From 37e1c0ee776dbf5566bcdf620c61b239fab99c11 Mon Sep 17 00:00:00 2001 From: Kees van Kempen Date: Thu, 17 Nov 2022 11:42:17 +0100 Subject: [PATCH] 09: Run copied code --- Exercise sheet 9/exercise_sheet_09.ipynb | 46 ++++++++- Exercise sheet 9/latticescalar.py | 106 ++++++++++++--------- Exercise sheet 9/preliminary_simulation.h5 | Bin 0 -> 10976 bytes 3 files changed, 103 insertions(+), 49 deletions(-) create mode 100644 Exercise sheet 9/preliminary_simulation.h5 diff --git a/Exercise sheet 9/exercise_sheet_09.ipynb b/Exercise sheet 9/exercise_sheet_09.ipynb index d5110a1..4f47597 100644 --- a/Exercise sheet 9/exercise_sheet_09.ipynb +++ b/Exercise sheet 9/exercise_sheet_09.ipynb @@ -20,12 +20,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "be2b5237", "metadata": {}, "outputs": [], "source": [ - "NAME = \"\"\n", + "NAME = \"Kees van Kempen\"\n", "NAMES_OF_COLLABORATORS = \"\"" ] }, @@ -191,6 +191,46 @@ "for $\\lambda=1.5$, $\\kappa=0.12$, $w=3$, and $1000$ measurements, together with optional parameters $\\delta$ and numbers of sweeps, as command line arguments and stores the relevant simulation outcomes in an hdf5-file. **(40 pts)**" ] }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1c6c1b73", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import h5py\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "# Load data\n", + "output_filename = \"preliminary_simulation.h5\"\n", + "with h5py.File(output_filename,'r') as f:\n", + " handler = f[\"mean-magn\"]\n", + " mean_magn = np.array(handler)\n", + " kappas = handler.attrs[\"kappas\"]\n", + "\n", + "# Plotterdeplotterdeplot\n", + "plt.figure()\n", + "plt.errorbar(kappas,[m[0] for m in mean_magn],yerr=[m[1] for m in mean_magn],fmt='-o')\n", + "plt.xlabel(r\"$\\kappa$\")\n", + "plt.ylabel(r\"$|m|$\")\n", + "plt.title(r\"Absolute field average on $3^4$ lattice, $\\lambda = 1.5$\")\n", + "plt.show()" + ] + }, { "cell_type": "markdown", "id": "d4640925", @@ -238,7 +278,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, diff --git a/Exercise sheet 9/latticescalar.py b/Exercise sheet 9/latticescalar.py index 45db780..90da42a 100644 --- a/Exercise sheet 9/latticescalar.py +++ b/Exercise sheet 9/latticescalar.py @@ -1,71 +1,85 @@ import numpy as np +import argparse +import time +import h5py + +starttime = time.asctime() + rng = np.random.default_rng() -import matplotlib.pylab as plt -%matplotlib inline def potential_v(x,lamb): - '''Compute the potential function V(x).''' - return lamb*(x*x-1)*(x*x-1)+x*x + '''Compute the potential function V(x).''' + return lamb*(x*x-1)*(x*x-1)+x*x def neighbor_sum(phi,s): - '''Compute the sum of the state phi on all 8 neighbors of the site s.''' - w = len(phi) - return (phi[(s[0]+1)%w,s[1],s[2],s[3]] + phi[(s[0]-1)%w,s[1],s[2],s[3]] + - phi[s[0],(s[1]+1)%w,s[2],s[3]] + phi[s[0],(s[1]-1)%w,s[2],s[3]] + - phi[s[0],s[1],(s[2]+1)%w,s[3]] + phi[s[0],s[1],(s[2]-1)%w,s[3]] + - phi[s[0],s[1],s[2],(s[3]+1)%w] + phi[s[0],s[1],s[2],(s[3]-1)%w] ) + '''Compute the sum of the state phi on all 8 neighbors of the site s.''' + w = len(phi) + return (phi[(s[0]+1)%w,s[1],s[2],s[3]] + phi[(s[0]-1)%w,s[1],s[2],s[3]] + + phi[s[0],(s[1]+1)%w,s[2],s[3]] + phi[s[0],(s[1]-1)%w,s[2],s[3]] + + phi[s[0],s[1],(s[2]+1)%w,s[3]] + phi[s[0],s[1],(s[2]-1)%w,s[3]] + + phi[s[0],s[1],s[2],(s[3]+1)%w] + phi[s[0],s[1],s[2],(s[3]-1)%w] ) def scalar_action_diff(phi,site,newphi,lamb,kappa): - '''Compute the change in the action when phi[site] is changed to newphi.''' - return (2 * kappa * neighbor_sum(phi,site) * (phi[site] - newphi) + - potential_v(newphi,lamb) - potential_v(phi[site],lamb) ) + '''Compute the change in the action when phi[site] is changed to newphi.''' + return (2 * kappa * neighbor_sum(phi,site) * (phi[site] - newphi) + + potential_v(newphi,lamb) - potential_v(phi[site],lamb) ) def scalar_MH_step(phi,lamb,kappa,delta): - '''Perform Metropolis-Hastings update on state phi with range delta.''' - site = tuple(rng.integers(0,len(phi),4)) - newphi = phi[site] + rng.uniform(-delta,delta) - deltaS = scalar_action_diff(phi,site,newphi,lamb,kappa) - if deltaS < 0 or rng.uniform() < np.exp(-deltaS): - phi[site] = newphi - return True - return False + '''Perform Metropolis-Hastings update on state phi with range delta.''' + site = tuple(rng.integers(0,len(phi),4)) + newphi = phi[site] + rng.uniform(-delta,delta) + deltaS = scalar_action_diff(phi,site,newphi,lamb,kappa) + if deltaS < 0 or rng.uniform() < np.exp(-deltaS): + phi[site] = newphi + return True + return False def run_scalar_MH(phi,lamb,kappa,delta,n): - '''Perform n Metropolis-Hastings updates on state phi and return number of accepted transtions.''' - total_accept = 0 - for _ in range(n): - total_accept += scalar_MH_step(phi,lamb,kappa,delta) - return total_accept + '''Perform n Metropolis-Hastings updates on state phi and return number of accepted transtions.''' + total_accept = 0 + for _ in range(n): + total_accept += scalar_MH_step(phi,lamb,kappa,delta) + return total_accept def batch_estimate(data,observable,k): - '''Devide data into k batches and apply the function observable to each. - Returns the mean and standard error.''' - batches = np.reshape(data,(k,-1)) - values = np.apply_along_axis(observable, 1, batches) - return np.mean(values), np.std(values)/np.sqrt(k-1) + '''Devide data into k batches and apply the function observable to each. + Returns the mean and standard error.''' + batches = np.reshape(data,(k,-1)) + values = np.apply_along_axis(observable, 1, batches) + return np.mean(values), np.std(values)/np.sqrt(k-1) lamb = 1.5 kappas = np.linspace(0.08,0.18,11) width = 3 num_sites = width**4 delta = 1.5 # chosen to have ~ 50% acceptance -equil_sweeps = 1000 +equil_sweeps = 10 measure_sweeps = 2 -measurements = 2000 +measurements = 20 mean_magn = [] for kappa in kappas: - phi_state = np.zeros((width,width,width,width)) - run_scalar_MH(phi_state,lamb,kappa,delta,equil_sweeps * num_sites) - magnetizations = np.empty(measurements) - for i in range(measurements): - run_scalar_MH(phi_state,lamb,kappa,delta,measure_sweeps * num_sites) - magnetizations[i] = np.mean(phi_state) - mean, err = batch_estimate(np.abs(magnetizations),lambda x:np.mean(x),10) - mean_magn.append([mean,err]) + phi_state = np.zeros((width,width,width,width)) + run_scalar_MH(phi_state,lamb,kappa,delta,equil_sweeps * num_sites) + magnetizations = np.empty(measurements) + for i in range(measurements): + run_scalar_MH(phi_state,lamb,kappa,delta,measure_sweeps * num_sites) + magnetizations[i] = np.mean(phi_state) + mean, err = batch_estimate(np.abs(magnetizations),lambda x:np.mean(x),10) + mean_magn.append([mean,err]) -plt.errorbar(kappas,[m[0] for m in mean_magn],yerr=[m[1] for m in mean_magn],fmt='-o') -plt.xlabel(r"$\kappa$") -plt.ylabel(r"$|m|$") -plt.title(r"Absolute field average on $3^4$ lattice, $\lambda = 1.5$") -plt.show() +output_filename = 'preliminary_simulation.h5' +with h5py.File(output_filename,'a') as f: + if not "mean-magn" in f: + dataset = f.create_dataset("mean-magn", chunks=True, data=mean_magn) + # store some information as metadata for the data set + dataset.attrs["lamb"] = lamb + dataset.attrs["kappas"] = kappas + dataset.attrs["width"] = width + dataset.attrs["num_sites"] = num_sites + dataset.attrs["delta"] = delta + dataset.attrs["equil_sweeps"] = equil_sweeps + dataset.attrs["measure_sweeps"] = measure_sweeps + dataset.attrs["measurements"] = measurements + dataset.attrs["start time"] = starttime + dataset.attrs["stop time"] = time.asctime() diff --git a/Exercise sheet 9/preliminary_simulation.h5 b/Exercise sheet 9/preliminary_simulation.h5 new file mode 100644 index 0000000000000000000000000000000000000000..8017f1385eabdabf452e193b89dd79c6c644b062 GIT binary patch literal 10976 zcmeI0T}V_x6o6-4|5`VHO2~YW%^=Lkc2^-0_%QXg)@LbC9xyPm|;ca)98hlEm9fz6)7%sj=H^Jg}MOhB-B|5}5%=9Cz?)EgtY zK937}<)fC>U1{mb?2VRMgP4bGN4Yox?0d&J%I}<%tqZst#dlbm&0cTvxyW{uNB{{S z0VIF~kN^@u0{=DvJ-H(G|9*7;A4ZPa91fdvp8db(_dG@J7b`+rwhxuJfo`DDk*K>5 zb+xQ?!1@Ch9*svmg!YItqnYiUaGkaeG!G)a8`GB1(o!*pJwQ~e6v_CwBmmqMqe7Ek_}qJ7%mSIvuZ zl>(QIwkU0+?QxQD*i}Y)T zKkr}o+m%Asy#0qx&EF!p2dMLRx@^TRgDc;zaD&x!9k7!f_UG*R(~!@B<Gl4&!Gc4VV zuqPyd1dsp{Kmtf$Ap-939QU5c2T<8>9qC_N16_BH)L5Q&fL}B@7h$c1II;h9|44l^q>W_+o=<%ZH^zQOt!cSI^;PeSx;{eLyJZ!tzBfb6?upVG e(