST = 'localhost'\\nELASTICSEARCH_PORT = 9200\\n\\n# indexing configuration\\nOPENXC_INDEX = 'openxc'\\nBULK_SIZE = 10000\\n\\n# -------------------- Driver Graph Configuration --------------------------- #\\nTRANSFORM = {\\n -1: \\\"reverse\\\",\\n 0: \\\"neutral\\\",\\n 1: \\\"first\\\",\\n 2: \\\"second\\\",\\n 3: \\\"third\\\",\\n 4: \\\"fourth\\\",\\n 5: \\\"fifth\\\",\\n 6: \\\"sixth\\\",\\n 7: \\\"seventh\\\"\\n}\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6167481541633606,\n \"alphanum_fraction\": 0.6179706454277039,\n \"avg_line_length\": 24.96825408935547,\n \"blob_id\": \"d448706db4a7366e9df01bc24a3bcd33b3b2a90e\",\n \"content_id\": \"3953f2c03c0555f5d6b9335877a4313c332657f7\",\n \"detected_licenses\": [\n \"MIT\",\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1636,\n \"license_type\": \"permissive\",\n \"max_line_length\": 79,\n \"num_lines\": 63,\n \"path\": \"/app/plugins/driver_graph/src/views.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import Blueprint, render_template\\nfrom app.plugins.helpers import render_md\\n\\nfrom .algorithms.drivergraph import DriverGraph\\nfrom .algorithms.settings import DefaultSettings\\n\\nfrom .. import __path__\\n\\ndriverGraph = Blueprint(\\n \\\"driverGraph\\\",\\n __name__,\\n url_prefix='/driver_graph',\\n template_folder=\\\"../templates\\\"\\n)\\n\\n# --------------------- /driver_graph/ : Overview --------------------------- #\\n\\n\\n@driverGraph.route(\\\"/\\\")\\ndef index():\\n\\n html = render_md(__path__, \\\"README.md\\\")\\n\\n return render_template(\\\"driver_graph/index.html\\\", html=html)\\n\\n# --------------------- /driver_graph/datasets : Show available datasets ---- #\\n\\n\\n@driverGraph.route(\\\"/datasets\\\")\\ndef datasets():\\n # todo\\n return render_template(\\\"driver_graph/datasets.html\\\")\\n\\n# --------------------- /driver_graph/run : Main plugin function ------------ #\\n\\n\\n@driverGraph.route(\\\"/run\\\")\\ndef run():\\n driver = DriverGraph(max_nodes=30)\\n driver.settings = DefaultSettings\\n driver.create_digraph(\\\"/home/karim/OpenXC/uptown-west.json\\\")\\n graph_js = driver.vis_network(physics=True)\\n\\n return render_template(\\\"driver_graph/run.html\\\", graph_js=graph_js)\\n\\n# --------------------- /driver_graph/custom : custom functions ------------- #\\n\\n\\n@driverGraph.route(\\\"/custom\\\")\\ndef custom():\\n # some plugin spesific functions ...\\n\\n return render_template(\\\"driver_graph/custom.html\\\")\\n\\n# --------------------- /driver_graph/docs : Documentation ------------------ #\\n\\n\\n@driverGraph.route(\\\"/docs\\\")\\ndef docs():\\n # documentation in markdown\\n html = render_md(__path__, \\\"DOC.md\\\")\\n\\n return render_template(\\\"driver_graph/docs.html\\\", html=html)\\n\"\n },\n {\n \"alpha_fraction\": 0.6293660998344421,\n \"alphanum_fraction\": 0.6293660998344421,\n \"avg_line_length\": 27.10909080505371,\n \"blob_id\": \"25d9ed4e88eaee236d6da0607354aad313505700\",\n \"content_id\": \"592e3cbd589cd30e39b9cf5ba48b858c56b88f25\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1546,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 55,\n \"path\": \"/app/carboard/views/plugin.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import (\\n render_template, flash, redirect, url_for\\n)\\nimport flask_plugins\\nfrom flask_login import login_required\\n\\nfrom . import carboard\\n\\n# -------------------- /carboard/plugin/ : List of plugins ------------------ #\\n\\n\\n@carboard.route('/plugin/', methods=['GET'])\\n@login_required\\ndef indexPlugin():\\n plugins = flask_plugins.get_all_plugins()\\n try:\\n plugins = flask_plugins.get_all_plugins()\\n except Exception:\\n plugins = None\\n\\n # raise\\n\\n return render_template('carboard/plugin/index.html', plugins=plugins)\\n\\n# -------------------- /carboard/plugin/id : Show plugin -------------------- #\\n\\n\\n@carboard.route('/plugin/<identifier>', methods=['GET'])\\n@login_required\\ndef showPlugin(identifier):\\n try:\\n plugin = flask_plugins.get_plugin_from_all(identifier)\\n except Exception:\\n plugin = None\\n\\n return render_template('carboard/plugin/show.html', plugin=plugin)\\n\\n# -------------------- /carboard/plugin/id/enable : Enable plugin ----------- #\\n\\n\\n@carboard.route('/plugin/<identifier>/enable', methods=['GET'])\\n@login_required\\ndef enablePlugin(identifier):\\n try:\\n enabled = flask_plugins.get_plugin_from_all(identifier)\\n flash('Plugin {}, updated successfully.'.format(identifier), 'success')\\n except Exception:\\n enabled = None\\n\\n if enabled:\\n flash('Plugin {}, enabled successfully.'.format(identifier), 'success')\\n else:\\n flash('Plugin {}, not enabled.'.format(identifier), 'error')\\n\\n return redirect(url_for('carboard.indexPlugin'))\\n\"\n },\n {\n \"alpha_fraction\": 0.637442946434021,\n \"alphanum_fraction\": 0.6420091390609741,\n \"avg_line_length\": 33.761905670166016,\n \"blob_id\": \"0ee28b09de9bdd0df3db74a7f751fc1f4d996d1b\",\n \"content_id\": \"eb51ea394b4b02e03d1fe4d2459b227563865015\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4380,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 126,\n \"path\": \"/app/carboard/views/platform.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import (\\n render_template, request, flash, redirect, url_for, jsonify\\n)\\nfrom flask_login import login_required\\n\\nfrom . import carboard\\nfrom ..models.platform import Platform, PlatformSchema\\nfrom ..models.extrasignal import ExtrasignalSchema\\nfrom ..forms.platform import PlatformForm\\nfrom ..helpers import paginate, upload_file\\nfrom ..constants import PER_PAGE, PLATFORM_LOGO_DIR\\nfrom ...extensions import db\\n\\n# --------------------- /carboard/platform/ : List of platforms ------------------ #\\n\\n\\n@carboard.route('/platform/')\\n@login_required\\ndef indexPlatform():\\n platforms = Platform.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/platform/index.html', platforms=platforms)\\n\\n# ----------------------- /carboard/platform/id : Show platform ------------------- #\\n\\n\\n@carboard.route('/platform/<int:id>', methods=['GET'])\\n@login_required\\ndef showPlatform(id):\\n platform = Platform.query.get_or_404(id)\\n return render_template('carboard/platform/show.html', platform=platform)\\n\\n# ----------------------- /carboard/platform/id/json : Json platform object ------- #\\n\\n\\n@carboard.route('/platform/<int:id>/json', methods=['GET'])\\n@login_required\\ndef jsonPlatform(id):\\n platform = Platform.query.get_or_404(id)\\n platform_schema = PlatformSchema()\\n extrasignal_eschema = ExtrasignalSchema(many=True)\\n platform_result = platform_schema.dump(platform)\\n extrasignal_result = extrasignal_eschema.dump(platform.signals)\\n\\n return jsonify({\\n 'platform': platform_result.data,\\n 'signals': extrasignal_result.data\\n })\\n\\n# ---------------------- /carboard/platform/new : Add platform -------------------- #\\n\\n\\n@carboard.route('/platform/new', methods=['GET', 'POST'])\\n@login_required\\ndef newPlatform():\\n \\\"\\\"\\\" Add new platform \\\"\\\"\\\"\\n\\n form = PlatformForm()\\n\\n if form.validate_on_submit():\\n logo = upload_file(form.logo.data, PLATFORM_LOGO_DIR)\\n platform = Platform(\\n name=form.name.data,\\n mimetype=form.mimetype.data,\\n website=form.website.data,\\n description=form.description.data,\\n logo=logo\\n )\\n db.session.add(platform)\\n db.session.commit()\\n flash('Platform {}, added successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.indexPlatform'))\\n\\n return render_template('carboard/platform/new.html', form=form)\\n\\n# -------------------- /carboard/platform/id/edit : Edit platform ----------------- #\\n\\n\\n@carboard.route('/platform/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editPlatform(id):\\n \\\"\\\"\\\" Edit existing platform \\\"\\\"\\\"\\n platform = Platform.query.get_or_404(id)\\n oldLogo = platform.logo\\n form = PlatformForm(obj=platform)\\n if form.validate_on_submit():\\n form.populate_obj(platform)\\n logo = upload_file(form.logo.data, PLATFORM_LOGO_DIR)\\n if logo is None:\\n platform.logo = oldLogo\\n else:\\n platform.logo = logo\\n db.session.commit()\\n flash('Platform {}, updated successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.showPlatform', id=id))\\n\\n return render_template('carboard/platform/edit.html', form=form, id=id)\\n\\n# ------------------ /carboard/platform/id/delete : Delete platform --------------- #\\n\\n\\n@carboard.route('/platform/<int:id>/toggle', methods=['GET'])\\n@login_required\\ndef togglePlatform(id):\\n platform = Platform.query.get_or_404(id)\\n # getattr(platform, 'status', 0)\\n status = platform.status if platform.status is not None else 0\\n platform.status = 1 - status\\n db.session.commit()\\n msg = 'activated' if platform.status is 1 else 'deactivated'\\n flash('Platform {}, {} successfully.'.format(platform.name, msg), 'success')\\n return redirect(url_for('carboard.indexPlatform'))\\n\\n# ------------------ /carboard/platform/id/delete : Delete platform --------------- #\\n\\n\\n@carboard.route('/platform/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deletePlatform(id):\\n platform = Platform.query.get_or_404(id)\\n db.session.delete(platform)\\n db.session.commit()\\n flash('Platform {}, deleted successfully.'.format(platform.name), 'success')\\n return redirect(url_for('carboard.indexPlatform'))\\n\"\n },\n {\n \"alpha_fraction\": 0.6414941549301147,\n \"alphanum_fraction\": 0.6447283029556274,\n \"avg_line_length\": 37.89308166503906,\n \"blob_id\": \"270622067e0bc8aefedd5cbc4da6fd466327a255\",\n \"content_id\": \"7e2038a0d940ffee6d01c84da3c18c93cb728c07\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6184,\n \"license_type\": \"no_license\",\n \"max_line_length\": 115,\n \"num_lines\": 159,\n \"path\": \"/app/carboard/views/signalsource.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\n\\nfrom flask import (\\n render_template, request, flash, redirect, url_for\\n)\\nfrom flask_login import login_required\\nfrom sqlalchemy import or_\\n\\nfrom . import carboard\\nfrom ..models.signalsource import Signalsource\\nfrom ..forms.signalsource import SignalsourceForm\\nfrom ..helpers import paginate, CSVLoader\\nfrom ..constants import PER_PAGE, CSV_TEMP\\nfrom ...extensions import db\\nfrom ..forms.filesignal import FileSignalForm\\nfrom ..helpers import remove_csv, upload_csv\\n\\n\\n# --------------------- /carboard/signalsource/ : List of signalsources ----- #\\n\\n\\n@carboard.route('/signalsource/')\\n@login_required\\ndef indexSignalsource():\\n signalsources = Signalsource.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n start = (request.args.get('page', 1, type=int) - 1) * PER_PAGE + 1\\n return render_template('carboard/signalsource/index.html', signalsources=signalsources, start=start)\\n\\n\\n# ----------------------- /carboard/signalsource/id : Show signalsource ----- #\\n\\n\\n@carboard.route('/signalsource/<int:id>', methods=['GET'])\\n@login_required\\ndef showSignalsource(id):\\n signalsource = Signalsource.query.get_or_404(id)\\n return render_template('carboard/signalsource/show.html', signalsource=signalsource)\\n\\n\\n# ---------------------- /carboard/signalsource/new : Add signalsource -------------------- #\\n\\n@carboard.route('/signalsource/new', methods=['GET', 'POST'])\\n@login_required\\ndef newSignalsource():\\n \\\"\\\"\\\" Add new signalsource \\\"\\\"\\\"\\n form = SignalsourceForm()\\n\\n if form.validate_on_submit():\\n ret = Signalsource.query.filter_by(name=form.name.data).first()\\n if ret:\\n flash('Signal source {}, already exists in the database.'.format(form.name.data), 'error')\\n return redirect(url_for('carboard.indexSignalsource'))\\n signalsource = Signalsource(\\n name=form.name.data,\\n description=form.description.data\\n )\\n db.session.add(signalsource)\\n db.session.commit()\\n flash('Signal source {}, added successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.indexSignalsource'))\\n\\n return render_template('carboard/signalsource/new.html', form=form)\\n\\n\\n# -------------------------------- /carboard/signalsource/bulk-add add all from file -------------#\\n\\n@carboard.route('/signalsource/bulk-add', methods=['GET', 'POST'])\\n@login_required\\ndef bulkAddSource():\\n form = FileSignalForm()\\n errors = None\\n if form.validate_on_submit():\\n try:\\n f = upload_csv(form.file.data, CSV_TEMP)\\n loader = CSVLoader(os.path.join(CSV_TEMP, form.file.data.filename))\\n res = loader.load()\\n already_there = False\\n for row in res:\\n ret = Signalsource.query.filter_by(name=row['Signal Source'])\\n if ret:\\n already_there = True # check to not add already added signal sources\\n continue\\n signal_source = Signalsource(\\n name=row['Signal Source'],\\n description=row['Description']\\n )\\n db.session.add(signal_source)\\n db.session.commit()\\n remove_csv(form.file.data.filename, CSV_TEMP)\\n except KeyError:\\n errors = ['Your file is Not well Formated, please review your file structure .']\\n if not errors and not already_there:\\n flash('Signal sources added Succesfully', 'success')\\n if already_there:\\n flash('Some Signal sources are not added because they are already in the database', 'warning')\\n return render_template('carboard/signalsource/bulk.html', form=form, errors=errors)\\n\\n\\n# -------------------- /carboard/signalsource/id/edit : Edit signalsource ----------------- #\\n\\n\\n@carboard.route('/signalsource/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editSignalsource(id):\\n \\\"\\\"\\\" Edit existing signalsource \\\"\\\"\\\"\\n signalsource = Signalsource.query.get_or_404(id)\\n form = SignalsourceForm(obj=signalsource)\\n if form.validate_on_submit():\\n form.populate_obj(signalsource)\\n db.session.commit()\\n flash('Signal source {}, updated successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.showSignalsource', id=id))\\n\\n return render_template('carboard/signalsource/edit.html', form=form, id=id)\\n\\n\\n# ------------------ /carboard/signalsource/id/delete : Delete signalsource --------------- #\\n\\n\\n@carboard.route('/signalsource/<int:id>/toggle', methods=['GET'])\\n@login_required\\ndef toggleSignalsource(id):\\n signalsource = Signalsource.query.get_or_404(id)\\n status = signalsource.status if signalsource.status is not None else 0\\n signalsource.status = 1 - status\\n db.session.commit()\\n msg = 'activated' if signalsource.status is 1 else 'deactivated'\\n flash('Signal source {}, {} successfully.'.format(signalsource.name, msg), 'success')\\n return redirect(url_for('carboard.indexSignalsource'))\\n\\n\\n# ------------------ /carboard/signalsource/id/delete : Delete signalsource --------------- #\\n\\n\\n@carboard.route('/signalsource/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteSignalsource(id):\\n signalsource = Signalsource.query.get_or_404(id)\\n db.session.delete(signalsource)\\n db.session.commit()\\n flash('Signal source {}, deleted successfully.'.format(signalsource.name), 'success')\\n return redirect(url_for('carboard.indexSignalsource'))\\n\\n\\n# ------------------ /carboard/signalsource/search?table_seach=? : Search for signal source --------------- #\\n\\n@carboard.route('/signalsource/search')\\n@login_required\\ndef searchSignalSource():\\n param = request.args.get('table_search')\\n signalsources = Signalsource.query.filter(\\n or_(Signalsource.name.like('%' + param + '%'), Signalsource.description.like('%' + param + '%'))).paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/signalsource/search.html', signalsources=signalsources)\\n\"\n },\n {\n \"alpha_fraction\": 0.45069825649261475,\n \"alphanum_fraction\": 0.4549301862716675,\n \"avg_line_length\": 30.81944465637207,\n \"blob_id\": \"4bb6cdb6047aa7f6b765bb8b406c6486977961cd\",\n \"content_id\": \"07202559b90a57671c5a3d7fef6bcb573288ada8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"HTML\",\n \"length_bytes\": 2363,\n \"license_type\": \"no_license\",\n \"max_line_length\": 121,\n \"num_lines\": 72,\n \"path\": \"/templates/importer/records.html\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"{% extends \\\"layout.html\\\" %}\\r\\n\\r\\n{% from \\\"importer/macros.html\\\" import render_field, info_box, box_header %}\\r\\n\\r\\n{% block title %}\\r\\n vCar importer manager :: Records | {{ super() }}\\r\\n{% endblock %}\\r\\n\\r\\n{% block content_header %}\\r\\n <section class=\\\"content-header\\\">\\r\\n <h1>\\r\\n Import wizard\\r\\n </h1>\\r\\n <ol class=\\\"breadcrumb\\\">\\r\\n <li><a href=\\\"{{ url_for('carboard.index') }}\\\"><i class=\\\"fa fa-dashboard\\\"></i>Home</a></li>\\r\\n <li><a href=\\\"#\\\">Importer</a></li>\\r\\n <li class=\\\"active\\\">Import wizard</li>\\r\\n </ol>\\r\\n </section>\\r\\n{% endblock content_header %}\\r\\n\\r\\n{% block content %}\\r\\n<div class=\\\"row\\\">\\r\\n <div class=\\\"col-md-12\\\">\\r\\n <div class=\\\"callout callout-vcar\\\">\\r\\n <i class=\\\"callout-icon\\\">Step 4 of 5</i>\\r\\n <h4>Upload trace files</h4>\\r\\n <p>Upload one or multiple trace files according to the choosed platform.</p>\\r\\n </div>\\r\\n </div>\\r\\n</div>\\r\\n\\r\\n<div class=\\\"row\\\">\\r\\n <div class=\\\"col-xs-12\\\">\\r\\n <div class=\\\"box box-solid vcar-box\\\">\\r\\n {{ box_header('Uploader manager') }}\\r\\n <div class=\\\"box-body\\\">\\r\\n <form id=\\\"upload\\\" method=\\\"post\\\" action=\\\"{{ url_for('importer.getfile') }}\\\" enctype=\\\"multipart/form-data\\\">\\r\\n <div id=\\\"drop\\\">\\r\\n Drop Here<br>\\r\\n <a>Browse</a>\\r\\n <input type=\\\"file\\\" name=\\\"openxc\\\" multiple />\\r\\n </div>\\r\\n <ul><!-- The file uploads will be shown here --></ul>\\r\\n </form>\\r\\n </div>\\r\\n <div class=\\\"box-footer\\\">\\r\\n <a class=\\\"pull-right btn btn-vcar-action\\\" href=\\\"#\\\" id=\\\"next\\\">\\r\\n <i class=\\\"fa fa-chevron-right\\\"></i>\\r\\n Submit\\r\\n </a>\\r\\n </div>\\r\\n <form id=\\\"send_files\\\" method=\\\"post\\\" action=\\\"{{ url_for('importer.records') }}\\\">\\r\\n <!-- hidden inputs goes here -->\\r\\n </form>\\r\\n </div>\\r\\n </form>\\r\\n </div>\\r\\n</div>\\r\\n{%- endblock content %}\\r\\n\\r\\n{% block scripts %}\\r\\n {{ super() }}\\r\\n <script type=\\\"text/javascript\\\">\\r\\n $(function() {\\r\\n $('#next').click(function(e) {\\r\\n e.preventDefault();\\r\\n $(\\\"#send_files\\\").submit();\\r\\n });\\r\\n });\\r\\n </script>\\r\\n{% endblock %}\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6872586607933044,\n \"alphanum_fraction\": 0.6988416910171509,\n \"avg_line_length\": 18.923076629638672,\n \"blob_id\": \"77ffcdfa748c773d589f5dada2b3ffae3c2abebe\",\n \"content_id\": \"285c6a8a97f71d8c4207a273aa17d9f5b11b272f\",\n \"detected_licenses\": [\n \"MIT\",\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 259,\n \"license_type\": \"permissive\",\n \"max_line_length\": 44,\n \"num_lines\": 13,\n \"path\": \"/app/plugins/driver_graph/__init__.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from os import getcwd\\nfrom app.plugin import AppPlugin\\nfrom .src.views import driverGraph\\n\\n__plugin__ = \\\"DriverGraph\\\"\\n__version__ = \\\"1.0.0\\\"\\n__path__ = getcwd()\\n\\n\\nclass DriverGraph(AppPlugin):\\n\\n def setup(self):\\n self.register_blueprint(driverGraph)\\n\"\n },\n {\n \"alpha_fraction\": 0.5469200015068054,\n \"alphanum_fraction\": 0.5506620407104492,\n \"avg_line_length\": 25.571428298950195,\n \"blob_id\": \"ef8ce1f4d0f2d09ccee076022b19bad8bd357c3c\",\n \"content_id\": \"330ced43e4b589d0196eb431fbd7e354d54bd9ea\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3474,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 126,\n \"path\": \"/app/importer/platforms/openxc/indexing_org.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import redis\\r\\nimport json\\r\\nfrom time import strftime, sleep\\r\\nfrom datetime import datetime\\r\\nfrom elasticsearch import Elasticsearch, helpers\\r\\nfrom .constants import *\\r\\nfrom .helpers import correct_value, correct_time\\r\\nfrom ...transformer import Transformer\\r\\n\\r\\n# ----------------- Setting up tools --------------------------------------- #\\r\\nred = redis.StrictRedis(\\r\\n\\thost = REDIS_HOST,\\r\\n\\tport = REDIS_PORT,\\r\\n\\tdb = REDIS_DB\\r\\n)\\r\\n\\r\\ndef task_info(name='info'):\\r\\n pubsub = red.pubsub()\\r\\n pubsub.subscribe(name)\\r\\n for message in pubsub.listen():\\r\\n # print(\\\"[+] Message: {}\\\".format(message))\\r\\n yield 'data: {}\\\\n\\\\n'.format(message['data'])\\r\\n\\r\\n# ----------------- Indexing with Bulk : Large files ----------------------- #\\r\\n\\r\\ndef index_bulk(traces, metadata, bulk_size=BULK_SIZE):\\r\\n\\tred.publish('info', 'Hello word! ')\\r\\n\\ttry:\\r\\n\\t\\tes = Elasticsearch([{\\r\\n\\t\\t\\t'host': ELASTICSEARCH_HOST,\\r\\n\\t\\t\\t'port': ELASTICSEARCH_PORT\\r\\n\\t\\t}])\\r\\n\\r\\n\\t\\tindex = OPENXC_INDEX\\r\\n\\t\\ttype = \\\"platform\\\"\\r\\n\\t\\tsleep(2)\\r\\n\\t\\tred.publish('info', 'Inisializing Elasticsearch ...')\\r\\n\\t\\tes.cluster.health(wait_for_status='yellow', request_timeout=30)\\r\\n\\t\\tred.publish('info', 'creating index if it dosn\\\\'t exist ...')\\r\\n\\t\\tmapping = {\\r\\n\\t\\t\\ttype: {\\r\\n\\t\\t\\t\\t\\\"properties\\\": {\\r\\n\\t\\t\\t\\t\\t\\\"user\\\": {\\r\\n\\t\\t\\t\\t\\t\\t\\\"type\\\": \\\"integer\\\"\\r\\n\\t\\t\\t\\t\\t},\\r\\n\\t\\t\\t\\t\\t\\\"vehicle\\\": {\\r\\n\\t\\t\\t\\t\\t\\t\\\"type\\\": \\\"integer\\\"\\r\\n\\t\\t\\t\\t\\t},\\r\\n\\t\\t\\t\\t\\t\\\"driver\\\": {\\r\\n\\t\\t\\t\\t\\t\\t\\\"type\\\": \\\"integer\\\"\\r\\n\\t\\t\\t\\t\\t},\\r\\n\\t\\t\\t\\t\\t\\\"class\\\": {\\r\\n\\t\\t\\t\\t\\t\\t\\\"type\\\": \\\"string\\\"\\r\\n\\t\\t\\t\\t\\t},\\r\\n\\t\\t\\t\\t\\t\\\"name\\\": {\\r\\n\\t\\t\\t\\t\\t\\t\\\"type\\\": \\\"string\\\",\\r\\n\\t\\t\\t\\t\\t\\t\\\"index\\\": \\\"not_analyzed\\\"\\r\\n\\t\\t\\t\\t\\t},\\r\\n\\t\\t\\t\\t\\t\\\"value\\\": {\\r\\n\\t\\t\\t\\t\\t\\t\\\"type\\\": \\\"string\\\",\\r\\n\\t\\t\\t\\t\\t\\t\\\"index\\\": \\\"not_analyzed\\\"\\r\\n\\t\\t\\t\\t\\t},\\r\\n\\t\\t\\t\\t\\t\\\"timestamp\\\": {\\r\\n\\t\\t\\t\\t\\t\\t\\\"type\\\": \\\"date\\\"\\r\\n\\t\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t}\\r\\n\\t\\t}\\r\\n\\t\\t# es.indices.delete(index=index, ignore=404)\\r\\n\\t\\tif not es.indices.exists(index=index):\\r\\n\\t\\t\\tes.indices.create(index=index, ignore=400)\\r\\n\\t\\t\\tes.indices.put_mapping(index=index, doc_type=type, body=mapping)\\r\\n\\r\\n\\t\\t# inisialise a Transformer instance + set the platform attribute\\r\\n\\t\\ttr = Transformer();\\r\\n\\t\\ttr.setPlatformById(metadata['platform']);\\r\\n\\t\\tfor trace in traces:\\r\\n\\t\\t\\ti = 1\\r\\n\\t\\t\\tred.publish('info', 'indexing the file: {}'.format(trace))\\r\\n\\t\\t\\tactions = []\\r\\n\\t\\t\\twith open(trace, 'r') as f:\\r\\n\\t\\t\\t\\tfor line in f:\\r\\n\\t\\t\\t\\t\\tsource = json.loads(line)\\r\\n\\t\\t\\t\\t\\tsignal = tr.getInfo(source.get('name'))\\r\\n\\r\\n\\t\\t\\t\\t\\tif signal is not None:\\r\\n\\t\\t\\t\\t\\t\\taction = {\\r\\n\\t\\t\\t\\t\\t\\t\\t\\\"_index\\\": index,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\\"_type\\\": type,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\\"_id\\\": i,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\\"_source\\\": {\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\\"user\\\": metadata['user'],\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\\"vehicle\\\": metadata['vehicle'],\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\\"driver\\\": metadata['driver'],\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\\"class\\\": signal['class'],\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\\"name\\\": signal['name'],\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\\"value\\\": source.get('value'),\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\\"timestamp\\\": correct_time(source.get('timestamp'))\\r\\n\\t\\t\\t\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\t\\t\\tactions.append(action)\\r\\n\\t\\t\\t\\t\\t\\tif i % bulk_size == 0:\\r\\n\\t\\t\\t\\t\\t\\t\\thelpers.bulk(es, actions)\\r\\n\\t\\t\\t\\t\\t\\t\\tred.publish('info', '{} events indexed'.format(i))\\r\\n\\t\\t\\t\\t\\t\\t\\tactions = []\\r\\n\\t\\t\\t\\t\\t\\ti += 1\\r\\n\\t\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t\\t\\\"\\\"\\\" Signal not included, should we remove it !! \\\"\\\"\\\"\\r\\n\\t\\t\\t\\t\\t\\tpass\\r\\n\\r\\n\\t\\t\\t\\t\\t# ,overwrite_existing=True\\r\\n\\t\\t\\t\\t\\t# es.index(index=index, doc_type=type, id=i, body=line)\\r\\n\\t\\t\\tif i < bulk_size:\\r\\n\\t\\t\\t\\thelpers.bulk(es, actions)\\r\\n\\t\\t\\tred.publish('info', 'File {} indexed'.format(trace))\\r\\n\\t\\t\\tred.publish('info', 'end_trace'.format(trace))\\r\\n\\t\\tred.publish('info', 'end_all')\\r\\n\\t\\tsleep(2)\\r\\n\\t\\tred.connection_pool.disconnect()\\r\\n\\t\\treturn True\\r\\n\\t\\tsys.exit()\\r\\n\\texcept Exception as e:\\r\\n\\t\\tprint('Exception: ', e)\\r\\n\\t\\tred.publish('info', 'Exception: {}'.format(e))\\r\\n\\t\\treturn False\\r\\n\\t\\tsys.exit()\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.46082594990730286,\n \"alphanum_fraction\": 0.5218062400817871,\n \"avg_line_length\": 22.990739822387695,\n \"blob_id\": \"e09b993ba147ef6654456bb5106bef315a172456\",\n \"content_id\": \"54bebddb8df437f3c9c2f16b5089d89d5018f8c3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5182,\n \"license_type\": \"no_license\",\n \"max_line_length\": 106,\n \"num_lines\": 216,\n \"path\": \"/tests/elastic.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from elasticsearch import Elasticsearch, helpers\\n\\nes = Elasticsearch()\\nindex = \\\"openxc\\\"\\ntype = \\\"driver_1\\\"\\nes.cluster.health(wait_for_status='yellow', request_timeout=1)\\n# count documents\\n# count = es.count(index=index, doc_type=type)\\n# print(count)\\n# get one document\\n# data = es.get(index=index, doc_type=type, id=200)\\n# print(data)\\n# search one signal\\nbody = {\\n \\\"query\\\": {\\n \\\"match\\\": {\\n 'name': 'accelerator_pedal_position'\\n }\\n }\\n}\\n# search = es.search(index=index, doc_type=type, body=body, _source=False)\\n\\n# print(search)\\n\\n# Query for all results (no matching criteria)\\n# res = es.search(index=index, body={\\\"query\\\": {\\\"match_all\\\": {}}})\\n# print (res['hits']['total'])\\n# print (res['hits']['hits'][100]['_source']['name'])\\n\\n# bulk\\nbody = {\\n '_op_type': 'delete', # values are (index, create, delete, update)\\n '_index': 'index-name',\\n '_type': 'document',\\n '_id': 42,\\n}\\n\\n# if not es.exists(index=index, doc_type=type):\\n# print(\\\"NOOOO\\\")\\n# if es.exists(index=index, doc_type=type):\\n# print(\\\"YEEES\\\")\\n# Mapping\\n\\nmapping = {\\n type: {\\n \\\"properties\\\": {\\n \\\"name\\\": {\\n \\\"type\\\": \\\"string\\\"\\n },\\n \\\"value\\\": {\\n \\\"type\\\": \\\"double\\\"\\n },\\n \\\"timestamp\\\": {\\n \\\"type\\\": \\\"date\\\",\\n \\\"format\\\": \\\"epoch_millis\\\"\\n }\\n }\\n }\\n}\\n# if not es.indices.exists(index=index):\\n# es.indices.create(index=index, ignore=400)\\n# es.indices.put_mapping(index=index, doc_type=type, body=mapping)\\n# x = \\\"1364323978.53600\\\"\\n# t = x.split('.')\\n# data = {\\n# \\\"name\\\": \\\"accelerator_pedal_position\\\",\\n# \\\"timestamp\\\": '{}{}'.format(t[0], t[1][:3]),\\n# \\\"value\\\": 53.5\\n# }\\n# es.index(index=index, doc_type=type, id=1, body=data)\\n# print(\\\"index creared\\\")\\n# else:\\n# es.indices.delete(index=index, ignore=404)\\n\\n\\ndef correct_time(timestamp):\\n try:\\n timestamp = str(timestamp)\\n print(timestamp)\\n t = timestamp.split('.')\\n return '{}{}'.format(t[0], t[1][:3].ljust(3, '0'))\\n except:\\n return timestamp.split('.')[0]\\n\\nx = correct_time(1361454774.657980)\\nprint(x)\\n# 13614547982\\n# 136145482189\\n# 136145484557\\n# 136145486924\\n# 136145489296\\n# 136145491667\\n# 136145494043\\n# 136145496416\\n# 136145498789\\n# 13614550116\\n# 13614550353\\n# 136145505902\\n\\n# put_mapping(*args, **kwargs)\\n# http://elasticsearch-py.readthedocs.io/en/master/api.html#elasticsearch.client.IndicesClient.put_mapping\\n# https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-put-mapping.html\\nmapping = '''\\n {\\n \\\"mappings\\\":{\\n \\\"logs_june\\\":{\\n \\\"_timestamp\\\":{\\n \\\"enabled\\\":\\\"true\\\"\\n },\\n \\\"properties\\\":{\\n \\\"logdate\\\":{\\n \\\"type\\\":\\\"date\\\",\\n \\\"format\\\":\\\"dd/MM/yyy HH:mm:ss\\\"\\n }\\n }\\n }\\n }\\n }\\n'''\\n# self.elastic_con.indices.create(index='test-index', ignore=400, body=mapping)\\n# 2\\n# conntect es\\n# es = Elasticsearch()\\n# delete index if exists\\n# if es.indices.exists(config.elastic_urls_index):\\n# es.indices.delete(index=config.elastic_urls_index)\\n# index settings\\nsettings = {\\n \\\"settings\\\": {\\n \\\"number_of_shards\\\": 1,\\n \\\"number_of_replicas\\\": 0\\n },\\n \\\"mappings\\\": {\\n \\\"urls\\\": {\\n \\\"properties\\\": {\\n \\\"url\\\": {\\n \\\"type\\\": \\\"string\\\"\\n }\\n }\\n }\\n }\\n}\\n# create index\\n# es.indices.create(index=config.elastic_urls_index, ignore=400, body=settings)\\n\\n# Notes\\n# curl -XGET 'http://localhost:9200/openxc/driver_1/1/_source'\\n# curl --head 'http://localhost:9200/openxc/driver_1/1/_source'\\n# curl -XGET 'http://localhost:9200/openxc/driver_1/1?fields=name,value'\\n# curl -XGET 'http://localhost:9200/openxc/driver_1/1?_source=false'\\n# curl -XGET 'http://localhost:9200/openxc/driver_1/_search?pretty&q=name:vehicle_speed'\\n# curl -XGET 'http://localhost:9200/openxc/driver_1/_search?pretty&size=10000'\\n\\n{\\n \\\"size\\\": 0,\\n \\\"aggs\\\": {\\n \\\"2\\\": {\\n \\\"date_histogram\\\": {\\n \\\"field\\\": \\\"timestamp\\\",\\n \\\"interval\\\": \\\"1s\\\",\\n \\\"time_zone\\\": \\\"Europe/London\\\",\\n \\\"min_doc_count\\\": 1,\\n \\\"extended_bounds\\\": {\\n \\\"min\\\": 1361454733109,\\n \\\"max\\\": 1361454981775\\n }\\n },\\n \\\"aggs\\\": {\\n \\\"1\\\": {\\n \\\"max\\\": {\\n \\\"field\\\": \\\"value\\\"\\n }\\n }\\n }\\n }\\n },\\n \\\"highlight\\\": {\\n \\\"pre_tags\\\": [\\n \\\"@kibana-highlighted-field@\\\"\\n ],\\n \\\"post_tags\\\": [\\n \\\"@/kibana-highlighted-field@\\\"\\n ],\\n \\\"fields\\\": {\\n \\\"*\\\": {}\\n },\\n \\\"require_field_match\\\": false,\\n \\\"fragment_size\\\": 2147483647\\n },\\n \\\"query\\\": {\\n \\\"filtered\\\": {\\n \\\"query\\\": {\\n \\\"query_string\\\": {\\n \\\"analyze_wildcard\\\": true,\\n \\\"query\\\": \\\"*name=\\\\\\\"vehicle_speed\\\\\\\"\\\"\\n }\\n },\\n \\\"filter\\\": {\\n \\\"bool\\\": {\\n \\\"must\\\": [\\n {\\n \\\"range\\\": {\\n \\\"timestamp\\\": {\\n \\\"gte\\\": 1361454733109,\\n \\\"lte\\\": 1361454981775,\\n \\\"format\\\": \\\"epoch_millis\\\"\\n }\\n }\\n }\\n ],\\n \\\"must_not\\\": []\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5970037579536438,\n \"alphanum_fraction\": 0.6029962301254272,\n \"avg_line_length\": 30.785715103149414,\n \"blob_id\": \"c9a9dac2fa2877d99670a9030937d28fe7235fef\",\n \"content_id\": \"2fd03b73fa5c6c89d3812e60fd562d58b5b95342\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1335,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 42,\n \"path\": \"/app/carboard/models/extrasignal.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom marshmallow import Schema, fields\\nfrom .signal import SignalSchema\\nfrom ...extensions import db\\n\\n# -------------------------------- Extrasignal Model ------------------------------- #\\n\\n\\nclass Extrasignal(db.Model):\\n \\\"\\\"\\\" Extrasignal Model: \\\"\\\"\\\"\\n __tablename__ = 'extrasignals'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.String(255))\\n storage = db.Column(db.String(255))\\n\\n signal_id = db.Column(db.Integer, db.ForeignKey('signals.id'))\\n signal = db.relationship(\\\"Signal\\\", back_populates=\\\"extrasignals\\\")\\n #platform_id = db.Column(db.Integer, db.ForeignKey('platforms.id'))\\n #platform = db.relationship('Platform', back_populates=\\\"signals\\\")\\n\\n status = db.Column(db.SmallInteger, default=1)\\n created = db.Column(db.DateTime(), default=datetime.utcnow())\\n\\n def __init__(self, name, signal_id, storage, status=1):\\n self.name = name\\n self.signal_id = signal_id\\n self.storage = storage\\n self.status = status\\n\\n def __repr__(self):\\n return str(self.name)\\n\\n# -------------------------------- Extrasignal Model ------------------------------- #\\n\\n\\nclass ExtrasignalSchema(Schema):\\n \\\"\\\"\\\" Extra signals schema \\\"\\\"\\\"\\n\\n id = fields.Int(dump_only=True)\\n name = fields.Str()\\n signal = fields.Nested(SignalSchema)\\n\"\n },\n {\n \"alpha_fraction\": 0.46034297347068787,\n \"alphanum_fraction\": 0.4643622636795044,\n \"avg_line_length\": 27.272727966308594,\n \"blob_id\": \"8045d3af655218575403ad8202f9ea5444cb9cb9\",\n \"content_id\": \"81f4202554a5da1ccc79dc7bc5d6c1194d753d1f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3732,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 132,\n \"path\": \"/app/carboard/forms/dataset.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom wtforms import FormField, FieldList, StringField, SelectField\\nfrom wtforms.fields.html5 import URLField\\nfrom flask_wtf.file import FileAllowed, FileField\\nfrom wtforms.validators import ValidationError, Optional, DataRequired, Regexp, Length, url\\nfrom ..models.dataset import Dataset\\nfrom .article import ArticleForm\\n\\n# ------------------------ custom validation methods ------------------------ #\\n\\n\\ndef dataset_exist(form, field):\\n if Dataset.select().where(Dataset.name == field.data).exists():\\n raise ValidationError('Dataset already exists !')\\n\\n# ---------------------------- Dataset form classes ------------------------- #\\n\\n\\nclass DatasetForm(FlaskForm):\\n \\\"\\\"\\\" Dataset add/edit FlaskForm \\\"\\\"\\\"\\n name = StringField(\\n 'Name',\\n validators=[\\n DataRequired(),\\n Length(min=3, max=50),\\n Regexp(\\n r'^[a-zA-Z_\\\\- ]+$',\\n message=(\\\"Dataset name is not correct !\\\")\\n ),\\n ]\\n )\\n slug = StringField(\\n 'Slug',\\n validators=[\\n Optional(),\\n Length(min=3, max=50),\\n Regexp(\\n r'^[a-z_]+$',\\n message=(\\\"Dataset slug is not correct !\\\")\\n ),\\n ]\\n )\\n description = StringField(\\n 'Description',\\n validators=[\\n DataRequired(),\\n ]\\n )\\n author = StringField(\\n 'Author',\\n )\\n lab = StringField(\\n 'Lab',\\n )\\n website = StringField(\\n 'Website',\\n )\\n\\n\\nclass FeedDatasetForm(FlaskForm):\\n \\\"\\\"\\\" Dataset add/edit FlaskForm \\\"\\\"\\\"\\n\\n where = SelectField(\\n 'Where is your dataset files?',\\n coerce=int,\\n choices=[\\n (0, 'Pick a choice ...'),\\n (1, 'In my local machine'),\\n (2, 'On a remote server'),\\n (3, 'In vCar platform server')\\n ],\\n validators=[DataRequired()]\\n )\\n\\n # local_type = SelectField(\\n # 'What is your data?',\\n # coerce=int,\\n # choices=[\\n # (0, 'Pick a choice ...'),\\n # (1, 'One file'),\\n # (2, 'One zip file'),\\n # (3, 'Multiple files')\\n # ],\\n # validators=[DataRequired()]\\n # )\\n # local_file = FileField(\\n # 'Now select your file', validators=[\\n # FileAllowed(['json', 'csv', 'txt'], 'File format not supported !')\\n # ]\\n # )\\n # local_zip = FileField(\\n # 'Now select your zip file', validators=[\\n # FileAllowed(['zip'], 'only zip files are supported !')\\n # ]\\n # )\\n\\n remote = URLField(\\n 'Specify file link',\\n validators=[\\n Optional(),\\n url(message='Sorry, this is not a valid URL,')\\n ],\\n # default='http://example.com/file.json'\\n )\\n\\n vcar = StringField(\\n 'Path',\\n validators=[Optional()],\\n # default='http://example.com/file.json'\\n )\\n\\n \\\"\\\"\\\"\\n ---------------------------------------------------\\n Dependences :\\n from wtforms import FormField, FieldList\\n ---------------------------------------------------\\n Nested FlaskForm :\\n article = FormField(ArticleForm)\\n article = FieldList(FormField(ArticleForm))\\n ---------------------------------------------------\\n Jinja implementation :\\n {{ form.hidden_tag() }}\\n {{ render_field(form.name) }}\\n {{ render_field(form.description) }}\\n {{ render_field(form.author) }}\\n ...\\n\\n {% for subform in form.article %}\\n {{ render_field(subform) }}\\n {% endfor %}\\n ---------------------------------------------------\\n \\\"\\\"\\\"\\n\"\n },\n {\n \"alpha_fraction\": 0.4590461254119873,\n \"alphanum_fraction\": 0.4634525775909424,\n \"avg_line_length\": 33.39449691772461,\n \"blob_id\": \"7c2cfffae91ff44abce08eb9426a2004b4f3dcc6\",\n \"content_id\": \"0ccfbbfec55ff54c61e62e8edc30202364c62116\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3858,\n \"license_type\": \"no_license\",\n \"max_line_length\": 135,\n \"num_lines\": 109,\n \"path\": \"/app/importer/platforms/openxc/indexing.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import redis\\r\\nimport json\\r\\nfrom time import strftime, sleep\\r\\nfrom datetime import datetime\\r\\nfrom elasticsearch import Elasticsearch, helpers\\r\\nfrom .constants import *\\r\\nfrom .helpers import correct_value, correct_time\\r\\nfrom ...transformer import Transformer\\r\\n\\r\\n# ----------------- Setting up tools --------------------------------------- #\\r\\nred = redis.StrictRedis(\\r\\n host=REDIS_HOST,\\r\\n port=REDIS_PORT,\\r\\n db=REDIS_DB\\r\\n)\\r\\n\\r\\n\\r\\ndef my_handler(message):\\r\\n # print('MY HANDLER: {}'.format(message['data']))\\r\\n yield 'data: {}\\\\n\\\\n'.format(message['data'])\\r\\n\\r\\n\\r\\ndef task_info(name='info'):\\r\\n p = red.pubsub(ignore_subscribe_messages=True)\\r\\n p.subscribe('info')\\r\\n # p.subscribe(**{'info': msg_handler})\\r\\n # thread = p.run_in_thread(sleep_time=0.001)\\r\\n # thread.stop()\\r\\n while True:\\r\\n msg = p.get_message()\\r\\n if msg:\\r\\n yield 'data: {}\\\\n\\\\n'.format(msg['data'])\\r\\n sleep(1)\\r\\n\\r\\n # pubsub = red.pubsub()\\r\\n # pubsub.subscribe(name)\\r\\n # for message in pubsub.listen():\\r\\n # # print(\\\"[+] Message: {}\\\".format(message))\\r\\n # yield 'data: {}\\\\n\\\\n'.format(message['data'])\\r\\n\\r\\n# ----------------- Indexing with Bulk : Large files ----------------------- #\\r\\n\\r\\n\\r\\ndef index_bulk(traces, metadata, bulk_size=BULK_SIZE):\\r\\n red.publish('info', 'Hello word! ')\\r\\n try:\\r\\n es = Elasticsearch([{\\r\\n 'host': ELASTICSEARCH_HOST,\\r\\n 'port': ELASTICSEARCH_PORT\\r\\n }])\\r\\n\\r\\n index = OPENXC_INDEX\\r\\n type = \\\"platform\\\"\\r\\n es.cluster.health(wait_for_status='yellow', request_timeout=30)\\r\\n\\r\\n # es.indices.delete(index=index, ignore=404)\\r\\n if not es.indices.exists(index=index):\\r\\n es.indices.create(index=index, ignore=400)\\r\\n es.indices.put_mapping(index=index, doc_type=type, body=MAPPING)\\r\\n\\r\\n # inisialise a Transformer instance + set the platform attribute\\r\\n tr = Transformer()\\r\\n tr.setPlatformById(metadata['platform'])\\r\\n\\r\\n for trace in traces:\\r\\n i = 1\\r\\n actions = []\\r\\n with open(trace, 'r') as f:\\r\\n for line in f:\\r\\n source = json.loads(line)\\r\\n signal = tr.getInfo(source.get('name'))\\r\\n\\r\\n if signal is not None:\\r\\n action = {\\r\\n \\\"_index\\\": index,\\r\\n \\\"_type\\\": type,\\r\\n \\\"_id\\\": i,\\r\\n \\\"_source\\\": {\\r\\n \\\"user\\\": metadata['user'],\\r\\n \\\"vehicle\\\": metadata['vehicle'],\\r\\n \\\"driver\\\": metadata['driver'],\\r\\n \\\"class\\\": signal['class'],\\r\\n \\\"name\\\": signal['name'],\\r\\n \\\"value\\\": source.get('value'),\\r\\n \\\"timestamp\\\": correct_time(source.get('timestamp'))\\r\\n }\\r\\n }\\r\\n actions.append(action)\\r\\n if i % bulk_size == 0:\\r\\n helpers.bulk(es, actions)\\r\\n actions = []\\r\\n i += 1\\r\\n else:\\r\\n \\\"\\\"\\\" Signal not included, should we remove it !! \\\"\\\"\\\"\\r\\n pass\\r\\n\\r\\n if i < bulk_size:\\r\\n helpers.bulk(es, actions)\\r\\n red.publish(\\r\\n 'info', 'Background indexing task [<b>{}</b> file<small>(s)</small>] has been completed successfully.'.format(len(traces)))\\r\\n # sleep(1)\\r\\n # red.connection_pool.disconnect()\\r\\n return True\\r\\n sys.exit()\\r\\n except Exception as e:\\r\\n print('Exception: ', e)\\r\\n red.publish('info', 'Exception: {}'.format(e))\\r\\n return False\\r\\n sys.exit()\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5380747318267822,\n \"alphanum_fraction\": 0.5380747318267822,\n \"avg_line_length\": 24.309091567993164,\n \"blob_id\": \"9f6d39064624b716ccf4cc6c1531cb21eb201497\",\n \"content_id\": \"292f2a74fa88fea8b9e16a595393825f949082ff\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1392,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 55,\n \"path\": \"/app/carboard/forms/vehicle.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom flask_wtf.file import FileAllowed, FileField\\nfrom wtforms import StringField, SelectField\\nfrom wtforms.validators import Optional, DataRequired, Regexp, Length\\n\\nfrom ..models.vehicle import Vehicle\\n\\n# ------------------------ custom validation methods ------------------------ #\\n\\n\\ndef vehicle_exist(form, field):\\n if Vehicle.select().where(Vehicle.name == field.data).exists():\\n raise ValidationError('Vehicle already exists !')\\n\\n# ---------------------------- Vehicle form classes ---------------------------- #\\n\\nclass VehicleForm(FlaskForm):\\n \\\"\\\"\\\" Vehicle add/edit FlaskForm \\\"\\\"\\\"\\n user_id = SelectField(\\n 'User',\\n coerce=int,\\n validators=[\\n DataRequired(),\\n ]\\n )\\n brand_id = SelectField(\\n 'Brand',\\n coerce=int,\\n validators=[\\n DataRequired(),\\n ]\\n )\\n model_id = SelectField(\\n 'Model',\\n coerce=int,\\n )\\n driver_id = SelectField(\\n 'Driver',\\n coerce=int,\\n )\\n image = FileField(\\n 'Picture', validators=[\\n FileAllowed(['jpg', 'jpeg', 'png', 'gif'], 'Images only plz :) ')\\n ]\\n )\\n\\nclass VehicleStatusForm(FlaskForm):\\n \\\"\\\"\\\" Edit vehicle status FlaskForm \\\"\\\"\\\"\\n status_id = SelectField(\\n 'Status',\\n coerce=int,\\n validators=[\\n DataRequired(),\\n ]\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.8484848737716675,\n \"alphanum_fraction\": 0.8484848737716675,\n \"avg_line_length\": 13.666666984558105,\n \"blob_id\": \"423db6513bd6edc448c832a46bc8a4a510e04e2d\",\n \"content_id\": \"568503a3f87728fc7ad6270ca05381eb72a9bcc9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 264,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 18,\n \"path\": \"/requirements2.txt\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"elasticsearch\\nFlask\\nFlask-Bcrypt\\nFlask-Cache\\nFlask-DebugToolbar\\nFlask-Login\\nFlask-Mail\\nFlask-Migrate\\nFlask-Images\\nFlask-Redis\\nFlask-Script\\nFlask-SQLAlchemy\\nFlask-WTF\\nFlask-Misaka\\nmarshmallow\\nnetworkx\\nPyYAML\\nhttps://github.com/vcar/flask-plugins/archive/master.zip\\n\"\n },\n {\n \"alpha_fraction\": 0.5739071369171143,\n \"alphanum_fraction\": 0.5795403122901917,\n \"avg_line_length\": 33.13846206665039,\n \"blob_id\": \"90ff4b06ef950bd5057bd1d971f0b509dafcdb5f\",\n \"content_id\": \"faee1957e8370b75384bd96b551b351b804f2278\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4438,\n \"license_type\": \"no_license\",\n \"max_line_length\": 109,\n \"num_lines\": 130,\n \"path\": \"/app/importer/helpers.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nimport re\\nfrom unicodedata import normalize\\nfrom uuid import uuid4\\nfrom time import strftime\\nfrom datetime import datetime\\n\\nfrom flask import request, url_for, session, redirect, flash\\nfrom werkzeug import secure_filename\\n\\nfrom config.config import DefaultConfig\\nfrom .constants import UPLOAD_TRACE_DIR, ALLOWED_TRACE_EXTENSIONS\\n\\n# ------------------------------ View Helpers ------------------------------- #\\n\\n\\ndef upload_file(file_data, upload_dir, oldFile=None):\\n \\\"\\\"\\\" Upload Files \\\"\\\"\\\"\\n if file_data:\\n file = secure_filename(\\n str(uuid4()) + os.path.splitext(file_data.filename)[1].lower())\\n path = os.path.join(upload_dir, strftime(\\\"%Y/%m\\\"))\\n # os.makedirs(path, exist_ok=True) Py 3.4+\\n make_dir(path)\\n file_data.save(\\\"/\\\".join([path, file]))\\n if oldFile is not None:\\n pass\\n return \\\"/\\\".join([strftime(\\\"%Y/%m\\\"), file])\\n return None\\n\\n\\ndef slugify(text, delim=u'-'):\\n \\\"\\\"\\\"Generates an slightly worse ASCII-only slug.\\\"\\\"\\\"\\n if type(text) == str:\\n text = text.decode()\\n result = []\\n _punct_re = re.compile(r'[\\\\t !\\\"#$%&\\\\'()*\\\\-/<=>?@\\\\[\\\\\\\\\\\\]^_`{|},.]+')\\n for word in _punct_re.split(text.lower()):\\n word = normalize('NFKD', word).encode('ascii', 'ignore')\\n if word:\\n result.append(word)\\n return unicode(delim.join(result))\\n\\n\\ndef choices(Model, placeholder=None, cond=1, orderField='name'):\\n if cond:\\n c = [(b.id, b) for b in Model.query.filter_by(\\n status=cond).order_by(orderField).all()]\\n else:\\n c = [(b.id, b) for b in Model.query.order_by(orderField).all()]\\n if placeholder is not None:\\n c.insert(0, ('0', placeholder))\\n else:\\n c.insert(0, ('0', 'Select an option ...'))\\n return c\\n\\n\\ndef make_dir(dir_path):\\n \\\"\\\"\\\" Make recursive directories \\\"\\\"\\\"\\n try:\\n if not os.path.exists(dir_path) and not os.path.isdir(dir_path):\\n os.makedirs(dir_path)\\n except Exception as e:\\n raise e\\n\\n\\ndef redirect_back():\\n \\\"\\\"\\\" Redirect back to last url \\\"\\\"\\\"\\n return request.args.get('next') or request.referrer or url_for('index')\\n\\n\\ndef get_current_time():\\n return datetime.utcnow()\\n\\n\\ndef allowed_extensions(filename):\\n \\\"\\\"\\\" check if the file extension is allowed \\\"\\\"\\\"\\n return '.' in filename and filename.rsplit('.', 1)[1] in DefaultConfig.ALLOWED_AVATAR_EXTENSIONS\\n\\n\\ndef checkSteps(step):\\n if step >= 1:\\n if 'import' not in session or 'user' not in session['import'] or 'platform' not in session['import']:\\n flash('Please choose a platform first.', 'success')\\n return redirect(url_for('importer.platform'))\\n elif step == 1:\\n return True\\n if step >= 2:\\n if 'vehicle' not in session['import']:\\n flash('Please choose a vehicle or select an anonymous one.', 'success')\\n return redirect(url_for('importer.vehicle'))\\n elif step == 2:\\n return True\\n if step >= 3:\\n if 'driver' not in session['import']:\\n flash('Please choose a driver or select an anonymous one.', 'success')\\n return redirect(url_for('importer.driver'))\\n elif step == 3:\\n return True\\n if step > 4:\\n if 'files' not in session['import']:\\n flash('Please upload some files before getting here', 'success')\\n return redirect(url_for('importer.records'))\\n elif step == 4:\\n return True\\n\\n\\ndef upload_trace(input='file', user_id=0):\\n \\\"\\\"\\\"Upload a trace file\\\"\\\"\\\"\\n result = {'status': False, 'message': 'No input file'}\\n if input not in request.files:\\n return result\\n file = request.files[input]\\n if file.filename == '':\\n return result\\n if '.' not in file.filename:\\n result['message'] = 'File without any extension'\\n return result\\n if file.filename.rsplit('.', 1)[1] not in ALLOWED_TRACE_EXTENSIONS:\\n result['message'] = 'Extension not allowed'\\n return result\\n directory = \\\"/\\\".join([UPLOAD_TRACE_DIR, str(user_id)])\\n directory = \\\"/\\\".join([directory, strftime(\\\"%Y/%m\\\")])\\n make_dir(directory)\\n file_path = \\\"/\\\".join([directory, secure_filename(str(uuid4()) +\\n os.path.splitext(file.filename)[1].lower())])\\n file.save(file_path)\\n return {'status': True, 'message': file_path}\\n\\n# ------------------------------- Form Helpers ------------------------------ #\\n\"\n },\n {\n \"alpha_fraction\": 0.5741350650787354,\n \"alphanum_fraction\": 0.5831960439682007,\n \"avg_line_length\": 29.128204345703125,\n \"blob_id\": \"ddee825a4d4b3c2de402beef3a15955aa2f89c76\",\n \"content_id\": \"819e9cf65ab780aa669e7d935fb01489deda2ce7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1214,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 39,\n \"path\": \"/app/carboard/models/file.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from uuid import uuid4\\r\\nfrom datetime import datetime\\r\\nfrom ...extensions import db\\r\\n\\r\\n\\r\\n# ------------------------------ File Model -------------------------------- #\\r\\n\\r\\n\\r\\nclass File(db.Model):\\r\\n\\r\\n __tablename__ = 'files'\\r\\n\\r\\n id = db.Column(db.Integer, primary_key=True)\\r\\n\\r\\n user_id = db.Column(db.Integer, db.ForeignKey('users.id'), nullable=False)\\r\\n user = db.relationship('User', backref=db.backref('files', lazy='dynamic'))\\r\\n\\r\\n filename = db.Column(db.String(255))\\r\\n path = db.Column(db.String(255), nullable=False, unique=True)\\r\\n processed = db.Column(db.SmallInteger, default=0)\\r\\n created = db.Column(db.DateTime(), default=datetime.utcnow())\\r\\n\\r\\n def __init__(self, user_id, filename=None, path=None, processed=0):\\r\\n self.filename = filename\\r\\n self.path = path\\r\\n self.user_id = user_id\\r\\n self.processed = processed\\r\\n\\r\\n def is_processed(self):\\r\\n return self.processed == 1\\r\\n\\r\\n def __repr__(self):\\r\\n return '<File {}>'.format(self.filename)\\r\\n\\r\\n @classmethod\\r\\n def get_by_user_id(cls, user_id, processed=0):\\r\\n return cls.query.filter(\\r\\n File.user_id == user_id and File.processed == processed\\r\\n ).all()\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6300984621047974,\n \"alphanum_fraction\": 0.6347866654396057,\n \"avg_line_length\": 33.40322494506836,\n \"blob_id\": \"799855806acf1aeda8f77202edec9ae77e49bd49\",\n \"content_id\": \"4ae27ae6d1f94c1d8a4e202c1ea6c603e057162a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2133,\n \"license_type\": \"no_license\",\n \"max_line_length\": 93,\n \"num_lines\": 62,\n \"path\": \"/config/config.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\n\\n\\n# --------------------------- Default Configuration ------------------------- #\\nclass DefaultConfig(object):\\n # Project name\\n PROJECT = \\\"vCar\\\"\\n # Turns on debugging features in Flask\\n DEBUG = True\\n # secret key\\n SECRET_KEY = \\\"Zfe&45f4y5fhgse6f4e&@+-%_r4oij5hgfa&#@\\\"\\n # Redis database url\\n REDIS_URL = \\\"redis://:password@localhost:6379/0\\\"\\n # Flask Session type\\n SESSION_TYPE = 'filesystem'\\n # Configuration for the Flask-Bcrypt extension\\n BCRYPT_LEVEL = 12\\n # Application root directory\\n APP_ROOT = os.path.dirname(os.path.abspath('.'))\\n # Application email\\n MAIL_FROM_EMAIL = \\\"misaki.yooko@gmail.com\\\"\\n # Upload directory\\n UPLOAD_DIR = \\\"static/uploads/\\\"\\n # Avater upload directory\\n UPLOAD_AVATAR_DIR = os.path.join(UPLOAD_DIR, 'avatars/')\\n ALLOWED_AVATAR_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif'])\\n # Instance folder path\\n INSTANCE_FOLDER_PATH = os.path.join(os.path.abspath('.'), 'instance')\\n # INSTANCE_FOLDER_PATH = os.path.join('/home/karim/OpenXC/Dashboard/Flask/instance')\\n # INSTANCE_FOLDER_PATH = os.path.join('I:/home/karim/OpenXC/Dashboard/Flask', 'instance')\\n UPLOAD_USER = \\\"/uploads/avatars/\\\"\\n UPLOAD_BRAND = \\\"/uploads/brands/\\\"\\n UPLOAD_DRIVER = \\\"/uploads/drivers/\\\"\\n UPLOAD_VEHICLE = \\\"/uploads/vehicles/\\\"\\n UPLOAD_RECORDS = \\\"/uploads/records/\\\"\\n UPLOAD_PLATFORM = \\\"/uploads/platforms/\\\"\\n # Cache configuration\\n CACHE_TYPE = 'null'\\n CACHE_DEFAULT_TIMEOUT = 1\\n TEMPLATES_AUTO_RELOAD = True\\n # ToolbarExtention Configuration\\n DEBUG_TB_ENABLED = True\\n DEBUG_TB_INTERCEPT_REDIRECTS = False\\n DEBUG_TB_TEMPLATE_EDITOR_ENABLED = True\\n DEBUG_TB_PROFILER_ENABLED = True\\n\\n\\n# ---------------------------- Debug Configuration -------------------------- #\\n\\n\\nclass DebugConfig(DefaultConfig):\\n # Turns on debugging features in Flask\\n DEBUG = True\\n # secret key\\n SECRET_KEY = \\\"devlopement key\\\"\\n # Avater upload directory\\n UPLOAD_AVATAR_DIR = \\\"static/uploads/avatars/\\\"\\n # Instance folder path\\n INSTANCE_FOLDER_PATH = os.path.join('/tmp', 'instance')\\n # Cache configuration\\n CACHE_TYPE = 'null'\\n CACHE_DEFAULT_TIMEOUT = 60\\n\"\n },\n {\n \"alpha_fraction\": 0.5079169869422913,\n \"alphanum_fraction\": 0.5100281238555908,\n \"avg_line_length\": 29.395721435546875,\n \"blob_id\": \"d10b8c890b520e103cf2bc657ab2655e290b0c23\",\n \"content_id\": \"c2357f66b54ca503698930d3f55139bc1c30cb1a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5684,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 187,\n \"path\": \"/app/dashboard/views.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nfrom os.path import basename\\nimport json\\nfrom werkzeug import secure_filename\\nfrom threading import Thread, active_count\\nfrom flask import (\\n Blueprint, render_template, request, flash, redirect, url_for, Response\\n)\\nfrom flask_login import (\\n login_user, logout_user, login_required, current_user\\n)\\n\\nfrom .models import Trace\\nfrom .drivergraph import DriverGraph\\nfrom .helpers import upload_trace, debug\\nfrom .tasks import task_info, openxc_task\\nfrom ..extensions import db\\n\\nfrom elasticsearch import Elasticsearch, helpers\\n\\n\\ndashboard = Blueprint('dashboard', __name__, url_prefix='/dashboard')\\n\\n\\n# -------------------- /dashboard : Dashboard home page -------------------- #\\n\\n# @dashboard.route('/stream')\\n# def stream():\\n# return Response(task_info(), mimetype=\\\"text/event-stream\\\")\\n\\n\\n@dashboard.route('/')\\n@login_required\\ndef index():\\n return render_template('dashboard/index.html', user=current_user)\\n\\n# ------------------------- /import : Import Addons ------------------------- #\\n\\n\\n@dashboard.route('/import')\\n@login_required\\ndef import_index():\\n return render_template('dashboard/import/index.html', user=current_user)\\n\\n# __________________________________________________________________________ #\\n# | | #\\n# | OPENXC | #\\n# | | #\\n# __________________________________________________________________________ #\\n\\n# ---------------- /dashboard/import/openxc : User home page --------------- #\\n\\n\\n@dashboard.route('/import/openxc', methods=['GET', 'POST'])\\n@login_required\\ndef import_openxc():\\n if request.method == 'POST':\\n openxc = upload_trace('openxc', current_user.id)\\n if openxc['status'] is True:\\n trace = Trace(\\n user_id=current_user.id,\\n filename=basename(openxc['message']),\\n path=openxc['message']\\n )\\n db.session.add(trace)\\n db.session.commit()\\n return json.dumps(openxc)\\n return render_template('dashboard/import/openxc.html', user=current_user)\\n\\n# --------------- /import/openxc/process : Process openxc file ------------- #\\n\\n\\n@dashboard.route('/import/openxc/process', methods=['GET', 'POST'])\\n@login_required\\ndef process_openxc():\\n if request.method == 'POST':\\n traces = request.form.getlist('traces')\\n thread = Thread(target=openxc_task, args=[traces, current_user.id])\\n thread.daemon = True\\n thread.start()\\n # thread.join(1)\\n # print(active_count())\\n else:\\n return redirect(url_for('dashboard.import_openxc'))\\n return render_template(\\n 'dashboard/import/openxc_porcess.html',\\n user=current_user\\n )\\n\\n# ------------------------- /openxc : Openxc Dashboard ---------------------- #\\n\\n\\n@dashboard.route('/openxc/', methods=['GET'])\\n@login_required\\ndef openxc():\\n info = {\\n index: 'openxc',\\n type: 'driver_{}'.format(current_user.id)\\n }\\n return render_template(\\n 'dashboard/openxc.html',\\n user=current_user,\\n elastic=info\\n )\\n\\n# -------------------------- /openxc/map : Openxc Map ----------------------- #\\n\\n\\n@dashboard.route('/openxc/map', methods=['GET'])\\n@login_required\\ndef openxc_map():\\n info = {\\n index: 'openxc',\\n type: 'driver_{}'.format(current_user.id)\\n }\\n return render_template(\\n 'dashboard/openxc_map.html',\\n user=current_user,\\n elastic=info\\n )\\n\\n# ------------------------ /openxc/graph : Openxc Graph --------------------- #\\n\\n\\n@dashboard.route('/openxc/graph', methods=['GET'])\\n@login_required\\ndef openxc_graph():\\n index = \\\"openxc\\\"\\n type = \\\"driver_{}\\\".format(current_user.id)\\n size = 10000\\n es = Elasticsearch([{\\n 'host': 'localhost',\\n 'port': 9200\\n }])\\n es.cluster.health(wait_for_status='yellow', request_timeout=1)\\n if es.indices.exists(index=index):\\n body = {\\n \\\"sort\\\": [\\n {\\\"timestamp\\\": {\\\"order\\\": \\\"asc\\\"}}\\n ],\\n \\\"query\\\": {\\n \\\"bool\\\": {\\n \\\"should\\\": [\\n {\\\"term\\\": {\\\"name\\\": 'vehicle_speed'}},\\n {\\\"term\\\": {\\\"name\\\": 'engine_speed'}},\\n {\\\"term\\\": {\\\"name\\\": 'fuel_level'}},\\n {\\\"term\\\": {\\\"name\\\": 'torque_at_transmission'}},\\n {\\\"term\\\": {\\\"name\\\": 'transmission_gear_position'}}\\n ]\\n }\\n }\\n }\\n res = es.search(index=index, doc_type=type, size=size, body=body)\\n # print(\\\"Got {} Hits:\\\".format(res['hits']['total']))\\n # for hit in res['hits']['hits']:\\n # print(\\\"%(timestamp)s %(name)s: %(value)s\\\" % hit[\\\"_source\\\"])\\n if res['hits']['total'] > 0:\\n driver = DriverGraph()\\n driver.build_digraph(res)\\n graph = driver.vis_network(True)\\n\\n info = {\\n index: 'openxc',\\n type: 'driver_{}'.format(current_user.id)\\n }\\n return render_template(\\n 'dashboard/openxc_graph.html',\\n user=current_user,\\n elastic=info,\\n graph=graph\\n )\\n\\n# ---------------------------- /kill : kill Server ------------------------- #\\n\\n\\n# @dashboard.route('/kill')\\n# def killo():\\n# func = request.environ.get('werkzeug.server.shutdown')\\n# if func is None:\\n# raise RuntimeError('Not running with the Werkzeug Server')\\n# func()\\n# return \\\"Flask Development Server killed successfully!\\\"\\n\\n\\n@dashboard.route('/restart')\\ndef restart_server():\\n os.kill(os.getpid(), signal.SIGHUP)\\n\"\n },\n {\n \"alpha_fraction\": 0.47234365344047546,\n \"alphanum_fraction\": 0.479186475276947,\n \"avg_line_length\": 22.591928482055664,\n \"blob_id\": \"d464a6001503075c14ed71d30bd654b1dec4c621\",\n \"content_id\": \"a06572ad10ff156f4150fbfa0527122f7a983beb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5261,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 223,\n \"path\": \"/app/carboard/forms/user.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom wtforms import StringField, PasswordField, BooleanField, HiddenField, SelectField\\nfrom wtforms.validators import (\\n DataRequired, Regexp, ValidationError, Email, Length, EqualTo\\n)\\nfrom flask_wtf.file import FileAllowed, FileField\\nfrom ..models.user import User\\nfrom ..constants import ROLES\\n\\n# ------------------------ custom validation methods ------------------------ #\\n\\n\\ndef name_exist(form, field):\\n if User.query.filter_by(username=field.data).exists():\\n raise ValidationError('User already exists !')\\n\\n\\ndef email_exist(form, field):\\n if User.query.filter_by(email=field.data).exists():\\n raise ValidationError('Email already exists !')\\n\\n\\n# ---------------------------- User form classes ---------------------------- #\\n\\nclass UserForm(FlaskForm):\\n \\\"\\\"\\\" User add/edit FlaskForm \\\"\\\"\\\"\\n fullname = StringField(\\n 'Full name',\\n validators=[\\n Length(min=3, max=50),\\n Regexp(\\n r'^[a-zA-Z0-9 ,._-]+$',\\n message=(\\\"Full name is not correct !\\\")\\n )\\n ]\\n )\\n username = StringField(\\n 'Username',\\n validators=[\\n DataRequired(),\\n Length(min=3, max=50),\\n Regexp(\\n r'^[a-zA-A0-9_]+$',\\n message=(\\\"Username is not correct !\\\")\\n ),\\n # name_exist\\n ]\\n )\\n email = StringField(\\n 'Email',\\n validators=[\\n DataRequired(),\\n Email(),\\n # email_exist\\n ]\\n )\\n password = PasswordField(\\n 'Password',\\n validators=[\\n DataRequired(),\\n Length(min=4),\\n EqualTo('password2', message='Password must much !')\\n ]\\n )\\n password2 = PasswordField(\\n 'Confirm Password',\\n validators=[\\n DataRequired()\\n ]\\n )\\n avatar = FileField(\\n 'Avatar', validators=[\\n FileAllowed(['jpg', 'png', 'gif'], 'Images only plz :)')\\n ]\\n )\\n role = SelectField(\\n 'Role',\\n coerce=int,\\n choices=ROLES,\\n validators=[\\n DataRequired(),\\n ]\\n )\\n\\n\\nclass RegisterForm(FlaskForm):\\n \\\"\\\"\\\" RegisterForm FlaskForm \\\"\\\"\\\"\\n username = StringField(\\n 'Username',\\n validators=[\\n DataRequired(),\\n Length(min=3, max=50),\\n Regexp(\\n r'^[a-zA-Z0-9_]+$',\\n message=(\\\"Username is not correct !\\\")\\n ),\\n # name_exist\\n ]\\n )\\n email = StringField(\\n 'Email',\\n validators=[\\n DataRequired(),\\n Email(),\\n # email_exist\\n ]\\n )\\n password = PasswordField(\\n 'Password',\\n validators=[\\n DataRequired(),\\n Length(min=4),\\n EqualTo('password2', message='Password must much !')\\n ]\\n )\\n password2 = PasswordField(\\n 'Confirm Password',\\n validators=[\\n DataRequired()\\n ]\\n )\\n avatar = FileField(\\n 'Avatar', validators=[\\n FileAllowed(['jpg', 'png', 'gif'], 'Images only plz :) ')\\n ]\\n )\\n\\n\\nclass LoginForm(FlaskForm):\\n \\\"\\\"\\\"Login FlaskForm\\\"\\\"\\\"\\n # next = HiddenField()\\n login = StringField(\\n 'Username or email',\\n validators=[\\n DataRequired()\\n ]\\n )\\n password = PasswordField(\\n 'Password',\\n validators=[\\n DataRequired()\\n ]\\n )\\n remember = BooleanField('Remember me?')\\n # submit = SubmitField('Sign in')\\n\\n\\nclass RecoverPasswordForm(FlaskForm):\\n email = StringField(\\n 'Email',\\n validators=[\\n DataRequired(),\\n Email()\\n ]\\n )\\n\\n\\nclass ChangePasswordForm(FlaskForm):\\n activation_key = HiddenField()\\n password = PasswordField(\\n 'Password',\\n validators=[\\n DataRequired(),\\n Length(min=4),\\n EqualTo('password2', message='Password must much !')\\n ]\\n )\\n password2 = PasswordField(\\n 'Confirm Password',\\n validators=[\\n DataRequired()\\n ]\\n )\\n\\n\\nclass ReauthForm(FlaskForm):\\n password = PasswordField(\\n 'Password',\\n validators=[\\n DataRequired(),\\n Length(min=4),\\n EqualTo('password2', message='Password must much !')\\n ]\\n )\\n\\n\\nclass ProfileForm(FlaskForm):\\n fullname = StringField(\\n 'Full name',\\n validators=[\\n Length(min=3, max=50),\\n Regexp(\\n r'^[a-zA-Z0-9,._-]+$',\\n message=(\\\"Full name is not correct !\\\")\\n ),\\n name_exist\\n ]\\n )\\n username = StringField(\\n 'Username',\\n validators=[\\n DataRequired(),\\n Length(min=3, max=50),\\n Regexp(\\n r'^[a-zA-A0-9_]+$',\\n message=(\\\"Username is not correct !\\\")\\n ),\\n name_exist\\n ]\\n )\\n email = StringField(\\n 'Email',\\n validators=[\\n DataRequired(),\\n Email(),\\n email_exist\\n ]\\n )\\n avatar = FileField(\\n 'Avatar', validators=[\\n FileAllowed(['jpg', 'png', 'gif'], 'Images only plz :)')\\n ]\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.4688202142715454,\n \"alphanum_fraction\": 0.4733578860759735,\n \"avg_line_length\": 38.255733489990234,\n \"blob_id\": \"eecce5a182ec848b3c6c0b13548cea0b17ae94ec\",\n \"content_id\": \"a1609c5517c4bbc3f8e4bf61ec1e2dc96fc3f75d\",\n \"detected_licenses\": [\n \"MIT\",\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 22258,\n \"license_type\": \"permissive\",\n \"max_line_length\": 81,\n \"num_lines\": 567,\n \"path\": \"/app/plugins/driver_graph/src/algorithms/drivergraph.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nfrom __future__ import print_function\\nfrom __future__ import unicode_literals\\nimport os\\nimport copy\\nimport json\\nimport yaml\\nfrom random import choice\\nimport networkx as nx\\nfrom networkx.drawing.nx_pydot import write_dot, read_dot\\nfrom networkx.readwrite import json_graph\\nimport networkx.algorithms.isomorphism as iso\\n\\nfrom .settings import DefaultSettings\\nfrom .settings import DATA, WEIGHT, COLORS\\n\\n# ___________________________ Start : DriverGraph ___________________________ #\\n\\n\\nclass DriverGraph:\\n \\\"\\\"\\\" this class create a driver graph using registred\\n driver traces, after an abstraction algorithm wich\\n is configured using the settings configuration\\n \\\"\\\"\\\"\\n\\n def __init__(self, **kwargs):\\n \\\"\\\"\\\"inisialize the DriverGraph with optional arguments\\\"\\\"\\\"\\n self.name = kwargs.get('name', 'Driver Bihavior Graph')\\n self.settings = kwargs.get('settings', DefaultSettings)\\n self.max_nodes = kwargs.get('max_nodes', None)\\n self.node_id = kwargs.get('init_node', 0)\\n self.G = kwargs.get('graph', None) # Networkx graph\\n self.last_id = 0 # internal param\\n # ====================================================================== #\\n\\n @property\\n def settings(self):\\n return self._settings\\n\\n @settings.setter\\n def settings(self, settings):\\n self._settings = settings\\n # ====================================================================== #\\n\\n def __str__(self):\\n return \\\"<DriverGraph name: {}, time: {}, settings: {}>\\\".format(\\n self.name, self.time, self.settings\\n )\\n # ====================================================================== #\\n\\n def show_attributes(self):\\n \\\"\\\"\\\"Show DriverGraph attributes\\\"\\\"\\\"\\n print(\\\"\\\\nATTRIBUTES:\\\")\\n for node_id, attributes in self.G.nodes(data=True):\\n print(\\\"Node {} :: [\\\".format(node_id), end='')\\n for name, value in attributes.items():\\n print(\\\"{} |\\\".format(value), end=' ')\\n print(\\\"]\\\")\\n\\n def show_edges(self):\\n \\\"\\\"\\\"Show DriverGraph edges\\\"\\\"\\\"\\n print(\\\"\\\\nEDGES :\\\")\\n for id1, id2 in self.G.edges():\\n # if id1 == 4 and id2 == 6:\\n # print(self.G.node[id1])\\n # print(self.G.node[id2])\\n print(\\\"({}, {}, {})\\\".format(\\n id1, id2, self.G[id1][id2]['weight']\\n ), end=', ')\\n print()\\n\\n def debug(self, id=None):\\n if id is None:\\n for node_id, attributes in self.G.nodes(data=True):\\n print('-------------- Node {} --------------'.format(node_id))\\n for att, value in attributes.items():\\n print(\\\">>[[{}]] {} : {}\\\".format(id, att, value))\\n else:\\n print('.', end=\\\"\\\")\\n\\n def graph_summary(self):\\n print(\\\"\\\\nNodes: {}\\\".format(len(self.G.nodes(data=True))))\\n print(\\\"\\\\nEdges: {}\\\".format(len(self.G.edges())))\\n # ====================================================================== #\\n\\n def create_digraph(self, filename=None):\\n \\\"\\\"\\\"Create a Directed Graph from the openxc traces\\\"\\\"\\\"\\n if filename is None:\\n raise ValueError('Plz provide an openxc trace file.')\\n\\n data = DATA\\n self._init_graph()\\n with open(filename, 'r') as f:\\n for line in f:\\n temp = json.loads(line)\\n signal = temp.get('name')\\n value = self._invoke_ranges(signal, temp.get('value'))\\n if value is None:\\n continue\\n data = copy.deepcopy(data)\\n if signal in data:\\n data[signal] = value\\n # data[signal] = str(value)\\n if self._create_node(data) is False:\\n break\\n print('Done')\\n # ====================================================================== #\\n\\n def _create_node(self, data):\\n \\\"\\\"\\\"Create node if doesn't exist, and create or update edges\\\"\\\"\\\"\\n if self.max_nodes is not None and self.node_id >= self.max_nodes:\\n return False\\n # print(\\\"---------------------------------------------------->\\\")\\n id = self._find_node(data)\\n # print(\\\"---------------------------------------------------->\\\")\\n # self.debug(self.node_id)\\n if id is None:\\n self.node_id += 1\\n self.G.add_node(self.node_id, data)\\n self.G.add_edge(self.last_id, self.node_id, weight=WEIGHT)\\n # print(self.G.nodes())\\n # print(self.G.edges())\\n self.last_id = self.node_id\\n # print(self.node_id, self.last_id)\\n else:\\n if self.G.has_edge(self.last_id, id):\\n # print(\\\"Edge exist\\\")\\n self.G[self.last_id][id]['weight'] += WEIGHT\\n else:\\n pass\\n # print(\\\"New Edge\\\")\\n self.G.add_edge(self.last_id, id, weight=WEIGHT)\\n # print(self.G.edges())\\n self.last_id = id\\n # ====================================================================== #\\n\\n def _init_graph(self):\\n \\\"\\\"\\\" inisialize the graph with the first ZERO node\\\"\\\"\\\"\\n # self.G = nx.DiGraph(name=self.name)\\n self.G = nx.DiGraph()\\n self.G.add_node(self.node_id, DATA)\\n # ====================================================================== #\\n\\n def _find_node(self, data):\\n \\\"\\\"\\\"\\n Loop throw the whole graph!, to check if a node hase the same\\n attributes as the data argument, if so return the node id,\\n otherwise return None.\\n \\\"\\\"\\\"\\n for node_id, attributes in self.G.nodes(data=True):\\n # print(\\\">> {} | {}\\\".format(node_id, attributes))\\n if data == attributes:\\n # if node_id >= 5:\\n # print(\\\"data == attributes\\\")\\n # print(node_id, ' -- ' ,end=\\\"\\\")\\n # for att, value in attributes.items(): print(value ,end=\\\"\\\")\\n # print(' -- ' ,end=\\\"\\\")\\n # for att, value in data.items(): print(value ,end=\\\"\\\")\\n # print()\\n return node_id\\n else:\\n # if node_id >= 5:\\n # print(\\\"data <> attributes\\\")\\n # print(node_id, ' -- ', end=\\\"\\\")\\n # for att, value in attributes.items(): print(value , end=\\\"\\\")\\n # print(' -- ', end=\\\"\\\")\\n # for att, value in data.items(): print(value , end=\\\"\\\")\\n # print()\\n pass\\n return None\\n # ====================================================================== #\\n\\n def _invoke_ranges(self, signal, value):\\n \\\"\\\"\\\"get the range value from the corret signal\\\"\\\"\\\"\\n value = str(value).lower()\\n if signal == \\\"fuel_level\\\":\\n return self._get_range_key(value, self.settings.FUEL_EFFICIENCY)\\n elif signal == \\\"transmission_gear_position\\\":\\n return self._get_range_key(value, self.settings.GEAR_POSITION)\\n elif signal == \\\"vehicle_speed\\\":\\n return self._get_range_key(value, self.settings.VEHICLE_SPEED)\\n elif signal == \\\"engine_speed\\\":\\n return self._get_range_key(value, self.settings.ENGINE_SPEED)\\n elif signal == \\\"torque_at_transmission\\\":\\n return self._get_range_key(value, self.settings.TORQUE)\\n # elif signal == \\\"accelerator_pedal_position\\\":\\n # return self._get_range_key(value, self.settings.ACCEL_PEDAL)\\n # elif signal == \\\"steering_wheel_angle\\\":\\n # return self._get_range_key(value, self.settings.STEERING)\\n # elif signal == \\\"brake_pedal_status\\\":\\n # return self._get_range_key(value, self.settings.PARKING_BRAKE)\\n else:\\n # print(\\\"\\\"\\\"\\n # Error: The signal <<{}>> is not included in the algorithm!\\\\n\\n # Please take a look at the _INVOKE_RANGES function to add it\\\\n\\n # [line 112].\\n # \\\"\\\"\\\".format(signal))\\n return None\\n # ====================================================================== #\\n\\n def _get_range_key(self, value, d):\\n \\\"\\\"\\\"retrun the key from the settings list\\\"\\\"\\\"\\n try:\\n value = round(float(value), 2)\\n debug_key = \\\"\\\"\\n for key in d:\\n if d[key][0] <= value <= d[key][1]:\\n # print(\\\"V({}) =>\\\".format(key), end=' ')\\n return key\\n debug_key = key\\n print(\\\"Warning: {} Not Found for {}!!\\\".format(value, d[debug_key]))\\n return -99\\n except:\\n for key in d:\\n if d[key] == value:\\n # print(\\\"G({}) ====>\\\".format(key), end=' ')\\n return key\\n print(\\\"Warning: value {} Not Found in any range !\\\".format(value))\\n return -99\\n # ====================================================================== #\\n\\n def _ranges2real(self, signal, value):\\n \\\"\\\"\\\"get the real value from signal range \\\"\\\"\\\"\\n value = int(value)\\n if value == 0:\\n return 0\\n if signal == \\\"fuel_level\\\":\\n return json.dumps(self.settings.FUEL_EFFICIENCY[value])\\n elif signal == \\\"transmission_gear_position\\\":\\n return json.dumps(self.settings.GEAR_POSITION[value])\\n elif signal == \\\"vehicle_speed\\\":\\n return json.dumps(self.settings.VEHICLE_SPEED[value])\\n elif signal == \\\"engine_speed\\\":\\n return json.dumps(self.settings.ENGINE_SPEED[value])\\n elif signal == \\\"torque_at_transmission\\\":\\n return json.dumps(self.settings.TORQUE[value])\\n elif signal == \\\"accelerator_pedal_position\\\":\\n return json.dumps(self.settings.ACCEL_PEDAL[value])\\n elif signal == \\\"steering_wheel_angle\\\":\\n return json.dumps(self.settings.STEERING[value])\\n elif signal == \\\"brake_pedal_status\\\":\\n return json.dumps(self.settings.PARKING_BRAKE[value])\\n else:\\n return None\\n # ====================================================================== #\\n\\n def factorize_graph(self, factor=1):\\n \\\"\\\"\\\"Show DriverGraph attributes\\\"\\\"\\\"\\n try:\\n for node_id, attributes in self.G.nodes(data=True):\\n for name, value in attributes.items():\\n self.G.node[node_id][name] = float(value) * factor\\n for id1, id2 in self.G.edges():\\n self.G[id1][id2]['weight'] *= factor\\n except ValueError as e:\\n print(\\\"Errror: \\\", e)\\n # ====================================================================== #\\n\\n def export_graphml(self, filename=None):\\n \\\"\\\"\\\"Export the DriverGraph to graphML format\\\"\\\"\\\"\\n if filename is not None:\\n nx.write_graphml(self.G, \\\"{}.graphml\\\".format(filename))\\n\\n def import_graphml(self, filename=None):\\n \\\"\\\"\\\"Import the DriverGraph from a graphML file\\\"\\\"\\\"\\n if filename is not None:\\n self.G = nx.read_graphml(filename)\\n # ====================================================================== #\\n\\n def export_json(self, filename=None):\\n \\\"\\\"\\\"Export the DriverGraph to Json format\\\"\\\"\\\"\\n if filename is not None:\\n g_json = json_graph.node_link_data(self.G)\\n json.dump(g_json, open(\\\"{}.json\\\".format(filename), 'w'), indent=4)\\n\\n def import_json(self, filename=None):\\n \\\"\\\"\\\"Import the DriverGraph from a Json file\\\"\\\"\\\"\\n if filename is not None:\\n with open(filename) as f:\\n js_graph = json.load(f)\\n self.G = json_graph.node_link_graph(js_graph)\\n # ====================================================================== #\\n\\n def export_gexf(self, filename=None):\\n \\\"\\\"\\\"Export the DriverGraph to GEXF format\\\"\\\"\\\"\\n if filename is not None:\\n nx.write_gexf(self.G, \\\"{}.gexf\\\".format(filename))\\n print(\\\"File exported\\\")\\n\\n def import_gexf(self, filename=None):\\n \\\"\\\"\\\"Import the DriverGraph from a GEXF file\\\"\\\"\\\"\\n if filename is not None:\\n self.G = nx.read_gexf(filename)\\n # ====================================================================== #\\n\\n def export_gxl(self, filename=None):\\n \\\"\\\"\\\"Export the DriverGraph to GXL format\\\"\\\"\\\"\\n if filename is not None:\\n write_dot(self.G, \\\"helpers/_tmp/xfile.dot\\\")\\n os.system(\\\"dot2gxl helpers/_tmp/xfile.dot -o {}\\\".format(filename))\\n os.system(\\\"sed -i -e '/name=\\\\\\\"name\\\\\\\"/ ,+2d' {}\\\".format(filename))\\n\\n def import_gxl(self, filename=None, digraph=True):\\n \\\"\\\"\\\"Import the DriverGraph from a GXL file\\\"\\\"\\\"\\n if filename is not None:\\n os.system(\\\"gxl2dot {} -o helpers/_tmp/ifile.dot\\\".format(filename))\\n self.G = read_dot(\\\"helpers/_tmp/ifile.dot\\\")\\n # ====================================================================== #\\n\\n def browse_sigma(self, filename=None):\\n \\\"\\\"\\\"View the Graph in borwser using sigmajs and GEXF format\\\"\\\"\\\"\\n self.export_gexf(\\\"helpers/sigma/driver_graph\\\")\\n os.system(\\\"cd helpers/sigma && python2 -m SimpleHTTPServer 8002\\\")\\n # ====================================================================== #\\n\\n def _build_vis_network(self, nodes, edges, physics=False):\\n # \\\"barnesHut\\\": {{\\\"centralGravity\\\": 1}},\\n html = \\\"\\\"\\\"\\n <script type=\\\"text/javascript\\\">\\n var nodes = new vis.DataSet({nodes});\\n var edges = new vis.DataSet({edges});\\n var container = document.getElementById(\\\"driver\\\");\\n var data = {{nodes: nodes, edges: edges}};\\n var options = {{\\n nodes: {{ size: 25, font: {{size: 14}} }},\\n edges: {{\\n font: {{size: 10, align: 'middle'}},\\n color: 'gray',\\n arrows: {{ to: {{enabled: true, scaleFactor: 0.5}} }},\\n smooth: {{enabled: true}}\\n }},\\n layout:{{\\n hierarchical: false\\n }},\\n physics: {{\\n enabled: {physics},\\n \\\"timestep\\\": 1,\\n stabilization: true\\n }}\\n }};\\n var network = new vis.Network(container, data, options);\\n </script>\\n \\\"\\\"\\\"\\n html = html.format(\\n nodes=json.dumps(nodes),\\n edges=json.dumps(edges),\\n physics=json.dumps(physics)\\n )\\n # filename = \\\"helpers/vis/index.html\\\"\\n # file = open(filename, \\\"w\\\")\\n # file.write(html)\\n # file.close()\\n\\n return html\\n\\n def vis_network(self, physics=False):\\n nodes = []\\n for node_id, attributes in self.G.nodes(data=True):\\n tmp = {'id': node_id, 'label': node_id, 'color': choice(COLORS)}\\n tmp['title'] = \\\"<pre>{}</pre>\\\".format(yaml.dump(\\n {k: self._ranges2real(k, v) for k, v in attributes.items()},\\n default_flow_style=False\\n ))\\n nodes.append(tmp)\\n edges = []\\n for id1, id2 in self.G.edges():\\n tmp = {\\n 'from': id1,\\n 'to': id2,\\n 'color': choice(COLORS)\\n }\\n try:\\n tmp['label'] = self.G[id1][id2]['weight']\\n except:\\n tmp['label'] = self.G[id1][id2][0]['weight']\\n edges.append(tmp)\\n return self._build_vis_network(nodes, edges, physics)\\n # os.system(\\\"cd helpers/vis && python2 -m SimpleHTTPServer 8001\\\")\\n # ====================================================================== #\\n\\n @staticmethod\\n def build_multiple(files=None, max_nodes=None, export=False):\\n if type(files) is list:\\n graphs = []\\n for file in files:\\n graph = DriverGraph(max_nodes=max_nodes, name=\\\"file\\\")\\n graph.create_digraph(filename=file)\\n graphs.append(graph)\\n if export is True:\\n filename = os.path.splitext(os.path.basename(file))[0]\\n graph.export_gxl(\\\"{}.gxl\\\".format(filename))\\n print(\\\"[{}]: {} nodes, edges: {}\\\".format(\\n filename, len(graph.G), len(graph.G.edges())\\n ))\\n return graphs\\n else:\\n raise ValueError(\\\"Please provide a list of trace files\\\")\\n # ====================================================================== #\\n\\n @staticmethod\\n def divide_graph(G=None, nparts=2):\\n if nparts < 1:\\n raise ValueError(\\\"you cant divide a graph by {}.\\\".format(nparts))\\n elif nparts == 1:\\n return self.G\\n else:\\n parts = [[] for i in range(nparts)]\\n # print(zip(G, part))\\n # for u, i in zip(G, part):\\n # G.remove_node(1)\\n # graph_size = len(G)\\n # print(graph_size)\\n # fist_node = [n for n, d in G.in_degree().items() if d == 0]\\n # if len(fist_node) != 1:\\n # raise ValueError(\\\"The graph has\\n # {} root nodes.\\\".format(len(fist_node)))\\n\\n # for i in nparts:\\n # print(i * graph_size / nparts)\\n # print(graph_size / nparts)\\n # # for i in range(i * graph_size / nparts, (i + 1) *\\n # graph_size / nparts):\\n # # print(i * graph_size / nparts)\\n # # for node_id, attributes in self.G.nodes(data=True):\\n # # print(i)\\n # pass\\n # # parts[i].append(u)\\n # # return objval, parts\\n # ====================================================================== #\\n\\n @staticmethod\\n def is_isomorphic(G1=None, G2=None, weight=None):\\n \\\"\\\"\\\"\\n - Structures that are the same except for relabelling\\n (can be the same using relabelling technique)\\n are called ISOMORPHIC structures.\\n - We can match also nodes attributes & edges attributes\\n edge attributes are implemented\\n - The function return True if G1 & G2 are isomorphic\\n False if not\\n \\\"\\\"\\\"\\n if G1 is None or G2 is None:\\n raise ValueError(\\\"Please provide two graphs.\\\")\\n if weight is None:\\n return nx.is_isomorphic(G1, G2)\\n else:\\n em = iso.numerical_edge_match('weight', weight)\\n return nx.is_isomorphic(G1, G2, edge_match=em)\\n\\n# ____________________________ End : DriverGraph ____________________________ #\\n\\nif __name__ == \\\"__main__\\\":\\n try:\\n print('working ...')\\n Files = [\\n # \\\"traces/usa/downtown-west.json\\\",\\n # \\\"traces/usa/downtown-west2.json\\\",\\n # \\\"traces/usa/uptown-west.json\\\",\\n # \\\"traces/usa/uptown-west2.json\\\",\\n # \\\"traces/usa/uptown-crossdown.json\\\",\\n # \\\"traces/usa/uptown-crosstown.json\\\",\\n # \\\"traces/usa/downtown-crosstown.json\\\",\\n # \\\"traces/usa/downtown-east.json\\\",\\n # \\\"traces/scenarios/localwithgps.json\\\",\\n # \\\"traces/scenarios/highway-speeding.json\\\",\\n ]\\n # graphs = DriverGraph.build_multiple(Files, max_nodes=None, export=True)\\n # driver = DriverGraph(max_nodes=99)\\n driver = DriverGraph()\\n # driver.create_digraph(\\\"traces/usa/downtown-crosstown.json\\\")\\n # driver.export_gxl(\\\"downtown-crosstown.gxl\\\")\\n driver.create_digraph(\\\"traces/usa/downtown-east.json\\\")\\n driver.export_gxl(\\\"downtown-east.gxl\\\")\\n print(len(driver.G))\\n print(len(driver.G.edges()))\\n # driver.export_json('downtown-crosstown')\\n # driver.show_attributes()\\n # driver.show_edges()\\n # driver.vis_network(True)\\n # saya = DriverGraph()\\n # driver.import_json(\\\"saya.json\\\")\\n # saya.import_graphml(\\\"saya.graphml\\\")\\n # driver.draw_graph('image.png')\\n # driver.browse_sigma()\\n # driver.export_gxl(\\\"graph.gxl\\\")\\n # driver.factorize_graph(1.5)\\n # driver.export_gxl(\\\"factorized.gxl\\\")\\n # print(\\\"-----\\\")\\n # print(len(driver.G))\\n # print(len(driver.G.edges()))\\n # print(len(driver.G))\\n # print(len(driver.G.edges()))\\n # driver.vis_network(True)\\n # driver.export_gxl(\\\"graph.gxl\\\")\\n # if DriverGraph.is_isomorphic(driver.G, saya.G):\\n # print(\\\"G1 & G2 are ISOMORPHIC\\\")\\n # else:\\n # print(\\\"G1 & G2 are NOT isomorphic\\\")\\n # driver.export_graphml('downtown-crosstown')\\n # driver.export_json('downtown-crosstown')\\n #\\n # driver.graph_summary()\\n # print('---------------')\\n # driver.divide_graph(driver.G, 4)\\n # print('---------------')\\n # driver.graph_summary()\\n except ValueError as e:\\n print(e)\\n # except:\\n # print(\\\"Error !!\\\")\\n\\n #\\n #\\n #\\n #\\n #\\n #\\n #\\n #\\n #\\n #\\n #\\n #\\n #\\n #\\n #\\n #\\n #\\n #\\n\\n # print(\\\"{} : {}\\\".format(temp.get('name'), temp.get('value')))\\n # print('***********************')\\n\\n # print('----')\\n # print(\\\"nam# print(item)\\n # print('----')e: {} | value: {}\\\".format(\\n # x.get('name', 'NAME'), x.get('value', 'VALUE')))\\n # while True:\\n # try:\\n # parser = ijsonself. (line)\\n # for prefix, event, value in parser:\\n # if prefix == \\\"name\\\":\\n # print(value)\\n # if prefix == \\\"value\\\":\\n # print(value)\\n # if prefix == \\\"timestamp\\\":\\n # print(value)\\n # except ValueError:\\n # line += next(f)\\n\\n # print(\\\"prefix: {} | value: {} | event: {}\\\".format(prefix, value, event))\\n # ret = {'builders': {}}\\n # if (prefix, event) == ('builders', 'map_key'):\\n # buildername = value\\n # ret['builders'][buildername] = {}\\n # elif prefix.endswith('.shortname'):\\n # ret['builders'][buildername]['shortname'] = value\\n #\\n # return ret\\n\\n # driver.create_digraph(\\\"traces/downtown-crosstown.json\\\")\\n # except Exception as e:\\n # print('>> {}'.format(e))\\n\\n #\\n # data_write_file = sys.argv[2]\\n # data_read_file = open(sys.argv[1])\\n\"\n },\n {\n \"alpha_fraction\": 0.8333333134651184,\n \"alphanum_fraction\": 0.8333333134651184,\n \"avg_line_length\": 24,\n \"blob_id\": \"391281fbc8e102385b22556afc3b0b88524a53d4\",\n \"content_id\": \"3a5db0490a57a4bf26fbdac5fdab201eafa8677d\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 24,\n \"license_type\": \"permissive\",\n \"max_line_length\": 24,\n \"num_lines\": 1,\n \"path\": \"/app/plugins/hello_world/src/__init__.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from .views import hello\"\n },\n {\n \"alpha_fraction\": 0.2944444417953491,\n \"alphanum_fraction\": 0.42679738998413086,\n \"avg_line_length\": 20.542253494262695,\n \"blob_id\": \"ab4252cad013d8b796d88d528e7b73aff18059e6\",\n \"content_id\": \"8ef5c99912192a59b2826e439c5c75438c6d70b6\",\n \"detected_licenses\": [\n \"MIT\",\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3060,\n \"license_type\": \"permissive\",\n \"max_line_length\": 56,\n \"num_lines\": 142,\n \"path\": \"/app/plugins/driver_graph/src/algorithms/settings.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nDATA = {\\n 'fuel_level': 0,\\n 'transmission_gear_position': 0,\\n 'vehicle_speed': 0,\\n 'engine_speed': 0,\\n 'torque_at_transmission': 0,\\n}\\n# 'accelerator_pedal_position': 0,\\n# 'steering_wheel_angle': 0,\\n# 'brake_pedal_status': 0,\\n\\nWEIGHT = 1\\n\\n# For key in range d\\n# if d[key][0] <= value <= d[key][1]:\\n# if d[key][0] <= value < d[key][1]:\\n# End For\\n\\n\\nclass DefaultSettings(object):\\n # 1 - fuel_level [ 0 => 120] (%)\\n FUEL_EFFICIENCY = {\\n 1: [0, 20],\\n 2: [20, 40],\\n 3: [40, 60],\\n 4: [60, 80],\\n 5: [80, 100],\\n 6: [100, 120]\\n }\\n # 2 - transmission gear position [ -1 => 7 ]\\n GEAR_POSITION = {\\n -1: \\\"reverse\\\",\\n 0: \\\"neutral\\\",\\n 1: \\\"first\\\",\\n 2: \\\"second\\\",\\n 3: \\\"third\\\",\\n 4: \\\"fourth\\\",\\n 5: \\\"fifth\\\",\\n 6: \\\"sixth\\\",\\n 7: \\\"seventh\\\"\\n }\\n # 3 - vehicle speed [ 0 => 800] (km/h)\\n VEHICLE_SPEED = {\\n 1: [0, 10],\\n 2: [10, 20],\\n 3: [20, 30],\\n 4: [30, 40],\\n 5: [40, 50],\\n 6: [50, 60],\\n 7: [60, 655],\\n 8: [655, 800]\\n }\\n # 4 - engine speed [ 0 => 20000] (RPM)\\n ENGINE_SPEED = {\\n 1: [0, 500],\\n 2: [500, 1000],\\n 3: [1000, 1500],\\n 4: [1500, 2000],\\n 5: [2000, 2500],\\n 6: [2500, 3000],\\n 7: [3000, 4000],\\n 8: [4000, 16382],\\n 9: [16382, 20000]\\n }\\n # 5 - torque at transmission [ -500 => 2000] (Nm)\\n TORQUE = {\\n 1: [-500, -100],\\n 2: [-100, 0],\\n 3: [0, 50],\\n 4: [50, 100],\\n 5: [100, 150],\\n 6: [150, 200],\\n 7: [200, 500],\\n 8: [500, 1500],\\n 9: [1500, 2000]\\n }\\n # 6 - accelerator pedal position [ 0 => 100] (%)\\n ACCEL_PEDAL = {\\n 1: [0, 10],\\n 2: [10, 20],\\n 3: [20, 30],\\n 4: [30, 40],\\n 5: [40, 50],\\n 6: [50, 60],\\n 7: [60, 100]\\n }\\n # 7 - steering wheel angle [ -600 => +600] (Degrees)\\n STEERING = {\\n 1: [-600, -300],\\n 2: [-300, 100],\\n 3: [100, 300],\\n 4: [300, 600]\\n }\\n # 8 - parking brake status [ True or False]\\n PARKING_BRAKE = {\\n 1: \\\"true\\\",\\n 2: \\\"false\\\"\\n }\\n # 9 - parking pedal status [ True or False]\\n PARKING_PEDAL = {\\n 1: \\\"true\\\",\\n 2: \\\"false\\\"\\n }\\n\\nCOLORS = [\\n '#FFFF00',\\n '#FB7E81',\\n '#7BE141',\\n '#6E6EFD',\\n '#C2FABC',\\n '#FFA807',\\n '#FFEB3B',\\n '#9C27B0',\\n '#FF5722',\\n '#673AB7',\\n '#00BCD4',\\n '#8BC34A',\\n '#FF5722',\\n '#6E6EFD',\\n '#607D8B',\\n]\\n\\n# All openxc data\\n# DATA = {\\n# 'accelerator_pedal_position': 0,\\n# 'brake_pedal_status': 0,\\n# 'button_state': 0,\\n# 'door_status': 0,\\n# 'engine_speed': 0,\\n# 'fuel_consumed_since_restart': 0,\\n# 'fuel_level': 0,\\n# 'headlamp_status': 0,\\n# 'ignition_status': 0,\\n# 'latitude': 0,\\n# 'longitude': 0,\\n# 'odometer': 0,\\n# 'steering_wheel_angle': 0,\\n# 'torque_at_transmission': 0,\\n# 'transmission_gear_position': 0,\\n# 'vehicle_speed': 0,\\n# 'windshield_wiper_status': 0\\n# }\\n\"\n },\n {\n \"alpha_fraction\": 0.6064339280128479,\n \"alphanum_fraction\": 0.6071184277534485,\n \"avg_line_length\": 35.525001525878906,\n \"blob_id\": \"3aae5abf440eceea54ea4f2274ba5c703d199175\",\n \"content_id\": \"97ff209ca20c33743de6a45d6a8536b4613f61fc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1461,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 40,\n \"path\": \"/app/dashboard/models.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from uuid import uuid4\\n\\nfrom .constants import STRING_LEN, PROCESSED, NOT_PROCESSED\\nfrom .helpers import get_current_time\\nfrom ..extensions import db\\n\\n# -------------------------------- Trace Model ------------------------------ #\\n\\n\\nclass Trace(db.Model):\\n\\n __tablename__ = 'traces'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n filename = db.Column(db.String(STRING_LEN))\\n path = db.Column(db.String(STRING_LEN), nullable=False, unique=True)\\n user_id = db.Column(db.Integer, db.ForeignKey('users.id'), nullable=False)\\n processed = db.Column(db.SmallInteger, default=NOT_PROCESSED)\\n created = db.Column(db.DateTime(), default=get_current_time())\\n user = db.relationship('User', backref=db.backref('traces', lazy='dynamic'))\\n\\n def __init__(self, user_id, filename=None, path=None, processed=NOT_PROCESSED):\\n self.filename = filename\\n self.path = path\\n self.user_id = user_id\\n self.processed = processed\\n\\n def is_processed(self):\\n return self.processed == PROCESSED\\n\\n def __repr__(self):\\n return '<Trace {}>'.format(self.filename)\\n\\n @classmethod\\n def get_by_user_id(cls, user_id, processed=NOT_PROCESSED):\\n return cls.query.filter(\\n Trace.user_id == user_id and Trace.processed == processed\\n ).all()\\n# (db.session.query(Address).filter_by(person_id=current_user.id).count())\\n# -------------------------------- Trace Model ------------------------------ #\\n\"\n },\n {\n \"alpha_fraction\": 0.3473523259162903,\n \"alphanum_fraction\": 0.3517491817474365,\n \"avg_line_length\": 34.83802795410156,\n \"blob_id\": \"59ecc059a7d7569a69c34a6e911cc3c84047833f\",\n \"content_id\": \"d2f49271e79da20d9cfe7f8d397c7deb3c6b0059\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5231,\n \"license_type\": \"no_license\",\n \"max_line_length\": 138,\n \"num_lines\": 142,\n \"path\": \"/app/importer/datasets/t-drive/elastic.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import redis\\r\\nimport csv\\r\\nimport copy\\r\\nimport json\\r\\nfrom time import strftime, sleep\\r\\nfrom elasticsearch import Elasticsearch, helpers\\r\\nfrom .constants import *\\r\\n\\r\\n# ----------------- Setting up tools --------------------------------------- #\\r\\nred = redis.StrictRedis(\\r\\n host=REDIS_HOST,\\r\\n port=REDIS_PORT,\\r\\n db=REDIS_DB\\r\\n)\\r\\n\\r\\n\\r\\ndef task_info(name='info'):\\r\\n p = red.pubsub(ignore_subscribe_messages=True)\\r\\n p.subscribe(name)\\r\\n thread = p.run_in_thread(sleep_time=0.001)\\r\\n # thread.stop()\\r\\n\\r\\n# def task_info(name='info'):\\r\\n# pubsub = red.pubsub()\\r\\n# pubsub.subscribe(name)\\r\\n# for message in pubsub.listen():\\r\\n# # print(\\\"[+] Message: {}\\\".format(message))\\r\\n# yield 'data: {}\\\\n\\\\n'.format(message['data'])\\r\\n\\r\\n# ----------------- Indexing with Bulk : Large files ----------------------- #\\r\\n\\r\\n\\r\\ndef index_bulk(traces, dataset, user_id, bulk_size=BULK_SIZE):\\r\\n try:\\r\\n es = Elasticsearch([{\\r\\n 'host': ELASTICSEARCH_HOST,\\r\\n 'port': ELASTICSEARCH_PORT\\r\\n }])\\r\\n\\r\\n index = dataset.slug\\r\\n type = \\\"dataset\\\"\\r\\n es.cluster.health(wait_for_status='yellow', request_timeout=30)\\r\\n mapping = {\\r\\n type: {\\r\\n \\\"properties\\\": {\\r\\n \\\"user\\\": {\\r\\n \\\"type\\\": \\\"integer\\\"\\r\\n },\\r\\n \\\"vehicle\\\": {\\r\\n \\\"type\\\": \\\"integer\\\"\\r\\n },\\r\\n \\\"driver\\\": {\\r\\n \\\"type\\\": \\\"integer\\\"\\r\\n },\\r\\n \\\"class\\\": {\\r\\n \\\"type\\\": \\\"string\\\"\\r\\n },\\r\\n \\\"name\\\": {\\r\\n \\\"type\\\": \\\"string\\\",\\r\\n \\\"index\\\": \\\"not_analyzed\\\"\\r\\n },\\r\\n \\\"value\\\": {\\r\\n \\\"type\\\": \\\"string\\\",\\r\\n \\\"index\\\": \\\"not_analyzed\\\"\\r\\n },\\r\\n \\\"timestamp\\\": {\\r\\n \\\"type\\\": \\\"date\\\",\\r\\n \\\"format\\\": \\\"yyyy-MM-dd HH:mm:ss\\\"\\r\\n }\\r\\n }\\r\\n }\\r\\n }\\r\\n\\r\\n if not es.indices.exists(index=index):\\r\\n es.indices.create(index=index, ignore=400)\\r\\n es.indices.put_mapping(index=index, doc_type=type, body=mapping)\\r\\n\\r\\n for trace in traces:\\r\\n actions = []\\r\\n i = 1\\r\\n ignored = 0\\r\\n with open(trace, 'r') as f:\\r\\n reader = csv.reader(f)\\r\\n for line in reader:\\r\\n try:\\r\\n vehicle = int(line[0])\\r\\n driver = int(line[0])\\r\\n lat = float(line[2])\\r\\n lng = float(line[3])\\r\\n timestamp = line[1]\\r\\n # adding Latitude\\r\\n actions.append({\\r\\n \\\"_index\\\": dataset.slug,\\r\\n \\\"_type\\\": 'dataset',\\r\\n \\\"_source\\\": {\\r\\n \\\"user\\\": user_id,\\r\\n \\\"vehicle\\\": vehicle,\\r\\n \\\"driver\\\": driver,\\r\\n \\\"class\\\": 3,\\r\\n \\\"name\\\": 'latitude',\\r\\n \\\"value\\\": lat,\\r\\n \\\"timestamp\\\": timestamp\\r\\n }\\r\\n })\\r\\n # adding Longitude\\r\\n actions.append({\\r\\n \\\"_index\\\": dataset.slug,\\r\\n \\\"_type\\\": 'dataset',\\r\\n \\\"_source\\\": {\\r\\n \\\"user\\\": user_id,\\r\\n \\\"vehicle\\\": vehicle,\\r\\n \\\"driver\\\": driver,\\r\\n \\\"class\\\": 3,\\r\\n \\\"name\\\": 'longitude',\\r\\n \\\"value\\\": lng,\\r\\n \\\"timestamp\\\": timestamp\\r\\n }\\r\\n })\\r\\n if i*2 % bulk_size == 0:\\r\\n helpers.bulk(es, actions)\\r\\n actions = []\\r\\n print(\\\" ============================================> Bulk : i = \\\" + str(i))\\r\\n i += 1\\r\\n except:\\r\\n ignored += 1\\r\\n if i < bulk_size:\\r\\n print(\\\" ============================================> Bulk Rest : i = \\\" + str(i))\\r\\n helpers.bulk(es, actions)\\r\\n # bulk the actions dict to elastic\\r\\n helpers.bulk(es, actions)\\r\\n # End processing files\\r\\n if ignored > 0:\\r\\n red.publish('info', 'background indexing completed with {} ignored line(s)'.format(ignored))\\r\\n else:\\r\\n red.publish('info', 'Background indexing task [<b>{}</b> file<small>(s)</small>] completed successfully.'.format(len(traces)))\\r\\n return True\\r\\n sys.exit()\\r\\n except Exception as e:\\r\\n print('Exception OUT: ', e)\\r\\n red.publish('info', 'Exception: {}'.format(e))\\r\\n return False\\r\\n sys.exit()\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5856115221977234,\n \"alphanum_fraction\": 0.5856115221977234,\n \"avg_line_length\": 27.95833396911621,\n \"blob_id\": \"8db8eff8fda8baf25e278bd728784656ab6d8b78\",\n \"content_id\": \"1b1e7748fcbefe61a19745c4c1dc88e50d0365d0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 695,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 24,\n \"path\": \"/app/importer/models.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from ..constants import STRING_LEN, ACTIVE\\nfrom ..helpers import get_current_time\\nfrom ...extensions import db\\n\\n# -------------------------------- Driver Model ------------------------------- #\\n\\n\\nclass Country(db.Model):\\n \\\"\\\"\\\" Country Model \\\"\\\"\\\"\\n __tablename__ = 'countries'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n title = db.Column(db.String(STRING_LEN), index=True)\\n code = db.Column(db.String(STRING_LEN))\\n status = db.Column(db.SmallInteger, default=ACTIVE)\\n created = db.Column(db.DateTime(), default=get_current_time())\\n\\n\\n def __init__(self, title, code):\\n self.title = title\\n self.code = code\\n\\n def __repr__(self):\\n return self.title\\n\"\n },\n {\n \"alpha_fraction\": 0.5254515409469604,\n \"alphanum_fraction\": 0.5303776860237122,\n \"avg_line_length\": 26.68181800842285,\n \"blob_id\": \"15e1e069fef1e5b94742b03f1a79ae0286769b5e\",\n \"content_id\": \"4bc3c861fb7ca510399759853fd535c677eb3d6d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 609,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 22,\n \"path\": \"/app/carboard/forms/drivetype.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom wtforms import StringField\\nfrom wtforms.validators import DataRequired, Regexp, Length\\n\\nfrom ..models.drivetype import DriveType\\n\\n# ----------------------------- DriveType form classes ---------------------------\\n\\n\\nclass DriveTypeForm(FlaskForm):\\n \\\"\\\"\\\" Driver type add/edit FlaskForm \\\"\\\"\\\"\\n name = StringField(\\n 'Driver type name',\\n validators=[\\n DataRequired(),\\n Length(min=3, max=50),\\n Regexp(\\n r'^[a-zA-Z ]+$',\\n message=(\\\"Driver type name is not correct !\\\")\\n ),\\n ]\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.40299883484840393,\n \"alphanum_fraction\": 0.4053056538105011,\n \"avg_line_length\": 44.6315803527832,\n \"blob_id\": \"136265614911504d5ed09380f14c16ee58482a17\",\n \"content_id\": \"c936ebdfa8bcd12b2093f9329a961454445debb7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"HTML\",\n \"length_bytes\": 4335,\n \"license_type\": \"no_license\",\n \"max_line_length\": 209,\n \"num_lines\": 95,\n \"path\": \"/templates/carboard/platform/index.html\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"{% extends \\\"layout.html\\\" %}\\n\\n{% from \\\"carboard/macros.html\\\" import render_pagination %}\\n\\n{% block title %}\\n List of platforms | {{ super() }}\\n{% endblock %}\\n\\n{% block content_header %}\\n\\t<section class=\\\"content-header\\\">\\n\\t\\t<h1>\\n\\t\\tList of platforms\\n\\t\\t</h1>\\n\\t\\t<ol class=\\\"breadcrumb\\\">\\n\\t\\t\\t<li><a href=\\\"#\\\"><i class=\\\"fa fa-dashboard\\\"></i>Home</a></li>\\n\\t\\t\\t<li><a href=\\\"#\\\">Platform</a></li>\\n\\t\\t\\t<li class=\\\"active\\\">List of platforms</li>\\n\\t\\t</ol>\\n\\t</section>\\n{% endblock content_header %}\\n\\n{% block content %}\\n\\n <div class=\\\"row\\\">\\n <div class=\\\"col-md-12\\\">\\n <div class=\\\"box\\\">\\n <div class=\\\"box-header\\\">\\n <div class=\\\"box-actions\\\">\\n <a href=\\\"{{ url_for('carboard.newPlatform') }}\\\" class=\\\"btn btn-primary\\\">\\n Add new platform\\n </a>\\n </div>\\n <div class=\\\"box-tools\\\">\\n <div class=\\\"input-group input-group-sm\\\" style=\\\"width: 150px;\\\">\\n <input type=\\\"text\\\" name=\\\"table_search\\\" class=\\\"form-control pull-right\\\" placeholder=\\\"Search\\\">\\n\\n <div class=\\\"input-group-btn\\\">\\n <button type=\\\"submit\\\" class=\\\"btn btn-default\\\">\\n <i class=\\\"fa fa-search\\\"></i>\\n </button>\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\\\"box-body no-padding\\\">\\n <table class=\\\"table table-hover\\\">\\n <tr>\\n <th>ID</th>\\n <th>Logo</th>\\n <th>Title</th>\\n <th>Website</th>\\n <th>Status</th>\\n <th>Actions</th>\\n\\n </tr>\\n\\n {% for platform in platforms.items %}\\n <tr>\\n <td><a href=\\\"{{ url_for('carboard.showPlatform', id=platform.id) }}\\\">{{ platform.id }}</a></td>\\n <td><a href=\\\"{{ url_for('carboard.showPlatform', id=platform.id) }}\\\">\\n {% if platform.logo %}\\n <img class=\\\"avatar table-avatar\\\" src=\\\"{{ resized_img_src(config.UPLOAD_PLATFORM + platform.logo, width=60) }}\\\" alt=\\\"{{ platform.name }}\\\">\\n {% else %}\\n <img class=\\\"avatar table-avatar\\\" src=\\\"/static{{ config.UPLOAD_PLATFORM}}/default.png\\\">\\n {% endif %}\\n </a></td>\\n <td>{{ platform.name }}</td>\\n <td>{{ platform.website }}</td>\\n <td>\\n {% if platform.status == 1 %}\\n <span class=\\\"label bg-green\\\">Activated</span>\\n {% else %}\\n <span class=\\\"label bg-red\\\">Disabled</span>\\n {% endif %}\\n </td>\\n <td>\\n <a href=\\\"{{ url_for('carboard.showPlatform', id=platform.id) }}\\\" class=\\\"btn btn-default btn-sm\\\" data-toggle=\\\"tooltip\\\" title=\\\"Show\\\"><i class=\\\"fa fa-eye\\\"></i></a>\\n\\n <a href=\\\"{{ url_for('carboard.togglePlatform', id=platform.id) }}\\\" class=\\\"btn btn-warning btn-sm\\\" data-toggle=\\\"tooltip\\\" title=\\\"Toggle status\\\"><i class=\\\"fa fa-toggle-on\\\"></i></a>\\n\\n <a href=\\\"{{ url_for('carboard.editPlatform', id=platform.id) }}\\\" class=\\\"btn btn-success btn-sm\\\" data-toggle=\\\"tooltip\\\" title=\\\"Edit\\\"><i class=\\\"fa fa-pencil\\\"></i></a>\\n\\n <a href=\\\"{{ url_for('carboard.deletePlatform', id=platform.id) }}\\\" class=\\\"btn btn-danger btn-sm\\\" data-toggle=\\\"tooltip\\\" title=\\\"Remove\\\"><i class=\\\"fa fa-trash\\\"></i></a>\\n </td>\\n </tr>\\n {% endfor %}\\n </table>\\n </div>\\n <div class=\\\"box-footer clearfix\\\">\\n {{render_pagination(platforms)}}\\n </div>\\n </div>\\n </div>\\n </div>\\n{% endblock %}\\n\"\n },\n {\n \"alpha_fraction\": 0.7322834730148315,\n \"alphanum_fraction\": 0.7322834730148315,\n \"avg_line_length\": 24.399999618530273,\n \"blob_id\": \"b7a3de9ef988daf5ea457b8480b0ca672c92b7e3\",\n \"content_id\": \"5e86556caebe8fb10615a1ae7e2920e86a590d2f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 127,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 5,\n \"path\": \"/app/vizboard/__init__.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import Blueprint\\n\\nvizboard = Blueprint('vizboard', __name__, url_prefix='/vizboard')\\n\\nfrom .explorer.views import *\\n\"\n },\n {\n \"alpha_fraction\": 0.8783783912658691,\n \"alphanum_fraction\": 0.8783783912658691,\n \"avg_line_length\": 36.5,\n \"blob_id\": \"6d89b2d0aa3026ec45069585c8a2d1309dd8d1fe\",\n \"content_id\": \"706f7d77d89330ef7c1c1b7f6e71794c28f9321c\",\n \"detected_licenses\": [\n \"MIT\",\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 74,\n \"license_type\": \"permissive\",\n \"max_line_length\": 37,\n \"num_lines\": 2,\n \"path\": \"/app/plugins/driver_graph/src/algorithms/__init__.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from .drivergraph import DriverGraph\\nfrom .settings import DefaultSettings\"\n },\n {\n \"alpha_fraction\": 0.6251664161682129,\n \"alphanum_fraction\": 0.6284953355789185,\n \"avg_line_length\": 30.621051788330078,\n \"blob_id\": \"a325810a5abf9ad45e60a395c8a2d17e78f7b42d\",\n \"content_id\": \"53eac5d82e265ddfb0636c5003bcd2d9563d536e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3004,\n \"license_type\": \"no_license\",\n \"max_line_length\": 89,\n \"num_lines\": 95,\n \"path\": \"/app/carboard/views/article.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import (\\n render_template, request, flash, redirect, url_for\\n)\\nfrom flask_login import login_required\\n\\nfrom . import carboard\\nfrom ..models.article import Article\\nfrom ..forms.article import ArticleForm\\nfrom ..constants import PER_PAGE\\nfrom ...extensions import db\\n\\n\\n# -------------------- /carboard/article/ : List of articles ---------------- #\\n\\n\\n@carboard.route('/article/')\\n@login_required\\ndef indexArticle():\\n articles = Article.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/article/index.html', articles=articles)\\n\\n\\n# -------------------- /carboard/article/id : Show article ------------------ #\\n\\n\\n@carboard.route('/article/<int:id>', methods=['GET'])\\n@login_required\\ndef showArticle(id):\\n article = Article.query.get_or_404(id)\\n return render_template('carboard/article/show.html', article=article)\\n\\n\\n# -------------------- /carboard/article/new : Add article ------------------ #\\n\\n\\n@carboard.route('/article/new/<int:dataset_id>', methods=['GET', 'POST'])\\n@carboard.route('/article/new', methods=['GET', 'POST'])\\n@login_required\\ndef newArticle(dataset_id=None):\\n \\\"\\\"\\\" Add new article \\\"\\\"\\\"\\n\\n form = ArticleForm()\\n\\n if form.validate_on_submit():\\n article = Article(\\n dataset_id=dataset_id,\\n name=form.name.data,\\n authors=form.authors.data,\\n abstract=form.abstract.data,\\n keywords=form.keywords.data,\\n publication_date=form.publication_date.data,\\n reference=form.reference.data,\\n link=form.link.data,\\n )\\n db.session.add(article)\\n db.session.commit()\\n flash('Article {}, added successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.indexArticle'))\\n\\n return render_template('carboard/article/new.html', form=form, dataset_id=dataset_id)\\n\\n\\n# -------------------- /carboard/article/id/edit : Edit article ------------- #\\n\\n\\n@carboard.route('/article/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editArticle(id):\\n \\\"\\\"\\\" Edit existing article \\\"\\\"\\\"\\n\\n article = Article.query.get_or_404(id)\\n form = ArticleForm(obj=article)\\n if form.validate_on_submit():\\n form.populate_obj(article)\\n db.session.commit()\\n flash('Article {}, updated successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.showArticle', id=id))\\n\\n return render_template('carboard/article/edit.html', form=form, id=id)\\n\\n\\n# -------------------- /carboard/article/id/delete : Delete article --------- #\\n\\n\\n@carboard.route('/article/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteArticle(id):\\n article = Article.query.get_or_404(id)\\n db.session.delete(article)\\n db.session.commit()\\n flash('Article {}, deleted successfully.'.format(article.name), 'success')\\n return redirect(url_for('carboard.indexArticle'))\\n\"\n },\n {\n \"alpha_fraction\": 0.41769281029701233,\n \"alphanum_fraction\": 0.42125728726387024,\n \"avg_line_length\": 40.702701568603516,\n \"blob_id\": \"7b961ca9fbc450c548ddef2f7ea37ac46969144a\",\n \"content_id\": \"634fe2e7d0d8887df2c51e4b149bc319efe39cf2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"HTML\",\n \"length_bytes\": 3086,\n \"license_type\": \"no_license\",\n \"max_line_length\": 195,\n \"num_lines\": 74,\n \"path\": \"/templates/carboard/article/index.html\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"{% extends \\\"layout.html\\\" %}\\n\\n{% from \\\"carboard/macros.html\\\" import render_pagination %}\\n\\n{% block title %}\\n List of articles | {{ super() }}\\n{% endblock %}\\n\\n{% block content_header %}\\n\\t<section class=\\\"content-header\\\">\\n\\t\\t<h1>\\n\\t\\tList of articles\\n\\t\\t</h1>\\n\\t\\t<ol class=\\\"breadcrumb\\\">\\n\\t\\t\\t<li><a href=\\\"#\\\"><i class=\\\"fa fa-dashboard\\\"></i>Home</a></li>\\n\\t\\t\\t<li><a href=\\\"#\\\">Article</a></li>\\n\\t\\t\\t<li class=\\\"active\\\">List of articles</li>\\n\\t\\t</ol>\\n\\t</section>\\n{% endblock content_header %}\\n\\n{% block content %}\\n\\n <div class=\\\"row\\\">\\n <div class=\\\"col-md-12\\\">\\n <div class=\\\"box\\\">\\n <div class=\\\"box-header\\\">\\n <div class=\\\"box-tools\\\">\\n <div class=\\\"input-group input-group-sm\\\" style=\\\"width: 150px;\\\">\\n <input type=\\\"text\\\" name=\\\"table_search\\\" class=\\\"form-control pull-right\\\" placeholder=\\\"Search\\\">\\n\\n <div class=\\\"input-group-btn\\\">\\n <button type=\\\"submit\\\" class=\\\"btn btn-default\\\">\\n <i class=\\\"fa fa-search\\\"></i>\\n </button>\\n </div>\\n </div>\\n </div>\\n </div>\\n <br>\\n <div class=\\\"box-body no-padding\\\">\\n <table class=\\\"table table-hover\\\">\\n <tr>\\n <th>ID</th>\\n <th>Name</th>\\n <th>Authors</th>\\n <th>Date of publication</th>\\n <th>Actions</th>\\n </tr>\\n\\n {% for article in articles.items %}\\n <tr>\\n <td><a href=\\\"{{ url_for('carboard.showArticle', id=article.id) }}\\\">{{ article.id }}</a></td>\\n <td><a href=\\\"{{ url_for('carboard.showArticle', id=article.id) }}\\\">{{ article.name | truncate(70) }}</a></td>\\n <td>{{ article.authors | truncate(35) }}</td>\\n <td>{{ article.publication_date }}</td>\\n <td>\\n <a href=\\\"{{ url_for('carboard.showArticle', id=article.id) }}\\\" class=\\\"btn btn-default btn-sm\\\" data-toggle=\\\"tooltip\\\" title=\\\"Show\\\"><i class=\\\"fa fa-eye\\\"></i></a>\\n\\n <a href=\\\"{{ url_for('carboard.editArticle', id=article.id) }}\\\" class=\\\"btn btn-success btn-sm\\\" data-toggle=\\\"tooltip\\\" title=\\\"Edit\\\"><i class=\\\"fa fa-pencil\\\"></i></a>\\n\\n <a href=\\\"{{ url_for('carboard.deleteArticle', id=article.id) }}\\\" class=\\\"btn btn-danger btn-sm\\\" data-toggle=\\\"tooltip\\\" title=\\\"Remove\\\"><i class=\\\"fa fa-trash\\\"></i></a>\\n </td>\\n </tr>\\n {% endfor %}\\n </table>\\n </div>\\n <div class=\\\"box-footer clearfix\\\">\\n {{render_pagination(articles)}}\\n </div>\\n </div>\\n </div>\\n </div>\\n{% endblock %}\\n\"\n },\n {\n \"alpha_fraction\": 0.49799197912216187,\n \"alphanum_fraction\": 0.5030120611190796,\n \"avg_line_length\": 25.91891860961914,\n \"blob_id\": \"a31bf86b9258cce9438d42cc4505dec1019b7453\",\n \"content_id\": \"094e00aae2c95ad68dc6730c25a3d63c40535ec2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 996,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 37,\n \"path\": \"/app/carboard/forms/country.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom wtforms import StringField\\nfrom wtforms.validators import DataRequired, Regexp, Length\\n\\nfrom ..models.country import Country\\n\\n# ------------------------ custom validation methods ------------------------ #\\n\\n\\ndef country_exist(form, field):\\n if Country.select().where(Country.title == field.data).exists():\\n raise ValidationError('Country already exists !')\\n\\n# ---------------------------- User form classes ---------------------------- #\\n\\n\\nclass CountryForm(FlaskForm):\\n \\\"\\\"\\\" Country add/edit FlaskForm \\\"\\\"\\\"\\n title = StringField(\\n 'Name',\\n validators=[\\n DataRequired(),\\n Length(min=3, max=50),\\n Regexp(\\n r'^[a-zA-Z ]+$',\\n message=(\\\"Country name is not correct !\\\")\\n ),\\n # country_exist()\\n ]\\n )\\n code = StringField(\\n 'Iso Code',\\n validators=[\\n DataRequired(),\\n Length(min=2, max=2),\\n ]\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.5804204344749451,\n \"alphanum_fraction\": 0.5857980847358704,\n \"avg_line_length\": 30.229007720947266,\n \"blob_id\": \"efc8012ae618975522cd866793c109d5caef7f4f\",\n \"content_id\": \"2d125043fd572da29217e861e5fe6556c7597eb1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8182,\n \"license_type\": \"no_license\",\n \"max_line_length\": 113,\n \"num_lines\": 262,\n \"path\": \"/app/carboard/helpers.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nimport re\\nimport csv\\nfrom unicodedata import normalize\\nfrom uuid import uuid4\\nfrom time import strftime\\nfrom datetime import datetime\\n\\nfrom flask import request, url_for\\nfrom werkzeug import secure_filename\\n\\nfrom app.carboard.models.dataset import Dataset\\nfrom app.carboard.models.platform import Platform\\nfrom app.carboard.models.signal import Signal\\nfrom config.config import DefaultConfig\\nfrom .constants import UPLOAD_DATASET_DIR, ALLOWED_DATASET_EXTENSIONS\\n\\n\\n# ------------------------------ View Helpers ------------------------------- #\\n\\n\\ndef paginate(query, max_per_page=25):\\n \\\"\\\"\\\" Helper to manage paginate configuration\\n it take as argument a query and return a\\n paginated query with given parametters\\n \\\"\\\"\\\"\\n # obtain pagination arguments from the URL's query string\\n page = request.args.get('page', 1, type=int)\\n per_page = min(\\n request.args.get('per_page', max_per_page, type=int),\\n max_per_page\\n )\\n # run the query with Flask-SQLAlchemy's pagination\\n p = query.paginate(page, per_page)\\n # build the pagination metadata to include in the response\\n pages = {\\n 'page': page,\\n 'per_page': per_page,\\n 'total': p.total,\\n 'pages': p.pages\\n }\\n if p.has_prev:\\n pages['prev_url'] = url_for(\\n request.endpoint, page=p.prev_num, per_page=per_page)\\n else:\\n pages['prev_url'] = None\\n if p.has_next:\\n pages['next_url'] = url_for(\\n request.endpoint, page=p.next_num, per_page=per_page)\\n else:\\n pages['next_url'] = None\\n pages['first_url'] = url_for(\\n request.endpoint, page=1, per_page=per_page)\\n pages['last_url'] = url_for(\\n request.endpoint, page=p.pages, per_page=per_page)\\n # return a dictionary as a response\\n return {'items': p.items, 'pages': pages}\\n\\n\\ndef upload_csv(file_data, upload_dir):\\n \\\"\\\"\\\" upload csv file (keeping the name cause it will be removed after processing \\\"\\\"\\\"\\n if file_data:\\n path = os.path.join(upload_dir)\\n file_data.save(\\\"/\\\".join([path, file_data.filename]))\\n return file_data.filename\\n\\n\\ndef remove_csv(filename, upload_dir):\\n \\\"\\\"\\\" delet the plugins csv file \\\"\\\"\\\"\\n path = os.path.join(upload_dir)\\n os.remove(\\\"/\\\".join([path, filename]))\\n return True\\n\\n\\ndef upload_file(file_data, upload_dir, oldFile=None):\\n \\\"\\\"\\\" Upload Files \\\"\\\"\\\"\\n if file_data:\\n file = secure_filename(str(uuid4()) + os.path.splitext(file_data.filename)[1].lower())\\n path = os.path.join(upload_dir, strftime(\\\"%Y/%m\\\"))\\n # os.makedirs(path, exist_ok=True) Py 3.4+\\n make_dir(path)\\n file_data.save(\\\"/\\\".join([path, file]))\\n if oldFile is not None:\\n pass\\n return \\\"/\\\".join([strftime(\\\"%Y/%m\\\"), file])\\n return None\\n\\n\\ndef upload_dsfile(input='file', dataset='_default'):\\n \\\"\\\"\\\"Upload a dataset file\\\"\\\"\\\"\\n result = {'status': False, 'message': 'No input file'}\\n if input not in request.files:\\n return result\\n file = request.files[input]\\n if file.filename == '':\\n return result\\n if '.' not in file.filename:\\n result['message'] = 'File without any extension'\\n return result\\n if file.filename.rsplit('.', 1)[1] not in ALLOWED_DATASET_EXTENSIONS:\\n result['message'] = 'Extension not allowed'\\n return result\\n directory = \\\"/\\\".join([UPLOAD_DATASET_DIR, str(dataset)])\\n make_dir(directory)\\n file_path = \\\"/\\\".join([directory, secure_filename(str(uuid4()) + os.path.splitext(file.filename)[1].lower())])\\n file.save(file_path)\\n return {'status': True, 'message': file_path}\\n\\n\\ndef slugify(text, delim=u'-'):\\n \\\"\\\"\\\"Generates an slightly worse ASCII-only slug.\\\"\\\"\\\"\\n result = []\\n _punct_re = re.compile(r'[\\\\t !\\\"#$%&\\\\'()*\\\\-/<=>?@\\\\[\\\\\\\\\\\\]^_`{|},.]+')\\n for word in _punct_re.split(text.lower()):\\n word = normalize('NFKD', word).encode('ascii', 'ignore')\\n if word:\\n result.append(word)\\n try:\\n return unicode(delim.join(result))\\n except:\\n d = d = bytes(delim, 'utf-8')\\n return str(d.join(result).decode(\\\"utf-8\\\"))\\n\\n\\ndef choices(Model, placeholder=None, cond=1, orderField='name'):\\n if cond:\\n c = [(b.id, b) for b in Model.query.filter_by(status=cond).order_by(orderField).all()]\\n else:\\n c = [(b.id, b) for b in Model.query.order_by(orderField).all()]\\n if placeholder is not None:\\n c.insert(0, ('0', placeholder))\\n else:\\n c.insert(0, ('0', 'Select an option ...'))\\n return c\\n\\n\\ndef platform_choices(placeholder=None, orderField='name'):\\n platforms = [(b.name, b) for b in Platform.query.filter_by(status=1).order_by(orderField).all()]\\n datasets = [(b.name, b) for b in Dataset.query.filter_by(status=1).order_by(orderField).all()]\\n ret = platforms + datasets\\n if placeholder:\\n ret.insert(0, ('0', placeholder))\\n else:\\n ret.insert(0, ('0', 'Select A Data Storatge'))\\n return ret\\n\\n\\ndef types():\\n return [\\n ('type', 'Select a Type ...'),\\n ('Numerical', 'Numerical'),\\n ('String', 'String'),\\n ('States', 'States'),\\n ('Path', 'Path')\\n ]\\n\\n\\ndef get_signal_from_form(form):\\n \\\"\\\"\\\" get a signal object from the given form \\\"\\\"\\\"\\n signal = Signal(\\n name=form.name.data,\\n signalclass_id=form.signalclass_id.data,\\n signalsource_id=form.signalsource_id.data,\\n unit=form.unit.data,\\n description=form.unit.description,\\n frequency=form.frequency.data,\\n type=form.type.data\\n )\\n if form.type.data == \\\"States\\\" or form.type.data == \\\"Numerical\\\":\\n if form.type.data == \\\"States\\\":\\n number = form.number.data\\n values = form.values.data\\n signal.range = values\\n else:\\n signal.range = \\\" - \\\".join([form.min_value.data, form.max_value.data])\\n return signal\\n else:\\n return signal\\n\\n\\ndef make_dir(dir_path):\\n \\\"\\\"\\\" Make recursive directories \\\"\\\"\\\"\\n try:\\n if not os.path.exists(dir_path) and not os.path.isdir(dir_path):\\n os.makedirs(dir_path)\\n except Exception as e:\\n raise e\\n\\n\\ndef redirect_back():\\n \\\"\\\"\\\" Redirect back to last url \\\"\\\"\\\"\\n return request.args.get('next') or request.referrer or url_for('index')\\n\\n\\ndef get_current_time():\\n return datetime.utcnow()\\n\\n\\ndef pretty_date(dt, default=None):\\n \\\"\\\"\\\" Returns string representing \\\"time since\\\" e.g. 5 hours ago etc.\\\"\\\"\\\"\\n\\n if default is None:\\n default = 'just now'\\n now = datetime.utcnow()\\n diff = now - dt\\n periods = (\\n (diff.days / 365, 'year', 'years'),\\n (diff.days / 30, 'month', 'months'),\\n (diff.days / 7, 'week', 'weeks'),\\n (diff.days, 'day', 'days'),\\n (diff.seconds / 3600, 'hour', 'hours'),\\n (diff.seconds / 60, 'minute', 'minutes'),\\n (diff.seconds, 'second', 'seconds'),\\n )\\n for period, singular, plural in periods:\\n if not period:\\n continue\\n if period == 1:\\n return u'%d %s ago' % (period, singular)\\n else:\\n return u'%d %s ago' % (period, plural)\\n return default\\n\\n\\ndef allowed_extensions(filename):\\n \\\"\\\"\\\" check if the file extension is allowed \\\"\\\"\\\"\\n return '.' in filename and filename.rsplit('.', 1)[1] in DefaultConfig.ALLOWED_AVATAR_EXTENSIONS\\n\\n\\n# ------------------------------- Form Helpers ------------------------------ #\\n\\n\\ndef find_id(model, field_name):\\n \\\"\\\"\\\" find the the given model by name, if not found return -1 \\\"\\\"\\\"\\n res = model.query.filter_by(name=field_name).first()\\n if res:\\n return res.id\\n return -1\\n\\n\\n# ------------------------------- CSV File Helper --------------------------- #\\n\\nclass CSVLoader:\\n ## initialize with the wanted file path\\n def __init__(self, path):\\n self.path = path\\n\\n ## give value in case of Non or empty\\n def sanitize(self, val):\\n if not val:\\n return \\\"UNDEFINED\\\"\\n return val\\n\\n ## load the contents of the file an generate a resulting Hash\\n def load(self):\\n list = []\\n with open(self.path, 'rU') as f:\\n r = csv.DictReader(f)\\n for a in r:\\n list.append(a)\\n # final result\\n return list\\n\"\n },\n {\n \"alpha_fraction\": 0.5522975921630859,\n \"alphanum_fraction\": 0.5584245324134827,\n \"avg_line_length\": 27.92405128479004,\n \"blob_id\": \"0f9e947e28c3942d307fb0c6656d396efdaac2c9\",\n \"content_id\": \"fb0c15712c9b6f65eb5e038a9968828befbfce01\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2285,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 79,\n \"path\": \"/app/dashboard/helpers.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nfrom time import strftime\\nfrom datetime import datetime\\n\\nfrom flask import request, url_for\\nfrom werkzeug import secure_filename\\n\\nfrom config.config import DefaultConfig\\nfrom .constants import UPLOAD_TRACE_DIR, ALLOWED_TRACE_EXTENSIONS, TRANSFORM\\n\\n# ------------------------------ View Helpers ------------------------------- #\\n\\n\\ndef upload_trace(input='file', user_id=0):\\n \\\"\\\"\\\"Upload a trace file\\\"\\\"\\\"\\n result = {'status': False, 'message': 'No input file'}\\n if input not in request.files:\\n return result\\n file = request.files[input]\\n if file.filename == '':\\n return result\\n if '.' not in file.filename:\\n result['message'] = 'extension not allowed'\\n return result\\n if file.filename.rsplit('.', 1)[1] not in ALLOWED_TRACE_EXTENSIONS:\\n result['message'] = 'extension not allowed'\\n return result\\n directory = \\\"/\\\".join([UPLOAD_TRACE_DIR, str(user_id)])\\n directory = \\\"/\\\".join([directory, strftime(\\\"%Y/%m\\\")])\\n make_dir(directory)\\n file_path = \\\"/\\\".join([directory, secure_filename(file.filename)])\\n file.save(file_path)\\n return {'status': True, 'message': file_path}\\n\\n\\ndef correct_value(value):\\n try:\\n return int(value)\\n except:\\n for k, v in TRANSFORM.items():\\n if value == v:\\n return k\\n return -99999\\n\\n\\ndef correct_time(timestamp):\\n try:\\n timestamp = str(timestamp)\\n t = timestamp.split('.')\\n return '{}{}'.format(t[0], t[1][:3].ljust(3, '0'))\\n except:\\n return timestamp.split('.')[0]\\n\\n\\ndef debug(obj):\\n print(\\\"\\\\n------------------ Start DEBUG ------------------\\\")\\n for a, v in obj.__dict__.iteritems():\\n print(\\\"\\\\t [{}] = {}\\\".format(a, v))\\n print(\\\"------------------- END DEBUG -------------------\\\\n\\\")\\n\\n\\ndef make_dir(dir_path):\\n \\\"\\\"\\\" Make recursive directories \\\"\\\"\\\"\\n try:\\n if not os.path.exists(dir_path) and not os.path.isdir(dir_path):\\n os.makedirs(dir_path)\\n except Exception as e:\\n raise e\\n\\n\\ndef redirect_back():\\n \\\"\\\"\\\" Redirect back to last url \\\"\\\"\\\"\\n return request.args.get('next') or request.referrer or url_for('index')\\n\\n\\ndef get_current_time():\\n return datetime.utcnow()\\n\\n# ------------------------------- Form Helpers ------------------------------ #\\n\"\n },\n {\n \"alpha_fraction\": 0.47676992416381836,\n \"alphanum_fraction\": 0.4800885021686554,\n \"avg_line_length\": 22.802631378173828,\n \"blob_id\": \"d793e3d7e15f124c6f2db35d430664a8511ad3e1\",\n \"content_id\": \"2ad5fb09526a2bc9c2cb41f000c7a30e94a5c1a6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1808,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 76,\n \"path\": \"/app/importer/forms.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom flask_wtf.file import FileAllowed, FileField\\nfrom wtforms import StringField, SelectField\\nfrom wtforms.validators import DataRequired, Regexp, Length\\nfrom .constants import DRIVER_GENDER\\n\\n\\n# ------------------------ custom validation methods ------------------------ #\\n\\n\\n# ---------------------------- Brand form classes ---------------------------- #\\n\\nclass VehicleForm(FlaskForm):\\n \\\"\\\"\\\" Vehicle add/edit FlaskForm \\\"\\\"\\\"\\n brand_id = SelectField(\\n 'Brand',\\n coerce=int,\\n validators=[\\n DataRequired(),\\n ]\\n )\\n model_id = SelectField(\\n 'Model',\\n coerce=int,\\n )\\n image = FileField(\\n 'Picture', validators=[\\n FileAllowed(['jpg', 'jpeg', 'png', 'gif'], 'Images only plz :) ')\\n ]\\n )\\n\\nclass DriverForm(FlaskForm):\\n \\\"\\\"\\\" Driver add/edit FlaskForm \\\"\\\"\\\"\\n gender = SelectField(\\n 'Gender',\\n coerce=int,\\n choices=DRIVER_GENDER,\\n validators=[\\n DataRequired(),\\n ]\\n )\\n fullname = StringField(\\n 'Fullname',\\n validators=[\\n Length(min=3, max=50),\\n Regexp(\\n r'^[a-zA-Z ]+$',\\n message=(\\\"Driver name is not correct !\\\")\\n ),\\n ]\\n )\\n country_id = SelectField(\\n 'Country',\\n coerce=int,\\n validators=[\\n DataRequired(),\\n ]\\n )\\n status_id = SelectField(\\n 'Status',\\n coerce=int,\\n validators=[\\n DataRequired(),\\n ]\\n )\\n city = StringField(\\n 'City',\\n validators=[\\n Length(min=2, max=50),\\n ]\\n )\\n avatar = FileField(\\n 'Avatar', validators=[\\n FileAllowed(['jpg', 'jpeg', 'png', 'gif'], 'Images only plz :) ')\\n ]\\n )\"\n },\n {\n \"alpha_fraction\": 0.5849056839942932,\n \"alphanum_fraction\": 0.5933962464332581,\n \"avg_line_length\": 30.64179039001465,\n \"blob_id\": \"63f3cabb0f857adfc4f77f4ef4fc76a447e153bc\",\n \"content_id\": \"ac9e6236fe1ae04f95f05a8d95516150a24bb834\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2120,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 67,\n \"path\": \"/app/carboard/models/platform.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import re\\nfrom datetime import datetime\\nfrom unicodedata import normalize\\n\\nfrom marshmallow import Schema, fields\\nfrom .extrasignal import ExtrasignalSchema\\nfrom ..constants import DEFAULT_PHOTO\\nfrom ...extensions import db\\n\\n# -------------------------------- Brand Model ------------------------------- #\\ndef slugify(text, delim=u'-'):\\n \\\"\\\"\\\"Generates an slightly worse ASCII-only slug.\\\"\\\"\\\"\\n result = []\\n _punct_re = re.compile(r'[\\\\t !\\\"#$%&\\\\'()*\\\\-/<=>?@\\\\[\\\\\\\\\\\\]^_`{|},.]+')\\n for word in _punct_re.split(text.lower()):\\n word = normalize('NFKD', word).encode('ascii', 'ignore')\\n if word:\\n result.append(word)\\n try:\\n return unicode(delim.join(result))\\n except:\\n d = d = bytes(delim, 'utf-8')\\n return str(d.join(result).decode(\\\"utf-8\\\"))\\n\\nclass Platform(db.Model):\\n \\\"\\\"\\\" Platform Model \\\"\\\"\\\"\\n __tablename__ = 'platforms'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.String(255))\\n slug = db.Column(db.String(255))\\n mimetype = db.Column(db.SmallInteger)\\n logo = db.Column(db.String(255), nullable=True)\\n description = db.Column(db.String(255))\\n website = db.Column(db.String(255), nullable=True)\\n\\n #signals = db.relationship(\\\"Extrasignal\\\")\\n\\n status = db.Column(db.SmallInteger, default=1)\\n created = db.Column(db.DateTime(), default=datetime.utcnow())\\n\\n def __init__(self, name, mimetype, description, website, logo=None):\\n self.name = name\\n self.slug = slugify(name)\\n self.mimetype = mimetype\\n self.logo = logo or DEFAULT_PHOTO\\n self.description = description\\n self.website = website\\n\\n def __repr__(self):\\n return self.name\\n\\n# -------------------------------- Brand Schema ------------------------------- #\\n\\n\\nclass PlatformSchema(Schema):\\n \\\"\\\"\\\" Platform Schema \\\"\\\"\\\"\\n\\n id = fields.Int(dump_only=True)\\n name = fields.Str()\\n slug = fields.Str()\\n mimetype = fields.Str()\\n logo = fields.Str()\\n signals = fields.Nested(ExtrasignalSchema)\\n description = fields.Str()\\n website = fields.Str()\\n created = fields.DateTime(dump_only=True)\\n\"\n },\n {\n \"alpha_fraction\": 0.48859935998916626,\n \"alphanum_fraction\": 0.49837133288383484,\n \"avg_line_length\": 18.125,\n \"blob_id\": \"4bde2d6e38e73bdd165915537b349bb1bb722711\",\n \"content_id\": \"e2e530c42da59ec8978c412d62d99427434163c6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 307,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 16,\n \"path\": \"/config/mail.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n# ----------------------------- Mail Configuration -------------------------- #\\n\\n\\nclass MailConfig(object):\\n\\n MAIL_USERNAME = 'misaki.yooko@gmail.com'\\n\\n MAIL_DEFAULT_SENDER = 'vCar <noreply@vcar.ma>'\\n\\n MAIL_SERVER = 'MAIL_SERVER', 'smtp.gmail.com'\\n\\n MAIL_PORT = 465\\n\\n MAIL_USE_SSL = True\\n\\n MAIL_USE_TLS = False\\n\"\n },\n {\n \"alpha_fraction\": 0.4707895517349243,\n \"alphanum_fraction\": 0.4707895517349243,\n \"avg_line_length\": 42.521278381347656,\n \"blob_id\": \"bab3aeb7e14c4ede4c122de4ec07fb65618ac9bc\",\n \"content_id\": \"769f0ff5a5496b38983f1f32af37cb680ddbf253\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"HTML\",\n \"length_bytes\": 4091,\n \"license_type\": \"no_license\",\n \"max_line_length\": 187,\n \"num_lines\": 94,\n \"path\": \"/templates/elements/sidebar.html\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"{% from \\\"elements/macros.html\\\" import is_active %}\\n\\n<a class=\\\"user-panel\\\" href=\\\"{{ url_for('importer.index') }}\\\">\\n <div class=\\\"pull-left image import-icon\\\">\\n <i class=\\\"fa fa-bandcamp\\\" aria-hidden=\\\"true\\\"></i>\\n </div>\\n <div class=\\\"pull-left info\\\">\\n <p>IMPORT DATA</p>\\n </div>\\n</a>\\n\\n<ul class=\\\"sidebar-menu\\\">\\n\\n <li CLASS=\\\"header\\\">V<strong>C</strong>AR DASHBOARD</li>\\n <li class=\\\"{ is_active('explorer.index') }}\\\">\\n <a href=\\\"{{ url_for('explorer.index') }}\\\">\\n <i class=\\\"fa fa-dashboard noor\\\"></i> <span>Explorer</span>\\n </a>\\n </li>\\n <li class=\\\"{{ is_active('explorer.index') }}\\\">\\n <a href=\\\"{{ url_for('explorer.index') }}\\\">\\n <i class=\\\"fa fa-map-o\\\"></i> <span>Google Map</span>\\n </a>\\n </li>\\n\\n {# Plugin links shows here #}\\n <li CLASS=\\\"header\\\">\\n VCAR Extensions\\n <span>\\n <a class=\\\"see-all\\\" href=\\\"{{ url_for('carboard.indexPlugin') }}\\\">view all</a>\\n </span>\\n </li>\\n\\n {% if plugins_menu is not none %}\\n {% for plugin in plugins_menu %}\\n {% if plugin.treeview %}\\n {% set status = {'treeview': '', 'menu': ''} %}\\n {% for item in plugin.menu %}\\n {% if request.path == url_for(item.action) %}\\n {% if status.update({'treeview': 'active', 'menu': 'menu-open'}) %} {% endif %}\\n {% endif %}\\n {% endfor %}\\n <li class=\\\"treeview {{ status.treeview }}\\\">\\n <a href=\\\"#\\\">\\n <i class=\\\"{{ plugin.iclass | default('fa fa-puzzle-piece')}}\\\"></i>\\n <span>{{ plugin.name }}</span>\\n <i class=\\\"fa fa-chevron-left pull-right\\\"></i>\\n </a>\\n <ul class=\\\"treeview-menu {{ status.menu }}\\\">\\n {% for item in plugin.menu %}\\n <li class=\\\"{{ is_active(item.action) }}\\\">\\n <a href=\\\"{{ url_for(item.action) }}\\\">\\n <i class=\\\"{{ item.iclass | default('fa fa-puzzle-piece')}}\\\"></i>\\n {{ item.name }}\\n </a>\\n </li>\\n {% endfor %}\\n </ul>\\n </li>\\n {% else %}\\n <li class=\\\"{{ is_active(plugin.menu.action) }}\\\">\\n <a href=\\\"{{ url_for(plugin.menu.action | string) }}\\\">\\n <i class=\\\"{{ plugin.menu.iclass | default('fa fa-puzzle-piece') }}\\\"></i> <span>{{ plugin.name }}</span>\\n </a>\\n </li>\\n {% endif %}\\n {% endfor %}\\n {% endif %}\\n\\n {# Data storage #}\\n <li class=\\\"header\\\">Data storage</li>\\n {# <li><a href=\\\"{{ url_for('importer.index') }}\\\"><i class=\\\"fa fa-bandcamp\\\"></i> Import Manger</a></li> #}\\n\\n <li class=\\\"{{ is_active('carboard.indexPlatform') }}\\\">\\n <a href=\\\"{{ url_for('carboard.indexPlatform') }}\\\"><i class=\\\"fa fa-leaf\\\"></i> <span>Platforms</span></a>\\n </li>\\n <li class=\\\"{{ is_active('carboard.indexDataset') }}\\\">\\n <a href=\\\"{{ url_for('carboard.indexDataset') }}\\\"><i class=\\\"fa fa-cube\\\"></i> <span>Datasets</span></a>\\n </li>\\n\\n <li class=\\\"{{ is_active('carboard.configuration') }}\\\">\\n <a href=\\\"{{ url_for('carboard.configuration') }}\\\"><i class=\\\"fa fa-cogs\\\"></i> <span>Configuration</span></a>\\n </li>\\n</ul>\\n\\n\\n<ul class=\\\"sidebar-menu buttom\\\">\\n <li class=\\\"header big-icon\\\">\\n <span><a href=\\\"#\\\" data-toggle=\\\"tooltip\\\" title=\\\"vCar License Policy\\\"><i class=\\\"fa fa-copyright\\\"></i></a></span>\\n <span><a href=\\\"#\\\" data-toggle=\\\"tooltip\\\" title=\\\"About vCar\\\"><i class=\\\"fa fa-info-circle\\\"></i></a></span>\\n <span><a href=\\\"#\\\" data-toggle=\\\"tooltip\\\" title=\\\"vCar Documentation\\\"><i class=\\\"fa fa-book\\\"></i></a></span>\\n <span><a class=\\\"power-off\\\" href=\\\"/kill\\\" data-toggle=\\\"confirmation\\\" data-container=\\\"body\\\" title=\\\"\\\" data-original-title=\\\"Shutdonw server?\\\"><i class=\\\"fa fa-power-off\\\"></i></a></span>\\n </li>\\n</ul>\\n\"\n },\n {\n \"alpha_fraction\": 0.5916727781295776,\n \"alphanum_fraction\": 0.5953250527381897,\n \"avg_line_length\": 30.471263885498047,\n \"blob_id\": \"5c55a8adaaa1a077aa66f402cb1befad76c9e6d2\",\n \"content_id\": \"6c0b4e43d00fd8e05815268e70ce92126b3fa80d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2738,\n \"license_type\": \"no_license\",\n \"max_line_length\": 84,\n \"num_lines\": 87,\n \"path\": \"/app/carboard/views/status.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import (\\n render_template, request, flash, redirect, url_for\\n)\\nfrom flask_login import login_required\\n\\nfrom . import carboard\\nfrom ..models.status import Status\\nfrom ..forms.status import StatusForm\\nfrom ..helpers import paginate\\nfrom ..constants import PER_PAGE\\nfrom ...extensions import db\\n\\n\\n# --------------------- /carboard/status/ : List of countries ------------------ #\\n\\n@carboard.route('/status/')\\n@login_required\\ndef indexStatus():\\n status = Status.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/status/index.html',\\n status=status\\n )\\n\\n# ----------------------- /carboard/status/id : Show status ------------------- #\\n\\n\\n@carboard.route('/status/<int:id>', methods=['GET'])\\n@login_required\\ndef showStatus(id):\\n status = Status.query.get_or_404(id)\\n return render_template('carboard/status/show.html',\\n status=status\\n )\\n\\n# ---------------------- /carboard/status/new : Add status -------------------- #\\n\\n\\n@carboard.route('/status/new', methods=['GET', 'POST'])\\n@login_required\\ndef newStatus():\\n \\\"\\\"\\\" Add new status \\\"\\\"\\\"\\n\\n form = StatusForm()\\n\\n if form.validate_on_submit():\\n status = Status(\\n title=form.title.data,\\n color=form.color.data\\n )\\n db.session.add(status)\\n db.session.commit()\\n flash('Status {}, added successfully.'.format(form.title.data), 'success')\\n return redirect(url_for('carboard.indexStatus'))\\n\\n return render_template('carboard/status/new.html', form=form)\\n\\n# -------------------- /carboard/status/id/edit : Edit status ----------------- #\\n\\n\\n@carboard.route('/status/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editStatus(id):\\n \\\"\\\"\\\" Edit existing status \\\"\\\"\\\"\\n status = Status.query.get_or_404(id)\\n form = StatusForm(obj=status)\\n if form.validate_on_submit():\\n form.populate_obj(status)\\n db.session.commit()\\n flash('Status {}, updated successfully.'.format(form.title.data), 'success')\\n return redirect(url_for('carboard.showStatus', id=id))\\n\\n return render_template('carboard/status/edit.html', form=form, id=id)\\n\\n# ------------------ /carboard/status/id/delete : Delete status --------------- #\\n\\n\\n@carboard.route('/status/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteStatus(id):\\n status = Status.query.get_or_404(id)\\n db.session.delete(status)\\n db.session.commit()\\n flash('Status {}, deleted successfully.'.format(status.title), 'success')\\n return redirect(url_for('carboard.indexStatus'))\\n\"\n },\n {\n \"alpha_fraction\": 0.6078697443008423,\n \"alphanum_fraction\": 0.6180461049079895,\n \"avg_line_length\": 34.095237731933594,\n \"blob_id\": \"f7ad17644b093d58e5f1e7752838e3537308c9b3\",\n \"content_id\": \"cd0353a0236727f2d76d20a580cee129ac1249ed\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1474,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 42,\n \"path\": \"/app/explorer/models.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom ..extensions import db\\n\\n# -------------------------------- Chart Model ------------------------------ #\\n\\n\\nclass Chart(db.Model):\\n \\\"\\\"\\\" Chart Model \\\"\\\"\\\"\\n __tablename__ = 'charts'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.String(255), index=True, unique=True)\\n token = db.Column(db.String(255), unique=True)\\n isValid = db.Column(db.SmallInteger, default=0)\\n error = db.Column(db.String(255), nullable=True)\\n query = db.Column(db.String(255), nullable=True)\\n\\n user_id = db.Column(db.Integer, db.ForeignKey('users.id'))\\n user = db.relationship('User')\\n\\n driver_id = db.Column(db.Integer, db.ForeignKey('drivers.id'))\\n driver = db.relationship('Driver')\\n\\n vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'))\\n vehicle = db.relationship('Vehicle')\\n\\n status = db.Column(db.SmallInteger, default=1)\\n deleted = db.Column(db.SmallInteger, default=0)\\n updated = db.Column(db.DateTime(), default=datetime.utcnow())\\n created = db.Column(db.DateTime(), default=datetime.utcnow())\\n\\n def __init__(self, name, user_id, driver_id, vehicle_id, query, **kwargs):\\n self.name = name\\n self.user_id = user_id\\n self.driver_id = driver_id\\n self.vehicle_id = vehicle_id\\n self.query = query\\n self.isValid = kwargs.get('isValid', True)\\n self.error = kwargs.get('error', None)\\n\\n def __repr__(self):\\n return self.name\\n\"\n },\n {\n \"alpha_fraction\": 0.5019304752349854,\n \"alphanum_fraction\": 0.5135135054588318,\n \"avg_line_length\": 23.66666603088379,\n \"blob_id\": \"9bc4013cd0414ad4e9e8280934202d57e1920c9e\",\n \"content_id\": \"62c63a06a0ac565908fadab209c6ad2cd990927e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 518,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 21,\n \"path\": \"/app/carboard/models/status.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from ...extensions import db\\n\\n# -------------------------------- Status Model ------------------------------- #\\n\\n\\nclass Status(db.Model):\\n \\\"\\\"\\\" Status Model:\\n The status of users and drivers.\\n \\\"\\\"\\\"\\n __tablename__ = 'status'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n title = db.Column(db.String(255))\\n color = db.Column(db.String(255))\\n\\n def __init__(self, title, color=\\\"primary\\\"):\\n self.title = title\\n self.color = color\\n\\n def __repr__(self):\\n return self.title\\n\"\n },\n {\n \"alpha_fraction\": 0.6079865097999573,\n \"alphanum_fraction\": 0.6136108040809631,\n \"avg_line_length\": 34.560001373291016,\n \"blob_id\": \"eb5d5b9074a749735c0c2a6519f28e8a4c1addca\",\n \"content_id\": \"da4ce5b6751c6fd2768f92bb76ab932c97a0cb22\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1778,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 50,\n \"path\": \"/app/carboard/models/record.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom ...extensions import db\\n\\n# -------------------------------- Record Model ------------------------------- #\\n\\n\\nclass Record(db.Model):\\n \\\"\\\"\\\" Record Model: \\\"\\\"\\\"\\n __tablename__ = 'records'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n\\n name = db.Column(db.String(255))\\n\\n user_id = db.Column(db.Integer, db.ForeignKey('users.id'))\\n user = db.relationship('User')\\n\\n drivetype_id = db.Column(db.Integer, db.ForeignKey('drivetypes.id'))\\n drivetype = db.relationship('DriveType')\\n\\n driver_id = db.Column(db.Integer, db.ForeignKey('drivers.id'), nullable=True)\\n driver = db.relationship('Driver')\\n\\n vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=True)\\n vehicle = db.relationship('Vehicle')\\n\\n status_id = db.Column(db.Integer, db.ForeignKey('status.id'))\\n status = db.relationship('Status')\\n\\n trace = db.Column(db.String(255))\\n start = db.Column(db.DateTime(), nullable=True)\\n end = db.Column(db.DateTime(), nullable=True)\\n description = db.Column(db.String(255), nullable=True)\\n created = db.Column(db.DateTime(), default=datetime.utcnow())\\n\\n def __init__(self, user_id, drivetype_id, trace, **kwargs):\\n self.vars = kwargs\\n self.user_id = user_id\\n self.drivetype_id = drivetype_id\\n self.trace = trace\\n self.name = kwargs.get('name', \\\"Record\\\")\\n self.driver_id = kwargs.get('driver_id', None)\\n self.vehicle_id = kwargs.get('vehicle_id', None)\\n self.status_id = kwargs.get('status_id', 1)\\n self.start = kwargs.get('start', None)\\n self.end = kwargs.get('end', None)\\n self.description = kwargs.get('description', None)\\n\\n def __repr__(self):\\n return '{} {}'.format(self.name, self.id)\\n\"\n },\n {\n \"alpha_fraction\": 0.5675675868988037,\n \"alphanum_fraction\": 0.5675675868988037,\n \"avg_line_length\": 17,\n \"blob_id\": \"5d37c636b3fec72ba9442334662758a3e7ffb043\",\n \"content_id\": \"30506a458cb50dd513be8e4d135acd575d1b62db\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 37,\n \"license_type\": \"no_license\",\n \"max_line_length\": 23,\n \"num_lines\": 2,\n \"path\": \"/app/importer/platforms/openxc/views.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\ndef help():\\n\\treturn 'Help text ...'\"\n },\n {\n \"alpha_fraction\": 0.602672278881073,\n \"alphanum_fraction\": 0.6054852604866028,\n \"avg_line_length\": 32.85714340209961,\n \"blob_id\": \"3292c0c2d96d64b8845b36ab94aa7a0c209864aa\",\n \"content_id\": \"f198778ccc2062b6028064691674b4db4435c96a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1422,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 42,\n \"path\": \"/app/carboard/models/vehicle.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom ...extensions import db\\n\\n# -------------------------------- Vehicle Model ------------------------------- #\\n\\n\\nclass Vehicle(db.Model):\\n \\\"\\\"\\\" Vehicle Model: \\\"\\\"\\\"\\n __tablename__ = 'vehicles'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n\\n brand_id = db.Column(db.Integer, db.ForeignKey('brands.id'))\\n brand = db.relationship('Brand')\\n\\n model_id = db.Column(db.Integer, db.ForeignKey('models.id'), nullable=True)\\n model = db.relationship('Model')\\n\\n image = db.Column(db.String(255), nullable=True)\\n\\n user_id = db.Column(db.Integer, db.ForeignKey('users.id'))\\n user = db.relationship('User', backref=db.backref('vehicles', lazy='dynamic'))\\n\\n driver_id = db.Column(db.Integer, db.ForeignKey('drivers.id'), nullable=True)\\n driver = db.relationship('Driver')\\n\\n status_id = db.Column(db.Integer, db.ForeignKey('status.id'))\\n status = db.relationship('Status')\\n\\n created = db.Column(db.DateTime(), default=datetime.utcnow())\\n\\n def __init__(self, brand_id, user_id, **kwargs):\\n self.vars = kwargs\\n self.brand_id = brand_id\\n self.user_id = user_id\\n self.model_id = kwargs.get('model_id', None)\\n self.driver_id = kwargs.get('driver_id', None)\\n self.status_id = kwargs.get('status_id', 1)\\n self.image = kwargs.get('image')\\n\\n def __repr__(self):\\n return '{} ({})'.format(self.brand, self.model)\\n\"\n },\n {\n \"alpha_fraction\": 0.5950000286102295,\n \"alphanum_fraction\": 0.5975000262260437,\n \"avg_line_length\": 22.52941131591797,\n \"blob_id\": \"6851764925d54d3196d807e4f3b06687be5550bd\",\n \"content_id\": \"64c545439a5f837b642b98d097c0956ec1f579e4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 400,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 17,\n \"path\": \"/app/plugins/helpers.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from os import path\\nfrom flask_misaka import markdown\\n\\n\\ndef render_md(filepath, file):\\n \\\"\\\"\\\"\\n Function takes as input a markdown file and generate\\n a rendred html file as output\\n \\\"\\\"\\\"\\n try:\\n with open(path.join(str(filepath[0]), str(file))) as fd:\\n mkdwn = fd.read()\\n html = markdown(mkdwn)\\n except Exception:\\n html = None\\n\\n return html\\n\"\n },\n {\n \"alpha_fraction\": 0.5080527067184448,\n \"alphanum_fraction\": 0.5090287923812866,\n \"avg_line_length\": 32.04838562011719,\n \"blob_id\": \"99144873fd56f978517dfcb43b23c18719ecfd82\",\n \"content_id\": \"b7fe20926541a0166600863234b20b9098414e1d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4098,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 124,\n \"path\": \"/app/importer/transformer.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import Flask\\nfrom flask_sqlalchemy import SQLAlchemy\\n# import main\\nfrom config.config import DefaultConfig\\nfrom config.database import DatabaseConfig\\nfrom ..extensions import db\\nfrom ..carboard.models.platform import Platform\\nfrom ..carboard.models.signal import Signal\\nfrom ..carboard.models.extrasignal import Extrasignal\\nfrom .helpers import slugify\\nfrom .constants import ACTIVE\\n# -------------------- Transformer Class : Version 0.1 ---------------------- #\\n\\n\\nclass Transformer:\\n\\n def __init__(self, **kwargs):\\n self.registerDB()\\n\\n def registerDB(self):\\n self.app = Flask(__name__)\\n self.app.config.from_object(DefaultConfig)\\n self.app.config.from_object(DatabaseConfig)\\n db = SQLAlchemy(self.app)\\n\\n def setPlatformById(self, platform_id):\\n with self.app.app_context():\\n self.platform = Platform.query.filter_by(id=platform_id, status=ACTIVE).first()\\n self.signals = self.getAvailable()\\n return self\\n\\n def setPlatformBySlug(self, platform_slug):\\n with self.app.app_context():\\n self.platform = Platform.query.filter_by(slug=platform_slug, status=ACTIVE).first()\\n self.signals = self.getAvailable()\\n return self\\n \\n def getAvailable(self):\\n extra = self.platform.signals\\n signals = {}\\n for x in extra:\\n if x.status == 1 and x.signal.status == 1:\\n signals[str(x.name)] = {\\n 'main': x.signal.name,\\n 'class': x.signal.signalclass.name\\n }\\n return signals\\n \\n def getInfo(self, name, ignore=True):\\n x = {}\\n if name in self.signals:\\n x['name'] = self.signals[name]['main']\\n x['class'] = self.signals[name]['class']\\n elif ignore == False:\\n x['name'] = name\\n x['class'] = 'unknown'\\n else:\\n x = None\\n return x\\n\\n def getMain(self, extra, ignore=None):\\n extra = slugify(extra)\\n extrasignal = None\\n with self.app.app_context():\\n extrasignal = Extrasignal.query.filter_by(name=extra, platform_id=self.platform.id).first()\\n ignore = self.ignore if ignore is None else ignore\\n\\n if extrasignal is not None:\\n main = extrasignal.signal\\n if main.status == ACTIVE and extrasignal.status == ACTIVE:\\n return {\\n 'name': main.name,\\n 'main': True,\\n 'class': main.signalclass.name,\\n 'error': None\\n }\\n elif not ignore:\\n return {\\n 'name': main.name,\\n 'main': True,\\n 'class': main.signalclass.name,\\n 'error': 'Signal `{}` is disabled but added to database anyway ! '.format(extra)\\n }\\n else:\\n return {\\n 'name': None,\\n 'main': None,\\n 'class': None,\\n 'error': 'Signal `{}` is not currently supported'.format(extra)\\n }\\n else:\\n if not ignore:\\n msg = extrasignal\\n return {\\n 'name': extra,\\n 'main': False,\\n 'class': 'unknown',\\n 'error': 'Signal `{}` is not currently supported but added to database anyway !'.format(extra)\\n }\\n else:\\n return {\\n 'name': None,\\n 'main': None,\\n 'class': None,\\n 'error': 'Signal `{}` is not currently supported'.format(extra)\\n }\\n\\n def getCorrectValue(self, value):\\n return value;\\n\\n def __str__(self):\\n str = \\\"\\\"\\n for k, v in self.vars.items():\\n str = str + \\\"{} : {} | \\\".format(k, v)\\n return str\\n\\n def showVars(self):\\n print(self.vars)\\n\\n def setVar(self, k, v):\\n self.vars[k] = v\\n\\n def getVar(self, k):\\n return self.vars.get(k, None)\\n\"\n },\n {\n \"alpha_fraction\": 0.43885281682014465,\n \"alphanum_fraction\": 0.44426408410072327,\n \"avg_line_length\": 33.867923736572266,\n \"blob_id\": \"2113de0f69cd75a955e28ff99dde661211825477\",\n \"content_id\": \"5f973634629c2ecd0b0c466091c4030f353cec56\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3696,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 106,\n \"path\": \"/app/dashboard/tasks.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import redis\\nimport json\\nfrom time import strftime, sleep\\nfrom datetime import datetime\\nfrom elasticsearch import Elasticsearch, helpers\\nfrom .constants import BULK_SIZE\\nfrom .helpers import correct_value, correct_time\\nred = redis.StrictRedis(host='localhost', port=6379, db=0)\\n\\n\\ndef task_info(name='info'):\\n pubsub = red.pubsub()\\n pubsub.subscribe(name)\\n for message in pubsub.listen():\\n # print(\\\"[+] Message: {}\\\".format(message))\\n yield 'data: {}\\\\n\\\\n'.format(message['data'])\\n# ---------------------------- Process openxc task -------------------------- #\\n\\n\\ndef openxc_task(traces, user_id):\\n try:\\n es = Elasticsearch([{\\n 'host': 'localhost',\\n 'port': 9200\\n }])\\n index = \\\"openxc\\\"\\n type = \\\"driver_{}\\\".format(user_id)\\n sleep(2)\\n red.publish('info', 'Inisializing Elasticsearch ...')\\n es.cluster.health(wait_for_status='yellow', request_timeout=1)\\n\\n red.publish('info', 'creating index if it dosn\\\\'t exist ...')\\n mapping = {\\n type: {\\n \\\"properties\\\": {\\n \\\"name\\\": {\\n \\\"type\\\": \\\"string\\\",\\n \\\"index\\\": \\\"not_analyzed\\\"\\n },\\n \\\"value\\\": {\\n \\\"type\\\": \\\"string\\\",\\n \\\"index\\\": \\\"not_analyzed\\\"\\n },\\n \\\"timestamp\\\": {\\n \\\"type\\\": \\\"date\\\"\\n }\\n }\\n }\\n }\\n # es.indices.delete(index=index, ignore=404)\\n if not es.indices.exists(index=index):\\n es.indices.create(index=index, ignore=400)\\n es.indices.put_mapping(index=index, doc_type=type, body=mapping)\\n\\n for trace in traces:\\n i = 1\\n red.publish('info', 'indexing the file: {}'.format(trace))\\n actions = []\\n with open(trace, 'r') as f:\\n for line in f:\\n source = json.loads(line)\\n action = {\\n \\\"_index\\\": index,\\n \\\"_type\\\": type,\\n \\\"_id\\\": i,\\n \\\"_source\\\": {\\n \\\"name\\\": source.get('name'),\\n # \\\"value\\\": correct_value(source.get('value')),\\n \\\"value\\\": source.get('value'),\\n \\\"timestamp\\\": correct_time(source.get('timestamp'))\\n }\\n }\\n actions.append(action)\\n if i % BULK_SIZE == 0:\\n helpers.bulk(es, actions)\\n red.publish('info', '{} events indexed'.format(i))\\n actions = []\\n i += 1\\n # ,overwrite_existing=True\\n # es.index(index=index, doc_type=type, id=i, body=line)\\n if i < BULK_SIZE:\\n helpers.bulk(es, actions)\\n red.publish('info', 'File {} indexed'.format(trace))\\n red.publish('info', 'end_trace'.format(trace))\\n red.publish('info', 'end_all')\\n print('[+] DONE ALL FILES')\\n red.publish('info', 'end_all')\\n red.connection_pool.disconnect()\\n return True\\n sys.exit()\\n except Exception as e:\\n print('Exception: ', e)\\n red.publish('info', 'Exception: {}'.format(e))\\n return False\\n sys.exit()\\n\\n\\n# def realtime_info(info=None):\\n# socketio.emit(\\n# 'send_info',\\n# {\\n# 'data': info.get('data', 'No data!'),\\n# 'status': info.get('status', 'GREEN')\\n# },\\n# namespace='/import'\\n# )\\n\"\n },\n {\n \"alpha_fraction\": 0.8214285969734192,\n \"alphanum_fraction\": 0.8214285969734192,\n \"avg_line_length\": 27,\n \"blob_id\": \"87a24bff92a898a314b1d8fd4bf159fb97449a5a\",\n \"content_id\": \"cbd45b293f8fda71ee62c155e75d5f526886a590\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 28,\n \"license_type\": \"no_license\",\n \"max_line_length\": 27,\n \"num_lines\": 1,\n \"path\": \"/app/carboard/__init__.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from .views import carboard\\n\"\n },\n {\n \"alpha_fraction\": 0.64236980676651,\n \"alphanum_fraction\": 0.6456981301307678,\n \"avg_line_length\": 37.519229888916016,\n \"blob_id\": \"9dd5b5e381c26249014d29b03d609631a742b6f5\",\n \"content_id\": \"1812f978380275e9f7d56d5686dd05c78ecadfff\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6009,\n \"license_type\": \"no_license\",\n \"max_line_length\": 124,\n \"num_lines\": 156,\n \"path\": \"/app/carboard/views/signalclass.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\n\\nfrom flask import (\\n render_template, request, flash, redirect, url_for\\n)\\n\\nfrom flask_login import login_required\\nfrom sqlalchemy import or_\\n\\nfrom ..forms.filesignal import FileSignalForm\\nfrom . import carboard\\nfrom ..models.signalclass import Signalclass\\nfrom ..forms.signalclass import SignalclassForm\\nfrom ..helpers import paginate, CSVLoader, find_id\\nfrom ..constants import PER_PAGE, CSV_TEMP\\nfrom ...extensions import db\\nfrom ..helpers import upload_csv, remove_csv\\n\\n\\n# --------------------- /carboard/signalclass/ : List of signalclasses ----- #\\n\\n\\n@carboard.route('/signalclass/')\\n@login_required\\ndef indexSignalclass():\\n signalclasses = Signalclass.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n start = (request.args.get('page', 1, type=int) - 1) * PER_PAGE + 1 # get the pagination number to render in the template\\n return render_template('carboard/signalclass/index.html', signalclasses=signalclasses, start=start)\\n\\n\\n# ----------------------- /carboard/signalclass/id : Show signalclass ----- #\\n\\n\\n@carboard.route('/signalclass/<int:id>', methods=['GET'])\\n@login_required\\ndef showSignalclass(id):\\n signalclass = Signalclass.query.get_or_404(id)\\n return render_template('carboard/signalclass/show.html', signalclass=signalclass)\\n\\n\\n# ---------------------- /carboard/signalclass/new : Add signalclass -------------------- #\\n\\n@carboard.route('/signalclass/bulk-add', methods=['GET', 'POST'])\\n@login_required\\ndef bulkAddClass():\\n form = FileSignalForm()\\n errors = None\\n if form.validate_on_submit():\\n f = upload_csv(form.file.data, CSV_TEMP)\\n loader = CSVLoader(os.path.join(CSV_TEMP, form.file.data.filename))\\n res = loader.load()\\n already_there = False\\n try:\\n for row in res:\\n ret = Signalclass.query.filter_by(name=row['Signal Class'])\\n if ret:\\n already_there = True\\n continue\\n signal_class = Signalclass(\\n name=row['Signal Class'],\\n description=row['Description']\\n )\\n db.session.add(signal_class)\\n db.session.commit()\\n remove_csv(form.file.data.filename, CSV_TEMP)\\n except KeyError:\\n errors = ['Your file is Not well Formated, please review your file structure .']\\n if not errors and not already_there:\\n flash('Signal classes added Succesfully', 'success')\\n if already_there:\\n flash('Some Signals are not added because they are already in the database', 'warning')\\n return render_template('carboard/signalclass/bulk.html', form=form, errors=errors)\\n\\n\\n@carboard.route('/signalclass/new', methods=['GET', 'POST'])\\n@login_required\\ndef newSignalclass():\\n \\\"\\\"\\\" Add new signalclass \\\"\\\"\\\"\\n form = SignalclassForm()\\n if form.validate_on_submit():\\n ret = Signalclass.query.filter_by(name=form.name.data).first()\\n if ret:\\n flash('Signal class {}, already exists in the database.'.format(form.name.data), 'error')\\n return redirect(url_for('carboard.indexSignalclass'))\\n signalclass = Signalclass(\\n name=form.name.data,\\n description=form.description.data\\n )\\n db.session.add(signalclass)\\n db.session.commit()\\n flash('Signal class {}, added successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.indexSignalclass'))\\n\\n return render_template('carboard/signalclass/new.html', form=form)\\n\\n\\n# -------------------- /carboard/signalclass/id/edit : Edit signalclass ----------------- #\\n\\n\\n@carboard.route('/signalclass/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editSignalclass(id):\\n \\\"\\\"\\\" Edit existing signalclass \\\"\\\"\\\"\\n signalclass = Signalclass.query.get_or_404(id)\\n form = SignalclassForm(obj=signalclass)\\n if form.validate_on_submit():\\n form.populate_obj(signalclass)\\n db.session.commit()\\n flash('Signal class {}, updated successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.showSignalclass', id=id))\\n\\n return render_template('carboard/signalclass/edit.html', form=form, id=id)\\n\\n\\n# ------------------ /carboard/signalclass/id/delete : Delete signalclass --------------- #\\n\\n\\n@carboard.route('/signalclass/<int:id>/toggle', methods=['GET'])\\n@login_required\\ndef toggleSignalclass(id):\\n signalclass = Signalclass.query.get_or_404(id)\\n status = signalclass.status if signalclass.status is not None else 0\\n signalclass.status = 1 - status\\n db.session.commit()\\n msg = 'activated' if signalclass.status is 1 else 'deactivated'\\n flash('Signal class {}, {} successfully.'.format(signalclass.name, msg), 'success')\\n return redirect(url_for('carboard.indexSignalclass'))\\n\\n\\n# ------------------ /carboard/signalclass/id/delete : Delete signalclass --------------- #\\n\\n\\n@carboard.route('/signalclass/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteSignalclass(id):\\n signalclass = Signalclass.query.get_or_404(id)\\n db.session.delete(signalclass)\\n db.session.commit()\\n flash('Signal class {}, deleted successfully.'.format(signalclass.name), 'success')\\n return redirect(url_for('carboard.indexSignalclass'))\\n\\n# ------------------ /carboard/signalclass/search?table_seach=?: Search for given signal class --------------- #\\n\\n@carboard.route('/signalclass/search', methods=['GET'])\\n@login_required\\ndef searchSignalClass():\\n param = request.args.get('table_search')\\n signalclasses = Signalclass.query.filter(\\n or_(Signalclass.name.like('%' + param + '%'), Signalclass.description.like('%' + param + '%'))).paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/signalclass/search.html', signalclasses=signalclasses)\\n\"\n },\n {\n \"alpha_fraction\": 0.5967857837677002,\n \"alphanum_fraction\": 0.6005165576934814,\n \"avg_line_length\": 37.29121017456055,\n \"blob_id\": \"baaa5176944e295085e8731dede29716da32b573\",\n \"content_id\": \"da1b08c91ab76f503c2693171c986df36aecd042\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6969,\n \"license_type\": \"no_license\",\n \"max_line_length\": 130,\n \"num_lines\": 182,\n \"path\": \"/app/carboard/views/signal.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import (\\n render_template, request, flash, redirect, url_for\\n)\\nfrom flask_login import login_required\\nimport os\\n\\nfrom sqlalchemy import or_\\n\\nfrom . import carboard\\nfrom ..models.signal import Signal\\nfrom ..models.signalclass import Signalclass\\nfrom ..models.signalsource import Signalsource\\nfrom ..forms.signal import SignalForm\\nfrom ..forms.filesignal import FileSignalForm\\nfrom ..helpers import paginate, choices, CSVLoader, find_id, types, get_signal_from_form\\nfrom ..constants import PER_PAGE, CSV_TEMP\\nfrom ...extensions import db\\nfrom ..helpers import upload_csv, remove_csv\\n\\n\\n# --------------------- /carboard/signal/ : List of signals ----- #\\n\\n\\n@carboard.route('/signal/')\\n@login_required\\ndef indexSignal():\\n signals = Signal.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n start = (request.args.get('page', 1, type=int) - 1) * PER_PAGE + 1 # get the pagination start number to render in the template\\n return render_template('carboard/signal/index.html', signals=signals, start=start)\\n\\n\\n# ----------------------- /carboard/signal/id : Show signal ----- #\\n\\n\\n@carboard.route('/signal/<int:id>', methods=['GET'])\\n@login_required\\ndef showSignal(id):\\n signal = Signal.query.get_or_404(id)\\n return render_template('carboard/signal/show.html', signal=signal)\\n\\n\\n# ---------------------- /carboard/signal/new : Add signal -------------------- #\\n\\n\\n@carboard.route('/signal/new', methods=['GET', 'POST'])\\n@login_required\\ndef newSignal():\\n \\\"\\\"\\\" Add new signal \\\"\\\"\\\"\\n form = SignalForm()\\n form.signalclass_id.choices = choices(Signalclass, 'Select signal class', 1)\\n form.signalsource_id.choices = choices(Signalsource, 'Select signal source', 1)\\n form.type.choices = types()\\n if form.validate_on_submit():\\n signal = get_signal_from_form(form);\\n db.session.add(signal)\\n db.session.commit()\\n flash('Signal {}, added successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.indexSignal'))\\n\\n return render_template('carboard/signal/new.html', form=form)\\n\\n\\n# ---------------------- /carboard/signal/bulk-add : Add signals from file -------------------- #\\n\\n@carboard.route('/signal/bulk-add', methods=['GET', 'POST'])\\n@login_required\\ndef bulkAdd():\\n form = FileSignalForm()\\n errors = None\\n print(request.form.getlist('files'))\\n if form.validate_on_submit():\\n f = upload_csv(form.file.data, CSV_TEMP)\\n loader = CSVLoader(os.path.join(CSV_TEMP, form.file.data.filename))\\n res = loader.load()\\n already_there = False\\n try:\\n for row in res:\\n print(row)\\n signal_class_id = find_id(Signalclass, row['Signal Class'])\\n signal_source_id = find_id(Signalsource, row['Signal Source'])\\n\\n if signal_class_id == -1 or signal_source_id == -1:\\n if not errors: errors = [] # lazy instatiation\\n errors.append('The Signal ' + row[\\n 'Signal Name'] + ' could not be added because of invalid Signal Class or Signal Source ')\\n continue\\n ret = Signal.query.filter_by(\\n name=row['Signal Name']).all() # check if a signal with the same name already exists\\n same = False\\n for sig in ret:\\n if sig.signalsource_id == signal_source_id:\\n same = True\\n break\\n if same:\\n already_there = True\\n continue\\n signal = Signal(\\n name=row['Signal Name'],\\n signalclass_id=signal_class_id,\\n signalsource_id=signal_source_id,\\n frequency=row['Frequency'],\\n type=row['Type'],\\n description=row['Description'],\\n unit=row['Unit']\\n )\\n signal.range = row['Range']\\n db.session.add(signal)\\n db.session.commit()\\n except KeyError:\\n errors = ['Your file is Not well Formated, please review your file structure .']\\n remove_csv(form.file.data.filename, CSV_TEMP)\\n if not errors: flash('Signals Added Successfully !', 'success')\\n if already_there:\\n flash('Some Signals are not added because they are already in the database', 'warning')\\n return render_template('carboard/signal/bulk.html', form=form, errors=errors)\\n\\n\\n# -------------------- /carboard/signal/id/edit : Edit signal ----------------- #\\n\\n\\n@carboard.route('/signal/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editSignal(id):\\n \\\"\\\"\\\" Edit existing signal \\\"\\\"\\\"\\n signal = Signal.query.get_or_404(id)\\n form = SignalForm(obj=signal)\\n print(form.data)\\n form.signalclass_id.choices = choices(Signalclass, 'Select signal class', 1)\\n form.signalsource_id.choices = choices(Signalsource, 'Select signal source', 1)\\n form.type.choices = types()\\n if form.validate_on_submit():\\n form.populate_obj(signal)\\n db.session.commit()\\n flash('Signal {}, updated successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.showSignal', id=id))\\n\\n return render_template('carboard/signal/edit.html', form=form, id=id)\\n\\n\\n# ------------------ /carboard/signal/id/delete : Delete signal --------------- #\\n\\n\\n@carboard.route('/signal/<int:id>/toggle', methods=['GET'])\\n@login_required\\ndef toggleSignal(id):\\n signal = Signal.query.get_or_404(id)\\n status = signal.status if signal.status is not None else 0\\n signal.status = 1 - status\\n db.session.commit()\\n msg = 'activated' if signal.status is 1 else 'deactivated'\\n flash('Signal {}, {} successfully.'.format(signal.name, msg), 'success')\\n return redirect(url_for('carboard.indexSignal'))\\n\\n\\n# ------------------ /carboard/signal/id/delete : Delete signal --------------- #\\n\\n\\n@carboard.route('/signal/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteSignal(id):\\n signal = Signal.query.get_or_404(id)\\n db.session.delete(signal)\\n db.session.commit()\\n flash('Signal {}, deleted successfully.'.format(signal.name), 'success')\\n return redirect(url_for('carboard.indexSignal'))\\n\\n# ------------------ /carboard/signal/search : signal lookup (by description or name) --------------- #\\n\\n@carboard.route('/signal/search', methods=['GET'])\\n@login_required\\ndef searchSignal():\\n param = request.args.get('table_search')\\n signals = Signal.query.filter(\\n or_(Signal.name.like('%' + param + '%'), Signal.description.like('%' + param + '%')))\\\\\\n .paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/signal/search.html', signals=signals)\\n\"\n },\n {\n \"alpha_fraction\": 0.5780312418937683,\n \"alphanum_fraction\": 0.5906362533569336,\n \"avg_line_length\": 33.020408630371094,\n \"blob_id\": \"9a492fccd9548024ae59843749c070cd8a101cbb\",\n \"content_id\": \"9b0963d9b97b7efab423e30b1b62366f383d0beb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1666,\n \"license_type\": \"no_license\",\n \"max_line_length\": 90,\n \"num_lines\": 49,\n \"path\": \"/app/carboard/models/dataset.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import re\\nfrom datetime import datetime\\n#from ..helpers import slugify\\nfrom unicodedata import normalize\\n\\nfrom ...extensions import db\\n\\n# -------------------------------- Brand Model ------------------------------ #\\n\\ndef slugify(text, delim=u'-'):\\n \\\"\\\"\\\"Generates an slightly worse ASCII-only slug.\\\"\\\"\\\"\\n result = []\\n _punct_re = re.compile(r'[\\\\t !\\\"#$%&\\\\'()*\\\\-/<=>?@\\\\[\\\\\\\\\\\\]^_`{|},.]+')\\n for word in _punct_re.split(text.lower()):\\n word = normalize('NFKD', word).encode('ascii', 'ignore')\\n if word:\\n result.append(word)\\n try:\\n return unicode(delim.join(result))\\n except:\\n d = d = bytes(delim, 'utf-8')\\n return str(d.join(result).decode(\\\"utf-8\\\"))\\nclass Dataset(db.Model):\\n \\\"\\\"\\\" Dataset Model \\\"\\\"\\\"\\n __tablename__ = 'datasets'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.String(255))\\n slug = db.Column(db.String(255))\\n description = db.Column(db.String(255))\\n author = db.Column(db.String(255), nullable=True)\\n lab = db.Column(db.String(255), nullable=True)\\n website = db.Column(db.String(255), nullable=True)\\n\\n article = db.relationship(\\\"Article\\\", uselist=False, back_populates=\\\"dataset\\\")\\n\\n status = db.Column(db.SmallInteger, default=1)\\n created = db.Column(db.DateTime(), default=datetime.utcnow())\\n\\n def __init__(self, name, description, slug=None, author=None, lab=None, website=None):\\n self.name = name\\n self.slug = slug if slug != '' else slugify(name, '_')\\n self.description = description\\n self.author = author\\n self.lab = lab\\n self.website = website\\n\\n def __repr__(self):\\n return self.name\"\n },\n {\n \"alpha_fraction\": 0.419380247592926,\n \"alphanum_fraction\": 0.42121848464012146,\n \"avg_line_length\": 44.33333206176758,\n \"blob_id\": \"f6a37bc13016cf26ff98afb567e3b74f30dbdb84\",\n \"content_id\": \"45d8010a6817d397c547b2463a3cc6f80ddaf1d9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"HTML\",\n \"length_bytes\": 3808,\n \"license_type\": \"no_license\",\n \"max_line_length\": 205,\n \"num_lines\": 84,\n \"path\": \"/templates/carboard/record/index.html\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"{% extends \\\"layout.html\\\" %}\\n\\n{% from \\\"carboard/macros.html\\\" import render_pagination %}\\n\\n{% block title %}\\n List of records | {{ super() }}\\n{% endblock %}\\n\\n{% block content_header %}\\n\\t<section class=\\\"content-header\\\">\\n\\t\\t<h1>\\n\\t\\tList of records\\n\\t\\t</h1>\\n\\t\\t<ol class=\\\"breadcrumb\\\">\\n\\t\\t\\t<li><a href=\\\"{{ url_for('carboard.index') }}\\\"><i class=\\\"fa fa-dashboard\\\"></i>Home</a></li>\\n\\t\\t\\t<li><a href=\\\"{{ url_for('carboard.indexRecord') }}\\\">Record</a></li>\\n\\t\\t\\t<li class=\\\"active\\\">List of records</li>\\n\\t\\t</ol>\\n\\t</section>\\n{% endblock content_header %}\\n\\n{% block content %}\\n\\n <div class=\\\"row\\\">\\n <div class=\\\"col-md-12\\\">\\n <div class=\\\"box\\\">\\n <div class=\\\"box-header\\\">\\n <div class=\\\"box-actions\\\">\\n <a href=\\\"{{ url_for('carboard.newRecord') }}\\\" class=\\\"btn btn-primary\\\">\\n Add new record\\n </a>\\n </div>\\n <div class=\\\"box-tools\\\">\\n <div class=\\\"input-group input-group-sm\\\" style=\\\"width: 150px;\\\">\\n <input type=\\\"text\\\" name=\\\"table_search\\\" class=\\\"form-control pull-right\\\" placeholder=\\\"Search\\\">\\n\\n <div class=\\\"input-group-btn\\\">\\n <button type=\\\"submit\\\" class=\\\"btn btn-default\\\">\\n <i class=\\\"fa fa-search\\\"></i>\\n </button>\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\\\"box-body no-padding\\\">\\n <table class=\\\"table table-hover\\\">\\n <tr>\\n <th>ID</th>\\n <th>Name</th>\\n <th>Record file</th>\\n <th>User</th>\\n <th>Drive type</th>\\n <th>Status</th>\\n <th>Actions</th>\\n </tr>\\n\\n {% for record in records.items %}\\n <tr>\\n <td><a href=\\\"{{ url_for('carboard.showRecord', id=record.id) }}\\\">{{ record.id }}</a></td>\\n <td>{{ record.name | default('--') }}</td>\\n <td><a href=\\\"/static{{ config.UPLOAD_RECORDS}}{{ record.trace}}\\\" target=\\\"_blank\\\">Get File</a></td>\\n <td>{{ record.user.username }}</td>\\n <td>{{ record.drivetype.name }}</td>\\n <td><span class=\\\"label bg-{{ record.status.color }}\\\">{{ record.status.title }}</span></td>\\n <td>\\n <a href=\\\"{{ url_for('carboard.showRecord', id=record.id) }}\\\" class=\\\"btn btn-default btn-sm\\\" data-toggle=\\\"tooltip\\\" title=\\\"Show\\\"><i class=\\\"fa fa-eye\\\"></i></a>\\n\\n <a href=\\\"{{ url_for('carboard.toggleRecord', id=record.id) }}\\\" class=\\\"btn btn-warning btn-sm\\\" data-toggle=\\\"tooltip\\\" title=\\\"Toggle status\\\"><i class=\\\"fa fa-toggle-on\\\"></i></a>\\n\\n <a href=\\\"{{ url_for('carboard.editRecord', id=record.id) }}\\\" class=\\\"btn btn-success btn-sm\\\" data-toggle=\\\"tooltip\\\" title=\\\"Edit\\\"><i class=\\\"fa fa-pencil\\\"></i></a>\\n\\n <a href=\\\"{{ url_for('carboard.deleteRecord', id=record.id) }}\\\" class=\\\"btn btn-danger btn-sm\\\" data-toggle=\\\"tooltip\\\" title=\\\"Remove\\\"><i class=\\\"fa fa-trash\\\"></i></a>\\n </td>\\n </tr>\\n {% endfor %}\\n </table>\\n </div>\\n <div class=\\\"box-footer clearfix\\\">\\n {{render_pagination(records)}}\\n </div>\\n </div>\\n </div>\\n </div>\\n{% endblock %}\\n\"\n },\n {\n \"alpha_fraction\": 0.8381876945495605,\n \"alphanum_fraction\": 0.8414239287376404,\n \"avg_line_length\": 16.16666603088379,\n \"blob_id\": \"366ec87651506558b0771b21ad3ba92c65fc169d\",\n \"content_id\": \"aac0bdbfb0352de358003fc56c246dca574270eb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 309,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 18,\n \"path\": \"/requirements3.txt\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"elasticsearch\\nFlask\\nFlask-Bcrypt\\nFlask-Cache\\nFlask-DebugToolbar\\nFlask-Login\\nFlask-Mail\\nFlask-Migrate\\nFlask-Redis\\nFlask-Script\\nFlask-SQLAlchemy\\nFlask-WTF\\nFlask-Misaka\\nmarshmallow\\nnetworkx\\nPyYAML\\nhttps://github.com/mikeboers/Flask-Images/archive/py3.zip\\nhttps://github.com/vcar/flask-plugins/archive/master.zip\\n\"\n },\n {\n \"alpha_fraction\": 0.6060874462127686,\n \"alphanum_fraction\": 0.6111111044883728,\n \"avg_line_length\": 32.17647171020508,\n \"blob_id\": \"eb1e776adb1be26feb555a982ecf5c8e509f2ad1\",\n \"content_id\": \"3449cba5979d061a951c708a7126fe322a0c116f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3384,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 102,\n \"path\": \"/app/carboard/views/country.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import (\\n render_template, request, flash, redirect, url_for\\n)\\nfrom flask_login import login_required\\n\\nfrom . import carboard\\nfrom ..models.country import Country\\nfrom ..forms.country import CountryForm\\nfrom ..helpers import paginate\\nfrom ..constants import PER_PAGE\\nfrom ...extensions import db\\n\\n\\n# --------------------- /carboard/country/ : List of countries ------------------ #\\n\\n@carboard.route('/country/')\\n@login_required\\ndef indexCountry():\\n countries = Country.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/country/index.html',\\n countries=countries\\n )\\n\\n# ----------------------- /carboard/country/id : Show country ------------------- #\\n\\n\\n@carboard.route('/country/<int:id>', methods=['GET'])\\n@login_required\\ndef showCountry(id):\\n country = Country.query.get_or_404(id)\\n return render_template('carboard/country/show.html',\\n country=country\\n )\\n\\n# ---------------------- /carboard/country/new : Add country -------------------- #\\n\\n\\n@carboard.route('/country/new', methods=['GET', 'POST'])\\n@login_required\\ndef newCountry():\\n \\\"\\\"\\\" Add new country \\\"\\\"\\\"\\n\\n form = CountryForm()\\n\\n if form.validate_on_submit():\\n country = Country(\\n title=form.title.data,\\n code=form.code.data\\n )\\n db.session.add(country)\\n db.session.commit()\\n flash('Country {}, added successfully.'.format(form.title.data), 'success')\\n return redirect(url_for('carboard.indexCountry'))\\n\\n return render_template('carboard/country/new.html', form=form)\\n\\n# -------------------- /carboard/country/id/edit : Edit country ----------------- #\\n\\n\\n@carboard.route('/country/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editCountry(id):\\n \\\"\\\"\\\" Edit existing country \\\"\\\"\\\"\\n country = Country.query.get_or_404(id)\\n form = CountryForm(obj=country)\\n if form.validate_on_submit():\\n form.populate_obj(country)\\n db.session.commit()\\n flash('Country {}, updated successfully.'.format(form.title.data), 'success')\\n return redirect(url_for('carboard.showCountry', id=id))\\n\\n return render_template('carboard/country/edit.html', form=form, id=id)\\n\\n# ------------------ /carboard/country/id/delete : Delete country --------------- #\\n\\n\\n@carboard.route('/country/<int:id>/toggle', methods=['GET'])\\n@login_required\\ndef toggleCountry(id):\\n country = Country.query.get_or_404(id)\\n # getattr(country, 'status', 0)\\n status = country.status if country.status is not None else 0;\\n country.status = 1 - status\\n db.session.commit()\\n msg = 'activated' if country.status is 1 else 'deactivated'\\n flash('Country {}, {} successfully.'.format(country.title, msg), 'success')\\n return redirect(url_for('carboard.indexCountry'))\\n\\n# ------------------ /carboard/country/id/delete : Delete country --------------- #\\n\\n\\n@carboard.route('/country/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteCountry(id):\\n country = Country.query.get_or_404(id)\\n db.session.delete(country)\\n db.session.commit()\\n flash('Country {}, deleted successfully.'.format(country.title), 'success')\\n return redirect(url_for('carboard.indexCountry'))\\n\"\n },\n {\n \"alpha_fraction\": 0.4561251103878021,\n \"alphanum_fraction\": 0.4610483646392822,\n \"avg_line_length\": 26.84677505493164,\n \"blob_id\": \"e7193cef92a958ea6a0e8739690836f80a468f5d\",\n \"content_id\": \"592b9aface79d7c43abfb780f50743e11cf15031\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 3453,\n \"license_type\": \"no_license\",\n \"max_line_length\": 95,\n \"num_lines\": 124,\n \"path\": \"/static/js/scripts/main.js\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/*\\n * Main JS application file : app.js\\n * =========================\\n */\\n\\n$(document).ready(function() {\\n // Switch Theme -------------------------------------------------------------\\n // if ($.cookie(\\\"dark-theme\\\")) {\\n // $('body').removeClass(\\\"light\\\").addClass(\\\"dark\\\");\\n // $('#switch-mode').html('<i class=\\\"fa fa-sun-o\\\"></i> Light mode');\\n // } else {\\n // $('body').removeClass(\\\"dark\\\").addClass(\\\"light\\\");\\n // $('#switch-mode').html('<i class=\\\"fa fa-moon-o\\\"></i> Dark mode');\\n // }\\n $('#switch-mode').click(function() {\\n if ($.cookie(\\\"dark-theme\\\")) {\\n $.removeCookie(\\\"dark-theme\\\", {\\n path: '/'\\n });\\n $('#switch-mode').html('<i class=\\\"fa fa-moon-o\\\"></i> Dark mode');\\n } else {\\n $.cookie(\\\"dark-theme\\\", 1, {\\n expires: 365,\\n path: '/'\\n });\\n $('#switch-mode').html('<i class=\\\"fa fa-sun-o\\\"></i> Light mode');\\n }\\n location.reload();\\n });\\n\\n // Switch Sidebar ---------------------------------------------------------\\n // if ($.cookie(\\\"sidebar-collapse\\\")) {\\n // $('body').removeClass(\\\"light\\\").addClass(\\\"dark\\\");\\n // $('#switch-mode').html('<i class=\\\"fa fa-sun-o\\\"></i> Light mode');\\n // } else {\\n // $('body').removeClass(\\\"dark\\\").addClass(\\\"light\\\");\\n // $('#switch-mode').html('<i class=\\\"fa fa-moon-o\\\"></i> Dark mode');\\n // }\\n $('#switch-sidebar').click(function() {\\n if ($.cookie(\\\"sidebar-collapse\\\")) {\\n $.removeCookie(\\\"sidebar-collapse\\\", {\\n path: '/'\\n });\\n $('#switch-sidebar').html('<i class=\\\"fa fa-minus-square-o\\\"></i> Collapse sidebar');\\n } else {\\n $.cookie(\\\"sidebar-collapse\\\", 1, {\\n expires: 365,\\n path: '/'\\n });\\n $('#switch-sidebar').html('<i class=\\\"fa fa-plus-square-o\\\"></i> Expand sidebar');\\n }\\n location.reload();\\n });\\n});\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nvar toastr = window.toastr;\\ntoastr.options = {\\n closeButton: false,\\n closeEasing: 'swing',\\n // showMethod: 'slideUp',\\n closeMethod: 'slideDown',\\n positionClass: 'toast-bottom-right',\\n progressBar: false\\n};\\n\\n/* turning flask flash messages into js popup notifications */\\n\\nwindow.popupMessages.forEach(function(m, i) {\\n var category = m[0];\\n var text = m[1];\\n setTimeout(function() {\\n switch (category) {\\n case 'success':\\n toastr.success(text);\\n break;\\n case 'warning':\\n toastr.warning(text);\\n break;\\n case 'error':\\n toastr.error(text);\\n break;\\n default:\\n toastr.info(text);\\n break;\\n }\\n }, (1 + i) * 1500);\\n});\\n\\n$(window).load(function() {\\n\\n $('.full').click(function(e) {\\n console.log(\\\"Clicked\\\");\\n $('.vcar-box').toggleClass('fullscreen');\\n });\\n\\n});\\n\\n$(function() {\\n\\n $('input').iCheck({\\n checkboxClass: 'icheckbox_square-orange',\\n radioClass: 'iradio_square-orange',\\n increaseArea: '20%' // optional\\n });\\n\\n // var iradio = $('.iradio_square-orange');\\n // if(iradio.hasClass('.checked')){\\n // \\tiradio.closest('.btn-vcar').addClass('active');\\n // }\\n\\n //Confirmation\\n $('[data-toggle=confirmation]').confirmation({\\n rootSelector: '[data-confirm=confirmation]',\\n });\\n\\n});\\n\"\n },\n {\n \"alpha_fraction\": 0.5389671325683594,\n \"alphanum_fraction\": 0.5389671325683594,\n \"avg_line_length\": 23.76744270324707,\n \"blob_id\": \"fb7793fb23684894a6ce6650975d975e87935973\",\n \"content_id\": \"447c64f2dcd99670a16a62c77c4a8bbe95994157\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1065,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 43,\n \"path\": \"/app/frontend/views.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nimport signal\\nfrom flask import Blueprint, request, render_template\\n\\nfrontend = Blueprint('frontend', __name__, url_prefix='/')\\n\\n# ------------------------------- /api/ : Api ------------------------------- #\\n\\n\\n@frontend.route('/')\\ndef index():\\n home = {\\n 'title': \\\"xx\\\",\\n 'sub_title': \\\"sub title\\\",\\n 'Other': \\\" bla bla bla ...\\\"\\n }\\n return render_template('frontend/index.html')\\n\\n\\n# ---------------------------- /kill : kill Server ------------------------- #\\n\\n\\n@frontend.route('about')\\ndef about():\\n return \\\"About : vCar Platform !\\\"\\n\\n\\n# ---------------------------- /kill : kill Server ------------------------- #\\n\\n\\n@frontend.route('kill')\\ndef killo():\\n func = request.environ.get('werkzeug.server.shutdown')\\n if func is None:\\n raise RuntimeError('Not running with the Werkzeug Server')\\n func()\\n return \\\"Flask Development Server killed successfully!\\\"\\n\\n\\n@frontend.route('restart')\\ndef restart_server():\\n os.kill(os.getpid(), signal.SIGHUP)\\n return \\\"Flask Development Server restarted successfully!\\\"\\n\"\n },\n {\n \"alpha_fraction\": 0.5096439123153687,\n \"alphanum_fraction\": 0.5126112699508667,\n \"avg_line_length\": 23.962963104248047,\n \"blob_id\": \"3c5e55c6c34403e5a9ed2be121d7d16c7d48db14\",\n \"content_id\": \"ed231a271f985e26e73b1a36217da63584c8b097\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1348,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 54,\n \"path\": \"/app/carboard/forms/article.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom wtforms import StringField, DateField\\nfrom wtforms.validators import ValidationError, DataRequired, Optional, Regexp, Length\\nfrom ..models.article import Article\\n\\n# ------------------------ custom validation methods ------------------------ #\\n\\n\\ndef article_exist(form, field):\\n if Article.select().where(Article.name == field.data).exists():\\n raise ValidationError('Article already exists !')\\n\\n# ---------------------------- Article form classes ------------------------- #\\n\\n\\nclass ArticleForm(FlaskForm):\\n \\\"\\\"\\\" Article add/edit FlaskForm \\\"\\\"\\\"\\n name = StringField(\\n 'Name',\\n validators=[\\n DataRequired(),\\n Length(min=3, max=255),\\n Regexp(\\n r'^[a-zA-Z_\\\\- ]+$',\\n message=(\\\"Article name is not correct !\\\")\\n ),\\n ]\\n )\\n abstract = StringField(\\n 'Abstract',\\n validators=[\\n DataRequired(),\\n ]\\n )\\n authors = StringField(\\n 'Authors',\\n )\\n keywords = StringField(\\n 'Keywords',\\n )\\n publication_date = DateField(\\n 'Date of publication',\\n format='%d/%m/%Y',\\n validators=[\\n Optional(),\\n ]\\n )\\n\\n reference = StringField(\\n 'Reference',\\n )\\n link = StringField(\\n 'Link to article',\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.3785078823566437,\n \"alphanum_fraction\": 0.4024640619754791,\n \"avg_line_length\": 23.74576187133789,\n \"blob_id\": \"6c91f9db43fd964e40359401a63933f16b11ec99\",\n \"content_id\": \"5bf76b0f45028a4428f115f216fb0feea108fdcd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1461,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 59,\n \"path\": \"/app/importer/platforms/openxc/constants.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n# -------------------- Redis constants -------------------------------------- #\\nREDIS_HOST = '127.0.0.1' # default config when runing inside a virtualenv\\n# REDIS_HOST = 'redis' # docker links\\nREDIS_PORT = 6379\\nREDIS_DB = 1\\n\\n# -------------------- Elasticsearch constants ------------------------------ #\\n\\n# basic configuration\\nELASTICSEARCH_HOST = '127.0.0.1' # default config when runing inside a virtualenv\\n# ELASTICSEARCH_HOST = 'elastic' # docker links\\nELASTICSEARCH_PORT = 9200\\n\\n# indexing configuration\\nOPENXC_INDEX = 'openxc'\\nBULK_SIZE = 10000\\n\\n# -------------------- Driver Graph Configuration --------------------------- #\\nTRANSFORM = {\\n -1: \\\"reverse\\\",\\n 0: \\\"neutral\\\",\\n 1: \\\"first\\\",\\n 2: \\\"second\\\",\\n 3: \\\"third\\\",\\n 4: \\\"fourth\\\",\\n 5: \\\"fifth\\\",\\n 6: \\\"sixth\\\",\\n 7: \\\"seventh\\\"\\n}\\n\\nMAPPING = {\\n \\\"platform\\\": {\\n \\\"properties\\\": {\\n \\\"user\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n },\\n \\\"vehicle\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n },\\n \\\"driver\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n },\\n \\\"class\\\": {\\n \\\"type\\\": \\\"string\\\"\\n },\\n \\\"name\\\": {\\n \\\"type\\\": \\\"string\\\",\\n \\\"index\\\": \\\"not_analyzed\\\"\\n },\\n \\\"value\\\": {\\n \\\"type\\\": \\\"string\\\",\\n \\\"index\\\": \\\"not_analyzed\\\"\\n },\\n \\\"timestamp\\\": {\\n \\\"type\\\": \\\"date\\\"\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.43503835797309875,\n \"alphanum_fraction\": 0.4544757008552551,\n \"avg_line_length\": 33.54545593261719,\n \"blob_id\": \"bb1f8eaa1602381a1358f7757141fa52962ce10e\",\n \"content_id\": \"8859099511b3b8d8ca2eb2366c61e7a91015e2bd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 3910,\n \"license_type\": \"no_license\",\n \"max_line_length\": 205,\n \"num_lines\": 110,\n \"path\": \"/static/js/scripts/uploader.js\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"$(function() {\\r\\n\\r\\n var ul = $('#upload ul');\\r\\n var acceptFileTypes = /^(application\\\\/json|text\\\\/plain|text\\\\/csv)$/i;\\r\\n var extensions = ['json', 'txt', 'csv', 'xml', 'mat', 'zip'];\\r\\n\\r\\n $('#drop a').click(function() {\\r\\n $(this).parent().find('input').click();\\r\\n });\\r\\n\\r\\n // Initialize the jQuery File Upload plugin\\r\\n $('#upload').fileupload({\\r\\n\\r\\n dropZone: $('#drop'),\\r\\n\\r\\n add: function(e, data) {\\r\\n var uploadError = true;\\r\\n var re = /(?:\\\\.([^.]+))?$/;\\r\\n var ext = re.exec(data.files[0]['name'])[1];\\r\\n // Check whether a file is accepted or not.\\r\\n\\r\\n if (ext != undefined && ext.length && (extensions.indexOf(ext) >= 0)) {\\r\\n uploadError = false;\\r\\n }\\r\\n if (!uploadError) {\\r\\n // if File Accepted :\\r\\n var tpl = $('<li class=\\\"working\\\"><input type=\\\"text\\\" value=\\\"0\\\" data-width=\\\"48\\\" data-height=\\\"48\\\" data-fgColor=\\\"#0788a5\\\" data-readOnly=\\\"1\\\" data-bgColor=\\\"#3e4043\\\" /><p></p><span></span></li>');\\r\\n tpl.find('p').text(data.files[0].name).append('<i>' + formatFileSize(data.files[0].size) + '</i>');\\r\\n data.context = tpl.appendTo(ul);\\r\\n tpl.find('input').knob({\\r\\n fgColor: \\\"#03A100\\\"\\r\\n });\\r\\n\\r\\n tpl.find('span').click(function() {\\r\\n if (tpl.hasClass('working')) {\\r\\n jqXHR.abort();\\r\\n }\\r\\n tpl.fadeOut(function() {\\r\\n tpl.remove();\\r\\n });\\r\\n });\\r\\n var jqXHR = data.submit();\\r\\n } else {\\r\\n // if Error\\r\\n var tpl = $('<li class=\\\"error working\\\"><div class=\\\"bad-extension\\\"></div><p></p><span></span></li>');\\r\\n tpl.find('p').text(data.files[0].name).append('<i>Extension not supported</i>');\\r\\n data.context = tpl.appendTo(ul);\\r\\n tpl.find('span').click(function() {\\r\\n tpl.fadeOut(function() {\\r\\n tpl.remove();\\r\\n });\\r\\n });\\r\\n }\\r\\n },\\r\\n\\r\\n progress: function(e, data) {\\r\\n\\r\\n // Calculate the completion percentage of the upload\\r\\n var progress = parseInt(data.loaded / data.total * 100, 10);\\r\\n\\r\\n // Update the hidden input field and trigger a change\\r\\n // so that the jQuery knob plugin knows to update the dial\\r\\n data.context.find('input').val(progress).change();\\r\\n\\r\\n if (progress == 100) {\\r\\n data.context.removeClass('working');\\r\\n }\\r\\n },\\r\\n\\r\\n fail: function(e, data) {\\r\\n // Something has gone wrong!\\r\\n data.context.addClass('error');\\r\\n\\r\\n },\\r\\n\\r\\n done: function(e, data) {\\r\\n var result = JSON.parse(data.result);\\r\\n if (result.status) {\\r\\n $('#send_files').append('<input type=\\\"hidden\\\" name=\\\"files\\\" value=\\\"' + result.message + '\\\" />');\\r\\n } else {\\r\\n data.context.addClass('error');\\r\\n data.context.find('p').find('i').html(\\\" \\\" + result.message).addClass('fa fa-exclamation-circle');\\r\\n }\\r\\n }\\r\\n\\r\\n });\\r\\n\\r\\n // Prevent the default action when a file is dropped on the window\\r\\n $(document).on('drop dragover', function(e) {\\r\\n e.preventDefault();\\r\\n });\\r\\n\\r\\n // Helper function that formats the file sizes\\r\\n function formatFileSize(bytes) {\\r\\n if (typeof bytes !== 'number') {\\r\\n return '';\\r\\n }\\r\\n\\r\\n if (bytes >= 1000000000) {\\r\\n return (bytes / 1000000000).toFixed(2) + ' GB';\\r\\n }\\r\\n\\r\\n if (bytes >= 1000000) {\\r\\n return (bytes / 1000000).toFixed(2) + ' MB';\\r\\n }\\r\\n\\r\\n return (bytes / 1000).toFixed(2) + ' KB';\\r\\n }\\r\\n\\r\\n});\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5564159154891968,\n \"alphanum_fraction\": 0.5652654767036438,\n \"avg_line_length\": 28.129032135009766,\n \"blob_id\": \"7b8277514c35a037cdd16c892f4cd6299112855a\",\n \"content_id\": \"bbdbcbba5fc6615ccd6ee258ba77bcb40e2510ef\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 904,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 31,\n \"path\": \"/app/carboard/forms/signalclass.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom wtforms import StringField, TextAreaField\\nfrom wtforms.validators import DataRequired, Regexp, Length\\n\\nfrom ..models.signalclass import Signalclass\\n# ------------------------ custom validation methods ------------------------ #\\n\\ndef signalclass_exist(form, field):\\n if Signalclass.select().where(Signalclass.name == field.data).exists():\\n raise ValidationError('Attribute already exists !')\\n\\n# ---------------------------- Driver form classes ---------------------------- #\\n\\n\\nclass SignalclassForm(FlaskForm):\\n \\\"\\\"\\\" Signalclass add/edit FlaskForm \\\"\\\"\\\"\\n name = StringField(\\n 'Name',\\n validators=[\\n DataRequired(),\\n Length(min=3, max=50),\\n # signalclass_exist()\\n ]\\n )\\n\\n description = TextAreaField(\\n 'Description',\\n validators=[\\n Length(min=0,max=1000)\\n ]\\n )\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6693877577781677,\n \"alphanum_fraction\": 0.6816326379776001,\n \"avg_line_length\": 17.846153259277344,\n \"blob_id\": \"876283a953a2af4c0c5861ec386eda8e4863641f\",\n \"content_id\": \"4657ecce0fa319da8d80618c15d361febb83ef21\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 245,\n \"license_type\": \"permissive\",\n \"max_line_length\": 38,\n \"num_lines\": 13,\n \"path\": \"/app/plugins/hello_world/__init__.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from os import getcwd\\nfrom app.plugin import AppPlugin\\nfrom .src.views import hello\\n\\n__plugin__ = \\\"HelloWorld\\\"\\n__version__ = \\\"1.0.0\\\"\\n__path__ = getcwd()\\n\\n\\nclass HelloWorld(AppPlugin):\\n\\n def setup(self):\\n self.register_blueprint(hello)\\n\"\n },\n {\n \"alpha_fraction\": 0.5905323624610901,\n \"alphanum_fraction\": 0.5935607552528381,\n \"avg_line_length\": 25.927038192749023,\n \"blob_id\": \"fdb1fc430e47b186d85f412e79f135548463fc66\",\n \"content_id\": \"eaa79974d810fec888714824c2eb98fdc9983e8a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6274,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 233,\n \"path\": \"/main.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import Flask, render_template, g\\nimport flask_plugins\\n\\nfrom config.config import DefaultConfig\\nfrom config.database import DatabaseConfig\\nfrom config.mail import MailConfig\\n\\nfrom app.frontend import frontend\\nfrom app.carboard import carboard\\nfrom app.importer import importer\\nfrom app.explorer import explorer\\n# from app.dashboard import dashboard\\nfrom app.api import api\\nfrom app.filters import filters\\n\\nfrom app.carboard.models.user import User\\nfrom app.extensions import (\\n db, migrate, mail, toolbar, images, login_manager, redis,\\n plugin_manager, socketio, misaka # , cache , session\\n)\\n\\n\\n__all__ = ['create_app']\\n\\n# ----------------------------- Blueprints List ----------------------------- #\\n\\nDEFAULT_BLUEPRINTS = (\\n api,\\n frontend,\\n carboard,\\n # dashboard,\\n importer,\\n explorer,\\n filters\\n)\\n\\n# ------------------------ Create a flask application ----------------------- #\\n\\n\\ndef create_app(config=None, app_name=None, blueprints=None):\\n \\\"\\\"\\\"Create a flask application.\\\"\\\"\\\"\\n\\n if app_name is None:\\n app_name = DefaultConfig.PROJECT\\n if blueprints is None:\\n blueprints = DEFAULT_BLUEPRINTS\\n # Create a flask application instance\\n app = Flask(\\n app_name,\\n # static_url_path='',\\n instance_path=DefaultConfig.INSTANCE_FOLDER_PATH,\\n instance_relative_config=True\\n )\\n # jinja configuration\\n app.jinja_env.trim_blocks = True\\n app.jinja_env.lstrip_blocks = True\\n # App configuration\\n configure_app(app, config)\\n # Static configuration\\n static_file_status(app)\\n # Before request\\n configure_hook(app)\\n # Register blueprints\\n configure_blueprints(app, blueprints)\\n # Inisialize extensions\\n configure_extensions(app)\\n # Template filters\\n configure_template_filters(app)\\n # Register plugins menu in g\\n register_plugins_menu(app)\\n # Error handling\\n configure_error_handlers(app)\\n\\n return app\\n\\n\\n# --------------------------- Minified static files ------------------------ #\\n\\ndef static_file_status(app, debug=None):\\n \\\"\\\"\\\"Use minified static files if debug is True\\\"\\\"\\\"\\n if debug is None:\\n debug = app.config['DEBUG']\\n minified = \\\".min\\\" if debug is False else \\\"\\\"\\n app.jinja_env.globals.update(minified=minified)\\n app.jinja_env.add_extension('jinja2.ext.do')\\n\\n# ----------------------------- App configuration --------------------------- #\\n\\n\\ndef configure_app(app, config=None):\\n \\\"\\\"\\\"App configuration.\\\"\\\"\\\"\\n\\n app.config.from_object(DefaultConfig)\\n app.config.from_object(DatabaseConfig)\\n app.config.from_object(MailConfig)\\n if config:\\n app.config.from_object(config)\\n\\n# ------------------------------- Before & After request -------------------- #\\n\\n\\ndef configure_hook(app):\\n @app.before_request\\n def before_request():\\n pass\\n\\n @app.after_request\\n def apply_caching(response):\\n response.headers['Cache-Control'] = 'no-cache, no-store, must-revalidate'\\n return response\\n# ---------------------------- Register blueprints ------------------------- #\\n\\n\\ndef configure_blueprints(app, blueprints):\\n \\\"\\\"\\\"Configure blueprints in views.\\\"\\\"\\\"\\n\\n for blueprint in blueprints:\\n app.register_blueprint(blueprint)\\n\\n# --------------------------- Inisialize extensions ------------------------ #\\n\\n\\ndef configure_extensions(app):\\n\\n # flask-sqlalchemy\\n db.init_app(app)\\n\\n # flask-migrate\\n migrate.init_app(app, db)\\n\\n # flask-mail\\n mail.init_app(app)\\n # flask-cache\\n # cache.init_app(app)\\n\\n # initialize debug toolbar\\n # toolbar.init_app(app)\\n\\n # initialize Images utils\\n images.init_app(app)\\n\\n # initialize FlaskRedis\\n redis.init_app(app)\\n\\n # initialize FlaskRedis\\n misaka.init_app(app)\\n\\n # initialize SocketIO\\n socketio.init_app(app)\\n\\n # initialize Session\\n # session.init_app(app)\\n\\n # initialize pluginManager\\n # plugin_manager.init_app(\\n # app,\\n # base_app_folder='app',\\n # plugin_folder=\\\"app/plugins\\\"\\n # )\\n plugin_manager.init_app(\\n app,\\n plugin_folder=\\\"app/plugins\\\",\\n plugin_import_path='app.plugins',\\n )\\n\\n # flask-login\\n login_manager.login_view = 'carboard.login'\\n login_manager.refresh_view = 'carboard.reauth'\\n\\n @login_manager.user_loader\\n def load_user(id):\\n return User.query.get(id)\\n login_manager.setup_app(app)\\n\\n# ------------------------------ Template filters -------------------------- #\\n\\n\\ndef configure_template_filters(app):\\n\\n @app.template_filter()\\n def pretty_date(value):\\n return pretty_date(value)\\n\\n @app.template_filter()\\n def format_date(value, format='%Y-%m-%d'):\\n return value.strftime(format)\\n\\n# ------------------------------ Plugins menus ----------------------------- #\\n\\n\\ndef convert_keys_to_string(dictionary):\\n \\\"\\\"\\\"Recursively converts dictionary keys to strings.\\\"\\\"\\\"\\n if not isinstance(dictionary, dict):\\n return dictionary\\n return dict((str(k), str(convert_keys_to_string(v)))\\n for k, v in dictionary.items())\\n\\n\\ndef register_plugins_menu(app):\\n\\n @app.context_processor\\n def configure_plugins_menu():\\n plugins = flask_plugins.get_all_plugins()\\n\\n plugins_menu = []\\n for plugin in plugins:\\n if plugin.options['favorite']:\\n plugins_menu.append({\\n 'id': str(plugin.identifier),\\n 'name': str(plugin.name),\\n 'iclass': plugin.options.get('iclass', 'fa fa-puzzle-piece'),\\n 'favorite': plugin.options.get('favorite', False),\\n 'treeview': plugin.options.get('treeview', False),\\n 'menu': plugin.options.get('menu', None),\\n })\\n return {'plugins_menu': plugins_menu}\\n\\n# ------------------------------- Error handling --------------------------- #\\n\\n\\ndef configure_error_handlers(app):\\n\\n @app.errorhandler(403)\\n def forbidden_page(error):\\n return render_template(\\\"errors/forbidden_page.html\\\"), 403\\n\\n @app.errorhandler(404)\\n def page_not_found(error):\\n return render_template(\\\"errors/page_not_found.html\\\"), 404\\n\\n @app.errorhandler(500)\\n def server_error_page(error):\\n return render_template(\\\"errors/server_error.html\\\"), 500\\n\"\n },\n {\n \"alpha_fraction\": 0.6039374470710754,\n \"alphanum_fraction\": 0.6137810945510864,\n \"avg_line_length\": 30.399999618530273,\n \"blob_id\": \"157e0e127f255feff0af887ff00185a3b85f2b80\",\n \"content_id\": \"cb68e824d60ba07b909c2d76b56529f3b9a6e628\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1727,\n \"license_type\": \"no_license\",\n \"max_line_length\": 109,\n \"num_lines\": 55,\n \"path\": \"/app/carboard/models/signal.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom marshmallow import Schema, fields\\nfrom ...extensions import db\\n\\n\\n# -------------------------------- Signal Model ------------------------------- #\\n\\n\\nclass Signal(db.Model):\\n \\\"\\\"\\\" Signal Model: \\\"\\\"\\\"\\n __tablename__ = 'signals'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.String(255))\\n description = db.Column(db.Text)\\n type = db.Column(db.String(255))\\n range = db.Column(db.String(255))\\n frequency = db.Column(db.String(255))\\n unit = db.Column(db.String(255))\\n\\n extrasignals = db.relationship(\\\"Extrasignal\\\")\\n\\n signalclass_id = db.Column(db.Integer, db.ForeignKey('signalclasses.id'))\\n signalclass = db.relationship(\\\"Signalclass\\\", back_populates=\\\"signals\\\")\\n\\n signalsource_id = db.Column(db.Integer, db.ForeignKey('signalsources.id'))\\n signalsource = db.relationship(\\\"Signalsource\\\", back_populates=\\\"signals\\\")\\n\\n status = db.Column(db.SmallInteger, default=1)\\n created = db.Column(db.DateTime(), default=datetime.utcnow())\\n\\n def __init__(self, name, signalclass_id, signalsource_id, frequency, type, description, unit, status=1 ):\\n self.name = name\\n self.signalclass_id = signalclass_id\\n self.signalsource_id = signalsource_id\\n self.status = status\\n self.frequency = frequency\\n self.type = type\\n self.description = description\\n self.unit = unit\\n\\n def __repr__(self):\\n return str(self.name)\\n\\n\\n# -------------------------------- Signal Model ----------------------------- #\\n\\n\\nclass SignalSchema(Schema):\\n \\\"\\\"\\\" Extra signals schema \\\"\\\"\\\"\\n\\n id = fields.Int(dump_only=True)\\n name = fields.Str()\\n signalclass = fields.Str()\\n signalsource = fields.Str()\\n\"\n },\n {\n \"alpha_fraction\": 0.6158388257026672,\n \"alphanum_fraction\": 0.6193122863769531,\n \"avg_line_length\": 32.47674560546875,\n \"blob_id\": \"84bc3f3b98034ed60709f6a6f74c5c12ff015cb2\",\n \"content_id\": \"e28bfba94241fb9777b32080ffbc3c27a5b88ef2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2879,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 86,\n \"path\": \"/app/carboard/views/drivetype.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import (\\n render_template, request, flash, redirect, url_for\\n)\\nfrom flask_login import login_required\\n\\nfrom . import carboard\\nfrom ..models.drivetype import DriveType\\nfrom ..forms.drivetype import DriveTypeForm\\nfrom ..helpers import paginate\\nfrom ..constants import PER_PAGE\\nfrom ...extensions import db\\n\\n\\n# --------------------- /carboard/drivetype/ : List of countries ------------------ #\\n\\n@carboard.route('/drivetype/')\\n@login_required\\ndef indexDriveType():\\n drivetypes = DriveType.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/drivetype/index.html',\\n drivetypes=drivetypes\\n )\\n\\n# ----------------------- /carboard/drivetype/id : Show drivetype ------------------- #\\n\\n\\n@carboard.route('/drivetype/<int:id>', methods=['GET'])\\n@login_required\\ndef showDriveType(id):\\n drivetype = DriveType.query.get_or_404(id)\\n return render_template('carboard/drivetype/show.html',\\n drivetype=drivetype\\n )\\n\\n# ---------------------- /carboard/drivetype/new : Add drivetype -------------------- #\\n\\n\\n@carboard.route('/drivetype/new', methods=['GET', 'POST'])\\n@login_required\\ndef newDriveType():\\n \\\"\\\"\\\" Add new driver type \\\"\\\"\\\"\\n\\n form = DriveTypeForm()\\n\\n if form.validate_on_submit():\\n drivetype = DriveType(\\n name=form.name.data\\n )\\n db.session.add(drivetype)\\n db.session.commit()\\n flash('Driver type {}, added successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.indexDriveType'))\\n\\n return render_template('carboard/drivetype/new.html', form=form)\\n\\n# -------------------- /carboard/drivetype/id/edit : Edit drivetype ----------------- #\\n\\n\\n@carboard.route('/drivetype/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editDriveType(id):\\n \\\"\\\"\\\" Edit existing driver type \\\"\\\"\\\"\\n drivetype = DriveType.query.get_or_404(id)\\n form = DriveTypeForm(obj=drivetype)\\n if form.validate_on_submit():\\n form.populate_obj(drivetype)\\n db.session.commit()\\n flash('Driver type {}, updated successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.showDriveType', id=id))\\n\\n return render_template('carboard/drivetype/edit.html', form=form, id=id)\\n\\n# ------------------ /carboard/drivetype/id/delete : Delete drivetype --------------- #\\n\\n\\n@carboard.route('/drivetype/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteDriveType(id):\\n drivetype = DriveType.query.get_or_404(id)\\n db.session.delete(drivetype)\\n db.session.commit()\\n flash('Driver type {}, deleted successfully.'.format(drivetype.name), 'success')\\n return redirect(url_for('carboard.indexDriveType'))\\n\"\n },\n {\n \"alpha_fraction\": 0.4955640137195587,\n \"alphanum_fraction\": 0.5044360160827637,\n \"avg_line_length\": 25.299999237060547,\n \"blob_id\": \"bf7b5431db1e30c928452dfa315dccda9555651d\",\n \"content_id\": \"029fbbe989dca0f19259f776646c7f7ca051d49a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 789,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 30,\n \"path\": \"/app/filters.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import jinja2\\nimport flask\\n\\nfrom .carboard.constants import ROLES, MIMETYPE\\n\\nfilters = flask.Blueprint('filters', __name__)\\n\\n# ---------------------- -- -- -- -- -- -- -- -- -- -- ---------------------- #\\n\\n\\n@jinja2.contextfilter\\n@filters.app_template_filter()\\ndef roles(context, value):\\n \\\"\\\"\\\" Return the Role name from ROLES list.\\\"\\\"\\\"\\n return ROLES[value][1]\\n# ---------------------- -- -- -- -- -- -- -- -- -- -- ---------------------- #\\n\\n\\n@jinja2.contextfilter\\n@filters.app_template_filter()\\ndef mimetypes(context, value):\\n \\\"\\\"\\\" Return the Format name from MIMETYPE list.\\\"\\\"\\\"\\n return MIMETYPE[value][1]\\n# ---------------------- -- -- -- -- -- -- -- -- -- -- ---------------------- #\\n\\n\\n@jinja2.contextfilter\\n@filters.app_template_filter()\\ndef status(context, value):\\n return 2\\n\"\n },\n {\n \"alpha_fraction\": 0.5395408272743225,\n \"alphanum_fraction\": 0.5497449040412903,\n \"avg_line_length\": 22.75757598876953,\n \"blob_id\": \"944d206274d05291e2ae630dde8d5bc5e53fbfbc\",\n \"content_id\": \"769c1dcc57cba0760a30819d85dc893f89e943a6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 784,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 33,\n \"path\": \"/app/carboard/forms/signalsource.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom wtforms import StringField, TextAreaField\\nfrom wtforms.validators import DataRequired, Regexp, Length\\n\\nfrom ..models.signalsource import Signalsource\\n\\n\\n# ------------------------ custom validation methods ------------------------ #\\n\\ndef signalsource_exist(form, field):\\n pass\\n\\n\\n# ---------------------------- Signalsource form classes -------------------- #\\n\\n\\nclass SignalsourceForm(FlaskForm):\\n \\\"\\\"\\\" Signalsource add/edit FlaskForm \\\"\\\"\\\"\\n name = StringField(\\n 'Name',\\n validators=[\\n DataRequired(),\\n Length(min=3, max=50),\\n # Signalsource_exist()\\n ]\\n )\\n\\n description = TextAreaField(\\n 'Description',\\n validators=[\\n Length(min=0, max=1000)\\n ]\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.6364645957946777,\n \"alphanum_fraction\": 0.63979572057724,\n \"avg_line_length\": 35.60975646972656,\n \"blob_id\": \"950a520400fcdb690e3bb8a3666677228cf5fba5\",\n \"content_id\": \"aefab2b419eab46a6c67f69531862d361d09c207\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4503,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 123,\n \"path\": \"/app/carboard/views/vehicle.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import (\\n render_template, request, flash, redirect, url_for\\n)\\nfrom flask_login import login_required\\n\\nfrom . import carboard\\nfrom ..models.vehicle import Vehicle\\nfrom ..models.user import User\\nfrom ..models.brand import Brand\\nfrom ..models.model import Model\\nfrom ..models.driver import Driver\\nfrom ..models.status import Status\\nfrom ..forms.vehicle import VehicleForm, VehicleStatusForm\\nfrom ..helpers import paginate, choices, upload_file\\nfrom ..constants import PER_PAGE, VEHICLE_LOGO_DIR\\nfrom ...extensions import db\\n\\n# --------------------- /carboard/vehicle/ : List of vehicles ------------------ #\\n\\n@carboard.route('/vehicle/')\\n@login_required\\ndef indexVehicle():\\n vehicles = Vehicle.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/vehicle/index.html', vehicles=vehicles)\\n\\n# ----------------------- /carboard/vehicle/id : Show vehicle ------------------- #\\n\\n\\n@carboard.route('/vehicle/<int:id>', methods=['GET'])\\n@login_required\\ndef showVehicle(id):\\n vehicle = Vehicle.query.get_or_404(id)\\n return render_template('carboard/vehicle/show.html', vehicle=vehicle)\\n\\n# ---------------------- /carboard/vehicle/new : Add vehicle -------------------- #\\n\\n\\n@carboard.route('/vehicle/new', methods=['GET', 'POST'])\\n@login_required\\ndef newVehicle():\\n \\\"\\\"\\\" Add new vehicle \\\"\\\"\\\"\\n\\n form = VehicleForm()\\n form.user_id.choices = choices(User, 'Select a user', 1, 'username')\\n form.brand_id.choices = choices(Brand, 'Select a brand')\\n form.model_id.choices = choices(Model, 'Select a model')\\n form.driver_id.choices = choices(Driver, 'Select a Driver', False, 'fullname')\\n\\n if form.validate_on_submit():\\n image = upload_file(form.image.data, VEHICLE_LOGO_DIR)\\n vehicle = Vehicle(\\n user_id=form.user_id.data,\\n brand_id=form.brand_id.data,\\n model_id=form.model_id.data,\\n driver_id=form.driver_id.data,\\n image=image\\n )\\n db.session.add(vehicle)\\n db.session.commit()\\n flash('Vehicle {}, added successfully.'.format(vehicle), 'success')\\n return redirect(url_for('carboard.indexVehicle'))\\n\\n return render_template('carboard/vehicle/new.html', form=form)\\n\\n# -------------------- /carboard/vehicle/id/edit : Edit vehicle ----------------- #\\n\\n\\n@carboard.route('/vehicle/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editVehicle(id):\\n \\\"\\\"\\\" Edit existing vehicle \\\"\\\"\\\"\\n vehicle = Vehicle.query.get_or_404(id)\\n oldImage = vehicle.image\\n\\n form = VehicleForm(obj=vehicle)\\n form.user_id.choices = choices(User, 'Select a user', 1, 'username')\\n form.brand_id.choices = choices(Brand, 'Select a brand')\\n form.model_id.choices = choices(Model, 'Select a model')\\n form.driver_id.choices = choices(Driver, 'Select a Driver', False, 'fullname')\\n\\n if form.validate_on_submit():\\n form.populate_obj(vehicle)\\n image = upload_file(form.image.data, VEHICLE_LOGO_DIR)\\n if image is None:\\n vehicle.image = oldImage\\n else:\\n vehicle.image = image\\n db.session.commit()\\n flash('Vehicle {}, updated successfully.'.format(vehicle), 'success')\\n return redirect(url_for('carboard.showVehicle', id=id))\\n\\n return render_template('carboard/vehicle/edit.html', form=form, id=id)\\n\\n# ------------------ /carboard/vehicle/id/delete : Delete vehicle --------------- #\\n\\n\\n@carboard.route('/vehicle/<int:id>/toggle', methods=['GET', 'POST'])\\n@login_required\\ndef toggleVehicle(id):\\n vehicle = Vehicle.query.get_or_404(id)\\n form = VehicleStatusForm(obj=vehicle)\\n form.status_id.choices = [(s.id, s) for s in Status.query.all()]\\n if form.validate_on_submit():\\n form.populate_obj(vehicle)\\n db.session.commit()\\n flash('Vehicle {}, updated successfully.'.format(vehicle.brand), 'success')\\n return redirect(url_for('carboard.showVehicle', id=id))\\n return render_template('carboard/vehicle/status.html', form=form, id=id)\\n\\n# ------------------ /carboard/vehicle/id/delete : Delete vehicle --------------- #\\n\\n\\n@carboard.route('/vehicle/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteVehicle(id):\\n vehicle = Vehicle.query.get_or_404(id)\\n db.session.delete(vehicle)\\n db.session.commit()\\n flash('Vehicle {}, deleted successfully.'.format(vehicle), 'success')\\n return redirect(url_for('carboard.indexVehicle'))\\n\"\n },\n {\n \"alpha_fraction\": 0.6046239137649536,\n \"alphanum_fraction\": 0.6095990538597107,\n \"avg_line_length\": 31.542856216430664,\n \"blob_id\": \"ccb2b87e62a52bbf934cd2f2cfe1ee5835f88e66\",\n \"content_id\": \"67bb4fcd076245e6ca931fab9a2b15f71660b7e3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3417,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 105,\n \"path\": \"/app/carboard/views/brand.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import (\\n render_template, request, flash, redirect, url_for\\n)\\nfrom flask_login import login_required\\n\\nfrom . import carboard\\nfrom ..models.brand import Brand\\nfrom ..forms.brand import BrandForm\\nfrom ..helpers import paginate, upload_file\\nfrom ..constants import PER_PAGE, BRAND_LOGO_DIR\\nfrom ...extensions import db\\n\\n# --------------------- /carboard/brand/ : List of brands ------------------ #\\n\\n@carboard.route('/brand/')\\n@login_required\\ndef indexBrand():\\n brands = Brand.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/brand/index.html', brands=brands)\\n\\n# ----------------------- /carboard/brand/id : Show brand ------------------- #\\n\\n\\n@carboard.route('/brand/<int:id>', methods=['GET'])\\n@login_required\\ndef showBrand(id):\\n brand = Brand.query.get_or_404(id)\\n return render_template('carboard/brand/show.html', brand=brand)\\n\\n# ---------------------- /carboard/brand/new : Add brand -------------------- #\\n\\n\\n@carboard.route('/brand/new', methods=['GET', 'POST'])\\n@login_required\\ndef newBrand():\\n \\\"\\\"\\\" Add new brand \\\"\\\"\\\"\\n\\n form = BrandForm()\\n\\n if form.validate_on_submit():\\n logo = upload_file(form.logo.data, BRAND_LOGO_DIR)\\n brand = Brand(\\n name=form.name.data,\\n code=form.code.data,\\n logo=logo\\n )\\n db.session.add(brand)\\n db.session.commit()\\n flash('Brand {}, added successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.indexBrand'))\\n\\n return render_template('carboard/brand/new.html', form=form)\\n\\n# -------------------- /carboard/brand/id/edit : Edit brand ----------------- #\\n\\n\\n@carboard.route('/brand/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editBrand(id):\\n \\\"\\\"\\\" Edit existing brand \\\"\\\"\\\"\\n brand = Brand.query.get_or_404(id)\\n oldLogo = brand.logo\\n form = BrandForm(obj=brand)\\n if form.validate_on_submit():\\n form.populate_obj(brand)\\n logo = upload_file(form.logo.data, BRAND_LOGO_DIR)\\n if logo is None:\\n brand.logo = oldLogo\\n else:\\n brand.logo = logo\\n db.session.commit()\\n flash('Brand {}, updated successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.showBrand', id=id))\\n\\n return render_template('carboard/brand/edit.html', form=form, id=id)\\n\\n# ------------------ /carboard/brand/id/delete : Delete brand --------------- #\\n\\n\\n@carboard.route('/brand/<int:id>/toggle', methods=['GET'])\\n@login_required\\ndef toggleBrand(id):\\n brand = Brand.query.get_or_404(id)\\n # getattr(brand, 'status', 0)\\n status = brand.status if brand.status is not None else 0\\n brand.status = 1 - status\\n db.session.commit()\\n msg = 'activated' if brand.status is 1 else 'deactivated'\\n flash('Brand {}, {} successfully.'.format(brand.name, msg), 'success')\\n return redirect(url_for('carboard.indexBrand'))\\n\\n# ------------------ /carboard/brand/id/delete : Delete brand --------------- #\\n\\n\\n@carboard.route('/brand/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteBrand(id):\\n brand = Brand.query.get_or_404(id)\\n db.session.delete(brand)\\n db.session.commit()\\n flash('Brand {}, deleted successfully.'.format(brand.name), 'success')\\n return redirect(url_for('carboard.indexBrand'))\\n\"\n },\n {\n \"alpha_fraction\": 0.6235954761505127,\n \"alphanum_fraction\": 0.6235954761505127,\n \"avg_line_length\": 23.920000076293945,\n \"blob_id\": \"50fb527bb7bea5fba74504e6b7cb3f9ca21078e9\",\n \"content_id\": \"4daf4cbad1ed6edf5b865b8c462b24490cf10d6d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1246,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 50,\n \"path\": \"/app/plugin.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import current_app\\nfrom flask_plugins import Plugin\\n\\n\\nclass AppPlugin(Plugin):\\n\\n \\\"\\\"\\\" Custom plugin class, it implements some useful methods\\n to deal with plugins\\n \\\"\\\"\\\"\\n\\n has_settings = False\\n\\n def register_blueprint(self, blueprint, **kwargs):\\n \\\"\\\"\\\"Register a plugin blueprint\\\"\\\"\\\"\\n current_app.register_blueprint(blueprint, **kwargs)\\n\\n def is_installed(self):\\n \\\"\\\"\\\"Check whether the plugin is installed\\\"\\\"\\\"\\n return self.has_settings is True\\n\\n def is_enabled(self):\\n \\\"\\\"\\\"Check whether the plugin is enabled\\\"\\\"\\\"\\n return self.has_settings is True\\n\\n def uninstallable(self):\\n \\\"\\\"\\\"Check whether the plugin is installed and can be uninstalled\\\"\\\"\\\"\\n return self.has_settings is True\\n\\n def enabled(self):\\n \\\"\\\"\\\"Enable plugin\\\"\\\"\\\"\\n return self.has_settings is True\\n\\n def disable(self):\\n \\\"\\\"\\\"Disable plugin\\\"\\\"\\\"\\n return self.has_settings is True\\n\\n @staticmethod\\n def all(self):\\n \\\"\\\"\\\"List all plugin\\\"\\\"\\\"\\n return True\\n\\n @staticmethod\\n def all_enabled(self):\\n \\\"\\\"\\\"List all enabled plugin\\\"\\\"\\\"\\n return True\\n\\n @staticmethod\\n def all_disabled(self):\\n \\\"\\\"\\\"List all disabled plugin\\\"\\\"\\\"\\n return True\\n\"\n },\n {\n \"alpha_fraction\": 0.6257582902908325,\n \"alphanum_fraction\": 0.630657970905304,\n \"avg_line_length\": 34.71666717529297,\n \"blob_id\": \"8fc569a2bd8dc77d2650bdb76cc22e9694d4423b\",\n \"content_id\": \"53f8908bd573cc395ead1879359207b45f9e51e9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4286,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 120,\n \"path\": \"/app/carboard/views/driver.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import (\\n render_template, request, flash, redirect, url_for\\n)\\nfrom flask_login import login_required\\n\\nfrom . import carboard\\nfrom ..models.driver import Driver\\nfrom ..models.user import User\\nfrom ..models.country import Country\\nfrom ..models.status import Status\\nfrom ..forms.driver import DriverForm\\nfrom ..helpers import paginate, choices, upload_file\\nfrom ..constants import PER_PAGE, DRIVER_LOGO_DIR\\nfrom ...extensions import db\\n\\n# --------------------- /carboard/driver/ : List of drivers ------------------ #\\n\\n@carboard.route('/driver/')\\n@login_required\\ndef indexDriver():\\n drivers = Driver.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/driver/index.html', drivers=drivers)\\n\\n# ----------------------- /carboard/driver/id : Show driver ------------------- #\\n\\n\\n@carboard.route('/driver/<int:id>', methods=['GET'])\\n@login_required\\ndef showDriver(id):\\n driver = Driver.query.get_or_404(id)\\n return render_template('carboard/driver/show.html', driver=driver)\\n\\n# ---------------------- /carboard/driver/new : Add driver -------------------- #\\n\\n\\n@carboard.route('/driver/new', methods=['GET', 'POST'])\\n@login_required\\ndef newDriver():\\n \\\"\\\"\\\" Add new driver \\\"\\\"\\\"\\n\\n form = DriverForm()\\n form.user_id.choices = choices(User, 'Select a user', 1, 'username')\\n form.country_id.choices = choices(Country, 'Select a country', 1, 'title')\\n form.status_id.choices = choices(Status, 'Select a status', False, 'title')\\n\\n if form.validate_on_submit():\\n avatar = upload_file(form.avatar.data, DRIVER_LOGO_DIR)\\n driver = Driver(\\n gender=form.gender.data,\\n fullname=form.fullname.data,\\n user_id=form.user_id.data,\\n status_id=form.status_id.data,\\n country_id=form.country_id.data,\\n city=form.city.data,\\n avatar=avatar\\n )\\n db.session.add(driver)\\n db.session.commit()\\n flash('Driver {}, added successfully.'.format(form.fullname.data), 'success')\\n return redirect(url_for('carboard.indexDriver'))\\n\\n return render_template('carboard/driver/new.html', form=form)\\n\\n# -------------------- /carboard/driver/id/edit : Edit driver ----------------- #\\n\\n\\n@carboard.route('/driver/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editDriver(id):\\n \\\"\\\"\\\" Edit existing driver \\\"\\\"\\\"\\n driver = Driver.query.get_or_404(id)\\n oldAvatar = driver.avatar\\n\\n form = DriverForm(obj=driver)\\n form.user_id.choices = choices(User, 'Select a user', 1, 'username')\\n form.country_id.choices = choices(Country, 'Select a country', 1, 'title')\\n form.status_id.choices = choices(Status, 'Select a status', False, 'title')\\n\\n if form.validate_on_submit():\\n form.populate_obj(driver)\\n avatar = upload_file(form.avatar.data, DRIVER_LOGO_DIR)\\n if avatar is None:\\n driver.avatar = oldAvatar\\n else:\\n driver.avatar = avatar\\n db.session.commit()\\n flash('Driver {}, updated successfully.'.format(form.fullname.data), 'success')\\n return redirect(url_for('carboard.showDriver', id=id))\\n\\n return render_template('carboard/driver/edit.html', form=form, id=id)\\n\\n# ------------------ /carboard/driver/id/delete : Delete driver --------------- #\\n\\n\\n@carboard.route('/driver/<int:id>/toggle', methods=['GET'])\\n@login_required\\ndef toggleDriver(id):\\n driver = Driver.query.get_or_404(id)\\n # getattr(driver, 'status', 0)\\n status = driver.status if driver.status is not None else 0\\n driver.status = 1 - status\\n db.session.commit()\\n msg = 'activated' if driver.status is 1 else 'deactivated'\\n flash('Driver {}, {} successfully.'.format(driver.fullname, msg), 'success')\\n return redirect(url_for('carboard.indexDriver'))\\n\\n# ------------------ /carboard/driver/id/delete : Delete driver --------------- #\\n\\n\\n@carboard.route('/driver/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteDriver(id):\\n driver = Driver.query.get_or_404(id)\\n db.session.delete(driver)\\n db.session.commit()\\n flash('Driver {}, deleted successfully.'.format(driver.fullname), 'success')\\n return redirect(url_for('carboard.indexDriver'))\\n\"\n },\n {\n \"alpha_fraction\": 0.6800000071525574,\n \"alphanum_fraction\": 0.6800000071525574,\n \"avg_line_length\": 20.428571701049805,\n \"blob_id\": \"bfb43119f76439252d4f55db210f1403a8fb5805\",\n \"content_id\": \"d0d8ec73d88f57b5fb830fa1c72f0e13fa7a08fb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 150,\n \"license_type\": \"no_license\",\n \"max_line_length\": 59,\n \"num_lines\": 7,\n \"path\": \"/app/api/__init__.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import Blueprint\\n\\n\\\"\\\"\\\"\\n This package should represent the vCar API main package\\n\\\"\\\"\\\"\\n\\napi = Blueprint('api', __name__, url_prefix='/api')\\n\"\n },\n {\n \"alpha_fraction\": 0.4884667694568634,\n \"alphanum_fraction\": 0.49027588963508606,\n \"avg_line_length\": 23.032608032226562,\n \"blob_id\": \"51d0cccc49c50e86a49b6572f29b0fc52104c7f3\",\n \"content_id\": \"06b35595815ddad9665e3bcac4a2b7d5a24599f5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2211,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 92,\n \"path\": \"/app/carboard/forms/record.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom flask_wtf.file import FileAllowed, FileField\\nfrom wtforms import StringField, SelectField, DateTimeField\\nfrom wtforms.validators import Optional, DataRequired, Regexp, Length\\n\\nfrom ..models.record import Record\\n\\n# ------------------------ custom validation methods ------------------------ #\\n\\n\\ndef vehicle_exist(form, field):\\n if Record.select().where(Record.name == field.data).exists():\\n raise ValidationError('Record already exists !')\\n\\n# ---------------------------- Record form classes ---------------------------- #\\n\\nclass RecordForm(FlaskForm):\\n \\\"\\\"\\\" Record add/edit FlaskForm \\\"\\\"\\\"\\n trace = FileField(\\n 'Record trace file', validators=[\\n DataRequired(),\\n FileAllowed(['json', 'txt', 'xml', 'csv', 'xsl'], 'File extension not allowed')\\n ]\\n )\\n name = StringField(\\n 'Name',\\n validators=[\\n Optional(),\\n Length(min=3, max=50),\\n Regexp(\\n r'^[a-zA-Z ]+$',\\n message=(\\\"Brand name is not correct !\\\")\\n ),\\n ]\\n )\\n user_id = SelectField(\\n 'User',\\n coerce=int,\\n validators=[\\n DataRequired(),\\n ]\\n )\\n drivetype_id = SelectField(\\n 'Drive Type',\\n coerce=int,\\n validators=[\\n Optional(),\\n ]\\n )\\n driver_id = SelectField(\\n 'Driver',\\n coerce=int, # str\\n validators=[\\n Optional(),\\n ]\\n )\\n vehicle_id = SelectField(\\n 'Driver',\\n coerce=int, # str\\n validators=[\\n Optional(),\\n ]\\n )\\n start = DateTimeField(\\n 'Starting record at',\\n validators=[\\n Optional(),\\n ]\\n )\\n end = DateTimeField(\\n 'End record at',\\n validators=[\\n Optional(),\\n ]\\n )\\n description = StringField(\\n 'Description',\\n validators=[\\n Optional(),\\n Length(min=3),\\n ]\\n )\\n\\nclass RecordStatusForm(FlaskForm):\\n \\\"\\\"\\\" Edit vehicle status FlaskForm \\\"\\\"\\\"\\n status_id = SelectField(\\n 'Status',\\n coerce=int,\\n validators=[\\n DataRequired(),\\n ]\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.6349206566810608,\n \"alphanum_fraction\": 0.6349206566810608,\n \"avg_line_length\": 14.25,\n \"blob_id\": \"17268a0b2139b10abfe52b741d4ae754b9aca411\",\n \"content_id\": \"feaea50a20548f560cb48d9bb0a912f992060b9f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 63,\n \"license_type\": \"no_license\",\n \"max_line_length\": 23,\n \"num_lines\": 4,\n \"path\": \"/app/importer/platforms/openxc/__init__.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from .views import help\\r\\n\\r\\ndef init():\\r\\n\\treturn 'init text ...'\"\n },\n {\n \"alpha_fraction\": 0.6328259706497192,\n \"alphanum_fraction\": 0.6365918517112732,\n \"avg_line_length\": 39.08176040649414,\n \"blob_id\": \"702c4ae983c9361fc2e46e6e939957a9a2106eca\",\n \"content_id\": \"ce68efdae9e2dabbe07658fa45efb3c341865e52\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6373,\n \"license_type\": \"no_license\",\n \"max_line_length\": 115,\n \"num_lines\": 159,\n \"path\": \"/app/carboard/views/extrasignal.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\n\\nfrom flask import (\\n render_template, request, flash, redirect, url_for\\n)\\nfrom flask_login import login_required\\nfrom sqlalchemy import or_\\n\\nfrom app.carboard.forms.filesignal import FileSignalForm\\nfrom . import carboard\\nfrom ..models.extrasignal import Extrasignal\\nfrom ..models.signal import Signal\\nfrom ..models.platform import Platform\\nfrom ..forms.extrasignal import ExtrasignalForm\\nfrom ..helpers import paginate, choices, remove_csv, find_id, CSVLoader, upload_csv, platform_choices\\nfrom ..constants import PER_PAGE, CSV_TEMP\\nfrom ...extensions import db\\n\\n# --------------------- /carboard/extrasignal/ : List of extrasignals ------------- #\\n\\n@carboard.route('/extrasignal/')\\n@login_required\\ndef indexExtrasignal():\\n extrasignals = Extrasignal.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n start = (request.args.get('page', 1, type=int) - 1) * PER_PAGE + 1\\n return render_template('carboard/extrasignal/index.html', extrasignals=extrasignals,start=start)\\n\\n# ----------------------- /carboard/extrasignal/id : Show extrasignal ------------------- #\\n\\n\\n@carboard.route('/extrasignal/<int:id>', methods=['GET'])\\n@login_required\\ndef showExtrasignal(id):\\n extrasignal = Extrasignal.query.get_or_404(id)\\n return render_template('carboard/extrasignal/show.html', extrasignal=extrasignal)\\n\\n# ---------------------- /carboard/extrasignal/new : Add extrasignal -------------------- #\\n\\n\\n@carboard.route('/extrasignal/new', methods=['GET', 'POST'])\\n@login_required\\ndef newExtrasignal():\\n \\\"\\\"\\\" Add new extrasignal \\\"\\\"\\\"\\n\\n form = ExtrasignalForm()\\n form.signal_id.choices = choices(Signal, 'Select main signal', 1)\\n form.storage.choices = platform_choices('Select a Data Storage', 'name')\\n\\n if form.validate_on_submit():\\n extrasignal = Extrasignal(\\n name=form.name.data,\\n signal_id=form.signal_id.data,\\n storage=form.storage.data,\\n )\\n db.session.add(extrasignal)\\n db.session.commit()\\n flash('Extra signal {}, added successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.indexExtrasignal'))\\n\\n return render_template('carboard/extrasignal/new.html', form=form)\\n\\n#------------------------- /carboard/extrasignal/bulk-add bulk add extra signals from file ------------------- #\\n\\n@carboard.route('/extrasignal/bulk-add', methods=['GET', 'POST'])\\n@login_required\\ndef bulkAddExtraSignal():\\n \\\"\\\"\\\" Add new extrasignal \\\"\\\"\\\"\\n form = FileSignalForm()\\n errors = None\\n if form.validate_on_submit():\\n f = upload_csv(form.file.data, CSV_TEMP)\\n loader = CSVLoader(os.path.join(CSV_TEMP, form.file.data.filename))\\n res = loader.load()\\n try:\\n for row in res:\\n signal_id = find_id(Signal, row['Main Signal'])\\n storage = row['Data Storage']\\n if signal_id == -1:\\n if not errors: errors = [] # lazy instatiation\\n errors.append('The Extra Signal ' + row[\\n 'Signal Name'] + ' could not be added because of invalid Main Signal or Data Storage ')\\n continue\\n extraSignal = Extrasignal(\\n name=row['Signal Name'],\\n signal_id=signal_id ,\\n storage=storage\\n )\\n db.session.add(extraSignal)\\n db.session.commit()\\n except KeyError:\\n errors = ['Your file is Not well Formated, please review your file structure .']\\n remove_csv(form.file.data.filename, CSV_TEMP)\\n if not errors: flash('Signals Added Successfully !', 'success')\\n return render_template('carboard/extrasignal/bulk.html', form=form, errors=errors)\\n\\n\\n\\n# -------------------- /carboard/extrasignal/id/edit : Edit extrasignal ----------------- #\\n\\n\\n@carboard.route('/extrasignal/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editExtrasignal(id):\\n \\\"\\\"\\\" Edit existing extrasignal \\\"\\\"\\\"\\n extrasignal = Extrasignal.query.get_or_404(id)\\n form = ExtrasignalForm(obj=extrasignal)\\n form.signal_id.choices = choices(Signal, 'Select main signal', 1)\\n form.storage.choices = platform_choices('Select a Data Storage','name')\\n\\n if form.validate_on_submit():\\n form.populate_obj(extrasignal)\\n print(form.data)\\n db.session.commit()\\n flash('Extra signal {}, updated successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.showExtrasignal', id=id))\\n\\n return render_template('carboard/extrasignal/edit.html', form=form, id=id)\\n\\n# ------------------ /carboard/extrasignal/id/delete : Delete extrasignal --------------- #\\n\\n\\n@carboard.route('/extrasignal/<int:id>/toggle', methods=['GET'])\\n@login_required\\ndef toggleExtrasignal(id):\\n extrasignal = Extrasignal.query.get_or_404(id)\\n # getattr(extrasignal, 'status', 0)\\n status = extrasignal.status if extrasignal.status is not None else 0\\n extrasignal.status = 1 - status\\n db.session.commit()\\n msg = 'activated' if extrasignal.status is 1 else 'deactivated'\\n flash('Extra signal {}, {} successfully.'.format(extrasignal.name, msg), 'success')\\n return redirect(url_for('carboard.indexExtrasignal'))\\n\\n# ------------------ /carboard/extrasignal/id/delete : Delete extrasignal --------------- #\\n\\n\\n@carboard.route('/extrasignal/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteExtrasignal(id):\\n extrasignal = Extrasignal.query.get_or_404(id)\\n db.session.delete(extrasignal)\\n db.session.commit()\\n flash('Extra signal {}, deleted successfully.'.format(extrasignal.name), 'success')\\n return redirect(url_for('carboard.indexExtrasignal'))\\n\\n# ------------------ /carboard/extrasignal/search : extra signal lookup (by description or name) --------------- #\\n\\n@carboard.route('/extrasignal/search', methods=['GET'])\\n@login_required\\ndef searchExtraSignal():\\n param = request.args.get('table_search')\\n extrasignals = Extrasignal.query.filter(Extrasignal.name.like('%' + param + '%')).paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/extrasignal/search.html', extrasignals=extrasignals)\\n\"\n },\n {\n \"alpha_fraction\": 0.5466330051422119,\n \"alphanum_fraction\": 0.5480639934539795,\n \"avg_line_length\": 35.33027648925781,\n \"blob_id\": \"6cbba07a41afa307137a7af23c94b47ef989dca4\",\n \"content_id\": \"5a262b629069bde373b6089ed9c00a1f6d66eaa4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 11880,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 327,\n \"path\": \"/app/importer/views.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import json\\nfrom os.path import basename\\nfrom importlib import import_module\\nfrom threading import Thread\\nfrom flask import (\\n Blueprint, Response, render_template, request, session, flash, redirect, url_for\\n)\\nfrom flask_login import (\\n login_user, logout_user, login_required, current_user\\n)\\n\\n# import .platforms.openxc.views\\nfrom ..extensions import db\\nfrom ..carboard.models.platform import Platform\\nfrom ..carboard.models.vehicle import Vehicle\\nfrom ..carboard.models.driver import Driver\\nfrom ..carboard.models.brand import Brand\\nfrom ..carboard.models.model import Model\\nfrom ..carboard.models.country import Country\\nfrom ..carboard.models.status import Status\\nfrom ..carboard.models.file import File\\n\\nfrom .helpers import checkSteps, choices, upload_file, upload_trace\\nfrom .forms import VehicleForm, DriverForm\\nfrom .transformer import Transformer\\nfrom .constants import VEHICLE_LOGO_DIR, DRIVER_LOGO_DIR\\n\\nfrom .platforms.openxc.indexing import task_info\\n\\nimporter = Blueprint('importer', __name__, url_prefix='/carboard/import')\\n\\n\\n# -------------------- /stream : Redis stream server ------------------------ #\\n\\n@importer.route('/stream')\\ndef stream():\\n return Response(task_info(), mimetype=\\\"text/event-stream\\\")\\n\\n# -------------------- / : Introduce import wizard -------------------------- #\\n\\n\\n@importer.route('/')\\n@login_required\\ndef index():\\n \\\"\\\"\\\" Import index page, containe wellcome text \\\"\\\"\\\"\\n return render_template('importer/index.html')\\n\\n# -------------------- /platform : Choose platform -------------------------- #\\n\\n\\n@importer.route('/platform', methods=['GET', 'POST'])\\n@login_required\\ndef platform():\\n \\\"\\\"\\\"Select upload files platform in order to process indexing correctly\\\"\\\"\\\"\\n platforms = Platform.query.filter_by(status=1).all()\\n if request.method == 'POST':\\n wizard_platform = request.form.get('platform', False)\\n if wizard_platform is not False:\\n session['import'] = {}\\n session['import']['user'] = current_user.id\\n session['import']['platform'] = int(request.form['platform'])\\n session.modified = True\\n return redirect(url_for('importer.vehicle'))\\n else:\\n session['import'] = {}\\n session.modified = True\\n # session.clear();\\n return render_template('importer/platform.html', platforms=platforms)\\n\\n# -------------------- /vehicle : Choose or add a vehicle ------------------- #\\n\\n\\n@importer.route('/vehicle', methods=['GET', 'POST'])\\n@login_required\\ndef vehicle():\\n \\\"\\\"\\\"Select or Add the vehicle used to generate traces\\\"\\\"\\\"\\n check = checkSteps(step=1)\\n if check is True:\\n # Preparing data\\n vehicle_selected = False\\n vehicles = Vehicle.query.filter_by(\\n user_id=current_user.id, status_id=1).all()\\n form = VehicleForm()\\n form.brand_id.choices = choices(Brand, 'Select a brand')\\n form.model_id.choices = choices(Model, 'Select a model')\\n\\n # Geting selected vehicle if any\\n if request.method == 'POST':\\n wizard_vehicle = request.form.get('vehicle', False)\\n if wizard_vehicle is not False:\\n session['import']['vehicle'] = int(request.form['vehicle'])\\n session.modified = True\\n vehicle_selected = True\\n return redirect(url_for('importer.driver'))\\n\\n # Adding a new vehicle if any\\n if vehicle_selected is False and form.validate_on_submit():\\n image = upload_file(form.image.data, VEHICLE_LOGO_DIR)\\n vehicle = Vehicle(\\n user_id=current_user.id,\\n brand_id=form.brand_id.data,\\n model_id=form.model_id.data,\\n image=image\\n )\\n db.session.add(vehicle)\\n db.session.commit()\\n session['import']['vehicle'] = vehicle.id\\n session.modified = True\\n flash('Vehicle {}, added successfully.'.format(vehicle), 'success')\\n return redirect(url_for('importer.driver'))\\n\\n # Rendring view template\\n return render_template(\\n 'importer/vehicle.html',\\n vehicles=vehicles,\\n form=form\\n )\\n else:\\n return check\\n\\n\\n# -------------------- /driver : Choose or add a driver --------------------- #\\n\\n@importer.route('/driver', methods=['GET', 'POST'])\\n@login_required\\ndef driver():\\n \\\"\\\"\\\"Select or Add the driver\\\"\\\"\\\"\\n check = checkSteps(step=2)\\n if check is True:\\n # preparing data\\n driver_selected = False\\n drivers = Driver.query.filter_by(\\n user_id=current_user.id, status_id=1).all()\\n form = DriverForm()\\n form.country_id.choices = choices(\\n Country, 'Select a country', 1, 'title')\\n form.status_id.choices = choices(\\n Status, 'Select a status', False, 'title')\\n # geting selected vehicle if any\\n if request.method == 'POST':\\n wizard_driver = request.form.get('driver', False)\\n if wizard_driver is not False:\\n session['import']['driver'] = int(request.form['driver'])\\n session.modified = True\\n driver_selected = True\\n return redirect(url_for('importer.records'))\\n # # adding a new vehicle if any\\n if driver_selected is False and form.validate_on_submit():\\n avatar = upload_file(form.avatar.data, DRIVER_LOGO_DIR)\\n driver = Driver(\\n gender=form.gender.data,\\n fullname=form.fullname.data,\\n user_id=current_user.id,\\n status_id=form.status_id.data,\\n country_id=form.country_id.data,\\n city=form.city.data,\\n avatar=avatar\\n )\\n db.session.add(driver)\\n db.session.commit()\\n session['import']['driver'] = driver.id\\n session.modified = True\\n flash('Driver {}, added successfully.'.format(driver), 'success')\\n return redirect(url_for('importer.records'))\\n\\n return render_template(\\n 'importer/driver.html',\\n drivers=drivers,\\n form=form\\n )\\n else:\\n return check\\n\\n# -------------------- /records : Uplaod and process record files ----------- #\\n\\n\\n@importer.route('/records', methods=['GET', 'POST'])\\n@login_required\\ndef records():\\n check = checkSteps(step=3)\\n if check is True:\\n if request.method == 'POST':\\n files = request.form.getlist('files')\\n if files:\\n \\\"\\\"\\\"\\n Process data indexing using the python module associated\\n with the selected platform.\\n Every platform folder should containe a module named: indexing.py\\n \\\"\\\"\\\"\\n try:\\n # Get the platform slug a.k.a module name\\n slug = Platform.query.get_or_404(\\n session['import']['platform']).slug\\n # Import the module\\n mod = import_module(\\n \\\".indexing\\\", 'app.importer.platforms.' + slug)\\n # Indexing function\\n index_function = 'index_bulk' # 'index'\\n # get a reference to the init function\\n init = getattr(mod, index_function)\\n # create a threaded job to index uploaded data according to\\n # it's platform\\n thread = Thread(target=init, args=[files, session['import']])\\n # thread.daemon = True\\n thread.start()\\n # thread.join(1)\\n # print(active_count())\\n flash('Your records are being indexed in background.', 'info')\\n return redirect(url_for('carboard.index'))\\n except:\\n raise\\n else:\\n session['import']['files'] = []\\n session.modified = True\\n flash('Please upload some files', 'success')\\n return redirect(url_for('importer.records'))\\n\\n return render_template('importer/records.html')\\n else:\\n return check\\n\\n# -------------------- /records : Uplaod record files ----------------------- #\\n\\n\\n@importer.route('/getfile', methods=['POST'])\\n@login_required\\ndef getfile():\\n \\\"\\\"\\\" Import trace files\\\"\\\"\\\"\\n if request.method == 'POST':\\n uploaded_file = upload_trace('openxc', current_user.id)\\n if uploaded_file['status'] is True:\\n file = File(\\n user_id=current_user.id,\\n filename=basename(uploaded_file['message']),\\n path=uploaded_file['message']\\n )\\n db.session.add(file)\\n db.session.commit()\\n\\n if \\\"files\\\" in session['import']:\\n files = session['import']['files']\\n else:\\n files = []\\n files.append(uploaded_file['message'])\\n session['import']['files'] = files\\n session.modified = True\\n return json.dumps(uploaded_file)\\n\\n# -------------------- /process : index files using elasticsearch ----------- #\\n\\n\\n@importer.route('/records', methods=['GET', 'POST'])\\n@login_required\\ndef process():\\n \\\"\\\"\\\"\\n Process data indexing using the python module associated\\n with the selected platform.\\n Every platform folder should containe a module named: indexing.py\\n \\\"\\\"\\\"\\n if request.method == 'POST':\\n files = request.form.getlist('files')\\n if files:\\n try:\\n # Get the platform slug a.k.a module name\\n slug = Platform.query.get_or_404(\\n session['import']['platform']).slug\\n # Import the module\\n mod = import_module(\\n \\\".indexing\\\", 'app.importer.platforms.' + slug)\\n # Indexing function\\n index_function = 'index_bulk' # 'index'\\n # get a reference to the init function\\n init = getattr(mod, index_function)\\n # create a threaded job to index uploaded data according to\\n # it's platform\\n\\n thread = Thread(target=init, args=[files, session['import']])\\n # thread.daemon = True\\n thread.start()\\n # thread.join(1)\\n # print(active_count())\\n except:\\n raise\\n else:\\n session['import']['files'] = []\\n session.modified = True\\n flash('Please upload some files before getting here', 'success')\\n return redirect(url_for('importer.records'))\\n\\n return render_template('importer/porcess.html')\\n\\n# -------------------- /carboard/user/id : Show user ------------------------ #\\n\\n\\n@importer.route('/help', methods=['GET'])\\ndef help():\\n # endpoints = [rule.rule for rule in app.url_map.iter_rules()\\n # if rule.endpoint !='static']\\n # return jsonify(dict(api_endpoints=endpoints))\\n # return render_template('dashboard/import/openxc_porcess.html')\\n tr = Transformer()\\n tr.setPlatformById(1)\\n a = 'Speeda'\\n x = tr.getInfo(a, ignore=False)\\n raise\\n\\n\\ndef dispatcher(data=None):\\n pass\\n # if data:\\n #\\n # else:\\n # return None\\n #\\n # if request.method == 'POST':\\n # files = request.form.getlist('files')\\n # if files:\\n # thread = Thread(target=openxc_task, args=[traces, current_user.id])\\n # thread.daemon = True\\n # thread.start()\\n # # thread.join(1)\\n # # print(active_count())\\n # raise\\n # else:\\n # session['import']['files'] = []\\n # flash('Please upload some files before getting here', 'success')\\n # return redirect(url_for('importer.records'))\\n\"\n },\n {\n \"alpha_fraction\": 0.5230333209037781,\n \"alphanum_fraction\": 0.525159478187561,\n \"avg_line_length\": 25.62264060974121,\n \"blob_id\": \"0a0096ed919569ef213be445d70a3cd8b0e4ead4\",\n \"content_id\": \"d167976bd5667c28778f3ede3be2a62e536a58c6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1411,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 53,\n \"path\": \"/app/carboard/forms/platform.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom flask_wtf.file import FileAllowed, FileField\\nfrom wtforms import StringField, SelectField\\nfrom wtforms.validators import DataRequired, Regexp, Length\\n\\nfrom ..models.platform import Platform\\nfrom ..constants import MIMETYPE\\n\\n# ------------------------ custom validation methods ------------------------ #\\n\\n\\ndef platform_exist(form, field):\\n if Platform.select().where(Platform.name == field.data).exists():\\n raise ValidationError('Platform already exists !')\\n\\n# ---------------------------- Platform form classes ---------------------------- #\\n\\n\\nclass PlatformForm(FlaskForm):\\n \\\"\\\"\\\" Platform add/edit FlaskForm \\\"\\\"\\\"\\n name = StringField(\\n 'Name',\\n validators=[\\n DataRequired(),\\n Length(min=3, max=50),\\n Regexp(\\n r'^[a-zA-Z_\\\\- ]+$',\\n message=(\\\"Platform name is not correct !\\\")\\n ),\\n ]\\n )\\n mimetype = SelectField(\\n 'Data format',\\n coerce=int,\\n choices=MIMETYPE,\\n validators=[\\n DataRequired(),\\n ]\\n )\\n description = StringField(\\n 'Description',\\n validators=[\\n DataRequired(),\\n ]\\n )\\n website = StringField(\\n 'Website',\\n )\\n logo = FileField(\\n 'Logo', validators=[\\n FileAllowed(['jpg', 'jpeg', 'png', 'gif'], 'Images only plz :) ')\\n ]\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.508369505405426,\n \"alphanum_fraction\": 0.5133292078971863,\n \"avg_line_length\": 20.223684310913086,\n \"blob_id\": \"1afd27ce488394be7509657efff571d51642310f\",\n \"content_id\": \"a32614f3b7d93dbc5c64fb2e55cd78783a6257fb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1613,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 76,\n \"path\": \"/app/carboard/forms/signal.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom wtforms import StringField, SelectField, TextAreaField\\nfrom wtforms.validators import DataRequired, Regexp, Length, ValidationError\\n\\nfrom ..models.signal import Signal\\n\\n\\n# ------------------------ custom validation methods ------------------------ #\\n\\ndef signal_exist(form, field):\\n if Signal.select().where(Signal.name == field.data).exists():\\n raise ValidationError('Attribute already exists !')\\n\\n\\n# ---------------------------- Driver form classes ---------------------------- #\\n\\n\\nclass SignalForm(FlaskForm):\\n \\\"\\\"\\\" Signal add/edit FlaskForm \\\"\\\"\\\"\\n name = StringField(\\n 'Name',\\n validators=[\\n DataRequired(),\\n Length(min=3, max=50),\\n # signal_exist()\\n ]\\n )\\n\\n type = SelectField(\\n 'Type',\\n validators=[\\n DataRequired()\\n ]\\n )\\n\\n unit = StringField(\\n 'Unit'\\n )\\n\\n min_value = StringField(\\n 'no_min_value'\\n )\\n max_value = StringField(\\n 'no_max_value'\\n )\\n\\n number = StringField(\\n 'no-number'\\n )\\n\\n values = StringField(\\n 'no-values'\\n )\\n frequency = StringField(\\n 'Frequency'\\n )\\n description = TextAreaField(\\n 'Description',\\n validators=[\\n Length(min=0, max=1000)\\n ]\\n )\\n signalclass_id = SelectField(\\n 'Signal class',\\n coerce=int,\\n validators=[\\n DataRequired(),\\n ]\\n )\\n signalsource_id = SelectField(\\n 'Signal source',\\n coerce=int,\\n validators=[\\n DataRequired(),\\n ]\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.4173261225223541,\n \"alphanum_fraction\": 0.43315285444259644,\n \"avg_line_length\": 23.75257682800293,\n \"blob_id\": \"53142acbb4f33736aa6a2f31295a913cd30179fe\",\n \"content_id\": \"b0cbaabd2950d064683dc22e57395835c1f7ac2f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2401,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 97,\n \"path\": \"/app/importer/constants.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nfrom config.config import DefaultConfig\\n# ----------------------------- Database constants -------------------------- #\\nMAPPING = {\\n \\\"platform\\\": {\\n \\\"properties\\\": {\\n \\\"user\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n },\\n \\\"vehicle\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n },\\n \\\"driver\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n },\\n \\\"class\\\": {\\n \\\"type\\\": \\\"string\\\"\\n },\\n \\\"name\\\": {\\n \\\"type\\\": \\\"string\\\",\\n \\\"index\\\": \\\"not_analyzed\\\"\\n },\\n \\\"value\\\": {\\n \\\"type\\\": \\\"string\\\",\\n \\\"index\\\": \\\"not_analyzed\\\"\\n },\\n \\\"timestamp\\\": {\\n \\\"type\\\": \\\"date\\\"\\n }\\n }\\n }\\n}\\n\\n\\n# ----------------------------- Database constants -------------------------- #\\nSTRING_LEN = 255\\n# ----------------------------- Default constants --------------------------- #\\nDEFAULT_AVATAR = \\\"default.jpg\\\"\\nDEFAULT_YEAR_ID = 1\\nDEFAULT_MODEL_ID = 1\\n# -------------------------------- User role -------------------------------- #\\nUSER = 0\\nADMIN = 1\\nRESEARCHER = 2\\nUSER_ROLES = [\\n (0, 'Select an option ...'),\\n (1, 'ADMIN'),\\n (2, 'USER'),\\n (3, 'Driver')\\n]\\n# ------------------------------- User status ------------------------------- #\\nDEFAULT_STATUS = 1\\nACTIVE = 1\\nINACTIVE = 0\\nUSER_STATUS = {\\n INACTIVE: 'inactive',\\n ACTIVE: 'active',\\n}\\n# ----------------------- Form validation configuration --------------------- #\\nUSERNAME_LEN_MIN = 4\\nUSERNAME_LEN_MAX = 25\\n\\nFULLNAME_LEN_MIN = 4\\nFULLNAME_LEN_MAX = 25\\n\\nPASSWORD_LEN_MIN = 4\\nPASSWORD_LEN_MAX = 16\\n\\nAGE_MIN = 1\\nAGE_MAX = 100\\n\\nMALE = 1\\nFEMALE = 2\\nSEX_TYPE = {\\n MALE: 'Male',\\n FEMALE: 'Female',\\n}\\nDRIVER_GENDER = [\\n (0, 'Select a gender'),\\n (1, 'Male'),\\n (2, 'Female'),\\n (3, 'Unknown')\\n]\\n# ------------------------------ View constants ----------------------------- #\\nPER_HOME_PAGE = 10\\nPER_PAGE = 20\\nUSER_LOGO_DIR = 'static/uploads/avatars/'\\nBRAND_LOGO_DIR = 'static/uploads/brands/'\\nPLATFORM_LOGO_DIR = 'static/uploads/platforms/'\\nDRIVER_LOGO_DIR = 'static/uploads/drivers/'\\nVEHICLE_LOGO_DIR = 'static/uploads/vehicles/'\\nDRIVE_FILES_DIR = 'static/uploads/records/'\\n\\n\\n# Trace upload directory\\nUPLOAD_TRACE_DIR = os.path.join(DefaultConfig.UPLOAD_DIR, 'files')\\nALLOWED_TRACE_EXTENSIONS = set(['txt', 'json', 'pdf', 'gif'])\\n\"\n },\n {\n \"alpha_fraction\": 0.7513368725776672,\n \"alphanum_fraction\": 0.7700534462928772,\n \"avg_line_length\": 40.66666793823242,\n \"blob_id\": \"047079874ee9e6a027cadb528e55b22eed70402e\",\n \"content_id\": \"1d6641178a6ae0b5ef4e73330ac1377c98dc1767\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Dockerfile\",\n \"length_bytes\": 374,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 9,\n \"path\": \"/Dockerfile\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"FROM python:2.7-alpine\\nLABEL maintainer=\\\"boubouh.karim@gmail.com\\\"\\nRUN apk --update add libxml2-dev libxslt-dev libffi-dev gcc musl-dev libgcc openssl-dev curl\\nRUN apk add jpeg-dev zlib-dev freetype-dev lcms2-dev openjpeg-dev tiff-dev tk-dev tcl-dev\\n# ADD . /vcar\\nCOPY ./requirements2.txt /vcar/requirements2.txt\\nWORKDIR /vcar\\nRUN pip install -r requirements2.txt\\nCMD [\\\"python\\\", \\\"manage.py\\\"]\"\n },\n {\n \"alpha_fraction\": 0.5580233931541443,\n \"alphanum_fraction\": 0.5610461235046387,\n \"avg_line_length\": 31.517093658447266,\n \"blob_id\": \"e064d32e8376fb14ee96ff6366567c2f8d5c90c7\",\n \"content_id\": \"12a896ab3f837480e33f753aad6f390a829476b6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7609,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 234,\n \"path\": \"/app/importer/views_org.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import (\\n render_template, request, flash, redirect, url_for\\n)\\nfrom flask_login import (\\n login_user, logout_user, login_required, current_user\\n)\\n\\nfrom . import carboard\\nfrom ..models.user import User\\nfrom ..forms.user import UserForm, LoginForm, RegisterForm\\nfrom ..helpers import paginate, upload_file\\nfrom ..constants import PER_PAGE, USER_LOGO_DIR\\nfrom ...extensions import db\\n\\n\\n# --------------------- /carboard/user/ : List of users ------------------ #\\n\\n@carboard.route('/user/')\\n@login_required\\ndef indexUser():\\n users = User.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/user/index.html', users=users)\\n\\n# ----------------------- /carboard/user/id : Show user ------------------- #\\n\\n\\n@carboard.route('/user/<int:id>', methods=['GET'])\\n@login_required\\ndef showUser(id):\\n user = User.query.get_or_404(id)\\n return render_template('carboard/user/show.html', user=user)\\n\\n# ---------------------- /carboard/user/new : Add user -------------------- #\\n\\n\\n@carboard.route('/user/new', methods=['GET', 'POST'])\\n@login_required\\ndef newUser():\\n \\\"\\\"\\\" Add new user \\\"\\\"\\\"\\n\\n form = UserForm()\\n\\n if form.validate_on_submit():\\n avatar = upload_file(form.avatar.data, USER_LOGO_DIR)\\n user = User(\\n fullname=form.fullname.data,\\n username=form.username.data,\\n email=form.email.data,\\n password=form.password.data,\\n role=form.role.data,\\n avatar=avatar\\n )\\n db.session.add(user)\\n db.session.commit()\\n flash('User {}, added successfully.'.format(form.username.data), 'success')\\n return redirect(url_for('carboard.indexUser'))\\n\\n return render_template('carboard/user/new.html', form=form)\\n\\n# -------------------- /carboard/user/id/edit : Edit user ----------------- #\\n\\n\\n@carboard.route('/user/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editUser(id):\\n \\\"\\\"\\\" Edit existing user \\\"\\\"\\\"\\n user = User.query.get_or_404(id)\\n oldAvatar = user.avatar\\n form = UserForm(obj=user)\\n if form.validate_on_submit():\\n form.populate_obj(user)\\n user.set_password(form.password.data)\\n avatar = upload_file(form.avatar.data, USER_LOGO_DIR)\\n if avatar is None:\\n user.avatar = oldAvatar\\n else:\\n user.avatar = avatar\\n db.session.commit()\\n flash('User {}, updated successfully.'.format(form.username.data), 'success')\\n return redirect(url_for('carboard.showUser', id=id))\\n\\n return render_template('carboard/user/edit.html', form=form, id=id)\\n\\n# ------------------ /carboard/user/id/delete : Delete user --------------- #\\n\\n\\n@carboard.route('/user/<int:id>/toggle', methods=['GET'])\\n@login_required\\ndef toggleUser(id):\\n user = User.query.get_or_404(id)\\n # getattr(user, 'status', 0)\\n status = user.status if user.status is not None else 0\\n user.status = 1 - status\\n db.session.commit()\\n msg = 'activated' if user.status is 1 else 'deactivated'\\n flash('User {}, {} successfully.'.format(user.username, msg), 'success')\\n return redirect(url_for('carboard.indexUser'))\\n\\n# ------------------ /carboard/user/id/delete : Delete user --------------- #\\n\\n\\n@carboard.route('/user/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteUser(id):\\n user = User.query.get_or_404(id)\\n db.session.delete(user)\\n db.session.commit()\\n flash('User {}, deleted successfully.'.format(user.username), 'success')\\n return redirect(url_for('carboard.indexUser'))\\n\\n# ----------------------- /carboard/login : User login page --------------------- #\\n\\n\\n@carboard.route('/login', methods=['GET', 'POST'])\\ndef login():\\n \\\"\\\"\\\" User login method \\\"\\\"\\\"\\n if current_user.is_authenticated:\\n return redirect(url_for('carboard.index'))\\n\\n form = LoginForm()\\n if form.validate_on_submit():\\n # request.form.get('login', 'default')\\n user, authenticated = User.authenticate(\\n form.login.data, form.password.data\\n )\\n if user and authenticated:\\n remember = form.remember.data == 'y'\\n if login_user(user, remember=remember):\\n flash(\\\"Logged in successfully\\\", 'success')\\n next = request.args.get('next')\\n # if not next_is_valid(next):\\n # return abort(400)\\n return redirect(next or url_for('carboard.index'))\\n else:\\n flash('Sorry, invalid login', 'error')\\n\\n return render_template('carboard/user/login.html', form=form)\\n\\n# ----------------------- /carboard/logout : User logout page ------------------- #\\n\\n\\n@carboard.route('/logout')\\n@login_required\\ndef logout():\\n logout_user()\\n flash('Logged out', 'success')\\n return redirect(url_for('frontend.index'))\\n\\n# ----------------------- /carboard/register : User register page --------------- #\\n\\n\\n@carboard.route('/register', methods=['GET', 'POST'])\\ndef register():\\n \\\"\\\"\\\" User Sigup method \\\"\\\"\\\"\\n if current_user.is_authenticated:\\n return redirect(url_for('carboard.index'))\\n\\n form = RegisterForm()\\n\\n if form.validate_on_submit():\\n avatar = upload_file(form.avatar.data, USER_LOGO_DIR)\\n user = User(\\n username=form.username.data,\\n email=form.email.data,\\n avatar=avatar,\\n password=form.password.data,\\n )\\n db.session.add(user)\\n db.session.commit()\\n flash('Yay, you successfully registered !', 'success')\\n if login_user(user):\\n return redirect(url_for('carboard.index'))\\n\\n return render_template('carboard/user/register.html', form=form)\\n\\n# ----------------------- /carboard/profile : User profile page ----------------- #\\n\\n\\n@carboard.route('/profile')\\ndef profile():\\n return render_template('carboard/user/profile.html', user=current_user)\\n\\n# ---------------------- /carboard/profile : Public user profile page ----------- #\\n\\n\\n@carboard.route('/<int:user_id>/profile')\\ndef public_profile(user_id):\\n user = User.get_by_id(user_id)\\n return render_template('carboard/user/profile.html', user=user)\\n\\n\\n# /\\\\/\\\\/\\\\/\\\\/\\\\\\\\/\\\\/\\\\/\\\\/\\\\\\\\/\\\\/\\\\/\\\\/\\\\\\\\/\\\\/\\\\/\\\\/\\\\\\\\/\\\\/\\\\/\\\\/\\\\\\\\/\\\\/\\\\/\\\\/\\\\\\\\/\\\\/\\\\/\\\\/\\\\\\\\/\\\\/\\\\\\\\/\\\\/\\\\/ #\\n# #\\n# ================== Admin Blueprint ===================== #\\n# #\\n# /\\\\/\\\\/\\\\/\\\\/\\\\\\\\/\\\\/\\\\/\\\\/\\\\\\\\/\\\\/\\\\/\\\\/\\\\\\\\/\\\\/\\\\/\\\\/\\\\\\\\/\\\\/\\\\/\\\\/\\\\\\\\/\\\\/\\\\/\\\\/\\\\\\\\/\\\\/\\\\/\\\\/\\\\\\\\/\\\\//\\\\/\\\\/\\\\/ #\\n\\n# admin = Blueprint('admin', __name__, url_prefix='/admin')\\n\\n\\n# ---------------------- /admin/index : Admin home page -------------------- #\\n\\n\\n# @admin.route('/')\\n# @login_required\\n# @admin_required\\n# def index():\\n# users = User.query.all()\\n# return render_template('admin/index.html', users=users, active='index')\\n#\\n#\\n# @admin.route('/users')\\n# @login_required\\n# @admin_required\\n# def users():\\n# users = User.query.all()\\n# return render_template('admin/users.html', users=users, active='users')\\n#\\n#\\n# @admin.route('/carboard/<int:user_id>', methods=['GET', 'POST'])\\n# @login_required\\n# @admin_required\\n# def user(user_id):\\n# user = User.query.filter_by(id=user_id).first_or_404()\\n# form = UserForm(obj=user, next=request.args.get('next'))\\n# if form.validate_on_submit():\\n# form.populate_obj(user)\\n# db.session.add(user)\\n# db.session.commit()\\n# flash('User updated.', 'success')\\n# return render_template('admin/user.html', user=user, form=form)\\n\"\n },\n {\n \"alpha_fraction\": 0.5292425751686096,\n \"alphanum_fraction\": 0.5340364575386047,\n \"avg_line_length\": 25.743589401245117,\n \"blob_id\": \"1143dc8e3b6adc938e00160fe955789b5a750584\",\n \"content_id\": \"ac647c07ed3ec2b2cc9cb50b25fec4bab81d6772\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1043,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 39,\n \"path\": \"/app/carboard/forms/model.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom wtforms import StringField, SelectField\\nfrom wtforms.ext.sqlalchemy.fields import QuerySelectField\\n\\nfrom wtforms.validators import DataRequired, Regexp, Length\\n\\nfrom ..models.model import Model\\nfrom ..models.brand import Brand\\n\\n# ------------------------ custom validation methods ------------------------ #\\n\\n\\ndef model_exist(form, field):\\n if Model.select().where(Model.name == field.data).exists():\\n raise ValidationError('Model already exists !')\\n\\n# ---------------------------- Model form classes ---------------------------- #\\n\\n\\nclass ModelForm(FlaskForm):\\n \\\"\\\"\\\" Model add/edit FlaskForm \\\"\\\"\\\"\\n name = StringField(\\n 'Name',\\n validators=[\\n DataRequired(),\\n Length(min=2, max=50),\\n Regexp(\\n r'^[a-zA-Z0-9 ]+$',\\n message=(\\\"Model name is not correct !\\\")\\n ),\\n ]\\n )\\n brand_id = SelectField(\\n 'Brand',\\n coerce=int,\\n validators=[\\n DataRequired(),\\n ]\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.8214285969734192,\n \"alphanum_fraction\": 0.8214285969734192,\n \"avg_line_length\": 27,\n \"blob_id\": \"b9f50a926da217cfa6dac64f6d7accf9b02f5fe1\",\n \"content_id\": \"ee45d2f96588cca8fd00708ee79daaaffcc10431\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 28,\n \"license_type\": \"no_license\",\n \"max_line_length\": 27,\n \"num_lines\": 1,\n \"path\": \"/app/importer/__init__.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from .views import importer\\n\"\n },\n {\n \"alpha_fraction\": 0.540826141834259,\n \"alphanum_fraction\": 0.5437079668045044,\n \"avg_line_length\": 25.69230842590332,\n \"blob_id\": \"ad3968971fa6a6e61da8b37b7111abe07a66d481\",\n \"content_id\": \"b3582e749c739930027cd5f434b71faff180ccb3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1041,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 39,\n \"path\": \"/app/carboard/forms/extrasignal.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom wtforms import StringField, SelectField\\nfrom wtforms.validators import DataRequired, Regexp, Length\\n\\nfrom ..models.extrasignal import Extrasignal\\n\\n# ------------------------ custom validation methods ------------------------ #\\n\\n\\ndef extrasignal_exist(form, field):\\n if Extrasignal.select().where(Extrasignal.name == field.data).exists():\\n raise ValidationError('Extra signal already exists !')\\n\\n# ---------------------------- User form classes ---------------------------- #\\n\\n\\nclass ExtrasignalForm(FlaskForm):\\n \\\"\\\"\\\" Extrasignal add/edit FlaskForm \\\"\\\"\\\"\\n name = StringField(\\n 'Name',\\n validators=[\\n DataRequired(),\\n Length(min=3, max=50),\\n # extrasignal_exist()\\n ]\\n )\\n signal_id = SelectField(\\n 'Main signal',\\n coerce=int,\\n validators=[\\n DataRequired(),\\n ]\\n )\\n storage = SelectField(\\n 'Data Storage (Optionel)',\\n validators = [\\n DataRequired(),\\n ]\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.614625096321106,\n \"alphanum_fraction\": 0.6195618510246277,\n \"avg_line_length\": 31.089109420776367,\n \"blob_id\": \"c2a0073d4107e3993fba80ae6e66bff786908077\",\n \"content_id\": \"35dd92d0c0447834bc554f2cc7dc9c78ad266a1d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3241,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 101,\n \"path\": \"/app/carboard/views/model.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import (\\n render_template, request, flash, redirect, url_for\\n)\\nfrom flask_login import login_required\\n\\nfrom . import carboard\\nfrom ..models.model import Model\\nfrom ..models.brand import Brand\\nfrom ..forms.model import ModelForm\\nfrom ..helpers import paginate, choices\\nfrom ..constants import PER_PAGE, BRAND_LOGO_DIR\\nfrom ...extensions import db\\n\\n# --------------------- /carboard/model/ : List of models ------------------ #\\n\\n\\n@carboard.route('/model/')\\n@login_required\\ndef indexModel():\\n models = Model.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/model/index.html', models=models)\\n\\n# ----------------------- /carboard/model/id : Show model ------------------- #\\n\\n\\n@carboard.route('/model/<int:id>', methods=['GET'])\\n@login_required\\ndef showModel(id):\\n model = Model.query.get_or_404(id)\\n return render_template('carboard/model/show.html', model=model)\\n\\n# ---------------------- /carboard/model/new : Add model -------------------- #\\n\\n\\n@carboard.route('/model/new', methods=['GET', 'POST'])\\n@login_required\\ndef newModel():\\n \\\"\\\"\\\" Add new model \\\"\\\"\\\"\\n\\n form = ModelForm()\\n form.brand_id.choices = choices(Brand)\\n\\n if form.validate_on_submit():\\n model = Model(\\n name=form.name.data,\\n brand_id=form.brand_id.data,\\n )\\n db.session.add(model)\\n db.session.commit()\\n flash('Model {}, added successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.indexModel'))\\n\\n return render_template('carboard/model/new.html', form=form)\\n\\n# -------------------- /carboard/model/id/edit : Edit model ----------------- #\\n\\n\\n@carboard.route('/model/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editModel(id):\\n \\\"\\\"\\\" Edit existing model \\\"\\\"\\\"\\n model = Model.query.get_or_404(id)\\n form = ModelForm(obj=model)\\n form.brand_id.choices = choices(Brand)\\n\\n if form.validate_on_submit():\\n form.populate_obj(model)\\n db.session.commit()\\n flash('Model {}, updated successfully.'.format(form.name.data), 'success')\\n return redirect(url_for('carboard.showModel', id=id))\\n\\n return render_template('carboard/model/edit.html', form=form, id=id)\\n\\n# ------------------ /carboard/model/id/delete : Delete model --------------- #\\n\\n\\n@carboard.route('/model/<int:id>/toggle', methods=['GET'])\\n@login_required\\ndef toggleModel(id):\\n model = Model.query.get_or_404(id)\\n status = model.status if model.status is not None else 0\\n model.status = 1 - status\\n db.session.commit()\\n msg = 'activated' if model.status is 1 else 'deactivated'\\n flash('Model {}, {} successfully.'.format(model.name, msg), 'success')\\n return redirect(url_for('carboard.indexModel'))\\n\\n# ------------------ /carboard/model/id/delete : Delete model --------------- #\\n\\n\\n@carboard.route('/model/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteModel(id):\\n model = Model.query.get_or_404(id)\\n db.session.delete(model)\\n db.session.commit()\\n flash('Model {}, deleted successfully.'.format(model.name), 'success')\\n return redirect(url_for('carboard.indexModel'))\\n\"\n },\n {\n \"alpha_fraction\": 0.4991212785243988,\n \"alphanum_fraction\": 0.5035149455070496,\n \"avg_line_length\": 26.095237731933594,\n \"blob_id\": \"3656a7e734327f8a3490e4bcc1b2eaf46ee67dda\",\n \"content_id\": \"de9e8618a09db26aeaa7561e4deabe15457b0df2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1138,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 42,\n \"path\": \"/app/carboard/forms/brand.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom flask_wtf.file import FileAllowed, FileField\\nfrom wtforms import StringField\\nfrom wtforms.validators import DataRequired, Regexp, Length\\n\\nfrom ..models.brand import Brand\\n\\n# ------------------------ custom validation methods ------------------------ #\\n\\n\\ndef brand_exist(form, field):\\n if Brand.select().where(Brand.name == field.data).exists():\\n raise ValidationError('Brand already exists !')\\n\\n# ---------------------------- Brand form classes ---------------------------- #\\n\\n\\nclass BrandForm(FlaskForm):\\n \\\"\\\"\\\" Brand add/edit FlaskForm \\\"\\\"\\\"\\n name = StringField(\\n 'Name',\\n validators=[\\n DataRequired(),\\n Length(min=3, max=50),\\n Regexp(\\n r'^[a-zA-Z ]+$',\\n message=(\\\"Brand name is not correct !\\\")\\n ),\\n ]\\n )\\n code = StringField(\\n 'Code',\\n validators=[\\n DataRequired(),\\n Length(min=2, max=2),\\n ]\\n )\\n logo = FileField(\\n 'Logo', validators=[\\n FileAllowed(['jpg', 'jpeg', 'png', 'gif'], 'Images only plz :) ')\\n ]\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.8518518805503845,\n \"alphanum_fraction\": 0.8518518805503845,\n \"avg_line_length\": 27,\n \"blob_id\": \"a6c9fb121422878aa02fa746ab6bb2ddf99d76b4\",\n \"content_id\": \"8c4daf0368a6d3a8dd2df0c96d6026a41c492f70\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 27,\n \"license_type\": \"no_license\",\n \"max_line_length\": 27,\n \"num_lines\": 1,\n \"path\": \"/app/explorer/__init__.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from .views import explorer\"\n },\n {\n \"alpha_fraction\": 0.6349801421165466,\n \"alphanum_fraction\": 0.6382884979248047,\n \"avg_line_length\": 35.86178970336914,\n \"blob_id\": \"b8e9c6acd70ab85debe1010dbdbdb042d1312a34\",\n \"content_id\": \"202c60def46f118a41566b5f8d50312766c46038\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4534,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 123,\n \"path\": \"/app/carboard/views/record.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import (\\n render_template, request, flash, redirect, url_for\\n)\\nfrom flask_login import login_required\\n\\nfrom . import carboard\\nfrom ..models.record import Record\\nfrom ..models.user import User\\nfrom ..models.drivetype import DriveType\\nfrom ..models.vehicle import Vehicle\\nfrom ..models.driver import Driver\\nfrom ..models.status import Status\\nfrom ..forms.record import RecordForm, RecordStatusForm\\nfrom ..helpers import paginate, choices, upload_file\\nfrom ..constants import PER_PAGE, DRIVE_FILES_DIR\\nfrom ...extensions import db\\n\\n# --------------------- /carboard/record/ : List of records ------------------ #\\n\\n@carboard.route('/record/')\\n@login_required\\ndef indexRecord():\\n records = Record.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', PER_PAGE, type=int),\\n )\\n return render_template('carboard/record/index.html', records=records)\\n\\n# ----------------------- /carboard/record/id : Show record ------------------- #\\n\\n\\n@carboard.route('/record/<int:id>', methods=['GET'])\\n@login_required\\ndef showRecord(id):\\n record = Record.query.get_or_404(id)\\n return render_template('carboard/record/show.html', record=record)\\n\\n# ---------------------- /carboard/record/new : Add record -------------------- #\\n\\n\\n@carboard.route('/record/new', methods=['GET', 'POST'])\\n@login_required\\ndef newRecord():\\n \\\"\\\"\\\" Add new record \\\"\\\"\\\"\\n\\n form = RecordForm()\\n form.user_id.choices = choices(User, 'Select a user', 1, 'username')\\n form.drivetype_id.choices = choices(DriveType, 'Select a drive type', False)\\n form.driver_id.choices = choices(Driver, 'Select a Driver', False, 'fullname')\\n form.vehicle_id.choices = choices(Vehicle, 'Select a vehicle', False, 'id')\\n\\n if form.validate_on_submit():\\n trace = upload_file(form.trace.data, DRIVE_FILES_DIR)\\n record = Record(\\n user_id=form.user_id.data,\\n drivetype_id=form.drivetype_id.data,\\n driver_id=form.driver_id.data,\\n vehicle_id=form.vehicle_id.data,\\n name=form.name.data,\\n start=form.start.data,\\n end=form.end.data,\\n description=form.description.data,\\n trace=trace\\n )\\n db.session.add(record)\\n db.session.commit()\\n flash('Record {}, added successfully.'.format(record.name), 'success')\\n return redirect(url_for('carboard.indexRecord'))\\n\\n return render_template('carboard/record/new.html', form=form)\\n\\n# -------------------- /carboard/record/id/edit : Edit record ----------------- #\\n\\n\\n@carboard.route('/record/<int:id>/edit', methods=['GET', 'POST'])\\n@login_required\\ndef editRecord(id):\\n \\\"\\\"\\\" Edit existing record \\\"\\\"\\\"\\n record = Record.query.get_or_404(id)\\n oldTrace = record.trace\\n\\n form = RecordForm(obj=record)\\n del form.trace\\n form.user_id.choices = choices(User, 'Select a user', 1, 'username')\\n form.drivetype_id.choices = choices(DriveType, 'Select a drive type', False)\\n form.driver_id.choices = choices(Driver, 'Select a Driver', False, 'fullname')\\n form.vehicle_id.choices = choices(Vehicle, 'Select a vehicle', False, 'id')\\n\\n if form.validate_on_submit():\\n form.populate_obj(record)\\n db.session.commit()\\n flash('Record {}, updated successfully.'.format(record.name), 'success')\\n return redirect(url_for('carboard.showRecord', id=id))\\n\\n return render_template('carboard/record/edit.html', form=form, id=id)\\n\\n# ------------------ /carboard/record/id/delete : Delete record --------------- #\\n\\n\\n@carboard.route('/record/<int:id>/toggle', methods=['GET', 'POST'])\\n@login_required\\ndef toggleRecord(id):\\n record = Record.query.get_or_404(id)\\n form = RecordStatusForm(obj=record)\\n form.status_id.choices = [(s.id, s) for s in Status.query.all()]\\n if form.validate_on_submit():\\n form.populate_obj(record)\\n db.session.commit()\\n flash('Record {}, updated successfully.'.format(record.name), 'success')\\n return redirect(url_for('carboard.showRecord', id=id))\\n return render_template('carboard/record/status.html', form=form, id=id)\\n\\n# ------------------ /carboard/record/id/delete : Delete record --------------- #\\n\\n\\n@carboard.route('/record/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteRecord(id):\\n record = Record.query.get_or_404(id)\\n db.session.delete(record)\\n db.session.commit()\\n flash('Record {}, deleted successfully.'.format(record.name), 'success')\\n return redirect(url_for('carboard.indexRecord'))\\n\"\n },\n {\n \"alpha_fraction\": 0.6031966805458069,\n \"alphanum_fraction\": 0.6115357875823975,\n \"avg_line_length\": 34.09756088256836,\n \"blob_id\": \"a97d93e935fd1128e80ee749a002f3d74748188d\",\n \"content_id\": \"95ad5c085ab8b2a76f645ed5c51052e5c8402dd3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1439,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 41,\n \"path\": \"/app/carboard/models/driver.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom ...extensions import db\\n\\n# -------------------------------- Driver Model ------------------------------- #\\n\\n\\nclass Driver(db.Model):\\n \\\"\\\"\\\" Driver Model:\\n The driver is the one who produce data records.\\n \\\"\\\"\\\"\\n __tablename__ = 'drivers'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n gender = db.Column(db.SmallInteger, default=0)\\n fullname = db.Column(db.String(255), default='Anonyme')\\n avatar = db.Column(db.String(255), nullable=True)\\n city = db.Column(db.String(255), nullable=True)\\n\\n status_id = db.Column(db.SmallInteger, db.ForeignKey('status.id'))\\n status = db.relationship('Status')\\n\\n user_id = db.Column(db.Integer, db.ForeignKey('users.id'))\\n user = db.relationship('User', backref=db.backref('drivers', lazy='dynamic'))\\n\\n country_id = db.Column(db.Integer, db.ForeignKey('countries.id'))\\n country = db.relationship('Country')\\n\\n created = db.Column(db.DateTime(), default=datetime.utcnow())\\n\\n def __init__(self, user_id, country_id, **kwargs):\\n self.vars = kwargs\\n self.gender = kwargs.get('gender', 3)\\n self.fullname = kwargs.get('fullname', 'Anonymous driver')\\n self.user_id = user_id\\n self.country_id = country_id\\n self.avatar = kwargs.get('avatar')\\n self.status_id = kwargs.get('status_id', 1)\\n self.city = kwargs.get('city')\\n\\n def __repr__(self):\\n return self.fullname\\n\"\n },\n {\n \"alpha_fraction\": 0.5982000827789307,\n \"alphanum_fraction\": 0.6029645204544067,\n \"avg_line_length\": 28.984127044677734,\n \"blob_id\": \"599893240b7f3d51ac757bf1cc6a52fc69b1d8da\",\n \"content_id\": \"f5a64d3fb4b2d8999c898d3865c3b46d226438f2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3778,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 126,\n \"path\": \"/app/explorer/views.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import (\\n Blueprint, render_template, request, flash, redirect, url_for\\n)\\nfrom flask_login import (\\n login_required, current_user\\n)\\n\\nfrom .models import Chart\\nfrom .forms import ChartForm\\nfrom .helpers import choices\\n\\nfrom ..carboard.models.vehicle import Vehicle\\nfrom ..carboard.models.driver import Driver\\nfrom ..extensions import db\\n\\n\\n# Declaring Explorer blueprint\\n\\nexplorer = Blueprint('explorer', __name__, url_prefix='/explorer')\\n\\n# --------------------- /explorer/ : List of charts ------------------- #\\n\\n\\n@explorer.route('/', methods=['GET'])\\n@login_required\\ndef index():\\n\\n return render_template('explorer/explorer.html')\\n\\n# --------------------- /explorer/ : List of charts ------------------- #\\n\\n\\n@explorer.route('/chart', methods=['GET'])\\n@login_required\\ndef indexChart():\\n charts = Chart.query.paginate(\\n page=request.args.get('page', 1, type=int),\\n per_page=request.args.get('per_page', 10, type=int),\\n )\\n return render_template('explorer/index.html', charts=charts)\\n\\n# ----------------------- /explorer/id : Show chart ------------------- #\\n\\n\\n@explorer.route('/chart/<int:id>', methods=['GET'])\\n@login_required\\ndef showChart(id):\\n chart = Chart.query.get_or_404(id)\\n return render_template('explorer/show.html', chart=chart)\\n\\n# ---------------------- /explorer/new : Add new chart ----------- #\\n\\n\\n@explorer.route('/chart/new', methods=['GET', 'POST'])\\n@login_required\\ndef newChart():\\n \\\"\\\"\\\" Add new chart\\\"\\\"\\\"\\n\\n form = ChartForm()\\n form.driver_id.choices = choices(Driver, 'Select a Driver', False, 'fullname')\\n form.vehicle_id.choices = choices(Vehicle, 'Select a vehicle')\\n\\n if form.validate_on_submit():\\n chart = Chart(\\n name=form.name.data,\\n user_id=current_user.id,\\n driver_id=form.driver_id.data,\\n vehicle_id=form.vehicle_id.data,\\n isValid=False,\\n error=\\\"Not initialized yet\\\"\\n )\\n db.session.add(chart)\\n db.session.commit()\\n return redirect(url_for('carboard.explorerChart'))\\n\\n return render_template('explorer/new.html', form=form)\\n\\n# -------------------- /explorer/id/edit : Edit chart ----------------- #\\n\\n\\n@explorer.route('/chart/<int:id>/explorer', methods=['GET', 'POST'])\\n@login_required\\ndef explorerChart():\\n \\\"\\\"\\\"Setup chart via explorer \\\"\\\"\\\"\\n\\n chart = Chart.query.get_or_404(id)\\n\\n # if form.validate_on_submit():\\n # trace = upload_file(form.trace.data, DRIVE_FILES_DIR)\\n # chart = Chart(\\n # user_id=form.user_id.data,\\n # driver_id=form.driver_id.data,\\n # vehicle_id=form.vehicle_id.data,\\n # name=form.name.data,\\n # )\\n # db.session.add(chart)\\n # db.session.commit()\\n # return redirect(url_for('carboard.explorerChart'))\\n\\n return render_template('explorer/new.html', chart=chart)\\n\\n# ------------------ /explorer/id/delete : Delete chart --------------- #\\n\\n\\n@explorer.route('/chart/<int:id>/toggle', methods=['GET', 'POST'])\\n@login_required\\ndef toggleChart(id):\\n chart = Chart.query.get_or_404(id)\\n status = chart.status if chart.status is not None else 0\\n chart.status = 1 - status\\n db.session.commit()\\n msg = 'activated' if chart.status is 1 else 'deactivated'\\n flash('Chart {}, {} successfully.'.format(chart.name, msg), 'info')\\n return redirect(url_for('carboard.indexChart'))\\n\\n# ------------------ /explorer/id/delete : Delete chart --------------- #\\n\\n\\n@explorer.route('/chart/<int:id>/delete', methods=['GET'])\\n@login_required\\ndef deleteChart(id):\\n chart = Chart.query.get_or_404(id)\\n db.session.delete(chart)\\n db.session.commit()\\n flash('Chart {}, deleted successfully.'.format(chart.name), 'info')\\n return redirect(url_for('carboard.indexChart'))\\n\"\n },\n {\n \"alpha_fraction\": 0.43623512983322144,\n \"alphanum_fraction\": 0.4384837746620178,\n \"avg_line_length\": 37.912498474121094,\n \"blob_id\": \"e052b46c074823176d1ad39fb8b188bf75e3af7e\",\n \"content_id\": \"020e82a41062beb6bfa61b9fa7e4de0e2403b841\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"HTML\",\n \"length_bytes\": 3113,\n \"license_type\": \"no_license\",\n \"max_line_length\": 128,\n \"num_lines\": 80,\n \"path\": \"/templates/carboard/signalsource/show.html\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"{% extends \\\"layout.html\\\" %}\\n\\n{% from \\\"carboard/macros.html\\\" import render_pagination %}\\n\\n{% block title %}\\n Signal source details | {{ super() }}\\n{% endblock %}\\n\\n{% block content_header %}\\n <section class=\\\"content-header\\\">\\n <h1>\\n Signal source details\\n </h1>\\n <ol class=\\\"breadcrumb\\\">\\n <li><a href=\\\"{{ url_for('carboard.index') }}\\\"><i class=\\\"fa fa-dashboard\\\"></i>Home</a></li>\\n <li><a href=\\\"{{ url_for('carboard.indexSignalsource') }}\\\">Signal Sources</a></li>\\n <li class=\\\"active\\\">Signal Source Details</li>\\n </ol>\\n </section>\\n{% endblock content_header %}\\n\\n\\n{% block content %}\\n <div class=\\\"row\\\">\\n <div class=\\\"col-md-12\\\">\\n <div class=\\\"box\\\">\\n\\n <div class=\\\"box-header with-border\\\">\\n <h3 class=\\\"box-title\\\">Signal source : {{ signalsource.name }}</h3>\\n </div>\\n\\n <div class=\\\"box-body\\\">\\n\\n <div class=\\\"line\\\">\\n <strong class=\\\"line-title\\\">ID</strong>\\n <p class=\\\"line-body\\\">{{ signalsource.id }}</p>\\n </div>\\n\\n <div class=\\\"line\\\">\\n <strong class=\\\"line-title\\\">Name</strong>\\n <p class=\\\"line-body\\\">{{ signalsource.name }}</p>\\n </div>\\n\\n <div class=\\\"line\\\">\\n <strong class=\\\"line-title\\\">Signals</strong>\\n <p class=\\\"line-body\\\">{{ signalsource.signals }}</p>\\n </div>\\n <div class=\\\"line\\\">\\n <strong class=\\\"line-title\\\">Description </strong>\\n <p class=\\\"line-body\\\">{{ signalsource.description or 'No description provided' }}</p>\\n </div>\\n\\n <div class=\\\"line\\\">\\n <strong class=\\\"line-title\\\">Status</strong>\\n <p class=\\\"line-body\\\">\\n {% if signalsource.status == 1 %}\\n <span class=\\\"label bg-green\\\">Activated</span>\\n {% else %}\\n <span class=\\\"label bg-red\\\">Disabled</span>\\n {% endif %}\\n </p>\\n </div>\\n\\n <div class=\\\"line\\\">\\n <strong class=\\\"line-title\\\">Creation</strong>\\n <p class=\\\"line-body\\\">{{ signalsource.created.strftime('%d-%m-%Y') }}</p>\\n </div>\\n\\n </div>\\n\\n <div class=\\\"box-footer form-footer\\\">\\n <a href=\\\"{{ url_for('carboard.editSignalsource', id=signalsource.id) }}\\\" class=\\\"btn btn-success\\\">Edit</a>\\n <a href=\\\"{{ url_for('carboard.deleteSignalsource', id=signalsource.id) }}\\\" class=\\\"btn btn-danger\\\">Delete</a>\\n <a href=\\\"{{ url_for('carboard.indexSignalsource') }}\\\" class=\\\"btn btn-default pull-right\\\">Back to\\n list</a>\\n </div>\\n </div>\\n </div>\\n </div>\\n{% endblock %}\\n\"\n },\n {\n \"alpha_fraction\": 0.6225000023841858,\n \"alphanum_fraction\": 0.6303571462631226,\n \"avg_line_length\": 30.81818199157715,\n \"blob_id\": \"5093e692ad6551c0523cf38ea01257bf530915b2\",\n \"content_id\": \"f344faee678f5be6a58902ab3a1b3d73c2306f71\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2800,\n \"license_type\": \"no_license\",\n \"max_line_length\": 104,\n \"num_lines\": 88,\n \"path\": \"/app/carboard/models/user.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from uuid import uuid4\\nfrom datetime import datetime\\nfrom flask_login import UserMixin\\nfrom flask_bcrypt import generate_password_hash, check_password_hash\\nfrom ..constants import DEFAULT_AVATAR, ROLE_USER, ROLE_ADMIN\\nfrom ...extensions import db\\n\\n\\n# -------------------------------- User Model ------------------------------- #\\n\\nclass User(db.Model, UserMixin):\\n \\\"\\\"\\\" User Model:\\n the user can be a person or even a campany, he/it can manage drivers\\n and vehicles.\\n \\\"\\\"\\\"\\n __tablename__ = 'users'\\n\\n id = db.Column(db.Integer, primary_key=True)\\n fullname = db.Column(db.String(255))\\n username = db.Column(db.String(255), nullable=False, unique=True)\\n email = db.Column(db.String(255), nullable=False, unique=True)\\n password = db.Column(db.String(255), nullable=False)\\n avatar = db.Column(db.String(255), nullable=True, default=DEFAULT_AVATAR)\\n role = db.Column(db.SmallInteger, default=ROLE_USER)\\n activation = db.Column(db.String(255))\\n status = db.Column(db.SmallInteger, default=1)\\n last_login = db.Column(db.DateTime(), default=datetime.utcnow())\\n created = db.Column(db.DateTime(), default=datetime.utcnow())\\n\\n def __init__(self, username, email, password, fullname=None, avatar=None, role=ROLE_USER, status=1):\\n self.fullname = fullname or username\\n self.username = username\\n self.email = email.lower()\\n self.password = generate_password_hash(password)\\n self.avatar = avatar\\n self.role = role\\n self.activation = str(uuid4())\\n self.status = status\\n\\n def set_password(self, password):\\n self.password = generate_password_hash(password)\\n\\n def check_password(self, password):\\n return check_password_hash(self.password, password)\\n\\n @property\\n def is_authenticated(self):\\n return True\\n\\n def is_active(self):\\n return self.status\\n\\n def is_anonymous(self):\\n return False\\n\\n def is_admin(self):\\n return self.role == ROLE_ADMIN\\n\\n def get_id(self):\\n return self.id\\n\\n def __repr__(self):\\n return '{} ({})'.format(self.username, self.email)\\n\\n def get_role(self):\\n return self.role\\n\\n def get_status(self):\\n return self.status\\n\\n @classmethod\\n def check_username(cls, username):\\n return cls.query.exists().where(User.username == username).scalar()\\n\\n @classmethod\\n def authenticate(cls, login, password):\\n user = cls.query.filter(\\n db.or_(User.username == login, User.email == login)\\n ).first()\\n if user:\\n authenticated = user.check_password(password)\\n else:\\n authenticated = False\\n return user, authenticated\\n\\n @classmethod\\n def get_by_id(cls, id):\\n return cls.query.filter(User.id == id).first()\\n\"\n },\n {\n \"alpha_fraction\": 0.5208333134651184,\n \"alphanum_fraction\": 0.5256410241127014,\n \"avg_line_length\": 26.130434036254883,\n \"blob_id\": \"8dcadf72dca6d731fd132e4fd21cd99e596db03c\",\n \"content_id\": \"6047eed6924a0c9b6a70a29660ed441c42551f72\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 624,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 23,\n \"path\": \"/app/carboard/forms/status.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_wtf import FlaskForm\\nfrom wtforms import StringField\\nfrom wtforms.validators import DataRequired, Regexp, Length\\n\\nfrom ..models.status import Status\\n\\n# ----------------------------- Status form classes ---------------------------\\n\\n\\nclass StatusForm(FlaskForm):\\n \\\"\\\"\\\" Status add/edit FlaskForm \\\"\\\"\\\"\\n title = StringField(\\n 'Status title',\\n validators=[\\n DataRequired(),\\n Length(min=3, max=50),\\n Regexp(\\n r'^[a-zA-Z ]+$',\\n message=(\\\"Status title is not correct !\\\")\\n ),\\n ]\\n )\\n color = StringField('Class color')\\n\"\n },\n {\n \"alpha_fraction\": 0.44807693362236023,\n \"alphanum_fraction\": 0.4692307710647583,\n \"avg_line_length\": 22.636363983154297,\n \"blob_id\": \"f9292e04d390f642a694e208a2cb936716ddd975\",\n \"content_id\": \"f6bdd7539507b802cf2190a73b50718e2ac85cb8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 520,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 22,\n \"path\": \"/app/importer/datasets/t-drive/helpers.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from .constants import TRANSFORM\\n\\n# -------------------- OpenXC Helpers --------------------------------------- #\\n\\n\\ndef correct_value(value):\\n try:\\n return int(value)\\n except:\\n for k, v in TRANSFORM.items():\\n if value == v:\\n return k\\n return -99999\\n\\n\\ndef correct_time(timestamp):\\n try:\\n timestamp = str(timestamp)\\n t = timestamp.split('.')\\n return '{}{}'.format(t[0], t[1][:3].ljust(3, '0'))\\n except:\\n return timestamp.split('.')[0]\\n\"\n },\n {\n \"alpha_fraction\": 0.6282051205635071,\n \"alphanum_fraction\": 0.6307692527770996,\n \"avg_line_length\": 26.85714340209961,\n \"blob_id\": \"e7e543acd6ccb8ea03ebce8f6b4203172e067fce\",\n \"content_id\": \"aa67bf12987c51d5fdb74111cf1ba05ed65a8789\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 390,\n \"license_type\": \"no_license\",\n \"max_line_length\": 93,\n \"num_lines\": 14,\n \"path\": \"/config/database.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from .config import DefaultConfig\\n\\n# -------------------------- Sqlalchemy Configuration ----------------------- #\\n\\n\\nclass DatabaseConfig(object):\\n\\n SQLALCHEMY_TRACK_MODIFICATIONS = True\\n\\n SQLALCHEMY_ECHO = False\\n\\n SQLALCHEMY_DATABASE_URI = 'sqlite:///' + DefaultConfig.INSTANCE_FOLDER_PATH + '/YooNa.db'\\n\\n # SQLALCHEMY_DATABASE_URI = 'mysql://user:pass@server/db?charset=utf8'\\n\"\n },\n {\n \"alpha_fraction\": 0.4947145879268646,\n \"alphanum_fraction\": 0.4947145879268646,\n \"avg_line_length\": 28.10769271850586,\n \"blob_id\": \"16730bab587beb021b21554e1d108249e019a602\",\n \"content_id\": \"4101731d530e5a16f961c71ae05d6706da636845\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1892,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 65,\n \"path\": \"/app/extensions.py\",\n \"repo_name\": \"vcar/vcar\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask_sqlalchemy import SQLAlchemy\\nfrom flask_migrate import Migrate\\nfrom flask_cache import Cache\\nfrom flask_mail import Mail\\nfrom flask_login import LoginManager\\nfrom flask_debugtoolbar import DebugToolbarExtension\\nfrom flask_images import Images\\nfrom flask_redis import FlaskRedis\\nfrom flask_plugins import PluginManager\\nfrom flask_misaka import Misaka\\nfrom flask_socketio import SocketIO\\n# from flask_session import Session\\n\\n# ------------------------- Inisialize Database object ---------------------- #\\n\\ndb = SQLAlchemy()\\n\\n# ------------------------- Inisialize Database object ---------------------- #\\n\\nmigrate = Migrate()\\n\\n# ------------------------- Inisialize Cache object ------------------------- #\\n\\ncache = Cache()\\n\\n# ------------------------- Inisialize Mail object -------------------------- #\\n\\nmail = Mail()\\n\\n# ------------------------- Inisialize Login object ------------------------- #\\n\\nlogin_manager = LoginManager()\\n\\n# ------------------------- Inisialize DebugToolbar object ------------------ #\\n\\ntoolbar = DebugToolbarExtension()\\n\\n# ------------------------- Inisialize Images object ------------------------ #\\n\\nimages = Images()\\n\\n# ------------------------- Inisialize SocketIO object ---------------------- #\\n\\nsocketio = SocketIO()\\n\\n# ------------------------- Inisialize Redis object ------------------------- #\\n\\nredis = FlaskRedis()\\n\\n# ------------------------- Inisialize PluginManager object ----------------- #\\n\\nplugin_manager = PluginManager()\\n\\n# ------------------------- Inisialize Misaka object ----------------- #\\n\\nmisaka = Misaka(tables=True, highlight=True)\\n\\n# ------------------------- Inisialize Session object ----------------------- #\\n\\\"\\\"\\\"\\n Flask-Session save session data on server with many options: filesystem,\\n Redis, Sqlite, ...\\n\\\"\\\"\\\"\\n# session = Session()\\n\\n# ------------------------------------ X ------------------------------------ #\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":112,"string":"112"}}},{"rowIdx":2983088,"cells":{"repo_name":{"kind":"string","value":"griffinfoster/artemis"},"repo_url":{"kind":"string","value":"https://github.com/griffinfoster/artemis"},"snapshot_id":{"kind":"string","value":"fc9dd1a5282dd388e49a63c4a0cf981f025d76da"},"revision_id":{"kind":"string","value":"bf445b7d2dc9127676503a4efd633d3492e6139a"},"directory_id":{"kind":"string","value":"3567127e47a287d6dad427c075ad384ef20df379"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-04-25T18:08:17.019014","string":"2020-04-25T18:08:17.019014"},"revision_date":{"kind":"timestamp","value":"2019-04-16T08:26:52","string":"2019-04-16T08:26:52"},"committer_date":{"kind":"timestamp","value":"2019-04-16T08:26:52","string":"2019-04-16T08:26:52"},"github_id":{"kind":"number","value":172974535,"string":"172,974,535"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6469648480415344,"alphanum_fraction":0.6576905250549316,"avg_line_length":41.125,"blob_id":"c37538fc6934037c762f3ca6dc6dfe9a3bff489e","content_id":"5c6fd245203dd6607e55cc3330d83651224e4cb1","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4382,"license_type":"permissive","max_line_length":276,"num_lines":104,"path":"/scripts/generateObsScripts.py","repo_name":"griffinfoster/artemis","src_encoding":"UTF-8","text":"#!/usr/bin/env python3\n\nfrom jinja2 import Environment, FileSystemLoader\nimport yaml\nimport os\nimport datetime\nimport numpy as np\n\n# HARDCODE\nLUMPTEMPLATE = 'LuMP_recorder.j2'\nLCUTEMPLATE = 'beamctl.j2'\nDATADIRROOT = '/local_data/ARTEMIS/'\n\nif __name__== \"__main__\":\n import argparse\n parser = argparse.ArgumentParser(\n description='''Generate an LCU beamctl script and LuMP recorder scripts from ARTEMIS3 YAML config files''')\n parser.add_argument('-o', '--obsConfigFile', help='YAML observation config file (required)')\n parser.add_argument('-s', '--srcConfigFile', help='YAML source config file (required)')\n parser.add_argument('-d', '--start_date', default=None, help='Start date and time to begin observation, if not used the observation begins when the script begins, which is not ideal when capturing all lanes, use the format: YYYY-MM-DDThh:mm:ssZ e.g. 2019-02-26T10:30:00Z')\n parser.add_argument('--templateDir', help='jinja2 template dir', default='../templates')\n parser.add_argument('-v', '--verbose', help='Verbose mode', action='store_true')\n parser.add_argument('--dry_run', help='Do not write scripts, just test out the generator script, useful with the -v option', action='store_true')\n parser.add_argument('--duration', help='Number of seconds to capture, default: 60', default=60)\n args = parser.parse_args()\n\n if args.obsConfigFile is None:\n print('ERROR: Observation config file is not set but is required, exiting')\n exit(1)\n if args.srcConfigFile is None:\n print('ERROR: Source config file is not set but is required, exiting')\n exit(1)\n obsConfigDict = yaml.load(open(args.obsConfigFile))\n srcConfigDict = yaml.load(open(args.srcConfigFile))\n\n # Useful variables\n dt = datetime.datetime.now()\n dataPath = dt.strftime('%Y%m%d_%H%M%S')\n generatorStartTime = str(dt)\n arrayMode = obsConfigDict['beamctl']['antennaset'].split('_')[0]\n decRad = srcConfigDict['DECJD'] * np.pi / 180.\n raRad = srcConfigDict['RAJD'] * np.pi / 180.\n dirStr = '%f.6,%f.6,J2000'%(raRad,decRad)\n\n # Generate LuMP scripts\n lumpDict = obsConfigDict['LuMP']\n\n lumpDict['generator_script'] = os.path.basename(__file__)\n lumpDict['generator_datetime'] = generatorStartTime\n lumpDict['anadir'] = dirStr\n lumpDict['digdir'] = dirStr\n lumpDict['duration'] = args.duration\n lumpDict['datadir'] = DATADIRROOT + '%s_%s'%(dataPath, srcConfigDict['NAME'])\n\n if not (args.start_date is None):\n lumpDict['opt_arg'] = '--start_date=%s'%args.start_date\n \n # build a config dict for each lane\n configBase = lumpDict.copy()\n configBase.pop('lane', None)\n\n configBase['sourcename_array'] = '[%s]*%i'%(srcConfigDict['NAME'], lumpDict['beamlets_per_lane'])\n configBase['rightascension_array'] = '[%f.6]*%i'%(raRad, lumpDict['beamlets_per_lane'])\n configBase['declination_array'] = '[%f.6]*%i'%(decRad, lumpDict['beamlets_per_lane'])\n configBase['epoch_array'] = '[J2000]*%i'%(lumpDict['beamlets_per_lane'])\n \n for lane in lumpDict['lane']:\n configLane = {**configBase, **lane}\n configLane['filename_base'] = '%s_lane%i'%(srcConfigDict['NAME'], lane['id'])\n \n #Load Jinja2 template\n env = Environment(loader = FileSystemLoader(args.templateDir), trim_blocks=True, lstrip_blocks=True)\n templateLuMP = env.get_template(LUMPTEMPLATE)\n renderText = templateLuMP.render(configLane)\n \n if args.verbose: print(renderText)\n\n outputFn = '%s_%s_lane%i.sh'%(srcConfigDict['NAME'], arrayMode, lane['id'])\n\n if not args.dry_run:\n print('Writing ' + outputFn)\n fh = open(outputFn,'w')\n fh.write(renderText)\n fh.close()\n\n # Generate LCU script\n lcuDict = obsConfigDict['beamctl']\n lcuDict['generator_script'] = os.path.basename(__file__)\n lcuDict['generator_datetime'] = generatorStartTime\n lcuDict['anadir'] = dirStr\n lcuDict['digdir'] = dirStr\n\n #Load Jinja2 template\n templateLCU = env.get_template(LCUTEMPLATE)\n renderText = templateLCU.render(lcuDict)\n \n if args.verbose: print(renderText)\n\n outputFn = '%s_%s_LCU.sh'%(srcConfigDict['NAME'], arrayMode)\n if not args.dry_run:\n print('Writing ' + outputFn)\n fh = open(outputFn,'w')\n fh.write(renderText)\n fh.close()\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6469648480415344,\n \"alphanum_fraction\": 0.6576905250549316,\n \"avg_line_length\": 41.125,\n \"blob_id\": \"c37538fc6934037c762f3ca6dc6dfe9a3bff489e\",\n \"content_id\": \"5c6fd245203dd6607e55cc3330d83651224e4cb1\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4382,\n \"license_type\": \"permissive\",\n \"max_line_length\": 276,\n \"num_lines\": 104,\n \"path\": \"/scripts/generateObsScripts.py\",\n \"repo_name\": \"griffinfoster/artemis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python3\\n\\nfrom jinja2 import Environment, FileSystemLoader\\nimport yaml\\nimport os\\nimport datetime\\nimport numpy as np\\n\\n# HARDCODE\\nLUMPTEMPLATE = 'LuMP_recorder.j2'\\nLCUTEMPLATE = 'beamctl.j2'\\nDATADIRROOT = '/local_data/ARTEMIS/'\\n\\nif __name__== \\\"__main__\\\":\\n import argparse\\n parser = argparse.ArgumentParser(\\n description='''Generate an LCU beamctl script and LuMP recorder scripts from ARTEMIS3 YAML config files''')\\n parser.add_argument('-o', '--obsConfigFile', help='YAML observation config file (required)')\\n parser.add_argument('-s', '--srcConfigFile', help='YAML source config file (required)')\\n parser.add_argument('-d', '--start_date', default=None, help='Start date and time to begin observation, if not used the observation begins when the script begins, which is not ideal when capturing all lanes, use the format: YYYY-MM-DDThh:mm:ssZ e.g. 2019-02-26T10:30:00Z')\\n parser.add_argument('--templateDir', help='jinja2 template dir', default='../templates')\\n parser.add_argument('-v', '--verbose', help='Verbose mode', action='store_true')\\n parser.add_argument('--dry_run', help='Do not write scripts, just test out the generator script, useful with the -v option', action='store_true')\\n parser.add_argument('--duration', help='Number of seconds to capture, default: 60', default=60)\\n args = parser.parse_args()\\n\\n if args.obsConfigFile is None:\\n print('ERROR: Observation config file is not set but is required, exiting')\\n exit(1)\\n if args.srcConfigFile is None:\\n print('ERROR: Source config file is not set but is required, exiting')\\n exit(1)\\n obsConfigDict = yaml.load(open(args.obsConfigFile))\\n srcConfigDict = yaml.load(open(args.srcConfigFile))\\n\\n # Useful variables\\n dt = datetime.datetime.now()\\n dataPath = dt.strftime('%Y%m%d_%H%M%S')\\n generatorStartTime = str(dt)\\n arrayMode = obsConfigDict['beamctl']['antennaset'].split('_')[0]\\n decRad = srcConfigDict['DECJD'] * np.pi / 180.\\n raRad = srcConfigDict['RAJD'] * np.pi / 180.\\n dirStr = '%f.6,%f.6,J2000'%(raRad,decRad)\\n\\n # Generate LuMP scripts\\n lumpDict = obsConfigDict['LuMP']\\n\\n lumpDict['generator_script'] = os.path.basename(__file__)\\n lumpDict['generator_datetime'] = generatorStartTime\\n lumpDict['anadir'] = dirStr\\n lumpDict['digdir'] = dirStr\\n lumpDict['duration'] = args.duration\\n lumpDict['datadir'] = DATADIRROOT + '%s_%s'%(dataPath, srcConfigDict['NAME'])\\n\\n if not (args.start_date is None):\\n lumpDict['opt_arg'] = '--start_date=%s'%args.start_date\\n \\n # build a config dict for each lane\\n configBase = lumpDict.copy()\\n configBase.pop('lane', None)\\n\\n configBase['sourcename_array'] = '[%s]*%i'%(srcConfigDict['NAME'], lumpDict['beamlets_per_lane'])\\n configBase['rightascension_array'] = '[%f.6]*%i'%(raRad, lumpDict['beamlets_per_lane'])\\n configBase['declination_array'] = '[%f.6]*%i'%(decRad, lumpDict['beamlets_per_lane'])\\n configBase['epoch_array'] = '[J2000]*%i'%(lumpDict['beamlets_per_lane'])\\n \\n for lane in lumpDict['lane']:\\n configLane = {**configBase, **lane}\\n configLane['filename_base'] = '%s_lane%i'%(srcConfigDict['NAME'], lane['id'])\\n \\n #Load Jinja2 template\\n env = Environment(loader = FileSystemLoader(args.templateDir), trim_blocks=True, lstrip_blocks=True)\\n templateLuMP = env.get_template(LUMPTEMPLATE)\\n renderText = templateLuMP.render(configLane)\\n \\n if args.verbose: print(renderText)\\n\\n outputFn = '%s_%s_lane%i.sh'%(srcConfigDict['NAME'], arrayMode, lane['id'])\\n\\n if not args.dry_run:\\n print('Writing ' + outputFn)\\n fh = open(outputFn,'w')\\n fh.write(renderText)\\n fh.close()\\n\\n # Generate LCU script\\n lcuDict = obsConfigDict['beamctl']\\n lcuDict['generator_script'] = os.path.basename(__file__)\\n lcuDict['generator_datetime'] = generatorStartTime\\n lcuDict['anadir'] = dirStr\\n lcuDict['digdir'] = dirStr\\n\\n #Load Jinja2 template\\n templateLCU = env.get_template(LCUTEMPLATE)\\n renderText = templateLCU.render(lcuDict)\\n \\n if args.verbose: print(renderText)\\n\\n outputFn = '%s_%s_LCU.sh'%(srcConfigDict['NAME'], arrayMode)\\n if not args.dry_run:\\n print('Writing ' + outputFn)\\n fh = open(outputFn,'w')\\n fh.write(renderText)\\n fh.close()\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2983089,"cells":{"repo_name":{"kind":"string","value":"eldrad294/DjangoCookbook"},"repo_url":{"kind":"string","value":"https://github.com/eldrad294/DjangoCookbook"},"snapshot_id":{"kind":"string","value":"c2e8713b587aa19929db508b704e15b613fbfbc1"},"revision_id":{"kind":"string","value":"142f79501395b25fdc35aa4357b68601e962bc92"},"directory_id":{"kind":"string","value":"79cc2c5227693a0bd8785b4b1842ac057403a267"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-07-26T05:49:18.725844","string":"2023-07-26T05:49:18.725844"},"revision_date":{"kind":"timestamp","value":"2021-09-05T16:21:53","string":"2021-09-05T16:21:53"},"committer_date":{"kind":"timestamp","value":"2021-09-05T16:21:53","string":"2021-09-05T16:21:53"},"github_id":{"kind":"number","value":403265389,"string":"403,265,389"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"timestamp","value":"2021-09-05T09:21:55","string":"2021-09-05T09:21:55"},"gha_updated_at":{"kind":"timestamp","value":"2021-09-05T13:00:57","string":"2021-09-05T13:00:57"},"gha_pushed_at":{"kind":"timestamp","value":"2021-09-05T16:21:53","string":"2021-09-05T16:21:53"},"gha_language":{"kind":"string","value":"Python"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7460567951202393,"alphanum_fraction":0.7507886290550232,"avg_line_length":19.45161247253418,"blob_id":"0c3f9c124abb5e0385d52b45ecc6810c2eb04ca3","content_id":"a1888fc01d8b5243cdb86ce598708c6fc677cb2d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":634,"license_type":"no_license","max_line_length":83,"num_lines":31,"path":"/README.md","repo_name":"eldrad294/DjangoCookbook","src_encoding":"UTF-8","text":"# Django Cooking!\n\n## Setting up a Python virtual environment\n\nThrough cmd, run\n\n* pip install \n* cd DjangoCookbook\n* pipenv install django==3.2.7\n* pipenv shell\n\n## Creating the project structure\n\nThrough cmd, run\n\n* django-admin startproject DjangoCookbook . \n\n## Booting up the project\n\n* python manage.py runserver\n\n## Creating an app within the project\n\n* python manage.py startapp pages\n* Go to settings.py and add the app under INSTALLED_APPS\n\n## Adding a view\n\n* Add view logic inside of views.py\n* Create a url.py file under the app (pages)\n* Add the urlpatterns list structure and route the view to the url regex expression\n"},{"alpha_fraction":0.7488986849784851,"alphanum_fraction":0.757709264755249,"avg_line_length":27.5,"blob_id":"24bca71be4b1ad31d5c9f7de8e07ea90d73f9d28","content_id":"7b6fc0efbfcb96fcd0f4f29d099cb63d70490276","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":227,"license_type":"no_license","max_line_length":42,"num_lines":8,"path":"/posts/views.py","repo_name":"eldrad294/DjangoCookbook","src_encoding":"UTF-8","text":"from django.shortcuts import render\nfrom django.views.generic import ListView\nfrom .models import Post\n\nclass HomePageView2(ListView):\n model = Post\n template_name = 'home2.html'\n context_object_name = 'all_posts_list'"},{"alpha_fraction":0.6596858501434326,"alphanum_fraction":0.6910994648933411,"avg_line_length":26.428571701049805,"blob_id":"5f5b28727ba135d59947f19e85d462e7a3ecaefa","content_id":"fdc0b5c6ef0471c8175dc0d00703a4ffc3c19551","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":191,"license_type":"no_license","max_line_length":57,"num_lines":7,"path":"/posts/urls.py","repo_name":"eldrad294/DjangoCookbook","src_encoding":"UTF-8","text":"from django.urls import path\nfrom .views import HomePageView2\n\nurlpatterns = [\n path('', HomePageView2.as_view(), name='home2'),\n path('home2/', HomePageView2.as_view(), name='home2')\n]"}],"string":"[\n {\n \"alpha_fraction\": 0.7460567951202393,\n \"alphanum_fraction\": 0.7507886290550232,\n \"avg_line_length\": 19.45161247253418,\n \"blob_id\": \"0c3f9c124abb5e0385d52b45ecc6810c2eb04ca3\",\n \"content_id\": \"a1888fc01d8b5243cdb86ce598708c6fc677cb2d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 634,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 31,\n \"path\": \"/README.md\",\n \"repo_name\": \"eldrad294/DjangoCookbook\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Django Cooking!\\n\\n## Setting up a Python virtual environment\\n\\nThrough cmd, run\\n\\n* pip install \\n* cd DjangoCookbook\\n* pipenv install django==3.2.7\\n* pipenv shell\\n\\n## Creating the project structure\\n\\nThrough cmd, run\\n\\n* django-admin startproject DjangoCookbook . \\n\\n## Booting up the project\\n\\n* python manage.py runserver\\n\\n## Creating an app within the project\\n\\n* python manage.py startapp pages\\n* Go to settings.py and add the app under INSTALLED_APPS\\n\\n## Adding a view\\n\\n* Add view logic inside of views.py\\n* Create a url.py file under the app (pages)\\n* Add the urlpatterns list structure and route the view to the url regex expression\\n\"\n },\n {\n \"alpha_fraction\": 0.7488986849784851,\n \"alphanum_fraction\": 0.757709264755249,\n \"avg_line_length\": 27.5,\n \"blob_id\": \"24bca71be4b1ad31d5c9f7de8e07ea90d73f9d28\",\n \"content_id\": \"7b6fc0efbfcb96fcd0f4f29d099cb63d70490276\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 227,\n \"license_type\": \"no_license\",\n \"max_line_length\": 42,\n \"num_lines\": 8,\n \"path\": \"/posts/views.py\",\n \"repo_name\": \"eldrad294/DjangoCookbook\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.shortcuts import render\\nfrom django.views.generic import ListView\\nfrom .models import Post\\n\\nclass HomePageView2(ListView):\\n model = Post\\n template_name = 'home2.html'\\n context_object_name = 'all_posts_list'\"\n },\n {\n \"alpha_fraction\": 0.6596858501434326,\n \"alphanum_fraction\": 0.6910994648933411,\n \"avg_line_length\": 26.428571701049805,\n \"blob_id\": \"5f5b28727ba135d59947f19e85d462e7a3ecaefa\",\n \"content_id\": \"fdc0b5c6ef0471c8175dc0d00703a4ffc3c19551\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 191,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 7,\n \"path\": \"/posts/urls.py\",\n \"repo_name\": \"eldrad294/DjangoCookbook\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.urls import path\\nfrom .views import HomePageView2\\n\\nurlpatterns = [\\n path('', HomePageView2.as_view(), name='home2'),\\n path('home2/', HomePageView2.as_view(), name='home2')\\n]\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2983090,"cells":{"repo_name":{"kind":"string","value":"Jeezus101/jeezus101.github.io"},"repo_url":{"kind":"string","value":"https://github.com/Jeezus101/jeezus101.github.io"},"snapshot_id":{"kind":"string","value":"d0b8f0bdf07e8754895d4f8b551644226a6dd700"},"revision_id":{"kind":"string","value":"06d8f8de0dab33bda64df980ed013cb518c23088"},"directory_id":{"kind":"string","value":"aeb2027014a82ec4a32fdfd399ec84a73bccb637"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2018-11-07T12:56:53.281256","string":"2018-11-07T12:56:53.281256"},"revision_date":{"kind":"timestamp","value":"2018-09-02T21:18:01","string":"2018-09-02T21:18:01"},"committer_date":{"kind":"timestamp","value":"2018-09-02T21:18:01","string":"2018-09-02T21:18:01"},"github_id":{"kind":"number","value":116399579,"string":"116,399,579"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5317640900611877,"alphanum_fraction":0.5376487970352173,"avg_line_length":26.492647171020508,"blob_id":"11431d3624afbad1d8a63fd9ff6e79134028e8eb","content_id":"c4aab75c10b7e0017407b32f1eb239b20bff841c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":7479,"license_type":"no_license","max_line_length":88,"num_lines":272,"path":"/JavaScript.js","repo_name":"Jeezus101/jeezus101.github.io","src_encoding":"UTF-8","text":"// Initialization process\nfunction intialize() {\n // Debugging initialize\n // localStorage.initialize = true;\n if (localStorage.initialize == null || localStorage.initialize == \"true\") {\n localStorage.initialize = false;\n\n // Declare arrays\n var plotArr = new Array([]);\n var payArr = new Array([]);\n for (i = 1; i < 172; i++) {\n plotArr.push([i, , , , payArr]);\n }\n localStorage.setObj(1, plotArr);\n }\n}\n\n\n//--------------------------------------------------------------------------------------\n// Javascript to go to new page\nfunction loginPrompt() {\n // Prompt to confirm admin\n \tvar password = prompt(\"Please enter password\", \"\");\n \t\n \t// Checking if right password entered\n \tif (passwordCheck(password)) {\n \t\talert(\"Right password\");\n \t\n \t\n \t\n \t} else {\n \t\talert(\"Wrong password\");\n \t}\n\n}\n\n\n\n\n\n//--------------------------------------------------------------------------------------\n// Javascript to go to new page\nfunction gotoPlotPage() {\n var plot = arguments[0];\n localStorage.plot = plot; \n window.location.href = \"PlotPage.html\";\n}\n\n\n\n//--------------------------------------------------------------------------------------\n// Set details of plot page\nfunction setDetails() {\n // Set currently selected plot number \n var plot = localStorage.plot;\n document.getElementById(\"titleplot\").innerHTML = \"Plot \" + plot;\n document.getElementById(\"plot\").innerHTML = plot;\n\n\n // Get plot info array\n var plotArr = new Array([]);\n plotArr = localStorage.getObj(1);\n\n // Get array of current plot info\n var tempArr = new Array([]);\n tempArr = plotArr[plot];\n\n // Get array of payment table\n var payArr = new Array([]);\n payArr = tempArr[4];\n\n // Set payment table details\n var table = document.getElementById('PayTable');\n\n payArr.forEach(function (rowData) {\n var row = document.createElement('tr');\n rowData.forEach(function (cellData) {\n var cell = document.createElement('td');\n cell.appendChild(document.createTextNode(cellData));\n row.appendChild(cell);\n });\n table.appendChild(row);\n });\n\n // Set table headings to be non-editables\n //iterate through rows\n var row = document.getElementById(\"tableHeads\")\n row.contentEditable = \"false\";\n\n\n // Set plot info input object\n var price = document.getElementById(\"inputprice\");\n var buyer = document.getElementById(\"inputbuyer\");\n var notes = document.getElementById(\"inputnote\");\n\n // Set plot info input values (if any)\n if (tempArr[1] != \"\" && tempArr[1] != null) price.value = tempArr[1];\n if (tempArr[2] != \"\" && tempArr[2] != null) buyer.value = tempArr[2];\n if (tempArr[3] != \"\" && tempArr[3] != null) notes.value = tempArr[3];\n\n\n\n // Get table input objects\n var date = document.getElementById(\"inputdate\");\n var payment = document.getElementById(\"inputpayment\");\n var paynotes = document.getElementById(\"inputpaynote\");\n\n // Clear table input textboxes\n date.value = \"\";\n payment.value = \"\";\n paynotes.value = \"\";\n\n\n}\n\n//--------------------------------------------------------------------------------------\n// Dynamic 'Notes' textbox \nfunction Expand(obj) {\n if (!obj.savesize) obj.savesize = obj.size;\n obj.size = Math.max(obj.savesize, obj.value.length);\n if (!obj.savesize) obj.savesize = obj.size;\n obj.size = Math.max(obj.savesize, obj.value.length);\n}\n\n\n//--------------------------------------------------------------------------------------\n// Add payment\nfunction addPay() {\n // Get table input objects\n var date = document.getElementById(\"inputdate\");\n var payment = document.getElementById(\"inputpayment\");\n var notes = document.getElementById(\"inputpaynote\");\n\n if (date.value != \"\" && payment.value != \"\") {\n // Get table object\n var table = document.getElementById(\"PayTable\");\n // Get new row object\n var row = table.insertRow();\n // Get each cells of new row\n var cell1 = row.insertCell(0);\n var cell2 = row.insertCell(1);\n var cell3 = row.insertCell(2);\n\n // Insert values into cells\n cell1.innerHTML = date.value;\n cell2.innerHTML = payment.value;\n if (notes.value != \"\") {\n cell3.innerHTML = notes.value;\n } else {\n cell3.innerHTML = \"N/A\";\n }\n // Clear table input textboxes\n date.value = \"\";\n payment.value = \"\";\n notes.value = \"\";\n\n\n var plotArr = new Array([]);\n } else if (date.value == \"\") {\n alert(\"Please enter date of payment\")\n } else if (payment.value == \"\") {\n alert(\"Please enter amount of payment\")\n } else {\n alert(\"No payment information entered\")\n }\n}\n\n\n//--------------------------------------------------------------------------------------\n// Save plot info to persistence\nfunction savePlot() {\n \t// Prompt to confirm admin\n \tvar password = prompt(\"Please enter password\", \"\");\n\t\n \tif (passwordCheck(password)) {\n // Get plot info \n var plot = localStorage.plot;\n var price = document.getElementById(\"inputprice\");\n var buyer = document.getElementById(\"inputbuyer\");\n var notes = document.getElementById(\"inputnote\");\n\n\n // Get plots info array\n var plotArr = new Array([]);\n plotArr = localStorage.getObj(1);\n\n // Get array of current plot info\n var tempArr = new Array([]);\n tempArr = plotArr[plot];\n\n\n // Save plot info\n tempArr[1] = price.value;\n tempArr[2] = buyer.value;\n tempArr[3] = notes.value;\n\n\n\n // Get array of payment table\n var payArr = new Array([]);\n payArr = tempArr[4];\n\n // Get payment table info\n var payArr = new Array([]);\n var table = document.getElementById(\"PayTable\");\n var temppayArr, count = 0;\n //iterate through rows\n for (var i = 1, row; row = table.rows[i]; i++) {\n temppayArr = new Array([]);\n count = 0;\n //iterate through columns\n for (var j = 0, col; col = row.cells[j]; j++) {\n if (col.innerHTML == \"\") count++;\n temppayArr[j] = col.innerHTML;\n col.contentEditable = true;\n }\n if (count != 3 && temppayArr.length != 1) payArr[i - 1] = temppayArr;\n }\n\n\n // Update plot array\n tempArr[4] = payArr;\n plotArr[plot] = tempArr;\n\n // Save to local storage\n localStorage.setObj(1, plotArr);\n\n tempArr[0] = tempArr[0].toString();\n\n\t\n\t\talert(\"Hello\");\n \n\t\t// Go back to main page\n \t\twindow.location.href = \"index.html\";\n\n\t\t// If cancel clicked\n\t\t} else if(password == null){\n\t\t\n\t // If incorrect password\n \t} else { \n \talert(\"Incorrect password\");\n \t}\n \n \n \n\t\n \n}\n\n//--------------------------------------------------------------------------------------\n\n// Returns true if correct password\nfunction passwordCheck() {\n var enteredPassword = arguments[0];\n if (enteredPassword == \"394dha\") {\n return true;\n } else {\n return false;\n }\n}\n\n\n\n\n//--------------------------------------------------------------------------------------\n// Functions to handle arrays in localStorage\nStorage.prototype.setObj = function (key, obj) {\n return this.setItem(key, JSON.stringify(obj))\n}\nStorage.prototype.getObj = function (key) {\n return JSON.parse(this.getItem(key))\n}"},{"alpha_fraction":0.7241379022598267,"alphanum_fraction":0.7241379022598267,"avg_line_length":29,"blob_id":"2cb11e12c2f659901d4c928cd5b766caddd52ca7","content_id":"7c3362cd6615e6a2ecffa6e147272f523c810cb2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":29,"license_type":"no_license","max_line_length":29,"num_lines":1,"path":"/UserGrant.py","repo_name":"Jeezus101/jeezus101.github.io","src_encoding":"UTF-8","text":"SELECT USER(),CURRENT_USER();"},{"alpha_fraction":0.4140722155570984,"alphanum_fraction":0.4196762144565582,"avg_line_length":27.696428298950195,"blob_id":"d72d2ef054cf9b05ee3bb81e0484bece0debd135","content_id":"b48c0aaa1ecb6c12423133fa25fb9fd738dc7ee8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"PHP","length_bytes":1606,"license_type":"no_license","max_line_length":86,"num_lines":56,"path":"/PHP.php","repo_name":"Jeezus101/jeezus101.github.io","src_encoding":"UTF-8","text":"<?php\n\n echo \"Hello\";\n\n header('Content-Type: application/json');\n\n $aResult = array();\n\n if( !isset($_POST['functionname']) ) { $aResult['error'] = 'No function name!'; }\n\n if( !isset($_POST['arguments']) ) { $aResult['error'] = 'No function arguments!'; }\n \n switch($_POST['functionname']) {\n \n // Code to write to the database\n case 'write':\n \n $servername = \"den1.mysql6.gear.host\";\n $username = \"colonyplot\";\n $password = \"Ij56w_?F9kHR\";\n $dbname = \"Colony_DB\";\n\n // Create connection\n $conn = new mysqli($servername, $username, $password, $dbname);\n // Check connection\n if ($conn->connect_error) {\n die(\"Connection failed: \" . $conn->connect_error);\n } \n\n $sql = \"INSERT INTO Payment Details (Plot No., Date, Payment, Note)\n VALUES (1, '24th April', '3000', 'Hello')\";\n\n if ($conn->query($sql) === TRUE) {\n echo \"New record created successfully\";\n } else {\n echo \"Error: \" . $sql . \"<br>\" . $conn->error;\n }\n\n $conn->close();\n \n break;\n \n \n \n // Code to read from the database \n case 'read':\n \n break;\n \n default:\n $aResult['error'] = 'Not found function '.$_POST['functionname'].'!';\n break;\n } \n \n 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\"path\": \"/JavaScript.js\",\n \"repo_name\": \"Jeezus101/jeezus101.github.io\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Initialization process\\nfunction intialize() {\\n // Debugging initialize\\n // localStorage.initialize = true;\\n if (localStorage.initialize == null || localStorage.initialize == \\\"true\\\") {\\n localStorage.initialize = false;\\n\\n // Declare arrays\\n var plotArr = new Array([]);\\n var payArr = new Array([]);\\n for (i = 1; i < 172; i++) {\\n plotArr.push([i, , , , payArr]);\\n }\\n localStorage.setObj(1, plotArr);\\n }\\n}\\n\\n\\n//--------------------------------------------------------------------------------------\\n// Javascript to go to new page\\nfunction loginPrompt() {\\n // Prompt to confirm admin\\n \\tvar password = prompt(\\\"Please enter password\\\", \\\"\\\");\\n \\t\\n \\t// Checking if right password entered\\n \\tif (passwordCheck(password)) {\\n \\t\\talert(\\\"Right password\\\");\\n \\t\\n \\t\\n \\t\\n \\t} else {\\n \\t\\talert(\\\"Wrong password\\\");\\n \\t}\\n\\n}\\n\\n\\n\\n\\n\\n//--------------------------------------------------------------------------------------\\n// Javascript to go to new page\\nfunction gotoPlotPage() {\\n var plot = arguments[0];\\n localStorage.plot = plot; \\n window.location.href = \\\"PlotPage.html\\\";\\n}\\n\\n\\n\\n//--------------------------------------------------------------------------------------\\n// Set details of plot page\\nfunction setDetails() {\\n // Set currently selected plot number \\n var plot = localStorage.plot;\\n document.getElementById(\\\"titleplot\\\").innerHTML = \\\"Plot \\\" + plot;\\n document.getElementById(\\\"plot\\\").innerHTML = plot;\\n\\n\\n // Get plot info array\\n var plotArr = new Array([]);\\n plotArr = localStorage.getObj(1);\\n\\n // Get array of current plot info\\n var tempArr = new Array([]);\\n tempArr = plotArr[plot];\\n\\n // Get array of payment table\\n var payArr = new Array([]);\\n payArr = tempArr[4];\\n\\n // Set payment table details\\n var table = document.getElementById('PayTable');\\n\\n payArr.forEach(function (rowData) {\\n var row = document.createElement('tr');\\n rowData.forEach(function (cellData) {\\n var cell = document.createElement('td');\\n cell.appendChild(document.createTextNode(cellData));\\n row.appendChild(cell);\\n });\\n table.appendChild(row);\\n });\\n\\n // Set table headings to be non-editables\\n //iterate through rows\\n var row = document.getElementById(\\\"tableHeads\\\")\\n row.contentEditable = \\\"false\\\";\\n\\n\\n // Set plot info input object\\n var price = document.getElementById(\\\"inputprice\\\");\\n var buyer = document.getElementById(\\\"inputbuyer\\\");\\n var notes = document.getElementById(\\\"inputnote\\\");\\n\\n // Set plot info input values (if any)\\n if (tempArr[1] != \\\"\\\" && tempArr[1] != null) price.value = tempArr[1];\\n if (tempArr[2] != \\\"\\\" && tempArr[2] != null) buyer.value = tempArr[2];\\n if (tempArr[3] != \\\"\\\" && tempArr[3] != null) notes.value = tempArr[3];\\n\\n\\n\\n // Get table input objects\\n var date = document.getElementById(\\\"inputdate\\\");\\n var payment = document.getElementById(\\\"inputpayment\\\");\\n var paynotes = document.getElementById(\\\"inputpaynote\\\");\\n\\n // Clear table input textboxes\\n date.value = \\\"\\\";\\n payment.value = \\\"\\\";\\n paynotes.value = \\\"\\\";\\n\\n\\n}\\n\\n//--------------------------------------------------------------------------------------\\n// Dynamic 'Notes' textbox \\nfunction Expand(obj) {\\n if (!obj.savesize) obj.savesize = obj.size;\\n obj.size = Math.max(obj.savesize, obj.value.length);\\n if (!obj.savesize) obj.savesize = obj.size;\\n obj.size = Math.max(obj.savesize, obj.value.length);\\n}\\n\\n\\n//--------------------------------------------------------------------------------------\\n// Add payment\\nfunction addPay() {\\n // Get table input objects\\n var date = document.getElementById(\\\"inputdate\\\");\\n var payment = document.getElementById(\\\"inputpayment\\\");\\n var notes = document.getElementById(\\\"inputpaynote\\\");\\n\\n if (date.value != \\\"\\\" && payment.value != \\\"\\\") {\\n // Get table object\\n var table = document.getElementById(\\\"PayTable\\\");\\n // Get new row object\\n var row = table.insertRow();\\n // Get each cells of new row\\n var cell1 = row.insertCell(0);\\n var cell2 = row.insertCell(1);\\n var cell3 = row.insertCell(2);\\n\\n // Insert values into cells\\n cell1.innerHTML = date.value;\\n cell2.innerHTML = payment.value;\\n if (notes.value != \\\"\\\") {\\n cell3.innerHTML = notes.value;\\n } else {\\n cell3.innerHTML = \\\"N/A\\\";\\n }\\n // Clear table input textboxes\\n date.value = \\\"\\\";\\n payment.value = \\\"\\\";\\n notes.value = \\\"\\\";\\n\\n\\n var plotArr = new Array([]);\\n } else if (date.value == \\\"\\\") {\\n alert(\\\"Please enter date of payment\\\")\\n } else if (payment.value == \\\"\\\") {\\n alert(\\\"Please enter amount of payment\\\")\\n } else {\\n alert(\\\"No payment information entered\\\")\\n }\\n}\\n\\n\\n//--------------------------------------------------------------------------------------\\n// Save plot info to persistence\\nfunction savePlot() {\\n \\t// Prompt to confirm admin\\n \\tvar password = prompt(\\\"Please enter password\\\", \\\"\\\");\\n\\t\\n \\tif (passwordCheck(password)) {\\n // Get plot info \\n var plot = localStorage.plot;\\n var price = document.getElementById(\\\"inputprice\\\");\\n var buyer = document.getElementById(\\\"inputbuyer\\\");\\n var notes = document.getElementById(\\\"inputnote\\\");\\n\\n\\n // Get plots info array\\n var plotArr = new Array([]);\\n plotArr = localStorage.getObj(1);\\n\\n // Get array of current plot info\\n var tempArr = new Array([]);\\n tempArr = plotArr[plot];\\n\\n\\n // Save plot info\\n tempArr[1] = price.value;\\n tempArr[2] = buyer.value;\\n tempArr[3] = notes.value;\\n\\n\\n\\n // Get array of payment table\\n var payArr = new Array([]);\\n payArr = tempArr[4];\\n\\n // Get payment table info\\n var payArr = new Array([]);\\n var table = document.getElementById(\\\"PayTable\\\");\\n var temppayArr, count = 0;\\n //iterate through rows\\n for (var i = 1, row; row = table.rows[i]; i++) {\\n temppayArr = new Array([]);\\n count = 0;\\n //iterate through columns\\n for (var j = 0, col; col = row.cells[j]; j++) {\\n if (col.innerHTML == \\\"\\\") count++;\\n temppayArr[j] = col.innerHTML;\\n col.contentEditable = true;\\n }\\n if (count != 3 && temppayArr.length != 1) payArr[i - 1] = temppayArr;\\n }\\n\\n\\n // Update plot array\\n tempArr[4] = payArr;\\n plotArr[plot] = tempArr;\\n\\n // Save to local storage\\n localStorage.setObj(1, plotArr);\\n\\n tempArr[0] = tempArr[0].toString();\\n\\n\\t\\n\\t\\talert(\\\"Hello\\\");\\n \\n\\t\\t// Go back to main page\\n \\t\\twindow.location.href = \\\"index.html\\\";\\n\\n\\t\\t// If cancel clicked\\n\\t\\t} else if(password == null){\\n\\t\\t\\n\\t // If incorrect password\\n \\t} else { \\n \\talert(\\\"Incorrect password\\\");\\n \\t}\\n \\n \\n \\n\\t\\n \\n}\\n\\n//--------------------------------------------------------------------------------------\\n\\n// Returns true if correct password\\nfunction passwordCheck() {\\n var enteredPassword = arguments[0];\\n if (enteredPassword == \\\"394dha\\\") {\\n return true;\\n } else {\\n return false;\\n }\\n}\\n\\n\\n\\n\\n//--------------------------------------------------------------------------------------\\n// Functions to handle arrays in localStorage\\nStorage.prototype.setObj = function (key, obj) {\\n return this.setItem(key, JSON.stringify(obj))\\n}\\nStorage.prototype.getObj = function (key) {\\n return JSON.parse(this.getItem(key))\\n}\"\n },\n {\n \"alpha_fraction\": 0.7241379022598267,\n \"alphanum_fraction\": 0.7241379022598267,\n \"avg_line_length\": 29,\n \"blob_id\": \"2cb11e12c2f659901d4c928cd5b766caddd52ca7\",\n \"content_id\": \"7c3362cd6615e6a2ecffa6e147272f523c810cb2\",\n 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\\\"Hello\\\";\\n\\n header('Content-Type: application/json');\\n\\n $aResult = array();\\n\\n if( !isset($_POST['functionname']) ) { $aResult['error'] = 'No function name!'; }\\n\\n if( !isset($_POST['arguments']) ) { $aResult['error'] = 'No function arguments!'; }\\n \\n switch($_POST['functionname']) {\\n \\n // Code to write to the database\\n case 'write':\\n \\n $servername = \\\"den1.mysql6.gear.host\\\";\\n $username = \\\"colonyplot\\\";\\n $password = \\\"Ij56w_?F9kHR\\\";\\n $dbname = \\\"Colony_DB\\\";\\n\\n // Create connection\\n $conn = new mysqli($servername, $username, $password, $dbname);\\n // Check connection\\n if ($conn->connect_error) {\\n die(\\\"Connection failed: \\\" . $conn->connect_error);\\n } \\n\\n $sql = \\\"INSERT INTO Payment Details (Plot No., Date, Payment, Note)\\n VALUES (1, '24th April', '3000', 'Hello')\\\";\\n\\n if ($conn->query($sql) === TRUE) {\\n echo \\\"New record created successfully\\\";\\n } else {\\n echo \\\"Error: \\\" . $sql . \\\"<br>\\\" . $conn->error;\\n }\\n\\n $conn->close();\\n \\n break;\\n \\n \\n \\n // Code to read from the database \\n case 'read':\\n \\n break;\\n \\n default:\\n $aResult['error'] = 'Not found function '.$_POST['functionname'].'!';\\n break;\\n } \\n \\n \\n?>\"\n },\n {\n \"alpha_fraction\": 0.6666666865348816,\n \"alphanum_fraction\": 0.8095238208770752,\n \"avg_line_length\": 21,\n \"blob_id\": \"10ef09dc22f671dc0bd57b9f52a2958d495afca1\",\n \"content_id\": \"49c976b7520f059a32f2d5ccb88a988775405824\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 21,\n \"license_type\": \"no_license\",\n \"max_line_length\": 21,\n \"num_lines\": 1,\n \"path\": \"/README.md\",\n \"repo_name\": \"Jeezus101/jeezus101.github.io\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# jeezus101.github.io\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":4,"string":"4"}}},{"rowIdx":2983091,"cells":{"repo_name":{"kind":"string","value":"hung135/pandas_read_zipfile"},"repo_url":{"kind":"string","value":"https://github.com/hung135/pandas_read_zipfile"},"snapshot_id":{"kind":"string","value":"a5db3205e0521a57d745368761cdac213f85e853"},"revision_id":{"kind":"string","value":"1c6109eb5a9cbb904bd185479c97cf9f4ac34262"},"directory_id":{"kind":"string","value":"c22dcc00e7a766212fa7f2750bc279f5ce50b3ad"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-04-01T20:30:10.702785","string":"2020-04-01T20:30:10.702785"},"revision_date":{"kind":"timestamp","value":"2018-10-22T15:45:41","string":"2018-10-22T15:45:41"},"committer_date":{"kind":"timestamp","value":"2018-10-22T15:45:41","string":"2018-10-22T15:45:41"},"github_id":{"kind":"number","value":153607028,"string":"153,607,028"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5798319578170776,"alphanum_fraction":0.5935828685760498,"avg_line_length":28.08888816833496,"blob_id":"e49cec5197e5e5b12c609e0de9cba8be7abbd51c","content_id":"bb403939c61574b725e13e1e6921fa8cece0bcc0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1309,"license_type":"no_license","max_line_length":123,"num_lines":45,"path":"/read_sas7bdat.py","repo_name":"hung135/pandas_read_zipfile","src_encoding":"UTF-8","text":"\nimport zipfile\nimport os\nimport datetime as dt\nimport pandas\nimport io\nimport psutil\n\nprint(\"memory\", psutil.virtual_memory())\n\nwith open('/Users/hnguyen/Desktop/pum2013/unix_pus.zip') as archive:\n # zip.write('/Users/hnguyen/Desktop/pum2013/csv_hvt.zip','test')\n print(\"xxx1\")\n x = zipfile.ZipFile(archive)\n\n print(\"xxx2\")\n t={}\n t[\"filename\"]=\"\"\n t[\"size\"]=0\n for y in x.infolist():\n assert isinstance(y,zipfile.ZipInfo)\n print(y.filename,y.file_size)\n if y.file_size>t[\"size\"]:\n t[\"filename\"]=y.filename\n t[\"size\"]=y.file_size\n print(t)\n with io.BufferedReader(x.open(t[\"filename\"],'r')) as xfile:\n #with x.read(t[\"filename\"], 'r') as xfile:\n #print(t.datetime.now())\n #print(type(xfile))\n\n #print(type(x),len(x),len(y))\n #print(io.StringIO(x))\n #x=xfile.read()\n #zz=io.BytesIO(x.read(t[\"filename\"]))\n #zz=io.BytesIO(xfile.read())\n print('reading')\n zz=xfile.read()\n print(\"memory\",psutil.virtual_memory())\n print(\"file_type\",type(zz))\n df = pandas.read_sas(io.BytesIO(\"\".join(zz)), format='sas7bdat', encoding='iso-8859-1', chunksize=1, iterator=True)\n print(df)\n print(dt.datetime.now())\n\n\n print(\"memory\",psutil.virtual_memory())"},{"alpha_fraction":0.6105169057846069,"alphanum_fraction":0.6221033930778503,"avg_line_length":23.413043975830078,"blob_id":"e30b0c6a485a772da8451d6e18ca59acf4652c97","content_id":"56e69a15ba3b41790ceb3a2a5232621602e7668c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1122,"license_type":"no_license","max_line_length":63,"num_lines":46,"path":"/add_to_zipfile.py","repo_name":"hung135/pandas_read_zipfile","src_encoding":"UTF-8","text":"import zipfile\nimport os\nimport datetime as t\nimport pandas\nimport io\n\ndir_path=\"/Users/hnguyen/Desktop/pum2013\"\n\nx=os.listdir(dir_path)\nprint(type(x))\nfor i in x:\n pass\n #print(i)\n\nzip=zipfile.ZipFile('/Users/hnguyen/Desktop/csv_pus.zip','a')\nfor item in zip.infolist():\n assert isinstance(item,zipfile.ZipInfo)\n #print(item.filename,item.file_size)\nzip.close()\nwith open('/Users/hnguyen/Desktop/csv_pus.zip') as archive:\n#zip.write('/Users/hnguyen/Desktop/pum2013/csv_hvt.zip','test')\n print(\"xxx1\")\n x=zipfile.ZipFile(archive)\n print(\"xxx2\")\n #with io.BufferedReader(x.open('test','r')) as xfile:\n with x.open('ss13pusa.csv','r') as xfile:\n print(t.datetime.now())\n #x=io.BytesIO(xfile.readline())\n\n df = pandas.read_csv(xfile,chunksize=5)\n print(type(df))\n\n print(t.datetime.now())\n #print(x)\n print(type(x))\n\n assert isinstance(df,pandas.io.parsers.TextFileReader)\n print(type(df))\n print(df,\"xxxx\")\n for a in df:\n print(type(a))\n print(a)\n break\n\n\n print(t.datetime.now())"},{"alpha_fraction":0.5469223260879517,"alphanum_fraction":0.5554994940757751,"avg_line_length":29.507692337036133,"blob_id":"e982dd1719658aaa2132547fc4576486c9a7ea88","content_id":"ff7e9275bd4dc88ea15ffc8cad147f595f47f7c8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1982,"license_type":"no_license","max_line_length":80,"num_lines":65,"path":"/count_column_from_zip.py","repo_name":"hung135/pandas_read_zipfile","src_encoding":"UTF-8","text":"import zipfile\nimport os\nimport datetime as t\nimport pandas\nimport io\nimport re\n\ndir_path=\"/Volumes/500gb/2011_ACSSF_By_State_By_Sequence_Table_Subset/\"\nre_data_file= re.compile(r'^.*\\.csv$')\nx=os.listdir(dir_path)\ncolumn_counts=set()\nprint(type(x))\nfor i in x:\n\n print(i)\n dir=os.path.join(dir_path,i)\n if os.path.isdir(dir):\n sub=os.listdir(dir)\n for ii in sub:\n print(ii)\n zip=zipfile.ZipFile('/Users/hnguyen/Desktop/csv_pus.zip','a')\n for item in zip.infolist():\n assert isinstance(item,zipfile.ZipInfo)\n if re_data_file.search(item.filename):\n print(item.filename)\n with zip.open(item.filename,'r') as data_file:\n df = pandas.read_csv(data_file,chunksize=1)\n assert isinstance(df,pandas.io.parsers.TextFileReader)\n for chunk in df:\n assert isinstance(chunk,pandas.core.frame.DataFrame)\n #print(\"\\t\"+str(len(chunk.columns.values)))\n x.append(len(chunk.columns.values))\n break\n zip.close()\nprint(column_counts)\n\n\"\"\"\nwith open('/Users/hnguyen/Desktop/csv_pus.zip') as archive:\n#zip.write('/Users/hnguyen/Desktop/pum2013/csv_hvt.zip','test')\n print(\"xxx1\")\n x=zipfile.ZipFile(archive)\n print(\"xxx2\")\n #with io.BufferedReader(x.open('test','r')) as xfile:\n with x.open('ss13pusa.csv','r') as xfile:\n print(t.datetime.now())\n #x=io.BytesIO(xfile.readline())\n\n df = pandas.read_csv(xfile,chunksize=5)\n print(type(df))\n\n print(t.datetime.now())\n #print(x)\n print(type(x))\n\n assert isinstance(df,pandas.io.parsers.TextFileReader)\n print(type(df))\n print(df,\"xxxx\")\n for a in df:\n print(type(a))\n print(a)\n break\n\n\n print(t.datetime.now())\n\"\"\""}],"string":"[\n {\n \"alpha_fraction\": 0.5798319578170776,\n \"alphanum_fraction\": 0.5935828685760498,\n \"avg_line_length\": 28.08888816833496,\n \"blob_id\": \"e49cec5197e5e5b12c609e0de9cba8be7abbd51c\",\n \"content_id\": \"bb403939c61574b725e13e1e6921fa8cece0bcc0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1309,\n \"license_type\": \"no_license\",\n \"max_line_length\": 123,\n \"num_lines\": 45,\n \"path\": \"/read_sas7bdat.py\",\n \"repo_name\": \"hung135/pandas_read_zipfile\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nimport zipfile\\nimport os\\nimport datetime as dt\\nimport pandas\\nimport io\\nimport psutil\\n\\nprint(\\\"memory\\\", psutil.virtual_memory())\\n\\nwith open('/Users/hnguyen/Desktop/pum2013/unix_pus.zip') as archive:\\n # zip.write('/Users/hnguyen/Desktop/pum2013/csv_hvt.zip','test')\\n print(\\\"xxx1\\\")\\n x = zipfile.ZipFile(archive)\\n\\n print(\\\"xxx2\\\")\\n t={}\\n t[\\\"filename\\\"]=\\\"\\\"\\n t[\\\"size\\\"]=0\\n for y in x.infolist():\\n assert isinstance(y,zipfile.ZipInfo)\\n print(y.filename,y.file_size)\\n if y.file_size>t[\\\"size\\\"]:\\n t[\\\"filename\\\"]=y.filename\\n t[\\\"size\\\"]=y.file_size\\n print(t)\\n with io.BufferedReader(x.open(t[\\\"filename\\\"],'r')) as xfile:\\n #with x.read(t[\\\"filename\\\"], 'r') as xfile:\\n #print(t.datetime.now())\\n #print(type(xfile))\\n\\n #print(type(x),len(x),len(y))\\n #print(io.StringIO(x))\\n #x=xfile.read()\\n #zz=io.BytesIO(x.read(t[\\\"filename\\\"]))\\n #zz=io.BytesIO(xfile.read())\\n print('reading')\\n zz=xfile.read()\\n print(\\\"memory\\\",psutil.virtual_memory())\\n print(\\\"file_type\\\",type(zz))\\n df = pandas.read_sas(io.BytesIO(\\\"\\\".join(zz)), format='sas7bdat', encoding='iso-8859-1', chunksize=1, iterator=True)\\n print(df)\\n print(dt.datetime.now())\\n\\n\\n print(\\\"memory\\\",psutil.virtual_memory())\"\n },\n {\n \"alpha_fraction\": 0.6105169057846069,\n \"alphanum_fraction\": 0.6221033930778503,\n \"avg_line_length\": 23.413043975830078,\n \"blob_id\": \"e30b0c6a485a772da8451d6e18ca59acf4652c97\",\n \"content_id\": \"56e69a15ba3b41790ceb3a2a5232621602e7668c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1122,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 46,\n \"path\": \"/add_to_zipfile.py\",\n \"repo_name\": \"hung135/pandas_read_zipfile\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import zipfile\\nimport os\\nimport datetime as t\\nimport pandas\\nimport io\\n\\ndir_path=\\\"/Users/hnguyen/Desktop/pum2013\\\"\\n\\nx=os.listdir(dir_path)\\nprint(type(x))\\nfor i in x:\\n pass\\n #print(i)\\n\\nzip=zipfile.ZipFile('/Users/hnguyen/Desktop/csv_pus.zip','a')\\nfor item in zip.infolist():\\n assert isinstance(item,zipfile.ZipInfo)\\n #print(item.filename,item.file_size)\\nzip.close()\\nwith open('/Users/hnguyen/Desktop/csv_pus.zip') as archive:\\n#zip.write('/Users/hnguyen/Desktop/pum2013/csv_hvt.zip','test')\\n print(\\\"xxx1\\\")\\n x=zipfile.ZipFile(archive)\\n print(\\\"xxx2\\\")\\n #with io.BufferedReader(x.open('test','r')) as xfile:\\n with x.open('ss13pusa.csv','r') as xfile:\\n print(t.datetime.now())\\n #x=io.BytesIO(xfile.readline())\\n\\n df = pandas.read_csv(xfile,chunksize=5)\\n print(type(df))\\n\\n print(t.datetime.now())\\n #print(x)\\n print(type(x))\\n\\n assert isinstance(df,pandas.io.parsers.TextFileReader)\\n print(type(df))\\n print(df,\\\"xxxx\\\")\\n for a in df:\\n print(type(a))\\n print(a)\\n break\\n\\n\\n print(t.datetime.now())\"\n },\n {\n \"alpha_fraction\": 0.5469223260879517,\n \"alphanum_fraction\": 0.5554994940757751,\n \"avg_line_length\": 29.507692337036133,\n \"blob_id\": \"e982dd1719658aaa2132547fc4576486c9a7ea88\",\n \"content_id\": \"ff7e9275bd4dc88ea15ffc8cad147f595f47f7c8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1982,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 65,\n \"path\": \"/count_column_from_zip.py\",\n \"repo_name\": \"hung135/pandas_read_zipfile\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import zipfile\\nimport os\\nimport datetime as t\\nimport pandas\\nimport io\\nimport re\\n\\ndir_path=\\\"/Volumes/500gb/2011_ACSSF_By_State_By_Sequence_Table_Subset/\\\"\\nre_data_file= re.compile(r'^.*\\\\.csv$')\\nx=os.listdir(dir_path)\\ncolumn_counts=set()\\nprint(type(x))\\nfor i in x:\\n\\n print(i)\\n dir=os.path.join(dir_path,i)\\n if os.path.isdir(dir):\\n sub=os.listdir(dir)\\n for ii in sub:\\n print(ii)\\n zip=zipfile.ZipFile('/Users/hnguyen/Desktop/csv_pus.zip','a')\\n for item in zip.infolist():\\n assert isinstance(item,zipfile.ZipInfo)\\n if re_data_file.search(item.filename):\\n print(item.filename)\\n with zip.open(item.filename,'r') as data_file:\\n df = pandas.read_csv(data_file,chunksize=1)\\n assert isinstance(df,pandas.io.parsers.TextFileReader)\\n for chunk in df:\\n assert isinstance(chunk,pandas.core.frame.DataFrame)\\n #print(\\\"\\\\t\\\"+str(len(chunk.columns.values)))\\n x.append(len(chunk.columns.values))\\n break\\n zip.close()\\nprint(column_counts)\\n\\n\\\"\\\"\\\"\\nwith open('/Users/hnguyen/Desktop/csv_pus.zip') as archive:\\n#zip.write('/Users/hnguyen/Desktop/pum2013/csv_hvt.zip','test')\\n print(\\\"xxx1\\\")\\n x=zipfile.ZipFile(archive)\\n print(\\\"xxx2\\\")\\n #with io.BufferedReader(x.open('test','r')) as xfile:\\n with x.open('ss13pusa.csv','r') as xfile:\\n print(t.datetime.now())\\n #x=io.BytesIO(xfile.readline())\\n\\n df = pandas.read_csv(xfile,chunksize=5)\\n print(type(df))\\n\\n print(t.datetime.now())\\n #print(x)\\n print(type(x))\\n\\n assert isinstance(df,pandas.io.parsers.TextFileReader)\\n print(type(df))\\n print(df,\\\"xxxx\\\")\\n for a in df:\\n print(type(a))\\n print(a)\\n break\\n\\n\\n print(t.datetime.now())\\n\\\"\\\"\\\"\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2983092,"cells":{"repo_name":{"kind":"string","value":"le-dit-gaga/lbry"},"repo_url":{"kind":"string","value":"https://github.com/le-dit-gaga/lbry"},"snapshot_id":{"kind":"string","value":"78ea83a73636646a7af995abb4d75470a8fbf00a"},"revision_id":{"kind":"string","value":"7abfbc4c5fb38884f3b72897af58699746f09771"},"directory_id":{"kind":"string","value":"d54b736096f22009da26edf8f85bf1be1b712c81"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2019-01-28T15:38:40.508028","string":"2019-01-28T15:38:40.508028"},"revision_date":{"kind":"timestamp","value":"2017-03-27T19:11:23","string":"2017-03-27T19:11:23"},"committer_date":{"kind":"timestamp","value":"2017-03-27T19:11:23","string":"2017-03-27T19:11:23"},"github_id":{"kind":"number","value":86380118,"string":"86,380,118"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"timestamp","value":"2017-03-27T20:18:46","string":"2017-03-27T20:18:46"},"gha_updated_at":{"kind":"timestamp","value":"2017-03-27T20:09:13","string":"2017-03-27T20:09:13"},"gha_pushed_at":{"kind":"timestamp","value":"2017-03-27T19:11:33","string":"2017-03-27T19:11:33"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6030820608139038,"alphanum_fraction":0.6043315529823303,"avg_line_length":28.64197540283203,"blob_id":"71db5ea6c575c36b6f410f36bd00725c0158c8eb","content_id":"ec3d70f124ee18cb577493efbbe1901d506c8f1c","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2401,"license_type":"permissive","max_line_length":84,"num_lines":81,"path":"/lbrynet/analytics/api.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"import functools\nimport json\nimport logging\n\nfrom requests import auth\nfrom txrequests import Session\n\nfrom lbrynet import conf\nfrom lbrynet.analytics import utils\n\nlog = logging.getLogger(__name__)\n\n\ndef log_response(fn):\n def _log_error(failure):\n log.warning('Failed to send an analytics event. %s', failure.getTraceback())\n\n @functools.wraps(fn)\n def wrapper(*args, **kwargs):\n d = fn(*args, **kwargs)\n d.addErrback(_log_error)\n return d\n\n return wrapper\n\n\nclass Api(object):\n def __init__(self, session, url, write_key):\n self.session = session\n self.url = url\n self.write_key = write_key\n\n def post(self, endpoint, data):\n # there is an issue with a timing condition with keep-alive\n # that is best explained here: https://github.com/mikem23/keepalive-race\n #\n # If you make a request, wait just the right amount of time,\n # then make another request, the requests module may opt to\n # reuse the connection, but by the time the server gets it the\n # timeout will have expired.\n #\n # by forcing the connection to close, we will disable the keep-alive.\n assert endpoint[0] == '/'\n headers = {\"Connection\": \"close\"}\n return self.session.post(\n self.url + endpoint, json=data, auth=self.auth, headers=headers)\n\n @property\n def auth(self):\n return auth.HTTPBasicAuth(self.write_key, '')\n\n @log_response\n def batch(self, events):\n \"\"\"Send multiple events in one request.\n\n Each event needs to have its type specified.\n \"\"\"\n data = json.dumps({\n 'batch': events,\n 'sentAt': utils.now(),\n })\n log.debug('sending %s events', len(events))\n log.debug('Data: %s', data)\n return self.post('/batch', data)\n\n @log_response\n def track(self, event):\n \"\"\"Send a single tracking event\"\"\"\n log.debug('Sending track event: %s', event)\n return self.post('/track', event)\n\n @classmethod\n def new_instance(cls, session=None):\n \"\"\"Initialize an instance using values from the configuration\"\"\"\n if not session:\n session = Session()\n return cls(\n session,\n conf.settings['ANALYTICS_ENDPOINT'],\n utils.deobfuscate(conf.settings['ANALYTICS_TOKEN'])\n )\n"},{"alpha_fraction":0.5800642967224121,"alphanum_fraction":0.6083601117134094,"avg_line_length":39.921051025390625,"blob_id":"f85cdf4975ab1cb674aee1deaf08143e41fb05cf","content_id":"ac5ae41908ec1fe693f77d975f435f065c5d6a5d","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1555,"license_type":"permissive","max_line_length":126,"num_lines":38,"path":"/tests/unit/lbrynet_daemon/test_DaemonCLI.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"from twisted.trial import unittest\nfrom lbrynet.lbrynet_daemon import DaemonCLI\n\n\nclass DaemonCLITests(unittest.TestCase):\n def test_guess_type(self):\n self.assertEqual('0.3.8', DaemonCLI.guess_type('0.3.8'))\n self.assertEqual(0.3, DaemonCLI.guess_type('0.3'))\n self.assertEqual(3, DaemonCLI.guess_type('3'))\n self.assertEqual('VdNmakxFORPSyfCprAD/eDDPk5TY9QYtSA==', DaemonCLI.guess_type('VdNmakxFORPSyfCprAD/eDDPk5TY9QYtSA=='))\n self.assertEqual(0.3, DaemonCLI.guess_type('0.3'))\n self.assertEqual(True, DaemonCLI.guess_type('TRUE'))\n self.assertEqual(True, DaemonCLI.guess_type('true'))\n self.assertEqual(True, DaemonCLI.guess_type('True'))\n self.assertEqual(False, DaemonCLI.guess_type('FALSE'))\n self.assertEqual(False, DaemonCLI.guess_type('false'))\n self.assertEqual(False, DaemonCLI.guess_type('False'))\n\n def test_get_params(self):\n test_params = [\n 'b64address=VdNmakxFORPSyfCprAD/eDDPk5TY9QYtSA==',\n 'name=test',\n 'amount=5.3',\n 'n=5',\n 'address=bY13xeAjLrsjP4KGETwStK2a9UgKgXVTXu',\n 't=true',\n 'f=False',\n ]\n test_r = {\n 'b64address': 'VdNmakxFORPSyfCprAD/eDDPk5TY9QYtSA==',\n 'name': 'test',\n 'amount': 5.3,\n 'n': 5,\n 'address': 'bY13xeAjLrsjP4KGETwStK2a9UgKgXVTXu',\n 't': True,\n 'f': False,\n }\n self.assertDictEqual(test_r, DaemonCLI.get_params_from_kwargs(test_params))\n"},{"alpha_fraction":0.6121758818626404,"alphanum_fraction":0.6234498023986816,"avg_line_length":21.743589401245117,"blob_id":"9c0786f69c609bc3a7d577f9045eb358442a0a75","content_id":"0e77b3cfe7855d3f05c56ae8c9aac20b197dea35","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":887,"license_type":"permissive","max_line_length":70,"num_lines":39,"path":"/lbrynet/metadata/Fee.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"import logging\nimport fee_schemas\n\nfrom lbrynet.metadata.StructuredDict import StructuredDict\n\nlog = logging.getLogger(__name__)\n\n\nclass FeeValidator(StructuredDict):\n def __init__(self, fee):\n self._versions = [\n ('0.0.1', fee_schemas.VER_001, None)\n ]\n\n StructuredDict.__init__(self, fee, fee.get('ver', '0.0.1'))\n\n self.currency_symbol = self.keys()[0]\n self.amount = self._get_amount()\n self.address = self[self.currency_symbol]['address']\n\n def _get_amount(self):\n amt = self[self.currency_symbol]['amount']\n try:\n return float(amt)\n except TypeError:\n log.error('Failed to convert fee amount %s to float', amt)\n raise\n\n\nclass LBCFeeValidator(StructuredDict):\n pass\n\n\nclass BTCFeeValidator(StructuredDict):\n pass\n\n\nclass USDFeeValidator(StructuredDict):\n pass\n"},{"alpha_fraction":0.6009801626205444,"alphanum_fraction":0.6045911908149719,"avg_line_length":41.60439682006836,"blob_id":"df07479c2d0e65d1557f1f285362b336885c94db","content_id":"2869ada6182212a1f9c7be912443d086ff59995a","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3877,"license_type":"permissive","max_line_length":107,"num_lines":91,"path":"/lbrynet/lbrylive/client/LiveStreamProgressManager.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"# pylint: skip-file\nimport logging\nfrom lbrynet.core.client.StreamProgressManager import StreamProgressManager\nfrom twisted.internet import defer\n\n\nlog = logging.getLogger(__name__)\n\n\nclass LiveStreamProgressManager(StreamProgressManager):\n def __init__(self, finished_callback, blob_manager, download_manager, delete_blob_after_finished=False,\n download_whole=True, max_before_skip_ahead=5):\n self.download_whole = download_whole\n self.max_before_skip_ahead = max_before_skip_ahead\n StreamProgressManager.__init__(self, finished_callback, blob_manager, download_manager,\n delete_blob_after_finished)\n\n ######### IProgressManager #########\n\n def stream_position(self):\n blobs = self.download_manager.blobs\n if not blobs:\n return 0\n else:\n newest_known_blobnum = max(blobs.iterkeys())\n position = newest_known_blobnum\n oldest_relevant_blob_num = (max(0, newest_known_blobnum - self.max_before_skip_ahead + 1))\n for i in xrange(newest_known_blobnum, oldest_relevant_blob_num - 1, -1):\n if i in blobs and (not blobs[i].is_validated() and not i in self.provided_blob_nums):\n position = i\n return position\n\n def needed_blobs(self):\n blobs = self.download_manager.blobs\n stream_position = self.stream_position()\n if blobs:\n newest_known_blobnum = max(blobs.iterkeys())\n else:\n newest_known_blobnum = -1\n blobs_needed = []\n for i in xrange(stream_position, newest_known_blobnum + 1):\n if i in blobs and not blobs[i].is_validated() and not i in self.provided_blob_nums:\n blobs_needed.append(blobs[i])\n return blobs_needed\n\n ######### internal #########\n\n def _output_loop(self):\n\n from twisted.internet import reactor\n\n if self.stopped is True:\n if self.outputting_d is not None:\n self.outputting_d.callback(True)\n self.outputting_d = None\n return\n\n blobs = self.download_manager.blobs\n log.info(\"In _output_loop. last_blob_outputted: %s\", str(self.last_blob_outputted))\n if blobs:\n log.debug(\"Newest blob number: %s\", str(max(blobs.iterkeys())))\n if self.outputting_d is None:\n self.outputting_d = defer.Deferred()\n\n current_blob_num = self.last_blob_outputted + 1\n\n def finished_outputting_blob():\n self.last_blob_outputted += 1\n final_blob_num = self.download_manager.final_blob_num()\n if final_blob_num is not None and final_blob_num == self.last_blob_outputted:\n self._finished_outputting()\n self.outputting_d.callback(True)\n self.outputting_d = None\n else:\n reactor.callLater(0, self._output_loop)\n\n if current_blob_num in blobs and blobs[current_blob_num].is_validated():\n log.info(\"Outputting blob %s\", str(current_blob_num))\n self.provided_blob_nums.append(current_blob_num)\n d = self.download_manager.handle_blob(current_blob_num)\n d.addCallback(lambda _: finished_outputting_blob())\n d.addCallback(lambda _: self._finished_with_blob(current_blob_num))\n elif blobs and max(blobs.iterkeys()) > self.last_blob_outputted + self.max_before_skip_ahead - 1:\n self.last_blob_outputted += 1\n log.info(\"Skipping blob number %s due to knowing about blob number %s\",\n str(self.last_blob_outputted), str(max(blobs.iterkeys())))\n self._finished_with_blob(current_blob_num)\n reactor.callLater(0, self._output_loop)\n else:\n self.outputting_d.callback(True)\n self.outputting_d = None\n"},{"alpha_fraction":0.5547069907188416,"alphanum_fraction":0.6389495134353638,"avg_line_length":57.86238479614258,"blob_id":"b872fc3c3114a3030fa4f50f115bbb6852935127","content_id":"0cf6d8168cc87a7c9147019e104746ae03b866c4","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":12832,"license_type":"permissive","max_line_length":413,"num_lines":218,"path":"/tests/unit/core/test_Metadata.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"from twisted.trial import unittest\nfrom jsonschema import ValidationError\n\nfrom lbrynet.core import Error\nfrom lbrynet.metadata import Metadata\n\n\nclass MetadataTest(unittest.TestCase):\n def test_name_error_if_blank(self):\n with self.assertRaises(AssertionError):\n Metadata.verify_name_characters(\"\")\n\n def test_name_error_if_contains_bad_chrs(self):\n with self.assertRaises(Error.InvalidName):\n Metadata.verify_name_characters(\"wu tang\")\n with self.assertRaises(Error.InvalidName):\n Metadata.verify_name_characters(\"$wutang\")\n with self.assertRaises(Error.InvalidName):\n Metadata.verify_name_characters(\"#wutang\")\n\n def test_validation_error_if_no_metadata(self):\n metadata = {}\n with self.assertRaises(ValidationError):\n Metadata.Metadata(metadata)\n\n def test_validation_error_if_source_is_missing(self):\n metadata = {\n 'license': 'Oscilloscope Laboratories',\n 'description': 'Four couples meet for Sunday brunch only to discover they are stuck in a house together as the world may be about to end.',\n 'language': 'en',\n 'title': \"It's a Disaster\",\n 'author': 'Written and directed by Todd Berger',\n 'content-type': 'audio/mpeg',\n 'thumbnail': 'http://ia.media-imdb.com/images/M/MV5BMTQwNjYzMTQ0Ml5BMl5BanBnXkFtZTcwNDUzODM5Nw@@._V1_SY1000_CR0,0,673,1000_AL_.jpg',\n }\n with self.assertRaises(ValidationError):\n Metadata.Metadata(metadata)\n\n def test_metadata_works_without_fee(self):\n metadata = {\n 'license': 'Oscilloscope Laboratories',\n 'description': 'Four couples meet for Sunday brunch only to discover they are stuck in a house together as the world may be about to end.',\n 'language': 'en',\n 'title': \"It's a Disaster\",\n 'author': 'Written and directed by Todd Berger',\n 'sources': {\n 'lbry_sd_hash': '8d0d6ea64d09f5aa90faf5807d8a761c32a27047861e06f81f41e35623a348a4b0104052161d5f89cf190f9672bc4ead'},\n 'content-type': 'audio/mpeg',\n 'thumbnail': 'http://ia.media-imdb.com/images/M/MV5BMTQwNjYzMTQ0Ml5BMl5BanBnXkFtZTcwNDUzODM5Nw@@._V1_SY1000_CR0,0,673,1000_AL_.jpg',\n }\n m = Metadata.Metadata(metadata)\n self.assertFalse('fee' in m)\n\n def test_validation_error_if_invalid_source(self):\n metadata = {\n 'license': 'Oscilloscope Laboratories',\n 'description': 'Four couples meet for Sunday brunch only to discover they are stuck in a house together as the world may be about to end.',\n 'language': 'en',\n 'title': \"It's a Disaster\",\n 'author': 'Written and directed by Todd Berger',\n 'sources': {\n 'fake': 'source'},\n 'content-type': 'audio/mpeg',\n 'thumbnail': 'http://ia.media-imdb.com/images/M/MV5BMTQwNjYzMTQ0Ml5BMl5BanBnXkFtZTcwNDUzODM5Nw@@._V1_SY1000_CR0,0,673,1000_AL_.jpg',\n }\n with self.assertRaises(ValidationError):\n Metadata.Metadata(metadata)\n\n def test_validation_error_if_missing_v001_field(self):\n metadata = {\n 'license': 'Oscilloscope Laboratories',\n 'fee': {'LBC': {'amount': 50.0, 'address': 'bRQJASJrDbFZVAvcpv3NoNWoH74LQd5JNV'}},\n 'description': 'Four couples meet for Sunday brunch only to discover they are stuck in a house together as the world may be about to end.',\n 'language': 'en',\n 'author': 'Written and directed by Todd Berger',\n 'sources': {\n 'lbry_sd_hash': '8d0d6ea64d09f5aa90faf5807d8a761c32a27047861e06f81f41e35623a348a4b0104052161d5f89cf190f9672bc4ead'},\n 'content-type': 'audio/mpeg',\n 'thumbnail': 'http://ia.media-imdb.com/images/M/MV5BMTQwNjYzMTQ0Ml5BMl5BanBnXkFtZTcwNDUzODM5Nw@@._V1_SY1000_CR0,0,673,1000_AL_.jpg'\n }\n with self.assertRaises(ValidationError):\n Metadata.Metadata(metadata)\n\n def test_version_is_001_if_all_fields_are_present(self):\n metadata = {\n 'license': 'Oscilloscope Laboratories',\n 'description': 'Four couples meet for Sunday brunch only to discover they are stuck in a house together as the world may be about to end.',\n 'language': 'en',\n 'title': \"It's a Disaster\",\n 'author': 'Written and directed by Todd Berger',\n 'sources': {\n 'lbry_sd_hash': '8d0d6ea64d09f5aa90faf5807d8a761c32a27047861e06f81f41e35623a348a4b0104052161d5f89cf190f9672bc4ead'},\n 'content-type': 'audio/mpeg',\n 'thumbnail': 'http://ia.media-imdb.com/images/M/MV5BMTQwNjYzMTQ0Ml5BMl5BanBnXkFtZTcwNDUzODM5Nw@@._V1_SY1000_CR0,0,673,1000_AL_.jpg',\n }\n m = Metadata.Metadata(metadata, migrate=False)\n self.assertEquals('0.0.1', m.version)\n\n def test_validation_error_if_there_is_an_extra_field(self):\n metadata = {\n 'license': 'Oscilloscope Laboratories',\n 'description': 'Four couples meet for Sunday brunch only to discover they are stuck in a house together as the world may be about to end.',\n 'language': 'en',\n 'title': \"It's a Disaster\",\n 'author': 'Written and directed by Todd Berger',\n 'sources': {\n 'lbry_sd_hash': '8d0d6ea64d09f5aa90faf5807d8a761c32a27047861e06f81f41e35623a348a4b0104052161d5f89cf190f9672bc4ead'},\n 'content-type': 'audio/mpeg',\n 'thumbnail': 'http://ia.media-imdb.com/images/M/MV5BMTQwNjYzMTQ0Ml5BMl5BanBnXkFtZTcwNDUzODM5Nw@@._V1_SY1000_CR0,0,673,1000_AL_.jpg',\n 'MYSTERYFIELD': '?'\n }\n with self.assertRaises(ValidationError):\n Metadata.Metadata(metadata, migrate=False)\n\n def test_version_is_002_if_all_fields_are_present(self):\n metadata = {\n 'license': 'NASA',\n 'fee': {'USD': {'amount': 0.01, 'address': 'baBYSK7CqGSn5KrEmNmmQwAhBSFgo6v47z'}},\n 'ver': '0.0.2',\n 'description': 'SDO captures images of the sun in 10 different wavelengths, each of which helps highlight a different temperature of solar material. Different temperatures can, in turn, show specific structures on the sun such as solar flares, which are gigantic explosions of light and x-rays, or coronal loops, which are stream of solar material travelling up and down looping magnetic field lines',\n 'language': 'en',\n 'author': 'The SDO Team, Genna Duberstein and Scott Wiessinger',\n 'title': 'Thermonuclear Art',\n 'sources': {\n 'lbry_sd_hash': '8655f713819344980a9a0d67b198344e2c462c90f813e86f0c63789ab0868031f25c54d0bb31af6658e997e2041806eb'},\n 'nsfw': False,\n 'content-type': 'video/mp4',\n 'thumbnail': 'https://svs.gsfc.nasa.gov/vis/a010000/a012000/a012034/Combined.00_08_16_17.Still004.jpg'\n }\n m = Metadata.Metadata(metadata, migrate=False)\n self.assertEquals('0.0.2', m.version)\n\n def test_version_is_003_if_all_fields_are_present(self):\n metadata = {\n 'license': 'NASA',\n 'fee': {'USD': {'amount': 0.01, 'address': 'baBYSK7CqGSn5KrEmNmmQwAhBSFgo6v47z'}},\n 'ver': '0.0.3',\n 'description': 'SDO captures images of the sun in 10 different wavelengths, each of which helps highlight a different temperature of solar material. Different temperatures can, in turn, show specific structures on the sun such as solar flares, which are gigantic explosions of light and x-rays, or coronal loops, which are stream of solar material travelling up and down looping magnetic field lines',\n 'language': 'en',\n 'author': 'The SDO Team, Genna Duberstein and Scott Wiessinger',\n 'title': 'Thermonuclear Art',\n 'sources': {\n 'lbry_sd_hash': '8655f713819344980a9a0d67b198344e2c462c90f813e86f0c63789ab0868031f25c54d0bb31af6658e997e2041806eb'},\n 'nsfw': False,\n 'content_type': 'video/mp4',\n 'thumbnail': 'https://svs.gsfc.nasa.gov/vis/a010000/a012000/a012034/Combined.00_08_16_17.Still004.jpg'\n }\n m = Metadata.Metadata(metadata, migrate=False)\n self.assertEquals('0.0.3', m.version)\n\n def test_version_claimed_is_001_but_version_is_002(self):\n metadata = {\n 'license': 'NASA',\n 'fee': {'USD': {'amount': 0.01, 'address': 'baBYSK7CqGSn5KrEmNmmQwAhBSFgo6v47z'}},\n 'ver': '0.0.1',\n 'description': 'SDO captures images of the sun in 10 different wavelengths, each of which helps highlight a different temperature of solar material. Different temperatures can, in turn, show specific structures on the sun such as solar flares, which are gigantic explosions of light and x-rays, or coronal loops, which are stream of solar material travelling up and down looping magnetic field lines',\n 'language': 'en',\n 'author': 'The SDO Team, Genna Duberstein and Scott Wiessinger',\n 'title': 'Thermonuclear Art',\n 'sources': {\n 'lbry_sd_hash': '8655f713819344980a9a0d67b198344e2c462c90f813e86f0c63789ab0868031f25c54d0bb31af6658e997e2041806eb'},\n 'nsfw': False,\n 'content-type': 'video/mp4',\n 'thumbnail': 'https://svs.gsfc.nasa.gov/vis/a010000/a012000/a012034/Combined.00_08_16_17.Still004.jpg'\n }\n with self.assertRaises(ValidationError):\n Metadata.Metadata(metadata, migrate=False)\n\n def test_version_claimed_is_002_but_version_is_003(self):\n metadata = {\n 'license': 'NASA',\n 'fee': {'USD': {'amount': 0.01, 'address': 'baBYSK7CqGSn5KrEmNmmQwAhBSFgo6v47z'}},\n 'ver': '0.0.2',\n 'description': 'SDO captures images of the sun in 10 different wavelengths, each of which helps highlight a different temperature of solar material. Different temperatures can, in turn, show specific structures on the sun such as solar flares, which are gigantic explosions of light and x-rays, or coronal loops, which are stream of solar material travelling up and down looping magnetic field lines',\n 'language': 'en',\n 'author': 'The SDO Team, Genna Duberstein and Scott Wiessinger',\n 'title': 'Thermonuclear Art',\n 'sources': {\n 'lbry_sd_hash': '8655f713819344980a9a0d67b198344e2c462c90f813e86f0c63789ab0868031f25c54d0bb31af6658e997e2041806eb'},\n 'nsfw': False,\n 'content_type': 'video/mp4',\n 'thumbnail': 'https://svs.gsfc.nasa.gov/vis/a010000/a012000/a012034/Combined.00_08_16_17.Still004.jpg'\n }\n with self.assertRaises(ValidationError):\n Metadata.Metadata(metadata, migrate=False)\n\n def test_version_001_ports_to_003(self):\n metadata = {\n 'license': 'Oscilloscope Laboratories',\n 'description': 'Four couples meet for Sunday brunch only to discover they are stuck in a house together as the world may be about to end.',\n 'language': 'en',\n 'title': \"It's a Disaster\",\n 'author': 'Written and directed by Todd Berger',\n 'sources': {\n 'lbry_sd_hash': '8d0d6ea64d09f5aa90faf5807d8a761c32a27047861e06f81f41e35623a348a4b0104052161d5f89cf190f9672bc4ead'},\n 'content-type': 'audio/mpeg',\n 'thumbnail': 'http://ia.media-imdb.com/images/M/MV5BMTQwNjYzMTQ0Ml5BMl5BanBnXkFtZTcwNDUzODM5Nw@@._V1_SY1000_CR0,0,673,1000_AL_.jpg',\n }\n m = Metadata.Metadata(metadata, migrate=True)\n self.assertEquals('0.0.3', m.version)\n\n def test_version_002_ports_to_003(self):\n metadata = {\n 'license': 'NASA',\n 'fee': {'USD': {'amount': 0.01, 'address': 'baBYSK7CqGSn5KrEmNmmQwAhBSFgo6v47z'}},\n 'ver': '0.0.2',\n 'description': 'SDO captures images of the sun in 10 different wavelengths, each of which helps highlight a different temperature of solar material. Different temperatures can, in turn, show specific structures on the sun such as solar flares, which are gigantic explosions of light and x-rays, or coronal loops, which are stream of solar material travelling up and down looping magnetic field lines',\n 'language': 'en',\n 'author': 'The SDO Team, Genna Duberstein and Scott Wiessinger',\n 'title': 'Thermonuclear Art',\n 'sources': {\n 'lbry_sd_hash': '8655f713819344980a9a0d67b198344e2c462c90f813e86f0c63789ab0868031f25c54d0bb31af6658e997e2041806eb'},\n 'nsfw': False,\n 'content-type': 'video/mp4',\n 'thumbnail': 'https://svs.gsfc.nasa.gov/vis/a010000/a012000/a012034/Combined.00_08_16_17.Still004.jpg'\n }\n m = Metadata.Metadata(metadata, migrate=True)\n self.assertEquals('0.0.3', m.version)\n"},{"alpha_fraction":0.5833278298377991,"alphanum_fraction":0.587092936038971,"avg_line_length":42.62824249267578,"blob_id":"3dd2535dc1e1cecc134bc94aab93c8d443e61be5","content_id":"921c27dc7742724d2ccf07ab5c5c7e8ec8b3f375","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":15139,"license_type":"permissive","max_line_length":117,"num_lines":347,"path":"/lbrynet/lbrylive/client/LiveStreamMetadataHandler.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"# pylint: skip-file\nfrom collections import defaultdict\nimport logging\nfrom zope.interface import implements\nfrom twisted.internet import defer\nfrom twisted.python.failure import Failure\nfrom lbrynet import conf\nfrom lbrynet.core.client.ClientRequest import ClientRequest, ClientPaidRequest\nfrom lbrynet.lbrylive.LiveBlob import LiveBlobInfo\nfrom lbrynet.core.cryptoutils import get_lbry_hash_obj, verify_signature\nfrom lbrynet.interfaces import IRequestCreator, IMetadataHandler\nfrom lbrynet.core.Error import InsufficientFundsError, InvalidResponseError, RequestCanceledError\nfrom lbrynet.core.Error import NoResponseError, ConnectionClosedBeforeResponseError\n\n\nlog = logging.getLogger(__name__)\n\n\nclass LiveStreamMetadataHandler(object):\n implements(IRequestCreator, IMetadataHandler)\n\n def __init__(self, stream_hash, stream_info_manager, peer_finder, stream_pub_key, download_whole,\n payment_rate_manager, wallet, download_manager, max_before_skip_ahead=None):\n self.stream_hash = stream_hash\n self.stream_info_manager = stream_info_manager\n self.payment_rate_manager = payment_rate_manager\n self.wallet = wallet\n self.peer_finder = peer_finder\n self.stream_pub_key = stream_pub_key\n self.download_whole = download_whole\n self.max_before_skip_ahead = max_before_skip_ahead\n if self.download_whole is False:\n assert self.max_before_skip_ahead is not None, \\\n \"If download whole is False, max_before_skip_ahead must be set\"\n self.download_manager = download_manager\n self._peers = defaultdict(int) # {Peer: score}\n self._protocol_prices = {}\n self._final_blob_num = None\n self._price_disagreements = [] # [Peer]\n self._incompatible_peers = [] # [Peer]\n\n ######### IMetadataHandler #########\n\n def get_initial_blobs(self):\n d = self.stream_info_manager.get_blobs_for_stream(self.stream_hash)\n d.addCallback(self._format_initial_blobs_for_download_manager)\n return d\n\n def final_blob_num(self):\n return self._final_blob_num\n\n ######## IRequestCreator #########\n\n def send_next_request(self, peer, protocol):\n if self._finished_discovery() is False and self._should_send_request_to(peer) is True:\n p_r = None\n if not self._price_settled(protocol):\n p_r = self._get_price_request(peer, protocol)\n d_r = self._get_discover_request(peer)\n reserved_points = self._reserve_points(peer, protocol, d_r.max_pay_units)\n if reserved_points is not None:\n d1 = protocol.add_request(d_r)\n d1.addCallback(self._handle_discover_response, peer, d_r)\n d1.addBoth(self._pay_or_cancel_payment, protocol, reserved_points)\n d1.addErrback(self._request_failed, peer)\n if p_r is not None:\n d2 = protocol.add_request(p_r)\n d2.addCallback(self._handle_price_response, peer, p_r, protocol)\n d2.addErrback(self._request_failed, peer)\n return defer.succeed(True)\n else:\n return defer.fail(InsufficientFundsError())\n return defer.succeed(False)\n\n def get_new_peers(self):\n d = self._get_hash_for_peer_search()\n d.addCallback(self._find_peers_for_hash)\n return d\n\n ######### internal calls #########\n\n def _get_hash_for_peer_search(self):\n r = None\n if self._finished_discovery() is False:\n r = self.stream_hash\n log.debug(\"Info finder peer search response for stream %s: %s\", str(self.stream_hash), str(r))\n return defer.succeed(r)\n\n def _find_peers_for_hash(self, h):\n if h is None:\n return None\n else:\n d = self.peer_finder.find_peers_for_blob(h)\n\n def choose_best_peers(peers):\n bad_peers = self._get_bad_peers()\n return [p for p in peers if not p in bad_peers]\n\n d.addCallback(choose_best_peers)\n return d\n\n def _format_initial_blobs_for_download_manager(self, blob_infos):\n infos = []\n for blob_hash, blob_num, revision, iv, length, signature in blob_infos:\n if blob_hash is not None:\n infos.append(LiveBlobInfo(blob_hash, blob_num, length, iv, revision, signature))\n else:\n log.debug(\"Setting _final_blob_num to %s\", str(blob_num - 1))\n self._final_blob_num = blob_num - 1\n return infos\n\n def _should_send_request_to(self, peer):\n if self._peers[peer] < -5.0:\n return False\n if peer in self._price_disagreements:\n return False\n return True\n\n def _get_bad_peers(self):\n return [p for p in self._peers.iterkeys() if not self._should_send_request_to(p)]\n\n def _finished_discovery(self):\n if self._get_discovery_params() is None:\n return True\n return False\n\n def _get_discover_request(self, peer):\n discovery_params = self._get_discovery_params()\n if discovery_params:\n further_blobs_request = {}\n reference, start, end, count = discovery_params\n further_blobs_request['reference'] = reference\n if start is not None:\n further_blobs_request['start'] = start\n if end is not None:\n further_blobs_request['end'] = end\n if count is not None:\n further_blobs_request['count'] = count\n else:\n further_blobs_request['count'] = conf.settings['MAX_BLOB_INFOS_TO_REQUEST']\n log.debug(\"Requesting %s blob infos from %s\", str(further_blobs_request['count']), str(peer))\n r_dict = {'further_blobs': further_blobs_request}\n response_identifier = 'further_blobs'\n request = ClientPaidRequest(r_dict, response_identifier, further_blobs_request['count'])\n return request\n return None\n\n def _get_discovery_params(self):\n log.debug(\"In _get_discovery_params\")\n stream_position = self.download_manager.stream_position()\n blobs = self.download_manager.blobs\n if blobs:\n last_blob_num = max(blobs.iterkeys())\n else:\n last_blob_num = -1\n final_blob_num = self.final_blob_num()\n if final_blob_num is not None:\n last_blob_num = final_blob_num\n if self.download_whole is False:\n log.debug(\"download_whole is False\")\n if final_blob_num is not None:\n for i in xrange(stream_position, final_blob_num + 1):\n if not i in blobs:\n count = min(self.max_before_skip_ahead, (final_blob_num - i + 1))\n return self.stream_hash, None, 'end', count\n return None\n else:\n if blobs:\n for i in xrange(stream_position, last_blob_num + 1):\n if not i in blobs:\n if i == 0:\n return self.stream_hash, 'beginning', 'end', -1 * self.max_before_skip_ahead\n else:\n return self.stream_hash, blobs[i-1].blob_hash, 'end', -1 * self.max_before_skip_ahead\n return self.stream_hash, blobs[last_blob_num].blob_hash, 'end', -1 * self.max_before_skip_ahead\n else:\n return self.stream_hash, None, 'end', -1 * self.max_before_skip_ahead\n log.debug(\"download_whole is True\")\n beginning = None\n end = None\n for i in xrange(stream_position, last_blob_num + 1):\n if not i in blobs:\n if beginning is None:\n if i == 0:\n beginning = 'beginning'\n else:\n beginning = blobs[i-1].blob_hash\n else:\n if beginning is not None:\n end = blobs[i].blob_hash\n break\n if beginning is None:\n if final_blob_num is not None:\n log.debug(\"Discovery is finished. stream_position: %s, last_blob_num + 1: %s\", str(stream_position),\n str(last_blob_num + 1))\n return None\n else:\n log.debug(\"Discovery is not finished. final blob num is unknown.\")\n if last_blob_num != -1:\n return self.stream_hash, blobs[last_blob_num].blob_hash, None, None\n else:\n return self.stream_hash, 'beginning', None, None\n else:\n log.info(\"Discovery is not finished. Not all blobs are known.\")\n return self.stream_hash, beginning, end, None\n\n def _price_settled(self, protocol):\n if protocol in self._protocol_prices:\n return True\n return False\n\n def _update_local_score(self, peer, amount):\n self._peers[peer] += amount\n\n def _reserve_points(self, peer, protocol, max_infos):\n assert protocol in self._protocol_prices\n point_amount = 1.0 * max_infos * self._protocol_prices[protocol] / 1000.0\n return self.wallet.reserve_points(peer, point_amount)\n\n def _pay_or_cancel_payment(self, arg, protocol, reserved_points):\n if isinstance(arg, Failure) or arg == 0:\n self._cancel_points(reserved_points)\n else:\n self._pay_peer(protocol, arg, reserved_points)\n return arg\n\n def _pay_peer(self, protocol, num_infos, reserved_points):\n assert num_infos != 0\n assert protocol in self._protocol_prices\n point_amount = 1.0 * num_infos * self._protocol_prices[protocol] / 1000.0\n self.wallet.send_points(reserved_points, point_amount)\n self.payment_rate_manager.record_points_paid(point_amount)\n\n def _cancel_points(self, reserved_points):\n return self.wallet.cancel_point_reservation(reserved_points)\n\n def _get_price_request(self, peer, protocol):\n self._protocol_prices[protocol] = self.payment_rate_manager.get_rate_live_blob_info(peer)\n request_dict = {'blob_info_payment_rate': self._protocol_prices[protocol]}\n request = ClientRequest(request_dict, 'blob_info_payment_rate')\n return request\n\n def _handle_price_response(self, response_dict, peer, request, protocol):\n if not request.response_identifier in response_dict:\n return InvalidResponseError(\"response identifier not in response\")\n assert protocol in self._protocol_prices\n response = response_dict[request.response_identifier]\n if response == \"RATE_ACCEPTED\":\n return True\n else:\n log.info(\"Rate offer has been rejected by %s\", str(peer))\n del self._protocol_prices[protocol]\n self._price_disagreements.append(peer)\n return True\n\n def _handle_discover_response(self, response_dict, peer, request):\n if not request.response_identifier in response_dict:\n return InvalidResponseError(\"response identifier not in response\")\n response = response_dict[request.response_identifier]\n blob_infos = []\n if 'error' in response:\n if response['error'] == 'RATE_UNSET':\n return defer.succeed(0)\n else:\n return InvalidResponseError(\"Got an unknown error from the peer: %s\" %\n (response['error'],))\n if not 'blob_infos' in response:\n return InvalidResponseError(\"Missing the required field 'blob_infos'\")\n raw_blob_infos = response['blob_infos']\n log.info(\"Handling %s further blobs from %s\", str(len(raw_blob_infos)), str(peer))\n log.debug(\"blobs: %s\", str(raw_blob_infos))\n for raw_blob_info in raw_blob_infos:\n length = raw_blob_info['length']\n if length != 0:\n blob_hash = raw_blob_info['blob_hash']\n else:\n blob_hash = None\n num = raw_blob_info['blob_num']\n revision = raw_blob_info['revision']\n iv = raw_blob_info['iv']\n signature = raw_blob_info['signature']\n blob_info = LiveBlobInfo(blob_hash, num, length, iv, revision, signature)\n log.debug(\"Learned about a potential blob: %s\", str(blob_hash))\n if self._verify_blob(blob_info):\n if blob_hash is None:\n log.info(\"Setting _final_blob_num to %s\", str(num - 1))\n self._final_blob_num = num - 1\n else:\n blob_infos.append(blob_info)\n else:\n raise ValueError(\"Peer sent an invalid blob info\")\n d = self.stream_info_manager.add_blobs_to_stream(self.stream_hash, blob_infos)\n\n def add_blobs_to_download_manager():\n blob_nums = [b.blob_num for b in blob_infos]\n log.info(\"Adding the following blob nums to the download manager: %s\", str(blob_nums))\n self.download_manager.add_blobs_to_download(blob_infos)\n\n d.addCallback(lambda _: add_blobs_to_download_manager())\n\n def pay_or_penalize_peer():\n if len(blob_infos):\n self._update_local_score(peer, len(blob_infos))\n peer.update_stats('downloaded_crypt_blob_infos', len(blob_infos))\n peer.update_score(len(blob_infos))\n else:\n self._update_local_score(peer, -.0001)\n return len(blob_infos)\n\n d.addCallback(lambda _: pay_or_penalize_peer())\n\n return d\n\n def _verify_blob(self, blob):\n log.debug(\"Got an unverified blob to check:\")\n log.debug(\"blob_hash: %s\", blob.blob_hash)\n log.debug(\"blob_num: %s\", str(blob.blob_num))\n log.debug(\"revision: %s\", str(blob.revision))\n log.debug(\"iv: %s\", blob.iv)\n log.debug(\"length: %s\", str(blob.length))\n hashsum = get_lbry_hash_obj()\n hashsum.update(self.stream_hash)\n if blob.length != 0:\n hashsum.update(blob.blob_hash)\n hashsum.update(str(blob.blob_num))\n hashsum.update(str(blob.revision))\n hashsum.update(blob.iv)\n hashsum.update(str(blob.length))\n log.debug(\"hexdigest to be verified: %s\", hashsum.hexdigest())\n if verify_signature(hashsum.digest(), blob.signature, self.stream_pub_key):\n log.debug(\"Blob info is valid\")\n return True\n else:\n log.debug(\"The blob info is invalid\")\n return False\n\n def _request_failed(self, reason, peer):\n if reason.check(RequestCanceledError):\n return\n if reason.check(NoResponseError):\n self._incompatible_peers.append(peer)\n log.warning(\"Crypt stream info finder: a request failed. Reason: %s\", reason.getErrorMessage())\n self._update_local_score(peer, -5.0)\n peer.update_score(-10.0)\n if reason.check(ConnectionClosedBeforeResponseError):\n return\n return reason\n"},{"alpha_fraction":0.6459190845489502,"alphanum_fraction":0.6480988264083862,"avg_line_length":39.48039245605469,"blob_id":"dcecd2e92d0340a5f61dccc5f3a1abcf705000a2","content_id":"86aad2f7cc358a2a0f865382d2450eea5f6cf2d0","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4129,"license_type":"permissive","max_line_length":117,"num_lines":102,"path":"/lbrynet/lbrylive/StdoutDownloader.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"# pylint: skip-file\n# pylint: skip-file\n# This file is not maintained, but might be used in the future\n#\nimport logging\nimport sys\n\nfrom lbrynet.lbrylive.client.LiveStreamDownloader import LiveStreamDownloader\nfrom lbrynet.core.BlobManager import TempBlobManager\nfrom lbrynet.core.Session import Session\nfrom lbrynet.core.client.StandaloneBlobDownloader import StandaloneBlobDownloader\nfrom lbrynet.core.StreamDescriptor import BlobStreamDescriptorReader\nfrom lbrynet.lbrylive.PaymentRateManager import BaseLiveStreamPaymentRateManager\nfrom lbrynet.lbrylive.LiveStreamMetadataManager import DBLiveStreamMetadataManager\nfrom lbrynet.lbrylive.StreamDescriptor import save_sd_info\nfrom lbrynet.dht.node import Node\nfrom twisted.internet import task\n\n\nclass StdoutDownloader():\n \"\"\"This class downloads a live stream from the network and outputs it to standard out.\"\"\"\n def __init__(self, dht_node_port, known_dht_nodes,\n stream_info_manager_class=DBLiveStreamMetadataManager, blob_manager_class=TempBlobManager):\n \"\"\"\n @param dht_node_port: the network port on which to listen for DHT node requests\n\n @param known_dht_nodes: a list of (ip_address, dht_port) which will be used to join the DHT network\n\n \"\"\"\n\n self.session = Session(blob_manager_class=blob_manager_class,\n stream_info_manager_class=stream_info_manager_class,\n dht_node_class=Node, dht_node_port=dht_node_port, known_dht_nodes=known_dht_nodes,\n use_upnp=False)\n self.payment_rate_manager = BaseLiveStreamPaymentRateManager()\n\n def start(self):\n \"\"\"Initialize the session\"\"\"\n d = self.session.setup()\n return d\n\n def read_sd_file(self, sd_blob):\n reader = BlobStreamDescriptorReader(sd_blob)\n return save_sd_info(self.stream_info_manager, reader, ignore_duplicate=True)\n\n def download_sd_file_from_hash(self, sd_hash):\n downloader = StandaloneBlobDownloader(sd_hash, self.session.blob_manager,\n self.session.peer_finder, self.session.rate_limiter,\n self.session.wallet)\n d = downloader.download()\n return d\n\n def start_download(self, sd_hash):\n \"\"\"Start downloading the stream from the network and outputting it to standard out\"\"\"\n d = self.download_sd_file_from_hash(sd_hash)\n d.addCallbacks(self.read_sd_file)\n\n def start_stream(stream_hash):\n consumer = LiveStreamDownloader(stream_hash, self.session.peer_finder,\n self.session.rate_limiter, self.session.blob_manager,\n self.stream_info_manager, self.payment_rate_manager,\n self.session.wallet)\n return consumer.start()\n\n d.addCallback(start_stream)\n return d\n\n def shut_down(self):\n \"\"\"End the session\"\"\"\n d = self.session.shut_down()\n return d\n\n\ndef launch_stdout_downloader():\n\n from twisted.internet import reactor\n\n logging.basicConfig(level=logging.WARNING, filename=\"dl.log\")\n if len(sys.argv) == 3:\n downloader = StdoutDownloader(int(sys.argv[2]), [])\n elif len(sys.argv) == 5:\n downloader = StdoutDownloader(int(sys.argv[2]), [(sys.argv[3], int(sys.argv[4]))])\n else:\n print \"Usage: lbrynet-stdout-downloader <sd_hash> <peer_port> <dht_node_port>\" \\\n \" [<dht_bootstrap_host> <dht_bootstrap port>]\"\n sys.exit(1)\n\n def start_stdout_downloader():\n return downloader.start_download(sys.argv[1])\n\n def print_error(err):\n logging.warning(err.getErrorMessage())\n\n def shut_down():\n reactor.stop()\n\n d = task.deferLater(reactor, 0, downloader.start)\n d.addCallback(lambda _: start_stdout_downloader())\n d.addErrback(print_error)\n d.addCallback(lambda _: shut_down())\n reactor.addSystemEventTrigger('before', 'shutdown', downloader.shut_down)\n reactor.run()\n"},{"alpha_fraction":0.6639838814735413,"alphanum_fraction":0.6643863320350647,"avg_line_length":39.73770523071289,"blob_id":"fa28390b8ef3138279dd05d3e52e291a248fd310","content_id":"dd272e5e68d0c9afd8c0b4885e32e42ed16cd19b","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2485,"license_type":"permissive","max_line_length":97,"num_lines":61,"path":"/lbrynet/lbrynet_daemon/Publisher.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"import logging\nimport mimetypes\nimport os\n\nfrom twisted.internet import defer\nfrom lbrynet.core import file_utils\nfrom lbrynet.lbryfilemanager.EncryptedFileCreator import create_lbry_file\nfrom lbrynet.lbryfile.StreamDescriptor import publish_sd_blob\nfrom lbrynet.metadata.Metadata import Metadata\n\n\nlog = logging.getLogger(__name__)\n\n\nclass Publisher(object):\n def __init__(self, session, lbry_file_manager, wallet):\n self.session = session\n self.lbry_file_manager = lbry_file_manager\n self.wallet = wallet\n self.lbry_file = None\n\n @defer.inlineCallbacks\n def publish_stream(self, name, file_path, bid, metadata):\n log.info('Starting publish for %s', name)\n file_name = os.path.basename(file_path)\n with file_utils.get_read_handle(file_path) as read_handle:\n stream_hash = yield create_lbry_file(self.session, self.lbry_file_manager, file_name,\n read_handle)\n prm = self.session.payment_rate_manager\n self.lbry_file = yield self.lbry_file_manager.add_lbry_file(stream_hash, prm)\n sd_hash = yield publish_sd_blob(self.lbry_file_manager.stream_info_manager,\n self.session.blob_manager, self.lbry_file.stream_hash)\n if 'sources' not in metadata:\n metadata['sources'] = {}\n metadata['sources']['lbry_sd_hash'] = sd_hash\n metadata['content_type'] = get_content_type(file_path)\n metadata['ver'] = Metadata.current_version\n claim_out = yield self.make_claim(name, bid, metadata)\n self.lbry_file.completed = True\n yield self.lbry_file.load_file_attributes()\n yield self.lbry_file.save_status()\n defer.returnValue(claim_out)\n\n @defer.inlineCallbacks\n def update_stream(self, name, bid, metadata):\n my_claim = yield self.wallet.get_my_claim(name)\n updated_metadata = my_claim['value']\n for meta_key in metadata:\n updated_metadata[meta_key] = metadata[meta_key]\n claim_out = yield self.make_claim(name, bid, updated_metadata)\n defer.returnValue(claim_out)\n\n @defer.inlineCallbacks\n def make_claim(self, name, bid, metadata):\n validated_metadata = Metadata(metadata)\n claim_out = yield self.wallet.claim_name(name, bid, validated_metadata)\n defer.returnValue(claim_out)\n\n\ndef get_content_type(filename):\n return mimetypes.guess_type(filename)[0] or 'application/octet-stream'\n"},{"alpha_fraction":0.5804196000099182,"alphanum_fraction":0.5804196000099182,"avg_line_length":10.916666984558105,"blob_id":"384c18dbc1a329096d767adc86466c1fdcbe4e30","content_id":"c98eae7b2850bb2fa80a4bd5e3152b0c1be42fe0","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":143,"license_type":"permissive","max_line_length":27,"num_lines":12,"path":"/run_tests.sh","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"#!/bin/bash\n\nset -o xtrace\n\nif [ -z \"$@\" ]; then\n TESTS=tests\nelse\n TESTS=\"$@\"\nfi\n\nfind -iname \"*.pyc\" -delete\nPYTHONPATH=. trial $TESTS\n"},{"alpha_fraction":0.7478991746902466,"alphanum_fraction":0.7478991746902466,"avg_line_length":28.75,"blob_id":"847600997caa326c30aa15b3f40dbd5d643f1367","content_id":"280b51a721c737c01646c12c6f86a91383e49e7b","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":119,"license_type":"permissive","max_line_length":45,"num_lines":4,"path":"/lbrynet/analytics/constants.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"\"\"\"Constants for metrics\"\"\"\n\nBLOB_BYTES_UPLOADED = 'Blob Bytes Uploaded'\nBLOB_BYTES_AVAILABLE = 'Blob Bytes Available'\n"},{"alpha_fraction":0.6733220219612122,"alphanum_fraction":0.6741716265678406,"avg_line_length":43.41509246826172,"blob_id":"7a3caaf25cacdea6d34ab8f49583c92f628ead6e","content_id":"8b87ea6e3475ba03cb9e7af796c20abc7ad5413a","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2354,"license_type":"permissive","max_line_length":94,"num_lines":53,"path":"/lbrynet/lbrylive/PaymentRateManager.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"# pylint: skip-file\nclass BaseLiveStreamPaymentRateManager(object):\n def __init__(self, blob_info_rate, blob_data_rate=None):\n self.min_live_blob_info_payment_rate = blob_info_rate\n self.min_blob_data_payment_rate = blob_data_rate\n\n\nclass LiveStreamPaymentRateManager(object):\n def __init__(self, base_live_stream_payment_rate_manager, payment_rate_manager,\n blob_info_rate=None, blob_data_rate=None):\n self._base_live_stream_payment_rate_manager = base_live_stream_payment_rate_manager\n self._payment_rate_manager = payment_rate_manager\n self.min_live_blob_info_payment_rate = blob_info_rate\n self.min_blob_data_payment_rate = blob_data_rate\n self.points_paid = 0.0\n\n def get_rate_live_blob_info(self, peer):\n return self.get_effective_min_live_blob_info_payment_rate()\n\n def accept_rate_live_blob_info(self, peer, payment_rate):\n return payment_rate >= self.get_effective_min_live_blob_info_payment_rate()\n\n def get_rate_blob_data(self, peer, blobs):\n response = self._payment_rate_manager.strategy.make_offer(peer, blobs)\n return response.rate\n\n def accept_rate_blob_data(self, peer, blobs, offer):\n response = self._payment_rate_manager.strategy.respond_to_offer(offer, peer, blobs)\n return response.accepted\n\n def reply_to_offer(self, peer, blobs, offer):\n reply = self._payment_rate_manager.strategy.respond_to_offer(offer, peer, blobs)\n self._payment_rate_manager.strategy.offer_accepted(peer, reply)\n return reply\n\n def get_effective_min_blob_data_payment_rate(self):\n rate = self.min_blob_data_payment_rate\n if rate is None:\n rate = self._payment_rate_manager.min_blob_data_payment_rate\n if rate is None:\n rate = self._base_live_stream_payment_rate_manager.min_blob_data_payment_rate\n if rate is None:\n rate = self._payment_rate_manager.get_effective_min_blob_data_payment_rate()\n return rate\n\n def get_effective_min_live_blob_info_payment_rate(self):\n rate = self.min_live_blob_info_payment_rate\n if rate is None:\n rate = self._base_live_stream_payment_rate_manager.min_live_blob_info_payment_rate\n return rate\n\n def record_points_paid(self, amount):\n self.points_paid += amount\n"},{"alpha_fraction":0.6218905448913574,"alphanum_fraction":0.6231343150138855,"avg_line_length":26.724138259887695,"blob_id":"055db479f55db60cd9943c3fde8cbd4497051d51","content_id":"87f2d143349982640b27f51543083cac461929ce","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":804,"license_type":"permissive","max_line_length":75,"num_lines":29,"path":"/lbrynet/core/system_info.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"import platform\nimport json\n\nfrom urllib2 import urlopen\n\nfrom lbrynet import __version__ as lbrynet_version\nfrom lbrynet import build_type\nfrom lbryum.version import LBRYUM_VERSION as lbryum_version\n\n\ndef get_platform(get_ip=True):\n p = {\n \"processor\": platform.processor(),\n \"python_version\": platform.python_version(),\n \"platform\": platform.platform(),\n \"os_release\": platform.release(),\n \"os_system\": platform.system(),\n \"lbrynet_version\": lbrynet_version,\n \"lbryum_version\": lbryum_version,\n \"build\": build_type.BUILD, # CI server sets this during build step\n }\n\n if get_ip:\n try:\n p['ip'] = json.load(urlopen('http://jsonip.com'))['ip']\n except:\n p['ip'] = \"Could not determine IP\"\n\n return p\n"},{"alpha_fraction":0.5905511975288391,"alphanum_fraction":0.5905511975288391,"avg_line_length":25.45833396911621,"blob_id":"6561d13e2686a77ee05fce6c033b1cd973ff2817","content_id":"7643ebce9e0ae81e3aa4b84bef18593bd4c4316b","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":635,"license_type":"permissive","max_line_length":57,"num_lines":24,"path":"/lbrynet/analytics/track.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"import collections\n\n\nclass Track(object):\n \"\"\"Track and summarize observations of metrics.\"\"\"\n def __init__(self):\n self.data = collections.defaultdict(list)\n\n def add_observation(self, metric, value):\n self.data[metric].append(value)\n\n def summarize_and_reset(self, metric, op=sum):\n \"\"\"Apply `op` on the current values for `metric`.\n\n This operation also resets the metric.\n\n Returns:\n a tuple (should_send, value)\n \"\"\"\n try:\n values = self.data.pop(metric)\n return True, op(values)\n except KeyError:\n return False, None\n"},{"alpha_fraction":0.6789524555206299,"alphanum_fraction":0.6808923482894897,"avg_line_length":41.95833206176758,"blob_id":"a79ee9f45100e974d5c34ee64f915ee37ad2f33d","content_id":"46bf54f7a1c0faad8e29f66178d3dd9a614da5b3","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1031,"license_type":"permissive","max_line_length":93,"num_lines":24,"path":"/lbrynet/lbrylive/LiveBlob.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"# pylint: skip-file\nfrom lbrynet.cryptstream.CryptBlob import CryptStreamBlobMaker, CryptBlobInfo\nimport binascii\n\n\nclass LiveBlobInfo(CryptBlobInfo):\n def __init__(self, blob_hash, blob_num, length, iv, revision, signature):\n CryptBlobInfo.__init__(self, blob_hash, blob_num, length, iv)\n self.revision = revision\n self.signature = signature\n\n\nclass LiveStreamBlobMaker(CryptStreamBlobMaker):\n def __init__(self, key, iv, blob_num, blob):\n CryptStreamBlobMaker.__init__(self, key, iv, blob_num, blob)\n # The following is a placeholder for a currently unimplemented feature.\n # In the future it may be possible for the live stream creator to overwrite a blob\n # with a newer revision. If that happens, the 0 will be incremented to the\n # actual revision count\n self.revision = 0\n\n def _return_info(self, blob_hash):\n return LiveBlobInfo(blob_hash, self.blob_num, self.length, binascii.hexlify(self.iv),\n self.revision, None)\n"},{"alpha_fraction":0.8235294222831726,"alphanum_fraction":0.8235294222831726,"avg_line_length":24.5,"blob_id":"1e028a66dbdf366b81f80fdf7f40d9e96e7e3ef6","content_id":"1aafe01b46d7f258abf32cde6de250a2d2917385","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":153,"license_type":"permissive","max_line_length":35,"num_lines":6,"path":"/lbrynet/analytics/__init__.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"from constants import *\nfrom events import *\nfrom api import Api\nfrom track import Track\nfrom manager import Manager\nfrom logging_handler import Handler\n"},{"alpha_fraction":0.5729424953460693,"alphanum_fraction":0.5752821564674377,"avg_line_length":38.489131927490234,"blob_id":"a229599519524df5cde67f469778f0c0245f27e2","content_id":"2fce90b6b3676aaf484a7aad18ca820890a67ad0","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7266,"license_type":"permissive","max_line_length":106,"num_lines":184,"path":"/lbrynet/lbrylive/server/LiveBlobInfoQueryHandler.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"# pylint: skip-file\nimport logging\nfrom twisted.internet import defer\nfrom zope.interface import implements\nfrom lbrynet.interfaces import IQueryHandlerFactory, IQueryHandler\n\n\nlog = logging.getLogger(__name__)\n\n\nclass CryptBlobInfoQueryHandlerFactory(object):\n implements(IQueryHandlerFactory)\n\n def __init__(self, stream_info_manager, wallet, payment_rate_manager):\n self.stream_info_manager = stream_info_manager\n self.wallet = wallet\n self.payment_rate_manager = payment_rate_manager\n\n ######### IQueryHandlerFactory #########\n\n def build_query_handler(self):\n q_h = CryptBlobInfoQueryHandler(self.stream_info_manager, self.wallet, self.payment_rate_manager)\n return q_h\n\n def get_primary_query_identifier(self):\n return 'further_blobs'\n\n def get_description(self):\n return (\"Stream Blob Information - blob hashes that are associated with streams,\"\n \" and the blobs' associated metadata\")\n\n\nclass CryptBlobInfoQueryHandler(object):\n implements(IQueryHandler)\n\n def __init__(self, stream_info_manager, wallet, payment_rate_manager):\n self.stream_info_manager = stream_info_manager\n self.wallet = wallet\n self.payment_rate_manager = payment_rate_manager\n self.query_identifiers = ['blob_info_payment_rate', 'further_blobs']\n self.blob_info_payment_rate = None\n self.peer = None\n\n ######### IQueryHandler #########\n\n def register_with_request_handler(self, request_handler, peer):\n self.peer = peer\n request_handler.register_query_handler(self, self.query_identifiers)\n\n def handle_queries(self, queries):\n response = {}\n\n if self.query_identifiers[0] in queries:\n if not self.handle_blob_info_payment_rate(queries[self.query_identifiers[0]]):\n return defer.succeed({'blob_info_payment_rate': 'RATE_TOO_LOW'})\n else:\n response['blob_info_payment_rate'] = \"RATE_ACCEPTED\"\n\n if self.query_identifiers[1] in queries:\n further_blobs_request = queries[self.query_identifiers[1]]\n log.debug(\"Received the client's request for additional blob information\")\n\n if self.blob_info_payment_rate is None:\n response['further_blobs'] = {'error': 'RATE_UNSET'}\n return defer.succeed(response)\n\n def count_and_charge(blob_infos):\n if len(blob_infos) != 0:\n log.info(\"Responding with %s infos\", str(len(blob_infos)))\n expected_payment = 1.0 * len(blob_infos) * self.blob_info_payment_rate / 1000.0\n self.wallet.add_expected_payment(self.peer, expected_payment)\n self.peer.update_stats('uploaded_crypt_blob_infos', len(blob_infos))\n return blob_infos\n\n def set_field(further_blobs):\n response['further_blobs'] = {'blob_infos': further_blobs}\n return response\n\n def get_further_blobs(stream_hash):\n if stream_hash is None:\n response['further_blobs'] = {'error': 'REFERENCE_HASH_UNKNOWN'}\n return defer.succeed(response)\n start = further_blobs_request.get(\"start\")\n end = further_blobs_request.get(\"end\")\n count = further_blobs_request.get(\"count\")\n if count is not None:\n try:\n count = int(count)\n except ValueError:\n response['further_blobs'] = {'error': 'COUNT_NON_INTEGER'}\n return defer.succeed(response)\n\n if len([x for x in [start, end, count] if x is not None]) < 2:\n response['further_blobs'] = {'error': 'TOO_FEW_PARAMETERS'}\n return defer.succeed(response)\n\n inner_d = self.get_further_blobs(stream_hash, start, end, count)\n\n inner_d.addCallback(count_and_charge)\n inner_d.addCallback(self.format_blob_infos)\n inner_d.addCallback(set_field)\n return inner_d\n\n if 'reference' in further_blobs_request:\n d = self.get_stream_hash_from_reference(further_blobs_request['reference'])\n d.addCallback(get_further_blobs)\n return d\n else:\n response['further_blobs'] = {'error': 'NO_REFERENCE_SENT'}\n return defer.succeed(response)\n else:\n return defer.succeed({})\n\n ######### internal #########\n\n def handle_blob_info_payment_rate(self, requested_payment_rate):\n if not self.payment_rate_manager.accept_rate_live_blob_info(self.peer, requested_payment_rate):\n return False\n else:\n self.blob_info_payment_rate = requested_payment_rate\n return True\n\n def format_blob_infos(self, blobs):\n blob_infos = []\n for blob_hash, blob_num, revision, iv, length, signature in blobs:\n blob_info = {}\n if length != 0:\n blob_info['blob_hash'] = blob_hash\n blob_info['blob_num'] = blob_num\n blob_info['revision'] = revision\n blob_info['iv'] = iv\n blob_info['length'] = length\n blob_info['signature'] = signature\n blob_infos.append(blob_info)\n return blob_infos\n\n def get_stream_hash_from_reference(self, reference):\n d = self.stream_info_manager.check_if_stream_exists(reference)\n\n def check_if_stream_found(result):\n if result is True:\n return reference\n else:\n return self.stream_info_manager.get_stream_of_blob(reference)\n\n d.addCallback(check_if_stream_found)\n return d\n\n def get_further_blobs(self, stream_hash, start, end, count):\n ds = []\n if start is not None and start != \"beginning\":\n ds.append(self.stream_info_manager.get_stream_of_blob(start))\n if end is not None and end != 'end':\n ds.append(self.stream_info_manager.get_stream_of_blob(end))\n dl = defer.DeferredList(ds, fireOnOneErrback=True)\n\n def ensure_streams_match(results):\n for success, stream_of_blob in results:\n if stream_of_blob != stream_hash:\n raise ValueError(\"Blob does not match stream\")\n return True\n\n def get_blob_infos():\n reverse = False\n count_to_use = count\n if start is None:\n reverse = True\n elif end is not None and count_to_use is not None and count_to_use < 0:\n reverse = True\n if count_to_use is not None and count_to_use < 0:\n count_to_use *= -1\n if start == \"beginning\" or start is None:\n s = None\n else:\n s = start\n if end == \"end\" or end is None:\n e = None\n else:\n e = end\n return self.stream_info_manager.get_blobs_for_stream(stream_hash, s, e, count_to_use, reverse)\n\n dl.addCallback(ensure_streams_match)\n dl.addCallback(lambda _: get_blob_infos())\n return dl\n"},{"alpha_fraction":0.6283149719238281,"alphanum_fraction":0.6332109570503235,"avg_line_length":22.79611587524414,"blob_id":"984a672c66706205366a2c4c30820fc94a61aaa7","content_id":"6c3e503bb00ef8080deb6e7e29ada6c32de2f95e","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2451,"license_type":"permissive","max_line_length":99,"num_lines":103,"path":"/lbrynet/dht_scripts.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"from lbrynet.dht.node import Node\nimport binascii\nfrom twisted.internet import reactor, task\nimport logging\nimport sys\nfrom lbrynet.core.utils import generate_id\n\n\nlog = logging.getLogger(__name__)\n\n\ndef print_usage():\n print \"Usage:\\n%s UDP_PORT KNOWN_NODE_IP KNOWN_NODE_PORT HASH\"\n\n\ndef join_network(udp_port, known_nodes):\n lbryid = generate_id()\n\n log.info('Creating Node')\n node = Node(udpPort=udp_port, lbryid=lbryid)\n\n log.info('Joining network')\n d = node.joinNetwork(known_nodes)\n\n def log_network_size():\n log.info(\"Approximate number of nodes in DHT: %s\", str(node.getApproximateTotalDHTNodes()))\n log.info(\"Approximate number of blobs in DHT: %s\", str(node.getApproximateTotalHashes()))\n\n d.addCallback(lambda _: log_network_size())\n\n d.addCallback(lambda _: node)\n\n return d\n\n\ndef get_hosts(node, h):\n\n def print_hosts(hosts):\n print \"Hosts returned from the DHT: \"\n print hosts\n\n log.info(\"Looking up %s\", h)\n d = node.getPeersForBlob(h)\n d.addCallback(print_hosts)\n return d\n\n\ndef announce_hash(node, h):\n d = node.announceHaveBlob(h, 34567)\n\n def log_results(results):\n for success, result in results:\n if success:\n log.info(\"Succeeded: %s\", str(result))\n else:\n log.info(\"Failed: %s\", str(result.getErrorMessage()))\n\n d.addCallback(log_results)\n return d\n\n\ndef get_args():\n if len(sys.argv) < 5:\n print_usage()\n sys.exit(1)\n udp_port = int(sys.argv[1])\n known_nodes = [(sys.argv[2], int(sys.argv[3]))]\n h = binascii.unhexlify(sys.argv[4])\n return udp_port, known_nodes, h\n\n\ndef run_dht_script(dht_func):\n log_format = \"(%(asctime)s)[%(filename)s:%(lineno)s] %(funcName)s(): %(message)s\"\n logging.basicConfig(level=logging.DEBUG, format=log_format)\n\n udp_port, known_nodes, h = get_args()\n\n d = task.deferLater(reactor, 0, join_network, udp_port, known_nodes)\n\n def run_dht_func(node):\n return dht_func(node, h)\n\n d.addCallback(run_dht_func)\n\n def log_err(err):\n log.error(\"An error occurred: %s\", err.getTraceback())\n return err\n\n def shut_down():\n log.info(\"Shutting down\")\n reactor.stop()\n\n d.addErrback(log_err)\n d.addBoth(lambda _: shut_down())\n reactor.run()\n\n\ndef get_hosts_for_hash_in_dht():\n run_dht_script(get_hosts)\n\n\ndef announce_hash_to_dht():\n run_dht_script(announce_hash)\n"},{"alpha_fraction":0.6598342061042786,"alphanum_fraction":0.6616766452789307,"avg_line_length":36.431034088134766,"blob_id":"d7fb26c4c61f5e702806b904e66e78919a9621d6","content_id":"c8dbc9d9e33d5ce8235eef549de815ca27a3dcc3","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4342,"license_type":"permissive","max_line_length":95,"num_lines":116,"path":"/lbrynet/analytics/manager.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"from twisted.internet import defer\nfrom twisted.internet import task\n\nfrom lbrynet import conf\nfrom lbrynet.core import looping_call_manager\nfrom lbrynet.core.system_info import get_platform\n\nimport constants\nfrom api import Api\nfrom events import Events, make_context\nfrom track import Track\n\n\nclass Manager(object):\n def __init__(self, analytics_api, events_generator, track):\n self.analytics_api = analytics_api\n self.events_generator = events_generator\n self.track = track\n self.looping_call_manager = self.setup_looping_calls()\n self.is_started = False\n\n @classmethod\n def new_instance(cls, api=None, events=None):\n if api is None:\n api = Api.new_instance()\n if events is None:\n events = Events(\n make_context(get_platform(), conf.settings['wallet']),\n conf.settings.installation_id,\n conf.settings.get_session_id(),\n )\n return cls(api, events, Track())\n\n def update_events_generator(self, events_generator):\n self.events_generator = events_generator\n\n def _get_looping_calls(self):\n return [\n ('send_heartbeat', self._send_heartbeat, 60),\n ('update_tracked_metrics', self._update_tracked_metrics, 300),\n ]\n\n def setup_looping_calls(self):\n call_manager = looping_call_manager.LoopingCallManager()\n for name, fn, _ in self._get_looping_calls():\n call_manager.register_looping_call(name, task.LoopingCall(fn))\n return call_manager\n\n def start(self):\n if not self.is_started:\n for name, _, interval in self._get_looping_calls():\n self.looping_call_manager.start(name, interval)\n self.is_started = True\n\n def shutdown(self):\n self.looping_call_manager.shutdown()\n\n def send_server_startup(self):\n event = self.events_generator.server_startup()\n self.analytics_api.track(event)\n\n def send_server_startup_success(self):\n event = self.events_generator.server_startup_success()\n self.analytics_api.track(event)\n\n def send_server_startup_error(self, message):\n event = self.events_generator.server_startup_error(message)\n self.analytics_api.track(event)\n\n def send_download_started(self, id_, name, stream_info=None):\n event = self.events_generator.download_started(id_, name, stream_info)\n self.analytics_api.track(event)\n\n def send_download_errored(self, id_, name, stream_info=None):\n event = self.events_generator.download_errored(id_, name, stream_info)\n self.analytics_api.track(event)\n\n def send_download_finished(self, id_, name, stream_info=None):\n event = self.events_generator.download_finished(id_, name, stream_info)\n self.analytics_api.track(event)\n\n def send_error(self, message):\n event = self.events_generator.error(message)\n self.analytics_api.track(event)\n\n def register_repeating_metric(self, event_name, value_generator, frequency=300):\n lcall = task.LoopingCall(self._send_repeating_metric, event_name, value_generator)\n self.looping_call_manager.register_looping_call(event_name, lcall)\n lcall.start(frequency)\n\n def _send_heartbeat(self):\n heartbeat = self.events_generator.heartbeat()\n self.analytics_api.track(heartbeat)\n\n def _update_tracked_metrics(self):\n should_send, value = self.track.summarize_and_reset(constants.BLOB_BYTES_UPLOADED)\n if should_send:\n event = self.events_generator.metric_observed(constants.BLOB_BYTES_UPLOADED, value)\n self.analytics_api.track(event)\n\n def _send_repeating_metric(self, event_name, value_generator):\n result = value_generator()\n if_deferred(result, self._send_repeating_metric_value, event_name)\n\n def _send_repeating_metric_value(self, result, event_name):\n should_send, value = result\n if should_send:\n event = self.events_generator.metric_observed(event_name, value)\n self.analytics_api.track(event)\n\n\ndef if_deferred(maybe_deferred, callback, *args, **kwargs):\n if isinstance(maybe_deferred, defer.Deferred):\n maybe_deferred.addCallback(callback, *args, **kwargs)\n else:\n callback(maybe_deferred, *args, **kwargs)\n"},{"alpha_fraction":0.6016063094139099,"alphanum_fraction":0.6021162867546082,"avg_line_length":42.57777786254883,"blob_id":"54c775a30d36cdb93acd0ad340e7570806e370ac","content_id":"a59eff1cf068a2751c837467c97c07ab13250884","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7844,"license_type":"permissive","max_line_length":112,"num_lines":180,"path":"/lbrynet/lbrylive/client/LiveStreamDownloader.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"# pylint: skip-file\nimport binascii\nfrom lbrynet.core.StreamDescriptor import StreamMetadata\nfrom lbrynet.cryptstream.client.CryptStreamDownloader import CryptStreamDownloader\nfrom zope.interface import implements\nfrom lbrynet.lbrylive.client.LiveStreamMetadataHandler import LiveStreamMetadataHandler\nfrom lbrynet.lbrylive.client.LiveStreamProgressManager import LiveStreamProgressManager\nimport os\nfrom lbrynet.lbrylive.StreamDescriptor import save_sd_info\nfrom lbrynet.lbrylive.PaymentRateManager import LiveStreamPaymentRateManager\nfrom twisted.internet import defer, threads # , process\nfrom lbrynet.interfaces import IStreamDownloaderFactory\nfrom lbrynet.lbrylive.StreamDescriptor import LiveStreamType\n\n\nclass _LiveStreamDownloader(CryptStreamDownloader):\n\n def __init__(self, stream_hash, peer_finder, rate_limiter, blob_manager, stream_info_manager,\n payment_rate_manager, wallet):\n CryptStreamDownloader.__init__(self, peer_finder, rate_limiter, blob_manager,\n payment_rate_manager, wallet)\n self.stream_hash = stream_hash\n self.stream_info_manager = stream_info_manager\n self.public_key = None\n\n def set_stream_info(self):\n if self.public_key is None and self.key is None:\n\n d = self.stream_info_manager.get_stream_info(self.stream_hash)\n\n def set_stream_info(stream_info):\n public_key, key, stream_name = stream_info\n self.public_key = public_key\n self.key = binascii.unhexlify(key)\n self.stream_name = binascii.unhexlify(stream_name)\n\n d.addCallback(set_stream_info)\n return d\n else:\n return defer.succeed(True)\n\n\nclass LiveStreamDownloader(_LiveStreamDownloader):\n def __init__(self, stream_hash, peer_finder, rate_limiter, blob_manager, stream_info_manager,\n payment_rate_manager, wallet):\n _LiveStreamDownloader.__init__(self, stream_hash, peer_finder, rate_limiter, blob_manager,\n stream_info_manager, payment_rate_manager, wallet)\n\n\n def _get_metadata_handler(self, download_manager):\n return LiveStreamMetadataHandler(self.stream_hash, self.stream_info_manager,\n self.peer_finder, self.public_key, False,\n self.payment_rate_manager, self.wallet, download_manager, 10)\n\n def _get_progress_manager(self, download_manager):\n return LiveStreamProgressManager(self._finished_downloading, self.blob_manager, download_manager,\n delete_blob_after_finished=True, download_whole=False,\n max_before_skip_ahead=10)\n\n def _get_write_func(self):\n def write_func(data):\n if self.stopped is False:\n pass\n return write_func\n\n\nclass FullLiveStreamDownloader(_LiveStreamDownloader):\n def __init__(self, stream_hash, peer_finder, rate_limiter, blob_manager, stream_info_manager,\n payment_rate_manager, wallet):\n _LiveStreamDownloader.__init__(self, stream_hash, peer_finder, rate_limiter,\n blob_manager, stream_info_manager, payment_rate_manager,\n wallet)\n self.file_handle = None\n self.file_name = None\n\n def set_stream_info(self):\n d = _LiveStreamDownloader.set_stream_info(self)\n\n def set_file_name_if_unset():\n if not self.file_name:\n if not self.stream_name:\n self.stream_name = \"_\"\n self.file_name = os.path.basename(self.stream_name)\n\n d.addCallback(lambda _: set_file_name_if_unset())\n return d\n\n def stop(self, err=None):\n d = self._close_file()\n d.addBoth(lambda _: _LiveStreamDownloader.stop(self, err))\n return d\n\n def _start(self):\n if self.file_handle is None:\n d = self._open_file()\n else:\n d = defer.succeed(True)\n d.addCallback(lambda _: _LiveStreamDownloader._start(self))\n return d\n\n def _open_file(self):\n def open_file():\n self.file_handle = open(self.file_name, 'wb')\n return threads.deferToThread(open_file)\n\n def _get_metadata_handler(self, download_manager):\n return LiveStreamMetadataHandler(self.stream_hash, self.stream_info_manager,\n self.peer_finder, self.public_key, True,\n self.payment_rate_manager, self.wallet, download_manager)\n\n def _get_primary_request_creators(self, download_manager):\n return [download_manager.blob_requester, download_manager.blob_info_finder]\n\n def _get_write_func(self):\n def write_func(data):\n if self.stopped is False:\n self.file_handle.write(data)\n return write_func\n\n def _close_file(self):\n def close_file():\n if self.file_handle is not None:\n self.file_handle.close()\n self.file_handle = None\n return threads.deferToThread(close_file)\n\n\nclass FullLiveStreamDownloaderFactory(object):\n\n implements(IStreamDownloaderFactory)\n\n def __init__(self, peer_finder, rate_limiter, blob_manager, stream_info_manager, wallet,\n default_payment_rate_manager):\n self.peer_finder = peer_finder\n self.rate_limiter = rate_limiter\n self.blob_manager = blob_manager\n self.stream_info_manager = stream_info_manager\n self.wallet = wallet\n self.default_payment_rate_manager = default_payment_rate_manager\n\n def can_download(self, sd_validator):\n return True\n\n def make_downloader(self, metadata, options, payment_rate_manager):\n # TODO: check options for payment rate manager parameters\n prm = LiveStreamPaymentRateManager(self.default_payment_rate_manager,\n payment_rate_manager)\n\n def save_source_if_blob(stream_hash):\n if metadata.metadata_source == StreamMetadata.FROM_BLOB:\n d = self.stream_info_manager.save_sd_blob_hash_to_stream(stream_hash, metadata.source_blob_hash)\n else:\n d = defer.succeed(True)\n d.addCallback(lambda _: stream_hash)\n return d\n\n d = save_sd_info(self.stream_info_manager, metadata.validator.raw_info)\n d.addCallback(save_source_if_blob)\n\n def create_downloader(stream_hash):\n stream_downloader = FullLiveStreamDownloader(stream_hash, self.peer_finder, self.rate_limiter,\n self.blob_manager, self.stream_info_manager,\n prm, self.wallet)\n d = stream_downloader.set_stream_info()\n d.addCallback(lambda _: stream_downloader)\n return d\n\n d.addCallback(create_downloader)\n return d\n\n\ndef add_full_live_stream_downloader_to_sd_identifier(session, stream_info_manager, sd_identifier,\n base_live_stream_payment_rate_manager):\n downloader_factory = FullLiveStreamDownloaderFactory(session.peer_finder,\n session.rate_limiter,\n session.blob_manager,\n stream_info_manager,\n session.wallet,\n base_live_stream_payment_rate_manager)\n sd_identifier.add_stream_downloader_factory(LiveStreamType, downloader_factory)\n"},{"alpha_fraction":0.6493775844573975,"alphanum_fraction":0.6535269618034363,"avg_line_length":31.133333206176758,"blob_id":"65e329e0a66efcb809f43beee65530f13380c837","content_id":"c54cf0a441bfa442a907fdd96ad95653d381692f","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":482,"license_type":"permissive","max_line_length":71,"num_lines":15,"path":"/run_pylint.sh","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"#! /bin/bash\n\nset -eu\n\n# Ignoring distutils because: https://github.com/PyCQA/pylint/issues/73\n# TODO: as code quality improves, make pylint be more strict\npylint -E --disable=inherit-non-class --disable=no-member \\\n --ignored-modules=distutils \\\n --enable=unused-import \\\n --enable=bad-whitespace \\\n --enable=line-too-long \\\n --enable=trailing-whitespace \\\n --enable=missing-final-newline \\\n --enable=mixed-indentation \\\n lbrynet $@\n"},{"alpha_fraction":0.625345766544342,"alphanum_fraction":0.6517980694770813,"avg_line_length":40.31428527832031,"blob_id":"cb594fd3db8da6a903a9a04353028d545244cd95","content_id":"dd06247a73a67ab96e8ecfa31c17f01ed2b00cbd","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5784,"license_type":"permissive","max_line_length":126,"num_lines":140,"path":"/tests/unit/lbrynet_daemon/test_Daemon.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"import mock\nimport requests\nfrom tests.mocks import BlobAvailabilityTracker as DummyBlobAvailabilityTracker\nfrom tests import util\nfrom twisted.internet import defer\nfrom twisted.internet import reactor\nfrom twisted.trial import unittest\nfrom lbrynet.lbrynet_daemon import Daemon\nfrom lbrynet.core import Session, PaymentRateManager, Wallet\nfrom lbrynet.lbrynet_daemon.Daemon import Daemon as LBRYDaemon\nfrom lbrynet.lbrynet_daemon import ExchangeRateManager\nfrom lbrynet import conf\nfrom tests.mocks import mock_conf_settings, FakeNetwork\n\n\nclass MiscTests(unittest.TestCase):\n def test_get_lbry_electron_client_version_from_github(self):\n response = mock.create_autospec(requests.Response)\n # don't need to mock out the entire response from the api\n # but at least need 'tag_name'\n response.json.return_value = {\n \"url\": \"https://api.github.com/repos/lbryio/lbry/releases/3685199\",\n \"assets_url\": \"https://api.github.com/repos/lbryio/lbry/releases/3685199/assets\",\n \"html_url\": \"https://github.com/lbryio/lbry/releases/tag/v0.3.8\",\n \"id\": 3685199,\n \"tag_name\": \"v0.3.8\",\n \"prerelease\": False\n }\n with mock.patch('lbrynet.lbrynet_daemon.Daemon.requests') as req:\n req.get.return_value = response\n rv = Daemon.CheckRemoteVersion()\n rv._get_lbry_electron_client_version()\n self.assertEqual('0.3.8', rv.version)\n\n def test_error_is_thrown_if_prerelease(self):\n response = mock.create_autospec(requests.Response)\n response.json.return_value = {\n \"tag_name\": \"v0.3.8\",\n \"prerelease\": True\n }\n with mock.patch('lbrynet.lbrynet_daemon.Daemon.requests') as req:\n req.get.return_value = response\n rv = Daemon.CheckRemoteVersion()\n with self.assertRaises(Exception):\n rv._get_lbry_electron_client_version()\n\n def test_error_is_thrown_when_version_cant_be_parsed(self):\n with self.assertRaises(Exception):\n Daemon.get_version_from_tag('garbage')\n\n\ndef get_test_daemon(data_rate=None, generous=True, with_fee=False):\n if data_rate is None:\n data_rate = conf.ADJUSTABLE_SETTINGS['data_rate'][1]\n\n rates = {\n 'BTCLBC': {'spot': 3.0, 'ts': util.DEFAULT_ISO_TIME + 1},\n 'USDBTC': {'spot': 2.0, 'ts': util.DEFAULT_ISO_TIME + 2}\n }\n daemon = LBRYDaemon(None, None)\n daemon.session = mock.Mock(spec=Session.Session)\n daemon.exchange_rate_manager = ExchangeRateManager.DummyExchangeRateManager(rates)\n base_prm = PaymentRateManager.BasePaymentRateManager(rate=data_rate)\n prm = PaymentRateManager.NegotiatedPaymentRateManager(base_prm, DummyBlobAvailabilityTracker(),\n generous=generous)\n daemon.session.payment_rate_manager = prm\n metadata = {\n \"author\": \"fake author\",\n \"content_type\": \"fake/format\",\n \"description\": \"fake description\",\n \"license\": \"fake license\",\n \"license_url\": \"fake license url\",\n \"nsfw\": False,\n \"sources\": {\n \"lbry_sd_hash\": \"d2b8b6e907dde95245fe6d144d16c2fdd60c4e0c6463ec98b85642d06d8e9414e8fcfdcb7cb13532ec5454fb8fe7f280\"\n },\n \"thumbnail\": \"fake thumbnail\",\n \"title\": \"fake title\",\n \"ver\": \"0.0.3\"\n }\n if with_fee:\n metadata.update(\n {\"fee\": {\"USD\": {\"address\": \"bQ6BGboPV2SpTMEP7wLNiAcnsZiH8ye6eA\", \"amount\": 0.75}}})\n daemon._resolve_name = lambda _: defer.succeed(metadata)\n return daemon\n\n\nclass TestCostEst(unittest.TestCase):\n def setUp(self):\n mock_conf_settings(self)\n util.resetTime(self)\n\n def test_fee_and_generous_data(self):\n size = 10000000\n correct_result = 4.5\n daemon = get_test_daemon(generous=True, with_fee=True)\n self.assertEquals(daemon.get_est_cost(\"test\", size).result, correct_result)\n\n def test_fee_and_ungenerous_data(self):\n size = 10000000\n fake_fee_amount = 4.5\n data_rate = conf.ADJUSTABLE_SETTINGS['data_rate'][1]\n correct_result = size / 10 ** 6 * data_rate + fake_fee_amount\n daemon = get_test_daemon(generous=False, with_fee=True)\n self.assertEquals(daemon.get_est_cost(\"test\", size).result, correct_result)\n\n def test_generous_data_and_no_fee(self):\n size = 10000000\n correct_result = 0.0\n daemon = get_test_daemon(generous=True)\n self.assertEquals(daemon.get_est_cost(\"test\", size).result, correct_result)\n\n def test_ungenerous_data_and_no_fee(self):\n size = 10000000\n data_rate = conf.ADJUSTABLE_SETTINGS['data_rate'][1]\n correct_result = size / 10 ** 6 * data_rate\n daemon = get_test_daemon(generous=False)\n self.assertEquals(daemon.get_est_cost(\"test\", size).result, correct_result)\n\n\nclass TestJsonRpc(unittest.TestCase):\n def setUp(self):\n def noop():\n return None\n\n mock_conf_settings(self)\n util.resetTime(self)\n self.test_daemon = get_test_daemon()\n self.test_daemon.session.wallet = Wallet.LBRYumWallet(storage=Wallet.InMemoryStorage())\n self.test_daemon.session.wallet.network = FakeNetwork()\n self.test_daemon.session.wallet.get_best_blockhash = noop\n\n def test_status(self):\n d = defer.maybeDeferred(self.test_daemon.jsonrpc_status)\n d.addCallback(lambda status: self.assertDictContainsSubset({'is_running': False}, status))\n\n def test_help(self):\n d = defer.maybeDeferred(self.test_daemon.jsonrpc_help, command='status')\n d.addCallback(lambda result: self.assertSubstring('daemon status', result['help']))\n # self.assertSubstring('daemon status', d.result)\n"},{"alpha_fraction":0.3913043439388275,"alphanum_fraction":0.417391300201416,"avg_line_length":20.5625,"blob_id":"23b7179a8ed2d40fe6844d6b0721d3a71a14d401","content_id":"18efd64b60109b0bd1b61bc8acfaecfbbbdf89f1","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":345,"license_type":"permissive","max_line_length":57,"num_lines":16,"path":"/lbrynet/metadata/fee_schemas.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"VER_001 = {\n '$schema': 'http://json-schema.org/draft-04/schema#',\n 'title': 'LBRY fee schema 0.0.1',\n 'type': 'object',\n\n 'properties': {\n 'amount': {\n 'type': 'number',\n 'minimum': 0,\n 'exclusiveMinimum': True,\n },\n 'address': {\n 'type': 'string'\n }\n },\n}\n"},{"alpha_fraction":0.6666666865348816,"alphanum_fraction":0.6666666865348816,"avg_line_length":32.42856979370117,"blob_id":"fe6a73e9f1e9dfe39fa7938fff4e5d1fa1ba170e","content_id":"e7bdb4ac9b244ae6ce50f5ba5ca3ee61beb9ea12","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":468,"license_type":"permissive","max_line_length":72,"num_lines":14,"path":"/lbrynet/analytics/logging_handler.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"import logging\n\n\nclass Handler(logging.Handler):\n \"\"\"A logging handler that reports errors to the analytics manager\"\"\"\n def __init__(self, manager, level=logging.ERROR):\n self.manager = manager\n logging.Handler.__init__(self, level)\n\n def emit(self, record):\n # We need to call format to ensure that record.message and\n # record.exc_text attributes are populated\n self.format(record)\n self.manager.send_error(record)\n"},{"alpha_fraction":0.628040075302124,"alphanum_fraction":0.6745350360870361,"avg_line_length":30.772727966308594,"blob_id":"5b2a25afa4cf84b96edadfcb2e6088df68178ad2","content_id":"b1b7de8b0078421319b154b888736f09d1fc9e25","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1398,"license_type":"permissive","max_line_length":90,"num_lines":44,"path":"/lbrynet/metadata/Metadata.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"import logging\n\nfrom lbrynet.core import Error\nfrom lbrynet.metadata.StructuredDict import StructuredDict\nimport metadata_schemas\n\nlog = logging.getLogger(__name__)\nNAME_ALLOWED_CHARSET = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0987654321-'\n\n\ndef verify_name_characters(name):\n assert len(name) > 0, \"Empty uri\"\n invalid_characters = {c for c in name if c not in NAME_ALLOWED_CHARSET}\n if invalid_characters:\n raise Error.InvalidName(name, invalid_characters)\n return True\n\n\ndef migrate_001_to_002(metadata):\n metadata['ver'] = '0.0.2'\n metadata['nsfw'] = False\n\ndef migrate_002_to_003(metadata):\n metadata['ver'] = '0.0.3'\n if 'content-type' in metadata:\n metadata['content_type'] = metadata['content-type']\n del metadata['content-type']\n\n\nclass Metadata(StructuredDict):\n current_version = '0.0.3'\n\n _versions = [\n ('0.0.1', metadata_schemas.VER_001, None),\n ('0.0.2', metadata_schemas.VER_002, migrate_001_to_002),\n ('0.0.3', metadata_schemas.VER_003, migrate_002_to_003)\n ]\n\n def __init__(self, metadata, migrate=True, target_version=None):\n if not isinstance(metadata, dict):\n raise TypeError(\"{} is not a dictionary\".format(metadata))\n starting_version = metadata.get('ver', '0.0.1')\n\n StructuredDict.__init__(self, metadata, starting_version, migrate, target_version)\n"},{"alpha_fraction":0.600923478603363,"alphanum_fraction":0.602902352809906,"avg_line_length":26.816513061523438,"blob_id":"20be6b9cd3ef7dd7ce1b869ad1bc3b390e9a5605","content_id":"7e999ba104ecd2773ce50ca0f54010770e9a4397","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3032,"license_type":"permissive","max_line_length":90,"num_lines":109,"path":"/scripts/gen_api_docs.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\n# Generate docs: python gen_api_docs.py\n# See docs: pip install mkdocs; mkdocs serve\n# Push docs: mkdocs gh-deploy\n\nimport inspect\nimport os.path as op\nimport re\nimport sys\n\nfrom six import string_types\nfrom lbrynet.lbrynet_daemon.Daemon import Daemon\n\n\ndef _name(obj):\n if hasattr(obj, '__name__'):\n return obj.__name__\n elif inspect.isdatadescriptor(obj):\n return obj.fget.__name__\n\n\ndef _anchor(name):\n anchor = name.lower().replace(' ', '-')\n anchor = re.sub(r'[^\\w\\- ]', '', anchor)\n return anchor\n\n\n_docstring_header_pattern = re.compile(r'^([^\\n]+)\\n[\\-\\=]{3,}$', flags=re.MULTILINE)\n_docstring_parameters_pattern = re.compile(r'^([^ \\n]+) \\: ([^\\n]+)$', flags=re.MULTILINE)\n\n\ndef _replace_docstring_header(paragraph):\n \"\"\"Process NumPy-like function docstrings.\"\"\"\n\n # Replace Markdown headers in docstrings with light headers in bold.\n paragraph = re.sub(_docstring_header_pattern, r'*\\1*', paragraph)\n paragraph = re.sub(_docstring_parameters_pattern, r'\\n* `\\1` (\\2)\\n', paragraph)\n return paragraph\n\n\ndef _doc(obj):\n docstr = (inspect.getdoc(obj) or '').strip()\n return _replace_docstring_header(docstr) if docstr and '---' in docstr else docstr\n\n\ndef _is_public(obj):\n name = _name(obj) if not isinstance(obj, string_types) else obj\n if name:\n return not name.startswith('_')\n else:\n return True\n\n\ndef _is_defined_in_package(obj, package):\n if isinstance(obj, property):\n obj = obj.fget\n mod = inspect.getmodule(obj)\n if mod and hasattr(mod, '__name__'):\n name = mod.__name__\n return name.split('.')[0] == package\n return True\n\n\ndef _iter_doc_members(obj, package=None):\n for _, member in inspect.getmembers(obj):\n if _is_public(member):\n if package is None or _is_defined_in_package(member, package):\n yield member\n\n\ndef _iter_methods(klass, package=None):\n for member in _iter_doc_members(klass, package):\n if inspect.isfunction(member) or inspect.ismethod(member):\n if inspect.isdatadescriptor(member):\n continue\n if _name(member).startswith('jsonrpc_'):\n yield member\n\n\ndef _link(name, anchor=None):\n return \"[{name}](#{anchor})\".format(name=name, anchor=anchor or _anchor(name))\n\n\ndef main():\n curdir = op.dirname(op.realpath(__file__))\n path = op.realpath(op.join(curdir, '..', 'docs', 'index.md'))\n\n klass = Daemon\n\n # toc = ''\n doc = ''\n # Table of contents\n for method in _iter_methods(klass):\n method_name = _name(method)[len('jsonrpc_'):]\n method_doc = _doc(method)\n if \"DEPRECATED\" in method_doc:\n continue\n # toc += '* ' + _link(method_name, _anchor(method_name)) + \"\\n\"\n doc += '## ' + method_name + \"\\n\\n```text\\n\" + method_doc + \"\\n```\\n\\n\"\n\n text = \"# LBRY JSON-RPC API Documentation\\n\\n\" + doc\n\n with open(path, 'w+') as f:\n f.write(text)\n\n\nif __name__ == '__main__':\n sys.exit(main())\n"},{"alpha_fraction":0.645118236541748,"alphanum_fraction":0.647978663444519,"avg_line_length":41.98360824584961,"blob_id":"c0b0ef288607b91c7294900bf387d526ea886c1f","content_id":"2328715248651eb73f13aac760ca25fd17a9c7ee","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5244,"license_type":"permissive","max_line_length":108,"num_lines":122,"path":"/lbrynet/lbrylive/StdinUploader.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"# pylint: skip-file\n# pylint: skip-file\n# This file is not maintained, but might be used in the future\n#\nimport logging\nimport sys\nfrom lbrynet.lbrylive.LiveStreamCreator import StdOutLiveStreamCreator\nfrom lbrynet.core.BlobManager import TempBlobManager\nfrom lbrynet.core.Session import Session\nfrom lbrynet.core.server.BlobAvailabilityHandler import BlobAvailabilityHandlerFactory\nfrom lbrynet.core.server.BlobRequestHandler import BlobRequestHandlerFactory\nfrom lbrynet.core.server.ServerProtocol import ServerProtocolFactory\nfrom lbrynet.lbrylive.PaymentRateManager import BaseLiveStreamPaymentRateManager\nfrom lbrynet.lbrylive.LiveStreamMetadataManager import DBLiveStreamMetadataManager\nfrom lbrynet.lbrylive.server.LiveBlobInfoQueryHandler import CryptBlobInfoQueryHandlerFactory\nfrom lbrynet.dht.node import Node\nfrom twisted.internet import defer, task\n\n\nclass StdinUploader():\n \"\"\"This class reads from standard in, creates a stream, and makes it available on the network.\"\"\"\n def __init__(self, peer_port, dht_node_port, known_dht_nodes,\n stream_info_manager_class=DBLiveStreamMetadataManager, blob_manager_class=TempBlobManager):\n \"\"\"\n @param peer_port: the network port on which to listen for peers\n\n @param dht_node_port: the network port on which to listen for nodes in the DHT\n\n @param known_dht_nodes: a list of (ip_address, dht_port) which will be used to join the DHT network\n \"\"\"\n self.peer_port = peer_port\n self.lbry_server_port = None\n self.session = Session(blob_manager_class=blob_manager_class,\n stream_info_manager_class=stream_info_manager_class,\n dht_node_class=Node, dht_node_port=dht_node_port,\n known_dht_nodes=known_dht_nodes, peer_port=self.peer_port,\n use_upnp=False)\n self.payment_rate_manager = BaseLiveStreamPaymentRateManager()\n\n def start(self):\n \"\"\"Initialize the session and start listening on the peer port\"\"\"\n d = self.session.setup()\n d.addCallback(lambda _: self._start())\n\n return d\n\n def _start(self):\n self._start_server()\n return True\n\n def _start_server(self):\n query_handler_factories = [\n CryptBlobInfoQueryHandlerFactory(self.stream_info_manager, self.session.wallet,\n self.payment_rate_manager),\n BlobAvailabilityHandlerFactory(self.session.blob_manager),\n BlobRequestHandlerFactory(self.session.blob_manager, self.session.wallet,\n self.payment_rate_manager),\n self.session.wallet.get_wallet_info_query_handler_factory()\n ]\n\n self.server_factory = ServerProtocolFactory(self.session.rate_limiter,\n query_handler_factories,\n self.session.peer_manager)\n from twisted.internet import reactor\n self.lbry_server_port = reactor.listenTCP(self.peer_port, self.server_factory)\n\n def start_live_stream(self, stream_name):\n \"\"\"Create the stream and start reading from stdin\n\n @param stream_name: a string, the suggested name of this stream\n \"\"\"\n stream_creator_helper = StdOutLiveStreamCreator(stream_name, self.session.blob_manager,\n self.stream_info_manager)\n d = stream_creator_helper.create_and_publish_stream_descriptor()\n\n def print_sd_hash(sd_hash):\n print \"Stream descriptor hash:\", sd_hash\n\n d.addCallback(print_sd_hash)\n d.addCallback(lambda _: stream_creator_helper.start_streaming())\n return d\n\n def shut_down(self):\n \"\"\"End the session and stop listening on the server port\"\"\"\n d = self.session.shut_down()\n d.addCallback(lambda _: self._shut_down())\n return d\n\n def _shut_down(self):\n if self.lbry_server_port is not None:\n d = defer.maybeDeferred(self.lbry_server_port.stopListening)\n else:\n d = defer.succeed(True)\n return d\n\n\ndef launch_stdin_uploader():\n\n from twisted.internet import reactor\n\n logging.basicConfig(level=logging.WARNING, filename=\"ul.log\")\n if len(sys.argv) == 4:\n uploader = StdinUploader(int(sys.argv[2]), int(sys.argv[3]), [])\n elif len(sys.argv) == 6:\n uploader = StdinUploader(int(sys.argv[2]), int(sys.argv[3]), [(sys.argv[4], int(sys.argv[5]))])\n else:\n print \"Usage: lbrynet-stdin-uploader <stream_name> <peer_port> <dht_node_port>\" \\\n \" [<dht_bootstrap_host> <dht_bootstrap port>]\"\n sys.exit(1)\n\n def start_stdin_uploader():\n return uploader.start_live_stream(sys.argv[1])\n\n def shut_down():\n logging.debug(\"Telling the reactor to stop in 60 seconds\")\n reactor.callLater(60, reactor.stop)\n\n d = task.deferLater(reactor, 0, uploader.start)\n d.addCallback(lambda _: start_stdin_uploader())\n d.addCallback(lambda _: shut_down())\n reactor.addSystemEventTrigger('before', 'shutdown', uploader.shut_down)\n reactor.run()\n"},{"alpha_fraction":0.8666666746139526,"alphanum_fraction":0.8666666746139526,"avg_line_length":59,"blob_id":"f80b5b6576df9e2700938124d4e2ac5517f9778f","content_id":"8cd10066a4559c568ac9a15bd2ea1763ea98d7ed","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":120,"license_type":"permissive","max_line_length":61,"num_lines":2,"path":"/lbrynet/lbryfile/__init__.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"from lbrynet.lbryfile.StreamDescriptor import get_sd_info\nfrom lbrynet.lbryfile.StreamDescriptor import publish_sd_blob\n"},{"alpha_fraction":0.6454358696937561,"alphanum_fraction":0.64646315574646,"avg_line_length":37.71590805053711,"blob_id":"b5bb38c02f4bf1e42a62931f0282862a560ff7a8","content_id":"f0710c5fcac731681c5b68cd04be20e26d3438c4","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6814,"license_type":"permissive","max_line_length":115,"num_lines":176,"path":"/lbrynet/lbrylive/LiveStreamCreator.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"# pylint: skip-file\nfrom lbrynet.core.StreamDescriptor import BlobStreamDescriptorWriter\nfrom lbrynet.lbrylive.StreamDescriptor import get_sd_info\nfrom lbrynet.cryptstream.CryptStreamCreator import CryptStreamCreator\nfrom lbrynet.lbrylive.LiveBlob import LiveStreamBlobMaker\nfrom lbrynet.core.cryptoutils import get_lbry_hash_obj, get_pub_key, sign_with_pass_phrase\nfrom Crypto import Random\nimport binascii\nimport logging\nfrom lbrynet import conf\nfrom twisted.internet import interfaces, defer\nfrom twisted.protocols.basic import FileSender\nfrom zope.interface import implements\n\n\nlog = logging.getLogger(__name__)\n\n\nclass LiveStreamCreator(CryptStreamCreator):\n def __init__(self, blob_manager, stream_info_manager, name=None, key=None, iv_generator=None,\n delete_after_num=None, secret_pass_phrase=None):\n CryptStreamCreator.__init__(self, blob_manager, name, key, iv_generator)\n self.stream_hash = None\n self.stream_info_manager = stream_info_manager\n self.delete_after_num = delete_after_num\n self.secret_pass_phrase = secret_pass_phrase\n self.file_extension = conf.settings['CRYPTSD_FILE_EXTENSION']\n self.finished_blob_hashes = {}\n\n def _save_stream(self):\n d = self.stream_info_manager.save_stream(self.stream_hash, get_pub_key(self.secret_pass_phrase),\n binascii.hexlify(self.name), binascii.hexlify(self.key),\n [])\n return d\n\n def _blob_finished(self, blob_info):\n log.debug(\"In blob_finished\")\n log.debug(\"length: %s\", str(blob_info.length))\n sig_hash = get_lbry_hash_obj()\n sig_hash.update(self.stream_hash)\n if blob_info.length != 0:\n sig_hash.update(blob_info.blob_hash)\n sig_hash.update(str(blob_info.blob_num))\n sig_hash.update(str(blob_info.revision))\n sig_hash.update(blob_info.iv)\n sig_hash.update(str(blob_info.length))\n signature = sign_with_pass_phrase(sig_hash.digest(), self.secret_pass_phrase)\n blob_info.signature = signature\n self.finished_blob_hashes[blob_info.blob_num] = blob_info.blob_hash\n if self.delete_after_num is not None:\n self._delete_old_blobs(blob_info.blob_num)\n d = self.stream_info_manager.add_blobs_to_stream(self.stream_hash, [blob_info])\n\n def log_add_error(err):\n log.error(\"An error occurred adding a blob info to the stream info manager: %s\", err.getErrorMessage())\n return err\n\n d.addErrback(log_add_error)\n log.debug(\"returning from blob_finished\")\n return d\n\n def setup(self):\n \"\"\"Create the secret pass phrase if it wasn't provided, compute the stream hash,\n save the stream to the stream info manager, and return the stream hash\n \"\"\"\n if self.secret_pass_phrase is None:\n self.secret_pass_phrase = Random.new().read(512)\n\n d = CryptStreamCreator.setup(self)\n\n def make_stream_hash():\n hashsum = get_lbry_hash_obj()\n hashsum.update(binascii.hexlify(self.name))\n hashsum.update(get_pub_key(self.secret_pass_phrase))\n hashsum.update(binascii.hexlify(self.key))\n self.stream_hash = hashsum.hexdigest()\n return self.stream_hash\n\n d.addCallback(lambda _: make_stream_hash())\n d.addCallback(lambda _: self._save_stream())\n d.addCallback(lambda _: self.stream_hash)\n return d\n\n def publish_stream_descriptor(self):\n descriptor_writer = BlobStreamDescriptorWriter(self.blob_manager)\n d = get_sd_info(self.stream_info_manager, self.stream_hash, False)\n d.addCallback(descriptor_writer.create_descriptor)\n return d\n\n def _delete_old_blobs(self, newest_blob_num):\n assert self.delete_after_num is not None, \"_delete_old_blobs called with delete_after_num=None\"\n oldest_to_keep = newest_blob_num - self.delete_after_num + 1\n nums_to_delete = [num for num in self.finished_blob_hashes.iterkeys() if num < oldest_to_keep]\n for num in nums_to_delete:\n self.blob_manager.delete_blobs([self.finished_blob_hashes[num]])\n del self.finished_blob_hashes[num]\n\n def _get_blob_maker(self, iv, blob_creator):\n return LiveStreamBlobMaker(self.key, iv, self.blob_count, blob_creator)\n\n\nclass StdOutLiveStreamCreator(LiveStreamCreator):\n def __init__(self, stream_name, blob_manager, stream_info_manager):\n LiveStreamCreator.__init__(self, blob_manager, stream_info_manager, stream_name,\n delete_after_num=20)\n\n def start_streaming(self):\n stdin_producer = StdinStreamProducer(self)\n d = stdin_producer.begin_producing()\n\n def stop_stream():\n d = self.stop()\n return d\n\n d.addCallback(lambda _: stop_stream())\n return d\n\n\nclass FileLiveStreamCreator(LiveStreamCreator):\n def __init__(self, blob_manager, stream_info_manager, file_name, file_handle,\n secret_pass_phrase=None, key=None, iv_generator=None, stream_name=None):\n if stream_name is None:\n stream_name = file_name\n LiveStreamCreator.__init__(self, blob_manager, stream_info_manager, stream_name,\n secret_pass_phrase, key, iv_generator)\n self.file_name = file_name\n self.file_handle = file_handle\n\n def start_streaming(self):\n file_sender = FileSender()\n d = file_sender.beginFileTransfer(self.file_handle, self)\n\n def stop_stream():\n d = self.stop()\n return d\n\n d.addCallback(lambda _: stop_stream())\n return d\n\n\nclass StdinStreamProducer(object):\n \"\"\"This class reads data from standard in and sends it to a stream creator\"\"\"\n\n implements(interfaces.IPushProducer)\n\n def __init__(self, consumer):\n self.consumer = consumer\n self.reader = None\n self.finished_deferred = None\n\n def begin_producing(self):\n\n self.finished_deferred = defer.Deferred()\n self.consumer.registerProducer(self, True)\n self.resumeProducing()\n return self.finished_deferred\n\n def resumeProducing(self):\n if self.reader is not None:\n self.reader.resumeProducing()\n\n def stopProducing(self):\n if self.reader is not None:\n self.reader.stopReading()\n self.consumer.unregisterProducer()\n self.finished_deferred.callback(True)\n\n def pauseProducing(self):\n if self.reader is not None:\n self.reader.pauseProducing()\n\n def childDataReceived(self, fd, data):\n self.consumer.write(data)\n\n def childConnectionLost(self, fd, reason):\n self.stopProducing()\n"},{"alpha_fraction":0.5887850522994995,"alphanum_fraction":0.5908057689666748,"avg_line_length":32.837608337402344,"blob_id":"282c13013ab592f63e0dd23a011d04eb62c4bc54","content_id":"1aa2f4f1fbbc13be2ca202d0fcec8f09d75e2ff9","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3959,"license_type":"permissive","max_line_length":96,"num_lines":117,"path":"/lbrynet/cryptstream/CryptBlob.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"import binascii\nimport logging\nfrom Crypto.Cipher import AES\nfrom lbrynet import conf\nfrom lbrynet.core.BlobInfo import BlobInfo\n\n\nlog = logging.getLogger(__name__)\n\n\nclass CryptBlobInfo(BlobInfo):\n def __init__(self, blob_hash, blob_num, length, iv):\n BlobInfo.__init__(self, blob_hash, blob_num, length)\n self.iv = iv\n\n\nclass StreamBlobDecryptor(object):\n def __init__(self, blob, key, iv, length):\n self.blob = blob\n self.key = key\n self.iv = iv\n self.length = length\n self.buff = b''\n self.len_read = 0\n self.cipher = AES.new(self.key, AES.MODE_CBC, self.iv)\n\n def decrypt(self, write_func):\n\n def remove_padding(data):\n pad_len = ord(data[-1])\n data, padding = data[:-1 * pad_len], data[-1 * pad_len:]\n for c in padding:\n assert ord(c) == pad_len\n return data\n\n def write_bytes():\n if self.len_read < self.length:\n num_bytes_to_decrypt = greatest_multiple(len(self.buff), self.cipher.block_size)\n data_to_decrypt, self.buff = split(self.buff, num_bytes_to_decrypt)\n write_func(self.cipher.decrypt(data_to_decrypt))\n\n def finish_decrypt():\n assert len(self.buff) % self.cipher.block_size == 0\n data_to_decrypt, self.buff = self.buff, b''\n write_func(remove_padding(self.cipher.decrypt(data_to_decrypt)))\n\n def decrypt_bytes(data):\n self.buff += data\n self.len_read += len(data)\n write_bytes()\n\n d = self.blob.read(decrypt_bytes)\n d.addCallback(lambda _: finish_decrypt())\n return d\n\n\nclass CryptStreamBlobMaker(object):\n \"\"\"This class encrypts data and writes it to a new blob\"\"\"\n def __init__(self, key, iv, blob_num, blob):\n self.key = key\n self.iv = iv\n self.blob_num = blob_num\n self.blob = blob\n self.cipher = AES.new(self.key, AES.MODE_CBC, self.iv)\n self.buff = b''\n self.length = 0\n\n def write(self, data):\n max_bytes_to_write = conf.settings['BLOB_SIZE'] - self.length - 1\n done = False\n if max_bytes_to_write <= len(data):\n num_bytes_to_write = max_bytes_to_write\n done = True\n else:\n num_bytes_to_write = len(data)\n self.length += num_bytes_to_write\n data_to_write = data[:num_bytes_to_write]\n self.buff += data_to_write\n self._write_buffer()\n return done, num_bytes_to_write\n\n def close(self):\n log.debug(\"closing blob %s with plaintext len %s\", str(self.blob_num), str(self.length))\n if self.length != 0:\n self._close_buffer()\n d = self.blob.close()\n d.addCallback(self._return_info)\n log.debug(\"called the finished_callback from CryptStreamBlobMaker.close\")\n return d\n\n def _write_buffer(self):\n num_bytes_to_encrypt = (len(self.buff) // AES.block_size) * AES.block_size\n data_to_encrypt, self.buff = split(self.buff, num_bytes_to_encrypt)\n encrypted_data = self.cipher.encrypt(data_to_encrypt)\n self.blob.write(encrypted_data)\n\n def _close_buffer(self):\n data_to_encrypt, self.buff = self.buff, b''\n assert len(data_to_encrypt) < AES.block_size\n pad_len = AES.block_size - len(data_to_encrypt)\n padded_data = data_to_encrypt + chr(pad_len) * pad_len\n self.length += pad_len\n assert len(padded_data) == AES.block_size\n encrypted_data = self.cipher.encrypt(padded_data)\n self.blob.write(encrypted_data)\n\n def _return_info(self, blob_hash):\n return CryptBlobInfo(blob_hash, self.blob_num, self.length, binascii.hexlify(self.iv))\n\n\ndef greatest_multiple(a, b):\n \"\"\"return the largest value `c`, that is a multiple of `b` and is <= `a`\"\"\"\n return (a // b) * b\n\n\ndef split(buff, cutoff):\n return buff[:cutoff], buff[cutoff:]\n"},{"alpha_fraction":0.5834539532661438,"alphanum_fraction":0.584901750087738,"avg_line_length":34.036231994628906,"blob_id":"8a1642c9d7806ffa7a59d362c0928681bcb03b8a","content_id":"1977c263c0331732e200e45124ced15671e52b90","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4835,"license_type":"permissive","max_line_length":99,"num_lines":138,"path":"/lbrynet/lbrylive/StreamDescriptor.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"# pylint: skip-file\nimport binascii\nimport logging\nfrom lbrynet.core.cryptoutils import get_lbry_hash_obj, verify_signature\nfrom twisted.internet import defer, threads\nfrom lbrynet.core.Error import DuplicateStreamHashError, InvalidStreamDescriptorError\nfrom lbrynet.lbrylive.LiveBlob import LiveBlobInfo\nfrom lbrynet.interfaces import IStreamDescriptorValidator\nfrom zope.interface import implements\n\n\nlog = logging.getLogger(__name__)\n\n\nLiveStreamType = \"lbrylive\"\n\n\ndef save_sd_info(stream_info_manager, sd_info, ignore_duplicate=False):\n log.debug(\"Saving info for %s\", str(sd_info['stream_name']))\n hex_stream_name = sd_info['stream_name']\n public_key = sd_info['public_key']\n key = sd_info['key']\n stream_hash = sd_info['stream_hash']\n raw_blobs = sd_info['blobs']\n crypt_blobs = []\n for blob in raw_blobs:\n length = blob['length']\n if length != 0:\n blob_hash = blob['blob_hash']\n else:\n blob_hash = None\n blob_num = blob['blob_num']\n revision = blob['revision']\n iv = blob['iv']\n signature = blob['signature']\n crypt_blobs.append(LiveBlobInfo(blob_hash, blob_num, length, iv, revision, signature))\n log.debug(\"Trying to save stream info for %s\", str(hex_stream_name))\n d = stream_info_manager.save_stream(stream_hash, public_key, hex_stream_name,\n key, crypt_blobs)\n\n def check_if_duplicate(err):\n if ignore_duplicate is True:\n err.trap(DuplicateStreamHashError)\n\n d.addErrback(check_if_duplicate)\n\n d.addCallback(lambda _: stream_hash)\n return d\n\n\ndef get_sd_info(stream_info_manager, stream_hash, include_blobs):\n d = stream_info_manager.get_stream_info(stream_hash)\n\n def format_info(stream_info):\n fields = {}\n fields['stream_type'] = LiveStreamType\n fields['stream_name'] = stream_info[2]\n fields['public_key'] = stream_info[0]\n fields['key'] = stream_info[1]\n fields['stream_hash'] = stream_hash\n\n def format_blobs(blobs):\n formatted_blobs = []\n for blob_hash, blob_num, revision, iv, length, signature in blobs:\n blob = {}\n if length != 0:\n blob['blob_hash'] = blob_hash\n blob['blob_num'] = blob_num\n blob['revision'] = revision\n blob['iv'] = iv\n blob['length'] = length\n blob['signature'] = signature\n formatted_blobs.append(blob)\n fields['blobs'] = formatted_blobs\n return fields\n\n if include_blobs is True:\n d = stream_info_manager.get_blobs_for_stream(stream_hash)\n else:\n d = defer.succeed([])\n d.addCallback(format_blobs)\n return d\n\n d.addCallback(format_info)\n return d\n\n\nclass LiveStreamDescriptorValidator(object):\n implements(IStreamDescriptorValidator)\n\n def __init__(self, raw_info):\n self.raw_info = raw_info\n\n def validate(self):\n log.debug(\"Trying to validate stream descriptor for %s\", str(self.raw_info['stream_name']))\n hex_stream_name = self.raw_info['stream_name']\n public_key = self.raw_info['public_key']\n key = self.raw_info['key']\n stream_hash = self.raw_info['stream_hash']\n h = get_lbry_hash_obj()\n h.update(hex_stream_name)\n h.update(public_key)\n h.update(key)\n if h.hexdigest() != stream_hash:\n raise InvalidStreamDescriptorError(\"Stream hash does not match stream metadata\")\n blobs = self.raw_info['blobs']\n\n def check_blob_signatures():\n for blob in blobs:\n length = blob['length']\n if length != 0:\n blob_hash = blob['blob_hash']\n else:\n blob_hash = None\n blob_num = blob['blob_num']\n revision = blob['revision']\n iv = blob['iv']\n signature = blob['signature']\n hashsum = get_lbry_hash_obj()\n hashsum.update(stream_hash)\n if length != 0:\n hashsum.update(blob_hash)\n hashsum.update(str(blob_num))\n hashsum.update(str(revision))\n hashsum.update(iv)\n hashsum.update(str(length))\n if not verify_signature(hashsum.digest(), signature, public_key):\n raise InvalidStreamDescriptorError(\"Invalid signature in stream descriptor\")\n\n return threads.deferToThread(check_blob_signatures)\n\n def info_to_show(self):\n info = []\n info.append((\"stream_name\", binascii.unhexlify(self.raw_info.get(\"stream_name\"))))\n return info\n\n def get_length_of_stream(self):\n return None\n"},{"alpha_fraction":0.520906925201416,"alphanum_fraction":0.5965842008590698,"avg_line_length":40.378047943115234,"blob_id":"1a4186dfe8a45eebec69b1491e9f8c1d187e077c","content_id":"34529c18c488574d156a7d152284ba4ac435b32d","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3396,"license_type":"permissive","max_line_length":379,"num_lines":82,"path":"/tests/unit/lbrynet_daemon/test_ExchangeRateManager.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"from lbrynet.metadata import Fee\nfrom lbrynet.lbrynet_daemon import ExchangeRateManager\nfrom lbrynet import conf\nfrom lbrynet.core.Error import InvalidExchangeRateResponse\n\nfrom twisted.trial import unittest\nfrom twisted.internet import defer\nfrom tests import util\n\n\nclass FeeFormatTest(unittest.TestCase):\n def test_fee_created_with_correct_inputs(self):\n fee_dict = {\n 'USD': {\n 'amount': 10.0,\n 'address': \"bRcHraa8bYJZL7vkh5sNmGwPDERFUjGPP9\"\n }\n }\n fee = Fee.FeeValidator(fee_dict)\n self.assertEqual(10.0, fee['USD']['amount'])\n\n\nclass FeeTest(unittest.TestCase):\n def setUp(self):\n util.resetTime(self)\n\n def test_fee_converts_to_lbc(self):\n fee_dict = {\n 'USD': {\n 'amount': 10.0,\n 'address': \"bRcHraa8bYJZL7vkh5sNmGwPDERFUjGPP9\"\n }\n }\n rates = {\n 'BTCLBC': {'spot': 3.0, 'ts': util.DEFAULT_ISO_TIME + 1},\n 'USDBTC': {'spot': 2.0, 'ts': util.DEFAULT_ISO_TIME + 2}\n }\n manager = ExchangeRateManager.DummyExchangeRateManager(rates)\n result = manager.to_lbc(fee_dict).amount\n self.assertEqual(60.0, result)\n\nclass GoogleBTCFeedTest(unittest.TestCase):\n\n @defer.inlineCallbacks\n def test_handle_response(self):\n feed = ExchangeRateManager.GoogleBTCFeed()\n\n response = '// [ { \"id\": \"-2001\" ,\"t\" : \"USDBTC\" ,\"e\" : \"CURRENCY\" ,\"l\" : \"0.0008\" ,\"l_fix\" : \"\" ,\"l_cur\" : \"\" ,\"s\": \"0\" ,\"ltt\":\"\" ,\"lt\" : \"Feb 27, 10:21PM GMT\" ,\"lt_dts\" : \"2017-02-27T22:21:39Z\" ,\"c\" : \"-0.00001\" ,\"c_fix\" : \"\" ,\"cp\" : \"-0.917\" ,\"cp_fix\" : \"\" ,\"ccol\" : \"chr\" ,\"pcls_fix\" : \"\" } ]'\n out = yield feed._handle_response(response)\n self.assertEqual(0.0008, out)\n\n # check negative trade price throws exception\n response = '// [ { \"id\": \"-2001\" ,\"t\" : \"USDBTC\" ,\"e\" : \"CURRENCY\" ,\"l\" : \"-0.0008\" ,\"l_fix\" : \"\" ,\"l_cur\" : \"\" ,\"s\": \"0\" ,\"ltt\":\"\" ,\"lt\" : \"Feb 27, 10:21PM GMT\" ,\"lt_dts\" : \"2017-02-27T22:21:39Z\" ,\"c\" : \"-0.00001\" ,\"c_fix\" : \"\" ,\"cp\" : \"-0.917\" ,\"cp_fix\" : \"\" ,\"ccol\" : \"chr\" ,\"pcls_fix\" : \"\" } ]'\n with self.assertRaises(InvalidExchangeRateResponse):\n out = yield feed._handle_response(response)\n\n\n\n\nclass BittrexFeedTest(unittest.TestCase):\n def setUp(self):\n conf.initialize_settings()\n\n def tearDown(self):\n conf.settings = None\n\n @defer.inlineCallbacks\n def test_handle_response(self):\n feed = ExchangeRateManager.BittrexFeed()\n\n response ='{\"success\":true,\"message\":\"\",\"result\":[{\"Id\":6902471,\"TimeStamp\":\"2017-02-27T23:41:52.213\",\"Quantity\":56.12611239,\"Price\":0.00001621,\"Total\":0.00090980,\"FillType\":\"PARTIAL_FILL\",\"OrderType\":\"SELL\"},{\"Id\":6902403,\"TimeStamp\":\"2017-02-27T23:31:40.463\",\"Quantity\":430.99988180,\"Price\":0.00001592,\"Total\":0.00686151,\"FillType\":\"PARTIAL_FILL\",\"OrderType\":\"SELL\"}]}'\n out = yield feed._handle_response(response)\n expected= 1.0 / ((0.00090980+0.00686151) / (56.12611239+430.99988180))\n self.assertEqual(expected, out)\n\n response='{}'\n with self.assertRaises(InvalidExchangeRateResponse):\n out = yield feed._handle_response(response)\n\n response='{\"success\":true,\"result\":[]}'\n with self.assertRaises(InvalidExchangeRateResponse):\n out = yield feed._handle_response(response)\n\n\n\n"},{"alpha_fraction":0.5138888955116272,"alphanum_fraction":0.5509259104728699,"avg_line_length":32.230770111083984,"blob_id":"36203f7e4bb9a34111712546594bb064f8ba78e8","content_id":"06b9bcd9269bff5c4702578663736dc254e24e40","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1296,"license_type":"permissive","max_line_length":96,"num_lines":39,"path":"/tests/unit/analytics/test_events.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"from lbrynet.analytics import events\n\nfrom twisted.trial import unittest\n\nfrom tests import util\n\n\nclass EventsTest(unittest.TestCase):\n def setUp(self):\n util.resetTime(self)\n self.event_generator = events.Events('any valid json datatype', 'lbry123', 'session456')\n\n def test_heartbeat(self):\n result = self.event_generator.heartbeat()\n desired_result = {\n 'context': 'any valid json datatype',\n 'event': 'Heartbeat',\n 'properties': {'lbry_id': 'lbry123', 'session_id': 'session456'},\n 'timestamp': '2016-01-01T00:00:00Z',\n 'userId': 'lbry'\n }\n self.assertEqual(desired_result, result)\n\n def test_download_started(self):\n result = self.event_generator.download_started('1', 'great gatsby')\n desired_result = {\n 'context': 'any valid json datatype',\n 'event': 'Download Started',\n 'properties': {\n 'lbry_id': 'lbry123',\n 'session_id': 'session456',\n 'name': 'great gatsby',\n 'stream_info': None,\n 'download_id': '1'\n },\n 'timestamp': '2016-01-01T00:00:00Z',\n 'userId': 'lbry'\n }\n self.assertEqual(desired_result, result)\n"},{"alpha_fraction":0.5766825079917908,"alphanum_fraction":0.5784338116645813,"avg_line_length":30.2265625,"blob_id":"4d21e9e82527f74e05f616a6f66377afdeee789d","content_id":"203f2497350ab196c73cfb78f6fb413199b7efa3","detected_licenses":["LicenseRef-scancode-unknown-license-reference","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3997,"license_type":"permissive","max_line_length":90,"num_lines":128,"path":"/lbrynet/analytics/events.py","repo_name":"le-dit-gaga/lbry","src_encoding":"UTF-8","text":"import logging\n\nfrom lbrynet.core import utils\nfrom lbrynet.conf import LBRYUM_WALLET\n\nlog = logging.getLogger(__name__)\n\n\ndef get_sd_hash(stream_info):\n if not stream_info:\n return None\n try:\n return stream_info['sources']['lbry_sd_hash']\n except (KeyError, TypeError, ValueError):\n log.debug('Failed to get sd_hash from %s', stream_info, exc_info=True)\n return None\n\n\nclass Events(object):\n def __init__(self, context, installation_id, session_id):\n \"\"\"Contains all of the analytics events that can be sent\n\n Attributes:\n context: usually the output of `make_context`\n installation_id: id unique to this installation. Can be anything, but\n generally should be base58 encoded.\n session_id: id for tracking events during this session. Can be\n anything, but generally should be base58 encoded.\n \"\"\"\n self.context = context\n self.installation_id = installation_id\n self.session_id = session_id\n\n def update_context(self, context):\n self.context = context\n\n def server_startup(self):\n return self._event('Server Startup')\n\n def server_startup_success(self):\n return self._event('Server Startup Success')\n\n def server_startup_error(self, message):\n return self._event('Server Startup Error', {\n 'message': message,\n })\n\n def heartbeat(self):\n return self._event('Heartbeat')\n\n def download_started(self, *args, **kwargs):\n properties = download_properties(*args, **kwargs)\n return self._event('Download Started', properties)\n\n def download_errored(self, *args, **kwargs):\n properties = download_properties(*args, **kwargs)\n return self._event('Download Errored', properties)\n\n def download_finished(self, *args, **kwargs):\n properties = download_properties(*args, **kwargs)\n return self._event('Download Finished', properties)\n\n def error(self, log_record):\n \"\"\"Record when a log message of ERROR or higher was emitted\"\"\"\n properties = {\n 'message': log_record.message,\n 'module': log_record.module,\n 'lineno': log_record.lineno,\n 'name': log_record.name,\n 'traceback': log_record.exc_text,\n }\n return self._event('Error', properties)\n\n def metric_observed(self, metric_name, value):\n properties = {\n 'value': value,\n }\n return self._event(metric_name, properties)\n\n def _event(self, event, event_properties=None):\n return {\n 'userId': 'lbry',\n 'event': event,\n 'properties': self._properties(event_properties),\n 'context': self.context,\n 'timestamp': utils.isonow()\n }\n\n def _properties(self, event_properties=None):\n event_properties = event_properties or {}\n properties = {\n 'lbry_id': self.installation_id,\n 'session_id': self.session_id,\n }\n properties.update(event_properties)\n return properties\n\n\ndef make_context(platform, wallet):\n return {\n 'app': {\n 'name': 'lbrynet',\n 'version': platform['lbrynet_version'],\n 'python_version': platform['python_version'],\n 'build': platform['build'],\n 'wallet': {\n 'name': wallet,\n 'version': platform['lbryum_version'] if wallet == LBRYUM_WALLET else None\n },\n },\n # TODO: expand os info to give linux/osx specific info\n 'os': {\n 'name': platform['os_system'],\n 'version': platform['os_release']\n },\n 'library': {\n 'name': 'lbrynet-analytics',\n 'version': '1.0.0'\n },\n }\n\n\ndef download_properties(id_, name, stream_info=None):\n return {\n 'download_id': id_,\n 'name': name,\n 'stream_info': get_sd_hash(stream_info)\n }\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6030820608139038,\n \"alphanum_fraction\": 0.6043315529823303,\n \"avg_line_length\": 28.64197540283203,\n \"blob_id\": \"71db5ea6c575c36b6f410f36bd00725c0158c8eb\",\n \"content_id\": \"ec3d70f124ee18cb577493efbbe1901d506c8f1c\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2401,\n \"license_type\": \"permissive\",\n \"max_line_length\": 84,\n \"num_lines\": 81,\n \"path\": \"/lbrynet/analytics/api.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import functools\\nimport json\\nimport logging\\n\\nfrom requests import auth\\nfrom txrequests import Session\\n\\nfrom lbrynet import conf\\nfrom lbrynet.analytics import utils\\n\\nlog = logging.getLogger(__name__)\\n\\n\\ndef log_response(fn):\\n def _log_error(failure):\\n log.warning('Failed to send an analytics event. %s', failure.getTraceback())\\n\\n @functools.wraps(fn)\\n def wrapper(*args, **kwargs):\\n d = fn(*args, **kwargs)\\n d.addErrback(_log_error)\\n return d\\n\\n return wrapper\\n\\n\\nclass Api(object):\\n def __init__(self, session, url, write_key):\\n self.session = session\\n self.url = url\\n self.write_key = write_key\\n\\n def post(self, endpoint, data):\\n # there is an issue with a timing condition with keep-alive\\n # that is best explained here: https://github.com/mikem23/keepalive-race\\n #\\n # If you make a request, wait just the right amount of time,\\n # then make another request, the requests module may opt to\\n # reuse the connection, but by the time the server gets it the\\n # timeout will have expired.\\n #\\n # by forcing the connection to close, we will disable the keep-alive.\\n assert endpoint[0] == '/'\\n headers = {\\\"Connection\\\": \\\"close\\\"}\\n return self.session.post(\\n self.url + endpoint, json=data, auth=self.auth, headers=headers)\\n\\n @property\\n def auth(self):\\n return auth.HTTPBasicAuth(self.write_key, '')\\n\\n @log_response\\n def batch(self, events):\\n \\\"\\\"\\\"Send multiple events in one request.\\n\\n Each event needs to have its type specified.\\n \\\"\\\"\\\"\\n data = json.dumps({\\n 'batch': events,\\n 'sentAt': utils.now(),\\n })\\n log.debug('sending %s events', len(events))\\n log.debug('Data: %s', data)\\n return self.post('/batch', data)\\n\\n @log_response\\n def track(self, event):\\n \\\"\\\"\\\"Send a single tracking event\\\"\\\"\\\"\\n log.debug('Sending track event: %s', event)\\n return self.post('/track', event)\\n\\n @classmethod\\n def new_instance(cls, session=None):\\n \\\"\\\"\\\"Initialize an instance using values from the configuration\\\"\\\"\\\"\\n if not session:\\n session = Session()\\n return cls(\\n session,\\n conf.settings['ANALYTICS_ENDPOINT'],\\n utils.deobfuscate(conf.settings['ANALYTICS_TOKEN'])\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.5800642967224121,\n \"alphanum_fraction\": 0.6083601117134094,\n \"avg_line_length\": 39.921051025390625,\n \"blob_id\": \"f85cdf4975ab1cb674aee1deaf08143e41fb05cf\",\n \"content_id\": \"ac5ae41908ec1fe693f77d975f435f065c5d6a5d\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1555,\n \"license_type\": \"permissive\",\n \"max_line_length\": 126,\n \"num_lines\": 38,\n \"path\": \"/tests/unit/lbrynet_daemon/test_DaemonCLI.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from twisted.trial import unittest\\nfrom lbrynet.lbrynet_daemon import DaemonCLI\\n\\n\\nclass DaemonCLITests(unittest.TestCase):\\n def test_guess_type(self):\\n self.assertEqual('0.3.8', DaemonCLI.guess_type('0.3.8'))\\n self.assertEqual(0.3, DaemonCLI.guess_type('0.3'))\\n self.assertEqual(3, DaemonCLI.guess_type('3'))\\n self.assertEqual('VdNmakxFORPSyfCprAD/eDDPk5TY9QYtSA==', DaemonCLI.guess_type('VdNmakxFORPSyfCprAD/eDDPk5TY9QYtSA=='))\\n self.assertEqual(0.3, DaemonCLI.guess_type('0.3'))\\n self.assertEqual(True, DaemonCLI.guess_type('TRUE'))\\n self.assertEqual(True, DaemonCLI.guess_type('true'))\\n self.assertEqual(True, DaemonCLI.guess_type('True'))\\n self.assertEqual(False, DaemonCLI.guess_type('FALSE'))\\n self.assertEqual(False, DaemonCLI.guess_type('false'))\\n self.assertEqual(False, DaemonCLI.guess_type('False'))\\n\\n def test_get_params(self):\\n test_params = [\\n 'b64address=VdNmakxFORPSyfCprAD/eDDPk5TY9QYtSA==',\\n 'name=test',\\n 'amount=5.3',\\n 'n=5',\\n 'address=bY13xeAjLrsjP4KGETwStK2a9UgKgXVTXu',\\n 't=true',\\n 'f=False',\\n ]\\n test_r = {\\n 'b64address': 'VdNmakxFORPSyfCprAD/eDDPk5TY9QYtSA==',\\n 'name': 'test',\\n 'amount': 5.3,\\n 'n': 5,\\n 'address': 'bY13xeAjLrsjP4KGETwStK2a9UgKgXVTXu',\\n 't': True,\\n 'f': False,\\n }\\n self.assertDictEqual(test_r, DaemonCLI.get_params_from_kwargs(test_params))\\n\"\n },\n {\n \"alpha_fraction\": 0.6121758818626404,\n \"alphanum_fraction\": 0.6234498023986816,\n \"avg_line_length\": 21.743589401245117,\n \"blob_id\": \"9c0786f69c609bc3a7d577f9045eb358442a0a75\",\n \"content_id\": \"0e77b3cfe7855d3f05c56ae8c9aac20b197dea35\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 887,\n \"license_type\": \"permissive\",\n \"max_line_length\": 70,\n \"num_lines\": 39,\n \"path\": \"/lbrynet/metadata/Fee.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import logging\\nimport fee_schemas\\n\\nfrom lbrynet.metadata.StructuredDict import StructuredDict\\n\\nlog = logging.getLogger(__name__)\\n\\n\\nclass FeeValidator(StructuredDict):\\n def __init__(self, fee):\\n self._versions = [\\n ('0.0.1', fee_schemas.VER_001, None)\\n ]\\n\\n StructuredDict.__init__(self, fee, fee.get('ver', '0.0.1'))\\n\\n self.currency_symbol = self.keys()[0]\\n self.amount = self._get_amount()\\n self.address = self[self.currency_symbol]['address']\\n\\n def _get_amount(self):\\n amt = self[self.currency_symbol]['amount']\\n try:\\n return float(amt)\\n except TypeError:\\n log.error('Failed to convert fee amount %s to float', amt)\\n raise\\n\\n\\nclass LBCFeeValidator(StructuredDict):\\n pass\\n\\n\\nclass BTCFeeValidator(StructuredDict):\\n pass\\n\\n\\nclass USDFeeValidator(StructuredDict):\\n pass\\n\"\n },\n {\n \"alpha_fraction\": 0.6009801626205444,\n \"alphanum_fraction\": 0.6045911908149719,\n \"avg_line_length\": 41.60439682006836,\n \"blob_id\": \"df07479c2d0e65d1557f1f285362b336885c94db\",\n \"content_id\": \"2869ada6182212a1f9c7be912443d086ff59995a\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3877,\n \"license_type\": \"permissive\",\n \"max_line_length\": 107,\n \"num_lines\": 91,\n \"path\": \"/lbrynet/lbrylive/client/LiveStreamProgressManager.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# pylint: skip-file\\nimport logging\\nfrom lbrynet.core.client.StreamProgressManager import StreamProgressManager\\nfrom twisted.internet import defer\\n\\n\\nlog = logging.getLogger(__name__)\\n\\n\\nclass LiveStreamProgressManager(StreamProgressManager):\\n def __init__(self, finished_callback, blob_manager, download_manager, delete_blob_after_finished=False,\\n download_whole=True, max_before_skip_ahead=5):\\n self.download_whole = download_whole\\n self.max_before_skip_ahead = max_before_skip_ahead\\n StreamProgressManager.__init__(self, finished_callback, blob_manager, download_manager,\\n delete_blob_after_finished)\\n\\n ######### IProgressManager #########\\n\\n def stream_position(self):\\n blobs = self.download_manager.blobs\\n if not blobs:\\n return 0\\n else:\\n newest_known_blobnum = max(blobs.iterkeys())\\n position = newest_known_blobnum\\n oldest_relevant_blob_num = (max(0, newest_known_blobnum - self.max_before_skip_ahead + 1))\\n for i in xrange(newest_known_blobnum, oldest_relevant_blob_num - 1, -1):\\n if i in blobs and (not blobs[i].is_validated() and not i in self.provided_blob_nums):\\n position = i\\n return position\\n\\n def needed_blobs(self):\\n blobs = self.download_manager.blobs\\n stream_position = self.stream_position()\\n if blobs:\\n newest_known_blobnum = max(blobs.iterkeys())\\n else:\\n newest_known_blobnum = -1\\n blobs_needed = []\\n for i in xrange(stream_position, newest_known_blobnum + 1):\\n if i in blobs and not blobs[i].is_validated() and not i in self.provided_blob_nums:\\n blobs_needed.append(blobs[i])\\n return blobs_needed\\n\\n ######### internal #########\\n\\n def _output_loop(self):\\n\\n from twisted.internet import reactor\\n\\n if self.stopped is True:\\n if self.outputting_d is not None:\\n self.outputting_d.callback(True)\\n self.outputting_d = None\\n return\\n\\n blobs = self.download_manager.blobs\\n log.info(\\\"In _output_loop. last_blob_outputted: %s\\\", str(self.last_blob_outputted))\\n if blobs:\\n log.debug(\\\"Newest blob number: %s\\\", str(max(blobs.iterkeys())))\\n if self.outputting_d is None:\\n self.outputting_d = defer.Deferred()\\n\\n current_blob_num = self.last_blob_outputted + 1\\n\\n def finished_outputting_blob():\\n self.last_blob_outputted += 1\\n final_blob_num = self.download_manager.final_blob_num()\\n if final_blob_num is not None and final_blob_num == self.last_blob_outputted:\\n self._finished_outputting()\\n self.outputting_d.callback(True)\\n self.outputting_d = None\\n else:\\n reactor.callLater(0, self._output_loop)\\n\\n if current_blob_num in blobs and blobs[current_blob_num].is_validated():\\n log.info(\\\"Outputting blob %s\\\", str(current_blob_num))\\n self.provided_blob_nums.append(current_blob_num)\\n d = self.download_manager.handle_blob(current_blob_num)\\n d.addCallback(lambda _: finished_outputting_blob())\\n d.addCallback(lambda _: self._finished_with_blob(current_blob_num))\\n elif blobs and max(blobs.iterkeys()) > self.last_blob_outputted + self.max_before_skip_ahead - 1:\\n self.last_blob_outputted += 1\\n log.info(\\\"Skipping blob number %s due to knowing about blob number %s\\\",\\n str(self.last_blob_outputted), str(max(blobs.iterkeys())))\\n self._finished_with_blob(current_blob_num)\\n reactor.callLater(0, self._output_loop)\\n else:\\n self.outputting_d.callback(True)\\n self.outputting_d = None\\n\"\n },\n {\n \"alpha_fraction\": 0.5547069907188416,\n \"alphanum_fraction\": 0.6389495134353638,\n \"avg_line_length\": 57.86238479614258,\n \"blob_id\": \"b872fc3c3114a3030fa4f50f115bbb6852935127\",\n \"content_id\": \"0cf6d8168cc87a7c9147019e104746ae03b866c4\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 12832,\n \"license_type\": \"permissive\",\n \"max_line_length\": 413,\n \"num_lines\": 218,\n \"path\": \"/tests/unit/core/test_Metadata.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from twisted.trial import unittest\\nfrom jsonschema import ValidationError\\n\\nfrom lbrynet.core import Error\\nfrom lbrynet.metadata import Metadata\\n\\n\\nclass MetadataTest(unittest.TestCase):\\n def test_name_error_if_blank(self):\\n with self.assertRaises(AssertionError):\\n Metadata.verify_name_characters(\\\"\\\")\\n\\n def test_name_error_if_contains_bad_chrs(self):\\n with self.assertRaises(Error.InvalidName):\\n Metadata.verify_name_characters(\\\"wu tang\\\")\\n with self.assertRaises(Error.InvalidName):\\n Metadata.verify_name_characters(\\\"$wutang\\\")\\n with self.assertRaises(Error.InvalidName):\\n Metadata.verify_name_characters(\\\"#wutang\\\")\\n\\n def test_validation_error_if_no_metadata(self):\\n metadata = {}\\n with self.assertRaises(ValidationError):\\n Metadata.Metadata(metadata)\\n\\n def test_validation_error_if_source_is_missing(self):\\n metadata = {\\n 'license': 'Oscilloscope Laboratories',\\n 'description': 'Four couples meet for Sunday brunch only to discover they are stuck in a house together as the world may be about to end.',\\n 'language': 'en',\\n 'title': \\\"It's a Disaster\\\",\\n 'author': 'Written and directed by Todd Berger',\\n 'content-type': 'audio/mpeg',\\n 'thumbnail': 'http://ia.media-imdb.com/images/M/MV5BMTQwNjYzMTQ0Ml5BMl5BanBnXkFtZTcwNDUzODM5Nw@@._V1_SY1000_CR0,0,673,1000_AL_.jpg',\\n }\\n with self.assertRaises(ValidationError):\\n Metadata.Metadata(metadata)\\n\\n def test_metadata_works_without_fee(self):\\n metadata = {\\n 'license': 'Oscilloscope Laboratories',\\n 'description': 'Four couples meet for Sunday brunch only to discover they are stuck in a house together as the world may be about to end.',\\n 'language': 'en',\\n 'title': \\\"It's a Disaster\\\",\\n 'author': 'Written and directed by Todd Berger',\\n 'sources': {\\n 'lbry_sd_hash': '8d0d6ea64d09f5aa90faf5807d8a761c32a27047861e06f81f41e35623a348a4b0104052161d5f89cf190f9672bc4ead'},\\n 'content-type': 'audio/mpeg',\\n 'thumbnail': 'http://ia.media-imdb.com/images/M/MV5BMTQwNjYzMTQ0Ml5BMl5BanBnXkFtZTcwNDUzODM5Nw@@._V1_SY1000_CR0,0,673,1000_AL_.jpg',\\n }\\n m = Metadata.Metadata(metadata)\\n self.assertFalse('fee' in m)\\n\\n def test_validation_error_if_invalid_source(self):\\n metadata = {\\n 'license': 'Oscilloscope Laboratories',\\n 'description': 'Four couples meet for Sunday brunch only to discover they are stuck in a house together as the world may be about to end.',\\n 'language': 'en',\\n 'title': \\\"It's a Disaster\\\",\\n 'author': 'Written and directed by Todd Berger',\\n 'sources': {\\n 'fake': 'source'},\\n 'content-type': 'audio/mpeg',\\n 'thumbnail': 'http://ia.media-imdb.com/images/M/MV5BMTQwNjYzMTQ0Ml5BMl5BanBnXkFtZTcwNDUzODM5Nw@@._V1_SY1000_CR0,0,673,1000_AL_.jpg',\\n }\\n with self.assertRaises(ValidationError):\\n Metadata.Metadata(metadata)\\n\\n def test_validation_error_if_missing_v001_field(self):\\n metadata = {\\n 'license': 'Oscilloscope Laboratories',\\n 'fee': {'LBC': {'amount': 50.0, 'address': 'bRQJASJrDbFZVAvcpv3NoNWoH74LQd5JNV'}},\\n 'description': 'Four couples meet for Sunday brunch only to discover they are stuck in a house together as the world may be about to end.',\\n 'language': 'en',\\n 'author': 'Written and directed by Todd Berger',\\n 'sources': {\\n 'lbry_sd_hash': '8d0d6ea64d09f5aa90faf5807d8a761c32a27047861e06f81f41e35623a348a4b0104052161d5f89cf190f9672bc4ead'},\\n 'content-type': 'audio/mpeg',\\n 'thumbnail': 'http://ia.media-imdb.com/images/M/MV5BMTQwNjYzMTQ0Ml5BMl5BanBnXkFtZTcwNDUzODM5Nw@@._V1_SY1000_CR0,0,673,1000_AL_.jpg'\\n }\\n with self.assertRaises(ValidationError):\\n Metadata.Metadata(metadata)\\n\\n def test_version_is_001_if_all_fields_are_present(self):\\n metadata = {\\n 'license': 'Oscilloscope Laboratories',\\n 'description': 'Four couples meet for Sunday brunch only to discover they are stuck in a house together as the world may be about to end.',\\n 'language': 'en',\\n 'title': \\\"It's a Disaster\\\",\\n 'author': 'Written and directed by Todd Berger',\\n 'sources': {\\n 'lbry_sd_hash': '8d0d6ea64d09f5aa90faf5807d8a761c32a27047861e06f81f41e35623a348a4b0104052161d5f89cf190f9672bc4ead'},\\n 'content-type': 'audio/mpeg',\\n 'thumbnail': 'http://ia.media-imdb.com/images/M/MV5BMTQwNjYzMTQ0Ml5BMl5BanBnXkFtZTcwNDUzODM5Nw@@._V1_SY1000_CR0,0,673,1000_AL_.jpg',\\n }\\n m = Metadata.Metadata(metadata, migrate=False)\\n self.assertEquals('0.0.1', m.version)\\n\\n def test_validation_error_if_there_is_an_extra_field(self):\\n metadata = {\\n 'license': 'Oscilloscope Laboratories',\\n 'description': 'Four couples meet for Sunday brunch only to discover they are stuck in a house together as the world may be about to end.',\\n 'language': 'en',\\n 'title': \\\"It's a Disaster\\\",\\n 'author': 'Written and directed by Todd Berger',\\n 'sources': {\\n 'lbry_sd_hash': '8d0d6ea64d09f5aa90faf5807d8a761c32a27047861e06f81f41e35623a348a4b0104052161d5f89cf190f9672bc4ead'},\\n 'content-type': 'audio/mpeg',\\n 'thumbnail': 'http://ia.media-imdb.com/images/M/MV5BMTQwNjYzMTQ0Ml5BMl5BanBnXkFtZTcwNDUzODM5Nw@@._V1_SY1000_CR0,0,673,1000_AL_.jpg',\\n 'MYSTERYFIELD': '?'\\n }\\n with self.assertRaises(ValidationError):\\n Metadata.Metadata(metadata, migrate=False)\\n\\n def test_version_is_002_if_all_fields_are_present(self):\\n metadata = {\\n 'license': 'NASA',\\n 'fee': {'USD': {'amount': 0.01, 'address': 'baBYSK7CqGSn5KrEmNmmQwAhBSFgo6v47z'}},\\n 'ver': '0.0.2',\\n 'description': 'SDO captures images of the sun in 10 different wavelengths, each of which helps highlight a different temperature of solar material. Different temperatures can, in turn, show specific structures on the sun such as solar flares, which are gigantic explosions of light and x-rays, or coronal loops, which are stream of solar material travelling up and down looping magnetic field lines',\\n 'language': 'en',\\n 'author': 'The SDO Team, Genna Duberstein and Scott Wiessinger',\\n 'title': 'Thermonuclear Art',\\n 'sources': {\\n 'lbry_sd_hash': '8655f713819344980a9a0d67b198344e2c462c90f813e86f0c63789ab0868031f25c54d0bb31af6658e997e2041806eb'},\\n 'nsfw': False,\\n 'content-type': 'video/mp4',\\n 'thumbnail': 'https://svs.gsfc.nasa.gov/vis/a010000/a012000/a012034/Combined.00_08_16_17.Still004.jpg'\\n }\\n m = Metadata.Metadata(metadata, migrate=False)\\n self.assertEquals('0.0.2', m.version)\\n\\n def test_version_is_003_if_all_fields_are_present(self):\\n metadata = {\\n 'license': 'NASA',\\n 'fee': {'USD': {'amount': 0.01, 'address': 'baBYSK7CqGSn5KrEmNmmQwAhBSFgo6v47z'}},\\n 'ver': '0.0.3',\\n 'description': 'SDO captures images of the sun in 10 different wavelengths, each of which helps highlight a different temperature of solar material. Different temperatures can, in turn, show specific structures on the sun such as solar flares, which are gigantic explosions of light and x-rays, or coronal loops, which are stream of solar material travelling up and down looping magnetic field lines',\\n 'language': 'en',\\n 'author': 'The SDO Team, Genna Duberstein and Scott Wiessinger',\\n 'title': 'Thermonuclear Art',\\n 'sources': {\\n 'lbry_sd_hash': '8655f713819344980a9a0d67b198344e2c462c90f813e86f0c63789ab0868031f25c54d0bb31af6658e997e2041806eb'},\\n 'nsfw': False,\\n 'content_type': 'video/mp4',\\n 'thumbnail': 'https://svs.gsfc.nasa.gov/vis/a010000/a012000/a012034/Combined.00_08_16_17.Still004.jpg'\\n }\\n m = Metadata.Metadata(metadata, migrate=False)\\n self.assertEquals('0.0.3', m.version)\\n\\n def test_version_claimed_is_001_but_version_is_002(self):\\n metadata = {\\n 'license': 'NASA',\\n 'fee': {'USD': {'amount': 0.01, 'address': 'baBYSK7CqGSn5KrEmNmmQwAhBSFgo6v47z'}},\\n 'ver': '0.0.1',\\n 'description': 'SDO captures images of the sun in 10 different wavelengths, each of which helps highlight a different temperature of solar material. Different temperatures can, in turn, show specific structures on the sun such as solar flares, which are gigantic explosions of light and x-rays, or coronal loops, which are stream of solar material travelling up and down looping magnetic field lines',\\n 'language': 'en',\\n 'author': 'The SDO Team, Genna Duberstein and Scott Wiessinger',\\n 'title': 'Thermonuclear Art',\\n 'sources': {\\n 'lbry_sd_hash': '8655f713819344980a9a0d67b198344e2c462c90f813e86f0c63789ab0868031f25c54d0bb31af6658e997e2041806eb'},\\n 'nsfw': False,\\n 'content-type': 'video/mp4',\\n 'thumbnail': 'https://svs.gsfc.nasa.gov/vis/a010000/a012000/a012034/Combined.00_08_16_17.Still004.jpg'\\n }\\n with self.assertRaises(ValidationError):\\n Metadata.Metadata(metadata, migrate=False)\\n\\n def test_version_claimed_is_002_but_version_is_003(self):\\n metadata = {\\n 'license': 'NASA',\\n 'fee': {'USD': {'amount': 0.01, 'address': 'baBYSK7CqGSn5KrEmNmmQwAhBSFgo6v47z'}},\\n 'ver': '0.0.2',\\n 'description': 'SDO captures images of the sun in 10 different wavelengths, each of which helps highlight a different temperature of solar material. Different temperatures can, in turn, show specific structures on the sun such as solar flares, which are gigantic explosions of light and x-rays, or coronal loops, which are stream of solar material travelling up and down looping magnetic field lines',\\n 'language': 'en',\\n 'author': 'The SDO Team, Genna Duberstein and Scott Wiessinger',\\n 'title': 'Thermonuclear Art',\\n 'sources': {\\n 'lbry_sd_hash': '8655f713819344980a9a0d67b198344e2c462c90f813e86f0c63789ab0868031f25c54d0bb31af6658e997e2041806eb'},\\n 'nsfw': False,\\n 'content_type': 'video/mp4',\\n 'thumbnail': 'https://svs.gsfc.nasa.gov/vis/a010000/a012000/a012034/Combined.00_08_16_17.Still004.jpg'\\n }\\n with self.assertRaises(ValidationError):\\n Metadata.Metadata(metadata, migrate=False)\\n\\n def test_version_001_ports_to_003(self):\\n metadata = {\\n 'license': 'Oscilloscope Laboratories',\\n 'description': 'Four couples meet for Sunday brunch only to discover they are stuck in a house together as the world may be about to end.',\\n 'language': 'en',\\n 'title': \\\"It's a Disaster\\\",\\n 'author': 'Written and directed by Todd Berger',\\n 'sources': {\\n 'lbry_sd_hash': '8d0d6ea64d09f5aa90faf5807d8a761c32a27047861e06f81f41e35623a348a4b0104052161d5f89cf190f9672bc4ead'},\\n 'content-type': 'audio/mpeg',\\n 'thumbnail': 'http://ia.media-imdb.com/images/M/MV5BMTQwNjYzMTQ0Ml5BMl5BanBnXkFtZTcwNDUzODM5Nw@@._V1_SY1000_CR0,0,673,1000_AL_.jpg',\\n }\\n m = Metadata.Metadata(metadata, migrate=True)\\n self.assertEquals('0.0.3', m.version)\\n\\n def test_version_002_ports_to_003(self):\\n metadata = {\\n 'license': 'NASA',\\n 'fee': {'USD': {'amount': 0.01, 'address': 'baBYSK7CqGSn5KrEmNmmQwAhBSFgo6v47z'}},\\n 'ver': '0.0.2',\\n 'description': 'SDO captures images of the sun in 10 different wavelengths, each of which helps highlight a different temperature of solar material. Different temperatures can, in turn, show specific structures on the sun such as solar flares, which are gigantic explosions of light and x-rays, or coronal loops, which are stream of solar material travelling up and down looping magnetic field lines',\\n 'language': 'en',\\n 'author': 'The SDO Team, Genna Duberstein and Scott Wiessinger',\\n 'title': 'Thermonuclear Art',\\n 'sources': {\\n 'lbry_sd_hash': '8655f713819344980a9a0d67b198344e2c462c90f813e86f0c63789ab0868031f25c54d0bb31af6658e997e2041806eb'},\\n 'nsfw': False,\\n 'content-type': 'video/mp4',\\n 'thumbnail': 'https://svs.gsfc.nasa.gov/vis/a010000/a012000/a012034/Combined.00_08_16_17.Still004.jpg'\\n }\\n m = Metadata.Metadata(metadata, migrate=True)\\n self.assertEquals('0.0.3', m.version)\\n\"\n },\n {\n \"alpha_fraction\": 0.5833278298377991,\n \"alphanum_fraction\": 0.587092936038971,\n \"avg_line_length\": 42.62824249267578,\n \"blob_id\": \"3dd2535dc1e1cecc134bc94aab93c8d443e61be5\",\n \"content_id\": \"921c27dc7742724d2ccf07ab5c5c7e8ec8b3f375\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 15139,\n \"license_type\": \"permissive\",\n \"max_line_length\": 117,\n \"num_lines\": 347,\n \"path\": \"/lbrynet/lbrylive/client/LiveStreamMetadataHandler.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# pylint: skip-file\\nfrom collections import defaultdict\\nimport logging\\nfrom zope.interface import implements\\nfrom twisted.internet import defer\\nfrom twisted.python.failure import Failure\\nfrom lbrynet import conf\\nfrom lbrynet.core.client.ClientRequest import ClientRequest, ClientPaidRequest\\nfrom lbrynet.lbrylive.LiveBlob import LiveBlobInfo\\nfrom lbrynet.core.cryptoutils import get_lbry_hash_obj, verify_signature\\nfrom lbrynet.interfaces import IRequestCreator, IMetadataHandler\\nfrom lbrynet.core.Error import InsufficientFundsError, InvalidResponseError, RequestCanceledError\\nfrom lbrynet.core.Error import NoResponseError, ConnectionClosedBeforeResponseError\\n\\n\\nlog = logging.getLogger(__name__)\\n\\n\\nclass LiveStreamMetadataHandler(object):\\n implements(IRequestCreator, IMetadataHandler)\\n\\n def __init__(self, stream_hash, stream_info_manager, peer_finder, stream_pub_key, download_whole,\\n payment_rate_manager, wallet, download_manager, max_before_skip_ahead=None):\\n self.stream_hash = stream_hash\\n self.stream_info_manager = stream_info_manager\\n self.payment_rate_manager = payment_rate_manager\\n self.wallet = wallet\\n self.peer_finder = peer_finder\\n self.stream_pub_key = stream_pub_key\\n self.download_whole = download_whole\\n self.max_before_skip_ahead = max_before_skip_ahead\\n if self.download_whole is False:\\n assert self.max_before_skip_ahead is not None, \\\\\\n \\\"If download whole is False, max_before_skip_ahead must be set\\\"\\n self.download_manager = download_manager\\n self._peers = defaultdict(int) # {Peer: score}\\n self._protocol_prices = {}\\n self._final_blob_num = None\\n self._price_disagreements = [] # [Peer]\\n self._incompatible_peers = [] # [Peer]\\n\\n ######### IMetadataHandler #########\\n\\n def get_initial_blobs(self):\\n d = self.stream_info_manager.get_blobs_for_stream(self.stream_hash)\\n d.addCallback(self._format_initial_blobs_for_download_manager)\\n return d\\n\\n def final_blob_num(self):\\n return self._final_blob_num\\n\\n ######## IRequestCreator #########\\n\\n def send_next_request(self, peer, protocol):\\n if self._finished_discovery() is False and self._should_send_request_to(peer) is True:\\n p_r = None\\n if not self._price_settled(protocol):\\n p_r = self._get_price_request(peer, protocol)\\n d_r = self._get_discover_request(peer)\\n reserved_points = self._reserve_points(peer, protocol, d_r.max_pay_units)\\n if reserved_points is not None:\\n d1 = protocol.add_request(d_r)\\n d1.addCallback(self._handle_discover_response, peer, d_r)\\n d1.addBoth(self._pay_or_cancel_payment, protocol, reserved_points)\\n d1.addErrback(self._request_failed, peer)\\n if p_r is not None:\\n d2 = protocol.add_request(p_r)\\n d2.addCallback(self._handle_price_response, peer, p_r, protocol)\\n d2.addErrback(self._request_failed, peer)\\n return defer.succeed(True)\\n else:\\n return defer.fail(InsufficientFundsError())\\n return defer.succeed(False)\\n\\n def get_new_peers(self):\\n d = self._get_hash_for_peer_search()\\n d.addCallback(self._find_peers_for_hash)\\n return d\\n\\n ######### internal calls #########\\n\\n def _get_hash_for_peer_search(self):\\n r = None\\n if self._finished_discovery() is False:\\n r = self.stream_hash\\n log.debug(\\\"Info finder peer search response for stream %s: %s\\\", str(self.stream_hash), str(r))\\n return defer.succeed(r)\\n\\n def _find_peers_for_hash(self, h):\\n if h is None:\\n return None\\n else:\\n d = self.peer_finder.find_peers_for_blob(h)\\n\\n def choose_best_peers(peers):\\n bad_peers = self._get_bad_peers()\\n return [p for p in peers if not p in bad_peers]\\n\\n d.addCallback(choose_best_peers)\\n return d\\n\\n def _format_initial_blobs_for_download_manager(self, blob_infos):\\n infos = []\\n for blob_hash, blob_num, revision, iv, length, signature in blob_infos:\\n if blob_hash is not None:\\n infos.append(LiveBlobInfo(blob_hash, blob_num, length, iv, revision, signature))\\n else:\\n log.debug(\\\"Setting _final_blob_num to %s\\\", str(blob_num - 1))\\n self._final_blob_num = blob_num - 1\\n return infos\\n\\n def _should_send_request_to(self, peer):\\n if self._peers[peer] < -5.0:\\n return False\\n if peer in self._price_disagreements:\\n return False\\n return True\\n\\n def _get_bad_peers(self):\\n return [p for p in self._peers.iterkeys() if not self._should_send_request_to(p)]\\n\\n def _finished_discovery(self):\\n if self._get_discovery_params() is None:\\n return True\\n return False\\n\\n def _get_discover_request(self, peer):\\n discovery_params = self._get_discovery_params()\\n if discovery_params:\\n further_blobs_request = {}\\n reference, start, end, count = discovery_params\\n further_blobs_request['reference'] = reference\\n if start is not None:\\n further_blobs_request['start'] = start\\n if end is not None:\\n further_blobs_request['end'] = end\\n if count is not None:\\n further_blobs_request['count'] = count\\n else:\\n further_blobs_request['count'] = conf.settings['MAX_BLOB_INFOS_TO_REQUEST']\\n log.debug(\\\"Requesting %s blob infos from %s\\\", str(further_blobs_request['count']), str(peer))\\n r_dict = {'further_blobs': further_blobs_request}\\n response_identifier = 'further_blobs'\\n request = ClientPaidRequest(r_dict, response_identifier, further_blobs_request['count'])\\n return request\\n return None\\n\\n def _get_discovery_params(self):\\n log.debug(\\\"In _get_discovery_params\\\")\\n stream_position = self.download_manager.stream_position()\\n blobs = self.download_manager.blobs\\n if blobs:\\n last_blob_num = max(blobs.iterkeys())\\n else:\\n last_blob_num = -1\\n final_blob_num = self.final_blob_num()\\n if final_blob_num is not None:\\n last_blob_num = final_blob_num\\n if self.download_whole is False:\\n log.debug(\\\"download_whole is False\\\")\\n if final_blob_num is not None:\\n for i in xrange(stream_position, final_blob_num + 1):\\n if not i in blobs:\\n count = min(self.max_before_skip_ahead, (final_blob_num - i + 1))\\n return self.stream_hash, None, 'end', count\\n return None\\n else:\\n if blobs:\\n for i in xrange(stream_position, last_blob_num + 1):\\n if not i in blobs:\\n if i == 0:\\n return self.stream_hash, 'beginning', 'end', -1 * self.max_before_skip_ahead\\n else:\\n return self.stream_hash, blobs[i-1].blob_hash, 'end', -1 * self.max_before_skip_ahead\\n return self.stream_hash, blobs[last_blob_num].blob_hash, 'end', -1 * self.max_before_skip_ahead\\n else:\\n return self.stream_hash, None, 'end', -1 * self.max_before_skip_ahead\\n log.debug(\\\"download_whole is True\\\")\\n beginning = None\\n end = None\\n for i in xrange(stream_position, last_blob_num + 1):\\n if not i in blobs:\\n if beginning is None:\\n if i == 0:\\n beginning = 'beginning'\\n else:\\n beginning = blobs[i-1].blob_hash\\n else:\\n if beginning is not None:\\n end = blobs[i].blob_hash\\n break\\n if beginning is None:\\n if final_blob_num is not None:\\n log.debug(\\\"Discovery is finished. stream_position: %s, last_blob_num + 1: %s\\\", str(stream_position),\\n str(last_blob_num + 1))\\n return None\\n else:\\n log.debug(\\\"Discovery is not finished. final blob num is unknown.\\\")\\n if last_blob_num != -1:\\n return self.stream_hash, blobs[last_blob_num].blob_hash, None, None\\n else:\\n return self.stream_hash, 'beginning', None, None\\n else:\\n log.info(\\\"Discovery is not finished. Not all blobs are known.\\\")\\n return self.stream_hash, beginning, end, None\\n\\n def _price_settled(self, protocol):\\n if protocol in self._protocol_prices:\\n return True\\n return False\\n\\n def _update_local_score(self, peer, amount):\\n self._peers[peer] += amount\\n\\n def _reserve_points(self, peer, protocol, max_infos):\\n assert protocol in self._protocol_prices\\n point_amount = 1.0 * max_infos * self._protocol_prices[protocol] / 1000.0\\n return self.wallet.reserve_points(peer, point_amount)\\n\\n def _pay_or_cancel_payment(self, arg, protocol, reserved_points):\\n if isinstance(arg, Failure) or arg == 0:\\n self._cancel_points(reserved_points)\\n else:\\n self._pay_peer(protocol, arg, reserved_points)\\n return arg\\n\\n def _pay_peer(self, protocol, num_infos, reserved_points):\\n assert num_infos != 0\\n assert protocol in self._protocol_prices\\n point_amount = 1.0 * num_infos * self._protocol_prices[protocol] / 1000.0\\n self.wallet.send_points(reserved_points, point_amount)\\n self.payment_rate_manager.record_points_paid(point_amount)\\n\\n def _cancel_points(self, reserved_points):\\n return self.wallet.cancel_point_reservation(reserved_points)\\n\\n def _get_price_request(self, peer, protocol):\\n self._protocol_prices[protocol] = self.payment_rate_manager.get_rate_live_blob_info(peer)\\n request_dict = {'blob_info_payment_rate': self._protocol_prices[protocol]}\\n request = ClientRequest(request_dict, 'blob_info_payment_rate')\\n return request\\n\\n def _handle_price_response(self, response_dict, peer, request, protocol):\\n if not request.response_identifier in response_dict:\\n return InvalidResponseError(\\\"response identifier not in response\\\")\\n assert protocol in self._protocol_prices\\n response = response_dict[request.response_identifier]\\n if response == \\\"RATE_ACCEPTED\\\":\\n return True\\n else:\\n log.info(\\\"Rate offer has been rejected by %s\\\", str(peer))\\n del self._protocol_prices[protocol]\\n self._price_disagreements.append(peer)\\n return True\\n\\n def _handle_discover_response(self, response_dict, peer, request):\\n if not request.response_identifier in response_dict:\\n return InvalidResponseError(\\\"response identifier not in response\\\")\\n response = response_dict[request.response_identifier]\\n blob_infos = []\\n if 'error' in response:\\n if response['error'] == 'RATE_UNSET':\\n return defer.succeed(0)\\n else:\\n return InvalidResponseError(\\\"Got an unknown error from the peer: %s\\\" %\\n (response['error'],))\\n if not 'blob_infos' in response:\\n return InvalidResponseError(\\\"Missing the required field 'blob_infos'\\\")\\n raw_blob_infos = response['blob_infos']\\n log.info(\\\"Handling %s further blobs from %s\\\", str(len(raw_blob_infos)), str(peer))\\n log.debug(\\\"blobs: %s\\\", str(raw_blob_infos))\\n for raw_blob_info in raw_blob_infos:\\n length = raw_blob_info['length']\\n if length != 0:\\n blob_hash = raw_blob_info['blob_hash']\\n else:\\n blob_hash = None\\n num = raw_blob_info['blob_num']\\n revision = raw_blob_info['revision']\\n iv = raw_blob_info['iv']\\n signature = raw_blob_info['signature']\\n blob_info = LiveBlobInfo(blob_hash, num, length, iv, revision, signature)\\n log.debug(\\\"Learned about a potential blob: %s\\\", str(blob_hash))\\n if self._verify_blob(blob_info):\\n if blob_hash is None:\\n log.info(\\\"Setting _final_blob_num to %s\\\", str(num - 1))\\n self._final_blob_num = num - 1\\n else:\\n blob_infos.append(blob_info)\\n else:\\n raise ValueError(\\\"Peer sent an invalid blob info\\\")\\n d = self.stream_info_manager.add_blobs_to_stream(self.stream_hash, blob_infos)\\n\\n def add_blobs_to_download_manager():\\n blob_nums = [b.blob_num for b in blob_infos]\\n log.info(\\\"Adding the following blob nums to the download manager: %s\\\", str(blob_nums))\\n self.download_manager.add_blobs_to_download(blob_infos)\\n\\n d.addCallback(lambda _: add_blobs_to_download_manager())\\n\\n def pay_or_penalize_peer():\\n if len(blob_infos):\\n self._update_local_score(peer, len(blob_infos))\\n peer.update_stats('downloaded_crypt_blob_infos', len(blob_infos))\\n peer.update_score(len(blob_infos))\\n else:\\n self._update_local_score(peer, -.0001)\\n return len(blob_infos)\\n\\n d.addCallback(lambda _: pay_or_penalize_peer())\\n\\n return d\\n\\n def _verify_blob(self, blob):\\n log.debug(\\\"Got an unverified blob to check:\\\")\\n log.debug(\\\"blob_hash: %s\\\", blob.blob_hash)\\n log.debug(\\\"blob_num: %s\\\", str(blob.blob_num))\\n log.debug(\\\"revision: %s\\\", str(blob.revision))\\n log.debug(\\\"iv: %s\\\", blob.iv)\\n log.debug(\\\"length: %s\\\", str(blob.length))\\n hashsum = get_lbry_hash_obj()\\n hashsum.update(self.stream_hash)\\n if blob.length != 0:\\n hashsum.update(blob.blob_hash)\\n hashsum.update(str(blob.blob_num))\\n hashsum.update(str(blob.revision))\\n hashsum.update(blob.iv)\\n hashsum.update(str(blob.length))\\n log.debug(\\\"hexdigest to be verified: %s\\\", hashsum.hexdigest())\\n if verify_signature(hashsum.digest(), blob.signature, self.stream_pub_key):\\n log.debug(\\\"Blob info is valid\\\")\\n return True\\n else:\\n log.debug(\\\"The blob info is invalid\\\")\\n return False\\n\\n def _request_failed(self, reason, peer):\\n if reason.check(RequestCanceledError):\\n return\\n if reason.check(NoResponseError):\\n self._incompatible_peers.append(peer)\\n log.warning(\\\"Crypt stream info finder: a request failed. Reason: %s\\\", reason.getErrorMessage())\\n self._update_local_score(peer, -5.0)\\n peer.update_score(-10.0)\\n if reason.check(ConnectionClosedBeforeResponseError):\\n return\\n return reason\\n\"\n },\n {\n \"alpha_fraction\": 0.6459190845489502,\n \"alphanum_fraction\": 0.6480988264083862,\n \"avg_line_length\": 39.48039245605469,\n \"blob_id\": \"dcecd2e92d0340a5f61dccc5f3a1abcf705000a2\",\n \"content_id\": \"86aad2f7cc358a2a0f865382d2450eea5f6cf2d0\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4129,\n \"license_type\": \"permissive\",\n \"max_line_length\": 117,\n \"num_lines\": 102,\n \"path\": \"/lbrynet/lbrylive/StdoutDownloader.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# pylint: skip-file\\n# pylint: skip-file\\n# This file is not maintained, but might be used in the future\\n#\\nimport logging\\nimport sys\\n\\nfrom lbrynet.lbrylive.client.LiveStreamDownloader import LiveStreamDownloader\\nfrom lbrynet.core.BlobManager import TempBlobManager\\nfrom lbrynet.core.Session import Session\\nfrom lbrynet.core.client.StandaloneBlobDownloader import StandaloneBlobDownloader\\nfrom lbrynet.core.StreamDescriptor import BlobStreamDescriptorReader\\nfrom lbrynet.lbrylive.PaymentRateManager import BaseLiveStreamPaymentRateManager\\nfrom lbrynet.lbrylive.LiveStreamMetadataManager import DBLiveStreamMetadataManager\\nfrom lbrynet.lbrylive.StreamDescriptor import save_sd_info\\nfrom lbrynet.dht.node import Node\\nfrom twisted.internet import task\\n\\n\\nclass StdoutDownloader():\\n \\\"\\\"\\\"This class downloads a live stream from the network and outputs it to standard out.\\\"\\\"\\\"\\n def __init__(self, dht_node_port, known_dht_nodes,\\n stream_info_manager_class=DBLiveStreamMetadataManager, blob_manager_class=TempBlobManager):\\n \\\"\\\"\\\"\\n @param dht_node_port: the network port on which to listen for DHT node requests\\n\\n @param known_dht_nodes: a list of (ip_address, dht_port) which will be used to join the DHT network\\n\\n \\\"\\\"\\\"\\n\\n self.session = Session(blob_manager_class=blob_manager_class,\\n stream_info_manager_class=stream_info_manager_class,\\n dht_node_class=Node, dht_node_port=dht_node_port, known_dht_nodes=known_dht_nodes,\\n use_upnp=False)\\n self.payment_rate_manager = BaseLiveStreamPaymentRateManager()\\n\\n def start(self):\\n \\\"\\\"\\\"Initialize the session\\\"\\\"\\\"\\n d = self.session.setup()\\n return d\\n\\n def read_sd_file(self, sd_blob):\\n reader = BlobStreamDescriptorReader(sd_blob)\\n return save_sd_info(self.stream_info_manager, reader, ignore_duplicate=True)\\n\\n def download_sd_file_from_hash(self, sd_hash):\\n downloader = StandaloneBlobDownloader(sd_hash, self.session.blob_manager,\\n self.session.peer_finder, self.session.rate_limiter,\\n self.session.wallet)\\n d = downloader.download()\\n return d\\n\\n def start_download(self, sd_hash):\\n \\\"\\\"\\\"Start downloading the stream from the network and outputting it to standard out\\\"\\\"\\\"\\n d = self.download_sd_file_from_hash(sd_hash)\\n d.addCallbacks(self.read_sd_file)\\n\\n def start_stream(stream_hash):\\n consumer = LiveStreamDownloader(stream_hash, self.session.peer_finder,\\n self.session.rate_limiter, self.session.blob_manager,\\n self.stream_info_manager, self.payment_rate_manager,\\n self.session.wallet)\\n return consumer.start()\\n\\n d.addCallback(start_stream)\\n return d\\n\\n def shut_down(self):\\n \\\"\\\"\\\"End the session\\\"\\\"\\\"\\n d = self.session.shut_down()\\n return d\\n\\n\\ndef launch_stdout_downloader():\\n\\n from twisted.internet import reactor\\n\\n logging.basicConfig(level=logging.WARNING, filename=\\\"dl.log\\\")\\n if len(sys.argv) == 3:\\n downloader = StdoutDownloader(int(sys.argv[2]), [])\\n elif len(sys.argv) == 5:\\n downloader = StdoutDownloader(int(sys.argv[2]), [(sys.argv[3], int(sys.argv[4]))])\\n else:\\n print \\\"Usage: lbrynet-stdout-downloader <sd_hash> <peer_port> <dht_node_port>\\\" \\\\\\n \\\" [<dht_bootstrap_host> <dht_bootstrap port>]\\\"\\n sys.exit(1)\\n\\n def start_stdout_downloader():\\n return downloader.start_download(sys.argv[1])\\n\\n def print_error(err):\\n logging.warning(err.getErrorMessage())\\n\\n def shut_down():\\n reactor.stop()\\n\\n d = task.deferLater(reactor, 0, downloader.start)\\n d.addCallback(lambda _: start_stdout_downloader())\\n d.addErrback(print_error)\\n d.addCallback(lambda _: shut_down())\\n reactor.addSystemEventTrigger('before', 'shutdown', downloader.shut_down)\\n reactor.run()\\n\"\n },\n {\n \"alpha_fraction\": 0.6639838814735413,\n \"alphanum_fraction\": 0.6643863320350647,\n \"avg_line_length\": 39.73770523071289,\n \"blob_id\": \"fa28390b8ef3138279dd05d3e52e291a248fd310\",\n \"content_id\": \"dd272e5e68d0c9afd8c0b4885e32e42ed16cd19b\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2485,\n \"license_type\": \"permissive\",\n \"max_line_length\": 97,\n \"num_lines\": 61,\n \"path\": \"/lbrynet/lbrynet_daemon/Publisher.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import logging\\nimport mimetypes\\nimport os\\n\\nfrom twisted.internet import defer\\nfrom lbrynet.core import file_utils\\nfrom lbrynet.lbryfilemanager.EncryptedFileCreator import create_lbry_file\\nfrom lbrynet.lbryfile.StreamDescriptor import publish_sd_blob\\nfrom lbrynet.metadata.Metadata import Metadata\\n\\n\\nlog = logging.getLogger(__name__)\\n\\n\\nclass Publisher(object):\\n def __init__(self, session, lbry_file_manager, wallet):\\n self.session = session\\n self.lbry_file_manager = lbry_file_manager\\n self.wallet = wallet\\n self.lbry_file = None\\n\\n @defer.inlineCallbacks\\n def publish_stream(self, name, file_path, bid, metadata):\\n log.info('Starting publish for %s', name)\\n file_name = os.path.basename(file_path)\\n with file_utils.get_read_handle(file_path) as read_handle:\\n stream_hash = yield create_lbry_file(self.session, self.lbry_file_manager, file_name,\\n read_handle)\\n prm = self.session.payment_rate_manager\\n self.lbry_file = yield self.lbry_file_manager.add_lbry_file(stream_hash, prm)\\n sd_hash = yield publish_sd_blob(self.lbry_file_manager.stream_info_manager,\\n self.session.blob_manager, self.lbry_file.stream_hash)\\n if 'sources' not in metadata:\\n metadata['sources'] = {}\\n metadata['sources']['lbry_sd_hash'] = sd_hash\\n metadata['content_type'] = get_content_type(file_path)\\n metadata['ver'] = Metadata.current_version\\n claim_out = yield self.make_claim(name, bid, metadata)\\n self.lbry_file.completed = True\\n yield self.lbry_file.load_file_attributes()\\n yield self.lbry_file.save_status()\\n defer.returnValue(claim_out)\\n\\n @defer.inlineCallbacks\\n def update_stream(self, name, bid, metadata):\\n my_claim = yield self.wallet.get_my_claim(name)\\n updated_metadata = my_claim['value']\\n for meta_key in metadata:\\n updated_metadata[meta_key] = metadata[meta_key]\\n claim_out = yield self.make_claim(name, bid, updated_metadata)\\n defer.returnValue(claim_out)\\n\\n @defer.inlineCallbacks\\n def make_claim(self, name, bid, metadata):\\n validated_metadata = Metadata(metadata)\\n claim_out = yield self.wallet.claim_name(name, bid, validated_metadata)\\n defer.returnValue(claim_out)\\n\\n\\ndef get_content_type(filename):\\n return mimetypes.guess_type(filename)[0] or 'application/octet-stream'\\n\"\n },\n {\n \"alpha_fraction\": 0.5804196000099182,\n \"alphanum_fraction\": 0.5804196000099182,\n \"avg_line_length\": 10.916666984558105,\n \"blob_id\": \"384c18dbc1a329096d767adc86466c1fdcbe4e30\",\n \"content_id\": \"c98eae7b2850bb2fa80a4bd5e3152b0c1be42fe0\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 143,\n \"license_type\": \"permissive\",\n \"max_line_length\": 27,\n \"num_lines\": 12,\n \"path\": \"/run_tests.sh\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n\\nset -o xtrace\\n\\nif [ -z \\\"$@\\\" ]; then\\n TESTS=tests\\nelse\\n TESTS=\\\"$@\\\"\\nfi\\n\\nfind -iname \\\"*.pyc\\\" -delete\\nPYTHONPATH=. trial $TESTS\\n\"\n },\n {\n \"alpha_fraction\": 0.7478991746902466,\n \"alphanum_fraction\": 0.7478991746902466,\n \"avg_line_length\": 28.75,\n \"blob_id\": \"847600997caa326c30aa15b3f40dbd5d643f1367\",\n \"content_id\": \"280b51a721c737c01646c12c6f86a91383e49e7b\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 119,\n \"license_type\": \"permissive\",\n \"max_line_length\": 45,\n \"num_lines\": 4,\n \"path\": \"/lbrynet/analytics/constants.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Constants for metrics\\\"\\\"\\\"\\n\\nBLOB_BYTES_UPLOADED = 'Blob Bytes Uploaded'\\nBLOB_BYTES_AVAILABLE = 'Blob Bytes Available'\\n\"\n },\n {\n \"alpha_fraction\": 0.6733220219612122,\n \"alphanum_fraction\": 0.6741716265678406,\n \"avg_line_length\": 43.41509246826172,\n \"blob_id\": \"7a3caaf25cacdea6d34ab8f49583c92f628ead6e\",\n \"content_id\": \"8b87ea6e3475ba03cb9e7af796c20abc7ad5413a\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2354,\n \"license_type\": \"permissive\",\n \"max_line_length\": 94,\n \"num_lines\": 53,\n \"path\": \"/lbrynet/lbrylive/PaymentRateManager.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# pylint: skip-file\\nclass BaseLiveStreamPaymentRateManager(object):\\n def __init__(self, blob_info_rate, blob_data_rate=None):\\n self.min_live_blob_info_payment_rate = blob_info_rate\\n self.min_blob_data_payment_rate = blob_data_rate\\n\\n\\nclass LiveStreamPaymentRateManager(object):\\n def __init__(self, base_live_stream_payment_rate_manager, payment_rate_manager,\\n blob_info_rate=None, blob_data_rate=None):\\n self._base_live_stream_payment_rate_manager = base_live_stream_payment_rate_manager\\n self._payment_rate_manager = payment_rate_manager\\n self.min_live_blob_info_payment_rate = blob_info_rate\\n self.min_blob_data_payment_rate = blob_data_rate\\n self.points_paid = 0.0\\n\\n def get_rate_live_blob_info(self, peer):\\n return self.get_effective_min_live_blob_info_payment_rate()\\n\\n def accept_rate_live_blob_info(self, peer, payment_rate):\\n return payment_rate >= self.get_effective_min_live_blob_info_payment_rate()\\n\\n def get_rate_blob_data(self, peer, blobs):\\n response = self._payment_rate_manager.strategy.make_offer(peer, blobs)\\n return response.rate\\n\\n def accept_rate_blob_data(self, peer, blobs, offer):\\n response = self._payment_rate_manager.strategy.respond_to_offer(offer, peer, blobs)\\n return response.accepted\\n\\n def reply_to_offer(self, peer, blobs, offer):\\n reply = self._payment_rate_manager.strategy.respond_to_offer(offer, peer, blobs)\\n self._payment_rate_manager.strategy.offer_accepted(peer, reply)\\n return reply\\n\\n def get_effective_min_blob_data_payment_rate(self):\\n rate = self.min_blob_data_payment_rate\\n if rate is None:\\n rate = self._payment_rate_manager.min_blob_data_payment_rate\\n if rate is None:\\n rate = self._base_live_stream_payment_rate_manager.min_blob_data_payment_rate\\n if rate is None:\\n rate = self._payment_rate_manager.get_effective_min_blob_data_payment_rate()\\n return rate\\n\\n def get_effective_min_live_blob_info_payment_rate(self):\\n rate = self.min_live_blob_info_payment_rate\\n if rate is None:\\n rate = self._base_live_stream_payment_rate_manager.min_live_blob_info_payment_rate\\n return rate\\n\\n def record_points_paid(self, amount):\\n self.points_paid += amount\\n\"\n },\n {\n \"alpha_fraction\": 0.6218905448913574,\n \"alphanum_fraction\": 0.6231343150138855,\n \"avg_line_length\": 26.724138259887695,\n \"blob_id\": \"055db479f55db60cd9943c3fde8cbd4497051d51\",\n \"content_id\": \"87f2d143349982640b27f51543083cac461929ce\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 804,\n \"license_type\": \"permissive\",\n \"max_line_length\": 75,\n \"num_lines\": 29,\n \"path\": \"/lbrynet/core/system_info.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import platform\\nimport json\\n\\nfrom urllib2 import urlopen\\n\\nfrom lbrynet import __version__ as lbrynet_version\\nfrom lbrynet import build_type\\nfrom lbryum.version import LBRYUM_VERSION as lbryum_version\\n\\n\\ndef get_platform(get_ip=True):\\n p = {\\n \\\"processor\\\": platform.processor(),\\n \\\"python_version\\\": platform.python_version(),\\n \\\"platform\\\": platform.platform(),\\n \\\"os_release\\\": platform.release(),\\n \\\"os_system\\\": platform.system(),\\n \\\"lbrynet_version\\\": lbrynet_version,\\n \\\"lbryum_version\\\": lbryum_version,\\n \\\"build\\\": build_type.BUILD, # CI server sets this during build step\\n }\\n\\n if get_ip:\\n try:\\n p['ip'] = json.load(urlopen('http://jsonip.com'))['ip']\\n except:\\n p['ip'] = \\\"Could not determine IP\\\"\\n\\n return p\\n\"\n },\n {\n \"alpha_fraction\": 0.5905511975288391,\n \"alphanum_fraction\": 0.5905511975288391,\n \"avg_line_length\": 25.45833396911621,\n \"blob_id\": \"6561d13e2686a77ee05fce6c033b1cd973ff2817\",\n \"content_id\": \"7643ebce9e0ae81e3aa4b84bef18593bd4c4316b\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 635,\n \"license_type\": \"permissive\",\n \"max_line_length\": 57,\n \"num_lines\": 24,\n \"path\": \"/lbrynet/analytics/track.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import collections\\n\\n\\nclass Track(object):\\n \\\"\\\"\\\"Track and summarize observations of metrics.\\\"\\\"\\\"\\n def __init__(self):\\n self.data = collections.defaultdict(list)\\n\\n def add_observation(self, metric, value):\\n self.data[metric].append(value)\\n\\n def summarize_and_reset(self, metric, op=sum):\\n \\\"\\\"\\\"Apply `op` on the current values for `metric`.\\n\\n This operation also resets the metric.\\n\\n Returns:\\n a tuple (should_send, value)\\n \\\"\\\"\\\"\\n try:\\n values = self.data.pop(metric)\\n return True, op(values)\\n except KeyError:\\n return False, None\\n\"\n },\n {\n \"alpha_fraction\": 0.6789524555206299,\n \"alphanum_fraction\": 0.6808923482894897,\n \"avg_line_length\": 41.95833206176758,\n \"blob_id\": \"a79ee9f45100e974d5c34ee64f915ee37ad2f33d\",\n \"content_id\": \"46bf54f7a1c0faad8e29f66178d3dd9a614da5b3\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1031,\n \"license_type\": \"permissive\",\n \"max_line_length\": 93,\n \"num_lines\": 24,\n \"path\": \"/lbrynet/lbrylive/LiveBlob.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# pylint: skip-file\\nfrom lbrynet.cryptstream.CryptBlob import CryptStreamBlobMaker, CryptBlobInfo\\nimport binascii\\n\\n\\nclass LiveBlobInfo(CryptBlobInfo):\\n def __init__(self, blob_hash, blob_num, length, iv, revision, signature):\\n CryptBlobInfo.__init__(self, blob_hash, blob_num, length, iv)\\n self.revision = revision\\n self.signature = signature\\n\\n\\nclass LiveStreamBlobMaker(CryptStreamBlobMaker):\\n def __init__(self, key, iv, blob_num, blob):\\n CryptStreamBlobMaker.__init__(self, key, iv, blob_num, blob)\\n # The following is a placeholder for a currently unimplemented feature.\\n # In the future it may be possible for the live stream creator to overwrite a blob\\n # with a newer revision. If that happens, the 0 will be incremented to the\\n # actual revision count\\n self.revision = 0\\n\\n def _return_info(self, blob_hash):\\n return LiveBlobInfo(blob_hash, self.blob_num, self.length, binascii.hexlify(self.iv),\\n self.revision, None)\\n\"\n },\n {\n \"alpha_fraction\": 0.8235294222831726,\n \"alphanum_fraction\": 0.8235294222831726,\n \"avg_line_length\": 24.5,\n \"blob_id\": \"1e028a66dbdf366b81f80fdf7f40d9e96e7e3ef6\",\n \"content_id\": \"1aafe01b46d7f258abf32cde6de250a2d2917385\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 153,\n \"license_type\": \"permissive\",\n \"max_line_length\": 35,\n \"num_lines\": 6,\n \"path\": \"/lbrynet/analytics/__init__.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from constants import *\\nfrom events import *\\nfrom api import Api\\nfrom track import Track\\nfrom manager import Manager\\nfrom logging_handler import Handler\\n\"\n },\n {\n \"alpha_fraction\": 0.5729424953460693,\n \"alphanum_fraction\": 0.5752821564674377,\n \"avg_line_length\": 38.489131927490234,\n \"blob_id\": \"a229599519524df5cde67f469778f0c0245f27e2\",\n \"content_id\": \"2fce90b6b3676aaf484a7aad18ca820890a67ad0\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7266,\n \"license_type\": \"permissive\",\n \"max_line_length\": 106,\n \"num_lines\": 184,\n \"path\": \"/lbrynet/lbrylive/server/LiveBlobInfoQueryHandler.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# pylint: skip-file\\nimport logging\\nfrom twisted.internet import defer\\nfrom zope.interface import implements\\nfrom lbrynet.interfaces import IQueryHandlerFactory, IQueryHandler\\n\\n\\nlog = logging.getLogger(__name__)\\n\\n\\nclass CryptBlobInfoQueryHandlerFactory(object):\\n implements(IQueryHandlerFactory)\\n\\n def __init__(self, stream_info_manager, wallet, payment_rate_manager):\\n self.stream_info_manager = stream_info_manager\\n self.wallet = wallet\\n self.payment_rate_manager = payment_rate_manager\\n\\n ######### IQueryHandlerFactory #########\\n\\n def build_query_handler(self):\\n q_h = CryptBlobInfoQueryHandler(self.stream_info_manager, self.wallet, self.payment_rate_manager)\\n return q_h\\n\\n def get_primary_query_identifier(self):\\n return 'further_blobs'\\n\\n def get_description(self):\\n return (\\\"Stream Blob Information - blob hashes that are associated with streams,\\\"\\n \\\" and the blobs' associated metadata\\\")\\n\\n\\nclass CryptBlobInfoQueryHandler(object):\\n implements(IQueryHandler)\\n\\n def __init__(self, stream_info_manager, wallet, payment_rate_manager):\\n self.stream_info_manager = stream_info_manager\\n self.wallet = wallet\\n self.payment_rate_manager = payment_rate_manager\\n self.query_identifiers = ['blob_info_payment_rate', 'further_blobs']\\n self.blob_info_payment_rate = None\\n self.peer = None\\n\\n ######### IQueryHandler #########\\n\\n def register_with_request_handler(self, request_handler, peer):\\n self.peer = peer\\n request_handler.register_query_handler(self, self.query_identifiers)\\n\\n def handle_queries(self, queries):\\n response = {}\\n\\n if self.query_identifiers[0] in queries:\\n if not self.handle_blob_info_payment_rate(queries[self.query_identifiers[0]]):\\n return defer.succeed({'blob_info_payment_rate': 'RATE_TOO_LOW'})\\n else:\\n response['blob_info_payment_rate'] = \\\"RATE_ACCEPTED\\\"\\n\\n if self.query_identifiers[1] in queries:\\n further_blobs_request = queries[self.query_identifiers[1]]\\n log.debug(\\\"Received the client's request for additional blob information\\\")\\n\\n if self.blob_info_payment_rate is None:\\n response['further_blobs'] = {'error': 'RATE_UNSET'}\\n return defer.succeed(response)\\n\\n def count_and_charge(blob_infos):\\n if len(blob_infos) != 0:\\n log.info(\\\"Responding with %s infos\\\", str(len(blob_infos)))\\n expected_payment = 1.0 * len(blob_infos) * self.blob_info_payment_rate / 1000.0\\n self.wallet.add_expected_payment(self.peer, expected_payment)\\n self.peer.update_stats('uploaded_crypt_blob_infos', len(blob_infos))\\n return blob_infos\\n\\n def set_field(further_blobs):\\n response['further_blobs'] = {'blob_infos': further_blobs}\\n return response\\n\\n def get_further_blobs(stream_hash):\\n if stream_hash is None:\\n response['further_blobs'] = {'error': 'REFERENCE_HASH_UNKNOWN'}\\n return defer.succeed(response)\\n start = further_blobs_request.get(\\\"start\\\")\\n end = further_blobs_request.get(\\\"end\\\")\\n count = further_blobs_request.get(\\\"count\\\")\\n if count is not None:\\n try:\\n count = int(count)\\n except ValueError:\\n response['further_blobs'] = {'error': 'COUNT_NON_INTEGER'}\\n return defer.succeed(response)\\n\\n if len([x for x in [start, end, count] if x is not None]) < 2:\\n response['further_blobs'] = {'error': 'TOO_FEW_PARAMETERS'}\\n return defer.succeed(response)\\n\\n inner_d = self.get_further_blobs(stream_hash, start, end, count)\\n\\n inner_d.addCallback(count_and_charge)\\n inner_d.addCallback(self.format_blob_infos)\\n inner_d.addCallback(set_field)\\n return inner_d\\n\\n if 'reference' in further_blobs_request:\\n d = self.get_stream_hash_from_reference(further_blobs_request['reference'])\\n d.addCallback(get_further_blobs)\\n return d\\n else:\\n response['further_blobs'] = {'error': 'NO_REFERENCE_SENT'}\\n return defer.succeed(response)\\n else:\\n return defer.succeed({})\\n\\n ######### internal #########\\n\\n def handle_blob_info_payment_rate(self, requested_payment_rate):\\n if not self.payment_rate_manager.accept_rate_live_blob_info(self.peer, requested_payment_rate):\\n return False\\n else:\\n self.blob_info_payment_rate = requested_payment_rate\\n return True\\n\\n def format_blob_infos(self, blobs):\\n blob_infos = []\\n for blob_hash, blob_num, revision, iv, length, signature in blobs:\\n blob_info = {}\\n if length != 0:\\n blob_info['blob_hash'] = blob_hash\\n blob_info['blob_num'] = blob_num\\n blob_info['revision'] = revision\\n blob_info['iv'] = iv\\n blob_info['length'] = length\\n blob_info['signature'] = signature\\n blob_infos.append(blob_info)\\n return blob_infos\\n\\n def get_stream_hash_from_reference(self, reference):\\n d = self.stream_info_manager.check_if_stream_exists(reference)\\n\\n def check_if_stream_found(result):\\n if result is True:\\n return reference\\n else:\\n return self.stream_info_manager.get_stream_of_blob(reference)\\n\\n d.addCallback(check_if_stream_found)\\n return d\\n\\n def get_further_blobs(self, stream_hash, start, end, count):\\n ds = []\\n if start is not None and start != \\\"beginning\\\":\\n ds.append(self.stream_info_manager.get_stream_of_blob(start))\\n if end is not None and end != 'end':\\n ds.append(self.stream_info_manager.get_stream_of_blob(end))\\n dl = defer.DeferredList(ds, fireOnOneErrback=True)\\n\\n def ensure_streams_match(results):\\n for success, stream_of_blob in results:\\n if stream_of_blob != stream_hash:\\n raise ValueError(\\\"Blob does not match stream\\\")\\n return True\\n\\n def get_blob_infos():\\n reverse = False\\n count_to_use = count\\n if start is None:\\n reverse = True\\n elif end is not None and count_to_use is not None and count_to_use < 0:\\n reverse = True\\n if count_to_use is not None and count_to_use < 0:\\n count_to_use *= -1\\n if start == \\\"beginning\\\" or start is None:\\n s = None\\n else:\\n s = start\\n if end == \\\"end\\\" or end is None:\\n e = None\\n else:\\n e = end\\n return self.stream_info_manager.get_blobs_for_stream(stream_hash, s, e, count_to_use, reverse)\\n\\n dl.addCallback(ensure_streams_match)\\n dl.addCallback(lambda _: get_blob_infos())\\n return dl\\n\"\n },\n {\n \"alpha_fraction\": 0.6283149719238281,\n \"alphanum_fraction\": 0.6332109570503235,\n \"avg_line_length\": 22.79611587524414,\n \"blob_id\": \"984a672c66706205366a2c4c30820fc94a61aaa7\",\n \"content_id\": \"6c3e503bb00ef8080deb6e7e29ada6c32de2f95e\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2451,\n \"license_type\": \"permissive\",\n \"max_line_length\": 99,\n \"num_lines\": 103,\n \"path\": \"/lbrynet/dht_scripts.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from lbrynet.dht.node import Node\\nimport binascii\\nfrom twisted.internet import reactor, task\\nimport logging\\nimport sys\\nfrom lbrynet.core.utils import generate_id\\n\\n\\nlog = logging.getLogger(__name__)\\n\\n\\ndef print_usage():\\n print \\\"Usage:\\\\n%s UDP_PORT KNOWN_NODE_IP KNOWN_NODE_PORT HASH\\\"\\n\\n\\ndef join_network(udp_port, known_nodes):\\n lbryid = generate_id()\\n\\n log.info('Creating Node')\\n node = Node(udpPort=udp_port, lbryid=lbryid)\\n\\n log.info('Joining network')\\n d = node.joinNetwork(known_nodes)\\n\\n def log_network_size():\\n log.info(\\\"Approximate number of nodes in DHT: %s\\\", str(node.getApproximateTotalDHTNodes()))\\n log.info(\\\"Approximate number of blobs in DHT: %s\\\", str(node.getApproximateTotalHashes()))\\n\\n d.addCallback(lambda _: log_network_size())\\n\\n d.addCallback(lambda _: node)\\n\\n return d\\n\\n\\ndef get_hosts(node, h):\\n\\n def print_hosts(hosts):\\n print \\\"Hosts returned from the DHT: \\\"\\n print hosts\\n\\n log.info(\\\"Looking up %s\\\", h)\\n d = node.getPeersForBlob(h)\\n d.addCallback(print_hosts)\\n return d\\n\\n\\ndef announce_hash(node, h):\\n d = node.announceHaveBlob(h, 34567)\\n\\n def log_results(results):\\n for success, result in results:\\n if success:\\n log.info(\\\"Succeeded: %s\\\", str(result))\\n else:\\n log.info(\\\"Failed: %s\\\", str(result.getErrorMessage()))\\n\\n d.addCallback(log_results)\\n return d\\n\\n\\ndef get_args():\\n if len(sys.argv) < 5:\\n print_usage()\\n sys.exit(1)\\n udp_port = int(sys.argv[1])\\n known_nodes = [(sys.argv[2], int(sys.argv[3]))]\\n h = binascii.unhexlify(sys.argv[4])\\n return udp_port, known_nodes, h\\n\\n\\ndef run_dht_script(dht_func):\\n log_format = \\\"(%(asctime)s)[%(filename)s:%(lineno)s] %(funcName)s(): %(message)s\\\"\\n logging.basicConfig(level=logging.DEBUG, format=log_format)\\n\\n udp_port, known_nodes, h = get_args()\\n\\n d = task.deferLater(reactor, 0, join_network, udp_port, known_nodes)\\n\\n def run_dht_func(node):\\n return dht_func(node, h)\\n\\n d.addCallback(run_dht_func)\\n\\n def log_err(err):\\n log.error(\\\"An error occurred: %s\\\", err.getTraceback())\\n return err\\n\\n def shut_down():\\n log.info(\\\"Shutting down\\\")\\n reactor.stop()\\n\\n d.addErrback(log_err)\\n d.addBoth(lambda _: shut_down())\\n reactor.run()\\n\\n\\ndef get_hosts_for_hash_in_dht():\\n run_dht_script(get_hosts)\\n\\n\\ndef announce_hash_to_dht():\\n run_dht_script(announce_hash)\\n\"\n },\n {\n \"alpha_fraction\": 0.6598342061042786,\n \"alphanum_fraction\": 0.6616766452789307,\n \"avg_line_length\": 36.431034088134766,\n \"blob_id\": \"d7fb26c4c61f5e702806b904e66e78919a9621d6\",\n \"content_id\": \"c8dbc9d9e33d5ce8235eef549de815ca27a3dcc3\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4342,\n \"license_type\": \"permissive\",\n \"max_line_length\": 95,\n \"num_lines\": 116,\n \"path\": \"/lbrynet/analytics/manager.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from twisted.internet import defer\\nfrom twisted.internet import task\\n\\nfrom lbrynet import conf\\nfrom lbrynet.core import looping_call_manager\\nfrom lbrynet.core.system_info import get_platform\\n\\nimport constants\\nfrom api import Api\\nfrom events import Events, make_context\\nfrom track import Track\\n\\n\\nclass Manager(object):\\n def __init__(self, analytics_api, events_generator, track):\\n self.analytics_api = analytics_api\\n self.events_generator = events_generator\\n self.track = track\\n self.looping_call_manager = self.setup_looping_calls()\\n self.is_started = False\\n\\n @classmethod\\n def new_instance(cls, api=None, events=None):\\n if api is None:\\n api = Api.new_instance()\\n if events is None:\\n events = Events(\\n make_context(get_platform(), conf.settings['wallet']),\\n conf.settings.installation_id,\\n conf.settings.get_session_id(),\\n )\\n return cls(api, events, Track())\\n\\n def update_events_generator(self, events_generator):\\n self.events_generator = events_generator\\n\\n def _get_looping_calls(self):\\n return [\\n ('send_heartbeat', self._send_heartbeat, 60),\\n ('update_tracked_metrics', self._update_tracked_metrics, 300),\\n ]\\n\\n def setup_looping_calls(self):\\n call_manager = looping_call_manager.LoopingCallManager()\\n for name, fn, _ in self._get_looping_calls():\\n call_manager.register_looping_call(name, task.LoopingCall(fn))\\n return call_manager\\n\\n def start(self):\\n if not self.is_started:\\n for name, _, interval in self._get_looping_calls():\\n self.looping_call_manager.start(name, interval)\\n self.is_started = True\\n\\n def shutdown(self):\\n self.looping_call_manager.shutdown()\\n\\n def send_server_startup(self):\\n event = self.events_generator.server_startup()\\n self.analytics_api.track(event)\\n\\n def send_server_startup_success(self):\\n event = self.events_generator.server_startup_success()\\n self.analytics_api.track(event)\\n\\n def send_server_startup_error(self, message):\\n event = self.events_generator.server_startup_error(message)\\n self.analytics_api.track(event)\\n\\n def send_download_started(self, id_, name, stream_info=None):\\n event = self.events_generator.download_started(id_, name, stream_info)\\n self.analytics_api.track(event)\\n\\n def send_download_errored(self, id_, name, stream_info=None):\\n event = self.events_generator.download_errored(id_, name, stream_info)\\n self.analytics_api.track(event)\\n\\n def send_download_finished(self, id_, name, stream_info=None):\\n event = self.events_generator.download_finished(id_, name, stream_info)\\n self.analytics_api.track(event)\\n\\n def send_error(self, message):\\n event = self.events_generator.error(message)\\n self.analytics_api.track(event)\\n\\n def register_repeating_metric(self, event_name, value_generator, frequency=300):\\n lcall = task.LoopingCall(self._send_repeating_metric, event_name, value_generator)\\n self.looping_call_manager.register_looping_call(event_name, lcall)\\n lcall.start(frequency)\\n\\n def _send_heartbeat(self):\\n heartbeat = self.events_generator.heartbeat()\\n self.analytics_api.track(heartbeat)\\n\\n def _update_tracked_metrics(self):\\n should_send, value = self.track.summarize_and_reset(constants.BLOB_BYTES_UPLOADED)\\n if should_send:\\n event = self.events_generator.metric_observed(constants.BLOB_BYTES_UPLOADED, value)\\n self.analytics_api.track(event)\\n\\n def _send_repeating_metric(self, event_name, value_generator):\\n result = value_generator()\\n if_deferred(result, self._send_repeating_metric_value, event_name)\\n\\n def _send_repeating_metric_value(self, result, event_name):\\n should_send, value = result\\n if should_send:\\n event = self.events_generator.metric_observed(event_name, value)\\n self.analytics_api.track(event)\\n\\n\\ndef if_deferred(maybe_deferred, callback, *args, **kwargs):\\n if isinstance(maybe_deferred, defer.Deferred):\\n maybe_deferred.addCallback(callback, *args, **kwargs)\\n else:\\n callback(maybe_deferred, *args, **kwargs)\\n\"\n },\n {\n \"alpha_fraction\": 0.6016063094139099,\n \"alphanum_fraction\": 0.6021162867546082,\n \"avg_line_length\": 42.57777786254883,\n \"blob_id\": \"54c775a30d36cdb93acd0ad340e7570806e370ac\",\n \"content_id\": \"a59eff1cf068a2751c837467c97c07ab13250884\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7844,\n \"license_type\": \"permissive\",\n \"max_line_length\": 112,\n \"num_lines\": 180,\n \"path\": \"/lbrynet/lbrylive/client/LiveStreamDownloader.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# pylint: skip-file\\nimport binascii\\nfrom lbrynet.core.StreamDescriptor import StreamMetadata\\nfrom lbrynet.cryptstream.client.CryptStreamDownloader import CryptStreamDownloader\\nfrom zope.interface import implements\\nfrom lbrynet.lbrylive.client.LiveStreamMetadataHandler import LiveStreamMetadataHandler\\nfrom lbrynet.lbrylive.client.LiveStreamProgressManager import LiveStreamProgressManager\\nimport os\\nfrom lbrynet.lbrylive.StreamDescriptor import save_sd_info\\nfrom lbrynet.lbrylive.PaymentRateManager import LiveStreamPaymentRateManager\\nfrom twisted.internet import defer, threads # , process\\nfrom lbrynet.interfaces import IStreamDownloaderFactory\\nfrom lbrynet.lbrylive.StreamDescriptor import LiveStreamType\\n\\n\\nclass _LiveStreamDownloader(CryptStreamDownloader):\\n\\n def __init__(self, stream_hash, peer_finder, rate_limiter, blob_manager, stream_info_manager,\\n payment_rate_manager, wallet):\\n CryptStreamDownloader.__init__(self, peer_finder, rate_limiter, blob_manager,\\n payment_rate_manager, wallet)\\n self.stream_hash = stream_hash\\n self.stream_info_manager = stream_info_manager\\n self.public_key = None\\n\\n def set_stream_info(self):\\n if self.public_key is None and self.key is None:\\n\\n d = self.stream_info_manager.get_stream_info(self.stream_hash)\\n\\n def set_stream_info(stream_info):\\n public_key, key, stream_name = stream_info\\n self.public_key = public_key\\n self.key = binascii.unhexlify(key)\\n self.stream_name = binascii.unhexlify(stream_name)\\n\\n d.addCallback(set_stream_info)\\n return d\\n else:\\n return defer.succeed(True)\\n\\n\\nclass LiveStreamDownloader(_LiveStreamDownloader):\\n def __init__(self, stream_hash, peer_finder, rate_limiter, blob_manager, stream_info_manager,\\n payment_rate_manager, wallet):\\n _LiveStreamDownloader.__init__(self, stream_hash, peer_finder, rate_limiter, blob_manager,\\n stream_info_manager, payment_rate_manager, wallet)\\n\\n\\n def _get_metadata_handler(self, download_manager):\\n return LiveStreamMetadataHandler(self.stream_hash, self.stream_info_manager,\\n self.peer_finder, self.public_key, False,\\n self.payment_rate_manager, self.wallet, download_manager, 10)\\n\\n def _get_progress_manager(self, download_manager):\\n return LiveStreamProgressManager(self._finished_downloading, self.blob_manager, download_manager,\\n delete_blob_after_finished=True, download_whole=False,\\n max_before_skip_ahead=10)\\n\\n def _get_write_func(self):\\n def write_func(data):\\n if self.stopped is False:\\n pass\\n return write_func\\n\\n\\nclass FullLiveStreamDownloader(_LiveStreamDownloader):\\n def __init__(self, stream_hash, peer_finder, rate_limiter, blob_manager, stream_info_manager,\\n payment_rate_manager, wallet):\\n _LiveStreamDownloader.__init__(self, stream_hash, peer_finder, rate_limiter,\\n blob_manager, stream_info_manager, payment_rate_manager,\\n wallet)\\n self.file_handle = None\\n self.file_name = None\\n\\n def set_stream_info(self):\\n d = _LiveStreamDownloader.set_stream_info(self)\\n\\n def set_file_name_if_unset():\\n if not self.file_name:\\n if not self.stream_name:\\n self.stream_name = \\\"_\\\"\\n self.file_name = os.path.basename(self.stream_name)\\n\\n d.addCallback(lambda _: set_file_name_if_unset())\\n return d\\n\\n def stop(self, err=None):\\n d = self._close_file()\\n d.addBoth(lambda _: _LiveStreamDownloader.stop(self, err))\\n return d\\n\\n def _start(self):\\n if self.file_handle is None:\\n d = self._open_file()\\n else:\\n d = defer.succeed(True)\\n d.addCallback(lambda _: _LiveStreamDownloader._start(self))\\n return d\\n\\n def _open_file(self):\\n def open_file():\\n self.file_handle = open(self.file_name, 'wb')\\n return threads.deferToThread(open_file)\\n\\n def _get_metadata_handler(self, download_manager):\\n return LiveStreamMetadataHandler(self.stream_hash, self.stream_info_manager,\\n self.peer_finder, self.public_key, True,\\n self.payment_rate_manager, self.wallet, download_manager)\\n\\n def _get_primary_request_creators(self, download_manager):\\n return [download_manager.blob_requester, download_manager.blob_info_finder]\\n\\n def _get_write_func(self):\\n def write_func(data):\\n if self.stopped is False:\\n self.file_handle.write(data)\\n return write_func\\n\\n def _close_file(self):\\n def close_file():\\n if self.file_handle is not None:\\n self.file_handle.close()\\n self.file_handle = None\\n return threads.deferToThread(close_file)\\n\\n\\nclass FullLiveStreamDownloaderFactory(object):\\n\\n implements(IStreamDownloaderFactory)\\n\\n def __init__(self, peer_finder, rate_limiter, blob_manager, stream_info_manager, wallet,\\n default_payment_rate_manager):\\n self.peer_finder = peer_finder\\n self.rate_limiter = rate_limiter\\n self.blob_manager = blob_manager\\n self.stream_info_manager = stream_info_manager\\n self.wallet = wallet\\n self.default_payment_rate_manager = default_payment_rate_manager\\n\\n def can_download(self, sd_validator):\\n return True\\n\\n def make_downloader(self, metadata, options, payment_rate_manager):\\n # TODO: check options for payment rate manager parameters\\n prm = LiveStreamPaymentRateManager(self.default_payment_rate_manager,\\n payment_rate_manager)\\n\\n def save_source_if_blob(stream_hash):\\n if metadata.metadata_source == StreamMetadata.FROM_BLOB:\\n d = self.stream_info_manager.save_sd_blob_hash_to_stream(stream_hash, metadata.source_blob_hash)\\n else:\\n d = defer.succeed(True)\\n d.addCallback(lambda _: stream_hash)\\n return d\\n\\n d = save_sd_info(self.stream_info_manager, metadata.validator.raw_info)\\n d.addCallback(save_source_if_blob)\\n\\n def create_downloader(stream_hash):\\n stream_downloader = FullLiveStreamDownloader(stream_hash, self.peer_finder, self.rate_limiter,\\n self.blob_manager, self.stream_info_manager,\\n prm, self.wallet)\\n d = stream_downloader.set_stream_info()\\n d.addCallback(lambda _: stream_downloader)\\n return d\\n\\n d.addCallback(create_downloader)\\n return d\\n\\n\\ndef add_full_live_stream_downloader_to_sd_identifier(session, stream_info_manager, sd_identifier,\\n base_live_stream_payment_rate_manager):\\n downloader_factory = FullLiveStreamDownloaderFactory(session.peer_finder,\\n session.rate_limiter,\\n session.blob_manager,\\n stream_info_manager,\\n session.wallet,\\n base_live_stream_payment_rate_manager)\\n sd_identifier.add_stream_downloader_factory(LiveStreamType, downloader_factory)\\n\"\n },\n {\n \"alpha_fraction\": 0.6493775844573975,\n \"alphanum_fraction\": 0.6535269618034363,\n \"avg_line_length\": 31.133333206176758,\n \"blob_id\": \"65e329e0a66efcb809f43beee65530f13380c837\",\n \"content_id\": \"c54cf0a441bfa442a907fdd96ad95653d381692f\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 482,\n \"license_type\": \"permissive\",\n \"max_line_length\": 71,\n \"num_lines\": 15,\n \"path\": \"/run_pylint.sh\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#! /bin/bash\\n\\nset -eu\\n\\n# Ignoring distutils because: https://github.com/PyCQA/pylint/issues/73\\n# TODO: as code quality improves, make pylint be more strict\\npylint -E --disable=inherit-non-class --disable=no-member \\\\\\n --ignored-modules=distutils \\\\\\n --enable=unused-import \\\\\\n --enable=bad-whitespace \\\\\\n --enable=line-too-long \\\\\\n --enable=trailing-whitespace \\\\\\n --enable=missing-final-newline \\\\\\n --enable=mixed-indentation \\\\\\n lbrynet $@\\n\"\n },\n {\n \"alpha_fraction\": 0.625345766544342,\n \"alphanum_fraction\": 0.6517980694770813,\n \"avg_line_length\": 40.31428527832031,\n \"blob_id\": \"cb594fd3db8da6a903a9a04353028d545244cd95\",\n \"content_id\": \"dd06247a73a67ab96e8ecfa31c17f01ed2b00cbd\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5784,\n \"license_type\": \"permissive\",\n \"max_line_length\": 126,\n \"num_lines\": 140,\n \"path\": \"/tests/unit/lbrynet_daemon/test_Daemon.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import mock\\nimport requests\\nfrom tests.mocks import BlobAvailabilityTracker as DummyBlobAvailabilityTracker\\nfrom tests import util\\nfrom twisted.internet import defer\\nfrom twisted.internet import reactor\\nfrom twisted.trial import unittest\\nfrom lbrynet.lbrynet_daemon import Daemon\\nfrom lbrynet.core import Session, PaymentRateManager, Wallet\\nfrom lbrynet.lbrynet_daemon.Daemon import Daemon as LBRYDaemon\\nfrom lbrynet.lbrynet_daemon import ExchangeRateManager\\nfrom lbrynet import conf\\nfrom tests.mocks import mock_conf_settings, FakeNetwork\\n\\n\\nclass MiscTests(unittest.TestCase):\\n def test_get_lbry_electron_client_version_from_github(self):\\n response = mock.create_autospec(requests.Response)\\n # don't need to mock out the entire response from the api\\n # but at least need 'tag_name'\\n response.json.return_value = {\\n \\\"url\\\": \\\"https://api.github.com/repos/lbryio/lbry/releases/3685199\\\",\\n \\\"assets_url\\\": \\\"https://api.github.com/repos/lbryio/lbry/releases/3685199/assets\\\",\\n \\\"html_url\\\": \\\"https://github.com/lbryio/lbry/releases/tag/v0.3.8\\\",\\n \\\"id\\\": 3685199,\\n \\\"tag_name\\\": \\\"v0.3.8\\\",\\n \\\"prerelease\\\": False\\n }\\n with mock.patch('lbrynet.lbrynet_daemon.Daemon.requests') as req:\\n req.get.return_value = response\\n rv = Daemon.CheckRemoteVersion()\\n rv._get_lbry_electron_client_version()\\n self.assertEqual('0.3.8', rv.version)\\n\\n def test_error_is_thrown_if_prerelease(self):\\n response = mock.create_autospec(requests.Response)\\n response.json.return_value = {\\n \\\"tag_name\\\": \\\"v0.3.8\\\",\\n \\\"prerelease\\\": True\\n }\\n with mock.patch('lbrynet.lbrynet_daemon.Daemon.requests') as req:\\n req.get.return_value = response\\n rv = Daemon.CheckRemoteVersion()\\n with self.assertRaises(Exception):\\n rv._get_lbry_electron_client_version()\\n\\n def test_error_is_thrown_when_version_cant_be_parsed(self):\\n with self.assertRaises(Exception):\\n Daemon.get_version_from_tag('garbage')\\n\\n\\ndef get_test_daemon(data_rate=None, generous=True, with_fee=False):\\n if data_rate is None:\\n data_rate = conf.ADJUSTABLE_SETTINGS['data_rate'][1]\\n\\n rates = {\\n 'BTCLBC': {'spot': 3.0, 'ts': util.DEFAULT_ISO_TIME + 1},\\n 'USDBTC': {'spot': 2.0, 'ts': util.DEFAULT_ISO_TIME + 2}\\n }\\n daemon = LBRYDaemon(None, None)\\n daemon.session = mock.Mock(spec=Session.Session)\\n daemon.exchange_rate_manager = ExchangeRateManager.DummyExchangeRateManager(rates)\\n base_prm = PaymentRateManager.BasePaymentRateManager(rate=data_rate)\\n prm = PaymentRateManager.NegotiatedPaymentRateManager(base_prm, DummyBlobAvailabilityTracker(),\\n generous=generous)\\n daemon.session.payment_rate_manager = prm\\n metadata = {\\n \\\"author\\\": \\\"fake author\\\",\\n \\\"content_type\\\": \\\"fake/format\\\",\\n \\\"description\\\": \\\"fake description\\\",\\n \\\"license\\\": \\\"fake license\\\",\\n \\\"license_url\\\": \\\"fake license url\\\",\\n \\\"nsfw\\\": False,\\n \\\"sources\\\": {\\n \\\"lbry_sd_hash\\\": \\\"d2b8b6e907dde95245fe6d144d16c2fdd60c4e0c6463ec98b85642d06d8e9414e8fcfdcb7cb13532ec5454fb8fe7f280\\\"\\n },\\n \\\"thumbnail\\\": \\\"fake thumbnail\\\",\\n \\\"title\\\": \\\"fake title\\\",\\n \\\"ver\\\": \\\"0.0.3\\\"\\n }\\n if with_fee:\\n metadata.update(\\n {\\\"fee\\\": {\\\"USD\\\": {\\\"address\\\": \\\"bQ6BGboPV2SpTMEP7wLNiAcnsZiH8ye6eA\\\", \\\"amount\\\": 0.75}}})\\n daemon._resolve_name = lambda _: defer.succeed(metadata)\\n return daemon\\n\\n\\nclass TestCostEst(unittest.TestCase):\\n def setUp(self):\\n mock_conf_settings(self)\\n util.resetTime(self)\\n\\n def test_fee_and_generous_data(self):\\n size = 10000000\\n correct_result = 4.5\\n daemon = get_test_daemon(generous=True, with_fee=True)\\n self.assertEquals(daemon.get_est_cost(\\\"test\\\", size).result, correct_result)\\n\\n def test_fee_and_ungenerous_data(self):\\n size = 10000000\\n fake_fee_amount = 4.5\\n data_rate = conf.ADJUSTABLE_SETTINGS['data_rate'][1]\\n correct_result = size / 10 ** 6 * data_rate + fake_fee_amount\\n daemon = get_test_daemon(generous=False, with_fee=True)\\n self.assertEquals(daemon.get_est_cost(\\\"test\\\", size).result, correct_result)\\n\\n def test_generous_data_and_no_fee(self):\\n size = 10000000\\n correct_result = 0.0\\n daemon = get_test_daemon(generous=True)\\n self.assertEquals(daemon.get_est_cost(\\\"test\\\", size).result, correct_result)\\n\\n def test_ungenerous_data_and_no_fee(self):\\n size = 10000000\\n data_rate = conf.ADJUSTABLE_SETTINGS['data_rate'][1]\\n correct_result = size / 10 ** 6 * data_rate\\n daemon = get_test_daemon(generous=False)\\n self.assertEquals(daemon.get_est_cost(\\\"test\\\", size).result, correct_result)\\n\\n\\nclass TestJsonRpc(unittest.TestCase):\\n def setUp(self):\\n def noop():\\n return None\\n\\n mock_conf_settings(self)\\n util.resetTime(self)\\n self.test_daemon = get_test_daemon()\\n self.test_daemon.session.wallet = Wallet.LBRYumWallet(storage=Wallet.InMemoryStorage())\\n self.test_daemon.session.wallet.network = FakeNetwork()\\n self.test_daemon.session.wallet.get_best_blockhash = noop\\n\\n def test_status(self):\\n d = defer.maybeDeferred(self.test_daemon.jsonrpc_status)\\n d.addCallback(lambda status: self.assertDictContainsSubset({'is_running': False}, status))\\n\\n def test_help(self):\\n d = defer.maybeDeferred(self.test_daemon.jsonrpc_help, command='status')\\n d.addCallback(lambda result: self.assertSubstring('daemon status', result['help']))\\n # self.assertSubstring('daemon status', d.result)\\n\"\n },\n {\n \"alpha_fraction\": 0.3913043439388275,\n \"alphanum_fraction\": 0.417391300201416,\n \"avg_line_length\": 20.5625,\n \"blob_id\": \"23b7179a8ed2d40fe6844d6b0721d3a71a14d401\",\n \"content_id\": \"18efd64b60109b0bd1b61bc8acfaecfbbbdf89f1\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 345,\n \"license_type\": \"permissive\",\n \"max_line_length\": 57,\n \"num_lines\": 16,\n \"path\": \"/lbrynet/metadata/fee_schemas.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"VER_001 = {\\n '$schema': 'http://json-schema.org/draft-04/schema#',\\n 'title': 'LBRY fee schema 0.0.1',\\n 'type': 'object',\\n\\n 'properties': {\\n 'amount': {\\n 'type': 'number',\\n 'minimum': 0,\\n 'exclusiveMinimum': True,\\n },\\n 'address': {\\n 'type': 'string'\\n }\\n },\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6666666865348816,\n \"alphanum_fraction\": 0.6666666865348816,\n \"avg_line_length\": 32.42856979370117,\n \"blob_id\": \"fe6a73e9f1e9dfe39fa7938fff4e5d1fa1ba170e\",\n \"content_id\": \"e7bdb4ac9b244ae6ce50f5ba5ca3ee61beb9ea12\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 468,\n \"license_type\": \"permissive\",\n \"max_line_length\": 72,\n \"num_lines\": 14,\n \"path\": \"/lbrynet/analytics/logging_handler.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import logging\\n\\n\\nclass Handler(logging.Handler):\\n \\\"\\\"\\\"A logging handler that reports errors to the analytics manager\\\"\\\"\\\"\\n def __init__(self, manager, level=logging.ERROR):\\n self.manager = manager\\n logging.Handler.__init__(self, level)\\n\\n def emit(self, record):\\n # We need to call format to ensure that record.message and\\n # record.exc_text attributes are populated\\n self.format(record)\\n self.manager.send_error(record)\\n\"\n },\n {\n \"alpha_fraction\": 0.628040075302124,\n \"alphanum_fraction\": 0.6745350360870361,\n \"avg_line_length\": 30.772727966308594,\n \"blob_id\": \"5b2a25afa4cf84b96edadfcb2e6088df68178ad2\",\n \"content_id\": \"b1b7de8b0078421319b154b888736f09d1fc9e25\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1398,\n \"license_type\": \"permissive\",\n \"max_line_length\": 90,\n \"num_lines\": 44,\n \"path\": \"/lbrynet/metadata/Metadata.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import logging\\n\\nfrom lbrynet.core import Error\\nfrom lbrynet.metadata.StructuredDict import StructuredDict\\nimport metadata_schemas\\n\\nlog = logging.getLogger(__name__)\\nNAME_ALLOWED_CHARSET = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0987654321-'\\n\\n\\ndef verify_name_characters(name):\\n assert len(name) > 0, \\\"Empty uri\\\"\\n invalid_characters = {c for c in name if c not in NAME_ALLOWED_CHARSET}\\n if invalid_characters:\\n raise Error.InvalidName(name, invalid_characters)\\n return True\\n\\n\\ndef migrate_001_to_002(metadata):\\n metadata['ver'] = '0.0.2'\\n metadata['nsfw'] = False\\n\\ndef migrate_002_to_003(metadata):\\n metadata['ver'] = '0.0.3'\\n if 'content-type' in metadata:\\n metadata['content_type'] = metadata['content-type']\\n del metadata['content-type']\\n\\n\\nclass Metadata(StructuredDict):\\n current_version = '0.0.3'\\n\\n _versions = [\\n ('0.0.1', metadata_schemas.VER_001, None),\\n ('0.0.2', metadata_schemas.VER_002, migrate_001_to_002),\\n ('0.0.3', metadata_schemas.VER_003, migrate_002_to_003)\\n ]\\n\\n def __init__(self, metadata, migrate=True, target_version=None):\\n if not isinstance(metadata, dict):\\n raise TypeError(\\\"{} is not a dictionary\\\".format(metadata))\\n starting_version = metadata.get('ver', '0.0.1')\\n\\n StructuredDict.__init__(self, metadata, starting_version, migrate, target_version)\\n\"\n },\n {\n \"alpha_fraction\": 0.600923478603363,\n \"alphanum_fraction\": 0.602902352809906,\n \"avg_line_length\": 26.816513061523438,\n \"blob_id\": \"20be6b9cd3ef7dd7ce1b869ad1bc3b390e9a5605\",\n \"content_id\": \"7e999ba104ecd2773ce50ca0f54010770e9a4397\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3032,\n \"license_type\": \"permissive\",\n \"max_line_length\": 90,\n \"num_lines\": 109,\n \"path\": \"/scripts/gen_api_docs.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\n# Generate docs: python gen_api_docs.py\\n# See docs: pip install mkdocs; mkdocs serve\\n# Push docs: mkdocs gh-deploy\\n\\nimport inspect\\nimport os.path as op\\nimport re\\nimport sys\\n\\nfrom six import string_types\\nfrom lbrynet.lbrynet_daemon.Daemon import Daemon\\n\\n\\ndef _name(obj):\\n if hasattr(obj, '__name__'):\\n return obj.__name__\\n elif inspect.isdatadescriptor(obj):\\n return obj.fget.__name__\\n\\n\\ndef _anchor(name):\\n anchor = name.lower().replace(' ', '-')\\n anchor = re.sub(r'[^\\\\w\\\\- ]', '', anchor)\\n return anchor\\n\\n\\n_docstring_header_pattern = re.compile(r'^([^\\\\n]+)\\\\n[\\\\-\\\\=]{3,}$', flags=re.MULTILINE)\\n_docstring_parameters_pattern = re.compile(r'^([^ \\\\n]+) \\\\: ([^\\\\n]+)$', flags=re.MULTILINE)\\n\\n\\ndef _replace_docstring_header(paragraph):\\n \\\"\\\"\\\"Process NumPy-like function docstrings.\\\"\\\"\\\"\\n\\n # Replace Markdown headers in docstrings with light headers in bold.\\n paragraph = re.sub(_docstring_header_pattern, r'*\\\\1*', paragraph)\\n paragraph = re.sub(_docstring_parameters_pattern, r'\\\\n* `\\\\1` (\\\\2)\\\\n', paragraph)\\n return paragraph\\n\\n\\ndef _doc(obj):\\n docstr = (inspect.getdoc(obj) or '').strip()\\n return _replace_docstring_header(docstr) if docstr and '---' in docstr else docstr\\n\\n\\ndef _is_public(obj):\\n name = _name(obj) if not isinstance(obj, string_types) else obj\\n if name:\\n return not name.startswith('_')\\n else:\\n return True\\n\\n\\ndef _is_defined_in_package(obj, package):\\n if isinstance(obj, property):\\n obj = obj.fget\\n mod = inspect.getmodule(obj)\\n if mod and hasattr(mod, '__name__'):\\n name = mod.__name__\\n return name.split('.')[0] == package\\n return True\\n\\n\\ndef _iter_doc_members(obj, package=None):\\n for _, member in inspect.getmembers(obj):\\n if _is_public(member):\\n if package is None or _is_defined_in_package(member, package):\\n yield member\\n\\n\\ndef _iter_methods(klass, package=None):\\n for member in _iter_doc_members(klass, package):\\n if inspect.isfunction(member) or inspect.ismethod(member):\\n if inspect.isdatadescriptor(member):\\n continue\\n if _name(member).startswith('jsonrpc_'):\\n yield member\\n\\n\\ndef _link(name, anchor=None):\\n return \\\"[{name}](#{anchor})\\\".format(name=name, anchor=anchor or _anchor(name))\\n\\n\\ndef main():\\n curdir = op.dirname(op.realpath(__file__))\\n path = op.realpath(op.join(curdir, '..', 'docs', 'index.md'))\\n\\n klass = Daemon\\n\\n # toc = ''\\n doc = ''\\n # Table of contents\\n for method in _iter_methods(klass):\\n method_name = _name(method)[len('jsonrpc_'):]\\n method_doc = _doc(method)\\n if \\\"DEPRECATED\\\" in method_doc:\\n continue\\n # toc += '* ' + _link(method_name, _anchor(method_name)) + \\\"\\\\n\\\"\\n doc += '## ' + method_name + \\\"\\\\n\\\\n```text\\\\n\\\" + method_doc + \\\"\\\\n```\\\\n\\\\n\\\"\\n\\n text = \\\"# LBRY JSON-RPC API Documentation\\\\n\\\\n\\\" + doc\\n\\n with open(path, 'w+') as f:\\n f.write(text)\\n\\n\\nif __name__ == '__main__':\\n sys.exit(main())\\n\"\n },\n {\n \"alpha_fraction\": 0.645118236541748,\n \"alphanum_fraction\": 0.647978663444519,\n \"avg_line_length\": 41.98360824584961,\n \"blob_id\": \"c0b0ef288607b91c7294900bf387d526ea886c1f\",\n \"content_id\": \"2328715248651eb73f13aac760ca25fd17a9c7ee\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5244,\n \"license_type\": \"permissive\",\n \"max_line_length\": 108,\n \"num_lines\": 122,\n \"path\": \"/lbrynet/lbrylive/StdinUploader.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# pylint: skip-file\\n# pylint: skip-file\\n# This file is not maintained, but might be used in the future\\n#\\nimport logging\\nimport sys\\nfrom lbrynet.lbrylive.LiveStreamCreator import StdOutLiveStreamCreator\\nfrom lbrynet.core.BlobManager import TempBlobManager\\nfrom lbrynet.core.Session import Session\\nfrom lbrynet.core.server.BlobAvailabilityHandler import BlobAvailabilityHandlerFactory\\nfrom lbrynet.core.server.BlobRequestHandler import BlobRequestHandlerFactory\\nfrom lbrynet.core.server.ServerProtocol import ServerProtocolFactory\\nfrom lbrynet.lbrylive.PaymentRateManager import BaseLiveStreamPaymentRateManager\\nfrom lbrynet.lbrylive.LiveStreamMetadataManager import DBLiveStreamMetadataManager\\nfrom lbrynet.lbrylive.server.LiveBlobInfoQueryHandler import CryptBlobInfoQueryHandlerFactory\\nfrom lbrynet.dht.node import Node\\nfrom twisted.internet import defer, task\\n\\n\\nclass StdinUploader():\\n \\\"\\\"\\\"This class reads from standard in, creates a stream, and makes it available on the network.\\\"\\\"\\\"\\n def __init__(self, peer_port, dht_node_port, known_dht_nodes,\\n stream_info_manager_class=DBLiveStreamMetadataManager, blob_manager_class=TempBlobManager):\\n \\\"\\\"\\\"\\n @param peer_port: the network port on which to listen for peers\\n\\n @param dht_node_port: the network port on which to listen for nodes in the DHT\\n\\n @param known_dht_nodes: a list of (ip_address, dht_port) which will be used to join the DHT network\\n \\\"\\\"\\\"\\n self.peer_port = peer_port\\n self.lbry_server_port = None\\n self.session = Session(blob_manager_class=blob_manager_class,\\n stream_info_manager_class=stream_info_manager_class,\\n dht_node_class=Node, dht_node_port=dht_node_port,\\n known_dht_nodes=known_dht_nodes, peer_port=self.peer_port,\\n use_upnp=False)\\n self.payment_rate_manager = BaseLiveStreamPaymentRateManager()\\n\\n def start(self):\\n \\\"\\\"\\\"Initialize the session and start listening on the peer port\\\"\\\"\\\"\\n d = self.session.setup()\\n d.addCallback(lambda _: self._start())\\n\\n return d\\n\\n def _start(self):\\n self._start_server()\\n return True\\n\\n def _start_server(self):\\n query_handler_factories = [\\n CryptBlobInfoQueryHandlerFactory(self.stream_info_manager, self.session.wallet,\\n self.payment_rate_manager),\\n BlobAvailabilityHandlerFactory(self.session.blob_manager),\\n BlobRequestHandlerFactory(self.session.blob_manager, self.session.wallet,\\n self.payment_rate_manager),\\n self.session.wallet.get_wallet_info_query_handler_factory()\\n ]\\n\\n self.server_factory = ServerProtocolFactory(self.session.rate_limiter,\\n query_handler_factories,\\n self.session.peer_manager)\\n from twisted.internet import reactor\\n self.lbry_server_port = reactor.listenTCP(self.peer_port, self.server_factory)\\n\\n def start_live_stream(self, stream_name):\\n \\\"\\\"\\\"Create the stream and start reading from stdin\\n\\n @param stream_name: a string, the suggested name of this stream\\n \\\"\\\"\\\"\\n stream_creator_helper = StdOutLiveStreamCreator(stream_name, self.session.blob_manager,\\n self.stream_info_manager)\\n d = stream_creator_helper.create_and_publish_stream_descriptor()\\n\\n def print_sd_hash(sd_hash):\\n print \\\"Stream descriptor hash:\\\", sd_hash\\n\\n d.addCallback(print_sd_hash)\\n d.addCallback(lambda _: stream_creator_helper.start_streaming())\\n return d\\n\\n def shut_down(self):\\n \\\"\\\"\\\"End the session and stop listening on the server port\\\"\\\"\\\"\\n d = self.session.shut_down()\\n d.addCallback(lambda _: self._shut_down())\\n return d\\n\\n def _shut_down(self):\\n if self.lbry_server_port is not None:\\n d = defer.maybeDeferred(self.lbry_server_port.stopListening)\\n else:\\n d = defer.succeed(True)\\n return d\\n\\n\\ndef launch_stdin_uploader():\\n\\n from twisted.internet import reactor\\n\\n logging.basicConfig(level=logging.WARNING, filename=\\\"ul.log\\\")\\n if len(sys.argv) == 4:\\n uploader = StdinUploader(int(sys.argv[2]), int(sys.argv[3]), [])\\n elif len(sys.argv) == 6:\\n uploader = StdinUploader(int(sys.argv[2]), int(sys.argv[3]), [(sys.argv[4], int(sys.argv[5]))])\\n else:\\n print \\\"Usage: lbrynet-stdin-uploader <stream_name> <peer_port> <dht_node_port>\\\" \\\\\\n \\\" [<dht_bootstrap_host> <dht_bootstrap port>]\\\"\\n sys.exit(1)\\n\\n def start_stdin_uploader():\\n return uploader.start_live_stream(sys.argv[1])\\n\\n def shut_down():\\n logging.debug(\\\"Telling the reactor to stop in 60 seconds\\\")\\n reactor.callLater(60, reactor.stop)\\n\\n d = task.deferLater(reactor, 0, uploader.start)\\n d.addCallback(lambda _: start_stdin_uploader())\\n d.addCallback(lambda _: shut_down())\\n reactor.addSystemEventTrigger('before', 'shutdown', uploader.shut_down)\\n reactor.run()\\n\"\n },\n {\n \"alpha_fraction\": 0.8666666746139526,\n \"alphanum_fraction\": 0.8666666746139526,\n \"avg_line_length\": 59,\n \"blob_id\": \"f80b5b6576df9e2700938124d4e2ac5517f9778f\",\n \"content_id\": \"8cd10066a4559c568ac9a15bd2ea1763ea98d7ed\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 120,\n \"license_type\": \"permissive\",\n \"max_line_length\": 61,\n \"num_lines\": 2,\n \"path\": \"/lbrynet/lbryfile/__init__.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from lbrynet.lbryfile.StreamDescriptor import get_sd_info\\nfrom lbrynet.lbryfile.StreamDescriptor import publish_sd_blob\\n\"\n },\n {\n \"alpha_fraction\": 0.6454358696937561,\n \"alphanum_fraction\": 0.64646315574646,\n \"avg_line_length\": 37.71590805053711,\n \"blob_id\": \"b5bb38c02f4bf1e42a62931f0282862a560ff7a8\",\n \"content_id\": \"f0710c5fcac731681c5b68cd04be20e26d3438c4\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6814,\n \"license_type\": \"permissive\",\n \"max_line_length\": 115,\n \"num_lines\": 176,\n \"path\": \"/lbrynet/lbrylive/LiveStreamCreator.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# pylint: skip-file\\nfrom lbrynet.core.StreamDescriptor import BlobStreamDescriptorWriter\\nfrom lbrynet.lbrylive.StreamDescriptor import get_sd_info\\nfrom lbrynet.cryptstream.CryptStreamCreator import CryptStreamCreator\\nfrom lbrynet.lbrylive.LiveBlob import LiveStreamBlobMaker\\nfrom lbrynet.core.cryptoutils import get_lbry_hash_obj, get_pub_key, sign_with_pass_phrase\\nfrom Crypto import Random\\nimport binascii\\nimport logging\\nfrom lbrynet import conf\\nfrom twisted.internet import interfaces, defer\\nfrom twisted.protocols.basic import FileSender\\nfrom zope.interface import implements\\n\\n\\nlog = logging.getLogger(__name__)\\n\\n\\nclass LiveStreamCreator(CryptStreamCreator):\\n def __init__(self, blob_manager, stream_info_manager, name=None, key=None, iv_generator=None,\\n delete_after_num=None, secret_pass_phrase=None):\\n CryptStreamCreator.__init__(self, blob_manager, name, key, iv_generator)\\n self.stream_hash = None\\n self.stream_info_manager = stream_info_manager\\n self.delete_after_num = delete_after_num\\n self.secret_pass_phrase = secret_pass_phrase\\n self.file_extension = conf.settings['CRYPTSD_FILE_EXTENSION']\\n self.finished_blob_hashes = {}\\n\\n def _save_stream(self):\\n d = self.stream_info_manager.save_stream(self.stream_hash, get_pub_key(self.secret_pass_phrase),\\n binascii.hexlify(self.name), binascii.hexlify(self.key),\\n [])\\n return d\\n\\n def _blob_finished(self, blob_info):\\n log.debug(\\\"In blob_finished\\\")\\n log.debug(\\\"length: %s\\\", str(blob_info.length))\\n sig_hash = get_lbry_hash_obj()\\n sig_hash.update(self.stream_hash)\\n if blob_info.length != 0:\\n sig_hash.update(blob_info.blob_hash)\\n sig_hash.update(str(blob_info.blob_num))\\n sig_hash.update(str(blob_info.revision))\\n sig_hash.update(blob_info.iv)\\n sig_hash.update(str(blob_info.length))\\n signature = sign_with_pass_phrase(sig_hash.digest(), self.secret_pass_phrase)\\n blob_info.signature = signature\\n self.finished_blob_hashes[blob_info.blob_num] = blob_info.blob_hash\\n if self.delete_after_num is not None:\\n self._delete_old_blobs(blob_info.blob_num)\\n d = self.stream_info_manager.add_blobs_to_stream(self.stream_hash, [blob_info])\\n\\n def log_add_error(err):\\n log.error(\\\"An error occurred adding a blob info to the stream info manager: %s\\\", err.getErrorMessage())\\n return err\\n\\n d.addErrback(log_add_error)\\n log.debug(\\\"returning from blob_finished\\\")\\n return d\\n\\n def setup(self):\\n \\\"\\\"\\\"Create the secret pass phrase if it wasn't provided, compute the stream hash,\\n save the stream to the stream info manager, and return the stream hash\\n \\\"\\\"\\\"\\n if self.secret_pass_phrase is None:\\n self.secret_pass_phrase = Random.new().read(512)\\n\\n d = CryptStreamCreator.setup(self)\\n\\n def make_stream_hash():\\n hashsum = get_lbry_hash_obj()\\n hashsum.update(binascii.hexlify(self.name))\\n hashsum.update(get_pub_key(self.secret_pass_phrase))\\n hashsum.update(binascii.hexlify(self.key))\\n self.stream_hash = hashsum.hexdigest()\\n return self.stream_hash\\n\\n d.addCallback(lambda _: make_stream_hash())\\n d.addCallback(lambda _: self._save_stream())\\n d.addCallback(lambda _: self.stream_hash)\\n return d\\n\\n def publish_stream_descriptor(self):\\n descriptor_writer = BlobStreamDescriptorWriter(self.blob_manager)\\n d = get_sd_info(self.stream_info_manager, self.stream_hash, False)\\n d.addCallback(descriptor_writer.create_descriptor)\\n return d\\n\\n def _delete_old_blobs(self, newest_blob_num):\\n assert self.delete_after_num is not None, \\\"_delete_old_blobs called with delete_after_num=None\\\"\\n oldest_to_keep = newest_blob_num - self.delete_after_num + 1\\n nums_to_delete = [num for num in self.finished_blob_hashes.iterkeys() if num < oldest_to_keep]\\n for num in nums_to_delete:\\n self.blob_manager.delete_blobs([self.finished_blob_hashes[num]])\\n del self.finished_blob_hashes[num]\\n\\n def _get_blob_maker(self, iv, blob_creator):\\n return LiveStreamBlobMaker(self.key, iv, self.blob_count, blob_creator)\\n\\n\\nclass StdOutLiveStreamCreator(LiveStreamCreator):\\n def __init__(self, stream_name, blob_manager, stream_info_manager):\\n LiveStreamCreator.__init__(self, blob_manager, stream_info_manager, stream_name,\\n delete_after_num=20)\\n\\n def start_streaming(self):\\n stdin_producer = StdinStreamProducer(self)\\n d = stdin_producer.begin_producing()\\n\\n def stop_stream():\\n d = self.stop()\\n return d\\n\\n d.addCallback(lambda _: stop_stream())\\n return d\\n\\n\\nclass FileLiveStreamCreator(LiveStreamCreator):\\n def __init__(self, blob_manager, stream_info_manager, file_name, file_handle,\\n secret_pass_phrase=None, key=None, iv_generator=None, stream_name=None):\\n if stream_name is None:\\n stream_name = file_name\\n LiveStreamCreator.__init__(self, blob_manager, stream_info_manager, stream_name,\\n secret_pass_phrase, key, iv_generator)\\n self.file_name = file_name\\n self.file_handle = file_handle\\n\\n def start_streaming(self):\\n file_sender = FileSender()\\n d = file_sender.beginFileTransfer(self.file_handle, self)\\n\\n def stop_stream():\\n d = self.stop()\\n return d\\n\\n d.addCallback(lambda _: stop_stream())\\n return d\\n\\n\\nclass StdinStreamProducer(object):\\n \\\"\\\"\\\"This class reads data from standard in and sends it to a stream creator\\\"\\\"\\\"\\n\\n implements(interfaces.IPushProducer)\\n\\n def __init__(self, consumer):\\n self.consumer = consumer\\n self.reader = None\\n self.finished_deferred = None\\n\\n def begin_producing(self):\\n\\n self.finished_deferred = defer.Deferred()\\n self.consumer.registerProducer(self, True)\\n self.resumeProducing()\\n return self.finished_deferred\\n\\n def resumeProducing(self):\\n if self.reader is not None:\\n self.reader.resumeProducing()\\n\\n def stopProducing(self):\\n if self.reader is not None:\\n self.reader.stopReading()\\n self.consumer.unregisterProducer()\\n self.finished_deferred.callback(True)\\n\\n def pauseProducing(self):\\n if self.reader is not None:\\n self.reader.pauseProducing()\\n\\n def childDataReceived(self, fd, data):\\n self.consumer.write(data)\\n\\n def childConnectionLost(self, fd, reason):\\n self.stopProducing()\\n\"\n },\n {\n \"alpha_fraction\": 0.5887850522994995,\n \"alphanum_fraction\": 0.5908057689666748,\n \"avg_line_length\": 32.837608337402344,\n \"blob_id\": \"282c13013ab592f63e0dd23a011d04eb62c4bc54\",\n \"content_id\": \"1aa2f4f1fbbc13be2ca202d0fcec8f09d75e2ff9\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3959,\n \"license_type\": \"permissive\",\n \"max_line_length\": 96,\n \"num_lines\": 117,\n \"path\": \"/lbrynet/cryptstream/CryptBlob.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import binascii\\nimport logging\\nfrom Crypto.Cipher import AES\\nfrom lbrynet import conf\\nfrom lbrynet.core.BlobInfo import BlobInfo\\n\\n\\nlog = logging.getLogger(__name__)\\n\\n\\nclass CryptBlobInfo(BlobInfo):\\n def __init__(self, blob_hash, blob_num, length, iv):\\n BlobInfo.__init__(self, blob_hash, blob_num, length)\\n self.iv = iv\\n\\n\\nclass StreamBlobDecryptor(object):\\n def __init__(self, blob, key, iv, length):\\n self.blob = blob\\n self.key = key\\n self.iv = iv\\n self.length = length\\n self.buff = b''\\n self.len_read = 0\\n self.cipher = AES.new(self.key, AES.MODE_CBC, self.iv)\\n\\n def decrypt(self, write_func):\\n\\n def remove_padding(data):\\n pad_len = ord(data[-1])\\n data, padding = data[:-1 * pad_len], data[-1 * pad_len:]\\n for c in padding:\\n assert ord(c) == pad_len\\n return data\\n\\n def write_bytes():\\n if self.len_read < self.length:\\n num_bytes_to_decrypt = greatest_multiple(len(self.buff), self.cipher.block_size)\\n data_to_decrypt, self.buff = split(self.buff, num_bytes_to_decrypt)\\n write_func(self.cipher.decrypt(data_to_decrypt))\\n\\n def finish_decrypt():\\n assert len(self.buff) % self.cipher.block_size == 0\\n data_to_decrypt, self.buff = self.buff, b''\\n write_func(remove_padding(self.cipher.decrypt(data_to_decrypt)))\\n\\n def decrypt_bytes(data):\\n self.buff += data\\n self.len_read += len(data)\\n write_bytes()\\n\\n d = self.blob.read(decrypt_bytes)\\n d.addCallback(lambda _: finish_decrypt())\\n return d\\n\\n\\nclass CryptStreamBlobMaker(object):\\n \\\"\\\"\\\"This class encrypts data and writes it to a new blob\\\"\\\"\\\"\\n def __init__(self, key, iv, blob_num, blob):\\n self.key = key\\n self.iv = iv\\n self.blob_num = blob_num\\n self.blob = blob\\n self.cipher = AES.new(self.key, AES.MODE_CBC, self.iv)\\n self.buff = b''\\n self.length = 0\\n\\n def write(self, data):\\n max_bytes_to_write = conf.settings['BLOB_SIZE'] - self.length - 1\\n done = False\\n if max_bytes_to_write <= len(data):\\n num_bytes_to_write = max_bytes_to_write\\n done = True\\n else:\\n num_bytes_to_write = len(data)\\n self.length += num_bytes_to_write\\n data_to_write = data[:num_bytes_to_write]\\n self.buff += data_to_write\\n self._write_buffer()\\n return done, num_bytes_to_write\\n\\n def close(self):\\n log.debug(\\\"closing blob %s with plaintext len %s\\\", str(self.blob_num), str(self.length))\\n if self.length != 0:\\n self._close_buffer()\\n d = self.blob.close()\\n d.addCallback(self._return_info)\\n log.debug(\\\"called the finished_callback from CryptStreamBlobMaker.close\\\")\\n return d\\n\\n def _write_buffer(self):\\n num_bytes_to_encrypt = (len(self.buff) // AES.block_size) * AES.block_size\\n data_to_encrypt, self.buff = split(self.buff, num_bytes_to_encrypt)\\n encrypted_data = self.cipher.encrypt(data_to_encrypt)\\n self.blob.write(encrypted_data)\\n\\n def _close_buffer(self):\\n data_to_encrypt, self.buff = self.buff, b''\\n assert len(data_to_encrypt) < AES.block_size\\n pad_len = AES.block_size - len(data_to_encrypt)\\n padded_data = data_to_encrypt + chr(pad_len) * pad_len\\n self.length += pad_len\\n assert len(padded_data) == AES.block_size\\n encrypted_data = self.cipher.encrypt(padded_data)\\n self.blob.write(encrypted_data)\\n\\n def _return_info(self, blob_hash):\\n return CryptBlobInfo(blob_hash, self.blob_num, self.length, binascii.hexlify(self.iv))\\n\\n\\ndef greatest_multiple(a, b):\\n \\\"\\\"\\\"return the largest value `c`, that is a multiple of `b` and is <= `a`\\\"\\\"\\\"\\n return (a // b) * b\\n\\n\\ndef split(buff, cutoff):\\n return buff[:cutoff], buff[cutoff:]\\n\"\n },\n {\n \"alpha_fraction\": 0.5834539532661438,\n \"alphanum_fraction\": 0.584901750087738,\n \"avg_line_length\": 34.036231994628906,\n \"blob_id\": \"8a1642c9d7806ffa7a59d362c0928681bcb03b8a\",\n \"content_id\": \"1977c263c0331732e200e45124ced15671e52b90\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4835,\n \"license_type\": \"permissive\",\n \"max_line_length\": 99,\n \"num_lines\": 138,\n \"path\": \"/lbrynet/lbrylive/StreamDescriptor.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# pylint: skip-file\\nimport binascii\\nimport logging\\nfrom lbrynet.core.cryptoutils import get_lbry_hash_obj, verify_signature\\nfrom twisted.internet import defer, threads\\nfrom lbrynet.core.Error import DuplicateStreamHashError, InvalidStreamDescriptorError\\nfrom lbrynet.lbrylive.LiveBlob import LiveBlobInfo\\nfrom lbrynet.interfaces import IStreamDescriptorValidator\\nfrom zope.interface import implements\\n\\n\\nlog = logging.getLogger(__name__)\\n\\n\\nLiveStreamType = \\\"lbrylive\\\"\\n\\n\\ndef save_sd_info(stream_info_manager, sd_info, ignore_duplicate=False):\\n log.debug(\\\"Saving info for %s\\\", str(sd_info['stream_name']))\\n hex_stream_name = sd_info['stream_name']\\n public_key = sd_info['public_key']\\n key = sd_info['key']\\n stream_hash = sd_info['stream_hash']\\n raw_blobs = sd_info['blobs']\\n crypt_blobs = []\\n for blob in raw_blobs:\\n length = blob['length']\\n if length != 0:\\n blob_hash = blob['blob_hash']\\n else:\\n blob_hash = None\\n blob_num = blob['blob_num']\\n revision = blob['revision']\\n iv = blob['iv']\\n signature = blob['signature']\\n crypt_blobs.append(LiveBlobInfo(blob_hash, blob_num, length, iv, revision, signature))\\n log.debug(\\\"Trying to save stream info for %s\\\", str(hex_stream_name))\\n d = stream_info_manager.save_stream(stream_hash, public_key, hex_stream_name,\\n key, crypt_blobs)\\n\\n def check_if_duplicate(err):\\n if ignore_duplicate is True:\\n err.trap(DuplicateStreamHashError)\\n\\n d.addErrback(check_if_duplicate)\\n\\n d.addCallback(lambda _: stream_hash)\\n return d\\n\\n\\ndef get_sd_info(stream_info_manager, stream_hash, include_blobs):\\n d = stream_info_manager.get_stream_info(stream_hash)\\n\\n def format_info(stream_info):\\n fields = {}\\n fields['stream_type'] = LiveStreamType\\n fields['stream_name'] = stream_info[2]\\n fields['public_key'] = stream_info[0]\\n fields['key'] = stream_info[1]\\n fields['stream_hash'] = stream_hash\\n\\n def format_blobs(blobs):\\n formatted_blobs = []\\n for blob_hash, blob_num, revision, iv, length, signature in blobs:\\n blob = {}\\n if length != 0:\\n blob['blob_hash'] = blob_hash\\n blob['blob_num'] = blob_num\\n blob['revision'] = revision\\n blob['iv'] = iv\\n blob['length'] = length\\n blob['signature'] = signature\\n formatted_blobs.append(blob)\\n fields['blobs'] = formatted_blobs\\n return fields\\n\\n if include_blobs is True:\\n d = stream_info_manager.get_blobs_for_stream(stream_hash)\\n else:\\n d = defer.succeed([])\\n d.addCallback(format_blobs)\\n return d\\n\\n d.addCallback(format_info)\\n return d\\n\\n\\nclass LiveStreamDescriptorValidator(object):\\n implements(IStreamDescriptorValidator)\\n\\n def __init__(self, raw_info):\\n self.raw_info = raw_info\\n\\n def validate(self):\\n log.debug(\\\"Trying to validate stream descriptor for %s\\\", str(self.raw_info['stream_name']))\\n hex_stream_name = self.raw_info['stream_name']\\n public_key = self.raw_info['public_key']\\n key = self.raw_info['key']\\n stream_hash = self.raw_info['stream_hash']\\n h = get_lbry_hash_obj()\\n h.update(hex_stream_name)\\n h.update(public_key)\\n h.update(key)\\n if h.hexdigest() != stream_hash:\\n raise InvalidStreamDescriptorError(\\\"Stream hash does not match stream metadata\\\")\\n blobs = self.raw_info['blobs']\\n\\n def check_blob_signatures():\\n for blob in blobs:\\n length = blob['length']\\n if length != 0:\\n blob_hash = blob['blob_hash']\\n else:\\n blob_hash = None\\n blob_num = blob['blob_num']\\n revision = blob['revision']\\n iv = blob['iv']\\n signature = blob['signature']\\n hashsum = get_lbry_hash_obj()\\n hashsum.update(stream_hash)\\n if length != 0:\\n hashsum.update(blob_hash)\\n hashsum.update(str(blob_num))\\n hashsum.update(str(revision))\\n hashsum.update(iv)\\n hashsum.update(str(length))\\n if not verify_signature(hashsum.digest(), signature, public_key):\\n raise InvalidStreamDescriptorError(\\\"Invalid signature in stream descriptor\\\")\\n\\n return threads.deferToThread(check_blob_signatures)\\n\\n def info_to_show(self):\\n info = []\\n info.append((\\\"stream_name\\\", binascii.unhexlify(self.raw_info.get(\\\"stream_name\\\"))))\\n return info\\n\\n def get_length_of_stream(self):\\n return None\\n\"\n },\n {\n \"alpha_fraction\": 0.520906925201416,\n \"alphanum_fraction\": 0.5965842008590698,\n \"avg_line_length\": 40.378047943115234,\n \"blob_id\": \"1a4186dfe8a45eebec69b1491e9f8c1d187e077c\",\n \"content_id\": \"34529c18c488574d156a7d152284ba4ac435b32d\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3396,\n \"license_type\": \"permissive\",\n \"max_line_length\": 379,\n \"num_lines\": 82,\n \"path\": \"/tests/unit/lbrynet_daemon/test_ExchangeRateManager.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from lbrynet.metadata import Fee\\nfrom lbrynet.lbrynet_daemon import ExchangeRateManager\\nfrom lbrynet import conf\\nfrom lbrynet.core.Error import InvalidExchangeRateResponse\\n\\nfrom twisted.trial import unittest\\nfrom twisted.internet import defer\\nfrom tests import util\\n\\n\\nclass FeeFormatTest(unittest.TestCase):\\n def test_fee_created_with_correct_inputs(self):\\n fee_dict = {\\n 'USD': {\\n 'amount': 10.0,\\n 'address': \\\"bRcHraa8bYJZL7vkh5sNmGwPDERFUjGPP9\\\"\\n }\\n }\\n fee = Fee.FeeValidator(fee_dict)\\n self.assertEqual(10.0, fee['USD']['amount'])\\n\\n\\nclass FeeTest(unittest.TestCase):\\n def setUp(self):\\n util.resetTime(self)\\n\\n def test_fee_converts_to_lbc(self):\\n fee_dict = {\\n 'USD': {\\n 'amount': 10.0,\\n 'address': \\\"bRcHraa8bYJZL7vkh5sNmGwPDERFUjGPP9\\\"\\n }\\n }\\n rates = {\\n 'BTCLBC': {'spot': 3.0, 'ts': util.DEFAULT_ISO_TIME + 1},\\n 'USDBTC': {'spot': 2.0, 'ts': util.DEFAULT_ISO_TIME + 2}\\n }\\n manager = ExchangeRateManager.DummyExchangeRateManager(rates)\\n result = manager.to_lbc(fee_dict).amount\\n self.assertEqual(60.0, result)\\n\\nclass GoogleBTCFeedTest(unittest.TestCase):\\n\\n @defer.inlineCallbacks\\n def test_handle_response(self):\\n feed = ExchangeRateManager.GoogleBTCFeed()\\n\\n response = '// [ { \\\"id\\\": \\\"-2001\\\" ,\\\"t\\\" : \\\"USDBTC\\\" ,\\\"e\\\" : \\\"CURRENCY\\\" ,\\\"l\\\" : \\\"0.0008\\\" ,\\\"l_fix\\\" : \\\"\\\" ,\\\"l_cur\\\" : \\\"\\\" ,\\\"s\\\": \\\"0\\\" ,\\\"ltt\\\":\\\"\\\" ,\\\"lt\\\" : \\\"Feb 27, 10:21PM GMT\\\" ,\\\"lt_dts\\\" : \\\"2017-02-27T22:21:39Z\\\" ,\\\"c\\\" : \\\"-0.00001\\\" ,\\\"c_fix\\\" : \\\"\\\" ,\\\"cp\\\" : \\\"-0.917\\\" ,\\\"cp_fix\\\" : \\\"\\\" ,\\\"ccol\\\" : \\\"chr\\\" ,\\\"pcls_fix\\\" : \\\"\\\" } ]'\\n out = yield feed._handle_response(response)\\n self.assertEqual(0.0008, out)\\n\\n # check negative trade price throws exception\\n response = '// [ { \\\"id\\\": \\\"-2001\\\" ,\\\"t\\\" : \\\"USDBTC\\\" ,\\\"e\\\" : \\\"CURRENCY\\\" ,\\\"l\\\" : \\\"-0.0008\\\" ,\\\"l_fix\\\" : \\\"\\\" ,\\\"l_cur\\\" : \\\"\\\" ,\\\"s\\\": \\\"0\\\" ,\\\"ltt\\\":\\\"\\\" ,\\\"lt\\\" : \\\"Feb 27, 10:21PM GMT\\\" ,\\\"lt_dts\\\" : \\\"2017-02-27T22:21:39Z\\\" ,\\\"c\\\" : \\\"-0.00001\\\" ,\\\"c_fix\\\" : \\\"\\\" ,\\\"cp\\\" : \\\"-0.917\\\" ,\\\"cp_fix\\\" : \\\"\\\" ,\\\"ccol\\\" : \\\"chr\\\" ,\\\"pcls_fix\\\" : \\\"\\\" } ]'\\n with self.assertRaises(InvalidExchangeRateResponse):\\n out = yield feed._handle_response(response)\\n\\n\\n\\n\\nclass BittrexFeedTest(unittest.TestCase):\\n def setUp(self):\\n conf.initialize_settings()\\n\\n def tearDown(self):\\n conf.settings = None\\n\\n @defer.inlineCallbacks\\n def test_handle_response(self):\\n feed = ExchangeRateManager.BittrexFeed()\\n\\n response ='{\\\"success\\\":true,\\\"message\\\":\\\"\\\",\\\"result\\\":[{\\\"Id\\\":6902471,\\\"TimeStamp\\\":\\\"2017-02-27T23:41:52.213\\\",\\\"Quantity\\\":56.12611239,\\\"Price\\\":0.00001621,\\\"Total\\\":0.00090980,\\\"FillType\\\":\\\"PARTIAL_FILL\\\",\\\"OrderType\\\":\\\"SELL\\\"},{\\\"Id\\\":6902403,\\\"TimeStamp\\\":\\\"2017-02-27T23:31:40.463\\\",\\\"Quantity\\\":430.99988180,\\\"Price\\\":0.00001592,\\\"Total\\\":0.00686151,\\\"FillType\\\":\\\"PARTIAL_FILL\\\",\\\"OrderType\\\":\\\"SELL\\\"}]}'\\n out = yield feed._handle_response(response)\\n expected= 1.0 / ((0.00090980+0.00686151) / (56.12611239+430.99988180))\\n self.assertEqual(expected, out)\\n\\n response='{}'\\n with self.assertRaises(InvalidExchangeRateResponse):\\n out = yield feed._handle_response(response)\\n\\n response='{\\\"success\\\":true,\\\"result\\\":[]}'\\n with self.assertRaises(InvalidExchangeRateResponse):\\n out = yield feed._handle_response(response)\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5138888955116272,\n \"alphanum_fraction\": 0.5509259104728699,\n \"avg_line_length\": 32.230770111083984,\n \"blob_id\": \"36203f7e4bb9a34111712546594bb064f8ba78e8\",\n \"content_id\": \"06b9bcd9269bff5c4702578663736dc254e24e40\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1296,\n \"license_type\": \"permissive\",\n \"max_line_length\": 96,\n \"num_lines\": 39,\n \"path\": \"/tests/unit/analytics/test_events.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from lbrynet.analytics import events\\n\\nfrom twisted.trial import unittest\\n\\nfrom tests import util\\n\\n\\nclass EventsTest(unittest.TestCase):\\n def setUp(self):\\n util.resetTime(self)\\n self.event_generator = events.Events('any valid json datatype', 'lbry123', 'session456')\\n\\n def test_heartbeat(self):\\n result = self.event_generator.heartbeat()\\n desired_result = {\\n 'context': 'any valid json datatype',\\n 'event': 'Heartbeat',\\n 'properties': {'lbry_id': 'lbry123', 'session_id': 'session456'},\\n 'timestamp': '2016-01-01T00:00:00Z',\\n 'userId': 'lbry'\\n }\\n self.assertEqual(desired_result, result)\\n\\n def test_download_started(self):\\n result = self.event_generator.download_started('1', 'great gatsby')\\n desired_result = {\\n 'context': 'any valid json datatype',\\n 'event': 'Download Started',\\n 'properties': {\\n 'lbry_id': 'lbry123',\\n 'session_id': 'session456',\\n 'name': 'great gatsby',\\n 'stream_info': None,\\n 'download_id': '1'\\n },\\n 'timestamp': '2016-01-01T00:00:00Z',\\n 'userId': 'lbry'\\n }\\n self.assertEqual(desired_result, result)\\n\"\n },\n {\n \"alpha_fraction\": 0.5766825079917908,\n \"alphanum_fraction\": 0.5784338116645813,\n \"avg_line_length\": 30.2265625,\n \"blob_id\": \"4d21e9e82527f74e05f616a6f66377afdeee789d\",\n \"content_id\": \"203f2497350ab196c73cfb78f6fb413199b7efa3\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-unknown-license-reference\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3997,\n \"license_type\": \"permissive\",\n \"max_line_length\": 90,\n \"num_lines\": 128,\n \"path\": \"/lbrynet/analytics/events.py\",\n \"repo_name\": \"le-dit-gaga/lbry\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import logging\\n\\nfrom lbrynet.core import utils\\nfrom lbrynet.conf import LBRYUM_WALLET\\n\\nlog = logging.getLogger(__name__)\\n\\n\\ndef get_sd_hash(stream_info):\\n if not stream_info:\\n return None\\n try:\\n return stream_info['sources']['lbry_sd_hash']\\n except (KeyError, TypeError, ValueError):\\n log.debug('Failed to get sd_hash from %s', stream_info, exc_info=True)\\n return None\\n\\n\\nclass Events(object):\\n def __init__(self, context, installation_id, session_id):\\n \\\"\\\"\\\"Contains all of the analytics events that can be sent\\n\\n Attributes:\\n context: usually the output of `make_context`\\n installation_id: id unique to this installation. Can be anything, but\\n generally should be base58 encoded.\\n session_id: id for tracking events during this session. Can be\\n anything, but generally should be base58 encoded.\\n \\\"\\\"\\\"\\n self.context = context\\n self.installation_id = installation_id\\n self.session_id = session_id\\n\\n def update_context(self, context):\\n self.context = context\\n\\n def server_startup(self):\\n return self._event('Server Startup')\\n\\n def server_startup_success(self):\\n return self._event('Server Startup Success')\\n\\n def server_startup_error(self, message):\\n return self._event('Server Startup Error', {\\n 'message': message,\\n })\\n\\n def heartbeat(self):\\n return self._event('Heartbeat')\\n\\n def download_started(self, *args, **kwargs):\\n properties = download_properties(*args, **kwargs)\\n return self._event('Download Started', properties)\\n\\n def download_errored(self, *args, **kwargs):\\n properties = download_properties(*args, **kwargs)\\n return self._event('Download Errored', properties)\\n\\n def download_finished(self, *args, **kwargs):\\n properties = download_properties(*args, **kwargs)\\n return self._event('Download Finished', properties)\\n\\n def error(self, log_record):\\n \\\"\\\"\\\"Record when a log message of ERROR or higher was emitted\\\"\\\"\\\"\\n properties = {\\n 'message': log_record.message,\\n 'module': log_record.module,\\n 'lineno': log_record.lineno,\\n 'name': log_record.name,\\n 'traceback': log_record.exc_text,\\n }\\n return self._event('Error', properties)\\n\\n def metric_observed(self, metric_name, value):\\n properties = {\\n 'value': value,\\n }\\n return self._event(metric_name, properties)\\n\\n def _event(self, event, event_properties=None):\\n return {\\n 'userId': 'lbry',\\n 'event': event,\\n 'properties': self._properties(event_properties),\\n 'context': self.context,\\n 'timestamp': utils.isonow()\\n }\\n\\n def _properties(self, event_properties=None):\\n event_properties = event_properties or {}\\n properties = {\\n 'lbry_id': self.installation_id,\\n 'session_id': self.session_id,\\n }\\n properties.update(event_properties)\\n return properties\\n\\n\\ndef make_context(platform, wallet):\\n return {\\n 'app': {\\n 'name': 'lbrynet',\\n 'version': platform['lbrynet_version'],\\n 'python_version': platform['python_version'],\\n 'build': platform['build'],\\n 'wallet': {\\n 'name': wallet,\\n 'version': platform['lbryum_version'] if wallet == LBRYUM_WALLET else None\\n },\\n },\\n # TODO: expand os info to give linux/osx specific info\\n 'os': {\\n 'name': platform['os_system'],\\n 'version': platform['os_release']\\n },\\n 'library': {\\n 'name': 'lbrynet-analytics',\\n 'version': '1.0.0'\\n },\\n }\\n\\n\\ndef download_properties(id_, name, stream_info=None):\\n return {\\n 'download_id': id_,\\n 'name': name,\\n 'stream_info': get_sd_hash(stream_info)\\n }\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":33,"string":"33"}}},{"rowIdx":2983093,"cells":{"repo_name":{"kind":"string","value":"annelovepeace/DEND-Project-3-Cloud-Data-Warehouse-AWS"},"repo_url":{"kind":"string","value":"https://github.com/annelovepeace/DEND-Project-3-Cloud-Data-Warehouse-AWS"},"snapshot_id":{"kind":"string","value":"6a3d301c6e0b0c9bc3cdd8a9fb9621f4c787139f"},"revision_id":{"kind":"string","value":"0d23bbddda786ec5a635bda53332eeed1616be51"},"directory_id":{"kind":"string","value":"88ef36ef3adf4eef184fc490d40bb9f644c6334c"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-01-08T20:13:29.342363","string":"2022-01-08T20:13:29.342363"},"revision_date":{"kind":"timestamp","value":"2019-08-01T03:28:15","string":"2019-08-01T03:28:15"},"committer_date":{"kind":"timestamp","value":"2019-08-01T03:28:15","string":"2019-08-01T03:28:15"},"github_id":{"kind":"number","value":197876700,"string":"197,876,700"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.38746315240859985,"alphanum_fraction":0.39980342984199524,"avg_line_length":44.336631774902344,"blob_id":"e917082768ef091c03157a9a92084b0dbb0c9d59","content_id":"d765a2cd26357b1118358d7ed4b3efac3f482343","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":9157,"license_type":"no_license","max_line_length":181,"num_lines":202,"path":"/sql_queries.py","repo_name":"annelovepeace/DEND-Project-3-Cloud-Data-Warehouse-AWS","src_encoding":"UTF-8","text":"import configparser\n\n\n# CONFIG\nconfig = configparser.ConfigParser()\nconfig.read('dwh.cfg')\n\n# DROP TABLES\n\nstaging_events_table_drop = \"DROP TABLE IF EXISTS staging_events\"\nstaging_songs_table_drop = \"DROP TABLE IF EXISTS staging_songs\"\nsongplay_table_drop = \"DROP TABLE IF EXISTS songplay\"\nuser_table_drop = \"DROP TABLE IF EXISTS users\"\nsong_table_drop = \"DROP TABLE IF EXISTS song\"\nartist_table_drop = \"DROP TABLE IF EXISTS artist\"\ntime_table_drop = \"DROP TABLE IF EXISTS time\"\n\n# CREATE TABLES\n\nstaging_events_table_create = (\"\"\"CREATE TABLE IF NOT EXISTS staging_events (\n se_eventid INT IDENTITY(0,1) PRIMARY KEY,\n se_artist VARCHAR(255),\n se_auth VARCHAR(255),\n se_firstname VARCHAR(255),\n se_gender VARCHAR(1),\n se_iteminsession INT,\n se_lastname VARCHAR(255),\n se_length NUMERIC,\n se_level VARCHAR(255),\n se_location VARCHAR(255),\n se_method VARCHAR(255),\n se_page VARCHAR(255),\n se_registration VARCHAR(255),\n se_sessionid INT,\n se_song VARCHAR(255),\n se_status INT,\n se_ts BIGINT,\n se_useragent VARCHAR(255),\n se_userid INT\n );\n \"\"\")\n\nstaging_songs_table_create = (\"\"\"CREATE TABLE IF NOT EXISTS staging_songs (\n song_id VARCHAR(255) PRIMARY KEY,\n num_songs INT,\n title VARCHAR(255),\n artist_name VARCHAR(255),\n artist_latitude NUMERIC,\n year INT,\n duration NUMERIC,\n artist_id VARCHAR(255),\n artist_longitude NUMERIC,\n artist_location VARCHAR(255)\n );\n \"\"\")\n\nsongplay_table_create = (\"\"\"CREATE TABLE IF NOT EXISTS songplay (\n sp_songplayid INT IDENTITY(0,1) PRIMARY KEY,\n sp_starttime TIMESTAMP NOT NULL,\n sp_userid INT NOT NULL,\n sp_level VARCHAR(255),\n sp_songid VARCHAR(255) NOT NULL,\n sp_artistid VARCHAR(255) NOT NULL,\n sp_sessionid INT,\n sp_location VARCHAR(255),\n sp_useragent VARCHAR(255)\n );\n \"\"\")\n\nuser_table_create = (\"\"\"CREATE TABLE IF NOT EXISTS users (\n u_userid int PRIMARY KEY,\n u_firstname VARCHAR(255),\n u_lastname VARCHAR(255),\n u_gender VARCHAR(255),\n u_level VARCHAR(255)\n );\n \"\"\")\n\nsong_table_create = (\"\"\"CREATE TABLE IF NOT EXISTS song (\n s_songid VARCHAR(255) PRIMARY KEY,\n s_title VARCHAR(255),\n s_artistid VARCHAR(255) NOT NULL,\n s_year INT,\n s_duration NUMERIC\n );\n \"\"\")\n\nartist_table_create = (\"\"\"CREATE TABLE IF NOT EXISTS artist (\n a_artistid VARCHAR(255) PRIMARY KEY,\n a_name VARCHAR(255),\n a_location VARCHAR(255),\n a_latitude NUMERIC,\n a_longitude NUMERIC\n );\n \"\"\")\n\ntime_table_create = (\"\"\"CREATE TABLE IF NOT EXISTS time (\n t_starttime TIMESTAMP PRIMARY KEY,\n t_hour INT,\n t_day INT,\n t_week INT,\n t_month INT,\n t_year INT,\n t_weekday INT\n );\n \"\"\")\n\n# STAGING TABLES\n\nstaging_events_copy = (\"\"\"\n copy staging_events from '{}'\n credentials 'aws_iam_role={}'\n region 'us-west-2'\n compupdate off\n JSON '{}'\n \"\"\").format(config.get('S3', 'LOG_DATA'),\n config.get('IAM_ROLE', 'ARN'),\n config.get('S3', 'LOG_JSONPATH'))\n\nstaging_songs_copy = (\"\"\"\n copy staging_songs from '{}'\n credentials 'aws_iam_role={}'\n region 'us-west-2'\n compupdate off\n JSON 'auto'\n \"\"\").format(config.get('S3', 'SONG_DATA'),\n config.get('IAM_ROLE', 'ARN'))\n\n# FINAL TABLES\n\nsongplay_table_insert = (\"\"\"INSERT INTO songplay (sp_starttime, sp_userid,\n sp_level, sp_songid,\n sp_artistid, sp_sessionid,\n sp_location, sp_useragent)\n SELECT DISTINCT\n timestamp 'epoch' + se_ts/1000 *\n interval '1 second' AS sp_starttime,\n se_userid,\n se_level,\n song_id,\n artist_id,\n se_sessionid,\n se_location,\n se_useragent\n FROM staging_events se, staging_songs ss\n WHERE se.se_page='NextSong'\n and se.se_song=ss.title\n \"\"\")\n\nuser_table_insert = (\"\"\"INSERT INTO users (u_userid, u_firstname, u_lastname, u_gender, u_level)\n SELECT DISTINCT\n se_userid,\n se_firstname,\n se_lastname,\n se_gender,\n se_level\n FROM staging_events\n WHERE se_page='NextSong'\n \"\"\")\n\nsong_table_insert = (\"\"\"INSERT INTO song (s_songid, s_title, s_artistid, s_year, s_duration)\n SELECT DISTINCT\n song_id,\n title,\n artist_id,\n year,\n duration\n FROM staging_songs\n \"\"\")\n\nartist_table_insert = (\"\"\"INSERT INTO artist (a_artistid, a_name, a_location, a_latitude, a_longitude)\n SELECT DISTINCT\n artist_id,\n artist_name,\n artist_location,\n artist_latitude,\n artist_longitude\n FROM staging_songs\n \"\"\")\n\ntime_table_insert = (\"\"\"INSERT INTO time (t_starttime, t_hour, t_day, t_week,\n t_month, t_year, t_weekday)\n SELECT DISTINCT\n t_starttime,\n EXTRACT(h from t_starttime) AS t_hour,\n EXTRACT(d from t_starttime) AS t_day,\n EXTRACT(w from t_starttime) AS t_week,\n EXTRACT(mon from t_starttime) AS t_month,\n EXTRACT(y from t_starttime) AS t_year,\n EXTRACT(dw from t_starttime) AS t_weekday\n FROM (SELECT distinct timestamp 'epoch' +\n se_ts/1000 * interval '1 second'\n AS t_starttime\n FROM staging_events)\n \"\"\")\n\n# QUERY LISTS\n\ncreate_table_queries = [staging_events_table_create, staging_songs_table_create, songplay_table_create, user_table_create, song_table_create, artist_table_create, time_table_create]\ndrop_table_queries = [staging_events_table_drop, staging_songs_table_drop, songplay_table_drop, user_table_drop, song_table_drop, artist_table_drop, time_table_drop]\ncopy_table_queries = [staging_events_copy, staging_songs_copy]\ninsert_table_queries = [songplay_table_insert, user_table_insert, song_table_insert, artist_table_insert, time_table_insert]"},{"alpha_fraction":0.6665777564048767,"alphanum_fraction":0.6887170076370239,"avg_line_length":40.655555725097656,"blob_id":"017050373f813ddc568a929444b621f810cee910","content_id":"ccaa53ecfa2f9bcbfbbc9c8e9701ddef3129cfdd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":3749,"license_type":"no_license","max_line_length":284,"num_lines":90,"path":"/README.md","repo_name":"annelovepeace/DEND-Project-3-Cloud-Data-Warehouse-AWS","src_encoding":"UTF-8","text":"# Project Summary\n\n### Background\nThis project is to help a startup called <strong>Sparkify</strong> to understand what songs users are listening to. Sparkify has been collecting on songs and user activity on their new music streaming app. \nAs their use base and song database grow, they decide to move their processes and data onto cloud with two Amazon Web Services <strong>S3</strong> (simple storage service) and <strong>Redshift</strong> (Internet hosting service and data warehouse product). \n\n\n### Datasets\nTwo datasets that reside in S3:\n\n<ol>\n <li><strong>Song Dataset</strong></li>\n > Each file is in JSON format and contains metadata about a song and the artist of that song. <br>\n > File names are like 'song_data/A/B/C/TRABCEI128F424C983.json' etc. <br>\n > In each file, data are like: {\"num_songs\": 1, \"artist_id\": \"ARJIE2Y1187B994AB7\", \"artist_latitude\": null, \"artist_longitude\": null, \"artist_location\": \"\", \"artist_name\": \"Line Renaud\", \"song_id\": \"SOUPIRU12A6D4FA1E1\", \"title\": \"Der Kleine Dompfaff\", \"duration\": 152.92036, \"year\": 0}\n <li><strong>Log Dataset</strong></li>\n > Each file is in JSON format and contains logs on user activity on the app <br>\n > File names are like 'log_data/2018/11/2018-11-12-events.json' etc. <br>\n > below is an example of what the data in a log file, 2018-11-12-events.json, looks like.\n</ol>\n\n\n \n\n### Tasks\n1. **Redshift cluster setup**\n - launch a readshift database dc2.large cluster with 4 nodes\n - create an IAM role that has read access to S3\n2. **Designing tables in _'sql_queries.py'_**\n - design fact and dimension tables for a star schema\\\n fact table: songplays\\\n dimension tables: users, songs, artists, time\n - design staging tables: staging_events, staging_songs\n - write a CREATE statement for each of these tables\n - write a DROP statements to drop tables if the tables already exist\n3. **Creating tables in _'create_tables.py'_**\n - connect to the database\n - create fact, dimension, staging tables\n4. **Buidling ETL pipeline in _'etl.py'_**\n - implement the logic to load data from S3 to staging tables on Redshift\n - implement the logic to load data from staging tables to analytics tables on Redshift\n5. **Cluster cleanup**\n - remember to delete the Redshift cluster when finish\n\n---\n# To run the Python scripts\n 1. Get _[Cluster]_ and _[IAM_ROLE]_ from Redshift to fill dwh.cfg file\n 2. Click _File_ -> _New_ -> _Console_ at top menu bar\n 3. Select kernel _Python3_\n 4. Type `%run create_tables.py` in the console cell\n 5. Click _Run_ -> _Run selected cell_ at top menu bar\n 6. Type `%run etl.py` in the console cell\n 7. Click _Run_ -> _Run selected cell_ at top menu bar\n \n \n---\n# Files in the repository\nThe project workspace includes five files:\n- **_create_tables.py_** creates the fact and dimension tables for the star schema in Redshift.\n- **_etl.py_** loads data from S3 into staging tables on Redshift and then process that data into the fact and dimension tables on Redshift.\n- **_sql_queries.py_** contains all sql queries, which are imported into the last two files above.\n- **_dwh.cfg_** stores cluster, IAM role, S3 file path information, which are imported into the last three files above.\n- **_README.md_** this file provides discussion on the project.\n \n\n---\n# Results\n\n**Analysis on songplay table**\n\n1. The most popular song is 'You're The One'\n\n query:\n \n  \n \n result:\n \n \n\n \n2. The most popular artistis 'Dwight Yoakam'\n\n query:\n \n  \n\n result:\n\n \n"}],"string":"[\n {\n \"alpha_fraction\": 0.38746315240859985,\n \"alphanum_fraction\": 0.39980342984199524,\n \"avg_line_length\": 44.336631774902344,\n \"blob_id\": \"e917082768ef091c03157a9a92084b0dbb0c9d59\",\n \"content_id\": \"d765a2cd26357b1118358d7ed4b3efac3f482343\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 9157,\n \"license_type\": \"no_license\",\n \"max_line_length\": 181,\n \"num_lines\": 202,\n \"path\": \"/sql_queries.py\",\n \"repo_name\": \"annelovepeace/DEND-Project-3-Cloud-Data-Warehouse-AWS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import configparser\\n\\n\\n# CONFIG\\nconfig = configparser.ConfigParser()\\nconfig.read('dwh.cfg')\\n\\n# DROP TABLES\\n\\nstaging_events_table_drop = \\\"DROP TABLE IF EXISTS staging_events\\\"\\nstaging_songs_table_drop = \\\"DROP TABLE IF EXISTS staging_songs\\\"\\nsongplay_table_drop = \\\"DROP TABLE IF EXISTS songplay\\\"\\nuser_table_drop = \\\"DROP TABLE IF EXISTS users\\\"\\nsong_table_drop = \\\"DROP TABLE IF EXISTS song\\\"\\nartist_table_drop = \\\"DROP TABLE IF EXISTS artist\\\"\\ntime_table_drop = \\\"DROP TABLE IF EXISTS time\\\"\\n\\n# CREATE TABLES\\n\\nstaging_events_table_create = (\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS staging_events (\\n se_eventid INT IDENTITY(0,1) PRIMARY KEY,\\n se_artist VARCHAR(255),\\n se_auth VARCHAR(255),\\n se_firstname VARCHAR(255),\\n se_gender VARCHAR(1),\\n se_iteminsession INT,\\n se_lastname VARCHAR(255),\\n se_length NUMERIC,\\n se_level VARCHAR(255),\\n se_location VARCHAR(255),\\n se_method VARCHAR(255),\\n se_page VARCHAR(255),\\n se_registration VARCHAR(255),\\n se_sessionid INT,\\n se_song VARCHAR(255),\\n se_status INT,\\n se_ts BIGINT,\\n se_useragent VARCHAR(255),\\n se_userid INT\\n );\\n \\\"\\\"\\\")\\n\\nstaging_songs_table_create = (\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS staging_songs (\\n song_id VARCHAR(255) PRIMARY KEY,\\n num_songs INT,\\n title VARCHAR(255),\\n artist_name VARCHAR(255),\\n artist_latitude NUMERIC,\\n year INT,\\n duration NUMERIC,\\n artist_id VARCHAR(255),\\n artist_longitude NUMERIC,\\n artist_location VARCHAR(255)\\n );\\n \\\"\\\"\\\")\\n\\nsongplay_table_create = (\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS songplay (\\n sp_songplayid INT IDENTITY(0,1) PRIMARY KEY,\\n sp_starttime TIMESTAMP NOT NULL,\\n sp_userid INT NOT NULL,\\n sp_level VARCHAR(255),\\n sp_songid VARCHAR(255) NOT NULL,\\n sp_artistid VARCHAR(255) NOT NULL,\\n sp_sessionid INT,\\n sp_location VARCHAR(255),\\n sp_useragent VARCHAR(255)\\n );\\n \\\"\\\"\\\")\\n\\nuser_table_create = (\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS users (\\n u_userid int PRIMARY KEY,\\n u_firstname VARCHAR(255),\\n u_lastname VARCHAR(255),\\n u_gender VARCHAR(255),\\n u_level VARCHAR(255)\\n );\\n \\\"\\\"\\\")\\n\\nsong_table_create = (\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS song (\\n s_songid VARCHAR(255) PRIMARY KEY,\\n s_title VARCHAR(255),\\n s_artistid VARCHAR(255) NOT NULL,\\n s_year INT,\\n s_duration NUMERIC\\n );\\n \\\"\\\"\\\")\\n\\nartist_table_create = (\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS artist (\\n a_artistid VARCHAR(255) PRIMARY KEY,\\n a_name VARCHAR(255),\\n a_location VARCHAR(255),\\n a_latitude NUMERIC,\\n a_longitude NUMERIC\\n );\\n \\\"\\\"\\\")\\n\\ntime_table_create = (\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS time (\\n t_starttime TIMESTAMP PRIMARY KEY,\\n t_hour INT,\\n t_day INT,\\n t_week INT,\\n t_month INT,\\n t_year INT,\\n t_weekday INT\\n );\\n \\\"\\\"\\\")\\n\\n# STAGING TABLES\\n\\nstaging_events_copy = (\\\"\\\"\\\"\\n copy staging_events from '{}'\\n credentials 'aws_iam_role={}'\\n region 'us-west-2'\\n compupdate off\\n JSON '{}'\\n \\\"\\\"\\\").format(config.get('S3', 'LOG_DATA'),\\n config.get('IAM_ROLE', 'ARN'),\\n config.get('S3', 'LOG_JSONPATH'))\\n\\nstaging_songs_copy = (\\\"\\\"\\\"\\n copy staging_songs from '{}'\\n credentials 'aws_iam_role={}'\\n region 'us-west-2'\\n compupdate off\\n JSON 'auto'\\n \\\"\\\"\\\").format(config.get('S3', 'SONG_DATA'),\\n config.get('IAM_ROLE', 'ARN'))\\n\\n# FINAL TABLES\\n\\nsongplay_table_insert = (\\\"\\\"\\\"INSERT INTO songplay (sp_starttime, sp_userid,\\n sp_level, sp_songid,\\n sp_artistid, sp_sessionid,\\n sp_location, sp_useragent)\\n SELECT DISTINCT\\n timestamp 'epoch' + se_ts/1000 *\\n interval '1 second' AS sp_starttime,\\n se_userid,\\n se_level,\\n song_id,\\n artist_id,\\n se_sessionid,\\n se_location,\\n se_useragent\\n FROM staging_events se, staging_songs ss\\n WHERE se.se_page='NextSong'\\n and se.se_song=ss.title\\n \\\"\\\"\\\")\\n\\nuser_table_insert = (\\\"\\\"\\\"INSERT INTO users (u_userid, u_firstname, u_lastname, u_gender, u_level)\\n SELECT DISTINCT\\n se_userid,\\n se_firstname,\\n se_lastname,\\n se_gender,\\n se_level\\n FROM staging_events\\n WHERE se_page='NextSong'\\n \\\"\\\"\\\")\\n\\nsong_table_insert = (\\\"\\\"\\\"INSERT INTO song (s_songid, s_title, s_artistid, s_year, s_duration)\\n SELECT DISTINCT\\n song_id,\\n title,\\n artist_id,\\n year,\\n duration\\n FROM staging_songs\\n \\\"\\\"\\\")\\n\\nartist_table_insert = (\\\"\\\"\\\"INSERT INTO artist (a_artistid, a_name, a_location, a_latitude, a_longitude)\\n SELECT DISTINCT\\n artist_id,\\n artist_name,\\n artist_location,\\n artist_latitude,\\n artist_longitude\\n FROM staging_songs\\n \\\"\\\"\\\")\\n\\ntime_table_insert = (\\\"\\\"\\\"INSERT INTO time (t_starttime, t_hour, t_day, t_week,\\n t_month, t_year, t_weekday)\\n SELECT DISTINCT\\n t_starttime,\\n EXTRACT(h from t_starttime) AS t_hour,\\n EXTRACT(d from t_starttime) AS t_day,\\n EXTRACT(w from t_starttime) AS t_week,\\n EXTRACT(mon from t_starttime) AS t_month,\\n EXTRACT(y from t_starttime) AS t_year,\\n EXTRACT(dw from t_starttime) AS t_weekday\\n FROM (SELECT distinct timestamp 'epoch' +\\n se_ts/1000 * interval '1 second'\\n AS t_starttime\\n FROM staging_events)\\n \\\"\\\"\\\")\\n\\n# QUERY LISTS\\n\\ncreate_table_queries = [staging_events_table_create, staging_songs_table_create, songplay_table_create, user_table_create, song_table_create, artist_table_create, time_table_create]\\ndrop_table_queries = [staging_events_table_drop, staging_songs_table_drop, songplay_table_drop, user_table_drop, song_table_drop, artist_table_drop, time_table_drop]\\ncopy_table_queries = [staging_events_copy, staging_songs_copy]\\ninsert_table_queries = [songplay_table_insert, user_table_insert, song_table_insert, artist_table_insert, time_table_insert]\"\n },\n {\n \"alpha_fraction\": 0.6665777564048767,\n \"alphanum_fraction\": 0.6887170076370239,\n \"avg_line_length\": 40.655555725097656,\n \"blob_id\": \"017050373f813ddc568a929444b621f810cee910\",\n \"content_id\": \"ccaa53ecfa2f9bcbfbbc9c8e9701ddef3129cfdd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 3749,\n \"license_type\": \"no_license\",\n \"max_line_length\": 284,\n \"num_lines\": 90,\n \"path\": \"/README.md\",\n \"repo_name\": \"annelovepeace/DEND-Project-3-Cloud-Data-Warehouse-AWS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Project Summary\\n\\n### Background\\nThis project is to help a startup called <strong>Sparkify</strong> to understand what songs users are listening to. Sparkify has been collecting on songs and user activity on their new music streaming app. \\nAs their use base and song database grow, they decide to move their processes and data onto cloud with two Amazon Web Services <strong>S3</strong> (simple storage service) and <strong>Redshift</strong> (Internet hosting service and data warehouse product). \\n\\n\\n### Datasets\\nTwo datasets that reside in S3:\\n\\n<ol>\\n <li><strong>Song Dataset</strong></li>\\n > Each file is in JSON format and contains metadata about a song and the artist of that song. <br>\\n > File names are like 'song_data/A/B/C/TRABCEI128F424C983.json' etc. <br>\\n > In each file, data are like: {\\\"num_songs\\\": 1, \\\"artist_id\\\": \\\"ARJIE2Y1187B994AB7\\\", \\\"artist_latitude\\\": null, \\\"artist_longitude\\\": null, \\\"artist_location\\\": \\\"\\\", \\\"artist_name\\\": \\\"Line Renaud\\\", \\\"song_id\\\": \\\"SOUPIRU12A6D4FA1E1\\\", \\\"title\\\": \\\"Der Kleine Dompfaff\\\", \\\"duration\\\": 152.92036, \\\"year\\\": 0}\\n <li><strong>Log Dataset</strong></li>\\n > Each file is in JSON format and contains logs on user activity on the app <br>\\n > File names are like 'log_data/2018/11/2018-11-12-events.json' etc. <br>\\n > below is an example of what the data in a log file, 2018-11-12-events.json, looks like.\\n</ol>\\n\\n\\n \\n\\n### Tasks\\n1. **Redshift cluster setup**\\n - launch a readshift database dc2.large cluster with 4 nodes\\n - create an IAM role that has read access to S3\\n2. **Designing tables in _'sql_queries.py'_**\\n - design fact and dimension tables for a star schema\\\\\\n fact table: songplays\\\\\\n dimension tables: users, songs, artists, time\\n - design staging tables: staging_events, staging_songs\\n - write a CREATE statement for each of these tables\\n - write a DROP statements to drop tables if the tables already exist\\n3. **Creating tables in _'create_tables.py'_**\\n - connect to the database\\n - create fact, dimension, staging tables\\n4. **Buidling ETL pipeline in _'etl.py'_**\\n - implement the logic to load data from S3 to staging tables on Redshift\\n - implement the logic to load data from staging tables to analytics tables on Redshift\\n5. **Cluster cleanup**\\n - remember to delete the Redshift cluster when finish\\n\\n---\\n# To run the Python scripts\\n 1. Get _[Cluster]_ and _[IAM_ROLE]_ from Redshift to fill dwh.cfg file\\n 2. Click _File_ -> _New_ -> _Console_ at top menu bar\\n 3. Select kernel _Python3_\\n 4. Type `%run create_tables.py` in the console cell\\n 5. Click _Run_ -> _Run selected cell_ at top menu bar\\n 6. Type `%run etl.py` in the console cell\\n 7. Click _Run_ -> _Run selected cell_ at top menu bar\\n \\n \\n---\\n# Files in the repository\\nThe project workspace includes five files:\\n- **_create_tables.py_** creates the fact and dimension tables for the star schema in Redshift.\\n- **_etl.py_** loads data from S3 into staging tables on Redshift and then process that data into the fact and dimension tables on Redshift.\\n- **_sql_queries.py_** contains all sql queries, which are imported into the last two files above.\\n- **_dwh.cfg_** stores cluster, IAM role, S3 file path information, which are imported into the last three files above.\\n- **_README.md_** this file provides discussion on the project.\\n \\n\\n---\\n# Results\\n\\n**Analysis on songplay table**\\n\\n1. The most popular song is 'You're The One'\\n\\n query:\\n \\n  \\n \\n result:\\n \\n \\n\\n \\n2. The most popular artistis 'Dwight Yoakam'\\n\\n query:\\n \\n  \\n\\n result:\\n\\n \\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2983094,"cells":{"repo_name":{"kind":"string","value":"Parsely/elasticsearch-hll-rollups"},"repo_url":{"kind":"string","value":"https://github.com/Parsely/elasticsearch-hll-rollups"},"snapshot_id":{"kind":"string","value":"6650e3fefd7de2f5a5af32fcd2d02e60bbdd4524"},"revision_id":{"kind":"string","value":"17fa722aa53c2918831326c53512bca4f11fa301"},"directory_id":{"kind":"string","value":"c5f2e7ed920131680a7fad9835b92266bb134e1d"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-08-27T05:35:11.316722","string":"2023-08-27T05:35:11.316722"},"revision_date":{"kind":"timestamp","value":"2019-09-16T20:46:02","string":"2019-09-16T20:46:02"},"committer_date":{"kind":"timestamp","value":"2019-09-16T20:46:02","string":"2019-09-16T20:46:02"},"github_id":{"kind":"number","value":208894205,"string":"208,894,205"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6778169274330139,"alphanum_fraction":0.7473591566085815,"avg_line_length":55.79999923706055,"blob_id":"b9faaf04776abc9f9b716fd50d87cc4827acf4d5","content_id":"b06c462deb80567c14f35b3c63d0f8b87e7d8c9b","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":2272,"license_type":"permissive","max_line_length":227,"num_lines":40,"path":"/benchmarks/README.md","repo_name":"Parsely/elasticsearch-hll-rollups","src_encoding":"UTF-8","text":"# To get running\n\n## Generate some data\n\n`generate_data.py` will handle generating documents with various numbers\nof UUIDs in them:\n\n1. Tiny: 100k docs, 1 to 4 ids -- `python generate_data.py 100000 --num-services 100 --jobs-per-service 1000 --jobs-per-container 2 --job-count-variation 100 > rally-tracks/documents-tiny.json`\n2. Small: 100k docs, 50 to 150 ids -- `python generate_data.py 100000 --num-services 100 --jobs-per-service 1000 --jobs-per-container 100 --job-count-variation 50 > rally-tracks/documents-small.json`\n3. Med: 100k docs, 400 to 600 ids -- `python generate_data.py 100000 --num-services 100 --jobs-per-service 1000 --jobs-per-container 500 --job-count-variation 20 > rally-tracks/documents-med.json`\n4. Large: 100k docs, 1440 to 2160 ids -- `python generate_data.py 100000 --num-services 100 --jobs-per-service 1000 --jobs-per-container 1800 --job-count-variation 20 > rally-tracks/documents-large.json`\n5. XLarge: 100k docs, 2250 to 2750 ids -- `python generate_data.py 100000 --num-services 100 --jobs-per-service 1000 --jobs-per-container 2500 --job-count-variation 10 > rally-tracks/documents-xlarge.json`\nNOTE: HLL Precision 11 has an LC -> HLL threshold of 1800\n\n`track.json` expects `documents_tiny.json` right now. If you want to\nuse a different file, you'll need to edit that.\n\n\n## Put this at the bottom ~/.rally/rally.ini\n\nThis will enable the plugin within esrally. Use your own path, obviously.\n\n\n```\n[distributions]\nrelease.cache = true\nplugin.elasticsearch-hll-rollups.release.url=file:///Users/kfb/src/ct/elasticsearch-hll-rollups/build/distributions/elasticsearch-hll-rollups-0.0.1.zip\n```\n\n## Run `esrally`\n\nFor HLLs: `esrally --track-path=./rally-tracks/cardinality-perf --distribution-version=6.8.2 --elasticsearch-plugins=\"elasticsearch-hll-rollups\" --on-error=abort --car=\"4gheap\" --challenge=cardinality-hll`\n\nFor a baseline using `long`s: `esrally --track-path=./rally-tracks/cardinality-perf --distribution-version=6.8.2 --elasticsearch-plugins=\"elasticsearch-hll-rollups\" --on-error=abort --car=\"4gheap\" --challenge=cardinality-long`\n\n## TODO:\n\n- [ ] Generate files and put them in S3\n- [ ] Analyze some Parse.ly diagnostic data to generate a realistic sample\n- [ ] Figure out a better way to switch between data files.\n"},{"alpha_fraction":0.6712213754653931,"alphanum_fraction":0.6749936938285828,"avg_line_length":49.12604904174805,"blob_id":"d24a1fb2d7ea4c947da4f0e3112cafd63058da25","content_id":"e89a905dcfe195aa31ba7be12a785a290b5fe101","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":11929,"license_type":"permissive","max_line_length":149,"num_lines":238,"path":"/src/test/java/com/parsely/elasticsearch/index/mapper/hll/HLLFieldMapperTests.java","repo_name":"Parsely/elasticsearch-hll-rollups","src_encoding":"UTF-8","text":"/*\n * Licensed to Elasticsearch under one or more contributor\n * license agreements. See the NOTICE file distributed with\n * this work for additional information regarding copyright\n * ownership. Elasticsearch licenses this file to you under\n * the Apache License, Version 2.0 (the \"License\"); you may\n * not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n * KIND, either express or implied. See the License for the\n * specific language governing permissions and limitations\n * under the License.\n */\n\npackage com.parsely.elasticsearch.index.mapper.hll;\n\nimport com.parsely.elasticsearch.search.aggregations.hll.HyperLogLogPlusPlus;\nimport org.apache.lucene.index.DocValuesType;\nimport org.apache.lucene.index.IndexOptions;\nimport org.apache.lucene.index.IndexableField;\nimport org.elasticsearch.common.Strings;\nimport org.elasticsearch.common.bytes.BytesReference;\nimport org.elasticsearch.common.compress.CompressedXContent;\nimport org.elasticsearch.common.xcontent.XContentFactory;\nimport org.elasticsearch.common.xcontent.XContentType;\nimport org.elasticsearch.index.IndexService;\nimport org.elasticsearch.index.mapper.DocumentMapper;\nimport org.elasticsearch.index.mapper.DocumentMapperParser;\nimport org.elasticsearch.index.mapper.MapperParsingException;\nimport org.elasticsearch.index.mapper.ParsedDocument;\nimport org.elasticsearch.index.mapper.SourceToParse;\nimport org.elasticsearch.index.query.QueryShardContext;\nimport org.elasticsearch.indices.mapper.MapperRegistry;\nimport org.elasticsearch.plugins.MapperPlugin;\nimport org.elasticsearch.plugins.Plugin;\nimport org.elasticsearch.test.ESSingleNodeTestCase;\nimport org.elasticsearch.test.InternalSettingsPlugin;\nimport org.junit.Before;\n\nimport java.io.IOException;\nimport java.util.Collection;\nimport java.util.Collections;\nimport java.util.function.Supplier;\nimport java.util.stream.IntStream;\n\nimport static org.hamcrest.Matchers.containsString;\n\npublic class HLLFieldMapperTests extends ESSingleNodeTestCase {\n\n MapperRegistry mapperRegistry;\n IndexService indexService;\n DocumentMapperParser parser;\n\n @Before\n public void setup() {\n indexService = createIndex(\"test\");\n mapperRegistry = new MapperRegistry(\n Collections.singletonMap(HLLFieldMapper.CONTENT_TYPE, new HLLFieldMapper.TypeParser()),\n Collections.emptyMap(), MapperPlugin.NOOP_FIELD_FILTER);\n Supplier<QueryShardContext> queryShardContext = () -> {\n return indexService.newQueryShardContext(0, null, () -> { throw new UnsupportedOperationException(); }, null);\n };\n parser = new DocumentMapperParser(indexService.getIndexSettings(), indexService.mapperService(), indexService.getIndexAnalyzers(),\n indexService.xContentRegistry(), indexService.similarityService(), mapperRegistry, queryShardContext);\n }\n\n @Override\n protected Collection<Class<? extends Plugin>> getPlugins() {\n return pluginList(InternalSettingsPlugin.class);\n }\n\n private String getTestMappingString() throws IOException {\n return Strings.toString(XContentFactory.jsonBuilder().startObject().startObject(\"test-doc-type\")\n .startObject(\"properties\").startObject(\"test-hll-field\")\n .field(\"type\", \"hll\")\n .endObject().endObject().endObject().endObject());\n }\n\n private String getTestMappingString(long precision) throws IOException {\n return Strings.toString(XContentFactory.jsonBuilder().startObject().startObject(\"test-doc-type\")\n .startObject(\"properties\").startObject(\"test-hll-field\")\n .field(\"type\", \"hll\")\n .field(\"precision\", precision)\n .endObject().endObject().endObject().endObject());\n }\n\n public void testDefaults() throws Exception {\n // Create the mapper and verify the default value for precision\n DocumentMapper mapper = parser.parse(\"test-doc-type\", new CompressedXContent(getTestMappingString()));\n HLLFieldMapper hllMapper = (HLLFieldMapper) mapper.mappers().getMapper(\"test-hll-field\");\n assertEquals(HLLFieldMapper.Defaults.PRECISION, hllMapper.fieldType().getPrecision());\n\n // Parse a test document and validate output\n ParsedDocument parsedDoc = mapper.parse(SourceToParse.source(\"test\", \"test-doc-type\", \"1\", BytesReference.bytes(XContentFactory.jsonBuilder()\n .startObject()\n .field(\"test-hll-field\", \"foo\")\n .endObject()),\n XContentType.JSON));\n IndexableField[] fields = parsedDoc.rootDoc().getFields(\"test-hll-field\");\n IndexableField field = fields[0];\n assertEquals(IndexOptions.NONE, field.fieldType().indexOptions());\n assertEquals(DocValuesType.BINARY, field.fieldType().docValuesType());\n // TODO: Is it worth checking the bytes here so we know if the underlying HLL implementation changes?\n assertEquals(7, field.binaryValue().length);\n }\n\n public void testChangingPrecision() throws Exception {\n int[] numbers = IntStream.range(0, 10000).toArray();\n BytesReference docBytes = BytesReference.bytes(XContentFactory.jsonBuilder()\n .startObject()\n .field(\"test-hll-field\", numbers)\n .endObject());\n\n // Create the mapper and verify precision is 18\n DocumentMapper mapper = parser.parse(\"test-doc-type\", new CompressedXContent(getTestMappingString(18)));\n HLLFieldMapper hllMapper = (HLLFieldMapper) mapper.mappers().getMapper(\"test-hll-field\");\n assertEquals(18, hllMapper.fieldType().getPrecision());\n\n // Parse the test document and validate output bytes length\n ParsedDocument parsedDoc = mapper.parse(SourceToParse.source(\"test\", \"test-doc-type\", \"1\", docBytes, XContentType.JSON));\n IndexableField field = parsedDoc.rootDoc().getFields(\"test-hll-field\")[0];\n assertEquals(40000, field.binaryValue().length);\n\n // Create the mapper and verify precision is now 14\n mapper = parser.parse(\"test-doc-type\", new CompressedXContent(getTestMappingString(10)));\n hllMapper = (HLLFieldMapper) mapper.mappers().getMapper(\"test-hll-field\");\n assertEquals(10, hllMapper.fieldType().getPrecision());\n\n // Parse a test document and validate output\n parsedDoc = mapper.parse(SourceToParse.source(\"test\", \"test-doc-type\", \"1\", docBytes, XContentType.JSON));\n field = parsedDoc.rootDoc().getFields(\"test-hll-field\")[0];\n assertEquals(1026, field.binaryValue().length);\n }\n\n public void testPrecisionTooLow() throws Exception {\n // Create mapper where precision is one too low.\n int precision = HyperLogLogPlusPlus.MIN_PRECISION - 1;\n try {\n DocumentMapper mapper = parser.parse(\"test-doc-type\", new CompressedXContent(getTestMappingString(precision)));\n } catch (MapperParsingException e) {\n assertTrue(e.getMessage().contains(\"Setting [precision] must be greater than\"));\n }\n }\n\n public void testPrecisionTooHigh() throws Exception {\n // Create mapper where precision is one too high.\n int precision = HyperLogLogPlusPlus.MAX_PRECISION + 1;\n try {\n DocumentMapper mapper = parser.parse(\"test-doc-type\", new CompressedXContent(getTestMappingString(precision)));\n } catch (MapperParsingException e) {\n assertTrue(e.getMessage().contains(\"Setting [precision] must be less than\"));\n }\n }\n\n public void testDocValuesSettingNotAllowed() throws Exception {\n String mapping = Strings.toString(XContentFactory.jsonBuilder().startObject().startObject(\"type\")\n .startObject(\"properties\").startObject(\"field\")\n .field(\"type\", \"hll\")\n .field(\"doc_values\", false)\n .endObject().endObject().endObject().endObject());\n try {\n parser.parse(\"type\", new CompressedXContent(mapping));\n fail(\"expected a mapper parsing exception\");\n } catch (MapperParsingException e) {\n assertTrue(e.getMessage().contains(\"Setting [doc_values] cannot be modified\"));\n }\n\n // Even setting to the default is not allowed, the setting is invalid\n mapping = Strings.toString(XContentFactory.jsonBuilder().startObject().startObject(\"type\")\n .startObject(\"properties\").startObject(\"field\")\n .field(\"type\", \"hll\")\n .field(\"doc_values\", true)\n .endObject().endObject().endObject().endObject());\n try {\n parser.parse(\"type\", new CompressedXContent(mapping));\n fail(\"expected a mapper parsing exception\");\n } catch (MapperParsingException e) {\n assertTrue(e.getMessage().contains(\"Setting [doc_values] cannot be modified\"));\n }\n }\n\n public void testIndexSettingNotAllowed() throws Exception {\n String mapping = Strings.toString(XContentFactory.jsonBuilder().startObject().startObject(\"type\")\n .startObject(\"properties\").startObject(\"field\")\n .field(\"type\", \"hll\")\n .field(\"index\", \"not_analyzed\")\n .endObject().endObject().endObject().endObject());\n try {\n parser.parse(\"type\", new CompressedXContent(mapping));\n fail(\"expected a mapper parsing exception\");\n } catch (MapperParsingException e) {\n assertTrue(e.getMessage().contains(\"Setting [index] cannot be modified\"));\n }\n\n // even setting to the default is not allowed, the setting is invalid\n mapping = Strings.toString(XContentFactory.jsonBuilder().startObject().startObject(\"type\")\n .startObject(\"properties\").startObject(\"field\")\n .field(\"type\", \"hll\")\n .field(\"index\", \"no\")\n .endObject().endObject().endObject().endObject());\n try {\n parser.parse(\"type\", new CompressedXContent(mapping));\n fail(\"expected a mapper parsing exception\");\n } catch (MapperParsingException e) {\n assertTrue(e.getMessage().contains(\"Setting [index] cannot be modified\"));\n }\n }\n\n public void testEmptyName() throws Exception {\n String mapping = Strings.toString(XContentFactory.jsonBuilder().startObject().startObject(\"type\")\n .startObject(\"properties\").startObject(\"\")\n .field(\"type\", \"hll\")\n .endObject().endObject().endObject().endObject());\n\n IllegalArgumentException e = expectThrows(IllegalArgumentException.class,\n () -> parser.parse(\"type\", new CompressedXContent(mapping))\n );\n assertThat(e.getMessage(), containsString(\"name cannot be empty string\"));\n }\n\n public void testArrayOfItems() throws Exception {\n DocumentMapper mapper = parser.parse(\"test-doc-type\", new CompressedXContent(getTestMappingString()));\n ParsedDocument parsedDoc = mapper.parse(SourceToParse.source(\"test\", \"test-doc-type\", \"1\", BytesReference.bytes(XContentFactory.jsonBuilder()\n .startObject()\n .field(\"test-hll-field\", new String[] {\"foo\", \"bar\", \"baz\"})\n .endObject()),\n XContentType.JSON));\n IndexableField[] fields = parsedDoc.rootDoc().getFields(\"test-hll-field\");\n IndexableField field = fields[0];\n // TODO: Is it worth checking the bytes here so we know if the underlying HLL implementation changes?\n assertEquals(15, field.binaryValue().length);\n }\n}"},{"alpha_fraction":0.5967890024185181,"alphanum_fraction":0.6061926484107971,"avg_line_length":29.27777862548828,"blob_id":"ca1832aefbdf543596a921709ba63020ba827968","content_id":"cad82322be594cc91bb982ebfa4a99911058234e","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4360,"license_type":"permissive","max_line_length":96,"num_lines":144,"path":"/benchmarks/generate_data.py","repo_name":"Parsely/elasticsearch-hll-rollups","src_encoding":"UTF-8","text":"import argparse\nimport json\nimport random\nimport sys\nimport uuid\nimport mmh3\n\nfrom itertools import cycle\n\n\ndef _init(num_services, jobs_per_service):\n output = []\n for _ in range(num_services):\n output.append(\n {\n \"name\": f\"service-{str(uuid.uuid4())}\",\n \"jobs\": [\n mmh3.hash64(str(uuid.uuid4()))[0] for _ in range(jobs_per_service)\n ],\n }\n )\n return output\n\n\ndef generate_data(\n num_containers,\n num_services,\n jobs_per_service,\n jobs_per_container,\n container_job_count_variation_pct,\n running_job_pct,\n):\n services = cycle(_init(num_services, jobs_per_service))\n variance = int(jobs_per_container * (container_job_count_variation_pct / 100))\n min_jobs = max(jobs_per_container - variance, 1)\n max_jobs = jobs_per_container + variance\n running_job_pct = running_job_pct / 100\n\n for _ in range(num_containers):\n svc = next(services)\n all_jobs = random.choices(svc[\"jobs\"], k=random.randint(min_jobs, max_jobs))\n running_jobs = random.choices(all_jobs, k=int(len(all_jobs) * running_job_pct))\n yield {\n \"service_id\": svc[\"name\"],\n \"container_id\": f\"container-{str(uuid.uuid4())}\",\n \"all_jobs\": all_jobs,\n \"all_jobs_len\": len(all_jobs),\n \"running_jobs\": running_jobs,\n \"running_jobs_len\": len(running_jobs),\n }\n\n\ndef main(\n num_containers,\n num_services,\n jobs_per_service,\n jobs_per_container,\n container_job_count_variation_pct,\n running_job_pct,\n):\n generator = generate_data(\n num_containers,\n num_services,\n jobs_per_service,\n jobs_per_container,\n container_job_count_variation_pct,\n running_job_pct,\n )\n for _ in range(num_containers):\n print(json.dumps(next(generator)))\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\n usage=\"python generate_data.py\",\n description=\"\"\"\n Generate data for esrally tracks.\n\n Generates data that could be diagnostic info from a queue/worker\n style system which is useful for testing high cardinality sets in\n ES.\n\n The model creates a number of services, which each have a set of\n jobs. Each ES doc generated models the state of a container\n running one of the services. It'll have a subset of jobs that\n it knows about, some of which are running.\n\n Jobs can be shared between containers. This means a\n cardinality agg would be needed to know how many jobs are handled\n by any given service.\n \"\"\",\n formatter_class=argparse.RawTextHelpFormatter,\n )\n parser.add_argument(\n \"num_containers\",\n metavar=\"NUM_CONTAINERS\",\n type=int,\n help=\"The number of containers (ES docs) to model.\",\n )\n parser.add_argument(\n \"--num-services\",\n metavar=\"NUM_SERVICES\",\n type=int,\n help=\"Number of services to model. [default: NUM_CONTAINERS/10]\",\n )\n parser.add_argument(\n \"--jobs-per-service\",\n type=int,\n help=\"The number of jobs (UUIDs) assigned to each service. [default: NUM_CONTAINERS/2]\",\n )\n parser.add_argument(\n \"--jobs-per-container\",\n type=int,\n default=50,\n help=\"The number of jobs (UUIDs) assigned to each container (ES doc). Will be \"\n \"affected by job count variation as well. [default: 50]\",\n )\n parser.add_argument(\n \"--job-count-variation\",\n type=int,\n default=10,\n help=\"How much job counts should vary between docs. Should be between 0 and 100. \"\n \"For example a value of 10 gives a +/- 10%% variation in the length of all_jobs \"\n \"between docs. [default: 10]\",\n )\n parser.add_argument(\n \"--running-job-percentage\",\n type=int,\n default=10,\n help=\"Percent of `all_jobs` that should be in `running_jobs`. [default: 10]\",\n )\n args = parser.parse_args()\n\n num_services = args.num_services or int(args.num_containers / 10)\n jobs_per_service = args.jobs_per_service or int(args.num_containers / 2)\n\n main(\n args.num_containers,\n num_services,\n jobs_per_service,\n args.jobs_per_container,\n args.job_count_variation,\n args.running_job_percentage,\n )\n"},{"alpha_fraction":0.6989619135856628,"alphanum_fraction":0.7162629961967468,"avg_line_length":22.432432174682617,"blob_id":"43cec23fc36c042fb4359a5fbe0cbb18d0a9c262","content_id":"12c86a32ada070d05ec5420bcd14197c5cf7ca7d","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Gradle","length_bytes":867,"license_type":"permissive","max_line_length":121,"num_lines":37,"path":"/build.gradle","repo_name":"Parsely/elasticsearch-hll-rollups","src_encoding":"UTF-8","text":"buildscript {\n repositories {\n mavenLocal()\n mavenCentral()\n jcenter()\n }\n\n dependencies {\n classpath \"org.elasticsearch.gradle:build-tools:6.8.2\"\n }\n}\n\ngroup = 'com.parsely'\nversion = '0.0.1'\n\napply plugin: 'java'\napply plugin: 'idea'\napply plugin: 'elasticsearch.esplugin'\n\n\ndependencies {\n compile \"org.elasticsearch:elasticsearch:6.8.2\"\n compile \"org.elasticsearch.plugin:aggs-matrix-stats-client:6.8.2\"\n testCompile \"org.elasticsearch.test:framework:6.8.2\"\n}\n\ncheckstyleMain.enabled = false\ncheckstyleTest.enabled = false\n\n\nlicenseFile = rootProject.file('LICENSE.txt')\nnoticeFile = rootProject.file('NOTICE.txt')\n\nesplugin {\n description 'The Mapper HLL plugin allows you to compute serialized HLLs at index-time and to store them in the index.'\n classname 'com.parsely.elasticsearch.plugin.HLLRollupPlugin'\n}\n"},{"alpha_fraction":0.7680890560150146,"alphanum_fraction":0.771799623966217,"avg_line_length":61.19230651855469,"blob_id":"7af05bbcf8fe2d5e00d36ec376406cc419196154","content_id":"7e97c376891c94701c101867e595de640226da76","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1617,"license_type":"permissive","max_line_length":117,"num_lines":26,"path":"/README.md","repo_name":"Parsely/elasticsearch-hll-rollups","src_encoding":"UTF-8","text":"# elasticsearch-hll-rollups\n\nElasticsearch plugins to support \"HLL Rollups\". The goal is to store the computed HyperLogLogPlusPlus sketch\ninstead of raw values. This serialized version is different from Elasticsearch, which will store all values and\nthen compute the HLL at query time. What this plugin loses is flexibility (precision is fixed at index-time) it gains\nin disk savings and overall scalability for very high-cardinality fields.\n\n## State of the Project\n\nThis is an initial implementation of the idea which runs on Elasticsearch 6.8.2. We've validated the correctness\nof the plugin, but there's a lot more work to be done in terms of optimizations and code cleanups. Our team\ndoesn't write a lot of Java, so there's likely more than a few issues to be worked through.\n\nThere's also a lot of code copied from the ES codebase. I'm sure there's a lot to be done cleaning\nthat up so we only keep or rewrite the bits we need. Since there was a cardinality agg already there\nit made to most sense to follow that model.\n\nTODO:\n\n- [ ] Rename `precision` to `precisionThreshold` so it matches the param for `cardinality`.\n- [ ] Consider skipping the HLL step when indexing LC-sized datasets. It may speed up indexing and\n later querying to instead store either the raw values of the mmh3 hash that the LC uses.\n- [ ] Write up a section about how storing the HLL can help with GDPR compliance (and validate this assumption).\n- [ ] Figure out how reindexing is going to work with all of this. Do we need a way to return a base64 encoded\n HLL to the user? \n- [ ] Many more tests for the aggregation code.\n"},{"alpha_fraction":0.6279229521751404,"alphanum_fraction":0.6444291472434998,"avg_line_length":40.5523796081543,"blob_id":"fb1737fab590d1ee9b649d526757ca3e93e8a40d","content_id":"dc4a3932131990a8c45659a42cc95ebe6250a32f","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":4362,"license_type":"permissive","max_line_length":113,"num_lines":105,"path":"/src/test/java/com/parsely/elasticsearch/search/aggregations/metrics/HyperLogLogPlusPlusTests.java","repo_name":"Parsely/elasticsearch-hll-rollups","src_encoding":"UTF-8","text":"/*\n * Licensed to Elasticsearch under one or more contributor\n * license agreements. See the NOTICE file distributed with\n * this work for additional information regarding copyright\n * ownership. Elasticsearch licenses this file to you under\n * the Apache License, Version 2.0 (the \"License\"); you may\n * not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n * KIND, either express or implied. See the License for the\n * specific language governing permissions and limitations\n * under the License.\n */\n\npackage com.parsely.elasticsearch.search.aggregations.metrics;\n\nimport com.carrotsearch.hppc.BitMixer;\nimport org.elasticsearch.common.util.BigArrays;\nimport com.parsely.elasticsearch.search.aggregations.hll.HyperLogLogPlusPlus;\nimport org.elasticsearch.test.ESTestCase;\n\nimport org.elasticsearch.common.io.stream.InputStreamStreamInput;\nimport org.elasticsearch.common.io.stream.OutputStreamStreamOutput;\n\nimport static com.parsely.elasticsearch.search.aggregations.hll.HyperLogLogPlusPlus.MAX_PRECISION;\nimport static org.hamcrest.Matchers.closeTo;\n\nimport java.io.ByteArrayInputStream;\nimport java.io.ByteArrayOutputStream;\nimport java.io.IOException;\n\nimport static org.apache.logging.log4j.Level.INFO;\n\n\npublic class HyperLogLogPlusPlusTests extends ESTestCase {\n\n private HyperLogLogPlusPlus makeRandomSketch(int size) {\n // utility to make a random HLL of a given size\n HyperLogLogPlusPlus counts = new HyperLogLogPlusPlus(MAX_PRECISION, BigArrays.NON_RECYCLING_INSTANCE, 0);\n for (int i = 0; i < size; i++) {\n final int n = randomInt(100_000_000);\n final long hash = BitMixer.mix64(n);\n counts.collect(0, hash);\n }\n return counts;\n\n }\n\n public void testReadWrite() {\n // We'll test reading and writing HLL bytes for different cardinality sizes\n // ranging from 1 to 10 million. Beyond 10M, it takes awhile to generate the\n // random data.\n int[] sizes = new int[] {\n 1,\n 10,\n 100,\n 1_000,\n 10_000,\n 100_000,\n 1_000_000, // 1 million\n 10_000_000 // 10 million\n };\n\n for (int size : sizes) {\n // Our goal with the `baos` and `osso` is to just turn the `HyperLogLogPlusPlus.writeTo(...)`\n // call into a byte[]\n ByteArrayOutputStream baos = new ByteArrayOutputStream();\n OutputStreamStreamOutput osso = new OutputStreamStreamOutput(baos);\n HyperLogLogPlusPlus counts = makeRandomSketch(size);\n long cardinality = counts.cardinality(0);\n long precision = counts.precision();\n long maxBucket = counts.maxBucket();\n logger.printf(INFO, \"%25s: [%d]\", \"Initial size of\", size);\n logger.printf(INFO,\"%25s: [%d]\", \"Initial cardinality of\", cardinality);\n try {\n counts.writeTo(0, osso);\n } catch (IOException e) {\n fail();\n }\n // now we have the HLL as a byte[]\n byte[] hllBytes = baos.toByteArray();\n logger.printf(INFO,\"%25s: [%d]\", \"Storing bytes on-disk\", hllBytes.length);\n logger.printf(INFO,\"---\");\n // Our goal with `bais` and `issi` is to deserialize the byte[] into the HLL using\n // the `HyperLogLogPlusPlus.readFrom(...)` method\n ByteArrayInputStream bais = new ByteArrayInputStream(hllBytes);\n InputStreamStreamInput issi = new InputStreamStreamInput(bais);\n try {\n counts = HyperLogLogPlusPlus.readFrom(issi, BigArrays.NON_RECYCLING_INSTANCE);\n } catch (IOException e) {\n fail();\n }\n // now `counts` is our deserialized HLL; this assertion\n // confirms it by comparing their values\n assertEquals(cardinality, counts.cardinality(0));\n assertEquals(precision, counts.precision());\n assertEquals(maxBucket, counts.maxBucket());\n }\n }\n}"}],"string":"[\n {\n \"alpha_fraction\": 0.6778169274330139,\n \"alphanum_fraction\": 0.7473591566085815,\n \"avg_line_length\": 55.79999923706055,\n \"blob_id\": \"b9faaf04776abc9f9b716fd50d87cc4827acf4d5\",\n \"content_id\": \"b06c462deb80567c14f35b3c63d0f8b87e7d8c9b\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 2272,\n \"license_type\": \"permissive\",\n \"max_line_length\": 227,\n \"num_lines\": 40,\n \"path\": \"/benchmarks/README.md\",\n \"repo_name\": \"Parsely/elasticsearch-hll-rollups\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# To get running\\n\\n## Generate some data\\n\\n`generate_data.py` will handle generating documents with various numbers\\nof UUIDs in them:\\n\\n1. Tiny: 100k docs, 1 to 4 ids -- `python generate_data.py 100000 --num-services 100 --jobs-per-service 1000 --jobs-per-container 2 --job-count-variation 100 > rally-tracks/documents-tiny.json`\\n2. Small: 100k docs, 50 to 150 ids -- `python generate_data.py 100000 --num-services 100 --jobs-per-service 1000 --jobs-per-container 100 --job-count-variation 50 > rally-tracks/documents-small.json`\\n3. Med: 100k docs, 400 to 600 ids -- `python generate_data.py 100000 --num-services 100 --jobs-per-service 1000 --jobs-per-container 500 --job-count-variation 20 > rally-tracks/documents-med.json`\\n4. Large: 100k docs, 1440 to 2160 ids -- `python generate_data.py 100000 --num-services 100 --jobs-per-service 1000 --jobs-per-container 1800 --job-count-variation 20 > rally-tracks/documents-large.json`\\n5. XLarge: 100k docs, 2250 to 2750 ids -- `python generate_data.py 100000 --num-services 100 --jobs-per-service 1000 --jobs-per-container 2500 --job-count-variation 10 > rally-tracks/documents-xlarge.json`\\nNOTE: HLL Precision 11 has an LC -> HLL threshold of 1800\\n\\n`track.json` expects `documents_tiny.json` right now. If you want to\\nuse a different file, you'll need to edit that.\\n\\n\\n## Put this at the bottom ~/.rally/rally.ini\\n\\nThis will enable the plugin within esrally. Use your own path, obviously.\\n\\n\\n```\\n[distributions]\\nrelease.cache = true\\nplugin.elasticsearch-hll-rollups.release.url=file:///Users/kfb/src/ct/elasticsearch-hll-rollups/build/distributions/elasticsearch-hll-rollups-0.0.1.zip\\n```\\n\\n## Run `esrally`\\n\\nFor HLLs: `esrally --track-path=./rally-tracks/cardinality-perf --distribution-version=6.8.2 --elasticsearch-plugins=\\\"elasticsearch-hll-rollups\\\" --on-error=abort --car=\\\"4gheap\\\" --challenge=cardinality-hll`\\n\\nFor a baseline using `long`s: `esrally --track-path=./rally-tracks/cardinality-perf --distribution-version=6.8.2 --elasticsearch-plugins=\\\"elasticsearch-hll-rollups\\\" --on-error=abort --car=\\\"4gheap\\\" --challenge=cardinality-long`\\n\\n## TODO:\\n\\n- [ ] Generate files and put them in S3\\n- [ ] Analyze some Parse.ly diagnostic data to generate a realistic sample\\n- [ ] Figure out a better way to switch between data files.\\n\"\n },\n {\n \"alpha_fraction\": 0.6712213754653931,\n \"alphanum_fraction\": 0.6749936938285828,\n \"avg_line_length\": 49.12604904174805,\n \"blob_id\": \"d24a1fb2d7ea4c947da4f0e3112cafd63058da25\",\n \"content_id\": \"e89a905dcfe195aa31ba7be12a785a290b5fe101\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 11929,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 238,\n \"path\": \"/src/test/java/com/parsely/elasticsearch/index/mapper/hll/HLLFieldMapperTests.java\",\n \"repo_name\": \"Parsely/elasticsearch-hll-rollups\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/*\\n * Licensed to Elasticsearch under one or more contributor\\n * license agreements. See the NOTICE file distributed with\\n * this work for additional information regarding copyright\\n * ownership. Elasticsearch licenses this file to you under\\n * the Apache License, Version 2.0 (the \\\"License\\\"); you may\\n * not use this file except in compliance with the License.\\n * You may obtain a copy of the License at\\n *\\n * http://www.apache.org/licenses/LICENSE-2.0\\n *\\n * Unless required by applicable law or agreed to in writing,\\n * software distributed under the License is distributed on an\\n * \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\\n * KIND, either express or implied. See the License for the\\n * specific language governing permissions and limitations\\n * under the License.\\n */\\n\\npackage com.parsely.elasticsearch.index.mapper.hll;\\n\\nimport com.parsely.elasticsearch.search.aggregations.hll.HyperLogLogPlusPlus;\\nimport org.apache.lucene.index.DocValuesType;\\nimport org.apache.lucene.index.IndexOptions;\\nimport org.apache.lucene.index.IndexableField;\\nimport org.elasticsearch.common.Strings;\\nimport org.elasticsearch.common.bytes.BytesReference;\\nimport org.elasticsearch.common.compress.CompressedXContent;\\nimport org.elasticsearch.common.xcontent.XContentFactory;\\nimport org.elasticsearch.common.xcontent.XContentType;\\nimport org.elasticsearch.index.IndexService;\\nimport org.elasticsearch.index.mapper.DocumentMapper;\\nimport org.elasticsearch.index.mapper.DocumentMapperParser;\\nimport org.elasticsearch.index.mapper.MapperParsingException;\\nimport org.elasticsearch.index.mapper.ParsedDocument;\\nimport org.elasticsearch.index.mapper.SourceToParse;\\nimport org.elasticsearch.index.query.QueryShardContext;\\nimport org.elasticsearch.indices.mapper.MapperRegistry;\\nimport org.elasticsearch.plugins.MapperPlugin;\\nimport org.elasticsearch.plugins.Plugin;\\nimport org.elasticsearch.test.ESSingleNodeTestCase;\\nimport org.elasticsearch.test.InternalSettingsPlugin;\\nimport org.junit.Before;\\n\\nimport java.io.IOException;\\nimport java.util.Collection;\\nimport java.util.Collections;\\nimport java.util.function.Supplier;\\nimport java.util.stream.IntStream;\\n\\nimport static org.hamcrest.Matchers.containsString;\\n\\npublic class HLLFieldMapperTests extends ESSingleNodeTestCase {\\n\\n MapperRegistry mapperRegistry;\\n IndexService indexService;\\n DocumentMapperParser parser;\\n\\n @Before\\n public void setup() {\\n indexService = createIndex(\\\"test\\\");\\n mapperRegistry = new MapperRegistry(\\n Collections.singletonMap(HLLFieldMapper.CONTENT_TYPE, new HLLFieldMapper.TypeParser()),\\n Collections.emptyMap(), MapperPlugin.NOOP_FIELD_FILTER);\\n Supplier<QueryShardContext> queryShardContext = () -> {\\n return indexService.newQueryShardContext(0, null, () -> { throw new UnsupportedOperationException(); }, null);\\n };\\n parser = new DocumentMapperParser(indexService.getIndexSettings(), indexService.mapperService(), indexService.getIndexAnalyzers(),\\n indexService.xContentRegistry(), indexService.similarityService(), mapperRegistry, queryShardContext);\\n }\\n\\n @Override\\n protected Collection<Class<? extends Plugin>> getPlugins() {\\n return pluginList(InternalSettingsPlugin.class);\\n }\\n\\n private String getTestMappingString() throws IOException {\\n return Strings.toString(XContentFactory.jsonBuilder().startObject().startObject(\\\"test-doc-type\\\")\\n .startObject(\\\"properties\\\").startObject(\\\"test-hll-field\\\")\\n .field(\\\"type\\\", \\\"hll\\\")\\n .endObject().endObject().endObject().endObject());\\n }\\n\\n private String getTestMappingString(long precision) throws IOException {\\n return Strings.toString(XContentFactory.jsonBuilder().startObject().startObject(\\\"test-doc-type\\\")\\n .startObject(\\\"properties\\\").startObject(\\\"test-hll-field\\\")\\n .field(\\\"type\\\", \\\"hll\\\")\\n .field(\\\"precision\\\", precision)\\n .endObject().endObject().endObject().endObject());\\n }\\n\\n public void testDefaults() throws Exception {\\n // Create the mapper and verify the default value for precision\\n DocumentMapper mapper = parser.parse(\\\"test-doc-type\\\", new CompressedXContent(getTestMappingString()));\\n HLLFieldMapper hllMapper = (HLLFieldMapper) mapper.mappers().getMapper(\\\"test-hll-field\\\");\\n assertEquals(HLLFieldMapper.Defaults.PRECISION, hllMapper.fieldType().getPrecision());\\n\\n // Parse a test document and validate output\\n ParsedDocument parsedDoc = mapper.parse(SourceToParse.source(\\\"test\\\", \\\"test-doc-type\\\", \\\"1\\\", BytesReference.bytes(XContentFactory.jsonBuilder()\\n .startObject()\\n .field(\\\"test-hll-field\\\", \\\"foo\\\")\\n .endObject()),\\n XContentType.JSON));\\n IndexableField[] fields = parsedDoc.rootDoc().getFields(\\\"test-hll-field\\\");\\n IndexableField field = fields[0];\\n assertEquals(IndexOptions.NONE, field.fieldType().indexOptions());\\n assertEquals(DocValuesType.BINARY, field.fieldType().docValuesType());\\n // TODO: Is it worth checking the bytes here so we know if the underlying HLL implementation changes?\\n assertEquals(7, field.binaryValue().length);\\n }\\n\\n public void testChangingPrecision() throws Exception {\\n int[] numbers = IntStream.range(0, 10000).toArray();\\n BytesReference docBytes = BytesReference.bytes(XContentFactory.jsonBuilder()\\n .startObject()\\n .field(\\\"test-hll-field\\\", numbers)\\n .endObject());\\n\\n // Create the mapper and verify precision is 18\\n DocumentMapper mapper = parser.parse(\\\"test-doc-type\\\", new CompressedXContent(getTestMappingString(18)));\\n HLLFieldMapper hllMapper = (HLLFieldMapper) mapper.mappers().getMapper(\\\"test-hll-field\\\");\\n assertEquals(18, hllMapper.fieldType().getPrecision());\\n\\n // Parse the test document and validate output bytes length\\n ParsedDocument parsedDoc = mapper.parse(SourceToParse.source(\\\"test\\\", \\\"test-doc-type\\\", \\\"1\\\", docBytes, XContentType.JSON));\\n IndexableField field = parsedDoc.rootDoc().getFields(\\\"test-hll-field\\\")[0];\\n assertEquals(40000, field.binaryValue().length);\\n\\n // Create the mapper and verify precision is now 14\\n mapper = parser.parse(\\\"test-doc-type\\\", new CompressedXContent(getTestMappingString(10)));\\n hllMapper = (HLLFieldMapper) mapper.mappers().getMapper(\\\"test-hll-field\\\");\\n assertEquals(10, hllMapper.fieldType().getPrecision());\\n\\n // Parse a test document and validate output\\n parsedDoc = mapper.parse(SourceToParse.source(\\\"test\\\", \\\"test-doc-type\\\", \\\"1\\\", docBytes, XContentType.JSON));\\n field = parsedDoc.rootDoc().getFields(\\\"test-hll-field\\\")[0];\\n assertEquals(1026, field.binaryValue().length);\\n }\\n\\n public void testPrecisionTooLow() throws Exception {\\n // Create mapper where precision is one too low.\\n int precision = HyperLogLogPlusPlus.MIN_PRECISION - 1;\\n try {\\n DocumentMapper mapper = parser.parse(\\\"test-doc-type\\\", new CompressedXContent(getTestMappingString(precision)));\\n } catch (MapperParsingException e) {\\n assertTrue(e.getMessage().contains(\\\"Setting [precision] must be greater than\\\"));\\n }\\n }\\n\\n public void testPrecisionTooHigh() throws Exception {\\n // Create mapper where precision is one too high.\\n int precision = HyperLogLogPlusPlus.MAX_PRECISION + 1;\\n try {\\n DocumentMapper mapper = parser.parse(\\\"test-doc-type\\\", new CompressedXContent(getTestMappingString(precision)));\\n } catch (MapperParsingException e) {\\n assertTrue(e.getMessage().contains(\\\"Setting [precision] must be less than\\\"));\\n }\\n }\\n\\n public void testDocValuesSettingNotAllowed() throws Exception {\\n String mapping = Strings.toString(XContentFactory.jsonBuilder().startObject().startObject(\\\"type\\\")\\n .startObject(\\\"properties\\\").startObject(\\\"field\\\")\\n .field(\\\"type\\\", \\\"hll\\\")\\n .field(\\\"doc_values\\\", false)\\n .endObject().endObject().endObject().endObject());\\n try {\\n parser.parse(\\\"type\\\", new CompressedXContent(mapping));\\n fail(\\\"expected a mapper parsing exception\\\");\\n } catch (MapperParsingException e) {\\n assertTrue(e.getMessage().contains(\\\"Setting [doc_values] cannot be modified\\\"));\\n }\\n\\n // Even setting to the default is not allowed, the setting is invalid\\n mapping = Strings.toString(XContentFactory.jsonBuilder().startObject().startObject(\\\"type\\\")\\n .startObject(\\\"properties\\\").startObject(\\\"field\\\")\\n .field(\\\"type\\\", \\\"hll\\\")\\n .field(\\\"doc_values\\\", true)\\n .endObject().endObject().endObject().endObject());\\n try {\\n parser.parse(\\\"type\\\", new CompressedXContent(mapping));\\n fail(\\\"expected a mapper parsing exception\\\");\\n } catch (MapperParsingException e) {\\n assertTrue(e.getMessage().contains(\\\"Setting [doc_values] cannot be modified\\\"));\\n }\\n }\\n\\n public void testIndexSettingNotAllowed() throws Exception {\\n String mapping = Strings.toString(XContentFactory.jsonBuilder().startObject().startObject(\\\"type\\\")\\n .startObject(\\\"properties\\\").startObject(\\\"field\\\")\\n .field(\\\"type\\\", \\\"hll\\\")\\n .field(\\\"index\\\", \\\"not_analyzed\\\")\\n .endObject().endObject().endObject().endObject());\\n try {\\n parser.parse(\\\"type\\\", new CompressedXContent(mapping));\\n fail(\\\"expected a mapper parsing exception\\\");\\n } catch (MapperParsingException e) {\\n assertTrue(e.getMessage().contains(\\\"Setting [index] cannot be modified\\\"));\\n }\\n\\n // even setting to the default is not allowed, the setting is invalid\\n mapping = Strings.toString(XContentFactory.jsonBuilder().startObject().startObject(\\\"type\\\")\\n .startObject(\\\"properties\\\").startObject(\\\"field\\\")\\n .field(\\\"type\\\", \\\"hll\\\")\\n .field(\\\"index\\\", \\\"no\\\")\\n .endObject().endObject().endObject().endObject());\\n try {\\n parser.parse(\\\"type\\\", new CompressedXContent(mapping));\\n fail(\\\"expected a mapper parsing exception\\\");\\n } catch (MapperParsingException e) {\\n assertTrue(e.getMessage().contains(\\\"Setting [index] cannot be modified\\\"));\\n }\\n }\\n\\n public void testEmptyName() throws Exception {\\n String mapping = Strings.toString(XContentFactory.jsonBuilder().startObject().startObject(\\\"type\\\")\\n .startObject(\\\"properties\\\").startObject(\\\"\\\")\\n .field(\\\"type\\\", \\\"hll\\\")\\n .endObject().endObject().endObject().endObject());\\n\\n IllegalArgumentException e = expectThrows(IllegalArgumentException.class,\\n () -> parser.parse(\\\"type\\\", new CompressedXContent(mapping))\\n );\\n assertThat(e.getMessage(), containsString(\\\"name cannot be empty string\\\"));\\n }\\n\\n public void testArrayOfItems() throws Exception {\\n DocumentMapper mapper = parser.parse(\\\"test-doc-type\\\", new CompressedXContent(getTestMappingString()));\\n ParsedDocument parsedDoc = mapper.parse(SourceToParse.source(\\\"test\\\", \\\"test-doc-type\\\", \\\"1\\\", BytesReference.bytes(XContentFactory.jsonBuilder()\\n .startObject()\\n .field(\\\"test-hll-field\\\", new String[] {\\\"foo\\\", \\\"bar\\\", \\\"baz\\\"})\\n .endObject()),\\n XContentType.JSON));\\n IndexableField[] fields = parsedDoc.rootDoc().getFields(\\\"test-hll-field\\\");\\n IndexableField field = fields[0];\\n // TODO: Is it worth checking the bytes here so we know if the underlying HLL implementation changes?\\n assertEquals(15, field.binaryValue().length);\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5967890024185181,\n \"alphanum_fraction\": 0.6061926484107971,\n \"avg_line_length\": 29.27777862548828,\n \"blob_id\": \"ca1832aefbdf543596a921709ba63020ba827968\",\n \"content_id\": \"cad82322be594cc91bb982ebfa4a99911058234e\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4360,\n \"license_type\": \"permissive\",\n \"max_line_length\": 96,\n \"num_lines\": 144,\n \"path\": \"/benchmarks/generate_data.py\",\n \"repo_name\": \"Parsely/elasticsearch-hll-rollups\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import argparse\\nimport json\\nimport random\\nimport sys\\nimport uuid\\nimport mmh3\\n\\nfrom itertools import cycle\\n\\n\\ndef _init(num_services, jobs_per_service):\\n output = []\\n for _ in range(num_services):\\n output.append(\\n {\\n \\\"name\\\": f\\\"service-{str(uuid.uuid4())}\\\",\\n \\\"jobs\\\": [\\n mmh3.hash64(str(uuid.uuid4()))[0] for _ in range(jobs_per_service)\\n ],\\n }\\n )\\n return output\\n\\n\\ndef generate_data(\\n num_containers,\\n num_services,\\n jobs_per_service,\\n jobs_per_container,\\n container_job_count_variation_pct,\\n running_job_pct,\\n):\\n services = cycle(_init(num_services, jobs_per_service))\\n variance = int(jobs_per_container * (container_job_count_variation_pct / 100))\\n min_jobs = max(jobs_per_container - variance, 1)\\n max_jobs = jobs_per_container + variance\\n running_job_pct = running_job_pct / 100\\n\\n for _ in range(num_containers):\\n svc = next(services)\\n all_jobs = random.choices(svc[\\\"jobs\\\"], k=random.randint(min_jobs, max_jobs))\\n running_jobs = random.choices(all_jobs, k=int(len(all_jobs) * running_job_pct))\\n yield {\\n \\\"service_id\\\": svc[\\\"name\\\"],\\n \\\"container_id\\\": f\\\"container-{str(uuid.uuid4())}\\\",\\n \\\"all_jobs\\\": all_jobs,\\n \\\"all_jobs_len\\\": len(all_jobs),\\n \\\"running_jobs\\\": running_jobs,\\n \\\"running_jobs_len\\\": len(running_jobs),\\n }\\n\\n\\ndef main(\\n num_containers,\\n num_services,\\n jobs_per_service,\\n jobs_per_container,\\n container_job_count_variation_pct,\\n running_job_pct,\\n):\\n generator = generate_data(\\n num_containers,\\n num_services,\\n jobs_per_service,\\n jobs_per_container,\\n container_job_count_variation_pct,\\n running_job_pct,\\n )\\n for _ in range(num_containers):\\n print(json.dumps(next(generator)))\\n\\n\\nif __name__ == \\\"__main__\\\":\\n parser = argparse.ArgumentParser(\\n usage=\\\"python generate_data.py\\\",\\n description=\\\"\\\"\\\"\\n Generate data for esrally tracks.\\n\\n Generates data that could be diagnostic info from a queue/worker\\n style system which is useful for testing high cardinality sets in\\n ES.\\n\\n The model creates a number of services, which each have a set of\\n jobs. Each ES doc generated models the state of a container\\n running one of the services. It'll have a subset of jobs that\\n it knows about, some of which are running.\\n\\n Jobs can be shared between containers. This means a\\n cardinality agg would be needed to know how many jobs are handled\\n by any given service.\\n \\\"\\\"\\\",\\n formatter_class=argparse.RawTextHelpFormatter,\\n )\\n parser.add_argument(\\n \\\"num_containers\\\",\\n metavar=\\\"NUM_CONTAINERS\\\",\\n type=int,\\n help=\\\"The number of containers (ES docs) to model.\\\",\\n )\\n parser.add_argument(\\n \\\"--num-services\\\",\\n metavar=\\\"NUM_SERVICES\\\",\\n type=int,\\n help=\\\"Number of services to model. [default: NUM_CONTAINERS/10]\\\",\\n )\\n parser.add_argument(\\n \\\"--jobs-per-service\\\",\\n type=int,\\n help=\\\"The number of jobs (UUIDs) assigned to each service. [default: NUM_CONTAINERS/2]\\\",\\n )\\n parser.add_argument(\\n \\\"--jobs-per-container\\\",\\n type=int,\\n default=50,\\n help=\\\"The number of jobs (UUIDs) assigned to each container (ES doc). Will be \\\"\\n \\\"affected by job count variation as well. [default: 50]\\\",\\n )\\n parser.add_argument(\\n \\\"--job-count-variation\\\",\\n type=int,\\n default=10,\\n help=\\\"How much job counts should vary between docs. Should be between 0 and 100. \\\"\\n \\\"For example a value of 10 gives a +/- 10%% variation in the length of all_jobs \\\"\\n \\\"between docs. [default: 10]\\\",\\n )\\n parser.add_argument(\\n \\\"--running-job-percentage\\\",\\n type=int,\\n default=10,\\n help=\\\"Percent of `all_jobs` that should be in `running_jobs`. [default: 10]\\\",\\n )\\n args = parser.parse_args()\\n\\n num_services = args.num_services or int(args.num_containers / 10)\\n jobs_per_service = args.jobs_per_service or int(args.num_containers / 2)\\n\\n main(\\n args.num_containers,\\n num_services,\\n jobs_per_service,\\n args.jobs_per_container,\\n args.job_count_variation,\\n args.running_job_percentage,\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.6989619135856628,\n \"alphanum_fraction\": 0.7162629961967468,\n \"avg_line_length\": 22.432432174682617,\n \"blob_id\": \"43cec23fc36c042fb4359a5fbe0cbb18d0a9c262\",\n \"content_id\": \"12c86a32ada070d05ec5420bcd14197c5cf7ca7d\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Gradle\",\n \"length_bytes\": 867,\n \"license_type\": \"permissive\",\n \"max_line_length\": 121,\n \"num_lines\": 37,\n \"path\": \"/build.gradle\",\n \"repo_name\": \"Parsely/elasticsearch-hll-rollups\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"buildscript {\\n repositories {\\n mavenLocal()\\n mavenCentral()\\n jcenter()\\n }\\n\\n dependencies {\\n classpath \\\"org.elasticsearch.gradle:build-tools:6.8.2\\\"\\n }\\n}\\n\\ngroup = 'com.parsely'\\nversion = '0.0.1'\\n\\napply plugin: 'java'\\napply plugin: 'idea'\\napply plugin: 'elasticsearch.esplugin'\\n\\n\\ndependencies {\\n compile \\\"org.elasticsearch:elasticsearch:6.8.2\\\"\\n compile \\\"org.elasticsearch.plugin:aggs-matrix-stats-client:6.8.2\\\"\\n testCompile \\\"org.elasticsearch.test:framework:6.8.2\\\"\\n}\\n\\ncheckstyleMain.enabled = false\\ncheckstyleTest.enabled = false\\n\\n\\nlicenseFile = rootProject.file('LICENSE.txt')\\nnoticeFile = rootProject.file('NOTICE.txt')\\n\\nesplugin {\\n description 'The Mapper HLL plugin allows you to compute serialized HLLs at index-time and to store them in the index.'\\n classname 'com.parsely.elasticsearch.plugin.HLLRollupPlugin'\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7680890560150146,\n \"alphanum_fraction\": 0.771799623966217,\n \"avg_line_length\": 61.19230651855469,\n \"blob_id\": \"7af05bbcf8fe2d5e00d36ec376406cc419196154\",\n \"content_id\": \"7e97c376891c94701c101867e595de640226da76\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1617,\n \"license_type\": \"permissive\",\n \"max_line_length\": 117,\n \"num_lines\": 26,\n \"path\": \"/README.md\",\n \"repo_name\": \"Parsely/elasticsearch-hll-rollups\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# elasticsearch-hll-rollups\\n\\nElasticsearch plugins to support \\\"HLL Rollups\\\". The goal is to store the computed HyperLogLogPlusPlus sketch\\ninstead of raw values. This serialized version is different from Elasticsearch, which will store all values and\\nthen compute the HLL at query time. What this plugin loses is flexibility (precision is fixed at index-time) it gains\\nin disk savings and overall scalability for very high-cardinality fields.\\n\\n## State of the Project\\n\\nThis is an initial implementation of the idea which runs on Elasticsearch 6.8.2. We've validated the correctness\\nof the plugin, but there's a lot more work to be done in terms of optimizations and code cleanups. Our team\\ndoesn't write a lot of Java, so there's likely more than a few issues to be worked through.\\n\\nThere's also a lot of code copied from the ES codebase. I'm sure there's a lot to be done cleaning\\nthat up so we only keep or rewrite the bits we need. Since there was a cardinality agg already there\\nit made to most sense to follow that model.\\n\\nTODO:\\n\\n- [ ] Rename `precision` to `precisionThreshold` so it matches the param for `cardinality`.\\n- [ ] Consider skipping the HLL step when indexing LC-sized datasets. It may speed up indexing and\\n later querying to instead store either the raw values of the mmh3 hash that the LC uses.\\n- [ ] Write up a section about how storing the HLL can help with GDPR compliance (and validate this assumption).\\n- [ ] Figure out how reindexing is going to work with all of this. Do we need a way to return a base64 encoded\\n HLL to the user? \\n- [ ] Many more tests for the aggregation code.\\n\"\n },\n {\n \"alpha_fraction\": 0.6279229521751404,\n \"alphanum_fraction\": 0.6444291472434998,\n \"avg_line_length\": 40.5523796081543,\n \"blob_id\": \"fb1737fab590d1ee9b649d526757ca3e93e8a40d\",\n \"content_id\": \"dc4a3932131990a8c45659a42cc95ebe6250a32f\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 4362,\n \"license_type\": \"permissive\",\n \"max_line_length\": 113,\n \"num_lines\": 105,\n \"path\": \"/src/test/java/com/parsely/elasticsearch/search/aggregations/metrics/HyperLogLogPlusPlusTests.java\",\n \"repo_name\": \"Parsely/elasticsearch-hll-rollups\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/*\\n * Licensed to Elasticsearch under one or more contributor\\n * license agreements. See the NOTICE file distributed with\\n * this work for additional information regarding copyright\\n * ownership. Elasticsearch licenses this file to you under\\n * the Apache License, Version 2.0 (the \\\"License\\\"); you may\\n * not use this file except in compliance with the License.\\n * You may obtain a copy of the License at\\n *\\n * http://www.apache.org/licenses/LICENSE-2.0\\n *\\n * Unless required by applicable law or agreed to in writing,\\n * software distributed under the License is distributed on an\\n * \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\\n * KIND, either express or implied. See the License for the\\n * specific language governing permissions and limitations\\n * under the License.\\n */\\n\\npackage com.parsely.elasticsearch.search.aggregations.metrics;\\n\\nimport com.carrotsearch.hppc.BitMixer;\\nimport org.elasticsearch.common.util.BigArrays;\\nimport com.parsely.elasticsearch.search.aggregations.hll.HyperLogLogPlusPlus;\\nimport org.elasticsearch.test.ESTestCase;\\n\\nimport org.elasticsearch.common.io.stream.InputStreamStreamInput;\\nimport org.elasticsearch.common.io.stream.OutputStreamStreamOutput;\\n\\nimport static com.parsely.elasticsearch.search.aggregations.hll.HyperLogLogPlusPlus.MAX_PRECISION;\\nimport static org.hamcrest.Matchers.closeTo;\\n\\nimport java.io.ByteArrayInputStream;\\nimport java.io.ByteArrayOutputStream;\\nimport java.io.IOException;\\n\\nimport static org.apache.logging.log4j.Level.INFO;\\n\\n\\npublic class HyperLogLogPlusPlusTests extends ESTestCase {\\n\\n private HyperLogLogPlusPlus makeRandomSketch(int size) {\\n // utility to make a random HLL of a given size\\n HyperLogLogPlusPlus counts = new HyperLogLogPlusPlus(MAX_PRECISION, BigArrays.NON_RECYCLING_INSTANCE, 0);\\n for (int i = 0; i < size; i++) {\\n final int n = randomInt(100_000_000);\\n final long hash = BitMixer.mix64(n);\\n counts.collect(0, hash);\\n }\\n return counts;\\n\\n }\\n\\n public void testReadWrite() {\\n // We'll test reading and writing HLL bytes for different cardinality sizes\\n // ranging from 1 to 10 million. Beyond 10M, it takes awhile to generate the\\n // random data.\\n int[] sizes = new int[] {\\n 1,\\n 10,\\n 100,\\n 1_000,\\n 10_000,\\n 100_000,\\n 1_000_000, // 1 million\\n 10_000_000 // 10 million\\n };\\n\\n for (int size : sizes) {\\n // Our goal with the `baos` and `osso` is to just turn the `HyperLogLogPlusPlus.writeTo(...)`\\n // call into a byte[]\\n ByteArrayOutputStream baos = new ByteArrayOutputStream();\\n OutputStreamStreamOutput osso = new OutputStreamStreamOutput(baos);\\n HyperLogLogPlusPlus counts = makeRandomSketch(size);\\n long cardinality = counts.cardinality(0);\\n long precision = counts.precision();\\n long maxBucket = counts.maxBucket();\\n logger.printf(INFO, \\\"%25s: [%d]\\\", \\\"Initial size of\\\", size);\\n logger.printf(INFO,\\\"%25s: [%d]\\\", \\\"Initial cardinality of\\\", cardinality);\\n try {\\n counts.writeTo(0, osso);\\n } catch (IOException e) {\\n fail();\\n }\\n // now we have the HLL as a byte[]\\n byte[] hllBytes = baos.toByteArray();\\n logger.printf(INFO,\\\"%25s: [%d]\\\", \\\"Storing bytes on-disk\\\", hllBytes.length);\\n logger.printf(INFO,\\\"---\\\");\\n // Our goal with `bais` and `issi` is to deserialize the byte[] into the HLL using\\n // the `HyperLogLogPlusPlus.readFrom(...)` method\\n ByteArrayInputStream bais = new ByteArrayInputStream(hllBytes);\\n InputStreamStreamInput issi = new InputStreamStreamInput(bais);\\n try {\\n counts = HyperLogLogPlusPlus.readFrom(issi, BigArrays.NON_RECYCLING_INSTANCE);\\n } catch (IOException e) {\\n fail();\\n }\\n // now `counts` is our deserialized HLL; this assertion\\n // confirms it by comparing their values\\n assertEquals(cardinality, counts.cardinality(0));\\n assertEquals(precision, counts.precision());\\n assertEquals(maxBucket, counts.maxBucket());\\n }\\n }\\n}\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":6,"string":"6"}}},{"rowIdx":2983095,"cells":{"repo_name":{"kind":"string","value":"UnityRohan/ytdownloader-web"},"repo_url":{"kind":"string","value":"https://github.com/UnityRohan/ytdownloader-web"},"snapshot_id":{"kind":"string","value":"339871d3447c590fd901be46306d637b77ff825a"},"revision_id":{"kind":"string","value":"c392d553787f21b904ba7897369f0956cde27a0f"},"directory_id":{"kind":"string","value":"2bf255583098fb772ba31943fd7a5a39b623807c"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-02-05T22:07:07.106970","string":"2023-02-05T22:07:07.106970"},"revision_date":{"kind":"timestamp","value":"2020-12-25T22:51:58","string":"2020-12-25T22:51:58"},"committer_date":{"kind":"timestamp","value":"2020-12-25T22:51:58","string":"2020-12-25T22:51:58"},"github_id":{"kind":"number","value":324441841,"string":"324,441,841"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7822580933570862,"alphanum_fraction":0.7842742204666138,"avg_line_length":32.13333511352539,"blob_id":"7e1e6318cc977ea78684d00adb382830c7aeef79","content_id":"68a20374c286b7b3745322f888c3bd0004406ca8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":496,"license_type":"no_license","max_line_length":118,"num_lines":15,"path":"/README.md","repo_name":"UnityRohan/ytdownloader-web","src_encoding":"UTF-8","text":"# ytdownloader-flask\nA simple one file Flask app that utilizes the Python package `pytube` to return YouTube videos available for download.\n<br>\nBased on my other YouTube downloader project: https://github.com/UnityRohan/ytdownloader\n\n### Required Modules\nThe only module required so far is pytube:\n`pip install pytube`\nor\n`pip3 install pytube`\n\n### Information on the pytube package\nPyPi Page: https://pypi.org/project/pytube/\n<br>\nDocs: https://python-pytube.readthedocs.io/en/latest/index.html"},{"alpha_fraction":0.6651933789253235,"alphanum_fraction":0.6817679405212402,"avg_line_length":35.20000076293945,"blob_id":"bea111583caecd6e5a7a5c1e21d1fbc356759506","content_id":"a6568251b9d0227ad9bea57bdb276192254ac347","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":905,"license_type":"no_license","max_line_length":142,"num_lines":25,"path":"/app.py","repo_name":"UnityRohan/ytdownloader-web","src_encoding":"UTF-8","text":"from flask import Flask, render_template, send_file\nfrom pytube import YouTube, exceptions\nimport string\nimport random\n\napp = Flask(__name__)\n\n@app.route(\"/download/<id>\")\ndef download(id):\n url = f'https://youtube.com/watch?v={id}'\n try:\n video_object = YouTube(url) \n except exceptions.RegexMatchError:\n return \"Invalid URL. After /download/, add the ID of the video. For example:\\n localhost:5000/download/ATOX9uMKtC4\"\n\n if video_object.length > 1800:\n return \"That video is too long! Your video can be up to 30 minutes long.\" \n\n rand_name = ''.join(random.choice(string.ascii_letters) for i in range(15))\n video_object.streams.filter(progressive=True).order_by('resolution').desc().first().download(output_path=\"downloaded\", filename=rand_name)\n return send_file(f\"downloaded\\\\{rand_name}.mp4\")\n \n\nif __name__ == \"__main__\":\n app.run(debug=True)\n"}],"string":"[\n {\n \"alpha_fraction\": 0.7822580933570862,\n \"alphanum_fraction\": 0.7842742204666138,\n \"avg_line_length\": 32.13333511352539,\n \"blob_id\": \"7e1e6318cc977ea78684d00adb382830c7aeef79\",\n \"content_id\": \"68a20374c286b7b3745322f888c3bd0004406ca8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 496,\n \"license_type\": \"no_license\",\n \"max_line_length\": 118,\n \"num_lines\": 15,\n \"path\": \"/README.md\",\n \"repo_name\": \"UnityRohan/ytdownloader-web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# ytdownloader-flask\\nA simple one file Flask app that utilizes the Python package `pytube` to return YouTube videos available for download.\\n<br>\\nBased on my other YouTube downloader project: https://github.com/UnityRohan/ytdownloader\\n\\n### Required Modules\\nThe only module required so far is pytube:\\n`pip install pytube`\\nor\\n`pip3 install pytube`\\n\\n### Information on the pytube package\\nPyPi Page: https://pypi.org/project/pytube/\\n<br>\\nDocs: https://python-pytube.readthedocs.io/en/latest/index.html\"\n },\n {\n \"alpha_fraction\": 0.6651933789253235,\n \"alphanum_fraction\": 0.6817679405212402,\n \"avg_line_length\": 35.20000076293945,\n \"blob_id\": \"bea111583caecd6e5a7a5c1e21d1fbc356759506\",\n \"content_id\": \"a6568251b9d0227ad9bea57bdb276192254ac347\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 905,\n \"license_type\": \"no_license\",\n \"max_line_length\": 142,\n \"num_lines\": 25,\n \"path\": \"/app.py\",\n \"repo_name\": \"UnityRohan/ytdownloader-web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import Flask, render_template, send_file\\nfrom pytube import YouTube, exceptions\\nimport string\\nimport random\\n\\napp = Flask(__name__)\\n\\n@app.route(\\\"/download/<id>\\\")\\ndef download(id):\\n url = f'https://youtube.com/watch?v={id}'\\n try:\\n video_object = YouTube(url) \\n except exceptions.RegexMatchError:\\n return \\\"Invalid URL. After /download/, add the ID of the video. For example:\\\\n localhost:5000/download/ATOX9uMKtC4\\\"\\n\\n if video_object.length > 1800:\\n return \\\"That video is too long! Your video can be up to 30 minutes long.\\\" \\n\\n rand_name = ''.join(random.choice(string.ascii_letters) for i in range(15))\\n video_object.streams.filter(progressive=True).order_by('resolution').desc().first().download(output_path=\\\"downloaded\\\", filename=rand_name)\\n return send_file(f\\\"downloaded\\\\\\\\{rand_name}.mp4\\\")\\n \\n\\nif __name__ == \\\"__main__\\\":\\n app.run(debug=True)\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2983096,"cells":{"repo_name":{"kind":"string","value":"dimsumlabs/ml_workshop"},"repo_url":{"kind":"string","value":"https://github.com/dimsumlabs/ml_workshop"},"snapshot_id":{"kind":"string","value":"c6c716c7bf6c26b28acb1303e8c0902e8d3e2944"},"revision_id":{"kind":"string","value":"b7bbd83bbb72a31bd29f64f592fc08f06e289f24"},"directory_id":{"kind":"string","value":"63545a162d9fdb303c108d8b59742bc401cfe750"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2016-09-05T18:26:35.312942","string":"2016-09-05T18:26:35.312942"},"revision_date":{"kind":"timestamp","value":"2014-11-17T10:45:46","string":"2014-11-17T10:45:46"},"committer_date":{"kind":"timestamp","value":"2014-11-17T10:45:46","string":"2014-11-17T10:45:46"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7418162822723389,"alphanum_fraction":0.7444561719894409,"avg_line_length":39.27659606933594,"blob_id":"da7d8c60e6d2a65a59eb5b38ab0625e17026beeb","content_id":"4a2d16f0193465bf8a65280275e9eebaed7a811e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1894,"license_type":"no_license","max_line_length":213,"num_lines":47,"path":"/README.mkd","repo_name":"dimsumlabs/ml_workshop","src_encoding":"UTF-8","text":"# SCRAPY WORKSHOP SETUP\nYou need ```ipython``` and ```scrapy```, python mysql bindings ```mysqldb``` and mysql server ```mysql``` or ```mariadb``` (to run a mysql server and write some data into a database table from within our scraper).\n\nShould be rather easy to install those systemwide or with pip,\n```\npip install ipython Scrapy MySQL-python\n```\nTo install ```mysql-server``` on ubuntu for example,\n```\nsudo apt-get install mysql-server\n```\n\n# SKLEARN WORKSHOP SETUP\nIf you have ```scikit-learn```, ```matplotlib``` and ```ipython``` \ninstalled you can just run ```ipython notebook --pylab```. Otherwise there are\n3 different ways of installing the tools below. \n\n- If you're on Windows or Mac OSX I recommend setting up a VM. \n- If you're running a Linux distro with package manager I recommend either \n install the depencies system wide via the package manager or setting up\n a virtual python environment and installing the tools via pip.\n\n## Set Up a VM\n+ install ```Virtualbox``` from https://www.virtualbox.org/wiki/Downloads\n+ download a ubuntu virtualbox image from http://virtualboxes.org/images/ubuntu/, 12.04 or newer recommended\n+ add the appliance to virtualbox, click Machine -> Add, then navigate to the *.vbox file\n+ once added, fire up the vm, log in with username/password provided on virtualboxes.org\n+ in a terminal, run ```sudo apt-get update```\n+ followed by ```sudo apt-get install ipython python-matplotlib python-sklearn```\n\n## Debian / Ubuntu\nRun \n```\nsudo apt-get install ipython python-matplotlib python-sklearn\n```\n\n## In a virtual environment\nIf inside a virtual environment run\n```\npip install numpy scipy matplotlib ipython scikit-learn\n```\nyou may need to install some additional system level\ndependencies if this command fails.\n\n## Start the ipython notebook server\nRun ```ipython notebook --pylab``` from within this repository and open a new\nnotebook.\n\n"},{"alpha_fraction":0.7355931997299194,"alphanum_fraction":0.7423728704452515,"avg_line_length":35.875,"blob_id":"bce7740e65765ba7913423473573c24ab765e8cc","content_id":"44cb3d3a1a0c5fdafba954cdbbf5d88c6b476f65","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"SQL","length_bytes":295,"license_type":"no_license","max_line_length":82,"num_lines":8,"path":"/scrapy/mysql_setup.sql","repo_name":"dimsumlabs/ml_workshop","src_encoding":"UTF-8","text":"CREATE DATABASE IF NOT EXISTS Scrapy;\nCREATE TABLE IF NOT EXISTS Scrapy.AirQuality (\n station VARCHAR(32),\n timestamp DATETIME,\n air_quality INT,\n PRIMARY KEY (station, timestamp));\nGRANT ALL ON Scrapy.AirQuality TO 'scrapy'@'localhost' IDENTIFIED BY 'some_password';\nFLUSH PRIVILEGES;\n"},{"alpha_fraction":0.5271238684654236,"alphanum_fraction":0.5271238684654236,"avg_line_length":31.5,"blob_id":"9f9609a781051bb9ac687db4cbddce8822036d14","content_id":"e57159bf13e4cfd25e605e4d1f33da7d10f02991","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":977,"license_type":"no_license","max_line_length":74,"num_lines":30,"path":"/scrapy/airq/airq/pipelines.py","repo_name":"dimsumlabs/ml_workshop","src_encoding":"UTF-8","text":"# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html\nfrom MySQLdb import connect\nfrom scrapy import log\n\nHOST = 'localhost'\nUSER = 'scrapy'\nPASSWD = 'some_password'\nDB = 'Scrapy'\n\n\nclass SQL(object):\n def open_spider(self, spider):\n self.dbcon = connect(host=HOST, \n user=USER, \n passwd=PASSWD,\n db=DB)\n self.cursor = self.dbcon.cursor()\n\n def process_item(self, item, spider):\n self.cursor.execute(\"INSERT INTO AirQuality (station, timestamp, \"\n \"air_quality) VALUES (%s, %s, %s)\", (item['station'], \n item['timestamp'],\n item['air_quality']))\n return item\n\n def close_spider(self, spider):\n self.dbcon.commit()\n\n\n"},{"alpha_fraction":0.5415282249450684,"alphanum_fraction":0.5423588156700134,"avg_line_length":28.75,"blob_id":"53e8c0dbcb393187f21b9c2452c01776607194a1","content_id":"c4264c60654e63f91b6541d4564799eeb5c2c486","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1204,"license_type":"no_license","max_line_length":81,"num_lines":40,"path":"/scrapy/airq/airq/spiders/airq_spider.py","repo_name":"dimsumlabs/ml_workshop","src_encoding":"UTF-8","text":"from dateutil.parser import parse\n\nfrom scrapy import log # This module is useful for printing out debug information\nfrom scrapy.spider import Spider\nfrom scrapy.selector import Selector\n\nfrom airq.items import AirqItem\n\n\nclass AirqSpider(Spider):\n name = 'air_quality'\n allowed_domains = ['aqhi.gov.hk']\n start_urls = [\n 'http://www.aqhi.gov.hk/en.html',\n ]\n\n def parse(self, response):\n sel = Selector(response)\n stations = sel.xpath('//td[@class=\"tblCurrAQHI_tdName\"]/a/text()')\\\n .extract()\n values = [int(x) for x in \n sel.xpath('//td[@class=\"tblCurrAQHI_tdBand notSurrogate\"'\n ']/text()').extract()\n if x.isdigit()] \n t = sel.xpath('//tr[@class=\"tblCurrAQHI_trHeader\"]/td/text()'\n ).extract()[0]\n timestamp = parse(t)\n\n items = []\n\n for s, v in zip(stations, values):\n item = AirqItem()\n item['station'] = s\n item['air_quality'] = v\n item['timestamp'] = timestamp\n items.append(item)\n \n self.log(\"Got {} items.\".format(len(items)))\n\n return items\n \n\n"},{"alpha_fraction":0.7439516186714172,"alphanum_fraction":0.7493279576301575,"avg_line_length":37.153846740722656,"blob_id":"29e9719e4450cfb5787cd30eac35344dc03c6099","content_id":"1b13a3591d2691a72b21fcd1f37ec4e979e7e3dc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1488,"license_type":"no_license","max_line_length":119,"num_lines":39,"path":"/Event_Description.mkd","repo_name":"dimsumlabs/ml_workshop","src_encoding":"UTF-8","text":"# Introduction to Machine Learning / python / scikit-learn workshop\n\nThe idea is to help people get started with machine learning. It will be\ninteractive and we'll be using scikit-learn (scikit-learn.org) for the\nmachine Learning part and matplotlib (matplotlib.org) for visualizations.\n\nThis is the first time I'm doing this. It's gonna be (w)hacky. I won't cover\nmuch (read, any) theory. We'll use some publicly available datasets and play\naround with them.\n\n## Prerequisites:\n- Laptop\n- Do the following on your laptop\n + Download / install ```Virtualbox``` from https://www.virtualbox.org/wiki/Downloads\n + Download the ```NeuroDebian``` Virtual Machine here http://neuro.debian.net/debian/vm/NeuroDebian_7.2.0_amd64.ova\n + Open ```Virtualbox```\n + Follow http://neuro.debian.net/vm.html to import/create the VM\n- Or use your own scikit-learn / matplotlib installation\n- Some python if you want. Plenty of tutorials online, google found this: http://www.learnpython.org\n- Ability to look at a command prompt without nervous breakdown.\n\n\n## Topics are gonna be some of the following,\n- Basics of Machine Learning\n + Classification\n + Regression\n- Dimensionality Reduction\n + Principal Component Analysis\n + Implementation\n- Visualization\n- Linear Discriminant Analysis\n- Support Vector Machines\n + Grid Search\n- Ensembles\n + Random Forests\n- ...\n\nCost: free for DSL members, HKD 200 otherwise. Goes pretty much entirely towards rent and\nutilities of DSL.\n"}],"string":"[\n {\n \"alpha_fraction\": 0.7418162822723389,\n \"alphanum_fraction\": 0.7444561719894409,\n \"avg_line_length\": 39.27659606933594,\n \"blob_id\": \"da7d8c60e6d2a65a59eb5b38ab0625e17026beeb\",\n \"content_id\": \"4a2d16f0193465bf8a65280275e9eebaed7a811e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1894,\n \"license_type\": \"no_license\",\n \"max_line_length\": 213,\n \"num_lines\": 47,\n \"path\": \"/README.mkd\",\n \"repo_name\": \"dimsumlabs/ml_workshop\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# SCRAPY WORKSHOP SETUP\\nYou need ```ipython``` and ```scrapy```, python mysql bindings ```mysqldb``` and mysql server ```mysql``` or ```mariadb``` (to run a mysql server and write some data into a database table from within our scraper).\\n\\nShould be rather easy to install those systemwide or with pip,\\n```\\npip install ipython Scrapy MySQL-python\\n```\\nTo install ```mysql-server``` on ubuntu for example,\\n```\\nsudo apt-get install mysql-server\\n```\\n\\n# SKLEARN WORKSHOP SETUP\\nIf you have ```scikit-learn```, ```matplotlib``` and ```ipython``` \\ninstalled you can just run ```ipython notebook --pylab```. Otherwise there are\\n3 different ways of installing the tools below. \\n\\n- If you're on Windows or Mac OSX I recommend setting up a VM. \\n- If you're running a Linux distro with package manager I recommend either \\n install the depencies system wide via the package manager or setting up\\n a virtual python environment and installing the tools via pip.\\n\\n## Set Up a VM\\n+ install ```Virtualbox``` from https://www.virtualbox.org/wiki/Downloads\\n+ download a ubuntu virtualbox image from http://virtualboxes.org/images/ubuntu/, 12.04 or newer recommended\\n+ add the appliance to virtualbox, click Machine -> Add, then navigate to the *.vbox file\\n+ once added, fire up the vm, log in with username/password provided on virtualboxes.org\\n+ in a terminal, run ```sudo apt-get update```\\n+ followed by ```sudo apt-get install ipython python-matplotlib python-sklearn```\\n\\n## Debian / Ubuntu\\nRun \\n```\\nsudo apt-get install ipython python-matplotlib python-sklearn\\n```\\n\\n## In a virtual environment\\nIf inside a virtual environment run\\n```\\npip install numpy scipy matplotlib ipython scikit-learn\\n```\\nyou may need to install some additional system level\\ndependencies if this command fails.\\n\\n## Start the ipython notebook server\\nRun ```ipython notebook --pylab``` from within this repository and open a new\\nnotebook.\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7355931997299194,\n \"alphanum_fraction\": 0.7423728704452515,\n \"avg_line_length\": 35.875,\n \"blob_id\": \"bce7740e65765ba7913423473573c24ab765e8cc\",\n \"content_id\": \"44cb3d3a1a0c5fdafba954cdbbf5d88c6b476f65\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"SQL\",\n \"length_bytes\": 295,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 8,\n \"path\": \"/scrapy/mysql_setup.sql\",\n \"repo_name\": \"dimsumlabs/ml_workshop\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"CREATE DATABASE IF NOT EXISTS Scrapy;\\nCREATE TABLE IF NOT EXISTS Scrapy.AirQuality (\\n station VARCHAR(32),\\n timestamp DATETIME,\\n air_quality INT,\\n PRIMARY KEY (station, timestamp));\\nGRANT ALL ON Scrapy.AirQuality TO 'scrapy'@'localhost' IDENTIFIED BY 'some_password';\\nFLUSH PRIVILEGES;\\n\"\n },\n {\n \"alpha_fraction\": 0.5271238684654236,\n \"alphanum_fraction\": 0.5271238684654236,\n \"avg_line_length\": 31.5,\n \"blob_id\": \"9f9609a781051bb9ac687db4cbddce8822036d14\",\n \"content_id\": \"e57159bf13e4cfd25e605e4d1f33da7d10f02991\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 977,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 30,\n \"path\": \"/scrapy/airq/airq/pipelines.py\",\n \"repo_name\": \"dimsumlabs/ml_workshop\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Define your item pipelines here\\n#\\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\\n# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html\\nfrom MySQLdb import connect\\nfrom scrapy import log\\n\\nHOST = 'localhost'\\nUSER = 'scrapy'\\nPASSWD = 'some_password'\\nDB = 'Scrapy'\\n\\n\\nclass SQL(object):\\n def open_spider(self, spider):\\n self.dbcon = connect(host=HOST, \\n user=USER, \\n passwd=PASSWD,\\n db=DB)\\n self.cursor = self.dbcon.cursor()\\n\\n def process_item(self, item, spider):\\n self.cursor.execute(\\\"INSERT INTO AirQuality (station, timestamp, \\\"\\n \\\"air_quality) VALUES (%s, %s, %s)\\\", (item['station'], \\n item['timestamp'],\\n item['air_quality']))\\n return item\\n\\n def close_spider(self, spider):\\n self.dbcon.commit()\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5415282249450684,\n \"alphanum_fraction\": 0.5423588156700134,\n \"avg_line_length\": 28.75,\n \"blob_id\": \"53e8c0dbcb393187f21b9c2452c01776607194a1\",\n \"content_id\": \"c4264c60654e63f91b6541d4564799eeb5c2c486\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1204,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 40,\n \"path\": \"/scrapy/airq/airq/spiders/airq_spider.py\",\n \"repo_name\": \"dimsumlabs/ml_workshop\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from dateutil.parser import parse\\n\\nfrom scrapy import log # This module is useful for printing out debug information\\nfrom scrapy.spider import Spider\\nfrom scrapy.selector import Selector\\n\\nfrom airq.items import AirqItem\\n\\n\\nclass AirqSpider(Spider):\\n name = 'air_quality'\\n allowed_domains = ['aqhi.gov.hk']\\n start_urls = [\\n 'http://www.aqhi.gov.hk/en.html',\\n ]\\n\\n def parse(self, response):\\n sel = Selector(response)\\n stations = sel.xpath('//td[@class=\\\"tblCurrAQHI_tdName\\\"]/a/text()')\\\\\\n .extract()\\n values = [int(x) for x in \\n sel.xpath('//td[@class=\\\"tblCurrAQHI_tdBand notSurrogate\\\"'\\n ']/text()').extract()\\n if x.isdigit()] \\n t = sel.xpath('//tr[@class=\\\"tblCurrAQHI_trHeader\\\"]/td/text()'\\n ).extract()[0]\\n timestamp = parse(t)\\n\\n items = []\\n\\n for s, v in zip(stations, values):\\n item = AirqItem()\\n item['station'] = s\\n item['air_quality'] = v\\n item['timestamp'] = timestamp\\n items.append(item)\\n \\n self.log(\\\"Got {} items.\\\".format(len(items)))\\n\\n return items\\n \\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7439516186714172,\n \"alphanum_fraction\": 0.7493279576301575,\n \"avg_line_length\": 37.153846740722656,\n \"blob_id\": \"29e9719e4450cfb5787cd30eac35344dc03c6099\",\n \"content_id\": \"1b13a3591d2691a72b21fcd1f37ec4e979e7e3dc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1488,\n \"license_type\": \"no_license\",\n \"max_line_length\": 119,\n \"num_lines\": 39,\n \"path\": \"/Event_Description.mkd\",\n \"repo_name\": \"dimsumlabs/ml_workshop\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Introduction to Machine Learning / python / scikit-learn workshop\\n\\nThe idea is to help people get started with machine learning. It will be\\ninteractive and we'll be using scikit-learn (scikit-learn.org) for the\\nmachine Learning part and matplotlib (matplotlib.org) for visualizations.\\n\\nThis is the first time I'm doing this. It's gonna be (w)hacky. I won't cover\\nmuch (read, any) theory. We'll use some publicly available datasets and play\\naround with them.\\n\\n## Prerequisites:\\n- Laptop\\n- Do the following on your laptop\\n + Download / install ```Virtualbox``` from https://www.virtualbox.org/wiki/Downloads\\n + Download the ```NeuroDebian``` Virtual Machine here http://neuro.debian.net/debian/vm/NeuroDebian_7.2.0_amd64.ova\\n + Open ```Virtualbox```\\n + Follow http://neuro.debian.net/vm.html to import/create the VM\\n- Or use your own scikit-learn / matplotlib installation\\n- Some python if you want. Plenty of tutorials online, google found this: http://www.learnpython.org\\n- Ability to look at a command prompt without nervous breakdown.\\n\\n\\n## Topics are gonna be some of the following,\\n- Basics of Machine Learning\\n + Classification\\n + Regression\\n- Dimensionality Reduction\\n + Principal Component Analysis\\n + Implementation\\n- Visualization\\n- Linear Discriminant Analysis\\n- Support Vector Machines\\n + Grid Search\\n- Ensembles\\n + Random Forests\\n- ...\\n\\nCost: free for DSL members, HKD 200 otherwise. Goes pretty much entirely towards rent and\\nutilities of DSL.\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":5,"string":"5"}}},{"rowIdx":2983097,"cells":{"repo_name":{"kind":"string","value":"Jacetone/CS471"},"repo_url":{"kind":"string","value":"https://github.com/Jacetone/CS471"},"snapshot_id":{"kind":"string","value":"8180941685140fb1bffbf3074c55214b03b5f769"},"revision_id":{"kind":"string","value":"57138c949c3ec21d060740ce2bbe6e4eb38bb161"},"directory_id":{"kind":"string","value":"2610f71feeafee8b4ead01bf89113974aa64e15f"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-02-09T19:57:07.378945","string":"2022-02-09T19:57:07.378945"},"revision_date":{"kind":"timestamp","value":"2019-05-02T13:31:49","string":"2019-05-02T13:31:49"},"committer_date":{"kind":"timestamp","value":"2019-05-02T13:31:49","string":"2019-05-02T13:31:49"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.46222221851348877,"alphanum_fraction":0.5022222399711609,"avg_line_length":15.666666984558105,"blob_id":"7662c536b9d15240f8282c845db281d77233b0c2","content_id":"3d786b7eb7ce4196aea992f90c14ffb6b18ac293","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C","length_bytes":450,"license_type":"no_license","max_line_length":53,"num_lines":27,"path":"/HW1/src/ack.c","repo_name":"Jacetone/CS471","src_encoding":"UTF-8","text":"//Maitri Mangal\n//CS471 Assignment 1\n\n#include <stdio.h>\n\nint ack(int m, int n){\n\tif(m == 0){\n\t\treturn(n+1);\n\t}else if(n == 0 && m > 0){\n\t\treturn ack(m-1, 1);\n\t}else{\n\t\treturn ack(m-1, ack(m, n-1));\n\t}\n}\n\nint main(int argc, char **argv) {\n \n if (argc < 3) {\n printf(\"%s usage: [NUMBER] [NUMBER]\\n\", argv[0]);\n return 1;\n }\n int m = atoi(argv[1]);\n int n = atoi(argv[2]);\n int r = ack(m,n);\n printf(\"ACK(%d, %d) = %d\\n\", m, n, r);\n return 0;\n}\n"},{"alpha_fraction":0.5803921818733215,"alphanum_fraction":0.6666666865348816,"avg_line_length":14.9375,"blob_id":"e0f9566fb66c1c8a7133bbf82fa4d8ac35991564","content_id":"efdfe8c400261eb7ae53e5e73e3d7d69f141f1f3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Makefile","length_bytes":255,"license_type":"no_license","max_line_length":40,"num_lines":16,"path":"/HW2/src/Makefile","repo_name":"Jacetone/CS471","src_encoding":"UTF-8","text":"all: arith1 arith2\n\narith1: arith1.c\n\tgcc -g -Wall -Wextra -o arith1 arith1.c\n\narith2: arith2.c\n\tgcc -g -Wall -Wextra -o arith2 arith2.c\n\nq4: q4.c\n\tgcc -g -Wall -Wextra -o q4 q4.c\n\nq8: q8.c\n\tgcc -g -Wall -Wextra -o q8 q8.c\n\nclean:\n\trm arith1 arith2 q4 q8\n"},{"alpha_fraction":0.53899085521698,"alphanum_fraction":0.5596330165863037,"avg_line_length":14.034482955932617,"blob_id":"0e7eedbc9612e8b0c6cbc5ddf662b9e383e31a64","content_id":"eef6ab6cb3bd2bd296f850ce82ca31dd529958aa","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Ruby","length_bytes":436,"license_type":"no_license","max_line_length":56,"num_lines":29,"path":"/HW1/src/gcd_full.rb","repo_name":"Jacetone/CS471","src_encoding":"UTF-8","text":"#Maitri Mangal\n#CS471 Assignment 1\n\n#! /usr/bin/env ruby\n\ndef gcdI(i, j)\n\twhile i != j\n\t\tif i > j\n\t\t\ti = i - j\n\t\telse\n\t\t\tj = j - i\n\t\tend\n\tend\n\treturn i\nend\n\ndef gcdF(i, j)\n\treturn gcdF(i-j, j) if i > j\n\treturn i if i == j\n\treturn gcdF(i, j-i)\nend \n\nif ARGV.length != 2 \n puts \"gcd_full.rb usage: [NUMBER] [NUMBER]\" \n exit\nend\n\nputs \"gcdI() : \" + gcdI(ARGV[0].to_i, ARGV[1].to_i).to_s\nputs \"gcdF() : \" + gcdF(ARGV[0].to_i, ARGV[1].to_i).to_s\n"},{"alpha_fraction":0.5582706928253174,"alphanum_fraction":0.5770676732063293,"avg_line_length":18.703702926635742,"blob_id":"93024ae55b0a4f009f0ccaabd29d5381eed481ae","content_id":"407f64671c5c7cde5700ddd72dfed1abbfd5349e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":532,"license_type":"no_license","max_line_length":66,"num_lines":27,"path":"/HW1/src/gcd_full.py","repo_name":"Jacetone/CS471","src_encoding":"UTF-8","text":"#Maitri Mangal\n#CS471 Assignment 1\n\n#! /usr/bin/env python\nimport sys\n\ndef gcdI(i, j):\n\twhile i != j:\n\t\tif i > j:\n\t\t\ti = i - j\n\t\telse:\n\t\t\tj = j - i\n\treturn i\n\n#it is called gcdF instead of gcdR in the assignment page\ndef gcdF(i, j):\n\tif i > j : return gcdF(i-j, j)\n\telif i == j : return i\n\telse : return gcdF(i, j-i)\n\n\nif len(sys.argv) != 3:\n print(\"%s usage: [NUMBER] [NUMBER]\" % sys.argv[0])\n exit()\n\nprint(\"gcdI() : \"+ str( gcdI(int(sys.argv[1]), int(sys.argv[2]))))\nprint(\"gcdF() : \"+ str( gcdF(int(sys.argv[1]), int(sys.argv[2]))))\n"},{"alpha_fraction":0.6451612710952759,"alphanum_fraction":0.6451612710952759,"avg_line_length":9.333333015441895,"blob_id":"0dc21118e1427547f8f7ff808e1d48b26afa01b8","content_id":"580361c6376d60efe3500968b7d1171d48e259a3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C","length_bytes":62,"license_type":"no_license","max_line_length":24,"num_lines":6,"path":"/HW1/src/gcd.h","repo_name":"Jacetone/CS471","src_encoding":"UTF-8","text":"#ifndef GCD_H\n#define GCD_H\n\n\tint gcdI(int i, int j);\n\n#endif\n"},{"alpha_fraction":0.5176470875740051,"alphanum_fraction":0.5647059082984924,"avg_line_length":16.894737243652344,"blob_id":"cccbeccdb41cade82974f2c36719f3d7550e0b2c","content_id":"f9ee971373d6a9170b2a1dae2f66f429fc65266b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":340,"license_type":"no_license","max_line_length":64,"num_lines":19,"path":"/HW1/src/ack.py","repo_name":"Jacetone/CS471","src_encoding":"UTF-8","text":"#Maitri Mangal\n#CS471 Assignment 1\n\n#! /usr/bin/env python\nimport sys\n\ndef ack(m, n):\n\tif m == 0:\n\t\treturn n+1\n\telif n == 0 and m > 0:\n\t\treturn ack(m-1, 1)\n\telse:\t\n\t\treturn ack(m-1, ack(m, n-1))\n\nif len(sys.argv) != 3:\n print(\"%s usage: [NUMBER] [NUMBER]\" % sys.argv[0])\n exit()\n\nprint(\"ack() : \"+ str( ack(int(sys.argv[1]), int(sys.argv[2]))))\n"},{"alpha_fraction":0.5273972749710083,"alphanum_fraction":0.5787671208381653,"avg_line_length":16.176469802856445,"blob_id":"734d40e11fc165f30f1b6ee54257cded8b9654d7","content_id":"91041a0a0447ecf5f7cb2813ae1dec53e6105935","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Ruby","length_bytes":292,"license_type":"no_license","max_line_length":54,"num_lines":17,"path":"/HW1/src/ack.rb","repo_name":"Jacetone/CS471","src_encoding":"UTF-8","text":"#Maitri Mangal\n#CS471 Assignment 1\n\n#! /usr/bin/env ruby\n\ndef ack(m, n)\n\treturn n+1 if m == 0\n\treturn ack(m-1, 1) if n == 0 and m > 0\n\treturn ack(m-1, ack(m, n-1))\nend\n\nif ARGV.length != 2 \n puts \"ack.rb usage: [NUMBER] [NUMBER]\" \n exit\nend\n\nputs \"ack() : \" + ack(ARGV[0].to_i, ARGV[1].to_i).to_s\n"}],"string":"[\n {\n \"alpha_fraction\": 0.46222221851348877,\n \"alphanum_fraction\": 0.5022222399711609,\n \"avg_line_length\": 15.666666984558105,\n \"blob_id\": \"7662c536b9d15240f8282c845db281d77233b0c2\",\n \"content_id\": \"3d786b7eb7ce4196aea992f90c14ffb6b18ac293\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C\",\n \"length_bytes\": 450,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 27,\n \"path\": \"/HW1/src/ack.c\",\n \"repo_name\": \"Jacetone/CS471\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"//Maitri Mangal\\n//CS471 Assignment 1\\n\\n#include <stdio.h>\\n\\nint ack(int m, int n){\\n\\tif(m == 0){\\n\\t\\treturn(n+1);\\n\\t}else if(n == 0 && m > 0){\\n\\t\\treturn ack(m-1, 1);\\n\\t}else{\\n\\t\\treturn ack(m-1, ack(m, n-1));\\n\\t}\\n}\\n\\nint main(int argc, char **argv) {\\n \\n if (argc < 3) {\\n printf(\\\"%s usage: [NUMBER] [NUMBER]\\\\n\\\", argv[0]);\\n return 1;\\n }\\n int m = atoi(argv[1]);\\n int n = atoi(argv[2]);\\n int r = ack(m,n);\\n printf(\\\"ACK(%d, %d) = %d\\\\n\\\", m, n, r);\\n return 0;\\n}\\n\"\n },\n {\n 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14.034482955932617,\n \"blob_id\": \"0e7eedbc9612e8b0c6cbc5ddf662b9e383e31a64\",\n \"content_id\": \"eef6ab6cb3bd2bd296f850ce82ca31dd529958aa\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Ruby\",\n \"length_bytes\": 436,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 29,\n \"path\": \"/HW1/src/gcd_full.rb\",\n \"repo_name\": \"Jacetone/CS471\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#Maitri Mangal\\n#CS471 Assignment 1\\n\\n#! /usr/bin/env ruby\\n\\ndef gcdI(i, j)\\n\\twhile i != j\\n\\t\\tif i > j\\n\\t\\t\\ti = i - j\\n\\t\\telse\\n\\t\\t\\tj = j - i\\n\\t\\tend\\n\\tend\\n\\treturn i\\nend\\n\\ndef gcdF(i, j)\\n\\treturn gcdF(i-j, j) if i > j\\n\\treturn i if i == j\\n\\treturn gcdF(i, j-i)\\nend \\n\\nif ARGV.length != 2 \\n puts \\\"gcd_full.rb usage: [NUMBER] [NUMBER]\\\" \\n exit\\nend\\n\\nputs \\\"gcdI() : \\\" + gcdI(ARGV[0].to_i, ARGV[1].to_i).to_s\\nputs \\\"gcdF() : \\\" + gcdF(ARGV[0].to_i, ARGV[1].to_i).to_s\\n\"\n },\n {\n \"alpha_fraction\": 0.5582706928253174,\n \"alphanum_fraction\": 0.5770676732063293,\n \"avg_line_length\": 18.703702926635742,\n \"blob_id\": \"93024ae55b0a4f009f0ccaabd29d5381eed481ae\",\n \"content_id\": \"407f64671c5c7cde5700ddd72dfed1abbfd5349e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 532,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 27,\n \"path\": \"/HW1/src/gcd_full.py\",\n \"repo_name\": \"Jacetone/CS471\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#Maitri Mangal\\n#CS471 Assignment 1\\n\\n#! /usr/bin/env python\\nimport sys\\n\\ndef gcdI(i, j):\\n\\twhile i != j:\\n\\t\\tif i > j:\\n\\t\\t\\ti = i - j\\n\\t\\telse:\\n\\t\\t\\tj = j - i\\n\\treturn i\\n\\n#it is called gcdF instead of gcdR in the assignment page\\ndef gcdF(i, j):\\n\\tif i > j : return gcdF(i-j, j)\\n\\telif i == j : return i\\n\\telse : return gcdF(i, j-i)\\n\\n\\nif len(sys.argv) != 3:\\n print(\\\"%s usage: [NUMBER] [NUMBER]\\\" % sys.argv[0])\\n exit()\\n\\nprint(\\\"gcdI() : \\\"+ str( gcdI(int(sys.argv[1]), int(sys.argv[2]))))\\nprint(\\\"gcdF() : \\\"+ str( gcdF(int(sys.argv[1]), int(sys.argv[2]))))\\n\"\n },\n {\n \"alpha_fraction\": 0.6451612710952759,\n \"alphanum_fraction\": 0.6451612710952759,\n \"avg_line_length\": 9.333333015441895,\n \"blob_id\": \"0dc21118e1427547f8f7ff808e1d48b26afa01b8\",\n \"content_id\": \"580361c6376d60efe3500968b7d1171d48e259a3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C\",\n \"length_bytes\": 62,\n \"license_type\": \"no_license\",\n \"max_line_length\": 24,\n \"num_lines\": 6,\n \"path\": \"/HW1/src/gcd.h\",\n \"repo_name\": \"Jacetone/CS471\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#ifndef GCD_H\\n#define GCD_H\\n\\n\\tint gcdI(int i, int j);\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.5176470875740051,\n \"alphanum_fraction\": 0.5647059082984924,\n \"avg_line_length\": 16.894737243652344,\n \"blob_id\": \"cccbeccdb41cade82974f2c36719f3d7550e0b2c\",\n \"content_id\": \"f9ee971373d6a9170b2a1dae2f66f429fc65266b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 340,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 19,\n \"path\": \"/HW1/src/ack.py\",\n \"repo_name\": \"Jacetone/CS471\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#Maitri Mangal\\n#CS471 Assignment 1\\n\\n#! /usr/bin/env python\\nimport sys\\n\\ndef ack(m, n):\\n\\tif m == 0:\\n\\t\\treturn n+1\\n\\telif n == 0 and m > 0:\\n\\t\\treturn ack(m-1, 1)\\n\\telse:\\t\\n\\t\\treturn ack(m-1, ack(m, n-1))\\n\\nif len(sys.argv) != 3:\\n print(\\\"%s usage: [NUMBER] [NUMBER]\\\" % sys.argv[0])\\n exit()\\n\\nprint(\\\"ack() : \\\"+ str( ack(int(sys.argv[1]), int(sys.argv[2]))))\\n\"\n },\n {\n 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}\n]","isConversational":false},"num_files":{"kind":"number","value":7,"string":"7"}}},{"rowIdx":2983098,"cells":{"repo_name":{"kind":"string","value":"eddieczc/hw"},"repo_url":{"kind":"string","value":"https://github.com/eddieczc/hw"},"snapshot_id":{"kind":"string","value":"fef9d2cfd45232b4eaade56270b37bb2521c449b"},"revision_id":{"kind":"string","value":"1a6fe86d20a6fcaee7223089a931fca1758fa73b"},"directory_id":{"kind":"string","value":"7a68d8fb3684f1f335a029fea9eaf9e636eee3f1"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-09-05T20:00:31.139423","string":"2020-09-05T20:00:31.139423"},"revision_date":{"kind":"timestamp","value":"2019-11-07T10:00:14","string":"2019-11-07T10:00:14"},"committer_date":{"kind":"timestamp","value":"2019-11-07T10:00:14","string":"2019-11-07T10:00:14"},"github_id":{"kind":"number","value":220199897,"string":"220,199,897"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5540619492530823,"alphanum_fraction":0.6031560301780701,"avg_line_length":30.33699607849121,"blob_id":"a24b54705d2b5df9995b7040e39fb65479250478","content_id":"ffeb7fd818653b6638d99ea262e990661d43dd28","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8556,"license_type":"no_license","max_line_length":124,"num_lines":273,"path":"/report_BackgroundSubtractor_r1/compare.py","repo_name":"eddieczc/hw","src_encoding":"UTF-8","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport cv2\nimport imutils\n#import time\nimport os\n#import tensorflow as tf\nimport math\n\n\n#bgPath = \"output/1538990680298.jpg\"\n# cap = cv2.VideoCapture('/media/sf_VMshare/videos/t1.mp4')\n# cap = cv2.VideoCapture(0)\nwidth = 640 #int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) # float\nheight = 480 #int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # float\nprint(width, height)\n\ndisplayResize = 600\nFILE_OUTPUT = '../all.avi'\nminArea = 6000\n\nfgbg_mog = cv2.bgsegm.createBackgroundSubtractorMOG()\n#fgbg_mog = cv2.createBackgroundSubtractorMOG(history=60, detectShadows=True)\nfgbg_mog2 = cv2.createBackgroundSubtractorMOG2( detectShadows=True)\nfgbg_knn = cv2.createBackgroundSubtractorKNN( detectShadows=True)\nfgbg_gmg = cv2.bgsegm.createBackgroundSubtractorGMG()\n\nkernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))\n\nfourcc = cv2.VideoWriter_fourcc(*'MJPG')\nout = cv2.VideoWriter(FILE_OUTPUT,fourcc, 30.0, (int(width)*2,int(height*3)))\n\n\ndef preprocess(img): \n img = cv2.GaussianBlur(img, (111, 111), 0)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n\n return img\n\ndef posprocess(img):\n (T, img) = cv2.threshold(img, 90, 255, cv2.THRESH_BINARY)\n img = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,\\\n cv2.THRESH_BINARY,11,2)\n img = cv2.dilate(img, None, iterations=62)\n img = cv2.erode(img, None, iterations=62)\n img = cv2.dilate(img, None, iterations=8)\n\n return img\n\ndef findContours(img):\n _, cnts, _ = cv2.findContours(img.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n return cnts\n\n\ndef mse(img1, img2):\n mse2 = np.mean( (img1 - img2) ** 2 )\n if mse2 == 0:\n return 100\n return mse2\n\ndef psnr(img1, img2):\n mse = np.mean( (img1 - img2) ** 2 )\n if mse == 0:\n return 100\n PIXEL_MAX = 255.0\n return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))\n \n \n######################ˇ\n\nlastFrame = None\npath = '..\\input'\npath2 = '..\\groundtruth'\nfiles = []\nfiles2 = []\n# r=root, d=directories, f = files\nfiles_in=[]\nfiles_gt=[]\ngg=0\nggg=0\ngggg=0\nz=0\n\nfor r, d, f in os.walk(path):\n for file in f:\n if '.png' in file:\n files_in.append(os.path.join(r, file))\n \n gg+=1\n #print('------------')\n #(in_file)\n #print('------------')\n files_gt.append('0')\n for rr, dd, f2 in os.walk(path2):\n for file2 in f2:\n if file[-10::] == file2[-10::]:\n files_gt.pop()\n files_gt.append(os.path.join(rr, file2))\n ggg += 1\n \n \n\n#print('input:{}'.format(gg))\n#print('input:{}'.format(gg))\n#print('gound_truth:{}'.format(ggg))\n\n\n#print(len(files))\n#print(len(files2))\n\n#print(files_in)\n#print('------------------------------------------')\n#print('------------------------------------------')\n#print('------------------------------------------')\n#print('------------------------------------------')\n#print(files_gt)\n\n\n\n#for f3 in os.listdir(path):\n #print(f3)\n\n\nz=0\n#for f, f2 in zip(files,files2):\nfor f,f2 in zip(files_in,files_gt):\n\n frame = cv2.imread(f)\n\n gt = os.path.basename(f2)\n\n frame_preprocess = preprocess(frame)\n\n if(lastFrame is None): lastFrame = frame_preprocess\n\n\n #\n fgmask_diff = cv2.absdiff(frame_preprocess, lastFrame)\n #lastFrame = frame_preprocess\n fgmask_mog = fgbg_mog.apply(frame_preprocess)\n fgmask_mog2 = fgbg_mog2.apply(frame_preprocess)\n fgmask_knn = fgbg_knn.apply(frame_preprocess)\n fgmask_gmg = fgbg_gmg.apply(frame_preprocess)\n \n\n #\n fgmask_mog = cv2.morphologyEx(fgmask_mog, cv2.MORPH_OPEN, kernel)\n fgmask_mog2 = cv2.morphologyEx(fgmask_mog2, cv2.MORPH_OPEN, kernel)\n fgmask_knn = cv2.morphologyEx(fgmask_knn, cv2.MORPH_OPEN, kernel)\n fgmask_gmg = cv2.morphologyEx(fgmask_gmg, cv2.MORPH_OPEN, kernel)\n \n \n \n boundcolor = (0,255,0)\n fontsize = 1.5\n locY = 80\n b = np.zeros(fgmask_mog.shape[:2], dtype = \"uint8\")\n r = np.zeros(fgmask_mog.shape[:2], dtype = \"uint8\")\n \n z+=1\n \n\n fgmask_diff_rgb = cv2.merge([fgmask_diff, fgmask_diff, fgmask_diff])\n fgmask_mog_rgb = cv2.merge([fgmask_mog, fgmask_mog, fgmask_mog])\n fgmask_mog2_rgb = cv2.merge([fgmask_mog2, fgmask_mog2, fgmask_mog2]) \n fgmask_knn_rgb = cv2.merge([fgmask_knn, fgmask_knn, fgmask_knn]) \n #cv2.putText(fgmask_knn_rgb, \"KNN:{}\".format(y), (250, locY), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2) \n fgmask_gmg_rgb = cv2.merge([fgmask_gmg, fgmask_gmg, fgmask_gmg])\n \n \n d1 = 0\n d2 = 0\n d3 = 0\n d4 = 0\n d5 = 0\n m1 = 0\n m2 = 0\n m3 = 0\n m4 = 0\n m5 = 0\n \n if f2!='0':\n\n \n fgmask_diff_rgb_gray = cv2.cvtColor(fgmask_diff_rgb, cv2.COLOR_BGR2GRAY)\n fgmask_mog_rgb_gray = cv2.cvtColor(fgmask_mog_rgb, cv2.COLOR_BGR2GRAY)\n fgmask_mog2_rgb_gray = cv2.cvtColor(fgmask_mog2_rgb, cv2.COLOR_BGR2GRAY)\n fgmask_knn_rgb_gray = cv2.cvtColor(fgmask_knn_rgb, cv2.COLOR_BGR2GRAY)\n fgmask_gmg_rgb_gray = cv2.cvtColor(fgmask_mog2_rgb, cv2.COLOR_BGR2GRAY)\n \n cv2.imwrite('test1.png', fgmask_diff_rgb_gray)\n cv2.imwrite('test2.png', fgmask_mog_rgb_gray)\n cv2.imwrite('test3.png', fgmask_mog2_rgb_gray)\n cv2.imwrite('test4.png', fgmask_knn_rgb_gray)\n cv2.imwrite('test5.png', fgmask_gmg_rgb_gray)\n \n \n original = cv2.imread(f2)\n #original = preprocess(original)\n original = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY)\n cv2.imwrite('test.png', original)\n original = cv2.imread(\"test.png\",1)\n \n \n contrast1 = cv2.imread(\"test1.png\",1)\n contrast2 = cv2.imread(\"test2.png\",1)\n contrast3 = cv2.imread(\"test3.png\",1)\n contrast4 = cv2.imread(\"test4.png\",1)\n contrast5 = cv2.imread(\"test5.png\",1)\n \n \n m1 = mse(original,contrast1)\n m2 = mse(original,contrast2)\n m3 = mse(original,contrast3)\n m4 = mse(original,contrast4)\n m5 = mse(original,contrast5)\n #print(\"MSE\")\n #print(m1)\n \n d1 = psnr(original,contrast1)\n d2 = psnr(original,contrast2)\n d3 = psnr(original,contrast3)\n d4 = psnr(original,contrast4)\n d5 = psnr(original,contrast5)\n print(\"PSNR\")\n print(d1)\n \n\n cv2.putText(original, \"Ground Truth\", (135, 60), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2)\n cv2.imshow(\"Ground Truth\", imutils.resize(original, width=displayResize))\n \n cv2.putText(fgmask_diff_rgb, \"cv2.absdiff\", (150, locY), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2)\n cv2.putText(fgmask_diff_rgb, \"MSE:{} PSNR:{}\".format(m1,d1), (180, locY+50), cv2.FONT_HERSHEY_COMPLEX, 1, boundcolor, 2)\n \n cv2.putText(fgmask_mog_rgb, \"MOG:{}\".format(d2), (250, locY), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2)\n cv2.putText(fgmask_mog_rgb, \"MSE:{} PSNR:{}\".format(m2,d2), (180, locY+50), cv2.FONT_HERSHEY_COMPLEX, 1, boundcolor, 2)\n \n cv2.putText(fgmask_mog2_rgb, \"MOG2:{}\".format(d3), (240, locY), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2)\n cv2.putText(fgmask_mog2_rgb, \"MSE:{} PSNR:{}\".format(m3,d3), (180, locY+50), cv2.FONT_HERSHEY_COMPLEX, 1, boundcolor, 2)\n\n cv2.putText(fgmask_knn_rgb, \"KNN:{}\".format(d4), (250, locY), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2)\n cv2.putText(fgmask_knn_rgb, \"MSE:{} PSNR:{}\".format(m4,d4), (180, locY+50), cv2.FONT_HERSHEY_COMPLEX, 1, boundcolor, 2)\n\n cv2.putText(fgmask_gmg_rgb, \"GMG:{}\".format(d5), (250, locY), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2)\n cv2.putText(fgmask_gmg_rgb, \"MSE:{} PSNR:{}\".format(m5,d5), (180, locY+50), cv2.FONT_HERSHEY_COMPLEX, 1, boundcolor, 2)\n\n \n cv2.putText(frame, \"Original:{}\".format(z), (150, locY), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2)\n \n \n\n #print(frame.shape, fgmask_diff_rgb.shape)\n combined1 = np.hstack((frame,fgmask_knn_rgb))\n combined2 = np.hstack((fgmask_mog_rgb,fgmask_mog2_rgb ))\n combined3 = np.hstack((fgmask_gmg_rgb,fgmask_diff_rgb))\n combined = np.vstack((combined1, combined2, combined3))\n #print(combined.shape)\n #combined = imutils.resize(combined, width=1980)\n cv2.imshow(\"Combined\", imutils.resize(combined, width=displayResize))\n \n out.write(combined)\n \n k = cv2.waitKey(1) & 0xff\n if k == 27:\n break\n\n\n# cap.release()\ncv2.destroyAllWindows()\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5540619492530823,\n \"alphanum_fraction\": 0.6031560301780701,\n \"avg_line_length\": 30.33699607849121,\n \"blob_id\": \"a24b54705d2b5df9995b7040e39fb65479250478\",\n \"content_id\": \"ffeb7fd818653b6638d99ea262e990661d43dd28\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8556,\n \"license_type\": \"no_license\",\n \"max_line_length\": 124,\n \"num_lines\": 273,\n \"path\": \"/report_BackgroundSubtractor_r1/compare.py\",\n \"repo_name\": \"eddieczc/hw\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python3\\n# -*- coding: utf-8 -*-\\n\\nimport numpy as np\\nimport cv2\\nimport imutils\\n#import time\\nimport os\\n#import tensorflow as tf\\nimport math\\n\\n\\n#bgPath = \\\"output/1538990680298.jpg\\\"\\n# cap = cv2.VideoCapture('/media/sf_VMshare/videos/t1.mp4')\\n# cap = cv2.VideoCapture(0)\\nwidth = 640 #int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) # float\\nheight = 480 #int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # float\\nprint(width, height)\\n\\ndisplayResize = 600\\nFILE_OUTPUT = '../all.avi'\\nminArea = 6000\\n\\nfgbg_mog = cv2.bgsegm.createBackgroundSubtractorMOG()\\n#fgbg_mog = cv2.createBackgroundSubtractorMOG(history=60, detectShadows=True)\\nfgbg_mog2 = cv2.createBackgroundSubtractorMOG2( detectShadows=True)\\nfgbg_knn = cv2.createBackgroundSubtractorKNN( detectShadows=True)\\nfgbg_gmg = cv2.bgsegm.createBackgroundSubtractorGMG()\\n\\nkernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))\\n\\nfourcc = cv2.VideoWriter_fourcc(*'MJPG')\\nout = cv2.VideoWriter(FILE_OUTPUT,fourcc, 30.0, (int(width)*2,int(height*3)))\\n\\n\\ndef preprocess(img): \\n img = cv2.GaussianBlur(img, (111, 111), 0)\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\\n\\n\\n return img\\n\\ndef posprocess(img):\\n (T, img) = cv2.threshold(img, 90, 255, cv2.THRESH_BINARY)\\n img = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,\\\\\\n cv2.THRESH_BINARY,11,2)\\n img = cv2.dilate(img, None, iterations=62)\\n img = cv2.erode(img, None, iterations=62)\\n img = cv2.dilate(img, None, iterations=8)\\n\\n return img\\n\\ndef findContours(img):\\n _, cnts, _ = cv2.findContours(img.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\\n\\n return cnts\\n\\n\\ndef mse(img1, img2):\\n mse2 = np.mean( (img1 - img2) ** 2 )\\n if mse2 == 0:\\n return 100\\n return mse2\\n\\ndef psnr(img1, img2):\\n mse = np.mean( (img1 - img2) ** 2 )\\n if mse == 0:\\n return 100\\n PIXEL_MAX = 255.0\\n return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))\\n \\n \\n######################ˇ\\n\\nlastFrame = None\\npath = '..\\\\input'\\npath2 = '..\\\\groundtruth'\\nfiles = []\\nfiles2 = []\\n# r=root, d=directories, f = files\\nfiles_in=[]\\nfiles_gt=[]\\ngg=0\\nggg=0\\ngggg=0\\nz=0\\n\\nfor r, d, f in os.walk(path):\\n for file in f:\\n if '.png' in file:\\n files_in.append(os.path.join(r, file))\\n \\n gg+=1\\n #print('------------')\\n #(in_file)\\n #print('------------')\\n files_gt.append('0')\\n for rr, dd, f2 in os.walk(path2):\\n for file2 in f2:\\n if file[-10::] == file2[-10::]:\\n files_gt.pop()\\n files_gt.append(os.path.join(rr, file2))\\n ggg += 1\\n \\n \\n\\n#print('input:{}'.format(gg))\\n#print('input:{}'.format(gg))\\n#print('gound_truth:{}'.format(ggg))\\n\\n\\n#print(len(files))\\n#print(len(files2))\\n\\n#print(files_in)\\n#print('------------------------------------------')\\n#print('------------------------------------------')\\n#print('------------------------------------------')\\n#print('------------------------------------------')\\n#print(files_gt)\\n\\n\\n\\n#for f3 in os.listdir(path):\\n #print(f3)\\n\\n\\nz=0\\n#for f, f2 in zip(files,files2):\\nfor f,f2 in zip(files_in,files_gt):\\n\\n frame = cv2.imread(f)\\n\\n gt = os.path.basename(f2)\\n\\n frame_preprocess = preprocess(frame)\\n\\n if(lastFrame is None): lastFrame = frame_preprocess\\n\\n\\n #\\n fgmask_diff = cv2.absdiff(frame_preprocess, lastFrame)\\n #lastFrame = frame_preprocess\\n fgmask_mog = fgbg_mog.apply(frame_preprocess)\\n fgmask_mog2 = fgbg_mog2.apply(frame_preprocess)\\n fgmask_knn = fgbg_knn.apply(frame_preprocess)\\n fgmask_gmg = fgbg_gmg.apply(frame_preprocess)\\n \\n\\n #\\n fgmask_mog = cv2.morphologyEx(fgmask_mog, cv2.MORPH_OPEN, kernel)\\n fgmask_mog2 = cv2.morphologyEx(fgmask_mog2, cv2.MORPH_OPEN, kernel)\\n fgmask_knn = cv2.morphologyEx(fgmask_knn, cv2.MORPH_OPEN, kernel)\\n fgmask_gmg = cv2.morphologyEx(fgmask_gmg, cv2.MORPH_OPEN, kernel)\\n \\n \\n \\n boundcolor = (0,255,0)\\n fontsize = 1.5\\n locY = 80\\n b = np.zeros(fgmask_mog.shape[:2], dtype = \\\"uint8\\\")\\n r = np.zeros(fgmask_mog.shape[:2], dtype = \\\"uint8\\\")\\n \\n z+=1\\n \\n\\n fgmask_diff_rgb = cv2.merge([fgmask_diff, fgmask_diff, fgmask_diff])\\n fgmask_mog_rgb = cv2.merge([fgmask_mog, fgmask_mog, fgmask_mog])\\n fgmask_mog2_rgb = cv2.merge([fgmask_mog2, fgmask_mog2, fgmask_mog2]) \\n fgmask_knn_rgb = cv2.merge([fgmask_knn, fgmask_knn, fgmask_knn]) \\n #cv2.putText(fgmask_knn_rgb, \\\"KNN:{}\\\".format(y), (250, locY), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2) \\n fgmask_gmg_rgb = cv2.merge([fgmask_gmg, fgmask_gmg, fgmask_gmg])\\n \\n \\n d1 = 0\\n d2 = 0\\n d3 = 0\\n d4 = 0\\n d5 = 0\\n m1 = 0\\n m2 = 0\\n m3 = 0\\n m4 = 0\\n m5 = 0\\n \\n if f2!='0':\\n\\n \\n fgmask_diff_rgb_gray = cv2.cvtColor(fgmask_diff_rgb, cv2.COLOR_BGR2GRAY)\\n fgmask_mog_rgb_gray = cv2.cvtColor(fgmask_mog_rgb, cv2.COLOR_BGR2GRAY)\\n fgmask_mog2_rgb_gray = cv2.cvtColor(fgmask_mog2_rgb, cv2.COLOR_BGR2GRAY)\\n fgmask_knn_rgb_gray = cv2.cvtColor(fgmask_knn_rgb, cv2.COLOR_BGR2GRAY)\\n fgmask_gmg_rgb_gray = cv2.cvtColor(fgmask_mog2_rgb, cv2.COLOR_BGR2GRAY)\\n \\n cv2.imwrite('test1.png', fgmask_diff_rgb_gray)\\n cv2.imwrite('test2.png', fgmask_mog_rgb_gray)\\n cv2.imwrite('test3.png', fgmask_mog2_rgb_gray)\\n cv2.imwrite('test4.png', fgmask_knn_rgb_gray)\\n cv2.imwrite('test5.png', fgmask_gmg_rgb_gray)\\n \\n \\n original = cv2.imread(f2)\\n #original = preprocess(original)\\n original = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY)\\n cv2.imwrite('test.png', original)\\n original = cv2.imread(\\\"test.png\\\",1)\\n \\n \\n contrast1 = cv2.imread(\\\"test1.png\\\",1)\\n contrast2 = cv2.imread(\\\"test2.png\\\",1)\\n contrast3 = cv2.imread(\\\"test3.png\\\",1)\\n contrast4 = cv2.imread(\\\"test4.png\\\",1)\\n contrast5 = cv2.imread(\\\"test5.png\\\",1)\\n \\n \\n m1 = mse(original,contrast1)\\n m2 = mse(original,contrast2)\\n m3 = mse(original,contrast3)\\n m4 = mse(original,contrast4)\\n m5 = mse(original,contrast5)\\n #print(\\\"MSE\\\")\\n #print(m1)\\n \\n d1 = psnr(original,contrast1)\\n d2 = psnr(original,contrast2)\\n d3 = psnr(original,contrast3)\\n d4 = psnr(original,contrast4)\\n d5 = psnr(original,contrast5)\\n print(\\\"PSNR\\\")\\n print(d1)\\n \\n\\n cv2.putText(original, \\\"Ground Truth\\\", (135, 60), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2)\\n cv2.imshow(\\\"Ground Truth\\\", imutils.resize(original, width=displayResize))\\n \\n cv2.putText(fgmask_diff_rgb, \\\"cv2.absdiff\\\", (150, locY), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2)\\n cv2.putText(fgmask_diff_rgb, \\\"MSE:{} PSNR:{}\\\".format(m1,d1), (180, locY+50), cv2.FONT_HERSHEY_COMPLEX, 1, boundcolor, 2)\\n \\n cv2.putText(fgmask_mog_rgb, \\\"MOG:{}\\\".format(d2), (250, locY), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2)\\n cv2.putText(fgmask_mog_rgb, \\\"MSE:{} PSNR:{}\\\".format(m2,d2), (180, locY+50), cv2.FONT_HERSHEY_COMPLEX, 1, boundcolor, 2)\\n \\n cv2.putText(fgmask_mog2_rgb, \\\"MOG2:{}\\\".format(d3), (240, locY), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2)\\n cv2.putText(fgmask_mog2_rgb, \\\"MSE:{} PSNR:{}\\\".format(m3,d3), (180, locY+50), cv2.FONT_HERSHEY_COMPLEX, 1, boundcolor, 2)\\n\\n cv2.putText(fgmask_knn_rgb, \\\"KNN:{}\\\".format(d4), (250, locY), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2)\\n cv2.putText(fgmask_knn_rgb, \\\"MSE:{} PSNR:{}\\\".format(m4,d4), (180, locY+50), cv2.FONT_HERSHEY_COMPLEX, 1, boundcolor, 2)\\n\\n cv2.putText(fgmask_gmg_rgb, \\\"GMG:{}\\\".format(d5), (250, locY), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2)\\n cv2.putText(fgmask_gmg_rgb, \\\"MSE:{} PSNR:{}\\\".format(m5,d5), (180, locY+50), cv2.FONT_HERSHEY_COMPLEX, 1, boundcolor, 2)\\n\\n \\n cv2.putText(frame, \\\"Original:{}\\\".format(z), (150, locY), cv2.FONT_HERSHEY_COMPLEX, fontsize, boundcolor, 2)\\n \\n \\n\\n #print(frame.shape, fgmask_diff_rgb.shape)\\n combined1 = np.hstack((frame,fgmask_knn_rgb))\\n combined2 = np.hstack((fgmask_mog_rgb,fgmask_mog2_rgb ))\\n combined3 = np.hstack((fgmask_gmg_rgb,fgmask_diff_rgb))\\n combined = np.vstack((combined1, combined2, combined3))\\n #print(combined.shape)\\n #combined = imutils.resize(combined, width=1980)\\n cv2.imshow(\\\"Combined\\\", imutils.resize(combined, width=displayResize))\\n \\n out.write(combined)\\n \\n k = cv2.waitKey(1) & 0xff\\n if k == 27:\\n break\\n\\n\\n# cap.release()\\ncv2.destroyAllWindows()\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2983099,"cells":{"repo_name":{"kind":"string","value":"someii/chatbot"},"repo_url":{"kind":"string","value":"https://github.com/someii/chatbot"},"snapshot_id":{"kind":"string","value":"4848ae0f3e9ae0636aeca35050f2feb648923496"},"revision_id":{"kind":"string","value":"c3bd9b0311a4b6010ff78ecdc4aa7483a0f01527"},"directory_id":{"kind":"string","value":"6d1b8798aa27614ab65fad62e56a57e596abcf79"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-04-08T11:57:38.627553","string":"2020-04-08T11:57:38.627553"},"revision_date":{"kind":"timestamp","value":"2018-11-23T00:53:43","string":"2018-11-23T00:53:43"},"committer_date":{"kind":"timestamp","value":"2018-11-23T00:53:43","string":"2018-11-23T00:53:43"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6231883764266968,"alphanum_fraction":0.625,"avg_line_length":25.600000381469727,"blob_id":"f7210318bb6e6d2c816bda9b31bebbb17e8601b0","content_id":"1882b5c67464f3467102d3afe2b5852394c28d72","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":808,"license_type":"no_license","max_line_length":59,"num_lines":20,"path":"/3장-EchoBot-1.py","repo_name":"someii/chatbot","src_encoding":"UTF-8","text":"\"\"\"\r\n파이썬의 print(), input() 함수를 이용하여 가장 기초적인 대화 시스템을 만들자.\r\n\"\"\"\r\n\r\n# 가장 기초적인 부분으로써 print( ) 함수로 사용자에게 질문을 유도하자.\r\nprint('Say Something...')\r\n\r\n# 사용자로부터 질문을 받아들이자. input( ) 함수 사용\r\nuser_message = input()\r\n\r\n# 질문에 대한 답변을 하자. 지금은 일단 EchoBack하자.\r\nprint(\"I can head you ! You said: \" + user_message)\r\n\r\n# 위 2가지를 아래와 같이 하나의 input( ) 함수로 처리할 수 있다.\r\n# user_message = input('Say Something...')\r\n# print(\"I can hear you! You said: \" + user_message)\r\n\r\n# 조금 다르게 하려면 아래와 같이 변수를 사용하자.\r\n# bot_message = \"I can hear you! You said: \" + user_message\r\n# print(bot_message)\r\n"},{"alpha_fraction":0.5656565427780151,"alphanum_fraction":0.5656565427780151,"avg_line_length":25.61111068725586,"blob_id":"f7d4308d7466d30e441843061ba008bf431f4ff8","content_id":"2073ee219944ded6a682af4f595911773627d510","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":641,"license_type":"no_license","max_line_length":58,"num_lines":18,"path":"/3장-EchoBot-2.py","repo_name":"someii/chatbot","src_encoding":"UTF-8","text":"\"\"\"\r\n제어문을 사용해서 연속적으로, 여러 종류의 질문에 답변해보자.\r\n사용자와 계속적으로 질의/응답을 진행하기 위해서 반복문(while)을 사용하자.\r\n다양한 질문을 처리하기 위해 if 문을 사용하자.\r\n\"\"\"\r\n\r\nwhile True:\r\n user_message = input('Say Something...')\r\n user_message = user_message.lower()\r\n if user_message == 'bye' or user_message == 'see you':\r\n print('bye')\r\n break\r\n elif user_message == 'hi':\r\n print('hi')\r\n elif user_message == 'good to see you':\r\n print('nice to meet you')\r\n else:\r\n print(\"i don't get it\")"},{"alpha_fraction":0.5585617423057556,"alphanum_fraction":0.5856176614761353,"avg_line_length":16.372549057006836,"blob_id":"889f7ceed9c264a1c960242382f3845f9ac2e347","content_id":"0a4d0f88d191d796a2b73409ac808e4bd722fd29","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4377,"license_type":"no_license","max_line_length":59,"num_lines":153,"path":"/3장-function-1.py","repo_name":"someii/chatbot","src_encoding":"UTF-8","text":"# 특정한 일만 하는 함수\r\n'''\r\n함수는 반복되는 작업에 의한 불필요한 작업을 줄이고, \r\n프로그램의 재 사용성을 높일 수 있도록 해주며, \r\n또한 긴 프로그램을 작성할 때, 연관된 부분끼리 함수라는 단위로 묶음으로써 \r\n긴 소스 코드들을 효율적으로 관리할 수 있다.\r\n함수의 기능은 음악 악보에서 ‘후렴’이라고 생각하면 된다. \r\n예를 들어, 애국가 1절을 부르고, (후렴)을 부르고, \r\n또 2절을 부르고 (후렴)을 부르는 경우, \r\n매번 후렴을 반복해서 악보에 쓰기 귀찮으니까, \r\n후렴이라고 뭉쳐놓고, (후렴) 부르세요… 라고 말만하면 되는 것처럼, \r\n함수의 기본적인 역할은 반복되는 부분의 재 사용성을 높이는 역할이다.\r\n'''\r\ndef greet1():\r\n print('Hi')\r\n\r\ngreet1()\r\n\r\n# 값을 반환하는 함수\r\ndef greet2():\r\n return 'Hello'\r\n\r\nprint(greet2())\r\n\r\n# 값을 전달받는 함수\r\n'''\r\n수학에서 사용하는 삼각 함수 중에서 사인 함수는 sin(30)과 같이 괄호에 숫자로 값을 전달한다. \r\n그러면 sin( ) 함수는 전달받은 값에 해당하는 sin 값을 반환한다.\r\nC 언어에서도 함수에 값을 전달하고 싶은 경우가 있다. \r\n우리가 사용한 print(“Hello, World”) 함수 호출은 \r\nprintf( ) 함수에게 문자열을 넘겨준 것이다. \r\n이렇게 함수에 전달하는 값을 매개 변수(parameter, 파라미터)라고 한다. \r\n아래와 같이 함수에 매개 변수를 사용할 경우에는, \r\n함수를 “정의”할 때 매개 변수의 개수, 종류, 이름 등을 정해야한다. \r\n매개 변수: 파라미터(parameter)라고도 한다. \r\n함수를 사용할 때 괄호 속에 적어서 넘겨주는 값을 말한다. \r\n매개 변수라는 용어는 호출하는 함수(caller)와 호출되는 함수(callee) 사이에 \r\n데이터를 연결(매개)해준다는 의미.\r\n\r\n'''\r\ndef greet3(message):\r\n print(message)\r\n\r\nprint('Say Hi!')\r\n\r\n\r\n# 값을 전달받고, 또, 반환하는 함수\r\n'''\r\n파라미터도 전달하고, 반환값도 있다.\r\n'''\r\ndef sum(a, b):\r\n return a+b\r\n\r\nc = sum(1, 2)\r\nprint(c)\r\n\r\na = 2\r\nb = 4\r\nc = sum(a, b)\r\nprint(c)\r\n\r\n# 반환값이 없는 함수의 반환값은 무엇일까?\r\n'''\r\n함수의 끝에 명시적인 return 문장이 없거나,\r\nreturn 문장만 있고, 실제로 반환하는 값이 없는 경우에는\r\n자동으로 None 값을 반환한다.\r\n즉, 파이썬의 모든 함수는 값을 반환하도록 되어있다.\r\n'''\r\ndef greet4():\r\n print('Hi')\r\n return\r\n\r\nprint(greet1())\r\nprint(greet4())\r\n\r\n# 아래의 코드를 보자.\r\n# 이 문장은 TEST를 출력한 후, # NONE을 출력한다. \r\n# 즉, PRINT( ) 함수는 NONE이라는 값을 반환한다는 의미다.\r\n# 이 내용은 함수 부분에서 자세하게 다시 설명한다.\r\nprint(print('test')\r\n\r\n\r\n# 2개 이상의 값을 반환하고 싶을 때는\r\ndef arithmetic_operation(a, b):\r\n return a+b, a-b, a*b, a/b\r\n\r\nc = arithmetic_operation(1, 2)\r\nprint(type(c))\r\nprint(c)\r\n\r\na, b, c, d = arithmetic_operation(1,2)\r\nprint(a, b, c, d)\r\n\r\n\r\n# 위치 인자(positional parameter)와 키워드 인자(keyword parameter)\r\ndef print_message(a, b, c):\r\n print(a, b, c)\r\n\r\nprint_message(1, 2, 3)\r\nprint_message(a=1, b=2, c=3)\r\nprint_message(c=1, b=2, a=3)\r\nprint_message(1, b=2, c=3)\r\n# print_message(1, b=2, 3)\r\n\r\n\r\n# 파라미터의 디폴트 값 설정\r\ndef greet4(message='Nice to meet you'):\r\n print(message)\r\n\r\ngreet4('hi')\r\ngreet4()\r\n\r\n# 디폴트 파라미터 값 설정 시 주의\r\ndef greet5(name, day, year='2018'):\r\n print('Hi '+name+'. Today is '+ day + '. ' + year)\r\n\r\ngreet5('Ben', 'Monday', '2018')\r\ngreet5('Ben', 'Monday')\r\n\r\n# 아래와 같이 디폴트 값을 설정한 파라미터 이후에는 디폴트값을 설정하지 않는 파라미터를 사용할 수 없다.\r\n'''\r\ndef greet6(name, year='2018', day):\r\n print('Hi '+name+'. Today is '+ day + '. ' + year)\r\n\r\ngreet5('Ben', 'Monday', '2018')\r\ngreet5('Ben', 'Monday')\r\n'''\r\n\r\n# 함수 내부의 지역 변수의 범위\r\na = 10\r\ndef test1(a):\r\n a = a + 1\r\n\r\ntest1(a)\r\nprint(a)\r\n\r\n\r\na = 10\r\ndef test2(a):\r\n a = a + 1\r\n return a\r\n\r\na = test2(a)\r\nprint(a)\r\n\r\n\r\na = 10\r\ndef test3():\r\n global a # 가급적 global 명령어는 사용하지 말자\r\n a = a + 1\r\n\r\ntest3()\r\nprint(a)"},{"alpha_fraction":0.6242424249649048,"alphanum_fraction":0.6272727251052856,"avg_line_length":20.827587127685547,"blob_id":"7cae45a9eda4af143f733940dff23a75f7fdc17e","content_id":"c20c0203dbe4f9f451f030ce3758b6cd4d418138","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":750,"license_type":"no_license","max_line_length":66,"num_lines":29,"path":"/3장-ChatBot-3.py","repo_name":"someii/chatbot","src_encoding":"UTF-8","text":"\"\"\"\r\n변수와 format 스트링을 이용해서 조금 제너럴하게 만들어보자.\r\n\"\"\"\r\n\r\nname = \"Python\"\r\nweather = \"cloudy\"\r\n\r\n# Define a dictionary with the predefined responses\r\nresponses = {\r\n \"what's your name?\": \"my name is {0}\".format(name),\r\n \"what's today's weather?\": \"the weather is {0}\".format(weather),\r\n \"default\": \"default message\"\r\n}\r\n\r\n\r\ndef respond(user_message):\r\n # dictionary에 존재하는 질문인지 검사\r\n if user_message in responses:\r\n bot_message = responses[user_message]\r\n else:\r\n bot_message = responses['default']\r\n \r\n return bot_message\r\n\r\n\r\n# 이제 반복문을 사용해보자\r\nwhile True:\r\n user_message = input('Say Something:')\r\n print(respond(user_message))"},{"alpha_fraction":0.6016260385513306,"alphanum_fraction":0.6016260385513306,"avg_line_length":20.035715103149414,"blob_id":"d7d3ac5af6f1885e227b4a5881383ef41ec19322","content_id":"585ce2d96193936e3c82dfb051c3a974da123c20","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":719,"license_type":"no_license","max_line_length":45,"num_lines":28,"path":"/3장-ChatBot-1.py","repo_name":"someii/chatbot","src_encoding":"UTF-8","text":"\"\"\"\r\ndictionary 자료형을 사용해서, \r\n특정 질문에 정해진 답변을 해보자\r\n\"\"\"\r\n\r\n# dictionary 자료형을 사용해서 질문과 응답 세트를 만들자.\r\nresponses = {\r\n \"what's your name?\": \"my name is ChatBot\",\r\n \"what's today's weather?\": \"It's sunny.\",\r\n \"default\": \"any other question?\"\r\n}\r\n\r\n\r\ndef respond(user_message):\r\n # dictionary에 존재하는 질문인지 검사\r\n if user_message in responses:\r\n bot_message = responses[user_message]\r\n else:\r\n bot_message = responses['default']\r\n \r\n return bot_message\r\n\r\n\r\nwhile True:\r\n user_message = input('Say Something:')\r\n if user_message == 'bye':\r\n break\r\n print(respond(user_message))"},{"alpha_fraction":0.5698924660682678,"alphanum_fraction":0.5854241251945496,"avg_line_length":22.676469802856445,"blob_id":"3d8dfc54a7443bc1f021f0363e5c54fedbdbe350","content_id":"cff5db1cf112bf7cee9e5dd71270aa45d252998f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":967,"license_type":"no_license","max_line_length":82,"num_lines":34,"path":"/3장-ChatBot-2-2.py","repo_name":"someii/chatbot","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Sep 11 11:30:40 2018\r\n\r\n@author: jungkeechul\r\n\"\"\"\r\n\r\n\"\"\"\r\ndictionary 자료형을 사용해서, \r\n특정 질문에 정해진 답변을 해보자\r\n\"\"\"\r\n\r\n# dictionary 자료형을 사용해서 질문과 응답 세트를 만들자.\r\nresponses = {\r\n (\"hello\", \"hi\", \"greetings\", \"sup\", \"what's up\",): \"hi~\",\r\n (\"name\", \"name please\", \"your name\", \"what's your name?\"): \"my name is ChatBot\",\r\n (\"weather\", \"hows weather\", \"what's today's weather?\"): \"It's sunny.\"\r\n }\r\n\r\n\r\ndef respond(user_message):\r\n # dictionary에 존재하는 모든 key(tuple)에 대해서\r\n for user_messages in responses:\r\n # 해당하는 tuple에 질문이 포함되는지\r\n if user_message in user_messages:\r\n return responses[user_messages]\r\n\r\n\r\nwhile True:\r\n user_message = input('Say Something:')\r\n if user_message == 'bye':\r\n print(\"See you soon ~\")\r\n break\r\n print(respond(user_message))"},{"alpha_fraction":0.6161616444587708,"alphanum_fraction":0.6161616444587708,"avg_line_length":24.157894134521484,"blob_id":"8fcd9ac172aef0d82db792a9b92e88bbcc14cde4","content_id":"3301c73923b03f42029434317a8569f6e7f7b200","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":543,"license_type":"no_license","max_line_length":74,"num_lines":19,"path":"/3장-ChatBot-2.py","repo_name":"someii/chatbot","src_encoding":"UTF-8","text":"\"\"\"\r\n조금 더 사용자의 질문에 유연하게 대처할 수 있도록 해보자.\r\n\"\"\"\r\n\r\ngreeting_words = (\"hello\", \"hi\", \"greetings\", \"sup\", \"what's up\",)\r\ngreeting_responses = [\"'sup bro\", \"hey\", \"*nods*\", \"hey you get my snap?\"]\r\n\r\nimport random\r\n\r\n\r\ndef check_for_greeting(user_message):\r\n for word in user_message.split(' '):\r\n if word.lower() in greeting_words:\r\n return random.choice(greeting_responses)\r\n\r\n\r\nwhile True:\r\n user_message = input('Say Something:')\r\n print(check_for_greeting(user_message))"},{"alpha_fraction":0.4656616449356079,"alphanum_fraction":0.4773869216442108,"avg_line_length":20.037036895751953,"blob_id":"6b672eb5fb4e58adb7c7f29b07c59ae1b42f1a23","content_id":"3c37034ab0d4396b3e844983f1ff6dea7f893f62","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":621,"license_type":"no_license","max_line_length":59,"num_lines":27,"path":"/3장-Echobot_guess_what.py","repo_name":"someii/chatbot","src_encoding":"UTF-8","text":"\r\n\"\"\"\r\n스무 고개 게임을 만들어보자.\r\n\"\"\"\r\ndef is_correct(guess, target):\r\n if guess == target:\r\n print(\"Your guess {0} is Correct!\\n\".format(guess))\r\n return True\r\n elif guess < target:\r\n print(\"Higher~\")\r\n return False\r\n elif guess > target:\r\n print(\"Lower~\") \r\n return False\r\n\r\ntarget = 77\r\n\r\ndef guess():\r\n while True:\r\n guess = input('Guess What between 1 to 100: ')\r\n if guess == '':\r\n break\r\n else:\r\n if is_correct(int(guess), target):\r\n break\r\n\r\nif __name__ == \"__main__\":\r\n guess()\r\n"},{"alpha_fraction":0.5460218191146851,"alphanum_fraction":0.5460218191146851,"avg_line_length":23.239999771118164,"blob_id":"3d284bf363736b5aba7718ce90c2ab9acd401c84","content_id":"03fc7f9198e8f2e3b80f0668a25b08b5653212c9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":741,"license_type":"no_license","max_line_length":58,"num_lines":25,"path":"/3장-EchoBot-3.py","repo_name":"someii/chatbot","src_encoding":"UTF-8","text":"\"\"\"\r\n이제 함수를 사용해보자. \r\n챗봇의 응답을 생성하는 함수를 만들것이다.\r\n\"\"\"\r\n\r\n# 함수를 사용해보자. 아래는 respond( )라는 함수를 정의하고 있다.\r\ndef respond(user_message):\r\n if user_message == 'bye' or user_message == 'see you':\r\n return('bye')\r\n elif user_message == 'hi':\r\n return('hi')\r\n elif user_message == 'good to see you':\r\n return('nice to meet you')\r\n else:\r\n return(\"i don't get it\")\r\n\r\n\r\nwhile True:\r\n user_message = input('Say Something...')\r\n if user_message == '':\r\n break\r\n else:\r\n user_message = user_message.lower()\r\n chatbot_message = respond(user_message)\r\n print(chatbot_message)\r\n \r\n "}],"string":"[\n {\n \"alpha_fraction\": 0.6231883764266968,\n \"alphanum_fraction\": 0.625,\n \"avg_line_length\": 25.600000381469727,\n \"blob_id\": \"f7210318bb6e6d2c816bda9b31bebbb17e8601b0\",\n \"content_id\": \"1882b5c67464f3467102d3afe2b5852394c28d72\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 808,\n \"license_type\": \"no_license\",\n \"max_line_length\": 59,\n \"num_lines\": 20,\n \"path\": \"/3장-EchoBot-1.py\",\n \"repo_name\": \"someii/chatbot\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\r\\n파이썬의 print(), input() 함수를 이용하여 가장 기초적인 대화 시스템을 만들자.\\r\\n\\\"\\\"\\\"\\r\\n\\r\\n# 가장 기초적인 부분으로써 print( ) 함수로 사용자에게 질문을 유도하자.\\r\\nprint('Say Something...')\\r\\n\\r\\n# 사용자로부터 질문을 받아들이자. input( ) 함수 사용\\r\\nuser_message = input()\\r\\n\\r\\n# 질문에 대한 답변을 하자. 지금은 일단 EchoBack하자.\\r\\nprint(\\\"I can head you ! You said: \\\" + user_message)\\r\\n\\r\\n# 위 2가지를 아래와 같이 하나의 input( ) 함수로 처리할 수 있다.\\r\\n# user_message = input('Say Something...')\\r\\n# print(\\\"I can hear you! You said: \\\" + user_message)\\r\\n\\r\\n# 조금 다르게 하려면 아래와 같이 변수를 사용하자.\\r\\n# bot_message = \\\"I can hear you! You said: \\\" + user_message\\r\\n# print(bot_message)\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5656565427780151,\n \"alphanum_fraction\": 0.5656565427780151,\n \"avg_line_length\": 25.61111068725586,\n \"blob_id\": \"f7d4308d7466d30e441843061ba008bf431f4ff8\",\n \"content_id\": \"2073ee219944ded6a682af4f595911773627d510\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 641,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 18,\n \"path\": \"/3장-EchoBot-2.py\",\n \"repo_name\": \"someii/chatbot\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\r\\n제어문을 사용해서 연속적으로, 여러 종류의 질문에 답변해보자.\\r\\n사용자와 계속적으로 질의/응답을 진행하기 위해서 반복문(while)을 사용하자.\\r\\n다양한 질문을 처리하기 위해 if 문을 사용하자.\\r\\n\\\"\\\"\\\"\\r\\n\\r\\nwhile True:\\r\\n user_message = input('Say Something...')\\r\\n user_message = user_message.lower()\\r\\n if user_message == 'bye' or user_message == 'see you':\\r\\n print('bye')\\r\\n break\\r\\n elif user_message == 'hi':\\r\\n print('hi')\\r\\n elif user_message == 'good to see you':\\r\\n print('nice to meet you')\\r\\n else:\\r\\n print(\\\"i don't get it\\\")\"\n },\n {\n \"alpha_fraction\": 0.5585617423057556,\n \"alphanum_fraction\": 0.5856176614761353,\n \"avg_line_length\": 16.372549057006836,\n \"blob_id\": \"889f7ceed9c264a1c960242382f3845f9ac2e347\",\n \"content_id\": \"0a4d0f88d191d796a2b73409ac808e4bd722fd29\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4377,\n \"license_type\": \"no_license\",\n \"max_line_length\": 59,\n \"num_lines\": 153,\n \"path\": \"/3장-function-1.py\",\n \"repo_name\": \"someii/chatbot\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# 특정한 일만 하는 함수\\r\\n'''\\r\\n함수는 반복되는 작업에 의한 불필요한 작업을 줄이고, \\r\\n프로그램의 재 사용성을 높일 수 있도록 해주며, \\r\\n또한 긴 프로그램을 작성할 때, 연관된 부분끼리 함수라는 단위로 묶음으로써 \\r\\n긴 소스 코드들을 효율적으로 관리할 수 있다.\\r\\n함수의 기능은 음악 악보에서 ‘후렴’이라고 생각하면 된다. \\r\\n예를 들어, 애국가 1절을 부르고, (후렴)을 부르고, \\r\\n또 2절을 부르고 (후렴)을 부르는 경우, \\r\\n매번 후렴을 반복해서 악보에 쓰기 귀찮으니까, \\r\\n후렴이라고 뭉쳐놓고, (후렴) 부르세요… 라고 말만하면 되는 것처럼, \\r\\n함수의 기본적인 역할은 반복되는 부분의 재 사용성을 높이는 역할이다.\\r\\n'''\\r\\ndef greet1():\\r\\n print('Hi')\\r\\n\\r\\ngreet1()\\r\\n\\r\\n# 값을 반환하는 함수\\r\\ndef greet2():\\r\\n return 'Hello'\\r\\n\\r\\nprint(greet2())\\r\\n\\r\\n# 값을 전달받는 함수\\r\\n'''\\r\\n수학에서 사용하는 삼각 함수 중에서 사인 함수는 sin(30)과 같이 괄호에 숫자로 값을 전달한다. \\r\\n그러면 sin( ) 함수는 전달받은 값에 해당하는 sin 값을 반환한다.\\r\\nC 언어에서도 함수에 값을 전달하고 싶은 경우가 있다. \\r\\n우리가 사용한 print(“Hello, World”) 함수 호출은 \\r\\nprintf( ) 함수에게 문자열을 넘겨준 것이다. \\r\\n이렇게 함수에 전달하는 값을 매개 변수(parameter, 파라미터)라고 한다. \\r\\n아래와 같이 함수에 매개 변수를 사용할 경우에는, \\r\\n함수를 “정의”할 때 매개 변수의 개수, 종류, 이름 등을 정해야한다. \\r\\n매개 변수: 파라미터(parameter)라고도 한다. \\r\\n함수를 사용할 때 괄호 속에 적어서 넘겨주는 값을 말한다. \\r\\n매개 변수라는 용어는 호출하는 함수(caller)와 호출되는 함수(callee) 사이에 \\r\\n데이터를 연결(매개)해준다는 의미.\\r\\n\\r\\n'''\\r\\ndef greet3(message):\\r\\n print(message)\\r\\n\\r\\nprint('Say Hi!')\\r\\n\\r\\n\\r\\n# 값을 전달받고, 또, 반환하는 함수\\r\\n'''\\r\\n파라미터도 전달하고, 반환값도 있다.\\r\\n'''\\r\\ndef sum(a, b):\\r\\n return a+b\\r\\n\\r\\nc = sum(1, 2)\\r\\nprint(c)\\r\\n\\r\\na = 2\\r\\nb = 4\\r\\nc = sum(a, b)\\r\\nprint(c)\\r\\n\\r\\n# 반환값이 없는 함수의 반환값은 무엇일까?\\r\\n'''\\r\\n함수의 끝에 명시적인 return 문장이 없거나,\\r\\nreturn 문장만 있고, 실제로 반환하는 값이 없는 경우에는\\r\\n자동으로 None 값을 반환한다.\\r\\n즉, 파이썬의 모든 함수는 값을 반환하도록 되어있다.\\r\\n'''\\r\\ndef greet4():\\r\\n print('Hi')\\r\\n return\\r\\n\\r\\nprint(greet1())\\r\\nprint(greet4())\\r\\n\\r\\n# 아래의 코드를 보자.\\r\\n# 이 문장은 TEST를 출력한 후, # NONE을 출력한다. \\r\\n# 즉, PRINT( ) 함수는 NONE이라는 값을 반환한다는 의미다.\\r\\n# 이 내용은 함수 부분에서 자세하게 다시 설명한다.\\r\\nprint(print('test')\\r\\n\\r\\n\\r\\n# 2개 이상의 값을 반환하고 싶을 때는\\r\\ndef arithmetic_operation(a, b):\\r\\n return a+b, a-b, a*b, a/b\\r\\n\\r\\nc = arithmetic_operation(1, 2)\\r\\nprint(type(c))\\r\\nprint(c)\\r\\n\\r\\na, b, c, d = arithmetic_operation(1,2)\\r\\nprint(a, b, c, d)\\r\\n\\r\\n\\r\\n# 위치 인자(positional parameter)와 키워드 인자(keyword parameter)\\r\\ndef print_message(a, b, c):\\r\\n print(a, b, c)\\r\\n\\r\\nprint_message(1, 2, 3)\\r\\nprint_message(a=1, b=2, c=3)\\r\\nprint_message(c=1, b=2, a=3)\\r\\nprint_message(1, b=2, c=3)\\r\\n# print_message(1, b=2, 3)\\r\\n\\r\\n\\r\\n# 파라미터의 디폴트 값 설정\\r\\ndef greet4(message='Nice to meet you'):\\r\\n print(message)\\r\\n\\r\\ngreet4('hi')\\r\\ngreet4()\\r\\n\\r\\n# 디폴트 파라미터 값 설정 시 주의\\r\\ndef greet5(name, day, year='2018'):\\r\\n print('Hi '+name+'. Today is '+ day + '. ' + year)\\r\\n\\r\\ngreet5('Ben', 'Monday', '2018')\\r\\ngreet5('Ben', 'Monday')\\r\\n\\r\\n# 아래와 같이 디폴트 값을 설정한 파라미터 이후에는 디폴트값을 설정하지 않는 파라미터를 사용할 수 없다.\\r\\n'''\\r\\ndef greet6(name, year='2018', day):\\r\\n print('Hi '+name+'. Today is '+ day + '. ' + year)\\r\\n\\r\\ngreet5('Ben', 'Monday', '2018')\\r\\ngreet5('Ben', 'Monday')\\r\\n'''\\r\\n\\r\\n# 함수 내부의 지역 변수의 범위\\r\\na = 10\\r\\ndef test1(a):\\r\\n a = a + 1\\r\\n\\r\\ntest1(a)\\r\\nprint(a)\\r\\n\\r\\n\\r\\na = 10\\r\\ndef test2(a):\\r\\n a = a + 1\\r\\n return a\\r\\n\\r\\na = test2(a)\\r\\nprint(a)\\r\\n\\r\\n\\r\\na = 10\\r\\ndef test3():\\r\\n global a # 가급적 global 명령어는 사용하지 말자\\r\\n a = a + 1\\r\\n\\r\\ntest3()\\r\\nprint(a)\"\n },\n {\n \"alpha_fraction\": 0.6242424249649048,\n \"alphanum_fraction\": 0.6272727251052856,\n \"avg_line_length\": 20.827587127685547,\n \"blob_id\": \"7cae45a9eda4af143f733940dff23a75f7fdc17e\",\n \"content_id\": \"c20c0203dbe4f9f451f030ce3758b6cd4d418138\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 750,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 29,\n \"path\": \"/3장-ChatBot-3.py\",\n \"repo_name\": \"someii/chatbot\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\r\\n변수와 format 스트링을 이용해서 조금 제너럴하게 만들어보자.\\r\\n\\\"\\\"\\\"\\r\\n\\r\\nname = \\\"Python\\\"\\r\\nweather = \\\"cloudy\\\"\\r\\n\\r\\n# Define a dictionary with the predefined responses\\r\\nresponses = {\\r\\n \\\"what's your name?\\\": \\\"my name is {0}\\\".format(name),\\r\\n \\\"what's today's weather?\\\": \\\"the weather is {0}\\\".format(weather),\\r\\n \\\"default\\\": \\\"default message\\\"\\r\\n}\\r\\n\\r\\n\\r\\ndef respond(user_message):\\r\\n # dictionary에 존재하는 질문인지 검사\\r\\n if user_message in responses:\\r\\n bot_message = responses[user_message]\\r\\n else:\\r\\n bot_message = responses['default']\\r\\n \\r\\n return bot_message\\r\\n\\r\\n\\r\\n# 이제 반복문을 사용해보자\\r\\nwhile True:\\r\\n user_message = input('Say Something:')\\r\\n print(respond(user_message))\"\n },\n {\n \"alpha_fraction\": 0.6016260385513306,\n \"alphanum_fraction\": 0.6016260385513306,\n \"avg_line_length\": 20.035715103149414,\n \"blob_id\": \"d7d3ac5af6f1885e227b4a5881383ef41ec19322\",\n \"content_id\": \"585ce2d96193936e3c82dfb051c3a974da123c20\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 719,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 28,\n \"path\": \"/3장-ChatBot-1.py\",\n \"repo_name\": \"someii/chatbot\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\r\\ndictionary 자료형을 사용해서, \\r\\n특정 질문에 정해진 답변을 해보자\\r\\n\\\"\\\"\\\"\\r\\n\\r\\n# dictionary 자료형을 사용해서 질문과 응답 세트를 만들자.\\r\\nresponses = {\\r\\n \\\"what's your name?\\\": \\\"my name is ChatBot\\\",\\r\\n \\\"what's today's weather?\\\": \\\"It's sunny.\\\",\\r\\n \\\"default\\\": \\\"any other question?\\\"\\r\\n}\\r\\n\\r\\n\\r\\ndef respond(user_message):\\r\\n # dictionary에 존재하는 질문인지 검사\\r\\n if user_message in responses:\\r\\n bot_message = responses[user_message]\\r\\n else:\\r\\n bot_message = responses['default']\\r\\n \\r\\n return bot_message\\r\\n\\r\\n\\r\\nwhile True:\\r\\n user_message = input('Say Something:')\\r\\n if user_message == 'bye':\\r\\n break\\r\\n print(respond(user_message))\"\n },\n {\n \"alpha_fraction\": 0.5698924660682678,\n \"alphanum_fraction\": 0.5854241251945496,\n \"avg_line_length\": 22.676469802856445,\n \"blob_id\": \"3d8dfc54a7443bc1f021f0363e5c54fedbdbe350\",\n \"content_id\": \"cff5db1cf112bf7cee9e5dd71270aa45d252998f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 967,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 34,\n \"path\": \"/3장-ChatBot-2-2.py\",\n \"repo_name\": \"someii/chatbot\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\r\\n\\\"\\\"\\\"\\r\\nCreated on Tue Sep 11 11:30:40 2018\\r\\n\\r\\n@author: jungkeechul\\r\\n\\\"\\\"\\\"\\r\\n\\r\\n\\\"\\\"\\\"\\r\\ndictionary 자료형을 사용해서, \\r\\n특정 질문에 정해진 답변을 해보자\\r\\n\\\"\\\"\\\"\\r\\n\\r\\n# dictionary 자료형을 사용해서 질문과 응답 세트를 만들자.\\r\\nresponses = {\\r\\n (\\\"hello\\\", \\\"hi\\\", \\\"greetings\\\", \\\"sup\\\", \\\"what's up\\\",): \\\"hi~\\\",\\r\\n (\\\"name\\\", \\\"name please\\\", \\\"your name\\\", \\\"what's your name?\\\"): \\\"my name is ChatBot\\\",\\r\\n (\\\"weather\\\", \\\"hows weather\\\", \\\"what's today's weather?\\\"): \\\"It's sunny.\\\"\\r\\n }\\r\\n\\r\\n\\r\\ndef respond(user_message):\\r\\n # dictionary에 존재하는 모든 key(tuple)에 대해서\\r\\n for user_messages in responses:\\r\\n # 해당하는 tuple에 질문이 포함되는지\\r\\n if user_message in user_messages:\\r\\n return responses[user_messages]\\r\\n\\r\\n\\r\\nwhile True:\\r\\n user_message = input('Say Something:')\\r\\n if user_message == 'bye':\\r\\n print(\\\"See you soon ~\\\")\\r\\n break\\r\\n print(respond(user_message))\"\n },\n {\n \"alpha_fraction\": 0.6161616444587708,\n \"alphanum_fraction\": 0.6161616444587708,\n \"avg_line_length\": 24.157894134521484,\n \"blob_id\": \"8fcd9ac172aef0d82db792a9b92e88bbcc14cde4\",\n \"content_id\": \"3301c73923b03f42029434317a8569f6e7f7b200\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 543,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 19,\n \"path\": \"/3장-ChatBot-2.py\",\n \"repo_name\": \"someii/chatbot\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\r\\n조금 더 사용자의 질문에 유연하게 대처할 수 있도록 해보자.\\r\\n\\\"\\\"\\\"\\r\\n\\r\\ngreeting_words = (\\\"hello\\\", \\\"hi\\\", \\\"greetings\\\", \\\"sup\\\", \\\"what's up\\\",)\\r\\ngreeting_responses = [\\\"'sup bro\\\", \\\"hey\\\", \\\"*nods*\\\", \\\"hey you get my snap?\\\"]\\r\\n\\r\\nimport random\\r\\n\\r\\n\\r\\ndef check_for_greeting(user_message):\\r\\n for word in user_message.split(' '):\\r\\n if word.lower() in greeting_words:\\r\\n return random.choice(greeting_responses)\\r\\n\\r\\n\\r\\nwhile True:\\r\\n user_message = input('Say Something:')\\r\\n print(check_for_greeting(user_message))\"\n },\n {\n \"alpha_fraction\": 0.4656616449356079,\n \"alphanum_fraction\": 0.4773869216442108,\n \"avg_line_length\": 20.037036895751953,\n \"blob_id\": \"6b672eb5fb4e58adb7c7f29b07c59ae1b42f1a23\",\n \"content_id\": \"3c37034ab0d4396b3e844983f1ff6dea7f893f62\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 621,\n \"license_type\": \"no_license\",\n \"max_line_length\": 59,\n \"num_lines\": 27,\n \"path\": \"/3장-Echobot_guess_what.py\",\n \"repo_name\": \"someii/chatbot\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\r\\n\\\"\\\"\\\"\\r\\n스무 고개 게임을 만들어보자.\\r\\n\\\"\\\"\\\"\\r\\ndef is_correct(guess, target):\\r\\n if guess == target:\\r\\n print(\\\"Your guess {0} is Correct!\\\\n\\\".format(guess))\\r\\n return True\\r\\n elif guess < target:\\r\\n print(\\\"Higher~\\\")\\r\\n return False\\r\\n elif guess > target:\\r\\n print(\\\"Lower~\\\") \\r\\n return False\\r\\n\\r\\ntarget = 77\\r\\n\\r\\ndef guess():\\r\\n while True:\\r\\n guess = input('Guess What between 1 to 100: ')\\r\\n if guess == '':\\r\\n break\\r\\n else:\\r\\n if is_correct(int(guess), target):\\r\\n break\\r\\n\\r\\nif __name__ == \\\"__main__\\\":\\r\\n guess()\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5460218191146851,\n \"alphanum_fraction\": 0.5460218191146851,\n \"avg_line_length\": 23.239999771118164,\n \"blob_id\": \"3d284bf363736b5aba7718ce90c2ab9acd401c84\",\n \"content_id\": \"03fc7f9198e8f2e3b80f0668a25b08b5653212c9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 741,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 25,\n \"path\": \"/3장-EchoBot-3.py\",\n \"repo_name\": \"someii/chatbot\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\r\\n이제 함수를 사용해보자. \\r\\n챗봇의 응답을 생성하는 함수를 만들것이다.\\r\\n\\\"\\\"\\\"\\r\\n\\r\\n# 함수를 사용해보자. 아래는 respond( )라는 함수를 정의하고 있다.\\r\\ndef respond(user_message):\\r\\n if user_message == 'bye' or user_message == 'see you':\\r\\n return('bye')\\r\\n elif user_message == 'hi':\\r\\n return('hi')\\r\\n elif user_message == 'good to see you':\\r\\n return('nice to meet you')\\r\\n else:\\r\\n return(\\\"i don't get it\\\")\\r\\n\\r\\n\\r\\nwhile True:\\r\\n user_message = input('Say Something...')\\r\\n if user_message == '':\\r\\n break\\r\\n else:\\r\\n user_message = user_message.lower()\\r\\n chatbot_message = respond(user_message)\\r\\n print(chatbot_message)\\r\\n \\r\\n \"\n 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Tneciv/Poseidon | https://github.com/Tneciv/Poseidon | f7ba5070396cc2e36804307140176aaec8661f4e | 4cbd7ee02d412b5d8a779218877d3fa58aaef452 | c8dbfd84e14d6551841c452387a98d13f37ea575 | refs/heads/master | 2021-01-23T03:53:09.305156 | 2017-12-18T09:11:20 | 2017-12-18T09:11:20 | 86,129,675 | 0 | 0 | MIT | 2017-03-25T03:31:15 | 2017-05-11T15:16:19 | 2017-12-18T09:11:20 | Java | [
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Henchel-Santillan/rt-sudoku | https://github.com/Henchel-Santillan/rt-sudoku | 27e83b2c70aeb78cb934bad2e8a132bc928c9182 | 239ca1ec9c005148cf518783e52648eca2f2db4b | 2a477097af6e38df7c5d8a402882949258767d24 | refs/heads/main | 2023-07-13T18:51:53.189942 | 2021-08-24T19:09:37 | 2021-08-24T19:09:37 | 361,771,423 | 0 | 0 | null | null | null | null | null | [
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clash402/flash-cards | https://github.com/clash402/flash-cards | 9c3c85b4b20f41936a2c7d014c0b8fd1470934a4 | 6e27c2b32df44078f19466e59d1d767b8805f558 | 7d8cdcc1274dd3157323bb478c3a72598e82a6ac | refs/heads/master | 2023-03-05T05:07:32.778078 | 2021-01-28T18:20:51 | 2021-01-28T18:20:51 | 333,847,140 | 0 | 0 | null | null | null | null | null | [
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pengler/TravelTime | https://github.com/pengler/TravelTime | 9e151a842dbb9dd3950e1f503f7e6deda2df1fb6 | 674909724a55fb87bae5b37ccda9e20b71fba680 | 0f4ff925a088d7982b8e88f6132fe9183c21e9a7 | refs/heads/master | 2020-03-30T14:57:59.234437 | 2019-02-26T03:25:22 | 2019-02-26T03:25:22 | 151,342,689 | 0 | 0 | null | null | null | null | null | [
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pjrcisco/train_example | https://github.com/pjrcisco/train_example | 31987a3ee187cf4bc0b6095643dfa554059adcfc | dc50d22930e8c3b612e9301fa5b86f387597d2ce | bb5a5922df4b2af6dd6fa81a29b4f13ac35ceffa | refs/heads/master | 2021-01-20T19:30:00.595184 | 2016-07-06T06:58:30 | 2016-07-06T06:58:30 | 62,691,585 | 0 | 0 | null | null | null | null | null | [
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fasterastv2/Google-Search | https://github.com/fasterastv2/Google-Search | 6b642c40660732ed6339e4d633573b6257f33330 | 6d6cd8190a343b5c729cee289c86a0573d7fda1e | 7870f5bf5c88a7215a3fe7ae69d9843ee104a09d | refs/heads/main | 2023-02-04T14:54:44.662242 | 2020-12-20T12:35:37 | 2020-12-20T12:35:37 | 323,066,260 | 1 | 0 | null | null | null | null | null | [
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joeydumont/mellotron | https://github.com/joeydumont/mellotron | 19e9f0594f374c5b5f3076fb2415595df831170b | e38fad179553f4a1eee3d96e313488f9d803bfbb | 425f9e82149314dadd516d8810cf0a58057a390c | refs/heads/develop | 2020-03-31T21:58:07.835957 | 2018-11-24T03:14:28 | 2018-11-24T03:14:28 | 152,599,987 | 1 | 2 | null | 2018-10-11T13:53:53 | 2018-11-24T03:14:38 | 2018-11-24T03:18:51 | C++ | [
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WaitingLin/MNIST | https://github.com/WaitingLin/MNIST | a80f9cb7796ab2c7af9351ee66b57852239c3a27 | 746278fdba633b3a2604a6fa7b6f723bdd857709 | cf64f284535ab5e085a8daf8f40c634a89c4de87 | refs/heads/master | 2021-08-19T00:07:19.406121 | 2017-11-24T08:40:55 | 2017-11-24T08:40:55 | 111,895,206 | 0 | 0 | null | null | null | null | null | [
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yhfudev/vs4p | https://github.com/yhfudev/vs4p | a91877c10741b2c3ae1d8c244b7a3c3e89327c89 | 0e2fda2a4458a6c85d64928322b758c1db5cc4d1 | 32faf3896733731c98fe7096e9b2f9b647d917f5 | refs/heads/master | 2021-01-10T01:06:15.815797 | 2008-12-17T21:10:07 | 2008-12-17T21:10:07 | 44,403,549 | 0 | 0 | null | null | null | null | null | [
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ramuvu/test_bdd | https://github.com/ramuvu/test_bdd | 84963db6e06449affa88f8fecaf638ebb8cd56a0 | 45a1bd7690cd454b816cb8b83bea6e2b40e19d6e | edf29652c2943e6d738da1ed5e9acfcb9c952d31 | refs/heads/master | 2023-07-14T12:02:21.321395 | 2021-07-30T14:56:08 | 2021-07-30T14:56:08 | 390,731,059 | 0 | 0 | null | null | null | null | null | [
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HipolitoJr17/Programs | https://github.com/HipolitoJr17/Programs | fb088afba8c66ae8c25223d1c898400a42c19434 | 9ccd5fc76927fa9bb8469d53037d7acdda6900ad | debbbeddce839bf8e39ad0209c3a54896497ec60 | refs/heads/main | 2023-03-21T16:23:45.921616 | 2021-03-09T23:25:29 | 2021-03-09T23:25:29 | 343,942,699 | 0 | 0 | null | null | null | null | null | [
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zchumager/pythreads | https://github.com/zchumager/pythreads | bac6d47b851a45f8f031cb10c33becc5e6e272d7 | 61b83a5d6b03207ffc0e2f9f9b2470bb5d3867ce | 46ed686d027a7288c63970efbe26cfdf566db039 | refs/heads/main | 2023-03-12T11:00:49.303345 | 2021-02-24T02:22:43 | 2021-02-24T02:22:43 | 341,752,235 | 0 | 0 | null | null | null | null | null | [
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minnaf/nyc-mhtn-ds-060319-lectures | https://github.com/minnaf/nyc-mhtn-ds-060319-lectures | 34ff11edcf3b1cbd1570a32a15faa128f126921b | 43c96d39d6c5d5b6c65e855337d733ecfc1c0bb2 | 5ed616ff1401883fb821f3daf0650f7554ace527 | refs/heads/master | 2020-05-31T03:16:42.994956 | 2019-08-14T14:57:50 | 2019-08-14T14:57:50 | 190,079,679 | 0 | 0 | null | 2019-06-03T20:44:56 | 2019-06-03T20:38:40 | 2019-06-03T17:27:38 | null | [
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AbooMardiiyah/indonesian-speech-recognition | https://github.com/AbooMardiiyah/indonesian-speech-recognition | 557610a584b6bc246d991aaf80547ed0f768d4ae | 789e9ff1c2d381e511faced460ea9bba7c9a4a9a | e061847e9bdf4601d9cdc6613fb87e52092a9a47 | refs/heads/main | 2023-06-18T05:35:26.859154 | 2021-07-12T11:29:16 | 2021-07-12T11:29:16 | null | 0 | 0 | null | null | null | null | null | [
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adamburford/COP4533 | https://github.com/adamburford/COP4533 | 6f00c06b8334d8c4ab76c2a56f24ab0231b8ace8 | d40e2e67fbe7c4dc9888117b5a40c213de40ee58 | 6d326f17e37811efd01c4fa0eacb281e3aeb65d9 | refs/heads/master | 2023-01-19T06:37:47.878983 | 2020-11-30T05:18:54 | 2020-11-30T05:18:54 | 310,965,682 | 0 | 0 | null | null | null | null | null | [
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Pritesh7/temp | https://github.com/Pritesh7/temp | 72f5f43c6afdf6bd163a3ed09f4b67e6778dd741 | 4a41483985366eb905143760a29eab0f2313b00d | f78c5d9a391379e448e33fd67078dbf6de0e52bf | refs/heads/master | 2022-12-18T04:25:26.736518 | 2020-08-24T07:16:05 | 2020-08-24T07:16:05 | 288,214,948 | 0 | 0 | null | null | null | null | null | [
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basketballrelativity/py_ball | https://github.com/basketballrelativity/py_ball | 84ccb8a06ce3e79ed128d6872dafe5f6891d5991 | 1727f6a761261990e4a4fd5601f1342648e1c6dc | e046f959a249ab687b3ff270def3c9ec9babb5ec | refs/heads/master | 2023-05-26T19:50:07.694897 | 2023-02-02T01:48:31 | 2023-02-02T01:48:31 | 152,098,891 | 96 | 16 | MIT | 2018-10-08T15:01:46 | 2023-01-31T19:40:17 | 2023-02-02T01:47:27 | Python | [
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lukedala/KibbleBit-RaspberryPI-Config | https://github.com/lukedala/KibbleBit-RaspberryPI-Config | 1033c1f9d8e94b7e39188996020de8844deab38a | 6af0f298a4f7e1f1447cca3ac9bf7be8579419d0 | 70b722029154bfe24940def0d42cfbf4c13b668e | refs/heads/master | 2021-01-21T08:33:07.911839 | 2017-03-15T03:51:02 | 2017-03-15T03:51:02 | 91,631,495 | 0 | 0 | null | null | null | null | null | [
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hieubz/TaggingApp | https://github.com/hieubz/TaggingApp | 01955589ff07146b264f3319e27191577c7cf384 | 49f1fbd239cc4a57637c7a4d35a52212cac757c6 | 7d16c815701edef3b440c5e70b4802844f8849bf | refs/heads/master | 2021-06-24T04:00:53.813535 | 2017-07-28T16:05:29 | 2017-07-28T16:05:29 | null | 0 | 0 | null | null | null | null | null | [
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shengjiangtou/parabint | https://github.com/shengjiangtou/parabint | 37af27cedbe18450a0ace5e85e2e34591960ea67 | 2662d4bf0fbd831cdefca48863b00d1ae087457a | e1540f78a810195dc6c0af6895a106bef171c02c | refs/heads/master | 2020-11-29T23:54:59.255497 | 2017-06-12T07:32:17 | 2017-06-12T07:32:17 | null | 0 | 0 | null | null | null | null | null | [
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c3iox/leetcode | https://github.com/c3iox/leetcode | a67e490b77c5dc71e1a3e5cbbb6b89aa7bfb5a30 | fa91193922ff3e34728cac782532cc5fc606a2f6 | 40a3c54a4fa70c6ae19d560874c0cf61e4041202 | refs/heads/main | 2023-04-27T09:11:43.407647 | 2021-05-22T12:43:05 | 2021-05-22T12:43:05 | 369,804,850 | 0 | 0 | null | null | null | null | null | [
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denizurgun/graduation-project | https://github.com/denizurgun/graduation-project | 41b6dcc5ec8b3172716f680ff31dc2bd973839f1 | fa0ef22454050d9de207daef38862c5ec702d7c7 | 93286557f5baeb8e7d9c841247ddf9b41b6d7d43 | refs/heads/main | 2023-05-30T16:49:20.381955 | 2021-06-20T16:04:27 | 2021-06-20T16:04:27 | 378,682,986 | 0 | 0 | null | null | null | null | null | [
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GarvinChanderia/WindowsNotificationTrigger | https://github.com/GarvinChanderia/WindowsNotificationTrigger | a6ff4caeaa8e9725966cf420b3f0f3ee46933106 | 9ae5093987749abd05098ff7025f1a8f2a71b479 | c2d127675b94b7e6294e38eab1d9bde559b710af | refs/heads/main | 2023-07-14T23:32:54.706309 | 2021-09-06T18:22:33 | 2021-09-06T18:22:33 | 403,720,695 | 0 | 0 | null | null | null | null | null | [
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biteapple777/RPG-in-Python | https://github.com/biteapple777/RPG-in-Python | e1e459bba61fa3a953df4fb04c702533cc3a3b2c | a875210fd63e7cb69bf4d411992667c93e02a5f0 | 714123b28257fec46ae8ed0cb75fa6049071cc3d | refs/heads/master | 2020-02-27T04:19:39.976636 | 2016-06-12T08:36:10 | 2016-06-12T08:36:10 | 56,909,019 | 0 | 0 | null | 2016-04-23T08:32:32 | 2016-04-23T08:32:32 | 2016-06-12T08:36:10 | null | [
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Waightman/pyerz | https://github.com/Waightman/pyerz | da13741885aee9cc90694f8d10e908d6d184ec78 | 03311ce81dda7cba69f2eb0f0bf9c2c5a282a297 | b20f9602b61cc05e20ecd4a7123284b083377dee | refs/heads/master | 2023-03-24T09:31:52.528388 | 2021-01-10T01:43:00 | 2021-01-10T01:43:00 | null | 0 | 0 | null | null | null | null | null | [
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Panako14/mi_primer_programa_2 | https://github.com/Panako14/mi_primer_programa_2 | 5e399b229e66b863c110d3b167e6f16abd4e8c85 | 4f470a3a50506f0721f039c0547e5aacb2fe2e8e | 8cc063b282947812664face15ae94a6a1884c6fc | refs/heads/master | 2020-03-13T08:39:48.902653 | 2018-06-22T14:27:27 | 2018-06-22T14:27:27 | 131,047,982 | 0 | 0 | null | null | null | null | null | [
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C00kiie/youtubeSearch_archive | https://github.com/C00kiie/youtubeSearch_archive | de75c36aeec59ee619f36246908631ef81a76881 | 857ed3cf13ab72f5a28b01c813bbd4d412c58efd | 61579a3a27dd293e67e26393781d0723fccd08fd | refs/heads/master | 2022-04-19T10:46:59.223462 | 2020-04-19T06:15:47 | 2020-04-19T06:15:47 | 256,924,096 | 2 | 0 | null | null | null | null | null | [
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Valentin2771/SimpleBanking | https://github.com/Valentin2771/SimpleBanking | 6299b19ec4095ef511231f51fdf7d90e5d2a26a9 | f32fed95d6699acef29f1a144235ad80b2676cdf | 7ed59604095126d6fbae2edaab42593b5f8c69d4 | refs/heads/main | 2022-12-19T17:42:42.905374 | 2020-10-24T03:54:36 | 2020-10-24T03:54:36 | 306,798,227 | 0 | 0 | null | null | null | null | null | [
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1447964530/CVPR2021_PFNet | https://github.com/1447964530/CVPR2021_PFNet | 469c706450868f1d90a6b3d831e7f554e0fbf31e | 2c4cab0730e6a0619fad79092f0b34f71c3b56c4 | cb05382a1863fa9ac6f0cf1dba24e8781b0d33ec | refs/heads/main | 2023-07-25T13:03:25.976051 | 2021-09-05T04:11:06 | 2021-09-05T04:11:06 | null | 0 | 0 | null | null | null | null | null | [
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murtraja/django-sensor-server | https://github.com/murtraja/django-sensor-server | 72fd09141ff84eee2a2e75db474b0ecc208070a0 | 3ed329af2d38ed0d1809cd89e254b93e7fcc4daf | 8441383d17d70fdc57ff2d54352f336d0f666c50 | refs/heads/master | 2021-01-10T06:51:51.676488 | 2015-09-24T11:25:59 | 2015-09-24T11:25:59 | 43,062,338 | 0 | 0 | null | null | null | null | null | [
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rrifaldiu/line-bot-medik | https://github.com/rrifaldiu/line-bot-medik | 02b19ac07291575926c23a93d22f6761cd11e961 | bfcf6570e62d841a6b3fe13adf55ac9779ffad62 | df6fa4fa52d4708ae51ea2b47d5d1fa9f7b6d83e | refs/heads/master | 2021-09-02T05:00:05.729420 | 2017-12-30T15:09:20 | 2017-12-30T15:09:20 | 115,486,950 | 0 | 0 | null | null | null | null | null | [
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michjk/Question_Classifier_Pytorch | https://github.com/michjk/Question_Classifier_Pytorch | fd6930316acdb29189f0e29e9666802d9f669ef6 | dead77242f069f37c04e2013e526130d8cdc93c3 | ab9bf463f061780d76d813e74a072dde299787d0 | refs/heads/refactor | 2023-02-23T22:02:40.101352 | 2022-05-26T05:48:19 | 2022-05-26T05:48:19 | 116,722,798 | 4 | 2 | null | 2018-01-08T20:10:55 | 2022-05-26T05:48:23 | 2023-02-15T18:35:34 | Python | [
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CORRUPTOR2037/last-word-project | https://github.com/CORRUPTOR2037/last-word-project | ff0c1637ebef753cecc33ad20365790cb705f2ee | 7a4b890fcae02ac5e534d90a2581db11b66ae2fa | 695f4b5af5498e9516671fa8f649b6af4a652c69 | refs/heads/master | 2020-09-25T18:08:13.093550 | 2019-12-30T16:45:16 | 2019-12-30T16:45:16 | 226,060,616 | 1 | 0 | null | null | null | null | null | [
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zhangquanit/fastapp | https://github.com/zhangquanit/fastapp | 01449ad8c42d9866ea5e20dcc55ffc42c3fbde62 | 10f3906790b33736aabf0d59324a5e30cddbae84 | d2444eeb13c2c4693d4a5c37c16bc18532e9c229 | refs/heads/master | 2023-07-27T10:41:48.018353 | 2021-09-18T09:21:38 | 2021-09-18T09:21:38 | 277,781,324 | 0 | 0 | null | null | null | null | null | [
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davidfang/dolphin | https://github.com/davidfang/dolphin | ef4dc29a1f096dd4b945636930ca5a0d50fdb1ae | c492b369b963ded2865b96d29e8c0a895082f0ac | 6e9cc00ee5df466b55a8b56670ef1988e190a1ab | refs/heads/master | 2020-04-12T21:51:57.908934 | 2018-07-25T09:06:12 | 2018-07-25T09:06:12 | null | 0 | 0 | null | null | null | null | null | [
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shanirub/blinkandtouch | https://github.com/shanirub/blinkandtouch | 58b91871fe0322665ee04e634658497be4dc9e59 | f4abc830c85e0e4c1b5e8afda1bfd73e114e08e7 | 858a3e88eee1d0f06d3d338ce7754a100d4f032a | refs/heads/master | 2021-05-21T05:20:43.744635 | 2020-05-24T14:41:30 | 2020-05-24T14:41:30 | 252,563,457 | 0 | 0 | null | null | null | null | null | [
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gusbacos/UAVPreparer | https://github.com/gusbacos/UAVPreparer | 97a7a95496db66fb17fcf30a8e4d74e1b52a9adc | 171b08af2aae2e19f467bf5efbefa679eabeb612 | 0a3a2e133dd26b45dfede937d54d24cf700dc82e | refs/heads/main | 2023-04-23T11:47:51.048575 | 2021-05-06T08:13:43 | 2021-05-06T08:13:43 | 364,549,559 | 0 | 0 | null | null | null | null | null | [
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ChristianSchuessler/Robot-Project | https://github.com/ChristianSchuessler/Robot-Project | 666644a0b5391665360fde14c33180f600c66b79 | 1abf05f5f8c73ee5f549591428d5ef3b1fdd5e6a | e7763867e5324bec9d8785cef3d2897ee33456c6 | refs/heads/master | 2020-03-22T14:11:10.812389 | 2018-08-19T14:21:56 | 2018-08-19T14:21:56 | 140,159,326 | 0 | 0 | null | null | null | null | null | [
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Creature-03/Reddit-Bot-Customizable | https://github.com/Creature-03/Reddit-Bot-Customizable | 107a5f6a9d25071692e9811834551c7e4407768c | 66252f0b3b243abebd1058db0576b648c03f7ce9 | 829de733045f6caaa93fd5eb3156e40d439b53fd | refs/heads/main | 2023-02-19T19:29:23.679992 | 2021-01-16T05:04:33 | 2021-01-16T05:04:33 | 330,088,821 | 0 | 0 | null | null | null | null | null | [
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niuwan1/PU-gbdt | https://github.com/niuwan1/PU-gbdt | 4b8f680c392279e370ad8c9fa9507830c3037730 | e4d3f3783a3db0a0cdc5b88469525f85fc9e7e99 | df7bb92372ac22ca0ac51695552f42429e23d5f8 | refs/heads/master | 2022-09-19T20:58:54.005220 | 2020-06-06T00:31:27 | 2020-06-06T00:31:27 | null | 0 | 0 | null | null | null | null | null | [
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MonsieurShaik/nltk-tutorials | https://github.com/MonsieurShaik/nltk-tutorials | 95b3ee88311cf0abc3479b67467f71534d8260eb | fa3361dc3f939629ec925c7cb5aa5b6092e16e6f | 9197a63774ea1e1ddda73c3cbff953fd7fdda9bc | refs/heads/master | 2018-02-09T12:35:55.550063 | 2017-07-11T07:24:16 | 2017-07-11T07:24:16 | 96,747,196 | 1 | 0 | null | null | null | null | null | [
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jstestsuite/ros-test | https://github.com/jstestsuite/ros-test | c33e244794287e2665cace847a9faf1b596ddf92 | b0bb5b06c1c18b804c0f0a4e2deb4d88be894d1f | bdf3c98e33c6bc9c8e0332043cc5a3a3f5e8ea42 | refs/heads/master | 2020-04-11T16:54:41.401198 | 2019-02-13T00:44:14 | 2019-02-13T00:44:14 | 161,938,209 | 0 | 0 | null | null | null | null | null | [
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my-poi/machina | https://github.com/my-poi/machina | 05104812d7bc32c7eba7fa2567498754164ee32a | a84e5f530a03acc36cadcf8002c40c25dfeb41d8 | a9cba816576883e7d679f85bbf7166c8b8250fc5 | refs/heads/master | 2021-01-10T06:53:55.307126 | 2018-02-16T09:24:00 | 2018-02-16T09:24:00 | 69,585,258 | 2 | 0 | null | null | null | null | null | [
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mike7105/RenameFiles | https://github.com/mike7105/RenameFiles | e6cc50b9150ea83df7c2a9ecb584157b34bd987d | 5dc1aefea90ccd5ce667f577b8c254fd6f783cc8 | 0a0e701c406ad4b30aa055b8e83a38b59d5f1aa0 | refs/heads/master | 2023-08-15T14:52:48.591776 | 2021-10-06T20:59:11 | 2021-10-06T20:59:11 | 414,353,345 | 0 | 0 | null | null | null | null | null | [
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yuyurun/note_tag | https://github.com/yuyurun/note_tag | 0ab5dda70de803118b374b76e66f3ce4c3f0acea | d5d7dd5cc4939af8bcfb4d30b2a2a0b94aecb527 | 96ccd9b0b76657b4f7a06d65d4c9fe9fcc29a0ec | refs/heads/master | 2020-05-09T18:15:42.767498 | 2019-08-25T14:19:08 | 2019-08-25T14:19:08 | 181,334,733 | 0 | 0 | null | null | null | null | null | [
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srinicoder035/Temperature-Predictor | https://github.com/srinicoder035/Temperature-Predictor | ea431022ebd640cddcfc5107bc94df17167c4db5 | 858bafcd018c7a6a7b82f33f3585e2f81c56e98d | 7db1cb71b85313786f422f4eea7a08b9ae85eb08 | refs/heads/master | 2020-09-23T07:25:04.923881 | 2019-12-02T18:09:31 | 2019-12-02T18:09:31 | 225,438,555 | 0 | 0 | null | null | null | null | null | [
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Soundpruf/block_party-server | https://github.com/Soundpruf/block_party-server | 7ea703b17326612b8ac4ac894f10e475be9756eb | bf180a8b83b460b902d751bfaa4143f3b5e8bb5b | 924181628e23c92fc275f8fd880be21a5faafada | refs/heads/master | 2023-05-11T00:32:04.177366 | 2018-01-14T05:18:01 | 2018-01-14T05:18:01 | 116,161,921 | 1 | 0 | null | null | null | null | null | [
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vcar/vcar | https://github.com/vcar/vcar | 3635bab9032baab1616c7b722b2640cb087bc0d5 | cfdb105f7cc290a60d9f3ddc21608bf6c351811e | 00c379007e67c5b2e19891e6d80177df5fe651b5 | refs/heads/master | 2022-02-02T17:54:31.007578 | 2018-01-03T11:40:00 | 2018-01-03T11:40:00 | 90,060,759 | 0 | 0 | null | null | null | null | null | [
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griffinfoster/artemis | https://github.com/griffinfoster/artemis | fc9dd1a5282dd388e49a63c4a0cf981f025d76da | bf445b7d2dc9127676503a4efd633d3492e6139a | 3567127e47a287d6dad427c075ad384ef20df379 | refs/heads/master | 2020-04-25T18:08:17.019014 | 2019-04-16T08:26:52 | 2019-04-16T08:26:52 | 172,974,535 | 0 | 0 | null | null | null | null | null | [
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Jeezus101/jeezus101.github.io | https://github.com/Jeezus101/jeezus101.github.io | d0b8f0bdf07e8754895d4f8b551644226a6dd700 | 06d8f8de0dab33bda64df980ed013cb518c23088 | aeb2027014a82ec4a32fdfd399ec84a73bccb637 | refs/heads/master | 2018-11-07T12:56:53.281256 | 2018-09-02T21:18:01 | 2018-09-02T21:18:01 | 116,399,579 | 0 | 0 | null | null | null | null | null | [
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annelovepeace/DEND-Project-3-Cloud-Data-Warehouse-AWS | https://github.com/annelovepeace/DEND-Project-3-Cloud-Data-Warehouse-AWS | 6a3d301c6e0b0c9bc3cdd8a9fb9621f4c787139f | 0d23bbddda786ec5a635bda53332eeed1616be51 | 88ef36ef3adf4eef184fc490d40bb9f644c6334c | refs/heads/master | 2022-01-08T20:13:29.342363 | 2019-08-01T03:28:15 | 2019-08-01T03:28:15 | 197,876,700 | 0 | 0 | null | null | null | null | null | [
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Parsely/elasticsearch-hll-rollups | https://github.com/Parsely/elasticsearch-hll-rollups | 6650e3fefd7de2f5a5af32fcd2d02e60bbdd4524 | 17fa722aa53c2918831326c53512bca4f11fa301 | c5f2e7ed920131680a7fad9835b92266bb134e1d | refs/heads/master | 2023-08-27T05:35:11.316722 | 2019-09-16T20:46:02 | 2019-09-16T20:46:02 | 208,894,205 | 2 | 0 | null | null | null | null | null | [
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UnityRohan/ytdownloader-web | https://github.com/UnityRohan/ytdownloader-web | 339871d3447c590fd901be46306d637b77ff825a | c392d553787f21b904ba7897369f0956cde27a0f | 2bf255583098fb772ba31943fd7a5a39b623807c | refs/heads/master | 2023-02-05T22:07:07.106970 | 2020-12-25T22:51:58 | 2020-12-25T22:51:58 | 324,441,841 | 0 | 0 | null | null | null | null | null | [
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dimsumlabs/ml_workshop | https://github.com/dimsumlabs/ml_workshop | c6c716c7bf6c26b28acb1303e8c0902e8d3e2944 | b7bbd83bbb72a31bd29f64f592fc08f06e289f24 | 63545a162d9fdb303c108d8b59742bc401cfe750 | refs/heads/master | 2016-09-05T18:26:35.312942 | 2014-11-17T10:45:46 | 2014-11-17T10:45:46 | null | 0 | 0 | null | null | null | null | null | [
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Jacetone/CS471 | https://github.com/Jacetone/CS471 | 8180941685140fb1bffbf3074c55214b03b5f769 | 57138c949c3ec21d060740ce2bbe6e4eb38bb161 | 2610f71feeafee8b4ead01bf89113974aa64e15f | refs/heads/master | 2022-02-09T19:57:07.378945 | 2019-05-02T13:31:49 | 2019-05-02T13:31:49 | null | 0 | 0 | null | null | null | null | null | [
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someii/chatbot | https://github.com/someii/chatbot | 4848ae0f3e9ae0636aeca35050f2feb648923496 | c3bd9b0311a4b6010ff78ecdc4aa7483a0f01527 | 6d1b8798aa27614ab65fad62e56a57e596abcf79 | refs/heads/master | 2020-04-08T11:57:38.627553 | 2018-11-23T00:53:43 | 2018-11-23T00:53:43 | null | 0 | 0 | null | null | null | null | null | [
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